Comprehensive Evaluation of the IFloodS Radar Rainfall Products for Hydrologic Applications

Abstract

This study describes the generation and testing of a reference rainfall product created from field campaign datasets collected during the NASA Global Precipitation Measurement (GPM) mission Ground Validation Iowa Flood Studies (IFloodS) experiment. The study evaluates ground-based radar rainfall (RR) products acquired during IFloodS in the context of building the reference rainfall product. The purpose of IFloodS was not only to attain a high-quality ground-based reference for the validation of satellite rainfall estimates but also to enhance understanding of flood-related rainfall processes and the predictability of flood forecasting. We assessed the six RR estimates (IFC, Q2, CSU-DP, NWS-DP, Stage IV, and Q2-Corrected) using data from rain gauge and disdrometer networks that were located in the broader field campaign area of central and northeastern Iowa. We performed the analyses with respect to time scales ranging from 1 h to the entire campaign period in order to compare the capabilities of each RR product and to characterize the error structure at scales that are frequently used in hydrologic applications. The evaluation results show that the Stage IV estimates perform superior to other estimates, demonstrating the need for gauge-based bias corrections of radar-only products. This correction should account for each product’s algorithm-dependent error structure that can be used to build unbiased rainfall products for the campaign reference. We characterized the statistical error structures (e.g., systematic and random components) of each RR estimate and used them for the generation of a campaign reference rainfall product. To assess the hydrologic utility of the reference product, we performed hydrologic simulations driven by the reference product over the Turkey River basin. The comparison of hydrologic simulation results demonstrates that the campaign reference product performs better than Stage IV in streamflow generation.

The Role of Weather Radar in Rainfall Estimation and Its Application in Meteorological and Hydrological Modelling—A Review

“…It is because the quantitative estimation of the radar rainfall retrievals strongly influences model results. Studies with physically-based models have focused on two main sources of uncertainty: uncertainty in rainfall input [188][189][190] originated from the systematic errors produced in the process of Z-R transformation, and uncertainty in model parameters [191,192]. Investigations on radar hydrology are more frequently focused on rainfall input uncertainty.…”

“…In a trade-off between the added error of the radar rainfall derivation chain and the improvement on the radar rainfall estimates, the bias adjustment by means of rain gauge networks has been extensively accepted for applications on radar hydrology, while efforts have been made to reduce the negative effects of relative calibration on radar composites, as in Seo et al [193]. For instance, uncertainty in radar rainfall estimates was evaluated by Seo et al [188] using different radar rainfall products that differ on the data composition (i.e., only radar-based product vs. rain gauge bias-adjusted radar product). The study demonstrated the need for bias-adjusted radar estimates related to the Iowa Flood Studies (IFloodS) experiment.…”

Development and Evaluation of the Raindrop Size Distribution Parameters for the NASA Global Precipitation Measurement Mission Ground Validation Program

“…The 2DVD was the primary instrument used in this study to derive the relationships between the radar observables and gamma DSD parameters. The datasets included four GPM field campaigns: the Midlatitude Continental Convective Clouds experiment (MC3E) (Jensen et al 2016); the Iowa Flood Studies (IFloodS) (Seo et al 2018); the Integrated Precipitation and Hydrology Experiment (IPHEx) (Duan et al 2015); the Olympic Mountain Experiment (OLYMPEx) (Houze et al 2017); and extended (longer than 1 year) data collections made at two NASA facilities: Marshall Space Flight Center, University of Alabama at Huntsville (MSFC-UAH), and NASA Goddard Space Flight Center Wallops Flight Facility (GSFC-WFF). These datasets represent a range of midlatitude meteorological regimes including midcontinental deep convection (MC3E), flood-producing frontal and mesoscale convective systems (IFloodS), warm-season Appalachian area orographic enhancements from valley to mountain (IPHEx), West Coast cold-season orographic enhancements from ocean to mountain (OLYMPEx), as well as a year-long sampling of midlatitude continental inland (MSFC-UAH) and coastal mid-Atlantic region precipitation (GSFC-WFF).…”

Streamflow Forecasting without Models

“…limited only to the observational and estimation errors). In reality, the rainfall forecasts are subject to considerable uncertainty, especially for the longer lead times (e.g., Seo et al 2018).…”