Statistical verification of numerical weather prediction models for quantitative precipitation forecast

In this study, the potential of two numerical weather prediction (NWP) models in quantitative precipitation forecasting over a tropical region is examined. The precipitation forecasts produced from the Fifth Generation Penn State/NCAR Mesoscale (MM5) and Weather Research and Forecasting (WRF) models...

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Published in:2011 IEEE Colloquium on Humanities, Science and Engineering, CHUSER 2011
Main Author: Wardah T.; Kamil A.A.; Sahol Hamid A.B.; Maisarah W.W.I.
Format: Conference paper
Language:English
Published: 2011
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858981805&doi=10.1109%2fCHUSER.2011.6163865&partnerID=40&md5=310d064fe2018464178175ee74d0a7f4
id 2-s2.0-84858981805
spelling 2-s2.0-84858981805
Wardah T.; Kamil A.A.; Sahol Hamid A.B.; Maisarah W.W.I.
Statistical verification of numerical weather prediction models for quantitative precipitation forecast
2011
2011 IEEE Colloquium on Humanities, Science and Engineering, CHUSER 2011


10.1109/CHUSER.2011.6163865
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858981805&doi=10.1109%2fCHUSER.2011.6163865&partnerID=40&md5=310d064fe2018464178175ee74d0a7f4
In this study, the potential of two numerical weather prediction (NWP) models in quantitative precipitation forecasting over a tropical region is examined. The precipitation forecasts produced from the Fifth Generation Penn State/NCAR Mesoscale (MM5) and Weather Research and Forecasting (WRF) models are statistically verified with the observed rain in Kelantan River Basin, Malaysia. The statistical verification indicates that the root mean squared error (RMSE) increases for higher rainfall rate, though the models have performed quite satisfactorily in certain cases during heavy rainfalls that cause flood. It is also shown that the longer the rainfall forecast duration, the higher the probability of detection (POD) and the lesser probability to be the false alarm ratio (FAR) © 2011 IEEE.


English
Conference paper

author Wardah T.; Kamil A.A.; Sahol Hamid A.B.; Maisarah W.W.I.
spellingShingle Wardah T.; Kamil A.A.; Sahol Hamid A.B.; Maisarah W.W.I.
Statistical verification of numerical weather prediction models for quantitative precipitation forecast
author_facet Wardah T.; Kamil A.A.; Sahol Hamid A.B.; Maisarah W.W.I.
author_sort Wardah T.; Kamil A.A.; Sahol Hamid A.B.; Maisarah W.W.I.
title Statistical verification of numerical weather prediction models for quantitative precipitation forecast
title_short Statistical verification of numerical weather prediction models for quantitative precipitation forecast
title_full Statistical verification of numerical weather prediction models for quantitative precipitation forecast
title_fullStr Statistical verification of numerical weather prediction models for quantitative precipitation forecast
title_full_unstemmed Statistical verification of numerical weather prediction models for quantitative precipitation forecast
title_sort Statistical verification of numerical weather prediction models for quantitative precipitation forecast
publishDate 2011
container_title 2011 IEEE Colloquium on Humanities, Science and Engineering, CHUSER 2011
container_volume
container_issue
doi_str_mv 10.1109/CHUSER.2011.6163865
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858981805&doi=10.1109%2fCHUSER.2011.6163865&partnerID=40&md5=310d064fe2018464178175ee74d0a7f4
description In this study, the potential of two numerical weather prediction (NWP) models in quantitative precipitation forecasting over a tropical region is examined. The precipitation forecasts produced from the Fifth Generation Penn State/NCAR Mesoscale (MM5) and Weather Research and Forecasting (WRF) models are statistically verified with the observed rain in Kelantan River Basin, Malaysia. The statistical verification indicates that the root mean squared error (RMSE) increases for higher rainfall rate, though the models have performed quite satisfactorily in certain cases during heavy rainfalls that cause flood. It is also shown that the longer the rainfall forecast duration, the higher the probability of detection (POD) and the lesser probability to be the false alarm ratio (FAR) © 2011 IEEE.
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