Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA/NSSL Ground Radar–Based National Mosaic QPE

Abstract

Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a 3-month data sample in the southern part of the United States. The primary contribution of this study is the presentation of the detailed steps required to derive a trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relies on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors are revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfallrate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall-rate estimates from other sensors on board low-earth-orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.

Snowfall Detectability of Nasa's Cloudsat: The First Cross-Investigation of Its 2c-Snow-Profile Product and National Multi-Sensor Mosaic Qpe (Nmq) Snowfall Data

“…Our confidence in using the NMQ (or NEXRAD) data partially comes from its superior performance with liquid precipitation measurement, which has been validated by many studies using either surface observations or cross-validation with other remote sensing sensors [7,17,18]. The NMQ rainfall products have also been used as the benchmark to quantify the spaceborne rainfall measurements [5][6][7]. The NMQ snowfall algorithm has a similar radar data processing scheme to the NMQ rainfall algorithm except it applies a different temperature criterion for snowfall identification and a different Z-S relation for snowfall rate estimation.…”

“…Since June 2006, the NMQ system has been generating high-resolution nation-wide 2D/3D QPE products that have been utilized in various applications associated with weather and precipitation. Particularly, those QPE products have been applied as the benchmark for the ground validation of precipitation measured from space [5][6][7]. Since July 2013 the NMQ system has been upgraded to the multi-radar/multi-sensor system (MRMS), which accommodates the recent dual-polarization upgrade of NEXRAD radars.…”

Evaluation and Uncertainty Estimation of the Latest Radar and Satellite Snowfall Products Using SNOTEL Measurements over Mountainous Regions in Western United States

“…To date, much effort has gone into evaluating precipitation retrievals by comparing with ground observations (e.g., [8][9][10][11][12]), while few studies have investigated the errors in snowfall retrievals. Kirstetter et al (2015) [13] performed a quantitative evaluation of the MRMS Reflectivity-Snow Water Equivalent (SWE) relationship, and noted significant underestimation relative to the precipitation gauges in Hydrometeorological Automated Data System (HADS).…”

The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

“…With regard to precipitation it should be mentioned that, although the reliable knowledge of its intensity and accumulation is essential for understanding the global hydrological and energy cycles, precipitation estimate (from satellite and from the surface) is complicated by several factors: the large variability of the precipitation in time and space, the conversion of satellite measurements into quantitative precipitation estimates, uncertainties associated to rain gauges (and to their spatial distribution) and radar measurements (i.e., attenuation, beam-blocking), and their unavailability in several regions in the world and over ocean (Mugnai et al, 1993;Iturbide-Sanchez et al, 2011;Bennartz and Petty, 2001;Tian et al, 2009;Kirstetter et al, 2012). …”