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, 23–28 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, 23–28 Apr 2023, EGU23-3038, https://doi.org/10.5194/egusphere-egu23-3038, 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, 23–28 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, 23–28 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, 23–28 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, 23–28 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, 23–28 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, 23–28 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, 23–28 Apr 2023, EGU23-15927, https://doi.org/10.5194/egusphere-egu23-15927, 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, 23–28 Apr 2023, EGU23-3781, https://doi.org/10.5194/egusphere-egu23-3781, 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, 23–28 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, 23–28 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, 23–28 Apr 2023, EGU23-1687, https://doi.org/10.5194/egusphere-egu23-1687, 2023.

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, 23–28 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, 23–28 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, 23–28 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, 23–28 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, 23–28 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, 23–28 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, 23–28 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, 23–28 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, 23–28 Apr 2023, EGU23-9199, https://doi.org/10.5194/egusphere-egu23-9199, 2023.

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, 23–28 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 (x_b) and the analysis (x_a) 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, x_b and x_a) 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, 23–28 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, 23–28 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, 23–28 Apr 2023, EGU23-17383, https://doi.org/10.5194/egusphere-egu23-17383, 2023.