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| 001 | 1037646 | ||
| 005 | 20250203124512.0 | ||
| 024 | 7 | _ | |a 10.5194/gmd-16-1467-2023 |2 doi |
| 024 | 7 | _ | |a 1991-959X |2 ISSN |
| 024 | 7 | _ | |a 1991-9603 |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-00811 |2 datacite_doi |
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| 100 | 1 | _ | |a Rojas-Campos, Adrian |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Deep learning models for generation of precipitation maps based on numerical weather prediction |
| 260 | _ | _ | |a Katlenburg-Lindau |c 2023 |b Copernicus |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Numerical weather prediction (NWP) models are atmospheric simulations that imitate the dynamics of the atmosphere and provide high-quality forecasts. One of the most significant limitations of NWP is the elevated amount of computational resources required for its functioning, which limits the spatial and temporal resolution of the outputs. Traditional meteorological techniques to increase the resolution are uniquely based on information from a limited group of interest variables. In this study, we offer an alternative approach to the task where we generate precipitation maps based on the complete set of variables of the NWP to generate high-resolution and short-time precipitation predictions. To achieve this, five different deep learning models were trained and evaluated: a baseline, U-Net, two deconvolution networks and one conditional generative model (Conditional Generative Adversarial Network; CGAN). A total of 20 independent random initializations were performed for each of the models. The predictions were evaluated using skill scores based on mean absolute error (MAE) and linear error in probability space (LEPS), equitable threat score (ETS), critical success index (CSI) and frequency bias after applying several thresholds. The models showed a significant improvement in predicting precipitation, showing the benefits of including the complete information from the NWP. The algorithms doubled the resolution of the predictions and corrected an over-forecast bias from the input information. However, some new models presented new types of bias: U-Net tended to mid-range precipitation events, and the deconvolution models favored low rain events and generated some spatial smoothing. The CGAN offered the highest-quality precipitation forecast, generating realistic outputs and indicating possible future research paths. |
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| 536 | _ | _ | |a Earth System Data Exploration (ESDE) |0 G:(DE-Juel-1)ESDE |c ESDE |x 1 |
| 536 | _ | _ | |a Verbundprojekt DeepRain: Effiziente Lokale Niederschlagsvorhersage durch Maschinelles Lernen (01IS18047A) |0 G:(BMBF)01IS18047A |c 01IS18047A |x 2 |
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| 700 | 1 | _ | |a Langguth, Michael |0 P:(DE-Juel1)180790 |b 1 |
| 700 | 1 | _ | |a Wittenbrink, Martin |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Pipa, Gordon |0 P:(DE-HGF)0 |b 3 |
| 773 | _ | _ | |a 10.5194/gmd-16-1467-2023 |g Vol. 16, no. 5, p. 1467 - 1480 |0 PERI:(DE-600)2456725-5 |n 5 |p 1467 - 1480 |t Geoscientific model development |v 16 |y 2023 |x 1991-959X |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1037646/files/gmd-16-1467-2023.pdf |y OpenAccess |
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