1. Validation
Specialized ground based instruments are better suited for observing fog, but are extremely limited by their lack of spatial coverage.
Conventional surface observations are generally limited to populated areas (e.g. very few maritime observations) and are sometimes difficult to physically interpret.
Despite the limitations of each data source, we will try to take advantage of the strength of each potential validation data set.
Validation results are summarized on the next several slides.

2. Validation (fog/low stratus detection)
Accuracy statistics as a function of FLS probability threshold
LIFR Probability (All Clds)
IFR Probability (All Clds)
MVFR Probability (All Clds)
LIFR Probability (Liq. Clds)
IFR Probability (Liq. Clds)
MVFR Probability (Liq. Clds)

3. Validation (fog/low stratus detection)
Max. Algorithm Accuracy (threshold)
LIFR
All Pixels
Liquid Pixels Only
Daytime LIFR
93.1% (32%)
95.9% (74%)
Nighttime LIFR
89.7% (46%)
91.0% (48%)
Terminator LIFR
88.3% (32%)
89.7% (47%)
IFR
All Pixels
Liquid Pixels Only
Daytime IFR
90.6% (55%)
93.8% (60%)
Nighttime IFR
87.5% (48%)
89.9% (62%)
Terminator IFR
85.8% (48%)
88.2% (60%)
MVFR
All Pixels
Liquid Pixels Only
Daytime MVFR
85.1% (69%)
88.1% (74%)
Nighttime MVFR
85.0% (55%)
88.3% (61%)
Terminator MVFR
82.6% (61%)
85.2% (61%)
# of pts
All Pixels
Liquid Pixels Only
Daytime
492,977
298,480
Nighttime
564,029
278,042
Terminator
92,654
49,083
The validation data set was comprised of 12, 24-hr periods (1 day from each month) of GOES-13 data collocated with surface observations
The accuracy of the Enterprise GS LIFR, IFR and MVFR algorithms exceed the 70% F&PS accuracy specification during both day and night.

4. Validation (fog/low stratus detection)
CSI statistics as a function of FLS probability threshold
LIFR Probability (All Clds)
IFR Probability (All Clds)
MVFR Probability (All Clds)
LIFR Probability (Liq. Clds)
IFR Probability (Liq. Clds)
MVFR Probability (Liq. Clds)

5. Validation (fog/low stratus detection)
Algorithm Accuracy at Max CSI (threshold)
LIFR
All Pixels
Liquid Pixels Only
Daytime LIFR
91.4% (9%)
94.0% (9%)
Nighttime LIFR
85.5% (12%)
87.8% (13%)
Terminator LIFR
84.6% (9%)
86.9% (10%)
IFR
All Pixels
Liquid Pixels Only
Daytime IFR
89.0% (33%)
92.8% (36%)
Nighttime IFR
85.4% (26%)
88.2% (31%)
Terminator IFR
83.6% (28%)
86.8% (30%)
MVFR
All Pixels
Liquid Pixels Only
Daytime MVFR
84.2% (57%)
86.9% (58%)
Nighttime MVFR
84.0% (43%)
87.1% (47%)
Terminator MVFR
81.6% (47%)
83.7% (47%)
# of pts
All Pixels
Liquid Pixels Only
Daytime
492,977
298,480
Nighttime
564,029
278,042
Terminator
92,654
49,083
The validation data set was comprised of 12, 24-hr periods (1 day from each month) of GOES-13 data collocated with surface observations
Even at the probability threshold that yields the highest CSI (skill) the accuracy of the Enterprise GS LIFR, IFR and MVFR algorithms exceed the 70% F&PS accuracy specification during both day and night.

6. Validation (fog/low stratus detection)
Nighttime Reliability tables
LIFR
IFR
MVFR

7. Validation (fog/low stratus detection)
Daytime Reliability tables
LIFR
IFR
MVFR

8. Validation (fog/low stratus detection)
Terminator Reliability tables
LIFR
IFR
MVFR

9. Validation
The acoustic SODAR (Sonic Detection And Ranging) is an upwardly pointing parabolic antenna that emits an audible signal whose return signal is proportional to the vertical gradient of air density. As such, the SODAR is capable of detecting the base of the inversion, which defines the top of the stratus deck.
A combination of ceilometer and SODAR measurements are used to infer the geometric boundaries of low clouds in the San Francisco Bay Area.
This network of instruments is operated by the FAA and NWS.

10. Validation
The combination of SODAR and ceilometer data can be used to validate the fog depth product.
fog depth

11. Validation (fog depth)
Initial performance estimates indicate that the 500 m accuracy will be readily achieved (bias < 32 m). The strong correlations indicate that the spatial and temporal patterns are informative.
Daytime
Nighttime

12. Validation: Summary
The validation of the fog/low stratus products show that:
The fog depth algorithm easily meets the 500 m accuracy requirement.
The LIFR, IFR and MVFR probability products easily meet the 70% accuracy requirement