Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems
Radar data with high spatiotemporal resolution and automatic weather station (AWS) data are used in the data assimilation experiment to improve the precipitation forecast of a numerical model. The numerical model considered in this study is the Weather Research and Forecasting (WRF) model with doubl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/3/605 |
| _version_ | 1797485001132998656 |
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| author | Jeong-Ho Bae Ki-Hong Min |
| author_facet | Jeong-Ho Bae Ki-Hong Min |
| author_sort | Jeong-Ho Bae |
| collection | DOAJ |
| description | Radar data with high spatiotemporal resolution and automatic weather station (AWS) data are used in the data assimilation experiment to improve the precipitation forecast of a numerical model. The numerical model considered in this study is the Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated the radar equivalent reflectivity factor using high resolution WRF and compared it with radar observations in South Korea. To compare the precipitation forecast characteristics of the three-dimensional variational (3D-Var) assimilation of radar data, four experiments were performed based on the scales of precipitation systems. Comparison of the 24 h accumulated rainfall with surface observation data, contoured frequency by altitude diagram (CFAD), time–height cross sections (THCS), and vertical hydrometeor profiles was used to evaluate the accuracy of the simulation of precipitation. The model simulations were performed with and without 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The combined effect of the radar and AWS data assimilation experiment improved the location of the precipitation area and rainfall intensity compared to the control run. There is a noticeable scale dependence in the improvement of precipitation systems. Improvements in simulating mesoscale convective systems were larger compared to synoptically driven precipitation systems. |
| first_indexed | 2024-03-09T23:13:30Z |
| format | Article |
| id | doaj.art-3cbdd89bcebf4771b79146fb4caa4b84 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T23:13:30Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3cbdd89bcebf4771b79146fb4caa4b842023-11-23T17:40:30ZengMDPI AGRemote Sensing2072-42922022-01-0114360510.3390/rs14030605Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation SystemsJeong-Ho Bae0Ki-Hong Min1National Typhoon Center, Korea Meteorological Administration, Seogwipo 63614, KoreaSchool of Earth System Sciences, Kyungpook National University, Daegu 41566, KoreaRadar data with high spatiotemporal resolution and automatic weather station (AWS) data are used in the data assimilation experiment to improve the precipitation forecast of a numerical model. The numerical model considered in this study is the Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated the radar equivalent reflectivity factor using high resolution WRF and compared it with radar observations in South Korea. To compare the precipitation forecast characteristics of the three-dimensional variational (3D-Var) assimilation of radar data, four experiments were performed based on the scales of precipitation systems. Comparison of the 24 h accumulated rainfall with surface observation data, contoured frequency by altitude diagram (CFAD), time–height cross sections (THCS), and vertical hydrometeor profiles was used to evaluate the accuracy of the simulation of precipitation. The model simulations were performed with and without 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The combined effect of the radar and AWS data assimilation experiment improved the location of the precipitation area and rainfall intensity compared to the control run. There is a noticeable scale dependence in the improvement of precipitation systems. Improvements in simulating mesoscale convective systems were larger compared to synoptically driven precipitation systems.https://www.mdpi.com/2072-4292/14/3/605radarprecipitation3D-Vardata assimilationWRF |
| spellingShingle | Jeong-Ho Bae Ki-Hong Min Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems Remote Sensing radar precipitation 3D-Var data assimilation WRF |
| title | Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems |
| title_full | Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems |
| title_fullStr | Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems |
| title_full_unstemmed | Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems |
| title_short | Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems |
| title_sort | forecast characteristics of radar data assimilation based on the scales of precipitation systems |
| topic | radar precipitation 3D-Var data assimilation WRF |
| url | https://www.mdpi.com/2072-4292/14/3/605 |
| work_keys_str_mv | AT jeonghobae forecastcharacteristicsofradardataassimilationbasedonthescalesofprecipitationsystems AT kihongmin forecastcharacteristicsofradardataassimilationbasedonthescalesofprecipitationsystems |