The accurate knowledge of variations of melt ponds is important for understanding the Arctic energy budget due to its albedo–transmittance–melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) over Arctic sea ice using all seven spectral bands of MODIS surface reflectance. We construct a robust ensemble-based deep neural network and use in-situ MPF observations collected from multiple sources as the target data to train the network. We examine the potential influence of using sea ice concentration (SIC) from different sources as additional target data (besides MPF) on the MPF retrieval. The results suggest that the inclusion of SIC has a minor impact on MPF retrieval. Based on this, we create a new MPF data from 2000 to 2019 (the longest data in our knowledge). The validation shows that our new MPF data is in good agreement with the observations. We further compare the new MPF dataset with the previously published MPF datasets. It is found that the evolution of the new MPF is similar to previous MPF data throughout the melting season, but the new MPF data is in relatively better agreement with the observations in terms of correlations and root mean squared errors (RMSE), and also has the smallest value in the first half of the melting season.

A newly designed thermal/vacuum (T/V) prelaunch multiangle calibration test is proposed for Microwave Humidity and Temperature Sounder (MWHTS) onboard the Feng Yun 3D satellite. Compared with other existing prelaunch calibration tests designed at fix scan angle, this test is first proposed to analyze the measurements of MWHTS from multiscan angles. A set of comprehensive calibration method is established and used for calibration data processing. The high-quality data preprocessing methods are performed. Essential calibration coefficients such as cold bias, hot bias, and nonlinearity coefficients, and calibration results such as calibration residual error, sensitivity, and calibration accuracy of MWHTS are all obtained and analyzed in MWHTS's six observing positions. The measured sensitivity value of channel 2 is 1.5 K while the measured sensitivities of the others range from 0.18 to 0.57 K. The calibration accuracy of channel 2 is 1.5 K while the calibration accuracies of the others range from 0.32 to 0.7 K. According to the discrepancy of multiangle calibration test results, the radiation leakage and the response lag for special channels are discovered for the first time in T/V test, and the reasons of these two special phenomena are fully analyzed. Moreover, the measurements of the T/V test with fast changing instrument temperatures are analyzed and the applicability of nonlinearity coefficients obtained in T/V test for in-orbit calibration is verified. In conclusion, MWHTS meets the basic design requirements of sensitivity and calibration accuracy, the changing of the MWHTS observing positions does not impact the calibration coefficients and results, and the nonlinearity coefficients obtained in T/V test are valid when instrument temperature experiences some fast changing.

THz Atmospheric Limb Sounder (TALIS) is a microwave limb sounder being developed for atmospheric vertically resolved profile observations by the National Space Science Center, Chinese Academy of Sciences (NSSC, CAS). It is designed to measure temperature and chemical species such as O₃, HCl, ClO, N₂O, NO, NO₂, HOCl, H₂O, HNO₃, HCN, CO, SO₂, BrO, HO₂, H₂CO, CH₃Cl, CH₃OH, and CH₃CN with a high vertical resolution from about 10 to 100 km to improve our comprehension of atmospheric chemistry and dynamics and to monitor the man-made pollution in the atmosphere. Four heterodyne radiometers including several FFT spectrometers of 2 GHz bandwidth with 2 MHz resolution are employed to obtain the atmospheric thermal emission in broad spectral regions centred near 118, 190, 240, and 643 GHz. A theoretical simulation is performed to estimate the retrieval precision of the main targets and to compare them with that of Aura MLS standard spectrometers. Single scan measurement and averaged measurement are considered in the simulation, respectively. The temperature profile can be obtained with a precision of <2 K for a single scan from 10 to 60 km by using the 118 GHz radiometer, and the 240 and 643 GHz radiometers can provide temperature information in the upper troposphere. Chemical species such as H₂O, O₃, and HCl show a relatively good single scan retrieval precision of <20 % over most of the useful range and ClO, N₂O, and HNO₃ can be retrieved with a precision of <50 %. The other species should be retrieved by using averaged measurements because of the weak intensity and/or low abundance.

This study compares wind speeds derived from five satellite microwave radiometers with those directly observed by buoy-mounted anemometers and the global analyses produced by the European Center for Medium-Range Weather Forecasts (ECMWF) model. Buoy comparisons yield wind speed root mean square errors of 0.82 m/s for WindSat, 1.45 m/s for SSMIS F16, 1.39 m/s for SSMIS F17, 1.43 m/s for AMSR-E, and 1.45 m/s for AMSR2. The overall mean bias for each satellite is typically <0.25 m/s when averaged over all selected buoys for a given study time. The satellite wind speeds are underestimated with respect to the buoy observations at a band of the tropical Pacific Ocean from −8°S to 4°N. The mean buoy–satellite difference as a function of year is always <0.4 m/s, except for SSMIS F16. The selected satellite wind speeds show an obvious seasonal characteristic at high latitudes. In comparison with the ECMWF data, some obviously positive differences exist at high southern latitudes in January and at high northern latitudes in July.

Haiyang-2B (HY-2B) is the second marine dynamic environment satellite of China. Sea surface temperature (SST) products from the scanning microwave radiometer (SMR) onboard HY-2B satellite are evaluated against in situ measurements. Approximately, ten months of data are used for the initial evaluation, from January 15, 2019 to November 15, 2019. The temporal and spatial windows for collocation are 30 min and 25 km, respectively, which produce 450 416 matchup pairs between HY-2B/SMR and in situ SSTs. The statistical comparison of the entire data set shows that the mean bias is −0.13 °C (SMR minus buoy), and the corresponding root-mean-square error (RMSE) is 1.06 °C. Time series of collocations for the SST difference shows that a good agreement is found between HY-2B/SMR and in situ SSTs after June 15, revealing a mean bias and an RMSE of only 0.09 °C and 0.72 °C, respectively. A three-way error analysis is conducted between the SMR, Global Precipitation Measurement Microwave Imager (GMI), and in situ SSTs. Individual standard deviations are found to be 0.41 °C for the GMI SST, 0.15 °C for the in situ SST, and 1.03 °C for the SMR SST. The results indicate that the HY2B/SMR SST products need to be improved during the period from January 15, 2019 to June 15, 2019.

The shallow neural network (SNN) is a popular algorithm in atmospheric parameters retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger nonlinear mapping capability compared to SNN and has great potential for applications in microwave remote sensing. The Microwave Humidity and Temperature Sounder (Beijing, China, MWHTS) onboard the Fengyun-3 (FY-3) satellite has the ability to independently retrieve atmospheric temperature and humidity profiles. A study on the application of DNN in retrieving atmospheric temperature and humidity profiles from MWHTS was carried out. Three retrieval schemes of atmospheric parameters in microwave remote sensing based on DNN were performed in the study of bias correction of MWHTS observation and the retrieval of the atmospheric temperature and humidity profiles using MWHTS observations. The experimental results show that, compared with SNN, DNN can obtain better bias-correction results when applied to MWHTS observation, and can obtain higher retrieval accuracy of temperature and humidity profiles in all three retrieval schemes. Meanwhile, DNN shows higher stability than SNN when applied to the retrieval of temperature and humidity profiles. The comparative study of DNN and SNN applied in different atmospheric parameter retrieval schemes shows that DNN has a more superior performance.