This study applies the Advanced Radiative Transfer Modeling System (ARMS), which was developed to accelerate the uses of Fengyun satellite data in weather, climate, and environmental applications in China, to characterize the biases of seven infrared (IR) bands of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Chinese geostationary meteorological satellite, Fengyun–4A. The AGRI data are quality controlled to eliminate the observations affected by clouds and contaminated by stray lights during the mid–night from 1600 to 1800 UTC during spring and autumn. The mean biases, computed from AGRI IR observations and ARMS simulations from the National Center for Environmental Prediction (NCEP) Final analysis data (FNL) as input, are within −0.7–1.1 K (0.12–0.75 K) for all seven IR bands over the oceans (land) under clear–sky conditions. The biases show seasonal variation in spatial distributions at bands 11–13, as well as a strong dependence on scene temperatures at bands 8–14 and on satellite zenith angles at absorption bands 9, 10, and 14. The discrepancies between biases estimated using FNL and the European Center for Medium–Range Weather Forecasts Reanalysis–5 (ERA5) are also discussed. The biases from water vapor absorption bands 9 and 10, estimated using ERA5 over ocean, are smaller than those from FNL. Such discrepancies arise from the fact that the FNL data are colder (wetter) than the ERA5 in the middle troposphere (upper–troposphere).

This study explores the possibility of improving the impact of the Advanced Himawari Imager (AHI) clear‐sky radiance data assimilation (DA), focusing on cloud detection. First, the performance of the “clear‐channel” detection scheme of the minimum residual (MR) method embedded in the Gridpoint Statistical Interpolation (GSI) DA system is compared with the performances of the CLouds from Advanced Very High Resolution Radiometer Extended (CLAVR‐x) cloud processing system and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud‐product‐generating algorithm. The MR scheme does not reliably identify optically thin clouds along cloud edges. The MR‐estimated cloud‐top pressures are often too high for upper‐level clouds, rendering some cloud‐contaminated channels falsely clear. An infrared‐only AHI cloud mask (ACM) algorithm is added to the MR scheme to perform a so‐called double cloud detection (DCD). The DCD scheme adds nine ACM tests for selecting clear pixels and two thin cloud tests for rejecting pixels affected by upper‐level clouds. For a 1‐month period, we show the positive impacts of assimilating AHI infrared channels on short‐term forecasts of temperature and humidity using the DCD scheme rather than the MR scheme. Improvements in the DCD experiment extend more vertically, horizontally, and temporally than those in the MR experiment during the 48‐hr forecasting time. In terms of daily variations in forecasting performance, the DCD experiment consistently improves while the MR experiment fluctuates between improvement and degradation. Such improvements come from an elimination of those data having negative observation‐minus‐background values of large magnitudes due to cloud contamination, which causes positive biases in humidity analyses.

MicroWave Radiation Imagers (MWRIs) onboard the afternoon FengYun‐3B (FY‐3B) and midmorning FengYun‐3C (FY‐3C) satellites provide global observations almost four times a day at any location. This study examines the diurnal cycle of the liquid water path (LWP) over the southeast Pacific Ocean using MWRI data. A double‐difference calibration method is first used to evaluate the MWRI calibration bias based on Tropical Rainfall Measuring Mission Microwave Imager (TMI) measurements. LWP diurnal cycle characteristics are derived from FY‐3B/C MWRI data measured in 2014. The diurnal variation of LWP from combined FY‐3B/C MWRI measurements are consistent with that derived from TMI data. Maximum and minimum LWPs occurred in the early morning and afternoon, respectively. The largest diurnal amplitude (~40% of the mean LWP) was located near 85^oW/20^oS. The diurnal amplitude varied from season to season. MWRI data can provide important constraints for models simulating the cloud diurnal cycle.

Accurate information on microwave land surface emissivity (MLSE) is important for satellite data assimilation. In this paper, a new random forest (RF) algorithm is developed for retrieving MLSE under all-sky conditions. Using Level-1 brightness temperature data from the FengYun-3D (FY-3D) Microwave Radiation Imager (MWRI) in 2022, two global MLSE daily product data sets, clear-sky (FY-3D1) and clear/cloudy (FY-3D2), were obtained by using one-dimensional variational method and microwave radiative transfer method, respectively. Based on the global spatiotemporal consistency assessment, a high-quality daily MLSE training dataset for the Tibetan Plateau was selected from the two data sets. Then, ten land surface parameters from routine observation were chosen as input features to the RF model, to simulate the MLSE under all-sky conditions in the Tibetan Plateau. The results show that both FY-3D1 and FY-3D2 MLSE data sets are comparable to the international mainstream MLSE products in quality, while the clear sky FY-3D1 is likely to be better than the clear/cloudy FY-3D2 MLSE. Land surface roughness, vegetation optical thickness, normalized vegetation index, and land cover type are identified as the most important factors affecting MLSE in the Tibetan Plateau. The RF model can effectively simulate the MLSE in the frequency range of 10.65∼89.0 GHz under all-sky conditions. The coefficients of determination (R2) for horizontal polarization and vertical polarization range from 0.86 (10.65GHz) to 0.91(18.7GHz) and from 0.60(10.65GHz) to 0.74(89.0GHz), respectively. The root-mean-square errors (RMSE) for horizontal polarization and vertical polarization range from 0.017 (23.8GHz) to 0.023(10.65GHz) and from 0.016 (10.65GHz) to 0.019(89.0GHz), respectively. These results indicate that machine learning is likely to be an effective method for future all-sky simulation of MLSE.

The cloud-top-phase climatology over the western North Pacific (WNP) has received little attention. Using 3 years (2017–2019) of cloud-top-phase products from the Advanced Himawari Imager onboard the Japanese Himawari-8 satellite, this study examines the seasonal and diurnal variations in the cloud-top phase over the WNP. Results show that over the low- and mid-latitude maritime regions, ice (water) clouds occur more (less) frequently during boreal winter than summer. Water clouds are more likely to be related to moisture conditions in the lower troposphere than to the underlying sea surface temperature. Owing to the combined effects of moist air mass transport and ocean currents (topography), the WNP region east of Hokkaido (the Sichuan Basin) has a high frequency of water clouds in summer (winter). Furthermore, supercooled water cloud populations have a clear seasonal cycle. The fraction of water clouds that are supercooled appears to be modulated by the near-surface air temperature. A diurnal cycle is seen in ice-cloud populations, which are highest in the late afternoon over both ocean and land except for the Sichuan Basin where summer nocturnal precipitation is typical. The occurrences of continental water clouds peak at noon in summer but early morning (around sunrise) in winter. An increase in the frequency of continental summer water clouds around noon is found to be associated with variations in both the cloud-top elevation of already-existing water clouds and new formations of boundary-layer clouds.

Observation bias and error characteristics are the preconditions for the effective assimilation of observation data. In this paper, the bias and error characteristics of the AHI (Advanced Himawari Imager (AHI) and AGRI (Multi-Channel Advanced Geostationary Orbit Radiation Imager (AGRI) data are compared and analyzed, with an emphasis on the observations obtained from land areas. The results show that the observation errors of the two instruments for the ocean area have a good channel consistency over ocean areas, which are all with errors of about 0.6 K; however, the bias and error are significantly affected by the land-surface types and terrain heights over land. For most of the AHI channels, the bias in urban-area bias is smaller than that of those of other surface types, while that of the AGRI data exhibits just the opposite trend, with obviously larger biases in urban areas. However, the observation errors of these two instruments in urban areas are significantly smaller than those of other surface types. The biases of the two instruments do not extensively change much with the terrain height, only slightly decreasing when the height is above 1000 m; however, the observation errors increase obviously with the increase of terrain heights. The difference between the two instruments is that the observation error of the AHI data tends to be stable and stabilizes above 1000 m, while that of the AGRI data is relatively stable below 500 m. The observation errors of the CO₂-sensitive channels of the two instruments over the land areas are obviously smaller than those of other near-surface channels, which may indicate that the data obtained in these two CO₂ channels have good application prospects for assimilation over land.