Gang Ma

Fast and accurate calculation of atmospheric transmittance is essential for infrared atmospheric remote sensing and satellite data assimilation. However, fast radiative transfer models show significant errors in strong water vapor absorption channels (e.g., near 6.7 µm). An important reason is the numerical instability encountered during the regression of transmittance coefficients when dealing with lower and middle atmosphere. To address this, this study proposes an optimized scheme to calculate atmospheric transmittance vertical profiles for the RTTOV (Radiative Transfer for TOVS) fast transmittance algorithm. The method introduces a physically motivated transmittance threshold to sub-select training samples and employs a cumulative transmittance-based weighting factor within a weighted least squares regression to recalibrate the transmittance coefficients. It aims to optimize the calculation scheme for transmittance coefficients of the Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) instrument onboard the Fengyun-3F satellite (FY-3F). The method is assessed by calculations on the training profile datasets provided within the RTTOV model framework. By comparing transmittance and brightness temperature calculations at 6.7 µm from this method with those from a line-by-line model and observations from HIRAS-II, the results show that the accuracy of the forward model for the 6.7 µm absorption channel is significantly enhanced by applying a threshold-based noise reduction method. This improvement enhances the stability and reliability of the transmittance calculations for this strong absorption band. Further accuracy enhancements are obtained by incorporating weighting corrections into the calculations of transmittance coefficients. The root mean square error (RMSE) and bias of the observation minus background (OMB) time series for FY-3F HIRAS-II demonstrate that the transmittance coefficient calculation scheme with weighting factor correction improves the forward model accuracy, which is more consistent with RTTOV simulation results. The mean OMB bias at the 6.7 µm absorption-peak channel is closer to zero than that of RTTOV, whereas the OMB biases at the channels on either side of the absorption peak remain consistent with those of RTTOV.

Fast and accurate calculation of atmospheric transmittance is essential for infrared atmospheric remote sensing and satellite data assimilation. However, fast radiative transfer models show significant errors in strong water vapour absorption channels (e.g., near 6.7 μm). An important reason is that the numerical instability encountered during the regression of transmittance coefficients when dealing with lower and middle atmosphere. To address this, this study proposes an optimized scheme to calculate atmospheric transmittance vertical profiles for the RTTOV (Radiative Transfer for TOVS) fast transmittance algorithm. The method introduces a physically motivated transmittance threshold to sub-select training samples and employs cumulative transmittance-based weighting factor within a weighted least squares regression to recalibrate the transmittance coefficients. It aims to optimize the calculation scheme for transmittance coefficients of the Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) instrument onboard Fengyun 3F satellite (FY-3F). The method is assessed by calculations on the training profile datasets provided within the RTTOV model framework. By comparing transmittance and brightness temperature calculations at 6.7 microns from this method with those from a line-by-line model and observations from HIRAS-II, the results show that the accuracy of the forward model for the 6.7 μm absorption channel is significantly enhanced by applying a threshold-based noise reduction method. This improvement enhances the stability and reliability of the transmittance calculations for this strong absorption band. Further accuracy enhancements are obtained by incorporating weighting corrections into the calculations of transmittance coefficients. The root mean square error (RMSE) and bias of the observation minus background (OMB) time series for FY-3F HIRAS-II demonstrate that the transmittance coefficient calculation scheme with weighting factor correction improves the forward model accuracy, which is more consistent with RTTOV simulation results. The OMB bias at the 6.7 μm absorption peak channel performs better than that of RTTOV, while the OMB bias on both sides of the 6.7 μm absorption peak channel remains consistent with RTTOV.

Fast and accurate calculation of atmospheric transmittance is essential for infrared atmospheric remote sensing and satellite data assimilation. However, fast radiative transfer models show significant errors in strong water vapour absorption channels (e.g., near 6.7 μm). An important reason is that the numerical instability encountered during the regression of transmittance coefficients when dealing with lower and middle atmosphere. To address this, this study proposes an optimized scheme to calculate atmospheric transmittance vertical profiles for the RTTOV (Radiative Transfer for TOVS) fast transmittance algorithm. The method introduces a physically motivated transmittance threshold to sub-select training samples and employs cumulative transmittance-based weighting factor within a weighted least squares regression to recalibrate the transmittance coefficients. It aims to optimize the calculation scheme for transmittance coefficients of the Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) instrument onboard Fengyun 3F satellite (FY-3F). The method is assessed by calculations on the training profile datasets provided within the RTTOV model framework. By comparing transmittance and brightness temperature calculations at 6.7 microns from this method with those from a line-by-line model and observations from HIRAS-II, the results show that the accuracy of the forward model for the 6.7 μm absorption channel is significantly enhanced by applying a threshold-based noise reduction method. This improvement enhances the stability and reliability of the transmittance calculations for this strong absorption band. Further accuracy enhancements are obtained by incorporating weighting corrections into the calculations of transmittance coefficients. The root mean square error (RMSE) and bias of the observation minus background (OMB) time series for FY-3F HIRAS-II demonstrate that the transmittance coefficient calculation scheme with weighting factor correction improves the forward model accuracy, which is more consistent with RTTOV simulation results. The OMB bias at the 6.7 μm absorption peak channel performs better than that of RTTOV, while the OMB bias on both sides of the 6.7 μm absorption peak channel remains consistent with RTTOV.

Fast and accurate calculation of atmospheric transmittance is essential for infrared atmospheric remote sensing and satellite data assimilation. However, fast radiative transfer models show significant errors in strong water vapour absorption channels (e.g., near 6.7 μm). An important reason is that the numerical instability encountered during the regression of transmittance coefficients when dealing with lower and middle atmosphere. To address this, this study proposes an optimized scheme to calculate atmospheric transmittance vertical profiles for the RTTOV (Radiative Transfer for TOVS) fast transmittance algorithm. The method introduces a physically motivated transmittance threshold to sub-select training samples and employs cumulative transmittance-based weighting factor within a weighted least squares regression to recalibrate the transmittance coefficients. It aims to optimize the calculation scheme for transmittance coefficients of the Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) instrument onboard Fengyun 3F satellite (FY-3F). The method is assessed by calculations on the training profile datasets provided within the RTTOV model framework. By comparing transmittance and brightness temperature calculations at 6.7 microns from this method with those from a line-by-line model and observations from HIRAS-II, the results show that the accuracy of the forward model for the 6.7 μm absorption channel is significantly enhanced by applying a threshold-based noise reduction method. This improvement enhances the stability and reliability of the transmittance calculations for this strong absorption band. Further accuracy enhancements are obtained by incorporating weighting corrections into the calculations of transmittance coefficients. The root mean square error (RMSE) and bias of the observation minus background (OMB) time series for FY-3F HIRAS-II demonstrate that the transmittance coefficient calculation scheme with weighting factor correction improves the forward model accuracy, which is more consistent with RTTOV simulation results. The OMB bias at the 6.7 μm absorption peak channel performs better than that of RTTOV, while the OMB bias on both sides of the 6.7 μm absorption peak channel remains consistent with RTTOV.

Fast and accurate calculation of atmospheric transmittance is essential for infrared atmospheric remote sensing and satellite data assimilation. However, fast radiative transfer models show significant errors in strong water vapour absorption channels (e.g., near 6.7 μm). An important reason is that the numerical instability encountered during the regression of transmittance coefficients when dealing with lower and middle atmosphere. To address this, this study proposes an optimized scheme to calculate atmospheric transmittance vertical profiles for the RTTOV (Radiative Transfer for TOVS) fast transmittance algorithm. The method introduces a physically motivated transmittance threshold to sub-select training samples and employs cumulative transmittance-based weighting factor within a weighted least squares regression to recalibrate the transmittance coefficients. It aims to optimize the calculation scheme for transmittance coefficients of the Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) instrument onboard Fengyun 3F satellite (FY-3F). The method is assessed by calculations on the training profile datasets provided within the RTTOV model framework. By comparing transmittance and brightness temperature calculations at 6.7 microns from this method with those from a line-by-line model and observations from HIRAS-II, the results show that the accuracy of the forward model for the 6.7 μm absorption channel is significantly enhanced by applying a threshold-based noise reduction method. This improvement enhances the stability and reliability of the transmittance calculations for this strong absorption band. Further accuracy enhancements are obtained by incorporating weighting corrections into the calculations of transmittance coefficients. The root mean square error (RMSE) and bias of the observation minus background (OMB) time series for FY-3F HIRAS-II demonstrate that the transmittance coefficient calculation scheme with weighting factor correction improves the forward model accuracy, which is more consistent with RTTOV simulation results. The OMB bias at the 6.7 μm absorption peak channel performs better than that of RTTOV, while the OMB bias on both sides of the 6.7 μm absorption peak channel remains consistent with RTTOV.

To enhance the utilization and assimilation of the observations from the Geostationary Interferometric Infrared Sounder (GIIRS) into the China Meteorological Administration (CMA) Global Forecast System (CMA-GFS), a novel three-dimensional cloud detection (3DCD) method for GIIRS data has been recently developed. This study is an extension of the 3DCD method. In the original 3DCD (3DCD-OLD) method, the ideal atmosphere profile and observational error are used to construct a lookup table. In this study, the actual profiles and stricter threshold are used to improve the 3DCD method (3DCD-NEW). We aim to investigate the benefits of using the 3DCD-NEW and compare its impact on the analysis and forecasting through 1-month cycling and forecast experiments. Preliminary assessment results indicate that the number of assimilated GIIRS observations using the 3DCD-NEW is approximately double that of using completely clear-sky detection. The analysis fields of geopotential height, temperature, specific humidity, zonal wind, and meridional wind are neutral for the 3DCD-NEW when compared to the 3DCD-OLD. A neutral-to-positive impact is observed in the forecast fields of geopotential height, temperature, and vector winds over the Northern Hemisphere, tropics, and East Asia during the initial 7-day forecast period when compared to 3DCD-OLD. Significance Statement: The three-dimensional cloud detection (3DCD) method of Geostationary Interferometric Infrared Sounder (GIIRS) has been developed recently; this study is an extension work of the 3DCD method. In the improved 3DCD method (3DCD-NEW), the actual profile is used instead of the lookup table derived from the ideal atmosphere profile in the original 3DCD method (3DCD-OLD). The number of assimilated GIIRS observation by using 3DCD method is about twice than that in the CTRL experiment (using the completely clear-sky cloud detection). Compared to 3DCD-OLD, a neutral-to-positive impact is observed in the forecast fields of geopotential height, temperature, and vector winds over the Northern Hemisphere, tropics, and East Asia during the initial 7-day forecast period.

Satellite microwave-sounding radiometer data assimilation under clear-sky conditions typically requires the exclusion of precipitation-affected field-of-view (FOV) regions. However, the traditional scatter index (SI) and cloud liquid water path (CLWP)-based precipitation sounding algorithms from earlier NOAA microwave sounders are built on window channels which are not available from FY-3C/D MWTS-II. To address this limitation, this study establishes a nonlinear relationship between multispectral visible/infrared data from the FY-2F geostationary satellite and microwave sounding channels using an artificial intelligence (AI)-driven approach. The methodology involves three key steps: (1) The spatiotemporal integration of FY-2F VISSR-derived products with NOAA-19 AMSU-A microwave brightness temperatures was achieved through the GEO-LEO pixel fusion algorithm. (2) The fused observations were used as a training set and input into a random forest model. (3) The performance of the RF_SI method was evaluated by using individual cases and time series observations. Results demonstrate that the RF_SI method effectively captures the horizontal distribution of microwave scattering signals in deep convective systems. Compared with those of the NOAA-19 AMSU-A traditional SI and CLWP-based precipitation sounding algorithms, the accuracy and sounding rate of the RF_SI method exceed 94% and 92%, respectively, and the error rate is less than 3%. Also, the RF_SI method exhibits consistent performance across diverse temporal and spatial domains, highlighting its robustness for cross-platform precipitation screening in microwave data assimilation.

Microwave hyperspectral sounders are key instruments for next-generation meteorological satellites, designed to replace broadband sounders such as ATMS, MWTS, and MWHS. A major challenge in their design is determining the optimal channel bandwidth configuration to maximize the use of absorption spectra and enhance vertical atmospheric profiling. This study evaluates two variable-bandwidth strategies: (1) a generalized variable-bandwidth approach, which applies multiple fixed bandwidth combinations across different frequency bands, and (2) a dynamic bandwidth strategy, which differentiates channel bandwidths within the same frequency band for absorption peak and wing regions. The retrieval performance of various bandwidth configurations is analyzed for lower- and upper-atmospheric temperature and humidity observations. Results indicate that for upper-atmosphere temperature retrievals, the 60 GHz and 118 GHz bands achieve higher accuracy with a 10 MHz bandwidth. For lower-atmosphere temperature retrievals, where most information originates from the O₂ absorption line’s wing region, a wider bandwidth reduces observation costs while maintaining comparable accuracy. Water vapor retrievals, which primarily rely on the 118 GHz wing region and 183 GHz absorption lines, show minimal sensitivity to bandwidth configurations. A novel channel configuration method is proposed, integrating both absorption peak and wing regions into a single optimized scheme. Experimental results demonstrate that the 10+10+100 MHz (Peak) + 50+100+100 MHz (Wing) configuration achieves the best retrieval accuracy across different atmospheric layers, significantly improving both temperature and humidity profiling compared to existing broadband sounders. The advantages of using variable channel bandwidth hyperspectral microwave radiometers for joint retrieval of temperature and humidity profiles have been demonstrated from both theoretical analysis and quantitative retrieval (Optimal estimation method, OEM), providing theoretical support and technical guidance for the development of the next generation of spaceborne hyperspectral microwave radiometers.

2411号台风“摩羯”于2024年9月登陆中国东南沿海地区, 风云三号D星(FY-3D)微波湿度计(MWHS-II)成功监测到该台风. 本研究在天气研究与预报模式数据同化系统(Weather Research and Forecasting model Data Assimilation system, WRFDA)中构建了晴空条件下FY-3D MWHS-II资料三维变分同化框架, 通过设置118 GHz, 183 GHz单频段以及双频段联合同化及预报试验方案, 系统评估了微波资料同化对台风“摩羯”的强度、路径及降水预报效果. 试验表明, 同化FY-3D MWHS-II资料有效提升了分析场质量, 对台风强度、路径和降水的预报也产生了积极影响. 台风预报方面, 同化118 GHz和183 GHz通道对台风路径预报分别改进了17.18%和13.39%, 洋面区域的183 GHz同化更使得路径预报改进率达到14.59%; 降水预报方面, MWHS-II资料同化显著提升初期24 h内中小雨量级(50 mm)预报表现出独特优势; 183 GHz通道的改进效应在24 h后逐步显现, 而双频段联合同化方案展现出综合优势, 各量级降水的降水分数技能得分(Fractions Skill Scores, FSS)相比控制试验提升显著. FY-3D MWHS-II的118 GHz和183 GHz频段在不同预报要素上的差异化改进效果, 突显了FY-3D MWHS-II资料在区域台风数值预报中的独特应用价值及应用潜力.