This paper reports on the early mission performance of the radar and other major aspects of the CloudSat mission. The Cloudsat cloud profiling radar (CPR) has been operating since 2 June 2006 and has ...proven to be remarkably stable since turn‐on. A number of products have been developed using these space‐borne radar data as principal inputs. Combined with other A‐Train sensor data, these new observations offer unique, global views of the vertical structure of clouds and precipitation jointly. Approximately 11% of clouds detected over the global oceans produce precipitation that, in all likelihood, reaches the surface. Warm precipitating clouds are both wetter and composed of larger particles than nonprecipitating clouds. The frequency of precipitation increases significantly with increasing cloud depth, and the increased depth and water path of precipitating clouds leads to increased optical depths and substantially more sunlight reflected from precipitating clouds compared to than nonprecipitating warm clouds. The CloudSat observations also provide an authoritative estimate of global ice water paths. The observed ice water paths are larger than those predicted from most climate models. CloudSat observations also indicate that clouds radiatively heat the global mean atmospheric column (relative to clear skies) by about 10 Wm−2. Although this heating appears to be contributed almost equally by solar and infrared absorption, the latter contribution is shown to vary significantly with latitude being influenced by the predominant cloud structures of the different region in questions.
Present‐day shortcomings in the representation of upper tropospheric ice clouds in general circulation models (GCMs) lead to errors in weather and climate forecasts as well as account for a source of ...uncertainty in climate change projections. An ongoing challenge in rectifying these shortcomings has been the availability of adequate, high‐quality, global observations targeting ice clouds and related precipitating hydrometeors. In addition, the inadequacy of the modeled physics and the often disjointed nature between model representation and the characteristics of the retrieved/observed values have hampered GCM development and validation efforts from making effective use of the measurements that have been available. Thus, even though parameterizations in GCMs accounting for cloud ice processes have, in some cases, become more sophisticated in recent years, this development has largely occurred independently of the global‐scale measurements. With the relatively recent addition of satellite‐derived products from Aura/Microwave Limb Sounder (MLS) and CloudSat, there are now considerably more resources with new and unique capabilities to evaluate GCMs. In this article, we illustrate the shortcomings evident in model representations of cloud ice through a comparison of the simulations assessed in the Intergovernmental Panel on Climate Change Fourth Assessment Report, briefly discuss the range of global observational resources that are available, and describe the essential components of the model parameterizations that characterize their “cloud” ice and related fields. Using this information as background, we (1) discuss some of the main considerations and cautions that must be taken into account in making model‐data comparisons related to cloud ice, (2) illustrate present progress and uncertainties in applying satellite cloud ice (namely from MLS and CloudSat) to model diagnosis, (3) show some indications of model improvements, and finally (4) discuss a number of remaining questions and suggestions for pathways forward.
An Eye on the Storm Hristova-Veleva, Svetla M.; Li, P. Peggy; Knosp, Brian ...
Bulletin of the American Meteorological Society,
10/2020, Volume:
101, Issue:
10
Journal Article
Peer reviewed
Open access
Tropical cyclones (TCs) are among the most destructive natural phenomena with huge societal and economic impact. They form and evolve as the result of complex multiscale processes and nonlinear ...interactions. Even today the understanding and modeling of these processes is still lacking. A major goal of NASA is to bring the wealth of satellite and airborne observations to bear on addressing the unresolved scientific questions and improving our forecast models. Despite their significant amount, these observations are still underutilized in hurricane research and operations due to the complexity associated with finding and bringing together semicoincident and semicontemporaneous multiparameter data that are needed to describe the multiscale TC processes. Such data are traditionally archived in different formats, with different spatiotemporal resolution, across multiple databases, and hosted by various agencies. To address this shortcoming, NASA supported the development of the Jet Propulsion Laboratory (JPL) Tropical Cyclone Information System (TCIS)—a data analytic framework that integrates model forecasts with multiparameter satellite and airborne observations, providing interactive visualization and online analysis tools. TCIS supports interrogation of a large number of atmospheric and ocean variables, allowing for quick investigation of the structure of the tropical storms and their environments. This paper provides an overview of the TCIS’s components and features. It also summarizes recent pilot studies, providing examples of how the TCIS has inspired new research, helping to increase our understanding of TCs. The goal is to encourage more users to take full advantage of the novel capabilities. TCIS allows atmospheric scientists to focus on new ideas and concepts rather than painstakingly gathering data scattered over several agencies.
Retrieval and validation of upper tropospheric ice water content (IWC) measurements with the Aura Microwave Limb Sounder (MLS) are described. The MLS version 2.2 (V2.2) IWC, derived from 240‐GHz ...cloud‐induced radiances (Tcir) at high tangent heights, is scientifically useful at 215–83 hPa. The V2.2 IWC represents a bulk cloud property averaged over a ∼300 × 7 × 4 km3 volume near the pointing tangent height. Precision, accuracy, and spatial resolution of the V2.2 IWC are determined through model simulations and comparisons with CloudSat observations. Comparisons of MLS V2.2 and CloudSat R03 IWCs are made for the months of January and July in terms of normalized probability density function (PDF). The differences between MLS and CloudSat IWC PDFs are generally less than 50% over the IWC range where the MLS technique is valid. At pressures <177 hPa and extratropical latitudes, the MLS V2.2 IWC exhibits a slightly low bias compared to CloudSat, part of which can be attributed to systematic errors in the MLS retrieval. Cloud inhomogeneity and particle size distribution are the leading sources of uncertainties in the V2.2 IWC.
The CloudSat cloud water content (CWC) profiles are sorted by a number of large‐scale parameters obtained from reanalysis and satellite observations, including 500 hPa vertical velocity, sea surface ...temperature and its gradient, surface divergence, precipitation, water vapor path, convective available potential energy and lower tropospheric static stability. The sorting is physics‐based and phenomenon‐oriented. We find different degrees of clustering of cloud vertical structure in various large‐scale regimes. The dominant modes are the deep and shallow clouds with peak CWC above 7 km and below 2 km, respectively, corresponding to distinctly different large‐scale regimes. A middle‐level peak of CWC around 5–7 km is discernible associated with the large‐scale conditions similar to the shallow clouds. This study provides the first quantitative and comprehensive view of tropical CWC distributions in large‐scale regimes. These results offer insights into cloud parameterizations and serve as new observational metrics for evaluation of cloud simulations in models.
THE CLOUDSAT MISSION AND THE A-TRAIN Stephens, Graeme L.; Vane, Deborah G.; Boain, Ronald J. ...
Bulletin of the American Meteorological Society,
12/2002, Volume:
83, Issue:
12
Journal Article
Peer reviewed
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 NASA'sAquaandAurasatellites, 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 profiles requires a combination of active and passive instruments, and this will be achieved by combining the radar data of CloudSat with data from other active and passive sensors of the constellation. This paper describes the underpinning science and general overview of the mission, provides some idea of the expected products and anticipated application of these products, and the potential capability of the A-Train for cloud observations. Notably, the CloudSat mission is expected to stimulate new areas of research on clouds. The mission also provides an important opportunity to demonstrate active sensor technology for future scientific and tactical applications. The CloudSat mission is a partnership between NASA's JPL, the Canadian Space Agency, Colorado State University, the U.S. Air Force, and the U.S. Department of Energy.
The radiative effects of upper tropospheric (UT) clouds observed by CloudSat and Aura MLS during June‐July‐August 2008 are examined and contrasted. We find that the UT cloud occurrence frequency ...observed by MLS is more than CloudSat by 4–10% in the tropical average and by 40∼60% near the tropopause in the deep convective regions. The clouds detected by MLS but missed by CloudSat (denoted as TCC) typically have visible optical thickness less than 0.2. TCC produce a tropical‐mean net warming of 3.5 W/m2 at the top‐of‐atmosphere and net cooling of 1.2 W/m2 at the surface. They induce a net radiative heating in the UT. Their heating rate at 200 hPa is ∼0.35 K/day in the tropical‐mean and ∼0.8 K/day over South Asia, which is about 3–4 times the clear‐sky radiative heating rate. Hence, they are potentially important in affecting the mass transport rates from the troposphere to the stratosphere.
The A‐Train satellite constellation has dramatically increased the temporal and spatial coverage of atmospheric ice water content estimates. The new data are derived by retrieval algorithms designed ...to estimate atmospheric cloud ice water content from remotely sensed measurements. Such retrieval algorithms rely on simplifying assumptions regarding the characteristics of ice particles in the atmosphere. In this study, the sensitivities of CloudSat ice water content retrievals to frozen particle characteristics are tested by generating CloudSat‐like retrievals from profiles of known ice water content. CloudSat actively measures vertical profiles of radar reflectivity in clouds with a 94‐GHz cloud‐profiling radar. Ice water content is retrieved in each cloudy profile at temperatures below 0°C. To assess the CloudSat radar‐only ice water content retrieval algorithm (version 5.0 in Release 3 R03 and version 5.1 in Release 4 R04 of 2B‐CWC‐RO), we apply a 94‐GHz reflectivity simulator to profiles of ice water content generated by a cloud‐resolving numerical model and comprising various frozen particle species (ice, snow, and graupel). The CloudSat ice water content retrieval algorithm is applied to the profiles of simulated reflectivity, and the results are compared to the modeled profiles of known frozen water mass. The results from each version of the algorithm are shown to be sensitive to the characteristics of the frozen particle size distributions and particle densities. Tests of version 5.0 indicate that height varying information could improve retrievals. Despite the addition of a height varying component implemented in version 5.1, similar positive biases are indicated in the tests of each algorithm.
Eyes on the Storms Hristova-Veleva, Svetla M.; Li, P. Peggy; Knosp, Brian ...
Bulletin of the American Meteorological Society,
03/2021, Volume:
102, Issue:
3
Journal Article
PDF
