Empiric Forecasting of Dominant Modes to Summer Precipitation Anomaly of Northeast Brazil

Rodrigo Lins da Rocha Júnior, Fabrício Daniel dos Santos Silva, Rafaela Lisboa Costa, Heliofábio Barros Gomes

Abstract


The main economic activities of the Brazilian Northeast (NEB) are affected by the region's highly variable climate, requiring research into seasonal climate forecasting. In a work we show the results obtained after analysing the relationship between the main modes of NEB rainfall variability and lagged oceanic and atmospheric variable fields, that is, preceding rainfall. Consistent relationships were found between sea surface temperature (SST) in the Equatorial Pacific, Equatorial Atlantic and South Atlantic with NEB rainfall. Rossby wave patterns over the North Pacific that propagate from west to east to the Intertropical Convergence Zone (ITCZ), Walker Cell and Upper Bolivia have also been identified. In this research, the main objective was to identify the physical basis for the construction of linear regression models capable of predicting seasonal summer rainfall in the NEB, from the relationships between predictor-predictor. The adjusted regression model performed well between simulations and observations based on validation metrics and can be reliably indicated for operational climate forecasting systems. 


Keywords


seasonal forecast, climate, precipitation, reliability

Full Text:

PDF

References


AghaKouchak, A.; Madadgar, S.; Shukla, S.; Cheng, L.; Hsu, K. L., 2016. Improving seasonal drought prediction in California by combining statistical and dynamical models. AGU Fall Meeting Abstracts.

Coelho, C. A. S., 2010. A new hybrid precipitation seasonal forecasting system for South America. In: XVI Congresso Brasileiro de Meteorologia, Belém-PA. A Amazônia e o Clima Global.

Gavrilov, A., Seleznev, A., Mukhin, D., Loskutov, E., Feigin, A., Kurths, J., 2019. Linear dynamical modes as new variables for data-driven ENSO forecast. Climate Dynamics 52, 2199-2216.

Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., Zhang, H-M., 2017. Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. Journal of Climate 30, 8179-8205.

Lucio, P. S., Silva, F. D. S., Fortes, L. T. G., Santos, L. A. R., Ferreira, D. B., Salvador, A. M., Balbino, H. T., Sarmanho, G. F., Santos, L. S. F. C., Lucas, E. W. M., Barbosa, T. F., Dias, P. L. S., 2010. Um Modelo Estocástico Combinado de Previsão Sazonal para a Precipitação no Brasil. Revista Brasileira de Meteorologia 25, 70-87.

Madadgar, S., AghaKouchak, A., Shukla, S., Wood, A. W., Cheng, L., Hsu, K-H., Svoboda, M., 2016. A hybrid statistical‐dynamical framework for meteorological drought prediction: Application to the southwestern United States. Water Resources Research 52, 5095-5110.

Podesta, G., Letson, D., Messina, C., Royce, F., Ferreyra, R. A., Jones, J., Hansen, J., Llovet, I., Grondona, M., O'Brien, J .J., 2002. Use of ENSO- related climate information in agricultural decision making in Argentina: a pilot experience. Agricultural Systems 74, 371-392.

Roger, C., Graham, S., de Hoedt C., 2000. The development and delivery of current seasonal climate forecasting capabilities in Australia. TermoIn: Hammer, Graeme et al. Applications of seasonal climate forecasting in agricultural and natural ecosystems. Springer Science & Business Media, 21, 67-75.

van den Dool, H., 2007 Empirical Methods in Short-Term Climate Prediction. New York: Oxford University Press.

Wang, B., Lee, J. Y., Kang, I. S., Shukla, J., Hameed, S. N., Park, C. K., 2007. Coupled predictability of seasonal tropical precipitation. CLIVAR Exchanges 12, 17–18.

Wang, B., Lee, J-Y., Xiang, B., 2015. Asian summer monsoon rainfall predictability: a predictable mode analysis. Climate Dynamics 44, 61-74.

Wu, S., Notaro, M., Vavrus, S., Mortensen, E., Montgomery, R., Piérola, J., Block, P., 2018. Efficacy of tendency and linear inverse models to predict southern Peru's rainy season precipitation. International Journal of Climatology 38, 2590-2604.

Xing, W., Wang, B., Yim, S-Y., 2014. Peak-summer East Asian rainfall predictability and prediction. Part I: Southeast Asia. Climate Dynamics, 1–13, doi:10.1007/s00382-014-2385-0.

Xing, W., Wang, B., Yim, S-Y., 2016. Long-Lead Seasonal Prediction of China Summer Rainfall Using an EOF-PLS Regression-Based Methodology. Journal of Climate 29, 1783-1796.

Xing, W., Wang, B., Yim, S-Y., 2016. Peak-summer East Asian rainfall predictability and prediction part I: Southeast Asia. Climate Dynamics 47, 1-13.

Yim, S-Y., Wang, B., Xing, W., 2014. Prediction of early summer rainfall over South China by a physical empirical model. Climate Dynamics 43, 1883-1891.

Yim, S-Y., Wang, B., Xing, W., 2016. Peak-summer East Asian rainfall predictability and prediction part II: extratropical East Asia. Climate Dynamics 47, 15-30.




DOI: https://doi.org/10.29150/jhrs.v9.6.p353-360

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexadores / Base de Dados:

 

Google Scholar

 

Journal of Hyperspectral Remote Sensing - eISSN: 2237-2202