Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and Ensemble
This numerical weather prediction study investigates the effects of data assimilation and ensemble prediction on the forecast accuracy of moderate and heavy rainfall over New Zealand. In order to ascertain the optimal implementation of state-of-the-art 3Dvar and 4Dvar data assimilation techniques, 1...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2015/460243 |
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| _version_ | 1832554253588103168 |
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| author | Yang Yang Phillip Andrews Trevor Carey-Smith Michael Uddstrom Mike Revell |
| author_facet | Yang Yang Phillip Andrews Trevor Carey-Smith Michael Uddstrom Mike Revell |
| author_sort | Yang Yang |
| collection | DOAJ |
| description | This numerical weather prediction study investigates the effects of data assimilation and ensemble prediction on the forecast accuracy of moderate and heavy rainfall over New Zealand. In order to ascertain the optimal implementation of state-of-the-art 3Dvar and 4Dvar data assimilation techniques, 12 different experiments have been conducted for the period from 13 September to 18 October 2010 using the New Zealand limited area model. Verification has shown that an ensemble based on these experiments outperforms all of the individual members using a variety of metrics. In addition, the rainfall occurrence probability derived from the ensemble is a good predictor of heavy rainfall. Mountains significantly affect the performance of this ensemble which provides better forecasts of heavy rainfall over the South Island than over the North Island. Analysis suggests that underestimation of orographic lifting due to the relatively low resolution of the model (~12 km) is a factor leading to this variability in heavy rainfall forecast skill. This study indicates that regional ensemble prediction with a suitably fine model resolution (≤5 km) would be a useful tool for forecasting heavy rainfall over New Zealand. |
| format | Article |
| id | doaj-art-d7c8f8be1e214a188937132b3a1eff85 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-d7c8f8be1e214a188937132b3a1eff852025-02-03T05:52:00ZengWileyAdvances in Meteorology1687-93091687-93172015-01-01201510.1155/2015/460243460243Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and EnsembleYang Yang0Phillip Andrews1Trevor Carey-Smith2Michael Uddstrom3Mike Revell4National Institute of Water and Atmospheric Research (NIWA), Private Bag 14901, Wellington 6021, New ZealandNational Institute of Water and Atmospheric Research (NIWA), Private Bag 14901, Wellington 6021, New ZealandNational Institute of Water and Atmospheric Research (NIWA), Private Bag 14901, Wellington 6021, New ZealandNational Institute of Water and Atmospheric Research (NIWA), Private Bag 14901, Wellington 6021, New ZealandNational Institute of Water and Atmospheric Research (NIWA), Private Bag 14901, Wellington 6021, New ZealandThis numerical weather prediction study investigates the effects of data assimilation and ensemble prediction on the forecast accuracy of moderate and heavy rainfall over New Zealand. In order to ascertain the optimal implementation of state-of-the-art 3Dvar and 4Dvar data assimilation techniques, 12 different experiments have been conducted for the period from 13 September to 18 October 2010 using the New Zealand limited area model. Verification has shown that an ensemble based on these experiments outperforms all of the individual members using a variety of metrics. In addition, the rainfall occurrence probability derived from the ensemble is a good predictor of heavy rainfall. Mountains significantly affect the performance of this ensemble which provides better forecasts of heavy rainfall over the South Island than over the North Island. Analysis suggests that underestimation of orographic lifting due to the relatively low resolution of the model (~12 km) is a factor leading to this variability in heavy rainfall forecast skill. This study indicates that regional ensemble prediction with a suitably fine model resolution (≤5 km) would be a useful tool for forecasting heavy rainfall over New Zealand.http://dx.doi.org/10.1155/2015/460243 |
| spellingShingle | Yang Yang Phillip Andrews Trevor Carey-Smith Michael Uddstrom Mike Revell Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and Ensemble Advances in Meteorology |
| title | Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and Ensemble |
| title_full | Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and Ensemble |
| title_fullStr | Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and Ensemble |
| title_full_unstemmed | Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and Ensemble |
| title_short | Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and Ensemble |
| title_sort | prediction of moderate and heavy rainfall in new zealand using data assimilation and ensemble |
| url | http://dx.doi.org/10.1155/2015/460243 |
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