Abstract
Abstract High-resolution rain fields are a prerequisite to many hydrometeorological studies. For some applications, the required resolution may be as fine as 1 km in space and 5 min in time. At these scales, rainfall is strongly intermittent, variable in space, and correlated in time because of the propagation of the rainy systems. This paper compares two interpolation approaches to generate high-resolution rain fields from rain gauge measurements: (i) a classic interpolation technique that consists in interpolating independently the rain intensities at each time step (Eulerian kriging) and (ii) a simple dynamic interpolation technique that incorporates the propagation of the rainy systems (Lagrangian kriging). For this latter approach, three propagation models are tested. The different interpolation techniques are evaluated over three climatically contrasted areas in West Africa where a multiyear 5-min rainfall dataset has been collected during the African Monsoon Multidisciplinary Analyses (AMMA) campaigns. The dynamic interpolation technique is shown to perform better than the classic approach for a majority of the rainy events. The performances of the three propagation models differ from one another, depending on the evaluation criteria used. One of them provides a satisfactory time of arrival of rainfall but slightly smooths the rain intensities. The two others reproduce well the rain intensities, but the time of arrival of the rain is sometimes delayed. The choice of an appropriate propagation algorithm will thus depend on the operational objectives underlying the rain field generation.
Highlights
IntroductionStudies dealing with rainfall at small space–time scales are usually based on data obtained from recording rain gauge (RG) networks and/or meteorological radars
Producing high-resolution rain fields is a key element in several domains: (i) studying the climatology of rainfall at fine space–time scales (e.g. Krajewski et al 2003; Moszkowicz 2000; Bacchi and Kottegoda 1995); (ii) modeling the hydrological processes on the continental surface because hydrologic, agronomic, or soil–vegetation– atmosphere transfer models require high-resolution forcingStudies dealing with rainfall at small space–time scales are usually based on data obtained from recording rain gauge (RG) networks and/or meteorological radars
In West Africa, which is the region studied in this paper, Mathon et al (2002) have calculated that 90% of the annual rainfall is produced by mesoscale convective systems (MCS) for the Sahelian band
Summary
Studies dealing with rainfall at small space–time scales are usually based on data obtained from recording rain gauge (RG) networks and/or meteorological radars The latter provide valuable estimation of rainfall spatial pattern but yield rainfall intensity estimations subject to strong uncertainties (Wilson and Brandes 1979; Krajewski et al 2010). VOLUME 12 sensor producing a direct and accurate measurement of rainfall intensities These point measurements need to be spatially interpolated to obtain the areal rain fields required for most of the applications listed above. This interpolation process must take into account two features characterizing small time step rain fields: (i) their great spatial variability, including an important intermittency, and (ii) a nonnegligible autocorrelation in time. In West Africa, which is the region studied in this paper, Mathon et al (2002) have calculated that 90% of the annual rainfall is produced by mesoscale convective systems (MCS) for the Sahelian band
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
