Data Product Development for Cold Cloud and Precipitation Process Analyses

Abstract

High-latitude regions play a critical role in defining Earth’s response under a warming climate. Model predictions suggest that it is these areas that are most susceptible to change and will experience the most dramatic temperature changes in response to increased atmospheric greenhouse gas concentrations. Cold cloud and precipitation microphysical processes help regulate many high-latitude climate feedback mechanisms. The proposed work develops data essential for investigating these cold-region/cold-season processes that connect ice and mixed-phase clouds with precipitation. Improved understanding of these processes will allow their more faithful representation in regional and global climate models and enable more accurate analysis of the impacts of changing climate on the Arctic.

These data will detail the transformation of ice- and mixed-phase cloud to precipitation by using a variational retrieval scheme applied to the extensive multi-sensor measurements provided by the Atmospheric Radiation Measurement program’s Climate Research Facility (ARM CRF) sites. The work exploits the complementary sensitivities of the available instrumentation and a new snow particle imaging system to characterize particles ranging in size from small pristine ice crystals to larger precipitating hydrometeors. This approach produces a complete, integrated picture of the transformation from cloud to precipitation. The investigators will 1) apply recent developments in the application of variational retrieval methods to active sensors such as radar, 2) employ new methods for evaluating a priori constraints on microphysical properties, and 3) adapt information content metrics to control measurement and algorithm selection in the retrieval. This approach builds on work by the investigators that explores distinct variational retrievals founded on the optimal estimation method for ice cloud, mixed-phase cloud, and snowfall using cloud and precipitation radars along with imaging disdrometers, passive microwave, visible, near-infrared and infrared measurements.
The variational approach provides estimates of cloud and precipitation properties consistent with available measurements as well as a set of error diagnostics quantifying confidence and expected uncertainties in the retrieval products. The accuracy of these diagnostics as well as of the retrieval results themselves depend on rigorous analysis of expected uncertainties that result from various retrieval algorithm assumptions (e.g., ice particle shape, particle scattering properties, surface albedo). Techniques for assessing these uncertainties have been developed by the investigators and can be extended readily to retrievals using ARM CRF observations.

The data product will be developed initially and evaluated for North Slope of Alaska Central Facility and Oliktok Point. Cloud radar observations (principally the Ka-band Zenith Radar) will provide the primary vertically-resolved information about snowfall and associated cloud. Application of the scanning precipitation and cloud radars (X-, Ka- and W-band) will be investigated as suitable data are available, but the initial product development is not critically dependent on those data. Multi-Angle Snowflake Camera observations will be used to construct constraints on particle properties. Microwave measurements (e.g, MWR, MWRP) will be considered as possible constraints on water path for combined active-passive retrievals. The resulting product will contain vertically-profiled cloud and precipitation microphysical properties and derived bulk properties (e.g., size distribution characteristics, water contents, precipitation rates) resolved in time consistent with the sampling protocols of the radars, lidars and other instruments.

The proposed product represents a substantial step forward, applying a sophisticated retrieval method that provides a sound basis for synthesizing observations from multiple instruments, allows for incorporating recent scene-dependent a priori microphysical information as available, and enables explicit quantification of uncertainties in retrieved and derived variables.