EARWiG: SST retrieval issues for High Latitudes Andy Harris Jonathan Mittaz NOAA-CICS University of Maryland Chris Merchant U Edinburgh

Issues for IR SST Air-Sea temperature difference… –Can be quite extreme (~10 K) –Far from the “average” of algorithm training sets Sea Ice –Hard to spot Cloud detection –Low sun angles = shadows –Complex cloud –Difficult in winter because temperatures are low

Regression retrieval For daytime, usually 11 & 12 micron Nighttime adds 3.9 micron

Sensitivity to ASTD 2-channel retrieval perturbed by ±5, ±10 K

Sensitivity to ASTD Range of ASTD is much greater at high latitudes Small deviations at low latitudes “save the day” for tropical SST retrievals

Ambiguity in channel relationships Linear retrieval depends on SST-T i  SST-T j When optical depths are low, channels become more similar (and even “cross over”)

“Flagship” heritage satellite SST product derived from AVHRR data Retrieval coefficients a i are derived empirically by regression of AVHRR channel brightness temperatures matched to in situ [buoy] data Coefficients are time-dependent (5-month rolling average) The AVHRR Pathfinder SST Pathfinder NLSST retrieval algorithm: NLSST = a 0 + a 1 T 11 + SST bg a 2 (T 11 – T 12 ) + (sec(ZA)-1)a 3 (T 11 – T 12 ) Direct regression minimizes effects of mis-calibration & sensor characterization for the training data “Gamma”

Simulated Pathfinder Retrieval Errors ERA-40 data Atmospheric profiles SSTs Pathfinder matchups Lat, lon, time, view angle Simulated matchup BTs CRTM SST Retrieval coefficients Simulated global BTs ERA-40 “matchup” subset CRTM Simulated Pathfinder SSTs (+ ERA-40 SSTs) N.B. Bias is Pathfinder SST – ERA-40 SST No Aerosols & ERA-40 data filtered for cloud fraction

Modeled Pathfinder Bias 1985 – 1999 What’s the spatial distribution of these seasonal biases?

Seasonal Geographic Distribution of Bias

The “Gamma” Parameter NLSST Gamma is very smooth – mirrors climatological SST “True” Gamma has more detailed structure N.B. Difference in Gammas must be multiplied by T 11 – T 12

How sensitive is NLSST to true SST? If SST changes by 1 K, does retrieved NLSST change by 1 K? CRTM provides tangent-linear derivatives Response of NLSST algorithm to a change in true SST is… Merchant, C.J., A.R. Harris, H. Roquet and P. Le Borgne, Retrieval characteristics of non- linear sea surface temperature from the Advanced Very High Resolution Radiometer, Geophys. Res. Lett., 36, L17604, 2009

NLSST Sensitivity to true SSTAir – Sea Temperature Difference

Summary IR retrieval in HL affected by air-sea temperature difference –Large excursions (far from training average) Little training data for empirical algorithms Emissivity effects –Clear atmosphere => greater impact –12 micron decrease with temperature For Geo-SST, HL always viewed at large incidence angles –Magnifies ASTD effects –Also emissivity (refractive index) differences become more prominent

Summary cont’d Pathfinder SSTs show evidence of seasonal ASTD- induced bias –Not particularly prevalent in S.H. Also, cloud detection issues –Low sun angles => shadowing –Nighttime cloud detection (winter) very challenging due to cold temperatures and complex cloud patterns –Sea ice complicates picture MW SST –Sea-ice (especially in melt season) AVHRR Calibration –Sensor is exposed as satellite comes out of eclipse

Backup slides

Early theory required SST – T i = k i F(atm) This allowed SST = k 2 T 1 – k 1 T 2 ——————————— (k 2 – k 1 ) And hence the “split- window” equation, mystique about channel differences, etc. Only need to assume SST – T i  SST – T j to get SST = a 0 +  a i T i Some refinements to account for non-linearity, scan angle: SST = a 0 + a 1 T 11 + SST bg a 2 (T 11 – T 12 ) + (sec(ZA)-1)a 3 (T 11 – T 12 ) “Gamma”