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Evaluation of statistical corrective methods to minimize bias at different time scales in a regional climate model driven data. imate model driven data.

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Title Evaluation of statistical corrective methods to minimize bias at different time scales in a regional climate model driven data. imate model driven data.
Not Available
 
Creator Kaur S, Jalota S K, Kaur H, Vashist B B, Jalota U R and Lubana P P S.
 
Subject Past climatic data, bias, bias correction methods, correction functions, time scale
 
Description Not Available
The regional climate models provide sufficient information of the climate data, which can be
used for simulating the impact of expected climate change on crop growth and hydrological processes.
But future climate data derived from such models often suffers from bias and is not ready to use per se
in crop growth/hydrological models, wherein reasonable and consistent meteorological daily input data
is a crucial factor. The present study concerns the assessment and minimization of the bias in the
PRECIS modeled data of maximum and minimum temperatures and rainfall for Ludhiana station,
representing central Punjab of India. The correction functions for three corrective methods i.e. difference,
modified difference and statistical bias correction at daily, monthly and annual time scales were developed
and validated to minimize the bias. Amongst these, correction functions derived using modified difference
method at daily time scale for rainfall and at monthly time scale for Tmax and Tmin were found to be the
superseding.
Not Available
 
Date 2020-01-23T04:18:53Z
2020-01-23T04:18:53Z
2014-12
 
Type Article
 
Identifier Kaur S, Jalota S K, Kaur H, Vashist B B, Jalota U R and Lubana P P S. 2015. Evaluation of statistical corrective methods to minimize bias at different time scales in a regional climate model driven data. Paper accepted in Journal of Agrometeorology. 17(1):29-35,2015
ISSN 0972 - 1665
http://krishi.icar.gov.in/jspui/handle/123456789/31096
 
Language English
 
Relation Not Available;
 
Publisher Association of Agrometeorologists