Potential and limits of satellite data for climate
issues
Hans von Storch12, Matthias Zahn12, Anne Blechschmidt2, Stiig Wilkenskjeld1, Heinz
Günther1 and Stephan Bakan23
EXTROP Virtual Institute
and
1
Institute for Coastal Research, GKSS Research Center, Germany
2
Meteorological Institute of the University of Hamburg, Germany
3
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Max-Planck Institute of Meteorology, Germany
Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
Laboratories (EARSeL),
Bern, Switzerland, 11-13 February 2008
Overview



Satellite products are useful – in some, even many cases
But the utility of satellite products in climate research is limited – by the
length of available time series and compromised by their homogeneity
Examples:
1) Analysis of polar low occurrence
2) Derivation of information about tails of distributions (extreme wave
heights)
Results from satellite-climate modeller interaction in the HGF virtual institute
EXTROP
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Impact of satellite data on forecast skill
Source: The Changing Earth (SP-1304, ESA, 2006)
Anomaly
correlation
Increase in anomaly correlation of 500hPa height forecasts during recent decades is to a large extent due to
the assimilation of satellite data
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
Laboratories (EARSeL),
Bern, Switzerland, 11-13 February 2008
Source: The Changing Earth (SP-1304, ESA, 2006)
Decline in Arctic sea ice extent
Minimum sea ice extent for the month of Sept. each year.
Ice extent is defined as area with an ice concentration >15%
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
Laboratories (EARSeL),
Bern, Switzerland, 11-13 February 2008
Source: The Changing Earth (SP-1304, ESA, 2006)
Global sea level rise
Sea level rise derived from several satellite altimeters
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Climate research deals with (changing) statistics of parameters characterising weather. It
deals to large extent with the inference of characteristic parameters such as spatial
disaggregated mean values or average occurrences of certain phenomena, extreme values,
spatial correlations, spectra and characteristic patterns, and sensitivities.
To do proper inference the data need to fulfil some properties.
1. The data must be representative of the considered statistical ensembles, i.e., the time
series must be long enough.
2. Second, the data should be homogeneous, i.e., the informational content should be the
same through the entire time series.
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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We examine two examples, which illustrate the potential and limit of using satellite
data – the first deals with scrutinizing the skill of a climate model, and the other
with the number of samples and accuracy needed to infer extreme value statistics
from satellite soundings.
PhD work done at the Virtual Institute EXTROP
by
•
Anne Blechschmidt (HOAPS data set)
•
Matthias Zahn (Polar Low simulations)
Stiig Wilkenskjeld (Simulation of satellite based inference of significant wave
height)
•
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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1st Case: Polar Lows
The task/aim is to determine the occurrence of polar lows in the sub-polar Atlantic in the
past decades. Eventually this will enable an assessment whether recent trends in frequency,
spatial distribution or intensity are consistent with climate change scenarios or not.
In-situ data for this purpose are not available; (passive or active) satellite data are
available only for a limited time.
On the other hand, downscaling strategies, involving a limited area atmospheric model
suitably embedded in global atmospheric re-analysis, are able to generate mesoscale
disturbances in climate mode simulations. We demonstrate the quality of the LAM
simulation by comparing the model simulation with the HOAPS climatology in a case study,
when high-quality satellite data are available.
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Two year climatology of polar lows
• Study area: Nordic Seas
• Visual inspection of AVHRR images
• Usage of HOAPS-S wind estimates (> 15 m/s required for meso-scale disturbance to count as
polar low)
• When no wind estimate is available, cases are classified as “PL-like”
• Problem: only two years of data screened (very work-intensive)
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Anne Blechschmidt
Key features of HOAPS 3
Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data
precipitation, evaporation and related sea surface and atmospheric state parameters over ice-free oceans
derived from the SSM/I (passive microwave) radiometer on board the polar orbiting DMSP satellites
precipitation, surface wind speed and near surface air humidity (among others) directly retrieved
evaporation is derived through a bulk transfer formula, for which the additionally necessary sea surface specific
humidity is calculated from the NOAA Pathfinder SST, which uses AVHRR data
18+ years of satellite data: 1987 – 2005
homogeneous time series, which uses all SSM/I instruments operating at the same time, after careful intercalibration during overlap periods
scan-based dataset (HOAPS-S)
gridded datasets, resolution 0.5, daily composites, pentad and monthly means (HOAPS-G, HOAPS-C)
third enhanced version available now through www.hoaps.org
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Anne Blechschmidt
From the available 2 years of analysis interesting properties
about Polar Lows may be extracted, such as
-
seasonal frequencies
-
locations of genesis and tracks
-
characteristics features such as distribution of diameters
2004
2005
Total: 90 PLs (75-comma, 15-spiral), 119 ‘‘PL-like‘‘
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Anne Blechschmidt
What to do when we want to determine the level of inter-annual and inter-decadal
variability, and even non-natural trends of the occurrence of Polar Lows?
Simulate the genesis and tracks of such meso-scale disturbances in a regional
climate model, which is run in “climate mode”, i.e., continuously across decades of
years using operational coarse-grid re-analysis as large-scale constraints and
boundary values.
Satellite data serve as validation tool to determine if the RCM is simulating the
disturbances in recent years reasonably well.
If they do, then the RCM output may be used for the purpose of determining
variability incl. trends.
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Case of 18 January 1998
Simulation with
regional climate
model CLM,
forced with
NCEP re-analysis
Added value of RCM –
complete field; may be
subject to spatial
filtering to enhance
meso-scale fature
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Matthias
Zahn
CLM9801-sn: 18.1.98, 0:00
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In CLM, the Polar Low's position is reproduced farther SE compared to HOAPS.
Note, that the HOAPS data is fragmentary (white fields) and at 0:00, no HOAPS
Remote Sensing of Changing Cryosphere, LandIce data
and Snoware
- Workshop
of the European
of Remote
Sensing position.
available
at Association
the Polar
Lows
Laboratories (EARSeL),
Bern, Switzerland, 11-13 February 2008
45 years simulations with CLM presently underway ….
Stay tuned and watch out for papers by Matthias Zahn
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Testing satellite inference by simulating the data observing and collection process in
data generated by a simulation model
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2nd case: Wave height in The North Atlantic
How many data of which accuracy are needed to derive good estimates of extreme
wave heights in the North Atlantic?
In regular overpasses, a radar satellite reports significant wave height in pixels with irregular
temporal sampling. The question is, how long these efforts have to be continued before useful
estimates of very high percentiles or expected maxima per time period can be made.
This is examined in the framework of a multi-year wave simulation run with realistic wind fields,
and a crude model describing the estimation errors, when the ground signal is monitored by the
satellite.
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Stiig Wilkenskjeld
Imagette wave height data
• ERS-1, ERS-2, TOPEX retrievals, imagettes (30 s) covering approximately 5 km x 10 km.
• Binned in 3o x 3o whenever available.
• For each box, means, percentiles and maxima are determined.
• Observational period is limited to two years.
Can one reasonably expect to derive representative statistics of significant
wave height by this set-up?
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Stiig Wilkenskjeld
Method
2. Simulating satellite‘s sampling sequence: storing simulated local H s data at locations and
times along a predescribed location/time network of three radar satellites (TOPEX, ERS-1,
ERS-2)
3. Binning area averages into 3o x 3o boxes, and deriving statistics for each box across time –
means, different percentiles and maximum
4. Emulating measuring uncertainty
•
considering only one, two or all three satellites
•
considering data from only two years instead of the full time period of 10 years
•
considering reduced sampling density in time: 1s (”altimeter mode”), 2s, 5s, 10s, 30s (”SAR
imagette” mode), 1 min., 2 min., 10 min.
• deriving from noisy radar images by adding Guassian noise to simulated H s (std. dev. ~ 0, 1, 2,
5, 10, 20% Hs.)
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Stiig Wilkenskjeld
Simulated data - ”SAFEDOR2/GKSS database:
• Significant wave height Hs in the North Atlantic
• Simulated with WAM using NCEP winds
• Almost 10 years (January 1990 – April 1999)
• 0.5o x 0.5o spatial resolution,
• 1-hourly temporal resolution
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Stiig Wilkenskjeld
Dependency on (simulated) satellites – maximum HS
Hs (m)
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Stiig Wilkenskjeld
2 years of sampling
Percentile
1s
30s
Hs (m): ERS1+2 after full period
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Stiig Wilkenskjeld
About 10 years of sampling
Percentile
1s
10s
Hs (m): ERS1+2 after full period
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Stiig Wilkenskjeld
Percentile
Dependency on temporal sampling
Hs (m): ERS1+2 after full period
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Stiig Wilkenskjeld
Percentile
Dependency on intensity of noise
Hs (m): ERS1+2 after full period, 30 s sampling
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Stiig Wilkenskjeld
• Satellite-statistics has been simulated to assess the influence of the statistical
undersampling.
• Reliable estimates for mean values and lower percentiles are fastly established (1 years).
• Estimates of higher (e.g. 99.9%) percentiles need long sampling times to converge to the
”real” values.
• A sample period of 30 seconds is sufficient to obtain the best estimates.
• Including measuring uncertainty affects significantly high percentiles
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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Stiig Wilkenskjeld
Overall conclusions
1. Satellite products are useful.
2. However, before inferring assessments about climatic conditions and climatic change,
the issues
- are the time series sufficiently long for doing so?
- do the time series, often concatenated from data sets from different carriers, suffer
from inhomogeneities?
have to be dealt with.
3. When time series are insufficient to be directly used for inference about climatic
conditions, the satellite products may serve as only tools to validate numerical models,
which may be used to deal with the climatic issues. This is in particular so, when
dealing with smaller scale features such as meso-scale disturbances.
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Remote Sensing of Changing Cryosphere, LandIce and Snow - Workshop of the European Association of Remote Sensing
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