Coupling a Neural Network with a Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban Rain Radars
Rainfall forecasting plays a key role in mitigating environmental risks in urban areas, which are subject to increasing hydrogeological risk due to transformations in the urban landscape. We present a new technique for probabilistic precipitation nowcasting by generating an ensemble of equiprobable...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Forecasting |
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| Online Access: | https://www.mdpi.com/2571-9394/4/4/46 |
| _version_ | 1797458592627949568 |
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| author | Marino Marrocu Luca Massidda |
| author_facet | Marino Marrocu Luca Massidda |
| author_sort | Marino Marrocu |
| collection | DOAJ |
| description | Rainfall forecasting plays a key role in mitigating environmental risks in urban areas, which are subject to increasing hydrogeological risk due to transformations in the urban landscape. We present a new technique for probabilistic precipitation nowcasting by generating an ensemble of equiprobable forecasts, which is especially useful for weather radars with limited spatial range, and that can be used operationally on devices with low computational capacity. The ensemble members are obtained by a novel stochastic noise generation process, consistent with the spatial scales not resolved by the prediction model, which allows continuous downscaling of the output of a deep generative neural network. Through an in-depth analysis of the results, for precipitation accumulated in the first hour, measured by all the most robust skill indicators, extended to an entire year of data at 5-min time resolution, we demonstrate that the proposed procedure is able to provide calibrated, reliable, and sharp ensemble rainfall forecasts with a quality comparable or superior to the state-of-the-art classical alternative optical flow technique. The ensemble generation procedure we propose is sufficiently general to be applied in principle to other deterministic architectures as well, thus enabling their generalization in probabilistic terms. |
| first_indexed | 2024-03-09T16:40:16Z |
| format | Article |
| id | doaj.art-9fa1fd72ccbd48b1b19d00eebce76e5b |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-09T16:40:16Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-9fa1fd72ccbd48b1b19d00eebce76e5b2023-11-24T14:52:54ZengMDPI AGForecasting2571-93942022-10-014484586510.3390/forecast4040046Coupling a Neural Network with a Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban Rain RadarsMarino Marrocu0Luca Massidda1CRS4, Center for Advanced Studies, Research and Development in Sardinia, Loc. Piscina Manna ed. 1, 09050 Pula, ItalyCRS4, Center for Advanced Studies, Research and Development in Sardinia, Loc. Piscina Manna ed. 1, 09050 Pula, ItalyRainfall forecasting plays a key role in mitigating environmental risks in urban areas, which are subject to increasing hydrogeological risk due to transformations in the urban landscape. We present a new technique for probabilistic precipitation nowcasting by generating an ensemble of equiprobable forecasts, which is especially useful for weather radars with limited spatial range, and that can be used operationally on devices with low computational capacity. The ensemble members are obtained by a novel stochastic noise generation process, consistent with the spatial scales not resolved by the prediction model, which allows continuous downscaling of the output of a deep generative neural network. Through an in-depth analysis of the results, for precipitation accumulated in the first hour, measured by all the most robust skill indicators, extended to an entire year of data at 5-min time resolution, we demonstrate that the proposed procedure is able to provide calibrated, reliable, and sharp ensemble rainfall forecasts with a quality comparable or superior to the state-of-the-art classical alternative optical flow technique. The ensemble generation procedure we propose is sufficiently general to be applied in principle to other deterministic architectures as well, thus enabling their generalization in probabilistic terms.https://www.mdpi.com/2571-9394/4/4/46probabilistic nowcastmeteorological radar datadeep learninggenerative neural networkoptical flowspatial downscaling |
| spellingShingle | Marino Marrocu Luca Massidda Coupling a Neural Network with a Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban Rain Radars Forecasting probabilistic nowcast meteorological radar data deep learning generative neural network optical flow spatial downscaling |
| title | Coupling a Neural Network with a Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban Rain Radars |
| title_full | Coupling a Neural Network with a Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban Rain Radars |
| title_fullStr | Coupling a Neural Network with a Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban Rain Radars |
| title_full_unstemmed | Coupling a Neural Network with a Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban Rain Radars |
| title_short | Coupling a Neural Network with a Spatial Downscaling Procedure to Improve Probabilistic Nowcast for Urban Rain Radars |
| title_sort | coupling a neural network with a spatial downscaling procedure to improve probabilistic nowcast for urban rain radars |
| topic | probabilistic nowcast meteorological radar data deep learning generative neural network optical flow spatial downscaling |
| url | https://www.mdpi.com/2571-9394/4/4/46 |
| work_keys_str_mv | AT marinomarrocu couplinganeuralnetworkwithaspatialdownscalingproceduretoimproveprobabilisticnowcastforurbanrainradars AT lucamassidda couplinganeuralnetworkwithaspatialdownscalingproceduretoimproveprobabilisticnowcastforurbanrainradars |