Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

Abstract

Abstract. Estimates of snow microphysical properties obtained by analyzing collections of individual particles are often limited to short timescales and coarse time resolution. Retrievals using disdrometer observations coincident with bulk measurements such as radar reflectivity and snowfall amounts may overcome these limitations; however, retrieval techniques using such observations require uncertainty estimates not only for the bulk measurements themselves, but also for the simulated measurements modeled from the disdrometer observations. Disdrometer uncertainties arise due to sampling and analytic errors and to the discrete, potentially truncated form of the reported size distributions. Imaging disdrometers such as the Snowflake Video Imager and 2-D Video Disdrometer provide remarkably detailed representations of snow particles, but view limited projections of their three-dimensional shapes. Particle sizes determined by such instruments underestimate the true dimensions of the particles in a way that depends, in the mean, on particle shape, also contributing to uncertainties. An uncertainty model that accounts for these uncertainties is developed and used to establish their contributions to simulated radar reflectivity and snowfall rate. Viewing geometry effects are characterized by a parameter, φ, that relates disdrometer-observed particle size to the true maximum dimension of the particle. Values and uncertainties for φ are estimated using idealized ellipsoidal snow particles. The model is applied to observations from seven snow events from the Canadian CloudSat/CALIPSO Validation Project (C3VP), a mid-latitude cold-season cloud and precipitation field experiment. Typical total uncertainties are 4 dB for reflectivity and 40-60 % for snowfall rate, are highly correlated, and are substantial compared to expected uncertainties for radar and precipitation gauge observations. The dominant sources of errors are viewing geometry effects and the discrete, truncated form of the size distributions. While modeled Ze-S relationships are strongly affected by assumptions about snow particle mass properties, such relationships are only modestly sensitive to φ owing to partially compensating effects on both the reflectivity and snowfall rate.

Estimating snow microphysical properties using collocated multisensor observations

“…For the short path lengths and observing conditions applicable to these observations, attenuation by gases and snow particles should be negligible [Matrosov, 1998]. Reflectivity for the Rayleigh sphere approximation can then be written as [e.g., Wood et al, 2013] …”

“…Disdrometer size distributions, then, are not based on D M but rather on some observed particle size D obs . Wood et al [2013] introduced a parameter to convert observed size distributions into distributions based on D M . Since may vary significantly depending on the disdrometer and snow particle shape, it is added to the state vector to be retrieved, giving…”

“…Lacking direct observations of , a priori values for and its variance were estimated based on the particle modeling described in Wood et al [2013] as 0.825 and 0.125 2 . The value 0.825 is appropriate based on the use of SVI feret diameter and a typical particle aspect ratio of 0.60 to 0.70 [Korolev and Isaac, 2003].…”

“…associated with a particular regime, an assumed value for was used to construct an equivalent exponential distribution based on the maximum dimension, D M;i [Wood et al, 2013]. Using this N(D M;i ) and the regime's radar reflectivity along with the assumed value for , the corresponding value for was found via (9).…”

CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities

“…In addition, the CloudSat 2C-SNOW-PROFILE (hereafter, 2C-SNOW), used extensively in this study, provides profiles of instantaneous liquid equivalent surface snowfall rate retrievals based on CloudSat orbital swath data. The 2C-SNOW product uses an optimal estimation procedure to extract snowfall rate from W-band radar reflectivity values, using a priori snow microphysical properties to constrain snowfall rate retrievals [36,37]. It is worth noting that 2C-SNOW surface snowfall rate (and associated SWC profile) is computed only for CPR profiles where the 2C-PRECIP CPR product indicates surface snow probable or certain, or if estimated liquid fraction is <10% (dry snow).…”