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 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.

MVFR Probability Cloud Type IFR Probability FLS Depth

9 Requirements VerticalRes.Horiz.Res.MappingAccuracyMsmnt.RangeMsmnt.AccuracyRefreshRate/Coverage TimeOption(Mode 3)RefreshRate Option(Mode 4)DataLatencyLong-TermStabilityProductMeasurement Precision 0.5 km (depth) 2 km1 kmFog/No Fog70% Correct Detection 15 min5 min159 secTBDUndefined for binary mask C – CONUS FD – Full DiskM - Mesoscale NameUser &PriorityGeographicCoverage(G, H, C,M)TemporalCoverageQualifiersProductExtentQualifierCloudCoverConditionsQualifierProductStatisticsQualifier Low Cloud and Fog GOES-RFDDay and nightQuantitative 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 VerticalRes.Horiz.Res.MappingAccuracyMsmnt.RangeMsmnt.AccuracyRefreshRate/Coverage TimeOption(Mode 3)RefreshRate Option(Mode 4)DataLatencyLong-TermStabilityProductMeasurement Precision 0.5 km (depth) 2 km1 kmFog/No Fog70% Correct Detection 15 min5 min159 secTBDUndefined for binary mask C – CONUS FD – Full DiskM - Mesoscale NameUser &PriorityGeographicCoverage(G, H, C,M)TemporalCoverageQualifiersProductExtentQualifierCloudCoverConditionsQualifierProductStatisticsQualifier Low Cloud and Fog GOES-RFDDay and nightQuantitative 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 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

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 SITE Yellowknife, NWT, Canada Tower 3 Tower 1 Tower 4 Tower 2 INSTRUMENTS FD12P and Sentry Vis

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/No70% 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 (SO 2 Detection)

23 Example Product Output Cordon Caulle (Chile), June – 17:00

24 Example Product Output Grimsvotn, May 22, :00 UTC GOES-R SO 2 Product OMI SO 2 Product Iceland The GOES-R and OMI products are in good agreement (more on this later)

25 Requirements and Product Qualifiers SO 2 Detection NameUser &PriorityGeographicCoverage(G, H, C, M)VerticalResolutionHorizontalResolutionMappingAccuracyMeasurementRangeMeasurementAccuracyProduct RefreshRate/CoverageTime (Mode 3)Product RefreshRate/CoverageTime (Mode 4)Vendor AllocatedGround LatencyProductMeasurementPrecision SO 2 Detection GOES-RFull DiskTotal Column 2 km1 kmBinary yes/no detection from 10 to 700 Dobson Units (DU) 70% correct detection Full disk: 60 min Full disk: 5 min 806 secN/A NameUser &PriorityGeographicCoverage(G, H, C, M)TemporalCoverageQualifiersProductExtentQualifierCloud CoverConditionsQualifierProductStatisticsQualifier SO 2 DetectionGOES-RFull DiskDay and nightQuantitative 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 SO 2 Detection NameUser &PriorityGeographicCoverage(G, H, C, M)VerticalResolutionHorizontalResolutionMappingAccuracyMeasurementRangeMeasurementAccuracyProduct RefreshRate/CoverageTime (Mode 3)Product RefreshRate/CoverageTime (Mode 4)Vendor AllocatedGround LatencyProductMeasurementPrecision SO 2 Detection GOES-RFull DiskTotal Column 2 km1 kmBinary yes/no detection from 10 to 700 Dobson Units (DU) 70% correct detection Full disk: 60 min Full disk: 5 min 806 secN/A NameUser &PriorityGeographicCoverage(G, H, C, M)TemporalCoverageQualifiersProductExtentQualifierCloud CoverConditionsQualifierProductStatisticsQualifier SO 2 DetectionGOES-RFull DiskDay and nightQuantitative 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 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 SO 2. OMI can detect SO 2 at levels less than 1 DU. Although OMI is very sensitive to SO 2, 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 SO 2 clouds observed by OMI. The Ozone Monitoring Instrument (OMI) is used as “truth” The ABI SO 2 mask is validated as a function of OMI SO 2 loading

29 Validation Approach  OMI SO 2 Quality Flag (QF) from the OMI data set is used to filter out poor quality SO 2 loading retrievals Before filteringAfter filtering Spurious data

Validation Approach 30 The OMI SO 2 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 SO 2 Validation The ABI SO 2 mask is validated as a function of OMI SO 2 loading. Only scenes that contained SO 2 clouds were used so this analysis reflects how the algorithm will perform in relevant situations The accuracy is 79% when the OMI SO 2 indicates 10 DU or more of SO 2. Accuracy Accuracy Requirement

33 SO 2 Validation While not required the true skill score was also evaluated. The true skill score is 0.59 when the OMI SO 2 indicates 10 DU or more of SO 2. The true skill score is 0.70 when the OMI SO 2 indicates 14 DU or more of SO 2. 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 SO 2 Detection algorithm provides a new capability to objectively detect SO 2 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 SO 2 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 SO 2 detection, will likely lead to improved SO 2 detection capabilities (relative to SEVIRI and MODIS). ABI channel 10 was specifically designed to help with SO 2 detection (no SO 2 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 SO 2 3). Improve quantitative estimates of SO 2 loading and possibly height