Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002–2012)

Abstract

Abstract. We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over the contiguous United States (CONUS) for the period 2002-2012. This comparison effort includes satellite multi-sensor data sets (bias-adjusted TMPA 3B42, near-real-time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation data sets are compared with surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (±6 %). However, differences at the RFC are more important in particular for near-real-time 3B42RT precipitation estimates (−33 to +49 %). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near-real-time counterpart 3B42RT. However, large biases remained for 3B42 over the western USA for higher average accumulation (≥ 5 mm day −1 ) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in. day −1 ) over the Pacific Northwest. Furthermore, the conditional analysis and a contingency analysis conducted illustrated the challenge in retrieving extreme precipitation from remote sensing estimates.

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

“…Nevertheless, the FAR values increase with temporal accumulation and are nearly comparable with those available in the literature (Sunilkumar et al, 2015). The study region being a semi-arid region with dry atmo- 32,18.8 46.6,18.8 50,17.8 47.7,13.8 42.5,38.3 49.2,38.3 53.3,31.6 45,40 POD 67.9,81.8 53.3,81.8 50.8,82.1 52.2,86.1 56.6,61.6 50.8,61.6 46.6,68.3 55,60 spheric conditions, evaporation of falling rain is found to be significant with higher fraction of virga rain (predominant during the SWM) (Radhakrishna et al, 2008;Saikranthi et al, 2014). Since MPEs depend mostly on cloud top temperature or ice scattering signature for deriving rainfall over the land, significant evaporation of falling rain and higher fraction of virga rain results larger FAR values (Sunilkumar et al, 2015).…”

“…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).…”

Remote sensing-based characterization of rainfall during atmospheric rivers over the central United States

“…Further, the performance of the products decreases as the events become more extreme. A more recent study by Prat and Nelson (2015) also highlights the poor performance of satellite and remote sensing products in capturing extreme rainfall. Further challenges arise due to the peculiar structure of narrow and long rain bands that have been observed in some ARs studied over the central United States (for example, Moore et al, 2012;Nayak et al, 2016).…”

“…As part of this study, we will focus on one radar-based and four satellite-based rainfall products and perform a comprehensive evaluation with respect to a rain-gage based reference product. While the evaluation of remote-sensing product is topic addressed in several previous studies (for example, AghaKouchak et al, 2011;Cai et al, 2015;Chen et al, 2013;Derin and Yilmaz, 2014;Prat and Nelson, 2015;Puca et al, 2014;Vila et al, 2009;Villarini and Krajewski, 2007;Villarini et al, 2009;Zhang et al, 2015), how well different products can represent AR rainfall is a topic that requires further investigation. AghaKouchak et al (2011) compared four satellite products and concluded that none of the products can be treated as best suited to characterize extreme rainfalls.…”

Toward an object‐based assessment of high‐resolution forecasts of long‐lived convective precipitation in the central U.S.

“…Though the Stage IV product has a history of use as the reference product for validation of both models and other observational data sets, it is not without its own uncertainties, some discussion of which can be found in Smalley et al [2014] and Prat and Nelson [2015].…”