1 GOES-R AWG Aviation Team: ABI Visibility Algorithm (VP) June 14, 2011 Presented By: R. Bradley Pierce 1 1 NOAA/NESDIS/STAR

2 Outline  Executive Summary (1 slide)  Algorithm Description (2-4 slides)  ADEB and IV&V Response Summary (1-2 slides)  Requirements Specification Evolution (2 slides)  Validation Strategy (3-5 slides)  Validation Results (3-5 slides)  Summary (1-2 slides)

3 Executive Summary  This ABI Visibility Algorithm requires input from ABI Aerosol Optical Depth (AOD) Cloud Optical Thickness (COT), and Fog/Low Cloud Probability retrievals and generates the Option 2 product of Visibility.  Version 3, which uses MODIS AOD/COT and GEOCAT Fog/Low Cloud Probability, was delivered in April. ATBD (80%) is on track for a June 30 th, 2011 delivery with Test Readiness Review (TRR) scheduled for July 20 th, 2011  ASOS validation tools have been developed and applied to 8 weeks of MODIS/GEOCAT data.  The algorithm precision (0.6 categories) meets specifications (1.5 categories) but the accuracy (59%) does not meet specifications (80%)

4 Algorithm Description

5 Algorithm Summary  Visibility is inversely proportional to extinction. For measurement of visibility in the daytime the NWS uses 1 : V = 3.0/σ Where V is the visibility (in km), and σ is the extinction coefficient (km -1 ) Optical depth  is defined as σx. Expressing visibility in terms of  gives: V = 3.0/(  /x)  The ABI Visibility algorithm uses Aerosol Optical Depth (AOD) to estimate  under clear-sky conditions and uses Cloud Optical Thickness (COT) to estimate  under cloudy conditions when Fog or Low Clouds have been detected.  The ABI Visibility algorithm uses NWS Planetary Boundary Layer (PBL) depth to estimate x under clear-sky conditions and uses retrieved Fog and Low Cloud depth to estimate x under cloudy conditions when Fog or Low Clouds have been detected. 1 Federal Standard Algorithms for Automated Weather Observing Systems used for Aviation Purposes, Office of the Federal Coordinator for Meteorological Services and Supporting Research (OFCM), DOC

6 Algorithm Summary (cont)  Statistical analysis of historical ASOS visibilities vs first Guess ABI aerosol and fog visibilities is used to determine Look Up Table (LUT) of monthly categorical bias corrections (offsets and scale factors) for aerosol and fog visibility AOD, COT, and Fog probabilities used in the historical analysis were generated using MODIS radiances and ABI retrievals provided by the Aerosols/Air Quality/Atmos. Chemistry Team (S. Kondragunta), Cloud Team (A. Heidinger), and Aviation Team (M. Pavolonis)  The algorithm requires auxilary MODIS V5.1 L2 AOD and COT retrievals and retrieves Fog Probability using MODIS L1 radiances  A “blended” visibility retrieval is constructed using a weighted combination of the First Guess and bias corrected visibility estimates for both aerosol and low- cloud/Fog visibilities. Optimal weighting is determined based on assessment of required categorical accuracy (percent correct classification), required precision (standard deviation of categorical error), Heidke Skill Score (fractional improvement relative to chance), and false alarm rate.

7 Visibility Processing Schematic

Example of Visibility Output June 4 th, 2011 MODIS Terra Visibility Retrieval 8 Smoke from Wallow fire in Arizona led to reduced visibility in NE Colorado and Nebraska. Regional enhancement of sulfate aerosols over due to stagnate conditions within a stationary high pressure system led to reduced visibility in the SE US.

9 Algorithm Changes from V1 to V3 The V3 algorithm uses MODIS L1 radiances to compute fog probability and uses auxilary L2 MODIS AOD and COT input to compute aerosol and fog extinction and fog depth (Version 1 used MODIS AOD and GOES COT) The threshold visual contrast used to convert extinction to visibility has been changed from 0.02 to 0.05 to be consistent with WMO and NWS definitions. The Version 3 Visibility algorithm LUTs are based on regressions against the harmonic mean 1-minute visibility instead of instantaneous 1-minute visibility to be consistent with NWS ASOS reporting. The Version 3 Aerosol Visibility algorithm uses regressions of ABI AOD and COT vs ASOS visibility to generate the Aerosol and Fog LUT to reduce the dependence on the GFS pbl and Fog depth in the regression statistics. The Version 3 Fog Visibility algorithm uses an 80% threshold for fog probability (Version 1 used a 50% threshold)

10 ADEB and IV&V Response Summary  ADEP has not reviewed the Visibility Algorithm since the 80% ATBD has not been delivered  ADEP review is scheduled for 08/23/11

11 Requirements NameUser &PriorityGeographicCoverage(G, H, C, M)VerticalResolutionHorizontalResolutionMappingAccuracyMeasurementRangeMeasurementAccuracyProduct RefreshRate/Coverage Time(Mode 3)Product RefreshRate/Coverage Time(Mode 4)Mission Product DataLatencyProduct MeasurementPrecision Visibility GOES-R Full Disk (Threshold) Global (Goal) N/A10 km5 kmClear (vis ≥ 30km) Moderate (10km ≤ Vis < 30 km) Low (2 km ≤ vis < 10 km) and Poor (vis < 2km) 80% correct classification 60 min 15 min1.5 categories

12 Qualifiers These are made with the following qualifiers.  Quantitative out to at least 70 degrees LZA and qualitative at larger LZA  Clear conditions down to feature of interest associated with threshold accuracy  AOD, COT and Fog Probability available

13 Validation Approach

14 Validation Approach  Primary validation dataset is NWS Automated Surface Observing Systems (ASOS) raw (1 minute) extinction measurements Archived at NCDC Class 9868 V3 visibility retrieval pixels have been collocated with ASOS harmonic mean 1-minute visibility measurements during May-June 2010  Secondary validation data sets include High Spectral Resolution Lidar (HSRL) 550 nanometer extinction coefficients, Solar Spectral Flux Radiometer (SSFR) Cloud Optical Depth retrievals, and airborne insitu cloud microphysics measurements

15 Validation Results

16 Histogram of ASOS & ABI Merged Aerosol and Fog categorical visibility classifications for 9868 coincident ASOS/MODIS measurement pairs during May-June Merged ABI V3 vs ASOS visibility May-June 2010 Validation Results The merged visibility retrieval results in a 59.2% categorical success rate and an estimated precision of 0.60.

17 Validation Results V3 Heidke Skill Score (Hit Rate) Skill Score and False Alarm Rate (May-June, 2010) V3 False Alarm Rate Visibility CategoryMerged retrieval 1 (Clear) (Moderate) (Low) (Poor) Visibility CategoryMerged retrieval 1 (Clear) (Moderate) (Low) (Poor) The V3 Visibility retrieval shows lower Skill and increased false alarm rates as visibility degrades from Clear to Poor *Heidke Skill Score measures the fractional improvement relative to chance

June 4, 2011 Validation of ABI V3 Visibility Retrieval Smoke from Wallow fire in Arizona led to reduced visibility in NE Colorado and Nebraska

Boulder, CO ABI visibility at Boulder, CO = 15km June 4, 2011 Validation of ABI V3 Visibility Retrieval

GVHSRL PBL visibility at MODIS overpass = 3.0/(0.8/4.) = 15km GVHSRL 1km visibility at MODIS overpass = 3.0/(0.1/1.) = 30km MODIS Overpass 17:35Z PBL depth = 4km AOD = 0.8 AOD = 0.1 1km June 4, 2011 Validation of ABI V3 Visibility Retrieval Boulder GVHSRL lidar data provided by Ed Eloranta (SSEC, HSRL PBL visibility at MODIS overpass = 3.0/(0.8/4.) = 15km (Good agreement!) HSRL 1km visibility at MODIS overpass = 3.0/(0.1/1.) = 30km (Bad agreement!)

GVHSRL PBL visibility at MODIS overpass = 3.0/(0.8/4.) = 15km GVHSRL 1km visibility at MODIS overpass = 3.0/(0.1/1.) = 30km MODIS Overpass 17:35Z PBL depth = 4km AOD = 0.8 AOD = 0.1 1km June 4, 2011 Validation of ABI V3 Visibility Retrieval Boulder GVHSRL lidar data provided by Ed Eloranta (SSEC, ABI visibility retrieval represents a column visibility and can be decoupled from ASOS surface measurement if aerosols are aloft or PBL is not well mixed. This makes it difficult to reach the required accuracy when surface ASOS measurements are used for validation. HSRL PBL visibility at MODIS overpass = 3.0/(0.8/4.) = 15km (Good agreement!) HSRL 1km visibility at MODIS overpass = 3.0/(0.1/1.) = 30km (Bad agreement!)

22 Summary  The ABI Visibility Algorithm provides a new capability for estimating visibility from geostationary orbit and complements existing ASOS surface measurements  Version 3 is delivered and the 80% ATBD is coming.  The precision of the V3 Algorithm (0.6) meets the required precisions of 1.5 classes. However, the categorical accuracy (59%) of the V3 visibility Algorithm does not meet the requirement (80%)  Further work needs to be done to improve the categorical accuracy of the visibility algorithm