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Configuration of Bayesian model averaging training window to improve seasonal rainfall ensemble prediction
Muharsyah R.a,b, Hadi T.W.a, Indratno S.W.a
a Atmospheric Sciences Research Group, Faculty of Earth Sciences and Technology, Institute Technology Bandung, Bandung, 40132, Indonesia
b Agency for Meteorology, Climatology and Geophysics of Republic of Indonesia (BMKG), Jakarta, 107720, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© the author(s)Bayesian Model Averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from ensemble prediction in the form of predictive Probability Density Function (PDF). It is known that BMA is able to improve the reliability of the probabilistic forecast of short- and medium-range rainfall forecasts. This study aims to develop the application of BMA to calibrate long-range forecast in order to improve the quality of the seasonal forecast in Indonesia. The seasonal forecast that has been used is monthly rainfall from the output of the ensemble prediction European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4). This model was calibrated against observational data at 26 stations of Agency for Meteorology, Climatology and Geophysics of Republic of Indonesia (BMKG) over Java Island in 1981 – 2018. BMA predictive PDF was generated with a Gamma distribution which was obtained based on Training Window Sequential (TWS) and Conditional (TWC) training windows. Output of BMA-TWC was slightly better than BMA-TWS. Nevertheless, both of them were superior to raw model ECS4. BMA-TWC or BMA-TWS output was varying depend on spatial and temporal, but in general, the best result was in the dry season and during the El Nino phase. BMA was able to improve the distribution characteristics of ensemble prediction. BMA also increased the skill, resolution, and reliability of probability forecast of Below Normal (BN) and Above Normal (AN). Furthermore, the reliability of BN and AN of BMA output were also have the categories of “still very useful” and “perfect” compared to raw model ECS4 that were in the “dangerous” and “not useful” categories. The reliability “still very useful” and “perfect” show that the probabilistic forecast of BN and AN event can be used for making decisions related to seasonal forecast especially over Java island.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Bayesian model averaging,Distribution characteristics,European center for medium-range weather forecasts,Long-range forecasts,Probabilistic forecasts,Probability density function (pdf),Probability forecasts,Statistical post processing[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This study is a part of the master thesis of the first author in Atmospheric Sciences Research Group, Faculty of Earth Sciences and Technology, Institute Technology Bandung and we would like thanks to The Center of Training and Education of BMKG for supporting the scholarship.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1755-1315/572/1/012034[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]