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.

Sea surface temperature (SST) is an important parameter for assessing sea-atmosphere energy interaction and understanding climate change. One of the primary approaches for obtaining global-scale SST is retrieving from satellite thermal infrared remote sensing data. However, it is challenging to accurately retrieve large-scale SST due to the complexity of retrieving the key intermediate parameter, i.e., sea surface emissivity (SSE), using the standard theoretical model. In this study, we proposed a simplified SSE estimation model based on the satellite view zenith angle (VZA) and wind speed and compared it with three commonly used SSE estimation models. Then, the retrieved SSTs based on those SSEs were validated againstin-situSSTs. Results show that the SSE from the proposed estimation model shows the highest consistency and the lowest biases with the theoretical values compared to the other three estimation models, especially in large VZAs.In-situobservation-based SST validation results show that the SST retrieved using the proposed SSE estimation model also achieves the highest accuracy compared to the other three SSTs, with a mean bias error of 0.08 K, and a root-mean-square error of 0.30 K, which is close to the official SST products. In conclusion, the proposed SSE estimation model shows good performance both in SSE estimating and SST retrieving. Furthermore, the proposed model has the potential to estimate SSE on large scales that can serve as a reference for obtaining SST from other similar sensors to promote the development of marine remote sensing.

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.

A widely used approach for all-weather land surface temperature (LST) estimation is the integration of satellite passive microwave (MW) and thermal infrared (TIR) remote sensing observations. However, there are still few methods for estimating near-real time (NRT) all-weather (AW) (NRT-AW) LST. Besides, estimation of the LST within the swath gap of the satellite MW images is still greatly limited. This letter proposes a so-called NRT-AW method for the estimation of NRT-AW LST. NRT-AW firstly fills up the brightness temperatures (BT) inside the AMSR2 swath gap. Then, the NRT AW LST is estimated by learning the mapping between the time series of AMSR2 BT and MODIS LST on the annual scales. The results of the application of NRT-AW in the Heihe River Basin (HRB) show that the NRT-AW LST is spatially continuous and highly consistent with the original MODIS LST with a standard deviation (STD) of 1.27–1.77 K. Validation based onin situLST indicates that the NRT-AW LST estimate has a root mean square error (RMSE) of 2.46–4.62 K. This method is beneficial for rapid mapping of all-weather LST over large areas and, thus, can satisfy associated applications.

Land Surface Temperature (LST) is a key factor in the land surface energy budget. The accuracy of the LST is affected by topographical fluctuations, observation time, and other factors. Thus, it is necessary to validate the retrieved LST products. In this study, the Fengyun-4A (FY-4A) LST was evaluated against the in-situ LST, which is collected from four ground sites in the Heihe River basin from August 1st, 2019 to December 31st, 2019. The results show that the root-mean-square error(RMSE) varies from 2.39 K to 4.07 K. Therefore, it is considered that FY-4A LST has good correlations with the in-situ LST. However, the FY-4A LST product has large systematic errors over some sites, e.g Jingyangling. The main reason is that the longwave radiometer has a scale mismatch between the pixels of FY-4A, and the scale mismatch can affect the representatives of measurements at pixel scales.