1. Spatial downscaling on gridded precipitation over India
Javed Akhter1* and Lalu Das2
1Department of Physics, Jadavpur University, Kolkata
2Department of Agril. Meteorology and Physics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, WB
Poster ID: PB-002
Abstract
A spatial downscaling technique has been implemented in the present study where bias corrected MME (Multi-model ensemble) of GCMs is further modulated by a multiplicative factor to produce fine scale mean precipitation patterns. The newly constructed BCS (Bias corrected scaled) models have obtained superior R, d, NSE and smaller RSR values in compared to the ordinary MME ,indicating better performance of these models.
Results
Introduction
During Validation Period ( )
Global Circulation Models (GCMs) are the primary tools in the climatic research. A number of improvements in the physics, numerical algorithms and configurations are implemented in the state-of-the-art CMIP5 GCMs compared to its previous generations. However, they are still not able to adequately capture the regional precipitation patterns. To bridge the gap between global and regional scale, different types of downscaling techniques have been emerged in the past few decades.
In this study, a combined approach is adopted where bias corrected GCM is tuned by a multiplicative scaling factor to produce fine scale mean precipitation patterns over the Indian Domain. The method used here is slightly different from the conventional Bias Correction Spatial Downscaling (BCSD) technique as both the bias correction and multiplication have been performed in the same grid resolution of observed data.
Materials and Methods
Study Area: Indian Domain( °N & °E)
Observational Data: IMD 0.25°× 0.25° gridded data(Pie et al 2014)
GCMs :Historical Simulations of CMIP5 models. (
Calibration period: ; Validation Period :
Methodology:
CMIP5 GCMs are first interpolated to the Observation grid using bi-linear interpolation.
Multi-model ensemble of these interpolated GCMs are then bias corrected using empirical quantile mapping approach. The values of the empirical cumulative distribution function (CDF) of observed and modelled time series for regularly spaced quantiles are estimated during calibration period and then these estimates are used to perform quantile mapping during validation period.
Transformation function:
Bias corrected models are further scaled with the quotient between the observed and model simulated means in the calibration period and finally we have obtained the bias corrected scaled (BCS) models.
For evaluation of models, several statistical metrics viz. spatial correlation(R), index of agreement (d-index), Nash-Sutcliffe efficiency (NSE) and Ratio of Root Mean Square Error to the standard deviation of the observations (RSR) has been used.
Season
Method R
d-index
NSE
RSR
JJAS
MME
0.57
0.73
-0.08
1.04
BCS
0.94
0.97
0.87
0.36
DJF
0.80
0.83
0.24
0.88
0.93
0.71
0.54
Annual
0.64
0.79
0.22
Po = Fo−1 (Fm (Pm))
Here Po and Pm denote observed and modelled precipitation,
Fm is the CDF of Pm and Fo-1 is the inverse CDF corresponding to Po.
Conclusions
BCS models have shown better agreement with observed spatial patterns as the Spatial correlation, d-index and NSE is much higher than ordinary MME.
Smaller values of RSR indicates smaller errors in BCS models than MME.
BCS models are relatively more reliable in simulating JJAS and Annual Rainfall than DJF which indicates these models may be used with more confidence in the seasons when accumulated rainfall is much higher.
References
Reclamation, 'Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections: Release of Hydrology Projections, Comparison with preceding Information, and Summary of User Needs', prepared by the U.S. Department of the Interior, Bureau of Reclamation, Technical Services Center, Denver, Colorado. 110 pp.
Pai DS et al (2014) Development of a very high spatial resolution (0.250 x 0.250) Long period ( ) daily gridded rainfall data set over the Indian region.Mausam, 65, 1, PP 1-18