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The Sentinel-3 satellites are equipped with dual-band Microwave Radiometers (MWR) to derive the wet tropospheric correction (WTC) for satellite altimetry. The deployed MWR lack the 18 GHz channel, which mainly provides information on the surface emissivity. Currently, this information is considered using additional parameters, one of which is the sea surface temperature (SST) extracted from static seasonal tables. Recent studies show that the use of a dynamic SST extracted from Numerical Weather Models (ERA5) improves the WTC retrieval. Given that Sentinel-3 carries on board the Sea and Land Surface Temperature Radiometer (SLSTR), from which SST observations are derived simultaneously with those of the Synthetic Aperture Radar Altimeter and MWR sensors, this study aims to develop a synergistic approach between these sensors for the WTC retrieval over open ocean. Firstly, the SLSTR-derived SSTs are evaluated against the ERA5 model; secondly, their impact on the WTC retrieval is assessed. The results show that using the SST input from SLSTR, instead of ERA5, has no impact on the WTC retrieval, both globally and regionally. Thus, for the WTC retrieval, there seems to be no advantage in having collocated SST and radiometer observations. Additionally, this study reinforces the fact that the use of dynamic SST leads to a significant improvement over the current Sentinel-3 WTC operational algorithms. [ABSTRACT FROM AUTHOR]

Satellite altimetry allows the estimation of accurate water surface heights only with accurate determination of all involved terms, namely, the wet tropospheric correction (WTC) or its symmetric value, the wet path delay (WPD). WPD is best determined from onboard microwave radiometer (MWR) measurements; however, the corresponding WPD retrievals become invalid close to land (e.g., coastal and inland waters). Alternative WPD sources are numerical weather models (NWMs), e.g., from the European Centre for Medium-Range Weather Forecasts (ECMWF). NWMs provide the parameters at 6-h intervals; however, ERA5 (the latest ECMWF reanalysis) provides hourly atmospheric parameters at $0.25^{\circ } \times 0.25^{\circ }$. The best spatial resolution is provided by the ECMWF operational model at $0.125^{\circ } \times 0.125^{\circ }$. Motivated by this new and improved temporal resolution, the focus of this paper is the global assessment of the impact of different temporal resolutions of ERA5 in the WPD computation. The same assessment is also performed concerning the spatial resolution of ERA5 and operational models. Aiming to identify the best compromise between spatial/temporal resolutions, accuracy, and computational time, WPDs computed using various combinations of these resolutions were inter-compared and compared with MWR-derived WPD. The results show that the global root mean square (RMS) of the differences between MWR and ERA5 is 1.2 cm and the effect of using 1-h intervals instead of 6-h intervals is small, significant only for latitudes 30°–60° S and 30°–60° N. Hourly intervals do not have a significant impact on the WPD from ERA5, being a temporal resolution of 3 h high enough to ensure the same accuracy of 1 h, showing that ERA5 cannot map the WPD short space and time scales. [ABSTRACT FROM AUTHOR]

Zenith tropospheric delays (ZTDs) computed at a network of 60 global navigation satellite system (GNSS) stations have been used to assess microwave radiometer (MWR) measurements from eight altimeter missions in coastal zones, where some of these observations become invalid. Results show that ZTDs are determined with an accuracy of a few millimeters; however, jumps are detected in some stations in standard products. The comparison between the MWR-derived wet tropospheric correction (WTC) and the GNSS-derived WTC at the nearby coastal stations illustrates the effect of land contamination in the MWR measurements and yields the distance from coast at which this contamination appears. This distance is different for the analyzed altimetric missions, due to their different footprint sizes and different MWR retrieval algorithms, varying from 10 to 30 km. The root mean square of the differences between GNSS and MWR-derived WTC, at the closest distance at which no land contamination occurs, is in the range of 1.6–1.9 cm for all missions. This coastal assessment also shows the ability of the GNSS-derived path delay plus algorithm to remove this land contamination and to improve the WTC retrieval. Aiming at inspecting the long-term stability of the MWR measurements, the comparisons with GNSS show nonsignificant differences and drifts less than 0.3 mm/year. Therefore, the GNSS-derived WTC is a useful independent source to inspect the land effects on MWR observations and to monitor the stability of these instruments, thus contributing to the retrieval of precise water surface heights from satellite altimetry. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study concerning the development of a sea state bias (SSB) model designed with three parameters exclusively derived from altimeter data and globally applied to all reference altimeter missions. The proposed technique, first tested for the Jason-1 mission, proves to have a good performance for a wide range of ocean conditions when compared with the state-of-the-art SSB corrections currently in use. In addition to the significant wave height ($H_{s}$) and wind speed ($U~_{{10}}$), a third predictor acting as a mediator parameter gathered by the mean wave period ($T_{z}$) has been used. Two different empirical algorithms for altimeter ocean wave period have been tested and implemented, improving the SSB model performance in some ocean regions. The methodology relies on nonparametric modulation and statistical techniques based on smoothing splines embedded in a generalized additive model. This SSB modeling approach shows good performance when applied to all reference missions, in particular to TOPEX and Jason-2 missions, slightly reducing the explained variance of sea-level anomaly (SLA) when compared with the established SSB models. The approach is computationally efficient, capable of generating a stable SSB model using a small training data set when little information is available, as is the case with the recent Jason-3 mission. Model performance is assessed by comparison with existing SSB corrections for each reference mission, intercomparisons during the period of the tandem phases, and by SLA variance analysis, providing a consistent set of SSB corrections for the four reference missions. [ABSTRACT FROM AUTHOR]

Sea Level is a very sensitive index of climate change since it integrates the impacts of ocean warming and ice mass loss from glaciers and the ice sheets. Sea Level has been listed as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). During the past 25 years, the sea level ECV has been measured from space by different altimetry missions that have provided global and regional observations of sea level variations. As part of the Climate Change Initiative (CCI) program of the European Space Agency (ESA) (established in 2010), the Sea Level project (SL_cci) aimed at providing an accurate and homogeneous long-term satellite-based sea level record. At the end of the first phase of the project (2010-2013), an initial version (v1.1) of the sea level ECV has been made available to users (Ablain et al., 2015). During the second phase (2014-2017), improved altimeter standards have been selected to produce new sea level products (called SL_cci v2.0) based on 9 altimeter missions for the period 1993-2015 (https://doi.org/10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612). Corresponding orbit solutions, geophysical corrections and altimeter standards used in this v2.0 dataset are described in details in Quartly et al. (2017). The present paper focuses on the description of the SL_cci v2.0 ECV and associated uncertainty and discusses how it has been validated. Various approaches have been used for the quality assessment such as internal validation, comparisons with sea level records from other groups and with in-situ measurements, sea level budget closure analyses and comparisons with model outputs. Compared to the previous version of the sea level ECV, we show that use of improved geophysical corrections, careful bias reduction between missions and inclusion of new altimeter missions lead to improved sea level products with reduced uncertainties at different spatial and temporal scales. However, there is still room for improvement since the uncertainties remain larger than the GCOS requirements. Perspectives for subsequent evolutions are also discussed. [ABSTRACT FROM AUTHOR]

Abstract. Sea level is an essential climate variable (ECV) that has a direct effect on many people through inundations of coastal areas, and it is also a clear indicator of climate changes due to external forcing factors and internal climate variability. Regional patterns of sea level change inform us on ocean circulation variations in response to natural climate modes such as El Niño and the Pacific Decadal Oscillation, and anthropogenic forcing. Comparing numerical climate models to a consistent set of observations enables us to assess the performance of these models and help us to understand and predict these phenomena, and thereby alleviate some of the environmental conditions associated with them. All such studies rely on the existence of long-term consistent high-accuracy datasets of sea level. The Climate Change Initiative (CCI) of the European Space Agency was established in 2010 to provide improved time series of some ECVs, including sea level, with the purpose of providing such data openly to all to enable the widest possible utilisation of such data. Now in its second phase, the Sea Level CCI project (SL_cci) merges data from nine different altimeter missions in a clear, consistent and well-documented manner, selecting the most appropriate satellite orbits and geophysical corrections in order to further reduce the error budget. This paper summarises the corrections required, the provenance of corrections and the evaluation of options that have been adopted for the recently released v2.0 dataset (https://doi.org/10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612). This information enables scientists and other users to clearly understand which corrections have been applied and their effects on the sea level dataset. The overall result of these changes is that the rate of rise of global mean sea level (GMSL) still equates to ∼ 3.2 mm yr−1 during 1992–2015, but there is now greater confidence in this result as the errors associated with several of the corrections have been reduced. Compared with v1.1 of the SL_cci dataset, the new rate of change is 0.2 mm yr−1 less during 1993 to 2001 and 0.2 mm yr−1 higher during 2002 to 2014. Application of new correction models brought a reduction of altimeter crossover variances for most corrections. [ABSTRACT FROM AUTHOR]

Due to its large space-time variability, the wet tropospheric correction (WTC) is still considered a significant error source in satellite altimetry. This paper presents the GNSS (Global Navigation Satellite Systems) derived Path Delay Plus (GPD+), the most recent algorithm developed at the University of Porto to retrieve improved WTC for radar altimeter missions. The GPD+ are WTC estimated by space-time objective analysis, by combining all available observations in the vicinity of the point: valid measurements from the on-board microwave radiometer (MWR), from GNSS coastal and island stations and from scanning imaging MWR on board various remote sensing missions. The GPD+ corrections are available both for missions which do not possess an on-board microwave radiometer such as CryoSat-2 (CS-2) and for all missions which carry this sensor, by addressing the various error sources inherent to the MWR-derived WTC. To ensure long-term stability of the corrections, the large set of radiometers used in this study have been calibrated with respect to the Special Sensor Microwave Imager (SSM/I) and the SSM/I Sounder (SSM/IS). The application of the algorithm to CS-2 and Geosat Follow-on (GFO), as representative altimetric missions without and with a MWR aboard the respective spacecraft, is described. Results show that, for both missions, the newWTC significantly reduces the sea level anomaly (SLA) variance with respect to the model-based corrections. For GFO, the new WTC also leads to a large reduction in SLA variance with respect to the MWR-derived WTC, recovering a large number of observations in the coastal and polar regions and full sets of tracks and several cycles when MWR measurements are missing or invalid. Overall, the algorithm allows the recovery of a significant number of measurements, ensuring the continuity and consistency of the correction in the open-ocean/coastal transition zone and at high latitudes. [ABSTRACT FROM AUTHOR]

A conceptually simple formulation is proposed for a new empirical sea state bias (SSB) model using information retrieved entirely from altimetric data. Nonparametric regression techniques are used, based on penalized smoothing splines adjusted to each predictor and then combined by a Generalized Additive Model. In addition to the significant wave height (SWH) and wind speed (U10), a mediator parameter designed by the mean wave period derived from radar altimetry, has proven to improve the model performance in explaining some of the SSB variability, especially in swell ocean regions with medium-high SWH and low U10. A collinear analysis of scaled sea level anomalies (SLA) variance differences shows conformity between the proposed model and the established SSB models. The new formulation aims to be a fast, reliable and flexible SSB model, in line with the well-settled SSB corrections, depending exclusively on altimetric information. The suggested method is computationally efficient and capable of generating a stable model with a small training dataset, a useful feature for forthcoming missions. [ABSTRACT FROM AUTHOR]

Originally designed for applications over the ocean, satellite altimetry has been proven to be a useful tool for hydrologic studies. Altimeter products, mainly conceived for oceanographic studies, often fail to provide atmospheric corrections suitable for inland water studies. The focus of this paper is the analysis of the main issues related with the atmospheric corrections that need to be applied to the altimeter range to get precise water level heights. Using the corrections provided on the Radar Altimeter Database System, the main errors present in the dry and wet tropospheric corrections and in the ionospheric correction of the various satellites are reported. It has been shown that the model-based tropospheric corrections are not modeled properly and in a consistent way in the various altimetric products. While over the ocean, the dry tropospheric correction (DTC) is one of the most precise range corrections, in some of the present altimeter products, it is the correction with the largest errors over continental water regions, causing large biases of several decimeters, and along-track interpolation errors up to several centimeters, both with small temporal variations. The wet tropospheric correction (WTC) from the on-board microwave radiometers is hampered by the contamination on the radiometer measurements of the surrounding lands, making it usable only in the central parts of large lakes. In addition, the WTC from atmospheric models may also have large errors when it is provided at sea level instead of surface height. These errors cannot be corrected by the user, since no accurate expression exists for the height variation of the WTC. Alternative and accurate corrections can be computed from in situ data, e.g., DTC from surface pressure at barometric stations and WTC from Global Navigation Satellite System permanent stations. The latter approach is particularly favorable for small lakes and reservoirs, where GNSS-derived WTC at a single location can be representative of the whole lake. For non-timely critical studies, for consistency and stability, model-derived tropospheric corrections from European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis ERA Interim, properly computed at surface height, are recommended. The instrument-based dual-frequency ionospheric correction may have errors related with the land contamination in the Ku and C/S bands, making it more suitable to use a model-based correction. The most suitable model-based ionospheric correction is the Jet Propulsion Laboratory (JPL) global ionosphere map (GIM) model, available after 1998, properly scaled to the altimeter height. Most altimeter products provide the GIM correction unreduced for the total electron content extending above the altitude of these satellites, thus overestimating the ionospheric correction by about 8%. Prior to 1998, the NIC09 (NOAA Ionosphere Climatology 2009) climatology provides the best accuracy. [ABSTRACT FROM AUTHOR]

Unlike most altimetric missions, CryoSat-2 is not equipped with an onboard microwave radiometer (MWR) to provide wet tropospheric correction (WTC) to radar altimeter measurements, thus, relying on a model-based one provided by the European Center for Medium-range Weather Forecasts (ECMWF). In the ambit of ESA funded project CP4O, an improved WTC for CryoSat-2 data over ocean is under development, based on a data combination algorithm (DComb) through objective analysis of WTC values derived from all existing global-scale data types. The scope of this study is the analysis and inter-calibration of the large dataset of total column water vapor (TCWV) products from scanning MWR aboard Remote Sensing (RS) missions for use in the WTC computation for CryoSat-2. The main issues regarding the computation of the WTC from all TCWV products are discussed. The analysis of the orbital parameters of CryoSat-2 and all other considered RS missions, their sensor characteristics and inter-calibration is presented, providing an insight into the expected impact of these datasets on the WTC estimation. The most suitable approach for calculating the WTC from TCWV is investigated. For this type of application, after calibration with respect to an appropriate reference, two approaches were found to give very similar results, with root mean square differences of 2 mm. [ABSTRACT FROM AUTHOR]

Abstract: The characterisation of the geostrophic surface flow field around the Cape Verde Archipelago in the northeast Atlantic Ocean with satellite altimeter data is presented. The aim is to analyse the main current systems present in the region 3°–30°N, 40°–10°W and their seasonal and interannual variability. A merged data set of Topex/Poseidon (T/P) and ERS-2 altimeter data for an 8-year period, beginning in June 1995, has been used and corrected sea surface heights were computed by applying a homogeneous set of relevant geophysical corrections. ERS-2 data were crossover adjusted to T/P. Monthly maps of sea level anomalies were created for the whole period and were used in the computation of monthly maps of absolute dynamic topography, geostrophic currents and eddy kinetic energy (EKE). The seasonal signal of the northeast Tropical Atlantic large-scale surface circulation appears as the prevailing cause of the variability in the region, particularly in the southernmost portion of the region being studied. This signal is also present in the flow field along the African coast and in the Guinea Dome. Regions of highest EKE values are clearly associated with the North Equatorial Counter-Current and with the currents along the African coast. The significant interannual variability found for 1998 seems to be associated with the 1997–1998 ENSO Pacific event, but other anomalous periods (1996–1997 and 2001–2002) uncorrelated with ENSO are also evident. [Copyright &y& Elsevier]

Wet path delay (WPD) for satellite altimetry has been provided from external sources, raising the need of converting this value between different altitudes. The only expression available for this purpose considers the same altitude reduction, irrespective of geographic location and time. The focus of this study is the modelling of the WPD altitude dependence, aiming at developing improved expressions. Using ERA5 pressure level fields (2010–2013), WPD vertical profiles were computed globally. At each location and for each vertical profile, an exponential function was fitted using least squares, determining the corresponding decay coefficient. The time evolution of these coefficients reveals regions where they are highly variable, making this modelling more difficult, and regions where an annual signal exists. The output of this modelling consists of a set of so-called University of Porto (UP) coefficients, dependent on geographic location and time. An assessment with ERA5 data (2014) shows that for the location where the Kouba coefficient results in a maximum Root Mean Square (RMS) error of 3.2 cm, using UP coefficients this value is 1.2 cm. Independent comparisons with WPD derived from Global Navigation Satellite Systems and radiosondes show that the use of UP coefficients instead of Kouba's leads to a decrease in the RMS error larger than 1 cm. [ABSTRACT FROM AUTHOR]

Sea level anomaly (SLA), provided globally by satellite altimetry, is considered a valuable proxy for detecting long-term changes of the global ocean, as well as short-term and annual variations. In this manuscript, monthly sea level anomaly grids for the period 1993–2013 are used to characterise the North Atlantic Ocean variability at inter-annual timescales and its response to the North Atlantic main patterns of atmospheric circulation variability (North Atlantic Oscillation, Eastern Atlantic, Eastern Atlantic/Western Russia, Scandinavian and Polar/Eurasia) and main driven factors as sea level pressure, sea surface temperature and wind fields. SLA variability and long-term trends are analysed for the North Atlantic Ocean and several sub-regions (North, Baltic and Mediterranean and Black seas, Bay of Biscay extended to the west coast of the Iberian Peninsula, and the northern North Atlantic Ocean), depicting the SLA fluctuations at basin and sub-basin scales, aiming at representing the regions of maximum sea level variability. A significant correlation between SLA and the different phases of the teleconnection patterns due to the generated winds, sea level pressure and sea surface temperature anomalies, with a strong variability on temporal and spatial scales, has been identified. Long-term analysis reveals the existence of non-stationary inter-annual SLA fluctuations in terms of the temporal scale. Spectral density analysis has shown the existence of long-period signals in the SLA inter-annual component, with periods of ~ 10, 5, 4 and 2 years, depending on the analysed sub-region. Also, a non-uniform increase in sea level since 1993 is identified for all sub-regions, with trend values between 2.05 mm/year, for the Bay of Biscay region, and 3.98 mm/year for the Baltic Sea (no GIA correction considered). The obtained results demonstrated a strong link between the atmospheric patterns and SLA, as well as strong long-period fluctuations of this variable in spatial and temporal scales. [ABSTRACT FROM AUTHOR]