A Roughness Correction for Aquarius Ocean Brightness Temperature Using the CONAE MicroWave Radiometer

Aquarius/SAC-D is a joint NASA/CONAE (Argentine Space Agency) Earth Sciences satellite mission to measure global sea surface salinity (SSS), using an L-band radiometer that measures ocean brightness temperature (Tb). The application of L-band radiometry to retrieve SSS is a difficult task, and therefore, precise Tb corrections are necessary to obtain accurate measurements. One of the major error sources is the effect of ocean roughness that warms the ocean Tb. The Aquarius (AQ) instrument (L-band radiometer/scatterometer) baseline approach uses the radar scatterometer to provide this ocean roughness correction, through the correlation of radar backscatter with the excess ocean emissivity. In contrast, this dissertation develops an ocean roughness correction for AQ measurements using the MicroWave Radiometer (MWR) instrument Tb measurements at Ka-band to remove the errors that are caused by ocean wind speed and direction. The new ocean emissivity radiative transfer model was tuned using one year (2012) of on-orbit combined data from the MWR and the AQ instruments that are collocated in space and time. The roughness correction in this paper is a theoretical Radiative Transfer Model (RTM) driven by numerical weather forecast model surface winds, combined with ancillary satellite data from WindSat and SSMIS, and environmental parameters from NCEP. This RTM provides an alternative approach for estimating the scatterometer-derived roughness correction, which is independent. The theoretical basis of the algorithm is described and results are compared with the AQ baseline scatterometer method. Also results are presented for a comparison of AQ SSS retrievals using both roughness corrections

Insights on the OAFlux ocean surface vector wind analysis merged from scatterometers and passive microwave radiometers (1987 onward)

Author Posting. © American Geophysical Union, 2014. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 119 (2014): 5244–5269, doi:10.1002/2013JC009648.A high-resolution global daily analysis of ocean surface vector winds (1987 onward) was developed by the Objectively Analyzed air-sea Fluxes (OAFlux) project. This study addressed the issues related to the development of the time series through objective synthesis of 12 satellite sensors (two scatterometers and 10 passive microwave radiometers) using a least-variance linear statistical estimation. The issues include the rationale that supports the multisensor synthesis, the methodology and strategy that were developed, the challenges that were encountered, and the comparison of the synthesized daily mean fields with reference to scatterometers and atmospheric reanalyses. The synthesis was established on the bases that the low and moderate winds (<15 m s−1) constitute 98% of global daily wind fields, and they are the range of winds that are retrieved with best quality and consistency by both scatterometers and radiometers. Yet, challenges are presented in situations of synoptic weather systems due mainly to three factors: (i) the lack of radiometer retrievals in rain conditions, (ii) the inability to fill in the data voids caused by eliminating rain-flagged QuikSCAT wind vector cells, and (iii) the persistent differences between QuikSCAT and ASCAT high winds. The study showed that the daily mean surface winds can be confidently constructed from merging scatterometers with radiometers over the global oceans, except for the regions influenced by synoptic weather storms. The uncertainties in present scatterometer and radiometer observations under high winds and rain conditions lead to uncertainties in the synthesized synoptic structures.The project is sponsored by the NASA Ocean Vector Wind Science Team (OVWST) activities under grant NNA10AO86G.2015-02-1

Improved Monitoring of the Changjiang River Plume in the East China Sea During the Monsoon Season Using Satellite Borne L-Band Radiometers

Measurement of sea surface salinity (SSS) from Satellite borne L-band (1.4 GHz, 21cm) radiometers (NASA Aquarius/SAC-D and ESA SMOS) in the East China Sea (ECS) is challenging due to the uncertainty of SSS caused by land thermal emissions in the antenna side lobes and because of strong radio frequency interference (RFI) due to illegally emitted man-made sources. RFI contamination in the ECS has gradually decreased because of the on-going international efforts to eliminate broadcasts in the protected L-band radio-astronomy frequency band. The present dissertation focuses on carefully eliminating the remaining RFI contamination in retrieved SSS, and masking out regions close to the coast that are likely contaminated by thermal emissions from the land. Afterward, observation of SSS during the summer monsoon season in the ECS was conducted to demonstrate low salinity (\u3c 28 psu) Changjiang Diluted Water (CDW) which is a mixture of Changjiang River (CR) plume mixing and the ambient ocean water causing ecosystem disruptions as far east as the Korean peninsula. In this study, during southeasterly wind, CDW was observed to be horizontally advected east-northeastward due to Ekman flow. In addition, monthly averaged Aquarius SSS presented one-month lagged robust relationship with freshwater flux. Despite limits on temporal information of SMOS, the detachment of CDW from its formation region and northeastward advection was successfully observed after the arrival of the tropical storm Matmo in the mainland China

The Impact of the Assimilation of Aquarius Sea Surface Salinity Data in the GEOS Ocean Data Assimilation System

Ocean salinity and temperature differences drive thermohaline circulations. These properties also play a key role in the ocean-atmosphere coupling. With the availability of L-band space-borne observations, it becomes possible to provide global scale sea surface salinity (SSS) distribution. This study analyzes globally the along-track (Level 2) Aquarius SSS retrievals obtained using both passive and active L-band observations. Aquarius alongtrack retrieved SSS are assimilated into the ocean data assimilation component of Version 5 of the Goddard Earth Observing System (GEOS-5) assimilation and forecast model. We present a methodology to correct the large biases and errors apparent in Version 2.0 of the Aquarius SSS retrieval algorithm and map the observed Aquarius SSS retrieval into the ocean models bulk salinity in the topmost layer. The impact of the assimilation of the corrected SSS on the salinity analysis is evaluated by comparisons with insitu salinity observations from Argo. The results show a significant reduction of the global biases and RMS of observations-minus-forecast differences at in-situ locations. The most striking results are found in the tropics and southern latitudes. Our results highlight the complementary role and problems that arise during the assimilation of salinity information from in-situ (Argo) and space-borne surface (SSS) observation

Theoretical modeling of dual-frequency scatterometer response: improving ocean wind and rainfall effects

Ocean surface wind is a key parameter of the Earth’s climate system. Occurring at the interface between the ocean and the atmosphere, ocean winds modulate fluxes of heat, moisture and gas exchanges. They reflect the lower branch of the atmospheric circulation and represent a major driver of the ocean circulation. Studying the long-term trends and variability of the ocean surface winds is of key importance in our effort to understand the Earth’s climate system and the causes of its changes. More than three decades of surface wind data are available from spaceborne scatterometer/radiometer missions and there is an ongoing effort to inter-calibrate all these measurements with the aim of building a complete and continuous picture of the ocean wind variability. Currently, spaceborne scatterometer wind retrievals are obtained by inversion algorithms of empirical Geophysical Model Functions (GMFs), which represent the relationship between ocean surface backscattering coefficient and the wind parameters. However, by being measurement-dependent, the GMFs are sensor-specific and, in addition, they may be not properly defined in all weather conditions. This may reduce the accuracy of the wind retrievals in presence of rain and it may also lead to inconsistencies amongst winds retrieved by different sensors. Theoretical models of ocean backscatter have the big potential of providing a more general and understandable relation between the measured microwave backscatter and the surface wind field than empirical models. Therefore, the goal of our research is to understand and address the limitations of the theoretical modeling, in order to propose a new strategy towards the definition of a unified theoretical model able to account for the effects of both wind and rain. In this work, it is described our approach to improve the theoretical modeling of the ocean response, starting from the Ku-band (13.4 GHz) frequency and then broadening the analysis at C-band (5.3 GHz) frequency. This research has revealed the need for new understanding of the frequency-dependent modeling of the surface backscatter in response to the wind-forced surface wave spectrum. Moreover, our ocean wave spectrum modification introduced to include the influences of the surface rain, allows the interpretation/investigation of the scatterometer observations in terms not only of the surface winds but also of the surface rain, defining an additional step needed to improve the wind retrievals algorithms as well as the possibility to jointly estimate wind and rain from scatterometer observations

Ocean Measurements from Space in 2025

Seasat, launched by the US National Aeronautics and Space Administration (NASA) in 1977, was the first dedicated ocean-viewing satellite. Since then, in addition to NASA, the space agencies of Europe, France, Canada, Germany, India, Japan, and China have all launched ocean-viewing sensors or dedicated ocean-viewing satellites. Properties currently measured from space are sea surface temperature; topography (height); salinity; significant wave height and wave spectra; surface wind speed and vectors; ocean color; continental and sea ice extent, "flow, deformation, thickness; ocean mass; and to a lesser extent, surface currents. By 2025, one additional measurement may become available—total surface currents—but the largest foreseen improvements are increased spatial and temporal resolution and increased accuracy for all the currently measured properties

2000 days of SMOS at the Barcelona Expert Centre: a tribute to the work of Jordi Font

Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission capable of measuring sea surface salinity and soil moisture from space. Its novel instrument (the L-band radiometer MIRAS) has required the development of new algorithms to process SMOS data, a challenging task due to many processing issues and the difficulties inherent in a new technology. In the wake of SMOS, a new community of users has grown, requesting new products and applications, and extending the interest in this novel brand of satellite services. This paper reviews the role played by the Barcelona Expert Centre under the direction of Jordi Font, SMOS co-principal investigator. The main scientific activities and achievements and the future directions are discussed, highlighting the importance of the oceanographic applications of the mission.Peer ReviewedPostprint (published version

Reduced ascending/descending pass bias in SMOS salinity data demonstrated by observing westward-propagating features in the South Indian Ocean

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite has been providing data, including sea surface salinity (SSS) measurements, for more than five years. However, the operational ESA Level 2 SSS data are known to have significant spatially and temporally varying biases between measurements from ascending passes (SSSA) and measurements from descending passes (SSSD). This paper demonstrates how these biases are reduced through the use of SSS anomalies. Climatology products are constructed using SMOS Level 2 data to provide daily, one-degree by one-degree climatologies separately for ascending and descending passes using a moving window approach (in time and space). The daily, one-degree products can then be averaged to provide values of climatological SSS at different spatial and/or temporal resolutions. The averaged values of the SMOS climatology products are in good general agreement with data from the World Ocean Atlas 2013. However, there are significant differences at high latitudes, as well as in coastal and dynamic regions, as found by previous studies. Both the mean and standard deviation of the differences between data from ascending passes and data from descending passes for the anomalies are reduced compared with those obtained using the original salinity values. Geophysical signals are clearly visible in the anomaly products and an example is shown in the Southern Indian Ocean of westward-propagating signals that we conclude represent the surface expression of Rossby waves or large-scale non-linear eddies. The signals seen in salinity data agree (in speed) with those from sea surface temperature and sea surface height and are consistent with previous studies

Spatial and temporal scales of variability in Tropical Atlantic sea surface salinity from the SMOS and Aquarius satellite missions

Taking advantage of the spatially dense, multi-year time series of global Sea Surface Salinity (SSS) from two concurrent satellite missions, the spatial and temporal decorrelation scales of SSS in the Tropical Atlantic 30°N–30°S are quantified for the first time from SMOS and Aquarius observations. Given the dominance of the seasonal cycle in SSS variability in the region, the length scales are calculated both for the mean and anomaly (i.e. seasonal cycle removed) SSS fields. Different 7–10 days composite SSS products from the two missions are examined to explore the possible effects of varying resolution, bias corrections and averaging characteristics. With the seasonal cycle retained, the SSS field is characterized by strongly anisotropic spatial variability. Homogeneous SSS variations in the Tropics have the longest zonal scales of over ~ 2000 km and long temporal scales of up to ~ 70–80 days, as shown by both SMOS and Aquarius. The longest meridional scales, reaching over ~ 1000 km, are seen in the South Atlantic between ~ 10°–25°S, most discernible in Aquarius data. The longest temporal scales of SSS variability are reported by both satellites to occur in the North-West Atlantic region 15°–30°N, at the Southern end of the Sargasso Sea, with SSS persisting for up to 150–200 days. The removal of the seasonal cycle results in a noticeable decrease in the spatio-temporal decorrelation scales over most of the basin. Overall, with the exception of the differences in the South Atlantic, there is general agreement between the spatial and temporal scales of SSS from the two satellites and different products, despite differences in individual product calibration and resolution characteristics. These new estimates of spatio-temporal decorrelation scales of SSS improve our knowledge of the processes and mechanisms controlling the Tropical Atlantic SSS variability, and provide valuable information for a wide range of oceanographic and modelling applications