Skill of a superensemble forecast over equatorial southeast Asia

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The performance of the multianalysis-multimodel superensemble weather forecast and its comparison with several other operational prediction models is investigated in this study for the Southeast Asian region. The superensemble technique has been proven to exhibit exceptional skills compared with individual or even ensemble average forecasts for parameters such as precipitation, motion and mass fields. Precipitation forecasts from the superensemble technique for the year of 2001 from this study show that its skill scores are better than the other global operational models employed. Its precipitation root-mean-square errors (RMSEs) are consistently lower than the other member models for the annual period examined. The equitable threat score, which indicates the association of the common rainfall threshold distribution between the forecast and observed best rainfall analysis from the Tropical Rainfall Measurement Mission and Special Sensor Microwave Imager dataset, is slightly underpredicted for light rain thresholds, but it still outperforms the other individual members or even the ensemble mean forecasts. Correlation analysis of the superensemble forecast against the observed is far higher than either the ensemble mean or the individual forecast member models and is maintained for up to 3-day forecasts. The motion field comparisons also exhibit that the superensemble forecasts are closer to the observed data than the other member models due to its low systematic errors at the 850 hPa pressure level. Both the precipitation and wind field analyses have shown that the Florida State University multianalysis-multimodel superensemble forecast provides the lowest RMSE, the highest correlation against the best-observed data and lowest systematic errors compared with the other participating operational models. These forecasts have the potential to provide better daily weather predictions over the Southeast Asian region, particularly during the early northeast monsoon that often causes heavy rainfall in the equatorial part of the Southeast Asian region.

Original languageEnglish
Pages (from-to)1963-1972
Number of pages10
JournalInternational Journal of Climatology
Volume24
Issue number15
DOIs
Publication statusPublished - Dec 2004

Fingerprint

rainfall
forecast
Southeast Asia
weather
SSM-I
prediction
wind field
monsoon
comparison
analysis
parameter
distribution
rain

Keywords

  • Motion field analysis
  • Multimodel-multianalysis
  • Precipitation bias
  • Precipitation forecast
  • Root-mean-square error
  • Southeast Asia
  • Superensemble

ASJC Scopus subject areas

  • Atmospheric Science

Cite this

Skill of a superensemble forecast over equatorial southeast Asia. / Mahmud, Mastura.

In: International Journal of Climatology, Vol. 24, No. 15, 12.2004, p. 1963-1972.

Research output: Contribution to journalArticle

@article{16d0c4ad468a4f1b9e50259126fddeb0,
title = "Skill of a superensemble forecast over equatorial southeast Asia",
abstract = "The performance of the multianalysis-multimodel superensemble weather forecast and its comparison with several other operational prediction models is investigated in this study for the Southeast Asian region. The superensemble technique has been proven to exhibit exceptional skills compared with individual or even ensemble average forecasts for parameters such as precipitation, motion and mass fields. Precipitation forecasts from the superensemble technique for the year of 2001 from this study show that its skill scores are better than the other global operational models employed. Its precipitation root-mean-square errors (RMSEs) are consistently lower than the other member models for the annual period examined. The equitable threat score, which indicates the association of the common rainfall threshold distribution between the forecast and observed best rainfall analysis from the Tropical Rainfall Measurement Mission and Special Sensor Microwave Imager dataset, is slightly underpredicted for light rain thresholds, but it still outperforms the other individual members or even the ensemble mean forecasts. Correlation analysis of the superensemble forecast against the observed is far higher than either the ensemble mean or the individual forecast member models and is maintained for up to 3-day forecasts. The motion field comparisons also exhibit that the superensemble forecasts are closer to the observed data than the other member models due to its low systematic errors at the 850 hPa pressure level. Both the precipitation and wind field analyses have shown that the Florida State University multianalysis-multimodel superensemble forecast provides the lowest RMSE, the highest correlation against the best-observed data and lowest systematic errors compared with the other participating operational models. These forecasts have the potential to provide better daily weather predictions over the Southeast Asian region, particularly during the early northeast monsoon that often causes heavy rainfall in the equatorial part of the Southeast Asian region.",
keywords = "Motion field analysis, Multimodel-multianalysis, Precipitation bias, Precipitation forecast, Root-mean-square error, Southeast Asia, Superensemble",
author = "Mastura Mahmud",
year = "2004",
month = "12",
doi = "10.1002/joc.1099",
language = "English",
volume = "24",
pages = "1963--1972",
journal = "International Journal of Climatology",
issn = "0899-8418",
publisher = "John Wiley and Sons Ltd",
number = "15",

}

TY - JOUR

T1 - Skill of a superensemble forecast over equatorial southeast Asia

AU - Mahmud, Mastura

PY - 2004/12

Y1 - 2004/12

N2 - The performance of the multianalysis-multimodel superensemble weather forecast and its comparison with several other operational prediction models is investigated in this study for the Southeast Asian region. The superensemble technique has been proven to exhibit exceptional skills compared with individual or even ensemble average forecasts for parameters such as precipitation, motion and mass fields. Precipitation forecasts from the superensemble technique for the year of 2001 from this study show that its skill scores are better than the other global operational models employed. Its precipitation root-mean-square errors (RMSEs) are consistently lower than the other member models for the annual period examined. The equitable threat score, which indicates the association of the common rainfall threshold distribution between the forecast and observed best rainfall analysis from the Tropical Rainfall Measurement Mission and Special Sensor Microwave Imager dataset, is slightly underpredicted for light rain thresholds, but it still outperforms the other individual members or even the ensemble mean forecasts. Correlation analysis of the superensemble forecast against the observed is far higher than either the ensemble mean or the individual forecast member models and is maintained for up to 3-day forecasts. The motion field comparisons also exhibit that the superensemble forecasts are closer to the observed data than the other member models due to its low systematic errors at the 850 hPa pressure level. Both the precipitation and wind field analyses have shown that the Florida State University multianalysis-multimodel superensemble forecast provides the lowest RMSE, the highest correlation against the best-observed data and lowest systematic errors compared with the other participating operational models. These forecasts have the potential to provide better daily weather predictions over the Southeast Asian region, particularly during the early northeast monsoon that often causes heavy rainfall in the equatorial part of the Southeast Asian region.

AB - The performance of the multianalysis-multimodel superensemble weather forecast and its comparison with several other operational prediction models is investigated in this study for the Southeast Asian region. The superensemble technique has been proven to exhibit exceptional skills compared with individual or even ensemble average forecasts for parameters such as precipitation, motion and mass fields. Precipitation forecasts from the superensemble technique for the year of 2001 from this study show that its skill scores are better than the other global operational models employed. Its precipitation root-mean-square errors (RMSEs) are consistently lower than the other member models for the annual period examined. The equitable threat score, which indicates the association of the common rainfall threshold distribution between the forecast and observed best rainfall analysis from the Tropical Rainfall Measurement Mission and Special Sensor Microwave Imager dataset, is slightly underpredicted for light rain thresholds, but it still outperforms the other individual members or even the ensemble mean forecasts. Correlation analysis of the superensemble forecast against the observed is far higher than either the ensemble mean or the individual forecast member models and is maintained for up to 3-day forecasts. The motion field comparisons also exhibit that the superensemble forecasts are closer to the observed data than the other member models due to its low systematic errors at the 850 hPa pressure level. Both the precipitation and wind field analyses have shown that the Florida State University multianalysis-multimodel superensemble forecast provides the lowest RMSE, the highest correlation against the best-observed data and lowest systematic errors compared with the other participating operational models. These forecasts have the potential to provide better daily weather predictions over the Southeast Asian region, particularly during the early northeast monsoon that often causes heavy rainfall in the equatorial part of the Southeast Asian region.

KW - Motion field analysis

KW - Multimodel-multianalysis

KW - Precipitation bias

KW - Precipitation forecast

KW - Root-mean-square error

KW - Southeast Asia

KW - Superensemble

UR - http://www.scopus.com/inward/record.url?scp=11844254821&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=11844254821&partnerID=8YFLogxK

U2 - 10.1002/joc.1099

DO - 10.1002/joc.1099

M3 - Article

VL - 24

SP - 1963

EP - 1972

JO - International Journal of Climatology

JF - International Journal of Climatology

SN - 0899-8418

IS - 15

ER -