Cloud and atmospheric properties strongly influence the mass and energy budgets of the Greenland Ice Sheet (GIS). To address critical gaps in the understanding of these systems, a new suite of cloud- and atmosphere-observing instruments has been installed on the central GIS as part of the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit (ICECAPS) project. During the first 20 months in operation, this complementary suite of active and passive ground-based sensors and radiosondes has provided new and unique perspectives on important cloud?atmosphere properties. High atop the GIS, the atmosphere is extremely dry and cold with strong near-surface static stability predominating throughout the year, particularly in winter. This low-level thermodynamic structure, coupled with frequent moisture inversions, conveys the importance of advection for local cloud and precipitation formation. Cloud liquid water is observed in all months of the year, even the particularly cold and dry winter, while annual cycle observations indicate that the largest atmospheric moisture amounts, cloud water contents, and snowfall occur in summer and under southwesterly flow. Many of the basic structural properties of clouds observed at Summit, Greenland, particularly for low-level stratiform clouds, are similar to their counterparts in other Arctic regions. The ICECAPS observations and accompanying analyses will be used to improve the understanding of key cloud?atmosphere processes and the manner in which they interact with the GIS. Furthermore, they will facilitate model evaluation and development in this data-sparse but environmentally unique region

OLYMPEX is a comprehensive field campaign to study how precipitation in Pacific storms is modified by passage over coastal mountains.

The objective of this work is to derive equivalent radar reflectivity factor-liquid equivalent snow rate (Z e -SR) power-law relations for snowfall using the C-band King City operational weather radar and a 2D video disdrometer (2DVD). The 2DVD provides two orthogonal views of each snow particle that falls through its 10 cm 3 10 cm virtual sensor area. The ''size'' parameter used here for describing the size distribution is based on the ''apparent'' volume computed from the two images, and an equivolume spherical diameter D app is defined. The determination of fall speed is based on matching two images corresponding to the same particle as it falls through two light planes separated by a precalibrated separation distance. A new ''rematching'' algorithm was developed to improve the quality of the fall speed versus D app as compared with the original matching algorithm provided by the manufacturer.The snow density is parameterized in the conventional power-law form r 5 aD app b , where a and b are assumed to be variable. To account for strong horizontal winds that tend to decrease the measured concentrations from the 2DVD, a third parameter g is introduced. The methodology estimates the three parameters (a, b, and g) by minimizing the difference between the radar-measured reflectivity and the equivalent reflectivity computed from the 2DVD in a least squares sense. The optimally determined values of a, b, and g are used to estimate the SR and the coefficient and exponent of the Z e 5 a(SR) b relation. For validation, the accumulation from the SR is compared with the manually recorded accumulations from the double-fence international reference (DFIR) gauge. The data were collected during the Canadian Cloudsat Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Validation Project (C3VP) conducted in Ontario, Canada, during the 2006/07 winter season. A total of seven snow days were analyzed and the accumulation intercomparisons gave a fractional standard deviation of 26% and normalized bias 2.1%. The range of the a and b values for the seven days appear reasonable and similar to conventional Z e -R relations.

No abstract

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.