Traditional nadir altimeters struggle with coastal water surface elevation (WSE) measurement and fine-scale river-estuary interactions, due to land-water signal interference and their wide inter-track spacing. The wide-swath Surface Water and Ocean Topography (SWOT) mission, using a new Ka-band radar interferometer, aims to address these issues by delivering 2D WSE measurements with unprecedented spatial resolution, accuracy, and precision. However, the mission's effectiveness in coastal WSE retrieval and its error characteristics remain unverified. This study leverages gauge and airborne LiDAR data to validate SWOT's WSE in the Bristol Channel and Severn Estuary. Assuming error-free in situ data, SWOT ocean products exhibit a standard deviation of difference (STD) of 13 cm within a 3 km radius of tide gauges. Compared to LiDAR, SWOT's PIXC measurements have STD of 37 cm, improving to 14 cm over 100 m grids and 9 cm over 1 km2 areas. This meets the SWOT science requirement of 10 cm STD at 1 km2 scale and extends satellite-based WSE monitoring into complex coastal environments.

The North Atlantic Climate System Integrated Study (ACSIS) was a large multidisciplinary research programme funded by the UK's Natural Environment Research Council (NERC). ACSIS ran from 2016 to 2022 and brought together around 80 scientists from seven leading UK-based environmental research institutes to deliver major advances in the understanding of North Atlantic climate variability and extremes. Here, we present an overview of the data generated by the ACSIS programme. The datasets described cover the North Atlantic Ocean, the atmosphere above it (including its composition), and Arctic sea ice. Atmospheric composition datasets include measurements from seven aircraft campaigns (45 flights in total, 0–10 km altitude range) in the northeastern Atlantic (∼ 15–55° N, ∼ 40° W–5° E) made at intervals of 6 months to 2 years between February 2017 and May 2022. The flights measured chemical species (including greenhouse gases; ozone precursors; and volatile organic compounds – VOCs) and aerosols (organic aerosol – OA; SO4; NH4; NO3; and non-sea salt chloride – nss-Cl) (https://doi.org/10.5285/6285564c34a246fc9ba5ce053d85e5e7, FAAM et al., 2024). Ground-based stations at the Cape Verde Atmospheric Observatory (CVAO), Penlee Point Atmospheric Observatory (PPAO), and Plymouth Marine Laboratory (PML) recorded ozone, ozone precursors, halocarbons, greenhouse gases (CO2 and methane), SO2, and photolysis rates (CVAO; http://catalogue.ceda.ac.uk/uuid/81693aad69409100b1b9a247b9ae75d5, National Centre for Atmospheric Science et al., 2010); O3 and CH4 (PPAO, https://catalogue.ceda.ac.uk/uuid/8f1ff8ea77534e08b03983685990a9b0 (Plymouth Marine Laboratory and Yang, 2017); and aerosols (PML, https://doi.org/10.5285/e74491c96ef24df29a9342a3d57b5939, Smyth, 2024), respectively. Complementary model simulations of atmospheric composition were performed with the UK Earth System Model (UKESM1) for the period from 1982 to 2020 using Coupled Model Intercomparison Project Phase 6 (CMIP6) historical forcing up to 2014 and Shared Socioeconomic Pathways (SSP) scenario SSP3-7.0 from 2015 to 2020. Model temperature and winds were relaxed towards ERA reanalysis. Monthly mean model data for ozone, NO, NO2, CO, methane, stratospheric ozone tracers, and 30 regionally emitted tracers are available for download (https://data.ceda.ac.uk/badc/acsis/UKESM1-hindcasts, Abraham, 2024). ACSIS also generated new ocean heat content diagnostics (https://doi.org/10/g6wm, https://doi.org/10/g8g2, Moat et al., 2021a–b) and gridded temperature and salinity based on objectively mapped Argo measurements (https://doi.org/10.5285/fe8e524d-7f04-41f3-e053-6c86abc04d51 King, 2023). An ensemble of atmosphere-forced global-ocean sea-ice simulations using the NEMO-CICE model was performed with horizontal resolutions of 1/4 and 1/12° covering the period from 1958 to 2020 using several different atmosphere-reanalysis-based surface forcing datasets, supplemented by additional global simulations and stand-alone sea-ice model simulations with advanced sea-ice physics using the CICE model (http://catalogue.ceda.ac.uk/uuid/770a885a8bc34d51ad71e87ef346d6a8, Megann et al., 2021e). Output is stored as monthly averages and includes 3D potential temperature, salinity, zonal velocity, meridional velocity, and vertical velocity; 2D sea-surface height, mixed-layer depth, surface heat, and freshwater fluxes; ice concentration and thickness; and a wide variety of other variables. In addition to the data presented here, we provide a very brief overview of several other datasets that were generated during ACSIS and have been described previously in the literature.

We propose a nonparametric minimum entropy method for estimating an optimal velocity from position time series, which may contain unknown noise, data gaps, loading effects, transients, outliers and step discontinuities. Although nonparametric, the proposed method is based on elementary statistical concepts familiar to least-squares and maximum-likelihood users. It seeks a constant velocity with a best possible (realistic) variance rather than a best variable velocity fit to the closest position data. We show, based on information theory, synthetic and real data, that minimum-entropy velocity estimation: (1) accounts for colored noise without assumptions about its distribution or the extent of its temporal correlations; (2) is unaffected by the series deterministic content such as an initial position and the heights of step discontinuities and insensitive to small-amplitude periodic variations and transients; (3) is robust against outliers and, for long time series, against step discontinuities and even slight non-stationarity of the noise; (4) does not involve covariance matrices or eigen/singular value analysis, thus can be implemented by a short and efficient software; (5) under no circumstances results in a velocity variance that decays as 1/N , where N is the number of observations. The proposed method is verified based on synthetic data and then applied to a few hundred NGL (Nevada Geodetic Lab) position time series of different characteristics, and the results are compared to those of the Median Interannual Difference Adjusted for Skewness (MIDAS) algorithm. The compared time series include continuous and linear ones used to test the agreement between the two methods in the presence of unknown noise, data gaps and loading effects, discontinuous but linear series selected to include the effect of a few (1–4) discontinuities, and nonlinear but continuous time series selected for including the effects of transients. Both the minimum-entropy and MIDAS methods are nonparametric in the sense that they only extract the velocity from a position time series with hardly any explicit assumptions about its noise distribution or correlation structure. Otherwise, the two methods differ in every single possible technical sense. Other than pointing to a close agreement between the derived velocities, the comparisons consistently revealed that minimum-entropy velocity uncertainties suggest a smaller degree of temporal correlations in the NGL time series than the MIDAS does.

Radio wave propagation involved in Global Navigation Satellite System Reflectometry (GNSS-R) is subject to atmospheric refraction. Even for ground-based tracking stations, in applications such as coastal sea-level altimetry, the interferometric or reflection-minus-direct effect might be significant. Although atmospheric propagation delays are best investigated numerically via raytracing, including reflections, such a procedure is not trivial. We have developed simpler closed formulas to account for atmospheric refraction in ground-based GNSS-R, validated against independent raytracing. We provide specific expressions for the two components of the atmospheric interferometric delay and corresponding altimetry correction components, parameterized in terms of refractivity and bending angle. Assessment results showed excellent agreement for both components. We define the interferometric slant factor used to map interferometric zenithal delays to individual satellites.

<p>We synthesize observational datasets and a state of the art forced global ocean model to construct a multidecadal upper ocean heat budget for the North Atlantic for the period 1950 to 2020. Using multiple independent estimates of the variables allows us to provide robust uncertainty estimates for each term. Time-varying ocean heat transport convergence dominates the budget on multidecadal timescales in all regions of the North Atlantic. In the subpolar region (north of 45N) we find that the heat transport convergence is dominated by geostrophic currents whereas in the subtropics (26-45N) advection by ageostrophic currents is also significant. The geostrophic advection is dominated (especially in the subpolar regions) by anomalous geostrophic currents acting on the mean temperature gradient. The timescale and spatial distribution of the anomalous geostrophic currents are consistent with basin scale ‘thermal’ Rossby waves propagating westwards/northwestwards in the subpolar gyre. Multidecadal changes in North Atlantic Changes in ocean heat storage directly affect the climate of the surrounding continents, and hence it is important to understanding the mechanism behind these.</p>

GNSS Interferometric Reflectometry (GNSS-IR) is a method that can be used to measure water levels. The frequency of the interference pattern created by direct and reflected GNSS signals is used to estimate the height of the GNSS antenna above the reflecting surface. In principle each rising and setting satellite arc that reflects off the water can be used, yielding ~350 water level measurements per day at sites that track the four major constellations and have a good view of the water. Two examples of the GNSS-IR method are presented, one at a coastal site in Australia and the other from the Ems river in Germany. Each site has collocated traditional tide gauge instrumentation. Open source GNSS-IR software is used to analyze the GNSS data from each site. Correlations between GNSS-IR with the traditional gauge are shown to be better than 0.99.

Multidecadal changes in North Atlantic Ocean heat storage directly affect the climate of the surrounding continents, and it is important to understand how and why the changes are taking place. Here we synthesize a wide range of observational datasets to construct an upper ocean heat budget for the period 1950 to 2020. Lead-lag correlation analysis of time series of ocean heat content, horizontal heat transport, sea surface temperature and air sea fluxes are used to infer the drivers North Atlantic heat content changes. We find systematic and interconnected migration of heat content anomalies around both subtropical and subpolar gyres and between the near surface and deep ocean on multidecadal timescales. We find a significant driving/active role for ocean circulation in these migrations throughout the North Atlantic. In contrast, air sea interaction mainly plays an active/driving role in the western subpolar Atlantic. Our use of multiple independent observational estimates of the variables allows us to provide robust error/uncertainty estimates for the evolution of the North Atlantic heat budget terms.

Mesoscale eddy-permitting ocean models will be needed as a component of climate ensemble projections most likely for the next decade or more. However, the kinetic energy and other measures of variability are typically an order of magnitude too weak at this nominal 0.25 degree lon-lat resolution. This is predominantly due to excessive gridscale damping of momentum, needed for computational stability, which is believed to kill a large fraction of the energy source of the kinetic energy inverse cascade. The KE inverse cascade is associated with the generation of intrinsic chaotic variability and ensemble spread, hence the estimation of potential predictability, but also with slower, larger-scale variability associated with climate. The familiar Gent and McWilliams (1990) eddy parameterisation is problematic when applied to eddy-permitting models, where eddies are partially resolved, and it also tends to damp variability rather than energise it. In response to this problem, several recent studies have focussed on the KE backscatter problem, which each attempt to increase the upscale transfer of KE, either deterministically or stochastically. Stochastic parameterisation of sub-gridscale eddies has recently become a popular approach in ocean modelling, having been used in atmospheric modelling for many years, but there is still a diverse range of approaches for constraining either the underlying physics (how the forcing is applied) or the statistics (the spatiotemporal signature of the forcing). This study explores some basic recipes for constructing the stochastic model from statistics of either observations or higher-resolution models. The stochastic forcing, representing the sub-gridscale effects of eddies in our eddy-permitting simulations, is also applied adiabatically – to mimic the predominant behaviour of mesoscale eddies in the ocean interior and to preserve large-scale watermasses. A theoretical challenge, which we explore, is to connect the applied, weakly imbalanced forcing, to a response in kinetic energy and upscale transfer. This must also be applied without generating numerical instability.

<div> <p><span>The Permanent Service for Mean Sea Level (PSMSL) is the internationally recognised global sea level data bank for long-term sea level change information from tide gauges, responsible for the collection, publication, analysis and interpretation of sea level data. There is a need both for more records in data sparse regions such as Antarctica, the Arctic and Africa, and for a low cost method for monitoring climate change through sea level.</span><span> </span></p> </div><div> <p><span>While tide gauge sensors themselves are not very expensive, the costs in operating them over a long period of time can be considerable. Sensors based in the water are prone to biofouling, and can require divers to access. Meanwhile, land-based sensors are exposed to damage from accidents, storms, and vandalism.</span><span> </span></p> </div><div> <p><span>The emerging field of GNSS (Global Navigation Satellite Systems, such as GPS, GLONASS, Galileo and BeiDou) interferometric reflectometry (GNSS-IR) provides an alternative way to measure sea level. Permanent GNSS receivers are routinely installed near the coast to monitor land movements, and we can infer sea level by comparing the direct signal to a GNSS with those reflected off the surface of the water. GNSS-IR does not yet match the accuracy of traditional tide gauges, but has the potential to be part of an affordable, effective monitoring system of water levels.</span><span> </span></p> </div><div> <p><span>Here we present a new data portal of sea level measured using GNSS-IR, developed as part of the EuroSea project. So far, we have extracted sea level data from over 250 GNSS receivers worldwide. At each site we provide a file of calculated sea levels, along with metadata about the site, some diagnostic plots, and links to the source of the original GNSS data. We have also created an interactive map to help investigate the footprint of a GNSS installed at any location.</span><span> </span></p> </div><div> <p><span>At present the portal is in a beta stage of development, and we hope to continue to make improvements, including hosting the data on a server with an API (ERDDAP) to allow interoperable access to data and metadata in a wide range of formats. We have carried out proof-of-concept tests that demonstrate that data can be provided in near real time, and aim to secure funding to allow us to add this in the future.</span><span> </span></p> </div>

FIGURE 1. An example of a visual “tide gauge” engraved on a harbor wall, showing tide level markings at the entrance to Canning Half-Tide Dock, Liverpool, relative to the Old Dock Sill datum, a reference datum defined around 1715 in terms of the sill of Liverpool’s first dock. Photo credit: Philip Woodworth, National Oceanography Centre As the name suggests, tide gauges were originally devised for the singular purpose of monitoring tidal fluctuations in sea level in order to aid safe navigation and port operations. Early tide gauges, such as that used by the famous dockmaster William Hutchinson at Liverpool in the late eighteenth century, consisted of little more than graduated markers on sea walls or posts, against which the sea surface could be measured by eye (Figure 1). These were used to record and then forecast the times and heights of high and low water each day; printed in local tide tables, they provided rudimentary information on variations in the tide. Within 50 years, automatic (or “self-registering”) stilling well and float systems were developed, consisting of a float housed in a large vertical tube, with an opening to the sea. The float would rise and fall with the sea surface and, by means of a pen connected to the float via a pulley system, its movements were captured on a paper chart fixed to a clock-driven chart recorder. This, for the first time, produced a continuous sea level trace, allowing other phenomena such as seiches, storm surges, and tsunamis to be clearly identified. Very high frequency variations in sea level, such as wave action, remained unsampled due to the damping effect of the stilling wells. Through continued operation of these gauges over many decades, evidence of longer-term hazards emerged from their records, such as climate change-related sea level rise (SLR), a topic that is now considered in the important regular assessments of the Intergovernmental Panel on Climate Change (IPCC). Over the past few decades, a transition to radar, acoustic, or pressure-based tide gauges, together with advances in data-logging capacity, has enabled high frequency sampling (~1 Hz) that is also necessary for monitoring wave action; in addition, the co-location of Global Navigation Satellite System (GNSS) receivers with tide gauges has allowed scientists to infer the contributions of vertical land motion to rates of SLR. As a result, modern tide gauge networks are better equipped to monitor a wide range of sea level phenomena and are, therefore, viewed as multi-hazard warning systems. Of course, robust warning systems demand a comprehensive network of monitoring stations together with coordinated and timely notifications of impending hazards. Sadly, the impetus for such developments has often been provided by natural disasters. The UK Tide Gauge Network (UKTGN), for example, was formed primarily for the purposes of storm surge monitoring and forecasting following the 1953 North Sea storm surge that led to the loss of ~2,400 lives. More recently, the devastating Sumatran tsunami of 2004 galvanized international cooperation, via the Intergovernmental Oceanographic Commission (IOC), to establish and augment hazard warning tide gauge networks in high-risk areas such as the Indian Ocean and the Caribbean and Mediterranean Seas and to upgrade to modern near-real-time data transmission methods such as the Inmarsat Broadband Global Area Network (BGAN) system. The BGAN system was originally custom built to retrieve data from the remote stations of the UK’s South Atlantic Tide Gauge Network, which was established with the primary scientific aim of monitoring variability in circumpolar ocean transport in the South Atlantic and Southern Ocean. However, the network is now also the primary means of tsunami detection in the remote Southwest Atlantic (Figure 2), where there is presently no coordinated international early warning system. This brings us to an important point about the role of tide gauges in hazard warning: while some gauges are embedded solely as operational tools alongside numerical models within dedicated tsunami and/or storm surge early warning systems, they can never truly achieve multi-hazard status without some scientific evaluation after data collection. Design levels for sea defenses required by planners and civil engineers can only be derived thorough risk assessments, using qualitycontrolled observational data to estimate the combined Tide Gauges: From Single Hazard to Multi-Hazard Warning Systems

Studies of global sea level often exclude Tide Gauges (TGs) in glaciated regions due to vertical land movement. Recent studies show that geodetic GNSS stations can be used to estimate sea level by taking advantage of the reflections from the ocean surface using GNSS Interferometric Reflectometry (GNSS-IR). This method has the immediate benefit that one can directly correct for bedrock movements as measured by the GNSS station. Here we test whether GNSS-IR can be used for measurements of inter annual sea level variations in Thule, Greenland, which is affected by sea ice and icebergs during much of the year. We do this by comparing annual average sea level variations using the two methods from 2008–2019. Comparing the individual sea level measurements over short timescales we find a root mean square deviation (RMSD) of 13 cm, which is similar to other studies using spectral methods. The RMSD for the annual average sea level variations between TG and GNSS-IR is large (18 mm) compared to the estimated uncertainties concerning the measurements. We expect that this is in part due to the TG not being datum controlled. We find sea level trends from GNSS-IR and TG of −4 and −7 mm/year, respectively. The negative trend can be partly explained by a gravimetric decrease in sea level as a result of ice mass changes. We model the gravimetric sea level from 2008–2017 and find a trend of −3 mm/year.

We systematically assimilate a wide range of historical sea level data from around the coast of Great Britain, much of it previously unpublished, into a single comprehensive framework. We show that this greatly increased dataset allows the construction of a robust and extended Mean Sea Level curve for Great Britain covering a period of more than two centuries, and confirms that the 19th century trend was much weaker than that in the 20th century and beyond. As well as attempting to maximise the amount of newly recovered sea level observations, we have also recovered the levelling metadata necessary to connect this 19th and early 20th century data with modern records. We adjust this data for known sources of variability and estimate overall uncertainties over the entire period. Data are processed in 36 regional clusters, before recombining to compute national statistics. We investigate the advantages of extending and adjusting the time series on sea level rise trends and low order variability. Confidence limits are improved by better than 60%. The weighted linear trend since 1900 for the fully adjusted data points from all clusters when averaged annually and adjusted for Glacial Isostatic Adjustment is 2.12 mm/year +/- 0.02 mm/year (1-sigma). The much lower trend estimated for the 19th Century alone is 0.24 +/- 0.12 mm/yr. There is an acceleration of 0.012 mm/yr(2) +/- 0.003 mm/yr(2) in the rate of rise over the period 1813 to 2018. These trends are quite sensitive to the GIA correction used, but their differences and accelerations are not.

GNSS reflectometry (GNSS-R) ability to remote sense the Earth’s surface is affected by an atmospheric bias, as pointed out by several recent studies. In particular, sea level altimetry retrievals are biased in proportion to the reflector height, while by-products, such as tidal amplitudes, are underestimated. Previously, we developed an atmospheric ray-tracing procedure to solve rigorously the three-point boundary value problem of ground-based GNSS-R observations. We defined the reflection-minus-direct or interferometric delay in terms of vacuum distance and radio length. We clarified the roles of linear and angular refraction in splitting the total delay in two components, along-path and geometric. We introduced for the first time two subcomponents of the atmospheric geometric delay, the geometry shift and geometric excess. Finally, we defined atmospheric altimetry corrections necessary for unbiased altimetry retrievals based on half of the rate of change of the atmospheric delays with respect to sine of elevation angle. Later, for users without access to ray-tracing software, we developed closed-form expressions for the atmospheric delay and altimetry correction. The first expression accounts for the angular component of refraction (bending), leading to a displaced specular reflection point. The second one accounts for the linear component (speed retardation) in a homogeneous atmosphere. The expressions are parametrized in terms of refractivity and elevation bending, which can be obtained from empirical models, such as the GPT2 or Bennet’s, or fine-tuned based on in situ pressure and temperature. We also provide a correction for the satellite elevation angle such that the refraction effect is nullified. We validated these expressions against rigorous ray-tracing results and showed that the discrepancy is caused by assumptions in the derivation of the closed formulas. We found the corrections to be beneficial even for small reflector heights, as approximated half of the atmospheric effect originates above the receiving antenna at low satellite elevation angles.

The CORS network is a volunteer-based network of Global Positioning System reference stations located mainly in the US and its territories. We discuss the most recent comprehensive reprocessing of all GPS data collected via this network since 1996. Daily data for GPS weeks 834 through 1933 were reprocessed leading to epoch 2010.0 co-ordinates and velocities of 3049 stations aligned to IGS14. The updated realization of the US National Spatial Reference System derived in this work has been in use since late 2019. As a validation of the results, the derived velocity field is compared to several other solutions and to three regional geophysical and geodetic velocity models. These comparisons uncovered unstable stations which move differently than the regional kinematics around them. Once these are ignored, we estimate the horizontal and vertical stability of this updated realization to be better than similar to 0.3 and similar to 0.6 mm/year, respectively. We use the position residuals and estimated uncertainties from this reprocessing to derive long-term stability measures for all active stations serving longer than 3 years. These measures exposed similar to 60 CORS with the poorest long-term stability, which have been consequently excluded from serving as mapping control.

Observing subdaily surface deformations is important to the interpretation of rapidly developing transient events. However, it is not known whether Global Navigation Satellite System (GNSS) is able to identify millimeter‐level transient displacements over various subdaily timescales. We studied non‐tidal ocean loading (NTOL) using 18 GNSS stations along the southern North Sea for November–December 2013 and compared 3‐h GPS/GLONASS displacements with NTOL predictions. It was found that they overall agreed well with a mean correlation coefficient of 0.6 and their vertical differences had an RMS of 5.7 mm, but a 10‐mm subsidence prediction for December 5th could only be marginally detected. Hence, the spatial coherence among the loading signatures at the 18 stations was harnessed to improve subdaily GNSS, and then the predicted displacements of 5–10‐mm over the subdaily timescales could be discriminated successfully. We envision that adding Galileo/BeiDou signals to GPS/GLONASS can further improve the resolution of subdaily GNSS, which can also enhance the spatial coherence of transient signals captured by regional GNSS stations.

Our ability to quantify the likelihood of present-day extreme sea level (ESL) events is limited by the length of tide gauge records around the UK, and this results in substantial uncertainties in return level curves at many sites. In this work, we explore the potential for a state-of-the-art climate model, HadGEM3-GC3, to help refine our understanding of present-day coastal flood risk associated with extreme storm surges, which are the dominant driver of ESL events for the UK and wider European shelf seas. We use a 483-year present-day control simulation from HadGEM3-GC3-MM (1/4∘ ocean, approx. 60 km atmosphere in mid-latitudes) to drive a north-west European shelf seas model and generate a new dataset of simulated UK storm surges. The variable analysed is the skew surge (the difference between the high water level and the predicted astronomical high tide), which is widely used in analysis of storm surge events. The modelling system can simulate skew surge events comparable to the catastrophic 1953 North Sea storm surge, which resulted in widespread flooding, evacuation of 32 000 people, and hundreds of fatalities across the UK alone, along with many hundreds more in mainland Europe. Our model simulations show good agreement with an independent re-analysis of the 1953 surge event at the mouth of the river Thames. For that site, we also revisit the assumption of skew surge and tide independence. Our model results suggest that at that site for the most extreme surges, tide–surge interaction significantly attenuates extreme skew surges on a spring tide compared to a neap tide. Around the UK coastline, the extreme tail shape parameters diagnosed from our simulation correlate very well (Pearson's r greater than 0.85), in terms of spatial variability, with those used in the UK government's current guidance (which are diagnosed from tide gauge observations), but ours have smaller uncertainties. Despite the strong correlation, our diagnosed shape parameters are biased low relative to the current guidance. This bias is also seen when we replace HadGEM3-GC3-MM with a reanalysis, so we conclude that the bias is likely associated with limitations in the shelf sea model used here. Overall, the work suggests that climate model simulations may prove useful as an additional line of evidence to inform assessments of present-day coastal flood risk.

Atmospheric delays are known to cause biases in Global Navigation Satellite System Reflectometry (GNSS-R) altimetry applications, such as for sea-level monitoring. The main quantity of interest is the reflection-minus-direct or interferometric atmospheric delay. Recently, we have presented a rigorous raytracing procedure to account for linear and angular refraction in conjunction with reflection as observed from near-surface platforms. Here, we demonstrate the feasibility of simplifying the ray trajectory by imposing a rectilinear wave propagation model. Two variants were assessed, based on the apparent or refracted satellite direction on the one hand and the geometric or vacuum conditions on the other hand. The former was shown to agree with rigorous results in terms of interferometric radio length while the latter agreed in terms of the interferometric vacuum distance. Upon a judicious combination of the best aspects of the two rectilinear cases, we have defined a mixed variant with excellent agreement with rigorous raytracing in terms of interferometric atmospheric delay. We further showed that mapping functions developed for GNSS positioning cannot be reused for GNSS-R purposes without adaptations. Otherwise, the total atmospheric delay may be underestimated by up to 50% at low elevation angles. The present work facilitates the adaptation of existing atmospheric raytracing software for GNSS-R purposes.

Rapid determination of sea level variations caused by tsunamis or major storm surges is important for coastal hazard mitigation. Coastal Global Navigation Satellite Systems (GNSS) stations at elevations less than ∼300 m can record time‐varying sea level changes by tracking signals that reflect from the sea surface, relative to direct signals from the satellites. We demonstrate that such GNSS stations can rapidly provide local sea level measurements for a near‐field tsunami, involving many hours of shelf resonance generated by the 2020 Shumagin earthquake as well as for the storm surge accompanying passage of the eye of Hurricane Laura over the Louisiana Coast. Coastal GNSS stations deployed to measure tectonic deformation and co‐seismic displacements can inexpensively augment the spatial sampling provided by tide gauges for measuring tsunami‐induced coastal resonance and storm surges. This information can guide response activities during the crucial initial hours of an event.

The Permanent Service for Mean Sea Level (PSMSL) is the internationally recognised global sea level data bank for long-term sea level change information from tide gauges, responsible for the collection, publication, analysis and interpretation of sea level data. The primary aim of PSMSL is to collate, archive and distribute long-term sea level information from tide gauges. There is a need both for more records in data sparse regions such as Antarctica, the Arctic and Africa, and for a low cost method for monitoring climate change through sea level.Recent studies have demonstrated the utility of ground-based GNSS Interferometric Reflectometry (GNSS-IR) for the observation of sea level. GNSS receivers suffer from multipath, but if the physical and geometric effects multipath has on the measured signals are understood then this knowledge can be used to measure other environmental parameters such as the sea surface reflection. The GNSS receiver can also determine vertical land motion.PSMSL has received funding to create an international archive to preserve and deliver GNSS-IR data and to integrate these data with existing sea level observing networks. We aim to create an efficient data delivery mechanism to allow the sea level community to access these new data and incorporate them into existing records. We will develop a data format and create and/or populate controlled vocabularies with the new parameters, site identifiers and other discovery metadata required.Currently, we have processed records from over 250 GNSS receivers across the globe: each will be made available alongside information detailing how the records were processed; which GNSS constellations, satellites and frequencies were used; and visual diagnostics of each site. In this presentation we will give a brief overview of the theory behind GNSS-IR, and present some of the content that we plan to include in the completed portal.