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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