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Relevant bibliographies by topics / Seasonal probabilistic forecast

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Journal articles
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Academic literature on the topic 'Seasonal probabilistic forecast'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Seasonal probabilistic forecast"

1

Lenssen, Nathan J. L., Lisa Goddard, and Simon Mason. "Seasonal Forecast Skill of ENSO Teleconnection Maps." Weather and Forecasting 35, no. 6 (2020): 2387–406. http://dx.doi.org/10.1175/waf-d-19-0235.1.

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AbstractEl Niño–Southern Oscillation (ENSO) is the dominant source of seasonal climate predictability. This study quantifies the historical impact of ENSO on seasonal precipitation through an update of the global ENSO teleconnection maps of Mason and Goddard. Many additional teleconnections are detected due to better handling of missing values and 20 years of additional, higher quality data. These global teleconnection maps are used as deterministic and probabilistic empirical seasonal forecasts in a verification study. The probabilistic empirical forecast model outperforms climatology in the

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Krakauer, Nir Y., Michael D. Grossberg, Irina Gladkova, and Hannah Aizenman. "Information Content of Seasonal Forecasts in a Changing Climate." Advances in Meteorology 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/480210.

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We study the potential value to stakeholders of probabilistic long-term forecasts, as quantified by the mean information gain of the forecast compared to climatology. We use as a case study the USA Climate Prediction Center (CPC) forecasts of 3-month temperature and precipitation anomalies made at 0.5-month lead time since 1995. Mean information gain was positive but low (about 2% and 0.5% of the maximum possible for temperature and precipitation forecasts, resp.) and has not increased over time. Information-based skill scores showed similar patterns to other, non-information-based, skill scor

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Wilks, Daniel S. "Comparison of Probabilistic Statistical Forecast and Trend Adjustment Methods for North American Seasonal Temperatures." Journal of Applied Meteorology and Climatology 53, no. 4 (2014): 935–49. http://dx.doi.org/10.1175/jamc-d-13-0294.1.

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AbstractThe three multivariate statistical methods of canonical correlation analysis, maximum covariance analysis, and redundancy analysis are compared with respect to their probabilistic accuracy for seasonal forecasts of gridded North American temperatures. Derivation of forecast error covariance matrices for the methods allows a probabilistic formulation for the forecasts, assuming Gaussian predictive distributions. The three methods perform similarly with respect to probabilistic forecast accuracy as reflected by the ranked probability score, although maximum covariance analysis may be pre

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Rodwell, Mark J., and Francisco J. Doblas-Reyes. "Medium-Range, Monthly, and Seasonal Prediction for Europe and the Use of Forecast Information." Journal of Climate 19, no. 23 (2006): 6025–46. http://dx.doi.org/10.1175/jcli3944.1.

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Abstract Operational probabilistic (ensemble) forecasts made at ECMWF during the European summer heat wave of 2003 indicate significant skill on medium (3–10 day) and monthly (10–30 day) time scales. A more general “unified” analysis of many medium-range, monthly, and seasonal forecasts confirms a high degree of probabilistic forecast skill for European temperatures over the first month. The unified analysis also identifies seasonal predictability for Europe, which is not yet realized in seasonal forecasts. Interestingly, the initial atmospheric state appears to be important even for month 2 o

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Rauch, Manuel, Jan Bliefernicht, Patrick Laux, Seyni Salack, Moussa Waongo, and Harald Kunstmann. "Seasonal Forecasting of the Onset of the Rainy Season in West Africa." Atmosphere 10, no. 9 (2019): 528. http://dx.doi.org/10.3390/atmos10090528.

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Seasonal forecasts for monsoonal rainfall characteristics like the onset of the rainy seasons (ORS) are crucial for national weather services in semi-arid regions to better support decision-making in rain-fed agriculture. In this study an approach for seasonal forecasting of the ORS is proposed using precipitation information from a global seasonal ensemble prediction system. It consists of a quantile–quantile-transformation for eliminating systematic differences between ensemble forecasts and observations, a fuzzy-rule based method for estimating the ORS date and graphical methods for an impr

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Madadgar, Shahrbanou, and Hamid Moradkhani. "A Bayesian Framework for Probabilistic Seasonal Drought Forecasting." Journal of Hydrometeorology 14, no. 6 (2013): 1685–705. http://dx.doi.org/10.1175/jhm-d-13-010.1.

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Abstract Seasonal drought forecasting is presented within a multivariate probabilistic framework. The standardized streamflow index (SSI) is used to characterize hydrologic droughts with different severities across the Gunnison River basin in the upper Colorado River basin. Since streamflow, and subsequently hydrologic droughts, are autocorrelated variables in time, this study presents a multivariate probabilistic approach using copula functions to perform drought forecasting within a Bayesian framework. The spring flow (April–June) is considered as the forecast variable and found to have the

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Lavaysse, C., J. Vogt, and F. Pappenberger. "Early warning of drought in Europe using the monthly ensemble system from ECMWF." Hydrology and Earth System Sciences Discussions 12, no. 2 (2015): 1973–2009. http://dx.doi.org/10.5194/hessd-12-1973-2015.

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Abstract. Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from ECMWF in predicting droughts over the European continent. The Standardized Precipitation Index (SPI) is used to quantify the onset and severity of droughts. It can be shown that on average the extended range forecast has greater skill than the seasonal forecast whilst both outperform clima

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Li, Yuan, Zhiyong Wu, Hai He, and Guihua Lu. "Deterministic and Probabilistic Evaluation of Sub-Seasonal Precipitation Forecasts at Various Spatiotemporal Scales over China during the Boreal Summer Monsoon." Atmosphere 12, no. 8 (2021): 1049. http://dx.doi.org/10.3390/atmos12081049.

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Skillful sub-seasonal precipitation forecasts can provide valuable information for both flood and drought disaster mitigations. This study evaluates both deterministic and probabilistic sub-seasonal precipitation forecasts of ECMWF, ECCC, and UKMO models derived from the Sub-seasonal to Seasonal (S2S) Database at various spatiotemporal scales over China during the boreal summer monsoon. The Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), is used as the reference dataset to evaluate the forecast skills of the models. The results suggest that skillful deterministic sub-season

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Kharin, V. V., W. J. Merryfield, G. J. Boer, and W. S. Lee. "A Postprocessing Method for Seasonal Forecasts Using Temporally and Spatially Smoothed Statistics." Monthly Weather Review 145, no. 9 (2017): 3545–61. http://dx.doi.org/10.1175/mwr-d-16-0337.1.

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A statistical postprocessing method for seasonal forecasts based on temporally and spatially smoothed climate statistics is introduced. The method uses information available from seasonal hindcasts initialized at the beginning of 12 calendar months. The performance of the method is tested within both deterministic and probabilistic frameworks using output from the ensemble of seasonal hindcasts produced by the Canadian Seasonal to Interannual Prediction System for the 30-yr period 1981–2010. Forecast skill improvements are found to be greater when forecast adjustment parameters estimated for i

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Min, Young-Mi, Vladimir N. Kryjov, and Chung-Kyu Park. "A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction." Weather and Forecasting 24, no. 3 (2009): 812–28. http://dx.doi.org/10.1175/2008waf2222140.1.

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Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system

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Dissertations / Theses on the topic "Seasonal probabilistic forecast"

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NETRANANDA, SAHU. "Impacts of Climate Variations on Seasonal Streamflows and Probabilistic Forecasts." 京都大学 (Kyoto University), 2012. http://hdl.handle.net/2433/161004.

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Book chapters on the topic "Seasonal probabilistic forecast"

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Terra, Rafael, and Walter E. Baethgen. "Lessons Learned in 25 Years of Informing Sectoral Decisions With Probabilistic Climate Forecasts." In Sub-Seasonal to Seasonal Prediction. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-811714-9.00021-8.

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Nguyen-Huy, Thong, Ravinesh C. Deo, Shahbaz Mushtaq, and Shahjahan Khan. "Probabilistic seasonal rainfall forecasts using semiparametric d-vine copula-based quantile regression." In Handbook of Probabilistic Models. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-816514-0.00008-4.

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Conference papers on the topic "Seasonal probabilistic forecast"

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Krishnamurti, T. N., and Vinay Kumar. "Downscaled multi-model superensemble and probabilistic forecasts of seasonal rains over the Asian monsoon belt." In SPIE Asia-Pacific Remote Sensing, edited by Tiruvalam N. Krishnamurti, Jhoon Kim, and Takashi Moriyama. SPIE, 2010. http://dx.doi.org/10.1117/12.871078.

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