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Assimilation of high resolution satellite data for improved ice cover
K. A. Scott, L. Wang, and D. ClausiDepartment of Systems Design Engineering, University of Waterloo, Canada
Contributions from Mark Buehner, Lynn Pogson and Tom Carrieres from CMC and CIS
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Outline
• Introduction to data assimilation• Benefits and challenges with using data from SAR (synthetic aperture radar)• Retrieval of observations
• Ice concentration from SAR• Ice/water from SAR
• Assimilation of observations – method and results• Related work using VIS/IR• Future work
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The data assimilation cycle
Source: T. Carrieres
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Updating the state estimate
• There are many methods used to carry out data assimilation• Most result in an analysis increment that can be written as:
• δx – analysis increment (xa –xb)• xa – analysis (optimal state)• xb – background state • y- observations• H – observation operator• B, R error covariance matrices
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Sea ice concentration from passive microwave data:Retrieval under cold conditions
Sea ice concentration from passive microwave data (ASI)
Source: Alec Casey, York U
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How can we use data from SAR to improve sea ice concentration estimates?
Observation PRO CON
Ice/water Mature methods exist to retrieve ice/water
Binary data – difficult to map to ice concentration
Ice concentration
Easy to assimilate –ice concentration is a model variable
Methods to retrieve ice concentration under development
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Study region for SAR – Gulf of St Lawrence (GSL)
• January 17th - 31st 2014• Freeze up, mostly new ice on the 17th,
thicker ice (30cm) by the 31st
• Observations:• SAR data
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Background state: Ice-Ocean-Atmosphere Model
• Output from the analysis system at CMC• Ice model – CICE• Ocean model – NEMO• Atmosphere - GEM
• Grid resolution approx 4km• Currently not coupled to the model
(calculating analyses)
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Retrieval of ice concentration from SAR imagery
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• Previous work has used engineered features in a neural network to estimate ice concentration (Karvonen, 2012, 2014)
• These features are dependent on region and season
Retrieval of ice concentration using SAR data
Adapted from Karvonen 2014
mean(HH)mean(HV)std(HH)std(HV)….
Input patch -
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How does a Convolutional Neural Network (CNN) learn ice concentration?
CNN
Feature vector from CNN
Input patch
-
Concentration from training data
Update weights
• CNN learns features directly from the images (LeCun, 2014)• May be easier to apply to different regions and seasons
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Features learned – 100 % Ice
Input patches100% ice
CNN
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Features learned – 100% Water
Input patches100% water
CNN
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Input patchesIce (20%)
Features learned – 20% ice
CNN
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CNN results: New Ice (January 17, 2014)
HH HV
CNNASI
new ice
Image analysis
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CNN results: Wind roughening
HH HH HV
Ice Concentration (CNN)
Ice/water
Icewater
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CNN results: Impact of patch size
25x25 - 10km x 10km35x35 - 14km x 14km45x45 - 18km x 18km55x55 - 22km x 22km
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Retrieval of ice/water from SAR imagery
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Retrieval of ice/water from SAR• Use image analyses as training data• Calculate a probability of ice and water using Bayes theorem• Threshold probabilities to obtain ice/water observations
Probability of iceIcewater
water
Ice
Scott et al. Telluse
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Data Assimilation
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Data assimilation approach
• x – analysis (optimal state)• xb – background state • y- observations• H – observation operator• B, R error covariance matrices
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Assimilation of ice/water from SARobservation
ice water
Is xb < 0.7 Is xb > 0.3
Assimilate0bs=0.7
AssimilateObs=0.3
ice
water
Yes Yes
discard discardNoNo
Scott et al., Tellus 2015
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Ice concentration analyses – January 17th
SAR IC analysis SAR ice/waterobservations
SAR IC observations
SAR ice/water analysis
Background state
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Ice concentration analyses – January 31th
SAR IC analysis SAR ice/water SAR imageSAR IC observations
SAR ice/water analysis
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Comparison between image analysis charts and DA output
Experiment Abs(ICchart-ICana)Background 0.245 +/- .0023SAR SIC 0.197 +/- .0017SAR Ice/water 0.212 +/- .0018
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Assimilation of AVHRR +NT2 ice concentration
Assimilation of NT2 ice concentration only
Related work assimilating data from AVHRR
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Related work assimilating data from AVHRR
Background state Analysis state
observations Analysis incrementScott et al. RSE 2012
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Future Work • Should we consider spatially correlated observation errors in the
assimilation of SAR image data?
Innovation (obs-bkg)
Increment with correlated R
Increment with correlated B
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Concluding remarks
• Both ice/water and ice concentration observations from SAR imagery can be retrieved and assimilated
• Impact of SAR observations on the sea ice state is positive• More research is required on the retrieval of ice concentration from SAR• These methods can also be applied to the assimilation of information over
lakes
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Extra Slides
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Ice thickness can also be retrieved from IR dataIce thickness can be retrieved from ice surface temperature (IST) using a heat balance equation
Ice surface temperature from MODIS
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Reasons for limited range of ice thickness values?
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• Choose state so concentration of ones in an interval (20points) increases linearly
Can we average ice/water obs to get ice concentration?
• Sample observations from the true state• If ICtrue =0 , obs=10+error• If ICtrue =1 , obs =20+error
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Scale=2
Scale=4
Can we average ice/water obs to get ice concentration?
True scale
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• A CNN applies multiple layers where each layer has 3 operations:• Convolution filter – produces a set of features• Nonlinear activation – selects features that continue to the next layer• Subsampling – reduces dimension of data set
How does a CNN learn ice concentration?
http://deeplearning.net/tutorial/lenet.html
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Motivation to use image analysis charts
CNN ice concentrationASI ice concentration
• CNN and ice/water from SAR trained using image analysis charts• ASI SIC uses passive microwave data, trained using Arctic conditions• ASI significantly underestimates ice concentration in the region
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• Based on 10.65 GHz (low frequency to reduce atmospheric contamination)• Use image analysis charts as training data make two histograms
• One that represents pr10 values for points where SIC(ImA) > 0.9• One that represents pr10 values for points where SIC(ImA) == 0
Ice tie point water tie point
Retrieval of ice concentration from passive microwave data - Method
Markus et al. 2009
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L1(SIC(ASI)-SIC(ImA))=0.305
ASI Ice concentration
PR10 ice concentration
Retrieval of ice concentration from passive microwave data – comparison with image analyses
L1(SIC(pr10)-SIC(ImA))=0.100
Ice concentration from image analysis chart
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L1(SIC(pr10)-SIC(ImA))=0.100L1(SIC(ASI)-SIC(ImA))=0.305
ASI Ice concentration
PR10 ice concentration
ASI Ice concentrationPR10 ice concentration
Retrieval of ice concentration from passive microwave data – comparison with image analyses