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results are the worst. On the other hand, individual gauge–radar and satellite products are not necessarily better than some reanalysis products, and they are occasionally even not as good as model results in some performance metrics. For instance, the JJA precipitation bias from PERSIANN (8) satellite-only product is highest among all products (including models). Therefore, for reanalysis product evaluations, several gauge–radar and satellite products along with their uncertainties should be used
results are the worst. On the other hand, individual gauge–radar and satellite products are not necessarily better than some reanalysis products, and they are occasionally even not as good as model results in some performance metrics. For instance, the JJA precipitation bias from PERSIANN (8) satellite-only product is highest among all products (including models). Therefore, for reanalysis product evaluations, several gauge–radar and satellite products along with their uncertainties should be used
rainfall datasets ( Fig. 4 ), highlighting the challenge of assessing model performance in West Africa when the spread in the reanalyzed conditions is so large. Also evident in the Taylor diagram for precipitation ( Fig. 4 ) is the lower spread in the models, the higher correlation between models, and the multiobservation mean (no correlation value is below 0.7), and the higher skill of the multimodel ensemble mean (for both AMIP and historical simulations). Fig . 3. Taylor diagram representing the
rainfall datasets ( Fig. 4 ), highlighting the challenge of assessing model performance in West Africa when the spread in the reanalyzed conditions is so large. Also evident in the Taylor diagram for precipitation ( Fig. 4 ) is the lower spread in the models, the higher correlation between models, and the multiobservation mean (no correlation value is below 0.7), and the higher skill of the multimodel ensemble mean (for both AMIP and historical simulations). Fig . 3. Taylor diagram representing the
Mission (TRMM 3B42) and the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), we conclude that almost all regions have a single diurnal peak, either in the afternoon or at night. The purpose of this paper is to examine the ability of a regional, convection-permitting atmospheric model to reproduce the diurnal cycle of rainfall over West Africa and capture the underlying physical processes. The performance issues of the current generation of GCMs and
Mission (TRMM 3B42) and the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), we conclude that almost all regions have a single diurnal peak, either in the afternoon or at night. The purpose of this paper is to examine the ability of a regional, convection-permitting atmospheric model to reproduce the diurnal cycle of rainfall over West Africa and capture the underlying physical processes. The performance issues of the current generation of GCMs and
in total in Nigeria, Benin, Ghana, and the Ivory Coast irrespective of the exact periods (see SM), assuming that the obtained climatologies give reasonable estimates of typical values and therefore allow evaluating models and satellite products. Minimum coverage is 5 years (Axim, Ghana) but most stations have substantially more data. For a gridded observational estimate and its uncertainty, two different Satellite Application Facility on Climate Monitoring (CM SAF) products for satellite
in total in Nigeria, Benin, Ghana, and the Ivory Coast irrespective of the exact periods (see SM), assuming that the obtained climatologies give reasonable estimates of typical values and therefore allow evaluating models and satellite products. Minimum coverage is 5 years (Axim, Ghana) but most stations have substantially more data. For a gridded observational estimate and its uncertainty, two different Satellite Application Facility on Climate Monitoring (CM SAF) products for satellite
corresponding training period. The above predictors may not be mathematically independent. However, the problem of multicollinearity that arises in a given predictor matrix, owing to lack of independence between levels and variables, was evaluated for individual regression models during the final model selection stage, as described in step 3 below. 2) Step 2: Construction of multimodel ensemble set Model development used a forward selection stepwise multiple linear regression procedure ( S-PLUS 2013 ) that
corresponding training period. The above predictors may not be mathematically independent. However, the problem of multicollinearity that arises in a given predictor matrix, owing to lack of independence between levels and variables, was evaluated for individual regression models during the final model selection stage, as described in step 3 below. 2) Step 2: Construction of multimodel ensemble set Model development used a forward selection stepwise multiple linear regression procedure ( S-PLUS 2013 ) that
. VALIDATION, RESULTS, AND IMPLEMENTATION. Model performance. Using only commonly available rainfall, PET, and topographical datasets and open-source GIS tools, EF5 simulates the maximum streamflow in the Okavango River at Rundu, Namibia, for each forecast period. EF5 is capable of accurately simulating the peak volume and the peak timing of the annual flooding at Rundu in both dry and wet years. During the calibration period, from 1 June 2003 to 31 December 2006, the Nash–Sutcliffe coefficient of model
. VALIDATION, RESULTS, AND IMPLEMENTATION. Model performance. Using only commonly available rainfall, PET, and topographical datasets and open-source GIS tools, EF5 simulates the maximum streamflow in the Okavango River at Rundu, Namibia, for each forecast period. EF5 is capable of accurately simulating the peak volume and the peak timing of the annual flooding at Rundu in both dry and wet years. During the calibration period, from 1 June 2003 to 31 December 2006, the Nash–Sutcliffe coefficient of model
simulations. The purpose of this paper is to better understand the precipitation changes that lead to these simulated reductions in growing-season days over East Africa, since a physical understanding of the change is useful for evaluating confidence in the projections. Background on East African rainy seasons is provided in the next section, along with an overview of projected changes from increased atmospheric greenhouse gas concentrations. The regional model simulations are described and their realism
simulations. The purpose of this paper is to better understand the precipitation changes that lead to these simulated reductions in growing-season days over East Africa, since a physical understanding of the change is useful for evaluating confidence in the projections. Background on East African rainy seasons is provided in the next section, along with an overview of projected changes from increased atmospheric greenhouse gas concentrations. The regional model simulations are described and their realism
to improve climate services. The training programs also provide an excellent opportunity to use NCEP and U.S. NMME model data and to evaluate the performance of the models especially in the tropics. Through this process, NMSs can develop effective weather and climate monitoring tools that enable decision making. Finally, one of the key missions of the CPC ID is to provide domestic and international agencies with access to real-time NCEP operational weather and climate forecasts for any given
to improve climate services. The training programs also provide an excellent opportunity to use NCEP and U.S. NMME model data and to evaluate the performance of the models especially in the tropics. Through this process, NMSs can develop effective weather and climate monitoring tools that enable decision making. Finally, one of the key missions of the CPC ID is to provide domestic and international agencies with access to real-time NCEP operational weather and climate forecasts for any given
for advancing weather and climate prediction over West Africa. To capture the diurnal cycle of rainfall correctly, climate models need to have an accurate representation of the determining physical processes. In Part II , the diurnal cycle of rainfall produced by convection-permitting simulations is analyzed to evaluate the extent to which the model correctly represents the important physical processes controlling the diurnal cycle of rainfall over West Africa. Acknowledgments The financial
for advancing weather and climate prediction over West Africa. To capture the diurnal cycle of rainfall correctly, climate models need to have an accurate representation of the determining physical processes. In Part II , the diurnal cycle of rainfall produced by convection-permitting simulations is analyzed to evaluate the extent to which the model correctly represents the important physical processes controlling the diurnal cycle of rainfall over West Africa. Acknowledgments The financial
question: given a change in the annual mean or annual cycle of SST, what is the response of the annual cycle of precipitation and to what extent can this explain changes in the coupled models? Using an atmospheric general circulation model (AGCM) forced with SST provides a simple framework to evaluate this question, but there are drawbacks to this approach. In particular, prescribing SST eliminates feedbacks between the ocean and atmosphere that are present in both the real climate and coupled models
question: given a change in the annual mean or annual cycle of SST, what is the response of the annual cycle of precipitation and to what extent can this explain changes in the coupled models? Using an atmospheric general circulation model (AGCM) forced with SST provides a simple framework to evaluate this question, but there are drawbacks to this approach. In particular, prescribing SST eliminates feedbacks between the ocean and atmosphere that are present in both the real climate and coupled models
