Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in Guangdong
This paper aims to assess the latest 1 km-grid Analysis Real Time (ART_1 km) precipitation product developed by the National Meteorological Information Center of China Meteorological Administration (CMA), which can provide great support for disaster weather monitoring and warning, intelligent grid f...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Torrential Rain and Disasters
2023-12-01
|
| Series: | 暴雨灾害 |
| Subjects: | |
| Online Access: | http://www.byzh.org.cn/cn/article/doi/10.12406/byzh.2022-242 |
| _version_ | 1797373838976090112 |
|---|---|
| author | Chunyan ZHANG Yanping ZHENG Peidong WANG Yizhi CHEN Sixiao YANG Shangyou XIE Gang XIANG |
| author_facet | Chunyan ZHANG Yanping ZHENG Peidong WANG Yizhi CHEN Sixiao YANG Shangyou XIE Gang XIANG |
| author_sort | Chunyan ZHANG |
| collection | DOAJ |
| description | This paper aims to assess the latest 1 km-grid Analysis Real Time (ART_1 km) precipitation product developed by the National Meteorological Information Center of China Meteorological Administration (CMA), which can provide great support for disaster weather monitoring and warning, intelligent grid forecasting and weather services. Observed precipitation data from the independent stations (including non-uploaded regional meteorological stations and hydrometric stations) that were not integrated into the ART_1 km precipitation product as well as precipitation classification inspection are used to assess the quality of this product during twenty disastrous rainstorm cases from May to August during 2019-2022 in Guangdong. The results show that the ART_1 km precipitation product successfully reproduces the precipitation location, strength, and trends in these cases, with the best performance in the Pearl River Delta, the east of eastern Guangdong, and the north of northern Guangdong. The stronger the precipitation, the greater the correlation as well as the root mean square error (RMSE) and mean error (ME) between the ART_1 km precipitation and the observed precipitation. When the hourly precipitation is not classified, about 60% of these independent stations present a correlation efficient ≥ 0.8, more than 90% of the stations present an RMSE within the range of [1.0, 5.0) mm, and more than 60% of the stations present a ME within ±0.1 mm. When the hourly precipitation is < 5 mm, most of the stations have a correlation efficient < 0.5, an RMSE within the range of [1.0, 5.0) mm, and a ME within [0.0, 0.5] mm. When the hourly precipitation is ≥ 20 mm, 42%~56% of the stations have a correlation efficient ≥ 0.5, and most of the stations have an RMSE ≥ 10 mm and a ME < 0 mm, even when the hourly precipitation is ≥ 50 mm, most of the stations have a ME < -10 mm. Overall, ART_1 km precipitation is usually underestimated at the independent stations, and integrating observations from more sites into producing ART_1 km precipitation is helpful to improve the quality of the products. |
| first_indexed | 2024-03-08T18:56:14Z |
| format | Article |
| id | doaj.art-cd4326f51ecb45ccb421c27f5597a22b |
| institution | Directory Open Access Journal |
| issn | 2097-2164 |
| language | zho |
| last_indexed | 2024-03-08T18:56:14Z |
| publishDate | 2023-12-01 |
| publisher | Editorial Office of Torrential Rain and Disasters |
| record_format | Article |
| series | 暴雨灾害 |
| spelling | doaj.art-cd4326f51ecb45ccb421c27f5597a22b2023-12-28T12:48:36ZzhoEditorial Office of Torrential Rain and Disasters暴雨灾害2097-21642023-12-0142667969110.12406/byzh.2022-242byzh-42-6-679Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in GuangdongChunyan ZHANG0Yanping ZHENG1Peidong WANG2Yizhi CHEN3Sixiao YANG4Shangyou XIE5Gang XIANG6Guangdong Meteorological Data Center, Guangzhou, 510080Guangdong Meteorological Data Center, Guangzhou, 510080Guangdong Meteorological Data Center, Guangzhou, 510080Guangdong Meteorological Data Center, Guangzhou, 510080Huizhou Meteorological Bureau, Huizhou, 516000Shantou Meteorological Bureau, Shantou, 515000Shaoyang Meteorological Bureau, Shaoyang, 422000This paper aims to assess the latest 1 km-grid Analysis Real Time (ART_1 km) precipitation product developed by the National Meteorological Information Center of China Meteorological Administration (CMA), which can provide great support for disaster weather monitoring and warning, intelligent grid forecasting and weather services. Observed precipitation data from the independent stations (including non-uploaded regional meteorological stations and hydrometric stations) that were not integrated into the ART_1 km precipitation product as well as precipitation classification inspection are used to assess the quality of this product during twenty disastrous rainstorm cases from May to August during 2019-2022 in Guangdong. The results show that the ART_1 km precipitation product successfully reproduces the precipitation location, strength, and trends in these cases, with the best performance in the Pearl River Delta, the east of eastern Guangdong, and the north of northern Guangdong. The stronger the precipitation, the greater the correlation as well as the root mean square error (RMSE) and mean error (ME) between the ART_1 km precipitation and the observed precipitation. When the hourly precipitation is not classified, about 60% of these independent stations present a correlation efficient ≥ 0.8, more than 90% of the stations present an RMSE within the range of [1.0, 5.0) mm, and more than 60% of the stations present a ME within ±0.1 mm. When the hourly precipitation is < 5 mm, most of the stations have a correlation efficient < 0.5, an RMSE within the range of [1.0, 5.0) mm, and a ME within [0.0, 0.5] mm. When the hourly precipitation is ≥ 20 mm, 42%~56% of the stations have a correlation efficient ≥ 0.5, and most of the stations have an RMSE ≥ 10 mm and a ME < 0 mm, even when the hourly precipitation is ≥ 50 mm, most of the stations have a ME < -10 mm. Overall, ART_1 km precipitation is usually underestimated at the independent stations, and integrating observations from more sites into producing ART_1 km precipitation is helpful to improve the quality of the products.http://www.byzh.org.cn/cn/article/doi/10.12406/byzh.2022-242art_1 km precipitationrainstormindependent stationsprecipitation classification inspectioncorrelationerror |
| spellingShingle | Chunyan ZHANG Yanping ZHENG Peidong WANG Yizhi CHEN Sixiao YANG Shangyou XIE Gang XIANG Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in Guangdong 暴雨灾害 art_1 km precipitation rainstorm independent stations precipitation classification inspection correlation error |
| title | Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in Guangdong |
| title_full | Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in Guangdong |
| title_fullStr | Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in Guangdong |
| title_full_unstemmed | Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in Guangdong |
| title_short | Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in Guangdong |
| title_sort | assessment of multi source observation merged 1 km grid precipitation product during the disastrous rainstorms in guangdong |
| topic | art_1 km precipitation rainstorm independent stations precipitation classification inspection correlation error |
| url | http://www.byzh.org.cn/cn/article/doi/10.12406/byzh.2022-242 |
| work_keys_str_mv | AT chunyanzhang assessmentofmultisourceobservationmerged1kmgridprecipitationproductduringthedisastrousrainstormsinguangdong AT yanpingzheng assessmentofmultisourceobservationmerged1kmgridprecipitationproductduringthedisastrousrainstormsinguangdong AT peidongwang assessmentofmultisourceobservationmerged1kmgridprecipitationproductduringthedisastrousrainstormsinguangdong AT yizhichen assessmentofmultisourceobservationmerged1kmgridprecipitationproductduringthedisastrousrainstormsinguangdong AT sixiaoyang assessmentofmultisourceobservationmerged1kmgridprecipitationproductduringthedisastrousrainstormsinguangdong AT shangyouxie assessmentofmultisourceobservationmerged1kmgridprecipitationproductduringthedisastrousrainstormsinguangdong AT gangxiang assessmentofmultisourceobservationmerged1kmgridprecipitationproductduringthedisastrousrainstormsinguangdong |