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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2014

Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

Cloud properties determined from satellite imager radiances provide a valuable source of information for nowcasting and weather forecasting. In recent years, it has been shown that assimilation of cloud top temperature, optical depth, and total water path can increase the accuracies of weather analyses and forecasts. Aircraft icing conditions can be accurately diagnosed in near-­‐real time (NRT) retrievals of cloud effective particle size, phase, and water path, providing valuable data for pilots. NRT retrievals of surface skin temperature can also be assimilated in numerical weather prediction models to provide more accurate representations of solar heating and longwave cooling at the surface, where convective initiation. These and other applications are being exploited more frequently as the value of NRT cloud data become recognized. At NASA Langley, cloud properties and surface skin temperature are being retrieved in near-­‐real time globally from both geostationary (GEO) and low-­‐earth orbiting (LEO) satellite imagers for weather model assimilation and nowcasting for hazards such as aircraft icing. Cloud data from GEO satellites over North America are disseminated through NCEP, while those data and global LEO and GEO retrievals are disseminated from a Langley website. This paper presents an overview of the various available datasets, provides examples of their application, and discusses the use of the various datasets downstream. Future challenges and areas of improvement are also presented.

Minnis, Patrick↗
NASA NTRS1993

Microphysical properties of the November 26 cirrus cloud retrieved by Doppler radar/IR radiometer technique

Gaining information about cirrus cloud microphysics requires development of remote sensing techniques. In an earlier paper. Matrosov et al. (1992) proposed a method to estimate ice water path (IWP) (i.e., vertically integrated ice mass content IMC) and characteristic particle size averaged through the cloud from combined groundbased measurements of radar reflectivities and IR brightness temperatures of the downwelling thermal radiation in the transparency region of 10-12 mu m. For some applications, the vertically averaged characteristic particle sizes and IWP could be the appropriate information to use. However, vertical profiles of cloud microphysical parameters can provide a better understanding of cloud structure and development. Here we describe a further development of the previous method by Matrosov et al. (1992) for retrieving vertical profiles of cirrus particle sizes and IMC rather than their vertically averaged values. In addition to measurements of radar reflectivities, the measurements of Doppler velocities are used in the new method. This provides us with two vertical profiles of measurements to infer two vertical profiles of unknowns, i.e., particle characteristic sizes and IMC. Simultaneous measurements of the IR brightness temperatures are still needed to resolve an ambiguity in particle size-fall velocity relationships.

Matrosov, Sergey Y.↗
NASA NTRS1984

Comparison of GLAS retrieved cloud fields with model generated rainfall fields

Monthly mean fractional cloud cover for January and February 1979, retrieved from SOP 1 of FGGE, are compared with the total precipitation field derived diagonally from the GLAS analysis/forecast system for the same time period. The breakdown of cloudiness into day (3 AM) and night (3 PM) is consistent with maps of outgoing long wave and short wave radiation inferred from AVHRR data. Of the many regions of coincidence, there is a particularly striking phenomenon: west of the coast of Peru, at about 20 deg S, there is a distinct small scale maximum in precipitation which coincides precisely with a maximum in the cloudiness field. This maximum in cloudiness and precipitation does not appear in the NOAA/NESS fields of albedo and outgoing long wave radiation which are normally sensitive to cloud fields. These low level clouds with warm tops are a mainly nocturnal phenomenon.

Susskind, J.↗
NASA NTRS2015

CHIMAERA System for Cloud Retrievals v 6.0.85

Organizers of the MODIS-VIIRS Science Team Meeting, held May 18-22, 2015 in Silver Spring, MD plan to post the presentations and posters to the NASA MODIS website: http:modis.gsfc.nasa.govsci_teammeetings201505index.php. The MODIS Science Team Meeting is held twice a year, so that the members of the science team may assemble and discuss data they have collected, ideas they have formed, and future issues that apply to the MODIS Mission.

CHIMAERA↗
NASA NTRS2017

The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition

Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, approximately 70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations.

MAIAC AO↗
NASA NTRS1993

Cirrus cloud retrievals from HIS observations during FIRE 2

This paper presents retrieval methods applied to HIS observations during FIRE II and doubling/adding model developed to simulate high-spectral resolution infrared radiances in a cloudy atmosphere. The capabilities of the retrieval methods and sensitivity studies of high-altitude aircraft based observations to cloud microphysical structure are conducted with the model.

Ackerman, S. A.↗
NASA NTRS2006

NASA-Langley Web-Based Operational Real-time Cloud Retrieval Products from Geostationary Satellites

At NASA Langley Research Center (LaRC), radiances from multiple satellites are analyzed in near real-time to produce cloud products over many regions on the globe. These data are valuable for many applications such as diagnosing aircraft icing conditions and model validation and assimilation. This paper presents an overview of the multiple products available, summarizes the content of the online database, and details web-based satellite browsers and tools to access satellite imagery and products.

Palikonda, Rabindra↗
NASA NTRS2013

Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters : Synthetic Sensor Radiance Formulation - Pt. 1

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

Simulations↗
NASA NTRS2018

PACE Technical Report Series, Volume 4: Cloud Retrievals in the PACE Mission: PACE Science Team Consensus Document

Earth is a complex dynamical system exhibiting continuous change in its atmosphere, ocean,and surface elements. Nearly all (99.97%) of the energy driving these systems is linked to the Sun. Measurements of reflected sunlight contain a unique signature of wavelength-specific scattering and absorption interactions occurring between incoming solar energy and atmospheric (molecules, aerosols,clouds) and surface features Clouds can affect significantly both shortwave and long wave radiation, depending on altitude/vertical structure, thermodynamic phase, and optical properties. Low, warm, and optically thick clouds predominantly have a cooling effect, while high, cold, optically thin clouds can cause warming by absorbing warmer radiation emitted from the surface and lower atmosphere.When the net difference between outgoing and incoming solar radiation is matched by the net infrared radiation emitted to space, the Earth's climate is in radiative balance. While radiative forcing components (GHGs, aerosols - direct and indirect) contribute to a net radiative imbalance, climate sensitivity is ultimately determined by the contribution of various system feed backs. The role of cloud feedback in a warming climate is currently the largest inter-model uncertainty in climate sensitivity and therefore in climate prediction [Bony and Dufresne 2005]. A comprehensive understanding of current cloud propertiesand dynamic/microphysical processes requires a global perspective from satellites.

Plankton, Aerosol, Cloud, ocean Ecosystem (PACE)↗
NASA NTRS1986

Retrieving cloud ice water content and geometrical thickness from microwave and infrared radiometric observations

Techniques are presented and their application illustrated for analysis of remotely sensed data collected with an aircraft carrying a multispectral cloud radiometer and an advanced microwave moisture sounder. The instruments were used on NASA high altitude flights to perform cloud field experiments. Sample IR and microwave brightness temperature data are provided as functions of the ice water path and of the ice water content. Quantitative models are described for deriving the cloud ice (or liquid) water content and the cloud geometric thickness from the radiometric data.

Wu, M.-L. C.↗
NASA NTRS1990

Using spatial coherence to retrieve cloud properties - ERBE experience and CERES applications

Cloud properties for individual ERBE scanner fields of view are obtained from simultaneous, collocated imagery data. The cloud properties are used to assess the performance of the ERBE maximum likelihood estimate of cloud conditions which ultimately influences estimates of the radiative fluxes at the top of the atmosphere. During EOS the analysis of MODIS observations within the fields of view of the CERES instruments promises to yield, in addition, estimates of surface radiative fluxes as well as the vertical distribution of radiative heating within the atmosphere.

Coakley, James A., Jr.↗
NASA NTRS1999

Multi-Spectral Cloud Property Retrieval

Despite numerous studies to retrieve cloud properties using infrared measurements the information content of the data has not yet been fully exploited. In an effort to more fully utilize the information content of infrared measurements, we have developed a multi-spectral technique for retrieving effective cloud particle size, optical depth and effective cloud temperature. While applicable to all cloud types, we begin by validating our retrieval technique through analysis of MS spectral radiances obtained during the SUCCESS field campaign over the ARM SGP CART facility, and compare our retrieval product with lidar and MODIS Airborne Simulator (MAS) measurement results. The technique is then applied to the Nimbus-4 MS infrared spectral measurements to obtain global cloud information.

Carlson, Barbara E.↗