scholarly journals A Database of Weekly Sea Ice Parcel Tracks Derived from Lagrangian Motion Data with Ancillary Data Products

Data ◽  
2017 ◽  
Vol 2 (3) ◽  
pp. 25 ◽  
Author(s):  
Matthew Tooth ◽  
Mark Tschudi
Keyword(s):  
Sea Ice ◽  
Ancillary Data ◽  
Motion Data ◽  
Data Products ◽  
Lagrangian Motion

scholarly journals Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation

2016 ◽  
Vol 10 (2) ◽  
pp. 761-774 ◽  
Author(s):  
Qinghua Yang ◽  
Martin Losch ◽  
Svetlana N. Losa ◽  
Thomas Jung ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Satellite Data ◽  
General Circulation ◽  
The Arctic ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Uncertainty Estimates ◽  
Ice Concentration ◽  
Data Products

Abstract. Data assimilation experiments that aim at improving summer ice concentration and thickness forecasts in the Arctic are carried out. The data assimilation system used is based on the MIT general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter. The effect of using sea ice concentration satellite data products with appropriate uncertainty estimates is assessed by three different experiments using sea ice concentration data of the European Space Agency Sea Ice Climate Change Initiative (ESA SICCI) which are provided with a per-grid-cell physically based sea ice concentration uncertainty estimate. The first experiment uses the constant uncertainty, the second one imposes the provided SICCI uncertainty estimate, while the third experiment employs an elevated minimum uncertainty to account for a representation error. Using the observation uncertainties that are provided with the data improves the ensemble mean forecast of ice concentration compared to using constant data errors, but the thickness forecast, based on the sparsely available data, appears to be degraded. Further investigating this lack of positive impact on the sea ice thicknesses leads us to a fundamental mismatch between the satellite-based radiometric concentration and the modeled physical ice concentration in summer: the passive microwave sensors used for deriving the vast majority of the sea ice concentration satellite-based observations cannot distinguish ocean water (in leads) from melt water (in ponds). New data assimilation methodologies that fully account or mitigate this mismatch must be designed for successful assimilation of sea ice concentration satellite data in summer melt conditions. In our study, thickness forecasts can be slightly improved by adopting the pragmatic solution of raising the minimum observation uncertainty to inflate the data error and ensemble spread.

scholarly journals Methods for biogeochemical studies of sea ice: The state of the art, caveats, and recommendations

Elem Sci Anth ◽  
2015 ◽  
Vol 3 ◽  
Author(s):  
Lisa A. Miller ◽  
Francois Fripiat ◽  
Brent G.T. Else ◽  
Jeff S. Bowman ◽  
Kristina A. Brown ◽  
...  
Keyword(s):  
Sea Ice ◽  
Primary Production ◽  
Chemical Elements ◽  
Numerical Models ◽  
Inert Gases ◽  
Biogeochemical Processes ◽  
Ancillary Data ◽  
Net Community Production ◽  
Wide Range ◽  
Almost All

Abstract Over the past two decades, with recognition that the ocean’s sea-ice cover is neither insensitive to climate change nor a barrier to light and matter, research in sea-ice biogeochemistry has accelerated significantly, bringing together a multi-disciplinary community from a variety of fields. This disciplinary diversity has contributed a wide range of methodological techniques and approaches to sea-ice studies, complicating comparisons of the results and the development of conceptual and numerical models to describe the important biogeochemical processes occurring in sea ice. Almost all chemical elements, compounds, and biogeochemical processes relevant to Earth system science are measured in sea ice, with published methods available for determining biomass, pigments, net community production, primary production, bacterial activity, macronutrients, numerous natural and anthropogenic organic compounds, trace elements, reactive and inert gases, sulfur species, the carbon dioxide system parameters, stable isotopes, and water-ice-atmosphere fluxes of gases, liquids, and solids. For most of these measurements, multiple sampling and processing techniques are available, but to date there has been little intercomparison or intercalibration between methods. In addition, researchers collect different types of ancillary data and document their samples differently, further confounding comparisons between studies. These problems are compounded by the heterogeneity of sea ice, in which even adjacent cores can have dramatically different biogeochemical compositions. We recommend that, in future investigations, researchers design their programs based on nested sampling patterns, collect a core suite of ancillary measurements, and employ a standard approach for sample identification and documentation. In addition, intercalibration exercises are most critically needed for measurements of biomass, primary production, nutrients, dissolved and particulate organic matter (including exopolymers), the CO2 system, air-ice gas fluxes, and aerosol production. We also encourage the development of in situ probes robust enough for long-term deployment in sea ice, particularly for biological parameters, the CO2 system, and other gases.

scholarly journals Underestimation of surface pCO2 and air-sea CO2 fluxes due to freshwater stratification in an Arctic shelf sea, Hudson Bay

Elem Sci Anth ◽  
2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohamed M. M. Ahmed ◽  
Brent G. T. Else ◽  
David Capelle ◽  
Lisa A. Miller ◽  
Tim Papakyriakou
Keyword(s):  
Sea Ice ◽  
River Runoff ◽  
Dissolved Inorganic Carbon ◽  
Stable Isotope Ratio ◽  
Total Alkalinity ◽  
Hudson Bay ◽  
Co2 Fluxes ◽  
Ancillary Data ◽  
Ice Melt ◽  
The Impact

The objective of this study is to quantify the impact of freshwater stratification on the vertical gradients of partial pressure of CO2 (pCO2) and estimates of air-sea CO2 exchange in Hudson Bay during peak sea-ice melt and river runoff. During the spring of 2018, we sampled water in Hudson Bay and Hudson Strait for dissolved inorganic carbon, total alkalinity, salinity, the oxygen stable isotope ratio in the water (δ18O), and other ancillary data. The coastal domain and regions close to the ice edge had significant vertical concentration gradients of pCO2 across the top meters of the ocean due to the presence of a stratified fresh layer at the surface. The pCO2 and salinity in the central (where sea-ice melt was significant) and the southeast (where river runoff and sea-ice melt were significant) side of the bay generally increased with depth, with average gradients of 4.5 μatm m–1 and 0.5 m–1, respectively. Ignoring these gradients causes a significant error in calculating air-sea CO2 fluxes, especially when using shipboard underway systems that measure pCO2 at several meters below the sea surface. We found that the oceanic CO2 sink in Hudson Bay is underestimated by approximately 50% if underway pCO2 system measurements are used without correction. However, we observed that these gradients do not persist for more than 5 weeks following ice melt. We have derived a linear correction for underway pCO2 measurements to account for freshwater stratification during periods of 1–5 weeks after ice breakup. Given the lack of measurements in stratified Arctic waters, our results provide a road map to better estimates of the important role of these regions in global carbon cycles.

scholarly journals Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison: Closed Ice and Ship-Based Observations

2019 ◽  
Author(s):  
Stefan Kern ◽  
Thomas Lavergne ◽  
Dirk Notz ◽  
Leif Toudal Pedersen ◽  
Rasmus Tage Tonboe ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Data Sets ◽  
Sea Ice Concentration ◽  
Data Set ◽  
Group I ◽  
Ice Concentration ◽  
Data Products ◽  
Group Ii ◽  
The Antarctic

Abstract. Accurate sea-ice concentration (SIC) data are a pre-requisite to reliably monitor the polar sea-ice covers. Over the last four decades, many algorithms have been developed to retrieve the SIC from satellite microwave radiometry, some of them applied to generate long-term data products. We report on results of a systematic inter-comparison of ten global SIC data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global winter-time near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the observed inter-product consistency and differences of the inter-comparison results. Group I consists of data sets using the self-optimizing EUMETSAT-OSISAF – ESA-CCI algorithms. Group II includes data using the NASA-Team 2 and Comiso-Bootstrap algorithms, and the NOAA-NSIDC sea-ice concentration climate data record (CDR). The standard NASA-Team and the ARTIST Sea Ice (ASI) algorithms are put into a separate group III because of their often quite diverse results. Within group I and II evaluation results and intra-product differences are mostly very similar. For instance, among group I products, SIA agrees within ±100 000 km2 in both hemispheres during maximum and minimum sea-ice cover. Among group II products, satellite- minus ship-based SIC differences agree within ±0.7 %. Standing out with large negative differences to other products and evaluation data is the standard NASA-Team algorithm, in both hemispheres. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to the 100 % reference SIC with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic only range from −0.2  to +0.9 %. The standard deviation is smaller in the Arctic for the quoted group I products: 1.9 % to 2.9 % and Antarctic: 2.5 % to 3.1 %, than for group II products: Arctic: 3.6 % to 5.0 %, Antarctic: 4.5 % to 5.4 %. Products of group I exhibit larger overall satellite- minus ship-based SIC differences than group II in both hemispheres. However, compared to group II, group I products’ standard deviations are smaller, correlations higher and evaluation results are less sensitive to seasonal changes. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to re-construct the un-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for, e.g., surface heat-flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and suggest advancing studies about the influence of these weather filters on SIA and SIE time-series and their trends.

scholarly journals Assessment of contemporary satellite sea ice thickness products for Arctic sea ice

2019 ◽  
Vol 13 (4) ◽  
pp. 1187-1213 ◽  
Author(s):  
Heidi Sallila ◽  
Sinéad Louise Farrell ◽  
Joshua McCurry ◽  
Eero Rinne
Keyword(s):  
Sea Ice ◽  
Region Of Interest ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Change Analysis ◽  
Sea Ice Thickness ◽  
Ship Traffic ◽  
Marginal Seas ◽  
Data Products ◽  
Operational Activities

Abstract. Advances in remote sensing of sea ice over the past two decades have resulted in a wide variety of satellite-derived sea ice thickness data products becoming publicly available. Selecting the most appropriate product is challenging given end user objectives range from incorporating satellite-derived thickness information in operational activities, including sea ice forecasting, routing of maritime traffic and search and rescue, to climate change analysis, longer-term modelling, prediction and future planning. Depending on the use case, selecting the most suitable satellite data product can depend on the region of interest, data latency, and whether the data are provided routinely, for example via a climate or maritime service provider. Here we examine a suite of current sea ice thickness data products, collating key details of primary interest to end users. We assess 8 years of sea ice thickness observations derived from sensors on board the CryoSat-2 (CS2), Advanced Very-High-Resolution Radiometer (AVHRR) and Soil Moisture and Ocean Salinity (SMOS) satellites. We evaluate the satellite-only observations with independent ice draft and thickness measurements obtained from the Beaufort Gyre Exploration Project (BGEP) upward looking sonar (ULS) instruments and Operation IceBridge (OIB), respectively. We find a number of key differences among data products but find that products utilizing CS2-only measurements are reliable for sea ice thickness, particularly between ∼0.5 and 4 m. Among data compared, a blended CS2-SMOS product was the most reliable for thin ice. Ice thickness distributions at the end of winter appeared realistic when compared with independent ice draft measurements, with the exception of those derived from AVHRR. There is disagreement among the products in terms of the magnitude of the mean thickness trends, especially in spring 2017. Regional comparisons reveal noticeable differences in ice thickness between products, particularly in the marginal seas in areas of considerable ship traffic.

scholarly journals Development of ground pipeline system for high-level scientific data products of the Hisaki satellite mission and its application to planetary space weather

2019 ◽  
Vol 9 ◽  
pp. A8 ◽  
Author(s):  
Tomoki Kimura ◽  
Atsushi Yamazaki ◽  
Kazuo Yoshioka ◽  
Go Murakami ◽  
Fuminori Tsuchiya ◽  
...  
Keyword(s):  
Solar System ◽  
Spectral Data ◽  
Temporal Resolution ◽  
Space Weather ◽  
Transport Processes ◽  
Light Curves ◽  
Pipeline System ◽  
Ancillary Data ◽  
Data Products ◽  
High Level

The Hisaki satellite is the first-ever space telescope mission dedicated to planetary sciences. Atmospheres and magnetospheres of our solar system planets are continuously monitored by the extreme ultraviolet (EUV) spectrometer onboard Hisaki. This paper describes a data pipeline system developed for processing high-level scientific and ancillary data products from the Hisaki mission. The telemetry data downlinked from the satellite are stored in a ground telemetry database, processed in the pipeline to imaging spectral data with a 1-min temporal resolution and ancillary data products, and then archived in a public database. The imaging spectra can be further reduced to higher-level data products for practical scientific use. For example, light curves of the power emitted from Jupiter’s aurora and plasma torus with a temporal resolution of 10-min can be reduced from the imaging spectral data; the reduced light curves reveal the transport processes of energy and mass in Jupiter’s magnetosphere and associated interplanetary solar wind conditions. Continuous monitoring with Hisaki will contribute considerably to our understanding of space weather relating to planets in our solar system.

scholarly journals Sea Ice Thickness Estimation Based on Regression Neural Networks Using L-Band Microwave Radiometry Data from the FSSCat Mission

2021 ◽  
Vol 13 (7) ◽  
pp. 1366
Author(s):  
Christoph Herbert ◽  
Joan Francesc Munoz-Martin ◽  
David Llaveria ◽  
Miriam Pablos ◽  
Adriano Camps
Keyword(s):  
Neural Networks ◽  
Soil Moisture ◽  
Sea Ice ◽  
Full Range ◽  
The Arctic ◽  
Ice Thickness ◽  
Ancillary Data ◽  
Sea Ice Thickness ◽  
Ground Truth Data ◽  
L Band

Several methods have been developed to provide polar maps of sea ice thickness (SIT) from L-band brightness temperature (TB) and altimetry data. Current process-based inversion methods to yield SIT fail to address the complex surface characteristics because sea ice is subject to strong seasonal dynamics and ice-physical properties are often non-linearly related. Neural networks can be trained to find hidden links among large datasets and often perform better on convoluted problems for which traditional approaches miss out important relationships between the observations. The FSSCat mission launched on 3 September 2020, carries the Flexible Microwave Payload-2 (FMPL-2), which contains the first Reflected Global Navigation Satellite System (GNSS-R) and L-band radiometer on board a CubeSat—designed to provide TB data on global coverage for soil moisture retrieval, and sea ice applications. This work investigates a predictive regression neural network approach with the goal to infer SIT using FMPL-2 TB and ancillary data (sea ice concentration, surface temperature, and sea ice freeboard). Two models—covering thin ice up to 0.6 m and full-range thickness—were separately trained on Arctic data in a two-month period from mid-October to the beginning of December 2020, while using ground truth data derived from the Soil Moisture and Ocean Salinity (SMOS) and Cryosat-2 missions. The thin ice and the full-range models resulted in a mean absolute error of 6.5 cm and 23 cm, respectively. Both of the models allowed for one to produce weekly composites of Arctic maps, and monthly composites of Antarctic SIT were predicted based on the Arctic full-range model. This work presents the first results of the FSSCat mission over the polar regions. It reveals the benefits of neural networks for sea ice retrievals and demonstrates that moderate-cost CubeSat missions can provide valuable data for applications in Earth observation.

scholarly journals Geometric remapping of particle distributions in the Discrete Element Model for Sea Ice (DEMSI v0.0)

2021 ◽  
Author(s):  
Adrian K. Turner ◽  
Kara J. Peterson ◽  
Dan Bolintineanu
Keyword(s):  
Sea Ice ◽  
Element Model ◽  
Discrete Element ◽  
Discrete Element Model ◽  
Earth System ◽  
System Models ◽  
Ice Edge ◽  
Ice Floes ◽  
Lagrangian Motion ◽  
Earth System Models

Abstract. A new sea ice dynamical core, the Discrete Element Model for Sea Ice (DEMSI), is under development for use in coupled Earth system models. DEMSI is based on the discrete element method, which models collections of ice floes as interacting Lagrangian particles. In basin-scale sea ice simulations the Lagrangian motion results in significant convergence and ridging, which requires periodic remapping of sea ice variables from a deformed particle configuration back to an undeformed initial distribution. At the resolution required for Earth system models we cannot resolve individual sea ice floes, so we adopt the sub-gridscale thickness distribution used in continuum sea ice models. This choice leads to a series of hierarchical tracers depending on ice fractional area or concentration that must be remapped consistently. The circular discrete elements employed in DEMSI help improve the computational efficiency at the cost of increased complexity in the effective element area definitions for sea ice cover that are required for the accurate enforcement of conservation. An additional challenge is the accurate remapping of element values along the ice edge, the location of which varies due to the Lagrangian motion of the particles. In this paper we describe a particle-to-particle remapping approach based on well-established geometric remapping ideas that enforces conservation, bounds-preservation, and compatibility between associated tracer quantities, while also robustly managing remapping at the ice edge. One element of the remapping algorithm is a novel optimization-based flux correction that enforces concentration bounds in the case of non-uniform motion. We demonstrate the accuracy and utility of the algorithm in a series of numerical test cases.

SAR and SARin Altimetry Processing on Demand for Cryosat-2 and Sentinel-3 at ESA G-POD

2020 ◽  
Author(s):  
Jérôme Benveniste ◽  
Salvatore Dinardo ◽  
Giovanni Sabatino ◽  
Marco Restano ◽  
Américo Ambrózio
Keyword(s):  
Sea Ice ◽  
Water Level ◽  
Coastal Zone ◽  
Open Ocean ◽  
Data Repository ◽  
Output Data ◽  
Inland Water ◽  
On Demand ◽  
Virtual Station ◽  
Data Products

<p>The scope of this presentation is to feature the G-POD SARvatore service to users for the exploitation of CryoSat-2 and Sentinel-3 data, which was designed and developed by the Altimetry Team in the R&D division at ESA-ESRIN. The G-POD service coined SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research & Exploitation) is a web platform that allows any scientist to process on-line, on-demand and with user-selectable configuration CryoSat-2 SAR/SARin and Sentinel-3 SAR data, from L1A (FBR) data products up to SAR/SARin Level-2 geophysical data products. <br>The G-POD graphical interface allows users to select a geographical area of interest within the time-frame related to the Cryosat-2 SAR/SARin FBR and Sentinel-3 L1A data products availability in the service catalogue. The processor prototype is versatile, allowing users to customize and to adapt the processing according to their specific requirements by setting a list of configurable options. Pre-defined processing configurations (Official CryoSat-2, Official Sentinel-3, Open Ocean, Coastal Zone, Inland Water (20Hz & 80Hz), Ice and Sea-Ice) are available. After the task submission, users can follow, in real time, the status of the processing. The output data products are generated in standard NetCDF format, therefore being compatible with the multi-mission “Broadview Radar Altimetry Toolbox” (BRAT, http://www.altimetry.info) and typical tools.<br>Initially, the processing was designed and optimized uniquely for open ocean studies. It was based on the SAMOSA model developed for the Sentinel-3 Ground Segment. However, since June 2015, the SAMOSA+ retracker is available as a dedicated retracker for coastal zone, inland water and sea-ice/ice-sheet. A new retracker (SAMOSA++) has been recently developed and will be made available in the future. The scope is to maximize the exploitation of CryoSat-2 and Sentinel-3 data over all surfaces providing user with specific processing options not available in the default processing chains.<br>Recent improvements include: 1) A Join & Share Forum to allow users to post questions and report issues (https://wiki.services.eoportal.org/tiki-custom_home.php); 2) A data repository to better support the growing Altimetry Community avoiding the redundant reprocessing of already processed data (https://wiki.services.eoportal.org/tiki-index.php?page=SARvatore+Data+Repository&highlight=repository); 3) A new function in the GUI allowing users to compute the geodetic distance between selected points on the map; 4) A new function in the GUI to filter the products search to a specific RON (Relative Orbit Number) and to a specific pass direction (Ascending or Descending). Furthermore, users will find in the folder SUM_RESDIR of the output data package a short summary report with information on the products that have not been processed and instructions on how to eventually try to re-process the missing data.<br>To respond to the request of hydrologists, and simulate data that a river gauge would provide, SARvatore  will soon include a post-processing service to convert water level estimates in L2 data to virtual station water level values,  which are typically required by hydrologists. Validation of SARvatore data over river targets will be presented to demonstrate the potential of both the SAMOSA+/++ retrackers and the innovative processing configurations not available in the default CryoSat-2 and Sentinel-3 processing chains.</p>

Sign in / Sign up

Export Citation Format

Share Document