24 October 2023
This Corrected Moments in Antenna Coordinates (CMAC) product includes corrections to the Colorado State University X-band precipitation radar related to beam blockage, de-aliased Doppler velocities, corrected reflectivity for liquid water path attenuation, differential phase corrected for non-uniform beam filling, and the integration of individual sweeps into volumes for the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign. For the precipitation estimation, empirical relationships of the equivalent radar reflectivity factor (Ze) to liquid-equivalent snowfall rates (Ze = aSb) or rainfall rates (Ze = aRb), are applied to the CMAC-corrected observations. The coefficients a and b vary with hydrometeor characteristics such as size distribution and density of snow crystals. Therefore, an ensemble approach with multiple a and b coefficients is used in order to better describe the spread within the precipitation estimates.
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As an evaluation VAP, it is requested that users of the data communicate closely with the VAP points of contact especially in communicating issues.
This VAP is useful for those interested in analyzing precipitation estimates, as well as dual-polarization radar moments within complex terrain. This is especially useful not only within meteorological applications but also hydrology.
From Bukovčić et al. (2018) and Matrosov et al. (2009), the initial liquid-equivalent snowfall rates chosen for this product are:
- Wolfe and Snider (2012): Z = 110S2
- WSR-88D High Plains: Z = 130S2
- Braham (1990) 1: Z = 67S1.28
- Braham (1990) 2: Z = 136S1.3
For additional information, including examples on VAP usage and methodology, notebooks relating to the SAIL campaign can be found on GitHub.
References
Matrosov SY, C Campbell, D Kingsmill, and E Sukovich. 2009. “Assessing Snowfall Rates from X-Band Radar Reflectivity Measurements.” Journal of Atmospheric and Oceanic Technology, 26, 2324–2339, doi:10.1175/2009JTECHA1238.1
Braham RR. 1990. “Snow Particle Size Spectra in Lake Effect Snows.” Journal of Applied Meteorology and Climatology, 29, 200–207, doi:10.1175/1520-0450(1990)029<0200:SPSSIL>2.0.CO;2.
Helmus JJ and SM Collis. 2016. “The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language.” Journal of Open Research Software, 4(1), doi:10.5334/jors.119.