Search

This paper describes a new gridded, daily 29-yr precipitation estimation dataset centered over Africa at 0.1° spatial resolution. Called the African Rainfall Climatology, version 2 (ARC2), it is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System (GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983-2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), with correlation coefficients of 0.86 over a 27-yr period. However, a marginal summer dry bias that occurs over West and East Africa is examined. Daily validation with independent gauge data shows RMSEs of 11.3, 13.4, and 14, respectively, for ARC2, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, version 6 (3B42v6), and the CPC morphing technique (CMORPH) for the West African summer season. The ARC2 RMSE is slightly higher for Ethiopia than those of CMORPH and 3B42v6. Both daily and monthly validations suggested that ARC2 underestimations may be attributed to the unavailability of daily GTS gauge reports in real time, and deficiencies in the satellite estimate associated with precipitation processes over coastal and orographic areas. However, ARC2 is expected to provide users with real-time monitoring of the daily evolution of precipitation, which is instrumental in improved decision making in famine early warning systems. [ABSTRACT FROM AUTHOR]

Abstract: The link between the African Monsoon systems and aerosol loading in Africa is studied using multi-year satellite observations of UV-absorbing aerosols and rain gauge measurements. The main aerosol types occurring over Africa are desert dust and biomass burning aerosols, which are UV-absorbing. The abundance of these aerosols over Africa is characterised in this paper using residues and Absorbing Aerosol Index (AAI) data from Global Ozone Monitoring Experiment (GOME) on board ERS-2 and SCanning Imaging Absorption SpectroMeter for Atmospheric ChartograpHY (SCIAMACHY) on board Envisat. Time series of regionally averaged residues from 1995 to 2008 show the seasonal variations of aerosols in Africa. Zonally averaged daily residues over Africa are related to monthly mean precipitation data and show monsoon-controlled atmospheric aerosol loadings. A distinction is made between the West African Monsoon (WAM) and the East African Monsoon (EAM), which have different dynamics, mainly due to the asymmetric distribution of land masses around the equator in the west. The seasonal variation of the aerosol distribution is clearly linked to the seasonal cycle of the monsoonal wet and dry periods in both studied areas. The residue distribution over Africa shows two distinct modes, one associated with dry periods and one with wet periods. During dry periods the residue varies freely, due to aerosol emissions from deserts and biomass burning events. During wet periods the residue depends linearly on the amount of precipitation, due to scavenging of aerosols and the prevention of aerosol emissions from the wet surface. This is most clear over east Africa, where the sources and sinks of atmospheric aerosols are controlled directly by the local climate, i.e. monsoonal precipitation. Here, the wet mode has a mean residue of −1.4 and the dry mode has a mean residue of −0.3. During the wet modes a reduction of one residue unit for every 160 mm monthly averaged precipitation was found. Shielding effects due to cloud cover may also play a role in the reduction of the residue during wet periods. A possible influence of aerosols on the monsoon, via aerosol direct and indirect effects, is plausible, but cannot directly be deduced from these data. [Copyright &y& Elsevier]

Near real-time precipitation is essential to many applications. In Africa, the lack of dense rain-gauge networks and ground weather radars makes the use of satellite precipitation products unavoidable. Despite major progresses in estimating precipitation rate from remote sensing measurements over the past decades, satellite precipitation products still suffer from quantitative uncertainties and biases compared to ground data. Consequently, almost all precipitation products are provided in two modes: a real-time mode (also called early-run or raw product) and a corrected mode (also called final-run, adjusted or post-processed product) in which ground precipitation measurements are integrated in algorithms to correct for bias, generally at a monthly timescale. This paper describes a new methodology to provide a near-real-time precipitation product based on satellite precipitation and soil moisture measurements. Recent studies have shown that soil moisture intrinsically contains information on past precipitation and can be used to correct precipitation uncertainties. The PrISM (Precipitation inferred from Soil Moisture) methodology is presented and its performance is assessed for five in situ rainfall measurement networks located in Africa in semi-arid to wet areas: Niger, Benin, Burkina Faso, Central Africa, and East Africa. Results show that the use of SMOS (Soil Moisture and Ocean Salinity) satellite soil moisture measurements in the PrISM algorithm most often improves the real-time satellite precipitation products, and provides results comparable to existing adjusted products, such as TRMM (Tropical Rainfall Measuring Mission), GPCC (Global Precipitation Climatology Centre) and IMERG (Integrated Multi-satellitE Retrievals for GPM), which are available a few weeks or months after their detection. [ABSTRACT FROM AUTHOR]