Assessing the Performance of Water Vapor Products from ERA5 and MERRA-2 during Heavy Rainfall in the Guangxi Region of China
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Published:2024-02-29
Issue:3
Volume:15
Page:306
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ISSN:2073-4433
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Container-title:Atmosphere
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language:en
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Short-container-title:Atmosphere
Author:
Huang Ning123ORCID, Fu Shiyang4, Chen Biyan12ORCID, Huang Liangke5ORCID, Jin Wenping6
Affiliation:
1. School of Geosciences and Info-Physics, Central South University, Changsha 410000, China 2. Laboratory of GeoHazards Perception, Cognition and Predication, Central South University, Changsha 410000, China 3. Hunan Engineering Research Center of BDS High Precision Satellite Navigation and Location Based Service, Changsha 410000, China 4. School of Land Science and Technology, China University of Geosciences, Beijing 100083, China 5. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China 6. Hunan Province Mapping and Science and Technology Investigation Institute, Changsha 410000, China
Abstract
Precipitable water vapor (PWV) is a crucial factor in regulating the Earth’s climate. Moreover, it demonstrates a robust correlation with precipitation. Situated in a region known for the generation and development of tropical cyclones, Guangxi in China is highly susceptible to floods triggered via intense rainfall. The atmospheric water vapor in this area displays prominent spatiotemporal features, thus posing challenges for precipitation forecasting. The water vapor products within the MERRA-2 and ERA5 reanalysis datasets present an opportunity to overcome constraints associated with low spatiotemporal resolution. In this study, the PWV data derived from GNSS and meteorological measurements in Guangxi from 2016 to 2018 were used to evaluate the accuracy of MERRA-2 and ERA5 water vapor products and their relationship with water vapor variations during extreme rainfall. Using GNSS PWV as a reference, the average bias of MERRA-2 PWV and ERA5 PWV for heavy rainfall was −0.22 mm and 1.84 mm, respectively, with average RMSE values of 3.72 mm and 3.31 mm. For severe rainfall, the average bias of MERRA-2 PWV and ERA5 PWV was −0.14 mm and 2.92 mm, respectively, with average RMSE values of 4.28 mm and 4.01 mm. During heavy rainfall days from Days 178 to 184 in 2017, the average bias of MERRA-2 PWV and ERA5 PWV was 0.92 mm and 2.42 mm, respectively, with average RMSE values of 4.04 mm and 3.40 mm. The accuracy was highest at the Guiping and Hechi stations and lowest at the Hezhou and Rongshui stations. Furthermore, when comparing MERRA-2/ERA5 PWV with GNSS PWV and actual precipitation, the trends in the variations of MERRA-2/ERA5 PWV were generally consistent with GNSS PWV and aligned with the increasing or decreasing trends of actual precipitation. In addition, ERA5 PWV exhibited high accuracy. Before the onset of heavy rainfall, PWV has a sharp surge. During heavy rainfall, PWV reaches its peak value. Subsequently, after the cessation of heavy rainfall, PWV tends to stabilize. Therefore, the reanalysis data of PWV can effectively reveal significant changes in water vapor and actual precipitation during periods of heavy rainfall in the Guangxi region.
Funder
National Key R&D Program of China National Natural Science Foundation of China Natural Science Foundation of Hunan Province, China Research Foundation of the Department of Natural Resources of Hunan Province Guangxi Natural Science Foundation of China
Reference50 articles.
1. Core Writing Team, Lee, H., and Romero, J. (2023). IPCC, 2023: Climate Change 2023: Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC. 2. Estimating Trends in Atmospheric Water Vapor and Temperature Time Series over Germany;Alshawaf;Atmos. Meas. Tech.,2017 3. Integrated Water Vapor during Active and Break Spells of Monsoon and Its Relationship with Temperature, Precipitation and Precipitation Efficiency over a Tropical Site;Jadala;Geod. Geodyn.,2022 4. Zhao, Q., Zhang, X., Wu, K., Liu, Y., Li, Z., and Shi, Y. (2022). Comprehensive Precipitable Water Vapor Retrieval and Application Platform Based on Various Water Vapor Detection Techniques. Remote Sens., 14. 5. Wang, H., Liu, Y., Liu, Y., Cao, Y., Liang, H., Hu, H., Liang, J., and Tu, M. (2022). Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon. Remote Sens., 14.
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