A Real-Time, Three-Dimensional, Rapidly Updating, Heterogeneous Radar Merger Technique for Reflectivity, Velocity, and Derived Products

Abstract

With the advent of real-time streaming data from various radar networks, including most Weather Surveillance Radars-1988 Doppler and several Terminal Doppler Weather Radars, it is now possible to combine data in real time to form 3D multiple-radar grids. Herein, a technique for taking the base radar data (reflectivity and radial velocity) and derived products from multiple radars and combining them in real time into a rapidly updating 3D merged grid is described. An estimate of that radar product combined from all the different radars can be extracted from the 3D grid at any time. This is accomplished through a formulation that accounts for the varying radar beam geometry with range, vertical gaps between radar scans, the lack of time synchronization between radars, storm movement, varying beam resolutions between different types of radars, beam blockage due to terrain, differing radar calibration, and inaccurate time stamps on radar data. Techniques for merging scalar products like reflectivity, and innovative, real-time techniques for combining velocity and velocity-derived products are demonstrated. Precomputation techniques that can be utilized to perform the merger in real time and derived products that can be computed from these three-dimensional merger grids are described.

Seasonal, Regional, and Storm-Scale Variability of Cloud-to-Ground Lightning Characteristics in Florida

“…The Warning Decision Support System-Integrated Information (WDSS-II; Lakshmanan et al 2007) software was used to examine lightning, radar, and model-derived information. These data sources were combined using WDSS-II to investigate storms that produced both wildfire ignitions and severe weather.…”

The advanced algorithm for tracking objects (AALTO)

“…This approach does not make the best use of data in areas with numerous radars such as multiple WSR-88Ds and terminal Doppler weather radars (TDWRs) and perhaps other radars such as the UMass collaborative adaptive sensing of the atmosphere (CASA; Junyent et al, 2010) radars. The single-radar limitations of SCIT and MDA are often avoided by first creating a mosaic of the product being analysed, such as radar reflectivity (the proportion of energy backscattered by targets such as hydrometeors) or radial velocity (the component of the mean motion of the targets towards or away from the radar), using an algorithm such as w2merger (Lakshmanan et al, 2006). However, the identification of objects in mosaicked reflectivity or radial velocity data requires new multi-radar algorithms that are likely based on local maxima or minima (Lakshmanan et al, 2009), whereas the present applications of AALTO have used objects identified by legacy algorithms such as SCIT and MDA .…”

Sensitive versus Rough Dependence under Initial Conditions in Atmospheric Flow Regimes

“…The radial velocity data is dealiased, and the azimuthal shear field is derived using the two-dimensional local, linear least squares derivative (LLSD) methodology described in [18] and implemented in the Warning Decision Support System-Integrated Information (WDSS-II: [19]) using a 750 m by 1500 m kernel. The native reflectivity and azimuthal shear fields are then converted to a Cartesian grid using the data merger package of [20], resulting in a 0.5 km grid of azimuthal shear values.…”