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Improve Our Understanding on the Effect of Lead Time Forecasting in Wrf Over Northern – Cameroon Region.

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Abstract A Weather Research and Forecasting (WRF) model at a horizontal resolution of 12 km has been analysed for the month of July 2018 over the North region of Cameroon. For the first time, a high-resolution WRF version 3.7 is being run operationally over this part of the country for wet weather forecast. In such a study, detailed validation of the WRF model is crucial. Therefore, the validation of mean parameters including wind distribution, relative humidity and rainfall, over the entire region within all the forecast lead time, is essential. Validation is done by comparing WRF outputs to ERA5, ARC2 and observed upper air data. It is found that the model captures accurately relative humidity and low level wind events with sufficiently shorter lead times (3 days), while the same performance is observed for extreme precipitable water and rainfall but at longer lead time as well as the diurnal variability of these parameters associated with wet season at all lead times. Furthermore, the accuracy of WRF in predicting spatiotemporal changes of some atmospheric variables decreases with increase in lead time.
Title: Improve Our Understanding on the Effect of Lead Time Forecasting in Wrf Over Northern – Cameroon Region.
Description:
Abstract A Weather Research and Forecasting (WRF) model at a horizontal resolution of 12 km has been analysed for the month of July 2018 over the North region of Cameroon.
For the first time, a high-resolution WRF version 3.
7 is being run operationally over this part of the country for wet weather forecast.
In such a study, detailed validation of the WRF model is crucial.
Therefore, the validation of mean parameters including wind distribution, relative humidity and rainfall, over the entire region within all the forecast lead time, is essential.
Validation is done by comparing WRF outputs to ERA5, ARC2 and observed upper air data.
It is found that the model captures accurately relative humidity and low level wind events with sufficiently shorter lead times (3 days), while the same performance is observed for extreme precipitable water and rainfall but at longer lead time as well as the diurnal variability of these parameters associated with wet season at all lead times.
Furthermore, the accuracy of WRF in predicting spatiotemporal changes of some atmospheric variables decreases with increase in lead time.

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