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Verification of forecast rainfall anomalies

Statistical downscaling is used to relate the large scale climate information with the local variables that is to find the relationship between the National Center of Environmental Prediction (NCEP) data with the ground data. This study examines the verification of forecast rainfall anomalies during...

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Main Authors: Pui, Kim Kho, Yusof, Fadhilah, Mohd. Daud, Zalina
Format: Article
Published: 2013
Subjects:
Q Science
Online Access:http://eprints.utm.my/id/eprint/40887/
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spelling my.utm.408872017-08-16T08:17:51Z http://eprints.utm.my/id/eprint/40887/ Verification of forecast rainfall anomalies Pui, Kim Kho Yusof, Fadhilah Mohd. Daud, Zalina Q Science Statistical downscaling is used to relate the large scale climate information with the local variables that is to find the relationship between the National Center of Environmental Prediction (NCEP) data with the ground data. This study examines the verification of forecast rainfall anomalies during November-December-January-February (NDJF). The ground data used is the 30 years NDJF rainfall for 40 stations while the NCEP data is the 20 grids point Sea Level Pressure (SLP). In this paper, Canonical correlation analysis (CCA) is used to find the maximum correlated pattern between two variables. CCA model is verified using the mean square error skill score and anomaly correlation coefficient and used to simulate the current rainfall using the General Circulation Model (GCM) data as predictors. This is so called the validation method. Due to appearance of some biases, the anomaly correlation coefficient is considerably higher than the skill score. These biases may relate to the penalty associated with retaining the Sea Level Pressure (SLP) in the meteorological features when such features are not predictable. 2013 Article PeerReviewed Pui, Kim Kho and Yusof, Fadhilah and Mohd. Daud, Zalina (2013) Verification of forecast rainfall anomalies. Matematika, 29 (1b). pp. 77-87. ISSN 0127-8274
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science
spellingShingle Q Science
Pui, Kim Kho
Yusof, Fadhilah
Mohd. Daud, Zalina
Verification of forecast rainfall anomalies
description Statistical downscaling is used to relate the large scale climate information with the local variables that is to find the relationship between the National Center of Environmental Prediction (NCEP) data with the ground data. This study examines the verification of forecast rainfall anomalies during November-December-January-February (NDJF). The ground data used is the 30 years NDJF rainfall for 40 stations while the NCEP data is the 20 grids point Sea Level Pressure (SLP). In this paper, Canonical correlation analysis (CCA) is used to find the maximum correlated pattern between two variables. CCA model is verified using the mean square error skill score and anomaly correlation coefficient and used to simulate the current rainfall using the General Circulation Model (GCM) data as predictors. This is so called the validation method. Due to appearance of some biases, the anomaly correlation coefficient is considerably higher than the skill score. These biases may relate to the penalty associated with retaining the Sea Level Pressure (SLP) in the meteorological features when such features are not predictable.
format Article
author Pui, Kim Kho
Yusof, Fadhilah
Mohd. Daud, Zalina
author_facet Pui, Kim Kho
Yusof, Fadhilah
Mohd. Daud, Zalina
author_sort Pui, Kim Kho
title Verification of forecast rainfall anomalies
title_short Verification of forecast rainfall anomalies
title_full Verification of forecast rainfall anomalies
title_fullStr Verification of forecast rainfall anomalies
title_full_unstemmed Verification of forecast rainfall anomalies
title_sort verification of forecast rainfall anomalies
publishDate 2013
url http://eprints.utm.my/id/eprint/40887/
_version_ 1643650585104220160
score 13.211869

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