Retrieval of ice cloud microphysical parameters using the CloudSat millimeter‐wave radar and tempera

@[TOC]Retrieval of ice cloud microphysical parameters using the CloudSat millimeter‐wave radar and temperature
电子科技大学格拉斯哥学院 2017级孙禹尧
Abstract
A new remote sensing retrieval of ice cloud microphysics has been developed for use with millimeter‐wave radar from ground‐, air‐, or space‐based sensors. Developed from an earlier retrieval that used measurements of radar reflectivity factor together with a priori information about the likely cloud targets, the new retrieval includes temperature information as well to assist in determining the correct region of state space, particularly for those size distribution parameters that are less constrained by the radar measurements. These algorithms have served as the ice cloud retrieval algorithms in Releases 3 and 4 of the CloudSat 2B‐CWC‐RO Standard Data Product. Several comparison studies have been performed on the previous and current retrieval algorithms: some involving tests of the algorithms on simulated radar data (based on actual cloud probe data or cloud resolving models) and others featuring statistical comparisons of the R04 2B‐CWC‐RO product (current algorithm) to ice cloud mass retrievals by other spaceborne, airborne, and ground‐based instruments or alternative algorithms using the same CloudSat radar data. Comparisons involving simulated radar data based on a database of cloud probe data showed generally good performance, with ice water content (IWC) bias errors estimated to be less than 40%. Comparisons to ice water content and ice water path estimates by other instruments are mixed. When the comparison is restricted to different retrieval approaches using the same CloudSat radar measurements, CloudSat R04 results generally agree with alternative IWC retrievals for IWC < 1000 mg m−3 at altitudes below 12 km but differ at higher ice contents and altitudes, either exceeding other retrievals or falling within a spread of retrieval values. Validation and reconciliation of all these approaches will continue to be a topic for further research.

Introduction
Clouds have a strong and complex influence on the Earth’s radiation budget and hydrological cycle and constitute the major source of uncertainty in predicting climate change and sensitivity [Intergovernmental Panel on Climate Change, 2001; Randall et al., 2007]. While the measurement of cloud occurrence and cloud properties at useful spatial and temporal scales is notoriously difficult [Stephens and Kummerow, 2007], the recent proliferation of new satellite platforms in the last few years [e.g., Eriksson et al., 2008; Stephens et al., 2002; Wu et al., 2009] is fostering a number of new approaches.

This paper describes the development, structure, and performance of remote sensing retrievals of ice cloud properties designed for use with the Cloud Profiling Radar (CPR) on the CloudSat satellite, which was launched in April 2006. The CPR measures copolar radar reflectivity (with no Doppler information), so retrievals must be based on reflectivity data alone or in combination with non‐CloudSat sources such as model data, climatological data, or observations from other instruments. A number of ice cloud retrievals have been reported in recent years, some using reflectivity alone [e.g., Sassen et al., 2002; Heymsfield et al., 2005; Sayres et al., 2008] and some adding temperature as an additional constraint [e.g., Liu and Illingworth, 2000; Hogan et al., 2006]. Many of these prescribe direct reflectivity–ice water content relations, usually in the form of a power‐law relation, allowing measured reflectivity values to be converted directly to ice water content. An alternative approach, being used in the CloudSat retrievals, is one of using an optimal estimation algorithm to retrieve parameters of the particle size distribution (PSD). This approach was used in the early liquid and ice cloud retrievals developed for CloudSat [Austin and Stephens, 2001; Benedetti et al., 2003]. Once the PSD parameters are retrieved, ice water content, effective radius, and other moments of the distribution are easily calculated. The retrievals in the current paper follow this practice, improving on the earlier retrieval described by Benedetti et al. [2003] and modifying it to use radar without an independent measurement of visible optical depth.

The current retrieval algorithm, using radar and temperature data, is described in section 2. Algorithm performance is addressed in section 3.
2. Retrieval Algorithm
The earliest prelaunch versions of the CloudSat ice retrieval followed the algorithm described by Benedetti et al. [2003], modified to use radar alone (with no optical depth input). These represented the ice cloud particles as a distribution of ice spheres of fixed density whose size was modeled using a three‐parameter modified gamma distribution. In the radar‐only (RO) retrieval case, the state vector (containing the unknowns to be retrieved) was composed of an array of characteristic diameter values (one of the three size distribution parameters) corresponding to the cloudy bins of the measured radar profile. Thus for a measurement vector containing p cloudy bins, the retrieval would solve for p values of the characteristic diameter. The other two size distribution parameters (the particle number concentration and the distribution width parameter) were assigned fixed values and uncertainties (based on climatology, field data, or other criteria); these forward model parameters were constrained to be height‐invariant. Once the elements of the state vector were determined, values of typical microphysical parameters such as effective radius and ice water content (IWC) were easily calculated in terms of the size distribution parameters. The remaining inputs to the retrieval consisted of an a priori vector and covariance matrix, corresponding to the best knowledge of the elements of the state vector before the measurement is made. These were determined in a manner similar to the forward model parameters. An augmented retrieval using a combination of radar plus visible optical depth (RVOD) in the measurements vector was constructed similarly. (This is in fact the case described by Benedetti et al. [2003].) This augmented version added the (height‐invariant) particle number concentration to the state vector, allowing this parameter to be retrieved rather than prescribed.
3. Algorithm Performance
While there is no all‐encompassing test of the performance of the two ice retrieval algorithms, several assessments and comparisons have been performed that give some insight.

Intercomparison Study
Heymsfield et al. [2008] compared a number of ice cloud microphysical retrieval algorithms by using simulated remote sensing measurements (radar reflectivity, optical depth, Doppler velocity, etc.) generated from a large database of cloud probe data. Because the remote sensing data were directly based on the particle data, this method allowed various retrievals to be compared without complications owing to instrument sampling. It was also possible to examine the performance of the various retrievals as a function of associated variables, such as the magnitude of the reflectivity, the temperature, the ice water content, and the optical depth.
4. Conclusions
A new remote sensing retrieval of ice cloud microphysics has been developed for use with millimeter‐wave radar from ground‐, air‐, or space‐based sensors. Developed from an earlier retrieval that used measurements of radar reflectivity factor together with a priori information about the likely cloud targets, the new retrieval includes temperature information as well to assist in determining the correct region of state space, particularly for those size distribution parameters that are less constrained by the radar measurements. Both retrievals are cast in an optimal estimation framework and may be considered as descendants of the algorithm reported by Benedetti et al. [2003]. The previous and current algorithms described in this paper have served as the ice cloud retrieval algorithms in Releases 3 and 4 (respectively) of the CloudSat 2B‐CWC‐RO Standard Data Product. Release 3 was a provisional product released on a limited basis; Release 4 was produced for the entire mission and is the current version at the time of this writing. CloudSat Release 4 2B‐CWC‐RO data (using the second algorithm) were released to the community in October 2007. Data are available for the entire mission from June 2006 to date (27 months so far), allowing statistical analysis of measurements through a complete annual cycle. A sample of such analyses are included in the present paper.

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