1

Product Overview(Fog/Low Cloud

Detection)

Example Product Output

2

Deadhorse

Areas of interest

Barrow

Arctic Ocean

Kaktovik

MVFR Probability

Surface observation at Barrow (in middle of an FLS deck) shows VFR conditions, while further east along the Arctic Ocean coast LIFR conditions are being reported

Deadhorse

Barrow

Arctic Ocean

Kaktovik

Notice how the traditional BTD FLS product would show the same signal (color) for both Barrow, Deadhorse, and Kaktovik

Deadhorse

Barrow

Kaktovik

The GOES-R MVFR probability product indicates a < 50% probability of MVFR at Barrow and a > 50% probability of MVFR at Deadhorse and Kaktovik. In general, the GOES-R product is more sensitive than the BTD to localized changes in ceiling.

Deadhorse

Barrow

Kaktovik

MVFR Probability

Deadhorse

Barrow

Kaktovik

The GOES-R FLS depth product shows that there is some spatial variability in cloud depth.

MV

FR

Pro

bab

ilit

yC

lou

d T

ype

IFR

Pro

bab

ilityF

LS

Dep

th

9

Requirements

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

10

Requirements

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

11

Validation Approach

12

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

13

Validation Results

Surface Observation-Based Fog Detection

Validation

14

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

15

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

16

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

17

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

20

Validation Results Summary

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)

21

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.

Prospects for future improvement: 1). Incorporate additional NWP fields (e.g. wind). 2). Incorporate LEO data. 3). Working on incorporating “valleyness” metric derived from DEM 4). Correct for thin cirrus clouds 5). Work towards an all weather MVFR and IFR probability capability

22

Product Overview(SO2 Detection)

23

Example Product Output

Cordon Caulle (Chile), June 06 2011 – 17:00

24

Example Product Output

Grimsvotn, May 22, 2010 - 13:00 UTC

GOES-R SO2 ProductOMI SO2 Product

Iceland

IcelandThe GOES-R and OMI products are in good agreement (more on this later)

25

Requirements and Product Qualifiers

SO2 Detection

Nam

e

User &

Priority

Geograp

hic

Coverage

(G, H

, C, M

)

Vertical

Resolu

tion

Horizon

tal

Resolu

tion

Map

pin

g

Accu

racy

Measu

remen

t

Ran

ge

Measu

remen

t

Accu

racy

Prod

uct R

efresh

Rate/C

overage Tim

e (Mod

e 3)

Prod

uct R

efresh

Rate/C

overage Tim

e (Mod

e 4)

Ven

dor A

llocated G

roun

d

Laten

cy

Prod

uct M

easurem

ent P

recision

SO2 Detection

GOES-R Full Disk Total Column

2 km 1 km Binary yes/no detection from 10 to 700 Dobson Units (DU)

70% correct detection

Full disk: 60 min

Full disk: 5 min

806 sec N/A

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

SO2 Detection GOES-R Full Disk Day and night Quantitative out to at least 70 degrees LZA and qualitative at larger LZA

Clear conditions down to feature of interest associated with threshold accuracy

Over specified geographic area

26

Requirements and Product Qualifiers

SO2 Detection

Nam

e

User &

Priority

Geograp

hic

Coverage

(G, H

, C, M

)

Vertical

Resolu

tion

Horizon

tal

Resolu

tion

Map

pin

g

Accu

racy

Measu

remen

t

Ran

ge

Measu

remen

t

Accu

racy

Prod

uct R

efresh

Rate/C

overage Tim

e (Mod

e 3)

Prod

uct R

efresh

Rate/C

overage Tim

e (Mod

e 4)

Ven

dor A

llocated G

roun

d

Laten

cy

Prod

uct M

easurem

ent P

recision

SO2 Detection

GOES-R Full Disk Total Column

2 km 1 km Binary yes/no detection from 10 to 700 Dobson Units (DU)

70% correct detection

Full disk: 60 min

Full disk: 5 min

806 sec N/A

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

SO2 Detection GOES-R Full Disk Day and night Quantitative out to at least 70 degrees LZA and qualitative at larger LZA

Clear conditions down to feature of interest associated with threshold accuracy

Over specified geographic area

27

Validation Approach

28

Validation ApproachValidation Approach

•OMI is a Dutch-Finnish instrument on board the Aura satellite in NASA’s A-Train.

•OMI uses measurements of backscattered solar UV radiation to detect SO2. OMI can detect SO2 at levels less than 1 DU.

•Although OMI is very sensitive to SO2, it only views a given area of earth once daily.

•The MODIS instrument on the Aqua satellite (also in the A-Train) observes the same area as OMI, which allows us to study any SO2

clouds observed by OMI.

The Ozone Monitoring Instrument (OMI) is used as “truth”

The ABI SO2 mask is validated as a function of OMI SO2 loading

29

Validation Approach Validation Approach

OMI SOOMI SO22 Quality Flag Quality Flag

(QF) from the OMI data (QF) from the OMI data set is used to filter out set is used to filter out poor quality SOpoor quality SO22 loading loading

retrievalsretrievals

Before filtering After filtering

Spurious data

Validation ApproachValidation Approach

30

•The OMI SO2 loading product and the GOES-R proxy data (MODIS or SEVIRI) are matched up in time and re-mapped to the same grid to allow for quantitative comparisons.

31

Validation Results

32

SO2 Validation•The ABI SO2 mask is validated as a function of OMI SO2 loading.

•Only scenes that contained SO2 clouds were used so this analysis reflects how the algorithm will perform in relevant situations

•The accuracy is 79% when the OMI SO2 indicates 10 DU or more of SO2.

Accuracy

Accuracy Requirement

33

SO2 Validation•While not required the true skill score was also evaluated.

•The true skill score is 0.59 when the OMI SO2 indicates 10 DU or more of SO2.

•The true skill score is 0.70 when the OMI SO2 indicates 14 DU or more of SO2.

True Skill Score

34

Validation Results Summary

F&PS requirement: Product Measurement Range

F&PS requirement: Product Measurement Accuracy

Validation results

Binary yes/no detection from 10 to 700 Dobson Units (DU)

70% correct detection

79% correct detection when loading is 10 DU or greater (0.70 true skill score when loading is 14 DU or greater)

34

35

Summary

The ABI SO2 Detection algorithm provides a new capability to objectively detect SO2 clouds, which may be an aviation hazard and impact climate, at high temporal resolution (this is the first quantitative geostationary capability).

The GOES-R AWG SO2 algorithm meets all performance and latency requirements.

The improved spatial and temporal resolution of the ABI, along with a 7.3 um band that is better suited for SO2 detection, will likely lead to improved SO2 detection capabilities (relative to SEVIRI and MODIS). ABI channel 10 was specifically designed to help with SO2 detection (no SO2 product = wasted instrument capability)

Prospects for future improvement: 1). Express results as a probability, not a mask. 2). Correct for high level ice and ash clouds using bands not sensitive to SO2 3). Improve quantitative estimates of SO2 loading and possibly height