AS1.1 | Numerical weather prediction, data assimilation and ensemble forecasting
Numerical weather prediction, data assimilation and ensemble forecasting
Convener: Haraldur Ólafsson | Co-conveners: Jian-Wen Bao, Lisa DegenhardtECSECS
| Mon, 15 Apr, 08:30–12:30 (CEST)
Room 0.11/12
Posters on site
| Attendance Mon, 15 Apr, 16:15–18:00 (CEST) | Display Mon, 15 Apr, 14:00–18:00
Hall X5
Orals |
Mon, 08:30
Mon, 16:15
This session welcomes papers on:

1) Forecasting and simulating high impact weather events - research on using advanced artificial intelligence and machine learning techniques to improve numerical weather model prediction of severe weather events (such as winter storms, tropical storms, and severe mesoscale convective storms);

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

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

4) Verification of model physics and forecast products against theories and observations;

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

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

7) Advances and challenges in applying data from various conventional and avant-garde observation platforms to evaluate and improve high-resolution simulations and forecasting.

8) Application of Artificial Intelligence / Machine Learning in weather forecasting in general

Orals: Mon, 15 Apr | Room 0.11/12

Chairpersons: Lisa Degenhardt, Haraldur Ólafsson
On-site presentation
Ivanka Stajner, Brian Gross, Vijay Tallapragada, Jason Levit, Raffaele Montuoro, Avichal Mehra, Daryl Kleist, and Fanglin Yang

National Oceanic and Atmospheric Administration’s (NOAA’s) Environmental Modeling Center (EMC) is a lead developer of operational Numerical Weather Prediction (NWP) systems at the National Weather Service (NWS), which are used for the protection of life and property and the enhancement of the economy. EMC transitions to operations and maintains more than 20 numerical prediction systems that are used by NWS, NOAA, other United States (U.S.) federal agencies, and various other stakeholders. These systems are developed through a close collaboration with academic, federal and commercial sector partners. 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 global domains.


NOAA’s operational predictions are transitioning to the Unified Forecast System (UFS) framework in order to simplify the operational prediction suite of modeling systems. 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 from local to global domains and predictive time scales ranging from sub-hourly analyses to seasonal predictions.  Disparate legacy 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 several years ago and is planned to continue over the next few years. Fewer resulting applications will consolidate NCEP’s Production Suite that shares a set of common scientific components and technical infrastructure.  This streamlined suite 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 couple of years including for example a new UFS-based hurricane application, recent advances in the use of satellite data and a new verification system. We will present EMC plans for the next few years, within the overall NOAA strategy, and how planned efforts link with other modeling efforts within NOAA, in the broader U.S. and international community.

How to cite: Stajner, I., Gross, B., Tallapragada, V., Levit, J., Montuoro, R., Mehra, A., Kleist, D., and Yang, F.: NOAA’s Environmental Modeling Center Update: Transitioning to Unified Forecast System Applications for Operations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12029,, 2024.

On-site presentation
Karolina Stanisławska and Olafur Rognvaldsson

Machine Learning (ML) became pervasive in every domain of the research, providing opportunities of modeling phenomena that were difficult to capture using known equations. From small models running on student computers, to giant LLMs trained on the whole Internet, ML models come in all shapes and sizes. To the meteorological community, one branch of this research stands out as revolutionary - ML-based global weather models.

ML-based global weather models lie on the opposite end of the spectrum compared to numerical weather prediction (NWP) models. Instead of representing the physics in a form of equations and solving these equations on the model grid, ML models are purely data-driven - even if they managed to represent physics internally, the inference of that physics would remain a black box.

Yet, these models underwent significant advancement in the past year - and three of them stand out - GraphCast (Google), ClimaX (Microsoft) and MetNet (Google). The former two, open-sourced for research purposes, are being tested currently at Belgingur. Having many years of experience with running and deploying NWP weather models, we notice how working with these models differs from working with the new class of ML-based (or data-driven) models.

This talk discusses essential differences between working with NWP and ML-based weather models. What we can, and what we cannot control? What does the process of working with such an ML model look like? What is the main advantage of an ML model run in production? What are the main obstacles in deploying an ML model and running it operationally?

With the current pace of the growth of Machine Learning models, we will be encountering them in our everyday work sooner or later. Knowing the challenges and opportunities of them will help us understand how to use them to our advantage.

How to cite: Stanisławska, K. and Rognvaldsson, O.: Running global Machine Learning weather models - challenges, observations and conclusions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15614,, 2024.

On-site presentation
James F. Kelly, Francis X. Giraldo, P. Alex Reinecke, Felipe Alves, Cory A. Barton, and Stephen D. Eckermann

The U.S. Navy is building a coupled thermosphere-ionosphere prediction system.  As part of this project, we are developing a new dynamical core (DyCore) extending from the ground to the exobase (~500 km).  The DyCore must be able to handle large variations in both temperature and composition, which motivates a new Horizontally Explicit Vertically Implicit (HEVI) time integrator.  Unlike traditional linear Implicit-EXplicit (IMEX) methods commonly used in numerical weather prediction (NWP), HEVI does not require a fixed reference state.  Our DyCore combines HEVI with a Specific Internal Energy Equation (SIEE) and a Spectral Element Method (SEM) spatial discretization to form a robust, whole-atmosphere model for the neutral atmosphere.  We present results for two test cases using the proposed DyCore: an idealized heating/cooling test extending into the middle thermosphere and a perturbation experiment yielding nonhydrostatic baroclinic instability. The idealized heating/cooling test, which is compared to corresponding results from the hydrostatic Navy Global Environmental Model (NAVGEM), demonstrates that HEVI is more robust than traditional linear IMEX methods.  The baroclinic instability test shows that HEVI, when combined with a banded lower-upper (LU) direct solve, is efficient and allows a large timestep.  These numerical results suggest that our HEVI-enabled DyCore is a good candidate for the proposed thermosphere-ionosphere prediction system.

This work was funded by the Office of Naval Research Marine Meteorology and Space Weather program.

How to cite: Kelly, J. F., Giraldo, F. X., Reinecke, P. A., Alves, F., Barton, C. A., and Eckermann, S. D.: Horizontally Explicit Vertically Implicit (HEVI) Time-Integrators for a Non-Hydrostatic Whole Atmosphere Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2375,, 2024.

On-site presentation
Ying Xie, Xiaofeng Wang, Yanqing Gao, Baode Chen, Ronald van der A, Jieying Ding, Wen Gu, Min Zhou, and Hongli Wang

Aerosols and droplets are the main factors of visibility reduction by scattering and absorbing light. For visibility predictions in operational NWP models, hydrometeors are often considered to be the dominant factor in the total extinction, whereas aerosol effects are usually simplified or omitted in models developed for relatively clean regions. In China, also many NWP studies related to visibility forecasting during haze-fog processes have been conducted, primarily focusing on severely polluted periods before 2018. These studies often employed visibility parameterizations that considered either aerosol extinction alone or hydrometeor extinction alone. Therefore, the significance of incorporating both aerosol and hydrometeor extinction into visibility forecasting during haze-fog processes remains uncertain, particularly under recent rapid changes in aerosol concentration, composition, and hygroscopicity in China.

In this study, we first use the 3-D meteorology fields from the Shanghai Meteorological Service WRF-ADAS Real-Time Modeling System (WARMS) to drive the Community Multiscale Air Quality (CMAQ) model. In this version, CMAQ is used in an off-line mode and visibility is diagnosed by combining extinctions due to hydrometeors and aerosols. Satellited derived NOx emissions using the Daily Emissions Constrained by Satellite Observations (DECSO) algorithm have been incorporated to give more up-to-date emissions. We analyze the results of a one-month forecasting period during the winter of 2021-2022 to assess the model's performance and understand the impact of hydrometeor and aerosol extinction on operational visibility forecasting. We find that for the city of Shanghai, aerosol extinction has a minor impact on the model’s performance when forecasting visibility below 1 km but becomes crucial for predictions spanning 1-10 km. Comparison against observations shows that the model well captures the general contributions from various chemical constituents with nitrate as the most important factor in aerosol extinction (~60%). Furthermore, our assessment of the North China Plain (NCP) highlights that in highly polluted areas aerosols could be significant for visibility below 1 km. Finally, we conduct case studies with the fully coupled WRF-Chem model and compare results with the offline WARMS-CMAQ system. Aerosol effects on fog and visibility forecasting due to feedbacks between aerosols, radiation, and cloud physics will be discussed.

How to cite: Xie, Y., Wang, X., Gao, Y., Chen, B., van der A, R., Ding, J., Gu, W., Zhou, M., and Wang, H.: Improving Visibility Forecasting during Haze-fog Processes in Shanghai and Eastern China: the Significance of Aerosol and Hydrometeor, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3686,, 2024.

On-site presentation
Nina Horat, Christopher Bülte, Julian Quinting, and Sebastian Lerch

Data-driven machine learning methods for weather forecasting have experienced a steep progress over the last years, with recent studies demonstrating substantial improvements over physics-based numerical weather prediction models. Beyond improved forecasts, the major advantages of purely data-driven models are their substantially lower computational costs and faster generation of forecasts, once a model has been trained. However, in contrast to ensemble forecasts from physical weather models, most efforts in data-driven weather forecasting have been limited to deterministic, point-valued predictions only, making it impossible to quantify forecast uncertainties which is crucial for optimal decision making in applications.

Our overarching aim is to evaluate and compare methods for creating probabilistic forecasts from data-driven weather models. The uncertainty quantification (UQ) approaches we compare are either based on generating ensemble forecasts from data-driven weather models via perturbations to the initial conditions, or based on statistical post-hoc UQ methods. The perturbation-based methods either leverage initial conditions from the ECMWF IFS ensemble, add random Gaussian noise to the deterministic initial conditions, or add random field perturbations based on past observations (Magnusson et al., 2009). The post-hoc approaches operate on deterministic forecasts and quantify forecast uncertainty using established post-processing methods, namely distributional regression networks (Rasp and Lerch, 2018) and isotonic distributional regression (Walz et al., 2022; Henzi et al., 2021).

Using forecasts from Pangu-Weather (Bi et al., 2023), we evaluate these UQ methods over Europe for selected user-relevant weather variables, such as wind speed at 10 m, temperature at 2 m, and geopotential height at 500 hPa. We focus on daily initialised Pangu-Weather forecasts for 2022 with a forecast horizon of up to 7 days and compare their performance against ECMWF IFS ensemble forecasts. Our results suggest that Pangu-Weather predictions combined with UQ approaches yield improvements over the ECMWF ensemble forecasts for lead times of up to 5 days in terms of the Continuous Ranked Probability Score. However, it strongly depends on the variable of interest which of the UQ methods performs best, none of the different UQ methods performs best over all variables and lead times. Post-hoc UQ methods tend to perform better for shorter lead times, while initial condition perturbations are superior for longer lead times, with in particular the random field method showing promising results.



  • Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X. and Tian, Q. (2023). Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619, 533–538.
  • Henzi, A., Ziegel, J. F. and Gneiting, T. (2021). Isotonic distributional regression. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83, 963–993.
  • Magnusson, L., Nycander, J. and Källén, E. (2009). Flow-dependent versus flow-independent initial perturbations for ensemble prediction. Tellus A: Dynamic Meteorology and Oceanography, 61, 194.
  • Rasp, S. and Lerch, S. (2018). Neural networks for postprocessing ensemble weather forecasts. Monthly Weather Review, 146, 3885–3900.
  • Walz, E.-M., Henzi, A., Ziegel, J. and Gneiting, T. (2022). Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-valued Model Output. Preprint, available at

How to cite: Horat, N., Bülte, C., Quinting, J., and Lerch, S.: Uncertainty quantification for data-driven weather models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5395,, 2024.

On-site presentation
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert

The Global Navigation Satellite System (GNSS) ground-based network in Europe is a comparatively dense network that provides valuable humidity information through Zenith Total Delays (ZTDs) and tropospheric gradients. ZTDs include information on column water vapor, while tropospheric gradients provide information on moisture distribution. Recently, we developed the tropospheric gradient operator (Zus et al., 2023) and implemented it in the Weather Research and Forecasting (WRF) model (Thundathil et al., 2023, under review).

We have conducted ZTD and tropospheric gradient assimilation experiments over a couple of periods, which lasted for two months. We will present our latest test period, the Benchmark Campaign organized within the European COST Action, in May and June 2013. Data from more than 250 GNSS stations in central Europe covering Germany, the Czech Republic, and part of Poland and Austria were assimilated during this period. The data assimilation (DA) system used a rapid update cycle of 3-dimensional variational DA with 6-hourly cycles for two months.

Our research methodology involved configuring a 0.1 x 0.1-degree mesh in the WRF model with 50 vertical levels up to 50 hPa for Europe. Model forcing was done with the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis. We conducted three runs, which included the assimilation of conventional datasets from ECMWF (or control run), ZTD added on top of the control run, and ZTD and gradients on top of the control run. We observed a significant reduction of the root mean square errors; we observed a 42 % and 16 % reduction for ZTDs and gradients in the ZTD assimilation run, which further reduced to 43 % and 21 % for ZTDs and gradients in the ZTD and gradient assimilation. Validation with the atmospheric reanalysis ERA5 and radiosondes revealed improvements in the lower troposphere.

We conducted an additional sensitivity experiment using a sparsely distributed GNSS network. This process involved reducing the station density from roughly 0.5 degrees to 1 degree by replacing the original network with one consisting of 100 stations. We found that the improvement in the humidity field with the assimilation of ZTD and gradients from the sparse station network (1-degree resolution) is roughly the same as in the humidity field with the assimilation of ZTD only from the dense station network (0.5-degree resolution). Therefore, the assimilation of gradients in addition to ZTDs is particularly interesting in regions with a few GNSS stations. It may also be considered a cost-effective way to increase the density of networks.

After preliminary testing of the GNSS ZTD plus gradient assimilation with WRF, we are ready to move to convective-scale assimilation using an ensemble-based approach over different regions and seasons. We will be presenting initial results from our high-resolution simulations.


Zus, F., Thundathil R., Dick G., and Wickert J. "Fast Observation Operator for Global Navigation Satellite System Tropospheric Gradients." Remote Sensing 15, no. 21 (2023): 5114.

Thundathil, R. M., Zus, F., Dick, G., and Wickert, J. "Assimilation of GNSS Tropospheric Gradients into the Weather Research and Forecasting Model Version 4.4.1", Geoscientific Model Development Discussion [preprint], in review, 2023.

How to cite: Thundathil, R., Zus, F., Dick, G., and Wickert, J.: Impact of GNSS tropospheric gradient assimilation and sensitivity analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5656,, 2024.

Virtual presentation
Bobby Antonio, Andrew McRae, Dave MacLeod, Fenwick Cooper, John Marsham, Laurence Aitchison, Tim Palmer, and Peter Watson

Existing weather models are known to have poor skill at forecasting rainfall over East Africa, where there are regular threats of drought and floods. Improved precipitation forecasts could reduce the effects of these extreme weather events and provide significant socioeconomic benefits to the region. We present a novel machine learning based method to improve precipitation forecasts in East Africa, using postprocessing based on a conditional generative adversarial network (cGAN). This addresses the challenge of realistically representing tropical rainfall in this region, where convection dominates and is poorly simulated in conventional global forecast models. We postprocess hourly forecasts made by the European Centre for Medium-Range Weather Forecasts Integrated Forecast System at 6-18h lead times, at 0.1o resolution. We combine the cGAN predictions with a novel neighbourhood version of quantile mapping, to integrate the strengths of both machine learning and conventional postprocessing. Our results indicate that the cGAN substantially improves the diurnal cycle of rainfall, and improves rainfall predictions up to the 99.9th percentile of rainfall. This improvement persists when evaluating against the 2018 March-May season, which had extremely high rainfall, indicating that the approach has some ability to generalise to more extreme conditions. We explore the potential for the cGAN to produce probabilistic forecasts and find that the spread of this ensemble broadly reflects the predictability of the observations, but is also characterised by a mixture of under- and over-dispersion. Overall our results demonstrate how the strengths of machine learning and conventional postprocessing methods can be combined, and illuminate what benefits machine learning approaches can bring to this region.

How to cite: Antonio, B., McRae, A., MacLeod, D., Cooper, F., Marsham, J., Aitchison, L., Palmer, T., and Watson, P.: Postprocessing East African rainfall forecasts using a generative machine learning model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2897,, 2024.

On-site presentation
Lei Zhang, Zeyi Niu, Fuzhong Weng, Peiming Dong, Wei Huang, and Jia Zhu

The Advanced Weather Research Forecast model (WRF-ARW) is used to investigate the potential impacts of assimilating the FengYun-4A (FY-4A) Geostationary Interferometric Infrared Sounder (GIIRS) long-wave temperature sounding channel data on prediction of Typhoon In-Fa (2021). In addition, a series of data assimilation experiments are conducted to demonstrate the added value of the FY-4A/GIIRS data assimilation for typhoon forecasts. It is shown that the higher spectral resolution and broader coverage of GIIRS radiance data can positively impact the model analysis and forecasts with larger temperature and moisture increments at the initial time of simulations, thus producing the better simulation for typhoon warm core aloft, vortex wind structure and spiral rainfall band. Moreover, the assimilation of the GIIRS data can also lead to better storm steering flows and consequently better typhoon track forecasts. Overall, the assimilation of FY-4A/GIIRS temperature sounding channel data shows some added values to improve the track and storm structure forecasts of Typhoon In-Fa.

How to cite: Zhang, L., Niu, Z., Weng, F., Dong, P., Huang, W., and Zhu, J.: Impacts of Direct Assimilation of the FY-4A/GIIRS Long-Wave Temperature Sounding Channel Data on Forecasting Typhoon In-Fa (2021), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6940,, 2024.

On-site presentation
Adam Gainford

Convective-scale ensembles are routinely used in operational centres around the world to produce probabilistic precipitation forecasts, but a lack of spread between members is providing forecasts that are frequently overconfident. This deficiency can be corrected by increasing spread, increasing forecast accuracy or both. A recent development in the Met Office forecasting system is the inclusion of Large-Scale Blending (LSB) in the convective-scale data assimilation scheme. This method aims to reduce the synoptic-scale forecast error in the analysis by reducing the influence of the convective-scale data assimilation at scales that are too large to be constrained by the limited domain. These scales are instead initialised using output from the global data assimilation scheme, which we expect to reduce the forecast error and, thus, improve the spread-skill relationship. In this study, we have quantified the impact of LSB on the spread-skill relationship of hourly precipitation accumulations by comparing forecast ensembles with and without LSB over a 17-day summer trial period. This trial found modest but significant improvements to the spread-skill relationship as calculated using metrics based on the Fractions Skill Score. Skill is improved for a lower precipitation centile by an average of 0.56% at the largest scales, but a corresponding degradation of spread limits the overall correction. The spread-skill disparity is reduced the most in the higher centiles due to a more muted spread response, with significant reductions of up to 0.40% obtained at larger scales. Case study analysis using a novel extension of the Localised Fractions Skill Score demonstrates how spread-skill improvements transfer to smaller-scale features, not just the scales that have been blended. There are promising signs that further spread-skill improvements can be made by implementing LSB more fully within the ensemble.

How to cite: Gainford, A.: Improvements in the spread-skill relationship of precipitation in a convective-scale ensemble through blending, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9034,, 2024.

On-site presentation
Joshua Wiggs, Joe Eyles, and Alice Lake

In modern forecasting it is now a common technique to use an ensemble of forecasts generated by Numerical Weather Prediction (NWP) models. This necessitates a statistical approach be taken when using these weather predictions to inform decision-making and leveraging probabilities in the production of forecasts. It is often required to take the spread of predictions made by NWPs in the ensemble and reduce these to a single value, a pseudo-deterministic forecast, analogous to a forecast made be a traditional deterministic NWP, in order to allow end users to make binary decisions often defined at a definite threshold. These values may be representative of a single physical parameter modelled (e.g. road surface temperature) or may combine multiple parameters in a physically consistent manner (e.g. the road surface temperature coupled to the depth of water on the road for calculating road state), and are used by stakeholders in a number of sectors often to inform safety critical decision making. Therefore, it is important to ensure that the methodology used to reduce the ensemble of predictions to a pseudo-deterministic forecast is as accurate as possible and can retain information related to the ensemble spread , whilst ensuring consistency in parameters through the spatial and temporal domain.

The Surface Transport Forecast (STF) system produces forecasts for different transport surfaces in response to NWP outputs. The STF system is architected such that it runs simultaneously for each member of the NWP forecast ensemble, producing a corresponding ensemble of STF predictions. This enables the computation of a pseudo-deterministic forecast, which retains the maximum amount of information provided by the NWP ensemble.

To reduce the STF ensemble to a pseudo-deterministic forecast a Kernel Density Estimation (KDE) is utilised to build Probability Density Functions (PDFs), which can be readily interrogated using standard statistical techniques. It is found that pseudo-deterministic forecasts, which are consistent across a combination of physical modelled parameters, can be determined using covariant techniques, ensuring the ensemble is reduced as late as possible in the forecast production keeping the maximum benefit provided by the forecast spread. We will present the numerical and computational implementation of the described method in our STF system. Further, we will analyse the pseudo-deterministic forecasts produced and verify the validity of results at specific locations using multiple years of road observations.

How to cite: Wiggs, J., Eyles, J., and Lake, A.: Creating a Pseudo-Deterministic Forecast for Surface Transport from an NWP Ensemble with Consistency Across Multiple Variables using KDE, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11209,, 2024.

Coffee break
Chairpersons: Haraldur Ólafsson, Jian-Wen Bao
On-site presentation
Nasim Alavi, Stephane Belair, Marco Carrera, Maria Abrahamowicz, Bernard Bilodeau, Dragan Simjanovski, Dorothee Charpentier, Bakr Badawy, and Sylvie Leroyer

A new land surface package developed at Environment and Climate Change Canada (ECCC) has been evaluated in the context of the medium-range global deterministic numerical weather prediction (NWP) system. The evaluation is performed by comparison of NWP forecasts against near-surface and

atmospheric analyses. The new land surface package includes i) new databases to specify soils and vegetation characteristics, ii) improved initialization of land surface variables by the assimilation of space-based remote sensing observations, and iii) a more sophisticated land surface scheme.

Evaluation for the screen-level air temperature and humidity indicates that the new land surface package resulted in smaller STDEs and larger temporal correlation between forecasts and analyses comparing to the current operational configuration. The improvement is greater for humidity than for air temperature.

Upper-air evaluation indicates that the impact of the new land surface package on the Planetary boundary layer (PBL) is substantial but more mixed, with large spatial variability in terms of its effect.

This study also investigated the physical and statistical links between near-surface and upper-air forecast errors at the medium range.

How to cite: Alavi, N., Belair, S., Carrera, M., Abrahamowicz, M., Bilodeau, B., Simjanovski, D., Charpentier, D., Badawy, B., and Leroyer, S.: Impact of a new land surface package in Canadian  numerical weather prediction system on the medium range weather forecast in the lower and upper atmosphere, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12345,, 2024.

On-site presentation
Vijay Tallapragada, Jeffrey Whitaker, and Jim Kinter

The UFS-R2O Project, which began in July 2020 as a five-year plan with deliverables for the first three years funded, has made significant progress in developing the medium-range and sub-seasonal to seasonal (MRW/S2S) predictions, a regional, high-resolution hourly-updating and convection-allowing ensemble system for prediction of short range severe weather (CAM/SRW), and a Hurricane Application developing a very high-resolution Hurricane Analysis and Forecast System (HAFS) with storm following moving nests.  The Project is organized with Application Teams and Development Teams interacting with each other to reflect the cross-cutting nature of the UFS components and infrastructure. It  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.  Most sIgnificant outcomes of the project thus far are the implementation of the HAFSv1 ahead of the schedule, and the development of a six-way global coupled (atmosphere/ ocean/ land/ sea-ice/ wave/ aerosol) modeling system, both within the UFS framework, major accomplishments from the community modeling perspective.  

The UFS-R2O Project has entered into its second phase (2023-2024), albeit with reduced funding, to continue the momentum built during the first phase.  While the first three years of the project were focused on engineering and infrastructure, Phase II 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.  In addition, Phase II of the project includes a new Seasonal Forecast System (SFS) Application Tean established to develop SFS v1 that will replace the legacy Climate Forecast System (CFSv2) currently in operations since 2011.

This presentation describes the outcomes of the UFS R2O Project for the first three years, and highlights the progress and plans for the Phase II.

How to cite: Tallapragada, V., Whitaker, J., and Kinter, J.: NOAA's Unified Forecast System Research to Operations (UFS R2O) Project Phase II - Accomplishments, Progress and Future Plans, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2930,, 2024.

On-site presentation
Mingjing Tong, Lucas Harris, Linjiong Zhou, Kun Gao, Alex Kaltenbaugh, and Baoqiang Xiang

The Geophysical Fluid Dynamics Laboratory (GFDL)’s System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD) model typically uses the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses to initialize its medium-range global forecasts. Both initial condition (IC) and forecast model have an impact on model prediction skills. The quality of the IC is partially determined by the model short-range forecast used as first guess in data assimilation. 

A data assimilation (DA) system has been developed for the global SHiELD to demonstrate the prediction skills of the model initialized from its own analysis. The DA system largely leverages the advanced DA techniques used in GFS and assimilates all the observations assimilated in GFS. Compared to the SHiELD forecasts initialized from GFS analysis, SHiELD forecast skill is significantly improved by using its own analysis. Tremendous improvement was found in the Southern Hemisphere with positive impact lasting up to 10 days. The DA system is also useful in identifying and understanding model errors. The most noticeable model error detected by the DA system originates from the TKE-EDMF boundary layer scheme. The model error leads to insufficient ensemble spread, which could not be fully addressed by the multiplicative inflation and stochastic physics schemes used in the system. Including two versions of the TKE EDMF scheme in the ensemble can alleviate the systematic model error, which further improves forecast skills. The use of the interchannel correlated observation errors for Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) was also investigated, which improves the forecast skill up to day 5 and further reduces the impact of the model error in the marine stratocumulus region. Further understanding of the model error associated with the TKE-EDMF scheme will be presented. 

How to cite: Tong, M., Harris, L., Zhou, L., Gao, K., Kaltenbaugh, A., and Xiang, B.: Improved Weather Predictions Through Data Assimilation for GFDL SHiELD, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13187,, 2024.

On-site presentation
Olafur Rognvaldsson and Karolina Stanislawska

Belgingur Ltd. has created a novel weather forecasting framework, called Weather On Demand – WOD, that is deployable in the cloud and on in-house hardware and which can be customised for any location world-wide at a very short notice.

The WOD framework is a distributed system for:

  • Running the WRF weather model for data-assimilation and forecasts by either triggering scheduled or on-demand jobs.
  • Gathering upstream weather forecasts and observations from a wide variety of sources.
  • Processing data for long to medium-term storage.
  • Making results available through APIs.
  • Making data files available to custom post-processors.

Much effort is put into starting processing as soon as the required data becomes available and in parallel when possible.

Recent additions to the WOD system include the potential of:

  • Optional use of the hybrid data assimilation techniques of the WRF Data Assimilation system [1, 2].
  • Set up a multi-domain dispersion forecast of volcanic ash and gases.
  • Use of the Verif [3] verification package to compare forecasts, both upstream and WOD, to observations.
  • Using different sources of initial data to that of the boundary forcing data.

On-going developments focuses on the use of in-situ UAV profiles and radar data as input to the WOD data assimilation system.

We have further started experimenting with using global models, both conventional NWP models as well as novel ML models (cf. abstract no. EGU24-15614).


[1] Xuguang Wang, Dale M. Barker, Chris Snyder, and Thomas M. Hamill, 2008: A hybrid ETKF–3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116–5131.

[2] Xuguang Wang, Dale M. Barker, Chris Snyder, and Thomas M. Hamill, 2008: A Hybrid ETKF–3DVAR Data Assimilation Scheme for the WRF Model. Part II: Real Observation Experiments. Mon. Wea. Rev., 136, 5132–5147.

[3] Nipen, T. N., R. B. Stull, C. Lussana, and I. A. Seierstad, 2023. Verif: A Weather-Prediction Verification Tool for Effective Product Development. Bulletin of the American Meteorological Society 104, 9; 10.1175/BAMS-D-22-0253.1.

How to cite: Rognvaldsson, O. and Stanislawska, K.: The Weather On Demand weather forecast framework - Recent developments and outlook, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16714,, 2024.

On-site presentation
Benjamin Doiteau, Florian Pantillon, Matthieu Plu, Laurent Descamps, and Thomas Rieutord

Cyclones provides the majority of water supplies in the Mediterranean and are essential elements of the climate of the region. The most intense of them lead to natural disasters because of their violent winds and extreme rainfall. Identifying systematic errors in the predictability of Mediterranean cyclones is therefore essential to better anticipate and prevent their impact. The aim of this work is to understand what processes determine their predictability. 

We investigate the predictability of Mediterranean cyclones in a systematic framework using an ensemble prediction system. First, a reference dataset of 2853 cyclones is obtained by tracking lows in the ERA5 reanalysis, using an algorithm developed for the North Atlantic and adapted for the Mediterranean region. Then we investigate their predictability using IFS ensemble reforecasts in a homogeneous configuration over 22 years (2000-2021). The predictability in the reforecasts is quantified using probabilistic scores on cyclones trajectories and on intensity (mean sea level pressure) and then crossed with explanatory variables such as geographic area, cyclone velocity, season and intensity.

The evolution of location error with lead time shows a two phases growth, until and beyond 72 h, which will be discussed. When crossing the location and intensity errors with the explanatory variables, we can identify the conditions leading to a poorer (respectively better) predictability. In particular the velocity of cyclones appears to play an important role in the predictability of the location, the slower the cyclone the better the predictability, while the season is shown to play a greater role on the predictability of the intensity. These characteristics are also dependant on the sub-region considered and on the intensity of the low itself, the deeper the cyclone, the poorer the predictability in both the location and the intensity.

How to cite: Doiteau, B., Pantillon, F., Plu, M., Descamps, L., and Rieutord, T.: What determines the predictability of a Mediterranean cyclone?  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13557,, 2024.

On-site presentation
Aaron Hill and Russ Schumacher

The prediction of excessive rainfall using numerical weather prediction (NWP) models is unequivocally difficult owing to the myriad of complexities that must be resolved (e.g., parent storm dynamics, microphysics) in order to forecast the placement and intensity of rainfall correctly. However, machine learning (ML) has provided a new avenue by which we can generate predictions of excessive rainfall with sufficient lead time to inform decision makers and planners to the threat of inclement weather. ML techniques are able to decode known long-standing relationships between environmental predictors and convective hazards from long historical records, and they have demonstrated tremendous value in predicting weather hazards at longer lead times (e.g., Hill et al. 2023). Further, continued effort by the meteorological community to explain ML models and their forecasts is building trust between developers and end users. As a result, their use in meteorological hazard forecasting is expanding, particularly into the medium range (e.g., 4-8 days) when forecasters are reliant on relatively coarse NWP models to create forecasts.


In this work, we are using Random Forests (RFs) to generate daily probabilistic forecasts of excessive rainfall at 1-8 day lead times. The RFs are trained using output from the Global Ensemble Forecast System and historical observations of excessive rainfall. Environmental parameters like precipitable water and CAPE, as well as modeled precipitation, are spatiotemporally arranged so the RFs can learn spatial and diurnal patterns that associate with excessive rainfall. The RF models are evaluated against a spatio-temporally varying climatology and show skill out to 7 days, and routinely outperform human-based forecasts past a 1-day lead time. In this presentation, we will highlight performance characteristics of the RFs into the medium-range (e.g., out to 8 days) and discuss the implications of excessive rainfall definitions in RF model training. Additionally, we will present an ensemble prediction framework that provides estimates of uncertainty and ranges of forecast solutions that operational forecasters desire at extended lead times.

How to cite: Hill, A. and Schumacher, R.: Medium-Range Excessive Rainfall Prediction with Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11708,, 2024.

On-site presentation
Sören Schmidt, Michael Riemer, and Tobias Selz

Atmospheric predictability is intrinsically limited by the upscale growth of initial small-scale, small-amplitude errors. For practical predictability, model error and initial-condition uncertainty also contribute significantly. The accurate representation and interactions of these factors within numerical weather prediction systems determine the extent to which forecast uncertainty is correctly modeled. An improved understanding of upscale error-growth mechanisms and their flow dependence in numerical weather prediction models has several implications: it enables more focused model verification and development, aids in recognizing limitations in emerging forecasts systems like machine-learning-based approaches, and may indicate when the intrinsic limit of predictability has been reached.

Studying the flow dependence of error growth requires a local perspective, which is not provided by the traditional spectral perspective on upscale error growth. We here take a complementary approach and apply a feature-based perspective. 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 experiments with the global prediction Model ICON from the German Weather Service. Errors in these experiments grow from differences in the seeding of a stochastic convection scheme. In a suite of experiments, this source of uncertainty competes with initial-condition uncertainty of varying magnitude. Evaluation of the process-specific error-growth rates allow the detailed quantification of the upscale-growth mechanisms. 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 in an upscale-error-growth experiment reproduces a previously found three-stage multi-scale upscale-growth paradigm. Illustration the importance of flow dependence, the growth rates from a single feature can substantially differ from the overall average. Further highlighting this importance, intrinsic limits of predictivity can be identified for some features even in the presence of substantial initial-condition uncertainty. The presentation will conclude with a comparison of error evolution in conventional numerical weather prediction systems to a data-driven, machine-learned model.

How to cite: Schmidt, S., Riemer, M., and Selz, T.: A feature-based framework to investigate atmospheric predictability., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17438,, 2024.

On-site presentation
Yuejian Zhu

Ensemble forecasts play a pivotal role in weather prediction, providing valuable insights into the inherent uncertainty of atmospheric processes. Strategies in ensemble construction involve generating multiple simulations by perturbing initial conditions, model parameters, or both. This diverse set of forecasts allows meteorologists to capture a range of possible future scenarios, acknowledging the inherent complexity of the atmosphere. Model resolution is a critical factor, influencing the representation of small-scale features and improving the overall accuracy of ensemble predictions. Additionally, forecast range-related issues address the challenge of extending predictions beyond a few days, where uncertainties tend to grow. Combining advanced statistical techniques with cutting-edge modeling technologies helps refine ensemble forecasts, enhancing our ability to anticipate and mitigate the impacts of weather-related events on society and the environment.

The investigation based operational global ensemble forecast system from NCEP, CMC, ECMWF and CMA to focus on the analyses of ensemble design that combined to the data assimilation for initial condition perturbation and various stochastic physical perturbations. The impact of model resolutions (both horizontal and vertical) will be addressed to the different atmospheric characteristics, such as forecast uncertainty, reliability and resolution. The forecast capability and predictability to the extreme events will be discussed from single model ensemble and multi-model ensemble. Finally, the 1st-moment and 2nd-moment ensemble forecast calibration will be demonstrated from traditional statistical method and machine learning based ensemble reforecasts

How to cite: Zhu, Y.: An assessment of prediction and predictability through the state-of-the-art global ensemble forecast systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6802,, 2024.

On-site presentation
Jane Roque, Arianna Valmassoi, and Jan Keller

Irrigation is one agricultural practice that contributes to maintain an optimal soil water content for crop development. Currently, farmers find this practice as an essential method for adapting to climate change. The Earth science community identified some irrigation effects beyond soil moisture and plant growth impact, as multiple studies found an influence on atmospheric variables such as 2 m temperature, relative humidity and even precipitation. Moreover, the effect of irrigation on the Earth’s system has been studied on various temporal and geographical scales and with different climate and land surface models. However, there are few studies that simulated the effect of irrigation on higher resolutions on a regional scale. Therefore, the aim of this study is to include the representation of irrigation processes in the operational ICON-nwp in Limited Area Mode on the EURO-CORDEX domain. The implementation of the current irrigation parameterization in ICON-nwp coupled with TERRA is an adaptation of the CHANNEL scheme developed by Valmassoi et al. (2020). We found suitable to include this scheme in the land surface and atmosphere interface of the icon-nwp-2.6.6-nwp0 version. The present study consists of four sensitivity experiments with different irrigation water amounts, namely 2.6 mmd-1, 6.7 mmd-1 and two fixed soil water contents, field capacity and saturation. All experiments have the same irrigation frequency (1 day), length (24 hours), and simulation period (May to August). The model settings for the experiments are 3 km resolution, 75 vertical levels and ICON boundary and initial conditions. The results from the difference between experiments and the control run demonstrate that ICON captures the irrigation effect on land surface atmospheric variables. As expected, soil moisture content increased on different magnitudes in all experiments. Moreover, 2 m temperature values dropped on average -0.74 K in irrigated areas. Likewise, energy fluxes were sensible to the different irrigation amounts.

How to cite: Roque, J., Valmassoi, A., and Keller, J.: Irrigation parameterization in the Operational Numerical Weather Prediction model ICON-nwp, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20366,, 2024.

On-site presentation
Chandra Kondragunta, Aaron Pratt, Kevin Garrett, Nicole Kurkowski, Wendy Sellers, and Valbona Kunkel

In 2016, the U. S. Congress created the Joint Technology Transfer Initiative (JTTI) program in the Office of Oceanic and Atmospheric Research (OAR), the research wing of the National Oceanic Atmospheric Administration (NOAA).  Within OAR, the Weather Program Office (WPO) is responsible for managing the JTTI program.  The main mission of this program is to continuously develop and transition the mature weather technologies from the research community to the National Weather Service (NWS) operations.  

JTTI selects promising Research to Operations (R2O) transition projects through two types of competitions: one for the external community (non-NOAA) that includes private, academic sectors and non-profit organizations through Notices of Funding Opportunities; and the other for the NOAA scientific community.  Additionally, the JTTI program collaborates with the NWS Office of Science and Technology Integration (OSTI) and provides funding for the Unified Forecasting System - R2O project and testbed activities.  JTTI-funded R2O projects cover three main frameworks within the NWS forecasting operations: the observational, modeling, and products and services frameworks.  The topics covered include data assimilation; convective scale weather modeling; stochastic physics; ensemble model building; hydrologic modeling; post-processing of model output on time scales ranging from hourly to subseasonal; high impact weather forecasting tools; artificial intelligence/machine learning; and social behavioral and economic science. To date, the JTTI program has funded 155 R2O projects and transitioned 20 projects to the NWS operations.  In this paper, we present the JTTI implementation process in NOAA and share some of the successful R2O stories.

How to cite: Kondragunta, C., Pratt, A., Garrett, K., Kurkowski, N., Sellers, W., and Kunkel, V.: Crossing the Valley of Death : Transitioning Weather Research to Operations in NOAA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20553,, 2024.

Posters on site: Mon, 15 Apr, 16:15–18:00 | Hall X5

Display time: Mon, 15 Apr 14:00–Mon, 15 Apr 18:00
Chairpersons: Jian-Wen Bao, Haraldur Ólafsson
Athira Krishnankutty Nair and Sarmistha Singh

Numerical weather prediction models are utilized to forecast extreme rainfall events at fine resolutions; however, these models possess inherent errors due to the parameterization and discretization of differential equations, which diminishes simulation accuracy. Recent advancements in machine learning methods indicate their potential capability to significantly enhance forecast results. In this study, multiple extreme rainfall events for the Pamba river basin during the Indian Summer Monsoon Period spanning 2-4 days were forecasted using the WRF model. Pamba, one of the flood-prone basins in southern states of India (Kerala), experiences severe flood events annually. While numerous studies have been conducted to simulate the Kerala flood of 2018, those demonstrating the application of high-resolution rainfall data for the Pamba river basin remain unexplored. Therefore, in this study, we simulated multiple storm events during the period from 2015 to 2018 using the WRF model at a high resolution (1 km * 1 km) and a temporal resolution of a one-hour interval. The WRF-simulated rainfall dataset was further post-processed using various machine learning algorithms, including Random Forest, Support Vector Machine, and extreme gradient boost (XGBoost), to reduce bias in the hourly forecast of extreme rainfall events. Several cross-validation training and testing procedures were carried out using various algorithms, and the forecasted and predicted results were compared with ERA5 hourly data of 10*10 km resolution. Results indicated that XGBoost, with hyperparameter tunings, substantially reduced the Root Mean Square Error (RMSE); it was able to reduce the RMSE by up to 50% across the river basin. This hybrid model will provide a more accurate forecast of hourly extreme rainfall during the Indian Summer Monsoon Period for Pamba river basin, with high resolution essential for flood forecasting and warning.

How to cite: Krishnankutty Nair, A. and Singh, S.: Improving extreme rainfall forecasting for a flood prone region: A hybrid modelling approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-857,, 2024.

Sebastián Estrada and Olga Lucia Quintero Montoya

The Atmospheric Boundary Layer Height (ABLH) is a fundamental parameter for many meteorological applications and climate change assessment and evaluation. A large number of methods for ABLH determination have been proposed; however, there is no sufficiently reliable and feasible method for this purpose. The rise of intelligence-based mathematical models for feature determination in data space has allowed their application to solve problems similar to ABLH determination. This article describes the development of a mathematical model based on artificial intelligence for ABLH determination, in which an introductory analysis of the data space from the point of view of machine learning, unsupervised, and supervised methods is presented. The methods explored are the mountain method, subtractive clustering, and the classic K-means and its soft counterpart, Fuzzy C-means. Furthermore, an analysis was conducted to determine which similarity function—whether Euclidean, Manhattan, Mahalanobis, or Cosine—best fits for ABLH estimation in each unsupervised method. For classification in a supervised fashion, the best suitable models, among others, are support vector machines and decision trees. Different internal metrics (Silhouette Index, Calinski-Harabasz score) and external metrics (root mean square error and adjusted Rand score), with a reference made by means of visual inspection by an expert, were used to evaluate the methods. The unsupervised mountain method with the Manhattan similarity function proved to be the most feasible, as it is a non-stochastic method, its computation time is reduced, and it does not require an ABLH reference. The data used was extracted from several sources: 83 days of quasi-continuous LIDAR data with 23,000 data points located at Brest, France, measured with a MiniMPL from the Meteo France LIDAR network, were used. An ABLH reference from a radiosonde adjacent to the site of the LIDAR system was used. The references range from October to December 2018. The root mean square error achieved for the whole dataset was 600 m for the mountain method. The presented method is shown to be effective for various atmospheric situations, regardless of their complexity.

How to cite: Estrada, S. and Quintero Montoya, O. L.: Development of a mathematical model for the determination of the atmospheric boundary layer height using artificial intelligence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1043,, 2024.

Hsu-Feng Teng

This study explores the potential impact of global navigation satellite system radio occultation (RO) data assimilation on the tropical cyclone (TC) intensity forecast over the western North Pacific. The forecast experiments are performed through a regional model for six TCs occurring in 2020. RO data are mainly obtained from the Constellation Observing System for Meteorology, Ionosphere, and Climate Mission II. The forecasts with and without assimilation of RO data are compared, and their difference is regarded as the impact of RO data on TC forecasts. Overall, the forecasts tend to underestimate the TC intensity relative to the best track data. Compared to the forecasts assimilating without RO data, forecasts assimilating with RO data improve the initial conditions and reduce the underestimation of TC intensity forecast by 13 kt and 16 hPa in subsequent forecasts. This intensity improvement is more significant for TCs developing in drier environments than those in moister environments. The main period of intensity increase is 48-24 h prior to TCs developing to the maximum intensity. The assimilation of RO data increases the moisture around the TC centers, especially at mid-levels (700-300 hPa). It also increases the low-level vorticity but reduces the mid-level vorticity around the TC centers. These characteristics favor TCs with stronger surface wind speed and lower sea surface pressure. In summary, this study highlights the positive contribution of RO data to TC intensity forecast and explores the potential mechanisms.

How to cite: Teng, H.-F.: Impact of radio occultation data assimilation on tropical cyclone intensity forecast over the western North Pacific, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4760,, 2024.

Renuka Prakash Shastri, Stefan Brönnimann, and Peter Stucki

One of the most hazardous windstorms was observed in Switzerland on February 15, 1925. The storm is categorized as a 'High-impact Foehn Storm' that affected all Foehn regions of Switzerland. All communities, stables, and houses were wholly or partially damaged in the canton of Glarus. In previous work, the Weather Research and Forecasting Model (WRF) was used for downscaling the storm from the Twentieth Century Reanalysis (20CRv2) down to a grid width of 3 km. While many storm features were realistically simulated, wind speeds in the Glarus Valley, where most damage occurred, remained well below the expected values. Here, we go one step further by using a Large-Eddy Simulation model (LES) to analyze whether high gust peaks would occur at the bottom of the valley. For this, the PArallelized Large-eddy simulation Model (PALMv6.0) is coupled to WRFv4.1.2. In the first stage, WRFv4.1.2 was downscaled to a resolution of 1x1 km2 by using the "Twentieth Century Reanalysis" (20CRv3) as a boundary condition. Three nested domains with resolutions 25km, 5 km, and 1 km were set up for the simulation experiment. The second stage involves downscaling PALMv6.0 to a resolution of 20 m by using the output of WRFv4.1.2 as a boundary condition. The simulation shows strong winds between Netstal and Näfels on Earth's surface. Peak gusts of 40 m/s and more hit the valley floor south of Näfels. Strong turbulence fields reaching the ground at high velocities are observed in the central valley in the south-north direction. The simulation shows good agreement with the damage described, and the simulated peak gusts easily reach the measured maxima of extreme storms. Being able to realistically simulate the local characteristics of a Foehn storm that occurred a century back opens a new window to quantitative analyses of past extremes and their impacts.

How to cite: Shastri, R. P., Brönnimann, S., and Stucki, P.: Numerical Investigation of High Impact Foehn storm in February 1925 using WRF and PALM models., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6813,, 2024.

Hee-Jeong Lim and Young-Hee Lee

We developed a rice paddy model based on the Noah LSM considering the standing water layer during the irrigation period. In the model, we adopted a consistent subcanopy process from thin to thick canopy conditions and considered small scalar roughness length of water surface in rice paddy field. We evaluated the model’s performance against observations from three rice paddy sites with different leaf area index (LAI) and water depths during the growing season. Two simulations were performed in an offline mode: the fixed irrigation simulation of Noah LSM with saturation moisture in the top two soil layers during the irrigation period (IRRI) and the developed model simulation (RICE). The evaluation results showed that RICE outperformed IRRI in the simulating ground, sensible (H) and latent heat (LH) fluxes and topsoil temperature (Tsoil) on hourly and diurnal time scales. Two sensitivity tests of RICE were performed in relation to the subcanopy resistance and standing water layer: RICE without consideration of small roughness length of water surface during the irritation period (BARE) and RICE with a constant standing water depth (FIX). The sensitivity tests showed that BARE calculated very low subcanopy resistance values when the sum of LAI and stem area index was less than 2 m2 m-2, which resulted in cold biases in the daily mean Tg and Tsoil and also led to overestimation of daily mean LH. There was no significant difference in RICE and FIX with hourly and seasonal time scale statistics, suggesting that H, LH,  Tg and Tsoil of the developed model are not sensitive to changes in water depth. The structure of the developed model was also discussed.

How to cite: Lim, H.-J. and Lee, Y.-H.: Development of Rice Paddy Model Based on Noah LSM: Consistent Parameterization of Subcanopy Resistance from the Ponded Water to Dense Rice Canopy , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7968,, 2024.

Haraldur Ólafsson and Negar Ekrami

Persistence is a natural first approximation or a baseline to seasonal temperature forecasting.  In the present study, winter and spring persistence in mean montly temperatures in the circumpolar Arctic is explored in long time-series of monthly mean data for the winter and spring seasons.

Locally, very high temporal correlations, as well as significant negative correlations are detected

Physically, the persistence may be traced to snow cover and sea-ice extent.  The variability in these factors may contribute directly to seasonal variability in the radiation budget as well as in surface fluxes, but there are also indirect, but detectable impacts upon regional circulation patterns.

How to cite: Ólafsson, H. and Ekrami, N.: Arctic temperature persistence in winter and spring and seasonal forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9797,, 2024.

Jian-Wen Bao, Sara Michelson, Haiqin Li, and Sungsu Park

It remains challenging to represent subgrid convection in weather and climate models at horizontal grid resolution across the gray zone, in which convective clouds are only partially resolved by the model dynamics and it is required for the representation of subgrid convection to have a generalized transitional behavior as the model’s horizontal resolution varies.  A practical approach for such a representation is to scale the eddy transport of physical properties from a conventional convection parameterization scheme by a quadradic function of the fractional area covered by convective updrafts in the grid cell (Arakawa and Wu, 2013).  Despite this approach’s popularity, its generalization is limited theoretically by the fact that the coarse-graining statistical analysis that gave rise to the approach involved only an idealized scenario of deep convection in quasi-equilibrium.  Additionally, when applying this approach, there is a theoretical ambiguity associated with the validity of conventional convection parameterizations for a fractional area covered by convective updrafts in the grid cell that is not close to zero.

An alternative approach for subgrid convection representation across the gray zone is to apply a unified plume scheme that treats subgrid convection as nonlocal asymmetric eddies due to unresolved convection relative to the grid-mean vertical flow (Park, 2014).  This unified plume scheme represents unresolved convection relative to the grid-mean vertical motion without relying on quasi-equilibrium assumptions in conventional convection parameterizations.  Its generalized transitional behavior across the gray zone is naturally controlled by the size of the plumes representing unresolved convection that varies with the model’s horizontal resolution.  It simulates all unresolved convective transport of atmospheric properties within a single steady framework, allowing multiple convective plumes.  It also includes the prognosis of unresolved cold pool and convection organization within the planetary boundary layer.  The unified plume scheme circumvents the theoretical limitation and ambiguity of the above approach based on conventional convection parameterization.  It also rectifies the lack of plume memory across the time step in conventional convection parameterizations.

This presentation will focus on an ongoing effort to experiment with the alternative unified approach for representing subgrid convection across the gray zone in NOAA’s Unified Forecast System.  Results from 1-D and 3-D case studies will be shown to highlight the advantage of the unified plume scheme.


Arakawa, A., and C.-M. Wu, 2013: A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I. J. Atmos. Sci., 70, 1977–1992.

Park, S., 2014: A unified convection scheme (UNICON). Part I: Formulation. J. Atmos. Sci., 71, 3902–3930.

How to cite: Bao, J.-W., Michelson, S., Li, H., and Park, S.: A Unified Representation of Subgrid Convection in NOAA’s Unified Forecast System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11953,, 2024.

JoongHyun Jo, Sun-Young Park, Kyo-Sun Sunny Lim, Wonbea Bang, and Gyuwon Lee

Cloud microphysics parameterizations are generally divided into two categories: bin models that explicitly calculate the evolution of the drop size distribution (DSD) and bulk models that represent the DSD with a specific function. The Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) scheme is one of the bulk microphysics options in the WRF model and is widely utilized for both research and operational purposes. In WDM6 scheme, the gamma form with a single static shape parameter is applied for the DSD of rain. This study adopts a generalized double-moment normalization method for the rain DSD in WDM6 scheme. Previous study mentions that the advantage of the generalized double-moment normalization method lies in its ability to singnificantly reduce the observed DSD scatter. Therefore, it can concisely represent the DSD with appropriate shape parameters, c and μ. The modified WDM6 is evaluated through simulations of an idealized 2D squall line and a summer precipitation case over the Korean peninsula. Based on similar experimental results from the original WDM6 and the modified WDM6 schemes, we can confirm that the generalized double-moment normalization method in the WDM6 scheme is properly implemented. We further collected the observed shape parameters suitable for the generalized double-moment DSD of rain over a two-year summer period (2018, 2019). The modified WDM6, with the observed shape parameters, simulates a more comparable spatial distribution of acummulated precipitation that occurred on 6 August 2013 with the observation, compared to the original WDM6. More detailed simulation results will be presented at the conference.


* This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT). (grant no.RS-2023-00208394).

How to cite: Jo, J., Park, S.-Y., Lim, K.-S. S., Bang, W., and Lee, G.: Implementation of the Generalized Double-Moment Normalization Method in the Cloud Microphysics Scheme, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13807,, 2024.

Evelyn Grell and Jian-Wen Bao

Planetary boundary layer (PBL) parameterizations using the eddy diffusivity - mass flux (EDMF) technique for turbulent mixing in the convective PBL have been popularly used in weather and climate models.  When including moist adjustment processes, some numerical implementations of the EDMF parameterization may result in unphysical solutions of cloud condensate, for example, negative cloud water quantities.  To solve this problem, a procedure to obtain a positive definite solution is proposed to solve the moist EDMF equations.  In this presentation, we will demonstrate the formulation of the solution procedure and show examples of its impact on the PBL mixing simulation using a single-column model.

How to cite: Grell, E. and Bao, J.-W.: A Positive-Definite Moist EDMF Parameterization Scheme for Turbulent Mixing in the PBL, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13978,, 2024.

Hae-Jin Kong, Ja-Rin Park, and Hyun Nam

Korea Institute of Atmospheric Prediction Systems (KIAPS) has developed a global forecasting system, Korean Integrated Model (KIM) and the model now operates with 12-km horizontal resolution. With plans to develop the numerical model in horizontally and vertically higher resolution, smoothed hybrid sigma-pressure (SMH) coordinate has applied to KIM to cover the influence of the terrain structure. The SMH is proposed to alleviate artificial circulations that horizontal pressure gradients and advection can be appeared along complex surfaces by reducing small-scale components more rapidly with height (Choi and Klemp, 2021). 
In this research, we focus on the prediction with higher-resolution topography in the SMH coordinate and it is revealed that more realistic data can be utilized than the previous topography adapted in hybrid sigma coordinate. The SMH coordinate could well reflect the steepness and roughness of complex region such as terrains near mountains without stability issue. To investigate the sensitivity to the detailed topographic data, case studies such as heatwave, cold surge and rainfall are dealt with especially in the Korean peninsula consisted of complex terrain. By considering more complex topography, the SMH coordinate performs better in capturing precipitation peak and temperature bias. In addition, it will be discussed that vertical propagation to the upper atmosphere is appropriately controlled due to the SMH coordinate. This study can contribute to the future work on adjusting diffusion coefficient by optimizing the SMH coordinate in much higher resolution.

How to cite: Kong, H.-J., Park, J.-R., and Nam, H.: Response of the SMoothed Hybrid sigma-pressure (SMH) coordinate to higher-resolution topographic data in Korean Integrated Model (KIM), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14761,, 2024.

A. Cem Çatal, Aysu Arık, M. Tuğrul Yılmaz, and İsmail Yücel

Weather Research and Forecasting (WRF) plays a crucial role in studying atmospheric dynamics and investigating the mesoscale weather prediction phenomena. However, WRF model offers lots of different configurations for physics, dynamics, and domain options that need to be investigated. From these configurations, domain options offer nesting techniques which may affect the fundamental structure and the performance of the simulations. Nesting options may impact the representation of fine-scale processes by increasing the resolution for the desired domain, compared to single-domain simulations. Existing studies on comparison of different nesting configurations in mesoscale domains are limited. This study presents a comparative analysis of three different nesting configurations in the WRF model over Türkiye. Accuracy of WRF-based short-term (24 to 48 hourly) temperature, wind, and precipitation forecasts over a 30-day period in November 2021 is investigated utilizing ground-station based observations. Three different model configurations are investigated: single domain, one-way feedback nested, and two-way feedback nested runs for the same time period and region. Root mean square error (RMSE), error standard deviation, and correlation coefficient were calculated for all three configurations. This study contributes to the optimization of nesting configurations in WRF mesoscale weather predictions, aiding decision-making processes reliant on accurate short-term forecasts in Türkiye.

How to cite: Çatal, A. C., Arık, A., Yılmaz, M. T., and Yücel, İ.: Impact of Nesting Techniques Over Short-Term WRF Forecast Accuracy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15467,, 2024.

Clément Dauvilliers, Anastase Charantonis, and Claire Monteleoni

Skillfully forecasting the evolution of tropical cyclones (TC) is crucial for
the human populations in areas at risk, and an essential indicator of a storm’s
potential impact is the Maximum Sustained Wind Speed, often referred to as
the cyclone’s intensity. Predicting the future intensity of ongoing storms is
traditionally done using statistical-dynamical methods such as (D)SHIPS and
LGEM, or as a byproduct of fully dynamical models such as the HWRF model.
Previous works have shown that deep learning models based on convolutional
neural networks can achieve comparable performances using infrared and/or
passive microwave satellite imagery as input. Recently, multi-task models have
highlighted that jointly learning the future intensity and other indicators such
as the TC size with shared network weights can improve the performance in the
context of intensity estimation. This ongoing work aims to evaluate which tasks
and architectures can lead to the best improvement for intensity forecasting.

How to cite: Dauvilliers, C., Charantonis, A., and Monteleoni, C.: Evaluating multi-task learning strategies for tropical cyclones itnensity forecasting from satellite images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18151,, 2024.

Chen Wang, Nedjeljka Žagar, and Sergiy Vasylkevych
Large initial uncertainties in the tropics are believed to compromise medium- and extended-range extratropical forecasts. A more reliable analysis of tropical Rossby and non-Rossby waves requires more tropical observations and improved data assimilation schemes. Wind observations are known to be more valuable than mass observations in the tropics, but it is not well-understood how different types of observations affect the accuracy of equatorial wave analysis and influence extratropical predictability. 
We investigate these questions by assimilating only wind or mass observations within the tropics using a perfect-model framework and a global model based on shallow-water equations and 3D-Var data assimilation. The mass-wind relationships of equatorial waves are built into the background-error covariance matrix with Rossby and non-Rossby waves as control variables in 3D-Var and prognostic variables in the forecast model.  Results demonstrate that wind observations are more efficient at reducing both tropical and extratropical forecast errors than mass observations. Adding mass-wind coupling further improves extratropical forecasts and it is especially beneficial for mass observations.Forecast benefits are quantified along latitude circles in terms of scales. A more accurate analysis of the equatorial Rossby waves is found to be the key for the propagation of observation impact from the tropics to midlatitudes. 

How to cite: Wang, C., Žagar, N., and Vasylkevych, S.: Benefits of initializing equatorial waves on extratropical forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18982,, 2024.

Songyou Hong, Jian-Wen Bao, Sara Michelson, Evelyn Grell, Mike Toy, Joe Olson, and Fanglin Yang

The lower tropospheric enhanced gravity wave drag (GWD) parameterization has been operational in Global Forecast System (GFS) since late 1990s. The scheme is based on Kim and Arakawa and further revised with the addition of flow blocking (Kim and Doyle). For UFSR2O project, there have been collaborative efforts to improve the GWD parameterization by revising the mountain induced GWD. Revisions include the updates in GWD and flow blocking (Choi and Hong), and turbulent orography form drag of Beljaars et al. Sensitivity experiments are performed to investigate the importance of partitioning GWD and flow blocking in the skill of medium-range forecasts. Alternative approach for TOFD (Richter et al.) is tested. Importance of the representation of sub-grid orography statistics is also examined. 

How to cite: Hong, S., Bao, J.-W., Michelson, S., Grell, E., Toy, M., Olson, J., and Yang, F.: Sensitivity Experiments of a Mountain-Induced Gravity Wave Drag Parameterizations for Global Weather Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12794,, 2024.

Takemasa Miyoshi, Yanina G. Skabar, Shigenori Otsuka, Arata Amemiya, Juan Ruiz, Tomoo Ushio, Hirofumi Tomita, Tomoki Ushiyama, and Masaya Konishi

This presentation provides recent research highlights of the project PREVENIR, including radar quantitative precipitation estimates (QPE), ensemble nowcasting, data assimilation, numerical weather prediction (NWP), and hydrological model prediction. 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 in Argentina. PREVENIR takes advantage of leading research on 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, QPE, nowcasting, BDA and NWP, hydrological model prediction, warning communications, public education, and capacity building. 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., Skabar, Y. G., Otsuka, S., Amemiya, A., Ruiz, J., Ushio, T., Tomita, H., Ushiyama, T., and Konishi, M.: Second Year Progress of PREVENIR: Japan-Argentina Cooperation Project for Heavy Rain and Urban Flood Disaster Prevention, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13504,, 2024.

Towards higher resolution data assimilation in ECMWF IFS
Žiga Zaplotnik, Massimo Bonavita, and Elias Holm
Tetsuo Nakazawa, Takemasa Miyoshi, Takashi Sakajo, and Kohei Takatama

Forecast and control are the two sides of a coin. Recent improvements in numerical weather prediction have led to the point where we can start discussing the control of complex, chaotic weather systems. The Japan’s Moonshot Goal 8 research and development (R&D) program or simply MS8 was launched in 2022 to control extreme weather events such as typhoons and torrential rains and to reduce damage from extreme winds and rains, so that we can realize a society safe from such disasters by 2050. As the important first step toward the next 3-decade R&D, MS8 prioritizes numerical simulation experiments to investigate the feasibility of weather control under the constraints of energy and technology within human’s capability in a foreseeable future. Thus far, MS8 achieved promising results to reduce a peak rainfall of heavy downpours, and more results are expected by ongoing efforts. MS8 also accelerates developing basic science and technologies for realizing weather control, such as advanced weather models, computational models of flood damage, and mathematical approaches to intervention optimization techniques for large dimensional systems. In addition, addressing ethical, legal, and social issues (ELSI) is essential and a priority in MS8. This presentation will provide an overview of MS8 with highlighting scientific results.


How to cite: Nakazawa, T., Miyoshi, T., Sakajo, T., and Takatama, K.: An overview of Japan’s Moonshot Goal 8 R&D program for controlling and modifying the weather by 2050, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13655,, 2024.

Chuanwen Wei, Wei Han, Yan Liu, Hao Hu, Huijuan Lu, Hongyi Xiao, and Dan Wang

Satellite sea surface wind fields can compensate for the shortcomings of conventional observation data, thereby improving the forecasting skills of global medium-range numerical weather models. China successfully launched the HY2D satellite carrying a Ku band microwave scatterometer (HSCAT) on May 19, 2021. It can provide a large amount of high-quality sea surface wind field data for numerical forecasting models. In order to test the potential application of HY2D sea surface wind field data in the Global Assimilation Forecasting System of the China Meteorological Administration (CMA-GFS). A three-step study was conducted, with the first step being the timeliness evaluation of HY2D wind, followed by the quality evaluation of HY2D wind using ERA5 and buoy data, and finally assessment of impacts of the HY2D wind assimilation on the analyses and forecasts. Two sets of assimilation experiments were conducted: the control experiment without HY2D wind (CTRL) and sensitivity experiment with HY2D wind based on a new quality control scheme (HY2D-FlagQC). The experimental period is from March 1, 2023 to April 1, 2023. The results show that the timeliness of HY2D wind field obtained through National Satellite Ocean Application Service (NSOAS) has been improved by about 20% compared to Koninklijk Nederlands Meteorologisch Instituut (KNMI). But the timeliness fluctuation is relatively large in terms of time and space. The root mean square error of HY2D wind field is less than 2m/s. After assimilating the HY2D wind, the analysis errors of the wind fields in the lower-middle troposphere of the tropics and the southern hemisphere (SH) are significantly reduced. Furthermore, assimilating the HY2D wind data can improve the forecast skill of wind, geopotential height, and temperature in the troposphere of the tropics and SH. 

How to cite: Wei, C., Han, W., Liu, Y., Hu, H., Lu, H., Xiao, H., and Wang, D.: Assimilation of HY-2D scatterometer wind field data in CMA-GFS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3462,, 2024.

Changliang Shao

Data assimilation is a widely used method for estimating the state and associated uncertainties in numerical models. While ensemble-based approaches are common, their computational expense arises from necessary ensemble integrations. This study improves the Weather Research and Forecasting–Advanced Research WRF (WRF-ARW) model by integrating it with the Parallel Data Assimilation Framework (PDAF) in a fully online mode. Through minimal modifications to the WRF-ARW code, an efficient data assimilation system is developed, leveraging parallelization and in-memory data transfers to minimize file I/O and model restarts during assimilation. The clear separation of concerns between method development and model application, facilitated by PDAF's model-agnostic structure, is an advantage. Evaluating the assimilation system through a twin experiment simulating a tropical cyclone reveals improved accuracy in temperature, U and V fields. The assimilation process incurs minimal overhead in run time compared to the model without data assimilation, demonstrating excellent parallel performance. Consequently, the online WRF-PDAF system proves to be an efficient framework for high-resolution mesoscale forecasting and reanalysis.

How to cite: Shao, C.: Augmenting WRF with PDAF for an Online Localized Ensemble Data Assimilation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4851,, 2024.

Zeyi Niu

The Cross-track Infrared Sounder (CrIS) observations (O) contributed greatly to numerical weather prediction. Further contribution depends on the success of all-sky data assimilation, which requires a method to produce realistic cloud/rain band structures from background fields (i. e., 6-h forecasts), and to remove large biases of all-sky simulation of brightness temperature in the presence of clouds. In this study, CrIS all-sky simulations of brightness temperatures at an arbitrarily selected window channel within Typhoon Hinnamnor (2022) are investigated. The 3-km Weather Research and Forecasting model with three microphysics schemes were used to produce 6-h background forecasts (B). The O−B statistic deviate greatly from Gaussian distribution with large biases in either water clouds, or thin ice clouds, or thick ice clouds within Typhoon Hinnamnor. By developing a linear regression function of three all-sky simulations of brightness temperature from 6-h forecasts with three microphysics schemes, the O−B statistics approximate a Gaussian normal distribution in water clouds, thin ice clouds and thick ice clouds. Taking the regression function that is established by a training dataset to combine 6-h background forecasts at later times, the cloud/rain band structures compared much more favorably with CrIS observations than those from an individual microphysics, and the O−B biases are significantly reduced. The work in this study to quantify and remove biases in background fields of brightness temperature and generating realistic typhoon cloud/rain band structures in background fields will allow a better description of center position, intensity and size to improve typhoon forecasts.

How to cite: Niu, Z.: Improving All-sky Simulations of Typhoon Cloud/Rain Band Structures of NOAA-20 CrIS Window Channel Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6917,, 2024.

Investigation of Cloud Simulations of a Low-echo Centroid Storm Using Cloud-Resolving Model Based on Satellite and Radar Observations
Nan Yang, Kefeng Zhu, and Ming Xue
Shin-Woo Kim, Taehyoun Shim, Ja-Young Hong, and Hye-Jin Park

The Korean Integrated Model (KIM) is a global numerical weather prediction (NWP) system developed by the first phase project of the Korea Institute of Atmospheric Prediction Systems (KIAPS) and has been used as the operational NWP system at the Korea Meteorological Administration (KMA) since April 2020. The second phase project of KIAPS aims at developing a next-generation NWP system to seamlessly predict from very short-range to extended medium-range. To improve the extended medium-range forecast, one of the main goals of KIAPS is to develop the ensemble prediction system with coupling to land, ocean, and sea ice. The production of extended medium-range reforecast data is necessary to understand the climatological characteristics and model biases of KIM. KIAPS developed an initial version of reforecasting system based on the KIM atmopheric model. The system has a spatial resolution of 50 km (NE090NP3) and consists of 91 vertical layers. We produce reforecast of the cold season cases for 20 years (from 2001 to 2020) and perform the diagnosis and verification of reforecast data. A suite of sensitivity experiments are also performed to investigate the impact of initial perturbations on the ensemble prediction system.

How to cite: Kim, S.-W., Shim, T., Hong, J.-Y., and Park, H.-J.: Development of extended medium-range reforecasting system based on the Korean Integrated Model (KIM), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15364,, 2024.

Peter Weston, Patricia de Rosnay, Christoph Herbert, and Ewan Pinnington

The CERISE (CopERnIcus climate change Service Evolution) project aims to develop land and coupled land-atmosphere data assimilation systems for the next generation of coupled reanalysis. This encompasses technical enhancements to the system architecture as well as scientific changes to improve the quality of the reanalyses.

Recent work has focussed on developing ensemble perturbation methods for the land-surface. The existing ensemble spread in model variables at and near the land-surface is known to be insufficient which can cause problems when assimilating interface observations in a coupled system. This is because the existing ensemble perturbations are mainly applied to upper air atmospheric variables. One way to increase the spread at the surface is to directly perturb land-surface parameters such as vegetation cover and leaf area index. Results from this approach are encouraging in offline and coupled experiments.

Another part of the project is to enhance the assimilation of passive microwave radiances over land. Currently the use of surface-sensitive passive microwave channels are largely limited to the ocean due to challenges in forward modelling of complex and heterogenous land surfaces. In CERISE, machine learning approaches are being explored to develop an observation operator to enable the use of these observations over land and snow surfaces.

Finally, developing quasi-strongly coupled land-atmosphere assimilation is a key objective of the project. Developments so far have focussed on technical changes to build a framework to allow stronger coupling than the current weakly coupled assimilation strategy. A summary of recent progress in the CERISE project will be presented.

How to cite: Weston, P., de Rosnay, P., Herbert, C., and Pinnington, E.: Enhanced coupled land-atmosphere data assimilation for reanalysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18054,, 2024.

Hyun-Joo Choi, Seok Hwan Kim, Baek-Min Kim, Myung-Seo Koo, Eek-Hyun Cho, and Young Cheol Kwon

The Korean Integrated Model (KIM) has been in operation at Korea Meteorological Administration (KMA) since April 2020 and its forecasting performance has been improved by updating model physical processes and data assimilation system. The model performance is comparable to that of the Unified Model run in parallel with the KIM at KMA during Boreal summer, but is relatively poor during the winter. One of the major biases in 5-day temperature forecasts for Norther Hemisphere winter is the low atmospheric cold bias over the Arctic region, and thus this study modifies the initialization of sea ice properties (sea ice thickness and temperature) to reduce the bias. First, the initial sea ice thickness data prescribed by climatology data produced using reanalysis data from the past 10 years (2000~2009) is replaced using the latest (2019~2021) reanalysis data. Second, the initial temperatures of the 1st and 2nd sea ice layers are set to the sea water freezing temperature instead of the currently applied first guess (background) sea ice temperatures. The effects of initialization modification on the medium-range forecasts of KIM are analyzed by performing two sets of experiments: cold start and warm cycle experiments without and with a data assimilation system in January 2022. The latest sea ice thickness initial data shows that sea ice thickness has decreased by about a factor of two. And its adoption by KIM increases surface and lower atmospheric temperatures in the Arctic sea ice region, alleviating cold biases in the region for both analysis and forecasts. In addition to sea ice thickness, sea ice temperature initialization modifications enhance Arctic warming and lead to greater improvement of cold bias. The warming effect in the lower Arctic is consistent in both cold start and warm cycle experiments. However, secondary effects induced by the Arctic warming occur significantly only in the warm cycle experiment and significantly affect forecasts fields not only in the polar region but also in the Southern Hemisphere and mid-latitude regions. Skill scores for medium-range forecasts in January 2022 are mostly improved (degraded) for the 12 UTC (00 UTC) initial conditions in the warm cycle experiment.

How to cite: Choi, H.-J., Kim, S. H., Kim, B.-M., Koo, M.-S., Cho, E.-H., and Kwon, Y. C.: Effects of initialization of sea ice properties on medium-range forecasts in the Korean Integrated Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18966,, 2024.

Peter Stucki, Lucas Pfister, Stefan Brönnimann, Yuri Brugnara, Chantal Hari, and Renate Varga

The “Year Without Summer” of 1816 was characterized by extraordinarily cold and wet periods in Central Europe, and it was associated with severe crop failures, famine, and socio-economic disruptions. From a modern perspective and beyond its tragic consequences, the summer of 1816 represents a rare occasion to analyze the adverse weather (and its impacts) after a major volcanic eruption. However, given the distant past, obtaining the high-resolution data needed for such studies is a challenge. In our approach, we use dynamical downscaling, in combination with 3D-variational data assimilation of early instrumental observations, for assessing a cold-air outbreak in early June 1816. 
Our downscaling simulations reproduce and explain meteorological processes well at regional to local scales, such as a foehn wind situation over the Alps with much lower temperatures on its northern side. Simulated weather variables, such as cloud cover or rainy days, are simulated in good agreement with (eye) observations and (independent) measurements, with small differences between the simulations with and without data assimilation. However, validations with partly independent station data show that simulations with assimilated pressure and temperature measurements are closer to the observations. In turn, data assimilation requires careful selection, preprocessing and bias-adjustment of the underlying observations. Our findings underline the great value of digitizing efforts of early instrumental data and provide novel opportunities to learn from extreme weather and climate events as far back as 200 years or more.

How to cite: Stucki, P., Pfister, L., Brönnimann, S., Brugnara, Y., Hari, C., and Varga, R.: Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without Summer 1816, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19593,, 2024.

Zhiquan Liu, Junmei Ban, and Ivette Banos

MPAS-JEDI, a relatively-new data assimilation (DA) system for the Model for Prediction Across Scales – Atmosphere (MPAS-A) based upon the Joint Effort for Data assimilation Integration (JEDI), allows to assimilate cloud-/precipitation-affected satellite microwave and infrared radiance data to analysis microphysical parameters, e.g., mixing ratios of hydrometeors. Global cycling DA experiments were conducted in the context of MPAS-JEDI’s hybrid-3DEnVar configured at 30km resolution with 80-member ensemble input at 60km that is produced using MPAS-JEDI's ensemble of 3DEnVar. The benchmark experiment assimilates conventional observations plus clear-sky radiances from AMSU-A and MHS. All-sky experiments add the assimilation of all-sky microwave (MW) radiances from AMSU-A’s and/or ATMS’s window channels over water as well as infrared (IR) channels of two geostationary sensors GOES-ABI and Himawari-AHI. In addition to the impact assessment on dynamic and thermodynamic variables, we investigated more the impact on cloud forecasts in terms of fitting to ABI/AHI radiance data at different wavelengths. The community radiative transfer model (CRTM) is used as the observation operator in both all-sky radiance DA and evaluation. The substantial positive impact on cloud forecasts was obtained with all-sky microwave DA (individually or collectively from AMSU-A and ATMS) in terms of a better forecast fitting to observed ABI/AHI channel 13's radiances up to 7 days, especially over tropical regions, where the day-1 forecast root-mean-square error is reduced up to 10%. Cloud forecast impact from assimilating all-sky ABI/AHI 3 water vapor channels' radiances is more limited although a clear benefit is seen for middle/upper troposphere moisture field, which is consistent with ABI/AHI water vapor channels' sensitivity height. Future research direction for all-sky MW and IR radiance DA with MPAS-JEDI will also be discussed.

How to cite: Liu, Z., Ban, J., and Banos, I.: Improving cloud forecasts with assimilation of cloud-/precipitation-affected microwave and infrared radiances using MPAS-JEDI, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7021,, 2024.

Strongly Coupled Land-Atmosphere Data Assimilation and Its Impact on Near-surface Weather Forecasting
Zhaoxia Pu and Kian Huang
Fedor Mesinger, Katarina Veljovic, Sin Chan Chou, Jorge L. Gomes, André A. Lyra, and Dusan Jovic

An experiment reported in Mesinger and Veljovic (JMSJ 2020) and at the preceding EGU General Assembly, showed an advantage of the Eta over its driver ECMWF ensemble members in placing 250 hPa jet stream winds east of the Rockies.  Verifications subsequent to 2020 confirmed this advantage.  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.  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 finite-volume vertical advection of the Eta, implemented in 2007, may be a significant contributor to this advantage.  In that 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.

Another likely and perhaps unique feature of the Eta contributing to that advantage is its sophisticated representation of topography, designed to arrive at the most realistic grid-cell values with no smoothing (Mesinger and Veljovic, MAAP 2017).

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 (Thuburn, 10.1007/978-3-642-11640-7 2011),” a comprehensive 2006 NCEP parallel test gave the opposite result.  With seemingly equal PBL schemes, the Eta showed a higher surface layer accuracy over high topography than the NMM, using a hybrid terrain-following system (Mesinger, BLM 2023).

Hundreds of thousands of the Eta forecasts and experiments performed demonstrate that the relaxation lateral boundary condition, almost universally used in regional climate models (RCMs), in addition to conflicting with the properties of the basic equations used, is unnecessary.  Similarly, so-called large scale or spectral nudging, frequently applied in RCMs, 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 Eta vs ECMWF results we refer to above.

Even so, to have large scales of a nested model ensemble members most times more accurate than those of their driver members, surely requires not only the absence of detrimental techniques, but also the use of a lateral boundary condition (LBC) scheme that is not inducing major errors.  The scheme of the Eta is at the outflow points of the boundary prescribing one less condition than at the inflow points (e.g., Mesinger and Veljovic, MAAP 2013), and has for that reason been referred to by McDonald (MWR 2003) as one of “fairly well-posed” schemes.

How to cite: Mesinger, F., Veljovic, K., Chou, S. C., Gomes, J. L., Lyra, A. A., and Jovic, D.: Eta features, additional to the vertical coordinate, deserving attention, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8324,, 2024.