Ethiopia is frequently affected by extreme rainfall events with devastating consequences for its society and economy. For example, they caused a deadly flash flood in Dire Dawa town in April 2020, and in Addis Ababa in August 2021. The frequency and severity of extreme rainfall events varies by region and season. Their variability depends mainly on the advection of moist air and the location and intensity of precipitation-bearing systems approaching Ethiopia. The westward propagation of low-pressure systems developing over the Indian Ocean and Arabian Sea as well as southerly moisture flow are widely known precipitation-producing features in this region.

Due to the vulnerability to extreme precipitation, especially over the orographically complex regions of Ethiopia, a very high resolution of Numerical Weather prediction (NWP) models with high quality initial conditions are required to enhance the advance warning time. The accuracy of initial states of the NWP models can be enhanced through advanced variational Data Assimilation (DA) techniques. With this aim, we simulated an extreme precipitation event using the Weather Research and Forecasting (WRF) model over the Horn of Africa centering the target region Ethiopia. The model is set up at approx. 2 km horizontal resolution and 100 vertical levels. The impact of DA is also evaluated using single observation tests and 3DVar simulations at three cycles. The 3DVar simulations are compared with the control run (without DA) and observations from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). This shows that the DA improves the results in comparison with the control run. Finally, we combined the Ensemble Transform Kalman Filter (ETKF) with the 3-Dimensional Variational DA.

The hybrid ETKF-3DVar (3DEnsVar) simulations are performed within a Rapid Update Cycle (RUC) with 6 hourly updates and 15 ensemble members. The 3DEnsVar simulations are compared with observations from Global Precipitation Measurement Mission (GPM), Climate Prediction Center morphing method (CMORPH), and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) products. Results indicate that the 3DEnsVar method is an appropriate DA method to advance extreme rainfall prediction over Ethiopia.

How to cite: Adgeh, T., Schwitalla, T., Warrach-Sagi, K., and Wulfmeyer, V.: Advanced Weather Forecasts for Ethiopia by Optimized Initialization using a 3DVAR hybrid approach, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-458, https://doi.org/10.5194/ems2024-458, 2024.

Climate change poses significant challenges for agriculture, impacting crop production with rising temperatures, altered precipitation patterns, and more frequent extreme weather events. In this context, data assimilation techniques in Numerical Weather Prediction (NWP) models could become essential tools. By integrating real-time observations into forecasts, data assimilation enhances accuracy, providing farmers with valuable insights to adapt to climate change effectively. Thus, observations from high-resolution networks are of interest for non-hydrostatic model simulations both for verification purposes and for data assimilation. Such observations can be influenced by sub-grid scale processes that cannot be represented in the model dynamics; moreover they can be subject to gross errors. Furthermore, initial errors can grow, during a non-hydrostatic simulation, along a large number of independent modes, influencing a variety of dynamic scales. Suitable automatic data quality control techniques are then necessary and their application can improve assimilation results by enabling the representation of weather features that the model can effectively simulate.

The MAGDA (Meteorological Assimilation from Galileo and Drones for Agriculture) Horizon Europe project (https://www.magdaproject.eu), started in 2022, has been developing a toolchain aimed at providing valuable weather and irrigation information to agricultural operators. At the scientific core of MAGDA activity lies the assimilation in non-hydrostatic NWP models of various sources of high-resolution observations, including in situ observations, GNSS (Global Navigation Satellite System), weather radar and meteodrones .

In this work we study the effect of assimilating high-resolution in situ observations in a short-range simulation with the WRF model of a convective event of interest for MAGDA agricultural applications. We aim to assess the impact of preliminary quality control checks on observations being assimilated. In particular, the Spatial Consistency Test based on Optimal Interpolation and Cross Validation can be effective in rejecting data affected by gross errors or large representativeness errors that could otherwise introduce detrimental noise in the model simulation.

This type of application paves the way for the inclusion of low-cost IoT (Internet of Things) sensors in the assimilation procedure, the "metIS hub" used in the Magda project for instance, but it can also be utilized for various other applications using low-cost sensors as observational tools.

How to cite: Lagasio, M., Uboldi, F., Oberto, E., and Milelli, M.: Testing automatic observation quality control for assimilation in a non-hydrostatic simulation in the framework of the MAGDA HE Project, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-262, https://doi.org/10.5194/ems2024-262, 2024.

Numerical weather prediction requires well-defined initial conditions at the interface between land and atmosphere. Weather centres such as the European Centre for Medium-Range Weather Forecasts (ECMWF) currently rely on a variety of data assimilation schemes to analyze different land variables in their operational forecast system.

Recent activities at ECMWF are towards a unified and more consistent land data assimilation system that can provide more accurate initial conditions for the atmospheric forecast. The first step is to replace the current 1D Optimal Interpolation (1D-OI) so far used for single-layer soil and snow temperature analyses and integrate these variables into the most advanced ensemble-based Simplified Extended Kalman Filter (SEKF) applied in operations for multi-layer soil moisture analysis.

The focus is on the technical developments to integrate the first-layer snow and multi-layer soil temperature into the SEKF control vector and on the evaluation of the new implementation with respect to the impact on the atmospheric forecast skill. A sensitivity analysis regarding Jacobians computed from the covariances between screen-level 2-metre temperature observation and soil and snow temperature reveals better representation of small-scale features associated to land heterogeneity when compared to the empirically obtained sensitivity used in the 1D-OI.

A series of NWP experiments were conducted over a three-month summer and winter period to test the benefit of several configurations of the SEKF. Compared to the 1D-OI, the SEKF leads to significant improvements in the 2-metre temperature forecast with seasonal differences in the verification against own analyses and to slightly improved results in the validation using independent synoptic observations. The work presented will lay the foundation for further developments including the integration of additional land variables, such as snow depth and vegetation-related variables, into the SEKF and the investigation of quasi-strong land-atmosphere coupling.

How to cite: Herbert, C., de Rosnay, P., Weston, P., and Fairbairn, D.: Towards unified land data assimilation at ECMWF: Soil and snow temperature analysis in the Simplified Extended Kalman Filter, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-525, https://doi.org/10.5194/ems2024-525, 2024.

ECMWF is now running its extended-range 101-member ensemble, with an approximate grid spacing of 36 km, every day from 00UTC, out to 46 days ahead. This is in conjunction with the 9 km medium-range 51-member ensemble that is run 4 times a day (out to 15 days ahead from 00 and 12 UTC, and 6 days ahead from 06 and 18 UTC). It means that there are effectively 152 ensemble members available from 00UTC in the overlap period, and even more than that if time-lagging is used to include earlier forecasts.

Now that the two ensembles are run every day, work is underway to investigate the potential benefit from combining them and to get a better understanding of how they differ. Many previous studies have already shown that an increase in ensemble size or a combination of ensembles improves skill, especially for longer lead times as members diverge more.

One of the difficulties of blending ensembles at two different resolutions is that each has different biases and representativeness of point observations. Hence the assumption that all members are equally likely is not true since they form two different distributions. That problem could be alleviated by calibrating one to the other or both to observations, which would involve applying statistical methods to a very large ensemble which is not a trivial undertaking.

The work presented here introduces a new, very simple, diagnostic that identifies cyclonic and anticyclonic regions using surface pressure or geopotential height, computed over scales ranging between 500 and 6000km. This has many benefits for assessing ensembles as well as more generally. The prediction of cyclonic and anticyclonic regions is one of the most fundamental aspects of a weather forecast to get correct and hence diagnose. The approach removes the need for any calibration because the scales are much larger than the grid spacing of each ensemble, enabling comparison with analyses processed in the same way. It is also possible to apply standard or spatial ensemble verification metrics, detect any systematic differences in the synoptic pattern between ensembles, and measure predictability over different scales. 

Once the diagnostic has been introduced, highlights of comparative behaviour and verification scores will be presented for the individual and combined ensembles using one year of forecasts, along with any selected cases that are helpful for understanding the overall scores.

How to cite: Roberts, N. and Hewson, T.: Combining ensembles using a new synoptic-scale curvature diagnostic, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-349, https://doi.org/10.5194/ems2024-349, 2024.

Precipitation forecasting for heavy-rainfall events over South China in the rainy season is still challenging due to large uncertainties. Convection-permitting ensemble forecasting is expected to address such uncertainties to improve forecasts of heavy rainfall. However, it is not yet clear how to optimally design convection-permitting ensembles by implementing perturbations in initial conditions (ICs). This study investigates the impacts of various IC perturbation methods on convection-permitting ensemble forecasting over South China in the rainy season. Specifically, downscaling, ensemble of data assimilation, time-lagging, and their combination were used to generate IC perturbations for 12-h convection-permitting ensemble forecasting for heavy-rainfall events over South China during the rainy season in 2013–2020. These events were classified as weak- and strong-forcing cases based on synoptic-scale forcing during the presummer rainy season and as landfalling tropical cyclone (TC) cases. Various IC perturbation methods represented different-source IC uncertainties and thus differed in multiscale characteristics of perturbations in vertical structures, horizontal distributions, and time evolution. Combination of various IC perturbation methods evidently increased perturbations or spreads of precipitation in both magnitude and location and thus improved the forecast-error estimation. Such an improvement was most and least evident for TC cases during the early and late forecasts, respectively, and was more evident for strong- than weak-forcing cases beyond 6 h. The growing spatial similarity among various IC perturbations caused the damping of the added value of combining various IC perturbations over each individual type of perturbations. The added value was attributable much more to the increasing magnitude of initial perturbations than to the increasing sources of IC uncertainties. However, the impacts of combining various sources of IC uncertainties on the added value was still nonnegligible although case-dependent for precipitation perturbations in terms of both magnitude and location. Combination of various IC perturbation methods generally improved both the ensemble-mean and probabilistic forecasts with case-dependent improvements. For heavy rainfall forecasting, 1–6-h improvements were most prominent for TC cases in terms of discrimination and accuracy, while 7–12-h improvements were least prominent for weak-forcing cases in terms of reliability and accuracy. In particular, the improvements in predicting weak-forcing cases increased with spatial errors. In contrast, for strong-forcing cases, the improvements were least and most prominent before and beyond 6 h, respectively.

How to cite: Zhang, X.: Impacts of Combining Various Types of Initial Perturbations on Convection-Permitting Ensemble Forecasting over South China during the Rainy Season, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-92, https://doi.org/10.5194/ems2024-92, 2024.

The European Meteorological Aircraft Derived Data Centre (EMADDC) is an operational centre in development to utilize all aircraft in the European airspace to derive upper air wind and temperature observations.

The lecture will provide a high level overview of EMADDC and focus on the origination, the methods used, the EMADDC data and use cases and the unique partnerships. Also, the latest developments concerning global geographical coverage will be presented.   

New air traffic control surveillance technologies such as Mode-S Enhanced Surveillance radars (Mode- S EHS) present great opportunities to obtain or derive large quantities of wind direction, wind speed and temperature observations in numbers unprecedented in the European region.

EMADDC is a EUMETNET program, led by the Royal Netherlands Meteorological Institute (KNMI), for the collection, processing and dissemination of quality controlled meteorological upper air observations, based on aircraft data. Observations derived from sensors onboard aircraft are more or less instantaneously collected by Mode-S EHS radars of Air Traffic Control Centres and by local Mode-S/ADS-B receivers. By agreement, EMADDC receives these observations from Air Navigation Service Providers and local Mode-S/ADS-B operators, such as UK Met Office and Air Support, directly.

EMADDC is a true partnership program linked to the meteorological and aeronautical domain and is supported by several commercial and non-commercial organisations. EMADDC is co-funded by the European Union via the Connecting Europe Facility (SESAR Deployment).

Each day approximately 25 million temperature and 35 million wind observations, quality controlled, are being made available to stakeholders for use in data assimilation by NWP centres such as ECMWF, or to be used operationally in meteorological forecasting offices.

Information about EMADDC is available at https://emaddc.com

How to cite: Sondij, J., de Haan, S., de Jong, P., Koutek, M., Stone, E., Mirza, A. K., Pearce, G., Stringer, S., Strajnar, B., and Dörnbach, T.: From research to operations: the EMADDC use case, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1153, https://doi.org/10.5194/ems2024-1153, 2024.

How to cite: Assimenidis, S., Bravo, M., Mercader, J., and Moré, J.: ONA-ENS, the multimodel sea wave prediction system of the SMC, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-320, https://doi.org/10.5194/ems2024-320, 2024.

AEMET-γSREPS is a 2.5km multi-NWP models and multi-boundaries LAM-EPS system operating up to 72 hours since 2016 over Iberian Peninsula, Canary Islands and Antarctica Peninsula which is quite extensively used by AEMET forecasting offices. The main γSREPS goal is to improve operationally issued forecasts and warmings with a consistent measure of their predictability.

The γSREPS 20 members come up crossing four regional mesoscale non-hydrostatic convection-permitting NWP models: HARMONIE-AROME (ACCORD-HIRLAM), ALARO (ACCORD-ALADIN), WRF-ARW (NCAR-NOAA) and NMMB (NCEP-NOAA); with five Global NWP models’ boundary conditions: ECMWF-IFS, NCEP-GFS, MétéoFrance-ARPÈGE, JMA-GSM (Japanese) and CMC-GEM (Canadian). Multi-model and multi-boundary approaches have been selected to take into account the NWP model and boundary condition uncertainties respectively. The combination of both prove to hold better skill-spread relationship than other EPS techniques based on uni-NWP models, especially for precipitation and/or convective High Impact Weather (HIW) events.

γSREPS has a big number of spatial and point products available for forecasting offices through an integrated visualisation framework called PANEL. Interestingly, the latter integrates them with other forecasting systems in AEMET such as deterministic HARMONE-AROME and ECMWF IFS and also its ensemble IFS-ENS, allowing forecasters to carry out a “poor man ensemble” conceptual prediction integration in order to issue the best possible predictions and warnings. Static spatial plots comprise mean, maximum and minimum fields, quartiles, probabilities, member “spaghettis” and EFI/SOT (Extreme Forecast Index). Precipitation “spaghetti” product depicting every member’s contour precipitation in a number of distinct thresholds, are very appreciated in forecasting offices because it allows them to evaluate spatial uncertainty and look for possible extreme events represented only by a few members. Moreover, the new dynamic visualisation based on ADAGUC facilitates zoom up to a very local region for detailed forecasts. For point-based products, a new generation of meteograms (gSREPSgrams) which includes the most extreme single member are produced along the probabilistic vertical-profile product, which shows the most unstable member. Both products try to highlight possible extreme events.

The planned foreseeable evolution of AEMET-γSREPS system is to increase the number of members, incorporating more boundary conditions such as the ones from Global ICON, and including more mesoscale NWP models like ICON-LAM and GEM-LAM.

How to cite: Callado-Pallarès, A., Gómez-Navarro, J. J., and Gil-Oliva, D.: AEMET-γSREPS: The Spanish Convection-permitting LAM-EPS on AEMET forecasting offices, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-873, https://doi.org/10.5194/ems2024-873, 2024.

Within the context of the “Global-to-Regional ICON digital twin” (GLORI) project, a convection-permitting ensemble forecasting is established in order to study the predictability of high-impact weather events with high-resolution modeling (up to 500 m) and the influence of the land-surface—atmosphere coupling mechanisms.

At the DWD, ICON-D2-EPS is the limited-area high-resolution component of the ICON modeling system, running as an ensemble of 20 members at 2 km horizontal resolution over Germany and surrounding areas. The perturbed initial conditions are provided by the km-scale ensemble data assimilation system KENDA, run at the same resolution, assimilating a wide range of observations, including radar-derived radar volumes. Boundary conditions are provided by ICON-EPS, the global ensemble with a refinement at 13 km over Europe and is refreshed every 3 hours.

In this work, we employ a nested domain with horizontal resolution of 1-km in the southern region of the ICON-D2, encompassing the Alps mountains. We run a 24-hour forecast simulation starting at 00UTC on the 21st of June 2022, with 20 ensemble members. The choice of the date is crucial as it corresponds to a day when the DWD recorded instances of heavy rain and hail in southern Germany.

In our study, we perform all experiments using a two-moment microphysics scheme. Additionally, we incorporate the standard operational model perturbations and subsequently analyze the influence of various convection schemes on the predictability of processes that lead to convection development. Specifically, we examine the behavior of the convection scheme in two configurations: shallow convection only and deep convection parameterization in the so-called gray-zone-tuning version. By selectively enabling and disabling these schemes, our goal is to evaluate their individual contributions to predictability.

Following this, we implement a tailored variant of the stochastically perturbed parameterization scheme (SPP) in ICON in order to delve into the influence of some uncertain parameters within either microphysics or turbulent parameterization, further advancing our understanding of its effects on the model performance.

How to cite: Parsakhoo, Z., Marsigli, C., Gebhardt, C., Seifert, A., Steinert, T., and Keller, J.: Studies of Convection-Permitting Ensemble Forecasting for ICON-D2 with a 1km Nest over the Alps, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-485, https://doi.org/10.5194/ems2024-485, 2024.

Many factors need to be considered when designing limited-area ensemble models to forecast at short spatio-temporal scales. However, since the main motivation to run them is to capture the inherent forecast uncertainty at small spatio-temporal scales, especially for severe weather cases, representing such uncertainty is key for producing skillful probabilistic forecasts. Arguably, initial and lateral boundary conditions are a major source of uncertainty for limited-area ensemble forecasts. Many strategies have been explored to initialise convective-scale ensembles but a configuration that is objectively better than others has not been found yet, whereas lateral boundary conditions are provided by a coarser ensemble. Also, limited attention has been given to the size of domains for limited-area models. A domain that is too small could prevent the nested model to generate finer scale and more physically realistic structures which arise from local surface forcing.

To this end, at the Met Office, we have started to run some tests to better understand the sensitivity of our convective-scale ensembles to the presence of high-resolution information at initialisation as well as to the size of the domain, this as a function of lead time and regime dependence.

• hourly time-lagged ensemble with initial conditions centred around the high-resolution deterministic to mimic the current operational ensemble (MOGREPS-UK), 
• downscaler from a global ensemble on the same domain as the hourly-cycling,
• downscaler from a global ensemble on an increased size domain. 

In all the different designs the ensemble consists of 18 members and runs four times a day. However, in the downscaler all the members are generated from a 6-hourly cycle with initial perturbations coming from the same parent ensemble forecast range, whereas in the hourly-cycling three new members are generated every hour, with five additional sets of initial conditions perturbations and high-resolution deterministic analyses.  

The aim of this study is then to assess the sensitivity of the probabilistic skill of heavy precipitation forecasts to these three different designs, both through a series of case studies and aggregate statistics over a nine months period (November 2022-August 2023), using ad-hoc verification measures, focussing on the spatial spread-skill relationship. By looking at different cases we will also assess whether there is any link between the probabilistic skill and the synoptic weather patterns, in order to make more informed decisions about which ensemble is the most skilful.

How to cite: Cafaro, C. and Webster, S.: Designing future Met Office regional ensemble prediction systems: the impact of initial and later boundary conditions on spread-skill relationship for heavy precipitation forecasts., EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-247, https://doi.org/10.5194/ems2024-247, 2024.

On 18 August 2022, Europe was hit by an extreme convective phenomenon. In the second half of the night, a derecho formed in the Mediterranean and successively hit several countries such as Spain, France, Italy, Slovenia, Austria and the Czech Republic. This exceptional meteorological event, with wind gusts reaching a maximum of 62.2 m/s, caused extensive damage in its path and led to numerous fatalties in several countries with the highest death tolls reported in Corsica and in the Alps.

The aim of our work is to better understand and document various aspects of the predictability of such extreme phenomena. Using the operational convective ensemble forecasting system of Météo-France AROME-EPS, we built a large ensemble of hundreds of forecasts. The initial states of the forecasts come from the 26 (25 perturbed + 1 unperturbed) analyses of the operational AROME ensemble of data assimilations and the 35 (34 perturbed + 1 unperturbed) lateral boundary conditions come from the operational global ensemble prediction system of Météo-France, leading to a 910-members convective-scale forecast ensemble. The automatic bow echo detection tool developed by Mounier et al (2022) has been used to examine several aspects of the predictability of the derecho. First, we evaluated whether using hundreds of members in an ensemble prediction system improves our ability to anticipate the occurrence of the derecho. We examined several aspects of the predictability of the phenomenon, such as its trajectory, chronology and intensity. We also assessed the ability of this extended ensemble to produce more consistent probabilistic forecasts over time than the operational ensemble forecasting system. Finally, we examined the sensitivity of the forecasts ability to predict such an event to their initial states and to their coupling members.

How to cite: descamps, L. and mounier, A.: On some aspects of the predictability of the 18 August 2022 derecho using a 910-members convective scale ensemble prediction system, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-79, https://doi.org/10.5194/ems2024-79, 2024.

The use of ensemble prediction systems (EPS) enables the quantification of forecast uncertainty. However, the use of EPS is challenging due to the large amount of information it provides. Forecasts from EPS are typically summarised using statistical measures (such as quantiles maps). Although this mathematical representation is effective in capturing the ensemble distribution, it lacks physical consistency, which raises issues for many applications of EPS in an operational context. Following the application of a bow echo detection tool appreciated by forecasters at Meteo-France, we propose two different approaches for providing physically consistent synthesis of French convection-permitting AROME-EPS forecasts.

The first approach is similar to bow echo detection but it is applied to supercell. Supercell can especially produce hail, strong winds or tornadoes. To summarise the risk of supercell in AROME-EPS forecasts, a convolutional neural network has been trained to automatically detect these supercells in AROME-EPS members based on updraft helicity, reflectivity and hail diagnosis. Then, different synthesis plots are produced, based on these detections. A case study will be presented to better understand the usefulness of these plots.

The second approach is a rainfall synthesis. The aim is to automatically classify the members into different classes pre-defined by rainfall climatology. To design a rainfall synthesis, the procedure can be divided into two parts. The first step aims to extract relevant features from each EPS member to reduce the problem dimensionality. Then, clustering is performed based on these features. The originality of our work is to leverage the capabilities of deep learning for feature extraction. For this purpose, we use a convolutional autoencoder (CAE) to learn an optimal low-dimensional representation (also called latent space representation) of the input forecast field. The clustering task is then accomplished using a SOM algorithm. In this work, the algorithm is developed to work on 1-hour accumulated rainfall from AROME-EPS. The rainfall synthesis plot summarises information concerning rainfall positions, the number of members in each class, and rainfall intensities. The rainfall synthesis will be presented for a case study in this presentation.

The two methods proposed are shown to provide an additional and complementary information, useful for facilitating the human expertise. In addition, their design is generic enough to be applied to other events and variables.

How to cite: Mounier, A. and Soulard-Fischer, L.: Extraction of information for forecasters in convective-scale ensembles: supercell detection and rainfall synthesis, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-121, https://doi.org/10.5194/ems2024-121, 2024.