This paper describes the calibration of the CryoSat-2 interferometer, whose principal purpose is to accurately measure the height of the Antarctic and Greenland ice sheets. A sequence of CryoSat-2 data acquisitions over the tropical and midlatitude oceans were obtained between June and September 2010, from the SIRAL "A" and redundant SIRAL "B" radars operating in their "SARIN" mode, during a sequence of satellite rolls between -0.6° and 0.4°. Using the arrival angle of the echo relative to the interferometer baseline, the attitude of the satellite determined by the star trackers, and estimates of the ocean surface across-track slope from the EGM08 geoid, we determined the errors in the interferometer estimate of surface slope as functions of the roll angle and ocean surface waveheight. These were found to be in close agreement with the theoretical description. The scale factor of the interferometric measurement of angle was determined to be 0.973 ± 0.002. We estimate the accuracy of the across-track slope measurement of the interferometer by applying this scale factor to the measured phase. In applying this scale factor to the measurements, the across-track slope of the marine geoid was obtained with an accuracy of 26 μrad at 10 km and 10 μrad at 1000 km. We conclude that the instrument performance considerably exceeds that needed for the accurate determination of height over the sloping surfaces of the continental ice sheets. The results also demonstrate that CryoSat-2 provides the first observations of the instantaneous vector gradient of the ocean surface, and that the normal-incidence interferometric configuration has a greater potential for the measurement of the ocean across-track slope than has been previously recognized.
Recent advances in numerical modeling, satellite data, and coastal processes, together with the rapid evolution of CMEMS products and the increasing pressures on coastal zones, suggest the timeliness of extending such products toward the coast. The CEASELESS EU H2020 project combines Sentinel and in-situ data with high-resolution models to predict coastal hydrodynamics at a variety of scales, according to stakeholder requirements. These predictions explicitly introduce land discharges into coastal oceanography, addressing local conditioning, assimilation memory and anisotropic error metrics taking into account the limited size of coastal domains. This article presents and discusses the advances achieved by CEASELESS in exploring the performance of coastal models, considering model resolution and domain scales, and assessing error generation and propagation. The project has also evaluated how underlying model uncertainties can be treated to comply with stakeholder requirements for a variety of applications, from storm-induced risks to aquaculture, from renewable energy to water quality. This has led to the refinement of a set of demonstrative applications, supported by a software environment able to provide met-ocean data on demand. The article ends with some remarks on the scientific, technical and application limits for CMEMS-based coastal products and how these products may be used to drive the extension of CMEMS toward the coast, promoting a wider uptake of CMEMS-based predictions.
This chapter describes altimeter wind speed observations, their quality, and their importance for monitoring the quality of modeled surface wind the altimeter significant wave height (SWH), which is four times the square root of the total wave energy. It summarizes the applications of altimeter wind and wave observations and presents a few developments in satellite sea state measurements. The necessary calibration and validation of a satellite sensor requires large amounts of ground truth that should cover the full range of possible events. In particular, the number of reliable wave in situ measurements is very limited and, because of financial restrictions, dedicated field campaigns are possible only at a few sites. For the purpose of altimeter wind speed verification, an in situ wind speed measurement is only trusted if it is associated with an acceptable SWH value. One plausible reason for the seasonal cycle in the altimeter bias may be related to the presence of slicks.
A surface wind speed retrieval algorithm from Ku-band radar altimeter backscatter coefficients is presented. It was derived using two-months of ENVISAT altimeter backscatter data collocated with ECMWF model and in situ wind speeds and was extensively verified for ENVISAT, ERS-2, and Jason-1/2 against model and in situ wind data. The algorithm performs better than the two-parameter (backscatter and significant wave height, SWH) algorithms of Jason-1/2. The success of the current algorithm raises a question regarding the usefulness of SWH alone as a second parameter for retrievals. It is argued that SWH alone is not the proper choice to represent the sea-state conditions.
Scatterometer and altimeter wind data are very important for data assimilation and verification of numerical weather prediction models. Standard deviation of absolute random errors can be estimated using the triple collocation technique. However, error correlations between various wind sources (e.g., due to data assimilation) complicate the error estimation. A method is used to alleviate the impact of error correlations between the scatterometer and the model that assimilates such data. Using twenty-two datasets of triplet composed of Jason-2 altimeter, Metop-A/B scatterometers (ASCAT-A/B, respectively), and ECMWF model analysis and forecasts (1 altimeter × 2 scatterometers × 11 model analysis and forecasts = 22 datasets) covering a period of two years from August 2013 to July 2015, the correlation coefficient between the errors of scatterometers and the model analysis was found to be about 0.33 for those datasets. This correlation reduces with forecast lead time until it almost vanishes at day seven. Altimeter and scatterometer errors are not correlated. The standard deviation of wind speed random errors of Jason-2, ASCAT-A/B, and the IFS analysis are estimated as 0.7, 0.8, and 0.9 m/s, respectively. As expected, there was no difference between ASCAT-A and ASCAT-B results.
In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the "Green" Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments' development and satellite missions' evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion.
Satellite with ARgos and ALtiKa (SARAL)/AltiKa is the first oceanographic radar altimeter that operates in Ka-band which is very sensitive to the atmospheric factors like liquid water and water vapor. An empirical recipe is proposed to partially compensate for this impact for the purpose of ocean surface wind speed (SWS) evaluation. The results compare well with numerical prediction model fields and in situ measurements. SARAL SWS computed using the proposed recipe shows very low bias (below 0.4 m/s). The random error at a scale of 75 km is estimated at about 1.0 m/s, which is very close to that of the Ku-band altimeters.
Abstract. Sea state data are of major importance for climate studies, marine engineering, safety at sea and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state climate data record, the Global Climate Observing System has listed “Sea State” as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a programme of the European Space Agency, whose objective is to realise the full potential of global Earth observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project. The Sea State CCI dataset v1 is freely available on the ESA CCI website (http://cci.esa.int/data, last access: 25 August 2020) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/ (last access: 25 August 2020). Three products are available: a multi-mission along-track L2P product (http://dx.doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1, Piollé et al., 2020a), a daily merged multi mission along-track L3 product (http://dx.doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b, Piollé et al., 2020b) and a multi-mission monthly gridded L4 product (http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478, Piollé et al., 2020c).
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