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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 NTRS2002

Failure Mode Identification Through Clustering Analysis

Research has shown that nearly 80% of the costs and problems are created in product development and that cost and quality are essentially designed into products in the conceptual stage. Currently, failure identification procedures (such as FMEA (Failure Modes and Effects Analysis), FMECA (Failure Modes, Effects and Criticality Analysis) and FTA (Fault Tree Analysis)) and design of experiments are being used for quality control and for the detection of potential failure modes during the detail design stage or post-product launch. Though all of these methods have their own advantages, they do not give information as to what are the predominant failures that a designer should focus on while designing a product. This work uses a functional approach to identify failure modes, which hypothesizes that similarities exist between different failure modes based on the functionality of the product/component. In this paper, a statistical clustering procedure is proposed to retrieve information on the set of predominant failures that a function experiences. The various stages of the methodology are illustrated using a hypothetical design example.

Arunajadai, Srikesh G.↗
NASA NTRS2011

Space-Time Data fusion for Remote Sensing Applications

NASA has been collecting massive amounts of remote sensing data about Earth's systems for more than a decade. Missions are selected to be complementary in quantities measured, retrieval techniques, and sampling characteristics, so these datasets are highly synergistic. To fully exploit this, a rigorous methodology for combining data with heterogeneous sampling characteristics is required. For scientific purposes, the methodology must also provide quantitative measures of uncertainty that propagate input-data uncertainty appropriately. We view this as a statistical inference problem. The true but notdirectly- observed quantities form a vector-valued field continuous in space and time. Our goal is to infer those true values or some function of them, and provide to uncertainty quantification for those inferences. We use a spatiotemporal statistical model that relates the unobserved quantities of interest at point-level to the spatially aggregated, observed data. We describe and illustrate our method using CO2 data from two NASA data sets.

carbon dioxide↗
NASA NTRS2012

Filling-in of Far-Red and Near-Infrared Solar Lines by Terrestrial and Atmospheric Effects: Simulations and Space-Based Observations from SCHIAMACHY and GOSAT

Mapping of terrestrial vegetation fluorescence from space is of interest because it can potentially provide global information on the functional status of vegetation including light use efficiency and global primary productivity that can be used for global carbon cycle modeling. Space-based measurement of solar-induced chlorophyll fluorescence is challenging, because its signal is small as compared with the much larger reflectance signal. Ground- and aircraft-based approaches have made use of the dark and spectrally-wide 02-A (approx 760 nm) and O2-B (approx 690 nm) atmospheric features to detect the weak fluorescence signal. More recently, Joiner et a1. and Frankenberg et a1. focused on longer-wavelength solar Fraunhofer lines that can be observed with space-based instruments such as the currently operational GOSAT. They showed that fluorescence can be detected using Fraunhofer lines away from the far-red chlorophyll-a fluorescence peak even when the surface is relatively bright. Here, we build on that work by developing methodology to correct for instrumental artifacts that produce false filling-in signals that can bias fluorescence retrievals. We also examine other potential sources of filling-in at far-red and NIR wavelengths. Another objective is to explore the possibility of making fluorescence measurements from space with lower spectral resolution instrumentation than the GOSAT interferometer. We focus on the 866 nm Ca II solar Fraunhofer line. Very few laboratory and ground-based measurements of vegetation fluorescence have been reported at wavelengths longer than 800 mn. Some results of fluorescence measurements of corn leaves acquired in the laboratory using polychromatic excitation at wavelengths shorter than 665 nm show that at 866 nm, the measured signal is of the order of 0.1-0.2 mw/sq m/nm/sr. In this work we use the following satellite observations: We use SCIAMACHY channel 5 in nadir mode that covers wavelengths between 773 and 1063 nm at a spectral resolution of 0.54 nm. GOSAT has two instrument packages: the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (CAI). We use TANSO-FTS band 1, which extends from approximately 758 to 775 mn and we use cloud fraction derived from the CAL We compare satellite-derived fluorescence with the Enhanced Vegetation Index (EVI), an Aqua/MODIS-derived vegetation reflectance-based index that indicates relative greenness and is used to infer photosynthetic function.

Joiner, J.↗
NASA NTRS2012

Filling-in of Far-Red and Near-Infrared Solar Lines by Terrestrial and Atmospheric Effects: Simulations and Space-Based Observations from SCIAMACHY and GOSAT

Mapping of terrestrial vegetation fluorescence from space is of interest because it can potentially provide global information on the functional status of vegetation including light use efficiency and global primary productivity that can be used for global carbon cycle modeling. Space-based measurement of solar-induced chlorophyll fluorescence is challenging, because its signal is small as compared with the much larger reflectance signal. Ground- and aircraft-based approaches have made use of the dark and spectrally-wide O2-A ( approx 760 nm) and O2-B (approx 690 nm) atmospheric features to detect the weak fluorescence signal. More recently, Joiner et al. and Frankenberg et al. focused on longer-wavelength solar Fraunhofer lines that can be observed with space-based instruments such as the currently operational GOSAT. They showed that fluorescence can be detected using Fraunhofer lines away from the far-red chlorophyll-a fluorescence peak even when the surface is relatively bright. Here, we build on that work by developing methodology to correct for instrumental artifacts that produce false filling-in signals that can bias fluorescence retrievals. We also examine other potential sources of filling-in at far-red and NIR wavelengths. Another objective is to explore the possibility of making fluorescence measurements from space with lower spectral resolution instrumentation than the GOSAT interferometer. We focus on the 866nm Ca II solar Fraunhofer line. Very few laboratory and ground-based measurements of vegetation fluorescence have been reported at wavelengths longer than 800 nm. Some results of fluorescence measurements of corn leaves acquired in the laboratory using polychromatic excitation at wavelengths shorter than 665nm show that at 866 nm, the measured signal is of the order of 0.1-0.2 mW/sq m/nm/sr. In this work, we use the following satellite observations: We use SCIAMACHY channel 5 in nadir mode that covers wavelengths between 773 and 1063nm at a spectral resolution of 0.54 nm. GOSAT has two instrument packages: the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (CAI). We use TANSO-FTS band 1, which extends from approximately 758 to 775nm and we use cloud fraction derived from the CAI. We compare satellite-derived fluorescence with the Enhanced Vegetation Index (EVI), an Aqua/MODIS-derived vegetation reflectance-based index that indicates relative greenness and is used to infer photosynthetic function.

Joiner, J.↗
NASA NTRS2015

On the Feasibility of Monitoring Carbon Monoxide in the Lower Troposphere from a Constellation of Northern Hemisphere Geostationary Satellites (PART 1)

By the end of the current decade, there are plans to deploy several geostationary Earth orbit (GEO) satellite missions for atmospheric composition over North America, East Asia and Europe with additional missions proposed. Together, these present the possibility of a constellation of geostationary platforms to achieve continuous time-resolved high-density observations over continental domains for mapping pollutant sources and variability at diurnal and local scales. In this paper, we use a novel approach to sample a very high global resolution model (GEOS-5 at 7 km horizontal resolution) to produce a dataset of synthetic carbon monoxide pollution observations representative of those potentially obtainable from a GEO satellite constellation with predicted measurement sensitivities based on current remote sensing capabilities. Part 1 of this study focuses on the production of simulated synthetic measurements for air quality OSSEs (Observing System Simulation Experiments). We simulate carbon monoxide nadir retrievals using a technique that provides realistic measurements with very low computational cost. We discuss the sampling methodology: the projection of footprints and areas of regard for geostationary geometries over each of the North America, East Asia and Europe regions; the regression method to simulate measurement sensitivity; and the measurement error simulation. A detailed analysis of the simulated observation sensitivity is performed, and limitations of the method are discussed. We also describe impacts from clouds, showing that the efficiency of an instrument making atmospheric composition measurements on a geostationary platform is dependent on the dominant weather regime over a given region and the pixel size resolution. These results demonstrate the viability of the "instrument simulator" step for an OSSE to assess the performance of a constellation of geostationary satellites for air quality measurements.

GEOS-5↗
NASA NTRS2018

Remote Sensing of Evaporation

Evaporation of water from the Earth's surface into the atmosphere is central to the terrestrial energy, water and carbon cycle. Remote sensing approaches to measure evaporation (ET) combine observable inputs to the energy and water balance within statistical or process-based methodologies. These ET products draw on satellite observations from across the electro-magnetic spectrum. The ET retrievals are grouped in two main categories. One category includes approaches that combine meteorological descriptions of potential evaporation with a range of strategies to estimate evaporative stress. A second category includes surface energy balance approaches that retrieve latent heat from the thermal signatures. For each category a practical implementation example is described, including an outlook on progress towards multi-model assessment of global evaporation.

Transpiration↗
NASA NTRS2016

Phase-Retrieval Uncertainty Estimation and Algorithm Comparison for the JWST-ISIM Test Campaign

Phase retrieval, the process of determining the exitpupil wavefront of an optical instrument from image-plane intensity measurements, is the baseline methodology for characterizing the wavefront for the suite of science instruments (SIs) in the Integrated Science Instrument Module (ISIM) for the James Webb Space Telescope (JWST). JWST is a large, infrared space telescope with a 6.5-meter diameter primary mirror. JWST is currently NASA's flagship mission and will be the premier space observatory of the next decade. ISIM contains four optical benches with nine unique instruments, including redundancies. ISIM was characterized at the Goddard Space Flight Center (GSFC) in Greenbelt, MD in a series of cryogenic vacuum tests using a telescope simulator. During these tests, phase-retrieval algorithms were used to characterize the instruments. The objective of this paper is to describe the Monte-Carlo simulations that were used to establish uncertainties (i.e., error bars) for the wavefronts of the various instruments in ISIM. Multiple retrieval algorithms were used in the analysis of ISIM phase-retrieval focus-sweep data, including an iterativetransform algorithm and a nonlinear optimization algorithm. These algorithms emphasize the recovery of numerous optical parameters, including low-order wavefront composition described by Zernike polynomial terms and high-order wavefront described by a point-by-point map, location of instrument best focus, focal ratio, exit-pupil amplitude, the morphology of any extended object, and optical jitter. The secondary objective of this paper is to report on the relative accuracies of these algorithms for the ISIM instrument tests, and a comparison of their computational complexity and their performance on central and graphical processing unit clusters. From a phase-retrieval perspective, the ISIM test campaign includes a variety of source illumination bandwidths, various image-plane sampling criteria above and below the Nyquist- Shannon critical sampling value, various extended object sizes, and several other impactful effects.

Design Analysis↗
NASA NTRS2021

Comparison of soil dielectric mixing models for Soil Moisture Retrieval using SMAP Brightness Temperature over croplands in India

The accurate estimation of soil moisture (SM) using microwave remote sensing depends mostly on careful selection of retrieval parameters among which the soil dielectric mixing model is the important one. These models are often categorized into empirical, semi-empirical or volumetric based on their methodologies and input data requirements. To study in detail, the comparative performance of four dielectric mixing models -- Wang & Schmugge model, Hallikainen model, Dobson model and Mironov model were used with Soil Moisture Active Passive (SMAP) L-band brightness temperature and Single Channel Algorithm for SM retrieval over agricultural landscapes in India. The highest performance statistics combination in terms of Root Mean Square Error (RMSE), correlation coefficient (R^(2)) and percentage bias (PBIAS) against the concurrent in-situ SM measurements were calculated at the selected validation sites. The overall results indicate that the best performance was given by the Mironov model (RMSE = 0.07 cu. m/cu. m), followed by Wang & Schmugge model (RMSE = 0.08 cu. m3/cu. m), Hallikainen model (RMSE = 0.09 cu. m/cu. m), Dobson model (RMSE = 0.10 cu. m/cu. m) and original SMAP radiometer SM (RMSE = 0.12 cu. m/cu. m). Findings of this study provides important insights into application and performance of dielectric mixing models in mapping surface SM variations. This study also underlines the pivotal role of local conditions for SM retrieval which should be carefully included in the algorithms.

Swati Suman↗
NASA NTRS2009

An Evaluation of Soil Moisture Retrievals Using Aircraft and Satellite Passive Microwave Observations during SMEX02

The Soil Moisture Experiments conducted in Iowa in the summer of 2002 (SMEX02) had many remote sensing instruments that were used to study the spatial and temporal variability of soil moisture. The sensors used in this paper (a subset of the suite of sensors) are the AQUA satellite-based AMSR-E (Advanced Microwave Scanning Radiometer- Earth Observing System) and the aircraft-based PSR (Polarimetric Scanning Radiometer). The SMEX02 design focused on the collection of near simultaneous brightness temperature observations from each of these instruments and in situ soil moisture measurements at field- and domain- scale. This methodology provided a basis for a quantitative analysis of the soil moisture remote sensing potential of each instrument using in situ comparisons and retrieved soil moisture estimates through the application of a radiative transfer model. To this end, the two sensors are compared with respect to their estimation of soil moisture.

Bolten, John D.↗
NASA NTRS2019

SST Improvements in the NASA GMAO Weather Analysis and Prediction System

It is well known that the sea surface temperature (SST) exhibits a diurnal cycle. Since January 2017, the NASA GMAO's Weather Analysis & Prediction System (https://gmao.gsfc.nasa.gov/weather_prediction/) includes a model for this diurnal cycle, a cool-skin layer and assimilates for skin SST. Data assimilation for the skin SST is carried out along with the 3-D state of the atmosphere, primarily using infrared radiance measurements, that are sensitive to the cool-skin and diurnal warming. In the context of Numerical Weather Prediction (NWP), this implies that the skin SST from our system is tightly coupled to the overlaying atmosphere and constrained by all the available atmospheric and surface-sensitive observations (in situ and satellite brightness temperatures). Comparisons of the SST diurnal cycle with other SST retrievals (SEVIRI (Spinning Enhanced Visible & InfraRed Imager) and AMSR-2 (Advanced Microwave Scanning Radiometer-2)) has shown mostly similar diurnal variation, however, the former decays at a faster rate. This poster presents our SST plus atmosphere assimilation methodology, and recent improvements to the skin SST model to address the known issues. Global, hourly estimates of the cool-skin and diurnal warm layer fields would be of interest to the SST retrieval community.

Akella, Santha↗
NASA NTRS2012

Improved Impact of Atmospheric Infrared Sounder (AIRS) Radiance Assimilation in Numerical Weather Prediction

Improvements to global and regional numerical weather prediction (NWP) have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) that mimics the analysis methodology, domain, and observational datasets for the regional North American Mesoscale (NAM) model run at the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) are run to examine the impact of each type of AIRS data set. The first configuration will assimilate the AIRS radiance data along with other conventional and satellite data using techniques implemented within the operational system; the second configuration will assimilate AIRS retrieved profiles instead of AIRS radiances in the same manner. Preliminary results of this study will be presented and focus on the analysis impact of the radiances and profiles for selected cases.

Zavodsky, Bradley↗
NASA NTRS2005

Atmospheric Soundings from AIRS/AMSU in Partial Cloud Cover

Simultaneous use of AIRS/AMSU-A observations allow for the determination of accurate atmospheric soundings under partial cloud cover conditions. The methodology involves the determination of the radiances AIRS would have seen if the AIRS fields of view were clear, called clear column radiances, and use of these radiances to infer the atmospheric and surface conditions giving rise to these clear column radiances. Susskind et al. demonstrate via simulation that accurate temperature soundings and clear column radiances can be derived from AIRS/AMSU-A observations in cases of up to 80% partial cloud cover, with only a small degradation in accuracy compared to that obtained in clear scenes. Susskind and Atlas show that these findings hold for real AIRS/AMSU-A soundings as well. For data assimilation purposes, this small degradation in accuracy is more than offset by a significant increase in spatial coverage (roughly 50% of global cases were accepted, compared to 3.6% of the global cases being diagnosed as clear), and assimilation of AIRS temperature soundings in partially cloudy conditions resulted in a larger improvement in forecast skill than when AIRS soundings were assimilated only under clear conditions. Alternatively, derived AIRS clear column radiances under partial cloud cover could also be used for data assimilation purposes. Further improvements in AIRS sounding methodology have been made since the results shown in Susskind and Atlas . A new version of the AIRS/AMSU-A retrieval algorithm, Version 4.0, was delivered to the Goddard DAAC in February 2005 for production of AIRS derived products, including clear column radiances. The major improvement in the Version 4.0 retrieval algorithm is with regard to a more flexible, parameter dependent, quality control. Results are shown of the accuracy and spatial distribution of temperature-moisture profiles and clear column radiances derived from AIRS/AMSU-A as a function of fractional cloud cover using the Version 4.0 algorithm. Use of the Version 4.0 AIRS temperature profiles increased the positive forecast impact arising from AIRS retrievals relative to what was shown in Susskind and Atlas .

Susskind, Joel↗
NASA NTRS2005

Using information Theory in Optimal Test Point Selection for Health Management in NASA's Exploration Vehicles

In this paper, we will present a new methodology that measures the "worth" of deploying an additional testing instrument (sensor) in terms of the amount of information that can be retrieved from such measurement. This quantity is obtained using a probabilistic model of RLV's that has been partially developed in the NASA Ames Research Center. A number of correlated attributes are identified and used to obtain the worth of deploying a sensor in a given test point from an information-theoretic viewpoint. Once the information-theoretic worth of sensors is formulated and incorporated into our general model for IHM performance, the problem can be formulated as a constrained optimization problem where reliability and operational safety of the system as a whole is considered. Although this research is conducted specifically for RLV's, the proposed methodology in its generic form can be easily extended to other domains of systems health monitoring.

Mehr, Ali Farhang↗
NASA NTRS2015

Global Ocean Evaporation: How Well Can We Estimate Interannual to Decadal Variability?

Evaporation from the world's oceans constitutes the largest component of the global water balance. It is important not only as the ultimate source of moisture that is tied to the radiative processes determining Earth's energy balance but also to freshwater availability over land, governing habitability of the planet. Here we focus on variability of ocean evaporation on scales from interannual to decadal by appealing to three sources of data: the new MERRA-2 (Modern-Era Retrospective analysis for Research and Applications -2); climate models run with historical sea-surface temperatures, ice and atmospheric constituents (so-called AMIP experiments); and state-of-the-art satellite retrievals from the Seaflux and HOAPS (Hamburg Ocean-Atmosphere Parameters and Fluxes from Satellite) projects. Each of these sources has distinct advantages as well as drawbacks. MERRA-2, like other reanalyses, synthesizes evaporation estimates consistent with observationally constrained physical and dynamical models-but data stream discontinuities are a major problem for interpreting multi-decadal records. The climate models used in data assimilation can also be run with lesser constraints such as with SSTs and sea-ice (i.e. AMIPs) or with additional, minimal observations of surface pressure and marine observations that have longer and less fragmentary observational records. We use the new ERA-20C reanalysis produced by ECMWF embodying the latter methodology. Still, the model physics biases in climate models and the lack of a predicted surface energy balance are of concern. Satellite retrievals and comparisons to ship-based measurements offer the most observationally-based estimates, but sensor inter-calibration, algorithm retrieval assumptions, and short records are dominant issues. Our strategy depends on maximizing the advantages of these combined records. The primary diagnostic tool used here is an analysis of bulk aerodynamic computations produced by these sources and uses a first-order Taylor series analysis of wind speed, SST, near-surface stability and relative humidity variations around climatology to gauge the importance of these components. We find that the MERRA-2 evaporation record is strongly influenced by the availability of wind speed and humidity from passive microwave imagers beginning in the late 1980s as well as by the SST record. The trend over the period 1980 to present is nearly 10%. AMIP or the ERA-20C trends are much smaller. We find that ENSO-related signals involving both wind speed and thermodynamic variability remain the primary signal in the latter and are confirmed by satellite retrievals. We present uncertainty estimates based on the various data sources and discuss the implications for GEWEX water and energy budget science challenges.

Robertson, Franklin R.↗
NASA NTRSDate not supplied

A Radiometric Consistent Spectral Fingerprinting Algorithm for Continuity Products of Hyperspectral Sounders

A radiometric consistent climate fingerprinting methodology has been developed to derive long-term temperature, water vapor, cloud, trace gases, and surface skin temperature anomaly time series from the hyper-spectral sounder measurements of multiple platforms. The spectral fingerprinting methodology requires the use of radiative kernels that are radiometrically consistent with observations. Radiative kernels are built using space-time averaged Jacobians that are physically retrieved from observations under all sky conditions. The physical retrieval algorithm uses the Principal Component based Radiative Transfer Model (PCRTM) for the forward simulation. The incorporation of multiple scattering simulation in PCRTM allows the direct radiative relationship between single field-of-view (FOV) radiance observations and corresponding thermal dynamic variables including cloud properties to be established. Therefore, radiance ?closure? can be achieved under all-sky conditions by the fingerprinting scheme. This methodology has been used to derive climate anomalies from the space-time averaged spectra of AIRS/AMSU and CrIS/ATMS. The use of a consistent fingerprinting scheme provides an effective mean of generating continuity product by merging observations from different platforms and therefore facilitating the long-term climate trend study.

Wan Wu↗
NASA NTRS2006

Cloud Radiative Forcing at the ARM Climate Research Facility: Technique, Validation, and Comparison to Satellite-derived Diagnostic Quantities - Part 1

It has been hypothesized that continuous ground-based remote sensing measurements from active and passive remote sensors combined with regular soundings of the atmospheric thermodynamic structure can be combined to describe the effects of clouds on the clear sky radiation fluxes. We critically test that hypothesis in this paper and a companion paper (Part II). Using data collected at the Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) site sponsored by the U.S. Department of Energy, we explore an analysis methodology that results in the characterization of the physical state of the atmospheric profile at time resolutions of five minutes and vertical resolutions of 90 m. The description includes thermodynamics and water vapor profile information derived by merging radiosonde soundings with ground-based data, and continues through specification of the cloud layer occurrence and microphysical and radiative properties derived from retrieval algorithms and parameterizations. The description of the atmospheric physical state includes a calculation of the infrared and clear and cloudy sky solar flux profiles. Validation of the methodology is provided by comparing the calculated fluxes with top of atmosphere (TOA) and surface flux measurements and by comparing the total column optical depths to independently derived estimates. We find over a 1-year period of comparison in overcast uniform skies, that the calculations are strongly correlated to measurements with biases in the flux quantities at the surface and TOA of less than 10% and median fractional errors ranging from 20% to as low as 2%. In the optical depth comparison for uniform overcast skies during the year 2000 where the optical depth varies over 3 orders of magnitude we find a mean positive bias of 46% with a median bias of less than 10% and a 0.89 correlation coefficient. The slope of the linear regression line for the optical depth comparison is 0.86 with a normal deviation of 20% about this line. In addition to a case study where we examine the cloud radiative effects at the TOA, surface and atmosphere by a middle latitude synoptic-scale cyclone, we examine the cloud top pressure and optical depth retrievals of ISCCP and LBTM over a period of 1 year. Using overcast period from the year 2000, we find that the satellite algorithms tend to bias cloud tops into the middle troposphere and underestimate optical depth in high optical depth events (greater than 100) by as much as a factor of 2.

Mace, Gerald G.↗
NASA NTRS1994

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application

One essential parameter used in the estimation of radiative and turbulent heat fluxes from satellite data is surface temperature. Sea and land surface temperature (SST and LST) retrieval algorithms that utilize the thermal infrared portion of the spectrum have been developed, with the degree of success dependent primarily upon the variability of the surface and atmospheric characteristics. However, little effort has been directed to the retrieval of the sea ice surface temperature (IST) in the Arctic and Antarctic pack ice or the ice sheet surface temperature over Antarctica and Greenland. The reason is not one of methodology, but rather our limited knowledge of atmospheric temperature, humidity, and aerosol vertical, spatial and temporal distributions, the microphysical properties of polar clouds, and the spectral characteristics of snow, ice, and water surfaces. Over the open ocean the surface is warm, dark, and relatively homogeneous. This makes SST retrieval, including cloud clearing, a fairly straightforward task. Over the ice, however, the surface within a single satellite pixel is likely to be highly heterogeneous, a mixture of ice of various thicknesses, open water, and snow cover in the case of sea ice. Additionally, the Arctic is cloudy - very cloudy - with typical cloud cover amounts ranging from 60-90 percent. There are few observations of cloud cover amounts over Antarctica. The goal of this research is to increase our knowledge of surface temperature patterns and magnitudes in both polar regions, by examining existing data and improving our ability to use satellite data as a monitoring tool. Four instruments are of interest in this study: the AVHRR, ATSR, SMMR, and SSM/I. Our objectives are as follows. Refine the existing AVHRR retrieval algorithm defined in Key and Haefliger (1992; hereafter KH92) and applied elsewhere. Develop a method for IST retrieval from ATSR data similar to the one used for SST. Further investigate the possibility of estimating surface temperature from passive microwave data (in conjunction with AVHRR clear sky samples) through the use of 'effective emissivities' and physical relationships between skin temperature and subsurface temperature. Use the general method outlined in MK93 to calculate a 12-year record of clear sky equivalent surface temperatures, or possibly all-sky snow-ice interface physical temperatures, from SMMR and SSM/I, compare these temperatures to climatologies, ECMWF modeled surface temperatures, and surface temperatures predicted by a 2-D ice model. And intercompare several ice surface retrieval methods and validate them against ground measurements from the Swiss Camp on the Greenland ice sheet. Additionally, we intend to develop a surface temperature product based on AVHRR data and possibly blended with drifting buoy and meteorological station temperatures.

Key, Jeff↗
NASA NTRS2006

Aerosol Indirect Effect Studies at Southern Great Plains During the May 2003 Intensive Operations Period

During May 2003 the Department of Energy's Atmospheric Radiation Measurement Program conducted an Intensive Operations Period (IOP) to measure the radiative effects of aerosol and clouds. A suite of both in situ and remote sensing measurements were available to measure aerosol and cloud parameters. This paper has three main goals: First, it focuses on comparison between in situ retrievals of the radiatively important drop effective radius r(sub e) and various satellite, airborne, and surface remote sensing retrievals of the same parameter. On 17 May 2003, there was a fortuitous, near-simultaneous sampling of a stratus cloud by five different methods. The retrievals of r(sub e) agree with one another to within approx.20%, which is approximately the error estimate for most methods. Second, a methodology for deriving a best estimate of r(sub e) from these different instruments, with their different physical properties and sampling volumes, is proposed and applied to the 17 May event. Third, the paper examines the response of r(sub e) to changes in aerosol on 3 days during the experiment and examines the consistency of remote sensing and in situ measurements of the effect of aerosol on r(sub e). It is shown that in spite of the generally good agreement in derived r(sub e), the magnitude of the response of r(sub e), to changes in aerosol is quite sensitive to the method of retrieving r(sub e) and to the aerosol proxy for cloud condensation nuclei. Nonphysical responses are sometimes noted, and it is suggested that further work needs to be done to refine these techniques.

Feingold, Graham↗