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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Covariation of Mesoscale Ocean Color and Sea-Surface Temperature Patterns in the Sargasso Sea

During the lifetime of the Coastal Zone Color Scanner, there were 21 instances in which both satellite-derived ocean color and sea-surface temperature are simultaneously available over large areas of the Sargasso Sea. These images reveal close correspondence between mesoscale structures observed in temperature and pigment fields. In general, higher (lower) pigment biomass occurs in mesoscale features consisting of cold (warm) temperature anomalies. This relationship is consistent with the idea that upward displacement of isopycnals at the base of the euphotic zone by mesoscale eddies is an important mechanism of nutrient supply in the region.

McGillicuddy, D. J.↗
NASA NTRS1985

Intercomparison of Sea-surface Temperature Anomaly Fields

Sea-surface temperature anomaly fields produced from analysis of HIRS2/MSU data at GLAS for the months November 1979, December 1981, March 1982, and July 1982 were compared to those produced from data from ships, AVHRR, and SMMR in a series of NASA sponsored sea-surface temperature workshops. The methods used by GLAS to analyze the HIRS2/MSU data were made to the algorithm used to retrieve sea and land surface temperatures and in the cloud filtering algorithm. One of the improvements involved generating weights for a given sounding, which decreased with increasing cloudiness and increasing humidity. These weights should be used in generating monthly mean fields. The workshop generated statistics for the weighted HIRS2/MSU field only for the month of July 1982.

Susskind, J.↗
DOE OSTI2022

A Positive Low Cloud–Sea Surface Temperature Feedback in the East Asian Marginal Seas during El Niño Mature Winters and Their Following Spring

During the winter and subsequent spring of an El Niño year, the East Asian marginal sea (EAMS) exhibits positive sea surface temperature anomalies (SSTAs) and fewer low clouds, while the western North Pacific experiences negative SSTAs. In this study, we suggest that the positive SSTAs in EAMS are maintained by a positive low cloud–SST feedback. In neutral winters and springs, the EAMS is covered by low clouds, which have a cooling effect on surface temperatures. During an El Niño year, a western North Pacific anomalous anticyclone is established, and along its northwestern flank, there are favorable conditions for convergence of moisture and weaker surface latent heat flux over the EAMS. Once a positive SSTA has been established, a further reduction of turbulent mixing results in less low cloud and enhanced solar heating of the ocean mixed layer; this reinforces and maintains both the positive SSTA and the lack of low cloud via a positive feedback mechanism. Further, the concurrent increase of low cloud–SST feedback and anticyclone circulation strengths is evident in the coupled-model simulations from phase 6 of the Coupled Model Intercomparison Project. Furthermore, sensitivity experiments, performed with the atmospheric components of Community Earth System Model (CESM2), reveal that a positive SSTA helps to maintain the western North Pacific anomalous anticyclone. Four pacemaker-coupled experiments by CESM2, with sea surface temperature in the equatorial Pacific restored to the observational anomalies plus the model climatology and altered low cloud feedback over EAMS, suggest that the low cloud–SST feedback results in more than the maintenance of a positive SSTA over the EAMS: the positive feedback is also a previously overlooked mechanism for the maintenance of the western North Pacific anomalous anticyclone.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Mean sea surface and geoid gradient comparisons with TOPEX altimeter data

Cycles 4 to 54 of TOPEX data have been analyzed through comparisons with the mean sea surface given on the disturbed geophysical data record (GDR). Two inverted barometer correction procedures were considered for the data reduction. One used a constant atmospheric pressure for all data while the one adopted for use, for most computations, introduced a cycle average pressure. The maximum difference between the two estimates was 3.0 cm with a clear annual signal. With the modified correction the TOPEX sea surface was compared to The Ohio State University (OSU) mean sea surface, given on the GDR, to estimate three translations ( delta x = -2.3 cm; delta y = 25.0 cm; delta z = -0.3 cm) and a bias (43.3 cm) between the two surfaces. The only significant translation is delta y which indicates the reference frame of the TOPEX system differs from that used in the OSU mean sea surface system. The bias between the TOPEX mean sea surface and the OSU mean sea surface was used to estimate an equatorial radius of 6,378,136.55 m based on an 18-cm biased estimate of the TOPEX altimeter. Examination of the average difference, by cycle, between the TOPEX sea surface and the OSU mean sea surface suggested a bias change of 3.1 +/- 2.2 mm/yr with a positive sign indicating the average ocean surface is rising or the altimeter measured distance is decreasing. Models were implemented that solved directly for a bias, bias rate annual/semiannual, and tide correction terms. The computations indicated that a simultaneous solution for this bias, bias rate, and annual/semiannual terms gave the most accurate results. Nonsimultaneous solutions led to slightly different bias rate values. The root mean square difference between the TOPEX sea surface and OSU sea surface, after translation and bias correction, was +/- 17 cm for a typical cycle. Some locations were indentified where the difference could reach 2.3 cm and were repeated over several cycles indicating errors in the mean sea surface. Most of the large differences occur in regions lacking altimeter data prior to the TOPEX/POSEIDON mission and/or areas of significant bathymetric signature. Geoid gradients are needed for the reduction of individual track data to a reference track. The accuracy of the determination of such gradients was determined through the comparison of predicted along-track gradients to the observed gradients. Among four mean sea surfaces tested the best agreement was found with the OSU mean sea surface placed on the TOPEX geophysical data record.

Rapp, Richard H.↗
NASA NTRS1992

A comprehensive comparison between skin and multi-channel sea surface temperatures as derived from AVHRR

Global maps of Satellite Measured Surface Skin Temperature (SMSST), MultiChannel Sea Surface Temperature (MCSST) and Cross Product Sea Surface Temperature (CPSSR) are computed from imagery of the Advanced Very High Resolution Radiometer (AVHRR) for five fortnights in 1984/85. They demonstrate systematic latitudinal variations and regional extremes of the differences with greatest root mean square values of 0.7K between MCSST and SMSST found at low latitudes. Mean differences range from 0.1K between CPSST and SMSST to 0.45K between MCSST and SMSST. A part of the differences might be attributed to the skin cooling at the sea surface. A parameterization of the bulk skin temperature difference is derived from in situ field measurements. This can be used to find a relation between satellite measured bulk and skin temperatures.

Schluessel, Peter↗
NASA NTRS2012

Sea Surface Salinity : Research Challenges and Opportunities

Sea surface salinity (SSS) can be important in regulating sea surface temperature (SST). Two technological breakthrough satellite SSS missions, Aquarius and Soil Moisture and Ocean Salinity (SMOS), are currently producing high-quality SSS data. This paper provides an overview of the importance of SSS for weather and climate applications and describes the Aquarius and SMOS missions. The newness of adequately sampled SSS data prompted a first-time at-sea field campaign devoted to improved understanding of SSS variations.

Aquarius↗
NASA NTRS1997

LITE Measurements of Sea Surface Directional Reflectance

The dependence of sea surface directional reflectance on surface wind speed from space-based lidar measurements of sea surface backscatter. In particular lidar measurements in the nidir angle range from 10-30 degrees appear to be most sensitive to surface wind speed variability in the regime below 10m/s.

Sea Surface Wind Speed↗
NASA NTRS2001

Observations of Sea Surface Mean Square Slope During the Southern Ocean Waves Experiment

For the Southern Ocean Waves Experiment (SOWEX), conducted in June 1992 out of Hobart, Tasmania, the NASA Scanning Radar Altimeter (SRA) was shipped to Australia and installed on a CSIRO Fokker F-27 research aircraft instrumented to make comprehensive surface layer measurements of air-sea interaction fluxes. The SRA sweeps a radar beam of P (two-way) half-power width across the aircraft ground track over a swath equal to 0.8 of the aircraft height, simultaneously measuring the backscattered power at its 36 GHz (8.3 mm) operating frequency and the range to the sea surface at 64 cross-track positions. In realtime, the slant ranges are multiplied by the cosine of the off-nadir incidence angles (including the effect of aircraft roll attitude) to determine the vertical distances from the aircraft to the sea surface. These distances are subtracted from the aircraft height to produce a sea-surface elevation map, which is displayed on a monitor in the aircraft to enable real-time assessments of data quality and wave properties. The sea surface mean square slope (mss), which is predominantly caused by the short waves, was determined from the backscattered power falloff with incidence angle measured by the SRA in the plane normal to the aircraft heading. On each flight, data were acquired at 240 m altitude while the aircraft was in a 7 degree roll attitude, interrogating off-nadir incidence angles from -15 degrees through nadir to +29 degrees. The aircraft turned azimuthally through 810 degrees in this attitude, mapping the azimuthal dependence of the backscattered power falloff with incidence angle. Two sets of turning data were acquired on each day, before and after the aircraft measured wind stress at low altitude (12 meters to 65 meters). Wave topography and backscattered power for mss were also acquired during those level flight segments whenever the aircraft altitude was above the SRA minimum range of 35 m. Data were collected over a wide range of wind and sea conditions, from quiescent to gale force winds with 9 meter wave height.

Walsh, E. J.↗
NASA NTRS1984

Global mean sea surface based upon SEASAT altimeter data

A global mean sea surface based upon the SEASAT altimeter data was derived. A combination of crossing arc techniques, accurate SEASAT reference orbits, and a previously computed GOES-3/SEASAT mean sea surface were used in the computation process. This mean sea surface provides a basis for the determination of global ocean circulation patterns and for detailed analysis of the Earth's internal structure. A contour map of the global mean sea surface is presented.

Marsh, J. G.↗
NASA NTRS1994

Relationship between clouds and sea surface temperatures in the western tropical Pacific

Analysis of four years of earth radiation budget, cloud, and sea surface temperature data confirms that cloud parameters change dramatically when and where sea surface temperatures increase above approximately 300K. These results are based upon monthly mean values within 2.5 deg x 2.5 deg grid points over the 'warm pool' region of the western tropical Pacific. The question of whether sea surface temperatures are influenced, in turn, by the radiative effects of these clouds (Ramanathan and Collins) is less clear. Such a feedback, if it exists, is weak. The reason why clouds might have so little influence, despite large changes in their longwave and shortwave radiative effects, might be that the sea surface responds to both the longwave heating and the shortwave cooling effects of clouds, and the two effects nearly cancel. There are strong correlations between the rate of change of sea surface temperature and any of the radiation budget parameters that are highly correlated with the incident solar flux-implying that season and latitude are the critical factors determining sea surface temperatures. With the seasonal or both seasonal and latitudinal variations removed, the rate of change of sea surface temperature shows no correlation with cloud-related parameters in the western tropical Pacific.

Arking, Albert↗
NASA NTRS2006

The Aquarius Scatterometer: An Active System for Measuring Surface Roughness for Sea-Surface Brightness Temperature Correction

The Aquarius scatterometer is a total-power L-band radar system for estimating ocean surface roughness. Its measurements will enable the removal of wind effects from the Aquarius radiometer ocean-surface brightness temperature measurements being used to retrieve ocean salinity. The Aquarius scatterometer is a relatively simple, low-spatial resolution power-detecting radar, without ranging capability. But to meet its science requirement, it must be very stable, with repeatability on the order of 0.1 dB over several days, and calibrated accuracy to this level over several months. Data from this instrument over land as well as ocean areas will be available for a variety of geophysical applications.

scatterometers↗
NASA NTRS1993

An atlas of monthly mean distributions of SSMI surface wind speed, ARGOS buoy drift, AVHRR/2 sea surface temperature, and ECMWF surface wind components during 1991

The following monthly mean global distributions for 1991 are presented with a common color scale and geographical map: 10-m height wind speed estimated from the Special Sensor Microwave Imager (SSMI) on a United States Air Force Defense Meteorological Satellite Program (DMSP) spacecraft; sea surface temperature estimated from the advanced very high resolution radiometer (AVHRR/2) on a U.S. National Oceanic and Atmospheric Administration (NOAA) spacecraft; Cartesian components of free-drifting buoys which are tracked by the ARGOS navigation system on NOAA satellites; and Cartesian components of the 10-m height wind vector computed by the European Center for Medium-Range Weather Forecasting (ECMWF). Charts of monthly mean value, sampling distribution, and standard deviation value are displayed. Annual mean distributions are displayed.

Halpern, D.↗
NASA NTRS1993

An atlas of monthly mean distributions of SSMI surface wind speed, ARGOS buoy drift, AVHRR/2 sea surface temperature, and ECMWF surface wind components during 1990

The following monthly mean global distributions for 1990 are proposed with a common color scale and geographical map: 10-m height wind speed estimated from the Special Sensor Microwave Imager (SSMI) on a United States (US) Air Force Defense Meteorological Satellite Program (DMSP) spacecraft; sea surface temperature estimated from the advanced very high resolution radiometer (AVHRR/2) on a U.S. National Oceanic and Atmospheric Administration (NOAA) spacecraft; Cartesian components of free drifting buoys which are tracked by the ARGOS navigation system on NOAA satellites; and Cartesian components on the 10-m height wind vector computed by the European Center for Medium-Range Weather Forecasting (ECMWF). Charts of monthly mean value, sampling distribution, and standard deviation values are displayed. Annual mean distributions are displayed.

Halpern, D.↗
NASA NTRS2019

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO Seasonal Forecast System

El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite sea surface salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (North American Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF (Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive satellite) V4.1 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by satellite at approximately 1-centimeter depth) and in situ measurements (typically measured by Argo floats at 3-meters depth). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from GMAO S2S spring reanalyses that assimilate/withhold along-track (L2) SSS. In particular, we contrast forecasts of the big 2015 El Nino, the 2017 La Nina and the 2018 weak El Nino. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrade the mixed layer depth leading to more accurate coupled air/sea interaction. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP, allows a unique opportunity to compare and contrast forecasts initialized with the benefit of these two satellite SSS observation types. We assess the impact of gridded satellite sea surface salinity observations on dynamical ENSO forecasts for the big 2015 El Nino.

Hackert, E.↗
NASA NTRS1995

Global climatology and variability of potential new production estimated from remote sensing of sea-surface temperature

During this project we have collected numerous shipboard data-bases of oceanic nitrate and silicate versus temperature for both equatorial and coastal upwelling regions. These cruises all have accompanying N-15 measurements of new production. The inverse relationships between nutrients and temperatures have been determined and are being used to obtain surface nutrient fields from sea surface temperatures measured remotely by satellite borne sensors- i.e. AVHRR data from NOAA satellites contained in the MCSST data set for the world ocean provided by the University of Miami. The images and data derived from space in this way show the strong seasonal fluctuations and interannual el Nino fluctuations of the nitrate field. the nitrate data has been used to make estimates of new production for the equatorial pacific which are compared with shipboard measurements when available. The importance of silicate as a nutrient driving new production and the ratio of nitrate to silicate has been discovered to be crucial to better understand the causes of new production variability, so we have added these parameters to our study and have begun to make estimates of these for the equatorial Pacific, derived from the weekly averaged sea surface temperatures (SSTs).

Dugdale, Richard C.↗
NASA NTRS1991

The mean sea surface height and geoid along the Geosat subtrack from Bermuda to Cape Cod

The mean sea-surface height and the gravitational geoid are presently estimated via near-surface velocity changes and concurrent sea-level changes along an ascending Geosat subtrack. The velocity measurements were made on three traverses, within ten days, of a Geosat subtrack, by means of an acoustic Doppler current profiler (ADCP). The mean sea-surface height was estimated as the difference between the instantaneous sea-surface height from ADCP and the Geosat residual sea level. In order to minimize mesoscale errors in the estimate, the along-track geoid estimate was computed as the difference between mean sea-level height from the Geosat Exact Repeat Mission and an estimate of the mean sea-surface height.

Kelly, Kathryn A.↗