| 001 | 911598 | ||
| 005 | 20240712100951.0 | ||
| 024 | 7 | _ | |a 10.5194/gmd-2022-118 |2 doi |
| 024 | 7 | _ | |a 2128/32685 |2 Handle |
| 037 | _ | _ | |a FZJ-2022-04857 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 910 |
| 100 | 1 | _ | |a Lu, Yen-Sen |0 P:(DE-Juel1)164851 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Optimization of weather forecasting for cloud cover over the European domain using the meteorological component of the Ensemble for Stochastic Integration of Atmospheric Simulations version 1.0 |
| 260 | _ | _ | |a Katlenburg-Lindau |c 2022 |b Copernicus |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1669038376_14265 |2 PUB:(DE-HGF) |
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| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 520 | _ | _ | |a In this study, we present an expansive sensitivity analysis of physics configurations for cloud cover using the Weather Forecasting and Research Model (WRF V3.7.1) on the European domain. The experiments utilize the meteorological part of a large ensemble framework known as the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-met). The experiments first seek the best deterministic WRF physics configuration by simulating over 1,000 combinations of microphysics, cumulus parameterization, planetary boundary layer physics (PBL), surface layer physics, radiation scheme and land surface models. The results on six different test days are compared to CMSAF satellite images from EUMETSAT. We then selectively conduct stochastic simulations to assess the best choice for ensemble forecasts. The results indicate a high variability in terms of physics and parameterization. The combination of Goddard, WSM6, or CAM5.1 microphysics with MYNN3 or ACM2 PBL exhibited the best performance in Europe. For probabilistic simulations, the combination of WSM6 and SBU–YL microphysics with MYNN2 and MYNN3 showed the best performance, capturing the cloud fraction and its percentiles with 32 ensemble members. This work also demonstrates the capability and performance of ESIAS-met for large ensemble simulations and sensitivity analysis. |
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| 773 | _ | _ | |a 10.5194/gmd-2022-118 |0 PERI:(DE-600)2456729-2 |x 1991-9611 |y 2022 |t Geoscientific model development discussions |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/911598/files/Preprint.pdf |y OpenAccess |
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