Data / Science Data Products / VAPs

SQUIRE

Surface QUantitatIve pRecipitation Estimation (SQUIRE)

Evaluation VAP

SQUIRE builds off the Corrected Moments in Antenna Coordinates (CMAC) product, which includes corrections 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 precipitation estimation, empirical relationships of the equivalent radar reflectivity factor (Z_e) to liquid-equivalent snowfall rates (Z_e = aS^b) or rainfall rates (Z_e = aR^b), 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.

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 gridded precipitation data. This is especially useful not only within meteorological applications but also hydrology. The earth system modeling community may use this to compare with liquid precipitation estimates from simulations.

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 = 67S^1.28
Braham (1990) 2: Z = 136S^1.3

Within SQUIRE, CMAC precipitation estimates are then gridded to a Cartesian grid using nearest neighbor interpolation, with 250-meter grid spacing (horizontal and vertical), with a spatial domain of 20 kilometers (x) x 20 kilometers (y) x 5 kilometers (z), all in units of distance from the radar. The lowest vertical level is calculated, and the data set is subset for this vertical level, with the fields valid at the lowest vertical level for each grid point.

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.