Challenges in Weather and Climate Modelling: from model development via verification to operational perspectives


Challenges in Weather and Climate Modelling: from model development via verification to operational perspectives
Conveners: Estíbaliz Gascón, Daniel Reinert, Balázs Szintai | Co-conveners: Emily Gleeson, Chiara Marsigli, Manfred Dorninger
Lightning talks
| Wed, 08 Sep, 09:00–12:30 (CEST)

Lightning talks: Wed, 8 Sep

Chairperson: Daniel Reinert
From short range regional NWP to climate modelling
Alfons Callado-Pallarès

SRNWP-EPS module/project into EUMETNET NWP Cooperation Programme has as main goals facilitating and coordinating the cooperation on developing reliable mesoscale convection-permitting ensemble systems (LAM-EPS) in Europe, and, at the same time, grouping efforts developing tools which can be smoothly applied to any LAM-EPS. This is motivated by the fact that the development of LAM-EPS capabilities in Europe is crucial for forecasting a range of weather phenomena and in particular for improving HIW (High Impact Weather) prediction. Due   to the latter, the current SRNWP-EPS 2019-2023 phase is focused on extreme events.

The project results as a survey on products for high-impact weather forecasting and the R2O (Research to Operations) LAM-EPS applications will be presented. The three main R2O forecasting tools developed as project requirements are: calibration of daily and  12 hours extremes for variables such as 10 metres maximum wind gusts, maximum accumulated precipitation, maximum and minimum2m temperatures; the forecasting post-processing LAM-EPS products devoted to HIW forecasting and focused on aeronautics such as icing, thunderstorms’ diagnostic and classification, clear-air turbulence and fog; and tools to apply in an affordable way an Extreme Forecast Index (EFI) and Shift of Tales Index (SOT) on LAM-EPSs.

Moreover, an off-line database of European convection-permitting LAM-EPS ensembles has been established at ECMWF, which archives convection related parameters close to the surface. The aim of LAM-EPS database is to foster coordinate research and collaborations around LAM-EPSs in order to improve HIW events bringing together all European LAM-NWP consortia (ALADIN, HIRLAM, COSMO, LACE, MetOffice partners, etc.). At the time of writing, nine participants are currently archiving since 1st of June of 2020: MOGREPS-UK (MetOffice), MEPS (MetCoOp), γSREPS (AEMET), IT-EPS (ItAF-REMET), IREPS (Met Éireann), COMEPS (DMI), MF-AromeEps (MétéoFrance), RMI-EPS (RMI) and ICON-D2-EPS (DWD). The SRNWP-EPS convection-permitting LAM-EPS database is currently being used by project research sub-groups, for example to check multi-ensemble performance or comparing two LAM-EPSs in their common overlapping area.

How to cite: Callado-Pallarès, A.: EUMETNET SRNWPEPS-EPS project and convection-permitting LAM-EPS database, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-363, https://doi.org/10.5194/ems2021-363, 2021.

Dario Nicolì, Alessio Bellucci, Paolo Ruggieri, Panos Athanasiadis, Giusy Fedele, and Silvio Gualdi

After the early pioneering studies during the 2000s, and the first coordinated multi-model effort within the framework of the 5th Coupled Model Inter-comparison Project (CMIP5) in early 2010s, decadal climate predictions are now entering a more mature phase of their historical development. Near-term climate prediction activities have been recently endorsed by the World Climate Research Programme (WCRP) as one of the Grand Challenges in climate science research, and the Lead Centre for Annual-to-Decadal Climate Prediction, collecting hindcasts and forecasts from several contributing centres worldwide has been established by the WMO.

Here we present results from the CMIP6 DCPP-A decadal hindcasts produced with the CMCC decadal prediction system (CMCC DPS), based on the fully-coupled CMCC-CM2-SR5 dynamical model. A 10-member suite of 10-year retrospective forecasts, initialized every year from 1960 to 2019, is performed using a full-field initialization strategy.

The predictive skill for key quantities is assessed and compared with a non-initialized historical simulation, so as to verify the added value of initialization. In particular, the CMCC DPS is capable to skilfully reproduce past-climate surface temperature over the North Atlantic ocean, the Indian ocean and the Western Pacific ocean, as well as over most part of the continents. Beyond the contribution of the climate change, predictive skill emerges, among other regions, for the subpolar North Atlantic sea-surface temperatures, resembling the imprint of the extra-tropical part of the Atlantic Multidecadal Variability.

In terms of precipitation, CMCC DPS is able to capture most of the decadal variability over the Northern part of the Eurasian continent. Indeed, a set of regional diagnostics is aimed to investigate the process at stake behind this high predictive skill.

How to cite: Nicolì, D., Bellucci, A., Ruggieri, P., Athanasiadis, P., Fedele, G., and Gualdi, S.: Predicting Climate Change over the multi-annual range: a perspective from CMCC Decadal Prediction System, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-150, https://doi.org/10.5194/ems2021-150, 2021.

Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid

Irrigation is a common land use practice to adapt agriculture to unsuitable climatic conditions. It is highly relevant to ensure food production. Due to the growing population and its food demand in the future, as well as due to climate change, the irrigated areas are expected to increase globally. Therefore, it is important to understand the effects of irrigation on the climate system. Irrigation of cropland alters the biogeophysical properties of the land surface and the soil. Due to the land-atmosphere interactions, these alterations have the potential to affect the atmosphere directly or through feedback processes. Various studies point out that the effects of irrigation, like temperature reduction, are particularly pronounced on local to regional scales where they bear a mitigation potential to regional climate change.

This study aims to investigate the effects of irrigation on the regional climate. To model these effects, we developed and implemented a new flexible irrigation parameterization into the regional climate model REMO. In our setup, REMO is interactively coupled to the mosaic-based vegetation module iMOVE, enabling the calculation of irrigation effects and feedbacks on land, vegetation, and atmosphere. Multiple simulations for specific climatic conditions with and without the new irrigation parameterization are conducted on 0.11° resolution for the ”Greater Alpine Region“, which includes some of Europe‘s most intensively irrigated areas like the Po valley in Northern Italy. The differences between these simulations are analyzed to identify and quantify irrigation effects on atmospheric processes.

The new irrigation parameterization will be introduced and the analysis of the irrigation effects on the regional climate in the “Greater Alpine Region” will be presented.

How to cite: Asmus, C., Hoffmann, P., Pietikäinen, J.-P., Böhner, J., and Rechid, D.: Modeling irrigation effects on the regional climate in the "Greater Alpine Region" using a newly developed parameterization, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-176, https://doi.org/10.5194/ems2021-176, 2021.

Tomas Halenka and Gaby Langendijk

Cities play a fundamental role on climate at local to regional scales through modification of heat and moisture fluxes, as well as affecting local atmospheric chemistry and composition, alongside air-pollution dispersion. Vice versa, regional climate change impacts urban areas and is expected to increasingly affect cities and their citizens in the upcoming decades. Simultaneously, the share of the population living in urban areas is growing, and is projected to reach about 70% of the world population up to 2050. Thus, cities are becoming one of the most vulnerable environments under climate change. This is especially critical in connection to extreme events, for instance heat waves with extremely high temperatures exacerbated by the urban heat island effect, in particular during night-time, with significant consequences for human health.

This is clearly a problem of regional to local scales and local impacts, which cannot be addressed by GCMs and till now are not included commonly in regional simulations. Already in 2013, the CORDEX community identified cities to be one of the prime scientific challenges. Therefore, we proposed this topic to become an activity at CORDEX platform, within the framework of so called flagship pilot studies, which was accepted and the FPS URB-RCC activity has been started in May this year. The proposed activity relies on specific data, for the land-use parameterization and observations, which are available from previous campaigns, to perform the expected simulations and to validate them. This FPS action is connected to the certain extent to SDGs (Sustainable Development Goals) on sustainable cities (#11), climate action (#13) and health (#3), providing information for risk management in these aspects to urban stakeholders. Moreover, urbanization is important for many groups participating in CORDEX activities and goes across CORDEX domains as big cities appear in each of them. Clearly, careful selection of targeted coordinated simulations are going to be performed.

The main goal of this FPS is to understand the effect of urban areas on the regional climate, as well as the impact of regional climate change on cities, with the help of coordinated experiments with urbanized RCMs. While the urban climate with all the complex processes has been studied for decades, there is a significant gap to incorporate this knowledge into RCMs. This FPS aims to bridge this gap, leading the way to include urban parameterization schemes as a standard component in RCM simulations, especially at  high resolutions

How to cite: Halenka, T. and Langendijk, G.: URBan environments and Regional Climate Change (URB-RCC) – new CORDEX FPS on Urbanization, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-478, https://doi.org/10.5194/ems2021-478, 2021.

Eoghan Keany, Geoffrey Bessardon, and Emily Gleeson

To represent surface thermal, turbulent and humidity exchanges, Numerical Weather Prediction (NWP) systems require a land-cover classification map to calculate sur-face parameters used in surface flux estimation. The latest land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAMNWP system for operational weather forecasting is ECOCLIMAP-SG (ECO-SG). The first evaluation of ECO-SG over Ireland suggested that sparse urban areas are underestimated and instead appear as vegetation areas (1). While the work of (2) on land-cover classification helps to correct the horizontal extent of urban areas, the method does not provide information on the vertical characteristics of urban areas. ECO-SG urban classification implicitly includes building heights (3), and any improvement to ECO-SG urban area extent requires a complementary building height dataset.

Openly accessible building height data at a national scale does not exist for the island of Ireland. This work seeks to address this gap in availability by extrapolating the preexisting localised building height data across the entire island. The study utilises information from both the temporal and spatial dimensions by creating band-wise temporal aggregation statistics from morphological operations, for both the Sentinel-1A/B and Sentinel-2A/B constellations (4). The extrapolation uses building height information from the Copernicus Urban Atlas, which contains regional coverage for Dublin at 10 m x10 m resolution (5). Various regression models were then trained on these aggregated statistics to make pixel-wise building height estimates. These model estimates were then evaluated with an adjusted RMSE metric, with the most accurate model chosen to map the entire country. This method relies solely on freely available satellite imagery and open-source software, providing a cost-effective mapping service at a national scale that can be updated more frequently, unlike expensive once-off private mapping services. Furthermore, this process could be applied by these services to reduce costs by taking a small representative sample and extrapolating the rest of the area. This method can be applied beyond national borders providing a uniform map that does not depends on the different private service practices facilitating the updates of global or continental land-cover information used in NWP.


(1) G. Bessardon and E. Gleeson, “Using the best available physiography to improve weather forecasts for Ireland,” in Challenges in High-Resolution Short Range NWP at European level including forecaster-developer cooperation, European Meteorological Society, 2019.

(2) E. Walsh, et al., “Using machine learning to produce a very high-resolution land-cover map for Ireland, ” Advances in Science and Research,  (accepted for publication).

(3) CNRM, "Wiki - ECOCLIMAP-SG" https://opensource.umr-cnrm.fr/projects/ecoclimap-sg/wiki

(4) D. Frantz, et al., “National-scale mapping of building height using sentinel-1 and sentinel-2 time series,” Remote Sensing of Environment, vol. 252, 2021.

(5) M. Fitrzyk, et al., “Esa Copernicus sentinel-1 exploitation activities,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2019.

How to cite: Keany, E., Bessardon, G., and Gleeson, E.: Leveraging machine learning to produce a cost-effective national building height map of Ireland, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-48, https://doi.org/10.5194/ems2021-48, 2021.

Geoffrey Bessardon and Emily Gleeson

A good representation of surface processes is essential for weather forecasting as it is where most of the thermal, turbulent and humidity exchanges occur. One of the main goals in numerical weather prediction (NWP) at Met Éireann is to improve short-term weather forecasts. One aspect of this work involves improving the description of the Earth’s surface in the models used for short-range weather forecasting. Sample surface information in weather models includes land cover types, tree heights, soil types and Leaf Area Index (LAI).

Met Éireann is involved in developing the HARMONIE-AROME configuration of the shared ALADIN-HIRLAM NWP system for operational weather forecasting. Surface processes and physiography issues cause some of the limitations in the performance of HARMONIE-AROME [1]. The current cycle of HARMONIE-AROME, cycle 43, uses the latest version of the ECOCLIMAP [2] land cover map, ECOCLIMAP-SG [3]. ECOCLIMAP-SG, contrary to previous versions, uses external tree height, albedo and LAI data inputs. The choice of LAI input is important for the wind forecasts as the roughness length over vegetation, which is inversely proportional to wind speeds at the surface, depends on LAI and tree height. 

There are two multiyear climatologies suggested as input for ECOCLIMAP-SG [3]: the 2014-2016 Copernicus satellite LAI data at 300 m-resolution, and the 1999-2016 Copernicus satellite LAI data at 1 km-resolution, brought to 300 m resolution using a Kalman filter. Sensitivity testing for the June 2018 drought over Ireland using HARMONIE-AROME cycle 43 showed that LAI multi-year climatologies are not appropriate for representing the LAI during extreme events such as a drought. The implementation of ECOCLIMAP-SG, (including multi-year climatological LAI values) leads to cold biases and an over-prediction of wind speeds [4]. Simulations using near-real-time LAI values are thus necessary to assess the potential for assimilating LAI value in an operational forecasting setup. This led to establishing a special project running through 2021 at ECMWF consisting of a series of 16 1-month long HARMONIE-AROME cycle43 simulations comparing the use of multi-year climatologies and near-real-time data. The chosen months were selected across the 4 seasons showing the different behaviour of the real-time LAI compared to the multiyear climatologies [5]. This work presents the initial results of this project.

[1] Bengtsson, L., et al,. (2017). The HARMONIE–AROME Model Configurationin the ALADIN–HIRLAM NWP System. Monthly Weather Review, 145(5), 1919–1935.
[2] Masson, V., et al,. (2003). A global database of land surface parameters at 1-km resolution in meteorological and climate models. Journal of Climate.
[3] CNRM. (n.d.). Wiki - ECOCLIMAP-SG  https://opensource.umr-cnrm.fr/projects/ecoclimap-sg/wiki
[4] Bessardon, G., Gleeson, E., (2020) Physiography sensitivity testing over Ireland. Retrieved from http://www.umr-cnrm.fr/aladin/spip.php?article344
[5] Bessardon, G., Gleeson, E., (2021) LAI sensitivity testing in HARMONIE-AROME for NWP forecasting for Ireland. Retrieved from https://www.ecmwf.int/en/research/special-projects/spiebess-2021



How to cite: Bessardon, G. and Gleeson, E.: LAI sensitivity testing in HARMONIE-AROME for NWP forecasting for Ireland, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-49, https://doi.org/10.5194/ems2021-49, 2021.

Martina Tudor, Ivica Janeković, and Stjepan Ivatek-Šahdan

The season of late summer and autumn is favourable for intensive precipitation events (IPE) in the central Mediterranean. During that period the sea surface is warm and contributes to warming and moistening of the lowest portion of the atmosphere, particularly the planetary boundary layer (PBL). Adriatic sea is surrounded by mountains and the area often receives substantial amounts of precipitation in short time (24 hours). The IPEs are a consequence of convection triggered by topography acting on the southerly flow that has brought the unstable air to the coastline. Improvement in prediction of high impact weather events is one of the goals of The Hydrological cycle in the Mediterranean eXperiment (HyMeX). This study examines how precipitation patterns change in response to different SST forcing. We focus on the IPEs that occurred on the eastern Adriatic coast during the first HyMeX Special observing period (SOP1, 6 September to 5 November 2012). The operational forecast model ALADIN uses the same SST as the global meteorological model (ARPEGE from Meteo France), as well as the forecast lateral boundary conditions (LBCs). First we assess the SST used by the operational atmospheric model ALADIN and compare it to the insitu measurements, ROMS ocean model, OSTIA and MUR analyses. Results of this assessment show that SST in the eastern Adriatic was overestimated by up to 10 K during HyMeX SOP1 period. Then we examine the sensitivity of 8 km and 2 km resolution forecasts of IPEs to the changes in the SST during whole SOP1 with special attention to the intensive precipitation event in Rijeka. Forecast runs in both resolutions are performed for the whole SOP1 using different SST fields prescribed at initial time and kept constant during the model forecast. Categorical verification of 24h accumulated precipitation did not show substantial improvement in verification scores when more realistic SST was used. Furthermore, the results show that the impact of introducing improved SST in the analysis on the precipitation forecast varies for different cases. There is generally a larger sensitivity to the SST in high resolution than in the lower one, although the forecast period of the latter is longer.

How to cite: Tudor, M., Janeković, I., and Ivatek-Šahdan, S.: Impact of SST on heavy rainfall events on eastern Adriatic during SOP1 of HyMeX, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-440, https://doi.org/10.5194/ems2021-440, 2021.

Martina Tudor, Ivica Janeković, and Stjepan Ivatek-Šahdan

In February 2012 a strong wind event over the Adriatic Sea lead to extreme air-sea interactions. The extreme event consisted of strong to severe cold wind (bura) event that last over three weeks accompanied by accumulation of snow on the coastline in the beginning of the event. As a result, the sea surface cooled rapidly and mixed throughtout the vertical column. The ALADIN System operational forecast used the sea surface temperature (SST) available from the global meteorological model ARPEGE. It was a practical solution for an operational application, since the same source is used also for forecast lateral boundary conditions (LBCs). Here we first compare the SST available from operational global atmospheric models ARPEGE and IFS to in situ measurements, SST analyses and ocean model output. The global models underestimated the SST cooling while the available satellite based observations do not resolve the eastern Adriatic coastline due to numerous islands. Both global models overestimated SST over the Adriatic for up to 10oC when compared to in situ measurements. Afterwards, we runset of forecasts using the SST from the OSTIA and MUR analyses or the ROMS ocean model forecast. The impact of changing SST field on forecast atmospheric fields is mostly reflected on precipitation. Model forecasts more precipitation using warmer SST. Changes in fluxes of evaporation, heat and momentum through the sea surface reflect the changes in the SST. Too warm SST yields too intense fluxes. Colder SST in the Velebit Channel causes reduced momentum flux that allows stronger bura jets above the open sea away from coastlines.

How to cite: Tudor, M., Janeković, I., and Ivatek-Šahdan, S.: Impact of SST on the NWP forecast over Adriaticduring the exceptional bura outbreak in February 2012, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-441, https://doi.org/10.5194/ems2021-441, 2021.

Martina Tudor and Stjepan Ivatek-Šahdan

The fields that describe surface properties, from terrain height to vegetation types can have substantial impact on NWP model forecast, especially on the model variables close to the surface. These fields can be computed from different databases. Higher resolution of the terrain height database and higher quality of input data leads to a better representation of the terrain height and other surface fields, especially as NWP models move to a higher resolution. Here we use ALARO configuration of the ALADIN System with TOUCANS turbulence scheme (prognostic TKE) with nonhydrostatic dynamics in 2km resolution over Croatia. The model domain contains Dinaric Alps mountains and Adriatic sea.  The existing operational NWP application uses fields from an old database that is insufficient to properly describe the surface in 2km grid spacing. The fields describing topography, such as terrain height, land sea mask, subgrid terrain variability including surface roughness are computed from a new database in substantially higher resolution. The new fields describing the surface characteristics are more realistic, but also substantially different from the fields used before.  However, the model, including the turbulence parametrisation, was tuned using the old database. Therefore, the subsequent model forecast was not automatically improved when the fields from the new database were used. Tuning only one parameter in a scheme is substantial work, but tuning the whole model with a large number of tuning parametres is daunting. Therefore, the computation of surface roughness and other parameters was tuned in order to improve the 10m wind forecast. Decreased surface roughness does not always lead to higher surface wind speeds and vice versa.

How to cite: Tudor, M. and Ivatek-Šahdan, S.: Improving surface representation and consequences in NWP forecast, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-479, https://doi.org/10.5194/ems2021-479, 2021.

Sylvain Cros, Martial Haeffelin, Felipe Toledo, Dupont Jean-Charles, and Badosa Jordi

By reducing the atmospheric visibility, fog events have strong impacts on several humans activities. Transport security, military operations, air quality forecast and solar energy production are critical activities considering fog dissipation time as a high valuable information.

Fog dissipation occurs through these two following processes. (1) An adiabatic cloud elevation converts the fog into a low stratus, increasing the visibility at ground level while keeping an overcast sky. (2) A radiative warming can break through a large continuous fog deck. Then, the cleared area increases progressively by heating the ground of the neighboured fog covered area.

These two events are particularly difficult to forecast using NWP models as many non-linear local processes at short-time scale are involved. Moreover, current network of fog presence sensors is too scarce to analyse and/or anticipate the phenomena. Subsequent images of geostationary meteorological satellite offer a high temporal resolution that enables to monitor large fog decks and detect punctual clear areas that induce dissipation (case 2). However, fog detection using satellite images suffers from a lack of distinction between fog and very low stratus.

In this work, we explored the potential of MSG SEVIRI radiometer through radiance observations and more advanced cloud products to analyse fog events effectively observed at the SIRTA atmospheric observatory (Palaiseau, France). We assumed that, during these events, pixels classified as “very low cloud” according to SAF-NWC algorithm were covered by fog. We monitored the evolution of these pixels using a cloud index derived from HRV channels, providing a more detailed spatial distribution of cloud cover during day time. We analysed the evolution of brightness temperature spatial gradient from the SEVIRI infrared window channel (IR 10.8µm). We isolated cases where ground warming situation could anticipate an irreversible fog dissipation. Then we deduced some fog dissipation forecasting principles.

This approach has the potential to provide to users information on morning fog sustainability with a higher accuracy and finer temporal resolution than NWP. Ongoing work focuses on characterizing favourable situations for accurate forecasts, while further predictors are investigated using recent products providing a smart distinction between fog and low stratus using SEVIRI images.

How to cite: Cros, S., Haeffelin, M., Toledo, F., Jean-Charles, D., and Jordi, B.: Fog dissipation through ground warming monitored by satellite image : an approach to support regional forecasting, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-361, https://doi.org/10.5194/ems2021-361, 2021.

Chairperson: Estíbaliz Gascón
Forecast verification
Sebastian Buschow and Petra Friederichs

Many atmospheric phenomena like fronts, convection and turbulence leave a distinct imprint on the spatial structure of meteorological fields such as precipitation, wind and temperature. Whether or not a forecast model is able to realistically simulate the resulting spatial correlation patterns is therefore a relevant question for model developers, forecasters and end users alike. Highly resolved numerical models have the potential to achieve this goal, but their realism is often difficult to assess objectively due to the sheer amount of data and wide variety of possible error contributions. 

While some existing verification methods measure an overall “structure” error, most of these approaches are limited to precipitation fields and fail to produce specific, interpretable judgements. Here, we introduce a new structural verification technique based on the dual-tree complex wavelet transformation: The SAD-scores explicitly quantify how well the observed spatial Scales, degrees of Anisotropy and preferred Directions are represented by the simulation. Directional aspects in particular have previously often been neglected, but can be important in assessing the realism of predicted fronts, convergence lines and organized convection. 

Unlike many established techniques, SAD is applicable not only to precipitation but to any meteorological field of interest. General verdicts like “the structure was predicted poorly” can be resolved into specific statements like “the modelled convection was too small in scale” or “the simulated front was too linear and rotated by an angle of X degrees”. The localized nature of the wavelets furthermore allows us to conveniently display the structural properties on a map. Lastly, making use of the inverse wavelet transform, we show how the detected structural errors can potentially be corrected, thereby leading the way towards future post-processing applications.

How to cite: Buschow, S. and Friederichs, P.: Making forecasters SAD: Verification of Scale, Anisotropy and Direction using wavelets., EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-325, https://doi.org/10.5194/ems2021-325, 2021.

Sam Allen, Chris Ferro, and Frank Kwasniok

The objective assessment of forecasts plays an integral role in the development of a prediction system. Scoring rules condense all information regarding forecast performance into a single numerical value, providing a convenient framework to objectively rank and compare competing prediction schemes. However, the value of a forecast to its user will depend on how it is to be used, and it is therefore necessary to consider several different characteristics of a forecast’s performance. Although scoring rules provide only a single measure of forecast accuracy, they can often be decomposed into components that each assess a distinct aspect of a forecast, such as its calibration or information content. These decompositions of scores provide additional feedback to the forecaster, which can be used to identify strengths and limitations in the prediction scheme, and, in turn, help to improve future forecasts. But these aspects of forecast quality could themselves depend on several factors, such as the time of the year, the spatial location, or on the value of the forecast itself, and it is therefore useful to evaluate the performance of a forecast under different circumstances; if a forecaster were able to identify situations in which their forecasts perform particularly poorly, then they could more easily develop their forecast strategy to account for these deficiencies. To help forecasters identify such situations, we introduce a novel decomposition of scoring rules that allows for a more rigorous examination of the sources of information in a forecast whilst simultaneously quantifying the magnitude of conditional forecast biases. We apply this decomposition to MeteoSwiss COSMO-2E forecasts for the occurrence of moderately extreme weather events and illustrate how the additional information provided by this decomposition can be used to design more appropriate statistical post-processing techniques.

How to cite: Allen, S., Ferro, C., and Kwasniok, F.: A sequential decomposition of proper scoring rules, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-336, https://doi.org/10.5194/ems2021-336, 2021.

Barbara Casati, Vincent Fortin, Franck Lespinas, and Dikraa Khedhaouiria

Numerical Model Prediction (NWP) verification against station measurements from a surface network is affected by sub-tile representativeness issues. Moreover, the station network is often not representative of the whole verification domain (e.g. usually coastal stations are predominant) and large unpopulated regions (such as oceans, Polar regions, deserts) are under-sampled. Verification against gridded analyses mitigate these issues, since they partially address the sub-tile representativeness, and sample homogeneously the verification domain. Moreover, gridded analyses merge station network measurements to radar and satellite retrieval estimates, in a physical coherent fashion, over the same NWP grid. Verification against own analysis, despite quite convenient, is however hampered by its dependence on the NWP background model, which renders the verification “incestuous”, further than being affected by the uncertainties introduced by retrieval algorithms and Data Assimilation (DA) procedures.

In this study we investigate the use of a gridded NWP own analysis for verification, by applying a mask to reduce the background model contribution. The mask weights the verification scores to account for the amounts of observations assimilated and their associated uncertainty, as estimated from DA. We illustrate the approach by using the Canadian Precipitation Analysis (CaPA), which assimilates station measurements, radar and satellite-based (IMERG) observations. The CaPA confidence (weighting) mask is dynamic and changes depending on the daily available (assimilated) observations, and on their corresponding DA error statistics; it is defined as

                                             mask = 1 - var(A-O)/var(B-O)

where A=analysis, B=Background, O=observations. Where the analysis is identical to the background model, the weighting mask is zero.

We evaluate the Canadian Regional Deterministic Prediction System (RDPS), which is the NWP system used as background model for CaPA. As expected, the verification results obtained by using the weighting mask lay between the verification results obtained verifying against the analysis over the full domain, and the results obtained verifying against station measurements. The effects of sub-tile representativeness are quantified by comparing verification results against station measurements to verification results against CaPA for the grid-points co-located with the stations. Finally, the comparison of the verification results against CaPA over the full domain versus the verification results against CaPA for the grid-points co-located with stations, estimates to which extent the station network is representative of the full domain.

The approach aims to propose a simple -yet effective- better practice for verification against own analysis.

How to cite: Casati, B., Fortin, V., Lespinas, F., and Khedhaouiria, D.: NWP verification against own analysis by using a Data Assimilation confidence mask, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-395, https://doi.org/10.5194/ems2021-395, 2021.

Christopher Steele, Ben Perryman, Philip Gill, and Teresa Hughes

Having the ability to stratify a model’s performance by weather type is not only beneficial to a weather forecaster when making decisions, but it is also important for end users, whether they be scientists looking to improve the model, or a customer wishing to know the value of a forecast under a specific set of circumstances.

At the MET Office, Decider is a tool which assigns a dominant weather type to a set of ensemble members, to predict the probability of a weather type occurring. The weather type is chosen from either a set of 30 or 8 sub-types, where a weather type is pre-determined objectively by clustering a 154 year record of sea level pressure anomaly fields.  

There is also a record of daily weather type classifications derived from analysis fields and so information of model performance for these weather types could be invaluable in reducing model error if combined with the predictions from Decider.

Early trials of assessing model performance by weather type revealed that larger errors occur when the weather type persisted for a single day, rather than longer timescales, and so this suggests that it would be beneficial to examine weather type transition periods.

To examine this, we expand the weather type methodology to include multiple time periods. The current methodology uses 12Z analyses to identify the weather type, and so we first assess model performance as a sensitivity study to the analysis time.

Transition days are identified when the weather type changes during a pre-defined validation period, which allows separation into either night/day weather type transitions, or a change in weather type over a full 24-hour period.

We will present early results of this work and demonstrate the impact of model performance when stratifying by regime transitions.

How to cite: Steele, C., Perryman, B., Gill, P., and Hughes, T.: Assessing model performance by weather regime transitions, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-92, https://doi.org/10.5194/ems2021-92, 2021.

Michael Sharpe, Joseph Battershill, and Katherine Hurst

The UK Met Office manages its commitment to the public through the Public Weather Service and an important factor in public safety and concern is extreme weather events. Therefore, a new Key Performance Indicator is being introduced, related to the ability with which extreme events are correctly identified. The Threshold Weighted Continuous Ranked Probability Score (twCRPS) is used to make this assessment by determining how well site-specific Met Office ensemble-based probabilistic forecast solutions predict relative-extreme events. The threshold weighted version of the Mean Absolute Error (twMAE) is the deterministic equivalent to the twCRPS. The twMAE is used for the assessment of the deterministic model output that currently appears on the Met Office App and website.

Gridded numerical ensemble model data is generated by MOGREPS (the Met Office Global and Regional Ensemble Prediction System). A new program of post-processing work has been undertaken in recent years (IMPROVER) to replace the system of post-processing currently employed by the Met Office. IMPROVER applies a series of post-processing steps to generate both probabilistic and deterministic forecasts and site-specific data is generated from these model fields. Verification of the model output is undertaken at each post-processing stage to ensure that every step is having the expected impact on the performance of the model. To date however, these assessments have concentrated on the performance of more typical conditions rather than the ability with which more extreme events are identified.

This session outlines very recent work to assess the ability with which raw MOGREPS data and data generated by various of the post-processing stages of IMPROVER, predict relative-extreme events at observation sites throughout the UK. The twMAE and twCRPS are used for this assessment, where in both cases, the threshold weighting function is defined in terms of a distribution formed by sampling the numerical value corresponding to a chosen relatively extreme percentile from the observed 30-year climatology of each UK site.

How to cite: Sharpe, M., Battershill, J., and Hurst, K.: Post-processing and its effect on relative-extreme event identification, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-94, https://doi.org/10.5194/ems2021-94, 2021.

Michele Salmi, Chiara Marsigli, and Manfred Dorninger

During the last decade, the constant improvement in computational capacity led to the development of the first limited-area, kilometer-scale ensemble prediction systems (L-EPS). The COSMO-D2 EPS (now ICON-D2) is the operational L-EPS at the German weather service (DWD) and has a spatial resolution of around 2km. This grid resolution allows large scale, deep convective processes such as thunderstorms or heavy showers to be handled explicitly, without any physical parametrization necessary. Special parameters involving both clouds (micro-)physics and large scale lifting – such as the Lighting Potential Index, or LPI – have also been developed in order to try and bring the forecasting of deep convection and therefore also of lightning activity to a new level of spatial accuracy. With such high-precision forecasts comes however also a much higher error potential, at least for gridpoint-verification. The use of this high resolution setup in an ensemble prediction system might however bring huge benefits in terms of skill and predictability. This work is a preliminary attempt to apply innovative verification approaches such as the dispersion Fractions Skill Score (dFSS) or the ensemble-SAL (eSAL) to the LPI in the COSMO-D2 EPS. Aim of this work is to assess the relationship between the ensemble error and the ensemble dispersion at different spatial scales. For the summer months 2019 the COSMO-D2 EPS shows a general tendency to underestimate the unpredictability of the lightning events, though the spread-error relationship varies greatly for different forecast lead times. With the help of the dFSS, one can also express this relationship in terms of skillful scales. On average, the system produces a useful forecast during the afternoon hours for horizontal scales of around 200 km. However, the ensemble members show an average horizontal dispersion that lies around half of that value, at more or less 100 km.

How to cite: Salmi, M., Marsigli, C., and Dorninger, M.: Predictability analysis and skillful scale verification of the Lightning Potential Index (LPI) in the COSMO-D2 high resolution ensemble system, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-260, https://doi.org/10.5194/ems2021-260, 2021.

Evelyn Müller, Jan Hoffmann, and Dennis Schulze

Actual, continuously available information on the accuracy of forecasts can support both weather services and users of forecasts in quality assurance during operations and identify systematic weaknesses. Comparing the forecast success of different forecasting methods allows decision makers in the weather service and on the user side to evaluate the cost-benefit ratio of available forecasting approaches, be it different models, DMO and post-processing, or different providers. Finally, in addition to on-off experiments for version comparison, the success of developments to the forecast system can be seen in the comparison of time series of verification results against those of other forecasts. 

From the development of the forecasting process to daily operations to the use of forecasts in subsequent industry applications, stakeholders have very different questions about the quality of weather forecasts. From the weather room, there is a particular need for up-to-date information on the previous day's forecast success and rapid access to case verification analyses following unusual events. Especially in B2B, case-specific comparison with the success of other forecasts is also in demand. For management, on the other hand, longer-term trends in forecast quality are the focus of interest. Finally, users often base their choice of a forecasting provider not only on procurement costs and convenience of access, but also take into account the current forecast accuracy of their relevant parameters, in their region, in the forecast horizon relevant to them. Especially weather-sensitive industries such as road weather services, energy production and transmission, but also media often agree with forecast suppliers on continuous monitoring of forecast quality. 

We present different perspectives and questions and show possible answers as use cases in a verification portal.

How to cite: Müller, E., Hoffmann, J., and Schulze, D.: Verification as a service to bring more transparency on forecast accuracy to weather services and users, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-456, https://doi.org/10.5194/ems2021-456, 2021.

Jonas Bhend, Jean-Christophe Orain, Vera Schönenberger, Christoph Spirig, Lionel Moret, and Mark Liniger

Verification is a core activity in weather forecasting. Insights from verification are used for monitoring, for reporting, to support and motivate development of the forecasting system, and to allow users to maximize forecast value. Due to the broad range of applications for which verification provides valuable input, the range of questions one would like to answer can be very large. Static analyses and summary verification results are often insufficient to cover this broad range. To this end, we developed an interactive verification platform at MeteoSwiss that allows users to inspect verification results from a wide range of angles to find answers to their specific questions.

We present the technical setup to achieve a flexible yet performant interactive platform and two prototype applications: monitoring of direct model output from operational NWP systems and understanding of the capabilities and limitations of our pre-operational postprocessing. We present two innovations that illustrate the user-oriented approach to comparative verification adopted as part of the platform. To facilitate the comparison of a broad range of forecasts issued with varying update frequency, we rely on the concept of time of verification to collocate the most recent available forecasts at the time of day at which the forecasts are used. In addition, we offer a matrix selection to more flexibly select forecast sources and scores for comparison. Doing so, we can for example compare the mean absolute error (MAE) for deterministic forecasts to the MAE and continuous ranked probability scores of probabilistic forecasts to illustrate the benefit of using probabilistic forecasts.

How to cite: Bhend, J., Orain, J.-C., Schönenberger, V., Spirig, C., Moret, L., and Liniger, M.: Comparative verification in complex topography, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-246, https://doi.org/10.5194/ems2021-246, 2021.

Sabrina Wahl and Jan D. Keller

Weather and climate simulations based on numerical models provide 4-dimensional reconstructions of multiple meteorological parameters describing the atmopsheric state. Yet, the vast majority of evaluation studies focus on the evaluation of single parameters in time and space without looking at the statistical multivariate dependence between the parameters. This, however, is necessary especially with respect to specific events where two or more parameters are involved, i.e., so called compound events. Previous studies have investigated the representation of natural hazards such as wildfires, heat stress, droughts by evaluating corresponding indices based on two or more parameters. Thereby the evaluation process stays in a single parameter framework with well established verification methods at hand. 

In this work, we present a more sophisticated and generalized approach to investigate physical dependencies between parameters by employing copula theory. With this method, we aim at evaluating the multivariate statistical dependence between two parameters (i.e. the copula) separately from their marginal distributions. This separation enables a more detailed investigation of compound indices (CI) based on the involved parameters. The differences in CI derived from model simulations and observations can now be related to deficiencies of the numerical model due to (i) a misrepresentation of the marginal distributions of the contributing variables, (ii) a misrepresentation of the statistical dependence between the parameters (the copula), (iii) or both. While the method is applicable to all combinations of two parameters, we will present the results of a specific joint copula-based evaluation of temperature and humidity which are the basis for natural hazards mentioned above.

How to cite: Wahl, S. and Keller, J. D.: Evaluation of multi-parameter dependencies in weather and climate simulations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-141, https://doi.org/10.5194/ems2021-141, 2021.

Maksim Iakunin, Niklas Wagner, Alexander Graf, Klaus Görgen, and Stefan Kollet

In many of today’s resource management and climate change adaptation challenges, versatile and  reliable numerical model simulations are the basis for informed decision making. The integration of multiple compartmental  models into simulation platforms allows us to reproduce interacting geosystem processes and thereby solve a wide range of problems in a variety of applications. The Terrestrial System Modelling Platform (TSMP, https://www.terrsysmp.org) is an integrated regional Earth system model that simulates processes from the groundwater across the land surface to the top of the atmosphere on multiple spatio-temporal scales. TSMP consists of the COSMO (Consortium for Small-scale Modeling) atmospheric model, the CLM (Community Land Model), and the hydrologic model ParFlo, coupled through OASIS3-MCT. TSMP is used in various studies from climate change simulations to near-real time forecasting and monitoring. Here we present the results of the evaluation of the TSMP in a monitoring setup, providing daily forecasts with a lead time of 10days of the atmospheric, surface, and groundwater states and fluxes for a heterogeneous mid mountain-ranges area in Western Germany. The model domain covers an area of 150km x 150km at 1km (atmosphere) and 0.5km (land surface and subsurface) resolution. The simulated data is compared with observations from the TERENO (Terrestrial Environmental Observatories, https://www.tereno.net) Eifel/Lower Rhine Valley network. This TERENO observatory comprises a total area of 2354 km² and provides data from a very dense measurement network of 12 climate stations, 6 eddy covariance stations, 6 lysimeter stations, and 13 cosmic-ray neutron stations. To assess the quality and suitability of the TSMP as a monitoring system of the geosystem’s state and evolution with agricultural applications in mind,  forecasts from July 2019 to October 2020 are analyzed with reference to the observations. Results show that the TSMP can well represent the main subsurface hydrological and relevant meteorological features.

How to cite: Iakunin, M., Wagner, N., Graf, A., Görgen, K., and Kollet, S.: Evaluation of daily forecasts by the coupled Terrestrial Systems Modelling Platform (TSMP) over a small convection-permitting model domain in Central Europe, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-291, https://doi.org/10.5194/ems2021-291, 2021.


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