The ability of ground-based in situ and remote sensing observations to constrain microphysical properties for dry snow is examined using a Bayesian optimal estimation retrieval method. Power functions describing the variation of mass and horizontally projected area with particle size and a parameter related to particle shape are retrieved from near-Rayleigh radar reflectivity, particle size distribution, snowfall rate, and size-resolved particle fall speeds. Algorithm performance is explored in the context of instruments deployed during the Canadian CloudSat CALIPSO Validation Project, but the algorithm is adaptable to other similar combinations of sensors. Critical estimates of observational and forward model uncertainties are developed and used to quantify the performance of the method using synthetic cases developed from actual observations of snow events. In addition to illustrating the technique, the results demonstrate that this combination of sensors provides useful constraints on the mass parameters and on the coefficient of the area power function but only weakly constrains the exponent of the area power function and the shape parameter. Information content metrics show that about two independent quantities are measured by the suite of observations and that the method is able to resolve about eight distinct realizations of the state vector containing the mass and area power function parameters. Alternate assumptions about observational and forward model uncertainties reveal that improved modeling of particle fall speeds could contribute substantial improvements to the performance of the method.
[1] The ability of CloudSat to detect precipitation in cold season cloud systems is examined using data from the Environment Canada C band weather radar at King City, Ontario. The factors complicating the comparison are the time mismatch, the differences in sensitivity, and the changes to the geometry of cross section with range from the ground radar, W band radar attenuation, and the effect of ground clutter. A total of 40 overpasses with precipitation were observed over the King City radar from September 2006 to April 2007. In about 14% of the precipitation profiles, time mismatches were diagnosed. When these cases were removed, the skill scores of the CloudSat precipitation occurrence product were excellent. The most frequent cause of a false detection was an incorrect precipitation threshold in the algorithm. The most frequent cause of a miss in detection was ground clutter removal of valid echoes by the algorithm. Overall, the CloudSat algorithm handled the effect of attenuation very well. Improvement to the algorithm would arise from a better tuning of the precipitation threshold, a threshold of À10 dBZ instead of À18 dBZ being more appropriate for winter storms in the Great Lakes area, and more effective ground clutter filtering in the lowest four range bins of the CloudSat data. The methodology employed here and the 1456 verified precipitation profiles from CloudSat can serve as a framework for a test bed to evaluate precipitation products from CloudSat.
An attenuation-based method to retrieve vertical profiles of rainfall rates from height derivatives/gradients of CloudSat nadir-pointing W-band reflectivity measurements is discussed. This method takes advantage of the high attenuation of W-band frequency signals in rain and the low variability of nonattenuated reflectivity due to strong non-Rayleigh scattering from rain drops. The retrieval uncertainties could reach 40%–50%. The suggested method is generally applicable to rainfall rates (R) in an approximate range from about 2–3 to about 20–25 mm h−1. Multiple scattering noticeably affects the gradients of CloudSat measurements for R values greater than about 5 mm h−1. To avoid a retrieval bias caused by multiple-scattering effects, a special correction for retrievals is introduced. For rainfall rates greater than about 25 mm h−1, the influence of multiple scattering gets overwhelming, and the retrievals become problematic, especially for rainfalls with higher freezing-level altitudes. The attenuation-based retrieval method was applied to experimental data from CloudSat covering the range of rainfall rates. CloudSat retrievals were compared to the rainfall estimates available from a National Weather Service ground-based scanning precipitation radar operating at S band. Comparisons between spaceborne and conventional radar rainfall retrievals were generally in good agreement and indicated the mutual consistency of both quantitative precipitation estimate types. The suggested CloudSat rainfall retrieval method is immune to the absolute calibration of the radar and to attenuation caused by the melting layer and snow regions. Since it does not require surface returns, it is applicable to measurements above both land and water surfaces.
[1] Precipitation events were examined at Fort Simpson, Northwest Territories, Canada, during the autumn and winter of 1998 and during the spring of 1999 with a variety of observational tools, including a polarimetric radar. This location is characterized by a relatively small amount of precipitation (annual average of 450 mm), with approximately half being in the form of snow. During the observational periods, precipitation was produced within multilayered cloud systems with heights ranging up to 10 km, and instances of light snow were associated with either low (<2.5 km) or high (up to 10 km) clouds. Precipitation over the observational periods was typically produced in banded structures, was sometimes reduced because of subcloud evaporation or sublimation, and in the winter was often in the form of individual crystals. A state-of-the-art weather forecasting model was often poor at simulating some of the critical features of the precipitation events, such as cloud top height and precipitation amount. In addition, it was shown that with the sensitive CloudSat radar, $17% of overpasses will be associated with the occurrence of detectable precipitation at Fort Simpson, but with the less sensitive Global Precipitation Measurement (GPM) radar, much of the precipitation will be undetected.
[1] This work presents a study of midlevel, mixed-phase clouds using satellite (remote sensing) and aircraft (in situ) observations. In this study, we analyze coincident multisatellite and in situ aircraft measurements of three mixed-phase cloud cases during an intensive field experiment (C3VP/CLEX-10) to better understand the microphysics and radiative properties and provide a foundation for the improvement of the satellite retrieval algorithms for these clouds. For the selected cases, various aspects observed from different instruments are presented and compared for these clouds. It is found that many areas in the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud phase product classified as "unknown" are more appropriately classified as "mixed phase" based on CloudSat and CALIPSO data as well as C3VP/CLEX-10 aircraft measurements. The aircraft measurements show that a significant amount of supercooled liquid water exists at or near cloud top at very low temperatures for these midlevel, mixed-phase clouds, contrary to the assumptions used in the CloudSat retrieval algorithms. The spatial distribution of liquid water content and ice water content and other cloud properties are examined for both the satellite remote sensing and in situ probe measurements. CloudSat and airborne radar reflectivity data are also compared through a structure function analysis. Radiative transfer simulations based on the aircraft and satellite observations indicate the importance of proper assignment of cloud phase within retrieval algorithms and numerical models, which use similar assumptions.
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