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At least 109 records · Page 6
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NASA NTRS1979

Estimation of mean sea surfaces in the North Atlantic, the Pacific and the Indian Ocean using GEOS-3 altimeter data

The sea surface heights above the reference ellipsoid were determined for several regions of the world's ocean using data from the radar altimeter on board the GEOS-3 satellite in conjunction with precise orbital position information derived from laser data. The resolution of the estimated sea surfaces varied from 0.25 degrees off the east coast of the United States to about 2 degrees in the Indian Ocean near Australia. The rms crossover discrepancy after adjustment varied from 30 cm to 70 cm depending on geographic location. Comparison of the altimeter derived mean sea surface in the North Atlantic with the 5 x 5 ft GEM-8 detailed gravimetric geoid indicated a relative consistency of better than one meter.

Marsh, J. G.↗
NASA NTRS1997

Monthly Maps of Sea Surface Height in the North Atlantic and Zonal Indices for the Gulf Stream Using TOPEX/Poseidon Altimeter Data

Monthly Maps of sea surface height are constructed for the North Atlantic Ocean using TOPEX/Poseidon altimeter data. Mean sea surface height is reconstructed using a weighted combination of historical, hydrographic data and a synthetic mean obtained by fitting a Gaussian model of the Gulf Stream jet to altimeter data. The resultant mean shows increased resolution over the hydrographic mean, and incorporates recirculation information that is absent in the synthetic mean. Monthly maps, obtained by adding the mean field to altimeter sea surface height residuals, are used to derive a set of zonal indices that describe the annual cycle of meandering as well as position and strength of the Gulf Stream.

GULF STREAM↗
NASA NTRS2018

The Impact of Satellite Sea Surface Salinity for Prediction of the Coupled Indo-Pacific System

Here we assess the impact of satellite sea surface salinity (SSS) observations on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts. The baseline experiment assimilates satellite sea level (SL), sea surface temperature (SST), and in situ subsurface temperature and salinity observations (Tz, Sz). These baseline experiments are then compared with experiments that additionally assimilate Aquarius (version 5.0 Lilly and Lagerloef, 2008) and SMAP (version 2.0 Meissner and Wentz, 2016) SSS. Twelve-month forecasts are initialized for each month from September 2011 to September 2017. We find that including satellite SSS significantly improves NINO3.4 sea surface temperature anomaly validation over 0-8 month forecast lead-times and removing the salty bias from SMAP data helps to extend useful forecasts out to 12 month lead-times.

SSS↗
NASA NTRS1990

Mean sea surface and variability of the Gulf of Mexico using Geosat altimetry data

Geosat Exact Repeat Mission (ERM) altimetric measurements of the sea surface height in the Gulf of Mexico are used to determine the mean sea surface height with respect to the ellipsoid and mesoscale variability along Geosat ground tracks in the gulf for the time period from November 8, 1986 to November 25, 1988. A mean surface generated using the Geosat ERM along-track mean is calculated and contrasted with a previously derived mean surface determined using GEOS 3 and Seasat crossover differences. This provides a first look at the variability in the mean between the time periods of 1987-1988 and 1975-1978. In addition, the along-track mesoscale variability time series has been produced from the Geosat ERM data set by using a robust orbit-error removal algorithm to determine the variability of the sea-surface height with respect to the along-track mean. Good qualitative and quantitative agreement with previous in situ observations in the region is found. This study demonstrates the potential of satellite altimetry for oceanographic studies of the Gulf of Mexico.

Leben, Robert R.↗
NASA NTRS1977

Sea-surface temperature and salinity mapping from remote microwave radiometric measurements of brightness temperature

A technique to measure remotely sea surface temperature and salinity was demonstrated with a dual frequency microwave radiometer system. Accuracies in temperature of 1 C and in salinity of part thousand for salinity greater than 5 parts per thousand were attained after correcting for the influence of extraterrestrial background radiation, atmospheric radiation and attenuation, sea-surface roughness, and antenna beamwidth. The radiometers, operating at 1.43 and 2.65 GHz, comprise a third-generation system using null balancing and feedback noise injection. Flight measurements from an aircraft at an altitude of 1.4 km over the lower Chesapeake Bay and coastal areas of the Atlantic Ocean resulted in contour maps of sea-surface temperature and salinity with a spatial resolution of 0.5 km.

Hans-Juergen, C. B.↗
NASA NTRS2020

Satellite Sea Surface Salinity Observations Impact on El Niño/Southern Oscillation Predictions: Case Studies from the NASA GEOS Seasonal Forecast System

El Niño/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would have great societal benefit. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean salinity. Satellite sea surface salinity (SSS), combined with temperature, help to improve the estimates of ocean density changes and associated near-surface mixing. For the first time, we assess the impact of satellite SSS observations for improving near-surface dynamics within ocean reanalyses and how these initializations impact dynamical ENSO forecasts using NASA’s coupled forecast system (GEOS-S2S-2). For all initialization experiments, all available sea level and in situ temperature and salinity observations are assimilated. Separate observing system experiments (OSE) additionally assimilate Aquarius, and SMAP, SMOS, and these datasets combined. We highlight the impact of satellite SSS on ocean reanalyses by comparing experiments with and without the application of SSS assimilation. Next, we compare case studies of coupled forecasts for the big 2015 El Niño, the 2017 La Niña, and the weak El Niño in 2018 that are initialized from GEOS-S2S-2 spring reanalyses that assimilate and withhold along-track SSS. For each of these ENSO-event case studies, assimilation of satellite SSS improves the forecast validation with respect to observed NINO3.4 anomalies (or at least reduces the forecast uncertainty). Satellite SSS assimilation improved characterization of the mixed layer depth leading to more accurate coupled air/sea interaction and better forecasts. These results further underline the value of satellite SSS assimilation into operational forecast systems.

Eric Hackert↗
NASA NTRS1985

Production of global sea surface temperature fields for the Jet Propulsion Laboratory workshop comparisons

Sea surface temperature (SST) is measured from space by the advanced very high resolution radiometer (AVHRR), scanning multichannel microwave radiometer (SMMR), high resolution infrared sounder (HIRS) and VISSR atmospheric sounder (VAS). Typical accuracies have been reported from 0.5 C regionally to 2.0 C on a global basis. To evaluate the accuracy of the satellite-derived sea surface temperatures, a series of three workshops was organized to provide uniform data reduction and analysis. The analytical techniques used to intercompare satellite and in situ measurements are described in detail. Selected results showed the overall average rms errors were in the range 0.5-1.0 C.

Hilland, J. E.↗
NASA NTRS1983

Equatorial long waves in geostationary satellite observations and in a multichannel sea surface temperature analysis

Geostationary satellite observations of a zonally oriented sea surface temperature front in the eastern equatorial Pacific were made between 1975 and 1981. Long waves appeared along the front mainly during the summer and fall, except during 1976, the year of an El Nino. The waves have averaged periods of 25 days and wavelengths of 1000 km. At the end of 1981, the long waves also were detected in a new sea surface temperature analysis based on multichannel infrared measurements from a polar-orbiting satellite. This quantitative analysis may improve the ability to resolve low-frequency equatorial wave motions from satellite observations.

Legeckis, R.↗
NASA NTRS2012

Estimating Sea Surface Salinity and Wind Using Combined Passive and Active L-Band Microwave Observations

Several L-band microwave radiometer and radar missions have been, or will be, operating in space for land and ocean observations. These include the NASA Aquarius mission and the Soil Moisture Active Passive (SMAP) mission, both of which use combined passive/ active L-band instruments. Aquarius s passive/active L-band microwave sensor has been designed to map the salinity field at the surface of the ocean from space. SMAP s primary objectives are for soil moisture and freeze/thaw detection, but it will operate continuously over the ocean, and hence will have significant potential for ocean surface research. In this innovation, an algorithm has been developed to retrieve simultaneously ocean surface salinity and wind from combined passive/active L-band microwave observations of sea surfaces. The algorithm takes advantage of the differing response of brightness temperatures and radar backscatter to salinity, wind speed, and direction, thus minimizing the least squares error (LSE) measure, which signifies the difference between measurements and model functions of brightness temperatures and radar backscatter. The algorithm uses the conjugate gradient method to search for the local minima of the LSE. Three LSE measures with different measurement combinations have been tested. The first LSE measure uses passive microwave data only with retrieval errors reaching 1 to 2 psu (practical salinity units) for salinity, and 1 to 2 m/s for wind speed. The second LSE measure uses both passive and active microwave data for vertical and horizontal polarizations. The addition of active microwave data significantly improves the retrieval accuracy by about a factor of five. To mitigate the impact of Faraday rotation on satellite observations, the third LSE measure uses measurement combinations invariant under the Faraday rotation. For Aquarius, the expected RMS SSS (sea surface salinity) error will be less than about 0.2 psu for low winds, and increases to 0.3 psu at 25 m/s wind speed for warm waters (25 C). To achieve the required 0.2 psu accuracy, the impact of sea surface roughness (e.g. wind-generated ripples) on the observed brightness temperature has to be corrected to better than one tenth of a degree Kelvin. With this algorithm, the accuracy of retrieved wind speed will be high, varying from a few tenths to 0.6 m/s. The expected direction accuracy is also excellent (less than 10 ) for mid to high winds, but degrades for lower speeds (less than 7 m/s).

Yueh, Simon H.↗
NASA NTRS1982

Aircraft measurement of sea surface temperature during the West Coast Experiment

During the West Coast Experiment in March 1977, a test was conducted to ascertain the effectiveness of using remote sensing techniques to estimate sea surface temperature (SST) from infrared (IR) emissions of the sea surface. Aircraft flights were made over three buoys moored in southern California coastal waters, and data was collected of sea surface emissions at thermal IR wavelengths (7.95-13.5 micrometers). SST obtained from the remote sensing measurements were compared with in situ SST measured with thermistors mounted on the buoys. The remotely determined SST were from 1.4-2.9 C lower than the in situ measurements. Several factors are discussed that could account for the differences.

Schieldge, J. P.↗
NASA NTRS2019

Sea Surface Salinity Distribution in the Southern Ocean as Observed from Space

Large-scale spatial and temporal variabilities of sea surface salinity (SSS) in the Southern Ocean from 2011 to 2017 were studied using products derived from microwave sensors on board Aquarius, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active and Passive (SMAP) satellites. Four products, three from Aquarius and one from SMOS, were evaluated and shown to be generally consistent within 0.3 to 0.6 psu and agree favorably with in situ measurements. However, although the Aquarius products show consistent seasonality of SSS with high values of 34.45 psu in October and low values of 33.40 psu in May, theSMOS and SMAP products lack such seasonal variations. This may be caused by larger uncertainties in the SMOS and SMAP data due in part to the lack of concurrent scatterometer measurements that is used to correct for roughness effects. The four products provide similar spatial distributions of SSS with root mean square difference from 0.25 to 0.58 psu. Differences among Aquarius products are mainly due to varying salinity retrieval algorithms, smoothing, and masking of sea ice, while the SMOS product showed the highest SSS deviation that is likely due to the bias adjustment done on the data set. Our analyses show that SSS in the Southern Ocean region has significant meridional variations with the lowest SSS near the ice edge and highest at lower latitudes. The SSS is also lowest in summer indicating the predominant influence of sea ice and glacial melt, but it stays low near ice edges even during the growth season. Salinity together with temperature is the key parameters that control the state and circulation of the World's oceans. The large‐scale distribution of the sea surface salinity of the Southern Ocean is quantified for the first time using Aquarius, Soil Moisture and Ocean Salinity, and Soil Moisture Active and Passive satellite microwave data. Seasonal and interannual changes in the distribution as affected by the sea ice cover, surface temperature, and precipitation are evaluated. Comparative studies were done, using four different products that are publicly available, in conjunction with in situ observations to gain insights into the true nature of the distribution and how consistently the sea surface salinity is depicted by the different products. There are general consistencies in the products, and discrepancies are attributed to different algorithms, smoothing techniques, and sea ice masking. Aquarius data are shown to have higher accuracy than Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive products in part because of having concurrent scatterometer that provides accurate correction to roughness effects.

Garcia-Eidell, Cynthia↗
NASA NTRS1975

Sea surface determination from space: The GSFC geoid

The determination of the sea surface/geoid and its relative variation were investigated and results of the altimeter experiment on Skylab to test the geoid are discussed. The spaceborne altimeter on Skylab revealed that the sea surface of the world's oceans can be measured with an accuracy in the meter range. Surface variations are discussed as they relate to those computed from satellite orbital dynamics and ground based gravity data. The GSFC geoid was constructed from about 400,000 satellite tracking data (range, range rate, angles) and about 20,000 ground gravity observations. One of the last experiments on Skylab was to measure and/or test this geoid over almost one orbit. It was found that the computed water surface deviates between 5 to 20 m from the measured one. Further outlined are the influence of orbital errors on the sea surface, and numerical examples are given based upon real tracking data. Orbital height error estimates were computed for geodetic type satellites and are found to be in the order of 0.2 to 5 meters.

Vonbun, F. O.↗
NASA NTRS1979

Effect of a Pacific sea-surface temperature anomaly on the circulation over North America: A numerical experiment with the GLAS model

A numerical experiment was carried out to determine the effect of sea surface temperature anomalies over the Pacific on the circulation over North America. The sea surface temperature (SST) anomaly pattern chosen for this study was similar to the one observed during January 1977. It is shown that a cold sea surface temperature anomaly over the Pacific produces a strong southward flow over the United States and colder temperature in eastern Canada and the United States, as it was observed during the 1977 winter. The results indicate that the SST anomaly over the Pacific can produce a significant downstream effect over the continental United States.

Shukla, J.↗
NASA NTRS1994

Evaporation and solar irradiance as regulators of sea surface temperature in annual and interannual changes

Seven years of net surface solar irradiance (S) derived from cloud information provided by the International Satellite Cloud Climatology Project (ISCCP) and 4 years of surface latent heat flux (E) derived from the observations of the special sensor microwave imager (SSM/I) were used to examine the relation between surface heat fluxes and sea surface temperature (T(sub s)) in their global geographical distribution, seasonal cycle, and interannual variation. The relations of seasonal changes imply that evaporation cooling is significant over most of the ocean and that solar heating is the main drive for the change of T(sub s) away from the equatorial wave guide where ocean dynamics may be more important. However, T(sub s) is not the most direct and significant factor in the seasonal changes of S and E over most of the ocean; the solar incident angle may be more important to S, and wind speed and air humidity are found to correlate better with E. Significant local correlations between anomalies of T(sub s) and S and between anomalies of T(sub s) and E are found in the central equatorial Pacific; both types of correlation are negative. The influence of ocean dynamics in changing T(sub s) in the tropical ocean cannot be ignored.

Liu, W. Timothy↗
NASA NTRS2018

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO S2S 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 (National 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) V4 (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 the satellite at approximately 1 centimeter) and in situ measurements (typically measured by Argo floats at 3 meters). 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 the GMAO S2S reanalyses that assimilates/withholds along-track (L2) SSS. In particular, we contrast forecasts of the overestimated 2014 El Nino, the big 2015 El Nino, and the minor 2016 La Nina. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrades the mixed layer depth leading to more accurate coupled air/sea interaction.

Hackert, E.↗
NASA NTRSDate not supplied

Assessment of the Sentinel-6Michael Frielich extension to the TOPEX/Jason Sea Surface Height Climate Data Record referenced to ITRF2020

The recent launch of Sentinel-6 Michael Freilichoffers the possibility of continuing global mean sea level (GMSL) monitoring well into the currentdecade. In this presentation we report Sentinel-6/Jason-3inter-mission bias estimates based on direct sea surface height (SSH) collinear differences of near coincident measurements during the verification phase, and with comparisons to a global network of tide gauges. In an effort to provide a more consistent SSH time series and seamlesstransition to Sentinel-6, we have generated orbits for the entire time span based on the revised ITRF2020terrestrial reference frame.We reportthe efficacy ofcorrection algorithm revisions leading to the development of the NASA MEaSURE’s V6.0 sea surface height Climate Data Record(MEaSUREs -Integrated Multi-Mission Ocean Altimeter Data for Climate Research (MEaSUREs-SSH) | PO.DAAC / JPL / NASA).We provide an assessment of recent improvements to the accuracy of the 30+ year sea surface height time series, describe continuing calibration/validation activities, and evaluate the subsequent impact on current global mean sea level estimates, and the accounting of the ocean mass budget.

B D Beckley↗
NASA NTRS2019

Sea Surface Salinity Retrievals from Aquarius Using Neural Networks

Even though the Sea Surface Salinity (SSS) retrieved from Aquarius are generally very close to in-situ measurements, the level of similarity varies with the region and with the circumstances of the observations (wind speed, sea surface temperature, etc.). SSS is currently retrieved from the brightness temperatures measured by Aquarius and applying the current theoretical model for the propagation and emission of the natural thermal radiation. In this contribution we consider an alternative retrieval approach based on a Neural Network (NN) with the goal of improving the subsets of Aquarius SSS data that are in poorer agreement within-situ measurements. The subset considered here are the SSS retrieved at latitudes higher than 30 ̊. The output of the NN approach are compared against in-situ measurements using four statistical metrics (correlation coefficient, bias, RMSD and 5% trimmed range). The output of the NN and the nominal Aquarius SSS are compared against SSS values from in-situ measurements and from ocean models. From these comparisons it appears that the output of the NN matches the in-situ measurements better than the nominal Aquarius SSS.

Soldo, Yan↗