Assessing Satellite-Based Rainfall Estimates in Semiarid Watersheds Using the USDA-ARS Walnut Gulch Gauge Network and TRMM PR

Abstract

The rain gauge network associated with the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona provides a unique opportunity for direct comparisons of in situ measurements and satellite-based instantaneous rain rate estimates like those from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR). The WGEW network is the densest rain gauge network in the PR coverage area for watersheds greater than 10 km 2 . It consists of 88 weighing rain gauges within a 149-km 2 area. On average, approximately 10 gauges can be found in each PR field of view (;5-km diameter). All gauges are very well synchronized with 1-min reporting intervals. This allows generating very-high-temporal-resolution rain rate fields and obtaining accurate estimates of the area-average rain rate for the entire watershed and for a single PR field of view. In this study, instantaneous rain rate fields from the PR and the spatially interpolated gauge measurements (on a 100 m 3 100 m grid, updated every 1 min) are compared for all TRMM overpasses in which the PR recorded rain within the WGEW boundaries (25 overpasses during 1999-2010). The results indicate very good agreement between the fields with low bias values (,10%) and high correlation coefficients, especially for the near-nadir cases (.0.9). The correlation is high at overpass time but the peak occurs several minutes after the overpass, which can be explained by the fact that it takes several minutes for the raindrops to reach the gauge from the time they are observed by the PR. The correlation improves with the new version of the TRMM algorithm (V7). The study includes assessment of the accuracy of the reference products.

Radar subpixel-scale rainfall variability and uncertainty: lessons learned from observations of a dense rain-gauge network

“…Fiener et al (2009) installed a network consisting of 13 tipping-bucket rain gauges on a 1.4 km 2 area in Germany to determine the spatial variability of rainfall on a subkilometer scale, taking into account the wind's potential effect. The Walnut Gulch Experimental Watershed (WGEW), equipped with about 10 rain gauges per every TRMM Precipitation Radar pixel (∼ 5 km in diameter), was used by Amitai et al (2012) who conducted rain rate comparisons of these two resources for a semiarid climate. Several studies have explored the small-scale spatial variability of the rainfall drop size distribution (DSD) (Tapiador et al, 2010;Tokay et al, 2010).…”

Evaluation of Extreme Rain Estimates in the TRMM/PR Standard Product Version 7 Using High-Temporal-Resolution Rain Gauge Datasets over Japan

“…Amitai et al (2012) employed densely arranged high-temporal-resolution rain gauge networks over a small basin in Arizona, USA, to evaluate near-surface rain rate estimates (NSR) both in V6 and V7. Since approximately 10 gauges are available in a field-of-view (FOV) of PR, NSR can be compared with FOV-averaged rain gauge data.…”

“…Since approximately 10 gauges are available in a field-of-view (FOV) of PR, NSR can be compared with FOV-averaged rain gauge data. Amitai et al (2012) used rain gauge data with a temporal resolution as high as 1 minute and set a time lag between NSR and rain gauge data for their matchup. V6 and V7 show similar biases in NSR, but V7 is superior to V6 in terms of correlation coefficients.…”

Scoping a field experiment: error diagnostics of TRMM precipitation radar estimates in complex terrain as a basis for IPHEx2014

“…Recent advances toward high spatial and temporal resolution satellite-based quantitative precipitation estimation (QPE) make these estimates potentially attractive for flood forecasting and other operational hydrology studies (e.g., Barros, 2013, 2014, and references therein). Numerous studies have been conducted to compare satellite products against ground measurements to quantify errors and to improve retrieval algorithms (Amitai et al, 2009(Amitai et al, , 2012Barros et al, 2000;Kirstetter et al, 2013;Tao and Barros, 2010;Wolff and Fisher, 2008). For long-term monitoring, raingauges remain the most autonomous and affordable instruments, but large errors can be introduced in extrapolating point observations to represent areal means (Prasetia et al, 2012).…”

“…For long-term monitoring, raingauges remain the most autonomous and affordable instruments, but large errors can be introduced in extrapolating point observations to represent areal means (Prasetia et al, 2012). Considering the large uncertainties due to satellite temporal sampling and volume sampling discrepancies, and the challenges in accounting for atmospheric heterogeneity and landform complexity, direct comparison of satellitebased precipitation estimates with ground-based point measurements (e.g., raingauges) poses many challenges, especially at short timescales over small areas (< 1000 km; Amitai et al, 2012;Barros and Tao, 2008;Fisher, 2004; among many others).…”