Zhien Wang

THE CLOUDSAT MISSION AND THE A-TRAIN A New Dimension of Space-Based Observations of Clouds and Precipitation

CloudSat is a satellite experiment designed to measure the vertical structure of clouds from space. The expected launch of CloudSat is planned for 2004, and once launched, CloudSat will orbit in formation as part of a constellation of satellites (the A-Train) that includes NASAs Aqua and Aura satellites, a NASA–CNES lidar satellite (CALIPSO), and a CNES satellite carrying a polarimeter (PARASOL). A unique feature that CloudSat brings to this constellation is the ability to fly a precise orbit enabling the fields of view of the CloudSat radar to be overlapped with the CALIPSO lidar footprint and the other measurements of the constellation. The precision and near simultaneity of this overlap creates a unique multisatellite observing system for studying the atmospheric processes essential to the hydrological cycle. The vertical profiles of cloud properties provided by CloudSat on the global scale fill a critical gap in the investigation of feedback mechanisms linking clouds to climate. Measuring these profi...

Global distribution of cirrus clouds from CloudSat/Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements

[1] The cirrus clouds of the upper troposphere are globally widespread and are important regulators of the radiative balance, and hence climate, of the Earth-atmosphere system. Despite their wide distribution, however, cirrus are difficult to study from satellite radiance measurements or from scattered ground observing sites because they can occur as part of multilayered cloud systems and are characteristically optically thin. The need to better characterize the global distribution of cirrus clouds was therefore a major justification for the formation flying of the CloudSat and CALIPSO satellites, which support a cloud radar and polarization lidar, respectively. Measurements by these active remote sensors, when analyzed by appropriate algorithms, have the ability to identify and accurately measure the locations and heights of this category of clouds. The combined CloudSat/ Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data cirrus cloud algorithm used in this study is aimed at identifying those clouds that would likely be classified as cirrus by a surface weather observer: it is based on previous experience with multiple remote sensor approaches and knowledge gleaned from extensive surface lidar and radar observations of visually identified cirrus clouds with a minimum of a priori assumptions. We report on the global and seasonal frequencies of cirrus clouds, and on their heights and thicknesses obtained over the initial 1 year of data collected. We find a global average frequency of cirrus cloud occurrence of 16.7%. These new results are compared with other cirrus cloud climatologies and are interpreted in terms of local cirrus cloud formation mechanisms and the responsible global weather phenomena.

CALIPSO/CALIOP Cloud Phase Discrimination Algorithm

Abstract The current cloud thermodynamic phase discrimination by Cloud-Aerosol Lidar Pathfinder Satellite Observations (CALIPSO) is based on the depolarization of backscattered light measured by its lidar [Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)]. It assumes that backscattered light from ice crystals is depolarizing, whereas water clouds, being spherical, result in minimal depolarization. However, because of the relationship between the CALIOP field of view (FOV) and the large distance between the satellite and clouds and because of the frequent presence of oriented ice crystals, there is often a weak correlation between measured depolarization and phase, which thereby creates significant uncertainties in the current CALIOP phase retrieval. For water clouds, the CALIOP-measured depolarization can be large because of multiple scattering, whereas horizontally oriented ice particles depolarize only weakly and behave similarly to water clouds. Because of the nonunique depolarization–cloud ph...

Cloud Type and Macrophysical Property Retrieval Using Multiple Remote Sensors

A cloud detection algorithm based on ground-based remote sensors has been developed that can differentiate among various atmospheric targets such as ice and water clouds, virga, precipitation, and aerosol layers. Standard cloud type and macrophysical properties are identified by combining polarization lidar, millimeter-wave radar, infrared radiometer, and dual-channel microwave radiometer measurements. These algorithms are applied to measurements collected during 1998 from the Atmospheric Radiation Measurement Program Cloud and Radiation Test Bed site in north-central Oklahoma. The statistical properties of clouds for this year are presented, illustrating how extended-time remote sensing datasets can be converted to cloud properties of concern to climate research.

Thin Liquid Water Clouds: Their Importance and Our Challenge

Abstract Many of the clouds important to the Earths energy balance, from the Tropics to the Arctic, contain small amounts of liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP), when the LWP is small (i.e., < 100 g m−2; clouds with LWP less than this threshold will be referred to as “thin”). Thus, the radiative properties of these thin liquid water clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earths energy balance, and explain the difficulties in observing them. In particular, because these clouds are thin, potentially mixed phase, and often broken (i.e., have large 3D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison used data collected at the Atmospheric Radiation Measurement (ARM) S...

Toward Understanding of Differences in Current Cloud Retrievals of ARM Ground-Based Measurements

Accurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes.

Cirrus Cloud Microphysical Property Retrieval Using Lidar and Radar Measurements. Part I: Algorithm Description and Comparison with In Situ Data

Abstract A retrieval algorithm is described to estimate vertical profiles of cirrus-cloud ice water content (IWC) and general effective size Dge from combined lidar and radar measurements. In the algorithm, the lidar extinction coefficient σ is parameterized as σ = IWC[a0 + (a1/Dge)] and water equivalent radar reflectivity factor Ze is parameterized as Ze = C′(IWC/ρi)Dbge, where a0, a1, C′, and b are constants based on the assumption of a modified gamma size distribution and hexagonal ice crystals. A comparison of retrieved results from a cirrus-cloud case study with aircraft in situ measurements indicates that the algorithm can provide reliable cirrus cloud microphysical properties. A technique to estimate ice water path and layer-mean Dge is also developed using the optical depth and mean radar reflectivity factor of the cloud layer.

Evaluation of several A-Train ice cloud retrieval products with in situ measurements collected during the SPARTICUS campaign

AbstractIn this study several ice cloud retrieval products that utilize active and passive A-Train measurements are evaluated using in situ data collected during the Small Particles in Cirrus (SPARTICUS) field campaign. The retrieval datasets include ice water content (IWC), effective radius re, and visible extinction σ from CloudSat level-2C ice cloud property product (2C-ICE), CloudSat level-2B radar-visible optical depth cloud water content product (2B-CWC-RVOD), radar–lidar (DARDAR), and σ from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). When the discrepancies between the radar reflectivity Ze derived from 2D stereo probe (2D-S) in situ measurements and Ze measured by the CloudSat radar are less than 10 dBZe, the flight mean ratios of the retrieved IWC to the IWC estimated from in situ data are 1.12, 1.59, and 1.02, respectively for 2C-ICE, DARDAR, and 2B-CWC-RVOD. For re, the flight mean ratios are 1.05, 1.18, and 1.61, respectively. For σ, the flight mean ratios for...

Cirrus Cloud Microphysical Property Retrieval Using Lidar and Radar Measurements. Part II: Midlatitude Cirrus Microphysical and Radiative Properties

Abstract The lidar–radar algorithm described in Part I of this set of papers is applied to ∼1000 h of Raman lidar and millimeter wave cloud radar (MMCR) data collected at the Atmospheric Radiation Measurement program Southern Great Plains Clouds and Radiation Testbed site in Oklahoma during the period from November 1996 to November 2000. The resulting statistics of cirrus microphysical and radiative properties show that most cirrus clouds are optically thin (mean optical depth of 0.58 with a standard deviation of 0.67) with low ice water path (mean 12.19 g m−2 with a standard deviation of 19.0). The seasonal changes of cirrus properties are relatively small except for the general effective radius (Dge). Strong temperature dependencies of ice water content, Dge, and extinction coefficients are found in the dataset, which are well described by second-order polynomial functions. The temperature and thickness dependencies of the cirrus properties are studied in detail, providing information useful in the vali...

An Intercomparison of Microphysical Retrieval Algorithms for Upper-Tropospheric Ice Clouds

The large horizontal extent, with its location in the cold upper troposphere, and ice composition make cirrus clouds important modulators of the Earths radiation budget and climate. Cirrus cloud microphysical properties are difficult to measure and model because they are inhomogeneous in nature and their ice crystal size distribution and habit are not well characterized. Accurate retrievals of cloud properties are crucial for improving the representation of cloud-scale processes in largescale models and for accurately predicting the Earths future climate. A number of passive and active remote sensing retrieval algorithms exist for estimating the microphysical properties of upper-tropospheric clouds. We believe significant progress has been made in the evolution of these retrieval algorithms in the last decade; however, there is room for improvement. Members of the Atmospheric Radiation Measurement (ARM) program Cloud Properties Working Group are involved in an intercomparison of optical depth τ and ice ...