Skillful spatial scales and representativity of seasonal rainfall forecasts over Africa

How to cite this Dataset

Young, Matthew, Heinrich, Viola and Black, Emily (2020): Skillful spatial scales and representativity of seasonal rainfall forecasts over Africa. University of Reading. Dataset. https://doi.org/10.17864/1947.252

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

, Heinrich, Viola and Black, Emily

Contributors:

Asfaw, Dagmawi

Rights-holders:

University of Reading, Viola Heinrich

Data Publisher:

University of Reading

Publication Year:

2020

Data last accessed:

4 November 2024

DOI:

https://doi.org/10.17864/1947.252

Metadata Record URL:

https://researchdata.reading.ac.uk/id/eprint/252

Organisational units:

Science > School of Mathematical, Computational and Physical Sciences > NCAS
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

Rights:

Creative Commons Attribution 4.0 International

Data Availability:

OPEN

Additional details

Project Name:

Atmospheric hazard in developing Countries: Risk assessment and Early Warning (ACREW)

Funders:

Natural Environment Research Council (NE/R000034/1)

Resource Language:

English

Temporal coverage:

From 1 December 1993 To 31 November 2016

Geographic coverage:

Africa

Bounding Area:

N. Lat.	E. Long.	S. Lat.	W. Long.
35	52	-35	-20

Data collection method:

The dataset has been produced using:

1) Hindcasts (re-forecasts) of seasonal total rainfall over Africa from the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting System 5 (SEAS5). The hindcasts used are initialised at a lead time of 1 month ahead of the start of the season, cover the period 1993-2016 and have a horizontal resolution of 1º. The hindcasts have been obtained from the Copernicus Climate Change Service Climate Data Store at https://cds.climate.copernicus.eu/.

2) Observations of seasonal total rainfall from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) version 2.0 dataset available from https://www.chc.ucsb.edu/data. The CHIRPS data has a horizontal resolution of 0.05º and CHIRPSv2.0 estimates between 1993-2016 are used here to measure representativity.

The methodology used to produce this dataset using 1) and 2) is described in detail in Young, M., Heinrich, V., Black, E. and Asfaw, D. T. (2020) Optimal spatial scales for seasonal forecasts over Africa. Environmental Research Letters. In Press https://doi.org/10.1088/1748-9326/ab94e9.

Depositing User:

Mr Matthew Young

Date Deposited:

04 Jun 2020 09:13

Last Modified:

24 Aug 2023 04:18