This study investigates an extreme weather event that impacted the United Arab Emirates (UAE) in March 2016, using the Weather Research and Forecasting (WRF) model version 3.7.1 coupled with its hydrological modeling extension package (WRF-Hydro). Six-hourly forecasted forcing records at 0.5^∘ spatial resolution, obtained from the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS), are used to drive the three nested downscaling domains of both standalone WRF and coupled WRF–WRF-Hydro configurations for the recent flood-triggering storm. Ground and satellite observations over the UAE are employed to validate the model results. The model performance was assessed using precipitation from the Global Precipitation Measurement (GPM) mission (30 min, 0.1^∘ product), soil moisture from the Advanced Microwave Scanning Radiometer 2 (AMSR2; daily, 0.1^∘ product) and the NOAA Soil Moisture Operational Products System (SMOPS; 6-hourly, 0.25^∘ product), and cloud fraction retrievals from the Moderate Resolution Imaging Spectroradiometer Atmosphere product (MODATM; daily, 5 km product). The Pearson correlation coefficient (PCC), relative bias (rBIAS), and root-mean-square error (RMSE) are used as performance measures. Results show reductions of 24 % and 13 % in RMSE and rBIAS measures, respectively, in precipitation forecasts from the coupled WRF–WRF-Hydro model configuration, when compared to standalone WRF. The coupled system also shows improvements in global radiation forecasts, with reductions of 45 % and 12 % for RMSE and rBIAS, respectively. Moreover, WRF-Hydro was able to simulate the spatial distribution of soil moisture reasonably well across the study domain when compared to AMSR2-derived soil moisture estimates, despite a noticeable dry and wet bias in areas where soil moisture is high and low. Temporal and spatial variabilities of simulated soil moisture compare well to estimates from the NOAA SMOPS product, which indicates the model's capability to simulate surface drainage. Finally, the coupled model showed a shallower planetary boundary layer (PBL) compared to the standalone WRF simulation, which is attributed to the effect of soil moisture feedback. The demonstrated improvement, at the local scale, implies that WRF-Hydro coupling may enhance hydrological and meteorological forecasts in hyper-arid environments.

In this paper we coupled a crop growth model to the Weather Research and Forecasting model with its land surface model Noah‐MP and demonstrated the influence of the weather driven crop growth on land‐atmosphere (L‐A) feedback. An impact study was performed at the convection permitting scale of 3 km over Germany. While the leaf area index (LAI) in the control simulation was the same for all cropland grid cells, the inclusion of the crop growth model resulted in heterogeneous crop development with higher LAI and stronger seasonality. For the analyses of L‐A coupling, a two‐legged metric was applied based on soil moisture, latent heat flux and convective available potential energy. Weak atmospheric coupling is enhanced by the crop model, the terrestrial coupling determines the regions with the L‐A feedback. The inclusion of the crop model turns regions with no L‐A feedback on this path into regions with strong positive coupling. The number of non‐atmospherically controlled days between April and August is increased by 10–15 days in more than 50% of Germany. Our work shows that this impact results in a reduction of both cold bias and warm biases and thus improves the metrics of distributed added value of the monthly mean temperatures. The study confirms that the simulation of the weather driven annual phenological development of croplands for the regional climate simulations in mid‐latitudes is crucial due to the L‐A feedback processes and the currently observed and expected future change in phenological phases.

The impact of assimilating conventional and Global Positioning System (GPS) Zenith Total Delay (ZTD) data over France into the Weather Research and Forecasting (WRF) model is investigated during COPS IOP 9c. A convection‐permitting horizontal resolution of 3.6 km covering the whole of central Europe was chosen. Four different simulations were conducted to show the influence of assimilating different observation types on the forecast. Additionally, a comparison with the models COSMO‐DE and COSMO‐EU from the German Meteorological Service (DWD) was performed. The results show a clear positive impact of assimilating data into a convection‐permitting configuration of the WRF model over Europe. The additional assimilation of GPS‐ZTD data shows a further improvement of integrated water vapour (IWV) correlation, but the influence on prediction of precipitation was not necessarily clear. A comparison with radiosonde data shows a positive impact on the humidity structure after 12 hours, while an overestimation of precipitation still remains. A further comparison with the operational high‐resolution models of DWD during this COPS IOP reveals a strong dependence on the initial boundary‐layer wind field especially as the near‐surface wind field is better represented in the ECMWF analysis than in the COSMO analysis. As this is, to our knowledge, the first study applying a convection‐permitting configuration of WRF over Europe, it can be used as a guidance for further studies. Copyright © 2011 Royal Meteorological Society

The quantification of land–atmosphere coupling strength is still challenging, particularly in the atmospheric segment of the local coupling process chain. This is in part caused by a lack of spatially comprehensive observations of atmospheric temperature and specific humidity which form the verification basis for the common process-based coupling metrics. In this study, we aim at investigating where uncertainty in the atmospheric temperature and moisture affects the land–atmosphere coupling strength over Europe, and how changes in the mean temperature and moisture, as well as their vertical gradients, influence the coupling. For this purpose, we implemented systematic a posteriori modifications to the temperature and moisture fields from a regional climate simulation to create a spread in the atmospheric conditions. Afterwards, the process-based coupling metric convective triggering potential – low-level humidity index framework was applied to each modification case. Comparing all modification cases to the unmodified control case revealed that a strong coupling hotspot region in northeastern Europe was insensitive to temperature and moisture changes, although the number of potential coupling days varied by up to 20 d per summer season. The predominance of positive feedbacks remained unchanged in the northern part of the hotspot, and none of the modifications changed the frequent inhibition of feedbacks due to dry conditions in the atmosphere over the Mediterranean and the Iberian Peninsula. However, in the southern hotspot region in the north of the Black Sea, the dominant coupling class frequently switched between wet soil advantage and transition zone. Thus, both the coupling strength and the predominant sign of feedbacks were sensitive to changes in temperature and moisture in this region. This implies not only uncertainty in the quantification of land–atmosphere coupling strength but also the potential that climate-change-induced temperature and moisture changes considerably impact the climate there, because they also change the predominant atmospheric response to land surface wetness.

Extreme precipitation is a weather phenomenon with tremendous damaging potential for property and human life. As the intensity and frequency of such events is projected to increase in a warming climate, there is an urgent need to advance the existing knowledge on extreme precipitation processes, statistics and impacts across scales. To this end, a working group within the Germany-based project, ClimXtreme, has been established to carry out multidisciplinary analyses of high-impact events. In this work, we provide a comprehensive assessment of the 29 June 2017 heavy precipitation event (HPE) affecting the Berlin metropolitan region (Germany), from the meteorological, impacts and climate perspectives, including climate change attribution. Our analysis showed that this event occurred under the influence of a mid-tropospheric trough over western Europe and two shortwave surface lows over Britain and Poland (Rasmund and Rasmund II), inducing relevant low-level wind convergence along the German–Polish border. Over 11 000 convective cells were triggered, starting early morning 29 June, displacing northwards slowly under the influence of a weak tropospheric flow (10 m s⁻¹ at 500 hPa). The quasi-stationary situation led to totals up to 196 mm d⁻¹, making this event the 29 June most severe in the 1951–2021 climatology, ranked by means of a precipitation-based index. Regarding impacts, it incurred the largest insured losses in the period 2002 to 2017 (EUR 60 million) in the greater Berlin area. We provide further insights on flood attributes (inundation, depth, duration) based on a unique household-level survey data set. The major moisture source for this event was the Alpine–Slovenian region (63 % of identified sources) due to recycling of precipitation falling over that region 1 d earlier. Implementing three different generalised extreme value (GEV) models, we quantified the return periods for this case to be above 100 years for daily aggregated precipitation, and up to 100 and 10 years for 8 and 1 h aggregations, respectively. The conditional attribution demonstrated that warming since the pre-industrial era caused a small but significant increase of 4 % in total precipitation and 10 % for extreme intensities. The possibility that not just greenhouse-gas-induced warming, but also anthropogenic aerosols affected the intensity of precipitation is investigated through aerosol sensitivity experiments. Our multi-disciplinary approach allowed us to relate interconnected aspects of extreme precipitation. For instance, the link between the unique meteorological conditions of this case and its very large return periods, or the extent to which it is attributable to already-observed anthropogenic climate change.

In this study, we present a five‐member Weather Research and Forecasting (WRF) physics ensemble over the Arabian Peninsula on the convection‐permitting (CP) scale and investigate the ability to simulate convection and precipitation by varying the applied cloud microphysics and planetary boundary layer (PBL) parametrizations. The study covers a typical precipitation event ocurring during summertime over the eastern part of the United Arab Emirates (UAE). Our results show that the best results are obtained by using water‐ and ice‐friendly aerosols combined with aerosol‐aware Thompson cloud microphysics and the Mellor‐Yamada‐Nakanishi‐Niino (MYNN) PBL parametrization. The diurnal cycle of 2‐m temperature over the desert is well captured by all members, although a cold bias is present during the morning and evening transition. All members are capable of simulating the correct timing of the onset of convection. Simulations with the MYNN PBL and Thompson scheme produce the highest convective available potential energy (CAPE) and convective inhibition (CIN), associated with stronger mixing inside the PBL, leading to the formation of more dense liquid water clouds. The WDM6 microphysics scheme is not a suitable option, as there are hardly any liquid water clouds; mainly ice clouds are simulated. Precipitation is best captured by applying the MYNN and Thomspon scheme. Although the ensemble size is relatively small, this allows for the provision of cloud probability maps suitable for cloud‐seeding applications.