Editorial Type:
Article
Article Type:
Research Article

Extended-Range Forecasts of Areal-Averaged Rainfall over Saudi Arabia

Michael K. Tippett Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York, and Center of Excellence for Climate Change Research, Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia

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Mansour Almazroui Center of Excellence for Climate Change Research, Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia

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In-Sik Kang School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea, and Center of Excellence for Climate Change Research, Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia

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Print Publication:
01 Aug 2015
DOI:
https://doi.org/10.1175/WAF-D-15-0011.1
Page(s):
1090–1105
Restricted access

Abstract

The climate of Saudi Arabia is arid–semiarid with infrequent but sometimes intense rainfall, which can cause flooding. Interannual and intraseasonal precipitation variability in the region is related to ENSO and MJO tropical convection. The predictability of these tropical signals gives some expectation of skillful extended-range rainfall forecasts in the region. Here, the extent to which this predictability is realizable in the Climate Forecast System (CFS), version 2, a state-of-the-art coupled global ocean–atmosphere model, is assessed. While there are deficiencies in the forecast climatology likely related to orography and resolution, as well as lead-dependent biases, CFS represents the climatology of the region reasonably well. Forecasts of the areal average of rainfall over Saudi Arabia show that the CFS captures some features of a spring 2013 heavy rainfall event up to 10 days in advance and a transition from dry to wet conditions up to 20 days in advance. Analysis of a 12-yr (1999–2010) reforecast dataset shows that the CFS can skillfully predict the rainfall amount, the number of days exceeding a threshold, and the probability of heavy rainfall occurrence for forecast windows ranging from 1 to 30 days. While the probability forecasts show good discrimination, they are overconfident. Logistic regression based on the ensemble mean value improves forecast skill and reliability. Forecast probabilities have a clear relation with the MJO phase in the wet season, providing a physical basis for the observed forecast skill.

Denotes Open Access content.

Corresponding author address: M. K. Tippett, Dept. of Applied Physics and Applied Mathematics, Columbia University, Mail Code 4701, New York, NY 10027. E-mail: tippett@iri.columbia.edu

Abstract

The climate of Saudi Arabia is arid–semiarid with infrequent but sometimes intense rainfall, which can cause flooding. Interannual and intraseasonal precipitation variability in the region is related to ENSO and MJO tropical convection. The predictability of these tropical signals gives some expectation of skillful extended-range rainfall forecasts in the region. Here, the extent to which this predictability is realizable in the Climate Forecast System (CFS), version 2, a state-of-the-art coupled global ocean–atmosphere model, is assessed. While there are deficiencies in the forecast climatology likely related to orography and resolution, as well as lead-dependent biases, CFS represents the climatology of the region reasonably well. Forecasts of the areal average of rainfall over Saudi Arabia show that the CFS captures some features of a spring 2013 heavy rainfall event up to 10 days in advance and a transition from dry to wet conditions up to 20 days in advance. Analysis of a 12-yr (1999–2010) reforecast dataset shows that the CFS can skillfully predict the rainfall amount, the number of days exceeding a threshold, and the probability of heavy rainfall occurrence for forecast windows ranging from 1 to 30 days. While the probability forecasts show good discrimination, they are overconfident. Logistic regression based on the ensemble mean value improves forecast skill and reliability. Forecast probabilities have a clear relation with the MJO phase in the wet season, providing a physical basis for the observed forecast skill.

Denotes Open Access content.

Corresponding author address: M. K. Tippett, Dept. of Applied Physics and Applied Mathematics, Columbia University, Mail Code 4701, New York, NY 10027. E-mail: tippett@iri.columbia.edu
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