AVHRR Radiance Bias Correction Andy Harris, Jonathan Mittaz NOAA Cooperative Institute for Climate Studies University of Maryland Some concepts and some numbersSome concepts and some numbers Radiative transfer methodsRadiative transfer methods Physical bias correctionsPhysical bias corrections Bayesian cloud detectionBayesian cloud detection

Some concepts SST is a key environmental data record, and also comparatively easy to validateSST is a key environmental data record, and also comparatively easy to validate SST is traditionally retrieved using regression-based equations with in situ data used to trainSST is traditionally retrieved using regression-based equations with in situ data used to train –May get biases in remote regions with sparse/no in situ data –Also results in least confidence where satellite data have most impact New physically-based retrieval methodologies are reliant on accurate sensor calibration and characterizationNew physically-based retrieval methodologies are reliant on accurate sensor calibration and characterization Pursue physically-based methodologies to:Pursue physically-based methodologies to: –Develop techniques to identify & quantify AVHRR calibration and characterization errors post-launch –Improve SST retrieval capability –Feed back results to improve forward modeling for CRTM

Bias pattern for GOES-W similar to that predicted by radiative transfer Fixed viewing geometry of GOES emphasizes that single “global” linear retrieval equation is regionally sub-optimal Observed and RT-modeled SST biases RTM is a good way of predicting and removing such biases on a global basis prior to assimilation

July: bias in retrieved SST (all matchups)July: bias in retrieved SST (restricted matchups)July: bias change due to restricted matchups January: regional bias in retrieved SST (all matchups) January: regional bias in retrieved SST (restricted matchups) January: bias change due to restricted matchups Impact of restricted training data

RTM improvements: GOES-9 Case Study Unusually large scatter and warm bias at low atmospheric corrections may be due to diurnal warming Nighttime retrievals also show small trend vs atmospheric correction Updated RT model removes most of the trend Application of daytime coefficients to nighttime data gives small –ve bias (expected)

January 2001 Diurnal Cycle of SST using ERA-40 fluxes July 2001 Prominent seasonal cycle – effect must be taken into account to ensure bias-free SSTs in important climatic regions

Impact of spectral response error on RT modeling Impact is greater at high water vapor loadings Impact is greater at higher scan angles While top plot shows dependence on temperature, bottom plot is the key to identifying SRF error rather than calibration

Regional biases if 12 µm SRF is shifted by -5 cm -1 NLSST retrieval difference (true – error) Geographic impact of SRF shifts Regional biases in NLSST retrievals

Results of perturbing NOAA & 12 µm spectral responses Daytime split-window Nighttime split-window

In practice, split- window retrieval will be replace by physical retrieval method Triple-window uses adjusted filters as determined by analysis of 11 and 12 µm data Impact of adjusted spectral response Impact of adjusted spectral response

Value of triple-window Value of triple-window With three channels (3.7, 11 & 12 µm) the SST retrieval problem is essentially linear In the daytime, solar radiation contaminates the signal at 3.7 µm

Some Aspects of Calibration Some Aspects of Calibration Calibration difference between day and night due to “boost” in BB radiance from reflected solar 3.7 µm Digitization of PRTs resolvable if calibration is recalculated using a running full precision Variations in scene radiance also affect calibration…

Some Aspects of Calibration cont’d Some Aspects of Calibration cont’d Warm scene radiances boost perceived BB emission c.f. cold scenes Large-scale variation dominated by center- weighted scene radiance Small-scale variation shows more sensitivity where it might be anticipated “Correct” calibration can be obtained by estimating an efficiency for scattered scene radiance to remove dependency

Some Good News on Calibration Some Good News on Calibration “Correct” calibration [i.e. in night-time portion of orbit] is stable (FWHM is 0.05°K over 1 day, including PRT digitization noise) Most “Daytime” and some nighttimecalibration of 3.7µm is erroneous, but… Most “Daytime” and some nighttime calibration of 3.7µm is erroneous, but… Derive a single “best estimate” of calibration and apply to whole orbit

11µm BT image Bayesian P clear >0.99 Manually screened “truth” Threshold- based mask Bayesian Cloud Mask: Along-Track Scanning Radiometer

Calculated vs “Actual” Probability

Some results… MaskPPHRFARTSS P-clear < P-clear < P-clear < Standard Threshold mask Note that a hit rate of 97.2% (P clear < 0.99 → cloud) corresponds to a “failure to detect” rate of 2.8%Note that a hit rate of 97.2% (P clear < 0.99 → cloud) corresponds to a “failure to detect” rate of 2.8% Standard threshold-based mask fails to detect 3.7% of cloud-contaminated pixelsStandard threshold-based mask fails to detect 3.7% of cloud-contaminated pixels What is the impact of these “failures to detect” and “false arms”?

“False alarm” SST histogram “Failure to detect” SST histogram SST difference histogram Impact on retrieved SST Computed from 1°×1° cells Appropriate for “climate” SST analyses Cold clear ocean can sometimes be flagged as cloud Failures of Bayesian scheme tend to have less impact

Probability vs Error in SST Increasing Probability of Clear-Sky Bias and scatter improve with increased P clearBias and scatter improve with increased P clear Significant decrease in coverage as P clear → 1Significant decrease in coverage as P clear → 1

Summary SST is key environmental data record, and also comparatively easy to validate – a powerful tool for diagnosing AVHRR sensor performanceSST is key environmental data record, and also comparatively easy to validate – a powerful tool for diagnosing AVHRR sensor performance Pursue physically-based methodologies to provide: Pursue physically-based methodologies to provide: –Techniques to identify instrumental calibration and characterization post-launch –Improved AVHRR SST retrieval capability (inc. diurnal) –Feed back results to improve forward modeling Bayesian cloud detection a promising method for assigning quantitative errors to individual pixelsBayesian cloud detection a promising method for assigning quantitative errors to individual pixels