A42C-01 INVITED
Overview of A-Train Satellite Cloud Measurements
NASA satellites make a wide variety of cloud measurements for climate and meteorological research and prediction. The A-Train is a constellation of satellites in coordinated low earth orbits with an extensive array of sensors making a wide variety of complementary observations of the earth system. The satellite constellation provides synergistic measurements enabling data from several different satellites/sensors to be used together to obtain comprehensive information about various key components and processes of the earth system. The A-Train consists of the following satellites and sensors currently in operation: Aqua, launched 4 May 2002 carries: Atmospheric Infrared Sounder-high spectral resolution (2378 channels) grating spectrometer. Advanced Microwave Sounding Unit-15 channel microwave radiometer. Humidity Sounder for Brazil is a 4 channel microwave radiometer, which provided data until February 2003. Advanced Microwave Scanning Radiometer for EOS-12 channel, 6 frequency microwave radiometer. Moderate Resolution Imaging Spectroradiometer-36 band visible and infrared imaging spectroradiometer. Cloud's and the Earth's Radiant Energy System-3 channel scanning visible and infrared radiometers. Aura, launched 15 July 2004 carries: High Resolution Dynamics Limb Sounder-multi channel infrared radiometer. Microwave Limb Sounder-multi channel microwave radiometer. Ozone Monitoring Instrument-visible and ultra violet hyperspectral imaging spectrometers. Tropospheric Emission Spectrometer-high-resolution, infrared Fourier transform spectrometer. Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar-launched 18 December 2004 by the French space agency Centre National d'Etudes Spatiales (CNES) and carries a polarimeter. CloudSat and CALIPSO launched together 28 April 2005. CloudSat-US/Canadian cooperative project carries a 94 GHz nadir cloud profiling radar. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations-US/French mission carrying:. Cloud-Aerosol Lidar with Orthogonal Polarization-532 (polarized)and 1064 nm backscatter lidar Wide Field Camera-single channel (620-670 nm) nadir view imager. Imaging Infrared Radiometer-3 channel, nadir viewing, imaging radiometer. Two more satellites will be joining the A-Train: Orbiting Carbon Observatory-to launch in early 2009 and will carry a single instrument comprised of 3 high-resolution infrared grating spectrometers. Glory to launch in early 2009 will carry: Aerosol Polarimetry Sensor-which will measure 9 channels from 410 to 2200 nm at angles ranging from +60° to -80° with respect to nadir. Cloud Cameras-2 high spatial resolution cameras. Total Irradiance Monitor-high accuracy and precision solar radiometer The A-Train satellites fly in formation independently in polar orbits 705 km above Earth at an inclination of 98 degrees and cross over the equator a few minutes apart at approximately 1:30 p.m. local time. A-Train cloud measurements are made primarily by visible, infrared and microwave radiometers. Retrievals using these observations provide measurements of cloud areal and vertical distributions, properties, and radiative effects. Optically thin clouds, multi-layer clouds and clouds over cold bright surfaces, polar regions, represent particular challenges for passive sensors. Recently, measurements by active sensors such as lidar and radar provide detailed vertical distributions of clouds and cloud properties, complementing the passive sensors.
A42C-02 INVITED
Model Evaluations Using CloudSat and the A-Train Data
CloudSat was launched on April 28 2006 as a NASA Earth System Science Pathfinder satellite mission to fly the first satellite-based millimeter-wavelength cloud radar (94 GHz) to advance our understanding of cloud abundance, distribution, structure and radiative properties. One of the key CloudSat objectives is to spur improvements in climate prediction by providing critical observation against which model representations of clouds can be effective evaluated and constrained. This talk presents results of research that utilizes the new data from CloudSat in combination with other sensors of the A-Train to expose systematic errors in weather prediction and climate models of various types. Comparisons between cloud and precipitation statistics derived from A-Train observations and global clouds resolving models ranging from global cloud resolving models, the so called multi-scale modeling framework (MMF) to current forecast and climate models will be highlighted. http://cloudsat.atmos.colostate.edu/
A42C-03 INVITED
Using Satellite Measurements to Evaluate Clouds And Their Seasonal Variations in 10 Atmospheric General Circulation Models
To assess the current status of climate models in simulating clouds, basic cloud climatologies from 10 atmospheric general circulation models are compared with satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) and the Clouds and Earth¡¯s Radiant Energy System (CERES) program. An ISCCP simulator is employed in all models to facilitate the comparison. Models simulated a four-fold difference in high-top clouds. There are however also large uncertainties in satellite high thin clouds to effectively constrain the models. The majority of models only simulated thirty to forty percent of middle-top clouds in the ISCCP and CERES datasets. Some models only simulated less than a quarter of observed middle clouds. Half of the models underestimated low clouds while none overestimated them at a statistically significant level. When stratified in the optical thickness ranges, the majority of the models simulated optically thick clouds more than twice the satellite observations. Most models however underestimated optically intermediate and thin clouds. The grand mean of all models simulated about eighty percent of optical intermediate clouds and sixty percent of optically thin clouds in observations. Compensations of these clouds biases are used to explain the simulated longwave and shortwave cloud radiative forcing at the top of the atmosphere. Seasonal sensitivities of clouds are also analyzed to compare with observations. Models are shown to simulate seasonal variations better for high clouds than for low clouds. Latitudinal distribution of the seasonal variations correlate with satellite measurements at >0.9, 0.6 to 0.9, and ?0.2 to 0.7 respectively for high, middle and low clouds. Seasonal amplitudes of individual ISCCP cloud types differ among the models and with observations by as much as several hundred percent. The seasonal sensitivities of cloud types are found to strongly depend on the basic cloud climatology in the models. Models that systematically underestimate middle clouds also underestimate seasonal variations, while those that overestimate optically thick clouds also overestimate their seasonal sensitivities. Possible causes of the systematic cloud biases in the models are discussed. http://www.agu.org/pubs/crossref/2005/2004JD005021.shtml
A42C-04
Cloud Ice: A Climate Model Challenge With Signs and Expectations of Progress
Global climate models (GCMs), including those assessed in the IPCC AR4, exhibit considerable disagreement in the amount of cloud ice both in terms of the annual global mean as well as their spatial variability. Global measurements of cloud ice have been difficult due to the challenges involved in remotely sensing ice water content (IWC) and its vertical profile including complications associated with multi-level clouds, mixed-phases and multiple hydrometer types, the uncertainty in classifying ice particle size and shape for remote retrievals, and the relatively small time and space scales associated with deep convection. Together, these measurement difficulties make it a challenge to characterize and understand the mechanisms of ice cloud formation and dissipation. Fortunately, there are new observational resources recently established that can be expected to lead to considerable reduction in the observational uncertainties of cloud ice, and in turn improve the fidelity of model representations. Specifically, these include the Microwave Limb Sounder (MLS) on the Earth Observing System (EOS) Aura satellite, and the CloudSat and Calipso satellite missions, all of which fly in formation in what is referred to as the A-Train. Based on radar and limb-sounding techniques, these new satellite measurements provide a considerable leap forward in terms of the information gathered regarding upper-tropospheric cloud IWC as well as other macrophysical and microphysical properties. In this presentation, we describe the current state of GCM representations of cloud ice and their associated uncertainties, the nature of the new observational resources for constraining cloud ice values in GCMs, the challenges in making model-data comparisons with these data resources, and prospects for near-term improvements in model representations.
A42C-05
Using High Frequency Passive Microwave, A-Train, and TRMM Data to Evaluate Hydrometeor Structure in the NASA GEOS-5 Data Assimilation System
Validating water vapor and prognostic condensate in global models remains a challenging research task. Model parameterizations are still subject to a large number of tunable parameters; furthermore, accurate and representative in situ observations are very sparse, and satellite observations historically have significant quantitative uncertainties. Progress on improving cloud / hydrometeor fields in models stands to benefit greatly from the growing inventory of A-Train data sets. In the present study we are using a variety of complementary satellite retrievals of hydrometeors to examine condensate produced by the emerging NASA Modern Era Retrospective Analysis for Research and Applications, MERRA, and its associated atmospheric general circulation model GEOS-5. Cloud and precipitation are generated by both grid-scale prognostic equations and by the Relaxed Arakawa-Schubert (RAS) diagnostic convective parameterization. The high frequency channels (89 to 183.3 GHz) from AMSU-B and MHS on NOAA polar orbiting satellites are being used to evaluate the climatology and variability of precipitating ice from tropical convective anvils. Vertical hydrometeor structure from the Tropical Rainfall Measuring Mission (TRMM) and CloudSat radars are used to develop statistics on vertical hydrometeor structure in order to better interpret the extensive high frequency passive microwave climatology. Ice water path data from the Moderate Resolution Imaging Spectroradiometer, MODIS, are used to investigate relationships between upper level cloudiness and tropical deep convective anvils. Together these complementary measures of water substance from different sensors are used to evaluate cloud liquid / ice water path, gross aspects of vertical hydrometeor structure, and the relationship between cloud extent and surface precipitation in preliminary products of the MERRA reanalysis.
A42C-06 INVITED
Using A-Train observations to constrain and improve arctic cloud parameterizations
Many climate models appear to have significant biases in their representation of Arctic climate, but there is a dearth of reliable cloud observations to help constrain climate model parameterizations. A-Train observations provide a unique and useful tool for investigating the spatial distribution and the physical properties of Arctic clouds. We use A-Train radar (CloudSat) and lidar (CALIOP) observations to map Arctic cloud fraction, cloud top height, cloud thickness, and radar reflectivity from July 2006 to present. We also investigate the relationship between Arctic cloud properties and sea ice cover (AMSR-E). Using these new observations, we evaluate the representation of Arctic clouds in a state-of-the-art climate model, the Community Atmosphere Model (CAM). We present model-observation comparisons using traditional geophysical variables, as well as comparison of the raw observations to model-simulated observations (e.g., model-simulated radar reflectivity). Finally, we evaluate the sensitivity of modeled Arctic clouds to the paramterization of clouds and the atmospheric boundary layer. http://www.cgd.ucar.edu/cms/jenkay/
A42C-07
A Test of the Simulation of Tropical Convective Cloudiness by a Cloud-Resolving Model
The distribution of tropical high clouds produced by a doubly periodic three-dimensional cloud-resolving model is compared with satellite observations. The model is forced with steady forcing characteristic of tropical Pacific convective regions, and the model clouds are compared with satellite observations for the same regions. Clouds are divided into categories that represent convective cores, moderately thick anvil clouds and thin high clouds. The statistics of these clouds and their relationship to the precipitation rate are computed in a similar way for the model data and for observations from the MODIS and AMSR instruments on the Aqua Satellite. The model produces a good simulation of the relationship between the precipitation rate and optically thick cold clouds that represent convective cores. The model also does a reasonable job of simulating the abundance of thin cold clouds in the East and West Pacific ITCZ regions. The model produces too little anvil cloud by a factor of about 4, however. The observations show probability density functions for OLR and albedo with maxima that correspond to extended upper level cold clouds, whereas the model does not. The sensitivity to model parameters of the simulation of anvil cloud area per unit of precipitation is explored using a two-dimensional model. A set of cloud physics parameters is found that produces anvil cloud with realistic amounts and sensitivity to the precipitation rate, while preserving the good simulation of thick and thin cloud. http://www.atmos.washington.edu/~dennis/Papers.html
A42C-08
Use of satellite observation to evaluate cloud schemes
Mesoscale models offer an ideal framework for performing detailed and explicit simulations of cloud. First these models are able to follow the time evolution of different moments of the cloud distributions in the context of real meteorological conditions. Second the gridmesh of a mesoscale model is of the same size than a satellite pixel facilitating a comparison without it being necessary to resort to additional assumptions on size. Here we adopt a model-to-satellite approach, in which satellite brightness temperature (BT) images are directly compared to BTs computed from predicted model fields. The approach is especially powerful in identifying discrepancies of cloud cover forecasts with BTs at 10.8 μm. The model-to-satellite approach associated with the BT difference (BTD) technique can also verify specific forecasts such as cirrus cover and convective overshoots. Recent applications of the approach to the MSG observations will be shown. In particular, the BTD technique leads us to improve the cloud scheme of the Meso-NH model by tuning a critical parameter in a cirrus parameterization. This improvement yields a better predicted diurnal cycle of upper- tropospheric humidity in the Tropics. The model-to-satellite approach is further combined with the calculation of meteorological scores for an objective and long-term evaluation of the model forecasts. These recent applications of the approach to the MSG observations and the Meso-NH forecasts will be shown in the context of AMMA, TROCCINOX, and COPS field campaigns over West Africa, Brazil, and Western Europe, respectively. http://mesonh.aero.obs- mip.fr/chaboureau/PUB
A42C-09
A Joint Satellite and Global Cloud-Resolving Model Analysis of the 2006/07 Madden-Julian Oscillation
The Madden-Julian Oscillation (MJO) is often considered as a touchstone to test the performance of atmospheric general circulation models (AGCMs). The prescribed parameterization of cumulus convection is likely a major factor that hampers the flexible representation of tropical convection and associated large-scale circulation in AGCMs. The improved treatment of cumulus-scale physics in a way consistent with observations is crucial for further understanding and better model-reproducibility of the MJO. In this study, cloud and precipitation properties associated with the MJO are investigated for 32 days starting from December 15, 2006, based on a joint analysis of global cloud-resolving model simulation and satellite measurements. The model adopted here is the Nonhydrostatic ICosahedral Atmospheric Model (NICAM), where individual convective clouds are explicitly simulated across the entire globe. The simulated cloud/precipitation characteristics are assessed in comparison with satellite observation in the manner described below. Radiative transfer calculations are applied to the NICAM output to simulate 13.8-GHz radar echo and 10.8-μm brightness temperature. The synthesized radar and infrared "observations" are directly comparable with the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and Visible/Infrared Scanner (VIRS) measurements. The model result exhibits a slow, eastward propagation of convective areas in reasonable agreement with the satellite measurement, although the cloud model tends to excessively produce deep convection. The joint histogram of radar echo-top height and infrared Tb suggests that model-calculated snow may be overly abundant compared to the observation. More analysis focused on large-scale dynamics and the radar sensitivity to microphysics will be presented.
A42C-10 INVITED
Evaluating Colorado State University and NASA Goddard multi-scale modeling framework (MMF) representation of tropical cloud and precipitation structures using CloudSat data
Two multi-scale modeling frameworks (MMFs) recently developed at Colorado State University (CSU) and NASA Goddard are evaluated against the initial CloudSat radar observations in the simulation of tropical cloud and precipitation structures. Since MMF adopts a first-principle approach to representing the dynamics and physics of cloud-scale processes, model-data comparison becomes more straightforward, especially in characterizing structures of tropical convection and the associated cloudiness. A radar simulator package called QuickBeam is used to convert modeled hydrometeor profiles into radar reflectivities. Furthermore, novel diagnostic tools and regime classification are used that were recently developed to emphasize the unique nature of the new space- borne active sensors. It is found that both CSU and Goddard MMFs have difficulties in capturing some aspects of the structure and distribution of tropical cloud and precipitation systems. For example, the boundary-layer clouds in CSU MMF are too thick such that they look as if they were drizzling clouds from radar perspective. The Goddard MMF, on the other hand, simulates the drizzle regime that is too deep in vertical extent. For deep convection, both CSU and Goddard MMFs seem to underpredict the large radar echoes at high altitude, suggesting that the simulated deep convective towers do not transport enough large-size particles into the upper troposphere. Regional biases in the two models are also presented.