multi-satellite sea surface temperature data handbook
TRANSCRIPT
Multi-satellite Sea Surface Temperature Data Handbook
Issue 1.0, 22 January 2013
Edited by the Coastal & Marine Research Centre, University College Cork
2
DOCUMENT DETAILS
Author Jacob L. Høyer, Rory Scarrott
Document Title Product Data Handbook: Multi-satellite Sea Surface Temperature Data
Document Reference D180D_HB_ST1
Product Reference ST1
Issue 1.0
Date of Issue 22 January 2013
CHANGE RECORD
Version Date Change Description Author
0.1 17.12.2012 Document Template created RS
0.1 10.01.2013 First draft created JLH
0.2 15.01.2013 Edited & sent back for final inputs RS
1.0 22.01.2013 Version 1.0 completed RS
Front cover image credit: Staff Sergeant Val Gempis (USAF), ABC Action News, EUMETSAT, DMI
3
Contents
Summary ................................................................................................................................................. 5
Abbreviations and acronyms .............................................................................................................. 6
Glossary of Terms................................................................................................................................ 7
1. THE MULTI-SATELLITE SEA SURFACE TEMPERATURE DATA PRODUCT .......................................... 8
1.1 Introduction ............................................................................................................................ 8
1.2 Multi-satellite Sea Surface Temperature Data - the principals behind the data .................... 9
1.3 From Data to Product ........................................................................................................... 10
1.4 Product Validation ................................................................................................................ 12
1.5 Potential Uses of Multi-satellite SST Data in Storm Surge Applications ............................... 12
2. PRODUCT DETAILS ........................................................................................................................ 13
2.1 Technical Description ............................................................................................................ 13
2.2 Accessing the Product ........................................................................................................... 16
2.2.1 Viewing the product using the eSurge website ............................................................ 17
2.2.2 Download the product .................................................................................................. 19
2.2.3 Accessing the product outside eSurge .......................................................................... 19
2.3 Using the Product.................................................................................................................. 19
2.4 Constraints on Use ................................................................................................................ 20
3. FREQUENTLY ASKED QUESTIONS .................................................................................................. 21
4. FURTHER INFORMATION AND CONTACTS .................................................................................... 22
5. REFERENCES & FURTHER READING ............................................................................................... 23
ANNEX A : THE ESURGE PROJECT ..................................................................................................... 24
A.1 About eSurge ........................................................................................................................ 24
A.2 The eSurge Consortium ......................................................................................................... 25
4
List of Figures and Tables
Table 0-1 Acronyms and abbreviations
Table 0-2 Glossary of terms
Figure 1-1 Analysed Sea surface temperature (Kelvin) at 0.00m depth.
Figure 1-2 Timeline of observational datasets used in the OSTIA reanalysis.
Figure 1-3 Schematic of the OSTIA multi-satellite SST analysis system.
Figure 1-4
Global observation-minus-background RMS and mean differences for all observations for the OSTIA reanalysis.
Figure 2-1 Accessing the data on the eSurge website.
Figure 2-2 The data access page of the eSurge website
Figure 2-3 The Cyclone Sidr dataset inventory on the eSurge web-service.
Figure 2-4 Previewing the Cyclone Sidr inundation dataset using the Preview link
Figure 2.5 Viewing dataset metadata and information using OPeNDAP.
Figure 2-6 Exploring Cyclone Sidr datasets in the eSurge GIS web-viewer.
Figure 2-7 Downloading data using the OPeNDAP dataset information page.
5
Summary
Many storms and hurricanes over the ocean gain heat from the ocean, as they pass across the ocean. The use of correct SST fields is therefore crucial to the atmospheric numerical weather prediction models to correctly determine the heat and momentum fluxes across the air-sea interface. This is critical when understanding the development of storm surges as the atmospheric processes drive their generation.
The multi-satellite SST product has been produced by the UK-Meterological Office (UK-Met Office) through the MyOcean project. The product consists of daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg.x 0.05deg. horizontal resolution, using both in-situ and satellite observations.
This product has been provided for dissemination through the eSurge Project, funded by the European Space Agency under the ESA Data User Element (DUE) Programme. For more information on the eSurge project go to http://www.storm-surge.info/. More information about the project and the consortium is given in Annex A.
Also please note that this is considered the initial draft version of the Multi-satellite Sea Surface Temperature dataset handbook and subject to review by users. To this end, we have made available a quick online survey to gather your views on our handbooks, and shape them further into what you need as a user. The simple Survey Monkey ® questionnaire can be found at http://www.surveymonkey.com/s/XX9VH5S, and will take a maximum of five minutes.
6
Abbreviations and acronyms
Table 0-1 lists the acronyms and abbreviations used within this document.
Table 0-1: Acronyms and abbreviations
Acronym Meaning
AOI Area of Interest
ATSR-1 Along Track Scanning Radiometer 1
ATSR-2 Along Track Scanning Radiometer 2
AATSR Advanced Along-Track Scanning Radiometer
CMRC Coastal and Marine Research Centre
DMI Danmarks Meteorologiske Institut (Danish Meteorological Institute).
DUE Data User Elements
DUP Data User Programme
EO Earth Observation
ESA European Space Agency
EUMETSAT European Organisation for the Exploitation of Meteorological Satellites
GMES Global Monitoring for Environment and Security
ICOADS International Comprehensive Ocean-Atmosphere Data Set
KNMI Koninklijk Nederlands Meterologisch Instituut (Royal Netherlands Meteorological Institute)
NERC Natural Environment Research Council (UK)
NetCDF Network Common data Format
NOAA National Oceanic and Atmospheric Administration
NOC National Oceanography Centre
NRT Near Real Time
SAF Satellite Application Facility
UCC University College Cork
QC Quality Control
SSES Single Sensor Error Statistics
SST Sea Surface Temperature
OSI SAF Ocean and Sea Ice Satellite Application Facility (of EUMETSAT)
OSTIA Operational SST and Sea Ice Analysis
SST TAC MyOcean SST Thematic Assembly Centre
TAC Thematic Assembly Centre
7
Glossary of Terms
Table 0-2 lists some specialist terms and their interpretation within this document.
Table 0-2: Glossary of terms
Term Description Inundation The submergence of areas/features which typically lie above the water-line.
Storm surge An unexpectedly high water level brought on by unusual atmospheric conditions. Satellite artificial objects in orbit around the Earth which serve as platforms for Earth
Observation sensors Sea Surface Temperature
The skin temperature of the ocean surface water
Sensor A device that measures a physical quantity (in the case of many Earth Observation sensors - electromagnetic radiation) and converts it into a signal which can be read by an observer or by an instrument.
Validation Determining whether a model fits the data well.
8
1. THE MULTI-SATELLITE SEA SURFACE TEMPERATURE DATA PRODUCT
1.1 Introduction The multi-satellite SST product has been produced by the UK-Meterological Office (UK-Met Office) through the MyOcean project. The MyOcean project aims at developing, implementing and operating the GMES Marine Core Services. The MyOcean system is composed of various sub-systems, amongst which are located the Monitoring and Forecast Centres (MFCs), in charge of producing analyses and forecasts of the ocean state, and the Thematic Assembly Centres (TACs), in charge of producing satellite and in-situ observations based products. This document provides practical information to users on the SST TAC level 4 products over the global ocean, which are processed at the UK Met Office. More details are found in Martin, 2011, Donlon et al., 2012 and Robert-Jones, et al., 2012. An example of the domain is given below:
Figure 1-1: Analysed Sea surface temperature (Kelvin) at 0.00m depth. Valid at 1200 UTC on January 1st, 2000.
The satellite reanalysis covers from 1985 to 2007 and the data set is supplemented with the operational product from 2007 to present. The satellite products included in the reanalysis are shown in the figure below:
9
Figure 1-2: Timeline of observational datasets used in the OSTIA reanalysis. (Source: Martin & Matthew., 2011)
1.2 Multi-satellite Sea Surface Temperature Data - the principals behind the data
This section details the steps performed within the OSTIA reanalysis system to produce the daily SST fields shown schematically in the figure below. Details of the NRT OSTIA analysis procedure are provided in Donlon et al. (2012).
Figure 1-3: Schematic of the OSTIA multi-satellite SST analysis system. (Source: Robert-Jones et al., 2012)
10
1.3 From Data to Product Quality control and pre-processing of input data All satellite and in situ SST data valid for a particular day, with a 24-hour overlap on the days either side, are extracted from the observation data-sets. The input SST data undergo various quality control (QC) and processing steps:
• Pathfinder data which have a quality flag of 4 and higher are accepted • ATSR-1 data with flags of 3, 4 and 5 are accepted. ATSR-2 and AATSR data with flag 5 are
accepted. • ICOADS data which passed all their QC checks are accepted. • A diurnal check is carried out for (A) ATSR and ICOADS data whereby day-time data
(determined using a solar zenith angle calculation) with a wind-speed of less than 6m/s are rejected.
• The Single Sensor Error Statistics (SSES) biases supplied with the (A) ATSR data are removed from each pixel and the SSES standard deviation values are passed on to the next steps in the analysis chain.
• For the AATSR data, a skin-to-bulk correction factor is applied. Bias correction of input satellite data Satellite data can be biased for several reasons, including: atmospheric water vapour; atmospheric aerosol (dust); surface changes (e.g. extreme roughness); instrument calibration problems. These biases can lead to biases in the analysis if they are not treated in some way. OSTIA uses a bias correction system based on match-up statistics between satellite and reference measurements (which are assumed to be unbiased). The reference data-set is specified to be all in situ data and the ATSR-2/AATSR data. Bias corrections are carried out on the Pathfinder AVHRR data and the ATSR-1 data. For each satellite observation type to be calibrated:
• Match-ups are calculated between each reference data point and the satellite data-set (valid on the same day) with a spatial radius of 25km.
• A large scale objective analysis is calculated for each satellite observation type using the match-ups as pseudo-observations of the bias, and a background from the previous day’s bias analysis. The horizontal correlation scales are set to be 700km for this bias analysis.
• The bias analysis field is interpolated back to the satellite observation locations, and the bias subtracted from the satellite observation.
• The outcome of this process is a new version of the satellite data, which have been bias- corrected.
Creation of the L4 analysis and error estimate The process steps for achieving the L4 analysis and accompanying error estimation are as follows:
1. The main SST analysis uses a persistence based approach based on the use of the previous analysis field as a background with a relaxation to climatology. For each grid point and at each analysis time, a relaxation time scale is derived. For ice-free areas this time scale is ~10-days. SSTs under ice are relaxed toward 271.35 K with a shorter time scale of around 2 days. The time scale varies from ~13 days to ~2 days linearly with ice concentration from 0% to
11
100%. A digital Gaussian filter with a half-width of 4.7 km is applied to the background field to remove small scale noise.
2. The background field calculated using equation (1) and the bias corrected measurements (described previously) are then used to produce an analysis using a multi-scale Optimal Interpolation (OI) type scheme. An iterative procedure is used to calculate the OI solution that is both efficient and flexible when processing large numbers of observations.
3. The background error covariance matrix is split into two components, one of which has spatial correlation scales specified as 10km and the other of which has spatial correlation scales of 100km. Both components of the error have spatially varying variances.
4. The observation error covariance matrix is assumed to be a diagonal matrix (observation errors are uncorrelated with each other). The diagonal elements are specified using the SSES standard deviation values supplied with the GHRSST data.
5. The observation operator is used to transform from the analysis grid to observation space. A number of different observation operators have been developed for use in OSTIA in order to represent the full range of satellite observation footprints. In the case of microwave data for instance, the observation footprint is larger than the model grid, and the background gridded values which fall within the observation footprint are used to estimate the model equivalent of the observation.
6. Each SST analysis value is accompanied by an uncertainty estimate. Various methods of approximating analysis error exist. The OSTIA system uses an analysis quality (AQ) optimal interpolation approach to produce this estimate. In this scheme, a second optimal interpolation analysis is performed that is identical to the main SST analysis except that all observations are given a value of 1.0, the background field is set to zero, and the error estimates used in the main analysis (background & measurements) are preserved. This field is then combined with the background error variance estimates described above to produce an analysis error estimate at each grid point.
12
1.4 Product Validation
The user is referred to Robert-Jones et al., 2012 and Donlon et al., 2012 for further details. The figure below shows a global comparison of the satellite product against in situ observations.
Figure 1-4: Global observation-minus-background RMS (gray) and mean (black) differences for (a) all in situ observations, (b) ship observations, (c) drifting buoy observations, and (d) moored buoy observations for the OSTIA reanalysis. Daily statistics have been smoothed with a 5-day rolling mean. (Source: Robert-Jones et al., 2012)
1.5 Potential Uses of Multi-satellite Sea Surface Temperature Data in Storm Surge Applications
Many storms and hurricanes over the ocean gain heat from the ocean, as they pass across the ocean. The use of correct SST fields is therefore crucial to the atmospheric numerical weather prediction models to correctly determine the heat and momentum fluxes across the air-sea interface.
13
2. PRODUCT DETAILS
2.1 Technical Description
The Global Ocean-OSTIA global Sea Surface Temperature product provides daily gap-free maps of sea surface temperature, referred as L4 product, at 0.05deg.x 0.05deg. horizontal resolution, using both in-situ and satellite observations.
Variables:
• sea_ice_area_fraction
• sea_surface_temperature
Geographical coverage:
• Global Ocean
Spatial resolution: 0.05 ('deg')
Temporal resolution: daily
Temporal coverage: from 1985-2012
An example header of a high resolution L4 netCDF file (generated using ncdump) is given below.
netcdf 20000101-UKMO-L4HRfnd-GLOB-v01-fv02-OSTIARAN { dimensions: lon = 7200 ; lat = 3600 ; time = 1 ; variables: int time(time) ; time:long_name = "reference time of sst field" ; time:standard_name = "time" ; time:axis = "T" ; time:calendar = "Gregorian" ; time:units = "seconds since 1981-01-01 00:00:00" ; float lat(lat) ; lat:long_name = "latitude" ; lat:standard_name = "latitude" ; lat:axis = "Y" ; lat:units = "degrees_north" ; float lon(lon) ; lon:long_name = "longitude" ; lon:standard_name = "longitude" ; lon:axis = "X" ; lon:units = "degrees_east" ; short analysed_sst(time, lat, lon) ; analysed_sst:long_name = "analysed sea surface temperature"
14
; analysed_sst:standard_name = "sea_surface_temperature" ; analysed_sst:type = "foundation" ; analysed_sst:units = "kelvin" ; analysed_sst:_FillValue = -32768s ; analysed_sst:add_offset = 273.15f ; analysed_sst:scale_factor = 0.01f ; analysed_sst:valid_min = -300s ; analysed_sst:valid_max = 4500s ; short analysis_error(time, lat, lon) ; analysis_error:long_name = "estimated error standard deviation of analysed_sst" ; analysis_error:units = "kelvin" ; analysis_error:_FillValue = -32768s ; analysis_error:add_offset = 0.f ; analysis_error:scale_factor = 0.01f ; analysis_error:valid_min = 0s ; analysis_error:valid_max = 32767s ; byte sea_ice_fraction(time, lat, lon) ; sea_ice_fraction:long_name = "sea ice fraction" ; sea_ice_fraction:standard_name = "sea_ice_area_fraction" ; sea_ice_fraction:units = "1" ; sea_ice_fraction:_FillValue = -128b ; sea_ice_fraction:add_offset = 0.f ; sea_ice_fraction:scale_factor = 0.01f ; sea_ice_fraction:valid_min = 0b ; sea_ice_fraction:valid_max = 100b ; sea_ice_fraction:source = "EUMETSAT OSI-SAF" ; byte mask(time, lat, lon) ; mask:long_name = "sea/land/lake/ice field composite mask" ; mask:_FillValue = -128b ; mask:flag_values = 1b, 2b, 4b, 8b ; mask:flag_meanings = "sea land lake ice" ; mask:comment = "b0: 1=grid cell is open sea water\n", "b1: 1=land is present in this grid cell\n", "b2: 1=lake surface is present in this grid cell\n", "b3: 1=sea ice is present in this grid cell\n", "b4-b7: reserved for future grid mask data" ; // global attributes: :Conventions = "CF-1.0" ; :title = "OSTIA Sea Surface Temperature and Sea Ice Analysis" ; :DSD_entry_id = "UKMO-L4HRfnd-GLOB-OSTIARAN" ; :references = "None" ; :institution = "UK Met Office" ; :contact = "[email protected]" ; :GDS_version_id = "v1.0-rev1.7" ; :netcdf_version_id = "3.6.0-p1 of May 24 2005 12:38:59 $" ; :creation_date = "2010-11-07 UTC" ;
15
:product_version = "0.1" ; :history = "Created from sst:temperature from /data/nwp/ofrd/share/OSTIA/reanalysis/run/sguqe/STORE_20000101/20000101_sst .nc; error:/data/nw p/ofrd/share/OSTIA/reanalysis/run/sguqe/opdaily/datawg6/20000101-UKMOL4HRfnd- GLOB-v01-fv02-OSTIARAN_err.nc; sea ice:/data/nwp/ofrd/share/OSTIA/reanalysis/r un/sguqe/STORE_20000101/20000101_seaice.nc" ; :spatial_resolution = "6 km" ; :source_data = "Pathfinder AVHRR version 5, Reprocessed (A)ATSR, ICOADS in situ" ; :comment = "IMPORTANT usage statement.\n", "Unless otherwise agreed in writing, these data may be used for pure academic \n", "research only, with no commercial or other application and all usage must meet \n", "the Met Office Standard Terms and Conditions, which may be found here : \n", "http://www.metoffice.gov.uk/corporate/legal/tandc.html . The data may be used \n", "for a maximum period of 5 years. Reproduction of the data is permitted provided \n", "the following copyright statement is included: \n", "(C) Crown Copyright 2010, published by the Met Office. \n", "You must submit a completed reproduction license application form (here \n", "http://www.metoffice.gov.uk/corporate/legal/repro_licence.html ) before using \n", "the data. This only needs to be completed once for each user.\n", "WARNING Some applications are unable to properly handle signed byte values. If values are encountered > 127, please subtract 256 fr om this reported value" ; :start_date = "2000-01-01 UTC" ; :start_time = "00:00:00 UTC" ; :stop_date = "2000-01-02 UTC" ; :stop_time = "00:00:00 UTC" ; :southernmost_latitude = -90.f ; :northernmost_latitude = 90.f ; :westernmost_longitude = -180.f ; :easternmost_longitude = 180.f ; :file_quality_index = 0 ;
16
2.2 Accessing the Product
For many storm cases (events) where storm-surges are reported to have arisen, Inundation data have been collected and made available through the eSurge web-service (see www.storm-surge.info shown in figure 2-1). We would encourage users to look up these events and the suite of dataset products contained on the facility database.
Figure 2-1: Accessing the data on the eSurge website (tabs encircled)
Figure 2-2: The data access page of the eSurge website
17
2.2.1 Viewing the product using the eSurge website
The eSurge web-service provides users with a number of ways to explore and assess the utility of data they are interested in. Upon navigating to a storm surge event of interest (e.g. Cyclone Sidr shown in figure 2-3), the individual datasets can be previewed (figure 2-4) using the preview tab alongside the dataset name as shown encircled in figure 2-3. Alongside the dataset name also lies the link to view global attributes and dataset information through the OPeNDAP viewer (figure 2-5).
Alternatively, users can quick link to the eSurge GIS web-viewer, where they can visualise and explore all the datasets available concerning the event (figure 2-6)
Figure 2-3: The Cyclone Sidr dataset inventory on the eSurge web-service. Preview option links are encircled.
18
Figure 2-4: Previewing a Cyclone Sidr inundation dataset using the Preview link.
Figure 2-5: Viewing dataset metadata and information using OPeNDAP.
Figure 2-6: Exploring Cyclone Sidr datasets in the eSurge GIS web-viewer.
19
2.2.2 Download the product
If users are satisfied that the product suits their purposes, there is an option to download the data using the OPenDAP dataset information window (figure 2-7). Simply click on the “Get ASCII” or “Get Binary” tab to download the data. Note that the ability to download requires registration.
Figure 2-7: downloading data using the OPeNDAP dataset information page (tabs encircled).
2.2.3 Accessing the product outside eSurge
Outside of the eSurge-listed events, the multi-satellite SST product can be obtained from through the MyOcean project (www.myocean.org)
2.3 Using the Product
Useful tools to work with the NetCDF SST data are Python (with netCDF4 and matplotlib libraries) and IDV for plotting. For manipulating the data NCO offers many tools, and Python offers higher level tools as well as low level manipulation of the data. Of course commercial software as Matlab and IDL also provides all capabilities
20
2.4 Constraints on Use
Unless otherwise agreed in writing, these data may be used for pure academic research only, with no commercial or other application and all usage must meet the Met Office Standard Terms and Conditions, which may be found at http://www.metoffice.gov.uk/corporate/legal/tandc.html. The data may be used "for a maximum period of 5 years
Please cite Robert-Jones, et al., 2012 or Donlon et al., 2012 when using these data
21
3. FREQUENTLY ASKED QUESTIONS
This section will remain un-compiled for version 1.0 of this handbook.
This is where the eSurge consortium needs your input as users to guide what we do next. What questions do you have regarding the data? What clarifications do you need or regularly need when assessing other datasets? For which storm surge events would you like to see data gathered during Phase 2 of the eSurge project?
To this end, we have made available a quick online survey to gather your views on our handbooks, and shape them further into what you need as a user. The simple Survey Monkey ® questionnaire can be found at http://www.surveymonkey.com/s/XX9VH5S,, and will take a maximum of five minutes.
22
4. FURTHER INFORMATION AND CONTACTS
For queries regarding Multi-satellite Sea Surface Temperature data, please do not hesitate to contact:
Jacob L Høyer, DMI,
Email address: [email protected]
For queries regarding the eSurge Project, please do not hesitate to contact:
General eSurge contact: Phil Harwood
Website http://www.storm-surge.info
Email address: [email protected]
For queries regarding the European Space Agency (ESA) Data User Elements (DUE) Programme, see http://due.esrin.esa.int/
23
5. REFERENCES & FURTHER READING
Donlon, C., M. Martin, J. Stark, J. Roberts-Jones, E. Fiedler, and W. Wimmer, 2012: The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sens. Environ., 116, 140–158.
Martin & Matthew, 2011: PRODUCT USER MANUAL For OSTIA Level 4 SST reanalysis products over the global ocean SST-GLO-SST-L4-RAN-OBSERVATIONS-010-011
Roberts-Jones, Jonah, Emma Kathleen Fiedler, Matthew James Martin, 2012: Daily, Global, High-Resolution SST and Sea Ice Reanalysis for 1985–2007 Using the OSTIA System. J. Climate, 25, 6215–6232. doi: http://dx.doi.org/10.1175/JCLI-D-11-00648.1
24
ANNEX A: THE ESURGE PROJECT
A.1 About eSurge
Despite the potential utility of satellite data, the storm surge community has not made as much use of it as they could. Largely this is due to the lack of easy data access. Different datasets are stored in different locations, in different data formats and with different access requirements. eSurge aims to change this, bringing relevant datasets together in an east to use, web-accessible database of data products, downloadable in a standardised format.
The eSurge project is being run in two phases. During the initial Development Phase (Phase 1) we have built the database, known as SEARS (Surge Event Analysis and Repository Service), and populated it with initial data for a selection of historical surge events. This will give a useful library that can be used for assessing and improving the performance of numerical models. Whilst most of the datasets are already available, and just need to be imported into the database, others are being created during the project.
Following the launch of the SEARS database, eSurge will move into a Service Demonstration phase (Phase 2). During this phase we will continue to add more historical data, but will also look at making data available for surge events as they occur. The aim is to show that it is feasible to provide satellite data in near real time, so that it could potentially be used in forecasting and warning systems.
It is important to note that eSurge is not itself a forecasting and warning system, it is a system to make data available to forecasters. There are dedicated agencies (such as the UK Environment Agency) whose role it is to warn of impending flooding.
Making the data available is just part of the process of getting people to use it; we must also show the value of the data. To this end our partners at DMI and NOC will perform a series of experiments, focussing on the North Sea and North Indian Ocean. These will take existing models, such as DMI’s HBM model and NOC’s operational CS3X surge model, and will look at how incorporating satellite data could improve the models’ hindcast accuracy. These experiments will also investigate the best way to incorporate satellite data into models. This is a complex subject, and we do not expect to be able to resolve it in this project, but we aim to pave the way for future work
25
A.2 The eSurge Consortium
The eSurge consortium consists of Logica (UK), the National Oceanography Centre (UK), the Danish Meteorological Institute (DK), University College Cork’s Coastal and Marine Research Centre (IRL) and the Royal Dutch Meteorological Institute (NL).
Logica is now part of CGI. Founded in 1976, CGI is a global IT and business process services provider delivering business consulting, systems integration and outsourcing services. With 72,000 professionals operating in 400 offices in 40 countries, CGI fosters local accountability for client success while bringing global delivery capabilities to clients’ front doors. CGI applies a disciplined and creative approach to achieve an industry-leading track record of on-time, on-budget projects and to help clients leverage current investments while adopting new technology and business strategies. As a result of this approach, our average client satisfaction score for the past 10 years has measured consistently higher than 9 out of 10. We have a dedicated international Space and Satcoms business with over 300 specialists and a long track record in delivering mission critical software systems across the Space sector, and in particular for Navigation and GNSS systems. We have worked on many ESA Earth Observation projects, including GlobWave, CCI, GECA, PALSAR and many others.
The National Oceanography Centre (NOC) is a wholly owned centre of the Natural Environment Research Council (NERC). The NOC was formed by bringing together the NERC-managed activity at Liverpool’s Proudman Oceanographic Laboratory and the National Oceanography Centre, Southampton, creating the UK’s leading institution for sea level science, coastal and deep ocean research and technology development. The NOC hosts both the National Tidal and Sea Level Facility, and the Permanent Service for Mean Sea Level (since 1933), and contributes to the Storm Tide Forecasting Service (STFS), developing operational tide-surge models that provide UK coastal flood warning (in partnership with the Met Office and the Environment Agency). It has been at the forefront in developing interfaces to data sources and information. NOC have been involved in ESA funded projects such as COASTALT, GlobColour and GlobWave.
The Danish Meteorological Institute (DMI) is a public institute, providing meteorological, oceanographic and related services for the people of the Kingdom of Denmark (Denmark, the Faroe Islands and Greenland). DMI’s area of activity comprises forecasting and warning services as well as continuous monitoring of weather, sea state, climate, and related environmental conditions in the atmosphere, over land and in the sea. As such, it has national responsibility for carrying out storm
26
surge model forecasts and issuing warnings for Danish areas to the Danish coastal authorities and the public in general. DMI is part of the Baltic Sea Operational Oceanographic System (BOOS) and North West Shelf Operational Oceanographic System (NOOS). DMI play the role as the real-time in-situ sea level centre for the BOOS and NOOS communities. In the MyOcean project DMI leads the Baltic Model Forecasting Centre providing real time ocean forecasting for the Baltic Sea. DMI is part of the High Resolution Local Area Modelling (HIRLAM) developing consortium within numerical weather predictions. DMI is operationally running a number of numerical forecast models for European and Arctic regions, alongside regional and large scale ocean models (HBM and HYCOM). DMI is part of a collaboration developing a coupled atmosphere, ocean and sea ice climate model (EC-Earth), whilst a high resolution coupled ocean and ice forecast model (HYCOM/CICE) is currently being developed at the institute.
The Coastal and Marine Research Centre (CMRC) in University College Cork was established in 1994 to undertake research into coastal and marine resource management. It is part of the Environmental Research Institute (ERI) and the Irish Maritime and Energy Resource Cluster (IMERC).Research and consultancy in the CMRC is undertaken by staff with a range of specialist backgrounds, all of whom work collaboratively in a project orientated environment. The Centre’s expertise and skill sets are highly regarded both nationally and internationally. Fundamental and applied research in the CMRC is organised according to five specialist areas of interest: marine geomatics; applied remote sensing and GIS; marine and coastal governance; coastal processes and seabed mapping and marine ecology. The CMRC works with data from a wide range of satellite EO instruments including MERIS, MODIS, SAR and higher resolution optical datasets (e.g. Landsat, IRS, SPOT, and IKONOS) for land, coastal and marine applications. It lies at the forefront of geomatics research with Europe and internationally, with an ability to work with a variety of data in projects such as FP7 NETMAR, FP6 InterRisk and FP5 DISMAR. It has a track record of engaging end users and stakeholders in projects, organising the CoastColour users’ workshop in 2008 and, was part of the organising committee for the UK remote Sensing and Photogrammetric annual conference held at UCC in September 2010.
The Koninklijk Nederlands Meteorologisch Instituut, KNMI, (Royal Netherlands Meteorological Institute) is a government agency operating under the responsibility of the Dutch Ministry of Transport. It provides weather observations, weather forecasts and vital weather information, whilst carrying out applied and fundamental research in support of its operational tasks and as a global change research centre. Skilled and experienced groups, specialising in diverse topics such as instrument development and electronic read-out, automation, computing, operations control and quality control are employed within the institute, providing quality controlled, and cost effective data acquisition and data processing services. As an operational meteorological data centre and research institute in one, KNMI combines its international networks and collaborative projects in a practical way. It is an active member of the World Meteorological Organisation (Geneva, CH), the European Centre for Medium-range Weather Forecasts (Reading, UK) and the European Organisation for the Exploitation of Meteorological Satellites (Darmstadt, G), and Eumetsat's Ocean and Sea Ice Satellite Application Facility (SAF).
27
BLANK PAGE
28
For more information on this product please contact Jacob L. Høyer at DMI:
For more information on eSurge please contact Phillip Harwood, eSurge Project Manager, at