Thomas Meissner

The complex dielectric constant of pure and sea water from microwave satellite observations

We provide a new fit for the microwave complex dielectric constant of water in the salinity range between 0-40 ppt using two Debye relaxation wavelengths. For pure water, the fit is based on laboratory measurements in the temperature range between -20/spl deg/C and +40/spl deg/C including supercooled water and for frequencies up to 500 GHz. For sea water, our fit is valid for temperatures between -2/spl deg/C and +29/spl deg/C and for frequencies up to at least 90 GHz. At low frequencies, our new model is a modified version of the Klein-Swift model. We compare the results of the new fit with various other models and provide a validation using an extensive analysis of brightness temperatures from the Special Sensor Microwave Imager.

The Emissivity of the Ocean Surface Between 6 and 90 GHz Over a Large Range of Wind Speeds and Earth Incidence Angles

We present a model function for the emissivity of the wind roughened ocean surface for microwave frequencies between 6 and 90 GHz. It is an update, refinement, and extension of model functions we had developed previously. The basis of our analysis are brightness temperature (TB) measurements from the spaceborne microwave radiometer WindSat and the Special Sensor Microwave/Imager, which are collocated with independent measurements of surface wind speeds and directions. This allows the determination of the emissivity model function for Earth incidence angles (EIA) around 55°. We demonstrate that an essential part in the model development is the absolute calibration of the radiometer measurements over the ocean to the computed TB of the radiative transfer model, one of whose components the emissivity model function is. We combine our results with other established measurements for lower EIA and finally obtain a model function which can be used over the whole EIA range between 0° and 65°. Results for both the isotropic, wind direction independent part as well as the four Stokes parameters of the wind direction signal are presented. Special emphasis is made on the behavior at high wind speeds between 20 and 40 m/s by conducting a comparison with data from the step frequency microwave radiometer.

A 10 year intercomparison between collocated Special Sensor Microwave Imager oceanic surface wind speed retrievals and global analyses

To evaluate the scalar ocean surface wind speeds obtained from the Special Sensor Microwave Imager (SSM/I), we compare them over the time period from July 1987 through December 1997 with those from two global analyses: the National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Annual Reanlysis and the European Center for Medium-Range Weather Forecasts (ECMWF)/Tropical Ocean-Global Atmosphere Global Surface Analysis. We perform a statistical analysis for the whole globe and present time series analyses for selected geographical regions in connection with collocated wind speed difference maps. In order to evaluate further geographical biases observed in the SSM/I versus analyses comparisons we use wind speeds from the NASA scatterometer (NSCAT) for the 10 month period from September 1996 through June 1997 as a third data source. The value of the standard deviation for all collocated SSM/I 2 ECMWF wind speed differences is 2.1 m s 21 and for all collocated SSM/I 2 NCEP/NCAR reanalyis wind speed differences is 2.4 m s 21 . When taking monthly or yearly averages in each pixel, which has the effect of cancelling out small timescale wind speed fluctuations, the values are between 0.8 and 1.2 m s 21 , respectively. Global biases range between 20.05 and 10.55 m s 21 for the various SSM/I satellites. Our analysis allows us to identify regional biases for both the SSM/I and analyses winds. The NCEP/NCAR reanalysis wind speeds appear underestimated in the tropical Pacific and tropical Atlantic. ECMWF wind speeds appear underestimated near the southern Pacific islands NE of Australia. The analyses wind speeds are higher than the SSM/I wind speeds near the Argentinean coast. The SSM/I wind speeds appear high in the extratropical central and eastern Pacific and low in certain coastal regions with eastern boundary currents and in the Arabian Sea. The size of some of these biases are seasonally dependent.

An updated analysis of the ocean surface wind direction signal in passive microwave brightness temperatures

We analyze the wind direction signal for vertically (v) and horizontally (h) polarized microwave radiation at 37 GHz, 19 GHz, and 11 GHz; and an Earth incidence angle of 53/spl deg/. We use brightness temperatures from SSM/I and TMI and wind vectors from buoys and the QUIKSCAT scatterometer. The wind vectors are space and time collocated with the radiometer measurements. Water vapor, cloud water and sea surface temperature are obtained from independent measurements and are uncorrelated with the wind direction. We find a wind direction signal that is noticeably smaller at low and moderate wind speeds than a previous analysis had indicated. We attribute the discrepancy to errors in the atmospheric parameters that were present in the data set of the earlier study. We show that the polarization combination 2v-h is almost insensitive to atmospheric changes and agrees with the earlier results. The strength of our new signals agrees well with JPL aircraft radiometer measurements. It is significantly smaller than the prediction of the two-scale sea surface emission model for low and intermediate wind speeds.

Accuracy of Satellite Sea Surface Temperatures at 7 and 11 GHz

Satellite microwave radiometers capable of accurately retrieving sea surface temperature (SST) have provided great advances in oceanographic research. A number of future satellite missions are planned to carry microwave radiometers of various designs and orbits. While it is well known that the 11 GHz SST retrievals are less accurate than the 7 GHz retrievals, particularly in colder waters, it has not been demonstrated using existing microwave data. The Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) provides the means to examine the accuracies of SST retrievals using these channels in a systematic manner. In this paper, the accuracies of SSTs at 7 and 11 GHz are determined using two approaches: modeled and empirical. The modeled accuracies are calculated using an emissivity model and climatology SSTs, while empirical accuracies are estimated through validation of AMSR-E 7 and 11 GHz SST retrievals using over six years of data. It was found that the 7 GHz SST retrievals have less errors due to radiometer noise and geophysical errors than the 11 GHz retrievals at all latitudes. Additionally, while averaging the 11 GHz retrievals will diminish error due to uncorrelated radiometer noise, the geophysical error is still higher than for the 7 GHz retrievals, particularly at the higher latitudes.

Wind-Vector Retrievals Under Rain With Passive Satellite Microwave Radiometers

We have developed algorithms that retrieve ocean-surface wind speed and direction under rain using brightness-temperature (TB) measurements from passive satellite microwave radiometers. For accurate radiometer retrievals of wind speeds in the rain, it is essential to use TB signals at different frequencies, whose spectral signature makes it possible to find channel combinations that are sufficiently sensitive to wind speed but little or not sensitive to rain. The wind-speed retrieval accuracy of an algorithm that utilizes C-band frequencies and is trained for tropical cyclones ranges from 2.0 m/s in light rain to 4.0 m/s in heavy rain. We have also trained and tested global algorithms that are less accurate in tropical storms but can be applied under all conditions. The wind-direction retrieval accuracy degrades from about 10deg in light rain to 30deg at the onset of heavy rain. We compare the performance of wind-vector retrievals under rain from microwave radiometers with those from scatterometers and discuss advantages and shortcomings of both instruments. We have also analyzed the wind-induced sea-surface emissivity, including its wind-direction dependence for wind speeds up to 45 m/s.

Satellite and In Situ Salinity: Understanding Near-Surface Stratification and Subfootprint Variability

Remote sensing of salinity using satellite-mounted microwave radiometers provides new perspectives for studying ocean dynamics and the global hydrological cycle. Calibration and validation of these measurements is challenging because satellite and in situ methods measure salinity differently. Microwave radiometers measure the salinity in the top few centimeters of the ocean, whereas most in situ observations are reported below a depth of a few meters. Additionally, satellites measure salinity as a spatial average over an area of about 100 × 100 km 2 . In contrast, in situ sensors provide pointwise measurements at the location of the sensor. Thus, the presence of vertical gradients in, and horizontal variability of, sea surface salinity complicates comparison of satellite and in situ measurements. This paper synthesizes present knowledge of the magnitude and the processes that contribute to the formation and evolution of vertical and horizontal variability in near-surface salinity. Rainfall, freshwater plumes, and evaporation can generate vertical gradients of salinity, and in some cases these gradients can be large enough to affect validation of satellite measurements. Similarly, mesoscale to submesoscale processes can lead to horizontal variability that can also affect comparisons of satellite data to in situ data. Comparisons between satellite and in situ salinity measurements must take into account both vertical stratification and horizontal variability.

The emission and scattering of L‐band microwave radiation from rough ocean surfaces and wind speed measurements from the Aquarius sensor

In order to achieve the required accuracy in sea surface salinity (SSS) measurements from L-band radiometers such as the Aquarius/SAC-D or SMOS (Soil Moisture and Ocean Salinity) mission, it is crucial to accurately correct the radiation that is emitted from the ocean surface for roughness effects. We derive a geophysical model function (GMF) for the emission and backscatter of L-band microwave radiation from rough ocean surfaces. The analysis is based on radiometer brightness temperature and scatterometer backscatter observations both taken on board Aquarius. The data are temporally and spatially collocated with wind speeds from WindSat and F17 SSMIS (Special Sensor Microwave Imager Sounder) and wind directions from NCEP (National Center for Environmental Prediction) GDAS (Global Data Assimilation System). This GMF is the basis for retrieval of ocean surface wind speed combining L-band H-pol radiometer and HH-pol scatterometer observations. The accuracy of theses combined passive/active L-band wind speeds matches those of many other satellite microwave sensors. The L-band GMF together with the combined passive/active L-band wind speeds is utilized in the Aquarius SSS retrieval algorithm for the surface roughness correction. We demonstrate that using these L-band wind speeds instead of NCEP wind speeds leads to a significant improvement in the SSS accuracy. Further improvements in the roughness correction algorithm can be obtained by adding VV-pol scatterometer measurements and wave height (WH) data into the GMF.

Geolocation and pointing accuracy analysis for the WindSat sensor

Geolocation and pointing accuracy analyses of the WindSat flight data are presented. The two topics were intertwined in the flight data analysis and will be addressed together. WindSat has no unusual geolocation requirements relative to other sensors, but its beam pointing knowledge accuracy is especially critical to support accurate polarimetric radiometry. Pointing accuracy was improved and verified using geolocation analysis in conjunction with scan bias analysis. Two methods were needed to properly identify and differentiate between data time tagging and pointing knowledge errors. Matchups comparing coastlines indicated in imagery data with their known geographic locations were used to identify geolocation errors. These coastline matchups showed possible pointing errors with ambiguities as to the true source of the errors. Scan bias analysis of U, the third Stokes parameter, and of vertical and horizontal polarizations provided measurement of pointing offsets resolving ambiguities in the coastline matchup analysis. Several geolocation and pointing bias sources were incrementally eliminated resulting in pointing knowledge and geolocation accuracy that met all design requirements.

Polarization rotation and the third Stokes parameter: the effects of spacecraft attitude and Faraday rotation

The third Stokes parameter of ocean surface brightness temperatures measured by the WindSat instrument is sensitive to the rotation angle between the polarization vectors at the ocean surface and the instrument. This rotation angle depends on the spacecraft attitude (roll, pitch, yaw) as well as the Faraday rotation of the electromagnetic radiation passing through the Earths ionosphere. Analyzing the WindSat antenna temperatures, we find biases in the third Stokes parameter as function of the along-scan position of up to 1.5 K in all feedhorns. This points to a misspecification of the reported spacecraft attitude. A single attitude correction of -0.16/spl deg/ roll and 0.18/spl deg/ pitch for the whole instrument eliminates all the biases. We also study the effect of Faraday rotation at 10.7 GHz on the accuracy of the third Stokes parameter and the sea surface wind direction retrieval and demonstrate how this error can be corrected using values from the International Reference Ionosphere for the total electron content when computing Faraday rotation.