Sea Surface Salinity Retrieval Research Articles

The use of a semi-empirical emissivity model for a rough estimation of sea surface salinity from an airborne microwave radiometer

In preparation for the European Space Agency SMOS (Soil Moisture and Ocean Salinity) satellite mission, radiometric and oceanographic measurements were performed in December 2000 and January 2001 and in November 2001 from a fixed platform in the NW Mediterranean to improve the modelling of the sea surface emissivity at L-band and new semi-empirical models were derived. Now one of these models has been used to retrieve sea surface salinity from L-band radiometric data acquired with a different instrument and different location. These data were acquired in August 2003 over the continental shelf within the influence of the Rio de la Plata, from Argentina to Brazil, southern Atlantic ocean with the STARRS airborne radiometer. Results show that the radiometer is capable of realistically detecting natural variations in surface salinity even though the model was derived in very different oceanographic conditions and from data collected by a different instrument.

Overview of the SMOS Sea Surface Salinity Prototype Processor

The L-band interferometric radiometer onboard the Soil Moisture and Ocean Salinity mission will measure polarized brightness temperatures (Tb). The measurements are affected by strong radiometric noise. However, during a satellite overpass, numerous measurements are acquired at various incidence angles at the same location on the Earth's surface. The sea surface salinity (SSS) retrieval algorithm implemented in the Level 2 Salinity Prototype Processor (L2SPP) is based on an iterative inversion method that minimizes the differences between Tb measured at different incidence angles and Tb simulated by a full forward model. The iterative method is initialized with a first-guess surface salinity that is iteratively modified until an optimal fit between the forward model and the measurements is obtained. The forward model takes into account atmospheric emission and absorption, ionospheric effects (Faraday rotation), scattering of celestial radiation by the rough ocean surface, and rough sea surface emission as approximated by one of three models. Potential degradation of the retrieval results is indicated through a flagging strategy. We present results of tests of the L2SPP involving horizontally uniform scenes with no disturbing factors (such as sun glint or land proximity) other than wind-induced surface roughness. Regardless of the roughness model used, the error on the retrieved SSS depends on the location within the swath and ranges from 0.5 psu at the center of the swath to 1.7 psu at the edge, at 35 psu and 15degC. Dual-polarization (DP) mode provides a better correction for wind-speed (WS) biases than pseudofirst Stokes mode (ST1). For a WS bias of -1 mmiddots <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , the corresponding SSS bias at the center of the swath is equal to -0.3 psu in DP mode and to -0.5 psu in ST1 mode. The inversion methodology implicitly assumes that WS errors follow a Gaussian distribution, even though these errors should follow more closely a Rayleigh distribution. For this reason, the use of wind components, which typically exhibit Gaussian error distributions, may be preferred in the retrieval. However, the use of noisy wind components creates WS and SSS biases at low WSs (0.1 psu at 3 mmiddots <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ). At a sea surface temperature (SST) of 15degC, the retrieved SSS is weakly sensitive to the SST biases, with the SSS bias always lower than 0.3 psu for SST biases ranging from -0.5degC to -2degC. In DP mode, biases in the vertical total electron content (TEC) of the atmosphere result in SSS biases smaller than 0.2 psu. The pseudofirst Stokes mode is insensitive to TEC. Failure to fully account for sea surface roughness scattering effects in the computation of sky radiation contribution leads to a maximum SSS bias of 0.2 psu in the selected configuration, i.e., a descending orbit over the Northern Pacific in February. To achieve SSS biases that are smaller than 0.2 psu, special care must be taken to correct for biases at low WS and to ensure that the bias on the mean WS (averaged over 200 km times 200 km and ten days) remains smaller than 0.5 mmiddots <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> .

Improved Image Reconstruction Algorithms for Aperture Synthesis Radiometers

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission aims at producing global and frequent maps of SMOS and will be launched in 2008. SMOS' single payload is a new type of radiometer called Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) operating at L-band in which brightness temperature images are formed by a Fourier synthesis technique. However, in the alias-free field of view where the brightness temperature images are reconstructed, a bias is present which has been found to be higher for high-contrast brightness temperature scenes (coastlines) and lower for homogeneous scenes (all oceans or lands). This scene-dependent bias will ultimately limit the achievable accuracy of the retrieved geophysical parameters, and it is particularly critical for the retrieval of sea surface salinity. This paper presents a general analysis of the origin of this bias, which is found to be actually due to the different measurement errors in the instrument observables (visibility samples). An improvement of the image reconstruction algorithm is then presented to mitigate it. As compared with the previous algorithm versions, the proposed improved reconstruction algorithm further decomposes the visibility samples into some new terms: ocean and land/iced sea, instead of just the Earth's disk over the sky background. This decomposition aims at further reducing the contrast (high-frequency components) in the differential image and, therefore, minimizes the impact of multiplicative errors, improving the radiometric accuracy. In addition, this approach proves to perform the image reconstruction in part of the alias regions and improves the quality of the reconstruction close to the coastlines.

Issues About Retrieving Sea Surface Salinity in Coastal Areas From SMOS Data

This paper aims at studying the quality of the sea surface salinity (SSS) retrieved from soil moisture and ocean salinity (SMOS) data in coastal areas. These areas are characterized by strong and variable SSS gradients [several practical salinity units (psu)] on relatively small scales: the extent of river plumes is highly variable, typically at kilometric and daily scales. Monitoring this variability from SMOS measurements is particularly challenging because of their resolution (typically 30-100 km) and because of the contamination by the nearby land. A set of academic tests was conducted with a linear coastline and constant geophysical parameters, and more realistic tests were conducted over the Bay of Biscay. The bias of the retrieved SSS has been analyzed, as well as the root mean square (rms) of the bias, and the retrieved SSS compared to a numerical hydrodynamic model in the semirealistic case. The academic study showed that the Blackman apodization window provides the best compromise in terms of magnitude and fluctuations of the bias of the retrieved SSS. Whatever the type of vegetation cover, a strong negative bias, greater than 1 psu, was found when nearer than 36 km from the coast. Between 44 and 80 km, the type of vegetation cover has an impact of less than a factor 2 on the bias, and no influence further than 80 km from the coast. The semirealistic study conducted in the Bay of Biscay showed a bias over ten days lower than 0.2 psu for distances greater than 47 km, due to an averaging over various geometries (coastline orientation, swath orientation, etc.). The bias showed a weak dependence on the location of the grid point within the swath. Despite the noise on the retrieved SSS, contrasts due to the plume of the Loire River and the Gironde estuary remained detectable on ten-day averaged maps with an rms of 0.57 psu. Finally, imposing thresholds on the major axis of the measurements brought little improvement to the bias, whereas it increased the rms and could lead to strong swath restriction: a 49-km threshold on the major axis resulted in an effective swath of 800-900 km instead of 1200 km.

The Role of Averaging for Improving Sea Surface Salinity Retrieval from the Soil Moisture and Ocean Salinity (SMOS) Satellite and Impact of Auxiliary Data

Abstract Soil Moisture and Ocean Salinity (SMOS) was chosen as the European Space Agency’s second Earth Explorer Opportunity mission. One of the objectives is to retrieve sea surface salinity (SSS) from measured brightness temperatures (TBs) at L band with a precision of 0.2 practical salinity units (psu) with averages taken over 200 km by 200 km areas and 10 days [as suggested in the requirements of the Global Ocean Data Assimilation Experiment (GODAE)]. The retrieval is performed here by an inverse model and additional information of auxiliary SSS, sea surface temperature (SST), and wind speed (W). A sensitivity study is done to observe the influence of the TBs and auxiliary data on the SSS retrieval. The key role of TB and W accuracy on SSS retrieval is verified. Retrieval is then done over the Atlantic for two cases. In case A, auxiliary data are simulated from two model outputs by adding white noise. The more realistic case B uses independent databases for reference and auxiliary ocean parameters. For these cases, the RMS error of retrieved SSS on pixel scale is around 1 psu (1.2 for case B). Averaging over GODAE scales reduces the SSS error by a factor of 12 (4 for case B). The weaker error reduction in case B is most likely due to the correlation of errors in auxiliary data. This study shows that SSS retrieval will be very sensitive to errors on auxiliary data. Specific efforts should be devoted to improving the quality of auxiliary data.

A new empirical model for retrieving sea surface salinity in L band

Sea surface salinity is a key physical parameter in ocean science. It is important in the ocean remote sensing to retrieve sea surface salinity by the microwave probe technology. Based on the in situ measurement data and remote sensing data of the Yellow Sea, we have built a new empirical model in this paper, which can be used to retrieve sea surface salinity of the Yellow Sea by means of the brightness temperature of the sea water at L-band. In this model, the influence of the roughness of the sea surface is considered, and the retrieved result is in good agreement with the in situ measurement data, where the mean absolute error of the retrieved sea surface salinity is about 0.288 psu. This result shows that our model has greater retrieval precision compared with similar models.

A new algorithm for microwave radiometer remote sensing of sea surface salinity and temperature

The microwave radiation of the sea surface, which is denoted by the sea surface brightness temperature, is not only related with sea surface salinity (SSS) and temperature (SST), but also influenced by sea surface wind. The errors of wind detected by satellite sensor have significant influences on the accuracy of SSS and SST retrieval. The effects of sea surface wind on sea surface brightness temperature, i.e. Δ T h,v , and the relations among Δ T h,v , wind speed, sea surface temperature, sea surface salinity and incidence angle of observation are investigated. Based on the investigations, a new algorithm depending on the design of a single radiometer with double polarizations and multi-incidence angles is proposed. The algorithm excludes the influence of sea surface wind on SSS and SST retrieval, and provides a new method for remote sensing of SSS and SST.

Impact on Sea Surface Salinity Retrieval of Different Auxiliary Data Within the SMOS Mission

Aiming to provide sea surface salinity (SSS) maps with a spatiotemporal averaged accuracy of 0.1 psu (practical salinity units), the Soil Moisture and Ocean Salinity (SMOS) community is increasingly focusing on the determination of a robust inversion scheme to enable SSS retrieval from L-band brightness temperature data. In the framework of the Synergetic Aspects and Auxiliary Data Concepts for Sea Surface Salinity Measurements from Space project, efforts have been oriented toward a quantitative analysis of SSS retrieval using different auxiliary data sets. This paper aims to contribute to the assessment of the SMOS salinity retrieval error budget in view of the upcoming SMOS mission ground segment development. Aiming to do that, different models and auxiliary data to simulate and invert the brightness temperature data have been used. An estimation of the different auxiliary parameters' influence has been performed to quantitatively predict at what extent it is reasonable to expect to retrieve salinity once the brightness temperatures are directly measured by the sensor. Statistical distributions of the spatiotemporal averaged errors are provided

Removing the impact of wind direction on remote sensing of sea surface salinity

Using the small-slope approximation model of microwave emission of rough sea surface, the impacts of sea surface wind on brightness temperature variations generated by the surface roughness, i.e. ΔTh,v, are investigated. Here ΔT denotes the brightness temperature variation, and “h” and “v” denote the horizontal and vertical polarizations respectively. ΔTh,v has a linear relation with wind speed, sea surface temperature (SST) and sea surface salinity (SSS) respectively. Further more, the impact of wind direction on SSS retrieval, under small incidence angles, can be removed by calculating (ΔTh+ΔTv). These characteristics provide simple new ways to develop an SSS retrieval algorithm without wind direction factor.

Performance of sea surface salinity and soil moisture retrieval algorithms with different auxiliary datasets in 2-D L-band aperture synthesis interferometric radiometers

The Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Opportunity Mission was selected in May 1999 by the European Space Agency Earth Observation Programme Board to provide global and frequent soil moisture (SM) and sea surface salinity (SSS) maps. SMOS' single payload is the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) sensor, an L-band two-dimensional aperture synthesis interferometric radiometer with multiangular and polarimetric imaging capabilities. The definition of the SMOS Level 2 Processor requires the selection of the optimum operation mode (dual-polarization or full-polarimetric) for each application, the specification of the required auxiliary data, and the optimum retrieval algorithms. Using the SMOS simulator and based on the experience gained in previous works, this paper presents a study of the SM and SSS retrieval capabilities over homogeneous pixels, in the two modes of operation with different auxiliary data. It is found that SSS retrievals using the first Stokes parameter measured in the dual-polarization mode perform somewhat worse than using the vertical (T/sub vv/) and horizontal (T/sub hh/) brightness temperatures measured in the full-polarimetric mode, and the performance degrades for cold waters due to the lower sensitivity of the brightness temperature to SSS at low sea surface temperature (SST). Due to the larger angular variation of T/sub hh/ and T/sub vv/, SM retrievals using T/sub hh/ and T/sub vv/ measured in the full-polarimetric mode exhibit a significant better performance over bare soils than over vegetation-covered soils. Over vegetation-covered soils vegetation parameters (opacity and albedo) can be inferred over a 550-km swath width in the full-polarimetric mode. However, since the first Stokes parameter is independent of both geometric and Faraday rotations, it is very robust in the presence of instrumental and geophysical errors. In the SSS retrieval problem and in the SM retrieval problem (with T/sub hh/ and T/sub vv/ measured in the full-polarimetric mode), the performance of the retrieval algorithms tested is not significantly altered if the model parameters are not exactly known, but are left as adjustable parameters in the optimization process.

Retrieving sea surface salinity with multiangular L‐band brightness temperatures: Improvement by spatiotemporal averaging

The Soil Moisture and Ocean Salinity (SMOS) mission was selected in May 1999 by the European Space Agency to provide global and frequent soil moisture and sea surface salinity maps. SMOS' single payload is Microwave Imaging Radiometer by Aperture Synthesis (MIRAS), an L band two‐dimensional aperture synthesis interferometric radiometer with multiangular observation capabilities. Most geophysical parameter retrieval errors studies have assumed the independence of measurements both in time and space so that the standard deviation of the retrieval errors decreases with the inverse of square root of the number of measurements being averaged. This assumption is especially critical in the case of sea surface salinity (SSS), where spatiotemporal averaging is required to achieve the ultimate goal of 0.1 psu error. This work presents a detailed study of the SSS error reduction by spatiotemporal averaging, using the SMOS end‐to‐end performance simulator (SEPS), including thermal noise, all instrumental error sources, current error correction and image reconstruction algorithms, and correction of atmospheric and sky noises. The most important error sources are the biases that appear in the brightness temperature images. Three different sources of biases have been identified: errors in the noise injection radiometers, Sun contributions to the antenna temperature, and imaging under aliasing conditions. A calibration technique has been devised to correct these biases prior to the SSS retrieval at each satellite overpass. Simulation results show a retrieved salinity error of 0.2 psu in warm open ocean, and up to 0.7 psu at high latitudes and near the coast, where the external calibration method presents more difficulties.

Surface Salinity Retrieved from SMOS Measurements over the Global Ocean: Imprecisions Due to Sea Surface Roughness and Temperature Uncertainties

The Soil Moisture and Ocean Salinity (SMOS) mission recently led by the European Space Agency (ESA) intends to monitor soil moisture and sea surface salinity (SSS). Since the sensitivity of radiometric L-band signal to SSS is weak, measuring SSS with an acceptable accuracy is challenging: it requires both a very stable instrument and very precise corrections of other geophysical signals than the SSS affecting the L-band signal. Concentration is on the sea surface roughness and temperature (SST) effects and the extent to which they need to be corrected to optimize both SSS precision and retrieval complexity. In addition to uncertainties regarding SST and wind speed (W), realistic noise on the SMOS brightness temperatures (Tb's) are considered and possible consequences of Tb biases are examined. In most oceanic regions, random noise in W, SST, and Tb should not hamper the SMOS SSS retrieval within the Global Ocean Data Assimilation Experiment (GODAE) requirements (a precision better than 0.1 pss over 200 km 3 200 km and 10 days). However, minimizing systematic bias errors over the time scale at which the SSS products will be averaged is critical: the GODAE requirement will not be met if Tb's or W is biased in warm waters (258C) by 0.07 K and 0.3 m s 21 , respectively, and in cold waters (58C) by 0.03 K and 0.15 m s 21 , respectively, or if no a priori information on W is available. In order to minimize errors coming from the W natural variability, it is essential to use high-temporal-resolution wind data. The use of the first Stokes parameter instead of bipolarized Tb degrades the SSS precision by less than 10% in most regions, showing that Faraday rotation should not hamper SMOS SSS retrieval.

The WISE 2000 and 2001 field experiments in support of the SMOS mission: sea surface L-band brightness temperature observations and their application to sea surface salinity retrieval

Soil Moisture and Ocean Salinity (SMOS) is an Earth Explorer Opportunity Mission from the European Space Agency with a launch date in 2007. Its goal is to produce global maps of soil moisture and ocean salinity variables for climatic studies using a new dual-polarization L-band (1400-1427 MHz) radiometer Microwave Imaging Radiometer by Aperture Synthesis (MIRAS). SMOS will have multiangular observation capability and can be optionally operated in full-polarimetric mode. At this frequency the sensitivity of the brightness temperature (T/sub B/) to the sea surface salinity (SSS) is low: 0.5 K/psu for a sea surface temperature (SST) of 20/spl deg/C, decreasing to 0.25 K/psu for a SST of 0/spl deg/C. Since other variables than SSS influence the T/sub B/ signal (sea surface temperature, surface roughness and foam), the accuracy of the SSS measurement will degrade unless these effects are properly accounted for. The main objective of the ESA-sponsored Wind and Salinity Experiment (WISE) field experiments has been the improvement of our understanding of the sea state effects on T/sub B/ at different incidence angles and polarizations. This understanding will help to develop and improve sea surface emissivity models to be used in the SMOS SSS retrieval algorithms. This paper summarizes the main results of the WISE field experiments on sea surface emissivity at L-band and its application to a performance study of multiangular sea surface salinity retrieval algorithms. The processing of the data reveals a sensitivity of T/sub B/ to wind speed extrapolated at nadir of /spl sim/0.23-0.25 K/(m/s), increasing at horizontal (H) polarization up to /spl sim/0.5 K/(m/s), and decreasing at vertical (V) polarization down to /spl sim/-0.2 K/(m/s) at 65/spl deg/ incidence angle. The sensitivity of T/sub B/ to significant wave height extrapolated to nadir is /spl sim/1 K/m, increasing at H-polarization up to /spl sim/1.5 K/m, and decreasing at V-polarization down to -0.5 K/m at 65/spl deg/. A modulation of the instantaneous brightness temperature T/sub B/(t) is found to be correlated with the measured sea surface slope spectra. Peaks in T/sub B/(t) are due to foam, which has allowed estimates of the foam brightness temperature and, taking into account the fractional foam coverage, the foam impact on the sea surface brightness temperature. It is suspected that a small azimuthal modulation /spl sim/0.2-0.3 K exists for low to moderate wind speeds. However, much larger values (4-5 K peak-to-peak) were registered during a strong storm, which could be due to increased foam. These sensitivities are satisfactorily compared to numerical models, and multiangular T/sub B/ data have been successfully used to retrieve sea surface salinity.

Determination of sea surface salinity and wind speed by L-band microwave radiometry from a fixed platform

In May 1999, the European Space Agency (ESA) selected SMOS (Soil Moisture and Ocean Salinity) as an Earth Explorer Opportunity mission. One of its goals is the generation of global sea surface salinity (SSS) maps. The satellite sensor is an L-band interferometric radiometer with full-polarimetric capability called MIRAS. The retrieval of SSS from microwave measurements is based on the fact that the brightness temperature (TB ) of seawater is a function of the dielectric constant, temperature and sea surface state (roughness, foam…). The sensitivity of TB to SSS is maximum at L-band, but it is necessary to quantify the other effects to have reliable SSS retrieval. In order to improve the present understanding of these effects on TB , ESA sponsored the Wind and Salinity Experiment (WISE) 2000 and 2001 field campaigns. These experimental results are of great importance for the development of sea surface emissivity models that will be used in the future SMOS SSS retrieval algorithms. This paper presents the influence of the emissivity models on the derived SSS from the data obtained in both campaigns. It also presents the impact on the retrieved SSS of using in situ measured or satellite derived wind information, or even simultaneously estimating the wind speed from the measured multi-angular TB .

A new empirical model of sea surface microwave emissivity for salinity remote sensing

SMOS (Soil Moisture and Ocean Salinity) is a European Space Agency mission that aims at generating global ocean salinity maps with an accuracy of 0.1 psu, at spatial and temporal resolution suitable for climatic studies. The satellite sensor is an L‐band (1400–1427 MHz) aperture synthesis interferometric radiometer. Sea surface salinity (SSS) can be retrieved since the brightness temperature of sea water is dependent on the frequency, angle of observation, dielectric constant of sea water, sea surface temperature and sea surface state. This paper presents a new empirical sea water emissivity model at L‐band in which surface roughness effects are parameterized in terms of wind speed and significant wave height. For the SMOS mission these parameters can be obtained from external measurements and model diagnostics. An analysis has been done on the effect on SSS retrieval of different sources for this auxiliary information.

L-band sea surface emissivity: Preliminary results of the WISE-2000 campaign and its application to salinity retrieval in the SMOS mission

[1] Soil moisture and ocean salinity at surface level can be measured by passive microwave remote sensing at L-band. To provide global coverage data of soil moisture and ocean salinity with three-day revisit time, the Earth Explorer Opportunity Mission SMOS (Soil Moisture and Ocean Salinity) was selected by ESA (European Space Agency) in May 1999. SMOS' single payload is a Y-shaped 2-D aperture synthesis interferometric radiometer called MIRAS (Microwave Imaging Radiometer by Aperture Synthesis). SMOS presents some particular imaging peculiarities: variation of incidence and azimuth angles, different radiometric sensitivity and accuracy at each direction (pixels), and geometric polarization mixing. Therefore, the accuracy of the geophysical parameter retrieval depends on the knowledge of the angular dependence of the emissivity over a wide range of incidence and azimuth angles. The accuracy of the sea surface salinity retrievals depends on our capability to correct the wind-induced variation of the brightness temperatures. To better understand wind effects, ESA sponsored the WInd and Salinity Experiment 2000 (WISE-2000) from November 15, 2000, to January 16, 2001, in the Casablanca oil rig, at 40 km off the coast of Tarragona (Spain). This paper is divided into two parts. First, it presents the derived sensitivities of the brightness temperatures at vertical and horizontal polarizations with wind speed, and compares to Hollinger's measurements and numerical simulations. Second, these results are applied to the SMOS sea surface salinity (SSS) retrieval problem for different tracks within the swath. It is shown that, except for low SSS and sea surface temperature (SST), the retrieved SSS has a RMS error of approximately 1 psu in one satellite pass.

Issues concerning the sea emissivity modeling at L band for retrieving surface salinity

In order to prepare the sea surface salinity (SSS) retrieval in the frame of the Soil Moisture and Ocean Salinity (SMOS) mission we conduct sensitivity studies to quantify uncertainties on simulated brightness temperatures (Tb) related to uncertainties on sea surface and scattering modeling. Using a two‐scale sea surface emissivity model to simulate Tb at L band (1.4 GHz), we explore the influence on estimated SSS of the parameterization of the seawater permittivity, of the sea wave spectrum, of the choice of the two‐scale cutoff wavelength, and of adding swell to the wind sea. Differences between Tb estimated with various existing permittivity models are up to 1.5 K. Therefore a better knowledge of the seawater permittivity at L band is required. The influence of wind speed on Tb simulated with various parameterizations of the sea wave spectrum differs by up to a factor of two; for a wind speed of 7 m s−1 the differences on estimated SSS is several psu depending on the sea wave spectral model taken, so that sea spectrum is a major source of uncertainty in models. We find no noticeable effect on simulated Tb when changing the two‐scale cutoff wavelength and when adding swell to the wind sea for low to moderate incidence angles. The dependence of the wind‐induced Tb on SST and SSS being weak, we assess the error in SSS estimated assuming that the wind speed influence is independent of SST and SSS. We find errors on estimated SSS up to 0.5 psu for 20°C variation in SST. Therefore this assumption would induce regional biases when applied to global measurements.

Estimation of sea surface salinity in the Bay of Bengal using Outgoing Longwave Radiation

A new technique for retrieval of sea surface salinity (SSS) from space‐borne satellite measurements of Outgoing Longwave Radiation (OLR) through the ‘Effective Oceanic Layer (EOL)’ is explained in this paper. The OLR is used to study the convection over the land and ocean. This study is based on the idea that intense convection over the ocean is associated with warmer surface temperatures maintained by low surface salinities formed due to large riverine input and compounded by convection‐induced precipitation. This study augments the in situ SSS measurements. A preliminary assessment of deriving SSS from OLR is presented for the Bay of Bengal. It is envisaged that the SSS estimated in this way may be useful in improving the existing climatologies at least for those parts of the world's ocean where intense convection is the regular feature, such as the tropical Indian and Pacific Oceans.

Evaluation of IRI-95 to correct errors caused by faraday rotation in passive microwave remote sensing from space

Abstract Passive remote sensing from space at the long wavelength end of the microwave spectrum has the advantages of penetrating the atmosphere and responding to parameters of the ocean and earth's surface important for understanding earth's environment. Examples are the measurements of soil moisture and sea surface salinity. These measurements can be made at L-band (1.413 GHz) and are needed to understand energy exchange with the atmosphere at the surface. Sensor systems are under serious consideration to make these measurements. However, at L-band Faraday rotation in the ionosphere can be sufficient to cause errors in the retrieval of the surface parameters. This is especially a problem for the retrieval of sea surface salinity in the open ocean because the dynamic range of the measurement is relatively small. An important question in the design of such sensor systems is whether models can be used to correct Faraday rotation. As a first step toward addressing that question, IRI-95 is used to assess the order of magnitude of the Faraday rotation and the error it causes at L-band in the observed brightness temperature. Errors on the order of several Kelvin are possible. In addition, a limited evaluation of the model is made by comparing the effects of Faraday rotation using prediction and observed vertical total electron content.