Evaluation of Radar-Rainfall Products over Coastal Louisiana

Abstract

Radar-rainfall products provide valuable information for hydro-ecological modeling and ecosystem applications, especially over coastal regions that lack adequate in-situ rainfall observations. This study evaluates two radar-based rainfall products, the Multi-Sensor Stage IV and the Multi-Radar Multi-Sensor (MRMS), over the Louisiana coastal region in the United States. Surface reference rainfall observations from two independent rain gage networks were used in the analysis. The evaluation included distribution-based comparisons between radar and gage observations at different time scales (hourly to monthly), bias decomposition to quantify the contribution of different error sources, and conditional evaluation of systematic and random components of the estimation errors. Both products report large levels of random errors at the hourly scale; however, the performance of the radar-rainfall products improves significantly with the increase in time scales. After decomposing the total bias, the results show that the largest contributor to the overall bias in radar-rainfall products is false rainfall detection, followed by missed rainfall. The results also reveal that the Stage IV product experienced a significant improvement over the area in the past few years (post 2015) compared to earlier periods. The results have implications for ongoing and future coastal ecosystem modeling and planning studies.

Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations

“…It covers a domain that contains Lafayette parish (county) and its surrounding. Lafayette parish is fairly developed with a relatively dense population and has been frequently impacted by floods resulting from extreme rainfall events (Sharif et al, 2020). Additionally, the area lies in a very flat region due to its proximity to the Gulf of Mexico, this allows the water to stagnate on the ground and raise the water levels in the stream channels for elongated periods.…”

“…The National Severe Storm Laboratory (NSSL) implements this product. MRMS has been extensively evaluated and validated for various applications (e.g., Zhang et al, 2016;ElSaadani et al, 2018;Sharif et al, 2020) and is available since September 2015. The product is archived by Iowa State University's Iowa Environmental Mesonet (IEM) in GRIB format and is available at (https://mesonet.agron.iastate.edu/archive/).…”

“…The product is archived by Iowa State University's Iowa Environmental Mesonet (IEM) in GRIB format and is available at (https://mesonet.agron.iastate.edu/archive/). MRMS is an excellent source of continuous spatiotemporal rainfall observations over the entire United States and is widely used by the hydrologic community (Zhang et al 2016;Sharif et al, 2020). In this study, rainfall observations are an irreplaceable source of information to train the DL models due to the direct relationships between rainfall and SM (Norbiato et al, 2008;Fang et al, 2017).…”

Empirical Distribution of Conditional Errors in Radar Rainfall Products

“…The EEM functional form given by Equation was used in Ciach and Gebremichael (2004), Habib et al (2013), and Sharif et al (2020). All the other studies mentioned above, except that of Kirstetter et al (2015), used the PPM representation.…”

“…The conditional statistics of RR errors were first presented in Ciach and Gebremichael (2004), and the method was later described in detail by Ciach et al (2007). After that, conditional statistics have been applied to evaluations and intercomparisons of RR products (Cunha et al, 2015; Habib et al, 2013; Sharif et al, 2020), to analyses of RR error structure (Habib & Qin, 2013; Villarini et al, 2008), to examine effects of RR error structure on streamflow predictions (Cunha et al, 2012; Habib et al, 2008), to probabilistic rain rate estimation (Kirstetter et al, 2015), to probabilistic flash flood forecasting (Villarini et al, 2010), to generate ensembles of probable surface rainfall fields based on arrays of deterministic RR estimates (Villarini, Krajewski, Ciach, et al, 2009, 2014), and to account for RR errors in evaluation of satellite rainfall estimates (Villarini, Krajewski, & Smith, 2009).…”