J1j.4 Multiple Doppler Wind Analysis and Assimilation via 3dvar Using Simulated Observations of the Planned Casa Network and Wsr-88d Radars
نویسندگان
چکیده
The advantages of using Doppler weather radar to track and forecast mesoscale severe weather events are widely known to both meteorologists and the public. With the use of Doppler radar, meteorologists can provide better information to the public, ultimately saving lives and property, by remotely observing the internal structure of thunderstorms at high resolution. Despite its highly advanced capabilities, Doppler radar can only directly measure the spectrum width, reflectivity, and the radial component of wind velocity. Thus, there is no direct measurement of the three-dimensional (3-D) wind field. To completely understand and analyze the atmosphere, the 3-D wind field must be known. To solve this problem and provide the better surveillance of severe weather, A new National Science Foundation Engineering Research Center, the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), was established in 2003 to develop low-cost, high spatial density and dynamically adaptive networks of Doppler radars for sensing the lower atmosphere. This project is a joint effort between the University of Massachusetts-Amherst, the University of Oklahoma, Colorado State University, and the University of Puerto Rico. The first test bed to be deployed in Oklahoma named IP1-A will consist of four scanning polarimetric Doppler radars located on average 30 km apart with ranges of the same distance. The network was designed to maximize dual-Doppler wind coverage and at certain parts of the network, triple Doppler wind coverage is also available. By using two or multiple Doppler radars scanning the same atmospheric volume simultaneously, it is possible to determine the 3-D wind. Gao et al. (1999) described a variational approach (3DVAR) that uses mass continuity and smoothness constraints by incorporating them into a cost function yielding the 3-D wind. In this study, this 3DVAR analysis method is adapted to perform multiple Doppler wind analysis for CASA radars, together with data from the Oklahoma City (KTLX) and Fredrick, Oklahoma (KFRD)
منابع مشابه
The Analysis and Prediction of the 8–9 May 2007 Oklahoma Tornadic Mesoscale Convective System by Assimilating WSR-88D and CASA Radar Data Using 3DVAR
The Advanced Regional Prediction System (ARPS) model is employed to perform high-resolution numerical simulations of a mesoscale convective system and associated cyclonic line-end vortex (LEV) that spawned several tornadoes in central Oklahoma on 8–9 May 2007. The simulation uses a 1000 km 3 1000 km domain with 2-km horizontal grid spacing. The ARPS three-dimensional variational data assimilati...
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ii Abstract One of the goals of the National Science Foundation Engineering Research Center (ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) is to improve storm-scale numerical weather prediction (NWP) by collecting data with dense X-band radar network which provides high-resolution low-level coverage, and by assimilating such data into NWP models. During the first spring storm...
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In late 2006, the NSF Engineering Research Center (ERC) for Collaborative and Adaptive Sensing of the Atmosphere (CASA, McLaughlin et al. 2005) began its first integrated project (IP1, Brotzge et al. 2007). CASA-IP1 consists of a network of four x-band dual-polarization Doppler radars in southwest Oklahoma. During the spring of 2007, this radar network collected data from a number of severe con...
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The impact of radar and Oklahoma Mesonet data assimilation on the prediction of mesovortices in a tornadic mesoscale convective system (MCS) is examined. The radar data come from the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere’s (CASA) IP-1 radar network. The Advanced Regional Prediction S...
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