Level 2 rain-rate retrievals from the Microwave Radiation Imager (MWRI) on board the Chinese FengYun (FY)-3B satellite are verified using minute rainfall measurements from a dense automatic weather station (AWS) network over eastern China for the warm seasons (May–September) of 2012 and 2013. First, analyses of minute rainfall are constructed on a 0.05° latitude–longitude grid box through interpolation of quality-controlled gauge reports. Ground truth for the FY-3B rain-rate retrievals is defined as the 5-min mean rate centering at the satellite observation time and over the 0.05° latitude–longitude grid boxes falling into the target field-of-view (FOV) coverage determined with parallax correction. Parallax displacement is about the same as the height of cloud or half of the FY-3B FOV size. Parallax correction is crucial to ensure accurate evaluation and applications of the level 2 precipitation retrievals from FY-3B and other satellites, including the Global Precipitation Mission (GPM) core satellite, and should be implemented before the level 2 retrievals may be used as inputs to the level 3 integrated satellite precipitation analyses. FY-3B level 2 retrievals present good skills in detecting raining pixels and quantifying rain rate as retrievals from other PMW sensors. However, they tend to miss rainfall from warm and low clouds of small scales and underestimate (overestimate) heavy (light) precipitation. In particular, the limited maximum rain rate yields significant underestimation for many heavy rainfall events. Maximum rainfall detected by the FY-3B retrievals for the afternoon orbits is shifted by about 7–8 km toward the leeward direction, most likely caused by the displacement between the heavy rainfall and tallest cloud top.

High-resolution gridded 2 m air temperature datasets are important input data for global and regional climate change studies, agrohydrologic model simulations and numerical weather predictions, etc. In this study, the digital elevation model (DEM) is used to correct temperature forecasts produced by ECMWF. The multi-grid variation formulation method is then used to fuse the data from corrected temperature forecasts and ground automatic station observations. The fused dataset covers the area over (0–60°N, 70–140°S), where different underlying surfaces exist, such as plains, basins, plateaus, and mountains. The spatial and temporal resolutions are 1 km and 1 h, respectively. The comparison of the fusion data with the verification observations, including 2400 weather stations, indicates that the accuracy of the gridded temperature is superior to European Centre for Medium-Range Weather Forecasts (ECMWF) data. This is because a more significant number of stations and high-resolution terrain data are used to generate the fusion data than are utilized in the ECMWF. The obtained dataset can describe the temperature feature of peaks and valleys more precisely. Due to its continuous temporal coverage and consistent quality, the fusion dataset is one of China’s most widely used temperature datasets. However, data uncertainty will increase for areas with sparse observations and high mountains, and we must be cautious when using data from these areas.

A common approach used for multi-source observation data blending is the fusion method. This study assesses the applicability of the first-generation fusion sea surface temperature (SST) product of the China Meteorological Administration (CMA) in the Yellow–Bohai Sea region for numerical weather predictions. First, daily and 6 h fusion SST measurements are compared with data derived from 21 buoy sites for 2019 to 2020. The error analysis results show that the root-mean-square error (RMSE) of the daily SST ranges from 0.64 to 1.36 °C (overall RMSE of 0.996 °C). The RMSE of the 6 h SST varies from 0.64 to 1.73 °C (overall RMSE of 1.06 °C). According to the simulation result, the SST difference could affect the value and location distribution of liquid water content in the fog area. A lower SST is favorable for increasing the liquid water content, which fits the mechanisms of advection fog formation by warm air flowing over colder water.

Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are −0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

Optimum interpolation (OI) analysis is performed on the sea surface temperature (SST) products retrieved from the Fengyun-3C (FY-3C) visible and infrared radiometer (VIRR). The oriented elliptic correlation scale is employed on the SST data and then compared with those from the traditional rectangular correlation scale in the OI analysis. The results were also evaluated against the daily SST data from the in-situ buoys and the Reynolds Optimum Interpolation 1/4 Degree Daily SST (OISST) during 2015. The statistical metrics indicate that the SSTs determined using the oriented elliptic correlation scales have higher accuracy than those from rectangular correlation scales, as the root-mean-square error (RMSE) from 0.42°C to 0.38°C, which are higher than those obtained with the comparison with OISST (RMSE of 0.28°C). This assessment demonstrates that the original SST data from FY-3C VIRR are of poor quality due to the presence of unresolved errors, whereas the oriented elliptic correlation scales for SST demonstrate better performance than the rectangular correlation scales in the OI analysis.

Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods with oriented elliptic correlation scales to construct SST fields. Firstly, the model for the oriented elliptic correlation scale was established for SST analysis. Secondly, observation errors from each type of SST data source were estimated using the optimal matched datasets, and background field errors were calculated using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product was obtained using the Kalman filtering method, then the SST fields using the optimum interpolation (OI) method were chosen for comparison to validate results. The quality analysis for 2016 revealed that the Kalman analysis with a root-mean-square error (RMSE) of 0.3243 °C had better performance than did the OI analysis with a RMSE of 0.3911 °C, which was closer to the OISST product RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data.