Statistical Bias Correction Modelling for Seasonal Rainfall Forecast for the case of Bali Island

Agah D. Garnadi, Dedi Lealdi, Sri Nurdiati and Ardhasena Sopaheluwakan

No n3g2k, INA-Rxiv from Center for Open Science

Abstract: Rainfall is an element of climate which is highly influential to the agricultural sector. Rain pattern and distribution highly determines the sustainability of agricultural activities. Therefore, information on rainfall is very useful for agriculture sector and farmers in anticipating the possibility of extreme events which often cause failures of agricultural production. This research aims to identify the biases from seasonal forecast products from ECMWF (European Centre for Medium-Range Weather Forecasts) rainfall forecast and to build a transfer function in order to correct the distribution biases as a new prediction model using quantile mapping approach. We apply this approach to the case of Bali Island, and as a result, the use of bias correction methods in correcting systematic biases from the model gives better results. The new prediction model obtained with this approach is better than ever. We found generally that during rainy season, the bias correction approach performs better than in dry season.

Date: 2018-01-29
New Economics Papers: this item is included in nep-agr and nep-env
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Persistent link: https://EconPapers.repec.org/RePEc:osf:inarxi:n3g2k

DOI: 10.31219/osf.io/n3g2k

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