Adjusting Satellite Precipitation Data to Facilitate Hydrologic Modeling

Abstract

Significant concern has been expressed regarding the ability of satellite-based precipitation products such as the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 products (version 6) and the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center's (CPC) morphing technique (CMORPH) to accurately capture rainfall values over land. Problems exist in terms of bias, false-alarm rate (FAR), and probability of detection (POD), which vary greatly worldwide and over the conterminous United States (CONUS). This paper directly addresses these concerns by developing a methodology that adjusts existing TMPA products utilizing ground-based precipitation data. The approach is not a simple bias adjustment but a three-step process that transforms a satellite precipitation product. Ground-based precipitation is used to develop a filter eliminating FAR in the authors' adjusted product. The probability distribution function (PDF) of the satellite-based product is adjusted to the PDF of the ground-based product, minimizing bias. Failure of precipitation detection (POD) is addressed by utilizing a ground-based product during these periods in their adjusted product. This methodology has been successfully applied in the hydrological modeling of the San Pedro basin in Arizona for a 3-yr time series, yielding excellent streamflow simulations at a daily time scale. The approach can be applied to any satellite precipitation product (i.e., TRMM 3B42 version 7) and will provide a useful approach to quantifying precipitation in regions with limited ground-based precipitation monitoring.

Evaluation of IMERG and TRMM 3B43 Monthly Precipitation Products over Mainland China

“…China, located in eastern Asia, covers an area of approximately 9.6 million km 2 . China features a wide range of climatic patterns, ranging from tropical in the far south to subarctic in the far north and to alpine in the higher elevations of the Tibetan Plateau.…”

“…The availability of reliable and accurate precipitation data at regional and global scales is critical to the applications of meteorology, hydrology, and water resources management [1][2][3]. Precipitation exhibits small-scale variability and highly non-normal statistical behavior that requires frequent, closely spaced observations for adequate data representation.…”

Hydrological monitoring and seasonal forecasting: Progress and perspectives

“…Actually, the gauge-based meteorological data used for retrospective simulations are barely reported in real-time manner due to the time latency for quality controlling. Thus, a main challenge for an operational drought monitoring is the inconsistency of real-time meteorological forcings and gauge-based observations (Tang et al, 2009a;Tobin and Bennett, 2010;Sheffield et al, 2014;Nijssen et al, 2014;Zhang and Tang, 2015). To resolve this issue, Tang et al (2009a) proposed an index station percentile method (ISPM) to estimate the real-time precipitation of all index stations based on the limited number of real-time stations.…”

“…To resolve this issue, Tang et al (2009a) proposed an index station percentile method (ISPM) to estimate the real-time precipitation of all index stations based on the limited number of real-time stations. Alternatively, when the satellite data is used as real-time source of model drivers, an equal quantile mapping method was usually employed to remove its systematic bias relative to ground observations (Tobin and Bennett, 2010;Zhang and Tang, 2015). By doing so, the real-time hydrological estimates can be directly compared with the multi-decadal retrospective simulations and thus greatly benefits the real-time drought diagnosis and detection (Sheffield et al, 2014).…”

Analysis of adjustment of satellite precipitation for streamflow simulations

“…A number of approaches have been developed to adjust satellite precipitation mostly focusing on bias correction (Vila et al [4]). Additionally, Tobin and Bennett [5] developed a method that corrects for bias, lack of precipitation detection, and false alarms.…”