Evaluation of Environmental Moisture from NWP Models with Measurements from Advanced Geostationary Satellite Imager—A Case Study

Biases Characteristics Assessment of the Advanced Geosynchronous Radiation Imager (AGRI) Measurement on Board Fengyun–4A Geostationary Satellite

The Chinese Fengyun–4A geostationary meteorological satellite was successfully launched on 11 December 2016, carrying an Advanced Geostationary Radiation Imager (AGRI) to provide the observations of visible, near infrared, and infrared bands with improved spectral, spatial, and temporal resolution. The AGRI infrared observations can be assimilated into numerical weather prediction (NWP) data assimilation systems to improve the atmospheric analysis and weather forecasting capabilities. To achieve data assimilation, the first and crucial step is to characterize and evaluate the biases of the AGRI brightness temperatures in infrared channels 8–14. This study conducts the assessment of clear–sky AGRI full–disk infrared observation biases by coupling the RTTOV model and ERA Interim analysis. The AGRI observations are generally in good agreement with the model simulations. It is found that the biases over the ocean and land are less than 1.4 and 1.6 K, respectively. For bias difference between land and ocean, channels 11–13 are more obvious than water vapor channels 9–10. The fitting coefficient of linear regression tests between AGRI biases and sensor zenith angles manifests no obvious scan angle–dependent biases over ocean. All infrared channels observations are scene temperature–dependent over the ocean and land.

Evaluation of the Diurnal Variation of Upper Tropospheric Humidity in Reanalysis Using Homogenized Observed Radiances from International Geostationary Weather Satellites

A near global dataset of homogenized clear-sky 6.5-μm brightness temperatures (BTs) from international geostationary (GEO) weather satellites has recently been generated and validated. In this study, these radiance measurements are used to construct the diurnal variation of upper tropospheric humidity (UTH) and to evaluate these diurnal variations simulated by five reanalysis datasets over the 45° N–45° S region. The features of the diurnal variation described by the new dataset are comparable with previous observational studies that a land–sea contrast in the diurnal variation of UTH is exhibited. Distinct diurnal variations are observed over the deep convective regions where high UTH exists. The evaluation of reanalysis datasets indicates that reanalysis systems still have considerable difficulties in capturing the observed features of the diurnal variation of UTH. All five reanalysis datasets present the largest wet biases in the afternoon when the observed UTH experiences a diurnal minimum. Reanalysis can roughly reproduce the day–night contrast of UTH but with much weaker amplitudes and later peak time over both land and ocean. Comparison of the geographical distribution of the diurnal variation shows that both ERA5 and MERRA-2 could capture the larger diurnal variations over convective regions. However, the diurnal amplitudes are widely underestimated, especially over convective land regions, while the phase biases are relatively larger over open oceans. These results suggest that some deficiencies may exist in convection and cloud parameterization schemes in reanalysis models.