NACLIM Deliverable D32.28
Report on the documentation and description of the new
Arctic Ocean dataset combining SST and IST
This deliverable is a report documenting the applied data, developed methods and algorithms used to
construct the SST/IST dataset. Error estimates are supplied and the report will describe the mean climatology,
seasonality and interannual variability based on a 15 year period.
WP No.
3.2
WP title
Impact of Arctic initialization on forecast skill
Work duration1) 24
Nature of the
deliverable
x
Due delivery deadline: 30 October 2014
R= report
P= prototype
D= demonstrator
O= Other
Dissemination
level
x
PU = public
PP= restricted to other programme participants, including the Commission
services
RE= restricted to a group specified by the consortium, including the
Commission services
CO= confidential, only for members of the consortium, including the
Commission services
Authors of this report
DANMARKS METEOROLOGISKE INSTITUT (DMI): Gorm Dybkjær, Jacob Høyer, Rasmus Tonboe, Steffen
Olsen
NACLIM project is financed by the European Commission
through the 7th Framework Programme for Research
Theme 6 Environment
Grant Agreement 308299
Page 1
Index
1. Executive summary ....................................................................................................................................... 3
2. Project objectives .......................................................................................................................................... 4
3. Detailed report on the deliverable .................................................................................................................. 4
Intro..................................................................................................................................................................... 4
Input data ........................................................................................................................................................... 6
The AASTI data sets........................................................................................................................................ 8
AASTIs ........................................................................................................................................................... 8
AASTIw .......................................................................................................................................................... 9
AASTIc ......................................................................................................................................................... 10
Algorithms........................................................................................................................................................ 10
Surface temperature decision tree: ......................................................................................................... 10
Calibration ....................................................................................................................................................... 13
SST calibration ........................................................................................................................................... 13
IST calibration .............................................................................................................................................. 14
Validation .......................................................................................................................................................... 17
Comments and future plans ......................................................................................................................... 18
4. References (Bibliography) ........................................................................................................................... 19
4. Publications ............................................................................................................................................. 20
6. The delivery is delayed:  Yes  No ....................................................................................................... 21
7. Changes made and difficulties encountered, if any ...................................................................................... 22
8. Efforts for this deliverable ............................................................................................................................ 22
9. Sustainability ............................................................................................................................................... 22
10. Dissemination and uptake activities ........................................................................................................... 23
Uptake by the targeted audience ....................................................................................................................... 23
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1. Executive summary
This report describes a new climate record of Arctic and Antarctic Ice Surface Temperatures from thermal
Infrared satellite sensors (AASTI), covering high latitude Seas, Sea Ice and Ice Cap surface temperatures
based on satellite infrared measurements. The purposes of this integrated surface temperature data set is; to
establish a data set with optimal data consistency to minimize temporal and spatial biases and, to create a
climate record that enables studies of both large scale climate properties and that at the same time can resolve
high resolution temporal and spatial variability’s. AASTI is a supplement to existing data sets, with lower
resolution, but with longer temporal coverage.
The data set is available in 3 products: 1) The swath based surface temperature data set in original satellite
view projection and resolution (level-2), i.e. in ~0.05 arc degree resolution with multiple daily coverage
(AASTIs); 2) A resampled weekly surface temperature data set in level-3; i.e. a fixed grid in ~0.25 arc degree
resolution without interpolated (AASTIw); and 3) Monthly temperature climatology on a fixed grid of 0.25 arc
degree resolution (AASTIc).
A number of input data sets are used to establish AASTI. The primary data input are the satellite radiometer
infrared brightness temperature (Tb) data used to calculate surface temperatures. These data are derived from
a 28 year Global Area Coverage – Advanced Very High Resolution Radiometer (GAC-AVHRR) data set. The
areas of interest are the Polar Regions, above and below latitudes 40 degree North and South, respectively.
Data at mid and low latitudes are masked out.
The AASTI temperature algorithm is a suite of algorithms selected by surface temperature and sun-elevation,
where the primary choice of algorithm lies in the distinction between sea and ice surfaces. The algorithms for
sea and ice surface temperatures are further developments the best operating algorithms. Calibration of each
applied infrared instrument is done from simulated Top-Of-Atmosphere Tb’s, calculated by a radiative transfer
model. All calibration coefficients for all algorithms and for all instruments are included in this report for public
use.
Errors and biases of the data set in the three main temperature domains under selected conditions are: std. 3.0
K, bias -2.1 K for Arctic sea ice; std. 0.7 K, bias -0.3 K in open North Atlantic water and std. 2.6 K, bias 3.1 K at
Greenland Summit (see Validation section).
Future updates and further developments of AASTI are expected to be carried out in the EUMETSAT-OSISAF
consortium, in the coming projects phase, CDOP3.Further, the reprocessing system developed in this project
will be adapted to the operational products of OSISAF, as a level-2 processing system in Near Real Time
(NRT), using 1km Metop AVHRR data. The OSISAF level-2 output will hereafter form the basis for an Arctic
Level-4 surface temperature product in the MyOcean Version5 product portfolio.
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2. Project objectives
With this deliverable, the project has contributed to the achievement of the following objectives (see DOW
Section B.1.1):
Nr.
1.
Objective
Assessing the predictability and quantifying the uncertainty in forecasts of the North
Atlantic/Arctic Ocean surface state
2. Assessing the atmospheric predictability related to the North Atlantic/Arctic Ocean
surface state
3. Monitoring of volume, heat and fresh water transports across key sections in the
North Atlantic
4. Quantifying the benefit of the different ocean observing system components for the
initialization of decadal climate predictions
5. Establishing the impact of an Arctic initialization on the forecast skill in the North
Atlantic/European sector
6. Quantifying the impact of predicted North Atlantic upper ocean state changes on
the oceanic ecosystem
7. Quantifying the impact of predicted North Atlantic upper ocean state changes on
socioeconomic systems in European urban societies
8. Providing recommendations for observational and prediction systems
9. Providing recommendations for predictions of the oceanic ecosystem
10. Disseminating the key results to the climate service community and relevant
endusers/stakeholders
11. Constructing a dataset for sea surface and sea ice surface temperatures in the
Arctic
Yes
X
No
X
X
3. Detailed report on the deliverable
Intro
Climate monitoring has received increasing attention in recent years and climate researchers are continuously
demanding longer and better observation records to calculate climate trends and anomalies and for climate
model development and validation. A physical quantity that is of special interest in climate monitoring is the
global temperature. Here, the Sea Ice and Sea Surface Temperatures (IST/SST) play an important role,
because the temperature and the physical state of the ocean surface substantially affect the exchange of
energy between ocean and atmosphere.
Climate monitoring is of special interest in the Arctic, because the impact of global warming is estimated to be
a factor of 2 higher there, than the average global warming. That is a consequence of the so called Arctic
amplification, which is mainly caused by the albedo difference between water and ice/snow. Here a decreasing
sea ice area results in increased surface absorption of incoming shortwave radiation. This leads to reduced
new ice production throughout the freezing period, which makes it a self-reinforcing warming process, special
for the Arctic Region.
To simulate climate changes, ocean, ice and atmosphere models must be developed and validated on long
and reliable climate observation records. At very high latitudes, where the traditional observation network is
Page 4
sparse and long observation records are few, satellite observations have since the 1970s been used to fill in
the vast areas where in situ measurements do not exist. However, when dealing with long time series of
observations, one must address data inhomogeneities and systematically errors that can cause artificial trends
and errors in the data set. Data inhomogeneities and errors are often caused by mixing multiple input data
sources, by instrument degradation and/or by changing data processing procedures.
There exist a number of integrated surface temperature climate records that deal with such issues, e.g. The
Global Ice and Sea Surface Temperature data set HadISST (Rayner et al., 2003), NOAA’s Global integrated
Land and Sea surface temperature Analysis (Reynolds et al, 2007) and GISS Surface temperature analysis
from the Goddard Institute for Space Studies (Hansen et al., 2010). These data sets cover the periods 1856onwards, 1880-2006 and 1880-onwards, respectively. A wide range of data sources and observation
techniques are applied to cover the approximately 150 years of global temperature records and the
consistency of the data sets are ensured in the best possible ways. These data sets provide a range of
temperature products, typically ranging from 1 to 5 arc degrees in spatial resolution and from daily to monthly
values.
This report describes a new climate record of Arctic and Antarctic Ice Surface Temperatures from thermal
Infrared satellite sensors (AASTI), covering high Latitude Sea, Sea Ice and Ice Cap surface temperatures. The
purposes of this surface temperature data set is to establish a complementary data set to existing global
temperature records, with focus on enhanced data consistency and resolution. This temperature record differs
from existing temperature records on mainly 3 points: 1) AASTI is based on data from a single sensor-type,
thus optimizing the data consistency, 2) AASTI is a ultra-high temporal and spatial resolution climate record,
enabling for most high and low frequent climate analysis, and 3) AASTI is an integrated surface temperature
data set that eases temperature analysis and applications across different surface regimes. This surface
temperature data processing system is similar to the MODIS IST/SST products, since 2002 (Hall et al. 2004
and 2006), with the record length as a main difference.
The data set is based on one of the longest existing satellite records, namely data from the Advanced Very
High Resolution Radiometer (AVHRR) instruments on board a long series of NOAA satellites. This first version
of AASTI covers the period from 1982 to 2009 in approximately hourly temporal resolution and in
approximately 5 km spatial resolution. This is the coarser Global Area Coverage (GAC) version of the original
~1km AVHRR data. The data set is dedicated and calibrated to atmospheric conditions at high latitudes and it
covers for that reason only the regions north of 40 degree north and south of 40 degree south. AASTI consist
of 3 data sets, namely, a full resolution data set in satellite swath projection, level-2 (AASTIs), a resampled
weekly data set in a fixed geographical grid, level-3 (AASTIw), and a monthly climatology data set in a fixed
geographical grid (AASTIc).
Page 5
Figure 1 AASTIs swath, level-2 surface temperature map of surface and cloud temperatures for the area of interest, i.e.
above and below 40North and 40 South, respectively. Here projected onto a global map illustrating the vast coverage of 1
GAC swath.
Input data
A number of input data sets are used to establish AASTI and of these the primary data are the brightness
temperature (Tb) data that ultimately determines the surface temperatures. The Tb data are derived from a 28
year long Global Area Coverage (GAC) AVHRR data set, CLARA-A1 that is compiled by EUMETSAT’s
Climate Monitoring, Satellite Application Facility (see below, Karlsson et al., 2013; CMSAF). Throughout the 28
year long period, ten different AVHRR satellites have been orbiting 14 times per day and thus providing
approximately bi-hourly coverage of the poles. In figure 2 operation periods of the NOAA satellite are listed.
Metop02 and NOAA19 and newer AVHRR carrying satellites will be included for the next version of AASTI.
Cloud mask data and satellite-sun-earth geometry data are provided along with the CLARA-A1 data set.
Beside CLARA-A1 data, a number of other data sets are input to AASTI. Some data are nice-to-have, and thus
just passed on to the output, other data are used by the surface temperature algorithms. The added and niceto-have data are ice concentration, model air temperature and wind speed and finally, a land, water and icecap
mask. These data can be used to stratify and filter the AASTI data in accordance with specific interests. All
input data are listed and described below.
Page 6
Figure 2 NOAA and Metop satellites carrying the AVHRR sensor.
Input data:
 Brightness temperatures and associated data from GAC-AVHRR level-2 data. This data set is compiled by
the Climate Monitoring Satellite Application Facility (CMSAF) to be the input data set for the CM SAF
cLoud, Albedo and Radiation dataset from AVHRR data, CLARA-A1. (Karlsson et al., 2013).
a) Brightness Temperatures (Tb) from 3 bands with centre wave length at ~3.7 microns (mid infrared), ~11
and ~12 microns (thermal infrared). Continuously onboard calibrated measurements.
b) Cloud mask produced with the Polar Platform System (PPS) cloud processing package (Dybbroe et al.,
2005),
c) Solar and satellite geometry. Some geometrical data are used in the surface temperature algorithms
and others are added to the output data set for post processing purposes, section output data and
algorithms.
 Ice Concentration. Reprocessed data set of daily, global ice concentration data from Ocean and Sea Ice,
Satellite Facility, OSISAF, EUMETSAT (OSISAF_IC_reproc, 2014-09). This is an add-on data set and it is
not used by the surface temperatures algorithms.
 Air temperature. The 2 meter air temperature field from ERA-Interim, reanalysis data set (ERA_interim,
2014_09). This is an add-on data set and it is not used for surface temperatures calculations.
 Wind Speed. Calculated from the u and v wind speed component fields from ERA-Interim, reanalysis data
set (ERA_interim, 2014-09). This is an add-on data set and it is not used for surface temperatures
calculations.
 SST Climate. Daily gridded climate temperature values from OSTIA reprocessed data set (OSTIA, 201409). This data set is used as ‘first guess’ in the daytime SST algorithm (See ‘Algorithms’)
 Land mask from NOAA NESDIS NGDC global relief maps (NOAA_ngdc, 2014-09). From ‘ice_surface’ and
‘bedrock’ ETOPO1 data sets, a land, water/sea ice and land ice mask is produced. The procedure below is
used. Thresholds of 10m and -5m are chosen to minimize noise in the relief maps.
a) ICE CAP: ‘ice_surface’ - ‘bedrock’ > 10 m
b) WATER/SEAICE: ‘ice_surface’ - ‘bedrock’ <= -5 m
c) LAND: Where NO ICE CAP and NO WATER/SEAICE.
This is an add-on data set for stratification of land, water and ice caps (See ‘output data’). It is not used for
surface temperatures calculations.
Figure 3 Figure 3 The polar stereographic map shows average
integrated surface temperature plot of the northern hemisphere.
An example of AASTIw, gridded with data gaps( level-3).
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The AASTI data sets
The core of the 28 year long AASTI data record is the temperature data set calculated for the complete original
level-2 swath data set. However, handling the complete swath data set is very CPU and space consuming and
for many applications, gridded and averaged data sets are more suited and much easier to handle. For these
reasons, two gridded data sets have been calculated from the swath data sets; name a weekly gridded data
set and a monthly climatology data set.
The data sets:
1. AASTIs: A swath based surface temperature data set in original projection and resolution (level-2), i.e.
~0.05 arc degree resolution and multiply daily coverage (see figure 1 and data description below).
2. AASTIw: A weekly and gridded surface temperature data set (level-3), in 0.25 arc degree resolution
(see figure 3).
3. AASTIc: A Climatology data set of gridded and interpolated monthly mean temperature with a
resolution of 0.25 arc degree (see figure 7).
The AASTIs data set is targeted at model development, cal/val activities and for high frequency climate
analysis. The AASTIw is established for general climate studies like distributed trend analysis, and finally, the
AASTIc data set is a traditional monthly climatology reference data set of high latitude water and ice surfaces.
AASTIs
The AASTIs output format is adapted to GHRSST-Data-Specification, version2- revision5 (GDS, 2014-09) from
the GHRSST community (GHRSST, 2014-09) and it is compliant to the Climate and Forecast convention and
metadata, version 1.6 (CF conventions, CF, 2014-09).
AASTIs files include 17 variable, each covering a given GAC swath area North of 40 N and South of 40S, as
shown in figure 1.
Variable description:
 lat: Latitude.
 lon: Longitude.
 mask: sea, icecap and land mask, from NOAA NESDIS (see Input data).
 sea_ice_fraction; Sea ice concentration, from OSISAF (see Input data).
 surface_temperature: The integrated sea and ice skin temperature (see algorithm decision procedure
described in Algorithm).
 sea_surface_temperature: Sea surface skin temperature in 10-20 microns depth. This field is a subset of
the surface temperature field, above, and identical to the SST field in the GDS format description (GDS)
from the GHRSST community (GHRSST, 2014-9).
 st_dtime: a line-wise time offset in seconds, from the Indicative date/time (reference time). I.e. “Indicative
date/time + st_dtime” is the line-wise time of data acquisition.
 l2p_flags: A 2-BYTE bit-field including miscellaneous information. Bit no.0-5. Landmask – bit0, algorithm
bit 1-7, Cloudmask bit 8-15:
0. Land: Land - from the input GAC data set
1. No_algorithm_applied: Algorithm check failed: See algorithm decision tree, figure 4.
2. Sea_Surface_Temperature_day_algorithm: See algorithm decision tree, figure 4.
3. Sea_Surface_Temperature_night_algorithm: See algorithm decision tree, figure 4.
4. Sea_Surface_Temperature_twilight_algorithm: See algorithm decision tree, figure 4.
5. Ice_Surface_Temperature_algorithm: See algorithm decision tree, figure 4.
6. Marginal_Ice_Zone_Temperature_algorithm_sstday_ist: See algorithm decision tree, figure 4.
7. Marginal_Ice_Zone_Temperature_algorithm_sstnight_ist: See algorithm decision tree, figure 4.
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








8. Cloudmask_quality_high: Quality indicator from PPS cloud mask software (PPScloud, 2014-09).
9. Cloudmask_not_processed: No cloud mask data or corrupted data (PPScloud, 2014-09).
10. Cloud_free: no contamination by snow/ice covered surface, no contamination by clouds but
contamination by thin dust/volcanic clouds not checked (PPScloud, 2014-09).
11. Cloud_contaminated: Partly cloudy or semitransparent. May also include dust clouds or volcanic
plumes (PPScloud, 2014-09).
12. Cloud_filled: opaque clouds completely filling the FOV. May also include thick dust clouds or
volcanic plumes (PPScloud, 2014-09).
13. Snow_ice_contaminated: (PPScloud, 2014-09).
14. Undefined: Cloud Mask has been processed but not classified due to separability problems
(PPScloud, 2014-09).
15. Not in use.
quality_level: A 1BYTE bit field of relative quality indicators (GHRSST, 2007-09). Bit no.:
0. no_data
1. bad data
2. worst_quality
3. low_quality
4. acceptable_quality
5. best_quality
6. not in use
7. not in use
sses_standard_deviation: Single Sensor error statistics (GHRSST, 2007-09).
sses_bias: Single Sensor bias statistics (GHRSST, 2007-09).
satellite_zenith_angle: Also called view angle.
probability_of_water: Baysian approach - will be implemented in AASTI version2
probability_of_ice: Baysian approach - will be implemented in AASTI version2
wind: ERA-INTERIM reanalysis (ERA_interim, 2014-09)
t2m: 2m air Temperature from ERA-INTERIM reanalysis (ERA_interim, 2014-09)
solar_zenith_angle: Also called Sun elevation angle.
The AASTIs file name convention follows the GDS file format description (GDS, 2014-09):
<Indicative Date><Indicative Time>-<RDAC>-<Processing Level>_GHRSST-<SST Type>- <Product String><Additional Segregator>-v<GDS Version>-fv<File Version>.<File Type>
Where:
- Indicative Date: The data set, acquisition start date
- Indicative Time: The data set, acquisition start date
- RDAC: Place of creation
- Processing Level: Processing level code
- SST Type: Type of surface temperature
- Product String: The data set identification
- Additional Segregator: optional text – here describing the area of interest.
- GDS Version: Version 2
- File Version: The version of the output data set
- File Type: Type of output file.
An example of a AASTIs file name:
20070315092700-DMI_METNO-L2P_GHRSST-STskin-GAC_polar_SST_IST-noaa18_00000_10589-v02.0fv01.0.nc
AASTIw
The weekly averaged AASTI is calculated from all swath data that comply to a set of conditions (see below).
The data set is available in level-3, which is gridded data fields that can (and does) contain data gaps. AASTIw
Page 9
is available in 0.25 arc degree resolution on a regular lat-lon grid North of 40N and South of 40S. The data are
weekly averages of:





Mean, minimum and maximum temperature
Std of errors and bias
Mean wind and air temperatures (see example plot in figure 8, showing large temperature divergence
between air and skin temperature)
Ice concentration
Land-Ice-Sea mask
In case the minimum and/or maximum temperature for a given period is not represented statistically sound –
the grid point will be assigned a No-Data value.
The AASTIs data used to calculate the weekly temperature fields comply to these conditions:
 Cloud-mask: Cloud free and snow/ice contaminated Cloud Mask data.
 Cloud-mask: ‘High quality’cloud-mask data over water and both high and low quality cloud mask over ice.
 View-angle: Satellite-zenith angles are less than 55 degrees.
 Tolerance: Absolute difference between 2m air temperature (embedded model data) and radiometric
surface temperature must not exceed 10 K.
AASTIc
The AASTI surface temperature climatology data set is available in a regular lat-lon grid, North of 40N and
South of 40S, with 0.25 arc degree grid spacing. AASTIc. The climatology are monthly averaged temperature
fields based on AASTIw data, and the available variables are identical to the AASTIw data set, plus a monthly
surface temperature trend map, based on a linear trend analysis.
Algorithms
The AASTI algorithm is in fact a suite of algorithms that are selected by different configurations of temperature
and sun-zenith angle domains. The primary choice of algorithm lies in the distinction between sea and ice
surfaces. This distinction is made from a simple brightness temperature threshold. This is an approach
adapted from the integrated IST/SST algorithm CASSTA that was introduced by Vincent et al. (Vincent el al.
2008). The IST algorithm is subdivided into 3 temperature domains, each with its own algorithm, i.e. cold,
medium and warm ice algorithms. The SST algorithm is made up by a day, a night and a twilight algorithm
each triggered by the current sun-zenith angle. A temperature interval between the SST and IST algorithm
domains is defined as the marginal ice zone. The marginal ice zone surface temperature (MIZT) algorithm is a
scaled average of the IST and SST algorithms; hence, the MIZT is an average temperature for the mixed ice
and water surface area.
Surface temperature decision tree:
The algorithm selection and surface temperatures calculations are based on the three infrared channels of the
AVHRR instrument, the solar-zenith angle and a first guess of SST, here represented by the OSTIA daily
climatology (OSTIA, 2014):


T37, near infrared brightness temperature of AVHRR channel 3b with centre wavelength at ~3.7 microns.
T11, thermal infrared brightness temperature of AVHRR channel 4 with centre wavelength at ~11 microns.
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



T12, thermal infrared brightness temperature of AVHRR channel 5 with centre wavelength at ~12 microns.
Tclim, OSTIA daily climate value.
satza, sat-zenith angle.
sunza, sun-zenith angle
The IST algorithm (equation 1) is a split window algorithm, working within three domains (Key et al., 1997). The
algorithms coefficients are calibrated within 3 T11 temperature intervals (cold, medium and warm), for each
individual satellite. The calibration table for IST algorithms are shown in tables 3, 4 and 5, and the IST
temperature domains are:



ISTcold, cold ice calibration for T11 < 240K;
ISTmedium, medium ice calibration for 240K ≤ T11 < 260K
ISTwarm, warm ice calibration for T11 ≥ 260K;
𝐼𝑆𝑇 = 𝑎 + 𝑏 𝑇11 + 𝑐 (𝑇11 − 𝑇12) + 𝑑 (𝑇11 − 𝑇12) 𝑠𝑡𝑒𝑡𝑎,
(𝐸𝑞. 1)
1
Where 𝑠𝑡𝑒𝑡𝑎 = cos(𝑠𝑎𝑡𝑧𝑎) − 1
The sea surface temperature algorithms SSTday, SSTnight and SSTtwilight (equations 2, 3 and 4,
respectively) are, like the IST algorithm, calibrated for 3 domains, but in this case the domains are temporal:
 SSTday, day time calibration for sunza ≤ 90 degrees. The day time algorithm formalism is a slightly
modified version of the operational day time algorithm used in the OSISAF SST product (OSISAF 2014) to
deal with a slight bias for the North Atlantic area (Le Borgne et al., 2014).
 SSTnight, night time calibration for sunza ≥ 110 degrees. The night time algorithm formalism is identical to
the operational night time algorithm used in the OSISAF Global SST product (OSISAF 2014)
 SSTtwilight, twilight calibration for 110 degree ≥ sunza > 90 degree. SSTtwilight is scaled linearly between
SSTday and SSTnight, in accordance with the sunza (OSISAF 2014).
The SST day and night algorithm coefficients are shown in tables 1 and 2, respectively.
𝑆𝑆𝑇𝑑𝑎𝑦 = (𝑎 + 𝑏 𝑠𝑡𝑒𝑡𝑎) 𝑇11 + (𝑐 + 𝑑 𝑠𝑡𝑒𝑡𝑎 + 𝑒 𝑇𝑐𝑙𝑖𝑚)(𝑇11 − 𝑇12) + 𝑓 + 𝑔 𝑠𝑡𝑒𝑡𝑎,
𝑆𝑆𝑇𝑛𝑖𝑔ℎ𝑡 = (𝑎 + 𝑏 𝑠𝑡𝑒𝑡𝑎) 𝑇37 + (𝑐 + 𝑑 𝑠𝑡𝑒𝑡𝑎)(𝑇11 − 𝑇12) + 𝑒 + 𝑓 𝑠𝑡𝑒𝑡𝑎,
𝑆𝑆𝑇𝑡𝑤𝑖𝑙𝑖𝑔ℎ𝑡 = 0.05 (𝑠𝑢𝑛𝑧𝑎 − 90) 𝑆𝑆𝑇𝑛𝑖𝑔ℎ𝑡 − 0.05 (𝑠𝑢𝑛𝑧𝑎 − 110) 𝑆𝑆𝑇𝑑𝑎𝑦,
(𝐸𝑞. 2)
(𝐸𝑔. 3)
(𝐸𝑞. 4)
Finally, the surface temperature is also defined for the marginal ice zone, MIZT, and the algorithm is given in
equation 5. The MIZT is linearly scaled between SST and IST in the temperature interval of for
268.95 K ≤ T11 < 270.95 K.
𝑀𝐼𝑍𝑇 = 0.5 (𝑇11 − 268.95) 𝑆𝑆𝑇 − 0.5 (𝑇11 − 270.95)𝐼𝑆𝑇,
Adapted from Vincent et al. (2008)
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(𝐸𝑞. 5)
fail
GAC IR data
Test for data
validity, area of
interest.
GAC cloud and
geometry data
T11 < 270.95
T11 < 240.0
ISTcold
240 ≤ T11 < 260.0
ISTmedium
T11 ≥ 268.95
T11 ≥ 260.00
ISTwarm
satza < 90
SSTday
satza > 110 90 < satza ≤ 110
SSTtwilight
SSTnight
268.95 ≤ T11 < 270.95
MIZT
Surface Temperature
Figure 4AASTI surface temperature algorithm decision tree
A schematic overview of the full algorithm decision tree is given in figure 4. The input data are spectral AVHRR
data and satellite-sun-earth geometry information. After validity check (see below), the choice of algorithm is
taken based on temperature and sun elevation.
The data validity check is partly a check for area of interest, partly for data sanity and finally, a cloud check.
The area of interest is North of 40N and South of 40S - in between these 2 parallels, the data are truncated.
The sanity check ensures realistic temperature characteristics. A surface temperature is rejected if the data are
flagged missing or bad and if (T11-T12) > 2.0 for T11 > 268.95 K. This is an indication of ice fog. Further, a
surface temperature is rejected if the calculated surface temperature is less than T11, which is not possible,
and if 150 K > Surface Temperature > 350K, which are unrealistic surface temperatures. In case of rejections a
flags is set in the output file. This is fully explained in the AASTI netCDF file.
Following this logic a given pixel in the data set is either rejected, with a flag value or a valid surface
temperature value. In case of a valid temperature there is no guaranty that the value is ice or water. The
temperature value can also represent cloud or land with no ice. The temperature value must therefore be
checked for cloud, quality and a land mask. These data fields are described in the Output data section.
Page 12
Calibration
There are basically two ways to calibrate the surface temperature algorithms used in this data set: 1) To relate
satellite measurements to in situ observations, and 2) to relate modelled surface temperatures with modelled
top-of-atmosphere brightness temperatures, determined by a radiative transfer model (RTM). However, each
AVHRR sensor has slightly different response functions to incoming radiation, which demands a statistically
robust amount of calibration data for each of the 10 satellite sensors used in the data set. It is not feasible to
collect sufficiently and well distributed in situ observations from all areas of interest, to obtain sufficient
calibration statistics for all applied sensors. Calibration of both the IST and SST algorithms is therefore carried
out using the RTM-approach. All algorithm coefficients in the following are valid for temperatures in Kelvin.
SST calibration
Coefficients for the SST algorithms were generated using a simulated brightness temperature (Tb) dataset in
turn generated from a dataset containing 31,673 Arctic profiles (Francois et al., 2002). The simulated Tb’s were
generated from RTTOV (RTTOV) using 10 different satellite zenith angles (0.0, 36.87, 48.19, 55.15, 60, 63.61,
66.42, 68.68, 70.53, 72.08). Coefficients for each sensor and algorithm were determined using least squares
regression. The full calibration tables for SSTday and SSTnight algorithms are shown in tables 1 and 2,
respectively.
Table 1 SSTday algorithm coefficients.
sat_id
noaa07
noaa09
noaa11
noaa12
noaa14
noaa15
noaa16
noaa17
noaa18
noaa19
metop02
a
b
c
d
e
f
g
1.02262
1.02553
1.02431
1.02115
1.02333
1.02342
1.01645
1.02392
1.01987
1.01779
1.03039
0.00912
0.01313
0.01142
0.00822
0.00665
0.01009
0.00014
0.01025
0.00212
-0.00224
0.01749
-1.13547
-0.60318
-1.01728
-1.44226
-1.15131
-1.2144
-1.9598
-1.07042
-1.5313
-1.6994
-0.29966
0.29602
0.28305
0.27839
0.30361
0.29205
0.27806
0.35027
0.27846
0.30351
0.32175
0.25514
0.00969
0.00843
0.00957
0.0107
0.00888
0.01006
0.01213
0.00943
0.00996
0.01015
0.00629
-6.0159
-6.73886
-6.45182
-5.62978
-6.2254
-6.23032
-4.4509
-6.35402
-5.34952
-4.80906
-8.13237
-1.4482
-2.49523
-2.06865
-1.2229
-0.73695
-1.71728
0.91701
-1.73739
0.43173
1.61212
-3.73728
Table 2 SST night algorithm coefficients
sat_id
noaa07
noaa09
noaa11
noaa12
noaa14
noaa15
noaa16
noaa17
a
1.01728
1.01871
1.01866
1.01579
1.0164
1.01855
1.01531
1.01811
b
0.03037
0.03213
0.03148
0.02983
0.02915
0.02905
0.02385
0.03081
c
1.24856
1.40724
1.31134
1.1493
1.05074
1.20324
1.04917
1.22122
Page 13
d
0.08426
0.05423
0.06446
0.11497
0.10687
0.0755
0.14037
0.07635
e
-3.89277
-4.24249
-4.25333
-3.47731
-3.65356
-4.4194
-3.64534
-4.08931
f
-7.21553
-7.68699
-7.52029
-7.0459
-6.83634
-6.95533
-5.58498
-7.31389
noaa18
noaa19
metop02
1.01593
1.015
1.01937
0.02683
0.02405
0.03637
0.98969
0.90622
1.1998
0.11903
0.13705
0.0582
-3.55909
-3.37403
-4.45263
-6.23845
-5.5104
-8.87747
The day and night coefficients are defined in the SSTday and SSTnight algorithm in formulars 2 and 3,
respectively.
IST calibration
Like the calibration of the SST algorithms, the IST algorithm calibrations are carried out using modelled surface
and TOA brightness temperatures. The basis for the new coefficients is an Arctic profile database covering one
year (2011) of ERA Interim atmospheric data (ERAint, 2014-09).
The initial profile database has 8,695 profiles, see figure 6. Profiles were picked from a sample of 960 locations
each day of the year, at times 0, 6, 12 and 18 UTC. Each profile complies with a land-ratio of zero, surface
temperatures less than 272K, and a cloud cover of less than 10%. Simulated TOA brightness temperatures
associated with the ERA-interim surface temperatures for over 10 different satellite zenith angles (0.0, 36.87,
48.19, 55.15, 60, 63.61, 66.42, 68.68, 70.53, 72.08), where generated using RTTOV11 (RTTOV). Ultimately,
the simulated IST calibration data set consisted of 86,950 data points.
AVHRR channel T11
AVHRR channel T12
AVHRR channel T37
Figure 5 The snow emissivity as a function of viewing angle for all operational AVHRR
instruments since 1982.
Page 14
RTTOV11 includes a model for calculating sea surface emissivities but does not include a model for land or
sea ice. Instead, as a default, RTTOV will use hardcoded values of 0.98 (land) and 0.99 (sea ice), respectively,
if the user indicates that RTTOV should 'calculate' the surface emissivity.
To describe the snow emissivity as a function of the
incidence angle we have used a snow emission
model with a typical snow type as input. The model is
described in Dozier and Warren (1982) and it is using
snow surface density at 300 kg/m3 and snow grain
size at 100 microns. The simulated spectral emissivity
is folded with the spectral response function for each
of the channels T37, T11 and T12 and for each of the
AVHRR
instruments
and
used
as
the
angle
dependent snow surface emissivity in the RTTOV
simulations
for
estimating
the
IST
algorithm
coefficients. The calculated and applied angular
dependent emissivities are plotted in figure 5, for Figure 6 Calibration profile positions for all IC=90%
each channel and for each sensor.
Figure 7 Monthly minimum-mean temperature maps for March (left) and September (right), 2007.
The full calibration tables for the ISTcold, ISTmedium and ISTwarm algorithms are shown in tables 3, 4 and 5,
respectively. The IST algorithm coefficients are defined formular 1.
Page 15
Table 3 Calibration table for the ISTcold algorithm.
sat_id
noaa07
noaa09
noaa11
noaa12
noaa14
noaa15
noaa16
noaa17
noaa18
noaa19
metop02
A
-3.1518
-2.89434
-2.94537
-3.80957
-3.45832
-3.56173
-3.53659
-3.1728
-3.04596
-3.24543
-3.21614
b
1.01341
1.01238
1.01256
1.01635
1.01469
1.0152
1.01514
1.01353
1.01287
1.01375
1.01371
c
0.93564
0.73633
0.80477
1.08612
0.93275
1.07877
1.21312
0.8432
0.8523
0.93709
0.86601
d
0.02495
0.03392
0.02875
0.05628
0.00155
0.05581
0.06273
0.03168
-0.00213
0.00328
0.03649
c
1.52775
1.60136
1.55334
1.31951
1.27299
1.56022
1.14418
1.46846
1.1599
1.00743
1.44255
d
0.01233
-0.07294
-0.04305
0.15909
0.05608
0.09864
0.22182
0.00221
0.03534
0.11042
0.0237
Table 4 Calibration table for the ISTmedium algorithm.
sat_id
noaa07
noaa09
noaa11
noaa12
noaa14
noaa15
noaa16
noaa17
noaa18
noaa19
metop02
a
-3.3834
-3.96853
-3.52994
-2.45254
-3.19604
-3.47579
-1.64335
-3.41004
-2.46457
-2.08332
-3.20022
b
1.01366
1.01579
1.01408
1.01041
1.01309
1.01427
1.00726
1.01374
1.01
1.00873
1.01295
Table 5 Calibration table for the ISTwarm algorithm.
sat_id
noaa07
noaa09
noaa11
noaa12
noaa14
noaa15
noaa16
noaa17
noaa18
noaa19
metop02
a
-3.62886
-4.95859
-4.37347
-2.8603
-3.14519
-3.61679
-1.12456
-4.08373
-2.27026
-1.13714
-3.87652
b
1.01434
1.01917
1.01694
1.01175
1.01264
1.01467
1.00495
1.01601
1.00886
1.00476
1.01525
Page 16
c
1.49753
1.67851
1.60349
1.39748
1.24742
1.48129
1.33341
1.48153
1.24532
1.10571
1.46076
d
0.32873
0.28765
0.28351
0.37574
0.31873
0.40322
0.34313
0.30949
0.22732
0.2531
0.31115
Validation
The data set is not fully validated, because the AASTI production is ongoing in the time of writing. However, separate
years have been selected for a temporary validation of different temperature and surface regimes. The validation statistics
is summarized in table 6.
Table 6 AASTI validation statistics for the main surface domains under selected conditions: Greenland Summit:
Validation results are based on Greenland Summit air temperatures. Data are day-time, view-angles less than 60, 80%
cloud free cloud mask inside nearest 5x5 pixels; absolute difference with ERA-interim air temperatures smaller than 10K.
Antarctic Vostok: Validation results are based on Vostok air temperatures. Data are day-time; view-angles less than 55;
80% cloud free and high quality cloud mask inside nearest 5x5 pixels. North Atlantic SST: Validation results are based
on the ‘Francois’ matchup data base (Francois et al., 2002). Data are day-time; North of 60N; view-angles less than 60;
80% cloud free inside 5x5 pixels; absolute difference with ERA-interim air temperatures less than 5K; absolute difference
with OSTIA climate temperatures less than 5K. Arctic IST: Metop-02 AVHRR data using the calibration coefficients from
tables 3-5. Validation results are based on a quality screened (not thorough) Arctic buoy temperatures. Data are nighttime; view-angles less than 45degrees; cloud free and high quality cloud mask; Ice concentration greater that 95%;
absolute difference with ERA-interim air temperatures smaller than 10K.
Surface
regime
Summit,
Greenland
Vostok,
Antarctica
North
Atlantic SST
Arctic IST
Period
2007
2007
2007
2011
Observation
type
2m air
temperature
2m air
temperature
Buoy
temperature
Buoy
temperature
Std.error
r
Bias
-3.1
No. of
samples
495
Cloud
screening
PPS-cloud
2.6
0.95
2.2
0.92
-3.4
420
PPS-cloud
0.7
0.97
-0.3
2447
PPS-cloud
3.0
0.91
-2.1
3421
PPS-cloud
Source
GTS,
DMI
GTS,
DMI
Coriolis
Data set
GTS,
DMI
Four different surface regimes are validated. The first is the WMO meteorological station at Greenland summit
(wmo 04416), on the central part of the Greenland ice sheet. Here 2m air temperatures are compared with
AASTIs data. The AASTI data are screened by the PPS-cloud classification system (see Input data). The
Vostok validation scheme is partly comparable the Summit validation scheme. However, error statistics for
Vostok is split into two; data that are cloud screened by the PPS cloud mask and data cloud screened using
the automatic cloud detection system in Vostok. The automatic cloud detection system in Vostok compares
incoming shortwave radiation to a standard irradiation curve and estimated cloud cover from the ratio of these
two values. Buoy temperatures from the North Atlantic and the Arctic Ocean are neither real air nor true skin
temperatures, and thus, validation results can be dubious. Temperatures from floating buoys are a top level
sea temperature, rather than a skin temperature and buoy temperatures from sea ice embedded buoys can be
both air temperature and snow temperature at some depth and a mixture of the two.
As a reference for the error statistics, Stammer et al. (2007) considered 4K and 1.5K to be the error threshold
for global numerical weather prediction model to benefit from IST and SST observations in data assimilation
schemes.
Page 17
Figure 8 Mean September temperature difference between embedded ERA interim 2m air temperature and AASTI
surface temperature.
Comments and future plans
A new surface temperature climate data set (AASTI) has been build, targeted at high latitude sea, sea ice and
ice cap areas. The satellite based observation data set is dealing with essential issues of climate record
construction, namely spatial and temporal homogeneity and biases. The temporal and spatial homogeneity of
data coverage is pursued using a nearly identical number of daily satellite swaths from nearly identical
instruments, throughout the 28 year long climate data record. In-flight calibration of the AVHRR channels 3, 4
and 5 minimize the risk of systematically biases. No AVHRR inter-calibration has been applied to the infrared
data, only standard NOAA calibration methodologies are followed (Karlsson et al., 2013). However, the visible
AVHRR bands, here used for cloud masking, have undergone thorough inter-calibrations and trend corrections
(Karlsson et al., 2013). The next version of AASTI will be based on an improve input data set, CLARE-A2,
which will have improved cloud mask detection and the input data set will become an officially validated and
reviewed data set (Karlsson, Pers. Comm., 2013).
This surface temperature climate record is unique in 3 ways, 1) It covers the marginal ice zone, and 2) it is the
first reprocessed high resolution IST climate record, and 3) it is based on observations from a single sensortype. AASTI provide both multiply daily coverage in 5 km spatial resolution and weekly gridded data as well as
monthly sea ice temperature climatology. The climatology period is the full length of the 28 year input data set
from 1982 to 2009.
Occasional updates and further development of the AASTI climate record will be pursued through the OSISAF
consortium and by the continuation of MyOcean/Copernicus. The reprocessing system that is developed in this
Page 18
project will be adapted to the operational portfolio of OSISAF to a level 2 processing system for Metop AVHRR
data. The OSISAF level-2 output will form the basis for an Arctic Level-4 surface temperature product in
MyOcean.
4. References (Bibliography)
CF. http://cfconventions.org, 2014-09.
Dozier, J. and S.G. Warren. Effect of Viewing Angle on the Infrared BrightnessTemperature of Snow. WATER
RESOURCES RESEARCH, 18, 5, pp. 1424-1434, 1982.
Dybbroe, A., A. Thoss and K.-G. Karlsson: NWC SAF AVHRR cloud detection and analysis using dynamic
thresholds and radiative transfer modeling - Part I: Algorithm description, J. Appl. Meteor, 44, pp. 39-54,
2005a.
Dybbroe, A., A. Thoss and K.-G. Karlsson: NWC SAF AVHRR cloud detection and analysis using dynamic
thresholds and radiative transfer modeling - Part II: Tuning and validation, J. Appl. Meteor, 44, 55-71, 2005b.
Dybkjær, G., R. Tonboe, and J. L. Høyer. Arctic surface temperatures from Metop AVHRR compared to in situ
ocean and land data. Ocean Sci., 8, 959–970, doi:10.5194/os-8-959-2012, 2012.
ERAint. http://www.ecmwf.int/en/forecasts/datasets/era-interim-dataset-january-1979-present, 2014-09.
François C., A Brisson, P. Le Borgne, A. Marsouin. Definition of a radiosounding database for sea surface
brightness temperature simulations: Application to sea surface temperature retrieval algorithm determination.
Remote Sensing of Environment, 81, 2–3, pp 309–326, 2002.
GDS. https://www.ghrsst.org/documents/q/category/ghrsst-data-processing-specification-gds/, 2014-09
GHRSST. https://www.ghrsst.org, 2014-09
Hall, D. K., Key, J. R., Casey, K. A., Riggs, G. A., and Cavalieri, D.J.: Sea Ice Surface Temperature Product
from MODIS, IEEE T. Geosci. Remote., 42, 1076–1087, 2004.
Hall, D. K., V. V. Salomonson, and G. A. Riggs. MODIS/Aqua Sea Ice Extent and IST Daily L3 Global 4km
EASE-Grid Day. Version 5. Boulder, Colorado USA: National Snow and Ice Data Center. 2006.
Hansen, J., R. Ruedy, M. Sato, and K. Lo (2010), Global surface temperature change, Rev. Geophys., 48,
RG4004, doi:10.1029/2010RG000345. 2010.
Hollmann, Rainar. Personal Communication (mail), 2013.
Karlsson, K.-G., Personal Communication (mail), 2013.
Karlsson, K.-G., Riihelä, A., Müller, R., Meirink, J. F., Sedlar, J., Stengel, M., Lockhoff, M., Trentmann, J.,
Kaspar, F., Hollmann, R., and Wolters, E.: CLARA-A1: a cloud, albedo, and radiation dataset from 28 yr of
global AVHRR data, Atmos. Chem. Phys., 13, 5351-5367, doi:10.5194/acp-13-5351-2013, 2013.
Key, J. and Haefliger, M. Arctic Ice Surface Temperature Retrieval from AVHRR Thermal Channels, J.
Geophys. Res.-Oc. Atm., 97, 5885–5893, 1992.
Key, J. R., Collins, J. B., Fowler, C., and Stone, R. S.: High-Latitude Surface Temperature Estimates from
Thermal Satellite Data, Remote Sens. Environ., 61, 302–309, 1997.
Page 19
Le Borgne, Pierre, S.Péré and H. Roquet. METOP-A/AVHRR derived SST over the Arctic 6 years (2007-2012)
daytime results. Working Paper, Météo-France, CMS, 2014.
NOAA_ngdc. National Oceanic and Atmospheric Administration, National Geophysical Dtaa Centre.
http://www.ngdc.noaa.gov/mgg/global/global.html, 2014-09
NWCSAF. http://www.nwcsaf.org, 2014-09.
OSI SAF project team (2014). Low earth orbiter sea surface temperature product user manual.
Version 2.6 Prepared by Météo France.
OSISAF_IC_reproc. Product Manual for Global Sea Ice Concentration Reprocessing product.
Osisaf.met.no/docs, 2014-09.
OSTIA. http://podaac.jpl.nasa.gov/dataset/UKMO-L4HRfnd-GLOB-OSTIA , 2014-09.
PPScloud. The Polar Platform System package, Product User Manual for "Cloud Products".
http://www.nwcsaf.org/HD/MainNS.jsp, CMa-PGE01 v3.2, CT-PGE02 v2.2 & CTTH-PGE03 v2.2, 2014-09
Rayner, N.A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent and A.
Kaplan. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late
nineteenth century. Journal of Geophysical Research, 108, D14, doi:10.1029/2002JD002670, 2003.
Reynolds, R.W., T.M. Smith, C. Liu, D.B. Chelton, K.S. Casey and M.G. Schlax, 2007: Daily High-ResolutionBlended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496, DOI:10.1175/2007JCLI1824.1
RTTOV. http://nwpsaf.eu/deliverables/rtm/, 201410.
Smith, T. M., R.W.Reynolds , T.C.Peterson , and J.Lawrimore, Improvements to NOAA's Historical Merged
Land-Ocean Surface Temperature Analysis (1880-2006), J. Climate, 21, 2283-2293, 2008.
Stammer, D., Johanessen, J., LeTraon, P.-Y., Minnett, P., Roquet, H., and Srokosz, M.: Requirements for
Ocean Observations Rel-evant to post-EPS, EUMETSAT Position Paper: AEG Ocean Topography and Ocean
Imaging, 10 January 2007, version 3, 2007.
Vincent, R. F., Marsden, R. F., Minnett, P. J., Creber, K. A.M., and Buckley, J. R. Arctic waters and marginal
ice zones: A composite Arctic sea surface temperature algorithm using satellite thermal data, J. Geophys.
Res.-Oceans, 113, C04021, doi:10.1029/2007JC004353, 2008.
4. Publications
5.
Peer reviewed publications
1
Title
Authors
Title of the
periodical or the
series
Volum
e and
releva
nt
pages
Publisher
Year of
publicati
on
Permanent
identifiers[1] DOI
Is/Will
open
access
1provided
to this
publicatio
n?
Arctic surface
temperatures from
Metop AVHRR
Dybkjær, G.,
R. Tonboe,
and J. L.
Ocean Science
8
EGU journals
2012
doi:10.5194/os-8959-2012
yes
Check the NACLIM „Dissemination Plan“ on the open access requirements: http://naclim.zmaw.de/Deliverables.2224.0.html
Page 20
compared to in situ
ocean and land data
Characterisation and
quantification of
regional diurnal SST
cycles from SEVIRI
Observations and
modeling of the
diurnal SST cycle in
the North and Baltic
Seas
SST diurnal variability
in the North Sea and
the Baltic Sea
A bias correction
method for Arctic
satellite sea surface
temperature
observations
A cascade of warming
impacts brings bluefin
tuna to Greenland
waters
Simulations of the
snow covered sea ice
surface temperature
and microwave
effective temperature.
The simulated sea ice
thermal microwave
emission at window
and sounding
frequencies.
Høyer
Karagali, I.
and Høyer,
J. L.
Ocean Science
10
EGU journals
2014
doi:10.5194/os10-745-2014
yes
Karagali, I.,
and J. L.
Høyer
J. Geophys.
Res. Oceans
118
AGN
2013
doi:10.1002/jgrc.2 yes
0320
Karagali I.,
Jacob
Høyer,
Charlotte
Hasager
Høyer, J. L.,
Le Borgne,
P., &
Eastwood,
S.
MacKenzie,
B. R.,
Payne, M.
R., Boje, J.,
Høyer, J. L.
and
Siegstad, H.
Tonboe, R.
T., G.
Dybkjar, J. L.
Hoyer
Remote Sensing
of Environment
121
Elsevier
2012
DOI:
10.1016/j.rse.201
2.01.016
yes
Remote Sensing
of Environment
146
Elsevier
2014
DOI:
10.1016/j.rse.201
3.04.020
yes
Global Change
Biology
20
Wiley
2014
DOI:
10.1111/gcb.125
97
yes
Tellus
63A
Wiley
2011
DOI:
10.1111/j.16000870.2011.00530
.x
yes
Tonboe, R. T Tellus
62A
Wiley
2010
DOI:
10.1111/j.16000870.2010.00434
.x
yes
Publications in preparation OR submitted
In preparation
OR submitted?
Title
All authors
Title of the periodical or the
series
Is/Will open access be
provided to this publication?
In preparation
28 years of global Ice
Surface Temperature
data from GAC-AVHRR
data
Dybkjær, G., C. Luis,
R. Tonboe, J. L. Høyer
and S. Eastwood.
The Cryosphere
yes
6. The delivery is delayed:
 Yes
Page 21
 No
7. Changes made and difficulties encountered, if any
We have implemented the following (positive) changes, enhancing what we had originally promised in the
DOW:
 The climate record is extended to 28 years – change from the announced 15 years.
 The area of interest is extended to both hemispheres – change from Arctic coverage
 The sensor calibration scheme was comprehensively expanded by setting up and applying a Radiative
Transfer Model, the RTTOV11 RTM.
8. Efforts for this deliverable
Partner
Person-months
Period covered
8
1 November 2012-30 October 2014
DANMARKS
METEOROLOGISKE INSTITUT
8
Total
Total estimated effort for this deliverable (DOW) was 8 person-months. The names of the scientist involved are
mentioned on the cover page of this report.
9. Sustainability
We have managed to extend both the temporal and the spatial coverage of the climate data set as well as we
have enhanced the algorithm and calibration work compared to the initial intensions. This has been made
possible due to an informal partnership with the Norwegian NORMAP project (http://normap.nersc.no). The
overall goal of NORMAP is to create and maintain a data repository, including metadata of high latitudes and
Arctic regions based on Earth Observation data from polar orbiting satellites, to facilitate and stimulate high
quality and original multidisciplinary Earth System research, application and education in marine, polar and
climate sciences. NACLIM and NORMAP have thus clear mutual interests in present climate record, and
through this partnership, we managed to improve and extend the data set beyond the Description of Work. The
partnership will be an important element in the sustainability of this work. Cristina Luis and Steinar Eastwood
from the Norwegian Meteorological Institute (partner in NOPMAP) were our direct collaborators.
As indicate in the section 3, occasional updates and further development of the AASTI climate record will be
pursued through the OSISAF consortium and by the continuation of MyOcean/Copernicus. The reprocessing
system that is developed in this project will be adapted to the operational portfolio of OSISAF to a level 2
processing system for Metop AVHRR data. The OSISAF level-2 output will form the basis for an Arctic Level-4
surface temperature product in MyOcean.
It is planned that the EUMETSAT OSISAF consortium will play an important role in the future updates and
dissemination of this data set. However, that will depend upon the negotiations of the subsequent OSISAF
project period.
Finally, all data produced in this task are compliable to the format used by the acknowledged GHRSST Data
processing Specifications. GDS is used by more or less all SST-user and producer communities. This
ensures an easy user applicability of the 3 data sets produced is this task.
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10. Dissemination and uptake activities
The complete list of dissemination activities is available on the NACLIM website
http://naclim.zmaw.de/Dissemination.2509.0.html and on the ECAS portal, section “List of Dissemination
Activities”.
Uptake by the targeted audience
According to the DOW, your audience for this deliverable is:
X
The general public (PU)
X
The project partners, including the Commission services (PP)
A group specified by the consortium, including the Commission services (RE)
This reports is confidential, only for members of the consortium, including the Commission services
(CO)
How are you going to ensure the uptake of this deliverable by the targeted audience?
Through our outreach activities we have (and we will continuously) make sure the information about this data
set is getting out, and we are convinced that the uniqueness of the data set will let the message float.
Moreover, we expect the EUMETSAT-OSISAF infrastructure will host the data set as well as we expect
OSISAF to take over the responsibility of further development and updates of the data set in the future. We
anticipate therefore that information about the data set will be spread through the extensive EUMETSAT
network as well as through the external partnership with NORMAP.
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