Determination of characteristics of atmospheric motions from satellite multiwave remote sensing data

A method of determination of atmospheric dynamic characteristics from the data of remote sensing from a geostationary satellite is described. The method is based on the use of inhomogeneities in the concentration field of a conservative additive as tracers and on the application of correlation-extreme algorithms. Unlike the common methods used abroad, this method is able to determine not only the vector field of wind velocity but also the coefficient of turbulent diffusion and vorticity. Results of computations of the fields of the horizontal component of wind velocity and the effective coefficient of horizontal mesoscale turbulent diffusion from the Meteosat-8 SEVIRI water-vapor channel data are presented. It is shown that the average values of the effective coefficient of mesoscale horizontal turbulent diffusion in the areas with a predominantly turbulized air-mass motion are 1.5 times greater than in the areas where a laminar motion dominates. Specific features of the calculated values of the upper-troposphere dynamic characteristics in different stages of the North Atlantic TC Helene (September 2006) are analyzed.     

Method for determining atmospheric motion characteristics using measurements on geostationary meteorological satellites

The method for determining the dynamic characteristics of the atmosphere using the data of sounding from geostationary meteorological satellites developed by the authors and based on using inhomogeneities of the conservative admixture concentration field as tracers and on applying the correlation extreme algorithms is described in detail. The accuracies in calculating the horizontal wind velocity vector (V) and coefficient of horizontal mesoscale turbulent diffusion (K _d ) are estimated on the basis of processing the data of sounding the atmosphere with a SEVIRI (Spinning Enhanced Visible and Infrared Imager) radiometer on the Meteosat-8 and Meteosat-9 European geostationary meteorological satellites in the water vapor channels centered at 6.2 and 7.3 μm and on comparing the results with the data of independent observations and theoretical models. It is indicated that the accuracy in calculating V using the developed method almost coincides with the accuracy of the commonly used foreign methods. In contrast to the methods applied abroad, the developed method makes it possible to determine not only the wind velocity vector field but also the coefficient of mesoscale turbulent diffusion and vorticity on one scale of air mass motion.     

Absolute Calibration of the MTVZA-GY Microwave Radiometer Atmospheric Sounding Channels

Izvestiya, Atmospheric and Oceanic Physics - The microwave MTVZA-GY imager/sounder is one of the key instruments onboard the Meteor-M N2 satellite (launched in July, 2014). The MTVZA-GY data...     

Monitoring the total atmospheric ozone content using data collected by the Elektro-L Russian geostationary meteorological satellite

The possibility of remotely monitoring the total atmospheric ozone content (TAOC) using data from the multichannel geostationary scanning instrument (MGSI) aboard the Elektro-L no. 1 Russian meteorological satellite is explored. In addition to the MGSI measurements in three channels (8.2–9.2, 9.2–10.2, and 10.2–11.2 μm), data on the vertical temperature distributions in the ozone layer and the temperature and pressure at the underlying terrain level (satellite sensing results or forecast data) are used as additional predictors in the process of TAOC estimation. The TAOC estimates are constructed with the use of a regularized regression algorithm (ridge regression). The learning and check samples are formed using independent TAOC estimates based on the data gathered by the OMI instrument aboard the EOS Aura satellite. Numerical experiments in processing the actual MGSI data gathered over certain periods within the interval from November 2011 to August 2012 reveal the possibility of arranging regular monitoring of the TAOC fields with high spatial and temporal resolutions and an acceptable precision: the absolute value of relative mean deviations and the relative root-mean-square deviations of the estimates based on the MGSI data from the estimates based on the OMI data lie within intervals of 1–2% and 5–7%, respectively, depending on the underlying terrain type.