A Field Experiment on the Small-Scale Variability of Rainfall Based on a Network of Micro Rain Radars and Rain Gauges

Abstract

Small-scale summer rainfall variability in a semiarid zone was studied by deploying five vertically pointing Micro Rain Radars (MRRs) along a nearly straight line and by using 12 rain gauges in the study area of the Xilin River catchment in China. The spatial scales of 4 and 9 km correspond to the resolution of precipitation radar and rainfall products from satellites. The dataset of the MRRs and rain gauges covers two months in the summer of 2009. Three parameters, that is, spatial correlation, intermittency, and the coefficient of variation (CV), were used to describe the rainfall variability as based on the data from the MRRs and rain gauges. The probability of partial beamfilling in a 4-km (9 km) pixel over a 30-min temporal scale was 17%–20% (28%–37%). More accurate equipment can measure lower rainfall intermittency. For scales of 4 and 9 km, the median CV of the accumulation times that were longer than 3 h with rainfall > 1 mm was 0.17–0.42. The accuracy of areal rainfall measured by different quantities of equipment was also evaluated. One MRR was sufficient for measuring the daily areal rainfall at a 4-km scale, with a fraction of prediction within a factor of 2 of observations of 1.0 and a correlation coefficient of ≥0.58 when daily mean rainfall was >1 mm.

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

“…Earlier studies have shown the usefulness of such analysis in gauge-satellite comparisons, hydrological and meteorological modeling and setting up gauge networks (Habib et al, 2001;Krajewski et al, 2003;Ciach and Krajewski, 2006;Villarini et al, 2008;Liechti et al, 2012;Luini and Capsoni, 2012;Mandapaka and Qin, 2013;Li et al, 2014;Chen et al, 2015). Spearman correlation coefficients have been computed between each pair of rain gauge locations for different rain accumulation periods.…”

“…6 (a for the SWM and b for the NEM). The spatial correlation distance is obtained by fitting a modified exponential model on the data samples in correlograms (intergauge correlation coefficient vs. intergauge distance), as given by Ciach and Krajewski (2006),…”

“…Research networks with a high density of gauges, but covering a limited area, are becoming increasingly popular to understand the sub-grid and sub-daily scale variability of rainfall. Measurements from such networks are also useful for the validation of precipitation estimates from microwave radars and imagers (Krajewski et al, 2003;Habib et al, 2012;Tokay et al, 2014;Dzotsi et al, 2014;Chen et al, 2015). The complexity in small-scale variability of rainfall increases in hilly terrain (Zängl, 2007;Li et al, 2014).…”

“…The rainfall often becomes inhomogeneous due to topographic influence and is at times highly localized, resulting in large errors in the retrieved precipitation by passive/active remote sensors due to non-uniform beam-filling of precipitation within the satellite or radar pixel (Tokay and Ozturk, 2012). In order to understand the physical processes responsible for such variability, several studies examined the dependency of rainfall spatial variability (in terms of correlation distance, d o ) on rainfall regimes (Krajewski et al, 2003), seasons, spatial and temporal aggregation of data (Krajewski et al, 2003;Villarini et al, 2008;Luini and Capsoni, 2012;Chen et al, 2015;Prat and Nelson, 2015), sample size and extreme rain events (Habib et al, 2001) and geographical features like topography (Li et al, 2014). Proper quantification of spatial correlation distance mitigates the uncertainty in the upscaling of rainfall from point-to-areal and also helps in designing rain gauge networks (Bras and RodriguezIturbe, 1993;Villarini et al, 2008).…”

Study of spatiotemporal rainfall structure and optimized local radar rainfall application to urban watershed, Louisville, Kentucky, 2010-2014.

“…Historically, rainfall monitoring by ground-level rain gauges is considered the most reliable measurement system for hydrologic applications because it physically captures pluvial water. In hydrologic engineering and research, rain gauge measurements frequently serve as reference data for evaluation of indirect or remote sensing rainfall systems such as weather radars and satellites (Krajewski and Smith 2002;Seo and Breidenbach 2002;Habib 2012;Price 2014;Chen 2015;Fencl 2015). However, limitations must be considered since a single rain gauge is a point measurement with limited direct information defining spatial variation of rainfall across an area.…”