University of Reading Research Data Archive

Skillful spatial scales and representativity of seasonal rainfall forecasts over Africa

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Description

This dataset contains gridded estimates of the skillful spatial scales of seasonal rainfall forecasts (tercile probabilities) over Africa and a measure of how representative these skillful spatial scales are for anticipating local rainfall conditions (as described by terciles). The skillful spatial scales presented apply to tercile probability hindcasts (re-forecasts) of seasonal total rainfall derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting System 5 (SEAS5). The SEAS5 hindcasts analysed here are initialised at a lead time of 1 month ahead of the start of a given season. The representativity of the SEAS5 skillful spatial scales is computed using observed seasonal rainfall terciles from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) version 2.0 dataset. The skillful spatial scales and representativity data are combined to delineate regions where the SEAS5 seasonal hindcasts are both skillful and representative for anticipating local seasonal rainfall conditions. Note that the these data are specific to tercile probabilities derived from the SEAS5 hindcasts and different skillful scales and representativity results are likely to be found when applying this methodology to other dynamical weather forecasting systems and event/quantile categories.

A subset of this dataset is presented in Figure 6 of
Young, M., Heinrich, V., Black, E., and Asfaw, D., 2020: Optimal spatial scales for seasonal forecasts over Africa, Environmental Research Letters. In press https://doi.org/10.1088/1748-9326/ab94e9.

Resource Type: Dataset
Creators: Young, Matthew ORCID: https://orcid.org/0000-0001-9892-1032, Heinrich, Viola and Black, Emily
Contributors: Asfaw, Dagmawi
Rights-holders: University of Reading, Viola Heinrich
Data Publisher: University of Reading
Publication Date: 2020
Data last accessed: 10 August 2020
DOI: http://dx.doi.org/10.17864/1947.252
Metadata Record URL: https://researchdata.reading.ac.uk/id/eprint/252
Organisational units: Faculty of Science > School of Mathematical, Computational and Physical Sciences > NCAS
Faculty of Science > School of Mathematical, Computational and Physical Sciences > Department of Meteorology
Participating Organisations: University of Reading
Keywords: seasonal forecasts, rainfall, spatial scales, Africa, drought monitoring, early warning
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