#  2 SPICES project overview

SPICES is a research and innovation project under the H2020-EO-1-2014 New
ideas for Earth-relevant space applications call in 2014. and running from
June 2015 to May 2018. The main objective of the SPICES is to develop new
methods to retrieve sea ice parameters from existing (and imminent) satellite
sensors to provide enhanced products for polar operators and prediction
systems, specifically addressing extreme and unexpected conditions.

Automatic remote sensing products traditionally provide general information on
sea ice conditions such as ice extent and concentration. However, for ice
charting, tactical navigation and management of off-shore activities much more
important is to know and avoid hazardous sea ice conditions. In general, sea
ice hazards are related to sea ice thickness. More often than not polar ships
and off-shore platforms are only operating during summer seasons and in
certain regions. This is because they are designed to resist typical forces of
induced by pack ice, but they are not designed to resist the extreme sea ice
conditions.

Ongoing climate warming has manifested as shrinking and thinning of pack ice
in the Arctic. This is a primary driver for the increasing shipping, oil and
gas explorations and mining activities in the Arctic. However, severe sea ice
conditions still exist and in consequence many locations are impossible for
ship based operations. Moreover, year-to-year variability of sea ice is very
large and hazardous multiyear ice (MYI) floes sometimes appear also in
typically seasonally ice-free regions.

In order to response needs of increase polar activities, we propose to focus
on detection of sea ice extremes and automatic production of “sea ice
warnings” products. In particular, we aim for a detection of MYI floes in an
area composed mostly first-year ice from synthetic aperture radar (SAR),
heavily ridged ice regions from SAR, the thickest ice from radar altimeter
(RA) thickness profiles, regional anomalies of thick or thin ice via passive
microwave (PMW) data, sea ice areas vulnerable for the wave action, detection
of early/late melting season and improving capabilities to forecast seasonal
sea ice extremes.

#  3 Data Summary

This document describes Data Management Plan for the H2020 SPICES project,
including data used and generated by the project, data access for verification
and re-use by a third party, and activities required to maintain the research
data long-term such that it is available for re-use and preservation (data
curation).

##  3.1 SPICES data overview

The SPICES sea ice products are based on wide variety of Earth Observation
(EO) data obtained from spaceborne sensors, and numerical weather prediction
(NWP) model data. For sea ice product development and validation a wide
variety of in-situ snow and sea ice data are used, as well as some airborne
remote sensing data. Existing data repositories (including in-situ, satellite
and model data) and infrastructure within SPICES partners are utilized in the
SPICES research work.

Data products generated in SPICES are stored in several existing data
repositories, many of them are wellknown. Thus, SPICES will not build up a new
e-infrastructure for the data storage and preservation.

This EO data is available free-of-charge from many sources; Copernicus, ESA,
EUMETSAT, JAXA, NOAA and NASA. In general, all needed EO data are stored by
the SPICES consortium by sharing some data storing by the partners, i.e. raw
sensor data (level 1) are stored by one institute, and others have access to
it. Within the project lifetime it may be possible to use forthcoming ESA
Thematic Exploitation Platforms (TEP) which are central facilities for the EO
data storage and product generation - EO data users can run their product
algorithms at TEPs without the need for downloading the raw EO data.

Multi-parameter data from different in-situ observations (platforms) are
combined into co-located data sets per parameters (e.g. sea ice thickness,
snow depth, roughness, freeboard). The original data sets are collected from
the respective data source. The final co-located data sets are stored and
shared by SPICES. Data from autonomous platforms (buoys) are additionally
available through the international buoy networks IABP and IPAB.

Input and generated data are kept by the partners after the project for a time
(at least for five years) we could expect there to be a public interest and
usage, but for EO raw sensor data and pre-processed data (e.g. level 1
products requiring large storage space) this is not a necessity as the EO data
are also available from the EO satellite operators.

The total amount data stored by SPICES is several tens TBs (largest part is
satellite level 1 data).

All sea ice products (e.g. level 2 swath products and level 3 gridded products
over the Arctic) generated in the SPICES are freely available to public, both
during and after the SPICES project. The storage space required by the sea ice
products will be several hundred GBs.

A SPICES sea ice product contain a sea ice variable (or multiple variables)
data and at minimum geolocation information. Depending on sea ice variable,
quality fields on input data and variable value may be included. The typical
product formats are ASCII-text file, GeoTiff-image, netCDF-file and shape-file
(vector polygon data). In general, product format and standards follow those
used by national Ice Services, EUMETSAT OSI SAF, and Copernicus CMEMS.

The SPICES sea ice products are expected to be useful for scientists working
on sea ice remote sensing or sea ice modelling and forecasting, Arctic climate
change, or developing sea ice products for the Arctic ship navigation. The
products are also of interest for shipping and off-shore companies operating
in the Arctic and needing near real time information in their operations, and
for Arctic policy makers.

###  3.1.1 SPICES input data

In the SPICES project following external datasets are used: 1) in-situ snow
and sea ice data, 2) satellite EO data, 3) airborne remote sensing data, and
4) NWP model data. The used data are described shortly below.

####  In-situ data

In-situ data are collected from different sources / platforms and processed
into common data formats to generate co-location data sets sorted by sea ice
parameters. The following data sets and sources are used by SPICES:

  * Electromagnetic (EM) measurements of (total) sea ice thickness from ground based (EM31, GEM-2) and airborne (helicopter and airplane) applications. Such data sets are usually available from summer time icebreaker expeditions to Arctic and Antarctic sea ice. Sources: Pangaea. 

  

  * The EM data sets are accomplished by in-situ measurements of snow depth (survey data) and point measurements from drillings and stake measurements, as well as other physical properties of sea ice. Sources: Pangaea data sets, ITU & published literature & reports. 

  

  * Measurements from autonomous platforms (buoys) are coordinated through IABP and IPAB. These data provide time series of sea ice thickness, snow depth, air-snow-ice-water temperatures, drift speed and direction (derived from GPS positions) and other sea ice parameters. Sources: CRREL IMB web page, Meereisportal.de, Pangaea. 

  

  * Directional wave buoy data for validation of SAR-waves algorithms for extracting pancake ice thickness. Sources: various research cruises; data managed by CNR and UNIVPM. 

  

  * Visual ship-based sea ice observations following the Antarctic Sea Ice and Processes (ASPeCt) and the according Arctic (ASSIST) protocol: total and partial sea ice concentration (SIC) of the three thickest sea ice categories; for the latter also: sea ice thickness, snow depth, snow type, floe type and size, fraction of deformed ice, ridge height. Sources: ICDC data base at UB, the ASPECT home page http://www.aspect.aq, and data archived in Pangaea. 

  

  * Sea ice draft observations from Upward Looking Sonar (ULS), Weddell Sea (from PANGAEA: 

  

http://doi.pangaea.de/10.1594/PANGAEA.785565, Behrendt et al., ESSD, 2013).

  * Operation Ice Bridge data; see details at  _http://nsidc.org/data/icebridge/_

  

  * Norwegian Young Sea Ice Cruise (N-ICE2015); see details at  _http://www.npolar.no/en/projects/nice2015.html_

  

####  Satellite EO data

Satellite EO data used in the SPICES includes SAR imagery, microwave
radiometer data, microwave scatterometer data, radar altimeter (RA) data, and
optical imagery. This EO data is available free-of-charge from many sources;
Copernicus, ESA, EUMETSAT, JAXA, NOAA and NASA. The main time period for the
EO data to be used is autumn 2010 onwards due to availability of CryoSat-2
radar altimeter data (processed to sea ice thickness) which is essential for
many SPICES sea ice products. CryoSat-2 data has Arctic wide coverage unlike
earlier ENVISAT RA2 and ERS-1/2 radar altimeters. Below is a list used (not
complete) sensors:

  * SAR imagery: SENTINEL-1 SAR (source Copernicus), RADARSAT-2 SAR (Copernicus and national sources), ALOS-2 PALSAR-2 (ALOS-2 Research Announcement (RA) projects), COSMO-SkyMed (Copernicus, ESA Third Party Mission Program, AO projects), TerraSAR-X (Copernicus and AO projects), ENVISAT ASAR (ESA), ALOS PALSAR (MyOcean, RA projects). 

  

  * Microwave radiometer: SSMIS (NOAA/NASA), SMOS (ESA), SMAP (NASA), AMSR2 (JAXA). 

  

  * Microwave scatterometer: METOP ASCAT (EUMETSAT), SMAP (NASA), QuikSCAT, OSCAT. 

  

  * Radar altimeter: CryoSat-2 (ESA), SENTINEL-3 (Copernicus), SARAL / ALTIKA (CNES). 

  

  * Optical imagery: Landsat 5, 7, 8 (USGS), SENTINEL-2 (Copernicus), MODIS (NASA), VIIRS (NOAA), ENVISAT MERIS (ESA), SENTINEL-3 OLCI and SLTSR (Copernicus). 

  

Some of the used EO data are from sensors no longer operating, like ENVISAT
ASAR and MERIS, and QuikSCAT.

In addition to the satellite data, also following derived sea products are
utilized:

  * Melt pond fraction from MODIS: MODIS data (MODIS Surface Reflectance 1-Day L3 Global 500 m SIN Grid V005) of bands 1, 3 and 4 are use in an artificial neural network to obtain the melt pond cover fraction on the Arctic sea ice. The method uses the fact that for surface types melt ponds, sea ice, snow, and open water different reflectance values are measured in the above-mentioned MODIS frequency bands. An artificial neural network has been developed. The approach of Tschudi et al. (2008) has been used to obtain a training data set of typical reflectances for selected regions and typical steps of melt pond cover development. This data set was subsequently used to train the neural network. After evaluation of the training results it has been applied to MODIS reflectances of bands 1, 3, 4 projected into a 500 m grid-cell size polar-stereographic grid to classify abovementioned surface types. 

  

The surface type distribution obtained is analysed and converted into a 12.5
km x 12.5 km grid-cell size product, i.e. the melt-pond fraction per grid
cell. In order to obtain a relative melt-pond fraction, i.e. relative to the
actual SIC, the melt-pond fraction needs to be divided by SIC (here: 1 minus
open water fraction). The data set offered comprises, on the one hand, the
full set of melt pond fraction, its standard deviation, the open water
fraction and the number of usable 500 m grid cells per 12.5 km grid cell.
Those 12.5 km grid cells with less than 10% usable 500 m grid cells or more
than 85% open water fraction are flagged. On the other hand, we offer in
addition melt pond fraction, its standard deviation and the open water
fraction for almost clear-sky areas, i.e. 12.5 km grid cells with more than
90% usable 500 m grid cells; areas with more than 85% open water fraction are
again flagged. This is version v02 of this data set. It differs from version
v01 by a bias correction of the melt pond cover fraction and the open water
fraction which were biased by 8% and 3%, respectively in the old version.

  * Melt pond fraction from MERIS: The current dataset consists of daily averages of the melt pond fraction and broadband albedo for May-September 2011 retrieved from MERIS (Medium Resolution Imaging Spectrometer) swath Level 1b data over the ice-covered Arctic Ocean using the MPD retrieval (Zege et al. 2015). The data is gridded on a 12.5 km polar stereographic grid. The melt pond area fraction is retrieved via inversion of a forward model (Malinka et al. 2016). The MPD retrieval has been validated against field, ship-based and airborne measurements (Istomina et al. 2015a). Case studies and weekly trends are presented by Istomina et al. (2015b). 

  

####  Airborne remote sensing data

Airborne sea ice observations are available in the recent years from polar
research aircraft campaigns during spring time. Routinely collected data sets
are total (snow plus ice) thickness with airborne electromagnetic induction
sounding (AEM) and snow freeboard with airborne laser scanning (ALS). The
surveys are regionally focused on the western Arctic between the Fram Strait
as the eastern limit and the Beaufort/Chukchi Sea north of Alaska as the
western limit. European airborne activities are usually carried out in the
cold-month period between mid-March and early-May in the framework of
institute based funding and international collaboration (AWI, Environment
Canada, York University, University of Alaska, Fairbanks) and ESA supported
satellite validation activities. Data from polar research aircraft is
available since 2009 in irregular intervals and earlier also from helicopter
activities of more limited range back to in key regions (Lincoln Sea since
2004, Beaufort Sea since 2007).

Operation IceBridge provides access to their data sets (snow freeboard, snow
depth and derived thickness products) from their flight lines in the western
Arctic since 2009 (http://nsidc.org/icebridge/portal/). In SPICES we use
IceBridge L4 Sea Ice Freeboard, Snow Depth, and Thickness dataset (Version 1).
This data set contains derived geophysical data products including sea ice
freeboard, snow depth, and sea ice thickness measurements in Greenland and
Antarctica retrieved from IceBridge Snow Radar, Digital Mapping System (DMS),
Continuous Airborne Mapping By Optical Translator (CAMBOT), and Airborne
Topographic Mapper (ATM) data sets.

In summer, sea ice thickness of the snow free summer ice pack is available
from helicopter-borne AEM measurements in the Transpolar-Drift at irregular
intervals.

####  NWP model data

Atmospheric data for derivation of various sea ice products from the EO data
were extracted from the European Centre of Medium-Range Weather Forecast
(ECMWF) ERA-Interim reanalysis data.

#  4 FAIR data

Data management in SPICES will be carried out in accordance with guidelines
for FAIR data management in H2020. This means data collected or generated in
the project must be:

  * F (Findable) – “making data findable, including provisions for metadata” 

  

  * A (Accessible) – “making data openly accessible” 

  

  * I (Interoperable) – “making data interoperable” 

  

  * R (Reusable) – “increase data re-use (through clarifying licenses)” 

  

##  4.1 Making data findable, including provisions for metadata

A key element of making SPICES sea ice products findable is to ensure that all
datasets are accompanied with rich metadata describing the contents and how
data has been processed, as well providing a persistent identifier, if
possible, that uniquely identifies every dataset. Currently, we are not
planning to obtain Digital Object Identifiers for all sea ice products, but to
use mainly SPICES internal naming convention for the products. If DOIs are
required for some products, e.g. due a scientific publication, it is possible
to obtain them.

In the following the SPICES sea ice products are first described, and then the
standards and metadata for the products are introduced.

###  4.1.1 SPICES sea ice products

The data generated in the SPICES includes many intermediate products generated
by one WP and used as input data in another WP, and the final sea ice
products. Here we list and describe only those intermediate products which
contain easy usable data outside the SPICES project. We do not plan to give
public access to pre-processed (calibration, geocoding, noise reduction etc.)
satellite level 1/2 data due to a large public storing space (e.g. ftp-site)
this would require. The pre-processing methods will be documented in SPICES
deliverables, and thus, any third party can pre-process level 1/2 data as the
SPICES do using his/her own software tools. In the following the generated
datasets are divided to intermediate products based on satellite EO data, and
in some cases also on NWP data, those based on multiple datasets, and to final
SPICES sea ice products based on multiple datasets and sea ice models.

Some similar datasets exists by operational services or from previous or
current research projects (e.g. CryoSat-2 ice thickness data). SPICES products
will be in common formats best suitable for merging with different sea ice,
NWP and sea ice model products.

All sea ice products underpin SPICES scientific publications, and generation
of some products require development of new methods which will be themselves
published in scientific journals.

The SPICES sea ice products are described in Tables 4.1-4.3 below. Access to
the SPICES sea ice products are described in Section 4.2.

Table 4.1 SPICES datasets based on multiple input datasets.  
  
<table>  
<tr>  
<th>

**Name**

</th>  
<th>

**Deliverable**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Sea ice data along IMB buoy trajectories

</td>  
<td>

D1.2

</td>  
<td>

Time series of snow/ice parameters along buoy drift trajectories as ASCII
files in ESA CCI RRDP format. Parameters include snow thickness, snow density,
ice thickness, surface temperature, ice/snow interface temperature,
temperatures at standard levels in snow and ice.

</td> </tr>  
<tr>  
<td>

Sea ice data from OIB and CryoVex campaigns

</td>  
<td>

D1.3

</td>  
<td>

Time series of snow/ice parameters along ice drift trajectories as ASCII files
in ESA CCI RRDP format. Parameters snow thickness, snow density, ice
thickness, surface temperature, ice/snow interface temperature, temperatures
at standard levels in snow and ice.

</td> </tr>  
<tr>  
<td>

Co-located daily sea ice dataset along buoy and

ice drift tracks

</td>  
<td>

D1.4

</td>  
<td>

Time series of satellite and ERA Interim NWP data co-located with the buoy and
ice drift trajectories. Satellite data includes SMOS, ASCAT, IR, SMAP, OSCAT,
SSMIS, Sentinel-1, CryoSat-2, etc. NWP data every 6 hours including 2 m air
temperature, 10 m wind speed, radiation fluxes, etc.

</td> </tr> </table>

Table 4.2 SPICES datasets based on satellite EO and NWP datasets.  
  
<table>  
<tr>  
<th>

**Name**

</th>  
<th>

**Deliverable**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

SAR based sea ice

products

</td>  
<td>

D2.4

</td>  
<td>

Set of SAR based sea ice products (e.g. sea ice types, degree of deformation,
ice concentration) generated using developed novel algorithms for utilization
in other WPs.

</td> </tr>  
<tr>  
<td>

Arctic sea ice type

product from satellite RA

</td>  
<td>

D3.4

</td>  
<td>

Sea ice type classification(e.g. WMO sea ice types) based on radar altimeter
waveform data (waveform shape parameters).

</td> </tr>  
<tr>  
<td>

Arctic large scale sea ice dataset at the end of

winter

</td>  
<td>

D4.3

</td>  
<td>

Estimates of snow and ice parameters from snap shots or time series of NWP and
satellite data. Snow/ice parameters include ice types, snow thickness, snow
density and snow/ice interface temperature. The dataset has Arctic wide
coverage for the month of May during several years. SPICES uses this dataset
in ice thickness retrieval and seasonal sea ice forecasting.

</td> </tr>  
<tr>  
<td>

Arctic summer time albedo, melt pond fraction and sea ice concentration data

</td>  
<td>

D5.6

</td>  
<td>

Arctic summer time albedo, melt pond fraction and ice concentration dataset
for at least three years. Based on MERIS (2002-2012), AMSR-E and SMOS
(starting on 2010) and starting on 2015 on Sentinel-3 (optical) and AMSR2 and
SMOS/SMAP observations.

</td> </tr> </table>  
<table>  
<tr>  
<th>

Gridded product of SMOS and SMAP TB

</th>  
<th>

D6.1

</th>  
<th>

Gridded product of SMOS and SMAP brightness temperatures and uncertainties;
daily average, resolution 12-15 km. SMOS measures TB at a range of incidence
angles while SMAP uses a conical scan geometry and a constant incidence angle
at 40°. In order to generate a homogeneous SMOS/SMAP data product the SMOS TB
will be interpolated to the SMAP incidence angle of 40°.

</th> </tr>  
<tr>  
<td>

Gridded product of sea ice thickness from SMOS and SMAP

</td>  
<td>

D6.3

</td>  
<td>

The operational SMOS algorithm of UHAM will be adjusted for the use of SMOS
and SMAP TBs at a constant incidence angle. The ice thickness and its
uncertainty will be estimated from the TBs.

</td> </tr>  
<tr>  
<td>

Sea ice thickness from the SAR wave-spectrum

</td>  
<td>

D6.6

</td>  
<td>

Sea ice thickness estimation based on SAR wavespectrum analysis will be
applied to areas of frazilpancake (FP) ice during periods of new ice formation
and ice growth in regions of turbulence. Sentinel-1 C-band and Cosmo-SkyMed
X-band SAR images, in areas of the Arctic (Greenland Sea) and of Antarctica
(Ross Sea), will be used.

</td> </tr>  
<tr>  
<td>

Improved mean sea-

surface height product from RA

</td>  
<td>

D7.2

</td>  
<td>

An intermediate product of CryoSat-2 data processing is a sea-surface height
product. This will be made publicly available for various external
applications, e.g. in oceanography.

</td> </tr>  
<tr>  
<td>

Sea ice freeboard and thickness from CryoSat-2 with weekly resolution

</td>  
<td>

D7.4

</td>  
<td>

Sea-ice freeboard and thickness from CryoSat-2 data, and co-located snow depth
data, with weekly resolution.

</td> </tr>  
<tr>  
<td>

Operational sea ice freeboard and thickness data from synthetic aperture radar
altimetry

</td>  
<td>

D7.5

</td>  
<td>

Operational data product of sea ice thickness and freeboard from SAR altimetry
data. This data product will concentrate on regions of high interest, and it
will provide highest possible spatial and temporal resolution.

</td> </tr> </table>

Table 4.3 SPICES compiled datasets and sea ice forecasting datasets.  
  
<table>  
<tr>  
<th>

**Name**

</th>  
<th>

**Deliverable**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

Compilation of novel/improved sea ice products

</td>  
<td>

D8.1

</td>  
<td>

Compilation of improved/novel sea-ice products suitable for initialization and
evaluation of coupled sea ice forecasts. Contain e.g. L2 SMOS and CryoSat-2
thickness, CryoSat-2 along track sea ice concentration, sea ice drift and snow
thickness from EO data.

</td> </tr>  
<tr>  
<td>

Sea ice initial conditions using improved and novel products

</td>  
<td>

\-

</td>  
<td>

Sea ice initial conditions at the end of winter from improved and novel SPICES
sea ice products.

</td> </tr>  
<tr>  
<td>

SPICES coupled sea ice forecasts

</td>  
<td>

\-

</td>  
<td>

Coupled sea ice forecasts produced using improved SPICES initial conditions
data.

</td> </tr> </table>

###  4.1.2 Standards and metadata

A SPICES sea ice product contain a sea ice variable (or multiple variables)
data and at minimum geolocation information. Depending on sea ice variable and
data format, different information and quality fields on input data and
variable value may be included. The typical product formats are GeoTiff-image,
NetCDF-file, ASCII text file, and shapefile (vector polygon data). In general,
product formats and standards follow those used by national Ice Services,
EUMETSAT OSI SAF, and Copernicus CMEMS. NetCDF CF (Climate and Forecast)
Metadata Conventions (http://cfconventions.org/) are used where-ever
applicable. The NetCDF files include search keywords to optimize re-use
outside SPICES. All products have clearly stated version numbers.

As an example a SPICES product in NetCDF-format and based on EO data can have
following product information and quality fields (metadata):

  * Full name of the product. 

  

  * Geolocation information: details of the coordinate system (e.g. ellipsoid, reference longitude), corner coordinates in coordinate system, pixel size. 

  

  * List of satellite sensors and auxiliary datasets used. 

  

  * Full names of input data files, e.g. original EO data files. 

  

  * Version of input data. 

  

  * Product version. 

  

  * Product generation date and time in UTC. 

  

  * Product generation institution. 

  

  * Physical units of sea ice variables. 

  

  * Algorithm version for sea ice variable(s). 

  

  * Start and end times of satellite observations for the product. 

  

  * Acquisition time of satellite data for each pixel in the product. 

  

  * Contact information; email. 

  

  * Quality index for each pixel, if possible to derive: depends on the availability and coverage of the input datasets, quality of data processing, and weather and sea ice conditions. Quality parameter and its inputs depend on a sea ice variable. 

  

  * General quality parameter for the product. 

  

For EO based products in tiff-image format the following information can be
given in the filename:

  * Product name. 

  

  * Product version. 

  

  * Product generation date and time in UTC. 

  

Metadata as for NetCDF can be included as tiff-image format tags. For the
shapefile (vector polygons) data the metadata is included as a XML-file (e.g.
similar to ICEMAR format).

##  4.2 Making data openly accessible

All the sea ice datasets described in Section 4.1 originate from the SPICES
project and are made openly accessible. The SPICES datasets are available at
following existing data repositories:

  * WP1 RRDP at  _http://www.seaice.dk/SPICES/_

  

  * WP2 SAR based sea ice products – Zenodo research data repository; for details contact torill.hamre@nersc.no 

  

  * WP3 radar altimeter orbit data based products: sea ice types, freeboard, ice thickness, RIO index – NRT images of products at  _http://ice.fmi.fi_ ,  RIO product files at  _http://ice.fmi.fi/SPICES/d3.4/_ , for other data files contact eero.rinne@fmi.fi 

  

  * WP4 ice concentration and multi-year ice fraction from the optimal estimation algorithm, and snow thickness on sea ice from a regression model – NRT images are available through the DTU Java browser at  _www.seaice.dk_ .  At the moment (May 2018) the datasets are not available as netCDF or Geotiff files. 

  

  * WP5 albedo, melt pond fraction and SIC products are available at:  _https://seaice.uni-bremen.de/data/meris/mpf2.0/_ .  The optical and PM products are stored as separate files and are currently not merged together. The output grid is 12.5km NSIDC polar stereographic grid. 

  

  * WP6 SMOS and SMAP and brightness temperature and sea ice thickness products – ftp-site by UHH  _ftp://ftp-projects.cen.uni-hamburg.de/seaice/Projects/SPICES/_

  

  * WP7 CryoSat-2 products – www.meereisportal.de hosted by AWI, see Section 4.2.1 below. 

  

  * Inquiries on WP8 sea ice forecast datasets can be sent to Steffen Tietsche / ECMWF; _Steffen.Tietsche@ecmwf.int_ . Unfortunately, it is not possible to have public access to the full output data sets, as they are very large. 

  

These repositories have an open data policy and a data license for all
datasets. Some repositories may require registration.

The SPICES datasets can be easily read, processed and visualized using freely
available software tools (e.g. Python). A dataset will accompany a data user
manual, if needed.

A need for a data access committee was not foreseen in SPICES.

The licensing of the SPICES datasets is discussed in Section 4.4.

###  4.2.1 Sea-ice freeboard, thickness from CryoSat-2 and snow-depth with
weekly resolution

The weekly CryoSat-2 sea ice product (D7.4) is distributed as gridded fields
with a spatial resolution of 25 km in netCDF v4 gridded files following the
Climate & Forecast (CF) conventions. Two versions of the datasets exists, a
non-time critical (NTC) product with a timeliness of one month and a near-real
time (NRT) product with a timelines of two days. Both are based on different
ESA input datasets and available on a password-protected ftp site:  
  
<table>  
<tr>  
<th>

Server

</th>  
<th>

ftp://data.meereisportal.de

</th> </tr>  
<tr>  
<td>

Login

</td>  
<td>

user: altim pwd: altim

</td> </tr>  
<tr>  
<td>

CryoSat-2 weekly (NTC)

</td>  
<td>

altim/sea_ice/product/north/cryosat2/cs2awi-v2.0/l3c_weekly/

</td> </tr>  
<tr>  
<td>

CryoSat-2 weekly (NRT)

</td>  
<td>

altim/sea_ice/product/north/cryosat2/cs2awi-v2.0/Latest/l3c_weekly/

</td> </tr>  
<tr>  
<td>

File naming

</td>  
<td>

l3c-awi-seaice-cryosat2-<nrt|ntc>-nh25kmEASE2-<start>-<end>-fv2.0.nc

</td> </tr>  
<tr>  
<td>

</td>  
<td>

<start>, <end>: Start and end date in form of YYYYMMDD

</td> </tr>  
<tr>  
<td>

Parameters

</td>  
<td>

sea ice thickness: `sea_ice_thickness` sea ice freeboard: `sea_ice_freeboard`
snow depth: `snow_depth`

</td> </tr> </table>

The content of the netcdf files can be parsed with several script languages or
tools, e.g. panoply  ( _https://www.giss.nasa.gov/tools/panoply/_ )  for data
visualization.

##  4.3 Making data interoperable

All SPICES datasets described in Section 4.1 can be freely integrated to other
datasets and used freely in scientific studies and commercial activities
outside SPICES, i.e. full unrestricted re-use is allowed by any user.

The SPICES dataset formats and standards follow those used by national Ice
Services, EUMETSAT OSI SAF, and Copernicus CMEMS. NetCDF CF (Climate and
Forecast) Metadata Conventions

(http://cfconventions.org/) are used where-ever applicable.

##  4.4 Increase data re-use (clarifying licences)

A SPICES dataset will be openly shared at earliest when the related
deliverable has been accepted by EC and the deliverable has made publicly
available at the SPICES web-site (https://www.h2020spices.eu/publications/).
All datasets are targeted to be available by the end of the SPICES project.
The reuse of the SPICES datasets is not restricted in anyway after the SPICES
project has ended. Inquiries on the SPICES datasets can be still send to the
SPICES scientists (contact information is in the metadata) after the SPICES
project end. The datasets remains re-usable as long as they are not
scientifically outdated (better products become available due to development
of satellite sensors, models, algorithms, etc.).

All SPICES datasets can be easily read, processed and visualized using freely
available software tools (e.g. Python). Common commercial softwares, like
Matlab, can also be used.

Quality of SPICES datasets (e.g. absolute accuracy against validation data)
will be described in detail in related SPICES deliverables and scientific
publications. We don’t foresee any possible quality problems in pre-processing
of satellite sensor data for sea ice parameter retrievals.

The Creative Commons Attribution 4.0 International license (CC BY 4.0) is used
by the SPICES project for all openly shared SPICES datasets. An end-user is
free to:

  * Share — copy and redistribute the material in any medium or format. 

  

  * Adapt — remix, transform, and build upon the material for any purpose, even commercially. 

  

SPICES cannot revoke these freedoms as long as the end-user follow the license
terms.

For details see:  _https://creativecommons.org/licenses/by/4.0/legalcode_

#  5 Allocation of resources

The costs of making the SPICES datasets available in the formats described in
Section 4.1, and deposit them in data repositories, are eligible costs under
the H2020 Grant Agreement. The respective SPICES Work Packages are responsible
for ensuring that their datasets are uploaded to the data repositories.
General data management is part of WP9 (Management, Coordination and
Dissemination) and is led by Finnish Meteorological Institute (project
coordinator).

In general, all SPICES open access datasets are targeted for long-term secured
storage (at least five years after the project ending). If a data repository
requires decreasing storage space required by the SPICES datasets, then the
SPICES consortium decides which datasets are first deleted. The costs for long
term storage are estimated to be negligible.

#  6 Data security

The SPICES datasets will be stored in established data repositories with
secured funding for long term preservation and curation.

In case of total data loss in the data repositories the SPICES datasets can be
re-processed using input datasets described in Section 3.1.1 and softwares
coded in SPICES. It is assumed that the input datasets are available for a
long time, over ten years.

**7 Ethical aspects**

There are no ethical or legal issues that can have an impact on data sharing.