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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
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Outline
Aviation Team Executive Summary Algorithm Description ADEB and IV&V Response Summary Requirements Specification Evolution Validation Strategy Validation Results Summary
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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)
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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
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Algorithm Description
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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 - 500 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.
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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
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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
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P(Cfog | F) =P(Cfog) P(Fi |Cfog)
i=1
N
∏P(Cfog) P(Fi |Cfog)
i=1
N
∏ + P(Cnofog) P(Fi |Cnofog)i=1
N
∏
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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
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ΔZ = LWP /LWC
Estimating Fog Depth
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ΔZ = A[emsac(3.9μm)] + B
DaytimeLWP: from cloud teamLWC: assumed to be 0.06 g/m3 (Hess et al., 2009)
Nighttimeemsac: atmospherically corrected 3.9 μm pseudo-emissivityA, B: linear regression coefficients
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Fog/Low Cloud Detection Processing Schematic
Use Bayes model to determine the probability that any given pixel contains fog/low cloud
INPUT: ABI data, cloud phase output, cloud optical properties output, and ancillary data
Generate quality flags
Compute yes/no mask from probability
Calculate fog/low cloud depth
Output Fog/Low Cloud Products
Example Output
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Anchorage
Anchorage
Anchorage
Anchorage
Kodiak Island
Kodiak Island
Kodiak Island
Kodiak Island
AW
IPS
Scr
een
sho
tFog Mask Fog Depth
MVFR Prob Cloud Type
Example Output
13Probability of IFR Probability of MVFR
http://cimss.ssec.wisc.edu/geocat
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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
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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.
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Requirements and Product Qualifiers
Low Cloud and Fog
Vertical
Res.
Horiz.
Res.
Map
pin
g
Accu
racy
Msm
nt.
Ran
ge
Msm
nt.
Accu
racy
Refresh
Rate/C
overage Tim
e Op
tion (M
ode
3) Refresh
Rate O
ption
(Mod
e 4)
Data
Laten
cy
Lon
g-
Term
Stab
ility
Prod
uct M
easurem
ent P
recision
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
C – CONUS FD – Full Disk M - Mesoscale
Nam
e
User &
Priority
Geograp
hic
Coverage
(G, H
, C, M
)
Tem
poral C
overage Qu
alifiers
Prod
uct E
xtent Q
ualifier
Clou
d C
over Con
dition
s Q
ualifier
Prod
uct S
tatistics Qu
alifier
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
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Validation Approach
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Validation Approach
Validation sources:1). Ceiling height at standard surface stations2). CALIOP cloud boundaries3). Special SODAR equipped stations4). Fog focused field experiments
Validation method: Determine fog detection accuracy as a function of MVFR probability; Directly validate fog depth
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Validation Results
Surface Observation-Based Fog Detection
Validation
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Day (75%)Night (83%)Combined (81%)
Day (0.51)Night (0.59)Combined (0.59)
Comparisons to surface observations indicate that the 70% accuracy specification is being satisfied
Accuracy Peirces’s Skill Score
CALIOP-Based Fog Detection Validation
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Accuracy
Peirces’s Skill Score
Comparisons to CALIOP indicate that the 70% accuracy specification is being satisfied
Daytime Accuracy: 90%Nighttime Accuracy: 91%Overall Accuracy 91%
Overall skill: 0.61
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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
SODAR-based Fog Depth Validation
CALIOP-based Fog Depth Validation
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The GOES-R fog depth product was also compared to cloud depth information derived from CALIOP. A bias of -403 m was found.
FRAM-ICE RPOJECT SITEYellowknife, NWT,
Canada
Tower 3
Tower 1
Tower 4
Tower 2
INSTRUMENTSFD12P and Sentry Vis
Yellowknife
Yellowknife Yellowknife
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
Cirrus overlapping low clouds
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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)
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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