Since the launch of China’s Fengyun-3 (FY-3) satellite series in 2008, the on-board Microwave Temperature Sounders (MWTS) have provided critical atmospheric sounding data for numerical weather prediction and extreme weather monitoring. However, their application in climate research remains limited. To establish long-term Fundamental Climate Data Records (FCDRs) for FY-3 satellites—the core foundation of climate research—a comprehensive assessment of data consistency between China’s FY-3 satellites and the U.S. NOAA satellites is essential. This study systematically evaluates the inter-satellite consistency between FY-3 MWTS observations and three datasets of NOAA FCDRs, focusing on comparison of brightness temperatures and their anomalies over the extended period of 2009–2024. In accordance with the Global Climate Observing System (GCOS) Essential Climate Variables (ECVs) requirements (GCOS-245), the accuracy and stability of both operational and recalibrated FY-3 brightness temperatures are quantified on a global grid scale, using multiple statistical metrics including root mean square error (RMSE), standard deviation (SD), bias, and linear trend. The key findings are as follows. (1) FY-3 MWTS observations can effectively capture the characteristic seasonal cycles and vertical atmospheric structures of brightness temperatures, confirming their basic reliability in climate-related analyses. (2) Significant discontinuities in brightness temperatures are observed in the upper troposphere and lower stratosphere for earlier FY-3 satellites (FY-3A/3B/3C), indicating limitations in their long-term climate consistency, while newer satellites (FY-3D/3E/3F) show substantially improved consistency with NOAA FCDRs. (3) The recalibrated data of FY-3D exhibit a marked quality improvement, with RMSE reduced by 60% compared to FY-3D operational observations, and this recalibrated FY-3D dataset is recommended as the preferred choice for climate applications. (4) Even the operational (non-recalibrated) brightness temperature data of FY-3D achieves remarkable consistency with global benchmarks, boasting a global mean accuracy of 0.270 ± 0.039 K, which fully meets the accuracy thresholds specified by GCOS for ECVs. (5) For specific MWTS channels, the global mean brightness temperatures of Channel 4 (over oceans), Channel 6, and Channel 9 generally meet the stability requirements for climate monitoring, further supporting their utility in long-term climate studies. (6) Larger RMSEs of FY-3D operational data are identified in two key regions: the stratosphere over high latitudes and the mid troposphere over topographically complex areas (e.g., the Qinghai–Xizang Plateau, the Andes Mountains, and parts of Africa). These discrepancies are attributed to two technical factors: non-linearity in the MWTS calibration process and orbital drift of the FY-3 satellites. This study validates the climate monitoring capabilities of FY-3 MWTS observations, clarifies key directions for data quality improvement, and lays an important foundation for the transformation of this data from product generation to climate applications.

For numerical weather prediction (NWP), data assimilation (DA) combines short-term forecasts and various atmospheric observations to achieve optimal initial conditions, based on which subsequent forecasts are launched. With the rapid advancements in numerical models and observing systems, DA has been significantly evolved. Modern methods now can account for uncertainties of state variables across various spatiotemporal scales, incorporate multiscale observation error statistics, and enforce dynamical constrains and model balances. Meanwhile, observations from various platforms, such as ground-based, aircraft, and satellite, have been assimilated. These include data from polar-orbiting and geostationary satellites, radar-derived radial winds and reflectivity, Global Navigation Satellite System (GNSS) radio occultations, etc. To further utilize the advanced observing systems and DA techniques for high-impact weather predictions, target observation strategies have been developed to identify areas where additional observations can yield the greatest predict improvements. Based on the advancements of DA theories and methods, China’s operational systems have made significant progress, establishing advanced operational DA systems. Over the past decade, the forecast skill of 5-day global weather prediction has improved by approximately 15%. The article reviews a century of development in DA, and discusses future directions, including the advanced DA methods, operational frameworks, integration of novel observations, and the synergy between DA and artificial intelligence.

The observations from satellite microwave sounding instruments have been proven to significantly impact severe weather monitoring and numerical weather prediction. Recent research indicates that the maturation of digitally channelized technology enables hyperspectral microwave sounding. However, the specific effects of these hyperspectral channels on the retrieval of temperature and humidity profiles remain uncertain. In this study, a novel microwave to terahertz sounder (MTS) is explored. Specifically, the impacts of 50–60 GHz (V band) hyperspectral channels and 380–420 GHz (Y1 and Y2 band) terahertz channels on one-dimensional variational retrieval (1DVAR) results are investigated through various channel configurations. Initially, the information entropy of the channels is evaluated. When compared to the currently orbiting microwave sounders, the use of V-band hyperspectral channels can reduce the root-mean-square error (RMSE) of the retrieved temperature near the tropopause by approximately 14%. The inclusion of Y1 and Y2 band channels also positively contributes to the retrieved profiles. Compared to the currently deployed microwave sounders, this leads to a 2% reduction in temperature RMSE and a 5% reduction in humidity RMSE. The optimal channel configuration based on information entropy results in a temperature RMSE reduction of around 5.6% and a humidity RMSE reduction of 4.1%. Furthermore, the influence of observation noises on the retrieval results is examined. It is discovered that halving the noise can decrease the temperature RMSE by 13% and the humidity RMSE by 6%. Overall, the new sounding channels offer greater potential for enhancing temperature and humidity sounding, and they may potentially improve atmospheric measurements and the utilization of microwave observations in numerical weather prediction.

The ocean surface emissivity model plays a pivotal role in satellite data assimilation and the retrieval of ocean physical parameters. In our previous research, we developed a physical emissivity model featuring a polarized Bidirectional Reflectance Distribution Function (pBRDF-E). This model effectively ensures the consistency between surface emission and reflection parameters. However, it suffers from low computational efficiency. In this study, we introduce a fast ocean emissivity model, OceanEM. Leveraging the emissivity data output from the pBRDF-E model, OceanEM is developed by using a multilayer perceptron neural network. It can compute the polarization emissivity vector across a wide range of conditions: incidence angles from 0° to 80°, wind speeds from 2 to 50 m s⁻¹, sea surface temperatures from −2° to 30°C, sea surface salinities from 0 to 40 psu, and frequencies from 1.4 to 410 GHz. Alongside the FAST Microwave Emissivity Model (FASTEM6) and SURface Fast Emissivity Model for Ocean (SURFEM-ocean), OceanEM is integrated into the Advanced Radiative Transfer Modeling System (ARMS) as a user-selectable option. To validate the accuracy of OceanEM, we compare it with FASTEM6 and SURFEM-ocean using data from WindSAT, a polarimetric radiometer onboard the Coriolis satellite. The results show that the three models generally yield consistent simulations of WindSAT brightness temperatures. Specifically, for channels at 6.8 GHz, 10.7 GHz (both horizontal and vertical polarization), and 18.7 GHz (vertical polarization), OceanEM demonstrates higher accuracy than FASTEM6 but lower than SURFEM-ocean. Conversely, for channels of 18.7 GHz (horizontal polarization), 23.8 GHz, and 37.0 GHz (both horizontal and vertical polarization), OceanEM outperforms both FASTEM6 and SURFEM-ocean.

Cloud liquid water path is an important parameter in climate analysis and weather applications and is often retrieved from satellite microwave observations. The algorithm requires the data measured at two frequencies at 23.8 and 31.4 GHz. Using the Advanced Technology Microwave Sounder (ATMS) observation data, the physical and empirical algorithms for retrieving the cloud liquid water path were compared. It is shown that both algorithms can well depict the distribution of cloud phase clouds, although the magnitudes at some regions are different. The cloudy areas detected by the physical retrieval algorithm are more consistent with that shown in satellite visible images. The performance of the empirical algorithm, however, is more affected by the season, especially in the mid-high latitudes. The sensitivity analysis indicates that the errors of the physical algorithm are affected by cloud layer temperature, sea surface temperature and sea surface wind speed. The uncertainty in cloud layer temperature is likely a major source of errors in cloud liquid water retrievals. In addition, sea surface temperature errors influence the retrieval of clouds liquid water having a high amount while sea surface wind has more impacts on cloud liquid water having a low amount.

The reflection of ocean surface is often assumed azimuthally symmetric in the previous vector discrete ordinate radiative transfer (VDISORT) and many other radiative transfer solvers. This assumption can lead to obvious errors in the simulated radiances. In this study, the vector radiative transfer equation is solved with a polarized bidirectional reflection distribution function (pBRDF) for computing the surface-leaving radiation from the lower boundary. An azimuthally asymmetric pBRDF model at visible and infrared bands over oceans is fully coupled with the updated VDISORT model. The radiance at the ocean surface is combined with the contributions of atmospheric scattering and surface properties. It is shown that the radiance at the ocean surface also exhibits a strong angular dependence in the Stokes vector and the magnitudes of $I,Q,\mathrm{a}\mathrm{n}\mathrm{d}V$ increase for a larger azimuthal dependence of pBRDF. In addition, the solar position affects the peaks of sun glitter pattern, thus modulating the signal magnitudes and the angular distributions. As ocean wind increases, the reflection weakens with reduced magnitudes of Stokes parameters and less-varying angular distributions.

The original vector discrete ordinate radiative transfer (VDISORT) model takes into account Stokes radiance vector but derives its solution assuming azimuthal symmetric surface reflective matrix and atmospheric scattering phase matrix, such as the phase matrix derived from spherical particles or randomly oriented non-spherical particles. In this study, a new VDISORT is developed for general atmospheric scattering and boundary conditions. Stokes vector is decomposed into both sinusoidal and cosinusoidal harmonic modes, and the radiance at arbitrary viewing geometry is solved directly by adding two zero-weighted points in the Gaussian quadrature scheme. The complex eigenvalues in homogeneous solutions are also taken into full consideration. The accuracy of VDISORT model is comprehensively validated by four cases: Rayleigh scattering case, the spherical particle scattering case with the Legendre expansion coefficients of 0th–13th orders of the phase matrix (hereinafter L13), L13 with a polarized source, and the random-oriented oblate particle scattering case with the Legendre expansion coefficients of 0th–11th orders of the phase matrix (hereinafter L11). In all cases, the simulated radiances agree well with the benchmarks, with absolute biases less than 0.0065, 0.0006, and 0.0008 for Rayleigh, unpolarized L13, and L11, respectively. Since a polarized bidirectional reflection distribution function (pBRDF) matrix is used as the lower boundary condition, VDISORT is now able to handle fully coupled atmospheric and surface polarimetric radiative transfer processes.