EVALUATION OF STATISTICAL CORRECTIVE METHODS TO MINIMIZE BIAS WITH RESPECT TO OBSERVATORY DATA IN MODELLED CLIMATIC PARAMETERS
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Date
2017-07-24
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UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU
Abstract
Climate change refers to any systematic change in the long-term weather
parameters. The study aims at reducing the bias in the climatic parameter values obtained
from the satellite models by comparing with the data obtained from the observatories.
Nine years day wise data for the climatic parameters such as Rainfall, Maximum and
Minimum temperature are collected from the AICRP on Agro meteorology UAS GKVK
Bengaluru, for the study. The day wise data was converted to, standard meteorological
weekly and monthly data for the period wise analysis. Climatic parameter data obtain
from the observatories are always more accurate than those from the satellite model. Bias
correction methods such as difference method (DM) and modified difference method
(MDM) were attempted to minimize bias of the satellite model data compared to
observatories data. Best bias correction method is identified based on the coefficient of
variation. Results revealed that, among them MDM was better for all the three periods of
rainfall. Similarly, MDM is an ideal correction measure for daily maximum and
minimum temperature, for weekly and monthly maximum and minimum temperature
data DM was found to be an ideal measure. Probability distribution functions were
attempted for the climatic factors considered and best fit of them were identified using
chi square test. The best fitted probability distribution for the different periods were
identified as Gamma and Weibull distribution as most suitable for the rainfall data. For
Maximum and minimum temperature, no generalize single model was found as best fit.
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