Contrail Detection and Optical Properties Derived Using Infrared Satellite Data from MODIS Sarah Bedka 1 , Patrick Minnis 2 , David P. Duda 1 , Rabindra Palikonda 1 , Robyn Boeke 1 and Kristopher Bedka 1 1 Science Systems and Applications Inc., Hampton VA 2 NASA Langley Research Center, Hampton VA
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Contrail Detection and Optical Properties Derived Using Infrared Satellite Data from MODIS
Contrail Detection and Optical Properties Derived Using Infrared Satellite Data from MODIS. Sarah Bedka 1 , Patrick Minnis 2 , David P. Duda 1 , Rabindra Palikonda 1 , Robyn Boeke 1 and Kristopher Bedka 1 1 Science Systems and Applications Inc., Hampton VA - PowerPoint PPT Presentation
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Contrail Detection and Optical Properties Derived Using Infrared Satellite Data from MODIS
Sarah Bedka1, Patrick Minnis2, David P. Duda1, Rabindra Palikonda1, Robyn Boeke1 and Kristopher Bedka1
1 Science Systems and Applications Inc., Hampton VA2 NASA Langley Research Center, Hampton VA
Objectives
• Develop an automated Contrail Detection Algorithm (CDA) for identifying linear contrail features in MODIS data
• Determine appropriate brightness temperature difference (BTD) thresholds for CDA, and provide error estimates
• Retrieve contrail optical properties from MODIS observations and estimate the errors in the retrieved properties resulting from errors in the contrail mask
Contrail Detection Algorithm (CDA)
See talk by Dave Duda tomorrow morning for more details
10.8 μm BT10.8 – 12 μm BTD Contrail Mask
• Based on Mannstein et. al (1999)
• Contrails are minimally visible in the 10.8 μm image but quite visible in the 10.8-12 μm BTD image.
• Uses two IR channels (10.8 μm and 12 μm on MODIS) and applies a scene-invariant BTD threshold to identify possible contrail linear features
• Additional IR information from MODIS removes non-contrail linear features (e.g. cloud edges, surface features)
CDA Visual Analysis
Contrail Mask
• GUI-based tool allowed reviewers to examine MODIS IR and VIS information, as well as BTDs
• All cases contained contrails (223366 daytime, 68189 nighttime)
• A composite mask was created from the consensus of the 4 analyses, and is used as “truth”
Composite Contrail Mask
RED = Confirmed ContrailsGREEN = Added ContrailsBLUE = Deleted Contrails
Accuracy of the CDA was determined by visual analysis of 44 (21 daytime, 23 nighttime) MODIS granules by 4 reviewers
CDA Accuracy AssessmentProbability of Detection (POD): What fraction of the observed contrail pixels were correctly identified? Range = 0 -> 1 Perfect = 1
False Alarm Rate (FAR): What fraction of the predicted contrail pixels were incorrectly identified? Range = 0 -> 1 Perfect = 0
Frequency Bias: What is the relative frequency of predicted contrail pixels to observed contrail pixels? Range = 0 -> ∞ Perfect = 1
In general, a higher percentage of contrails were added during the daytime than at night. A higher percentage of contrails were deleted at night.
The contrail mask slightly underestimates the number of contrails during the day, and slightly overestimates it at night.The Probability of Detection (POD) was slightly lower in the winter than in the summer, due to lower contrast of contrails over a colder and/or possibly snow-covered surface. False Alarm Rate (FAR) was also slightly higher in the winter, and was significantly higher at night than during the day.
Jan Apr Jul Oct# Granules 6 5 7 5
% Contrail 0.371 0.491 0.181 0.277
POD 0.535 0.589 0.590 0.616FAR 0.455 0.345 0.389 0.413Frequency Bias
• Accurate contrail temperature and clear-sky BTDs are key to accurate retrievals
• 2-channel retrieval (11, 12 μm) may capture the data better than the traditional 3-channel retrieval
predicted clear-sky value
Retrieval of Contrail Optical PropertiesTechnique is to minimize the difference between the observed and calculated BTDs for 3 IR bands (3.9, 11, and 12 μm).