Multimodel based superensemble forecasts for short and medium range NWP over various regions of Africa |
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Authors: | J N Mutemi L A Ogallo T N Krishnamurti A K Mishra T S V Vijaya Kumar |
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Institution: | (1) Department of Meteorology, University of Nairobi, Nairobi, Kenya;(2) Department of Meteorology, Florida State University, Tallahassee, FL, USA |
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Abstract: | Summary This study examines the predictability of weather over several regions in Africa using a multimodel superensemble technique
developed at the Florida State University, which is an objective means of combining daily forecasts from multilevel global
models. It is referred to as FSUSE and up to 7 different models are used to construct the superensemble. The benchmark reanalysis
fields used are the precipitation data sets from CMORPH and all other global fields from ECMWF daily operational analysis.
The FSUSE works by using multiple linear regression to derive weights from a comparison of each member model forecast to the
benchmark analysis during a training period of the most recent 120 days, and these weights are passed to the forecast phase.
This procedure removes the bias of each model and allows for an optimal linear combination of the individual model forecasts
by taking account of the relative skill of each model to give a consensus forecast that is superior to the ensemble mean and
all the members.
Results show that bad models and poor analysis fields used during the training phase degrade the skill of the FSUSE. In the
forecasts of rainfall events over all regions of Africa, the FSUSE root-mean-square (R M S) error, equitable threat skill
score (E T S), and bias on the daily forecasts of rainfall were invariably superior to the best member model. The skills deteriorate
as the forecast lead time in days increases, with the degradation being most significant beyond day 3. In all cases, the bias
score of the FSUSE was approximately 1, while the anomaly correlation scores were to the order of 0.9. These scores indicate
the robustness of the FSUSE forecasts. Over East Africa, the FSUSE forecasts were consistent with the spatial-temporal pattern
of the Intertropical Convergence Zone (ITCZ), the main rain bearing synoptic mechanism across tropical Africa. Thus, in addition
to superior forecasts, the use of FSUSE based data sets may provide a better understanding of the dynamical processes within
the ITCZ over the region.
These results could be further improved if the daily series of operational analysis had included gauge data and if the resolution
were higher. It is hardly possible to get uniformly consistent and continuous daily observations over these diverse regions
of Africa. However, given the availability of the satellite based estimates of daily rainfall, such as CMORPH and global analysis
that are exchanged very fast nowadays, the FSUSE scheme for numerical weather predictions (N W P) provides useful medium range
weather forecasts in real-time. |
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