Performance of Multimodel Schemes for Seasonal Precipitation over Indian Region

This study uses downscaled rainfall datasets from 16 coupled climate models at high resolution of 25 km from 1987 to 2001. The multimodel superensemble scheme is widely tested for rainfall forecast over mid-latitude, subtropical, and, especially, various regions of the monsoonal belt. A well-known s...

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Main Authors: Vinay Kumar, Tirthankar Ghosh
Format: Article
Language:English
Published: Wiley 2018-01-01
Series:Advances in Meteorology
Online Access:http://dx.doi.org/10.1155/2018/5874270
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author Vinay Kumar
Tirthankar Ghosh
author_facet Vinay Kumar
Tirthankar Ghosh
author_sort Vinay Kumar
collection DOAJ
description This study uses downscaled rainfall datasets from 16 coupled climate models at high resolution of 25 km from 1987 to 2001. The multimodel superensemble scheme is widely tested for rainfall forecast over mid-latitude, subtropical, and, especially, various regions of the monsoonal belt. A well-known statistical estimation theoretic approach, namely, Best Linear Unbiased Estimator (BLUE), is examined on 16 member models. The results are compared with superensemble methodology based on various skill scores. Results show that BLUE is providing promising forecasts. As far as comparative studies are concerned BLUE and superensemble schemes compete and show their importance from normal years to extreme rainfall years. BLUE methodology is capable of predicting draughts very well compared with other multimodel schemes. One basic advantage of BLUE is computationally less expensive than superensemble scheme. These statistical schemes like downscaling, BLUE, and superensemble can improve rainfall forecasts further, if a dense rain gauge data is provided.
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institution Kabale University
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spelling doaj-art-7e0ff00a87854aaaa5c5052f537706d42025-02-03T01:09:44ZengWileyAdvances in Meteorology1687-93091687-93172018-01-01201810.1155/2018/58742705874270Performance of Multimodel Schemes for Seasonal Precipitation over Indian RegionVinay Kumar0Tirthankar Ghosh1Department of Physical and Environmental Sciences, Texas A&M University, Corpus Christi, TX 78412, USADepartment of Statistics, Visva Bharati University, Bolpur Santiniketan, West Bengal 731235, IndiaThis study uses downscaled rainfall datasets from 16 coupled climate models at high resolution of 25 km from 1987 to 2001. The multimodel superensemble scheme is widely tested for rainfall forecast over mid-latitude, subtropical, and, especially, various regions of the monsoonal belt. A well-known statistical estimation theoretic approach, namely, Best Linear Unbiased Estimator (BLUE), is examined on 16 member models. The results are compared with superensemble methodology based on various skill scores. Results show that BLUE is providing promising forecasts. As far as comparative studies are concerned BLUE and superensemble schemes compete and show their importance from normal years to extreme rainfall years. BLUE methodology is capable of predicting draughts very well compared with other multimodel schemes. One basic advantage of BLUE is computationally less expensive than superensemble scheme. These statistical schemes like downscaling, BLUE, and superensemble can improve rainfall forecasts further, if a dense rain gauge data is provided.http://dx.doi.org/10.1155/2018/5874270
spellingShingle Vinay Kumar
Tirthankar Ghosh
Performance of Multimodel Schemes for Seasonal Precipitation over Indian Region
Advances in Meteorology
title Performance of Multimodel Schemes for Seasonal Precipitation over Indian Region
title_full Performance of Multimodel Schemes for Seasonal Precipitation over Indian Region
title_fullStr Performance of Multimodel Schemes for Seasonal Precipitation over Indian Region
title_full_unstemmed Performance of Multimodel Schemes for Seasonal Precipitation over Indian Region
title_short Performance of Multimodel Schemes for Seasonal Precipitation over Indian Region
title_sort performance of multimodel schemes for seasonal precipitation over indian region
url http://dx.doi.org/10.1155/2018/5874270
work_keys_str_mv AT vinaykumar performanceofmultimodelschemesforseasonalprecipitationoverindianregion
AT tirthankarghosh performanceofmultimodelschemesforseasonalprecipitationoverindianregion