Piotr Domaszczynski

Hydro-NEXRAD Radar-Rainfall Estimation Algorithm Development, Testing and Evaluation

The Hydro-NEXRAD radar-rainfall estimation algorithms involve three main components: 1) preprocessing, 2) rain rate, and 3) rainfall accumulation. The preprocessing algorithm performs the quality control of reflectivity volume data and generates a hybrid scan. That is, reflectivity values for each azimuth and range bin are assigned from the several lowest elevation angles. It optionally estimates an azimuthdependent vertical reflectivity profile and performs a correction for range effects. The rain rate algorithm converts the corrected reflectivity to rainfall intensity. The user can specify any power-law type empirical relationship between reflectivity and rainfall intensity. The last step of rainfall estimation is to integrate consecutive rate scans for specific time duration ranging from 15 minutes to daily. The algorithm mimics real-time calculations and involves advection correction.

Towards Better Utilization of NEXRAD Data in Hydrology: An Overview of Hydro-NEXRAD

Witold F. Krajewski (corresponding author) Anton Kruger Charles Gunyon Radoslaw Goska Bong-Chul Seo Piotr Domaszczynski A. Allen Bradley IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IA 52242, USA E-mail: witold-krajewski@uiowa.edu James A. Smith Mary Lynn Baeck Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA Mohan K. Ramamurthy Jeffrey Weber Unidata Program Center, UCAR, Boulder, CO 80307, USA Stephen A. DelGreco National Climatic Data Center, Ashville, NC 28801, USA Ramon Lawrence Computer Science, University of British Columbia, Okanagan, BC V1V 1V7, Canada Matthias Steiner National Center for Atmospheric Research, Boulder, CO 80301, USA With a very modest investment in computer hardware and the open-source local data manager (LDM) software from University Corporation for Atmospheric Research (UCAR) Unidata Program Center, a researcher can receive a variety of NEXRAD Level III rainfall products and the unprocessed Level II data in real-time from most NEXRAD radars in the USA. Alternatively, one can receive such data from the National Climatic Data Center in Ashville, NC. Still, significant obstacles remain in order to unlock the full potential of the data. One set of obstacles is related to effective management of multi-terabyte datasets. A second set of obstacles, for hydrologists and hydrometeorologists in particular, is that the NEXRAD Level III products are not well suited for applications in hydrology. There is a strong need for the generation of high-quality products directly from the Level II data with well-documented steps that include quality control, removal of false echoes, rainfall estimation algorithms, coordinate conversion, georeferencing and integration with GIS. For hydrologists it is imperative that these procedures are basin-centered as opposed to radar-centered. The authors describe the Hydro-NEXRAD system that addresses the above challenges. With support from the National Science Foundation through its ITR program, the authors have developed a basin-centered framework for addressing all these issues in a comprehensive manner, tailored specifically for use of NEXRAD data in hydrology and hydrometeorology.

Hydro-NEXRAD: An Updated Overview and Metadata Analysis

Hydro-NEXRAD is a prototype system that allows hydrologists to obtain userspecified rainfall data for their research. These data are based on observations collected by the national network of WSR-88D radars, known as NEXRAD. Users interact with Hydro-NEXRAD through a web-based interface that has map-based components for spatial navigation, calendar- and time series plot components for temporal navigation and a menu-based component for selection of processing options. Through the interface, users browse the Hydro-NEXRAD metadata and select data of interest. As the system is approaching the point of being fully operational, the authors and a group of test users have evaluated several aspects of the system. Metadata remains very important for the system functionality. Radar-based, basinbased and point (for selected set of rain gauge locations) metadata serve multiple purposes: 1) enable users to efficiently search for subsets of data (SQL query, visual inspection), 2) provide information on quality of the collected data archive (missing or corrupt data), 3) and have a scientific value (basin-based metadata has a potential to be used as a precipitation input to hydrologic models).