Abstract
AbstractConvection-permitting ensemble prediction systems (CP-ENS) have been implemented in the midlatitudes for weather forecasting time scales over the past decade, enabled by the increase in computational resources. Recently, efforts are being made to study the benefits of CP-ENS for tropical regions. This study examines CP-ENS forecasts produced by the Met Office over tropical East Africa, for 24 cases in the period April–May 2019. The CP-ENS, an ensemble with parameterized convection (Glob-ENS), and their deterministic counterparts are evaluated against rainfall estimates derived from satellite observations (GPM-IMERG). The CP configurations have the best representation of the diurnal cycle, although heavy rainfall amounts are overestimated compared to observations. Pairwise comparisons between the different configurations reveal that the CP-ENS is generally the most skillful forecast for both 3- and 24-h accumulations of heavy rainfall (97th percentile), followed by the CP deterministic forecast. More precisely, probabilistic forecasts of heavy rainfall, verified using a neighborhood approach, show that the CP-ENS is skillful at scales greater than 100 km, significantly better than the Glob-ENS, although not as good as found in the midlatitudes. Skill decreases with lead time and varies diurnally, especially for CP forecasts. The CP-ENS is underspread both in terms of forecasting the locations of heavy rainfall and in terms of domain-averaged rainfall. This study demonstrates potential benefits in using CP-ENS for operational forecasting of heavy rainfall over tropical Africa and gives specific suggestions for further research and development, including probabilistic forecast guidance.
Highlights
In tropical Africa, unlike midlatitude locations, the main contribution to daily rainfall comes from deep convective systems (Fink et al 2017)
In an operational forecasting testbed environment that occurred during April–May 2019, convection-permitting ensemble forecasts were produced by the Met Office for tropical East Africa for the first time
Potential benefits of the CP ensemble were assessed compared to the driving global ensemble, first in terms of rainfall characteristics and by verifying probabilistic forecasts calculated using a neighborhood approach
Summary
In tropical Africa, unlike midlatitude locations, the main contribution to daily rainfall comes from deep convective systems (Fink et al 2017). The version of MOGREPS-G run operationally did not provide the diagnostics required for the forecasting testbed, so a limited-area model with global model configuration, including the convective parameterization scheme (Walters et al 2017) was nested within MOGREPS-G It is this limited area version (hereafter Glob-ENS), with the same horizontal grid spacing of MOGREPS-G, that will be used for comparison against the CP-ENS in this paper. In comparison against rain gauges, Dezfuli et al (2017a) found that GPMIMERG captured well the annual cycle and the diurnal cycle during the March–April–May ‘‘short rains’’ season over East Africa, which is the focus period of this study Both the CP-ENS and Glob-ENS rainfall fields were regridded to match the GPM-IMERG grid using the conservative method of the Climate and Forecast (cf) package (https://ncascms.github.io/cf-python/introduction.html). NEP will be lower than NP because the probability field has undergone more smoothing, as discussed previously
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