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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

On the relationship between water vapor over the oceans and sea surface temperature

Monthly mean precipitable water data obtained from passive microwave radiometry were correlated with the National Meteorological Center (NMC) blended sea surface temperature data. It is shown that the monthly mean water vapor content of the atmosphere above the oceans can generally be prescribed from the sea surface temperature with a standard deviation of 0.36 g/sq cm. The form of the relationship between precipitable water and sea surface temperature in the range T(sub s) greater than 18 C also resembles that predicted from simple arguments based on the Clausius-Clapeyron relationship. The annual cycle of the globally integrated mass of Scanning Multichannel Microwave Radiometer (SMMR) water vapor is shown to differ from analyses of other water vapor data in both phase and amplitude and these differences point to a significant influence of the continents on water vapor. Regional scale analyses of water vapor demonstrate that monthly averaged water vapor data, when contrasted with the bulk sea surface temperature relationship developed in this study, reflect various known characteristics of the time mean large-scale circulation over the oceans. A water vapor parameter is introduced to highlight the effects of large-scale motion on atmospheric water vapor. Based on the magnitude of this parameter, it is shown that the effects of large-scale flow on precipitable water vapor are regionally dependent, but for the most part, the influence of circulation is generally less than about + or - 20 percent of the seasonal mean.

Stephens, Graeme L.↗
NASA NTRS1990

On the relationship between water vapor over the oceans and sea surface temperature

Monthly mean precipitable water data obtained from passive microwave radiometry were correlated with the National Meteorological Center (NMC) blended sea surface temperature data. It is shown that the monthly mean water vapor content of the atmosphere above the oceans can generally be prescribed from the sea surface temperature with a standard deviation of 0.36 g/sq cm. The form of the relationship between precipitable water and sea surface temperature in the range T (sub s) greater than 18 C also resembles that predicted from simple arguments based on the Clausius-Clapeyron relationship. The annual cycle of the globally integrated mass of Scanning Multichannel Microwave Radiometer (SMMR) water vapor is shown to differ from analyses of other water vapor data in both phase and amplitude and these differences point to a significant influence of the continents on water vapor. Regional scale analyses of water vapor demonstrate that monthly averaged water vapor data, when contrasted with the bulk sea surface temperature relationship developed in this study, reflect various known characteristics of the time mean large-scale circulation over the oceans. A water vapor parameter is introduced to highlight the effects of large-scale motion on atmospheric water vapor. Based on the magnitude of this parameter, it is shown that the effects of large-scale flow on precipitable water vapor are regionally dependent, but for the most part, the influence of circulation is generally less than about + or - 20 percent of the seasonal mean.

Stephens, Graeme L.↗
NASA NTRS1971

Remote Sensing of Sea Surface Temperature from Earth Satellites

Techniques for measuring sea surface temperature using remote sensors in earth orbiting satellites are discussed. Specific reference is made to the infrared sensors carried by the TIROS Operational Satellite. The development of a method to examine a large number of measurements in order to differentiate the temperature effects of clouds is described. It is stated that the system used is completely objective and minimizes the influence of atmospheric absorption, cloud contamination, and instrumental noise on the inferred sea surface temperatures.

Rao, P. K.↗
NASA NTRS2012

Spatial and Temporal Variations in Titan's Surface Temperatures from Cassini CIRS Observations

We report a wide-ranging study of Titan's surface temperatures by analysis of the Moon's outgoing radiance through a spectral window in the thermal infrared at 19 mm (530/cm) characterized by lower atmospheric opacity. We begin by modeling Cassini Composite Infrared Spectrometer (CIRS) far infrared spectra collected in the period 2004-2010, using a radiative transfer forward model combined with a non-linear optimal estimation inversion method. At low-latitudes, we agree with the HASI near-surface temperature of about 94 K at 101S (Fulchignoni et al., 2005). We find a systematic decrease from the equator toward the poles, hemispherically asymmetric, of approx. 1 K at 60 deg. south and approx. 3 K at 60 deg. north, in general agreement with a previous analysis of CIRS data and with Voyager results from the previous northern winter. Subdividing the available database, corresponding to about one Titan season, into 3 consecutive periods, small seasonal changes of up to 2 K at 60 deg N became noticeable in the results. In addition, clear evidence of diurnal variations of the surface temperatures near the equator are observed for the first time: we find a trend of slowly increasing temperature from the morning to the early afternoon and a faster decrease during the night. The diurnal change is approx. 1.5 K, in agreement with model predictions for a surface with a thermal inertia between 300 and 600 J/ sq. m s (exp -1/2) / K. These results provide important constraints on coupled surface-atmosphere models of Titan's meteorology and atmospheric dynamic.

titan↗
NASA NTRSDate not supplied

Communicating Metrics of Land Surface Temperature Variability Using Multi-sensor Machine Learning

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Further, time series clustering approaches are examined with the objective of identifying key indicators of change in diurnal cycling and extremes on a continental scale. Communicating LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Duffy↗
NASA NTRS1999

GISS Analysis of Surface Temperature Changes

We describe the current GISS analysis of surface temperature change based primarily on meteorological station measurements. The global surface temperature in 1998 was the warmest in the period of instrumental data. The rate of temperature change is higher in the past 25 years than at any previous time in the period of instrumental data. The warmth of 1998 is too large and pervasive to be fully accounted for by the recent El Nino, suggesting that global temperature may have moved to a higher level, analogous to the increase that occurred in the late 1970s. The warming in the United States over the past 50 years is smaller than in most of the world, and over that period there is a slight cooling trend in the Eastern United States and the neighboring Atlantic ocean. The spatial and temporal patterns of the temperature change suggest that more than one mechanism is involved in this regional cooling.

Hansen, J.↗
NASA NTRS2016

Validation of AIRS V6 Surface Temperature over Greenland with GCN and NOAA Stations

This work compares the temporal and spatial characteristics of the AIRSAMSU (Atmospheric Infrared Sounder Advanced Microwave Sounding Unit A) Version 6 and MODIS (Moderate resolution Imaging Spectroradiometer) Collection 5 derived surface temperatures over Greenland. To estimate uncertainties in space-based surface temperature measurements, we re-projected the MODIS Ice Surface Temperature (IST) to 0.5 by 0.5 degree spatial resolution. We also re-gridded AIRS Skin Temperature (Ts) into the same grid but classified with different cloud conditions and surface types. These co-located data sets make intercomparison between the two instruments relatively straightforward. Using this approach, the spatial comparison between the monthly mean AIRS Ts and MODIS IST is in good agreement with RMS 2K for May 2012. This approach also allows the detection of any long-term calibration drift and the careful examination of calibration consistency in the MODIS and AIRS temperature data record. The temporal correlations between temperature data are also compared with those from in-situ measurements from GC-Net (GCN) and NOAA stations. The coherent time series of surface temperature evident in the correlation between AIRS Ts and GCN temperatures suggest that at monthly time scales both observations capture the same climate signal over Greenland. It is also suggested that AIRS surface air temperature (Ta) can be used to estimate the boundary layer inversion.

AIRS↗
NASA NTRS2014

An Analytic Function of Lunar Surface Temperature for Exospheric Modeling

We present an analytic expression to represent the lunar surface temperature as a function of Sun-state latitude and local time. The approximation represents neither topographical features nor compositional effects and therefore does not change as a function of selenographic latitude and longitude. The function reproduces the surface temperature measured by Diviner to within +/-10 K at 72% of grid points for dayside solar zenith angles of less than 80, and at 98% of grid points for nightside solar zenith angles greater than 100. The analytic function is least accurate at the terminator, where there is a strong gradient in the temperature, and the polar regions. Topographic features have a larger effect on the actual temperature near the terminator than at other solar zenith angles. For exospheric modeling the effects of topography on the thermal model can be approximated by using an effective longitude for determining the temperature. This effective longitude is randomly redistributed with 1 sigma of 4.5deg. The resulting ''roughened'' analytical model well represents the statistical dispersion in the Diviner data and is expected to be generally useful for future models of lunar surface temperature, especially those implemented within exospheric simulations that address questions of volatile transport.

moon↗
NASA NTRS2016

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth's surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1-2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called Rvalue metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative transfer was varied by imposing a bias on an existing regression. These scenarios were evaluated through the Rvalue technique, resulting in optimal bias values on top of this regression. In a next step, these optimal bias values were incorporated in order to re-calibrate the existing linear regression, resulting in a quasi-global uniform LST relation for day-time observations. In a final step, day-time soil moisture retrievals using the re-calibrated land surface temperature relation were again validated through the Rvalue technique. Results indicate an average increasing Rvalue of 16.5%, which indicates a better performance obtained through the re-calibration. This number was confirmed through an independent Triple Collocation verification over the same domain, demonstrating an average root mean square error reduction of 15.3%. Furthermore, a comparison against an extensive in situ database (679 stations) also indicates a generally higher quality for the re-calibrated dataset. Besides the improved day-time dataset, this study furthermore provides insights on the relative quality of soil moisture retrieved from AMSR-E's day- and night-time observations.

soil moisture↗
NASA NTRS1974

Atmospheric response to variations in sea surface temperature

An extended range prediction experiment was performed with the GISS atmospheric model on a global data to test the sensitivity of the model to sea surface temperature (SST) variation over a two-week forecast period. The use of an initial observed SST field in place of the climatological monthly mean sea temperatures for surface flux calculations in the model was found to have a significant effect on the predicted precipitation over the ocean, with enhanced convection computed over areas where moderately large warm SST anomalies are found. However, there was no detectable positive effect of the SST anomaly field on forecast quality. The influence of the SST anomalies on the daily predicted fields of pressure and geopotential is relatively insignificant up to about one week compared with the growth of prediction error, and is no greater over a two-week period than that resulting from random errors in the initial meteorological state. The 14-day average fields of sea level pressure and 500-mb height predicted by the model, appear to be similarly insensitive to anomalies of sea surface temperature.

Spar, J.↗
NASA NTRS1995

Observation of local cloud and moisture feedbacks over high ocean and desert surface temperatures

New data on clouds and moisture, made possible by reanalysis of weather satellite observations, show that the atmosphere reacts to warm clusters of very high sea surface temperatures in the western Pacific Ocean with increased moisture, cloudiness, and convection, suggesting a negative feedback limiting the sea surface temperature rise. The reverse was observed over dry and hot deserts where both moisture and cloudiness decrease, suggesting a positive feedback perpetuating existing desert conditions. In addition, the observations show a common critical surface temperature for both oceans and land; the distribution of atmospheric moisture is observed to reach a maximum value when the daily surface temperatures approach 304 +/- 1 K. These observations reveal complex dynamic-radiative interactions where multiple processes act simultaneously at the surface as well as in the atmosphere to regulate the feedback processes.

Chahine, Moustafa T.↗
NASA NTRS1989

Modeling surface temperature distributions in forest landscapes

A model of the frequency distributions of the spatial variability in surface temperature is presented. Surface temperature data are obtained from two daytime and two nighttime flights of the Thermal IR Multispectral Scanner (TIMS) over forest land in western Oregon in August, 1985. The temperature values are corrected for atmospheric attenuation and thermal radiation emission with the LOWTRAN-6 algorithm. The temperature distributions were modeled with a two-parameter beta probability density distribution and the fit of the model was evaluated by comparison with the TIMS data set. Use of the model's parameters to identify and classify surface types shows good discrimination among various surfaces for the daytime images, with less distinct discrimination for the nighttime images.

Holbo, H. R.↗
NASA NTRS1999

GISS Analysis of Surface Temperature Change

We describe the current GISS analysis of surface temperature change for the period 1880-1999 based primarily on meteorological station measurements. The global surface temperature in 1998 was the warmest in the period of instrumental data. The rate of temperature change was higher in the past 25 years than at any previous time in the period of instrumental data. The warmth of 1998 was too large and pervasive to be fully accounted for by the recent El Nino. Despite cooling in the first half of 1999, we suggest that the mean global temperature, averaged over 2-3 years, has moved to a higher level, analogous to the increase that occurred in the late 1970s. Warming in the United States over the past 50 years has been smaller than in most of the world, and over that period there was a slight cooling trend in the Eastern United States and the neighboring Atlantic Ocean. The spatial and temporal patterns of the temperature change suggest that more than one mechanism was involved in this regional cooling. The cooling trend in the United States, which began after the 1930s and is associated with ocean temperature change patterns, began to reverse after 1979. We suggest that further warming in the United States to a level rivaling the 1930s is likely in the next decade, but reliable prediction requires better understanding of decadal oscillations of ocean temperature.

Hansen, J.↗
NASA NTRS1983

Total ozone and surface temperature correlations during 1972 - 1981

Ten years of Dobson spectrophotometer total ozone measurements and surface temperature observations were used to construct monthly mean values of the two parameters. The variability of both parameters is greatest in the months of January and February. Indeed, in January there is an apparent correlation between high total ozone values and abnormally low surface temperatures. However, the correlation does not hold in February. By reviewing the history of stratospheric warmings during this period, it is argued that the ozone and surface temperature correlation is influenced by the advection or lack of advection of ozone rich arctic air resulting from sudden stratospheric warmings.

Parsons, C. L.↗
NASA NTRS1983

Comparison of winter-nocturnal geostationary satellite infrared-surface temperature with shelter-height temperature in Florida

Geostationary satellite surface temperatures derived from a Visible and Infrared Spin Scan Radiometer (VISSR) sensor (10.5 to 12.6 microns) were compared with 1.5-m air temperatures collected by a thermocouple on a traversing vehicle along rural highway transects in Florida, and with two fixed thermographs located in rural and agricultural areas. Statistical comparisons between satellite and 1.5-m observations yielded a mean correlation coefficient of 0.87 and an average sample standard deviation from regression of 1.57 C during clear nights for four winters (1978-1981). The satellite-temperature image of Lake Okeechobee was compared with its geographic outline for areal image registration. Manual overlays of temporal images were repeatable to within one pixel. Satellite-sensed water temperature of Lake Okeechobee was used as an indicator of satellite radiometer repeatability and stability.

Chen, E.↗