Infrared and Microwave Remote Sensing of Sea Surface Temperature

Gary A. Wick

NOAA Environmental Technology Laboratory

January 14, 2004

Outline

Motivation Basic SST Retrieval Methods Current Multi-Sensor Merging Efforts

Why SST?

Boundary Condition

– Weather Models

– Estimation of Heat Content and Heat Flux Climate Monitoring and Change Detection Naval Operations

Climate Anomalies

Courtesy: NOAA Climate Diagnostics Center

Why Satellites?

Courtesy: R. Reynolds, NOAA NCDC

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

Definition of SST

Interface SST Skin SST Sub-skin SST Near-Surface SST

or SSTDepth

Radiative Transfer Equation

Methods for SST Retrieval

Thermal Infrared Passive Microwave

Infrared Retrievals

Strengths

– High Accuracy

– High Resolution

– Long Heritage (over 20 years) Weaknesses

– Obscured by Clouds

– Atmospheric Corrections Required

Microwave Retrievals

Strengths

– Clouds Transparent

– Relatively Insensitive to Atmospheric Effects Weaknesses

– Sensitive to Surface Roughness

– Poorer Accuracy (?)

– Poorer Resolution

Spatial Coverage Differences

Infrared Retrieval Technique

Cloud Detection

Atmospheric Correction Multi-Channel SST

– TS = T1 + (T1 - T2)

– Multi-Frequency

– Multiple View

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

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

Infrared Sensors

AVHRR ATSR GOES Imager MODIS

Others

– GMS

– SEVIRI

– VIRS

Microwave Sensors

TMI AMSR WindSat

Multi-Sensor Blended SST

Current Projects Key Issues Sample Results

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

Next Generation SST

Created by Hiroshi Kawamura, Tohoku University, Japan http://www.ocean.caos.tohoku.ac.jp/~adeos/sst/

Blended SST Issues

Different product resolutions Different sensor error characteristics Different sampling times and effective depths Merging techniques

Error Characteristics – Overall Accuracy

Observed Differences Between Infrared and Microwave Products

Comparisons between the products show complex spatial and temporal differences

Sources of Product Differences

Diurnal Warming Effects

Skin Layer Effects

Courtesy: P. Minnett, U. Miami

Courtesy: S. Castro, U. Colorado

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

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

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

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?