The session welcomes papers on:

1) Forecasting and simulating high impact weather events - research on improvement of high-resolution numerical model prediction of severe weather events (such as winter storms, tropical storms, and severe mesoscale convective storms) using data from various observational platforms, evaluation of the impact of new remote sensing data;

2) Development and improvement of model numerics - basic research on advanced numerical techniques for weather and climate models (such as cloud resolving global model and high-resolution regional models specialized for extreme weather events on sub-synoptic scales);

3) Development and improvement of model physics - progress in research on advanced model physics parametrization schemes (such as stochastic physics, air-wave-oceans coupling physics, turbulent diffusion and interaction with the surface, sub-grid condensation and convection, grid-resolved cloud and precipitation, land-surface parametrization, and radiation);

4) Model evaluation - verification of model components and operational NWP products against theories and observations, regional and global re-analysis of past observations, diagnosis of data assimilation systems;

5) Data assimilation systems - progress in the development of data assimilation systems for operational applications (such as reanalysis and climate services), research on advanced methods for data assimilation on various scales (such as treatment of model and observation errors in data assimilation, and observational network design and experiments);

6) Ensemble forecasts and predictability - strategies in ensemble construction, model resolution and forecast range-related issues, and applications to data assimilation;

7) Advances and challenges in high-resolution simulations and forecasting.

Convener: Haraldur Ólafsson | Co-convener: Jian-Wen Bao
| Attendance Fri, 08 May, 08:30–12:30 (CEST)

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Chat time: Friday, 8 May 2020, 08:30–10:15

D2699 |
| solicited
Curtis Alexander

The next and final update to the deterministic Rapid Refresh, version 5 (RAPv5), and High-Resolution Rapid Refresh, version 4 (HRRRv4), is currently scheduled for an operational implementation at NOAA/NCEP in mid-2020. Numerous physics, dynamics and data assimilation changes are being included as part of this upgrade.  This presentation will discuss the scope of this implementation including  an emphasis on the development of an hourly-cycled regional 36-member storm-scale ensemble analysis system with demonstrated improvements to deterministic forecasts at the convective scale.  The design of this HRRR data assimilation system (HRRRDAS) will be motivated through choices to include multiple-scales of perturbations from both a global and this regional convection-allowing ensemble, scale-dependent use of different observation types in an Ensemble Kalman Filter and use of inflation to maintain ensemble spread.  While not included in the HRRRv4 operational implementation, an experimental storm-scale ensemble forecast system leveraging a subset of the 36 members will also be described.  Design of the HRRR ensemble (HRRRE) forecasts will be discussed including use of stochastic parameter perturbations across the single model physics suite along with comparisons to other storm-scale multi-model/physics ensemble designs highlighting both the benefits of this single-model/physics ensemble approach along with challenges in maintaining appropriate spread at longer forecast lengths.  This final configuration of the RAP/HRRR model systems will serve as an operational baseline for the transition to a regional FV3-based convection allowing application in a Unified Forecast System (UFS), known as the Rapid Refresh Forecast System (RRFS), and this transition will be described.

How to cite: Alexander, C.: Storm-Scale Ensemble Data Assimilation and Forecast Development for the High-Resolution Rapid Refresh (HRRR) and Future Applications in the Unified Forecast System, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22670, 2020

D2700 |
Takemasa Miyoshi, Takmi Honda, Shigenori Otsuka, Arata Amemiya, Yasumitsu Maejima, Yoshihiro Ishikawa, Hiromu Seko, Yoshito Yoshizaki, Naonori Ueda, Hirofumi Tomita, Yutaka Ishikawa, Shinsuke Satoh, Tomoo Ushio, Kana Koike, and Yasuhiko Nakada

The Japan’s Big Data Assimilation (BDA) project started in October 2013 and ended its 5.5-year period in March 2019. The direct follow-on project was accepted and started in April 2019 under the Japan Science and Technology Agency (JST) AIP (Advanced Intelligence Project) Acceleration Research, with emphases on the connection with AI technologies, in particular, an integration of DA and AI with high-performance computation (HPC). The BDA project aimed to fully take advantage of “big data” from advanced sensors such as the phased array weather radar (PAWR) and Himawari-8 geostationary satellite, which provide two orders of magnitude more data than the previous sensors. We have achieved successful case studies with newly-developed 30-second-update, 100-m-mesh numerical weather prediction (NWP) system based on the RIKEN’s SCALE model and local ensemble transform Kalman filter (LETKF) to assimilate PAWR in Osaka and Kobe. We have been actively developing the workflow for real-time weather forecasting in Tokyo in summer 2020. In addition, we developed two precipitation nowcasting systems with the every-30-second PAWR data: one with an optical-flow-based system, the other with a deep-learning-based system. We chose the convolutional Long Short Term Memory (Conv-LSTM) as a deep learning algorithm, and found it effective for precipitation nowcasting. The use of Conv-LSTM would lead to an integration of DA and AI with HPC. This presentation will include an overview of the BDA project toward a DA-AI-HPC integration under the new AIP Acceleration Research scheme, and recent progress of the project.

How to cite: Miyoshi, T., Honda, T., Otsuka, S., Amemiya, A., Maejima, Y., Ishikawa, Y., Seko, H., Yoshizaki, Y., Ueda, N., Tomita, H., Ishikawa, Y., Satoh, S., Ushio, T., Koike, K., and Nakada, Y.: Big Data Assimilation: Real-time Workflow for 30-second-update Forecasting and Perspectives toward DA-AI Integration, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2483, https://doi.org/10.5194/egusphere-egu2020-2483, 2020

D2701 |
Tijana Janjic, Yuefei Zeng, Alberto de Lozar, Yvonne Ruckstuhl, Ulrich Blahak, and Axel Seifert

Model error is one of major contributors to forecast uncertainty. In addition, statistical representations of possible model errors substantially affect the data assimilation results. We investigate variety of methods of taking into account model error in ensemble based convective scale data assimilation. This is done using the operational convection-permitting COSMO model and data assimilation system KENDA of German weather service, for a two-week convective period in May 2016 over Germany. Conventional and radar reflectivity observations are assimilated hourly by the LETKF. For example, to take into account the model error due to unresolved scales and processes, we use the additive noise with samples coming from the difference between high-resolution model run and low-resolution experiment. We compare this technique for assimilation of radar reflectivity data to other methods such as RTPS, warm bubble initialization, stochastic boundary layer perturbation and estimation of parameters. To further improve on additive noise technique, which consists of perturbing each ensemble member with a sample from a given distribution, we propose a more flexible approach in which the model error samples are treated as additional synthetic ensemble members that are used in the update step of data assimilation but are not forecasted. In this way, the rank of the model error covariance matrix can be chosen independently of the ensemble. This altered additive noise method is analyzed as well.

How to cite: Janjic, T., Zeng, Y., de Lozar, A., Ruckstuhl, Y., Blahak, U., and Seifert, A.: Representation of model error in convective scale data assimilation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11232, https://doi.org/10.5194/egusphere-egu2020-11232, 2020

D2702 |
Žiga Zaplotnik and Nedjeljka Žagar

In the operational NWP, the assimilation of ozone causes large wind and temperature analysis increments in the stratosphere to accommodate for the differences between background and observations. In such cases, unless the ozone feedback on dynamics is switched off, the strong-constraint 4D-Var internal dynamics without comprehensive bias correction makes spurious flow adjustments, especially in the regions with larger gradients in the tracer background field, and when there are insufficient constraints such as large background flow uncertainties and a lack of observations of dynamic variables. The wind-tracer feedback is also turned off for the aerosols and the trace gases. Thus, their useful information on the wind advection is not accounted for anywhere in the domain at any time instance. In this way, the tracer analysis quality is also deteriorated. Somewhat smarter, selective use of tracer information would be beneficial to alleviate unphysical analysis increments in certain regions and at the same time to retain the benefits of wind extraction in other areas.

Thus, we formulate the method for flow-dependent 4D-Var wind extraction, which switches the wind-tracer feedback on or off in the tracers’ tangent-linear model and wind adjoint model. The objective criterion for the selection is deduced from the ensemble of simulations and is based on the ratio of the tracer physical forcings’ uncertainty and the mean tracer advection rate. The numerical tests with an intermediate-complexity incremental 4D-Var system MADDAM show promising results for both wind and tracer analyses. We also demonstrate that the aerosols have theoretically an even larger potential as the carriers of the advection information than humidity due to larger relative spatial gradients, which are crucial for successful wind extraction. The flow-dependent wind extraction method is compared with the weak-constraint 4D-Var, where the tracer model error obtained from the ensemble implicitly controls the amount of wind-tracer coupling.

How to cite: Zaplotnik, Ž. and Žagar, N.: Inferring atmospheric dynamics from tracer observations in 4D-Var: flow-dependent aspects, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12103, https://doi.org/10.5194/egusphere-egu2020-12103, 2020

D2703 |
Marina Platonova

This work is devoted to the urgent task of assessing regional flows of greenhouse gases from the Earth's surface according to satellite observations. The article presents the practical and theoretical results of the first year of study in the PhD program, later they will be included in the final dissertation. Flows will be estimated based on the observational data assimilation system for a three-dimensional model of diffusive transport of gas components in the atmosphere (MOZART-4). Model for Ozone and Associated Chemical Indicators, Version 4 (MOZART-4) is an autonomous global model for the transport of chemicals in the atmosphere.

The development of a modern system for the assimilation of real satellite data for assessing greenhouse gas sources is currently a very important theoretical and practical area in science. The ensemble approach is relevant and has great potential for using both stochastic and variational methods. In the process of implementation, this is an order of magnitude simpler, since there are no cumbersome matrix calculations using the model.

To solve the problem of estimating methane flows, the parameter estimation problem was solved: an algorithm for data assimilation was developed; the Kalman filter with the transformation of the local ensemble was used as the basis for it. Using an example of a model problem, an algorithm for estimating the concentration of a passive impurity and a parameter is developed. The case was also considered when only one parameter can be estimated in the assimilation system. In this case it is considered that at the forecasting stage the parameter does not change, and the calculations in accordance with the transport model are included in the operator H, for example, as in Feng (2009,2017). H is the observation operator; transfers predicted values to observation points (and observed variables). For example, for satellite methane data, H includes:

  1. a) interpolation to the observation point;
  2. b) vertical averaging (using the middle core);
  3. c) if the observation data is obtained from a large time interval, then the operator H also includes a forecast for the model in time.

Numerical experiments were carried out with model and real data. Using numerical experiments with the model, it was shown that a large problem (global) can be solved sequentially by subregion, independently in each subregion, which allowed the use of MPI and OpenMP.

How to cite: Platonova, M.: Data assimilation system for estimating methane flows using satellite data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-661, https://doi.org/10.5194/egusphere-egu2020-661, 2019

D2704 |
Jing-Shan Hong, Wen-Jou Chen, Ying-Jhen Chen, Siou-Ying Jiang, and Chin-Tzu Fong

The FORMOSAT-7/COSMIC-2 (simplified as FS-7/C-2 in the following descriptions) is the constellation of satellites for meteorology, ionosphere, climatology, and space weather research. FS-7/C-2 was a joint Taiwan-U.S. satellite mission that makes use of the radio occultation (RO) measurement technique. FORMOSAT-7 is the successor of FORMOSAT-3 which was launched in 2006. the FORMOSAT-3 RO data has been shown to be extremely valuable for numerical weather prediction, such as improving the prediction of tropical cyclogenesis and reducing the typhoon track error. The follow-on FS-7/C-2 mission was launched on 25 June 2019, and is currently going through preliminary testing and evaluation. After it is fully deployed, FS-7/C-2 is expected to provide 6,000 GNSS (Global Navigation Satellite System) RO profiles per day between 40S and 40N.  

In this study, we conduct a preliminary evaluation of FS-7/C-2 GNSS RO data on heavy precipitation events associated with typhoon and southwesterly monsoon flows based on the operational NWP system of the Central Weather Bureau (CWB) in Taiwan. The FS-7/C-2 GNSS RO data are assimilated using a dual-resolution hybrid 3DEnVare system with a 15-3 km nested-grid configuration. In the 15km resolution domain, flow-dependent background error covariances (BECs) derived from the perturbation of ensemble adjustment Kalman filter (EAKF), will be used to conduct hybrid 3DEnVar analysis. In the 3 km resolution domain, the 15 km resolution flow-dependent BECs will be inserted to the 3 km grid using a Dual-Resolution (DR) technique, and then combined with 3 km resolution static BECs, to perform the high-resolution 3DEnVar analysis. The performance of the CWB operational NWP system on quantitative precipitation forecast of significant precipitation events with and without the assimilation of FS-7/C-2 GNSS RO data will be evaluated.

How to cite: Hong, J.-S., Chen, W.-J., Chen, Y.-J., Jiang, S.-Y., and Fong, C.-T.: Impact of FORMOSAT-7/COSMIC-2 RO on High-Resolution Hybrid 3DEnVar System at Central Weather Bureau of Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4161, https://doi.org/10.5194/egusphere-egu2020-4161, 2020

D2705 |
Vincenzo Mazzarella and Rossella Ferretti

Nowadays, the use of 4D-VAR assimilation technique has been investigated in several scientific papers with the aim of improving the localization and timing of precipitation in complex orography regions. The results show the positive impact in rainfall forecast but, the need to resolve the tangent linear and adjoint model makes the 4D-VAR computationally too expensive. Hence, it is used in operationally only in large forecast centres. To the aim of exploring a more reasonable method, a comparison between a cycling 3D-VAR, that needs less computational resources, and 4D-VAR techniques is performed for a severe weather event occurred in Central Italy. A cut-off low (992 hPa), located in western side of Sicily region, was associated with a strong south-easterly flow over Central Adriatic region, which supplied a large amount of warm and moist air. This mesoscale configuration, coupled with the Apennines mountain range that further increased the air column instability, produced heavy rainfall in Abruzzo region (Central Italy).

The numerical simulations are carried out using the Weather Research and Forecasting (WRF) model. In-situ surface and upper-air observations are assimilated in combination with radar reflectivity and radial velocity data over a high-resolution domain. Several experiments have been performed in order to evaluate the impact of 4D-VAR and cycling 3D-VAR in the precipitation forecast. In addition, a statistical analysis has been carried out to objectively compare the simulations. Two different verification approaches are used: Receiver Operating Characteristic (ROC) curve and Fraction Skill Score (FSS). Both statistical scores are calculated for different threshold values in the study area and in the sub-regions where the maximum rainfall occurred.

How to cite: Mazzarella, V. and Ferretti, R.: A comparison between 4D-VAR and cycling 3D-VAR methods for the simulation of a severe weather event in Central Italy. Preliminary results., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7228, https://doi.org/10.5194/egusphere-egu2020-7228, 2020

D2706 |
| solicited
Ivanka Stajner, Vijay Tallapragada, Yuejian Zhu, Henrique Alves, Jeff McQueen, Tom Hamill, Jeff Whitaker, Georg Grell, and Jason Levit

NCEP has implemented the first version of the Finite Volume Cubed Sphere (FV3) dynamic core based Global Forecast System (GFS v15) into operations in June 2019, replacing the spectral model-based GFS. This is the first instantiation of NOAA's Unified Forecast System (UFS), which is being built as a comprehensive coupled Earth system model using modern tools and software infrastructure (e.g., NEMS, NUOPC, and ESMF) to support research and operations. Advancements in model physics and data assimilation are in development using CCPP and JEDI frameworks. Testing and evaluation of UFS are facilitated through the development of unified workflow and METplus capabilities. All these initiatives involve significant engagement with the research community, with emphasis on more efficient and streamlined transition of research advances to operations (R2O).

The next major operational upgrade towards UFS is for the Global Ensemble Forecast System (GEFSv12) planned for implementation later this year.  Compared to the currently operational GEFS, the next GEFS version 12 incorporates the following advances: the same FV3 based global model and UFS infrastructure as in GFS,  higher resolution (~25km), increased membership (31), GFSv15 physics, advanced stochastic physics perturbations (SKEB and SPPT), and 2-tiered SST approach using SST anomalies from CFSv2 as input.  For the first time, GEFSv12 will provide ensemble based operational weather predictions for sub-seasonal scales with daily 00z forecasts going out to 35 days. GEFSv12 also comes with 20-year reanalysis, 30-year reforecasts and 3-year retrospective forecasts to support stakeholder needs for calibration and validation.  In addition, GEFSv12 absorbs the global wave ensembles and aerosol capabilities (control member only) through one-way coupling, taking major steps towards building a unified system and simplifying NCEP’s production suite.

This presentation describes the design and development of GEFSv12 and discusses results from the evaluation of the retrospective and reforecast experiments.  Significant improvements were noted in both deterministic and probabilistic forecast metrics for several variables including 500 hPa geopotential height anomaly correction, 850 hPa temperature and winds, near surface variables, precipitation, tropical cyclone tracks and intensity, and modes of variability including MJO and NAO.  Substantial improvements were also noted in the performance of wave ensemble and aerosol predictions.

This presentation also describes NOAA’s efforts towards accelerating further development of fully coupled UFS consisting of six component models of the Earth system: the FV3 dynamical core for the atmosphere, MOM6 for the ocean, Noah MP for the land surface, GOCART for aerosols, CICE5/CICE6 for sea ice and WW3 for ocean surface waves.  Combined with data assimilation advances, an ambitious goal of unifying both the high-resolution deterministic (GFSv17) and probabilistic (GEFSv13) predictions for global medium range and sub-seasonal time scales is planned to significantly advance the global prediction capabilities at NCEP.


How to cite: Stajner, I., Tallapragada, V., Zhu, Y., Alves, H., McQueen, J., Hamill, T., Whitaker, J., Grell, G., and Levit, J.: NOAA’s Unified Forecast System for Sub-Seasonal Predictions: Development and operational implementation plans of Global Ensemble Forecast System v12 (GEFSv12) at NCEP, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6212, https://doi.org/10.5194/egusphere-egu2020-6212, 2020

D2707 |
Dom Heinzeller, Grant Firl, Ligia Bernardet, Laurie Carson, Man Zhang, and Jack Kain

Improving numerical weather prediction systems depends critically on the ability to transition innovations from research to operations (R2O) and to provide feedback from operations to research (O2R). This R2O2R cycle, sometimes referred to as "crossing the valley of death", has long been identified as a major challenge for the U.S. weather enterprise.

As part of a broader effort to bridge this gap and advance U.S. weather prediction capabilities, the Developmental Testbed Center (DTC) with staff at NOAA and NCAR has developed the Common Community Physics Package (CCPP) for application in NOAA's Unified Forecasting System (UFS). The CCPP consists of a library of physical parameterizations and a framework, which interfaces the physics with atmospheric models based on metadata information and standardized interfaces. The CCPP physics library contains physical parameterizations from the current operational U.S. global, mesoscale and high-resolution models, future implementation candidates, and additional physics from NOAA, NCAR and other organizations. The range of physics options in the CCPP physics library enables the application of the UFS - as well as every other model using the CCPP - across scales, from now-casting to seasonal and from high-resolution regional to global ensembles.

While the initial development of the CCPP was centered around the FV3 (Finite-Volume Cubed-Sphere) dynamical core of the UFS, its focus has since widened. The CCPP is also used by the DTC Single Column Model to support a hierarchical testing strategy, and by the next generation NEPTUNE (Navy Environmental Prediction sysTem Utilizing the Numa corE) model of the Naval Research Laboratory. Further, and most importantly, NOAA and NCAR recently signed an agreement to jointly develop the CCPP framework as a single, standardized way to interface physics with their models of the atmosphere (and other compartments of the Earth system). This places the CCPP in the heart of several of the U.S. flagship models and opens the door for bringing innovations from a large research community into operations.

In this contribution, we will present a brief overview of the concept of the CCPP, its technical design and the requirements for parameterizations to be considered as CCPP-compliant. We will describe the integration of CCPP in the UFS and touch upon the challenges in creating a flexible modeling framework while maintaining high computational performance. We will also provide information on how to obtain, use and contribute to the CCPP, as well as on the future development of the CCPP framework and upcoming additions to the CCPP physics library.

How to cite: Heinzeller, D., Firl, G., Bernardet, L., Carson, L., Zhang, M., and Kain, J.: The Common Community Physics Package (CCPP): bridging the gap between research and operations to improve U.S. numerical weather prediction capabilities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-23, https://doi.org/10.5194/egusphere-egu2020-23, 2019

D2708 |
Georg Grell, Hannah Barnes, Saulo Freitas, and Haiqin Li

We will present some recent improvements to the GF parameterization. These include two features that were added to the Grell-Freitas (GF) Cumulus Parameterization to improve the representation of the particle size distribution and to allow parameterized deep convection to propagate. These also include the treatment of tracer transport, wet scavenging, and aqueous phase chemistry, and improvements on interactions with aerosols. A more complete implementation for transport and treatment of atmospheric composition variables was necessary to complement recent new developments at NOAA/ESRL coupling chemical modules within the NWP model.

Estimates of cloud water and ice crystal number concentrations are added to GF base on the water-friendly aerosol content, temperature, and the cloud water and ice crystal mixing ratios. This modification is designed to diminish the artificial modification of the particle size distribution that occurs when the single moment cumulus schemes are used with the double-moment microphysics schemes. Simulations demonstrate that the addition of GF ice number concentrations substantially increases ice content aloft in the tropics, which shifts the outgoing longwave radiation distribution towards colder brightness temperatures.

The key modification used to enable the propagation of parameterized deep convective is the addition of an advected scalar that represents the cloud base mass flux associated with GF downdrafts. Our implementation of this advected scalar allows the impact of downdrafts from previous time steps to foster propagation. Evaluation and tuning of the new downdraft mass advection term is ongoing.

How to cite: Grell, G., Barnes, H., Freitas, S., and Li, H.: Ongoing Development and Applications of the Grell-Freitas Cumulus Parameterization , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20855, https://doi.org/10.5194/egusphere-egu2020-20855, 2020

D2709 |
Emre Esenturk

A key and expensive part of coupled atmospheric chemistry-climate model simulations is the integration of gas phase chemistry, which involves dozens of species and hundreds of reactions. These species and reactions form a highly-coupled network of Differential Equations (DEs). There exists orders of magnitude variability in the lifetimes of the different species present in the atmosphere and so solving these DEs to obtain robust numerical solutions poses a “stiff problem”. With newer models having more species and increased complexity it is now becoming increasingly important to have chemistry solving schemes that reduce time but maintain accuracy.

A sound way to handle stiff systems is by using implicit DE solvers but the computational costs for such solvers are high due to internal iterative algorithms (e.g., Newton-Raphson (NR) methods). Here we propose an approach for implicit DE solvers that improves their convergence speed and robustness with relatively small modification in the code. We achieve this by using Quasi-Newton (QN) methods. We test our approach with numerical experiments on the UK Chemistry and Aerosol (UKCA) model, part of the UK Met Office Unified Model suite, run in both an idealized box-model environment and under realistic 3D atmospheric conditions. The box model tests reveal that the proposed method reduces the time spent in the solver routines significantly, with each QN call costing 27% of a call to the full NR routine. A series of experiments over a range of chemical environments was conducted with the box-model to find the optimal iteration steps to call the QN routine which result in the greatest reduction in the total number of NR iterations whilst minimising the chance of causing instabilities and maintaining solver accuracy. The 3D simulations show that our method for the chemistry solver, speeds up the chemistry routines by around 13%, resulting in a net improvement in overall run-time of the full model by approximately 3% with negligible loss in the accuracy (relative error of order 10-7) . The QN method also improves the robustness of the solver by significantly reducing (40% ) the number of grid cells which fail to converge hence avoiding unnecessary timestep adjustments. 

How to cite: Esenturk, E.: Improved numerical solvers for coupled chemistry-climate model simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20973, https://doi.org/10.5194/egusphere-egu2020-20973, 2020

D2710 |
Julia Jeworrek, Gregory West, and Roland Stull

Canada’s west coast topography plays a crucial role for the local precipitation patterns, which are often shaped by orographic lifting on one side of the mountains, and rain shadows on the other side. The hydroelectric infrastructure in southwest British Columbia (BC) relies heavily on the abundant rainfall of the wet season, but long lasting and heavy precipitation can cause local flooding and make reliable precipitation forecasts crucial for resource management, risk assessment, and disaster mitigation.

This research evaluates hourly precipitation forecasts from the Weather Research and Forecasting (WRF) model over the complex terrain of southwest BC. The model data includes a full year of daily runs across three nested domains (27-9-3 km). A selection of different parameterizations is systematically varied, including microphysics, cumulus, turbulence, and land-surface parameterizations. The resulting over 100 model configurations are evaluated with observations from ground-based quality-controlled precipitation gauges. The individual model skill of the precipitation forecasts is assessed with respect to different accumulation windows, forecast horizons, grid resolutions, and precipitation intensities. Furthermore, the ensemble mean and spread provide insight to the general error growth for precipitation forecasts in WRF.

Cumulus and microphysics parameterizations together determine the total precipitation in numerical weather prediction models and this study confirms the expectation that the combination of those physics parameterizations is most decisive for the precipitation forecasts. However, the boundary-layer and land-surface parameterizations have a secondary effect on precipitation skill. The verification shows that the WSM5 microphysics parameterization yields surprisingly competitive verification scores when compared to more sophisticated and computationally expensive parameterizations. Although, the scale-aware Grell-Freitas cumulus parameterization performs better for summer-time convective precipitation, the conventional Kain-Fritsch parameterization performs better for winter-time frontal precipitation, which contributes to the majority of the annual rainfall in southwest BC.

Throughout a 3-day forecast horizon mean absolute errors are observed to grow by ~5% per forecast day. Furthermore, this study indicates that coarser resolutions suffer from larger total biases and larger random error components, however, they have slightly higher correlation coefficients. The mid-size 9-km domain yields the highest relative hit rate for significant and extreme precipitation. Verification metrics improve exponentially with longer accumulation windows: On one side, hourly precipitation values are highly prone to double-penalty issues (where a timing error can, for example, result in an over-forecast error in one hour and an under-forecast in a subsequent hour); on the other side, extended accumulation windows can compensate for timing errors, but lose information about short-term rain intensities.

How to cite: Jeworrek, J., West, G., and Stull, R.: Predictability of Precipitation in Complex Terrain using the WRF Model with Varying Physics Parameterizations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-599, https://doi.org/10.5194/egusphere-egu2020-599, 2019

D2711 |
Eren Duzenli, Heves Pilatin, Ismail Yucel, Berina M. Kilicarslan, and M. Tugrul Yilmaz

Global numerical weather prediction models (NWP) such as the European Centre for Medium-Range Weather Forecasts (ECMWF) and Global Forecast System (GFS) generate atmospheric data for the entire world. However, these models provide the data at large spatiotemporal resolutions because of computational limitations. Weather Research and Forecasting (WRF) Model is one of the models, which is capable of dynamically downscaling the NWP models’ output. In this study, all combinations of 4 microphysics and 3 cumulus parametrization schemes, 2 planetary boundary layers (PBL), 2 initial and lateral boundary conditions and 2 horizontal grid spacing (i.e., an ensemble consisting of 96 different scenarios) are simulated to measure the sensitivity of WRF-derived precipitation against different model configurations. The sensitivity analyses are performed for 4 separate events. These events are selected among the extreme precipitation events in the Mediterranean (MED) and eastern Black Sea (EBLS) regions. For each region, a summer and an autumn event are chosen. Here, the fundamental aim is to determine the spatiotemporal differences in WRF input parameters that yield better outcomes. A total of 72 hours simulations are started 24 hours before the event day to avoid spin-up time error. The model is adjusted to produce hourly precipitation outputs. The relative performance of scenarios is measured using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method considering 5 categorical validation indices and 4 pairwise statistics calculated between the model estimations and the ground-based precipitation observations. According to the TOPSIS results, microphysics scheme, initial and lateral boundary condition, and horizontal grid spacing are substantially influential on WRF precipitation estimates, while cumulus parameterization has a comparatively low effect. The choice of PBL scheme is essential for the summer events, but the results of the autumn events are independent of PBL selection. WRF products are better for the events of the EBLS basin when ERA5 is used as the initial and lateral boundary condition. On the contrary, GFS is superior in the MED region. In terms of spatial resolution, 9 km horizontal grid spacing is commonly preferable for all the events rather than 3 km. Besides, the model underestimates the area-averaged precipitation amounts except for the MED-autumn incident. Still, the model is successful at catching the peak hours of all events. Moreover, the precipitation detection ability of WRF is better for the autumn months. The probability of detection index is higher than 0.5 at 35% of MED stations and 68% of EBLS stations for the autumn events. The local and convective summer events are investigated considering the event centers. Albeit relatively low relationships are defined for the MED-summer event, a statistically significant correlation is obtained between the central station of the EBLS-summer event and the closest grid for the predictions of 52 scenarios (i.e., 54% of the ensemble).

How to cite: Duzenli, E., Pilatin, H., Yucel, I., Kilicarslan, B. M., and Yilmaz, M. T.: Evaluation of the performance of WRF model in extreme precipitation estimation concerning the changing model configuration and the spatial and temporal variations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1026, https://doi.org/10.5194/egusphere-egu2020-1026, 2019

D2712 |
Stan Benjamin, Joseph Joseph Olson, Shan Sun, Georg Georg Grell, and Curtis Curtis Alexander

Subgrid-scale cloud representation and the closely related surface-energy balance continue to be a central challenge from subseasonal-to-seasonal models down to storm-scale models applied for forecast duration of only a few hours. Previously, NOAA/ESRL confirmed this issue from 3-km model (HRRR using WRF-ARW) for short-range forecasting including sub-grid-scale cloud representation up to a 25-km subseasonal model (FV3-GFS) testing a common suite of scale-aware physical parameterizations.  

In a major physics suite component -- modified representation of subgrid cloud water resulted in much improved agreement with radiation measurements as shown with 2018-2020 testing of the 3km HRRR model. Latest results will be shown using SURFRAD radiation and METAR ceiling observations, indicating much improved bias in downward solar radiation and in cloud location (via mean absolute error metric), as well as with 2m temperature and precipitation.

In addition, new evaluations with the same convection-allowing suite (“mesoscale” suite) of physical parameterizations revised further for subseasonal 30-day tests over summer and winter periods with the 25km NOAA FV3-GFS model. These results are compared with CERES-estimated cloud and downward solar radiation fields. The radiation results from this very preliminary subseasonal test with the ESRL-HRRR physics suite will be compared with previous subseasonal tests using the GFS physics suite and at different horizontal resolution.  This global application now confirms much better downward solar-radiation results over oceans for both January and June from a Nov-2019 version over a 2018 of the “mesoscale” suite.

Background: NOAA Earth System Research Laboratory, together with NCAR, has developed this parameterization suite (turbulent mixing, deep/shallow convection, 9-layer land/snow/vegetation/lake model) to improve PBL biases (temperature and moisture) including better representation of clouds and precipitation. This parameterization suite development has been accompanied by an effort for improved data assimilation of clouds, near-surface observations and radar for the atmosphere-land system.

Subgrid-scale cloud representation continues to be a central challenge from subseasonal-to-seasonal models down to storm-scale models applied for forecast duration of only a few hours.   Previously, NOAA/ESRL confirmed this issue from 3-km model (HRRR) for short-range forecasting including sub-grid-scale cloud representation up to a 60-km subseasonal model testing a common suite of scale-aware physical parameterizations.   Some progress has been made in 2018-2019 to substantially reduce cloud deficiency and excessive downward solar radiation at least over land areas.

Recent development and refinements to this common suite of physical parameterizations for scale-aware deep/shallow convection and boundary-layer mixing over this wide range of time and spatial scales will be reported in this presentation showing some progress. Evaluation of components of this suite is being evaluated for cloud/radiation (using SURFRAD, CERES, METAR ceiling) and near-surface (METAR, mesonet, aircraft, rawinsonde).

NOAA Earth System Research Laboratory, together with NCAR, has developed this parameterization suite (turbulent mixing, deep/shallow convection, 9-layer land/snow/vegetation model) to improve PBL biases (temperature and moisture) including better representation of clouds and precipitation. This parameterization suite development has been accompanied by an effort for improved data assimilation of clouds, near-surface observations and radar for the atmosphere-land system.  

The MYNN boundary-layer EDMF scheme (Olson, et al 2019), RUC land-surface model (Smirnova et al. 2016 MWR), Grell-Freitas scheme (2014, Atmos. Chem. Phys.), and aerosol-aware cloud microphysics (Thompson and Eidhammer 2015) have been applied and tested extensively for the NOAA hourly updated 3-km High-Resolution Rapid Refresh (HRRR) and 13-km Rapid Refresh model/assimilation systems over the United States and North America.   This mesoscale but also scale-aware suite is being tested,

How to cite: Benjamin, S., Joseph Olson, J., Sun, S., Georg Grell, G., and Curtis Alexander, C.: Common evaluation/evolution of cloud-radiation processes from 25km S2S to 3km NWP, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22677, https://doi.org/10.5194/egusphere-egu2020-22677, 2020

D2713 |
| solicited
Thomas Haiden

Increases in extra-tropical numerical weather prediction (NWP) skill over the last decades have been well documented. The role of the Arctic, defined here as the area north of 60N, in driving (or slowing) this improvement has however not been systematically assessed. To investigate this question, spatial patterns of changes in medium-range forecast error of ECMWF’s Integrated Forecast System (IFS) are analysed both for deterministic and ensemble forecasts. The robustness of these patterns is evaluated by comparing results for different parameters and levels, and by comparing them with the respective changes in ERA5 forecasts, which are based on a ‘frozen’ model version. In this way the effect of different atmospheric variability on the estimation of skill improvement can be minimized. It is shown to what extent the strength of the polar vortex as measured by the Arctic and North-Atlantic Oscillation (AO, NAO) influences the magnitude of forecast errors. Results may indicate whether recent and future changes in these indices, possibly driven in part by sea-ice decline, could systematically affect the longer-term evolution of medium-range forecast skill.

How to cite: Haiden, T.: The role of arctic forecast errors in the evolution of northern extra-tropical forecast skill, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3606, https://doi.org/10.5194/egusphere-egu2020-3606, 2020

D2714 |
Bing Fu, Yuejian Zhu, Xiaqiong Zhou, and Dingchen Hou

With the successful upgrade of its deterministic model GFS (v15) on June 12, 2019, NCEP has scheduled the implementation of its next Global Ensemble Forecast System (GEFS v12) in the summer of 2020. These two model upgrades on deterministic and ensemble forecast systems are substantially different from previous upgrades. A new dynamical core (FV3) is adopted for the first time in the NCEP operational models, replacing the previous spectral dynamical core. The previous 3-category Zhao-Carr microphysics scheme is also being replaced by a more advanced 6-category GFDL microphysics scheme. From an ensemble model perspective, the previous GEFS has already demonstrated great success in past decades for weather and week-2 prediction by providing reliable probabilistic forecasts. Recently, there has been a large demand for subseasonal prediction, and GEFS v12 forecasts will be extended to 35 days to cover this time range. To better represent large uncertainties associated with this time scale, SPPT (stochastic physics perturbed tendency) and SKEB (stochastic kinetic energy backscatter) stochastic schemes are taking the place of the original STTP (stochastic total tendency perturbation), and a prescribed SST generated from combination of NSST and bias corrected CFS forecasts is also applied to simulate the sub-seasonal variation of SST forcing.    

As a major system upgrade,  a 2.5-year retrospective run of GEFS v12 is carried out to evaluate the model performance. A 30-year reforecast will be provided to stakeholders and the public to calibrate the forecast. The improvement of predictability and prediction skill will be studied through various measurements across tropical to extratropical areas in terms of deterministic (ensemble mean) and probabilistic (ensemble distribution) forecasts. The characteristics of model systematic error will be identified from comparing the major changes of the two state-of-art ensemble systems. As GEFS serves the most crucial model guidance for 5-7 day hurricane forecasts in support of the NHC (National Hurricane Center) and other customers, model capability in predicting tropical cyclone track and intensity is also examined from the retrospective runs. The results show there are significant improvements for tropical cyclone track forecast in North Atlantic and the western North Pacific, in particular, the intensity forecast is improved remarkably in all the basins.

How to cite: Fu, B., Zhu, Y., Zhou, X., and Hou, D.: Evaluation of NCEP next Global Ensemble Forecast System (GEFS v12), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6379, https://doi.org/10.5194/egusphere-egu2020-6379, 2020

D2715 |
Alexandre M. Ramos, Pedro M. Sousa, Emanuel Dutra, and Ricardo M. Trigo

In recent years a strong relationship has been found between Atmospheric Rivers (ARs) and extreme precipitation and floods across western Europe, with some regions having 8 of their top 10 annual maxima precipitation events related to ARs. In the case of the Iberian Peninsula, the association between ARs and extreme precipitation days has also been well documented, particularly for western Iberia river basins.

Since ARs are often associated with high impact weather, it is important to study their medium-range predictability. Here we perform such an assessment using the ECMWF ensemble forecasts up to 15 days, for events that made landfall in western Iberian Peninsula during the winters spanning between 2012/2013 and 2015/16. IVT and precipitation from the 51 ensemble members of the ECMWF Integrated Forecasting System (IFS) ensemble (ENS) were processed over a domain including western Europe and contiguous North Atlantic Ocean.

Metrics concerning the ARs location, intensity and orientation were computed, in order to compare the predictive skill (for different prediction lead times) of IVT and precipitation analyses in the IFS. We considered several regional boxes over Western Iberia, where the presence of ARs is detected in analysis/forecasts, enabling the construction of contingency tables and probabilistic evaluation for further objective verification of forecast accuracy. Our results indicate that the ENS forecasts have skill to detect upcoming ARs events, which can be particularly useful to improve the prediction of associated hydrometeorological extremes. We also characterized how the ENS dispersion and confidence curves change with increasing forecast lead times for each sub-domain. We employed the standard ROC analysis to evaluate the probabilistic component of these predictions showing that for short lead times precipitation forecasts are more accurate than IVT forecasts, while for longer lead times this result is reversed (~10 days). Furthermore, we show that this reversal occurs at shorter lead times in areas where the ARs contribution is more relevant for winter precipitation totals (e.g. northwestern Iberia).



The work done was supported by the project Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (BeSafeSlide) funded by Fundação para a Ciência e a Tecnologia, Portugal (FCT, PTDC/GES-AMB/30052/2017). A.M.R. was also supported by the Scientific Employment Stimulus 2017 from FCT (CEECIND/00027/2017).

How to cite: Ramos, A. M., Sousa, P. M., Dutra, E., and Trigo, R. M.: Predictive skill of atmospheric rivers in the Iberian Peninsula , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7354, https://doi.org/10.5194/egusphere-egu2020-7354, 2020

D2716 |
Marco Rodrigo López López and Adrian Pedrozo-Acuña

Security against extreme rainfall events is a basic need for social and economic development. The climate projections suggest a changing world in the rainfall patterns, forecasting increasingly extreme rainfall and droughts events; nevertheless, there is a lot of uncertainty in the future hydrologic cycle of the basins, where rainfall is the more complicated weather phenomena to predict. To deal with this difficulty, process such as assimilation, a better description of weather phenomena and the use of ensembles have been developed. Such technologic advances have resulted in the use of Numerical Weather Prediction Models (NWP) and its chain with Ensemble Prediction Systems (EPS), which have been recognized as valuable tools for a good Warning System.

Currently, Mexico City is one of the largest metropolis of the world with more than 22 million of inhabitants and serious difficulties oh hydraulic infrastructure. The city depends completely on the sewage system to prevent and mitigate floods. For these reasons, this work proposes to evaluate the deterministic and meteorological ensemble precipitation forecasts issued by the European Centre for Medium Range Weather Forecasting (ECMWF) for two study cases: 1) Mexico Valley Basin and 2) Mexico City. For study case 1, the precipitation forecasts were compared against 24 hours accumulated observed rainfall, issued by CLICOM System (clicom-mex.cicese.mx) and for 2007 to 2014 period time. For study case 2, the forecast were compared against observed real-time precipitation data issued by the Hydrological Observatory of Engineering Institute (OHIIUNAM), using a lead-time and time step of 90 hours and 6 hours respectively; and carried out for the rainy season of years 2017 and 2018. For this, deterministic and probabilistic verification metrics were applied (Relative Operating Characteristic, Reliability Diagram and the Brier Score) in order to measure the quality and performance of the forecasts products and its potential use for floods prediction in Mexico City.

The evaluation of the results shows that the observed events are within the range of the probability distribution, which means that the EPS constitutes a good representation of the possible atmospheric scenarios along the time horizon. Metrics establish a greater reliability for forecast in the range of 2 to 10 mm of accumulated rainfall in 24 hours; in the other hand, there is a good discrimination and accuracy of observed and unobserved events of accumulated precipitation of 1 mm in 6 hours.

How to cite: López López, M. R. and Pedrozo-Acuña, A.: Verification of Probabilistic Precipitation Forecasts in Metropolitan Area of Valley of Mexico Using the ECMWF Ensemble Prediction System, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12046, https://doi.org/10.5194/egusphere-egu2020-12046, 2020

D2717 |
Tobias Necker, Martin Weissmann, Yvonne Ruckstuhl, Juan Ruiz, Takemasa Miyoshi, and Jeffrey Anderson

Current regional forecasting systems particularly aim at the forecast of convective events and related hazards. Most weather centers apply high-resolution ensemble forecasts that resolve convection explicitly but can only afford a limited ensemble size of less than 100 members. Given that the degrees of freedom of atmospheric models are several magnitudes higher implies sampling errors. Sampling errors and fast error growth on convective scales in turn lead to a low predictability. Consequently, improving initial conditions and subsequent forecasts requires a better understanding of error correlations in both space and time.
For this purpose, we conducted the first convective-scale 1000-member ensemble simulation over central Europe. Several 1000-member ensemble forecasts are investigated during a high impact weather period in summer 2016 using ensemble sensitivity analysis. Spatial and spatiotemporal correlations are used to quantify sampling errors on convective scales. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of different localization approaches. Those approaches include a standard distance-based localization technique and a statistical sampling error correction method as proposed by Anderson (2012). Our study highlights advantages and disadvantages of existing methods and emphasises the need of different localization approaches for different scales and variables. Several results are published in Necker et al (2020a) and (2020b).

How to cite: Necker, T., Weissmann, M., Ruckstuhl, Y., Ruiz, J., Miyoshi, T., and Anderson, J.: Sampling errors on convective scales: What can we learn from a 1000-member ensemble?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-273, https://doi.org/10.5194/egusphere-egu2020-273, 2019

D2718 |
Polly Schmederer, Irina Sandu, Thomas Haiden, Anton Beljaars, Martin Leutbecher, and Claudia Becker

ECMWF’s medium-range forecasts of near-surface weather parameters, such as 2 m temperature, humidity and 10 m wind speed, have become more skilful over the years, following the trend of improvements in the forecast skill of upper-air fields. However, they are still affected by systematic errors which have proved difficult to eliminate. Systematic forecast errors in temperature and humidity near the surface can be better understood by also examining errors higher up in the atmospheric boundary layer and in the soil. Meteorological observatories, also known as super-sites, provide long-term observational records of such vertical profiles. ECMWF started to use data from super-sites more systematically to evaluate the quality of forecasts in the lowest part of the atmosphere (up to 100m) and in the soil, in an attempt to disentangle sources of forecast error in near-surface weather parameters. Findings for 2-metre temperature errors in ECMWF forecasts at European super-sites suggest that the errors are partly the result of the model exchanging too much energy between the atmosphere and the land. However, the influence of other factors, such as errors resulting from the representation of vegetation in semi-arid areas and from small-scale variations in vegetation and soil type near measurement stations, mean that it is difficult to adjust the energy exchange in a way which leads to an overall error reduction on the European scale.

How to cite: Schmederer, P., Sandu, I., Haiden, T., Beljaars, A., Leutbecher, M., and Becker, C.: Evaluation of near-surface temperature forecasts against super-site observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15852, https://doi.org/10.5194/egusphere-egu2020-15852, 2020

D2719 |
Valerio Capecchi and Bernardo Gozzini

The main goal of the ECMWF Special Project SPITCAPE is to understand the information content of the current ensemble systems both at global and meso scales in re-forecasting past high-impact weather events. In particular one of the main questions addressed in the project is: what is the added value of running a high-resolution (namely convection-permitting) ensembles for high-impact weather events with respect to global ones?
Running operational Ensemble Prediction Systems (EPS) at the convection-permitting (CP) scale is currently on the agenda at a number of European weather forecasting services and research centres: UK Met Office, Météo France and DWD to mention a few. Moreover, in the framework of the activities of the forthcoming ItaliaMeteo agency, it is foreseen the development of a regional EPS at CP scale for the Italian domain.
Recently, it has been demonstrated that the baseline approach of dynamical downscaling using CP models nested in a global ensemble with a coarser horizontal resolution (e.g. 20 km) provides valuable information. Since the introduction of the IFS model cycle 41r2 in March 2016, the horizontal resolution of the ECMWF ensemble forecasts (ENS) is about 18 km and it is planned to be further increased up to 10 km in the next future
(after the installation of the new supercomputer in the Bologna data center). Thus, these higher-resolution global ENS data allow us to estimate the technical feasibility and value of the simple dynamical downscaling method to initialise limited-area and CP models (the WRF-ARW, MESO-NH and MOLOCH models in the present case) directly nested in the new ECMWF global ensemble.
We applied this pragmatic approach in re-forecasting two high-impact weather events occurred in Italy in recent years (the Cinque Terre flooding occurred in October 2011 and the flash flood of Genoa in November 2011) with the ENS global forecasts and the data produced with the WRF-ARW, MESO-NH and MOLOCH models. The skills of the forecasts in the short-range are evaluated in terms of Probability of Precipitation exceeding predefined rainfall thresholds. In the medium-range we report and discuss the forecast uncertainty (i.e. ensemble spread) of ENS at different starting dates. Besides the fact that both global and regional model data under-estimate rainfall maxima in the area of interest, results demonstrate that CP ensemble forecasts provide better predictions regarding the occurrence of extreme precipitations and the area most likely affected.
The comparison among results obtained with regional models contribute to the debate regarding the reliability of these models and their strengths and weaknesses with respect to: (I) the accuracy of the results for the two events considered, (II) the integration with ECMWF products, (III) the ease of implementation and (IV) the computational costs in view of a potential use for operational forecasting activities.

How to cite: Capecchi, V. and Gozzini, B.: Evaluating current convection-permitting ensembles for past high-impact weather events in Italy: results from the SPITCAPE Special Project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18941, https://doi.org/10.5194/egusphere-egu2020-18941, 2020

D2720 |
Paul Harasti

The Marine Meteorology Division of the U.S. Naval Research Laboratory (NRL) has developed and transitioned a 3DVAR reflectivity data assimilation (DA) system into operations at Fleet Numerical Meteorology and Oceanography Center (FNMOC), located in Monterey, California.  The system assimilates hourly, volumetric, radar reflectivity data into the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®)1 high-resolution NWP model within the ship-following COAMPS – On demand System (COAMPS-OS®)1.  Both Next-Generation Radar (NEXRAD) land-based radar data and U.S. Navy shipboard SPS-48/Hazardous Weather Detection and Display Capability (HWDDC) radar data are assimilated depending on their data coverage provided to the COAMPS® nested grids. The SPS-48/HWDDC units are installed on eighteen U.S. Navy aircraft carriers and amphibious assault ships, and when underway on a mission, the available units automatically transmit compressed, radar data files to FNMOC near the top of the hour.  Through previously reported NRL and FNMOC demonstrations, and more recent operationally testing at FNMOC, the COAMPS-OS® radar DA system’s nowcasting products have demonstrated their ability to provide improved predictions of precipitation events out to at least 6 hour forecasts compared to 3DVAR conventional DA into COAMPS® alone.  Shipboard SPS-48/HWDDC radar data and their assimilation into COAMPS-OS® at FNMOC provide critical environmental awareness in the data sparse oceanic regions of the world that the Navy warfighter encounters.

The SPS-48 radar is a S-band, phased-array, azimuthally scanning, air-search radar that scans electronically in elevation and completes a volume scan in four seconds. The HWDDC combines the volume scans into motion-compensated, one-minute composites with limited clutter filtering applied. The SPS-48 beams are combined to yield full PPI scans at 22 different elevation angles ranging from 0.1° to 24°. The azimuthal resolution of the data is 1° and the range resolution is 1 km. The maximum range for reflectivity (radial velocity) data is 250 (81) km.  The Doppler data are only produced for the lowest three elevation scans whereas reflectivity data are produced for all elevation scans; all these data are archived in Universal Format and compressed before dissemination to FNMOC.   Owing to the limited HWDDC Doppler data both in range and elevation, and the single-polarization of the SPS-48 radar waveform, reflectivity data quality control is particularly challenging.  New algorithms have been developed to handle sea clutter and constant power function artifacts, such as bullseyes and sun strobes.  There are two algorithms for sea clutter; the first one deals with anomolus propagation sea clutter caused by sea-water evaporation into the atmospheric surface layer, and the second one deals with the more widespread and distant sea clutter due to surface-based and elevated electromagnetic ducts resulting from trapped moist air under temperature inversions often encountered off the coasts of California and the Arabian Gulf region.  An overview of the ship-following COAMPS-OS® radar data quality control and assimilation system will be presented along with examples of quality controlled SPS-48/HWDDC radar data and the impact on COAMPS® forecast skill scores.


1 COAMPS and COAMPS-OS are registered trademarks of the U.S. Naval Research Laboratory

How to cite: Harasti, P.: Quality Control of Sea Clutter and Constant Power Function Artifacts for Operational U.S. Navy Shipboard Radar Data Assimilation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11989, https://doi.org/10.5194/egusphere-egu2020-11989, 2020

D2721 |
Minyou Kim, Keunhee Lee, and Yong Hee Lee

To be well prepared for rapidly-developing meteorological hazards in advance, quick and qualified information on real-time and very-short range (within 6 hours) forecasts is required. KLAPS (Korea Local Analysis and Prediction System) was developed for the operational very-short range forecasts in KMA (Korea Meteorological Administration), based on the LAPS (Local Analysis and Prediction System) from NOAA and WRF from NCAR in 2009. Recently, KLAPS is updated to use new observation datasets and physics schemes from KIM (Korea Integrated Model) to improve its very-short range precipitation forecast skills. New observation data sources (geostationary satellite, RADAR, ground-based GNSS(Global Navigation Satellite System), ceilometer, local radiosonde, etc.) are ingested into KLAPS in real-time to resolve rapidly developing mesoscale systems. Physics schemes (WDM7, KSAS(Kiaps SAS), RRTMG, Shing-Hong PBL, etc.) based on KIM physics package are implemented in KLAPS to support the high-resolution physics. The new KLAPS is now operated in 10-minute interval, so that it could provide 10-minute interval precipitation forecasts to the public(www.weather.go.kr) every 10 minutes. The advantages of 10-minute interval analysis and forecast system will be presented.

How to cite: Kim, M., Lee, K., and Lee, Y. H.: Development of 10-minute interval analysis and prediction system in KMA, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6451, https://doi.org/10.5194/egusphere-egu2020-6451, 2020

D2722 |
Palina Zaiko, Siarhei Barodka, and Aliaksandr Krasouski

Heavy precipitation forecast remains one of the biggest problems in numerical weather prediction. Modern remote sensing systems allow tracking of rapidly developing convective processes and provide additional data for numerical weather models practically in real time. Assimilation of Doppler weather radar data also allows to specify the position and intensity of convective processes in atmospheric numerical models.

The primary objective of this study is to evaluate the impact of Doppler  radar reflectivity and velocity assimilation in the WRF-ARW mesoscale model for the territory of Belarus in different seasons of the year. Specifically, we focus on the short-range numerical forecasting of mesoscale convective systems passage over the territory of Belarus in 2017-2019 with assimilated radar data.

Proceeding with weather radar observations available for our cases, we first perform the necessary processing of the raw radar data to eliminate noise, reflections and other kinds of clutter. For identification of non-meteorological noise fuzzy echo classification was used. Then we use the WRF-DA (3D-Var) system to assimilate the processed radar observations from 3 Belarusian Doppler weather radar in the WRF model. Assimilating both radar reflectivity and radial velocity data in the model we aim to better represent not only the distribution of clouds and their moisture content, but also the detailed dynamical aspects of convective circulation. Finally, we analyze WRF modelling output obtained with assimilated radar data and compare it with available meteorological observations and with other model runs (including control runs with no data assimilation or with assimilation of conventional weather stations data only), paying special attention to the accuracy of precipitation forecast 12 hours in advance.

How to cite: Zaiko, P., Barodka, S., and Krasouski, A.: Impact of Doppler radar reflectivity and velocity data assimilation on the quality of precipitation forecasting in Belarus in different seasons, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-779, https://doi.org/10.5194/egusphere-egu2020-779, 2019

D2723 |
Ting-Chi Wu, Milija Zupanski, Lewis Grasso, James Fluke, Heather Cronk, Anton Kliewer, Richard Schulte, Wesley Berg, Christian Kummerow, Philip Partain, and Steven Miller

Unlike large, expensive, and high-risk operational satellites, small/cube satellites (SmallSats) are a small, inexpensive, and a low-risk type of satellite. As a NOAA Cooperative Institute with specialties in satellite data processing and data assimilation, CIRA is funded by a Technology Maturation Program (TMP) research project to help NOAA exploit upcoming constellation of SmallSats to be considered for use in operations. In this research, a CSU-led technology demonstration mission entitled “the Temporal Experiment for Storms and Tropical System - Demonstration (TEMPTEST-D)” is used as an example to explore quick and agile methodologies to entrain SmallSats into the NOAA processing stream. Specifically, a workflow that enables TEMPEST-D data for assimilation into the NCEP Global Forecast System (GFS) with Finite-Volume Cube-Sphered (FV3) dycore (FV3GFS) under the Gridpoint Statistical Interpolation (GSI) based hybrid 4DEnVar system is established.

One objective of this TMP research project is to assess the impact of SmallSat data on NOAA modeling and assimilation systems used in operations. We begin by asking whether the use of TEMPEST-D data is as good as the use of those obtained from well-established operational satellite sensors. Since the radiometric specification of TEMPEST-D is similar to the Microwave Humidity Sounder (MHS), we address the above question by directly comparing the following three cycled FV3GFS data assimilation and forecasting experiments: 1) the control experiment, which includes all routinely assimilated observations, but only assimilates MHS from the NOAA-19 and MetOp-B platforms, 2) the AddMHS experiment, which is the control plus MHS from the MetOp-A platform, and 3) the AddTEMPEST experiment, which is the control plus TEMPEST-D.

By differentiating the AddMHS and AddTEMPEST experiments against the control experiment, we will be able to demonstrate that a cost-effective TEMPEST-D is as beneficial as a well-established operational satellite like MHS, in terms of aiding operational global weather forecasting. In addition, results from this research offers implications of the utility of a constellation of SmallSats microwave radiometers for global weather forecasting.  

How to cite: Wu, T.-C., Zupanski, M., Grasso, L., Fluke, J., Cronk, H., Kliewer, A., Schulte, R., Berg, W., Kummerow, C., Partain, P., and Miller, S.: Assessments of Assimilation of TEMPEST-D into the NCEP Global Forecast System, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1949, https://doi.org/10.5194/egusphere-egu2020-1949, 2020

D2724 |
Yudong Gao

High frequency (20 Hz) aircraft observations from the Government Flying Service of the Hong Kong Government, penetrating a tropical cyclone (TC) at low altitude over the South China Sea had an extremely inhomogeneous distribution. Today the remoting observations have been widely used, but our work demonstrates aircraft observations still play an important role in TCs forecasts.

To investigate an effective scheme for assimilating inhomogeneous aircraft observations, a multigrid 3D variation (m3DVAR) system, with the assistance of a bogus vortex, was employed. Track and intensity forecasts were improved by assimilating aircraft observations and bogus data. The assimilation of pressure (horizontal wind) was also found mainly to contribute to the large magnitude (sophisticated distribution) of increments.

These aircraft observations were also thinned by arithmetic means over different time intervals to identify structures of tropical cyclone at different scales. It is found that the thinning process can reduce serial correlation in observational errors and enhance the representation of aircraft observations. The changes in dynamic structures indicate that the imbalance generated from assimilating aircraft observations at the sub-grid scale can be alleviated by using longer time intervals of the arithmetic mean. Assimilating aircraft observations at the grid scale achieves optimal forecasts based on verifications against independent observations and investigations of environmental and ventilation flows.

In fact, the west Pacific had access to aircraft observations but these observations stopped in 1987. We hope we can call attentions of governors and scientists to reboot in situ observations on aircraft platform in the west Pacific by disseminating our results. This can be a significant benefit to improving the regional real-time forecasts and understanding the climate variabilities of TCs. We already had two publications related to the assimilation of aircraft observations (Gao et al., 2019; Gao et al., 2019).



Gao, Y, Xiao, H, Chan, PW, Hon, Kai kwong, Wan, Q, Ding, W. Application of the multigrid 3D variation method to a combination of aircraft observations and bogus data for Typhoon Nida (2016). Meteorol Appl. 2019; 26: 312– 323. https://doi.org/10.1002/met.1764.


Gao, Y.; Xiao, H.; Jiang, D.; Wan, Q.; Chan, P.W.; Hon, K.K.; Deng, G. Impacts of Thinning Aircraft Observations on Data Assimilation and Its Prediction during Typhoon Nida (2016). Atmosphere 2019, 10, 754. https://doi.org/10.3390/atmos10120754.

How to cite: Gao, Y.: Assimilation of aircraft observations in the South China Sea to improve forecasts of tropical cyclones, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2370, https://doi.org/10.5194/egusphere-egu2020-2370, 2020

D2725 |
Yang Yang, Dan Chen, and Xiujuan Zhao

To facilitate the future inclusion of aerosol-radiation interactions in the regional operational Numerical Weather Prediction (NWP) system – RMAPS-ST (adapted from Weather Research and Forecasting, WRF) at Institute of Urban Meteorology (IUM), China Meteorological Administration (CMA), the impacts of aerosol-radiation interactions on the forecast of surface radiation and meteorological parameters during a heavy pollution event (December 6th -10th, 2015) over northern China were investigated. The aerosol information was simulated by RMAPS-Chem (adapted from WRF model coupled with Chemistry, WRF-Chem) and then offline-coupled into Rapid Radiative Transfer Model for General Circulation Models (RRTMG) radiation scheme of WRF to enable the aerosol-radiation feedback in the forecast. To ensure the accuracy of high-frequent (hourly) updated aerosol optical depth (AOD) field, the temporal variations of simulated AOD at 550nm were evaluated against satellite and in-situ observation, which showed great consistency. Further comparison of PM2.5 with in-situ observation showed WRF-Chem reasonably captured the PM2.5 field in terms of spatial distribution and magnitude, with the correlation coefficients of 0.85, 0.89 and 0.76 at Beijing, Shijiazhuang and Tianjin, respectively. Forecasts with/without the hourly aerosol information were conducted further, and the differences of surface radiation, energy budget, and meteorological parameters were evaluated against surface and sounding observations. The offline-coupling simulation (with aerosol-radiation interaction active) showed a remarkable decrease of downward shortwave (SW) radiation reaching surface, thus helps to reduce the overestimated SW radiation during daytime. The simulated surface radiation budget has also been improved, with the biases of net surface radiation decreased by 85.3%, 50.0%, 35.4%, and 44.1% during daytime at Beijing, Tianjin, Taiyuan and Jinan respectively, accompanied by the reduction of sensible (16.1 W m−2, 18.5%) and latent (6.8 W m−2, 13.4%) heat fluxes emitted by the surface at noon-time. In addition, the cooling of 2-m temperature (~0.40 °C) and the decrease of horizontal wind speed near surface (~0.08 m s-1) caused by aerosol-radiation interaction over northern China helped to reduce the bias by ~73.9% and ~7.8% respectively, particularly during daytime. Further comparison indicated that the simulation implemented AOD could better capture the vertical structure of atmospheric wind. Accompanied with the lower planetary boundary layer and the increased atmospheric stability, both U and V wind at 850hPa showed the convergence which were unfavorable for pollutants dispersion. Since RMPAS-ST provides meteorological initial condition for RMPS-Chem, the changes of meteorology introduced by aerosol-radiation interaction would routinely impact the simulations of pollutants. These results demonstrated the profound influence of aerosol-radiation interactions on the improvement of predictive accuracy and the potential prospects to offline couple near-real-time aerosol information in regional RMAPS-ST NWP in northern China.

How to cite: Yang, Y., Chen, D., and Zhao, X.: Impacts of aerosol-radiation interaction on meteorological forecast over northern China by offline coupling the WRF-Chem simulated AOD into WRF: a case study during a heavy pollution event, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2354, https://doi.org/10.5194/egusphere-egu2020-2354, 2020

D2726 |
I-Han Chen, Jing-Shan Hong, Ya-Ting Tsai, and Chin-Tzu Fong

Taiwan, a subtropical island with steep mountains, is influenced by diverse weather systems, including typhoons, monsoons, frontal, and convective systems. Of these, the prediction of deep, moist convection here is particularly challenging due to complex topography and apparent landsea contrast. This study explored the benefits of assimilating surface observations on prediction of afternoon thunderstorms using a 2-km resolution WRF and WRFDA model system with rapid update cycles. Consecutive afternoon thunderstorm events during 30 June to 08 July 2017 are selected. Five experiments, consisting of 240 continuous cycles are designed to evaluate the data assimilation strategy and observation impact. Statistical results show that assimilating surface observations systematically improves the accuracy of wind and temperature prediction near the surface. Also, assimilating surface observations alone in one-hour intervals improves model quantitative precipitation forecast (QPF) skill, extending the forecast lead time in the morning. Furthermore, radar data assimilation can benefit by the additional assimilation of surface observations, particularly for improving the model QPF skill for large rainfall thresholds. An afternoon thunderstorm event that occurred on 06 July 2017 is further examined. By assimilating surface and radar observations, the model is able to capture the timing and location of the convection. Consequently, the accuracy of the predicted cold pool and outflow boundary is improved, when compared to the surface observations.

How to cite: Chen, I.-H., Hong, J.-S., Tsai, Y.-T., and Fong, C.-T.: Improving Operational Numerical Prediction of Afternoon Thunderstorms over Taiwan through Surface Data Assimilation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7244, https://doi.org/10.5194/egusphere-egu2020-7244, 2020

D2727 |
Rafaella - Eleni Sotiropoulou, Ioannis Stergiou, and Efthimios Tagaris

Optimizing the performance of numerical weather prediction models is a very complicated process due to the numerous parameterization choices provided to the user. In addition, improving the predictability of one model’s variable (e.g., temperature) does not necessarily imply the improvement of another (e.g., precipitation). In this work the Technique of Preference by Similarity to the Ideal Solution (TOPSIS) is suggested as a method to optimize the performance of a numerical weather prediction model. TOPSIS provides the ability of using multiple statistical measures as ranking criteria for multiple forecasting variables. The Weather Research and Forecasting model (WRF) is used here for application of TOPSIS in order to optimize the model’s performance by the combined assessment of temperature and precipitation over Europe. Six ensembles optimize model’s physics performance (i.e., microphysics, planetary boundary layer, cumulus scheme, Long–and Short– wave and Land Surface schemes). The best performing option for each ensemble is selected by using multiple statistical criteria as input for the TOPSIS method, based on the integration of entropy weights. The method adopted here illustrates the importance of an integrated evaluation of weather prediction models’ performance and suggests a pathway for its improvement.

Acknowledgments LIFE CLIMATREE project “A novel approach for accounting & monitoring carbon sequestration of tree crops and their potential as carbon sink areas” (LIFE14 CCM/GR/000635).

How to cite: Sotiropoulou, R.-E., Stergiou, I., and Tagaris, E.: A Methodology for Optimizing Numerical Weather Prediction Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4149, https://doi.org/10.5194/egusphere-egu2020-4149, 2020

D2728 |
Lanqian Li and Aimei Shao

Low-level wind shear could occur not only in rainy weather conditions but also in non-rainy weather conditions, which is dangerous to aircraft safety for its rapid changes in wind direction or velocity. Recently, dry wind shear occurred in non-rainy condition has drawn more and more attention. Rain-detecting Doppler radar has no capabilities in detecting dry wind shear occurred in non-rainy condition, while Doppler Lidar observations with higher spatial and temporal resolution provide valuable information for dry wind shear. For this, considering dry wind shear cases reported by pilots at Lanzhou Zhongchuan International Airport as study object, lidar observations (radial velocities) were assimilated along with surface data to improve the prediction skill of dry wind shear events.

All experiments were conducted with Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-VAR) system. Three-nested domains were employed with 1-km horizontal resolution in the innermost domain. The model was derived by the NCEP FNL data. Lidar data was processed and only assimilated in the innermost domain. Experimental results show that the low-level wind shear can not be found in the experimental results without lidar data assimilation, while lidar data assimilation experiment successfully represented wind shear small-scale characteristics and simulated radial wind pattern was close to lidar observation. In addition, assimilation cycles with short time intervals effectively improved simulation accuracy of wind shear events.

How to cite: Li, L. and Shao, A.: Impact of Lidar Data Assimilation on Analysis and Prediction of Low-level Wind Shears at Lanzhou Zhongchuan International Airport, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5030, https://doi.org/10.5194/egusphere-egu2020-5030, 2020

D2729 |
Lei Zhang and Baode Chen

Lacking of high-resolution observations over oceans is one of the major problems for the numerical simulation of the tropical cyclones (TC), especially for the tropical cyclone inner-core structure’s simulation. Satellite observations plays an important role in improving the forecast skills of numerical weather prediction (NWP) systems. Many studies have suggested that the assimilation of satellite radiance data can substantially improve the numerical weather forecast skills for global model. However, the performance of satellite radiance data assimilation in limited-area modeling systems is still controversial.

This study attempts to investigate the impact of assimilation of the Advanced Technology Microwave Sounder (ATMS) satellite radiances data and its role to improve the model initial condition and forecast of typhoon LEKIMA(2019) using a regional mesoscale model. In this study, detailed analysis of the data impact will be presented, also the results from different data assimilation methods and different data usage schemes will be discussed.

How to cite: Zhang, L. and Chen, B.: Improving numerical simulation of typhoon LEKIMA(2019) through assimilating ATMS radiance data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6256, https://doi.org/10.5194/egusphere-egu2020-6256, 2020

Chat time: Friday, 8 May 2020, 10:45–12:30

D2730 |
Der-Song Chen, Ling-Feng Hsiao, Jia-Hong Xie, Jing-Shan Hong, Chin-Tzu Fong, and Tien-Chiang Yeh

With violent wind and severe rainfall, the tropical cyclone is one of the most disastrous weather systems over ocean and the coastal area. To provide accurate tropical cyclone (TC) track and intensity forecasts is one of the most important tasks of the national weather service of countries affected. Taiwan is one of the areas frequently influenced by tropical cyclones. Improving the tropical cyclone forecast is the highest priority task of Taiwan’s Central Weather Bureau (CWB).

Recent improvement of the TC forecast is due to the improvement of the numerical weather prediction. A version of the Advanced Research Weather Research and Forecasting Model (WRF), named TWRF (Typhoon WRF), was developed and implemented in CWB for operational TC forecasting from 2011. During the years, partial update cycling, cyclone bogus scheme, relocation scheme, 3DVAR with outer loop, analysis blending scheme, new trigger Kain–Fritsch cumulus scheme, and so on have been studied and applied in TWRF (Hsiao et al. 2010, 2012, 2015) to improve the model. We also improved the model by changing the TWRF configuration from a triple nested to a double nested grid and increasing the model resolution from 45/15/5 km 45-levels to 15/3 km 52-levels from 2016. Results showed increasing the model resolution improving the track, intensity and rainfall forecast. However, The averaged 24/48/72 hours TC track forecast errors of TWRF are 91/147/223, 84/133/197, 74/127/215, 64/122/202, 70/120/194 and 70/122/180 km in year 2014, 2015, 2016, 2017, 2018 and 2019 respectively.

In this study, WRF Four-dimensional data assimilation (FDDA) is adopted to assimilate the temperature, pressure, water vapor content which processed from the FORMOSAT-7 constellation, high-temporal frequency atmospheric motion vector (AMV) retrieved from Himawari-8 satellite images and radar data to generate a model balanced TC structure and thermodynamic state at the initial time. The specific goal is to improve the track, structure and intensity prediction of TCs and their associated rainfall distribution in Taiwan. The detail will be presented in the conference.

Keywords: tropical cyclone, Himawari-8 AMV, Four-dimensional data assimilation, FORMOSAT-7, radar data.

Corresponding author address:

Der-Song Chen,  song@cwb.gov.tw

Central Weather Bureau, 64 Gongyuan Rd., Taipei, Taiwan, R.O.C., 10048.

How to cite: Chen, D.-S., Hsiao, L.-F., Xie, J.-H., Hong, J.-S., Fong, C.-T., and Yeh, T.-C.: Improve Tropical Cyclone Prediction of TWRF with the Application of Advanced Observation Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4327, https://doi.org/10.5194/egusphere-egu2020-4327, 2020

D2731 |
Hongqin Zhang and Xiangjun Tian

The system of multigrid NLS-4DVar data assimilation for Numerical Weather Prediction (SNAP) is established, building upon the multigrid NLS-4DVar assimilation scheme, the operational Gridpoint Statistical Interpolation (GSI)-based data-processing and observation operator and widely used numerical forecast model WRF (easily replaced by others global/regional model). The multigrid assimilation framework can adequately correct errors from large to small scales to achieve higher assimilation accuracy. Meanwhile, the multigrid strategy can accelerate iteration solution improving the computational efficiency. NLS-4DVar, as an advanced 4DEnVar method, employs the Gauss-Newton iterative method to handle the nonlinear of the 4DVar cost function and provides the flow-dependent background error covariance, which both contribute to the assimilation accuracy. The efficient local correlation matrix decomposition approach and its application in the fast localization scheme of NLS-4DVar and obviating the need of the tangent linear and adjoint model further improve the computational efficiency. The numerical forecast model of SNAP is any optional global/regional model, which makes the application of SNAP very flexible. The analysis variables of SNAP are rather the model state variables than the control variables adopted in the usual 4DVar system. The data-processing and observation operator modules are used from the National Centers for Environmental Prediction (NCEP) operational GSI analysis system, prominent in the various observation operators and the ability to assimilate multi-source observations. Currently, we have achieved the assimilation of conventional observations and we will continue to improve the assimilation of radar and satellite observations in the future. The performance of SNAP was investigated assimilating conventional observations used for the generation of the operational global atmospheric reanalysis product (CRA-40) by the National Meteorological Information Center of China Meteorological Administration. Cyclic assimilation experiments with two windows, which is 6-h for each window, are designed. The results of numerical experiments show that SNAP can absorb observations, improve initial field, and then improve precipitation forecast.

How to cite: Zhang, H. and Tian, X.: System of Multigrid NLS-4DVar Data Assimilation for Numerical Weather Prediction (SNAP):System Formulation and Preliminary Evaluation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3812, https://doi.org/10.5194/egusphere-egu2020-3812, 2020

D2732 |
Rosa Claudia Torcasio, Stefano Federico, Silvia Puca, Marco Petracca, Gianfranco Vulpiani, Luca Baldini, and Stefano Dietrich

The forecast of severe events at the local scale still remains challenging because of the multitude of physical processes involved on a wide range of scales. Improving the initial conditions (IC) of numerical weather prediction (NWP) models is a key point for good forecasting. Since limited-area models are nowadays operational at the kilometric scale (< 5 km), the assimilation of data from high-resolution space-time observations is crucial to correctly represent the state of the atmosphere at local scale.

Radar and lightning data are both useful to improve the IC of NWP models for several reasons. Radar data is available with a high spatio-temporal resolution and provides information on hydrometeors and wind, while lightning data locates convection both spatially and temporally accurate.

Recently, Federico et al. (2019) studied the impact of radar reflectivity factor and lightning data assimilation on the Very Short-Term Forecast (VSF) of the RAMS@ISAC NWP model for two intense precipitation events over Italy. They found that, despite an improvement of the rainfall VSF due to the assimilation of lightning and radar reflectivity factor data, the usefulness of the procedure is partially limited by the increase in false alarms, especially in case of high precipitation rates (> 50 mm/3h).

In this work, we apply the methodology proposed by Federico et al. (2019) to an intense precipitation event occurred in Italy in November 2019. The RAMS@ISAC meteorological model is used here, with a horizontal resolution of 3km.

RAMS@ISAC is initialized by a 3D-Var data assimilation scheme that uses both lightning and radar reflectivity factor data. Different 3D-Var data assimilation scheme settings are used to produce different ICs for the RAMS@ISAC model for the specific case.  The sensitivity of the precipitation field prediction to changes in these ICs will be discussed.

Keywords: lightning data assimilation, radar reflectivity factor data assimilation, very short-term forecast, numerical weather prediction


Federico, S., Torcasio, R. C., Avolio, E., Caumont, O., Montopoli, M., Baldini, L., Vulpiani, G., and Dietrich, S.: The impact of lightning and radar reflectivity factor data assimilation on the very short-term rainfall forecasts of RAMS@ISAC: application to two case studies in Italy, Nat. Hazards Earth Syst. Sci., 19, 1839–1864, https://doi.org/10.5194/nhess-19-1839-2019, 2019.

How to cite: Torcasio, R. C., Federico, S., Puca, S., Petracca, M., Vulpiani, G., Baldini, L., and Dietrich, S.: Radar and lightning data assimilation: the impact of different setting options discussed for a heavy precipitation event occurred in Italy., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7629, https://doi.org/10.5194/egusphere-egu2020-7629, 2020

D2733 |
Ling-Feng Hsiao and Feng-Ju Wang

The global numerical weather prediction (NWP) system based on the FV3GFS model jointly developed by U.S. National Centers for Environmental Prediction (NCEP) and Geophysical Fluid Dynamics Laboratory (GFDL) has been implemented in the Taiwan’s Central Weather Bureau (CWB) forecast system for the next generation global NWP operations. Currently, NCEP FV3GFS model provides land use dataset retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. The MODIS 20-category data is composed of roughly 12 km resolution data elements. However, the modified of the 20-class MODIS land cover dataset with a resolution of 500 m which defined by the International Geosphere-Biosphere Program (IGBP) is provided by WRF model. A significant difference between these two datasets is MODIS data from NCEP FV3GFS as being extremely urbanized in western Taiwan. In a case of weaker synoptic-scale forcing, the modified MODIS land cover dataset from WRF model result in a larger improvement in 2-m temperature and 2-m mixing ratio when compare to the surface observations over Taiwan. The reason results from the overestimation of urban area in NCEP FV3GFS model, which contains previous and low-resolution MODIS dataset. Moreover, there is a bias reduction in 10-m wind speed as well as thermal effects. The detailed results will be presented in the conference.

How to cite: Hsiao, L.-F. and Wang, F.-J.: Impact of MODIS land cover data on surface predictions over Taiwan in the FV3GFS model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6752, https://doi.org/10.5194/egusphere-egu2020-6752, 2020

D2734 |
Nedjeljka Žagar and Istvan Szunyogh

In this talk we use the data from an operational ensemble prediction system to investigate recent developments in practical predictability across scales. Furthermore, we separate the estimated forecast error data into components representing the two dominant regimes in the atmosphere, the Rossby and inertia-gravity regimes. The latter is used to discuss aspects of tropical predictability.

We define the practical predictability limit of a meteorological field (e.g., meridional wind at 500 hPa) or of a variability mode (e.g., the equatorial Kelvin wave) by the forecast time at which the root mean square (rms) forecast error normalized by its saturation value reaches a prescribed threshold value (e.g., 60%).

The investigative technique fits a parametric function to the curve that describes the growth of the rms error of the forecasts with forecast time for a sample of forecasts. The parametric model describes the functional dependence of the magnitude of the forecast error on the magnitude of the initial error. Thus, it can be used for the estimation of the forecast error reduction that can be achieved by reducing the magnitude of the analysis error by a presumed percentage. Likewise, it can be used for the quantitative attribution of the forecast improvements between the years to analysis or model improvements.

The calculations are carried out for the different spatial scales and the two regimes separately.

How to cite: Žagar, N. and Szunyogh, I.: Recent and potential future evolution of practical predictability across scales , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12466, https://doi.org/10.5194/egusphere-egu2020-12466, 2020

D2735 |
Haraldur Ólafsson

The accuracy of a large set of high-resolution wind speed forecasts with different lead times is assessed for different parts of orographic flows, including upstream blockings, gap winds, corner winds, wakes and downslope winds.  The by far largest errors are in areas of downslope windstorms, but there are also considerable errors in the other parts of orographic disturbances to the flow and they are greater than in non-orographic flows in the same region.  The errors are discussed in view of the different dynamics and kinematics of the flows.  They are partly related to intermittency of i.e. gravity waves as well as strong spatial gradients in the wind field.

How to cite: Ólafsson, H.: Uncertainties in forecasting orographic flows, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11997, https://doi.org/10.5194/egusphere-egu2020-11997, 2020

D2736 |
Iman Rousta and Haraldur Ólafsson

The Normalized Difference Vegetation Index (NDVI) has been retrieved and analyzed for Iceland.  There are only limited trends in the total integrated NDVI in the period 2001 - 2018.  However, there is a positive trend in recent decades in the occurrence of signal corresponding to woodland and forests.   Locally, there may however be great changes; some small deserts have turned green and systematic planting of trees in certain regions is well detectable. The impact, and the driver of these changes are discussed in the context of climate and the implication for thermally driven weather systems and local weather forecasting is explained. 

How to cite: Rousta, I. and Ólafsson, H.: Changes in land surface and weather forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22110, https://doi.org/10.5194/egusphere-egu2020-22110, 2020

D2737 |
Chiara Marsigli

The COSMO-D2-EPS ensemble is running operationally at DWD at a resolution of 2.2 km. In the framework of the transition from the COSMO to the ICON model for the limited-area applications, the ICON-D2-EPS ensemble is starting its pre-operational phase. Therefore, the perturbation strategy developed for COSMO-D2-EPS is adapted to the new ensemble.
In this work, the focus is on the initial conditions, which are provided by the first 20 analyses generated by a LETKF ensemble data assimilation system (KENDA).
The KENDA analyses present the advantage of providing perturbed initial conditions to the convection-permitting ensemble, where the perturbations contain also the information on the convection-permitting scale uncertainties. On the other hand, the KENDA analyses are optimised for the purpose of data assimilation. The ensemble of analyses which is the most suitable for initialising the next data assimilation cycle may not be the same which is the most suitable for initialising the weather forecast ensemble, e.g. in terms of spread.

The analyses generated by the KENDA cycle are evaluated from the point of view of their usage for ensemble forecasting initialisation. Their spread is computed for different variables, assessing also how it varies with the spatial scale and with the weather situation. Furthermore, the spread is compared to the error of the analyses and of the forecasts, in order to assess the ability of the analyses to describe the initial condition uncertainty. 
The growth of the differences between the members during the first hours of the forecasts is studied as well, in dependence on the weather situation.

The final aim of this work is to identify possible improvements for deriving the ensemble initial conditions from the KENDA analyses.

How to cite: Marsigli, C.: On the initial conditions of the ICON-D2-EPS ensemble: An analysis in terms of spread and skill., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10803, https://doi.org/10.5194/egusphere-egu2020-10803, 2020

D2738 |
Pirkka Ollinaho
Probabilistic forecasts provide information on how predictions of the atmospheric evolution may differ from the best guess solution provided by a deterministic forecast. Ensemble prediction systems generate this information through assessing uncertainties in both the model initial state and the model itself. In order to open up ensemble prediction research for a wider research community, we have recreated all 50+1 operational ECMWF ensemble initial states for OpenIFS. The data set covers one year (December 2016 to November 2017) twice a day. A range of model resolutions are provided to cover different research needs (TL159, TL399 and TL639). The probabilistic skill of OpenIFS ensembles using these initial states is showcased. A case study of typhoon Damrey, which severely affected Vietnam in 2017, will also be presented.

How to cite: Ollinaho, P.: Ensemble prediction with OpenIFS, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18197, https://doi.org/10.5194/egusphere-egu2020-18197, 2020

D2739 |
Jose Luis Sanchez, Pablo Melcon, Guillermo Merida, Andres Merino, Eduardo Garcia-Ortega, Jose Luis Marcos, Laura Lopez, Laura Sanchez-Muñoz, Francisco Valero, Javier Fernandez, Pedro Bolgiani, Maria Luisa Martin, Sergio Fernandez-Gonzalez, and Andres Navarro

Icing occurs when an unheated solid structure is exposed to liquid cloud droplets at temperatures below the freezing point. Supercooled liquid water (SLW) in the atmosphere can persist in a physically metastable state until coming into contact with a solid object “In-cloud icing” occurs when super cooled liquid droplets (SLD) like clouds collide with a structure or object and freezes.

Atmospheric icing prediction has gain attention in the last years. Despite the progress made in meteorology, both weather forecasting modelling and atmospheric observations through advanced experimental technologies, there are still limitations in the accurate forecast and detection of icing conditions. The GFA‐ULE group has carried out some NWPs. In a previous work, we investigated the capability of the Weather Research and Forecasting model to detect regions containing supercooled cloud drops, proposing a multiphysics ensemble approach. Four microphysics and two planetary boundary layer schemes were used. Morrison and Goddard parameterizations with the YSU scheme, yielded superior results in evaluating the presence of liquid water content.

Concerning the remote detection of icing conditions, some European research centres (i.e. DLR, CIRA, ONERA, INCAS) as well as University of Leon (GFA-ULE) already have nowcasting or forecasting activities for detection of clouds and icing conditions. In this work a multichannel, microwave radiometer (MMWR) was used to detect the appearance of SLW. Consequently, we present both comparison between indirect detection of SLW and the output obtained by WRF with the two combination of parametrizations selected.

In our work we have taken into account:

  1. The comparison has been made at different levels, from the ground up to 5000 meters high
  2. We have taken different thresholds of the SLW: 0.05, 0.1, 0.15, 0.20, 0.25 and 0.30 g m-3 because of the flight campaigns carried out previously, which revealed that the presence of low concentrations of SLW could lead to the appearance of aircraft icing.

The results show a good concordance between the number of events found by the MMWR and the result of the two numerical modeling performed. Therefore, everything seems to indicate that indirect detection by MMWR can be an accurate technology to detect the appearance of SLW and that the models can be qualitatively validated.

Acknowledgments: Data support came from the Atmospheric Physics Group, IMA, University of León, Spain, and the National Institute of  Aerospace Technology (INTA). This research was carried out in the framework of the SAFEFLIGHT project, financed by MINECO (CGL2016‐78702) and LE240P18 project (Junta de Castilla y León). We also thank R. Weigand for computer support.

How to cite: Sanchez, J. L., Melcon, P., Merida, G., Merino, A., Garcia-Ortega, E., Marcos, J. L., Lopez, L., Sanchez-Muñoz, L., Valero, F., Fernandez, J., Bolgiani, P., Martin, M. L., Fernandez-Gonzalez, S., and Navarro, A.: Evaluating the ability of a microwave radiometer and wrf to detect and simulate in-cloud icing conditions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14316, https://doi.org/10.5194/egusphere-egu2020-14316, 2020

D2740 |
Namgu Yeo, Eun-Chul Chang, and Ki-Hong Min

In this study, Korea Rapid Developing Thunderstorms (K-RDT) product from geostationary meteorological satellite which represents developing stage of convective cells is nudged to the Simplified Arakawa Schubert (SAS) deep convection scheme using a simple nudging technique in order to improve prediction skill of a heavy rainfall caused by mesoscale convective system over South Korea in the short-term forecast. Impact of the K-RDT information is investigated on the Global/Regional Integrated Model system (GRIMs) regional model program (RMP) system. For the selected heavy rainfall cases, the control run without nudging and two nudging experiments with different nudging period are performed. Although the simulated precipitations in the nudging experiments tend to depend on the distribution of convective cells detected in the K-RDT algorithm, the nudging experiment shows improved precipitation forecast than the control experiment. Particularly, the experiment with nudging for longer time produces better prediction skill. The results present that the small-scale convective cells from the K-RDT which are detected with a 1-km resolution have clear impacts to large-scale atmospheric fields. Therefore, it is suggested that utilizing small-scale information of convective system in the numerical weather prediction can have critical impact to improve forecast skill when the model system, which cannot properly represent sub-grid scale convections.

How to cite: Yeo, N., Chang, E.-C., and Min, K.-H.: Impact of the Korea Rapid Developing Thunderstorms (K-RDT) product nudging to the convective parameterization over the Korean Peninsula, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20986, https://doi.org/10.5194/egusphere-egu2020-20986, 2020

D2741 |
Efthimios Tagaris, Ioannis Stergiou, and Rafaella-Eleni Sotiropoulou

The Weather Research and Forecasting (WRF) model dynamically downscales NCEP FNL Operational Global Analysis data in order to assess the grid size resolution effect on the simulated variables. Simulations where conducted over Europe for the year 2015 using 36km, 12km and 4km grid size resolutions. The sensitivity analysis assesses the grid size resolution effect on the simulated mean, maximum and minimum daily temperatures as well as precipitation. The simulated data are evaluated using reanalysis dataset. The statistical variables used are the bias, mean absolute error, root mean square error and the index of agreement for each grid cell. Results show that model performance for mean and maximum temperature, is better when increasing the spatial resolution from 36Km to 12Km but no significant change is found when the spatial resolution is further increased to 4Km, in general. In addition, model performance for minimum temperatures and precipitation does not change significantly when moving to higher spatial resolution grids (i.e., 12Km and 4Km) compared to the 36Km domain,

Acknowledgments LIFE CLIMATREE project “A novel approach for accounting & monitoring carbon sequestration of tree crops and their potential as carbon sink areas” (LIFE14 CCM/GR/000635).

How to cite: Tagaris, E., Stergiou, I., and Sotiropoulou, R.-E.: WRF forecast sensitivity to spatial resolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4151, https://doi.org/10.5194/egusphere-egu2020-4151, 2020

D2742 |
mengjuan liu and Xu Zhang

A new scale-adaptive three-dimensional (3D) turbulent kinetic energy (TKE) subgrid mixing scheme is developed using the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) to address the gray-zone problem in the parameterization of subgrid turbulent mixing. This scheme is based on the full 3D TKE prognostic equation and combines the horizontal and vertical subgrid turbulent mixing into a single energetically consistent framework.

A series of real tropical cyclone(TC) simulations with varying horizontal resolutions from 9km to 1km are carried out to compare the performance of the 3D mixing scheme and the conventional 1D planetary boundary layer (PBL) schemes to the observations, including conventional ones such as radiosonde and surface synoptic observations, as well as intensive ones obtained during the landfall of TC, such as mobile boundary layer wind profiler and Dual-pol Doppler Radar. This study aims to determine if the new scheme performs appropriate on TC simulation, and to evaluate the sensitivity of TC simulation to boundary layer schemes.

How to cite: liu, M. and Zhang, X.: Application of a new scale-aware three-dimensional subgrid mixing parameterization on the simulations of tropical cyclone, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6581, https://doi.org/10.5194/egusphere-egu2020-6581, 2020

D2743 |
Danqiong Dai

  A crucial step of the application of WRF in regional climate research is selection of the proper combinations of physical parameterizations. In this study, we performed experiments in WRF to assess the predict skill of various parametrization schemes sets in simulating precipitation, temperature over the Haihe river basin. The experiments driven by ERA-INTERIM reanalysis data are performed for a period of summer (1 June to 31 August, 2016) in this domain with 13 km grid spacing. Fifty-eight members of physics combinations thoroughly covering five types of physics options are assessed against the available observational data by utilizing the multivariable integrated evaluation (MVIE) method. It is deduced that the best performing setup consists of CAM5.1 microphysics, MRF PBL, BMJ Cumulus, CAM Longwave/Shortwave radiation, and Noah Land Surface schemes. To identify the robustness of the optimal scheme set, the vector field evaluation (VFE) diagram for displaying all simulations reveals that the optimal one is distinguished from others by higher vector field similarity coefficient(Rν), smaller root mean square vector deviation(RMSVD). The model deviations spatially for the precipitation show a promising tendency that a strong overestimation about 5 mm/day for the default configuration evolves small biases of the optimal setup with a range between -1 and 1 mm/day, and the surface temperature forecasts have improved to some extent although not significant as that of precipitation. The temporally analysis of the spatial average of all simulations exhibits that for temperature the optimal setup is more approaching to the observational data, but for precipitation no remarkable difference between all simulation and the observations. Further analysis of the sensitivities of model output to different types of physics option suggests that, microphysics, PBL, and Cumulus schemes have more significant impact on the model performances measured by a multivariable integrated evaluation index (MIEI) than radiation scheme and Land Surface schemes.

How to cite: Dai, D.: Evaluation of the WRF Physics Ensemble using Multivariable Integrated Evaluation Approach over Haihe river basin in north China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8832, https://doi.org/10.5194/egusphere-egu2020-8832, 2020

D2744 |
Boriana Chtirkova and Elisaveta Peneva

The weather forecast of good quality is essential for the humans living and operating in the Bulgarian Antarctic Base. The numerical weather prediction models in southern high latitude regions still need improvement as the user community is limited, little test cases are documented and validation data are scarce. Not lastly, the challenge of distributing the output results under poor internet conditions has to be addressed.

The Bulgarian Antarctic Base (BAB) is located on the Livingstone Island coast at 62⁰S and 60⁰W. The influence of the Southern ocean is significant, thus important to be correctly taken into account in the numerical forecast. The modeling system is based on the WRF model, configured in three nested domains down to 1 km horizontal resolution, centered in BAB. The main objective of the study is to quantify the Sea Surface Temperature (SST) impact and to recommend the frequency and way to perform measurements of the SST near the base. The focus is on prediction of right initial time and period of “bad” weather events like storms, frontal zones, and severe winds. Several test cases are considered with available measurements of temperature, pressure and wind speed in BAB during the summer season in 2017. The numerical 3 days forecast is performed and the model skill to capture the basic meteorological events in this period is discussed. Sensitivity experiments to SST values in the nearby marine area are concluded and the SST influence on the model forecast quality is analyzed.

How to cite: Chtirkova, B. and Peneva, E.: The impact of SST on the weather forecast quality in the Bulgarian Antarctic Base area on Livingstone Island, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9347, https://doi.org/10.5194/egusphere-egu2020-9347, 2020

D2745 |
Matilda Hallerstig, Linus Magnusson, and Erik Kolstad

ECMWF HRES and Arome Arctic are the operational Numerical Weather Prediction models that forecasters in northern Norway use to predict Polar lows in the Nordic and Barents Seas. These type of lows are small, but intense mesoscale cyclones with strong, gusty winds and heavy snow showers. They cause hazards like icing, turbulence, high waves and avalanches that threaten offshore activity and coastal societies in the area. Due to their small size and rapid development, medium range global models with coarser resolutions such as ECMWF have not been able to represent them properly. This was only possible with short range high resolution regional models like Arome. When ECMWF introduced their new HRES deterministic model with 9 km grid spacing, the potential for more precise polar low forecasts increased. Here we use case studies and sensitivity tests to examine the ability of ECMWF HRES to represent polar lows. We also evaluate what added value the Arome Arctic model with 2.5 km grid spacing gives. For verification, we use coastal meteorological stations and scatterometer winds. We found that convection has a greater impact on model performance than horizontal resolution. We also see that Arome Arctic produces higher wind speeds than ECMWF HRES. To improve performance during polar lows for models with a horizontal grid spacing less than 10 km, it is therefore more important to improve the understanding and formulation of convective processes rather than simply increasing horizontal resolution.

How to cite: Hallerstig, M., Magnusson, L., and Kolstad, E.: Convection is key to better polar low forecasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10163, https://doi.org/10.5194/egusphere-egu2020-10163, 2020

D2746 |
Andrés Merino, Guillermo Mérida, Pablo Melcón, Laura López, José Luis Marcos, Carmen Victoria Romo, Neves Seoane, Andrés Navarro, Eduardo García-Ortega, and José Luis Sánchez Gómez

The airborne research center called CIAR is placed in the airfield of Rozas (Lugo, Spain). It is a center for experimentation and development of new Unmanned Aerial Vehicles. Since you need to have a good planning of the flights of the prototypes, it is necessary to have a good prediction of the wind at different levels of height.

To obtain a reliable database for wind at different vertical levels, three types of instruments have been used: anemometers installed at 10 meters high to determine surface wind, a sodar for levels below 150 meters and a wind radar for those between 200 and 3000 m high above the CIAR level.

Concerning the mesoscale modelling: we have used the WRF with 48 sigma levels and horizontal resolution of up to 3 x 3 km. Therefore, we have applied multiphysics ensemble techniques. Five combinations of microphysics schemes (AEROSOL THOMPSON, MORRISON 2 MOMENTS, THOMPSON, GODDARD and WRF 2 MOMENTS), three of PBL (MYNN3, YSU and MYJ), and two of Surface (NOAA and RUC) have been selected.

Once the wind data databases were obtained, by means of the different instrumentation indicated above, it has been compared with each of the 20 WRF scenarios. To visualize the results, Taylor diagrams have been used for the different heights.

In summary, some conclusions have been found:

  1. It’s necessary distinguish between low levels and those of slightly higher heights. On the surface, the scenarios with the PBL parameterizations called YSU and MYNN3 show better results.
  2. It seems that the microphysics schemes settings have a less importance in wind forecast, which is consistent with the physical interpretation.
  3. Above 200 meter, the 20 scenarios behave more satisfactorily with excellent correlation coefficients and low standard deviations


Data support came from the Atmospheric Physics Group, IMA, University of León, Spain, and the National Institute of Aerospace Technology (INTA). This research was carried out in the framework of the SAFEFLIGHT project, financed by MINECO (CGL2016‐78702) and LE240P18 project (Junta de Castilla y León). We also thank R. Weigand for computer support to the research group.

How to cite: Merino, A., Mérida, G., Melcón, P., López, L., Marcos, J. L., Romo, C. V., Seoane, N., Navarro, A., García-Ortega, E., and Sánchez Gómez, J. L.: Sensitivity analysis to physical parameterizations schemes applied for wind forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10586, https://doi.org/10.5194/egusphere-egu2020-10586, 2020

D2747 |
Geon Kang and Jae-Jin Kim

This study investigated the effects of trees on the pedestrian wind comfort in the Pukyong National University (PKNU) campus. For this, we implemented the tree’s drag parameterization scheme to a computational fluid dynamics (CFD) model and validated the simulated results against a field measurement. The CFD model well reproduced the measured wind speeds and TKEs in the downwind region of the trees, indicating successful implementation of the tree drag parameterization schemes. Besides, we compared the wind speeds, wind directions, and temperatures simulated by the CFD model coupled to the local data assimilation and prediction system (LDAPS), one of the numerical weather prediction models operated by the Korean Meteorological Administration (KMA) to those observed at the automated weather station (AWS). We performed the simulations for one week (00 UTC 2 – 23 UTC 9 August 2015). The LDAPS overestimated the observed wind speeds (RMSE = 1.81 m s–1), and the CFD model markedly improved the wind speed RMSE (1.16 m s–1). We applied the CFD model to the simulations of the trees' effects on pedestrian wind comfort in the PKNU campus in views of wind comfort criteria based on the Beaufort wind force scale (BWS). We will present the trees' effects on pedestrian wind comfort in the PKNU campus in detail.

How to cite: Kang, G. and Kim, J.-J.: Effects of Trees on Pedestrian Wind Comfort in an Urban Area Using a CFD model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12249, https://doi.org/10.5194/egusphere-egu2020-12249, 2020

D2748 |
Samir Pokhrel, Hasibur Rahaman, Hemantkumar Chaudhari, Subodh Kumar Saha, and Anupam Hazra

IITM provides seasonal monsoon rainfall forecast using modified CGCM CFSv2. The present operational CFSv2 initilized with the INCOIS-GODAS ocean analysis based on MOM4p0d and 3DVar assimilation schemes. Recently new Ocean analysis GODAS-Mom4p1 using Moduler Ocean Model (MOM) upgraded physical model MOM4p1 is generated. This analysis has shown improvement in terms of subsurface temperature, salinity , current as well as sea surface temperature (SST), sea surface salinity (SSS) and surface currents over the Indian Ocean domain with respect to present operational INCOIS-GODAS analysis (Rahaman et al. 2017;Rahman et al. 2019). This newly generated ocean analysis is used to initialize NCEP Climate Forecast System (CFSv2) for the retrospective run from 2011 to 2018. The simulated coupled run has shown improvement in both oceanic as well atmospheric parameters. The more realistic nature of coupled simulations across the atmosphere and ocean may be promising to get better forecast skill.

How to cite: Pokhrel, S., Rahaman, H., Chaudhari, H., Saha, S. K., and Hazra, A.: Impact of improved Ocean initial condition in Climate Forecast System (CFSv2) Hindcast run, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19593, https://doi.org/10.5194/egusphere-egu2020-19593, 2020

D2749 |
Ligia Bernardet, Grant Firl, Dom Heinzeller, Laurie Carson, Xia Sun, Linlin Pan, and Man Zhang

Contributions from the community (national laboratories, universities, and private companies) have the potential to improve operational numerical models and translate to better forecasts. However, researchers often have difficulty learning about the most pressing forecast biases that need to be addressed, running operational models, and funneling their developments onto the research-to-operations process. Common impediments are lack of access to current and portable model code, insufficient documentation and support, difficulty in finding information about forecast shortcomings and systematic errors, and unclear processes to contribute code back to operational centers. 

The U.S. Developmental Testbed Center (DTC) has the mission of connecting the research and operational Numerical Weather Prediction (NWP) communities. Specifically in the field of model physics, the DTC works on several fronts to foster the engagement of community developers with the Unified Forecast System (UFS) employed by the U.S. National Oceanic and Atmospheric Administration (NOAA).  As a foundational step, the UFS’ operational and developmental physical parameterizations and suites are now publicly distributed through the Common Community Physics Package (CCPP), a library of physics schemes and associated framework that enables their use with various models. The CCPP can be used for physics experimentation and development in a hierarchical fashion, with hosts ranging in complexity from a single-column model driven by experimental case studies to fully coupled Earth system models. This hierarchical capability facilitates the isolation of non-linear processes prior to their integration in complex systems. 

The first public release of a NOAA Unified Forecast System (UFS) application is expected for February 2020, with a focus on the Medium-Range Weather Application. This global configuration uses the CCPP and will be documented and supported to the community. To accompany future public releases, the DTC is creating a catalog of case studies to exemplify the most prominent model biases identified by the US National Weather Service. The case studies will be made available to the community, who will be able to rerun the cases, to test their innovations and document model improvements. 

In this poster we will summarize how we are using the UFS public release, the single-column model, the CCPP, and the incipient catalog of code studies to create stronger connections among the groups that diagnose, develop, and produce predictions using physics suites.

How to cite: Bernardet, L., Firl, G., Heinzeller, D., Carson, L., Sun, X., Pan, L., and Zhang, M.: Engaging the Community in the Development of Physics for NWP Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22093, https://doi.org/10.5194/egusphere-egu2020-22093, 2020

D2750 |
Emilie C. Iversen, Gregory Thompson, and Bjørn Egil Nygaard

Snow falling into a melting layer will eventually consist of a fraction of meltwater and hence change its characteristics in terms of size, shape, density, fall speed and stickiness. Given that these characteristics contribute to determine the phase and amount of precipitation reaching the ground, precisely predicting such are important in order to obtain accurate weather forecasts for which society depends on. For example, in hydrological modelling precipitation phase at the surface is a first-order driver of hydrological processes in a water shed. Also, melting snow exerts a possible threat to critical infrastructure because the wet, sticky snow may adhere to the structures and form heavy ice sleeves.

Most widely used bulk microphysical parameterization schemes part of numerical weather prediction models represent only purely solid or liquid hydrometeors, and so melting particle characteristics are either ignored or represented by parent species with simple conditions for behavior in the melting layer. The Thompson microphysics scheme is explicitly developed for forecasting winter conditions in real-time as part of the WRF model, and to maintain computational performance, the introduction of additional prognostic variables is undesirable. This research aims at improving the Thompson scheme with respect to melting snow characteristics using a physically based approximation for the snowflake melted fraction, as well as a new definition of melting level and melting particle fall velocity. A real 3D WRF case is set up to compare with in-situ measurements of hydrometeor size and fall velocity from a disdrometer and a vertically pointing Doppler radar deployed during the Olympic Mountain Experiment (OLYMPEX). The modified microphysics scheme is able to replicate the bimodal distribution of fall speed – diameter relations typical of mixed precipitation seen in disdrometer data, as well as the non-linear increase in snow fall speed with melted fraction through the melting layer.

How to cite: Iversen, E. C., Thompson, G., and Nygaard, B. E.: Improvements to melting snow behavior in an NWP bulk microphysics scheme, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21924, https://doi.org/10.5194/egusphere-egu2020-21924, 2020

D2751 |
Wei Huang, Mengjuan Liu, Xu Zhang, and Jian-wen Bao

It is well known that horizontal resolution has a great deal of impact on tropical cyclone simulations using numerical weather prediction models.  It is relatively less discussed in the literature how vertical resolution affects the solution convergence of tropical cyclone simulations.  In this study, the resolved kinetic energy spectrum, the Richardson number probability density function and resolved flow features are used as metrics to examine the behavior of solution convergence in tropical cyclone simulations using the Weather and Forecast Model (WRF).  It is found that for convective-scale simulations of a real tropical cyclone case with 3-km horizontal resolution, the model solution does not converge until a vertically stretched vertical resolution approaches 200 layers or more.  The results from this study confirm the results from a few previous studies that the subgrid turbulent mixing, particularly, the vertical mixing, plays a significant role in the behavior of model solution convergence with respect to vertical resolution.  They also provide a basis for the vertical grid configuration selection for the operational tropical cyclone model of Shanghai Meteorological Service.

How to cite: Huang, W., Liu, M., Zhang, X., and Bao, J.: The impact of vertical resolution on tropical cyclone simulation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6352, https://doi.org/10.5194/egusphere-egu2020-6352, 2020

D2752 |
Xu Zhang, Jian-Wen Bao, and Baode Chen

Numerical weather predictions (NWP) models are increasingly run using kilometer-scale horizontal grid spacing at which convection is partially resolved and the use of a subgrid convection parameterization scheme is still required. Traditionally, subgrid deep convection has been represented by mass flux-based convection parameterizations based on the ensemble-mean closure concept. Recently, a great effort has been made to develop scale-aware subgrid convection schemes that can be used in kilometer-scale NWP models. However, direct evaluation of these schemes is rarely done using coarse-grained large-eddy simulation (LES).

In this study, an idealized LES of deep moist convection is performed to assess the performance of three widely-used scale-aware subgrid convection schemes in the Weather Research and Forecast (WRF) model that is run at 3-km horizontal resolution. It is found that the simulations using the three schemes not only differ from each other but also do not converge to the coarse-grained LES, indicating that further investigation is required as to what “scale-awareness” means in theory and practice.

How to cite: Zhang, X., Bao, J.-W., and Chen, B.: Evaluation of scale-aware convection schemes at the kilometer-scale resolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5337, https://doi.org/10.5194/egusphere-egu2020-5337, 2020

D2753 |
Jian-Wen Bao, Sara Michelson, Lisa Bengtsson, Philip Pegion, Jeffrey Whitaker, and Cécile Penland

Modern numerical weather prediction (NWP) model forecasts for various applications require not only high-quality deterministic forecasts, but also information about forecast uncertainty.  An ensemble forecast is commonly used to provide an estimation of forecast uncertainty.  Since a great deal of the forecast uncertainty comes from dynamical processes not resolved or explicitly represented by NWP models, there is a need to correctly quantify and simulate NWP model uncertainty for an ensemble forecast to be useful and reliable.

We present an overview of a theoretical framework for simulating the uncertainty in unresolved physics in the NOAA Unified Forecast System (UFS).  This framework is derived from the connection in mathematical physics between the Mori-Zwanzig formalism and multidimensional Langevin processes.  It follows the correspondence principle, a philosophical guideline for new theory development, such that it can be shown that the previously implemented stochastic uncertainty quantification schemes in the UFS are particular cases of this framework.  We will show an example of how we have used this framework to develop a new process-level stochastic uncertainty quantification scheme in the UFS.  We will also present a preliminary performance comparison of these previously-implemented schemes with the newly-developed process-level scheme in the UFS ensemble predictions on short, medium and sub-seasonal time scales.

How to cite: Bao, J.-W., Michelson, S., Bengtsson, L., Pegion, P., Whitaker, J., and Penland, C.: The use of multidimensional Langevin processes for stochastic uncertainty quantification in the NOAA Unified Forecast System (UFS), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10827, https://doi.org/10.5194/egusphere-egu2020-10827, 2020

D2754 |
Evelyn Grell, Jian-Wen Bao, and Sara Michelson

In bulk microphysics schemes, the behavior of the multiple processes that compete for cloud water and ice can be likened to the predator-prey relationship seen in the natural world. These processes provide compensatory feedback between production processes of precipitating hydrometeors.

In this presentation, we demonstrate the sensitivity of the predator-prey processes in two commonly-used microphysics schemes of the Weather Research and Forecasting Model (WRF) to perturbations in aerosol loading, using the simulations of an idealized 2-D squall line and idealized shallow convection in the marine boundary layer. Diagnoses of the parameterized pathways for hydrometeor production microphysics budget analysis reveal that the compensatory feedback associated with the predator-prey processes are quite similar between the schemes. Overall, the compensatory feedback makes the response of a scheme to perturbations in aerosol loading smaller than the differences between the two schemes with the same aerosol loading. This indicates that there remains great uncertainty in modeling the aerosol-cloud interaction in weather and climate models. Alleviating this uncertainty requires better microphysics parameterizations as well as better observations of cloud microphysical properties.

How to cite: Grell, E., Bao, J.-W., and Michelson, S.: The Impact of Predator-Prey Processes in Bulk Microphysics Schemes on Simulated Aerosol-Cloud Interaction, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10896, https://doi.org/10.5194/egusphere-egu2020-10896, 2020

D2755 |
Han-Gyul Jin and Jong-Jin Baik

A new parameterization of the accretion of cloud water by snow for use in bulk microphysics schemes is derived by analytically solving the stochastic collection equation (SCE), where the theoretical collision efficiency for individual snowflake–cloud droplet pairs is applied. The snowflake shape is assumed to be nonspherical with the mass- and area-size relations suggested by an observational study. The performance of the new parameterization is compared to two parameterizations based on the continuous collection equation, one with the spherical shape assumption for snowflakes (SPH-CON), and the other with the nonspherical shape assumption employed in the new parameterization (NSP-CON). In box model simulations, only the new parameterization reproduces a relatively slow decrease in the cloud droplet number concentration which is predicted by the direct SCE solver. This results from considering the preferential collection of cloud droplets depending on their sizes in the new parameterization based on the SCE. In idealized squall-line simulations using a cloud-resolving model, the new parameterization predicts heavier precipitation in the convective core region compared to SPH-CON, and a broader area of the trailing stratiform rain compared to NSP-CON due to the horizontal advection of greater amount of snow in the upper layer. In the real-case simulations of a line-shaped mesoscale convective system that passed over the central Korean Peninsula, the new parameterization predicts higher frequencies of light precipitation rates and lower frequencies of heavy precipitation rates. The relatively large amount of upper-level snow in the new parameterization contributes to a broadening of the area with significant snow water path.

How to cite: Jin, H.-G. and Baik, J.-J.: A new parameterization of the accretion of cloud water by snow and its evaluation through simulations of mesoscale convective systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1994, https://doi.org/10.5194/egusphere-egu2020-1994, 2020

D2756 |
Wanchen Wu, Wei Huang, and Baode Chen

Considering aerosol effects via microphysics parameterization is an imperative work in high-resolution numerical weather prediction. This paper uses two bulk microphysics parameterizations, Aerosol-Aware Thompson and CLR schemes, with the Weather and Research Forecast model to study the impacts of aerosols and microphysics scheme on an idealized supercell storm. Our results show that the implementation of aerosols can successfully modify the cloud droplet size and influence the subsequent warm-rain, mixed-phase, and accumulated precipitation. It implies that aerosols can make numerous differences to cloud microphysics properties and processes but the uncertainty in the magnitude of aerosol effects is huge because the two schemes are different from each other since the warm-rain process including CCN activation and rainwater formation. On the other hand, it is also found that the two schemes make tremendous differences in the rainfall pattern and storm dynamics due to the presence of graupel below the freezing level. The Thompson scheme has hail-like graupel which can fall below the freezing level to chill the air temperature effectively, intensify the downdraft, and enhance the uplifting on the front of cold pools. The mean graupel size represented by the two schemes plays a much more important role than the fall-speed formula for the dynamical feedbacks. Our results suggest that particle size is the core of a myriad of microphysics processes and highly associated with key cloud and dynamical signatures.

How to cite: Wu, W., Huang, W., and Chen, B.: A comparison study of aerosol impacts on idealized supercell between two bulk microphysics parameterizations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6235, https://doi.org/10.5194/egusphere-egu2020-6235, 2020

D2757 |
Louis Kwan Shu Tse, Ka Ki Ng, Yuk Sing Lui, Chi Chiu Cheung, Wai Nang Leung, and Yun Fat Lam

    The model performance and run-time are two major concerns in numerical weather prediction. Both are substantially dependent on the grid specification, in particular, the number of grids, resolution and coverage of the refinement regions. In the Model for Prediction Across Scales - Atmosphere (MPAS-A), unstructured Voronoi mesh is used and the infrastructure, particularly the dynamic core, is implemented to support this flexible topology. However, only several standard meshes are available for download while customization is not supported. Moreover, the use of a globally-constant time-step (determined by the smallest grid) poses challenges on high resolution forecast using meshes with large resolution variation due to impractically long-running time. A Customizable Unstructured Mesh Generation (CUMG) and Hierarchical Time-Stepping (HTS) was developed in the ClusterTech Platform for Atmospheric Simulation (CPAS), offering a potential path for high-resolution local/regional forecast in MPAS-A’s framework. The CUMG algorithm enables local mesh refinement in arbitrary shape using user-defined horizontal resolution at any desired locations. Meshes with large resolution variation, for example, ranging from 128 km to 1 km can be generated. The resulting meshes are 100% well-staggered, and zero obtuse Delaunay triangle is guaranteed. The CPAS provides a web-based graphical user interface and no coding is needed for specifying the refinements. In real simulations, grids are integrated in time with heterogenous time-step according to their cell spacings using HTS. It reduces the model run-time tremendously, particularly for meshes with large resolution variation. 

    In this study, a comparison on the mesh quality, efficiency and performance of a CPAS customized 128-to-1 km mesh to the MPAS-A standard 60-to-3 km mesh with and without HTS was performed. Three historical weather conditions over southern China in 2018 were selected to evaluate their performance: (i) passage of a cold front (ii) heavy rainfall and (iii) passage of a tropical cyclone. In general, the CPAS 128-to-1 km mesh was found to have better quality over the MPAS-A 60-to-3 km mesh, namely cell quality, angle-based triangle quality, and triangle quality. Moreover, using HTS, the benchmarked saving of the total run-time for the CPAS 128-to-1 km mesh and MPAS-A 60-to-3 km mesh are 56.8% (2.33x speedup) and 16.5% (1.20x speedup), respectively. Furthermore, the model results were validated through comparison with the National Centers for Environmental Prediction (NCEP) Final (FNL) Operational Global Analysis. The 5-day simulation results of various forecast variables within the area of interest (a lat-long box covering 3 km refinement region of the MPAS-A 60-to-3 km mesh) with and without HTS for both meshes show comparable performance in all cases. The promising model performance along with remarkable speedup indicates the validity and feasibility of high resolution local/regional forecast using customized global variable-resolution meshes in an operational manner. 

How to cite: Tse, L. K. S., Ng, K. K., Lui, Y. S., Cheung, C. C., Leung, W. N., and Lam, Y. F.: Development of Customized Variable-Resolution CPAS for Meteorological Simulation , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6653, https://doi.org/10.5194/egusphere-egu2020-6653, 2020

D2758 |
Jiayi Lai

The next generation of weather and climate models will have an unprecedented level of resolution and model complexity, while also increasing the requirements for calculation and memory speed. Reducing the accuracy of certain variables and using mixed precision methods in atmospheric models can greatly improve Computing and memory speed. However, in order to ensure the accuracy of the results, most models have over-designed numerical accuracy, which results in that occupied resources have being much larger than the required resources. Previous studies have shown that the necessary precision for an accurate weather model has clear scale dependence, with large spatial scales requiring higher precision than small scales. Even at large scales the necessary precision is far below that of double precision. However, it is difficult to find a guided method to assign different precisions to different variables, so that it can save unnecessary waste. This paper will take CESM1.2.1 as a research object to conduct a large number of tests to reduce accuracy, and propose a new discrimination method similar to the CFL criterion. This method can realize the correlation verification of a single variable, thereby determining which variables can use a lower level of precision without degrading the accuracy of the results.

How to cite: Lai, J.: A Guiding Principles for Choosing Numerical Precision in Atmospheric Model based on CESM, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13445, https://doi.org/10.5194/egusphere-egu2020-13445, 2020

D2759 |
Yuanfu Xie

In order to provide multiple choices of dynamic cores for the next generation global numerical prediction system at Chinese Meteorological Administration (CMA), a Z-grid based dynamic core is under development. Among other important features of a Z-grid scheme, better dispersion relation, natural geostrophic adjustment and conservation attract numerical modeler’s interests. In this presentation, we will share the progress of such a development at CMA along with other dynamic cores, improving its accuracy on a sphere, efficient solvers and software design and implementation. We also developed some standard unit test cases for software reliability, which are also available and convenient for other dynamic cores. Some numerical experiment results will be presented as well.

How to cite: Xie, Y.: A Z-grid Based Dynamic Core for Global Numerical Prediction Model of Chinese Meteorological Agency, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21150, https://doi.org/10.5194/egusphere-egu2020-21150, 2020

D2760 |
Xinpeng Yuan


MPDATA method for non–uniform mesh


Xinpeng Yuan

State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences,China 

Meteorological Administration, Beijing 100081, China 


Keyword: Atmospheric dynamics, MPDATA, non–uniform mesh, precision

Abstract: MPDATA[1,2](multidimensional positive definite advection transport algorithm) is proposed by Piotr K. Smolarkiewicz in 1983. This method is used to efficiently solve the advection transport problem of non-negative thermodynamic variables (such as liquid water or water vapor) in the atmospheric dynamics model. This method has been proved to be an effective numerical solution to the advection transport problem for uniform meshes. However, since there is no uniform mesh division on the sphere, the traditional MPDATA method is faced with the incompatibility problem for the non-uniform and quasi-uniform meshing of the sphere, resulting in the numerical algorithm failing to reach the designed second-order accuracy. Firstly, this paper analyzes the insufficiency of traditional MPDATA methods for non-uniform grids. That is, the incompatibility of the first-order numerical scheme and the approximation of boundary derivative.Then the MPDATA method suitable for non-uniform grid is proposed. According to the characteristics of non-uniform grid and the characteristics of well-balance[3] central grid point algorithm, the MPDATA method suitable for 1-d and 2-d complex grid structure is designed. The consistency and positivity of the algorithm are proved by mathematical analysis. Finally, the theoretical proof is verified by numerical simulation.



[1] Smolarkiewicz P. A Simple Positive Definite Advection Scheme with Small Implicit Diffusion[J]. Monthly Weather Review. 1983.

[2] Smolarkiewicz P K, Szmelter J. MPDATA: An edge-based unstructured-grid formulation[J]. Journal of Computational Physics. 2005, 206(2): 624-649.

[3] Kurganov A, Levy D. Central-Upwind Schemes for the Saint-Venant System[J]. ESAIM: Mathematical Modelling and Numerical Analysis. 2002, 36(3): 397-425.


How to cite: Yuan, X.: MPDATA method for non–uniform mesh, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4005, https://doi.org/10.5194/egusphere-egu2020-4005, 2020