Novel Validation and Calibration Strategy for Total Precipitable Water Products of Fengyun-2 Geostationary Satellites

The latest batch of the Chinese Fengyun-2 (FY-2) geostationary satellites (i.e., FY-2F, FY-2G, and FY-2H) provides total precipitable water (TPW) products at high spatial and temporal resolutions. However, due to the lack of accuracy and performance evaluation for these products, a vast amount of valuable TPW data remains unused in atmospheric science research. To address this issue, this study aimed to propose and apply a validation strategy that incorporated a hemispheric vertical correction model (VCM) to obtain reliable evaluation results. With the help of reliable radiosonde and the state-of-the-art fifth generation of the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), this study was the first to assess the quality of the full-disk TPW products retrieved via the three FY-2 satellites from January 2019 to December 2020. In addition, this study analyzed the water vapor content, latitude, and elevation dependencies of FY-2 TPW retrieval error and explored the potential for improving the quality of each satellite TPW product through a linear calibration in the three test areas of northern and southern temperate zones and tropics. The results of this study were threefold. First, the accuracy of FY-2F and FY-2H TPW was superior to that of FY-2G. The root mean square error (RMSE) values of FY-2F, FY-2G, and FY-2H were 3.84, 4.46, and 3.73 mm and 2.30, 2.55, and 2.14 mm relative to the radiosonde and ERA5 data, respectively. Second, the TPW retrieval error of the FY-2 satellites decreased with the increasing latitude or decreasing elevation. Overall, FY-2G underestimated TPW, whereas FY-2F and FY-2H only underestimated TPW under wet conditions (i.e., TPW > 55 mm). Finally, the calibration potential of FY-2F and FY-2H was higher than that of FY-2G, and the bias, slope, and potential index (PI) values of FY-2G were lower than those of FY-2H and FY-2F in all the three test areas.

[1]  G. Blewitt,et al.  An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020 , 2023, Earth System Science Data.

[2]  Cuixian Lu,et al.  Precipitable water vapor fusion of MODIS and ERA5 based on convolutional neural network , 2022, GPS Solutions.

[3]  Zhizhao Liu,et al.  Enhanced all-weather precipitable water vapor retrieval from MODIS near-infrared bands using machine learning , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Xiongwei Ma,et al.  Retrieval of high spatial resolution precipitable water vapor maps using heterogeneous earth observation data , 2022, Remote Sensing of Environment.

[5]  Y. Wang,et al.  Assessment and calibration of FY-4A AGRI total precipitable water products based on CMONOC , 2022, Atmospheric Research.

[6]  Xiuqing Hu,et al.  Global evaluation of the precipitable-water-vapor product from MERSI-II (Medium Resolution Spectral Imager) on board the Fengyun-3D satellite , 2021, Atmospheric Measurement Techniques.

[7]  Zhizhao Liu,et al.  Radiance-based retrieval of total water vapor content from sentinel-3A OLCI NIR channels using ground-based GPS measurements , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[8]  Hongchang He,et al.  A Comprehensive Evaluation of Key Tropospheric Parameters from ERA5 and MERRA-2 Reanalysis Products Using Radiosonde Data and GNSS Measurements , 2021, Remote. Sens..

[9]  C. Castro,et al.  The Impact of Assimilating GPS Precipitable Water Vapor in Convective-Permitting WRF-ARW on North American Monsoon Precipitation Forecasts over Northwest Mexico , 2021, Monthly Weather Review.

[10]  Zhizhao Liu,et al.  Water vapor retrieval from MERSI NIR channels of Fengyun-3B satellite using ground-based GPS data , 2021 .

[11]  Shihao Tang,et al.  Validation of FY-4A AGRI layer precipitable water products using radiosonde data , 2021 .

[12]  Zhaoliang Zeng,et al.  Evaluation of Hourly PWV Products Derived From ERA5 and MERRA‐2 Over the Tibetan Plateau Using Ground‐Based GNSS Observations by Two Enhanced Models , 2021, Earth and Space Science.

[13]  C. Shi,et al.  Assessment and calibration of MODIS precipitable water vapor products based on GPS network over China , 2021, Atmospheric Research.

[14]  Lin Chen,et al.  Water Vapor Retrievals from Near-infrared Channels of the Advanced Medium Resolution Spectral Imager Instrument onboard the Fengyun-3D Satellite , 2020, Advances in Atmospheric Sciences.

[15]  J. Thepaut,et al.  The ERA5 global reanalysis , 2020, Quarterly Journal of the Royal Meteorological Society.

[16]  Michael Abrams,et al.  ASTER Global Digital Elevation Model (GDEM) and ASTER Global Water Body Dataset (ASTWBD) , 2020, Remote. Sens..

[17]  D. Long,et al.  An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach , 2020, Remote Sensing of Environment.

[18]  Timothy J. Schmit,et al.  Legacy Atmospheric Profiles and Derived Products From GOES‐16: Validation and Applications , 2019, Earth and Space Science.

[19]  Jia He,et al.  Comparison of Satellite-Derived Precipitable Water Vapor Through Near-Infrared Remote Sensing Channels , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[20]  L. Gimeno,et al.  Completeness of radiosonde humidity observations based on the Integrated Global Radiosonde Archive , 2019, Earth System Science Data.

[21]  Shihao Tang,et al.  An Operational Precipitable Water Vapor Retrieval Algorithm for Fengyun-2F/VLSSR Using a Modified Three-Band Physical Split-Window Method , 2019, Journal of Meteorological Research.

[22]  Witold Rohm,et al.  4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF , 2019, Atmospheric Measurement Techniques.

[23]  Xiaofeng Lu,et al.  The first validation of the precipitable water vapor of multisensor satellites over the typical regions in China , 2018 .

[24]  Maria João Costa,et al.  Validation of MODIS integrated water vapor product against reference GPS data at the Iberian Peninsula , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[25]  Zhiqing Zhang,et al.  Introducing the New Generation of Chinese Geostationary Weather Satellites, Fengyun-4 , 2017 .

[26]  Shihao Tang,et al.  An improved physical split-window algorithm for precipitable water vapor retrieval exploiting the water vapor channel observations , 2017 .

[27]  Yingyan Cheng,et al.  Water vapor‐weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend , 2016 .

[28]  A. Dai,et al.  Evaluation of atmospheric precipitable water from reanalysis products using homogenized radiosonde observations over China , 2015 .

[29]  Liang Chang,et al.  Calibration and Evaluation of Precipitable Water Vapor From MODIS Infrared Observations at Night , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Jun Li,et al.  Evaluation of the GOES-R ABI LAP Retrieval Algorithm Using the GOES-13 Sounder , 2014 .

[31]  Yoram J. Kaufman,et al.  Remote sensing of water vapor in the near IR from EOS/MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..

[32]  A M Russell,et al.  Science and technology. , 1972, Science.

[33]  Biyan Chen,et al.  Evaluating Precipitable Water Vapor Products from Fengyun-4A Meteorological Satellite using Radiosonde, GNSS, and ERA5 Data , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Zhizhao Liu,et al.  A Back Propagation Neural Network-Based Algorithm for Retrieving All-Weather Precipitable Water Vapor From MODIS NIR Measurements , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Zhizhao Liu,et al.  The First Validation of Sentinel-3 OLCI Integrated Water Vapor Products Using Reference GPS Data in Mainland China , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Wanqiang Yao,et al.  Two-Step Precipitable Water Vapor Fusion Method , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Qingzhi Zhao,et al.  Real-Time Rainfall Nowcast Model by Combining CAPE and GNSS Observations , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Tianhe Xu,et al.  Precipitable Water Vapor Retrieval Over Land From GCOM-W/AMSR2 Based on a New Integrated Method , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[39]  B. Holben,et al.  Precipitable water vapor over oceans from the Maritime Aerosol Network: Evaluation of global models and satellite products under clear sky conditions , 2019, Atmospheric Research.

[40]  Guojie Wang,et al.  Validation on MERSI/FY-3A precipitable water vapor product , 2018 .

[41]  Zhao Dong Retrieving precipitable water vapor based on FY-3A near-IR data , 2012 .

[42]  Bo G Leckner,et al.  The spectral distribution of solar radiation at the earth's surface—elements of a model , 1978 .