Disdrometers measure the distribution of raindrop sizes (DSD) and the associated rainfall rates/accumulation. However, raw disdrometer datasets may be difficult to interpret for model evaluation, radar monitoring or other precipitation studies because of requirements for careful quality control and processing to estimate key DSD properties of interest. The VDISQUANTS products use standard methods (e.g., Tokay et al., 2013, Wang et al., 2018) to filter spurious measurements and subsequently estimate several parameterized DSD and radar equivalent quantities of interest to include dual-polarization radar quantities and fits to gamma/exponential distributions. These efforts enable disdrometer datasets to become more useful and easily handled by modeling and observational studies, or toward routine instrumentation checks.
The VDISQUANTS VAP is applicable for rain (liquid media) precipitation studies. Although disdrometers provide one estimate for rainfall rate and associated DSD information, these instruments and this VDISQUANTS VAP are not necessarily a complete substitute for rain gauge measurements (e.g., ARM’s RAIN, MET TBRG and WBPLUVIO2 data streams) or adequate under snowfall/frozen media. Users are encouraged to consider other ARM instruments for estimates of ice/snow properties (e.g., MASC).
The VDISQUANTS VAP estimates DSD properties at high temporal (1 minute) resolution. The products are available for download at every fixed and ARM Mobile Facility (AMF) deployment having a deployed video or laser disdrometer (see also, LDQUANTS). Implementations of these algorithms are from PyDisdrometer (PyDSD), an open-source Python library for working with disdrometer data (Hardin and Guy 2017).