TITLE

Improving Seasonal Forecasting in the Low Latitudes

AUTHOR(S)
Paeth, Heiko; Girmes, Robin; Menz, Gunter; Hense, Andreas
PUB. DATE
July 2006
SOURCE
Monthly Weather Review;Jul2006, Vol. 134 Issue 7, p1859
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Seasonal forecast of climate anomalies holds the prospect of improving agricultural planning and food security, particularly in the low latitudes where rainfall represents a limiting factor in agrarian production. Present-day methods are usually based on simulated precipitation as a predictor for the forthcoming rainy season. However, climate models often have low skill in predicting rainfall due to the uncertainties in physical parameterization. Here, the authors present an extended statistical model approach using three-dimensional dynamical variables from climate model experiments like temperature, geopotential height, wind components, and atmospheric moisture. A cross-validated multiple regression analysis is applied in order to fit the model output to observed seasonal precipitation during the twentieth century. This model output statistics (MOS) system is evaluated in various regions of the globe with potential predictability and compared with the conventional superensemble approach, which refers to the same variable for predictand and predictors. It is found that predictability is highest in the low latitudes. Given the remarkable spatial teleconnections in the Tropics, a large number of dynamical predictors can be determined for each region of interest. To avoid overfitting in the regression model an EOF analysis is carried out, combining predictors that are largely in-phase with each other. In addition, a bootstrap approach is used to evaluate the predictability of the statistical model. As measured by different skill scores, the MOS system reaches much higher explained variance than the superensemble approach in all considered regions. In some cases, predictability only occurs if dynamical predictor variables are taken into account, whereas the superensemble forecast fails. The best results are found for the tropical Pacific sector, the Nordeste region, Central America, and tropical Africa, amounting to 50% to 80% of total interannual variability. In general, the statistical relationships between the leading predictors and the predictand are physically interpretable and basically highlight the interplay between regional climate anomalies and the omnipresent role of El Niño–Southern Oscillation in the tropical climate system.
ACCESSION #
21626172

 

Related Articles

  • Wavelet Analysis on the Variability, Teleconnectivity, and Predictability of the Seasonal Rainfall of Taiwan. Chun-Chao Kuo; Thian Yew Gan; Pao-Shan Yu // Monthly Weather Review;Jan2010, Vol. 138 Issue 1, p162 

    Using wavelet analysis, the variability and oscillations of November–January (NDJ) and January–March (JFM) rainfall (1974–2006) of Taiwan and seasonal sea surface temperature (SST) of the Pacific Ocean were analyzed. From the scale-average wavelet power (SAWP) computed for...

  • The Predictability of Rainfall over the Greater Horn of Africa. Part II: Prediction of Monthly Rainfall during the Long Rains. Nicholson, Sharon E. // Journal of Hydrometeorology;Oct2015, Vol. 16 Issue 5, p2001 

    Seasonal prediction of the boreal spring rains in the Greater Horn of Africa has been notoriously challenging. Predictability is markedly lower than during the autumnal rainy season. Part I of this article explored predictability at the seasonal scale, using multiple linear regression. However,...

  • Seasonal forecasting of Bangladesh summer monsoon rainfall using simple multiple regression model. RAHMAN, MD; RAFIUDDIN, M; ALAM, MD // Journal of Earth System Science;Apr2013, Vol. 122 Issue 2, p551 

    In this paper, the development of a statistical forecasting method for summer monsoon rainfall over Bangladesh is described. Predictors for Bangladesh summer monsoon (June-September) rainfall were identified from the large scale ocean-atmospheric circulation variables (i.e., sea-surface...

  • High-resolution rainfall variability simulated by the WRF RCM: application to eastern France. Marteau, Romain; Richard, Yves; Pohl, Benjamin; Smith, Carmela; Castel, Thierry // Climate Dynamics;Feb2015, Vol. 44 Issue 3/4, p1093 

    The Weather Research and Forecasting (WRF) model, driven laterally by ERA-Interim reanalyses, is used here to downscale rainfall, at relatively high resolution (~8 km) over Burgundy (eastern France), during the period 1989-2009. Regional simulations are compared to the Météo-France Station...

  • Linear and Nonlinear Statistical Downscaling for Rainfall Forecasting over Southeastern Brazil. Ramírez, María Cleofé Valverde; Ferreira, Nelson Jesus; de Campos Velho, Haroldo Fraga // Weather & Forecasting;Dec2006, Vol. 21 Issue 6, p969 

    In this work linear and nonlinear downscaling are developed to establish empirical relationships between the synoptic-scale circulation and observed rainfall over southeastern Brazil. The methodology uses outputs from the regional Eta Model; prognostic equations for local forecasting were...

  • The Tropical Rainfall Potential (TRaP) Technique. Part II: Validation. Ferraro, Ralph; Pellegrino, Paul; Turk, Michael; Wanchun Chen; Shuang Qiu; Kuligowski, Robert; Kusselson, Sheldon; Irving, Antonio; Kidder, Stan; Knaff, John // Weather & Forecasting;Aug2005, Vol. 20 Issue 4, p465 

    Satellite analysts at the Satellite Services Division (SSD) of the National Environmental, Satellite, Data, and Information Service (NESDIS) routinely generate 24-h rainfall potential for all tropical systems that are expected to make landfall within 24 to at most 36 h and are of tropical storm...

  • Rainfall Prediction Using Multiple Regression Technique. Ahmed, Imran; Menon, Shruti; K. B., Nikitha // International Journal of Applied Engineering Research;2013, Vol. 8 Issue 19, p2267 

    Recently significant amount of interest have been shown in area of forecasting. As it is a scientifically and technologically challenging problem it has to be tackled down. Several researches have been carried out in the area of forecasting. this paper an empirical approach is used over the...

  • Rainfall Forecast Analysis using Rough Set Attribute Reduction and Data Mining Methods. Sudha, M.; Valarmathi, B. // Agris On-Line Papers in Economics & Informatics;2014, Vol. 6 Issue 4, p145 

    Developments in information technology has enabled accumulation of large databases and most of the environmental, agricultural and medical databases consist of large quantity of real time observatory datasets of high dimension space. The curse to these high dimensional datasets is the spatial...

  • Validation of Improved TAMANN Neural Network for Operational Satellite-Derived Rainfall Estimation in Africa. Coppola, E.; Grimes, D. I. F.; Verdecchia, M.; Visconti, G. // Journal of Applied Meteorology & Climatology;Nov2006, Vol. 45 Issue 11, p1557 

    Real-time rainfall monitoring in Africa is of great practical importance for operational applications in hydrology and agriculture. Satellite data have been used in this context for many years because of the lack of surface observations. This paper describes an improved artificial neural network...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics