AS1.1
Orals: Mon, 24 Apr | Room M1
National Oceanic and Atmospheric Administration’s (NOAA’s) Environmental Modeling Center (EMC) is a lead developer of Numerical Weather Prediction (NWP) systems that also transitions to operations and maintains more than 20 numerical prediction systems that are used across the National Weather Service (NWS), the broader NOAA, by other United States (U.S.) federal agencies, and various other stakeholders. These systems are developed through a close collaboration with partners from the academic, federal and commercial sectors. EMC maintains, enhances and transitions-to-operations numerical forecast systems for weather, ocean, climate, land surface and hydrology, hurricanes, and air quality for the U.S. and the global community and for the protection of life and property and the enhancement of the economy.
NOAA’s Next Generation Global Prediction System (NGGPS) Project initiated a major shift in the development of operational Earth system predictions with a goal to simplify the National Centers for Environmental Prediction (NCEP) Production Suite using the Unified Forecast System (UFS) framework (https://ufscommunity.org/). EMC has taken a lead in further development and consolidation of NCEP’s operational systems into UFS based applications. The UFS is being designed as a community-based, comprehensive atmosphere-ocean-sea-ice-wave-aerosol-land coupled Earth modeling system with coupled data assimilation and ensemble capabilities, organized around applications spanning local to global domains and predictive time scales ranging from sub-hourly analyses to seasonal predictions. Disparate operational applications that have been developed and maintained by EMC in support of various stakeholder requirements are being transitioned to the UFS framework. The transition started a few years ago and is planned to continue over the next few years. The resulting applications will consolidate NCEP’s Production Suite into far fewer applications that share a set of common scientific components and technical infrastructure. This approach is expected to accelerate the transition of research into operations and simplify maintenance of operational systems.
This talk describes major development and operational implementation projects at EMC over the last few years and the plans for the next five years (2023-2027), how those fit within the broader strategic plans of NOAA, and how these projects link with other model-related projects internally within NOAA and with the broader U.S. and international modeling community.
How to cite: Stajner, I., Gross, B., Tallapragada, V., Levit, J., Chawla, A., Mehra, A., Kleist, D., and Yang, F.: Advancing Operational Modeling Systems at NOAA’s Environmental Modeling Center: Transitioning to Unified Forecast System Applications, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2982, https://doi.org/10.5194/egusphere-egu23-2982, 2023.
NOAA is collaborating with the US weather and climate science community to develop the next generation fully coupled earth system modeling capability for both research and operational forecast applications across different temporal and spatial scales. In this presentation we explore the possibility of running the UFS at convection-permitting resolution for global medium-range weather forecasting. A few sensitivity experiments were performed at a global uniform 3-km resolution with and without parameterized convection. Results were compared with the 13-km control experiments to investigate the impact of model resolution and convection parameterization on precipitation and cloud-radiation interaction. Aerosol indirect effect on clouds is also tested and evaluated within this framework to understand its sensitivity to model resolution and parameterized convection. Aerosol indirect effect occurs when aerosols act as cloud condensation nuclei and ice nuclei within clouds and consequently alter cloud radiative properties and cloud lifetime. Using the Thompson double-momentum microphysics scheme, the number concentrations of water friendly aerosol and ice friendly aerosol are either diagnosed from the MERRA2 aerosol climatology or predicted and advected with source and sink terms derived from the climatology. The relations between clouds, radiation and precipitation with and without the presence of aerosol indirect effects are analyzed for simulations made at both the control 13-km and experimental 3-km UFS model resolutions.
How to cite: Yang, F., Chen, A., and Moorthi, S.: Prototyping Convection-Permitting Global Weather Forecast and the Representation of Aerosol-Cloud-Radiation Interaction in the NOAA Unified Forecast System , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3038, https://doi.org/10.5194/egusphere-egu23-3038, 2023.
The Common Community Physics Package (CCPP) is a collection of atmospheric physical parameterizations and a framework that couples the physics for use in Earth system models. The CCPP Framework was developed by the U.S. Developmental Testbed Center (DTC) and is now an integral part of the Unified Forecast System (UFS). The UFS is a community-based, coupled, comprehensive Earth modeling system designed to support research and be the source system for NOAA‘s multi-scale operational numerical weather prediction applications. The CCPP is also being used in the experimental U.S. Navy Environmental Prediction sysTem Utilizing the NUMA corE (NEPTUNE, which employs a modified version of the Non-hydrostatic United Model for the Atmosphere dynamical core) and is currently being integrated into the Community Atmosphere Model (CAM) utilized in the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM).
A primary goal for this effort is to facilitate research and development of physical parameterizations, while simultaneously offering capabilities for use in operational models. The CCPP Framework supports configurations ranging from process studies to operational numerical weather prediction as it enables host models to assemble the parameterizations in flexible suites. Framework capabilities include flexibility with respect to the order in which schemes are called, ability to group parameterizations for calls in different parts of the host model, and ability to call some parameterizations more often than others. Furthermore, the CCPP is distributed with a single-column model (SCM) that can be used to test innovations, conduct hierarchical studies in which physics and dynamics are decoupled, and isolate processes to more easily identify issues associated with systematic model biases. The CCPP SCM can be driven using files in the DEPHY format (an internationally agreed-upon format for inputs and outputs of SCMs). This opens doors for collaborations using multiple initial and forcing datasets based on observational field campaigns. The CCPP SCM is also being updated to be forced by the UFS.
The CCPP v6.0.0 public release includes 23 primary parameterizations (and six supported suites), representing a wide range of meteorological and land-surface processes. Experimental versions of the CCPP also contain chemical schemes, making it possible to represent processes in which chemistry and meteorology are tightly coupled.
The CCPP is developed as open-source code and has received contributions from the wide community in the form of new schemes and innovations within existing schemes. In this presentation, we will provide an update on CCPP development and plans, as well as review existing resources for users and developers, such as the public releases, documentation, tutorial, and support mechanism. We will also provide information about the upcoming CCPP Visioning Workshop, indeed to be a forum for current and future CCPP users to learn about its capabilities and discuss requirements for new development.
How to cite: Bernardet, L., Firl, G., Swales, D., Zhang, M., Kavulich, M., Trahan, S., Li, W., Dudhia, J., and Ek, M.: Facilitating the development of complex models with the Common Community Physics Package and its Single-Column Model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3703, https://doi.org/10.5194/egusphere-egu23-3703, 2023.
Significant problems in numerical weather prediction modeling systems appear when the horizontal grid-spacing is between 20 km and 1 km and when deep convection is important. These scales are usually termed “Gray Scales”. Techniques have been developed so that the behavior of the convective parameterization changes with the horizontal grid spacing of the model; such parameterizations are said to become “scale-aware”. Commonly used techniques involve applying a scaling approach to smoothly transition from parameterized to resolved convection. These are similar to an elegantly simple mathematical method originally developed by Arakawa et al. (2011), which scales the convective tendencies in dependence on the convective area fraction. Here we show that the scaling approaches are flawed, since they fail to consider the fact that the impacts of deep convection on those scales are not limited to one grid box, and usually – because of the scaling -- leads to light precipitation covering too much area, as we have previously shown in HRRR simulations. Any scaling approach is especially flawed in areas of light forcing (such as daytime heating) and in the tropics, when the explicit microphysics parameterization is not yet producing precipitation. We will show examples of these problems and discuss possible solutions as applied to NOAA’s new RRFS storm-scale modeling system.
How to cite: Grell, G., Li, H., and Freitas, S.: Flaws of scale-aware techniques in convective parameterizations, as discovered in NOAA’s operational convection-allowing modeling systems: an attempt to improve them., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10514, https://doi.org/10.5194/egusphere-egu23-10514, 2023.
Atmospheric resolved-scale air flow (dynamics) and sub-grid parameterizations (physics) are two essential components of a weather or climate model. These two independent components are coupled and advanced using the same time step, either parallel or sequentially split. However, traditionally dynamics and physics are engineered in isolation and developed independently in models. As a result, many parts of the physics run at a physically-inappropriate time frequency or with heat transfers that are inconsistent with the dynamics, leading to errors. In addition, physical parameterizations should contain dynamical and non-dynamical processes. We believe there are compelling reasons that dynamical processes, if resolved, should be taken care of by the dynamical core.
Our study proposes a novel integrated dynamics-physics coupling framework (Zhou and Harris, 2022) within the GFDL (Geophysical Fluid Dynamics Laboratory) weather-to-seasonal prediction system SHiELD (System for High-resolution prediction on Earth-to-Local Domains; Harris et al., 2020) that promises to resolve the above issues. We will present our integrated coupling framework and the development of integrated physical parameterization for this framework in detail. The performance of forecast experiments using the modeling system SHiELD with this integrated coupling framework will be highlighted, focusing on large-scale circulation, cloud and precipitation, hurricane, and convective-storm predictions.
How to cite: Zhou, L. and Harris, L.: An Integrated Coupling Framework for Atmospheric Dynamics and Physics, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13, https://doi.org/10.5194/egusphere-egu23-13, 2023.
A better understanding of the thermodynamics of heavy precipitation events is necessary towards improving weather and climate models and quantifying the impact of climate variability on precipitation. However, there are limited observations available to assess the thermodynamics model structure within heavy precipitation conditions.
In 2009, the Earth Observation Group at ICE-CSIC/IEEC conceived the polarimetric radio occultations (GNSS-PRO) technique with the aim to obtain simultaneous measurements of the vertical structure of precipitation and its associated thermodynamic state. Based on the standard GNSS radio occultation technique (GNSS-RO), polarimetric RO consists of an identical instrument working at two orthogonal linear polarizations (H,V) instead of the conventional circularly polarized antenna. This allows us to measure the differential phase delay at both ports, hypothesized to be positive in the presence of asymmetric hydrometeors (large raindrops, snowflakes, ice aggregates). This technique is being tested for the first time on the proof-of-concept mission Radio Occultations and Heavy Precipitation (ROHP) aboard PAZ satellite, operating since 2018. The results of the first 4 years of PRO observations already showed sensitivity to heavy precipitation and its associated cloud structures.
Such technique provides high quality thermodynamic observations of water vapor, temperature and pressure with high vertical resolution, along with the vertical measurements of the phase delay linked to the precipitation structure. This study focuses on comparing these observations with the simulations based on the outputs of several operational models and reanalysis for a set of selected cases. The main objectives of the study are: (1) To check if the models reproduce the main features of the actual data; (2) to assess whether different models/schemes result in different GNSS PRO observables, and whether these differences are larger than the measurement uncertainty; and (3) to examine the utility of PAZ GNSS PRO observations for model validation and diagnosis.
This effort provides insight on future methods to assimilate the PRO profile alongside other conventional (non-polarimetric) RO data, including work towards building a forward operator. The exercise includes comparisons with ECWMF operational model, ERA-5 reanalysis, the operational NWP at the Japan Meteorological Agency, and a near-real-time implementation of the WRF regional model over the northeastern Pacific produced at the Center for Western Weather and Water Extremes (CW3E) called West WRF, forced with ECMWF and GFS.
How to cite: Padullés, R., Cardellach, E., Paz, A., Turk, F. J., Ao, C. O., Wang, K. N., de la Torre Juárez, M., Murphy, M. J., Haase, J. S., Lonitz, K., and Hotta, D.: A multi-center exercise on the sensitivity of PAZ GNSS Polarimetric RO for NWP modeling, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12177, https://doi.org/10.5194/egusphere-egu23-12177, 2023.
Atmospheric predictability is fundamentally limited by the upscale growth of initial small-scale, small-amplitude errors. Studying upscale error growth mechanism is essential to better understand this fundamental limitation. Upscale error growth is frequently investigated by spectral analysis. By design, however, spectral analysis is non-local. A local investigation of error growth in different flow configurations is desirable, though, to study the well-known flow dependence of error growth. We thus take here a complementary approach to spectral analysis and identify local regions of prominent errors as “error features”.
We have developed an automated algorithm to identify error features in gridded data and track their spatial and temporal evolution. Errors are considered in terms of potential vorticity (PV) and near the tropopause, where they maximize. A previously derived PV-error tendency equation is evaluated to quantify the different contributions to error growth in previously published upscale error growth experiments with the global prediction Model ICON from the German Weather Service. Errors in these experiments grow from very small initial-condition uncertainty (three orders of magnitude smaller than current-day uncertainty) and due to differences in the seeding of a stochastic convection scheme.
Spatial composites centered on the centroid of error features indicate that features are primarily generated ahead of an upper-tropospheric trough. The environment surrounding the features at the time of their first detection is characterized by locally enhanced lower to mid tropospheric moisture, latent heat release, and upper tropospheric divergence. Subsequently, this moist-diabatic nature of the error environment becomes gradually less prominent. The evaluating of process specific error growth rates enables to quantify the upscale growth mechanics in more detail. For this purpose, we integrate the growth rates over the respective area associated with an error feature. Examination of the combined growth rates of all features reproduces the previously found three-phased multi-scale upscale growth paradigm: Errors are first generated on the small scale by differences in latent heat release, then projected onto the tropopause region by associated differences in upper tropospheric divergent outflow, and finally amplified by nonlinear Rossby wave dynamics. The growth rates from a single feature, however, can substantially differ from the mean picture. Some features, e.g., go through the described stages in a cyclic sequence, and the main focus of the presentation will be on the differences between fast and slowly amplifying error features.
How to cite: Schmidt, S., Riemer, M., and Selz, T.: A feature based perspective on upscale error growth., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14671, https://doi.org/10.5194/egusphere-egu23-14671, 2023.
An experiment reported in Mesinger and Veljovic (JMSJ 2020) showed an
advantage of the Eta over its driver ECMWF ensemble members in placing 250 hPa jet
stream winds during a period of an upper tropospheric trough crossing the Rockies. A
byproduct of that experiment was that of the Eta ensemble switched to use sigma,
Eta/sigma, also achieving 250 hPa wind speed scores better than their driver members,
although to a lesser extent. Nevertheless, it follows that the Eta must include feature or
features additional to the eta coordinate responsible for this advantage over the
ECMWF.
An experiment we have done strongly suggests that the van Leer type vertical
advection of the Eta, implemented in 2007, is a significant contributor to this advantage.
In this experiment, having replaced a centered finite-difference Lorenz-Arakawa scheme
this finite-volume scheme enabled a successful simulation of an intense downslope
windstorm in the lee of the Andes.
While apparently a widespread opinion is that it is a disadvantage of terrain
intersecting coordinates that “vertical resolution in the boundary layer becomes reduced
at mountain tops as model grids are typically vertically stretched at higher altitudes,” a
very comprehensive 2006 NCEP parallel test gave just the opposite result. With
seemingly equal ABL schemes, the Eta showed a higher surface layer accuracy over
high topography than the NMM, using a hybrid terrain-following system (Mesinger, BLM
2022).
Hundreds of thousands of the Eta forecasts and experiments performed
demonstrate that the relaxation lateral boundary conditions almost universally used in
regional climate modeling (RCM)–in addition to conflicting with the properties of the
basic equations used–are unnecessary. Similarly, frequently applied in RCMs so-called
large scale or spectral nudging, being based on an ill-founded belief, should only be
detrimental if possible numerical issues of the limited area model used are addressed.
Note that this is confirmed by the results we refer to above.
How to cite: Mesinger, F., Veljovic, K., Chou, S. C., Gomes, J. L., Lyra, A. A., and Jovic, D.: Cut-cell Eta ensemble skill vs. ECMWF: Lessons learned, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17562, https://doi.org/10.5194/egusphere-egu23-17562, 2023.
Atmospheric conditions and instabilities affect directly the performance of modern large offshore wind farms and several offshore operations, particularly farther offshore in deep waters. However, our current knowledge regarding to the atmospheric processes over a wide range of spatiotemporal scales needs further improvements by the use of measurements, and sophisticated modelling of Marine Atmospheric Boundary Layer (MABL) processes relevant to the offshore wind energy. Processes like gravity waves, Open Convective Cells (OCCs), Low Level Jets (LLJs) affect both horizontal and vertical structures of MABL flow fields and the interactions between the ambient flow and offshore constructions. For example, LLJs are common physical processes over the Southern North Sea. These transient events occur during stably stratified atmosphere with jet cores at heights between 150 m and 300 m. Strong positive and negative shears are observed below and above the nose of LLJ (i.e a maxima in the vertical wind profile). Structure, timing, shape, and characteristics of LLJs influence the loads on turbines and the overall power generation of offshore wind parks. Therefore, precise modelling and measurement of these episodes are highly important.
While advanced measurement systems such as LiDAR provides important information on formation and characteristics of LLJs, such measurements are sparse in time and space. On the other hand, modelling tools are sensitive in prediction of LLJ characteristics such as LLJ’s height, spatial position, and timing, the choice of initial and boundary conditions, and planetary boundary layer schemes used in the Numerical Weather Prediction models (NWPs). Predictive skills of these models can be enhanced through assimilation of available quality observational data with NWPs like Weather Research and Forecasting (WRF) model.
In this study, we use the WRF model to model wind variability for a geographical area covering the FINO1 offshore meteorological met-mast and Alpha Ventus offshore wind park (in the Southern North Sea). We first examine the performance of WRF, with an appropriate configuration, in forecasting few LLJ events. We then apply a LiDAR-based data assimilation (for sometimes during 2015) and study how different DA techniques (namely observational nudging and 3DVAR) can improve the accuracy of wind forecasting and reduce the model uncertainity during the LLJ events.
How to cite: Bakhoday-Paskyabi, M., Bui, H., and Mohammadpour Penchah, M.: LiDAR-based data assimilation during offshore transient events, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16972, https://doi.org/10.5194/egusphere-egu23-16972, 2023.
Stochastic parameterisations are an important way to represent uncertainty in the deterministic forecasting models underlying ensemble prediction systems. Current stochastic parameterisation approaches use random correlation patterns that are unrelated to the atmospheric flow to induce coherent perturbations to parameterisations. Here we replace these patterns by accumulated tendency fields from parameterized physical processes in the HARMONIE-AROME system. Our rationale is that by perturbing the parameterisations with a field that reflects where parameterisations are most active, rather than random, the model obtains a more targeted increase in the degrees-of-freedom to represent forecasting uncertainty.
Here we study a large marine cold-air outbreak over the Norwegian Sea. Strong heat fluxes persisted near the ice edge, and shallow convection dominated in the center of the model domain. Perturbation fields are diagnosed from individual tendency diagnostics implemented in AROME-Arctic within ALERTNESS. Total physical tendencies for the horizontal winds, for temperature and humidity are accumulated with a time filtering throughout the 66 h forecast period.
Accumulated tendencies show overlapping and differing centers of activity. Wind parameterisations are active near the ice edge, and with smaller scale variability over land areas. Temperature tendency patterns show activity more confined to the ice edge, and the coast of northern Scandinavia. Such spatially coherent patterns of parameterisation activity are meaningfully related to current weather. To exploit the relation between parameterisation activity and weather patterns for ensemble perturbation, we conduct sensitivity tests of cloud parameterisation parameters in a single-column model version MUSC and the full model version. First results illustrate our progress towards the use of diagnostic perturbation patterns for stochastically perturbed perturbations in the HarmonEPS system.
How to cite: Sodemann, H., Kähnert, M., Remes, T. M., Ekrem, P., Grote, R., and Frogner, I.-L.: Potential of accumulated parameterisation tendencies from AROME-Arctic for stochastic parameterisation erturbation patterns, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15927, https://doi.org/10.5194/egusphere-egu23-15927, 2023.
The UFS-R2O Project, which began in July 2020 as a five-year plan with deliverables for the first two years funded, has made significant progress in developing the medium-range and sub-seasonal to seasonal (MRW/S2S) predictions, and a regional, high-resolution hourly-updating and convection-allowing ensemble system for prediction of short range severe weather (CAM/SRW). The Project fostered successful collaborations between the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center, several NOAA research labs, the National Center for Atmospheric Research (NCAR), the Naval Research Lab (NRL) and multiple universities and cooperative institutes, and facilitated the development of a six-way global coupled (atmosphere/ocean/land/sea-ice/wave/aerosol) modeling system within the UFS framework, a major accomplishment from the community modeling perspective.
The UFS-R2O Project has entered into the third year, albeit with reduced funding, to continue the momentum built during the first two years. A new Hurricane Application is added to the project to develop a very high-resolution Hurricane Analysis and Forecast System (HAFS) with storm following moving nests, and the project was reorganized with Application Teams and Development Teams interacting with each other to reflect the cross-cutting nature of the UFS components and infrastructure. While the first two years of the project were focused on engineering and infrastructure, Year 3 is primarily targeting systematic testing and evaluation of the prototype UFS configurations for selecting the candidates for potential transition to operations in the next few years.
Highlights of the progress accomplished thus far include: (1) finalizing the UFS coupled model configuration for MRW/S2S applications; (2) advances in weakly coupled data assimilation system that can update the ocean, sea-ice, land and atmospheric states; (3) development of a limited-area convective-scale short-range ensemble prediction system that will form the basis for the Rapid Refresh Forecast System (RRFS); and (4) transition to operations the Hurricane Analysis and Forecast System (HAFS) that will replace legacy hurricane models in operations.
This presentation describes the outcomes of the UFS R2O Project for the first two years, highlights the progress for Year 3, and presents plans for Year 4 and beyond..
How to cite: Tallapragada, V., Whitaker, J., and Kinter, J.: NOAA's Unified Forecast System Research to Operations (UFS R2O) Project- Accomplishments till date and Future Plans , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2430, https://doi.org/10.5194/egusphere-egu23-2430, 2023.
This presentation provides an overall summary of the project PREVENIR and recent activities about data assimilation and numerical weather prediction (NWP) research. PREVENIR is an international cooperation project between Argentina and Japan since 2022 for five years under the Science and Technology Research Partnership for Sustainable Development (SATREPS) program jointly funded by the Japan International Cooperation Agency (JICA) and the Japan Science and Technology Agency (JST). The main goal is to develop an impact-based early warning system for heavy rains and urban floods designed for two highly vulnerable urban basins in Argentina: one located in Buenos Aires Province and the other in Córdoba Province. PREVENIR takes advantage of leading research on simulations and Big Data Assimilation (BDA) with the Japan’s flagship supercomputer “Fugaku” and its predecessor “K” and develops a total package for disaster prevention, namely, monitoring, quantitative precipitation estimates (QPE), nowcasting, BDA and NWP, hydrological model prediction, warning communications, public education, and capacity building. Here, the Japanese leading institutions in the scientific research and operational services, i.e., RIKEN, Osaka University, the International Centre for Water Hazard and Risk Management (ICHARM), and the Japan Meteorological Agency (JMA) closely work with the Argentinian counterparts, i.e., the National Meteorological Service, the National Water Institute, and the National Research Council of Argentina under the strong support of JICA, JST, and Argentinian Foreign Affairs Ministry. Heavy rains and urban floods are important global issues under the changing climate. The total package for disaster prevention will be the first of its kind in Argentina and will provide useful tools and recommendations for the implementation of similar systems in other parts of the world.
How to cite: Miyoshi, T., Saulo, C., Otsuka, S., Ruiz, J., Skabar, Y. G., Amemiya, A., Ushio, T., Tomita, H., Ushiyama, T., and Konishi, M.: PREVENIR: Japan-Argentina Cooperation Project for Heavy Rain and Urban Flood Disaster Prevention, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8919, https://doi.org/10.5194/egusphere-egu23-8919, 2023.
The improvement of numerical weather forecasts is a key element to predict high-impact weather events, associated with deep moist convection. The observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets and their impact should be objectively evaluated. This can be efficiently estimated by the Forecast Sensitivity to Observation Impact (FSOI) methodology. In this study, we explore the application of the ensemble formulation of FSOI (EFSOI) in a convective scale regional data assimilation system over Sierras de Córdoba (Argentina), a data-sparse region with complex terrain characterized by the periodic occurrence of extreme precipitation and flash floods events. To evaluate the observation networks that result beneficial and detrimental for the forecast, the Weather Research and Forecasting model coupled with the Local Ensemble Transform Kalman Filter was used with 40 members. Convective scale analyses were obtained every 5 minutes, assimilating reflectivity data from a C-band radar and conventional and non-conventional surface weather stations (CSWS and NSWS). The experiment was initialized on December 13 at 23 UTC and ran for 5 hours, until December 14 03 UTC. The experiment conducted was a case study within the intensive observing period of the RELAMPAGO-CACTI field campaign that was carried out during the 2018-2019 austral warm season in the center of Argentina. An independent data assimilation cycle using more observations and a different configuration is used in the experiments as verifying truth for the computation of forecast errors in EFSOI.
Results showed that all the observation sources had, on average, a positive impact on the 30 minute forecasts with a positive impact rate above 50%. However, when observations impacts are analyzed by geographic location, different results are evidenced. Most of the surface stations that evidence a detrimental impact in forecasts are located in the northern part of the region, probably due to a misrepresentation of the thermodynamic environment. Regarding radar reflectivity observations, values of positive impact rate above 50% dominate over all the region, demonstrating that, in general, they reduce the forecast errors. The results suggest that the observations with values of reflectivity beneath 15 dBZ have a larger amount of beneficial observations in lower levels than in upper levels.
This methodology is an approximation to quantify the impact of reflectivity and surface observations on a convective permitting forecast over the region. The results of this (and future) work can help to identify observation data sources detrimental for the data assimilation system, suggesting data selection criteria to assess improvements in this regional convective-scale data assimilation system where nonconventional observations such as radar data plays an essential role.
How to cite: Casaretto, G., Dillon, M. E., Garcia Skabar, Y., Ruiz, J., Maldonado, P., and Sacco, M.: Ensemble Forecast Sensitivity to Observations Impact (EFSOI) of a high impact weather event using a convection permitting data assimilation system., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-415, https://doi.org/10.5194/egusphere-egu23-415, 2023.
We have published [ https://doi.org/10.1002/qj.4342 ] recent results on winter jet stream wind speed changes in the eastern North Atlantic: there is no change for the past 40 years but a statistically significant increase for the past roughly 20 years (2002-2020). The increase shows up in both the Global Aircraft Data Set (GADS) observations from flight data recorders and the ERA5 reanalysis. The wind speeds seem to track the North Atlantic Oscillation (NAO). We can consider four possibilities: ( 1 ) synoptic fluctuation; ( 2 ) improved aircraft routing, though inconsistent with NAO correlations; ( 3 ) greater number of automated aircraft observations; ( 4 ) actual secular change in the polar jet exit region of the atmosphere. This type of study must deal with subtleties of North Atlantic track system that includes aircraft step climbs. We will present newer results on the secular increase in automated aircraft observations and the effects of including more recent Northern Hemisphere winters (2021 through, possibly, 2023).
How to cite: Tenenbaum, J. and Williams, P.: Winter jet stream wind speed changes in the eastern North Atlantic, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1687, https://doi.org/10.5194/egusphere-egu23-1687, 2023.
With enhanced computational capacity, the treatment of subgrid processes in global Earth System Models (ESMs) has grown increasingly complex. Despite these enhancements, critical biases remain in the modeling of fundamental processes that govern both the mean climate and the development of extreme weather phenomena of high societal impact. Due to their potential to be better resolved in the next generation of ESMs, tropical cyclones (TCs) are extremes of particular interest.
The importance of the parameterization of momentum flux within the boundary layer (PBL) for modeled TC structure has been established for numerical models run at a variety of spatial scales. However, few studies have specifically explored the modulation of TC structure by the PBL parameterization in a coarser-resolution ESM. In this study, we evaluate the role of the PBL scheme on modeled TC structure in the Community Atmosphere Model version 6 (CAM6), which is the atmospheric component of the Community Earth System Model version 2 (CESM2). CAM6 employs the Cloud Layers Unified by Binormals (CLUBB) scheme. To enhance generalizability of turbulent processes, we apply an experimental version of CLUBB (CLUBBX) with a prognostic formulation of momentum flux and a regime-specific formulation for the dissipation of turbulent eddies.
We perform a sensitivity analysis, the Morris one-at-a-time (MOAT) method, to evaluate the influence of various tunable CLUBBX input parameters on process-based metrics that characterize TC structure in an idealized framework. We find that certain tunable CLUBBX parameters controlling vertical turbulent mixing in the PBL modulate key TC metrics like jet height, inflow angle, and surface heat flux. We further demonstrate that targeted perturbations to these influential parameters can reduce established ESM biases in modeled TC structure.
However, in a global ESM, the accurate depiction of individual TCs should not come at the expense of the model’s depiction of the mean climate. Therefore, it is important to understand how the calibration of CAM6-CLUBBX impacts other aspects of the global and regional climate. We therefore repeat the MOAT sensitivity analysis on global ESM simulations to evaluate how these CLUBBX input parameters impact process-based climate metrics on regional and global scales. We leverage an ensemble approach with short, initialized runs (Betacast) to allow for computational tractability.
We find that CLUBBX parameters that influence TC structure also influence various regionally and globally-averaged climate metrics, including thermodynamic profiles, cloud-radiative forcing, and surface wind stress, at short timescales (3 days). We further show that targeted perturbations to a handful of these influential input parameters can reduce global and regional biases in CAM6-CLUBBX at decadal timescales. We explore physical mechanisms for these demonstrated parameter sensitivities and discuss practical implications of targeted model tunings for long-term climate simulations.
How to cite: Nardi, K., Zarzycki, C., Larson, V., and Bryan, G.: The Role of Parameterized Momentum Flux on Biases in Tropical Cyclones and the Mean State in the Community Atmosphere Model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3545, https://doi.org/10.5194/egusphere-egu23-3545, 2023.
The representation of upper tropospheric deep convective divergent outflow (UTDCDO) is compared between ICON-simulations with convection-permitting and convection parameterised set-ups (1 and 13 km resolution) for a convective event over Germany and the Alps on June 10th-11th 2019. Three hypotheses on those UTDCDO have been formulated using idealised Large Eddy Simulations and are now tested on ICON in a convection-permitting set-up: 1. Dimensionality affects the magnitude of UTDCDO in ICON; 2. Convective aggregation and organisation affects the magnitude of those convective outflows in ICON and 3. Convective momentum transport does not affect the magnitude of UTDCDO. A moving box is used to integrate mesoscale divergence, precipitation rate and convective momentum transport. Additionally, ellipse fitting is used to make estimates of convective organisation (dimensionality, area of convective precipitation, etc.).
Variability in UTDCDO at a given net latent heating rate is reduced in ICON with parameterised deep convection, compared to the convection-permitting set-up. Hints, but no conclusive results are found on the effect of dimensionality on the magnitude of UT divergent deep convective outflows. An impact of convective organisation and aggregation on UTDCDO is significant in the dataset: as a consequence of outflow collisions, UTDCDO increases sub-linearly with net latent heating. We also found a statistical relation between normalised UTDCDO and normalised convective momentum transport.
How to cite: Groot, E., Kuntze, P., Miltenberger, A., and Tost, H.: Upper tropospheric convective outflow in ICON convection-permitting and parameterised set-up, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6532, https://doi.org/10.5194/egusphere-egu23-6532, 2023.
Data assimilation (DA) is a tool that is capable of combining observations and numerical weather models (NWMs) in an optimal manner. Current DA systems used by operational forecasting centres are constantly evolving and getting better than before. High-quality observations are very important for the accurate representation of variables in a weather model. In this study, we are incorporating Global Navigation Satellite System (GNSS) tropospheric gradients and Zenith Total Delays (ZTDs) into the Weather Research and Forecasting (WRF) model. WRF model has its operator already developed for the ZTDs and in this research, we are developing a new operator for the assimilation of tropospheric gradients. The assimilation of ZTDs, which are closely related to Integrated Water Vapor (IWV) above the GNSS station, has a positive impact on weather forecasts. On the other hand, tropospheric gradients are not yet assimilated by the weather agencies. Our research is based on a project titled “Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction” (EGMAP) focusing on the impact of GNSS tropospheric gradients and how it can be effectively used for operational forecasting of severe weather. EGMAP is funded by the German Research Foundation (DFG).
The observation operator currently in use for tropospheric gradients is based on a linear combination of ray-traced tropospheric delays (Zus et al., 2022). This observation operator is challenging to be implemented into an NWM DA system. We will thus rely on a more simple and fast observation operator which is based on the closed-form expression depending on the north–south and east–west horizontal gradients of atmospheric refractivity (Davis et al., 1993).
Initial testing of the operator is done on a 0.1 x 0.1-degree mesh configured over Central Europe in the WRF model with 50 vertical levels up to 50 hPa. The model configuration will be later upgraded to a convective-scale resolution after initial testing of the tropospheric gradient operator. Model forcing observations are derived from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data at 0.25-degree resolution. Conventional observations are obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) and are the base dataset for the assimilation studies. The conventional datasets used for assimilation are restricted to surface stations (SYNOP observations) and radiosondes. Additionally, observations from roughly 100 GNSS stations are assimilated at each DA cycle. Three experiments are conducted: 1) Control run with only conventional data; 2) ZTD assimilation on top of the control run, and; 3) ZTD and tropospheric gradient assimilation on top of the control run. Initial DA tests are being performed with an automated rapid update cycle DA framework with 6 hourly intervals based on a deterministic three-Dimensional Variational (3DVar) DA system for the testing of ZTDs and tropospheric gradients. The DA system will be later upgraded to a probabilistic one based on the Hybrid 3DVar-Ensemble Transform Kalman Filter (-ETKF, Thundathil et al., 2021) and 4DEnVar. The EGMAP project status and initial results from the impact of GNSS-ZTDs and tropospheric gradients will be presented.
How to cite: Thundathil, R. M., Zus, F., Dick, G., and Wickert, J.: Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction (EGMAP): Project status and Initial results, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5548, https://doi.org/10.5194/egusphere-egu23-5548, 2023.
A squall line system that occurred on 9-10 April 2016 over southern China was used to investigate the impact of incremental analysis update (IAU) initialization under the replay configuration on its forecasts. The ERA5 global reanalysis and the forecast field of the regional Weather Research and Forecasting (WRF) Model were used to construct the analysis increment. The results showed that IAU initialization reduced the imbalance caused by the introduction of the low-resolution global reanalysis into the high-resolution regional WRF model and retained the microphysical information in the forecast field of the regional WRF model, which reduced the spin-up time. Compared with the cold start run initialized directly by the ERA5 reanalysis, the linear structure and precipitation distribution of the squall line system using IAU initialization were closer to those in the observations. Further analyses indicated that the improvement of the squall line forecasts using IAU initialization was mainly related to the faster development of cold pool caused by retaining the microphysical information in the forecast field of the regional WRF model and the more favorable stratification conditions corrected by the IAU increment.
How to cite: gao, Y. and wang, X.: Impact of incremental analysis update initialization under the replay configuration on forecasts of a squall line event in southern China, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11619, https://doi.org/10.5194/egusphere-egu23-11619, 2023.
ECMWF recent improvements on scientific and technological fronts will be presented. In 2021 two new operational upgrades of the Integrated Forecasting System (IFS), cycles 47r2 and 47r3, have been introduced. In 2022 the ECMWF High-Performance Computing (HPC) facility has migrated from Reading, UK to a new data centre in Bologna, Italy, and on 18 October 2022 the operational system has been ported to a new supercomputer with enhanced capacity, that will pave the way for an increase in resolution in 2023 with the implementation of IFS cycle 48r1.
IFS Cycle 47r2 was first introduced on 11 May 2021 and its key features included changing the vertical resolution of the Ensemble forecast system (ENS) from 91 to 137 levels, the same used in the high-resolution forecast (HRES). This was made possible by introducing single precision arithmetic in both the HRES and ENS forecast systems. The single precision itself is neutral but enabled the ENS change which led to significant forecast skill improvement. Five months later, ECMWF introduced Cycle 47r3 operationally on 12 October 2021. This included major changes to the model physics that had been under development for several years. A more consistent formulation of boundary layer turbulence, new deep convection closure and cloud microphysics changes have increased the realism of the water cycle.
The next science upgrade, cycle 48r1, will be implemented in 2023 on our new HPC system in Bologna. This will see an enhancement of the ENS horizontal resolution to the TCo1279 grid (approximately 9km), the same resolution currently used by the HRES. There will also be an increase of the data assimilation resolution used in the incremental 4D-Var minimisation, and the use a new object orientated approach to run the 4D-Var atmospheric data assimilation (OOPS). Other important changes in 48r1 include running a daily 100 members extended range ensembles, introducing a new multi-layer snowpack model, and improving the atmospheric energy and water conservation.
Looking further ahead, future higher resolution capabilities will be accelerated by the digital twin developments under the European Commission Destination Earth programme, which will build km-scale capability for a range of potential future HPC architectures. Major efforts have been invested in the code scalability of the Integrated Forecasting System to be able to run on GPUs and investigating alternative dynamical core options. Data assimilation will evolve towards a fully coupled approach to maximise the exploitation of observations and benefit all components of the Earth system (atmosphere, land, ocean) in a consistent way. Machine Learning (ML) will be exploited to enhance the performance and efficiency of our systems.
Finally, our Copernicus partnership with the European Commission has just entered its second phase. Synergistic interactions between meteorology and composition will be pursued for the mutual benefit of both and preparatory steps for next ECMWF climate reanalysis, ERA6, and new seasonal forecasting system, SEAS6, have already started. Several major upgrades in ERA6 and SEAS6 will aim at mitigating against systematic model biases to produce climate records with significantly improved time consistency, and enhanced reliability for extended-range predictions.
How to cite: Balsamo, G., Rabier, F., Balmaseda, M., Bauer, P., Brown, A., Dueben, P., English, S., McNally, T., Pappenberger, F., Sandu, I., Thepaut, J.-N., and Wedi, N.: Recent progress and outlook for the ECMWF Integrated Forecasting System, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13110, https://doi.org/10.5194/egusphere-egu23-13110, 2023.
At Deutscher Wetterdienst (DWD), the SINFONY project has been set up to develop a seamless ensemble prediction system for convective-scale forecasting with forecast ranges of up to 12 hours. It combines Nowcasting (NWC) techniques with numerical weather prediction (NWP) in a seamless way. So far NWC and NWP run on two different IT-Infrastructure levels. Due to the data transfer between both infrastructures, this separation slows down SINFONY, makes it complex and prone to disturbances. These disadvantages are solved by applying the interconnected part of the SINFONY on one single architecture using a Docker Container.
With this aim in view a Docker-Container of the respective NWC components is created and executed on the infrastructure of NWP, the high performance linux computing cluster (HPC) of DWD. In test applications we already observed a speed up of roughly 20% by using the Container on the HPC-cluster instead of using NWC-Tools on the initial NWC IT-Architecture. The Container is already implemented in DWD’s experimental tool BACY for the assimilation cycle.
A major innovation of SINFONY is the rapid update cycle (RUC), an hourly refreshing NWP procedure with a Forecast range of 8 hours, which will be extended to 12 hours soon. The container will be implemented to the RUC and used for the subsequent combination of NWP and NWC forecasts.
In the presentation I will explain what a container is and discuss opportunities and risks of this technology. I will introduce how building the Container is integrated to the CICD procedures at DWD, how and where the Container is implemented to BACY and discuss latest results for the implementation to the RUC.
How to cite: Zacharuk, M., Welzbacher, C., Schnoor, I., Rathmann, N., Eser, C., Prill, F., and Blahak, U. and the SINFONY: Docker container in DWD's Seamless INtegrated FOrecastiNg sYstem (SINFONY) , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1463, https://doi.org/10.5194/egusphere-egu23-1463, 2023.
Accurate and precise weather forecasting is essential for a wide range of applications and industries, from agriculture to transportation to renewable energy. However, current weather models often struggle to represent the weather accurately due to limitations in spatial resolution. Global models with broad resolution are unable to represent small-scale weather features, such as convective thunderstorms or local wind patterns, while regional high resolution models are highly dependent on boundary conditions and typically provide forecasts for a small domain. To fill this gap, Meteomatics has developed the EURO1k model, the first pan-European weather model with a 1km2 resolution.
The EURO1k model consists of approximately 20 million grid points and is run 24 times per day, with a forecast horizon of 24 hours. It is based on the WRF (Weather Research and Forecasting) model and uses global ECMWF-IFS model data for boundary conditions. In addition to standard data sources such as weather stations, radar and satellite data, and radiosondes, the EURO1k model also ingests data from a network of Meteodrones, small unmanned aircraft systems (UAS) developed by Meteomatics which collect vertical atmospheric profiles up to 6000m in altitude. The high resolution of the EURO1k model allows it to accurately represent small-scale weather patterns, resulting in highly accurate and precise forecasts. This is evident in verifications against weather station observations, which show a very good agreement between model output and a range of weather variables including wind, temperature, and radiation.
Statistical analyses of EURO1k model output against observations from 5000 weather stations in Europe demonstrate better accuracy compared to other global and regional models. This has important implications for industry and the public. The EURO1k model can improve the forecasting of extreme weather events, allowing for better preparation and response. It can also enhance the prediction of renewable energy production, which depends on weather conditions. This increases the cost efficiency of renewable energies and help to reduce CO2 emissions. And, most importantly, it provides a more accurate and reliable weather forecast for communities across Europe. Overall, the EURO1k model represents a major advance in numerical weather prediction, bringing improved understanding and forecasting of the weather to a wide range of users.
How to cite: Pasquier, J. T., Rausch, J., Stauch, A., and Fengler, M.: EURO1k: A high-resolution European weather model developed by Meteomatics, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16670, https://doi.org/10.5194/egusphere-egu23-16670, 2023.
Infrasound waves generated by phenomena at the Earth’s surface can travel to these levels before returning to the surface and being detected. Observations like travel time, change in backazimuth angle, and trace velocity contain integrated information of all the levels the wave travelled through. These often include stratospheric and mesospheric levels which are otherwise poorly observed.
In this work we take a data assimilation technique, the Modulated Ensemble Transform Kalman Filter, which is commonly used in satellite data assimilation, and illustrate how it can be readily used for infrasound data assimilation. We highlight the similarities between the two problems, and the particular challenges in extracting information from summarised quantities. To our knowledge, this is the first work doing data assimilation with a full ray-tracing model as forward operator.
How to cite: Amezcua, J. and Näsholm, S. P.: Using satellite data assimilation techniques to combine infrasound observations and a full ray-tracing model to constrain atmospheric variables, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8665, https://doi.org/10.5194/egusphere-egu23-8665, 2023.
We explored the sensitivity of the atmosphere general circulation model OpenIFS to horizontal resolution and time step. We conducted a series of experiments at different horizontal resolutions (i.e., 100, 50, and 25 km) while maintaining the same time step (i.e., 15 minutes), and using different time steps (i.e., 60, 30 and 15 minutes) at 100 km horizontal resolution. We find that the zonal wind bias over the Southern Ocean has significantly reduces at high horizontal resolution (i.e., 25 km), and that this improvement is evident too when using a coarse resolution model with smaller time step (i.e., 15 min and 100 km horizontal resolution). There is also evidence of improvements in the mid-latitude westerly jet in the Northern Hemisphere too, which is also sensitive to both model time step and horizontal resolution. We have also found that the biases in wave speed and wave amplitude reduce when we shorten the model time step or increase the model horizontal resolution. Therefore, it is clear that the improvement in the highest horizontal resolution (i.e., 25 km) simulation is a combination of both the enhanced horizontal resolution and shorter time step. We speculate that the improvement in the surface zonal wind bias in the coarse resolution with shorter time step (i.e., 15 min and 100 km horizontal resolution) simulation is mostly due to shallow convection that is intensified at shorter time step. In addition, we have also noticed improvements in the surface-air temperature when a high resolution and a smaller time step; however, the precipitation bias is independent of the model’s horizontal resolution and time step.
We propose based on OpenIFS that by reducing the time step in a coarse resolution atmospheric model (at least in OpenIFS), one can alleviate the surface-wind biases in the extratropics that is important for e.g., climate modeling in the Southern Ocean sector.
How to cite: Savita, A., Kjellsson, J., Pilch Kedzierski, R., Park, W., Latif, M., and Wahl, S.: ECMWF-OpenIFS Climate Sensitivity to Horizontal Resolution and Time Step, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9199, https://doi.org/10.5194/egusphere-egu23-9199, 2023.
Australian weather forecasts use Numerical Weather Prediction (NWP) model output. Forecast accuracy is improved by assimilating a range of observational data which includes Australian Bureau of Meteorology station data. The significant investment by the Bureau of Meteorology in the national observing network, and the constant evolution of observational technologies, requires an ongoing assessment of the scientific value of the network components. Examining an objective measure of the impact of each assimilated observing system on the quality of short-term NWP forecasts can potentially guide planning and investment decisions related to network efficiency and effectiveness.
Traditional techniques for assessing the impact of observations in NWP are inflexible (i.e. they require dedicated trials) and computationally expensive, but a widely used technique, known as adjoint-based Forecast Sensitivity to Observations (FSO), can provide forecast impact information continuously, flexibly, and in near real-time. We use archived FSO data to assess the relative forecast impact of in-situ data for different instruments and variables. We use two case studies to examine the impact of 1) three upper-air measurement instruments - radiosondes, aircraft, and a wind profiler - through the atmosphere at Sydney Airport, and 2) Automatic Weather Station surface observations along the Great Barrier Reef. These studies aim to provide network planners with information that can guide observations rationalisation decisions.
How to cite: Rogers, C. and Tingwell, C.: Forecast sensitivity to the assimilation of observational data - two case studies for Australia , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14259, https://doi.org/10.5194/egusphere-egu23-14259, 2023.
The shape, sharpness and altitude of the extratropical tropopause (TP) is strongly linked to the position and the strength of the subtropical and polar jet streams that determine the weather in the midlatitudes. However, current numerical weather prediction models fail to correctly represent the sharpness of the TP (i.e., the gradients of wind and temperature). In this study, we address the question if and how the assimilation of radiosonde observations influences the TP representation and whether it acts to sharpen or smooth near near-tropopause gradient.
We investigate the influence by comparing temperature, Brunt-Väisälä frequency (N²) and wind profiles of the observations (y), the model background (xb) and the analysis (xa) in tropopause-relative coordinates.
In total, we analyse more than 9000 radiosondes that were assimilated by the European Centre for Medium-Range Weather Forecast’s Integrated Forecast System (ECMWF IFS) over Canada, the Northern Atlantic and Europe during a one-month period in fall 2016. To test whether the diagnosed influence is caused by the assimilated radiosondes, we conducted a data denial experiment that excluded 500 radiosondes that were launched in the framework of the North Atlantic Waveguide and Downstream EXperiment (NAWDEX) field campaign. In observation space, we investigate the departures (i.e., the differences between y, xb and xa) in the control run (CTR) with all radiosondes considered and the denial run (DEN) without the NAWDEX radiosondes.
The observed minimum temperature at the TP is overestimated in the background forecast (warm bias, ~1 K). Above, in a layer 0.5-2 km, the temperature is underestimated (~0.5 K). Consequently, the sharpness of the TP which is diagnosed by the maximum of N² is also underestimated. We show that data assimilation is able to improve the temperature and to slightly strengthen the TP in the analysis, particularly in situations where the observed and model TP altitude fairly agree. In the data denial experiment we show that this influence exists in the CTR, but not in the DEN run, and thus can be attributed to the assimilation of the radiosonde data.
Regarding wind, we find an underestimation of the maximum wind at and below the TP (0.5-1 m s-1) and demonstrate that the assimilation of radiosonde winds is able to improve the wind profile across the TP. The bias and the positive influence are found to be stronger in situation of strong wind, i.e., the jet stream.
Although data assimilation is able to improve wind and temperature gradients across the tropopause by pulling the background closer to the observations, the individual analysis profiles still underestimate the sharpness of the tropopause. The misrepresented TP in models may impact the quality of weather and climate projections.
How to cite: Krüger, K., Schäfler, A., Craig, G., and Weissmann, M.: The influence of radiosonde observations on the sharpness and altitude of the tropopause, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12877, https://doi.org/10.5194/egusphere-egu23-12877, 2023.
With the ongoing climate change, the Arctic region is experiencing rapid warming. This has a profound effect on the sea-ice cover and, as a result, on the surface albedo. Surface albedo has a large impact on the energy balance of the region: a decrease in surface albedo leads to increased absorption of solar radiation and thus higher temperatures, ultimately leading to the albedo decreasing further. Information on the surface albedo is therefore necessary for various applications and climate studies. Atmospheric reanalysis products answer this need, providing consistent multiyear datasets with good spatial coverage.
We have studied the Arctic sea-ice albedo in two reanalyses. First is ERA5, a global atmospheric reanalysis by the ECMWF (European Centre for Medium range Weather Forecasts). ERA5 has a horizontal resolution of 31 km, and sea-ice is modelled with a one-dimensional sea-ice parameterisation scheme.
The second reanalysis is CARRA (Copernicus Arctic Regional ReAnalysis), a regional atmospheric reanalysis covering a part of the Arctic with two overlapping domains: the western domain centred around Greenland and the eastern over the European Arctic. The horizontal resolution is 2.5 km, and similarly to ERA5, sea-ice is modelled with a one-dimensional thermodynamic sea-ice scheme.
We compare the surface albedo of these two reanalyses to the satellite-based black-sky surface albedo product of the CLARA-A2.1 dataset (CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data). Comparisons are made for April to September, 2000-2015, for the sea areas of the CARRA domains. In addition to a general assessment, four different regions within the domains are studied separately.
How to cite: Kallio-Myers, V., Batrak, Y., and Cheng, B.: Comparison of Arctic sea-ice albedo between CARRA and ERA5 reanalyses and satellite based CLARA-A2, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1510, https://doi.org/10.5194/egusphere-egu23-1510, 2023.
Points of interest are identified along the track. Most commonly these points are the locations of weather stations, as they are generally placed where weather conditions are of interest and the WOD system has additional machine learning interpolation mechanisms in development for weather stations. From this set, along with on-the-hour locations, a representative, refined, lower resolution track is assembled, for which high-resolution forecast data is pulled.
From that forecast data, the information most relevant to the user is highlighted. Any difficult conditions, as well as a segmented summary is generated in simple, succinct text, programmable in any language.
An ongoing extension of this feature is to develop a module that can create a simple text forecast for any user defined region.
The WOD software is maintained in Git and can be installed on suitable hardware in a matter of hours, bringing the full flexibility and power of the WRF modelling system at your fingertips.
How to cite: Guðmundsson, E. H., Rögnvaldsson, Ó., and Stanislawska, K.: Automatic generation of a text forecast along a track, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17383, https://doi.org/10.5194/egusphere-egu23-17383, 2023.
Posters on site: Mon, 24 Apr, 16:15–18:00 | Hall X5
This study explores the potential impact of global navigation satellite system (GNSS) radio occultation (RO) data on the performance of satellite radiance data assimilation for the tropical cyclone formation forecast over the western North Pacific. The forecast experiments of 32 tropical disturbances in September−October 2019 are performed through a regional model. Either assimilation of GNSS RO data, radiance data, or both of them can improve the forecast skill and environmental moisture of tropical cyclone formation. However, the interaction between radiance and GNSS RO data can further increase moisture throughout the forecast period, compared to the experiment with only radiance data assimilation. Moreover, the improved vorticity patterns are different between the experiments with GNSS RO data and with radiance data. When both the GNSS RO and radiance data are assimilated, the improved vorticity pattern tends to the pattern improved by GNSS RO data assimilation. This may be attributed to the anchoring effect of GNSS RO data on satellite radiance data assimilation. Although radiance data volume is much larger than GNSS RO data, the interaction between GNSS RO and radiance data in the data assimilation process can significantly improve forecast performance.
How to cite: Teng, H.-F., Kuo, Y.-H., and Done, J.: Impact of radio occultation data on satellite radiance data assimilation performance in tropical cyclone formation forecast over the western North Pacific, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1079, https://doi.org/10.5194/egusphere-egu23-1079, 2023.
Slope and aspect are important topographic elements for thermodynamics and dynamics of atmospheric circulation, especially for local radiation and topographic precipitation. We propose a simple realistic statistical method based on trigonometric function transformation to calculate sub-grid slope and aspect for describing the orographic characteristics of complex areas over the globe. It is found that the transformed conditional probability density function (PDF) conforms to the Gaussian distribution in most of the global areas (~98%), and this feature is not eliminated with the increasing of horizontal resolution. The reasonability of this method is tested over the Tibetan Plateau. The results show that the improvement ratio of surface solar radiation downward (SSRD) over the Tibetan Plateau improved significantly compared with the results from the grid average scheme, especially in autumn. The improvement of root mean square error (RMSE) is approximately 18.2 W/m2, and the improvement ratio reached 38.4%. The improvements of maximum and regional-averaged SSRD over the whole Tibetan Plateau were ~130 W/ m2 and ~44.3W/m2 respectively. Although we only consider the effect of sub-grid slope and aspect on solar shortwave radiation, which has a certain bias with the observation data, it is sufficient to prove the rationality of the statistical method compared with the unobstructed horizontal surfaces scheme (CTL). After that, we applied this sub-grid parameterization scheme for topographic vertical motion in CAM5 to revise the original vertical velocity by adding the topographic vertical motion and then resulting a significant improvement of simulation in precipitation over steep mountains.
How to cite: Wang, Y., Wang, L., Feng, J., and Song, Z.: A statistical description method of global sub-grid topography for numerical models, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1830, https://doi.org/10.5194/egusphere-egu23-1830, 2023.
In numerical models, the amount of clouds affects atmospheric temperature through interaction with radiation. In the Korean Integrated Model (KIM), which has been in operation at the Korea Meteorological Administration since April 2020, the amount of clouds is determined from prognostic equation consisting of source and sink terms by physical processes such as planetary boundary layer (PBL) mixing, convection, advection, condensation, and evaporation. In the control KIM version, the temperature forcing used for calculating the rate of changes in time of saturation specific humidity which determines formation of cloud area due to condensation is calculated by considering those from all physical processes, such as radiation, cumulus convection, and turbulence, as well as cloud microphysical processes. However, we found the inconsistency between the cloud fraction and mixing ratio by using the methodology, so in this study, we modify the temperature forcing from all physical processes into that only due to the microphysical process. It is confirmed that the change in the amount of clouds changes the temperature and humidity of the atmosphere through the interaction between physical processes such as radiation, which also affects precipitation.
In this study, to examine the effect of changes in cloud cover on precipitation in the Korean Peninsula, we perform one case study July 4, 2021 when precipitation in Gangwon occurs due to the convergence of air currents caused by east wind of high pressure in the eastern of Korea. Up to 172.5 mm of daily maximum precipitation was reported in the Gangwon region. In the 3-day forecast of the case, the control KIM underestimates inland precipitation. But the trend of under-estimation is improved by increasing the amount of precipitation when the cloud amount is modified. The increase in precipitation mainly occurs in the large-scale precipitation due to the microphysical process. This is because the cloud amount generally increases in the Asian area including the Korean Peninsula, which makes the environment favor to the precipitation, by decreasing the temperature through the radiative cooling, in turn resulting the decrease in saturation vapor pressure. For the statistical evaluation of the precipitation performance, precipitation verification is also performed for one month in July, and it is found that ETS (Equivalent Threat Score) performance against the reanalysis data on the Korean Peninsula is also improved.
How to cite: Park, Y., Lim, J.-O., Choi, H.-J., Cho, K., Baek, S.-K., and Cheong, S.-H.: Impact of changes in the cloud amount due to condensation processes on precipitation forecasting on the Korean Peninsula during the summer in the Korean Integrated Model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2466, https://doi.org/10.5194/egusphere-egu23-2466, 2023.
Ensemble sensitivity is a tool to quantitatively determine which initial conditions influence a forecast quantity of choice. This information can then be used to understand the sources and dynamics of forecast uncertainty, quantify the impact of observations (e.g., E-FSOI), and determine where to best deploy observations to improve the forecast (e.g., observation targetting and network design). The ensemble sensitivity is calculated from the covariances of the initial ensemble to the forecast ensemble. Unfortunately, these covariances are prone to sampling errors due to the limited ensemble size. The most common approach in data assimilation to mitigate sampling errors is to apply distance-based damping, i.e., localization. This poster explores how to localize the sensitivity correctly and how it differs from analysis localization. Using simplified problems, we highlight the benefits and drawbacks of sensitivity localization and discuss its usefulness to numerical weather prediction applications.
How to cite: Griewank, P. J., Necker, T., and Weissmann, M.: Ensemble sensitivity localization, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2827, https://doi.org/10.5194/egusphere-egu23-2827, 2023.
Since the dynamical core of Korean Integrated Model (KIM) was developed in the 1st phase (2011~2019) of KIAPS, we have been aiming to develop a variable resolution prediction system covering short to medium range in the 2nd phase (2020.9~2026). As a first step towards moving to km-scale resolution, we have increased the model resolution from 12 km to 8 km horizontally and 91 to 137 layers vertically. For increasing the resolution horizontally, dynamics core configurations and terrain elevation data were newly set up. For vertically, vertical coordinates of 137 layers followed that of the European Center for Intermediate Forecasting (ECMWF) Integrated Forecasting System (IFS), which has been increased vertical resolution throughout the troposphere and stratosphere comparing to 91 layers.
This study discusses the forecast impact of high-resolution KIM in terms of objective verification scores against observations and analyses. The overall conclusion for horizontal high-resolution is that it shows slightly positive in southern hemisphere and mainly neutral for northern hemisphere, but also some negative in tropics. One of distinguished results is increasing horizontal resolution leads to cooling in the temperature in the lower and upper troposphere. The cooling in the lower tropospheric over the tropics seems to come from smaller time step that has to be for smaller dx, which results in enlarged low cloud formation and thus more radiative cooling. In case of the upper troposphere, the cooling results from outgoing long-wave radiative cooling by decreasing hydrometeors in physical response to smaller grid spacing. The increase of vertical resolution had an effect of neutral to slight positive in northern hemisphere but showed significant degradation in tropics. To achieve the consistency and improvement for high-resolution model, it is necessary to understand the physical processes related to time step and horizontal and vertical grid spacing.
Acknowledgement
One of the authors, S-J Choi, wishes to acknowledge this study was supported by 2023 New Professor Support Program of Natural Science Research Institute in Gangneung-Wonju National University).
How to cite: Park, J.-R., Kong, H.-J., Nam, H., and Choi, S.-J.: Effects of increasing horizontal and vertical resolution in Korean Integrated Model (KIM), EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3025, https://doi.org/10.5194/egusphere-egu23-3025, 2023.
Assimilation of accurate external satellite-based water vapor data has been demonstrated to be able to improve the Numerical Weather Prediction (NWP) model’s forecasting performance. In this study, two types of near-infrared Precipitable Water Vapor (PWV) data from the Ocean and Land Colour Instrument (OLCI) of the Sentinel-3A and -3B satellites are assimilated into the Weather Research and Forecasting (WRF) model to enhance the weather forecasting performance over the South China area. One type is the raw PWV products obtained directly from the Sentinle-3A and -3B satellites; the other type is the Sentinel-3A and -3B PWV calibrated by PWV from Global Navigation Satellite System (GNSS). The Back Propagation Neural Network (BPNN) model is used in the PWV calibration.
Correspondingly, two WRF assimilation schemes are adopted to assimilate the Sentinel-3 raw PWV and Sentinel-3 calibrated PWV. Additionally, the WRF without data assimilation scheme is used as the background run to investigate the impact of data assimilation. Three WRF experimental schemes are performed for two periods, i.e., February 2020 (dry period) and July 2020 (wet period). The forecasting performance of these three WRF schemes are evaluated by observations from GNSS, radiosonde, and surface meteorological stations. The comparison results with GNSS PWV show that assimilation of Sentienl-3 calibrated PWV can improve PWV forecasting accuracy by 5.6% for February period for the first 6 h after data assimilation. Evaluated by radiosonde profiles, the assimilation of Sentinel-3 both raw and calibrated PWV can improve WRF humidity and temperature forecasting accuracy over the July period. The accuracy improvement can be up to 9.2% (at 2 km to 3 km of altitude) for water vapor mixing ratio and 6.0% for temperature (at 4 km to 5 km of altitude). The WRF rainfall evaluation result has been validated by rainfall observations from meteorological stations and it shows that the assimilation of Sentinel-3 PWV can improve the rainfall forecasting performance over the July period. The rainfall forecasting success rates of three schemes of assimilating no data, Sentinel-3 raw PWV, and Sentinel-3 calibrated PWV are 69.4%, 69.6%, and 71.1%, respectively, over the July 2020 in the South China region.
How to cite: Liu, Z., Gong, Y., Chan, P. W., and Hon, K. K.: The Contribution of GNSS Calibrated Water Vapor from Sentinel-3A/3B OLCI Sensors to Enhancing the WRF Forecasting Performance, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3066, https://doi.org/10.5194/egusphere-egu23-3066, 2023.
Urbanization results in drastic land alteration in which natural land cover is replaced by impermeable surfaces such as compacted soils, buildings and associated infrastructures. While the impact of urbanization on extreme rainfall is captured in satellite data to a great extent, its signal is frequently less obvious in station-level data. Also, the lack of local meteorological data hinders the development of adequate mitigation measures to reduce the impact of extreme rainfall scenarios. To regenerate the local meteorological data, numerical model-based simulations using global boundary conditions are required at finer Spatio-temporal scales. To this end, integrated land surface models which can provide the maximum likelihood of observed rainfall can be of great significance, especially in urban complexes. Weather Research Forecasting (WRF) model is one such numerical model that can lay down a framework to provide short-range weather forecasts by fixing site-specific physics-based parametrization schemes. This study demonstrates the application of the WRF model to provide building-scale weather forecasts based on the finer-scale Urban Canopy Model (UCM) and Local Climate Zonation (LCZ). The numerical modelling framework is set up for Bangalore city, India. Bangalore city is categorized as one of the major urban complexes with a total built-up area of 77.5%. The World Urban Database Access Portal Tool (WUDAPT), which is based on random forest classification of the ground truth training samples, is used to develop the LCZ database for the WRF model. A single-layer UCM is developed to indicate the importance of structural and aerial characteristics of static datasets with appropriate land features. WRF model runs are carried out based on global boundary conditions to provide a 24hr forecast with 3km and 1km spatial domain for the study area at an urban scale. The overall accuracy of 92% (for the built-up area) and 85% (for water bodies) is obtained for LCZs developed using the random forest classification in WUDAPT. In comparison to default configurations of WRF, the forecasts of WUDAPT-based LCZs have shown an improvement at both spatial and temporal scales. The bias (particularly the spatial shift) observed using the default WRF is reduced drastically, and the forecasts are well-matched with the observed Telemetric Rain Gauge (TRG) station rainfall datasets. Assessment of the maximum likelihood of extreme rainfall forecasts can provide a platform for the development of an integrated WRF hydrological configuration in the future. Such frameworks will be greatly beneficial for obtaining more accurate rainfall and flood forecasts.
How to cite: Gaddam, N., Wadhwa, A., Pentakota, L., Reghunath, G., and P Mujumdar, P.: Integration of the WRF Model With Fine-Scale Land Use Data to Simulate Extreme Rainfall Events, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3183, https://doi.org/10.5194/egusphere-egu23-3183, 2023.
The eddy diffusivity and mass flux (EDMF) scheme for simulating turbulent transport in the operational Global Forecast System (GFS) shows a behavior due to a physical and numerical inconsistency in the scheme's numerical procedure to obtain detrained cloud water due to the moist nonlocal mixing and its associated moist adjustment. One-dimensional simulations show that such inconsistency leads to an unphysical distribution of thermal tendencies and detrained cloud water near the simulated planetary boundary layer (PBL) top. To solve this problem, a new procedure to obtain a positive definite solution from the scheme is proposed to solve the EDMF equations in the scheme. We will show the impact of this new solution procedure on the GFS performance.
How to cite: Bao, J.-W., Grell, E., Michelson, S., and Hong, S.-Y.: A positive definite solution for an EDMF PBL scheme that includes a moist adjustment process, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3553, https://doi.org/10.5194/egusphere-egu23-3553, 2023.
An unprecedented heavy rainfall event occurred in Henan Province of central China during 19-20 July 2021. To investigate the impacts of predicted large-scale circulation on the regional convection-permitting prediction of this event, two sets of nested experiments with different convective parameterizations (GF and MSKF) in the outer domain and at convection-permitting resolution in the inner domain are performed with the Weather Research and Forecasting (WRF) model. The analysis found the prediction of “21.7” rainstorm at convection-permitting resolution in the inner domain is largely affected by convective scheme in the outer domain. The large-scale circulation forcing from the outer domain with different convective schemes is significantly different, which ultimately affects the circulation and precipitation in the refined region through lateral boundary forcing. The difference in regional prediction at convection-permitting resolution can be mitigated by adjusting convective latent heat parameterization in the outer domain. This work highlights that appropriately parameterizing convective latent heat is the key to provide reasonable large-scale forcing for regionally predicting this catastrophic heavy rainfall event at convection-permitting resolution, which may also be applicable to other events and other regions.
How to cite: Xu, M., Zhao, C., Gu, J., Feng, J., Li, G., and Guo, J.: Appropriately representing convective heating is critical for predicting catastrophic heavy rainfall in 2021 in Henan Province of China, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3781, https://doi.org/10.5194/egusphere-egu23-3781, 2023.
The Mediterranean Sea’s distinctive and complicated terrain and the surrounding high mountain systems create a favorable environment for cyclogenesis. Despite Mediterranean cyclones being relatively weaker in intensities, smaller sizes, shorter lifetimes, and rarer than mid-latitude cyclones that develop over open oceans, they strongly influence the Mediterranean climate, including extremes. Despite notable improvements in the fine-scale processes, regional models still have considerable uncertainty. They are related to various constraints such as physical parameterization (convection schemes and microphysics), boundary conditions, and horizontal/vertical resolution. This study is focused on the sensitivity to microphysics schemes and convective parametrization using the non-hydrostatic Weather Research and Forecasting (WRF) numerical weather prediction model for a recent intense Medicane, ‘IANOS’, which was formed over the central Mediterranean Sea during 15-21 September. IANOS attained its mature stage on days 17–18 September and landed over the coast of Greece on 18 September. The horizontal grid spacing of 3 km × 3 km and 39 sigma levels up to 25 hPa were taken on one single domain covering most of the Mediterranean Sea, part of Europe, northern Africa, and part of the eastern North Atlantic Ocean. The simulation of IANOS shows strong sensitivity to the initial conditions, and the simulations initialized on 15 September or later show reasonable skill. All the microphysics schemes, ‘with and without’ enabling the convection scheme, reproduce the IANOS characteristics reasonably well with notable inter-simulation differences in magnitude and location. The large discrepancy and inter-simulation difference is noticed in the track and intensity on 18 September before hitting the coast.
Keywords: IANOS, WRF, convection parametrization, microphysics
How to cite: Mishra, A. K., Jangir, B., and Strobach, E.: Simulation of the Mediterranean Cyclone ‘IANOS’ using non-hydrostatic Weather Research and Forecasting model: Sensitivity to Convection Parameterizations and Microphysics, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4128, https://doi.org/10.5194/egusphere-egu23-4128, 2023.
The characteristic adjustment time scale τ is always defined as the time allowed for dissipation of Convective Available Potential Energy (CAPE) in convective parameterization schemes. Previous studies indicate that, in the cloud ensemble, τ is one of the most important parameters that have the greatest influences on the global mean precipitation. Some research work has improved the Kain–Fritsch scheme in the regional model to realize the variable parameters. In the global model, some studies have used machine learning methods to optimize the parameters of the deep convection trigger function. However, changing constant parameters into variable parameters in the global model has not been explored. In our study, the Zhang-McFarlane (ZM) deep convection scheme is improved to realize the variable characteristic adjustment time scale parameter, so as to reduce the precipitation deviation in a global model. In the ZM deep convection scheme, τ is usually the default constant. While in this paper, we use CAPE to modulate τ and propose a calculation formula of τ. In the region where the mean precipitation amount bias is improved, the new scheme mainly increases the deep convective precipitation and reduces the large-scale and shallow convective precipitation. The modified scheme significantly improves the simulation of precipitation over the eastern equatorial Pacific Ocean and some steep terrain regions. The root mean square error of the mean precipitation amount over the eastern equatorial Pacific Ocean and the central Indo-Pacific Warm Pool in boreal summer is reduced after the new scheme is adopted in a global model with the horizontal resolution of 1° longitude and 1° latitude. Moreover, the simulations of precipitation over the Tibet Plateau and South America are also improved. The new scheme reduces the frequency of deep convective precipitation and increases the amount of deep convective precipitation each time.
How to cite: Wang, M. and Wang, L.: Simulation of Precipitation with a Variable Characteristic Adjustment Time Scale Parameter of Deep Convection in a Global AGCM, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4649, https://doi.org/10.5194/egusphere-egu23-4649, 2023.
The ensemble forecast system based on the Korea Integrated Model (KIM), which is developed for Korea’s own numerical weather prediction (NWP) model, has been in operation at Korean Meteorological Administration (KMA) since October 2021. KMA ensemble forecast system consists of 50 perturbation members (25 members for long-range forecast) and 1 control simulation. Four-dimensional LETKF (Local Ensemble Transform Kalman Filter) with additive and RTPS inflation scheme is used to make initial perturbation.
Evaluation of forecast scores shows that our operational ensemble forecast system is generally more skillful compared to the deterministic simulation as forecast time is longer. Also, forecast with increased ensemble size produces better representation of atmospheric fields especially in higher latitudes. Details of results from operational ensemble system and impacts of increased ensemble size will be discussed with introducing a brief overview of our ensemble forecast system and development plan in future.
How to cite: Kim, E.-J., Shin, H.-C., Park, J. I., Ha, J.-C., and Kwon, Y.-C.: Status and plan of ensemble forecast system in Korea Meteorological Administration (KMA), EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4730, https://doi.org/10.5194/egusphere-egu23-4730, 2023.
The sub-seasonal characteristics and prediction of rainfall over the Asian Monsoon Area during spring-summer transitional season (April-May-June) are investigated using a full set of hindcasts generated by the Dynamic Extended Range Forecast operational system version 2.0 (DERF2.0) of Beijing Climate Center, China Meteorological Administration. The onset and development of Asian summer monsoon and the seasonal migration of rain belt over East Asia can be well depicted by the model hindcasts at various leads. However, there exist considerable differences between model results and observations, and model biases depend not only on lead time, but also on the stage of monsoon evolution. In general, forecast skill drops with increasing lead time, but rises again after lead time becomes longer than 30 days, possibly associated with the effect of slowly-varying forcing or atmospheric variability. An abrupt turning point of bias development appears around mid-May, when bias growths of wind and precipitation exhibit significant changes over the northwestern Pacific and South Asia, especially over the Bay of Bengal and the South China Sea. This abrupt bias change is reasonably captured by the first two modes of multivariate empirical orthogonal function analysis, which reveals several important features associated with the bias change. This analysis may provide useful information for further improving model performance in sub-seasonal rainfall prediction.
How to cite: Li, Q., Wang, J., and Yang, S.: Sub-seasonal Variations and Predictions of Precipitation over the Asian Monsoon Area with BCC_DERF2.0 in Spring-Summer Transition Season, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6653, https://doi.org/10.5194/egusphere-egu23-6653, 2023.
The Arome numerical weather prediction system is routinely used for weather forecasting over the mountains of the French Alps, Pyrénées and Corsica. However, its skills at temperature forecasting are altered by several 2 m temperature biases: (1) a cold bias at high altitude, (2) a low-altitude warm bias occurring in stably stratified layers and (3) a warm bias during snowfall situations.
Targeted numerical simulations (successive activation of some dynamic, physical and assimilation modifications) were carried out on the day of January 12, 2021, a problematic snowy situation in the Arve valley (Haute-Savoie, French Alps).
Over this period, the operational version of Arome has a mean absolute error (MAE) of 2.3°C in the valley. The increase of vertical resolution does not improve the performance of the model in the valley. The MAE is nevertheless decreased from 1.4 to 1.1°C in the mid-altitude range and from 1.5 to 1.2°C above 2000 m. Conversly, the use of a new surface scheme (ISBA-DIF) associated with a more complex snowpack model (ISBA-ES) allows to better represent the arrival of the warm front in the valley and reduces the error (to 1.8°C) whatever the altitude. The current surface scheme therefore seems too simplistic to correctly model soil-atmosphere interactions in the mountains. Forcing Arome with full-day data assimilation also reduces the bias in the valley (to 2.0°C). However, this experiment deteriorates the scores in the mid-altitude and high-altitude mountains. Furthermore, the situation has a poor initial state as biases are present even before the snow event starts. This may point towards deficiencies in the assimilation of in-situ data in mountain regions, that should be overcome in future work.
These results show that the warm bias during this snowy event has multiple origins. A carefull analysis of other situations will be needed to confirm and correct theses biases.
How to cite: Préaux, D., Dombrowski-Etchevers, I., Gouttevin, I., and Seity, Y.: Study of the 2 m temperature bias of the numerical weather forecasting model Arome over the French Alps, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7481, https://doi.org/10.5194/egusphere-egu23-7481, 2023.
Abstract: With climate change, rainfall is expected to get more intense, leading to cities being increasingly at risk of urban flooding. Understanding local climate change over cities has therefore become a priority for the scientific community and city planners on building resilient cities and mitigating hydrometeorological disasters. Very high resolution (km-scale, ‘convection-permitting’) climate models are required to adequately represent cities and local rainfall extremes. Here we assess the Weather Research and Forecasting (WRF) model for simulating urban rainfall. Despite the wide application of WRF in rainfall simulations (including urban areas), there are limited investigations on the impact of the domain size and how to search for a suitable domain size over a particular city region.
To fill this knowledge gap, Newcastle upon Tyne is selected as the study area to simulate a summer heavy rainfall event with ERA5 (a fifth-generation dataset of global reanalysis developed by the European Centre for Medium-Range Weather Forecasts) as the input data and a radar product from the UK Met Office for validation. Accordingly, different domain sizes with the convection-permitting resolutions from 1 km to 4.5 km (increment: 0.5 km) are explored, and the hourly model outputs are compared with the radar observation data.
This study has proposed and tested a method to decide the most suitable domain size. By using eight assessment indexes (including pattern, cumulative time series, hourly time series, particular values (max/min/mean) as well as the seven statistical indicators of each data and overall data), there are two preliminary conclusions: 1) 200 km × 200 km is the best domain size for the single domain simulation; 2) For 200 km × 200 km or smaller domain sizes, higher resolution produces better results, but for 250 km × 250 km or large domain sizes, resolution sensitivity is opposite. Regarding next steps, the above procedure will be further investigated by applying it to more extreme rainfall case studies and to other cities in order to assess whether results here are generally applicable, and therefore the optimal domain configuration can be usefully applied to produce reliable urban rainfall simulations.
How to cite: Du, S., Zhuo, L., Kendon, E. J., and Han, D.: Exploring Domain Size for WRF High-Resolution Urban Rainfall Simulation, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8950, https://doi.org/10.5194/egusphere-egu23-8950, 2023.
Ensemble and hybrid ensemble-variational Data Assimilation (DA) methods incorporating ensemble-based flow-dependent error statistics into state estimation have emerged in recent decades. In a hybrid DA, the background error covariances are a combination of ensemble covariances and static climatology. The ensemble component provides flow-dependency and non-linear error growth critical for convective-scale models, and the static climatology mitigates the effects of a small ensemble size. Hybrid ensemble variational DA methods were recently implemented in the convective-scale NWP model AROME at Meteo-France.
We present our findings from testing Hybrid-3-Dimensional Variational Data Assimilation in convective-scale NWP model AROME over Austria. Given Austria's unique alpine orography, we investigate the impact of applying different weighting to flow-dependent covariances in hybrid DA for a summertime convection case over central Europe. In addition to the hybrid weights, we explore optimal ensemble size, the increase of ensemble size with a time-lagged approach as well as suitable localization settings. Finally, we compare our results to the 3-dimensional variational data assimilation (3DVar) operational model forecast of GeoSphere Austria and discuss the potential benefits, drawbacks, and challenges of using hybrid DA over traditional 3DVar.
Keywords: summertime convection; hybrid-3DEnVar; AROME NWP model; flow dependent background error covariance
How to cite: Jyoti, K., Weissmann, M., Griewank, P., and Meier, F.: Testing the AROME Hybrid 3DEnVar for convective-scale NWP over Austria, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12636, https://doi.org/10.5194/egusphere-egu23-12636, 2023.
The development of regional reanalyses aims at the provision of high-resolution data sets that are suitable for climate applications and climate services. As the desired high-resolution information can barely be provided by either synoptic or remote sensing observation data, a growing interest in high-quality regional reanalyses is recognisable. Particular demand arises from the renewable energy sector. Further quality gains are expected by using an ensemble approach, e.g. by making available the desired uncertainty information when moving towards higher resolution.
Within the framework of the Innovation Programme for applied Researches and Developments (IAFE) at Germany's national meteorological service (DWD) our project aims to develop and evaluate an operational ensemble-based regional reanalysis system incorporating the current NWP model of DWD (ICON). One final goal of the project is to provide a basic framework for user-oriented verification.
We first present the Basic Cycling Environment (BACY) being mainly characterized by its modularity, robustness, user-friendlyness as well as its high complexity. Thus, our future reanalysis system will be a certain BACY version with "frozen" specifications. To assess BACY specifications such as model resolution, number of ensemble members, domain size and choice of output variables NWP simulations will be performed and first simulation results will be presented.
How to cite: Kelbch, A., Spangehl, T., Borsche, M., Rösch, T., and Imbery, F.: Ensemble-based regional reanalysis system for Central Europe: Development framework and outlook, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13449, https://doi.org/10.5194/egusphere-egu23-13449, 2023.
Precipitation characteristics are expected to change in the future as a consequence of global climate change. For example, high-intensity precipitation is expected to become more frequent in some areas of the world. The short time scales and small spatial scales of intense precipitation events pose challenges for numerical weather prediction (NWP) models. Measurements of precipitation characteristics from in-situ and remote sensing instrumentation are often available at much higher time resolution than common NWP model output, and need to be aggregated for validation studies. Here we present a methodology to enable the comparison of precipitation observations and model output at the time scale of the model time steps. Our analysis is focused on an extreme, convective precipitation event during 30th July 2019 in Bergen, Norway (60.38ºN, 5.33ºE, 12 m a.s.l.). We use high-resolution measurements of precipitation characteristics from a Micro Rain Radar Metek MRR-2, an Ott Parsivel2 Disdrometer, and a TPS-3100 Hotplate Pluviometer. Model precipitation was extracted from the operational NWP model MetCoOp that uses a horizontal grid spacing of 2.5 km and 65 vertical levels as part of the HARMONIE AROME model configuration. Using DDH (Diagnostics par Domaines Horizontaux), a novel tool for extracting prognostic variables from the model at a time-step resolution, we extracted a detailed dataset from a NWP model reforecast at every time step (75s), for a 62.5 by 62.5 km subdomain centred around the measurement site. We characterised precipitation by investigating five parameters, namely rain rate, liquid water content, mean volume diameter, the normalised intercept parameter, and terminal fall velocity. The newly developed methodology enabled a direct comparison of the observed precipitation characteristics with corresponding parameters from the model prediction for the convective rainfall event. Despite a generally reasonable correspondence between all parameters in the model and observations, the model struggled with underestimation of rainfall intensity during the high-intensity periods. The onset and intensity of precipitation depended strongly on location for the investigated event. Higher time resolution provided more detailed insight into intensity, timing and spatial variability of the modelled precipitation compared to the more commonly used hourly interval. Our new methodology can be easily applied to other precipitation events, such as frontal rainfall events, and thus provide process-level understanding of precipitation characteristics simulated by high-resolution NWP models.
How to cite: Steinslid, M., Sodemann, H., and Kähnert, M.: Enabling the comparison of high-resolution precipitation observations with numerical weather prediction model simulations at every model time-step, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14882, https://doi.org/10.5194/egusphere-egu23-14882, 2023.
Numerical weather prediction (NWP) models are frequently used tools in operational weather forecasting. The NWP bases on current weather observations and processing of this data using computational models to forecast possible weather conditions. The aim of the study was to determine the optimal configuration of the Weather Research and Forecasting (WRF) model , version 4.2 (Skamarock et al. 2008), for more effective weather forecasting for the area of Poland. For model evaluation, we used observations from the IMWM-NRI network (above 50 meteorological stations). Numerical simulations were run using GFS model data was obtained from NOAA's NCEP servers. The WRF model was configured for a 3 km horizontal resolution grid, using unique parameterization settings for this model. Validation of forecast data was performed using statistical measures recommended by the WMO, e.g. mean error, mean absolute error, mean squared error, showing the values of forecast error. In this study, the model settings were configured based of other papers for Europe (Stergiou et al. 2017, Mooney et al. 2013, Kioutsioukis et al. 2016, Garcia-Diez et al. 2015, Carvalho et al. 2014, Santos Alamillos 2013), especially from its central part (Wałaszek et al. 2014, Kryza et al. 2017). The results of the work present statistical summaries of optimal model parameterization schemes, depending on their verifiability. Model configuration characterized by the best performance will be further examined over a longer time period (in the study, the average MAE for air temperature was 0.8°C). The research was funded by National Science Center (project number: 2017/27/N/ST10/00565)
How to cite: Kendzierski, S.: Influence of resolution and parameterization of the WRF model on the verifiability of weather forecast, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15595, https://doi.org/10.5194/egusphere-egu23-15595, 2023.
A new double-moment parameterization with in-cloud microphysical processes is developed for use in weather forecasting and climate studies. A main ingredient of the scheme utilizes a concept to represent the partial cloudiness effect on the microphysical processes, following the study of Kim and Hong (2018). The underlying assumption is that all the microphysical processes occur in a cloudy part of the grid box. Based on the long-term evaluation of the WRF Single-Moment (WSM) and WRF Double-Moment (WDM) schemes by WRF community, several revisions are made in microphysics terms, along with a newly introduced aerosol effect in ice processes. An aerosol-aware feature with prognostic aerosol emissions of sea salt, dust, anthropogenic and wildfire organic carbon for CCN is also designed. A mass-conserving Semi-Lagrangian sedimentation is re-configured for double-moment physics, which is superior to the conventional Eulerian algorithm in the context of the computational accuracy and numerical accuracy. The new scheme reproduces the storm structure in an idealized 2D testbed, accompanying better organized front-to-rear jets, cold pools, and convective updrafts, as compared to the results in the case of conventional microphysics. The wall-clock time is about a half in the US NOAA/GFS model, as compared to that of Thompson scheme.
How to cite: Hong, S., Li, H., Bao, J.-W., and Dudhia, J.: Development of a New Microphysics Scheme with In-Cloud Processes for Weather Forecasting, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16973, https://doi.org/10.5194/egusphere-egu23-16973, 2023.
The daily maximum and minimum temperatures are among the most relevant meteorological variables in weather forecasts and climate monitoring. Their spatial and temporal evolution from synoptical to decadal scales are driven by numerous physical processes and climate feedbacks. Despite the significant improvements in weather forecasting over the last decades, forecasts of daily temperature extremes are still hampered by systematic errors. In this work we perform an integrated evaluation of the daily temperature extremes of the (i) ECMWF ERA5 reanalyses and (ii) ECMWF operational weather forecasts. The observations for the evaluation are taken from the Global Historical Climatology Network (GHCN) addressing: (i) the long-term assessment of the analysis produced by the ERA5 reanalysis, comprising a 40-year period (from 1980 to 2019); and (ii) the assessment of the ECMWF operational forecasts for a 5- year period (from 2017 to 2021). The evaluation carried out is global, however considering the GHCN station distribution and temporal availability, particular focus was given to four regions: Europe, Australia, East and West United States. The results identify a general underestimation of the daily maximum and overestimation of the daily minimum temperatures in both ERA5 analysis and operational forecasts, highlighting a known limitation of the ECMWF model in underestimating the diurnal temperature range. Our results also indicate a reduction of the errors in ERA5 when comparing the latest decade with the 1980’s, which is likely to be associated with an enhanced quality of the analysis due to a higher constrain emerging from the satellite data. The ERA5 analysis outperforms 1 day-ahead weather forecasts, which show some degree of improvement in the considered 5-year period, being associated with model upgrades.
This work was developed in the framework of the CoCO2 project. CoCO2 project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 958927.
How to cite: Lopes, F., Dutra, E., and Boussetta, S.: Evaluation of Daily Temperature Extremes in the ECMWF ERA5 Reanalysis and Operational Weather Forecasts, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7064, https://doi.org/10.5194/egusphere-egu23-7064, 2023.
Accurate forecasting of solar irradiance is crucial for the integration of solar energy into the
power grid, power system planning, and the operation of solar power plants. The Weather
Research and Forecasting (WRF) model, with its solar radiation (WRF-Solar) extension, has
been used to forecast solar irradiance in various regions worldwide. However, the application
of the WRF-Solar model for global horizontal irradiance (GHI) forecasting in West Africa,
specifically in Ghana, has not been studied. This study aims to evaluate the performance of
the WRF-Solar model for GHI forecasting in Ghana, focusing on 3 health centers (Kologo,
Kumasi and Akwatia) for the year 2021. We applied a two one-way nested domain (D1=15
km and D2=3 km) to investigate the ability of the WRF solar model to forecast GHI up to 72
hours in advance under different atmospheric conditions. The initial and lateral boundary
conditions were taken from the ECMWF operational forecasts. In addition, the optical aerosol
depth (AOD) data at 550 nm from the Copernicus Atmosphere Monitoring Service (CAMS)
were considered. The study uses statistical metrics such as mean bias error (MBE), root mean
square error (RMSE), to evaluate the performance of the WRF-Solar model with the
observational data obtained from automatic weather stations in the three health centers in
Ghana. The results of this study will contribute to the understanding of the capabilities and
limitations of the WRF-Solar model for forecasting GHI in West Africa, particularly in
Ghana, and provide valuable information for stakeholders involved in solar energy generation
and grid integration towards optimized management of in the region.
Keywords: WRF-Solar; Global horizontal irradiance; Forecasting; West Africa; Ghana
How to cite: Sawadogo, W., Fersch, B., Bliefernicht, J., Meilinger, S., and Kunstmann, H.: Evaluating the Performance of WRF-Solar Model for 72-Hour Ahead Global Horizontal Irradiance Forecasting in West Africa: A Case Study of Ghana, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17484, https://doi.org/10.5194/egusphere-egu23-17484, 2023.
Posters virtual: Mon, 24 Apr, 16:15–18:00 | vHall AS
Airflow is one of the most important weather parameters in a city. It is important for the air quality, the city's heat balance, pedestrian comfort and the safety of high-rise buildings. Local flow at the scale of streets and districts is difficult or impossible to capture in regional weather models. Computational Fluid Dynamic models are the solution. In this paper, the OpenFoam model was used to model wind direction and speed in specific meteorological situations. The results were compared with measurement stations in Warsaw, and the model was improved on their basis. An averaged Navier Stokes turbulence model was used under steady-stable flow conditions. The Darcy-Forchheimer model was used to take vegetation into account. The poster presents the first results of analyzes related to the spatial distribution of wind direction and speed, delineates areas at risk of low air quality and compares it with the results from measuring stations. In addition to the basic model, a model containing ground thermals was also created to study the extent and intensity of the urban heat island and to study the phenomenon of smog during temperature inversion in selected meteorological conditions. A comparative analysis of both models was made. The first results show that it is possible quite accurately to map airflow in a city. It also indicates that some existing ventilation channels of the city have been blocked or limited due to new investments. The most important ventilation channel is the Vistula valley, which is 500-600 m wide in Warsaw. However, due to the terrain, its most important role is not fulfilled during prevailing westerly winds, and then the air quality decreases, especially at low wind speeds. In most cases, the northern districts are also generally better ventilated (spatial distribution of buildings, higher wind speeds) than the southern districts, but this is not always visible when assessing air quality. The immediate vicinity also influences the aspects of mechanical ventilation of the city and the way buildings are heated. Districts that theoretically should have better conditions for air exchange are often areas of single-family houses and independent boiler rooms. The city centre, despite tighter development, is heated by the municipal heating plant, and they are not direct emitters of pollution. Another aspect is vehicle traffic. In the city centre, more vehicle traffic is another pollutant emitter. For this reason, pollutants specific to heating and traffic were analysed separately. The general problem in high-resolution city-scale modelling is the use of adequate computational power. This initially precludes using CFD models in meteorological nowcasting and short-term modelling.
How to cite: Strzyzewski, T. and Jaczewski, A.: Modeling of wind conditions in Warsaw, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1128, https://doi.org/10.5194/egusphere-egu23-1128, 2023.
Weather forecasting in the Middle Ages was most likely mostly based on persistence, and there are indications that persistence and correlation between elements of the sensible weather, in particular fog, helped in navigation in the N-Atlantic during the Viking age.
Investigation of the weather in the CARRA dataset, produced by dynamic downscaling, reveals that the connection between wind directions and fog is different on the leg between Iceland and Greenland from what it is between Iceland and Norway. Consequently, the same navigational rules could not be applied on both these legs, making navigation from Iceland to Greenland even more difficult than navigation from Norway to Iceland. This, in addition to very high frequency of fog and of strong winds in the vicinity of Greenland, made sailing and navigation between Iceland and the Medieval Nordic settlements in Greenland exceptionally difficult.
How to cite: Ólafsson, H., Weitzel, P., Rousta, I., Soula, B., and Jacopin, L.: Methods of weather forecasting and navigation in the N-Atlantic in the Middle Ages tested with a modern NWP tool, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15208, https://doi.org/10.5194/egusphere-egu23-15208, 2023.
