Infrared and Microwave Remote Sensing of Sea Surface Temperature
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Transcript of Infrared and Microwave Remote Sensing of Sea Surface Temperature
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Infrared and Microwave Remote Sensing of Sea Surface Temperature
Gary A. Wick
NOAA Environmental Technology Laboratory
January 14, 2004
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Outline
Motivation Basic SST Retrieval Methods Current Multi-Sensor Merging Efforts
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Why SST?
Boundary Condition
– Weather Models
– Estimation of Heat Content and Heat Flux Climate Monitoring and Change Detection Naval Operations
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Climate Anomalies
Courtesy: NOAA Climate Diagnostics Center
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Why Satellites?
Courtesy: R. Reynolds, NOAA NCDC
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Desired Accuracy
WCRP (1985) - Tropics
– 0.3 K on 2° grid every 15 days Robinson et al. (1984) - Global SST Monitoring
– 0.05 K on 5° grid every 15 days
NPOESS SST EDR Objectives
– 0.1 K uncertainty at ~4 km resolution
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Definition of SST
Interface SST Skin SST Sub-skin SST Near-Surface SST
or SSTDepth
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Radiative Transfer Equation
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Methods for SST Retrieval
Thermal Infrared Passive Microwave
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Infrared Retrievals
Strengths
– High Accuracy
– High Resolution
– Long Heritage (over 20 years) Weaknesses
– Obscured by Clouds
– Atmospheric Corrections Required
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Microwave Retrievals
Strengths
– Clouds Transparent
– Relatively Insensitive to Atmospheric Effects Weaknesses
– Sensitive to Surface Roughness
– Poorer Accuracy (?)
– Poorer Resolution
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Spatial Coverage Differences
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Infrared Retrieval Technique
Cloud Detection
Atmospheric Correction Multi-Channel SST
– TS = T1 + (T1 - T2)
– Multi-Frequency
– Multiple View
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Algorithm Refinements
Additional path length term NLSST Use of multiple frequencies AND multiple view angles Independent estimate of water vapor content Iterative solution for both SST and
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Microwave Retrieval Technique
Environmental Scenes42,195 Radiosondes
5 Cloud ModelsSST Randomly Varied for 0 to 30 C
Wind Speed Randomly Varied from 0 to 20 m/sWind Direction Randomly Varied from 0 to 360
Complete Radiative Transfer Model
Simulated AMSR TB's
Truth: Ts, W, V, L
Gaussian Noise Added
Derive Coefficients for Multiple Linear Regression Algorithm
Withheld Data Set
Algorithm Coefficients
Run Algorithm
Evalulate Algorithm Peformance
Retrieved values for Ts, W, V, L
Performance and Cross Talk Statistics
Courtesy: Remote Sensing Systems
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Infrared Sensors
AVHRR ATSR GOES Imager MODIS
Others
– GMS
– SEVIRI
– VIRS
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Microwave Sensors
TMI AMSR WindSat
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Multi-Sensor Blended SST
Current Projects Key Issues Sample Results
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GODAE High-Resolution SST Pilot Project
Provide rapidly and regularly distributed, global, multi-sensor, high-quality SST products at a fine spatial and temporal resolution
– Most promising solution to combine complementary infrared and passive microwave satellite measurements with quality controlled in situ observations from ships and buoys
www.ghrsst-pp.org
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Next Generation SST
Created by Hiroshi Kawamura, Tohoku University, Japan http://www.ocean.caos.tohoku.ac.jp/~adeos/sst/
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Blended SST Issues
Different product resolutions Different sensor error characteristics Different sampling times and effective depths Merging techniques
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Error Characteristics – Overall Accuracy
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Observed Differences Between Infrared and Microwave Products
Comparisons between the products show complex spatial and temporal differences
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Sources of Product Differences
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Diurnal Warming Effects
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Skin Layer Effects
Courtesy: P. Minnett, U. Miami
Courtesy: S. Castro, U. Colorado
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NOAA Environmental Technology Laboratory
Blended Infrared andMicrowave SST
Using derived corrections, the infrared and microwave SST products can be more accurately merged into a new enhanced product.
Diurnal warming effects are aliased into the product if not corrected.
Strong winds off Somalia cause perceived overcooling and large swath edge effects are visible.
Bias(K)
RMS(K)
w/ Adj -0.01 0.61
w/o Adj 0.15 0.67
Accuracy of MergedProduct vs. Buoys
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Analyzed SST Product
Daily global (40 N – 40 S) 0.25 degree
Referenced to nighttime predawn value
Based on Reynolds and Smith Optimal Interpolation
Relative product uncertainties derived from difference analyses
Analysis Characteristics
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Analyzed Product Accuracy Summary
Product Bias (K) RMS (K)
Full Analysis 0.13 0.68
Night Obs Only -0.08 0.58
AVHRR Obs Only -0.01 0.56
TMI Obs Only 0.22 0.74
Refined diurnal corrections are the most needed improvement
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Summary
Complementary infrared and microwave SST products provide the opportunity for cross-validation and improved SST
Multiple sensor-related and geophysical effects lead to complex differences between the products
Optimal blending of the products requires careful treatment of the differences
Is blending correct?