Errors in radiometric remote sensing of sea-surface temperature and salinity
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Britt, C. L., Jr.↗
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The Global Ocean Observing System
A Global Ocean Observing System (GOOS) should be established now with international coordination (1) to address issues of global change, (2) to implement operational ENSO forecasts, (3) to provide the data required to apply global ocean circulation models, and (4) to extract the greatest value from the one billion dollar investment over the next ten years in ocean remote sensing by the world's space agencies. The objectives of GOOS will focus on climatic and oceanic predictions, on assessing coastal pollution, and in determining the sustainability of living marine resources and ecosystems. GOOS will be a complete system including satellite observations, in situ observations, numerical modeling of ocean processes, and data exchange and management. A series of practical and economic benefits will be derived from the information generated by GOOS. In addition to the marine science community, these benefits will be realized by the energy industries of the world, and by the world's fisheries. The basic oceanic variables that are required to meet the oceanic and predictability objectives of GOOS include wind velocity over the ocean, sea surface temperature and salinity, oceanic profiles of temperature and salinity, surface current, sea level, the extent and thickness of sea ice, the partial pressure of CO2 in surface waters, and the chlorophyll concentration of surface waters. Ocean circulation models and coupled ocean-atmosphere models can be used to evaluate observing system design, to assimilate diverse data sets from in situ and remotely sensed observations, and ultimately to predict future states of the system. The volume of ocean data will increase enormously over the next decade as new satellite systems are launched and as complementary in situ measuring systems are deployed. These data must be transmitted, quality controlled, exchanged, analyzed, and archived with the best state-of-the-art computational methods.
Kester, Dana↗
Assimilation of Satellite Sea Surface Temperature and Salinity Using the Local Ensemble Transform Kalman Filter Applied to the NASA GEOS Earth System Model
The NASA GEOS-S2S-2 system (Molod et al., 2020) uses the SST relaxation with the Level-4 SST products while the latest GEOS GEOS-S2S-3 system directly assimilates the Level-4 SST in the data assimilation step, yet no study has been conducted to understand the impact of this switch on the GEOS coupled analysis and short-range forecast. In addition, the GEOS-S2S-2/3 systems use the ocean model MOM5. Recently, the ocean model MOM6 is added to the GEOS ESM. This poster presents results of the first coupling of the ensemble data assimilation system to the GEOS MOM6.
Cheng Da↗
Freshening of the Labrador Sea Surface Waters in the 1990s: Another Great Salinity Anomaly
Both the observed and simulated time series of the Labrador Sea surface salinities show a major freshening event since the middles. It continues the series of decoder events of the 1970s and 1980s from which the freshening in the early 1970's was named as the Great Salinity Anomaly (GSA). These events are especially distinguishable in the late summer (August and September) time series. The observed data suggests that the 1990's freshening may equal the GSA in magnitude. This recent event is associated with a large reduction in the overturning rate between the early and latter part of the 1990s. Both the observations and model results indicate that the surface salinity conditions appear to be returning towards normal daring 1999 and 2000 in the coastal area, but offshore, the model predicts the freshening to linger on after peaking 1997.
Hakkinen, Sirpa↗
Seasat-A - A product of user interests
The paper describes how user requirements were integrated into the development of the Seasat-A program and shows how user requirements influenced sensor selection, mission planning, satellite systems design, and the end-to-end data system. User requirements included the measurement of ocean topography, surface winds, gravity waves, surface temperature, sea ice, salinity, and surface pressure. Particular attention is paid to the selection of sensors: compressed pulse radar altimeter, synthetic aperture imaging radar, microwave wind scatterometer, visible/infrared radiometer, and multispectral microwave radiometer.
Mccandless, S. W., Jr.↗
Assessing the Potential to Derive Air-Sea Freshwater Fluxes from Aquarius-Like Observations of Surface Salinity
A state-of-the-art numerical model is used to investigate the possibility of determining freshwater flux fields from temporal changes io sea-surface salinity (SSS), a goal of the satellite salinity-measuring mission, Aquarius/SAC-D. Because the estimated advective temporal scale is usually longer than the Aquarius/SAC-D revisit time, the possibility of producing freshwater flux estimates from temporal salinity changes is first examined by using a correlation analysis. For the mean seasonal cycle, the patterns of the correlations between the freshwater fluxes and surface salinity temporal tendencies are mainly zonally oriented, and are highest where the local precipitation is also relatively high. Nonseasonal (deviations from the monthly mean) correlations are highest along mid-latitude moon tracks and are relatively small in the tropics. The complex correlation patterns presented here suggest that a global retrieval of the difference between evaporation and precipitation (E-P) from salinity changes requires more complex techniques than a simple consideration of local balance with surface forcing.
Zhen, Li↗
Feasibility and Error Sources for Ocean Surface Salinity Measurements from Space
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observations radio frequencies↗
Aquarius/SAC-D Mission Overview
Aquarius/SAC-D is a cooperative international mission developed between the National Aeronautics and Space Administration (NASA) of United States of America (USA) and the Comision Nacional de Actividades Espaciales (CONAE) of Argentina. The overall mission objective is to contribute to the understanding of the total Earth system and the consequences of the natural and man-made changes in the environment of the planet. Major themes are: ocean surface salinity, water cycle, climate, natural hazards and cryosphere.
Aquarius↗
Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations
The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (<90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.
Machine Learning↗
The Dielectric Constant at P-Band for Salinity from 0 to 150 pss
Measurements have been made at P-band (0.707 GHz) to construct a model for the dielectric constant of sea water and extend the model for the dielectric constant to high salinity (50-150 pss). The measurements are part of research to develop a model for the dielectric constant suitable for future wide-bandwidth remote sensing of salinity and for application to water bodies such as the Great Salt Lake with salinity significantly above that found in the open ocean. Measurements have been made at temperatures from 2 to 30 °C and salinity from 0 to 138 pss. The data have been fit to a Debye model for the dielectric constant with a single resonance as has been employed at L-band (1.413 GHz) where remote sensing of salinity is currently done. Comparison with contemporary models developed from data at L-band indicates that the L-band model and new P-band model do well at both frequencies for salinity less than 50 pss but at higher values of salinity the L-band models diverge from the data. The data has also been used to test at high salinity the mathematical relationship between salinity and conductivity which is the basis for the practical salinity scale (pss).
Dielectric Constant↗
Precision ocean salinity measurements using the passive active L/S band aircraft instrument
Ocean salinity measurements using the Passive Active L/S-band instrument flying on the NCAR C-130 aircraft were made in July 2002 near Monterey, CA.
sea surface salinity microwave radiometer↗
Enhanced Resolution for Aquarius Salinity Retrieval near Land-Water Boundaries
A numerical reconstruction of the brightness temperature is examined as a potential way to improve the retrieval of salinity from Aquarius measurements closer to landwater boundaries. A test case using simulated ocean-land scenes suggest promise for the technique.
L-band↗
Applications of SMAP Data to Retrieval of Ocean Surface Wind and Salinity
We have examined the L-band radiometer and radar data from NASA’s Soil Moisture Active Passive (SMAP) mission for ocean research and applications. We find that the SMAP data are in excellent agreement with the geophysical model function (GMF) derived from the Aquarius data up to a wind speed of 20 ms-1. For severe wind conditions, the higher resolution data from SMAP allowed us to assess the sensitivity of L-band radiometer signals to hurricane force winds. We applied the L-band GMF to the retrieval of ocean surface wind and SSS from the SMAP data. Comparison with the European Center for Medium-Range Weather Forecasting, WindSat and RapidSCAT wind speeds suggests that SMAP’s radiometer wind speed reaches an excellent accuracy of about 1.1-1.7 ms-1 below a wind speed of 20 ms-1. We have also found that the maximum wind speed derived from the SMAP radiometer data can reach 140 knots for severe storms and are generally in good agreement with the hurricane track analysis and operational aircraft Stepped Frequency Microwave Radiometer wind speeds. The spatial patterns of the SMAP SSS agree well with climatological distributions, but exhibit several unique spatial and temporal features.
ocean surface wind↗
New developments in satellite oceanography and current measurements
Principal satellite remote sensing techniques and instruments are described and attention is given to the application of such techniques to ocean current measurement. The use of radiometers, satellite tracking drifters, and altimeters for current measurement is examined. Consideration is also given to other applications of satellite remote sensing in physical oceanography, including measurements of surface wind stress, sea state, tides, ice, sea surface temperature, salinity, ocean color, and oceanic leveling.
Huang, N. E.↗
Radiometer requirements for Earth-observation systems using large space antennas
Requirements are defined for Earth observation microwave radiometry for the decade of the 1990's by using large space antenna (LSA) systems with apertures in the range from 50 to 200 m. General Earth observation needs, specific measurement requirements, orbit mission guidelines and constraints, and general radiometer requirements are defined. General Earth observation needs are derived from NASA's basic space science program. Specific measurands include soil moisture, sea surface temperature, salinity, water roughness, ice boundaries, and water pollutants. Measurements are required with spatial resolution from 10 to 1 km and with temporal resolution from 3 days to 1 day. The primary orbit altitude and inclination ranges are 450 to 2200 km and 60 to 98 deg, respectively. Contiguous large scale coverage of several land and ocean areas over the globe dictates large (several hundred kilometers) swaths. Radiometer measurements are made in the bandwidth range from 1 to 37 GHz, preferably with dual polarization radiometers with a minimum of 90 percent beam efficiency. Reflector surface, root mean square deviation tolerances are in the wavelength range from 1/30 to 1/100.
Keafer, L. S., Jr.↗
Aquarius Reflector Surface Temperature Monitoring Test and Analysis
The presentation addresses how to infer the front side temperatures for the Aquarius L-band reflector based upon backside measurement sites. Slides discussing the mission objectives and design details are at the same level found on typical project outreach websites and in conference papers respectively. The test discussion provides modest detail of an ordinary thermal balance test using mockup hardware. The photographs show an off-Lab vacuum chamber facility with no compromising details.
Comision Nacional para el Uso Eficiente de la Ener↗
Passive microwave studies
The application of passive microwave techniques from airborne and surface platforms for oceanography measurements is discussed. The program is designed to determine: (1) the optimum system parameters, (2) measurement techniques, and (3) the physical phenomena to enable unambiguous all-weather determination of the sea surface properties of sea roughness, surface wind fields, temperature, salinity, sea ice, and pollution. The advantages and disadvantages of the airborne and fixed station operations are examined.
Hollinger, J. P.↗