Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon

Wang, Haishen; Liu, Yubao; Liu, Yue-Wei; Cao, Yanjie; Liang, Hong; Hu, Heng; Liang, Jiahao; Tu, Manhong

doi:10.3390/rs14010178

Cited by 10 publications

(5 citation statements)

References 41 publications

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“…Precipitable water vapor (PWV) is a fundamental measurement for quantifying the moisture content within the atmosphere [2][3][4]. Its condensation or energy release, mechanisms contribute to the formation of clouds, rain, snow, and other weather phenomena, thus serving as a crucial energy source for extreme weather events, such as heavy rainfall and typhoons [5][6][7][8][9]. Empirical research has consistently demonstrated a robust correlation between atmospheric PWV and rainfall because the occurrence of precipitation relies on an ample supply of water vapor [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Performance of Water Vapor Products from ERA5 and MERRA-2 during Heavy Rainfall in the Guangxi Region of China

Huang,

Fu,

Chen

et al. 2024

Atmosphere

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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.

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Section: Introductionmentioning

confidence: 99%

Assessing the Performance of Water Vapor Products from ERA5 and MERRA-2 during Heavy Rainfall in the Guangxi Region of China

Huang,

Fu,

Chen

et al. 2024

Atmosphere

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“…There is insufficient research on the parameterization scheme combinations of microphysics, cumulus, and boundary layer. In addition to the selection of parameterization schemes, the assimilation of Global Precipitation Measurement (GPM) microwave and GNSS water vapor can improve the initial and lateral boundary conditions, enhancing the prediction accuracy of typhoon track and intensity [36,37]. Li et al employed a continuous cycling assimilation method to analyze the influence of different Advanced TIROS Operational Vertical Sounder (ATOVS) satellites on the simulation of Typhoon Fanapi [38].…”

Section: Introductionmentioning

confidence: 99%

The WRF Simulation Influence of Assimilating GNSS Water Vapor and Parameterization Schemes on Typhoon Rumbia

Li,

Ma,

et al. 2024

Atmosphere

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The Weather Research and Forecasting (WRF) model was used to simulate Typhoon Rumbia in this paper. The sensitivity experiments were conducted with 16 different parameterization combination schemes, including four microphysics (WSM6, WSM5, Lin, and Thompson), two boundary layers (YSU and MYJ), and two cumulus convection (Kain–Fritsch and Grell–Freitas) schemes. The impacts of 16 parameterization combination schemes and the data assimilation (DA) of Global Navigation Satellite System (GNSS) water vapor were evaluated by the simulation accuracy of typhoon track and intensity. The results show that the typhoon track and intensity are significantly influenced by parameterization schemes of cumulus and boundary layers rather than microphysics. The averaged track error of Lin_KF_Y is 104.73 km in the entire 72-h simulation period. The track errors of all the other combination schemes are higher than Lin_KF_Y. During the entire 72-h, the averaged intensity error of Thompson_GF_M is 1.36 hPa. It is the lowest among all the combination schemes. As for data assimilation, the simulation accuracy of typhoon tracks can be significantly improved by adding the GNSS water vapor. Thompson_GF_M-DA combination scheme has the lowest average track error of 45.05 km in the initial 24 h. The Lin_KF_Y-DA combination scheme exhibits an average track error of 32.17 km on the second day, 28.03 km on the third day, and 35.33 km during 72-h. The study shows that the combination of parameterization schemes and the GNSS water vapor data assimilation significantly improve the initial conditions and the accuracy of typhoon predictions. The study results contribute to the selection of appropriate combinations of physical parameterization schemes for the WRF-ARW model in the mid-latitude region of the western Pacific coast.

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“…GNSS-derived PWV [13] has gained recognition for its reliability, real-time availability, high temporal resolution, and exceptional accuracy (less than 3 mm) [14]. High-precision GNSS PWV plays a crucial role in monitoring and forecasting extreme weather conditions, such as heavy rainfall [15,16] and typhoons [17,18], while also contributing to the analysis and prevention of natural disasters like earthquakes [19,20] and storm surges [21,22] through the provision of reliable data support Nonetheless, as a relatively recent technique, the retrieval of PWV from GNSS still requires extensive exploration and validation across diverse global spatial and temporal scales.…”

Section: Introductionmentioning

confidence: 99%

Investigating the ERA5-Based PWV Products and Identifying the Monsoon Active and Break Spells with Dense GNSS Sites in Guangxi, China

Liu,

Zhang,

Xiong

et al. 2023

Remote Sensing

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Precipitable water vapor (PWV) with high precision and high temporal resolution estimated by Global Navigation Satellite System (GNSS) is widely used in atmospheric research and weather forecasting. However, most previous works are not consensual concerning the characteristics of the PWV at different time scales and the identification of active and break spells during summ er monsoon climate in Guangxi, China. Taking radiosonde (RS) observations as reference, a strong correlation (R > 0.97) exists between GNSS PWV and RS PWV with a mean root mean square error (RMSE) of 2.68 mm. The annual, seasonal, monthly, and diurnal PWV variations of three years (2017, 2018 and 2020) over Guangxi in were comprehensively investigated using 104 GNSS stations and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5). The mean annual bias and RMSE between GNSS PWV and ERA5 PWV are −1.04 mm and 2.63 mm, respectively. The monthly bias and RMSE range are −0.77 to 3.87 mm, 1.32 to 4.45 mm, and the daily range is −1.41 to 1.07 mm and 1.11 to 5.02 mm, respectively. Additionally, the adopted average standardized rainfall anomaly criteria also identified 7/7/3 active spells and 5/3/7 break spells during the summer monsoon (June–September) from 2017 to 2020, respectively. During the three-year period, the daily amplitude ranges for active spells varied from 1.41 to 2.49 mm, 0.69 to 5.4 mm, and 0.88 to 1.41 mm, while the ranges for break spells were 2.45 to 6.76 mm, 1.66 to 8.17 mm, and 1.48 to 2.99 mm, respectively. The results show a superior performance of GNSS PWV compared to ERA5 PWV in Guangxi, and the maximum, minimum and occurrence time of PWV anomaly vary slightly with the season and the topography of stations. Despite temperature primarily exhibiting a negative correlation with rainfall, acting as a dampener, a positive correlation remains evident between PWV and rainfall. Therefore, densely distributed GNSS stations exhibit excellent capabilities in quantifying atmospheric water vapor and facilitating real-time monitoring of small and medium-scale weather phenomena.

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Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon

Cited by 10 publications

References 41 publications

Assessing the Performance of Water Vapor Products from ERA5 and MERRA-2 during Heavy Rainfall in the Guangxi Region of China

Assessing the Performance of Water Vapor Products from ERA5 and MERRA-2 during Heavy Rainfall in the Guangxi Region of China

The WRF Simulation Influence of Assimilating GNSS Water Vapor and Parameterization Schemes on Typhoon Rumbia

Investigating the ERA5-Based PWV Products and Identifying the Monsoon Active and Break Spells with Dense GNSS Sites in Guangxi, China

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