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Retrieval of Volcanic Sulfate Aerosols Optical Parameters from AHI Radiometer Data

Advances in Atmospheric Sciences 2024年第10期41卷 1953-1968页

作者： Andrei FILEI Olga GIRINA Aleksei SOROKIN Far-Eastern center of state research center for space hydrometeorology«planeta» Khabarovsk 680000Russia Institute of Volcanology and Seismology Far Eastern Branch of the Russian Academy of Sciences Petropavlovsk-Kamchatsky 683006Russia Computing center of the Far Eastern Branch of the Russian Academy of Sciences Khabarovsk 680000Russia

This paper presents a method for retrieving optical parameters from volcanic sulfate aerosols from the AHI radiometer on board the Himawari-8 *** proposed method is based on optical models for various mixtures of aerosol components from volcanic clouds,including ash particles,ice crystals,water drops,and sulfate aerosol *** application of multi-component optical models of various aerosol compositions allows for the optical thickness and mass loading of sulfate aerosol to be estimated in the sulfuric cloud formed after the Karymsky volcano eruption on 3 November 2021.A comprehensive analysis of the brightness temperatures of the sulfuric cloud in the infrared bands was performed,which revealed that the cloud was composed of a mixture of sulfate aerosol and water *** models of various aerosol compositions allows for the satellite-based estimation of optical parameters not only for sulfate aerosol but also for the whole aerosol mixture.

关键词： H_(2)SO_(4) volcano Himawari-8 mass loading volcanic ash optical thickness the Karymsky volcano

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Numerical Study of the Dynamic, Thermodynamic and Microstructural Parameters of Convective Clouds

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Natural Science 2018年第2期10卷 63-69页

作者： Vitaly A. Shapovalov Alexander V. Shapovalov Boris P. Koloskov Ruslan Kh. Kalov Valery N. Stasenko Department of Cloud Physics High-Mountain Geophysical Institute Nalchik Russia Autonomous Nonprofit Organization “Agency of Atmospheric Technologies” Moscow Russia state research center on space hydrometeorology planeta Moscow Russia

On the basis of a three-dimensional non stationary model of a convective cloud with detailed description of dynamic, thermodynamic and microphysical processes, numerical experiments were conducted to study the formation of parameters of convective clouds under unstable stratification of the atmosphere. Numerical experiments have been carried out to study the formation of convective processes in the atmosphere. The thermo hydrodynamic parameters in the zone of a thunderstorm cloud are determined, and regions with a vortex motion of air are identified. The main flows feeding the convective cloud in the mature stage are determined. Due to the means of visualization, the areas of formation and growth of precipitation particles are identified. In a three-dimensional form, the interaction of dynamic and thermodynamic processes is analyzed. The interaction of fields is manifested in the form of deformation of fields of thermodynamic parameters under the influence of dynamic processes. Trajectories of air streams around a cloud and the trajectories of drops in a cloud are determined. The results of numerical experiments confirm that dynamic processes significantly influence the formation of fields of thermodynamic parameters in the cloud, which also determine the course of microphysical processes and the nature of the growth of precipitation particles.

关键词： Numerical 3D Modeling Convective Processes in the Atmosphere Thermodynamic Parameters Microstructural Parameters Velocity Field of Air Flow Particles Trajectories

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The study of estimation method of broadband emissivity from EOS/MODIS data

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High Technology Letters 2014年第1期20卷 88-91页

作者：毛克彪 Ma Ying Shen Xinyi Sun Zhiwen He Tianjue Xia Lang Xu Tongren National Hulunber Grassland Ecosystem Observation and research Station Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural Sciences Guangzhou Institute of Geography Department of Geography University of Toronto hydrometeorology and Remote Sensing Laboratory The University of Oklahoma Institute No.503 of China Academy of space Technology state Key Laboratory of Remote Sensing Science School of GeographyBeijing Normal University

The broadband emissivity is an important parameter for estimating the energy balance of the Earth. This study focuses on estimating the window （8 -12 μm） emissivity from the MODIS （mod- erate-resolution imaging spectroradiometer） data, and two methods are built. The regression method obtains the broadband emissivity from MODllB1 - 5KM product, whose coefficient is developed by using 128 spectra, and the standard deviation of error is about 0.0118 and the mean error is about O. 0084. Although the estimation accuracy is very high while the broadband emissivity is estimated from the emissivity of bands 29, 31 and 32 obtained from MOD11B1 _ 5KM product, the standard deviations of errors of single emissivity in bands 29, 31, 32 are about 0.009 for MOD11B1 5KM product, so the total error is about O. 02 and resolution is about 5km × 5km. A combined radiative transfer model with dynamic learning neural network method is used to estimate the broadband emis- sivity from MODIS 1B data. The standard deviation of error is about 0.016, the mean error is about 0.01, and the resolution is about 1 km x 1 km. The validation and application analysis indicates that the regression is simpler and more practical, and estimation accuracy of the dynamic learning neural network method is higher. Considering the needs for accuracy and practicalities in application, one of them can be chosen to estimate the broadband emissivity from MODIS data.

关键词： moderate-resolution imaging spectroradiometer (MODIS) broadband emissivity land surface temperature

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