Xinya Gong

The Geostationary Interferometric Infrared Sounder (GIIRS) flying on Fengyun (FY)‐4B is the world's first operational geostationary hyperspectral infrared sounder, which can observe detailed atmospheric vertical structure with temporal continuity. It provides an unprecedented observational source for enhancing numerical weather prediction (NWP), especially for high‐impact weather events. Currently, GIIRS is limited to clear‐sky radiance assimilation in operational system, and a large amount of cloud‐affected data have not been effectively utilized. The cloud‐clearing technology can eliminate the cloud effects to obtain the cloud‐cleared radiances (CCRs), which is an alternative method to realize the assimilation of cloudy‐sky infrared observation. A high‐precision cloud mask retrieved from the Advanced Geostationary Radiation Imager (AGRI) is used to calculate the GIIRS CCRs in this study. A comparative analysis of the bias characteristics reveals that the CCRs exhibit larger standard deviation of O‐B than clear‐sky radiances. To evaluate the impact of GIIRS CCRs on NWP, a 1‐month cycle assimilation and forecast experiments are carried out based on China Meteorological Administration Global Forecast System (CMA‐GFS). The results show that CCRs increase the amount of assimilated GIIRS observations by nearly 50%. Compared with the control experiment, CCRs assimilation has a modest positive contribution to the analysis fields, including geopotential height, low‐level humidity, and wind fields. Additionally, it improves the forecast fields of geopotential height and precipitation skill scores, demonstrating better forecast potential in typhoon weather processes. This study provides valuable insights for the prospective operational application of FY‐4B GIIRS CCRs in NWP.

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard China’s Fengyun-4A geostationary meteorological satellite provides high-spectral-resolution infrared observations for targeted observing areas with high temporal resolution. Due to the high uncertainties in radiative transfer modeling of cloudy radiances, it is challenging to take full advantage of the thermodynamic information from GIIRS in all sky conditions. The Advanced Geostationary Radiation Imager (AGRI) onboard the same platform provides a variety of cloud products with high spatial resolution. A bias corrected optimal cloud-clearing (BCOCC) approach is introduced to generate GIIRS cloud-cleared radiances (CCRs) with the help of AGRI collocated clear radiances. The bias correction (BC) scheme is based on the inter-comparisons between GIIRS and AGRI for each field-of-view and different scene temperatures. The BC method ensures the radiometric consistency between GIIRS and AGRI. Evaluations of GIIRS CCRs show that the mean biases are 0.09 K, -0.06 K, and 0.06 K when compared with the three AGRI infrared bands, B12, B13 and B14. In addition, BCOCC significantly increases the data yields of successful CCRs by three times that of the optimal cloud-clearing approach without BC. For 15 days from September 16th to 30th, 2021, around 37% more GIIRS partially cloudy footprints than clear sky are cloud cleared successfully. The CCRs can be assimilated as clear radiances in numerical weather prediction models without worry of the cloud impact. This study provides evidence on the importance of placing an advanced hyperspectral IR sounder and imager onboard the same geostationary platform for better quantitative applications.

With high spatio‐temporal resolution geostationary hyperspectral infrared sounder (GeoHIS) observations, monitoring, and predicting rapidly changing weather events are expected to be improved with continuous information of 3D weather cube. However, due to the nature of radiation, clouds prevent an adequate retrieval of atmospheric thermodynamic information under clear‐sky conditions due to large uncertainties in the current radiative transfer model. Removing cloud effects from GeoHIS sub‐footprint is an alternative approach for enhancing clear radiance generation. Such cloud removal can be achieved through the optimal cloud‐clearing (OCC) method, which usually relies on the assumption of atmospheric spatial homogeneity. With high temporal resolution observations from GeoHIS, cloud removal can also be achieved through OCC but using the assumption of temporal homogeneity. This concept was demonstrated using 15‐min Geostationary Interferometric Infrared Sounder (GIIRS) observations. The longwave GIIRS clear radiances under partially cloud cover can be effectively produced with observation errors less than 0.02 ± 0.86 K and 0.04 ± 0.77 K for 11 and 12 μm, respectively.

Geostationary (GEO) imager Infrared (IR) bands are often intercalibrated with hyperspectral IR sounders onboard low earth orbit (LEO) satellites. Due to viewing geometry and time differences between GEO and LEO, the inter‐calibration is limited to certain viewing angles and times. When a GEO hyperspectral IR sounder and GEO imager are on the same platform, the inter‐calibration can be conducted from all angles and at all times. In this study, the Geostationary Interferometric Infrared Sounder (GIIRS) is used to inter‐calibrate the Advanced Geosynchronous Radiation Imager (AGRI), both onboard FengYun‐4A. Radiance inter‐comparisons between GIIRS and AGRI under homogeneous scenes, including both clear and cloudy situations, indicate that the differences are within 1 K. The differences have obvious dependence on scene radiance, less dependence on the cloudy situation, and the local zenith angle when it is less than 70°. However, they exhibit significant diurnal characteristics.

Typically, DCCs are identified by 11 µm band brightness temperature (BT11) lower than a fixed BT threshold. Another method of combining the brightness temperature difference (BTD) between a water vapor absorption channel and a window channel to its measurement noise ratio (BNR) is adopted and applied to DCC identification. This BNR method improves the DCC detections over the legacy method because it is less contaminated with high clouds not thick and bright enough. BNR detects fewer DCCs than BT11, but with more confidence. Using observations of the collocated Cross-track Infrared Sounder (CrIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) from 2017 to 2018, the results show BNR has better performances than BT11 for identifying the DCC and monitoring reflective solar bands. When comparing to BT11, BNR has more robust and invariant time series of monthly reflectance for all RSBs. Because BNR affects more on the left tails (less reflective) of the histograms than the mode reflectance, the improvement is more significant on the mean values than the modes. This method can be applied to other imagers with collocated advanced infrared sounders for detecting DCCs and monitoring the calibration stabilities of RSBs. Recently, the hyperspectral infrared atmospheric sounders onboard China’s next-generation FengYun satellites, i.e. the Geosynchronous Interferometric InfraRed Sounder (GIIRS) on the FengYun-4 geostationary satellite series and the Hyperspectral Infrared Atmospheric Sounder (HIRAS) on the FengYun-3 polar orbiting meteorological satellite series, are in operation. Flown onboard the same platforms, the collocated (consistent in time and space) infrared sounders and imagers, provide mount of match-up measurements for the study of methodology and process for synergistic use of both infrared sounder and imager for multiple applications. The findings will provide scientific evidences for further enhancements and applications of future FengYun satellites and its observing system.

As uniform and stable objects, deep convective clouds (DCCs) are often used to monitor the calibration stabilities of the reflective solar bands (RSBs) of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar‐orbiting Partnership. Traditionally, DCCs are identified by the legacy 11‐μm brightness temperature (BT11) threshold method. With the collocated Cross‐track Infrared Sounder (CrIS), a method of combining the BT difference between a water vapor absorption channel and a window channel to its measurement noise ratio (BNR) is adopted and applied to DCC identification. The BNR method improves the DCC detections over the legacy method because it is less contaminated with high clouds not thick and bright enough. Using observations from 2017 to 2018, the results show BNR has better performances than BT11 for identifying DCCs and monitoring RSBs. When comparing to BT11, BNR has more robust and invariant time series of monthly reflectance for all RSBs; that is, the standard deviation and total variation range (maximum‐minimum) are up to 47% smaller. Because BNR affects more on the left tails (less reflective) than the modes of the histograms of reflectance, the improvement is more significant on the mean reflectance than the mode. BNR detects fewer DCCs than BT11, but with more confidence. This allows the weekly and daily time series for monitoring RSBs in higher temporal frequencies. This method can be applied to other imagers with collocated advanced infrared sounders for detecting DCCs and monitoring the calibration stabilities of RSBs.

The Cross‐track Infrared Sounder (CrIS) onboard the Suomi National Polar‐orbiting Partnership (SNPP) has high radiometric accuracy, which can be used to intercompare and understand the radiometric performance of the thermal emissive bands of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the same platform. Previous studies usually selected uniform scenes to avoid potential uncertainties due to geolocation errors. This study focuses on all scenes (including nonuniform scenes), as well as CrIS subpixel cloudiness, local zenith angle and surface type, and their impacts on VIIRS/CrIS intercomparisons. A geolocation error correction scheme is applied to mitigate the geolocation mismatch between CrIS and VIIRS. Intercomparison results of three days from 19 to 21 September 2016, a quarterly warm up/cool down calibration period, show overall good agreement in terms of radiance biases (less than 0.2 K in terms of brightness temperature) between VIIRS and CrIS, for scene temperatures between 220 and 300 K; however, larger biases are evident outside of this range. The three factors—CrIS subpixel cloudiness, local zenith angle, and surface type—show weak impacts on VIIRS radiance biases. Both clear‐sky and overcast conditions have slightly different radiance biases from other partially cloudy conditions because of more contributions from colder or warmer scenes. The time series show the impact of warm up/cool down and the effectiveness of the correction algorithm for reprocessing VIIRS M15 and M16 data sets. The methods and tools can be applied to monitor and validate other infrared imager/sounder duos onboard the same platform.