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these events: 1) most importantly, ~100-yr-long time series of daily rainfall with very few gaps are available at both locations—a very rare situation in Africa, and 2) recently, the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR; Ashouri et al. 2015 ), a homogenized, 32-yr-long (1983–2014) SRFE dataset, was released. The PERSIANN-CDR algorithm uses homogenized infrared brightness temperatures and was trained in a neural
these events: 1) most importantly, ~100-yr-long time series of daily rainfall with very few gaps are available at both locations—a very rare situation in Africa, and 2) recently, the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR; Ashouri et al. 2015 ), a homogenized, 32-yr-long (1983–2014) SRFE dataset, was released. The PERSIANN-CDR algorithm uses homogenized infrared brightness temperatures and was trained in a neural
resolution, are scaled to the desired 0.125° latitude by 0.125° longitude resolution. Then, the resulting DEM is processed to remove any pits or sinks following the procedure outlined in Jenson and Domingue (1988) . Using a vector representation of the Okavango River, it and its largest tributaries are “burned” into the DEM by reducing the elevation values of all the DEM pixels in the river channel. A drainage direction map (DDM) is generated using the eight-direction flow-direction algorithm; this DDM
resolution, are scaled to the desired 0.125° latitude by 0.125° longitude resolution. Then, the resulting DEM is processed to remove any pits or sinks following the procedure outlined in Jenson and Domingue (1988) . Using a vector representation of the Okavango River, it and its largest tributaries are “burned” into the DEM by reducing the elevation values of all the DEM pixels in the river channel. A drainage direction map (DDM) is generated using the eight-direction flow-direction algorithm; this DDM
vegetation and surface energy and moisture fluxes. The purpose of this study is to determine how well each of the products represents precipitation and soil moisture over WGEW. With over 40 products being evaluated using 58 years of data, this work gives insight into the strengths and weaknesses of product multitude of measurement techniques, algorithms, and models. Section 2 briefly describes all of the products and the evaluation metrics. Sections 3 and 4 compare precipitation and soil moisture
vegetation and surface energy and moisture fluxes. The purpose of this study is to determine how well each of the products represents precipitation and soil moisture over WGEW. With over 40 products being evaluated using 58 years of data, this work gives insight into the strengths and weaknesses of product multitude of measurement techniques, algorithms, and models. Section 2 briefly describes all of the products and the evaluation metrics. Sections 3 and 4 compare precipitation and soil moisture
radiation transfer model ( Mueller et al. 2009 ; Posselt et al. 2012 ). The second dataset is the operational downwelling surface shortwave radiation derived using SEVIRI and Geostationary Earth Radiation Budget (GERB) measurements during 2007–15. The algorithm is based on a look-up table approach to derive the atmospheric transmission for cloud-free as well as cloudy sky ( Mueller et al. 2009 ). The main difference between the two datasets is the temporal consistency of the applied algorithms. SARAH
radiation transfer model ( Mueller et al. 2009 ; Posselt et al. 2012 ). The second dataset is the operational downwelling surface shortwave radiation derived using SEVIRI and Geostationary Earth Radiation Budget (GERB) measurements during 2007–15. The algorithm is based on a look-up table approach to derive the atmospheric transmission for cloud-free as well as cloudy sky ( Mueller et al. 2009 ). The main difference between the two datasets is the temporal consistency of the applied algorithms. SARAH
. Hodges , K. , and C. D. Thorncroft , 1997 : Distribution and statistics of African mesoscale convective weather systems based on the ISCCP Meteosat imagery . Mon. Wea. Rev. , 125 , 2821 – 2837 , doi: 10.1175/1520-0493(1997)125<2821:DASOAM>2.0.CO;2 . 10.1175/1520-0493(1997)125<2821:DASOAM>2.0.CO;2 Hodges , K. , D. Chappell , G. Robinson , and G. Yang , 2000 : An improved algorithm for generating global window brightness temperatures from multiple satellite infrared imagery . J
. Hodges , K. , and C. D. Thorncroft , 1997 : Distribution and statistics of African mesoscale convective weather systems based on the ISCCP Meteosat imagery . Mon. Wea. Rev. , 125 , 2821 – 2837 , doi: 10.1175/1520-0493(1997)125<2821:DASOAM>2.0.CO;2 . 10.1175/1520-0493(1997)125<2821:DASOAM>2.0.CO;2 Hodges , K. , D. Chappell , G. Robinson , and G. Yang , 2000 : An improved algorithm for generating global window brightness temperatures from multiple satellite infrared imagery . J
trends that could be associated with change in the zenith angle through time, and Fensholt and Proud (2012) conclude that the long Global Inventory Modeling and Mapping Studies (GIMMS) NDVI time series based on retrievals from the Advanced Very High Resolution Radiometer (AVHRR) does not deviate significantly in its quantification of trends in semiarid regions when compared with the more recent and improved algorithm used on Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals over the
trends that could be associated with change in the zenith angle through time, and Fensholt and Proud (2012) conclude that the long Global Inventory Modeling and Mapping Studies (GIMMS) NDVI time series based on retrievals from the Advanced Very High Resolution Radiometer (AVHRR) does not deviate significantly in its quantification of trends in semiarid regions when compared with the more recent and improved algorithm used on Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals over the
