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Radkevich, Alexander

Publications and source records attributed to Radkevich, Alexander.

5 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Radiative Heating Rates Computed With Clouds Derived From Satellite‐Based Passive and Active Sensors and their Effects on Generation of Available Potential Energy

Radiative heating rates computed with cloud properties derived from passive and active sensors are investigated. Zonal monthly radiative heating rate anomalies computed using both active and passive sensors show that larger variability in longwave cooling exists near the tropical tropopause and near the top of the boundary layer between ~50°N to ~50°S. Aerosol variability contributes to increases in shortwave heating rate variability. When zonal monthly mean cloud effects on the radiative heating rate computed with both active and passive sensors and those computed with passive sensor only are compared, the latter shows cooling and heating peaks corresponding to cloud top and base height ranges used for separating cloud types. The difference of these two sets of cloud radiative effect on heating rates in the middle to upper troposphere is larger than the radiative heating rate uncertainty estimated based on the difference of two active sensor radiative heating rate profile data products. In addition, radiative heating rate contribution to generation of eddy available potential energy is also investigated. Although radiation contribution to generation of eddy available potential energy averaged over a year and the entire globe is small, radiation increases the eddy available potential energy in the northern hemisphere during summer. Two key elements that longwave radiation contribute to the generation of eddy potential energy are (1) longitudinal temperature gradient in the atmosphere associated with land and ocean surface temperatures contrasts and absorption of longwave radiation emitted by the surface and (2) cooling near the cloud top of stratocumulus clouds.

Kato, Seiji↗
NASA NTRS2019

An Approach to Retrieve BRDF from Satellite and Airborne Measurements of Surface-Reflected Radiance Based on Decoupling of Atmospheric Radiative Transfer and Surface Reflection

Bi-directional Reflection Distribution Function (BRDF) defines anisotropy of the surface reflection. It is required to specify the boundary condition for radiative transfer (RT) modeling. Measurements of reflected radiance by satellite- and air-borne sensors provide information about anisotropy of surface reflection. Atmospheric correction needs to be performed to derive BRDF from the reflected radiance. Common approach for BRDF retrievals consists of the use of kernel-based BRDF and RT modeling that needs to be done anew at every step of the iterative process. The kernels’ weights are obtained by minimization of the difference between measured and modeled radiance. This study develops a new method of retrieving kernel-based BRDF that requires RT calculations to be done only once. The method employs the exact analytical expression of radiance at any atmospheric level through the solutions of two auxiliary atmosphere-only RT problems and the surface-reflected radiance at the surface level. The latter is related to BRDF and solutions of the auxiliary RT problems by a Fredholm integral equation of the second kind. The approach requires to perform RT calculations one time before the iterations. It can use observations taken at different atmospheric conditions assuming that surface conditions remain unchanged during the time span of observations. The algorithm accurately catches zero weights of the kernels that may be a concern if the number of kernels is greater than 3 in current mainstream approaches. The study presents numerical tests of the BRDF retrieval algorithm for various surface and atmospheric conditions.

Radkevich, Alexander↗
NASA NTRS2013

A Supplementary Clear-Sky Snow and Ice Recognition Technique for CERES Level 2 Products

Identification of clear-sky snow and ice is an important step in the production of cryosphere radiation budget products, which are used in the derivation of long-term data series for climate research. In this paper, a new method of clear-sky snow/ice identification for Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. The algorithm's goal is to enhance the identification of snow and ice within the Clouds and the Earth's Radiant Energy System (CERES) data after application of the standard CERES scene identification scheme. The input of the algorithm uses spectral radiances from five MODIS bands and surface skin temperature available in the CERES Single Scanner Footprint (SSF) product. The algorithm produces a cryosphere rating from an aggregated test: a higher rating corresponds to a more certain identification of the clear-sky snow/ice-covered scene. Empirical analysis of regions of interest representing distinctive targets such as snow, ice, ice and water clouds, open waters, and snow-free land selected from a number of MODIS images shows that the cryosphere rating of snow/ice targets falls into 95% confidence intervals lying above the same confidence intervals of all other targets. This enables recognition of clear-sky cryosphere by using a single threshold applied to the rating, which makes this technique different from traditional branching techniques based on multiple thresholds. Limited tests show that the established threshold clearly separates the cryosphere rating values computed for the cryosphere from those computed for noncryosphere scenes, whereas individual tests applied consequently cannot reliably identify the cryosphere for complex scenes.

Radkevich, Alexander↗