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Downscaling Of Precipitation Using Statistical Downscaling Model and Multiple Linear Regression Over Rajasthan State
Author(s) -
Poonam Mahla,
A. K. Lohani,
V. K. Chandola,
Aradhana Thakur,
C. D. Mishra,
Aparajita Singh
Publication year - 2019
Publication title -
current world environment
Language(s) - English
Resource type - Journals
eISSN - 2320-8031
pISSN - 0973-4929
DOI - 10.12944/cwe.14.1.09
Subject(s) - downscaling , hadcm3 , precipitation , environmental science , climatology , linear regression , atmospheric research , baseline (sea) , meteorology , climate change , regression analysis , general circulation model , statistics , geography , gcm transcription factors , mathematics , geology , oceanography
Statistical downscaling method is mainly practised to relate atmospheric circulation to surface variables for forecast and prediction of the regional climate. As we know in Rajasthan drought is the foremost problem due to scanty of rainfall. The core objective of the present study stands to prognosis rainfall variation also assess the recital of Multiple Linear Regression (MLR) to access the variation in rainfall. The data were analyzed using higher resolution atmospheric data which includes daily National Centers for Environmental Prediction (NCEP)/ National Center for Atmospheric Research (NCAR) reanalysis data and daily mean climate model result intended for A2 and B2 scenarios of the Hadley Centre Climate Model (HadCM3) model. The period from 1961-1990 used as a baseline due to the availability of adequate period which is required to establish a reliable climatology. Results of the study show an increasing trend of future precipitation tended for both A2 and B2 scenarios. From the study, it has been found that MLR model is more superior to downscale precipitation in most districts under study area.

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