Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand

Data assimilation with a Numerical Weather Prediction (NWP) model using an observation system in a regional area is becoming more prevalent for local weather forecasting activities to reduce the risk of disasters. In this study, we evaluated the predictive capabilities of multi-platform observation...

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Autores principales:	Thippawan Thodsan, Falin Wu, Kritanai Torsri, Thakolpat Khampuenson, Gongliu Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multi-platform data assimilation prediction of heavy rainfall 3DVAR predictability Thailand Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/d4797f77cb1e43449e853290203da330
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spelling	oai:doaj.org-article:d4797f77cb1e43449e853290203da3302021-11-25T16:45:34ZImpact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand10.3390/atmos121114972073-4433https://doaj.org/article/d4797f77cb1e43449e853290203da3302021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4433/12/11/1497https://doaj.org/toc/2073-4433Data assimilation with a Numerical Weather Prediction (NWP) model using an observation system in a regional area is becoming more prevalent for local weather forecasting activities to reduce the risk of disasters. In this study, we evaluated the predictive capabilities of multi-platform observation assimilation based on a WRFDA (Weather Research and Forecasting model data assimilation) system with 9 km grid spacing over the Kong-Chi basin (KCB), where tropical storms and heavy rainfall occur frequently. Data assimilation experiments were carried out with two assimilation schemes: (1) assimilating the combined multi-platform observations of PREPBUFR data from the National Centers for Environmental Prediction (NCEP) and Automatic Weather Stations (AWS) data from the National Hydroinformatics Data Center in Thailand, and (2) assimilating the AWS data only, which are referred to as DAALL and DAAWS, respectively. Assimilation experiments skill scores with lead times of 48 h and 72 h were evaluated by comparing their accumulated rainfall and mean temperatures every three hours in the AWS for heavy rainfall events that occurred on 28 July 2017 and 30 August 2019. The results show that the DAALL improved the statistical skill scores by improving the pattern and intensity of heavy rainfall events, and DAAWS also improved the model results of near-surface location forecasts. The accuracy of the two assimilations for 3 h of accumulated rainfall with a 5 mm threshold, was only above 70%, but the threat score was acceptable. Temperature observations and assimilation experiments fitted a significant correlation with a coefficient greater than 0.85, while the mean absolute errors, even at the 48 h lead times remained below 1.75 °C of the mean temperature. The variables of the AWS observations in real-time after combining them with the weather forecasting model were evaluated for unprecedented rain events in the KCB. The scores suggested that the assimilation of the multi-platform observations at the 48 h lead times has an impact on heavy rainfall prediction in terms of the threat score, compared to the assimilation of AWS data only. The reason for this could be that fewer observations of the AWS data affected the WRFDA model.Thippawan ThodsanFalin WuKritanai TorsriThakolpat KhampuensonGongliu YangMDPI AGarticlemulti-platform data assimilationprediction of heavy rainfall3DVARpredictabilityThailandMeteorology. ClimatologyQC851-999ENAtmosphere, Vol 12, Iss 1497, p 1497 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multi-platform data assimilation prediction of heavy rainfall 3DVAR predictability Thailand Meteorology. Climatology QC851-999
spellingShingle	multi-platform data assimilation prediction of heavy rainfall 3DVAR predictability Thailand Meteorology. Climatology QC851-999 Thippawan Thodsan Falin Wu Kritanai Torsri Thakolpat Khampuenson Gongliu Yang Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand
description	Data assimilation with a Numerical Weather Prediction (NWP) model using an observation system in a regional area is becoming more prevalent for local weather forecasting activities to reduce the risk of disasters. In this study, we evaluated the predictive capabilities of multi-platform observation assimilation based on a WRFDA (Weather Research and Forecasting model data assimilation) system with 9 km grid spacing over the Kong-Chi basin (KCB), where tropical storms and heavy rainfall occur frequently. Data assimilation experiments were carried out with two assimilation schemes: (1) assimilating the combined multi-platform observations of PREPBUFR data from the National Centers for Environmental Prediction (NCEP) and Automatic Weather Stations (AWS) data from the National Hydroinformatics Data Center in Thailand, and (2) assimilating the AWS data only, which are referred to as DAALL and DAAWS, respectively. Assimilation experiments skill scores with lead times of 48 h and 72 h were evaluated by comparing their accumulated rainfall and mean temperatures every three hours in the AWS for heavy rainfall events that occurred on 28 July 2017 and 30 August 2019. The results show that the DAALL improved the statistical skill scores by improving the pattern and intensity of heavy rainfall events, and DAAWS also improved the model results of near-surface location forecasts. The accuracy of the two assimilations for 3 h of accumulated rainfall with a 5 mm threshold, was only above 70%, but the threat score was acceptable. Temperature observations and assimilation experiments fitted a significant correlation with a coefficient greater than 0.85, while the mean absolute errors, even at the 48 h lead times remained below 1.75 °C of the mean temperature. The variables of the AWS observations in real-time after combining them with the weather forecasting model were evaluated for unprecedented rain events in the KCB. The scores suggested that the assimilation of the multi-platform observations at the 48 h lead times has an impact on heavy rainfall prediction in terms of the threat score, compared to the assimilation of AWS data only. The reason for this could be that fewer observations of the AWS data affected the WRFDA model.
format	article
author	Thippawan Thodsan Falin Wu Kritanai Torsri Thakolpat Khampuenson Gongliu Yang
author_facet	Thippawan Thodsan Falin Wu Kritanai Torsri Thakolpat Khampuenson Gongliu Yang
author_sort	Thippawan Thodsan
title	Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand
title_short	Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand
title_full	Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand
title_fullStr	Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand
title_full_unstemmed	Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand
title_sort	impact of the assimilation of multi-platform observations on heavy rainfall forecasts in kong-chi basin, thailand
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d4797f77cb1e43449e853290203da330
work_keys_str_mv	AT thippawanthodsan impactoftheassimilationofmultiplatformobservationsonheavyrainfallforecastsinkongchibasinthailand AT falinwu impactoftheassimilationofmultiplatformobservationsonheavyrainfallforecastsinkongchibasinthailand AT kritanaitorsri impactoftheassimilationofmultiplatformobservationsonheavyrainfallforecastsinkongchibasinthailand AT thakolpatkhampuenson impactoftheassimilationofmultiplatformobservationsonheavyrainfallforecastsinkongchibasinthailand AT gongliuyang impactoftheassimilationofmultiplatformobservationsonheavyrainfallforecastsinkongchibasinthailand
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Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand

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