For effective use of radar-based QPE for high-resolution flash flood forecasting in large urban areas, it is necessary to understand and assess how the errors in radar-based QPE may differ over different spatiotemporal scales of...
moreFor effective use of radar-based QPE for high-resolution flash flood forecasting in large urban areas, it is necessary to understand and assess how the errors in radar-based QPE may differ over different spatiotemporal scales of aggregation and how they may manifest in hydrologic simulations. Toward that end, we carry out comparative evaluation of QPEs from the Multisensor Precipitation Estimator (MPE), NEXRAD Digital Hybrid Scan Reflectivity (DHR), Q2 and CASA (Collaborative Adaptive Sensing of the Atmosphere) for the Dallas-Fort Worth Metroplex (DFW) and for the headwater catchments of the Upper Trinity River Basin in North Texas. We carry out scale-compatible intercomparison of the QPE products, compare them with rain gauge observations and assess the relative information content among the QPEs via error decomposition and orthogonal analysis. We then evaluate streamflow simulation using lumped and hydrologic models forced by the different QPEs. For lumped modeling, the Sacramento...
ABSTRACT Radar can monitor the atmospheric conditions of a wide area very quickly and provide advanced observations and warnings for the precipitation systems at high spatial resolution. Over the past two decades, significant progress has...
moreABSTRACT Radar can monitor the atmospheric conditions of a wide area very quickly and provide advanced observations and warnings for the precipitation systems at high spatial resolution. Over the past two decades, significant progress has been made in dual-polarization radar quantitative precipitation estimations (QPE). The polarimetric radar observations can provide more information on the drop size distribution and hydrometeor classifications over traditional Z-R methods. Among different rainfall algorithms, the Kdp-based QPE was proved to be immune to the partial beam blockage and hail contamination, and it is also less prone to the calibration errors. The networked Kdp-based QPE system developed by the U.S. National Science Foundation Engineering Research Center (NSF-ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) has shown a great improvement compared with state-of-the-art. The high spatial and temporal resolution rainfall products from CASA QPE system can serve as a reliable data input for distributed hydrological models. The Research Distributed Hydrologic Model (RDHM) developed by the U.S. National Weather Service (NWS) Office of Hydrologic Development (OHD) is a promising tool for generating streamflow and other hydrological information such as soil moisture, etc. It can incorporate the heat transfer (HT) dynamics with the Sacramento soil moisture accounting model (SAC) to simulate rainfall-runoff and channel routing models for routing streamflow. In this research, the SAC-HT model was forced using hourly rainfall estimates produced by the CASA X-band dual-polarization radar network, for the purpose of predicting hydrological response and dealing with the flash flood issues. This paper will present a brief overview of the CASA QPE system and its various products. Then, the impacts of CASA QPE on SAC-HT model are mainly focused on, by using the networked polarimetric radar observations collected in IP-1 test bed in Southwestern Oklahoma. The "first" observation in CASA's urban demonstration network being deployed in the populous Dallas-Fort Worth (DFW) metroplex is also expected for the hydrological analysis.
ABSTRACT The dual-polarization X-band radar network developed by the U.S. National Science Foundation Engineering Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) has shown great advantages for observing and prediction...
moreABSTRACT The dual-polarization X-band radar network developed by the U.S. National Science Foundation Engineering Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) has shown great advantages for observing and prediction of hazardous weather events in the lower atmosphere (1-3 km above ground level). The network is operating though a scanning methodology called DCAS, distributed collaborative adaptive sensing, which is designed to focus on particular interesting regions of the atmosphere and disseminate information for decision-making to multiple end-users, such as emergency managers and policy analysts. Since spring 2012, CASA and the North Central Texas Council of Governments (NCTCOG) have embarked the development of Dallas Fort Worth (DFW) urban remote sensing network, including 8-node of dual-polarization X-band radars, in the populous DFW Metroplex (pop. 6.3 million in 2010). The main goal of CASA DFW urban demonstration network is to protect the safety and prosperity of humans and ecosystems through research activities that include: 1) to demonstrate the DCAS operation paradigm developed by CASA; 2) to create high-resolution, three-dimensional mapping of the meteorological conditions; 3) to help the local emergency managers issue impacts-based warnings and forecasts for severe wind, tornado, hail, and flash flood hazards. The products of this radar network will include single and multi-radar data, vector wind retrieval, quantitative precipitation estimation and nowcasting, and numerical weather predictions. In addition, the high spatial and temporal resolution rainfall products from CASA can serve as a reliable data input for distributed hydrological models in urban area. This paper presents the information and communication link between radars, rainfall product generation, hydrologic model link and end user community in the Dallas Fort Worth Urban Network. Specific details of the Information and Communication Technologies (ICT) between the various subsystems are presented.
