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Abstract
Investigations of extreme rainfall events in the southern African region are limited by the paucity of the observational network. Furthermore, the lack of full radar coverage for South Africa makes quantitative precipitation estimation difficult. Therefore, numerical modeling represents the most effective method for improving the understanding of the mechanisms that contribute to extreme rainfall events in this region with the caveat that accurate validation of model simulations is hampered by the limited observations in the region. This paper describes an intense cutoff low event over South Africa that led to record rainfall and flash flooding along the south coast of the country and adjoining hinterland. Analyses from the Global Forecast System model showed that the cutoff aloft was accompanied by a strong low-level jet (LLJ) impinging onto the south coast where rainfall was heaviest, and that lapse rates were steep in the lower troposphere. Simulations of the event were carried out using a numerical model [i.e., the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)], which showed that severe convection occurred over the ocean on the right-hand side of the LLJ, and at its leading edge where it impinged on the coastal topography. This topography was also very important in providing additional forcing for the ascent of moist air. A factor separation technique was used to show that surface heat fluxes from the warm sea surface temperature (SST) of the Agulhas Current were important in enhancing low-level cyclogenesis, and that topography was important in maintaining the position of the low-level coastal depression, which led to favorable conditions for rainfall remaining in the same area for an extended period of time. It is suggested that improved representation of the tight topographic and SST gradients of the southern African region in NWP models or postprocessing systems would help to provide more accurate forecasts of the amount and location of heavy precipitation during cutoff low events where surface forcing is important.
Abstract
Investigations of extreme rainfall events in the southern African region are limited by the paucity of the observational network. Furthermore, the lack of full radar coverage for South Africa makes quantitative precipitation estimation difficult. Therefore, numerical modeling represents the most effective method for improving the understanding of the mechanisms that contribute to extreme rainfall events in this region with the caveat that accurate validation of model simulations is hampered by the limited observations in the region. This paper describes an intense cutoff low event over South Africa that led to record rainfall and flash flooding along the south coast of the country and adjoining hinterland. Analyses from the Global Forecast System model showed that the cutoff aloft was accompanied by a strong low-level jet (LLJ) impinging onto the south coast where rainfall was heaviest, and that lapse rates were steep in the lower troposphere. Simulations of the event were carried out using a numerical model [i.e., the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)], which showed that severe convection occurred over the ocean on the right-hand side of the LLJ, and at its leading edge where it impinged on the coastal topography. This topography was also very important in providing additional forcing for the ascent of moist air. A factor separation technique was used to show that surface heat fluxes from the warm sea surface temperature (SST) of the Agulhas Current were important in enhancing low-level cyclogenesis, and that topography was important in maintaining the position of the low-level coastal depression, which led to favorable conditions for rainfall remaining in the same area for an extended period of time. It is suggested that improved representation of the tight topographic and SST gradients of the southern African region in NWP models or postprocessing systems would help to provide more accurate forecasts of the amount and location of heavy precipitation during cutoff low events where surface forcing is important.
Abstract
The main objective of this study is to evaluate the uncertainties due to sample size associated with the estimation of the standardized precipitation index (SPI) and their impact on the level of confidence in drought monitoring in Africa using high-spatial-resolution data from short time series. To do this, two different rainfall datasets, each available on a monthly basis, were analyzed over four river basins in Africa—Oum er-Rbia, Limpopo, Niger, and eastern Nile—as well as at the continental level. The two precipitation datasets used were the Tropical Rainfall Measuring Mission (TRMM) satellite monthly rainfall product 3B43 and the Global Precipitation Climatology Centre full-reanalysis gridded precipitation dataset. A nonparametric resampling bootstrap approach was used to compute the confidence bands associated with the SPI estimation, which are essential for making a qualified assessment of drought events. The comparative analysis of different datasets suggests that for reliable drought monitoring over Africa it is feasible to use short time series of remote sensing precipitation data, such as those from TRMM, that have a higher spatial resolution than other gridded precipitation data. The proposed approach for drought monitoring has the potential to be used in support of decision making at both continental and subcontinental scales over Africa or over other regions that have a sparse distribution of rainfall measurement instruments.
Abstract
The main objective of this study is to evaluate the uncertainties due to sample size associated with the estimation of the standardized precipitation index (SPI) and their impact on the level of confidence in drought monitoring in Africa using high-spatial-resolution data from short time series. To do this, two different rainfall datasets, each available on a monthly basis, were analyzed over four river basins in Africa—Oum er-Rbia, Limpopo, Niger, and eastern Nile—as well as at the continental level. The two precipitation datasets used were the Tropical Rainfall Measuring Mission (TRMM) satellite monthly rainfall product 3B43 and the Global Precipitation Climatology Centre full-reanalysis gridded precipitation dataset. A nonparametric resampling bootstrap approach was used to compute the confidence bands associated with the SPI estimation, which are essential for making a qualified assessment of drought events. The comparative analysis of different datasets suggests that for reliable drought monitoring over Africa it is feasible to use short time series of remote sensing precipitation data, such as those from TRMM, that have a higher spatial resolution than other gridded precipitation data. The proposed approach for drought monitoring has the potential to be used in support of decision making at both continental and subcontinental scales over Africa or over other regions that have a sparse distribution of rainfall measurement instruments.
