Contrail Detection and Optical Properties Derived Using Infrared Satellite Data from MODIS

1. Contrail Detection and Optical Properties Derived Using Infrared Satellite Data from MODIS Sarah Bedka1, Patrick Minnis2, David P. Duda1, RabindraPalikonda1, Robyn Boeke1 and Kristopher Bedka1 1 Science Systems and Applications Inc., Hampton VA 2 NASA Langley Research Center, Hampton VA

2. 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

3. Contrail Detection Algorithm (CDA) • 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) See talk by Dave Duda tomorrow morning for more details Contrail Mask 10.8 μm BT 10.8 – 12 μm BTD

4. CDA Visual Analysis Accuracy of the CDA was determined by visual analysis of 44 (21 daytime, 23 nighttime) MODIS granules by 4 reviewers • GUI-based tool allowed reviewers to examine MODIS IR and VIS information, as well as BTDs • All cases containedcontrails (223366 daytime, 68189 nighttime) • A composite mask was created from the consensus of the 4 analyses, and is used as “truth” RED = Confirmed Contrails GREEN = Added Contrails BLUE = Deleted Contrails Contrail Mask Composite Contrail Mask

5. hits: truth=Y mask=Y misses: truth=Y mask=N false alarms: truth=N mask=Y CDA Accuracy Assessment hits POD = hits + misses Probability of Detection (POD): What fraction of the observed contrail pixels were correctly identified? Range = 0 -> 1Perfect = 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 false alarms FAR = hits + false alarms hits + false alarms BIAS = hits + misses POD/FAR/BIAS were calculated for each of 6 BTD thresholds

6. Probability of Detection/False Alarm Rate Nighttime Daytime false alarms hits FAR= POD = hits + false alarms hits + misses #Cases Jan = 6 Apr = 5 Jul = 7 Oct = 5 mask00 mask01 mask02 mask03 mask04 mask05 mask00 mask01 mask02 mask03 mask04 mask05 more conservative more sensitive thresholds used thresholds used more conservative more sensitive thresholds used thresholds used #Cases Jan = 8 Apr = 9 Jul = 1 Oct = 3 mask00 mask01 mask02 mask03 mask04 mask05 mask00 mask01 mask02 mask03 mask04 mask05

7. Frequency Bias Nighttime Observations Only Daytime Observations Only # Cases Jan = 6 Apr = 5 Jul = 7 Oct = 5 #Cases Jan = 8 Apr = 9 Jul = 1 Oct = 3 mask00 mask01 mask02 mask03 mask04 mask05 mask00 mask01 mask02 mask03 mask04 mask05 more conservative more sensitive thresholds used thresholds used more conservative more sensitive thresholds used thresholds used A frequency bias of 1 indicates that the mask neither overestimates nor underestimates the number of contrails.

8. Monthly CDA Performance Statistics Nighttime Observations Only Daytime Observations Only 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.

9. Retrieval of Contrail Optical Properties Technique is to minimize the difference between the observed and calculatedBTDsfor 3 IR bands (3.9, 11, and 12 μm). predicted clear-sky value • Uses 9 ice cloud models (Minniset.al 1998). • 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

10. Contrail Retrieved Optical Properties Overall Averages – 23 Daytime Granules • 223366 total contrail observations (116789(52%) confirmed, 82118(37%) added, 24459(11%) deleted) • Total τ (Pc*τc+Pa*τa-Pd*τd) is 0.58 for the 2-channel IR retrieval and 0.64 for the 3-channel • Total De is 84.1 (60.1) for the 2-channel (3-channel) retrieval

11. Contrail Retrieved Optical Properties Overall Averages – 21 Nighttime Granules • Average of 21 nighttime cases • 4-reviewer composite mask used • 68189 total contrail observations (31941 confirmed, 18159 added, 19240 deleted)

12. Future Work • Improve contrail optical property retrievals by: • better characterizing background temperature • improving contrail temperature estimates • expanding model parameterizations (from P. Yang) • Apply CDA to a larger data set to get an estimate of annual contrail coverage and trends over the Northern Hemisphere • Use CDA performance statistics from visual analysis to estimate errors in long term data sets