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Flat heat pipes rapidly transfer heat to and from experimental volumes. Heat pipe vapor chamber carries heat to and from thermo electric modules. Critical surface acts as evaporator or condenser in cloud physics experiments. Used as walls of spaceborne atmospheric cloud chambers. On Earth, used as isothermal floors for environmental test chambers.
Simultaneous measurements of the liquid water content and particle size have assumed an important role in cloud physics as they help elucidate the mechanism of cloud particle formation and the mechanism of air mass-mixing in stratus clouds. Such measurements can reveal the modification of cloud air masses by anthropogenic aerosol particles (Coakley et al. 1987, Durkee 1989). Studies of the climatic impact of these modification processes on cloud microphysics seems to be urgent for understanding mechanisms of climate change. General Circulation Model (GCM) simulations can be improved by introducing a parameterization of cloud optical properties in terms of integrated liquid water content (liquid water path) and particle size (Slingo 1989). Motivated by the above mentioned circumstances, remote sensing techniques were developed for simultaneously retrieving the cloud optical thickness and effective particle radius, from which the liquid water path can be inferred. Statistical features of the cloud optical thickness (or liquid water path) and effective particle size for marine stratocumulus clouds are presented. These results were obtained during 4 days (7, 10, 13, and 16 July 1987) of observations with the Multispectral Cloud Radiometer (ER-2) and the Thematic Mapper (LANDSAT-5) during the First ISCCP Regional Experiment (FIRE).
This talk provides an overview of the subject of absorption of solar radiation by clouds in the earth's atmosphere. The paper summarizes the available evidence which points to disagreements between theoretical and observed values of cloud absorption (and reflections). The importance of these discrepancies, particularly to remote sensing of clouds as well as to studies of cloud physics and earth radiation budgets, is emphasized. Existing cloud absorption and reflection measurements are reviewed and the persistent differences that exist between calculated and measured near-infrared cloud albedos are highlighted. Various explanations for these reflection and absorption discrepancies are discussed under two separate paths: a theoretician's approach and an experimentalist's approach. Examples for the former approach include model accuracy tests, large-droplet hypothesis, excess absorbing aerosol, enhanced water vapor continuum absorption, and effects of cloud inhomogeneity. The latter approach focuses on discussions of instrumental device, calibration, operational strategy, and signal/noise separation. A recommendation for future activities on this subject will be given.
Aircraft icing is a serious safety problem for the general aviation and some commuter transport airplanes. There has been tremendous growth in the commuter aviation industry in the last few years, Since these type of aircraft generally operate at lower altitudes they consequently spend a far greater proportion of their time operating in icing conditions. For the past thirty years airborne and ground based facilities have relied primarily on two types of cloud physics instrumentation to measure the characteristics of icing clouds: hot wire liquid water content probes and laser based particle sizing probes for the measurement of water droplet size. The instrumentation is severely limited by the technology that was developed during the 1970's and is quite large in size. The goal of this research is to develop one instrument with a wide bandwidth, better response time, higher resolution, user selectability, and small and lightweight. NASA Glenn Research Center, Droplet Measurement Technology, and Meteorology Society of Canada have developed a collaborative effort to develop such an instrument. This paper describes the development and test results of the prototype Icing Sensor Probe.
Clouds play a key role in regulating the hydrological cycle and the Earth's radiative energy budget. However, global climate models (GCMs) with a horizontal grid spacing on the order of 100 km have limitations in representing sub-grid cloud dynamics with spatial scales on the order of 1 km, leading to potential uncertainties in cloud radiative feedback on the global scale. In our research, we will leverage the capabilities of Deep Machine Learning (DML) methods to construct parameterizations of sub-grid volumetric cloud fraction (VCF), which is the frequency of occurrence on a grid volume accumulated in the horizontal and vertical directions. Our investigation delves into the intricate relationship between VCF obtained from the NASA CALIPSO-CloudSat-CERES-MODIS (CCCM) satellite observation data and 3-D MERRA-2 reanalysis meteorological profiling data (e.g., wind, relative humidity, temperature). Through a comprehensive one-year data training utilizing the Sequence to Sequence DML method, we have successfully disentangled the complicated cloud formation dynamics across diverse meteorological conditions through a day-to-day analysis framework. Preliminary findings reveal promising statistical agreements in geographical and vertical distributions and seasonal variations of volumetric cloud fraction between ML prediction and satellite measurements. These results underscore the aptitude of our DML model to discern underlying cloud physical processes and accurately represent sub-grid cloud formation dynamics. Additionally, we have also employed trained neural network to analyze uncertainties arising from errors in meteorological data, further enhancing the robustness of our VCF parameterization.
Airborne effluent measurements and cloud physical behavior for the May 21, 1977, Titan 3 launch from the Air Force Eastern Test Range, Fla. are presented. The monitoring program included airborne effluent measurements in situ in the launch cloud, visible and infrared photography of cloud growth and physical behavior, and limited surface collection of rain samples. Airborne effluent measurements included concentrations of HCl, NO, NOx, and aerosols as a function of time in the exhaust cloud. For the first time in situ particulate mass concentration and aerosol number density were measured as a function of time and size in the size range of 0.05 to 25 micro meters diameter. Measurement results were similar to those of earlier launch monitorings. Maximum HCl and NOx concentrations ranged from 10 ppm and 500 ppb, respectively, several minutes after launch to about 1 ppm and 100 ppb at 45 minutes after launch.
21 Mixed-phase clouds are widespread in the Arctic and have substantial impacts on the 22 surface energy budget, sea ice extent, and regional climate. Significant changes are found in the 23 modeled phase partitioning of Arctic mixed-phase clouds in the newly developed U.S. 24 Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model 25 version 1 (EAMv1) compared to its predecessor (CAM5/EAMv0). The mixed-phase clouds 26 simulated by EAMv1 is overly dominated by super-cool liquid which is often underestimated by 27 CAM5. Compared to CAM5, EAMv1 has made several important changes in its cloud physical 28 parameterizations, including using CLUBB to unify shallow convection, PBL turbulence, and 29 cloud macrophysics, as well as updating the MG two-moment cloud microphysics scheme and 30 using the CNT heterogeneous ice nucleation scheme to replace the Meyers scheme for mixed- 31 phase clouds. To understand which of the newly-implemented physical parameterizations is 32 responsible for the change of modeled mixed-phase cloud phase partitioning in EAMv1, 33 sensitivity tests are conducted by utilizing the short-term hindcast approach and results are 34 compared to the observations from the ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) 35 field campaign. 36 It is found that although each scheme contributes to the decrease of cloud ice water 37 content (IWC) and increase of cloud liquid water content (LWC), only the combined effect of 38 CLUBB and CNT can explain the significant underestimation of IWC. By examining the budgets 39 of cloud microphysical processes, CLUBB is found to play a dominant role due to the fact that 40 CLUBB does not diagnoses cloud ice phase water in its assumed joint PDF method and thus may 41 not provide sufficient cloud ice to initialize ice phase microphysical processes such as the growth 42 of ice crystals through vapor deposition. Although a more effective heterogeneous ice nucleation 43 may alleviate the underestimation of IWC, the ice phase process in CLUBB requires urgent 44 attention in the future model development.
This paper presents a comparison of volume extinction coefficients in tropical ice clouds retrieved from two instruments : the 532-nm Cloud Physics Lidar (CPL), and the in-situ probe Cloud Integrating Nephelometer (CIN). Both instruments were mounted on airborne platforms during the CRYSTAL-FACE campaign and took measurements in ice clouds up to 17km. Coincident observations from three cloud cases are compared : one synoptically-generated cirrus cloud of low optical depth, and two ice clouds located on top of convective systems. Emphasis is put on the vertical variability of the extinction coefficient. Results show small differences on small spatial scales (approx. 100m) in retrievals from both instruments. Lidar retrievals also show higher extinction coefficients in the synoptic cirrus case, while the opposite tendency is observed in convective cloud systems. These differences are generally variations around the average profile given by the CPL though, and general trends on larger spatial scales are usually well reproduced. A good agreement exists between the two instruments, with an average difference of less than 16% on optical depth retrievals.
Cloud physics is inevitably affected by the smoke particle (CCN, cloud condensation nuclei) size distribution below the clouds. Therefore, the size distributions parameterized as spectral bin microphysics are needed to explicitly study the effects of atmospheric aerosol concentration on cloud development, rainfall production, and rainfall rates for convective clouds. Recently, a detailed spectral--bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. The formulation for the explicit spectral-bin microphysical processes is based on solving stochastic kinetic equations for the size distribution functions of water droplets (i.e., cloud droplets and raindrops), and several types of ice particles [i.e., pristine ice crystals (columnar and plate-like), snow (dendrites and aggregates), graupel and frozen drops/hail]. Each type is described by a special size distribution function containing many categories (i.e., 33 bins). Atmospheric aerosols are also described using number density size-distribution functions.
During the Tropical Composition, Clouds, and Climate Coupling (TC4) experiment in July-August 2007, the NASA WB-57F and ER-2 aircraft made coordinated flights through a tropopause subvisible cirrus (SVC) layer off the Pacific Coast of Central America. The ER-2 aircraft was equipped with a remote sensing payload that included the cloud physics lidar (CPL). The WB-57F payload included cloud microphysical and trace gas measurements, and the aircraft made four vertical profiles through the SVC layer shortly after the ER-2 flew over. The in situ and remotely sensed data are used to quantify the meteorological and microphysical properties of the SVC layer, and these data are compared to the limited set of SVC measurements that have previously been made. It is found that the layer encountered was particularly tenuous, with optical depths (tau) between about 10(exp -4) and 10(exp -3). From the in situ and other meteorological data, radiative heating rate perturbations of approx.0.05-0.1 K/day are calculated. These heating rates are smaller than previous estimates for tropopause SVC, consistent with the smaller tau in the present study. Coverage statistics based on CPL data from other TC4 flights indicate that this cloud was not an outlier among the sampled population. SVC with properties similar to the one presented here are below the detection limit of space \based lidars such as CALIPSO, and a comparison with the TC4 statistics suggests that a majority (>50%) of tropopause SVC (with tau < 0.01) could be unaccounted for in studies using CALIPSO data.
Technical assistance continued in support of the Atmospheric Cloud Physics Laboratory is discussed. A study of factors affecting warm cloud formation showed that the time of formation during an arbitrary expansion is independent of carrier gas composition for ideal gases and independent of aerosol concentration for low concentrations of very small aerosols. Equipment and procedures for gravimetric evaluation of a precision saturator were laboratory tested. A numerical feasibility study was conducted for the stable levitation of charged solution droplets by an electric field in a one-g static diffusion chamber. The concept, operating principles, applications, limits, and sensitivity of the levitation technique are discussed.
Three boundary-layer cloud object types, stratus, stratocumulus and cumulus, that occurred over the Pacific Ocean during January-August 1998, are identified from the CERES (Clouds and the Earth s Radiant Energy System) single scanner footprint (SSF) data from the TRMM (Tropical Rainfall Measuring Mission) satellite. This study emphasizes the differences and similarities in the characteristics of each cloud-object type between the tropical and subtropical regions and among different size categories and among small geographic areas. Both the frequencies of occurrence and statistical distributions of cloud physical properties are analyzed. In terms of frequencies of occurrence, stratocumulus clouds dominate the entire boundary layer cloud population in all regions and among all size categories. Stratus clouds are more prevalent in the subtropics and near the coastal regions, while cumulus clouds are relatively prevalent over open ocean and the equatorial regions, particularly, within the small size categories. The largest size category of stratus cloud objects occurs more frequently in the subtropics than in the tropics and has much larger average size than its cumulus and stratocumulus counterparts. Each of the three cloud object types exhibits small differences in statistical distributions of cloud optical depth, liquid water path, TOA albedo and perhaps cloud-top height, but large differences in those of cloud-top temperature and OLR between the tropics and subtropics. Differences in the sea surface temperature (SST) distributions between the tropics and subtropics influence some of the cloud macrophysical properties, but cloud microphysical properties and albedo for each cloud object type are likely determined by (local) boundary-layer dynamics and structures. Systematic variations of cloud optical depth, TOA albedo, cloud-top height, OLR and SST with cloud object sizes are pronounced for the stratocumulus and stratus types, which are related to systematic variations of the strength of inversion with cloud object sizes, produced by large-scale subsidence. The differences in cloud macrophysical properties over small regions are significantly larger than those of cloud microphysical properties and TOA albedo, suggesting a greater control of (local) large-scale dynamics and other factors on cloud object properties. When the three cloud object types are combined, the relative population among the three types is the most important factor for determining the cloud object properties in a Pacific transect where the transition of boundary-layer cloud types takes place.
In order to improve operational safety and efficiency, the transportation industry, including aviation, has an urgent need for accurate diagnoses and predictions of clouds and associated weather conditions. Adverse weather accounts for 70% of all air traffic delays within the U.S. National Airspace System. The Federal Aviation Administration has determined that as much as two thirds of weather-related delays are potentially avoidable with better weather information and roughly 20% of all aviation accidents are weather related. Thus, it is recognized that an important factor in meeting the goals of the Next Generation Transportation System (NexGen) vision is the improved integration of weather information. The concept of a 4-D weather cube is being developed to address that need by integrating observed and forecasted weather information into a shared 4-D database, providing an integrated and nationally consistent weather picture for a variety of users and to support operational decision support systems. Weather analyses and forecasts derived using Numerical Weather Prediction (NWP) models are a critical tool that forecasters rely on for guidance and also an important element in current and future decision support systems. For example, the Rapid Update Cycle (RUC) and the recently implemented Rapid Refresh (RR) Weather Research and Forecast (WRF) models provide high frequency forecasts and are key elements of the FAA Aviation Weather Research Program. Because clouds play a crucial role in the dynamics and thermodynamics of the atmosphere, they must be adequately accounted for in NWP models. The RUC, for example, cycles at full resolution five cloud microphysical species (cloud water, cloud ice, rain, snow, and graupel) and has the capability of updating these fields from observations. In order to improve the models initial state and subsequent forecasts, cloud top altitude (or temperature, T(sub c)) derived from operational satellite data, surface observations of cloud base altitude, radar reflectivity, and lightning data are used to help build and remove clouds in the models assimilation system. Despite this advance and the many recent advances made in our understanding of cloud physical processes and radiative effects, many problems remain in adequately representing clouds in models. While the assimilation of cloud top information derived from operational satellite data has merit, other information is available that has not yet been exploited. For example, the vertically integrated cloud water content (CWC) or cloud water path (CWP) and cloud geometric thickness (delta Z) are standard products being derived routinely from operational satellite data. These and other cloud products have been validated under a variety of conditions. Since the uncertainties have generally been found to be less than those found in model analyses and forecasts, the satellite products should be suitable for data assimilation, provided an appropriate strategy can be developed that links the satellite-derived cloud parameters with cloud parameters specified in the model. In this paper, we briefly outline such a strategy and describe a methodology to retrieve cloud water content profiles from operational satellite data. Initial results and future plans are presented. It is expected that the direct assimilation of this new product will provide the most accurate depiction of the vertical distribution of cloud water ever produced at the high spatial and temporal resolution needed for short term weather analyses and forecasts.
Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter's ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.
The Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment (CRYSTAL-FACE) is a measurement campaign designed to investigate tropical Cirrus cloud physical properties and formation processes. Understanding the production of upper tropospheric cirrus clouds is essential for the successful modeling of 'he Earth's climate. The deployment phase will occur in July, 2002 in southern Florida, USA. Several aircraft will be used, including the ER-2 and Proteus for cloud remote sensing, the WB-57 and Citation for in situ cloud measurements, the P-3 with a Doppler radar for characterization of convective systems, and the Twin otter for sampling of inflow airmasses. In addition, numerous ground-based and satellite remote sensing measurements will be contributing. A central focus of the mission is improvement of our ability to model cirrus clouds with numerical models. Several research groups with a variety of model types (cloud-resolving models, mesoscale models, weather-prediction models, and general circulation models) will be participating. Our hope is to fully characterize several mulonimbus/cirrus anvil systems that can be used as case studies for testing and improvement of the models. The models will be used for investigating cirrus generation and dissipation processes and the sensitivity of tropical cirrus to convective intensity and aerosol properties. Ultimately, we expect this effort to improve our ability to represent tropical cirrus in GCMs. A general description of the CRYSTAL-FACE program will be presented, with an emphasis on the cloud modeling approach.
One of the main objectives of the Clouds and the Earth's Radiant Energy System (CERES) project is the retrieval of cloud physical and microphysical properties simultaneously with observations of broadband radiative fluxes. These cloud parameter sare used for three main purposes: 1) to provide data for radiation-cloud climate feedback studies; 2) to provide scene identification data for the construction and application of angular distribution models; and 3) to be used as input to radiative transfer calculations of intra-atmospheric fluxes
The Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment (CRYSTAL-FACE) was a measurement campaign designed to investigate tropical cirrus cloud physical properties and formation processes. Understanding the production of upper tropospheric cirrus clouds is essential for the successful modeling of the Earth's climate. The mission was staged in July 2002 with flights of 6 aircraft from Key West, Florida. Several aircraft were used for in situ and remote sensing of aerosols, ice crystals, meteorological fields, radiative fluxes, and gas concentrations. The NASA ER-2 and WB-57, the Proteus aircraft, owned by Northrop Grumman and operated by Scaled Composites, CIRPAS provided the DeHavilland UV-l8A, "Twin Otter" aircraft, the University of North Dakota provided a Cessna Citation II aircraft , and NSF supported the ELDORA radar onboard the Naval Research Laboratory P-3 aircraft. In this presentation, I will describe some of the flights, the conditions, and some of the results from the mission.
Provide, from space, the first global survey of cloud profiles (height, thickness) and cloud physical properties (water, ice, precipitation) needed to evaluate and improve the way clouds, moisture and energy are represented in global models used for weather forecasts and climate prediction.