Benefiting from the ability to penetrate clouds, microwave (MW) can theoretically achieve all-sky monitoring, thus making significant contributions to weather forecasts and climate models. Various MW sea surface emissivity (SSE) models have been applied to the products of spaceborne MW radiometers and assimilation models. However, these models do not consider the impact of rainfall on the MW SSE. In this study, rainfall-induced SSE change was inferred based on the brightness temperature (BT) observed by AMSR2 and in-situ ship measurement OceanRAIN. The neural network-based Fast Atmospheric Parameter Simulators (NN-FAPSs) were constructed for quickly calculating the atmospheric upwelling/downwelling BT and transmissivity and the errors of NN-FAPSs fitting on the SSE were below 0.001±0.001 and 0.01±0.03 at 6.925 GHz and 36.5 GHz, respectively. The SSE estimates show that rainfall causes an increase of SSE (e.g., up to 0.2 at 10.65 GHz), and this phenomenon is more pronounced at low wind speeds and high SST, corresponding to the effects of rainfall-induced local wind and SST on the dielectric constant. The error analysis shows that at 6.925, 7.3, and 10.65 GHz, the accuracy of the SSE is less affected by the accuracy of the input parameters as well as the fitting errors of the NN-FAPSs (Δε were below 0.01±0.01). Additionally, polynomial models for 6.925, 7.3, and 10.65 GHz are proposed to provide the first guess of SSE under rainfall conditions for improving the accuracy of cloud and rainfall products. It is believed that this work helps improve the understanding of rainfall-induced MW SSE changes.

Passive microwave brightness temperature (PMW BT) images acquired by PMW imagers onboard polar-orbit satellites suffer from large observations missing between adjacent orbits due to the swath width of images, i.e., PMW BT swath gaps, limiting the spatiotemporal integrity and application potential of PMW BT-generated products. Here, we propose a gap-filling method [i.e., physical indicators-guided CNN-LSTM (PICL)] for PMW BT images by physically guided selection of surface indicators with CNN-LSTM model, which is suitable for special underlying surfaces (e.g., seasonal permafrost and snow) using only BT data to generate spatially gapless PMW BT images. The core of PICL is to use the CNN-LSTM model to capture the relationship of BT time series, thereby filling the missing BT values via historical BT data. PICL is applied to 7, 10, 18.7, 36, and 89 GHz frequencies of Advanced Microwave Scanning Radiometer 2 (AMSR2) for the Tibetan Plateau (TP). Validation results show good accuracy of the PICL filled BT, with the root-mean-squared error (RMSE) ranging from 1.28 to 2.43 K (<89 GHz), and the accuracy decreases as the frequency increases. The reconstructed BT images agree well with the original AMSR2 BT images and show no obvious boundary effect. PICL also has a good ability in capturing the temporal trends and discontinuities caused by snow and seasonal permafrost. PICL only requires historical BT before the missing moment, highlighting its feasibility to be extended to other satellite PMW imagers. It enables the generation of spatially seamless products such as all-weather land surface temperature (LST) and soil moisture.

With the recent availability of realistic azimuthally randomly oriented (ARO) raindrops, it is essential to evaluate the difference of radiative transfer simulations with scattering between the ARO raindrops (i.e., Chebyshev rain drop and liquid spheroid) and the commonly used spherical raindrop. Here, the scattering module of radiative transfer for TOVS (RTTOV-SCATT) was evaluated by a reference atmospheric radiative transfer simulator (ARTS) for the frequencies between 10.65 and 36.5 GHz. The two vector scattering solvers used by ARTS were discrete ordinate iterative (DOIT) and RT4. Both scattering solver difference (SSD) and scattering property difference (SPD) are assessed. It is shown that the brightness temperature (BT) difference between RTTOV-SCATT and DOIT can exceed 2.5 K, statistically higher than the value obtained from previous studies. There exists a size parameter threshold that influences the trend of SSD, and this threshold increases slightly with frequency. RT4 tends to produce lower BTs than DOIT, and the biases range from −0.1 to −0.8 K for different frequencies and surface types. In addition, the permittivity model difference dominates the SPD (biases and STDs range from 0.1 to 0.6 K and 0.4–1.3 K, respectively). The nonspherical shape and orientation yield higher horizontal polarization BT and lower vertical polarization BT. The additional polarization difference introduced by ARO raindrops is 0–3.2 K as a function of total column integrated rain water content. Thus, it is believed that this study offers the error sources and will help the improvements of RTTOV-SCATT.

Aerosol optical depth (AOD) is closely related to atmospheric pollutants. However, a large number of missing values in satellite AOD severely limits its application. We proposed a spatiotemporal random forest (RF) model to estimate the missing AHI AOD in this study. In addition to the commonly used meteorological and topographic parameters, the spatiotemporal data and MERRA-2 AOD were introduced as the model inputs. Specifically, the training data was divided into multiple subsets based on the land cover types and local times to explicitly characterize the spatiotemporal variation of AOD. The validation results indicated that the RF model achieved promising results with RMSE of 0.03 to 0.17, MBE of −0.01 to 0.02, and R of 0.87 to 0.97 at different land cover types and local times.

Sea surface temperature (SST) is a vital parameter at the earth-atmosphere interface. Current method can derive an all-sky SST at resolution up to 6 km by integrating thermal infrared (TIR) and passive microwave (PMW) remote sensing. However, due to the swath gap of the polar-orbit PMW sensors, current TIR-PMW integrated SST are not spatial-seamless (i.e. all-sky available). By integrating GCOM-W1 AMSR2 and FengYun-3B MWRI observations, this paper fills the BT inside the swath gap to reconstruct a spatial-seamless PMW brightness temperature (BT) and then introduces Aqua MODIS SST to estimates a 4-km all-sky SST, the spatial resolution and coverage of which outperforms the current TIR-PMW integrated SST. Results show that the reconstructed BT highly agrees with the original BT and the estimated SST has accuracy of 0.67 K-0.72 K when validated against in-situ SST. This study would be beneficial for associated studies such as climate change on large scales.