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.
Highlights
Over the last decades, numerous long-term rainfall data sets have been developed using rain gauge (RG) precipitation measurements, remotely sensed quantitative precipitation estimates (QPEs), or combining different sensors, each of which have specific characteristics and limitations
Multisensor satellite-based products – PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks; Sorooshian et al, 2000) and variants PERSIANN-CDR (Climate Data Record; Ashouri et al, 2015), CMORPH (CPC MORPHing technique; Joyce et al, 2004), and TMPA (TRMM (Tropical Rainfall Measuring Mission) Multisatellite Precipitation Analysis; Huffman et al, 2007) – or ground-based radar rainfall estimates – NCEP (National Centers for Environmental Prediction) Stage IV (Lin and Mitchell, 2005) or, more recently, the National Mosaic and Multi-sensor QPE (NMQ/Q2) (Zhang et al, 2011) – provide an opportunity to broach the problem of sparse observations over land and/or ocean
The objective of this study is to provide a comparison of a suite of common QPEs derived from satellites, radars, and rain gauges data sets for the period 2002–2012 over the contiguous United States (CONUS)
Summary
Numerous long-term rainfall data sets have been developed using rain gauge (RG) precipitation measurements, remotely sensed (ground-based radars, satellites) quantitative precipitation estimates (QPEs), or combining different sensors, each of which have specific characteristics and limitations. There are a fair amount of studies available that compare the respective merit of the data sets described above either against each other or against other data sets used as a reference Those studies often investigate isolated events such as intense precipitation or focus on a time period that is limited by day, month, or season. The remotely sensed data sets will be compared against surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and estimations from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), which combines surface observations with a digital elevation model to account for the orographic enhancement of precipitation Both GHCN-D and PRISM will be used as a baseline for QPE product evaluations.
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