Isabelle Jobard

Comparing Satellite and Surface Rainfall Products over West Africa at Meteorologically Relevant Scales during the AMMA Campaign Using Error Estimates

Abstract Monsoon rainfall is central to the climate of West Africa, and understanding its variability is a challenge for which satellite rainfall products could be well suited to contribute to. Their quality in this region has received less attention than elsewhere. The focus is set on the scales associated with atmospheric variability, and a meteorological benchmark is set up with ground-based observations from the African Monsoon Multidisciplinary Analysis (AMMA) program. The investigation is performed at various scales of accumulation using four gauge networks. The seasonal cycle is analyzed using 10-day-averaged products, the synoptic-scale variability is analyzed using daily means, and the diurnal cycle of rainfall is analyzed at the seasonal scale using a composite and at the diurnal scale using 3-hourly accumulations. A novel methodology is introduced that accounts for the errors associated with the areal–time rainfall averages. The errors from both satellite and ground rainfall data are computed u...

Satellite estimation of the tropical precipitation using the METEOSTAT and SSM/I data

Abstract The purpose of this study is to test a satellite method for estimating precipitation using infrared and microwave data. The method is validated by comparing the rain estimates to the ground precipitation measurements, over continental tropical regions (West Africa). The rain estimation method used is based on an automatic classification algorithm combining infrared and microwave satellite data. The method takes advantage of both, the good time and space resolution of infrared satellite images and the rain related information retrieved from microwave images. This method of rain and cloud classification (RACC) gives homogeneous classes characterising the different types of clouds or rain-rates. A set of microwave images from the 86 GHz channel of the passive microwave radiometer SSM/I and of the coincident infrared images from Meteosat is used in the learning phase of the classification process, while the full set of half-hourly infrared images is needed for the application phase. The areal rain estimates are computed with the microwave-infrared combined RACC method for time periods ranging from one to twelve hours and for different areas up to 120 km × 120 km. The correlation with the ground rainfall data given by the raingauge network of a validation site in Niger are estimated and compared to the correlations obtained for the rain estimated derived from a method based on a single infrared threshold and using the Meteosat images only. The improvement brought by the RACC method is discussed.

A new index to estimate precipitation using cloud growing rate

A new index assessing the cloud growing rate is described in this paper. It has been designed to be integrated in rainfall estimation procedures. As the highest precipitation rates occur in growing convective cores, integrating this information should enhance the precipitation estimations. The index computation relies on image processing methods. It is composed of two steps: first a watershed segmentation is applied and then an original heritage process is performed. This second step, which is based on a simple matrix computation, is adapted to the watershed algorithm as it mitigates the over‐segmentation artefact. This cloud growing rate index is compared with a cooling index which is usually computed to help in identifying raining cells and it is demonstrated to be more efficient. Moreover a simple integration of this index in a widespread rainfall estimation method leads to an improvement of the diurnal cycle retrieval.

Combination of Satellite Microwave and Infrared Measurements for Rainfall Estimation

Satellite-based rainfall estimations are obtained from either microwave or infrared techniques but all these techniques have their own inherent limitations. Microwave radiances have a good physical connection with rainrate but the microwave observations from a single low-orbit satellite, (one or at most two images per day) are inadequate for the detection of rapidly changing precipitation distributions. Infrared images from geosynchronous-orbit satellites have high spatial and high temporal resolutions, but they do not have a direct physical connection with the instantaneous rainrate. However, infrared techniques provide reliable estimation of the rainfall amount accumulated during long time periods and averaged over large areas. To overcome the limitations of these techniques, different methods for estimating precipitation at various time and space scales, which combine low-orbit microwave and geostationary infrared satellite data, are developed. The Rain and Cloud Classification (RACC) method, based on an automatic classification procedure, is described; the method is applied to two case- studies corresponding to different climatic regions: tropical land in West Africa and sub-tropical Japan and surrounding oceanic regions, for which the infrared data from geostationary satellites (Meteosat, GMS) and the microwave data from the SSM/I radiometer on board a U.S. DMSP polar satellite are used. The comparisons of monthly and daily rainfall estimates with surface rainfall validation data are presented. The results show that the combined microwave-infrared technique performed well.