Seasonal precipitation forecast skill over Iran

Show simple item record Shirvani, Amin Landman, W.A. (Willem Adolf), 1964- 2017-05-10T06:49:41Z 2016-09
dc.description.abstract This paper examines the skill of seasonal precipitation forecasts over Iran using one two-tiered model, three National Multi-Model Ensemble (NMME) models, and two coupled ocean–atmosphere or one-tiered models. These models are, respectively, the ECHAM4.5 atmospheric model that is forced with sea surface temperature (SST) anomalies forecasted using constructed analogue SSTs (ECHAM4.5-SSTCA); the IRI-ECHAM4.5-DirectCoupled, the NASA-GMAO-062012 and the NCEP-CFSv2; and the ECHAM4.5 Modular Ocean Model version 3 (ECHAM4.5-MOM3-DC2) and the ECHAM4.5-GML-NCEP Coupled Forecast System (CFSSST). The precipitation and 850 hPa geopotential height fields of the forecast models are statistically downscaling to the 0.5∘ × 0.5∘ spatial resolution of the Global Precipitation Climatology Centre (GPCC) Version 6 gridded precipitation data, using model output statistics (MOS) developed through the canonical correlation analysis (CCA) option of the Climate Predictability Tool (CPT). Retroactive validations for lead times of up to 3 months are performed using the relative operating characteristic (ROC) and reliability diagrams, which are evaluated for above- and below-normal categories and defined by the upper and lower 75th and 25th percentiles of the data record over the 15-year test period of 1995/1996 to 2009/2010. The forecast models’ skills are also compared with skills obtained by (a) downscaling simulations produced by forcing the ECHAM4.5 with simultaneously observed SST, and (b) the 850 hPa geopotential height NCEP-NCAR (National Centers for Environmental Prediction-National Center for Atmospheric Research) reanalysis data. Downscaling forecasts from most models generally produce the highest skill forecast at lead times of up to 3 months for autumn precipitation – the October-November-December (OND) season. For most seasons, a high skill is obtained from ECHAM4.5-MOM3-DC2 forecasts at a 1-month lead time when the models’ 850 hPa geopotential height fields are used as the predictor fields. For this model and lead time, the Pearson correlation between the area-averaged of the observed and forecasts over the study area for the OND, November-December-January (NDJ), December-January-February (DJF) and January-February-March (JFM) seasons were 0.68, 0.62, 0.42 and 0.43, respectively en_ZA
dc.description.department Geography, Geoinformatics and Meteorology en_ZA
dc.description.embargo 2017-09-30
dc.description.librarian hb2017 en_ZA
dc.description.sponsorship The Fars Regional Water Organization en_ZA
dc.description.uri en_ZA
dc.identifier.citation Shirvani, A & Landman, WA 2016, 'Seasonal precipitation forecast skill over Iran', International Journal of Climatology, vol. 36, no. 4, pp. 1887-1900. en_ZA
dc.identifier.issn 0899-8418 (print)
dc.identifier.issn 1097-0088 (online)
dc.identifier.other 10.1002/joc.4467
dc.language.iso en en_ZA
dc.publisher Wiley en_ZA
dc.rights © 2015 Royal Meteorological Society. Wiley. This is the pre-peer reviewed version of the following article : Seasonal precipitation forecast skill over Iran,International Journal of Climatology in International Journal of Climatology, vol. 36, no. 4, pp. 1887-1900, 2016. doi : 10.1002/joc.4467. which has been published in final form at : http://onlinelibrary.wiley.comjournal/10.1002/(ISSN)1097-0088. en_ZA
dc.subject Statistical downscaling en_ZA
dc.subject Seasonal forecasting en_ZA
dc.subject Iran en_ZA
dc.subject National multi-model ensemble (NMME) en_ZA
dc.subject Sea surface temperature (SST) en_ZA
dc.subject General circulation model (GCM) en_ZA
dc.title Seasonal precipitation forecast skill over Iran en_ZA
dc.type Postprint Article en_ZA

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