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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Atmospheric Radiation Measurement (ARM)/CART site

The DOE's Atmospheric Radiation Measurement (ARM) goals are as follows: (1) to provide an experimental test bed for improving the treatment of radiative transfer in global climate models (GCM's) under all kinds of cloud cover; and (2) to improve the parameterization and modeling of cloud formation, maintenance, dissipation, and related processes in GCM's. The scientific requirements which are most critical to the objectives of ARM are discussed.

Pennell, Bill↗
DOE OSTI2024

Contributions From Cloud Morphological Changes to the Interannual Shortwave Cloud Feedback Based on MODIS and ISCCP Satellite Observations

The surface temperature-mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel “double decomposition method” that employs the “cloud radiative kernel” and “cloud regime” concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low- and mid-level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm-track clouds to thinner oceanic low-level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid- and low-level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies.

58 GEOSCIENCES↗
NASA NTRS1984

Outlook for research on subtropical marine stratiform clouds

A detailed description of the goals and methodology of the First International Satellite Cloud Cover Project Regional Experiment (FIRE) is presented. The purpose of the experiment is to develop physical models and parameterizations of fractional cloud cover over the Pacific Basin. In order to determine fractional cloud cover parameters, satellite observations by radar and lidar instruments will be combined with in situ measurements of the cloud-capped marine boundary layer. A description of a candidate experiment for the program is presented, and some general problems connected with the statistical characterization of satellite imagery are discussed.

Randall, D. A.↗
NASA NTRS1993

Scientific goals of the Cooperative Multiscale Experiment (CME)

Mesoscale Convective Systems (MCS) form the focus of CME. Recent developments in global climate models, the urgent need to improve the representation of the physics of convection, radiation, the boundary layer, and orography, and the surge of interest in coupling hydrologic, chemistry, and atmospheric models of various scales, have emphasized the need for a broad interdisciplinary and multi-scale approach to understanding and predicting MCS's and their interactions with processes at other scales. The role of mesoscale systems in the large-scale atmospheric circulation, the representation of organized convection and other mesoscale flux sources in terms of bulk properties, and the mutually consistent treatment of water vapor, clouds, radiation, and precipitation, are all key scientific issues concerning which CME will seek to increase understanding. The manner in which convective, mesoscale, and larger scale processes interact to produce and organize MCS's, the moisture cycling properties of MCS's, and the use of coupled cloud/mesoscale models to better understand these processes, are also major objectives of CME. Particular emphasis will be placed on the multi-scale role of MCS's in the hydrological cycle and in the production and transport of chemical trace constituents. The scientific goals of the CME consist of the following: understand how the large and small scales of motion influence the location, structure, intensity, and life cycles of MCS's; understand processes and conditions that determine the relative roles of balanced (slow manifold) and unbalanced (fast manifold) circulations in the dynamics of MCS's throughout their life cycles; assess the predictability of MCS's and improve the quantitative forecasting of precipitation and severe weather events; quantify the upscale feedback of MCS's to the large-scale environment and determine interrelationships between MCS occurrence and variations in the large-scale flow and surface forcing; provide a data base for initialization and verification of coupled regional, mesoscale/hydrologic, mesoscale/chemistry, and prototype mesoscale/cloud-resolving models for prediction of severe weather, ceilings, and visibility; provide a data base for initialization and validation of cloud-resolving models, and for assisting in the fabrication, calibration, and testing of cloud and MCS parameterization schemes; and provide a data base for validation of four dimensional data assimilation schemes and algorithms for retrieving cloud and state parameters from remote sensing instrumentation.

Cotton, William↗
NASA NTRS1990

Model simulations of rainout and washout from a warm stratiform cloud

A one-dimensional, time-dependent cloud model with parameterized microphysics is used to investigate the processes which control the rainout and washout of soluble gases from warm, precipitating stratiform clouds. Calculations are presented simulating the distributions of soluble species within and below the cloud layer and in the precipitating raindrops as a function of time and species' solubility. Calculations indicate that for species with low solubility, wet removal processes are relatively slow and thus do not significantly affect the species' gas-phase abundance. As a result, the removal of low-solubility species by rainout and washout is controlled by thermodynamic processes with the concentration of the species in cloud and rainwater largely determined by the species' solubility. For highly soluble species on the other hand, dissolution into cloud droplets and removal in rain is quite rapid and the abundance of highly soluble species within and below the cloud falls rapidly as soon as the precipitation begins.

Xing, Lin↗
NASA NTRS1990

Model simulations of rainout and washout from a warm stratiform cloud

Calculations utilizing a one-dimensional, time-dependent stratiform cloud model with parameterized microphysics to study the washout and rainout of soluble trace gases are presented. It is shown that the in-cloud concentrations of relatively insoluble species remain essentially unaffected by the presence of cloudwater and rainwater, while the concentrations of highly soluble species are rapidly depleted within the cloud. Due to this depletion it is shown that for highly soluble species the below-cloud scavenging via washout can dominate over in-cloud scavenging via rainout; in-cloud, aqueous-phase cloudwater and rainwater concentrations can differ considerably and, near the cloudbase, cloudwater concentrations can exceed rainwater concentrations by factors of two to ten.

Xing, Lin↗
NASA NTRS2009

Exploring Alternate Parameterizations for Snowfall with Validation from Satellite and Terrestrial Radars

Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single-moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a midlatitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.

Molthan, Andrew L.↗
NASA NTRS2009

Exploring Alternative Parameterizations for Snowfall with Validation from Satellite and Terrestrial Radars

Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. The combination of reliable cloud microphysics and radar reflectivity may constrain radiative transfer models used in satellite simulators during future missions, including EarthCARE and the NASA Global Precipitation Measurement. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a mid latitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.

Molthan, Andrew L.↗
NASA NTRS2003

Regional-Scale Modeling at NASA Goddard Space Flight Center

Over the past decade, the Goddard Mesoscale Modeling and Dynamics Group has used a popular regional scale model, MM5, to study precipitation processes. Our group is making contributions to the MM5 by incorporating the following physical and numerical packages: improved Goddard cloud processes, a land processes model (Parameterization for Land-Atmosphere-Cloud Exchange - PLACE), efficient but sophisticated radiative processes, conservation of hydrometeor mass (water budget), four-dimensional data assimilation for rainfall, and better computational methods for trace gas transport. At NASA Goddard, the MM5 has been used to study: (1) the impact of initial conditions, assimilation of satellite-derived rainfall, and cumulus parameterizations on rapidly intensifying oceanic cyclones, hurricanes and typhoons, (2) the dynamic and thermodynamic processes associated with the development of narrow cold frontal rainbands, (3) regional climate and water cycles, (4) the impact of vertical transport by clouds and lightning on trace gas distributiodproduction associated with South and North American mesoscale convective systems, (5) the development of a westerly wind burst (WWB) that occurred during the TOGA COARE and the diurnal variation of precipitation in the tropics, (6) a Florida sea breeze convective event and a Mid-US flood event using a sophisticated land surface model, (7) the influence of soil heterogeneity on land surface energy balance in the southwest GCIP region, (8) explicit simulations (with 1.33 to 4 km horizontal resolution) of hurricanes Bob (1991) and Bonnie (1998), (9) a heavy precipitation event over Taiwan, and (10) to make real time forecasts for a major NASA field program. In this paper, the modifications and simulated cases will be described and discussed.

Tao, W.-K.↗
NASA NTRSDate not supplied

A Machine Learning Approach to Determine Surface Radiative Fluxes based on CERES Observations

The Clouds and Earth’s Radiant Energy System (CERES) projects provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. An alternative data product, Fast Longwave and Shortwave radiative Flux (FLASHFlux), was created to provide data to the applied sciences and educational users. FLASHFlux provides Top-of-Atmosphere radiative fluxes, Clouds properties, and parameterized surface radiative fluxes within four days for footprint (Level 2) data. We investigate the use of Artificial Neural Network (ANN) using MODerate resolution Imaging Spectroradiometer (MODIS) derived clouds properties and meteorology from the Global Assimilation and Meteorology Office (GMAO) scaled to the CERES footprint from the CERES Clouds Radiative Swath (CRS) data product to compute surface radiative fluxes. We test ANN produce fluxes against surface fluxes produced from the Fu-Liou model used in CRS and the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) used in FLASHFlux. We also validated each model with ground-based observations. Furthermore, we investigate Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training and provide insight for future models. Advances in machine learning, along with increases in computational capabilities and available data allow us to estimate effects of unresolved processes in our climate without direct modeling. This work evaluates the ability to create accurate data-driven models to supplement or replace current models that estimate surface radiative fluxes.

Climatology↗
NASA NTRSDate not supplied

Novel Hourly-Resolved Global Cloud Property Composite from Operational Satellites Imagers

Numerous applications in satellite remote sensing of surface properties, atmospheric state, composition, and radiation require accurate knowledge of the location and characteristics of clouds. Operational satellite imager radiances are valuable for cloud detection and for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. This paper describes a new global high-resolution dataset of cloud properties made available for community use that is constructed from analyses of a constellation of meteorological satellite imagers. Data taken from Meteosat-8/9 and -11, Himawari-8/9, GOES-16, and -17/-18, Aqua, Terra, Suomi-NPP and NOAA-20 are processed and composited on a 3-km grid to provide hourly global coverage. A historical multi-year dataset is available to serve various needs including modeling challenges related to cloud evaluations and parameterizations. Near real-time data products are also currently available between 60N and 60S. Efforts are underway to operationalize polar orbiting satellite cloud detection methods for low-latency applications over polar regions. The cloud detection and retrieval algorithms have been developed over many years to support NASA weather and climate programs such as the Clouds and Earth’s Radiant Energy System (CERES). To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. A brief description of the methods highlighting the unique aspects of the SatCORPS data products will be presented along with information on their status and availability.

SatCORPS↗
NASA NTRS2007

Evaluation of a Cloud Resolving Model Using TRMM Observations for Multiscale Modeling Applications

The climate change simulation community is moving toward use of global cloud resolving models (CRMs), however, current computational resources are not sufficient to run global CRMs over the hundreds of years necessary to produce climate change estimates. As an intermediate step between conventional general circulation models (GCMs) and global CRMs, many climate analysis centers are embedding a CRM in each grid cell of a conventional GCM. These Multiscale Modeling Frameworks (MMFs) represent a theoretical advance over the use of conventional GCM cloud and convection parameterizations, but have been shown to exhibit an overproduction of precipitation in the tropics during the northern hemisphere summer. In this study, simulations of clouds, precipitation, and radiation over the South China Sea using the CRM component of the NASA Goddard MMF are evaluated using retrievals derived from the instruments aboard the Tropical Rainfall Measuring Mission (TRMM) satellite platform for a 46-day time period that spans 5 May - 20 June 1998. The NASA Goddard Cumulus Ensemble (GCE) model is forced with observed largescale forcing derived from soundings taken during the intensive observing period of the South China Sea Monsoon Experiment. It is found that the GCE configuration used in the NASA Goddard MMF responds too vigorously to the imposed large-scale forcing, accumulating too much moisture and producing too much cloud cover during convective phases, and overdrying the atmosphere and suppressing clouds during monsoon break periods. Sensitivity experiments reveal that changes to ice cloud microphysical parameters have a relatively large effect on simulated clouds, precipitation, and radiation, while changes to grid spacing and domain length have little effect on simulation results. The results motivate a more detailed and quantitative exploration of the sources and magnitude of the uncertainty associated with specified cloud microphysical parameters in the CRM components of MMFs.

Posselt, Derek J.↗
NASA NTRS2019

Extratropical Cyclone Clouds in the GFDL Climate Model: Diagnosing Biases and the Associated Causes

The clouds in southern hemisphere extratropical cyclones generated by the GFDL climate model are analyzed against MODIS, CloudSat and CALIPSO cloud and precipitation observations. Two model versions are used: one is a developmental version of AM4, a model GFDL will utilize for CMIP6, the other is the same model with a different parameterization of moist convection. Both model versions predict a realistic top-of-atmosphere cloud cover in the southern oceans, within 5% of the observations. However, an examination of cloud cover transects in extratropical cyclones reveals a tendency in the models to overestimate high-level clouds (by differing amounts) and underestimate cloud cover at low-levels (again by differing amounts), especially in the post-cold frontal (PCF) region, when compared to observations. Focusing on only the models, their differences in high and mid-level clouds are consistent with their differences in convective activity and relative humidity (RH), but the same is not true for the PCF region. In this region, RH is higher in the model with less cloud fraction. These seemingly contradictory cloud and RH differences can be explained by differences in the cloud parameterization tuning parameters that ensure radiative balance. In the PCF region, the model cloud differences are smaller than either of the model biases with respect to observations, suggesting other physics changes are needed to address the bias. The process-oriented analysis used to assess these model differences will soon be automated and shared.

Catherine M Naud↗
NASA NTRS1993

A modeling study of marine boundary layer clouds

Marine boundary layer (MBL) clouds are important components of the earth's climate system. These clouds drastically reduce the amount of solar radiation absorbed by the earth, but have little effect on the emitted infrared radiation on top of the atmosphere. In addition, these clouds are intimately involved in regulating boundary layer turbulent fluxes. For these reasons, it is important that general circulation models used for climate studies must realistically simulate the global distribution of the MBL. While the importance of these cloud systems is well recognized, many physical processes involved in these clouds are poorly understood and their representation in large-scale models remains an unresolved problem. The present research aims at the development and improvement of the parameterization of these cloud systems and an understanding of physical processes involved. This goal is addressed in two ways. One is to use regional modeling approach to validate and evaluate two-layer marine boundary layer models using satellite and ground-truth observations; the other is to combine this simple model with a high-order turbulence closure model to study the transition processes from stratocumulus to shallow cumulus clouds. Progress made in this effort is presented.

Wang, Shouping↗
NASA NTRS2008

Arctic Mixed-phase Clouds Simulated by a Cloud-Resolving Model: Comparison with ARM Observations and Sensitivity to Microphysics Parameterizations

Single-layer mixed-phase stratiform (MPS) Arctic clouds, which formed under conditions of large surface heat flux combined with general subsidence during a subperiod of the Atmospheric Radiation Measurement (ARM) Program Mixed-Phase Arctic Cloud Experiment (M-PACE), are simulated with a cloud resolving model (CRM). The CRM is implemented with either an advanced two-moment (M05) or a commonly used one-moment (L83) bulk microphysics scheme and a state-of-the-art radiative transfer scheme. The CONTROL simulation, that uses the M05 scheme and observed aerosol size distribution and ice nulei (IN) number concentration, reproduces the magnitudes and vertical structures of cloud liquid water content (LWC), total ice water content (IWC), number concentration and effective radius of cloud droplets as suggested by the M-PACE observations. It underestimates ice crystal number concentrations by an order of magnitude and overestimates effective radius of ice crystals by a factor of 2-3. The OneM experiment, that uses the L83 scheme, produces values of liquid water path (LWP) and ice plus snow water path (ISWP) that were about 30% and 4 times, respectively, of those produced by the CONTROL. Its vertical profile of IWC exhibits a bimodal distribution in contrast to the constant distribution of IWC produced in the CONTROL and observations.

Xu, Kuan-Man↗
DOE OSTI2026

Sensitivity of Anthropogenic Cloud Droplet Number Change to Preindustrial Emission Inventories and Physics Parameterizations

Understanding the preindustrial (PI) to present-day (PD) change in cloud droplet number concentration (ΔNd) is important to constrain the anthropogenic influence on clouds and aid future model projections. Perturbed parameter ensemble simulations of PI and PD conditions in the Energy Exascale Earth System Model (E3SMv3) reveal two locations with consistent negative ΔNd signal across parameter space–the Southeastern US and the United Kingdom–despite a trend of positive ΔNd globally from increased fossil fuel consumption. These negative ΔNd signals are driven by higher anthropogenic biofuel emissions in the PI. By varying the PI aerosol mass and/or number emissions across these data sets' uncertainty ranges, we can change the magnitude and sign of ΔNd and the effective radiative forcing from aerosol-cloud interactions (ERFaci) regionally and globally, with near-zero ΔNd leading to near-zero ERFaci. These results highlight the need to constrain PI emission uncertainty to better understand Earth system responses to aerosol-cloud interactions.

Brown, Hunter Y. [University of Wyoming, Laramie, ↗
NASA NTRSDate not supplied

A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data

Clouds play a crucial role in regulating the hydrologic cycle and Earth's radiative energy budget, yet they are often poorly represented in global climate models (GCMs). This study applies deep machine learning techniques to develop a physical parameterization of volumetric cloud fraction (VCF), the fraction of a 3-D grid volume occupied by clouds using satellite lidar-radar measurements. The neural network (NN) captures the complicated relationships between observed VCF profiles and collocated meteorological variables from MERRA-2 reanalysis data. Our results show that the NN model, particularly a sequence-to-sequence long short-term memory (LSTM) network with a sixfactor loss function, effectively learns the underlying cloud physical processes. The NN model outperforms MERRA-2 reanalysis in representing low-level clouds in tropical and subtropical regions and low- and middle-level clouds over midlatitude storm-track regions, and also improves VCF histograms. These improvements are reflected in the vertical distributions of zonally, meridionally, and globally averaged VCFs, geographic distributions of low-, middle-, and high-level clouds, and seasonal variations in monthly-mean VCF. Furthermore, the NN predictions effectively capture the El Niño-Southern Oscillation (ENSO) effects and other interannual variations. The NN parameterization is further evaluated through a sensitivity analysis, in which a single predictor is perturbed at a time. This reveals that relative humidity (RH) is the dominant factor influencing variations in globally averaged VCF at low and middle altitudes, followed by temperature. At higher altitudes, temperature becomes the primary driver of VCF through its effect on RH. Changes in wind components had minimal impact on globally averaged VCF.

Shan Zeng↗
NASA NTRS1983

Effects of high altitude clouds on the earth's infrared radiation flux

Attention is given to the results of a study of cirrus cloud properties which employed the Goddard Laboratory for Atmospheric Sciences' general circulation model and concentrated on the effects of the nonblackness of high clouds on the IR radiation flux. Although the thermal radiation flux is very sensitive to the treatment of cirrus optical properties in the IR, a more realistic assessment will depend on better parameterizations for cirrus cloud formation, persistence, and dissipation.

Wang, W.-C.↗