PowerPointPrsentation

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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 Max-Planck Institute of
Meteorology, Germany
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• 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
• Examples1) Analysis of polar low occurrence2)
  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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Impact of satellite data on forecast skill
Anomaly correlation
Source The Changing Earth (SP-1304, ESA, 2006)
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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Decline in Arctic sea ice extent
Source The Changing Earth (SP-1304, ESA, 2006)
Minimum sea ice extent for the month of Sept.
each year. Ice extent is defined as area with an
ice concentration gt15
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Global sea level rise
Source The Changing Earth (SP-1304, ESA, 2006)
Sea level rise derived from several satellite
altimeters
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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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• 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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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 (gt 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)

Anne Blechschmidt
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Key features of HOAPS 3Hamburg 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 inter-calibration 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

Anne Blechschmidt
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• 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

Total 90 PLs (75-comma, 15-spiral), 119
PL-like
Anne Blechschmidt
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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
Matthias Zahn
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CLM9801-sn 18.1.98, 000
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
000, no HOAPS data are available at the Polar
Lows position.
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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.
Stiig Wilkenskjeld
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• 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?

Stiig Wilkenskjeld
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• Method
• Simulating satellites sampling sequence
  storing simulated local Hs data at locations and
  times along a predescribed location/time network
  of three radar satellites (TOPEX, ERS-1, ERS-2)

• Method
• Simulating satellites sampling sequence
  storing simulated local Hs data at locations and
  times along a predescribed location/time network
  of three radar satellites (TOPEX, ERS-1, ERS-2)
• Binning area averages into 3o x 3o boxes, and
  deriving statistics for each box across time
  means, different percentiles and maximum
• 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 Hs (std. dev. 0, 1,
  2, 5, 10, 20 Hs.)

Stiig Wilkenskjeld
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• 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

Stiig Wilkenskjeld
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Dependency on (simulated) satellites maximum HS
Hs (m)
Stiig Wilkenskjeld
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2 years of sampling
Percentile
1s
30s
Hs (m) ERS12 after full period
Stiig Wilkenskjeld
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About 10 years of sampling
Percentile
1s
10s
Hs (m) ERS12 after full period
Stiig Wilkenskjeld
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Dependency on temporal sampling
Percentile
Hs (m) ERS12 after full period
Stiig Wilkenskjeld
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Dependency on intensity of noise
Percentile
Hs (m) ERS12 after full period, 30 s sampling
Stiig Wilkenskjeld
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• 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

Stiig Wilkenskjeld
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Overall conclusions

• Satellite products are useful.
• 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.