1. GOES-R AWG Aviation Team: Fog/Low Cloud Detection
Presented By: Michael Pavolonis
NOAA/NESDIS/Center for Satellite Applications and Research Advanced Satellite Product Branch
In Close Collaboration With: Corey Calvert
UW-CIMSS

2. Outline Aviation Team Executive Summary Algorithm Description
ADEB and IV&V Response Summary
Requirements Specification Evolution
Validation Strategy
Validation Results
Summary

3. Aviation Team Co-Chairs: Ken Pryor and Wayne Feltz Aviation Team
K. Bedka, J. Brunner, W. MacKenzie
J. Mecikalski, M. Pavolonis, B. Pierce
W. Smith, Jr., A. Wimmers, J. Sieglaff
Others
Walter Wolf (AIT Lead)
Shanna Sampson (Algorithm Integration)
Zhaohui Zhang (Algorithm Integration)
Thanks to many others as well, S. Wanzong, D. Hillger, etc. 3 3

4. Executive Summary
This ABI Fog/Low Cloud detection algorithm generates two Option 2 products (fog/low cloud mask and depth)
ABI channels 2, 7, and 14 are directly utilized in the algorithm. Many other channels (e.g. 10, 11, 15) are used indirectly through the cloud phase algorithm output.
Software Version 3 was delivered in March. ATBD (80%) and test datasets are scheduled to be delivered in June 2010
The algorithm uses spatial and spectral information to identify fog/low stratus clouds. The algorithm is capable of both day and night detection. Fog/low cloud depth is calculated for pixels positively identified by the fog mask
Validation Datasets: Surface observations of ceiling over CONUS and spaceborne lidar are used to validate the fog/low cloud mask. SODAR data from the San Francisco Bay areaand spaceborne lidar are used to validate the fog depth.
Validation studies indicate that both products are in compliance with the 100% accuracy specification
Slide 2

5. Algorithm Description
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6. What Is Fog? Aviation based fog/low cloud definition
Visual flight rules - ceiling > 3000 ft (914 m)
Marginal visual flight rules 1000 ft (305 m) < ceiling < 3000 ft (914 m)
Instrument flight rules ft (152 m) < ceiling < 1000 ft (305 m)
Low instrument flight rules - ceiling < 500 ft (152 m)
The GOES-R fog detection algorithm aims to identify clouds that produce MVFR, IFR, or LIFR conditions.

7. Algorithm Summary
Input Datasets: 1). ABI channels 2, 7,14, 2). GOES-R cloud mask, phase, and daytime optical properties, 3). Solar zenith angle, 4). Clear sky radiances and transmittances, 5). NWP forecast data (GFS), 6). Fog/low cloud probabilty LUT’s
A naïve Bayes probabilistic model is used to determine the probability of MVFR (or lower cloud base conditions)
The required yes/no fog mask is set by applying an objectively determined probability threshold
An estimate of fog depth (geometrical thickness) is derived using empirical relationships
Slide 2

8. Fog/Low Cloud Detection
Daytime predictors (only applied to cloudy, non-cirrus pixels):
3.9 μm reflectance
Surface temperature bias
3x3 spatial uniformity of 0.65 μm reflectance
Boundary layer RH from NWP
Nighttime predictors (only applied to non-cirrus pixels, includes clear sky):
3.9 μm pseudo-emissivity
Surface temperature bias
Boundary layer RH from NWP
Surface based observations of ceiling were used to train the naïve Bayes probabilistic model.
The class conditional probabilities are stored in LUT’s
The difference between the NWP surface temperature and the surface temperature calculated from the measured 11 um radiance.

9. Impact of Satellite/NWP Fusion
With NWP RH as predictor (0.52)
Without NWP as predictor (0.46)
Traditional BTD technique (0.43)
When boundary layer relative humidity information from the GFS is used as a predictor in the Bayes classifier, the maximum achievable skill score (Peirces’s skill score) increases significantly
This analysis also shows that the Bayes approach (with or without BL RH) is more skilled than the traditional nighttime BTD approach
How well did the classifier separate the “yes” events from the “no” events

10. Estimating Fog Depth Daytime LWP: from cloud team
LWC: assumed to be 0.06 g/m3 (Hess et al., 2009)
Fifth Algorithm Summary Slide
Nighttime
emsac: atmospherically corrected 3.9 μm pseudo-emissivity
A, B: linear regression coefficients

11. Fog/Low Cloud Detection Processing Schematic
INPUT: ABI data, cloud phase output, cloud optical properties output, and ancillary data
Use Bayes model to determine the probability that any given pixel contains fog/low cloud
Compute yes/no mask from probability
Calculate fog/low cloud depth
Fifth Algorithm Summary Slide
Generate quality flags
Output Fog/Low Cloud Products

12. Example Output AWIPS Screenshot Fog Mask Fog Depth MVFR Prob
Anchorage
Kodiak Island
Fog Mask
Fog Depth
AWIPS Screenshot
MVFR Prob
Cloud Type

13. Example Output http://cimss.ssec.wisc.edu/geocat Probability of IFR
Probability of MVFR

14. Algorithm Changes from 80% to 100%
Incorporated GFS boundary layer relative humidity into probabilistic model
Fine tuned satellite metrics used in daytime fog detection
Metadata output added
Quality flags standardized

15. ADEB and IV&V Response Summary
The ADEB suggested that we use boundary layer RH in the algorithm – as described earlier, we did integrate boundary layer RH into the algorithm
The ADEB suggested that we characterize the performance for ice fog conditions (results of this will be shown in the validation section)
All ATBD errors and clarification requests have been addressed.
State the qualifiers on your products. (if obvious , skip this)

16. Requirements and Product Qualifiers Low Cloud and Fog
Name
User &
Priority
Geographic
Coverage
(G, H, C, M)
Temporal Coverage Qualifiers
Product Extent Qualifier
Cloud Cover Conditions Qualifier
Product Statistics Qualifier
Low Cloud and Fog
GOES-R FD
Day and night
Quantitative out to at least 70 degrees LZA and qualitative beyond
Clear conditions down to feature of interest (no high clouds obscuring fog) associated with threshold accuracy
Over low cloud and fog cases with at least 42% occurrence in the region
Vertical
Res.
Horiz.
Mapping
Accuracy
Msmnt.
Range
Refresh
Rate/Coverage Time Option (Mode 3)
Refresh Rate Option (Mode 4)
Data
Latency
Long-
Term
Stability
Product Measurement Precision
0.5 km (depth)
2 km
1 km
Fog/No Fog
70% Correct Detection
15 min
5 min
159 sec
TBD
Undefined for binary mask
We requesting a change in the MRD such that the ceiling based aviation definition of fog is used.
C – CONUS FD – Full Disk M - Mesoscale

17. Validation Approach
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18. Validation Approach Validation sources:
1). Ceiling height at standard surface stations
2). CALIOP cloud boundaries
3). Special SODAR equipped stations
4). Fog focused field experiments
Validation method: Determine fog detection accuracy as a function of MVFR probability; Directly validate fog depth 18

19. Validation Results
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20. Surface Observation-Based Fog Detection Validation
Comparisons to surface observations indicate that the 70% accuracy specification is being satisfied
Accuracy
Peirces’s Skill Score
Day (75%)
Night (83%)
Combined (81%)
Day (0.51)
Night (0.59)
Combined (0.59)
Includes all seasons (land), including very cold scenes.

21. CALIOP-Based Fog Detection Validation
Accuracy
Comparisons to CALIOP indicate that the 70% accuracy specification is being satisfied
Peirces’s Skill Score
-Includes all seasons (land and water), including very cold scenes.
-The Heidke skill score is about 0.92
Daytime Accuracy: 90%
Nighttime Accuracy: 91%
Overall Accuracy 91%
Overall skill: 0.61

22. SODAR-based Fog Depth Validation
Comparisons with SODAR/ceilometer derived fog depth indicate a bias of ~30 m. More data points will be added to this analysis in the future.
Daytime
Nighttime
QC screening is challenging

23. CALIOP-based Fog Depth Validation
The GOES-R fog depth product was also compared to cloud depth information derived from CALIOP. A bias of -403 m was found.

24. FRAM-ICE RPOJECT SITE Yellowknife, NWT, Canada
INSTRUMENTS
FD12P and Sentry Vis
Tower 4
Tower 3
Tower 1
Tower 2
Remember that ADEB comment out fog over cold surface and ice fog? Here is how we are addressing it.

25. GOES-R Fog/Low Stratus Detection Over Yellowknife
This is a daytime scene covering the same area around Yellowknife, NWT and Great Slave Lake
The SW corner of this scene is shown to contain a large amount of thin, overlaying cirrus clouds (circled areas)
It should be noted that all the clouds in this scene were classified by the GOES-R cloud type algorithm as either mixed phase or ice clouds, which should be the case during an ice fog event
Yellowknife
Cirrus overlapping low clouds
If your algorithm works here, then it will work anywhere!
Yellowknife
Yellowknife

26. Fog / Low Cloud F&PS Requirements and Validation
Product Measurement Range
Product Measurement Accuracy
Fog Detection Validation Results
Product Vertical Resolution
Fog Depth Validation Results
Binary Yes/No
70% correct detection (100% of spec.)
1). 81% correct detection (using surface observations)
2). 91% using CALIOP
0.5 km (fog depth)
1). SODAR bias:
31 m (day)
25 m (night)
2). CALIOP bias:
403 m (overall)

27. Summary
The GOES-R ABI fog/low cloud detection algorithm provides a new capability for objective detection of hazardous aviation conditions created by fog/low clouds
The GOES-R AWG fog algorithm meets all performance and latency requirements.
Improved ABI spatial and temporal resolution will likely improve detection capabilities further
The 100% ATBD and code will be delivered to the AIT this summer.
Prospects for future improvement: 1). Incorporate additional NWP fields (e.g. wind). 2). Incorporate radar data. 3). Working on incorporating high spatial resolution surface elevation map 4). Correct for thin cirrus clouds 5). Use data fusion techniques and Bayes classifier to create all weather MVFR and IFR probabilities
Our hope is that GOES-R Risk Reduction will be willing and able to support additional development