Leonid M. Mitnik

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.

On the remote sensing of oceanic and atmospheric convection in the Greenland Sea by synthetic aperture radar

[1] In this paper we discuss characteristic properties of radar signatures of oceanic and atmospheric convection features in the Greenland Sea. If the water surface is clean (no surface films or ice coverage), oceanic and atmospheric features can become visible in radar images via a modulation of the surface roughness, and their radar signatures can be very similar. For an unambiguous interpretation and for the retrieval of quantitative information on current and wind variations from radar imagery with such signatures, theoretical models of current and wind phenomena and their radar imaging mechanisms must be utilized. We demonstrate this approach with the analysis of some synthetic aperture radar (SAR) images acquired by the satellites ERS-2 and RADARSAT-1. In one case, an ERS-2 SAR image and a RADARSAT-1 ScanSAR image exhibit pronounced cell-like signatures with length scales on the order of 10–20 km and modulation depths of about 5–6 dB and 9–10 dB, respectively. Simulations with a numerical SAR imaging model and various input current and wind fields reveal that the signatures in both images can be explained consistently by wind variations on the order of ±2.5 m/s, but not by surface current variations on realistic orders of magnitude. Accordingly, the observed features must be atmospheric convection cells. This is confirmed by visible typical cloud patterns in a NOAA AVHRR image of the test scenario. In another case, the presence of an oceanic convective chimney is obvious from in situ data, but no signatures of it are visible in an ERS-2 SAR image. We show by numerical simulations with an oceanic convection model and our SAR imaging model that this is consistent with theoretical predictions, since the current gradients associated with the observed chimney are not sufficiently strong to give rise to significant signatures in an ERS-2 SAR image under the given conditions. Further model results indicate that it should be generally difficult to observe oceanic convection features in the Greenland Sea with ERS-2 or RADARSAT-1 SAR, since their signatures resulting from pure wave-current interaction will be too weak to become visible in the noisy SAR images in most cases. This situation will improve with the availability of future high-resolution SARs such as RADARSAT-2 SAR in fine resolution mode (2004) and TerraSAR-X (2005), which will offer significantly reduced speckle noise fluctuations at comparable spatial resolutions and thus a much better visibility of small image intensity variations on spatial scales on the order of a few hundred meters. INDEX TERMS: 3314 Meteorology and Atmospheric Dynamics: Convective processes; 4275 Oceanography: General: Remote sensing and electromagnetic processes (0689); 4279 Oceanography: General: Upwelling and convergences; 4255 Oceanography: General: Numerical modeling;

Retrieval of atmospheric and ocean surface parameters from ADEOS‐II Advanced Microwave Scanning Radiometer (AMSR) data: Comparison of errors of global and regional algorithms

[1]xa0Retrieval of the sea surface temperature ts, wind speed W, total atmospheric water vapor content V, and total cloud liquid water content Q over the ocean from the simulated ADEOS-II AMSR data in the absence of precipitation is considered. The brightness temperatures (TB) with the vertical (V) and horizontal (H) polarizations at the AMSR frequencies ν = 6.9, 10.7, 18.7, 23.8, and 36.5 GHz were computed for the radiosonde and relevant data collected by research vessels. V and Q were retrieved from TB(23.8V) and TB(36.5V) with the physically based “global” (−1.6 < ts ≤ 31°C), “polar” (ts ≤ 15°C), and “tropical” (ts ≥ 24°C) algorithms under the assumption that ts values were derived from the measurements at 6.9 and 10.7 GHz with an error σts. The errors σV and σQ were estimated at several combinations of the difference ΔT36 = TB(36.5V) - TB(36.5H), radiometer noises ΔT and σts. At ΔT36 = 35 K, ΔT = 0.3 K, and σts = 1°C, σV = 1.5 kg/m2 and σQ = 0.029 kg/m2 for a global algorithm. Standard regression techniques were applied to retrieve ts and W from the simulated brightness temperatures for the cases TB(10.7V) ≤ 185 K. For a global data base, three-channel algorithm (6.9V, 6.9H, 10.7V or 10.7V) yields the ts and W errors equal to 0.40 and 0.58°C and 0.66 and 0.85 m/s as radiometer noises increase from 0.1 to 0.2 K at ν = 6.9 GHz and from 0.13 to 0.27 K at ν = 10.7 GHz.

New Possibilities for Geophysical Parameter Retrievals Opened by GCOM-W1 AMSR2

The new Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W1 satellite has additional - comparatively to its predecessor AMSR-E - two channels working in C-band. It is demonstrated in this paper that the measurements at these additional channels can be effectively used for rain pixel identification and rain rate (RR) estimation. It is shown that the rain radiation constituent to the total microwave radiation measured at C- and X-band channels can be calculated. After the rain radiation constituent having been excluded from the brightness temperature, sea surface wind speed (SWS) is possible to be retrieved as if it were no rain, using the retrieval algorithms, developed for non-rain conditions using physical modeling of brightness temperatures. The suggested approach has been applied to several case studies of tropical typhoons including Haiyan case. Sea surface wind speeds retrieved from AMSR2 have been compared with those from Soil Moisture and Ocean Salinity (SMOS) instrument. High correlation has been detected indicating the great potential of AMSR2 SWS retrievals in hurricanes.

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.

Structure and dynamics of the Sea of Okhotsk marginal ice zone from “ocean” satellite radar sensing data

Radar images of the Sea of Okhotsk ice cover are analyzed. The images were obtained by side looking radars (SLR) installed on the USSR “Ocean” series of satellites since September 1983. SLR operates at wavelength 3.15 cm at vertical polarization. A swath width is 460 km at a spatial resolution of 1–3 km. Satellite optical images, air reconnaissance, and meteorological information are used to interpret sea ice radar contrasts. The radar images permit monitoring of the formation and transformation of the ice cover and its displacement under the influence of winds and currents. Radar contrast is enhanced in the marginal ice zone on account of the great difference in the scattering properties of pancake ice and grease ice. In the marginal ice zone, different regular motions are registered for ice edge waves, ice bands and jets, eddies, and mushroomlike structures with dimensions ranging between 10–20 and 100–120 km.

Mesoscale atmospheric vortices in the Okhotsk and Bering Seas: results of satellite multisensor study

Winter mesoscale cyclones (MCs) are frequently formed over the northern Asian Marginal Seas. They are often associated with precipitation and severe winds causing ice drift and serious disturbance in fishery and transport operation at the sea. Mesocyclones are difficult to forecast because of their rapid evolution and movement. Climatological occurrence of mesoscale vortices in various areas is still poorly understood. They were investigated mainly in the Northern Atlantic Ocean, as well as in Gulf of Alaska and the Japan Sea in the Pacific Ocean. Favorable conditions for their development are also in the Bering and Okhotsk Seas and to the east of Kamchatka where MCs are frequently observed on satellite images. However, conventional network is here sparse and the published information on MCs is too limited. Thus the main sources of quantitative spatial data to examine these systems are satellite observations and fields of geophysical parameters retrieved from measurements conducted by various satellite sensors. The MCs were detected by screening Envisat ASAR archive images acquired over the Northwest Pacific in 2002–2006. High-resolution ASAR images of selected wind fields, surface analysis and upper-air analysis as well as with Aqua AMSRE-derived fields of total atmospheric water vapor content V, total cloud liquid water content Q and wind speed W.

Sea surface wind and Sea ice in the Barents Sea using microwave sensing data from Meteor-M N1 and GCOM-W1 satellites in January–March 2013

Application of satellite passive microwave sensing for the retrieval of key climatic parameters in the Barents Sea is considered. Fields of surface wind, atmosphere water vapor content and cloud liquid water content were found from MTVZA-GY radiometer onboard the Meteor-M N1 satellite and AMSR2 onboard the GCOM-W1 satellite with the use of original algorithms. The fields are in a good agreement with the ancillary remote and in situ measurements, which follows from the analysis of the evolution of the extra tropical and polar cyclones and cold air outbreaks with storm winds leading to intense air-sea interaction, and the formation and drift of sea ice.

Interpretation of SAR Signatures of the Sea Surface: A Multi-sensor Approach

A spaceborne Synthetic Aperture Radar (SAR) provides unique high-resolution views of the sea surface. These finely detailed images constitute some of the most complex and least understood data provided by remote sensing. The sea surface can appear featureless or contain the signatures of such diverse phenomena as surface and internal waves, upwelling, current boundaries, eddies, shallow water bathymetry, wind, storms, rainfall, convective rolls and cells, surface films and objects, and a wide variety of sea ice forms. The interpretation of SAR signatures can be vastly improved by the concurrent analysis of data collected by a suite of other visible, infrared and microwave sensors.

New Russian meteorological satellite Meteor-M N 2: sensing of the subsurface, surface and atmospheric characteristics by MTVZA-GY microwave imager/sounder

The Meteor-M N 2 spacecraft with microwave radiometer MTVZA-GY has been launched on July 8, 2014 on sun-synchronous orbit at an altitude of 830 km. MTVZA-GY is a 29 channel microwave imager/sounder for remote sensing of the ocean and land surface parameters as well as for measuring total atmospheric water vapor content, total cloud liquid water content, air temperature and humidity profiles. MTVZA GY operates at frequencies10-190 GHz. The total power radiometer configuration is employed. The antenna system of MTVZA-GY consists of an offset parabolic reflector of dimension 65 cm, illuminated by four feed-horns antenna. Results of vicarious calibration and longtime stability study are discussed. Globe MTVZA-GY data are presented. The examples of joint analysis of MTVZA-GY and other remote and ground-based observations of severe marine weather systems and Antarctica are discussed.