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An improved microwave gaseous absorption scheme based on statistical regression is proposed in this study. In the new scheme, Monochromatic Radiative Transfer Model (MonoRTM) replaces the Millimeter‐wave Propagation Model (MPM) to train the new scheme and the effect of real Spectral Response Functions is included in the training process. After the replacement, results of the new scheme are closer to observations from Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar‐orbiting Partnership satellite in low level channels while MPM has some advantages in upper level channels. Introducing ozone absorption can cause a systematic bias but results in small standard deviations in channels with frequency 183 ± 1.8 GHz and 183 ± 1 GHz. In addition, the new scheme updates the vertical interpolation of water vapor and optimizes the vertical distribution of Planck function. These updates can reduce biases caused by vertical interpolation, especially for water vapor absorption channels. The bias associated to vertical interpolation can reach 0.25 K whereas the new scheme can decrease it to 0.003 K. To further validate the accuracy of the new scheme, we apply the new scheme to Advanced Radiative transfer Modeling System and compare simulated results to RTTOV 13.2 under 37 and 137 level (L) atmosphere (atm). Observations from ATMS onboard NOAA‐20 are used as true values. Results show that the new scheme agrees with RTTOV 13.2 well in accuracy and performs even better in upper level channels and water vapor absorption channels. Plain Language Summary: Fast radiative transfer (RT) models play a crucial role in satellite data assimilation as well as remote sensing. Gaseous absorption calculation is a fundamental aspect of fast RT models. With the capability of new‐generation spaceborne microwave instruments to observe spectra beyond 200 GHz, Monochromatic Radiative Transfer Model (MonoRTM) is utilized to train the microwave gaseous absorption scheme. The absorption of oxygen, ozone, water vapor and nitrogen is considered. In addition, the mismatch of vertical coordinates between fast RT models and input profiles requires fast RT models to do vertical interpolation during simulations. Vertical interpolation may introduce notable biases in microwave water vapor absorption channels. In this study, we enhance the vertical interpolation of water vapor and optimize the vertical distribution of the Planck function to mitigate these biases. The accuracy of the new scheme is assessed by comparing it with MonoRTM and satellite observations. Key Points: A new microwave gaseous absorption scheme, trained using Monochromatic Radiative Transfer Model (MonoRTM), is developed and the effect of ozone and nitrogen absorption is evaluatedThe accuracy of the new scheme is enhanced through updates to water vapor interpolation and the vertical distribution of Planck functionAfter application in Advanced Radiative transfer Modeling System (ARMS), the new scheme aligns with RTTOV and gets greater accuracy in upper‐level and water vapor absorption channels [ABSTRACT FROM AUTHOR]

The use of remote sensing methods to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research. The Nanjing Joint Institute for Atmospheric Sciences (NJIAS) Himawari-8/9 Cloud Feature Dataset (HCFD) provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the 7-year period from April 2016 to December 2022. Multiple cloud variables, such as cloud mask, phase/type, top height, optical thickness, and particle effective radius, as well as snow, dust, and haze masks, were generated from the visible and infrared measurements of the Advanced Himawari Imager (AHI) on board the Japanese geostationary satellites Himawari-8 and Himawari-9 using a series of recently developed cloud retrieval algorithms. Verifications with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) 1 km cloud layer product and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 cloud product (MYD06) demonstrate that the NJIAS HCFD gives higher skill scores than the Japanese Himawari-8/9 operational cloud product for all cloud variables except for cloud particle effective radius. The NJIAS HCFD even outperforms the MYD06 in nighttime cloud detection; cloud-top height, pressure, and temperature estimation; and infrared-only cloud-top phase determination. All evaluations are performed at the nominal 2 km scale, not including the effects of sub-pixel cloudiness or very thin cirrus. Two examples are presented to demonstrate applications of the NJIAS HCFD for climate and typhoon research. The NJIAS HCFD has been published in the Science Data Bank (https://doi.org/10.57760/sciencedb.09950 , Zhuge 2023a; https://doi.org/10.57760/sciencedb.09953 , Zhuge 2023b; https://doi.org/10.57760/sciencedb.09954 , Zhuge 2023c; https://doi.org/10.57760/sciencedb.10158 , Zhuge 2023d; https://doi.org/10.57760/sciencedb.09945 , Zhuge 2023e). [ABSTRACT FROM AUTHOR]

The microwave humidity sounder (MWHS) onboard the Fengyun-3D satellite is providing the data for profiling atmospheric temperature and moisture and has become an important data source for improving the weather forecasts. In this article, three data sources are utilized for assessing the MWHS on-orbit performance, including Global Navigation Satellite System Occultation Sounder (GNOS), ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA)-Interim reanalysis, and Advanced Technology Microwave Sounder (ATMS) data. GNOS-retrieved atmospheric profiles and the reanalysis data are used as inputs to the community radiative transfer model (CRTM) for simulating the MWHS brightness temperatures at the top of the atmosphere in July 2018 for characterizing the instrument performance. Since ATMS is a well-calibrated microwave sounding instrument onboard both Suomi NPP and NOAA-20 satellites, its measurements are also collocated with MWHS data for a consensus analysis using the simultaneous nadir overpasses (SNOs) method. In comparing GNOS simulations, MWHS upper air temperature sounding channels (3–6) have relatively larger biases (less than 2.5 K) than the water vapor sounding channels. However, the standard deviation of the difference between observations and simulations (O-B) is larger for water vapor sounding channels. For ERA simulations, MWHS sounding channels exhibit negative biases similar to GNOS results but the standard deviation of O-B at the water vapor channels is much smaller. When compared with ATMS water vapor channels, MWHS biases are mostly negative and agree with those from ERA simulation. Thus, the large uncertainty in simulating MWHS water vapor sounding channels from GNOS could result from the poor input water vapor profiles and high water vapor variability in the lower troposphere. [ABSTRACT FROM AUTHOR]