Task Team Evolution of the Regional/Global Task Sharing Report 2023 (GHRSST24)

<p>Task team report to the GHRSST Science Team. This presentation was delivered at the GHRSST24 meeting, 16-20 October 2023.</p&gt

GHRSST-14 DAS-TAG Report

The DAS-TAG provides the informatics and data management expertise in emerging information technologies for the GHRSST community. It provides expertise in data and metadata formats and standards, fosters improvements for GHRSST data curation, experiments with new data processing paradigms, and evaluates services and tools for data usage. It provides a forum for producer and distributor data management issues and coordination

Towards improved analysis of short mesoscale sea level signals from satellite altimetry

Satellite altimeters routinely supply sea surface height (SSH) measurements, which are key observations for monitoring ocean dynamics. However, below a wavelength of about 70 km, along-track altimeter measurements are often characterized by a dramatic drop in signal-to-noise ratio, making it very challenging to fully exploit the available altimeter observations to precisely analyze small mesoscale variations in SSH. Although various approaches have been proposed and applied to identify and filter noise from measurements, no distinct methodology has emerged for systematic application in operational products. To best address this unresolved issue, the Copernicus Marine Environment Monitoring Service (CMEMS) actually provides simple band-pass filtered data to mitigate noise contamination of along-track SSH signals. More innovative and suitable noise filtering methods are thus left to users seeking to unveil small-scale altimeter signals. As demonstrated here, a fully data-driven approach is developed and applied successfully to provide robust estimates of noise-free Sea Level Anomaly (SLA) signals. The method combines Empirical Mode Decomposition (EMD), to help analyze non-stationary and non-linear processes, and an adaptive noise filtering technique inspired by Discrete Wavelet Transform (DWT) decompositions. It is found to best resolve the distribution of SLA variability in the 30–120 km mesoscale wavelength band. A practical uncertainty variable is attached to the denoised SLA estimates that accounts for errors related to the local signal-to-noise ratio, but also for uncertainties in the denoising process, which assumes that the SLA variability results in part from a stochastic process. For the available period, measurements from the Jason-3, Sentinel-3 and Saral/AltiKa missions are processed and analyzed, and their energy spectral and seasonal distributions characterized in the small mesoscale domain. In anticipation of the upcoming SWOT (Surface Water and Ocean Topography) mission data, the SASSA data set (Satellite Altimeter Short-scale Signals Analysis, Quilfen and Piolle, 2021) of denoised SLA measurements for three reference altimeter missions already yields valuable opportunities to evaluate global small mesoscale kinetic energy distributions

Brève introduction à la fouille de grandes bases de données océaniques

Les bases de données marines, alimentées par les satellites et les robots autonomes sous-marins comme les flotteurs du réseau Argo, sont de plus en plus grandes (plusieurs dizaines de gigaoctets et teraoctets) et rapidement évolutives (elles changent d’heure en heure). Cette augmentation spectaculaire de la dimension et de la complexité des données rend difficile leur exploitation avec les outils standards. Or, c’est à partir de l’analyse des données que les chercheurs pourront réaliser de nouvelles découvertes scientifiques sur la dynamique des océans, à grande et petite échelles, et les changements climatiques régionaux et globaux. L'école d’été OBIDAM14 visait à contribuer à lever ces verrous d’analyse en introduisant les méthodes de fouille de données aux scientifiques de la communauté de recherche en océanographie physique. Pour un des premiers évènements académiques sur ce thème en France, OBIDAM14 a permis de donner un aperçu de ces méthodes. Le comité scientifique de l'école présente ici un compte-rendu des interventions

Fouille de grandes bases de données océaniques: nouveaux défis et solutions. Compte-rendu factuel de l’école d’été OBIDAM14 organisée par l’Ifremer, le CNRS et Telecom Bretagne, 8-9 septembre 2014, Brest

Fouille de grandes bases de données océaniques : nouveaux défis et solutions. Compte-rendu factuel de l'école d'été OBIDAM14 organisée par l'Ifremer, le CNRS et Télécom Bretagne, 8-9 septembre 2014, Brest

A revised L-band radio-brightness sensitivity to extreme winds under tropical cyclones: The 5 year SMOS-Storm database

Five years of SMOS L-band brightness temperature data intercepting a large number of tropical cyclones (TCs) are analyzed. The storm-induced half-power radio-brightness contrast (ΔI) is defined as the difference between the brightness observed at a specific wind force and that for a smooth water surface with the same physical parameters. ΔI can be related to surface wind speed and has been estimated for ~ 300 TCs that intercept with SMOS measurements. ΔI, expressed in a common storm-centric coordinate system, shows that mean brightness contrast monotonically increases with increased storm intensity ranging from ~ 5 K for strong storms to ~ 24 K for the most intense Category 5 TCs. A remarkable feature of the 2D mean ΔI fields and their variability is that maxima are systematically found on the right quadrants of the storms in the storm-centered coordinate frame, consistent with the reported asymmetric structure of the wind and wave fields in hurricanes. These results highlight the strong potential of SMOS measurements to improve monitoring of TC intensification and evolution. An improved empirical geophysical model function (GMF) was derived using a large ensemble of co-located SMOS ΔI, aircraft and H*WIND (a multi-measurement analysis) surface wind speed data. The GMF reveals a quadratic relationship between ΔI and the surface wind speed at a height of 10 m (U10). ECMWF and NCEP analysis products and SMOS derived wind speed estimates are compared to a large ensemble of H*WIND 2D fields. This analysis confirms that the surface wind speed in TCs can effectively be retrieved from SMOS data with an RMS error on the order of 10 kt up to 100 kt. SMOS wind speed products above hurricane force (64 kt) are found to be more accurate than those derived from NWP analyses products that systematically underestimate the surface wind speed in these extreme conditions. Using co-located estimates of rain rate, we show that the L-band radio-brightness contrasts could be weakly affected by rain or ice-phase clouds and further work is required to refine the GMF in this context

OceanSODA-UNEXE: Gridded surface ocean carbonate system datasets in the Amazon and Congo River outflows

Within the European Space Agency funded Oceanographic datasets for acidification (OceanSODA) project, the University of Exeter (UNEXE) produced the OceanSODA-UNEXE dataset (v1.0) which is an optimal dataset of the surface ocean carbonate system in the Amazon and Congo River outflows. All four main carbonate system variables, total alkalinity (TA), dissolved inorganic carbon (DIC), the partial pressure of carbon dioxide (pCO2) and pH are provided on monthly 1° × 1° grids along with additional carbonate system parameters. The uncertainties within these data have been assessed using independent in situ database (Land et.al 2022). A paper detailing the methodology used to optimally construct and then evaluate this dataset is currently being written. Each netCDF4 dataset file contains 10 or more years of data; the full carbonate system is provided for 2010-2020 in the Amazon outflow (defined as 2°S and 24°N and between 70°W and 31°W) datasets and the full carbonate system is provided for the period 2002-2016 in the Congo outflow (defined as 10°S and 4°N and between 2°W and 16°E). Variables are stored on a 180° by 360° latitude grid with a time dimension (defined as the months from January 1957 to December 2021). Following the methodology of Land et al. (2019), TA and DIC were derived using empirical algorithms from the published literature that use combinations of inputs that include sea surface temperature (SST), sea surface salinity (SSS) datasets and nutrients (silicate (SiO4-), nitrate (NO3-), phosphate (PO4-) or dissolved oxygen (DO). TA and the inputs used to derive it (e.g. SST and SSS) are within the netCDF files prefixed with _TA, whereas DIC and the inputs used to derive it (SST and SSS) are within the netCDF files prefixed with _DIC. The full carbonate system equations (calculating for surface waters) were run twice with PyCO2SYS V1.7 (Humphreys et al., 2022), using the same TA, DIC, SiO4- and PO4- along with the SST and SST datasets from the respective DIC or TA netCDF files. The variables computed with PyCO2SYS are the carbonate ion (CO3-2), the bicarbonate ion (HCO3-), hydrogen ions (H+) ,pH on the total scale, pH on the free scale, pH on the seawater scale, the partial pressure of carbon dioxide (pCO2), the fugacity of carbon dioxide (fCO2),the saturation state of calcite and the saturation state of aragonite. A full list of variables and references for all input data can be found in Table 1. All variable fields have an associated uncertainty field; this uncertainty has the same abbreviated variable name along with the suffix uncertainty (e.g. TA_uncertainty). SST, SSS and nutrient input data uncertainties come from their respective dataset accuracy assessments and dataset references (Table 1). TA and DIC uncertainty is the combined standard uncertainty from the algorithm and input data evaluation determined using the methods of Land et al. (2019) which are consistent with the uncertainty methods of (JCGM, 2008). Uncertainties for the remaining variables were determined by propagating the TA, DIC, SST and SSS uncertainties through PyCO2SYS using a forward finite difference approach (Humphreys et al., 2022)

Reexamining the Estimation of Tropical Cyclones Radius of Maximum Wind from Outer Size with an Extensive Synthetic Aperture Radar Dataset

The radius of maximum wind (Rmax), an important parameter in tropical cyclones (TCs) ocean surface wind structure, is currently resolved by only a few sensors, so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semi-empirical model relying on an outer wind radius, intensity and latitude was fit to best-track data. In this study we revise this semi-empirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, Rmax observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an inter-calibrated dataset of medium-resolution radiometers and scatterometers are considered to revise the model coefficients. The new version of the model is then applied to the period 2010-2020 and yields Rmax reanalyses and trends more accurate than best-track data. SAR measurements corroborate that fundamental conservation principles constrain the radial wind structure on average, endorsing the physical basis of the model. Observations highlight that departures from the average conservation situation are mainly explained by wind profile shape variations, confirming the model’s physical basis, which further shows that radial inflow, boundary layer depth and drag coefficient also play roles. Physical understanding will benefit from improved observations of the near-core region from accumulated SAR observations and future missions. In the meantime, the revised model offers an efficient tool to provide guidance on Rmax when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes

FluxEngine: A flexible processing system for calculating atmosphere-ocean carbon dioxide gas fluxes and climatologies

This is the author accepted manuscript. The final version is available from the American Meteorological Society via the DOI in this record.The air-sea flux of greenhouse gases (e.g. carbon dioxide, CO2) is a critical part of the climate system and a major factor in the biogeochemical development of the oceans. More accurate and higher resolution calculations of these gas fluxes are required if we are to fully understand and predict our future climate. Satellite Earth observation is able to provide large spatial scale datasets that can be used to study gas fluxes. However, the large storage requirements needed to host such data can restrict its use by the scientific community. Fortunately, the development of cloud-computing can provide a solution. Here we describe an open source air-sea CO2 flux processing toolbox called the ‘FluxEngine’, designed for use on a cloud-computing infrastructure. The toolbox allows users to easily generate global and regional air-sea CO2 flux data from model, in situ and Earth observation data, and its air-sea gas flux calculation is user configurable. Its current installation on the Nephalae cloud allows users to easily exploit more than 8 terabytes of climate-quality Earth observation data for the derivation of gas fluxes. The resultant NetCDF data output files contain >20 data layers containing the various stages of the flux calculation along with process indicator layers to aid interpretation of the data. This paper describes the toolbox design, the verification of the air-sea CO2 flux calculations, demonstrates the use of the tools for studying global and shelf-sea air-sea fluxes and describes future developments.European Space Agency (ESA) Support to Science Element (STSE)OceanFlux Greenhouse Gases projectUK NERC CArbon/Nutrient DYnamics and FLuxes Over Shelf Systems (CANDYFLOSS) projec

A new generation of Tropical Cyclone Size measurements from space

Combined microwave brightness temperature measurements from recent L- and dual C-band satellite radiometers provide new estimates of surface wind speed structure in Tropical Cyclones which enhances temporal sampling capability for gale (34-knots), damaging (50-knots) and destructive (64-knots) wind radii. Wind radii estimates in Tropical Cyclones (TC) are crucial to help determine the TC wind structure for the production of effective warnings and to constrain initial conditions for a number of applications. In that context, we report on the capabilities of a new generation of satellite microwave radiometers operating at L-band frequency (~1.4 GHz) and dual C-band (~6.9 and 7.3 GHz). These radiometers provide wide swath (> 1000 km) coverage at a spatial resolution of ~40 km and revisit of ~3 days. L-band measurements are almost unaffected by rain and atmospheric effects, while dual C-band data offer an efficient way to significantly minimize these impacts. During storm conditions, increasing foam coverage and thickness at the ocean surface sufficiently modify the surface emissivity at these frequencies, and in turn the brightness temperature (Tb) measurements. Based on aircraft measurements, new geophysical model functions have been derived to infer reliable ocean surface wind speeds from measured Tb variations. Data from these sensors collected over 2010-2015 are shown to provide reliable estimates of the gale-force (34-kt), damaging (50-kt), and destructive winds (64-kt), within the Best-track wind radii uncertainty. Combined, and further associated with other available observations, these measurements can now provide regular quantitative and complementary surface wind information of interest for operational TC forecasting operations