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date accessioned2022-05-09T00:50:14Z
date available2022-05-09T00:50:14Z
date copyright10 Jan 2022
date issued2022
identifier otherWAF-D-21-0144.1.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4285615
description abstractThe onset of the rainy season is an important date for the mostly rain-fed agricultural practices in Vietnam. Subseasonal to seasonal (S2S) ensemble hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used to evaluate the predictability of the rainy season onset dates (RSODs) over five climatic subregions of Vietnam. The results show that the ECMWF model reproduces well the observed interannual variability of RSODs, with a high correlation ranging from 0.60 to 0.99 over all subregions at all lead times (up to 40 days) using five different RSOD definitions. For increasing lead times, forecasted RSODs tend to be earlier than the observed ones. Positive skill score values for almost all cases examined in all subregions indicate that the model outperforms the observed climatology in predicting the RSOD at subseasonal lead times (∼28–35 days). However, the model is overall more skillful at shorter lead times. The choice of the RSOD criterion should be considered because it can significantly influence the model performance. The result of analyzing the highest skill score for each subregion at each lead time shows that criteria with higher 5-day rainfall thresholds tend to be more suitable for the forecasts at long lead times. However, the values of mean absolute error are approximately the same as the absolute values of the mean error, indicating that the prediction could be improved by a simple bias correction. The present study shows a large potential to use S2S forecasts to provide meaningful predictions of RSODs for farmers.
titleThe Performance of ECMWF Subseasonal Forecasts to Predict the Rainy Season Onset Dates in Vietnam
typeJournal Paper
journal volume37
journal issue1
journal titleWeather and Forecasting
identifier doi10.1175/WAF-D-21-0144.1
page113–124
treeWeather and Forecasting:;2022:;volume( 037 ):;issue: 001
contenttypeFulltext


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