[1] A snowfall retrieval algorithm based on Bayes' theorem is developed using high-frequency microwave satellite data. In this algorithm, observational data from both airborne and surface-based radars are used to construct an a priori database of snowfall profiles. These profiles are then used as input to a forward radiative transfer model to obtain brightness temperatures at high microwave frequencies. In the radiative transfer calculations, two size distributions for snowflakes and ten observed atmospheric sounding profiles are used with snowfall profiles from observations. In addition, the scattering properties of the snowflakes are calculated on the basis of realistic nonspherical shapes using discrete dipole approximation. The algorithm is first verified by airborne microwave and radar observations and then applied to the Advanced Microwave Sounding Unit-B (AMSU-B) satellite data. The retrieved snowfall rates using AMSU-B data from three snowfall cases in the vicinity of Japan show reasonable agreement with surface radar observations with correlation coefficients of about 0.8, 0.6, and 0.96 for the three cases, respectively. The comparison results also suggest the algorithm performs better for dry and heavy snow cases, but is less accurate for wet and weak snow cases.
[1] Although snowfall is an important component of global precipitation in extratropical regions, satellite snowfall estimate is still in an early developmental stage, and existing satellite remote sensing techniques do not yet provide reliable estimates of snowfall over higher latitudes. Toward the goal of developing a global snowfall algorithm, in this study, a Bayesian technique has been tested for snowfall retrieval over land using highfrequency microwave satellite data. In this algorithm, observational data from satelliteand surface-based radars and in situ aircraft measurements are used to build the a priori database consisting of snowfall profiles and corresponding brightness temperatures. The retrieval algorithm is applied to the Advanced Microwave Sounding Unit-B data for snowfall cases that occurred over the Great Lakes region, and the results are compared with the surface radar data and daily snowfall data collected from National Weather Service stations. Although the algorithm is still at an ad hoc stage, the results show that the satellite retrievals compare well with surface measurements in the early winter season, when there is no accumulated snow on ground. However, for the late winter season, when snow constantly covers the ground, the snowfall retrievals become very noisy and show overestimation. Therefore, it is concluded that developing methods to efficiently remove surface snow cover contamination will be the major task in the future to improve the accuracy of satellite snowfall retrieval over land.
Meteorological clouds often exist in the liquid phase at temperatures below 0°C. Traditionally, satellite-derived information on cloud phase comes from narrow bands in the shortwave and thermal infrared, with sensitivity biased strongly toward cloud top. In situ observations suggest an abundance of clouds having supercooled liquid water at their tops but a predominantly ice phase residing below. Satellites may report these clouds simply as supercooled liquid, with no further information regarding the presence of a subcloud top ice phase. Here we describe a physical basis for the detection of liquid-top mixed-phase clouds from passive satellite radiometer observations. The algorithm makes use of reflected sunlight in narrow bands at 1.6 and 2.25 μm to optically probe below liquid-topped clouds and determine phase. Detection is predicated on differential absorption properties between liquid and ice particles, accounting for varying Sun/sensor geometry and cloud optical properties. When tested on numerical weather prediction model simulated cloud fields, the algorithm provided threat scores in the 0.6-0.8 range and false alarm rates in the 0.1-0.2 range. A case study based on surface and satellite observations of liquid-top mixed-phase clouds in northern Alaska was also examined. Preliminary results indicate promising potential for distinction between supercooled liquid-top phase clouds with and without an underlying mixed-phase component.
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