[1] A methodology, based on model simulations and neural networks inversion, is proposed to jointly retrieve sea surface wind speed, sea surface temperature, atmospheric water vapor content, cloud liquid water content, and total atmospheric absorption at 10.65 GHz using Advanced Microwave Scanning Radiometer 2 measurements. In particular, estimation of the total atmospheric absorption at 10.65 GHz, which can be done with high accuracy due to the not so strong influence of liquid water and especially water vapor, helps to refine a new filter to considerably reduce masking ocean areas for severe weather systems, characterized by high wind speeds and moderate atmospheric absorption, appropriate for studying winter extratropical cyclone and polar low systems. A polar low case study has demonstrated significant improvement in the coverage of the ocean area available for geophysical retrievals: Only less than 1% of high wind speed pixels were masked comparatively to the 40–70% masking given by other methods.

Sea-ice monitoring using long-term data from satellite passive microwave instruments allows one to make quantitative estimates of climatic trends. These numerical estimates depend on the...

Three mesocyclones (MCs) over the Russian (Eastern) Arctic are investigated using multispectral satellite remote sensing data, surface analysis maps, and reanalysis data. Advanced retrieval algorithms are used for estimating the geophysical parameter from satellite passive microwave measurements. These methods allow reconstructing in full the geophysical parameter fields characterizing polar lows. Synoptic analysis along with cloud image, atmospheric water vapor content, cloud liquid water content, and sea surface wind speed field analysis show that, while the Arctic sea ice retreats, new areas of open water appear where MCs can arise. A detailed study of several polar low cases reveals the typical conditions of their formation and development. Further studies are in demand due to the danger of MC extreme events for navigation, transport, and fishery operations in these unexplored regions.

The surface effective emissivities of Arctic sea ice are calculated using Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements. These emissivities are analyzed for stable winter conditions during the months of January–May and November and December of 2020 for several main sea ice types defined with the sea ice maps of the Arctic and Antarctic Research Institute (AARI). The sea ice emissivities are derived from the AMSR2 data using the radiation transfer model for a non-scattering atmosphere and ERA5 reanalysis data. The emissivities are analyzed only for areas of totally consolidated sea ice of definite types. Probability distribution functions are built for the emissivities and their functions for such sea ice types as nilas, young ice, thin first-year (FY) ice, medium FY ice, thick FY ice and multi-year ice. The emissivity variations with frequency are estimated for each of the considered sea ice type for all seven months. The variations are calculated both for the emissivities and for their gradients at the AMSR2 channel frequencies. Obtained emissivities turned out to be generally lower than reported previously in scientific studies, whereas the emissivity variability values proved to be much larger than was known before. For all FY ice types, at all the frequencies, an increase in the emissivity at the beginning of winter and its decrease by the end of May are observed. The emissivity gradients demonstrate noticeable decreases with sea ice age, and their values may be used in sea ice classification algorithms based on the AMSR2 data.

An advanced algorithm for atmospheric water vapor column (WVC) retrieval from the Advanced Microwave Scanning Radiometer (AMSR) measurements over the Arctic sea ice (SI) and open ocean waters is presented. The algorithm is built on the physical modeling of the brightness temperature (BT) of the microwave radiation of the SI-open ocean-atmosphere system at the AMSR frequencies and polarizations. The BTs are calculated using a data set of the SI, atmospheric, and oceanic parameters changing in the range of their natural variability in the Arctic, and using the SI microwave emission coefficients varied according to the published experimental data. The inverse operator explores neural networks (NNs), trained on an ensemble of modeled BTs. The algorithm is applied both to the AMSR-E and to the AMSR2 measurement data. Validation of the algorithm is performed with radiosonde (r/s) WVC measurements from the four Arctic coastal stations at different SI conditions during 2014-2017. The results of the application of the new algorithm to satellite radiometer measurements are also compared with the Era-Interim reanalysis WVC, as well as with other satellite WVC products, based on the data of the Moderate Resolution Imaging Spectrometer (MODIS) and on the data of the Advanced Microwave Sounding Unit-B (AMSU-B) for 2008 and 2015. To justify the usage of the Era-Interim WVC as a reference data set for the algorithm accuracy estimation in the Arctic area, Era-Interim WVC is also compared with the r/s WVC measurements.

A new algorithm is derived for rain rate (RR) estimation from Advanced Microwave Sounding Radiometer 2 (AMSR2) measurements taken at 6.9, 7.3, and 10.65 GHz. The algorithm is based on the numerical simulation of brightness temperatures (TB) for AMSR2 lower frequency channels, using a simplified radiation transfer model. Simultaneous meteorological and hydrological observations, supplemented with modeled values of cloud liquid water content and rain rate values, are used for the calculation of an ensemble of AMSR2TBs and RRs. Ice clouds are not taken into account. AMSR2 brightness temperature differences at C- and X-band channels are then used as inputs to train a neural network (NN) function for RR retrieval. Validation is performed against Tropical Rain Measurement Mission (TRMM) Microwave Instrument (TMI) RR products. For colocated AMSR2-TMI measurements, obtained within 10 min intervals, errors are about 1 mm/h. The new algorithm is applicable for RR estimation up to 20 mm/h. ForRR<2 mm/h the retrieval error is 0.3 mm/h. ForRR>10 mm/h the algorithm significantly underestimates TMI RR.