Elizaveta V. Zabolotskikh

Atmospheric Water Vapor and Cloud Liquid Water Retrieval Over the Arctic Ocean Using Satellite Passive Microwave Sensing

New algorithms for total atmospheric water vapor content (Q) and total cloud liquid water content (W) retrieval from satellite microwave radiometer data, based on neural networks (NNs) and applicable for high-latitude open-water areas, were developed. For algorithm development, a radiative transfer equation numerical integration was carried out for Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) channel characteristics for nonprecipitating conditions over the open ocean. Sets of sea surface temperatures less than 15°C, surface winds, and radiosonde (r/s) reports collected by Russian research vessels served as input data for integration. It was shown that NNs perform better than the conventional regression techniques. Q retrieval algorithms were validated both for the SSM/I and AMSR-E instruments using satellite radiometric measurements collocated in space and time with polar station r/s data. The resulting SSM/I and AMSR-E retrieval errors proved to be 1.09 and 0.90 kg/m2 correspondingly. For SSM/I Q retrievals, the algorithms were compared with the Wentz global operational algorithm. This comparison demonstrated the advantages of NN-based polar regional algorithms in comparison with the Wentz global one. The retrieval errors proved to be 1.34 and 1.90 kg/m2 ( ~ 40% worse) for the NN and Wentz algorithms correspondingly.

An Updated Geophysical Model for AMSR-E and SSMIS Brightness Temperature Simulations over Oceans

In this study, we considered the geophysical model for microwave brightness temperature (BT) simulation for the Atmosphere-Ocean System under non-precipitating conditions. The model is presented as a combination of atmospheric absorption and ocean emission models. We validated this model for two satellite instruments—for Advanced Microwave Sounding Radiometer-Earth Observing System (AMSR-E) onboard Aqua satellite and for Special Sensor Microwave Imager/Sounder (SSMIS) onboard F16 satellite of Defense Meteorological Satellite Program (DMSP) series. We compared simulated BT values with satellite BT measurements for different combinations of various water vapor and oxygen absorption models and wind induced ocean emission models. A dataset of clear sky atmospheric and oceanic parameters, collocated in time and space with satellite measurements, was used for the comparison. We found the best model combination, providing the least root mean square error between calculations and measurements. A single combination of models ensured the best results for all considered radiometric channels. We also obtained the adjustments to simulated BT values, as averaged differences between the model simulations and satellite measurements. These adjustments can be used in any research based on modeling data for removing model/calibration inconsistencies. We demonstrated the application of the model by means of the development of the new algorithm for sea surface wind speed retrieval from AMSR-E data.

Arctic Polar Low Detection and Monitoring Using Atmospheric Water Vapor Retrievals from Satellite Passive Microwave Data

An approach for detecting and tracking polar lows (PLs) is developed based on satellite passive microwave data from two sensors: Special Sensor Microwave Imager (SSM/I) on board the Defense Meteorological Satellite Program satellite and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) on board the Aqua satellite. This approach consists of two stages. During the first stage, the total atmospheric water vapor fields are retrieved from SSM/I and AMSR-E measurement data using precise Arctic polar algorithms, applicable over open water and having high retrieval accuracies under a wide range of environmental conditions previously developed. During the second stage, the vortex structures are detected by visual analysis in these fields, and PLs are identified and tracked. A few case studies are comprehensively conducted based on multisensor data usage. SSM/I and AMSR-E measurements and other satellite data, including visible, infrared, and synthetic aperture radar images, scatterometer wind fields, surface analysis maps, and reanalysis data, have been used for PL study. It has been shown that multisensor data provide the most complete information about these weather events. Through this, advantages of satellite passive microwave data are demonstrated.

Radio-Frequency Interference Identification Over Oceans for C- and X-Band AMSR2 Channels

A new method for radio-frequency interference (RFI) contamination identification over open oceans for the two C-subbands and X-band of Advanced Microwave Scanning Radiometer 2 (AMSR2) channel measurements is suggested. The method is based both on the AMSR2 brightness temperature (TB) modeling and on the analysis of AMSR2 measurements over oceans. The joint analysis of TB spectral differences allowed to identify the relations between them and the limits of their variability, which are ensured by the changes in the environmental conditions. It was found that the constraints, based on the ratio of spectral differences, are more regionally and seasonally independent than the spectral differences themselves. Although not all possible RFI combinations are considered, the developed simple criteria can be used to detect most RFI-contaminated pixels over the World Ocean for AMSR2 measurements in two C-subbands and the X-band.

Neural-Network based algorithm for ice concentration retrievals from satellite passive microwave data

Present algorithms for observing the multiyear ice cover are not accurate in multiyear fraction calculations, which is a significant disadvantage of the present system of global ice monitoring considering the fact that multiyear ice is one of the key indicators of changes in the Arctic climate. In this research regionally differing Neural Networks (NN)-based algorithms for total and multiyear Arctic sea ice concentration retrievals from Special Sensor Microwave Imager (SSM/I) data are developed using closed scheme of the numerical experiment. Era-40 Reanalysis data on atmospheric parameter profiles and sea ice temperature are used for the numerical integration of the radiation transfer of the microwave emission in the Atmosphere-Ocean-Ice System. The data on cloud liquid water content and cloud boundaries are modeled basing on the results of Arctic SHEBA experiment. Numerical values for first year and multiyear ice emissivities are taken from published experimental data. The calculated radiometer brightness temperature values are used for NN-based theoretical algorithm development. New weather filter is defined. The algorithms are validated for stable winter conditions using collocated SSM/I data and Synthetic Aperture Radar (SAR) images, classified by an ice expert.

New Satellite Passive Microwave Retrieval Algorithms and Their Application to Study Atmospheric Phenomena and Oceanic Parameters

The new high accuracy algorithms for atmospheric and oceanic parameter retrievals from satellite passive microwave measurement data, based on Neural Network (NN) technique, employed for the radiative transfer equation inversion, are considered in this study. Extensive calibration of Special Sensor Microwave Imager/Sounder (SSMIS) measurement data onboard Defense Meteorological Satellite Program (DMSP) satellites (F16, F17 and F18) is fulfilled against the geophysical model used for brightness temperature calculations. The application of these algorithms, along with previously developed, applicable to Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Sounding Radiometer - Earth Observing System (AMSR-E) instruments, to satellite data allows conducting various case studies, including synoptic-scale and mesoscale weather system (e.g. polar lows, extratropical and tropical cyclones) investigation. Several case studies of weather systems in tropical and polar regions are comprehensively considered, involving all available satellite and in-situ data, with an accent on satellite passive microwave data usage.

Monitoring winter marine weather systems using satellite multisensor observations and ground-based data

Satellite and in situ data were examined for insights into the behavior of water vapor, cloud liquid water and wind speed during formation and evolution of synoptic-scale and mesoscale cyclones and cold air outbreaks — weather systems, which are usually accompanied by gale winds and intensive air-sea interaction. Satellite measurements carried out at visible, infrared and microwave ranges were collected over the Northern Pacific and Northern Atlantic Oceans in winter allowed investigating both the large-scale structural features (the main and the secondary fronts, etc.) and the small- and fine- scale details of the frontal boundaries, organized convection in the marine boundary layer of the atmosphere, etc. Multisatellite approach improved temporal resolution and the possibility to trace the location and characteristics of weather systems including fast moving and fast evolving systems.

Arctic polar algorithms for atmospheric water parameter retrievals from satellite passive microwave data

The new algorithms for retrieval of total atmospheric water vapor content (Q) and total cloud liquid water content (W) from satellite microwave radiometer data, applicable for the Arctic Basin. These algorithms are based on the neural networks (NNs) regression technique employed for the inversion of a radiative transfer equation (RTE). For the algorithm development the numerical integration of RTE was carried out for the channel characteristics of a Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR-E), and brightness temperatures (TB) were simulated for non-precipitating conditions over the open ocean. Sets of sea surface temperatures (Ts), surface winds and radiosonde reports collected by Russian research vessels served as input data for the integration. Only data with Ts less than 15degC were selected for algorithm development. Simulated radiometer noise was added to the calculated values of TB. Once developed, using theoretically simulated values of TB, the Q algorithms were then validated both for SSM/I and AMSR-E retrievals using satellite radiometric measurements collocated in space and time with polar station radiosonde data. The resulting SSM retrieval error proved to be 1.1 kg/m2, AMSR-E retrieval error -0.9 kg/m2. Considered case study was the polar low in the Norwegian Sea occurred 30-31 January 2008. NOAA AVHRR, Terra and Aqua MODIS images, QuikSCAT-retrieved wind fields, Envisat ASAR images as well as weather maps were used as ancillary data to passive microwave retrievals to study this phenomenon.

Neural networks based algorithms for sea surface wind speed retrieval using SSM/I data and their validation

The algorithms for sea surface wind speed retrieval from Special Sensor Microwave/Imager data based on a neural networks approach and some physical limitations are considered. Different physical limitations, based on the atmospheric absorption and applied to training and testing data sets, are used. It is shown that the best results (root mean square difference=1.05 m/s) were obtained for the limitations of horizontal uniformity of the atmosphere with the absence of large absorption.