A physical model to estimate snowfall over land using AMSU‐B observations

Abstract

In this study, we present an improved physical model to retrieve snowfall rate over land using brightness temperature observations from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Microwave Sounder Unit-B (AMSU-B) at 89 GHz, 150 GHz, 183.3±1 GHz, 183.3±3 GHz, and 183.3±7 GHz. The retrieval model is applied to the New England blizzard of March 5, 2001 which deposited about 75 cm of snow over much of Vermont, New Hampshire, and northern New York.In this improved physical model, prior retrieval assumptions about snowflake shape, particle size distributions, environmental conditions, and optimization methodology have been updated. Here, single scattering parameters for snow particles are calculated with the Discrete-Dipole Approximation (DDA) method instead of assuming spherical shapes.Five different snow particle models (hexagonal columns, hexagonal plates, and three different kinds of aggregates) are considered. Snow particle size distributions are assumed to vary with air temperature and to follow aircraft measurements described by previous studies.Brightness temperatures at AMSU-B frequencies for the New England blizzard are calculated using these DDA calculated single scattering parameters and particle size distributions. The vertical profiles of pressure, temperature, relative humidity and hydrometeors are provided by MM5 model simulations. These profiles are treated as the a priori data base in the Bayesian retrieval algorithm. In algorithm applications to the blizzard data, calculated brightness temperatures associated with selected database profiles agree with AMSU-B observations to within about ±5 K at all five frequencies.Retrieved snowfall rates compare favorably with the near-concurrent National Weather Service (NWS) radar reflectivity measurements. The relationships between the NWS radar measured reflectivities Z e and retrieved snowfall rate R for a given snow particle model are derived by a histogram matching technique. All of these Z e -R relationships fall in the range of previously established Z e -R relationships for snowfall. This suggests that the current physical model developed in this study can reliably estimate the snowfall rate over land using the AMSU-B measured brightness temperatures.1

Rain or snow: hydrologic processes, observations, prediction, and research needs

“…Passive microwave remote sensing has the potential for discriminating between rainfall and snowfall because microwave radiation emitted by the Earth's surface propagates through all but the densest precipitating clouds, meaning that radiation at microwave wavelengths directly interacts with hydrometeors within clouds (Olson et al, 1996;Ardanuy, 1989). However, the remote sensing of precipitation in microwave wavelengths and the development of operational algorithms is dominated by research focused on rainfall (Arkin and Ardanuy, 1989); by comparison, snowfall detection and observation has received less attention (Noh et al, 2009;Kim et al, 2008). This is partly explained by examining the physical processes within clouds that attenuate the microwave signal.…”

Utilizing Spaceborne Radars to Retrieve Dry Snowfall

“…Unfortunately, routine surface measurements of snow are scarce in remote regions where snowfall frequently occurs, and these locales are also largely devoid of ground-based remote sensing observations that could provide useful information about frozen precipitation. Therefore, satellite-based microwave remote sensing remains the most viable option to obtain global snowfall information, and increasing attention is being dedicated to retrieve properties of snow via passive, active, and combined microwave observations (e.g., SkofronickJackson et al 2004;Noh et al 2006;Kim et al 2007;Liu 2008a;Grecu and Olson 2008). These recent research avenues are especially critical to prepare for the Global Precipitation Measurement (GPM) mission that is scheduled to launch early in the next decade and will include coincident active and passive measurements at higher latitudes.…”

Ice clouds microphysical retrieval using 94‐GHz Doppler radar observations: Basic relations within the retrieval framework

“…Even in the presence of the non-Rayleigh effects, which introduce some dependence on the particle shape and orientation, the masssize relationship, is still a key factor. This can be seen in the parameterization of the W-band backscattering as a function of mass proposed by Kim [2004]. Similarly, Zhang [2004] suggested that the most important factor controlling the backscattering intensity of random oriented crystal is the ratio of the ice volume equivalent radius to the maximum dimension D; this ratio is directly related to the density.…”

“…Some investigators assume the aspect ratios fixed, sizeindependent, while some of them use the published empirical dimensional relations [Jayaweera and Cottis, 1969;Auer and Veal, 1970;Heymsfield, 1972;Hobbs et al, 1974;Jayaweera and Ohtake, 1974;Mitchell and Arnott, 1994;Matrosov et al, 1996]. However, there are differences between the empirical relations for the same ice crystal habit, as shown in Kim [2004]. The most used are the dimensional relationships published by Auer and Veal [1970], derived from measurements at a mountain laboratory, and hence, their validity for high cloud particles may be questionable.…”