scholarly journals Improved Assessment of Atmospheric Water Vapor Content in the Himalayan Regions Around the Kullu Valley in India Using Landsat‐8 Data

2019 ◽  
Vol 55 (1) ◽  
pp. 462-475 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Water Vapor ◽  
Water Vapor Content ◽  
Atmospheric Water Vapor ◽  
Landsat 8 ◽  
Atmospheric Water ◽  
Kullu Valley

The determination of the total atmospheric water vapor content over the oceans using the MTVZA-GY microwave radiometer measurements

2020 ◽  
pp. 83-98
Author(s):  
Анжелика Андреевна Косторная ◽  
Алексей Николаевич Рублев ◽  
Владимир Викторович Голомолзин
Keyword(s):  
Water Vapor ◽  
Approximation Error ◽  
Water Vapor Content ◽  
Atmospheric Water Vapor ◽  
Microwave Range ◽  
Adaptive Search ◽  
Atmospheric Water ◽  
Water Vapor Absorption ◽  
Optimal Set

Представлена методика определения интегрального влагосодержания в безоблачной атмосфере над океанскими и морскими акваториями по измерениям микроволнового радиометра МТВЗА-ГЯ, устанавливаемого на российских гидрометеорологических спутниках серии “Метеор-М”. Определение влагосодержания осуществляется с помощью регрессий, предикторами которых являются измеренные интенсивности излучения в выбранных каналах радиометра. В их число могут входить каналы с рабочими спектральными диапазонами внутри и вне полос поглощения водяного пара. Адаптивный поиск оптимального набора каналов для различных районов земного шара проводится в зависимости от типа поверхности и климатической зоны. Критерием выбора каналов и вида регрессии является минимальная среднеквадратичная невязка получаемых оценок влагосодержания атмосферы с контрольными значениями, рассчитанными по данным реанализа Национального центра экологического прогнозирования (NCEP) и специальных атмосферных моделей, разработанных в Европейском центре среднесрочных прогнозов погоды (ECMWF) The determination of the total atmospheric water vapor content over the cloudless ocean using the MTVZA-GY measurements in microwave range is described. The microwave scanning radiometer MTVZA-GY is located on the Russian meteorological satellites “Meteor-M” and outgoing radiation of the surface-atmosphere system is measured in 29 channels. To calculate the integrated water vapor, the adaptive searching of the optimal set of channels using regression analysis was proposed. Frequencies that are not related to water-vapor absorption lines are used as predictors. The minimum of total approximation error was obtained for selected channels and corresponding regression coefficients values. The quality control of retrieval integrated water vapor (kg/m) was conducted with the help of the set of atmospheric profiles obtained by M. Matricardi and NCEP/NCAR Reanalysis as a priori data using the proposed method. Standard deviations (RMS) obtained by determined adaptive search for the predictors are about 3 kg/m2. Application of the method for cloudless water areas allowed finding a set of 6 channels MTVZA GY (18.7H, 23.8V, 23.8H, 57+0.32+0.025H, 57+0.32+0.01H и 183+1.4V) for which the RMS values are minimal - 4.4 kg/m. The use of all channels of the device in the search allows reducing the error in determining the integrated water vapor content. The proposed method for recovering the content of water vapor from measurements in the channels of the MTVZA-GYa device allows an adaptive search for an optimal set of channels for different regions of the globe and find the best combinations for various climatic zones and surface types

Determination of the atmospheric-water-vapor content in the 940-nm absorption band by use of moderate spectral-resolution measurements of direct solar irradiance

1998 ◽  
Vol 37 (21) ◽  
pp. 4678 ◽  
Author(s):  
Victoria E. Cachorro ◽  
Pilar Utrillas ◽  
Ricardo Vergaz ◽  
Plinio Durán ◽  
Angel M. de Frutos ◽  
...  
Keyword(s):  
Water Vapor ◽  
Absorption Band ◽  
Spectral Resolution ◽  
Solar Irradiance ◽  
Water Vapor Content ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water

scholarly journals Atmospheric water vapor retrieval from Landsat 8 thermal infrared images

2015 ◽  
Vol 120 (5) ◽  
pp. 1723-1738 ◽  
Author(s):  
Huazhong Ren ◽  
Chen Du ◽  
Rongyuan Liu ◽  
Qiming Qin ◽  
Guangjian Yan ◽  
...  
Keyword(s):  
Water Vapor ◽  
Thermal Infrared ◽  
Atmospheric Water Vapor ◽  
Landsat 8 ◽  
Infrared Images ◽  
Atmospheric Water ◽  
Thermal Infrared Images

scholarly journals Estimation of the Total Atmospheric Water Vapor Content and Land Surface Temperature Based on AATSR Thermal Data

Sensors ◽  
2008 ◽  
Vol 8 (3) ◽  
pp. 1832-1845 ◽  
Author(s):  
Tangtang Zhang ◽  
Jun Wen ◽  
Rogier Van der Velde ◽  
Xianhong Meng ◽  
Zhenchao Li ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Water Vapor Content ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water ◽  
Thermal Data

scholarly journals Estimating of total atmospheric water vapor content from MSG1-SEVIRI observations

2015 ◽  
Vol 8 (8) ◽  
pp. 8903-8923 ◽  
Author(s):  
A. Labbi ◽  
A. Mokhnache
Keyword(s):  
Water Vapor ◽  
Geographic Location ◽  
Water Vapor Content ◽  
Atmospheric Water Vapor ◽  
Radiosonde Data ◽  
Atmospheric Water ◽  
Ancillary Data ◽  
Climate Change Research ◽  
Quadratic Formula ◽  
The One

Abstract. In this work, we proposed a methodology to estimate total atmospheric water vapor content (TAWV) from observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first Meteosat Second Generation satellite (MSG1). The method used is called the split-window technique which requires only the data from the channels IR10.8 and IR12, therefore this method not requires any ancillary data. This method is based on the MSG1 observations of the same geographic location over land at two slightly different times during a period when the ground temperature is changing rapidly. The main contribution of the present work is to consider that the relationship between TAWV and the ratio of the two split-window channel transmittances (τ12/τ10.8) is a quadratic formula, this assumption is based on the "Roberts" approach simulations using MSG1-SEVIRI filter response functions for a 2311 atmospheric situations from the TIGR dataset. For validation, we have examined the accuracy of the TAWV estimated in this work by comparison with the data obtained from radiosonde and from aerosol robotic network (AERONET). On the one hand, the comparison with the radiosonde data show that the root mean square error (RMSE) equals 0.66 g cm−2, the standard deviation (SD) equals 0.59 g cm−2 and the correlation coefficient (R) equals 0.79. On the other hand, the comparison with the AERONET data show that the RMSE equals 0.42 g cm−2, the SD equals 0.29 g cm−2 and the R equals 0.82. Also, the comparison with another method demonstrates that the spatial variation of TAWV here is reasonable. We have concluded in this study that the TAWV can be determined from the MSG1-SEVIRI observations with accuracy acceptable which can be used for climate change research.

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