1. 1 Algorithm Theoretical Basis (Fog/Low Stratus) Presented by Michael Pavolonis Aviation Application Team STAR With significant contributions from: Corey Calvert (UW/CIMSS)

2. 2 Algorithm Theoretical Basis   Purpose: Provide product developers, reviewers and users with a theoretical description (scientific and mathematical) of the enterprise GS Fog/Low Stratus algorithm   Will be documented in the ATBD of enterprise GS Fog/Low Stratus products

3. 3 CDR Requirements Low Cloud and Fog NameUser &PriorityGeographicCoverage(G, H, C, M)VerticalRes.Horiz.Res.MappingAccuracyMsmnt.RangeMsmnt.AccuracyRefreshRate/CoverageTime Option(Mode 3)Refresh RateOption (Mode4)DataLatencyLong-TermStabilityProductMeasurementPrecision Fog/Low Stratus Cloud Enterprise GS FD0.5 km (depth) 4 km1 kmFog/No Fog70% Correct Detection 15 min5 min159 secTBDUndefined C – CONUS FD – Full DiskM - Mesoscale

4. 4 CDR Requirements Low Cloud and Fog NameUser &PriorityGeographicCoverage(G, H, C, M)TemporalCoverageQualifiersProductExtentQualifierCloud CoverConditionsQualifierProductStatisticsQualifier Fog/Low Stratus Clouds Enterprise GSFDDay and nightQuantitative out to at least 70 degrees LZA and qualitative beyond All cloud cover conditionsOver low cloud and fog cases with at least 42% occurrence in the region C – CONUS FD – Full DiskM - Mesoscale

5. 5 Algorithm Development - Development Strategy  Aviation based fog/low cloud definition  Visual flight rules  ceiling > 3000 ft and/or surface visibility > 5 sm  Marginal visual flight rules  1000 ft < ceiling < 3000 ft and/or 3 sm < sfc vis < 5 sm  Instrument flight rules  500 ft < ceiling < 1000 ft and/or 1 sm < sfc vis < 3 sm  Low instrument flight rules  ceiling < 500 ft and/or sfc vis < 1 sm

6. 6 Algorithm Development - Development Strategy  Aviation based fog/low cloud definition  Visual flight rules  ceiling > 3000 ft and/or surface visibility > 5 sm  Marginal visual flight rules  1000 ft < ceiling < 3000 ft and/or 3 sm < sfc vis < 5 sm  Instrument flight rules  500 ft < ceiling < 1000 ft and/or 1 sm < sfc vis < 3 sm  Low instrument flight rules  ceiling < 500 ft and/or sfc vis < 1 sm

7. 7 ADR Algorithm   There was no ADR performed for the Enterprise GS fog/low stratus products   However, an algorithm was developed to detect fog/low stratus under the GOES-R Future Capabilities project and has already gone through a CDR and TRR   This algorithm was created to mitigate several issues seen in the current 3.9-11μm BTD product currently in operations » »Issues with the 3.9-11μm BTD products – –Method only designed for nighttime conditions. – –False alarms over barren surfaces. – –Non-hazardous water clouds are often detected as fog/low cloud – –Thresholds and empirical relationship depends on exact sensor characteristics.

8. 8 CDR Algorithm Given that the F&PS requires fog/low stratus products to be produced during the day and night, our preferred solution is to utilize the algorithm developed under the GOES-R Future Capabilities project.Given that the F&PS requires fog/low stratus products to be produced during the day and night, our preferred solution is to utilize the algorithm developed under the GOES-R Future Capabilities project.

9. 9 Algorithm Objectives  F&PS  Meet the F&PS requirements for low cloud and fog.   Provide needed performance information to allow for proper use of our products.

10. 10 Low Cloud and Fog Sensor Inputs Future GOES Imager (ABI) Band Nominal Wavelength Range (μm) Nominal Central Wavelength (μm) Nominal Central Wavenumber (cm-1) Nominal sub-satellite IGFOV (km) Sample Use 10.45-0.490.47212771 20.59-0.690.64156250.5Fog detection 30.846-0.8850.865115611 41.371-1.3861.37872572 51.58-1.641.6162111 62.225 - 2.2752.2544442 73.80-4.003.9025642Fog detection and depth 85.77-6.66.1916162 96.75-7.156.9514392 107.24-7.447.3413622 118.3-8.78.511762 129.42-9.89.6110412 1310.1-10.610.359662 1410.8-11.611.28932Fog detection and depth 1511.8-12.812.38132 1613.0-13.613.37522 Current Input Expected Added InputPossible Added Input

11. 11 GOES-R ABI Low Cloud/Fog requires the following for each pixel: » »Calibrated/Navigated ABI brightness temperatures/radiances/reflectances » »Spectral response information » »Solar-view geometry (satellite zenith, relative azimuth, solar zenith) » »Geolocation (latitude, longitude) NameTypeDescriptionDimension Ch2 reflectanceinputCalibrated ABI level 1b reflectance for channel 2Scan grid (xsize, ysize) Ch 7 reflectanceInputCalibrated ABI level 1b reflectance for channel 7Scan grid (xsize, ysize) Ch7 brightness temp/ radiances input Calibrated ABI level 1b brightness temperatures for channel 7 Scan grid (xsize, ysize) Ch14 brightness temp/ radiances input Calibrated ABI level 1b brightness temperatures and radiances for channel 14 Scan grid (xsize, ysize) Fog and Low Cloud Input Sensor Input Details Possible Added Input

12. 12 NameTypeDescriptionDimension LatitudeInputABI LatitudeScan grid (xsize, ysize) LongitudeInputABI LongitudeScan grid (xsize, ysize) Glint angleInputGlint Zenith angleScan grid (xsize, ysize) Solar geometryinputABI solar zenith angleScan grid (xsize, ysize) View anglesinputABI view zenith and relative azimuth anglesScan grid (xsize, ysize) QC flagsinputABI quality control flags with level 1b dataScan grid (xsize, ysize) Fog and Low Cloud Input Sensor Input Details

13. 13 Sensor Inputs Channels used in low cloud/fog algorithm

14. 14 NameTypeDescriptionDimension Snow Maskinput Daily global snow and ice mask available at a horizontal resolution of 4km in the northern hemisphere (IMS) and 25km in the southern (SSMI) 4 km resolution Land MaskinputDerived from global land cover land types Scan grid (xsize, ysize) SSTinput NOAA 1/4° daily Optimum Interpolation Sea Surface Temperature analysis Scan grid (xsize, ysize) Coast Maskinput Created from global 1-km land/water mask used for collection 5. Differentiates coast at distances from 1 km – 10 km. 1 km resolution Surface emissivity InputSeebor 5-km surface emissivity database5-km resolution Digital elevationinput NGDC-GLOBE global digital elevation model with a horizontal resolution of 1km 1 km resolution ●Non-ABI Static Data Fog and Low Cloud Input Ancillary Input Details

15. 15 NameTypeDescriptionDimension Clear-sky TOA radiances Input TOA clear sky radiances for ABI channels 7 and 14 derived from PFAAST RTM Scan grid (xsize, ysize) Clear-sky radiance profiles Input Clear sky radiance profiles for ABI channels 7 and 14 derived from PFAAST RTM NWP grid (xsize, ysize, ivza, nprof) Atmospheric transmittance profiles Input Atmospheric transmittance profiles for ABI channels 7 and 14 derived from PFAAST RTM NWP grid (xsize, ysize, ivza, nprof) NWP temperature, RH and height profiles Input Profiles of NWP temperature, relative humidity and height for each cell. NWP grid (xsize, ysize) NWP/imager co- location Input X and Y cell number for each latitude and longitude Scan grid (xsize, ysize) NWP surface and troposphere levels Input NWP level for surface and troposphere for each pixel. NWP grid (xsize, ysize) NWP surface temperature Input Temperature of surface from NWP for each cell. NWP grid (xsize, ysize) ●Non-ABI Dynamic Data Fog and Low Cloud Algorithm Input Ancillary Input Details

16. 16 NameTypeDescriptionDimension Cloud MaskInputDerived ABI Cloud Mask Scan grid (xsize, ysize) Cloud Phase/TypeInputDerived ABI Cloud Phase and Type Output Scan grid (xsize, ysize) Daytime Microphysical Properties Input Derived ABI VIS/NIR Cloud Microphysical Properties Scan grid (xsize, ysize) ●Products required to run algorithm Fog and Low Cloud Algorithm Product Precedence Details

17. 17 Algorithm Output Output Name Description MVFR probability Probability that MVFR, or lower conditions are present IFR probability Probability that IFR, or lower conditions are present LIFR probability Probability that LIFR, or lower conditions are present Fog depth Fog geometrical depth in meters

18. 18 ABI Proxy Data for Fog/Low Cloud AWG Products Geostationary satellite datasets are used as ABI proxies in algorithm verification/validation  GOES N-P Imagers  Moderate Resolution Imaging Spectroradiometer (MODIS) The data from GOES is considered as a good proxy of ABI since noise levels are close to expected ABI channels in geostationary orbit covering the same geographic area as the ABI Spatial resolution (4 km) somewhat similar to ABI (2 km) Co-located with high quality ground truth The data from MODIS is considered as a good proxy of ABI since Spatial resolution (1 km) somewhat similar to ABI (2 km)

19. 19 MODIS Band Number MODIS Wavelength Range (μm) MODIS Central Wavelength (μm) MODIS Sensor Noise ABI Band Number ABI Wavelength Range (μm) ABI Central Wavelength (μm) 10.62 – 0.670.645128@ 21.8 W/m 2 20.59 - 0.690.64 203.66 – 3.843.750.05 K @ 300K73.80 - 4.003.90 3110.78 – 11.2811.030.05 K @ 300K1410.80 – 11.6011.20 ABI Proxy Data Used Current InputExpected Added InputPossible Added Input GOES Band Number GOES N-P Wavelength Range (μm) GOES N-P Central Wavelength (μm) GOES N-Ps Sensor Noise ABI Band Number ABI Wavelength Range (μm) ABI Central Wavelength (μm) 10.55 - 0.750.650.39@ 5.3 W/m 2 20.59 - 0.690.64 23.80 - 4.003.900.051 K @ 300K73.80 - 4.003.90 410.20 - 11.2010.700.053 K @ 300K1410.80 – 11.6011.20

20. 20 Retrieval Strategy (Naïve Bayesian Approach) Clear Sky RTM - Minimum channel requirement: 0.65, 3.9, 6.7/7.3, 11, and 12/13.3 μm -Previous image for temporal continuity (GEO only) -Cloud Phase + + Static Ancillary Data -DEM -Surface Type -Surface Emissivity Daily SST Data 0.25 degree OISST + NWP Naïve Bayesian Model IFR Probability NWP RH Profiles -RUC/RAP (2-3 hr forecast) or GFS (12 hr forecast) ***IMPORTANT: Other sources of relevant data (e.g. sfc obs) influence results through the model fields -Surface Temperature -Profiles of T and q -RUC/RAP (2-3 hr forecast) or GFS (12 hr forecast)

21. 21 Retrieval Strategy (Naïve Bayesian Approach) Naïve Bayesian Model probability an event occurs given no measured features probability an event does not occur given no measured features Conditional probability a set of features are observed given an event occurs probability an event occurs given a set of measured features Calculated climatology of event Trained using known data

22. 22 Retrieval Strategy (single layer liquid fog detection)   The ABI cloud phase/type product is used to identify liquid water clouds   For single layer liquid fog detection, the following physical properties are exploited: » »Since the top of fog/low stratus layers is often close to the ground, the temperature difference between the cloud and the surface temperature is typically small. » »Since fog/low stratus is not formed by adiabatic cooling brought about by spatially varying vertical motions, it tends to be horizontally uniform in albedo (e.g. reflectivity). » »Fog/low clouds are generally composed of small liquid droplets » »Fog/low clouds form in environments at or near saturation   Thus, the difference between the observed and clear sky surface temperature, the 3.9 micron reflectance and pseudo-emissivity, the standard deviation of reflectance in a 3 x 3 pixel array and low level moisture are used as fog detection metrics. The strength of the radiometric signal is also taken into account (via a cloud - clear reflectance difference or the 3.9/11  m nighttime signal).   As opposed to using specific fog/no fog thresholds, a training data set is used to assign a fog probability to each non-ice cloud pixel.   This approach does not rely on thresholds or metrics that are highly sensitive to the exact sensor characteristics, so it should not need to be extensively tuned when applied to actual ABI data.

23. 23 Retrieval Strategy (IFR detection when multi-layered or ice clouds are present)   The ABI cloud phase/type product is used to identify ice clouds, including multilayered ice clouds.   When multi-layered or ice clouds are present, the following physical property is exploited: » »Ice clouds, including multi-layered clouds, tend to be elevated and can obscure lower clouds and fog from a satellite point of view » »Fog/low clouds beneath ice clouds form in environments at or near saturation » »Precipitation from convective clouds can create IFR conditions at or near the surface   Satellites can not see through multiple cloud layers so if the fog/low cloud layer is not the highest cloud layer then it’s spectral information is not seen.   For this reason, only the low level RH information from NWP models is used to detect IFR conditions.   As opposed to using specific fog/no fog thresholds, a training data set is used to assign a fog probability to each multi-layered or ice pixel.   This approach does not rely on thresholds or metrics that are highly sensitive to the exact sensor characteristics, so it should not need to be extensively tuned when applied to actual ABI data.

24. 24 Retrieval Strategy (IFR detection in the terminator region)   At high daytime solar zenith angles accurate satellite data can be difficult to obtain due to scattering issues   The characteristics of fog/low stratus clouds usually do not drastically change in time so temporal data is used until accurate data is available at lower sun angles   A maximum of 2 scenes within 1 hr of the processed scene are used to temporarily fill gaps in the terminator region   Day-to-night transition »Temporal 3.9  m reflectance and the 0.65  m spatial uniformity metric are used for solar angles between 80-90°   Night-to-day transition »Temporal 3.9  m pseudo-emissivity data is used for solar angles between 85-90°   The NWP data is not dependent on solar angles so the modeled RH and sfc temperature bias are used for all pixels.  °  Cloud masking can also be difficult in the terminator region so a temporal cloud check is used for sun angles between 70-90° to re- classify ‘clear’ pixels as cloudy when fog/low stratus might be present   This approach does not rely on thresholds or metrics that are highly sensitive to the exact sensor characteristics, so it should not need to be extensively tuned when applied to actual ABI data.

25. 25 Retrieval Strategy (fog depth)   The fog depth retrieval is performed on all pixels not characterized as ice from the cloud type algorithm   During the day, the fog depth is computed using the retrieved liquid water path and an assumed liquid water content consistent with in-situ measurements of fog.   At night, a regression relationship, conceptually similar to the one developed by Ellrod, is used.   Fog depth retrievals are not possible in the day/night terminator.

26. 26 Retrieval Strategy (Fog/Low Cloud Detection)   The enterprise GS fog/low cloud detection algorithm can be broken into ????? separate steps. 1. 1.Using the cloud mask and cloud type/phase results to differentiate water from ice clouds, compute fog detection metrics for all pixels at night and cloudy pixels during the day 2. 2.Based on the metrics, determine the fog/low cloud probability for each pixel using previously constructed fog probability look-up tables. 3. 3.?????

27. 27 Retrieval Strategy (Fog/Low Cloud Detection)   The enterprise GS fog/low cloud detection algorithm can be broken into 4 separate steps. 1. 1.Using the cloud mask and cloud type/phase results to differentiate water from ice clouds, compute fog detection metrics for all pixels at night and cloudy pixels during the day   Based on the metrics, determine the fog/low cloud probability for each pixel using previously constructed fog probability look-up tables.  ?????

28. 28 Cloud Type/Phase Product Full disk cloud type results Water, Supercooled Water, Mixed Phase, Thick Ice, Thin Ice, Multilayered

29. 29 Physical Description For cloudy scenes, the radiometric surface temperature tends to be much lower than the SST or LST given by NWP, unless fog/low cloud is present. In which case, the difference is smaller. The radiometric surface temperature can be derived from the 11-  m radiance by re-arranging the infrared radiative transfer equation. Note that if one were to use the difference between the observed 11-  m brightness temperature and the surface temperature, the results will be sensitive to the water vapor loading, view angle, and sensor characteristics. The radiometric surface temperature is derived as follows: Metric 1: Difference between the radiometrically derived surface temperature and the NWP surface temperature [Day and Night]

30. 30 Physical Description Metric 1: Difference between the radiometrically derived surface temperature and the NWP surface temperature [Day and Night] : wavelength (11-  m in this case) R( ): observed radiance R sfc ( ): surface radiance T atm ( ): total atmospheric transmittance R atm ( ): atmospheric radiance  sfc ( ): surface emissivity B -1 ( ): inverse Planck Function T sfc : radiometric surface temperature T nwp : surface temperature from NWP T sfc_bias : the difference between the radiometric and NWP surface temperatures Assuming clear conditions

31. 31 Physical Description Full disk view of “surface temperature bias”

32. 32 Physical Description Full disk view of “surface temperature bias” Elevated clouds Low clouds

33. 33 Metric 2: Low-level Relative Humidity [Day and night] Fog/low clouds form in environments at or near saturation low-level RH information is determined using the surface RH and RH profile data from NWP models (RAP/GFS) The vertical ceiling requirement for fog/low clouds is 1000 ft so the maximum RH from the surface up to 1000 ft above ground level (AGL) is used as the RH metric Physical Description

34. 34 Physical Description Full disk view of Max RH in lowest 1000 ft layer AGL Dry low-level air Most low-level air at/near saturation

35. 35 Physical Description As stated earlier, fog/low cloud tends to be spatially uniform in albedo. Consider the standard deviation of a given parameter over a 3 x 3 pixel window. The standard deviation of the visible reflectance is a useful indicator of fog during the day. Issues arise near cloud edges where the standard deviations in the visible reflectance are large. To mitigate this issue, the “gradient filter” procedure is used to determine the local radiative center (LRC) for each cloudy pixel during the day. The gradient filter allows the spectral information from an interior pixel within the same cloud to represent pixels with a very weak cloud radiative signal or sub-pixel cloudiness associated with cloud edges. Metric 3: Spatial uniformity at the cloud LRC [Day only]

36. 36 Physical Description   Given a cloud parameter such as emissivity or reflectance, the LRC for a given pixel is defined as the location in the direction of the gradient vector upon which the gradient reverses or when a value greater than some threshold is found.   By definition, the gradient vector points from low to high values.   This concept is illustrated graphically on the next few slides. Metric 3: Spatial uniformity at the cloud LRC [Day only]

37. 37 Mathematical Description The tail of each vector represents the pixel of interest. The head of each vector represents the LRC of the pixel of interest.

38. 38 Mathematical Description The tail of each vector represents the pixel of interest. The head of each vector represents the LRC of the pixel of interest.

39. 39 Mathematical Description Zoomed in view of gradient vectors

40. 40 Mathematical Description Zoomed in view of gradient vectors

41. 41 Physical Description Full disk view of the spatial uniformity at the cloud LRC

42. 42 Physical Description Full disk view of the spatial uniformity at the cloud LRC

43. 43 Physical Description Full disk view of the spatial uniformity at the cloud LRC Spatially uniform clouds Non-spatially uniform clouds

44. 44 Physical Description Fog/low stratus clouds usually consist of small liquid water droplets compared to higher liquid water cloud layers. Smaller water droplets have a higher reflectivity at 3.9 μm than larger droplets, ice particles or snow. Thus, clouds with a relatively high 3.9 μm reflectance (e.g. small water droplets), high spatial uniformity in the visible reflectance, and a small surface temperature bias have a higher probability of being fog/low clouds. Metric 4: NIR (3.9 μm) reflectance [Day Only]

45. 45 Physical Description Metric 4: NIR (3.9 μm) reflectance [Day Only]

46. 46 Physical Description Metric 4: NIR (3.9 μm) reflectance [Day Only]

47. 47 Physical Description Metric 4: NIR (3.9 μm) reflectance [Day Only] Surface Snow Ice cloud Water cloud

48. 48 Physical Description Traditionally, the 3.9 - 11  m brightness temperature difference (BTD) has been used to detect fog/low cloud at night. While this BTD has been successfully used to make fog imagery, it is difficult to use for quantitative fog detection because of its dependence on moisture and view angle. Fortunately, these effects can be accounted for by deriving a radiometric parameter from the observed 3.9 and 11  m radiances. This radiometric parameter, called the “3.9-  m pseudo-emissivity” or ems (3.9  m), has less sensitivities to the spectral response functions and scene temperatures. ems(3.9  m) is derived as follows: Metric 5: The 3.9-  m pseudo-emissivity [Night Only]

49. 49 Physical Description Metric 5: The 3.9-  m pseudo-emissivity [Night Only] ems(3.9  m) is computed as follows: where and ems(3.9  m): the 3.9-  m pseudo-emissivity R sfc (3.9  m): 3.9-  m surface radiance B[3.9  m,BT sfc (11  m)]: the 3.9-  m blackbody surface radiance relative to the 11-  m surface brightness temperature : wavelength R( ): observed radiance R sfc ( ): surface radiance T atm ( ): total atmospheric transmittance R atm ( ): atmospheric radiance  sfc ( ): surface emissivity B -1 ( ): inverse Planck Function

50. 50 Physical Description Metric 5: The 3.9-  m pseudo-emissivity [Night Only] ems(3.9  m) is computed as follows: where and Converting the observed radiances to surface radiances (e.g. the radiance leaving the surface that is transmitted to the TOA) helps correct for water vapor absorption. Normalizing by the term in the denominator accounts for 11-  m temperature variability.

51. 51 Physical Description Full disk view of water cloud ems(3.9)

52. 52 Physical Description Full disk view of water cloud ems(3.9) Low values of ems(3.9) are generally associated with fog/low cloud

53. 53 Retrieval Strategy (Fog/Low Cloud Detection)   The enterprise GS fog/low cloud detection algorithm can be broken into 4 separate steps. 1. 1.Using the cloud mask and cloud type/phase results to differentiate water from ice clouds, compute fog detection metrics for all pixels at night and cloudy pixels during the day 2. 2.Based on the metrics, determine the fog/low cloud probability for each pixel using previously constructed fog probability look-up tables. 3. 3.?????

54. UPDATE LUT’s 54

55. 55 Physical Description Categorical probability look-up tables (LUT’s) were created for 2 different spectral/spatial metric combinations. Only fog/low stratus and non-fog/low stratus water cloud pixels were used to generate the satellite metric LUTs. All pixels were used to generate the NWP max low-level RH LUT’s Observations of surface visibility and cloud ceiling were used to classify fog/no fog in this training data set.

56. 56 Physical Description LUT Type 1: Visible reflectance x surface temperature difference x visible reflectance spatial uniformity [Day Only] Visible reflectance bin #3: 30 - 100% Water Surface Land Surface

57. 57 Physical Description LUT Type 2: ems ac (3.9) x surface temperature difference x 11-  m brightness temperature spatial uniformity [Night Only] ems ac (3.9) bin #4: 0.80 - 0.85 Water Surface Land Surface

58. 58 Physical Description LUT Type 3: surface temperature difference x 11-  m brightnes temperature spatial uniformity [Day or Night] Produces more false alarms, but works regardless of solar zenith angle Water Surface Land Surface

59. 59 Physical Description UPDATE THIS SLIDE During the day, LUT Type 1 is used (solar zenith angle < 85 o ). At night, LUT Type 2 is used (solar zenith angle > 90 o ). In the day/night terminator, LUT Type 3 is used (85 ≤ solar zenith angle ≤ 90). In order to convert the fog probability to a binary fog mask, a yes/no threshold of 0.75 is chosen in probability space. This threshold was chosen in order to have a low probability of false alarm.

60. 60 Retrieval Strategy Summary: Fog/Low Stratus Detection  The cloud mask and cloud type/phase products are used to identify water, supercooled water and mixed phase clouds.  A set of spatial and spectral fog/low stratus detection metrics are computed for each of these pixels.  For a given set of metrics, the probability of a given flight rule category is determined.  The fog/low stratus detection algorithm is computationally stable and efficient (~60 sec. on a full disk, ~5 minutes including cloud dependencies and upfront CPU time)

61. 61 Algorithm Output Full disk nighttime IFR probabilities for 1/10/2014 at 5:45 UTC

62. 62 Algorithm Output Zoom in Full disk nighttime IFR probabilities for 1/10/2014 at 5:45 UTC

63. 63 Algorithm Output The ABI algorithm detects much of what appears to be a large scale fog deck. nighttime IFR probabilities for 1/10/2014 at 5:45 UTC

64. 64 Algorithm Output Surface Observations: VFR MVFR IFR LIFR nighttime IFR probabilities for 1/10/2014 at 5:45 UTC MAKE IMAGE WITH SFC OBS

65. 65 Algorithm Output Full disk daytime IFR probabilities for 1/10/2014 at 17:45 UTC

66. 66 Algorithm Output Zoom in Full disk daytime IFR probabilities for 1/10/2014 at 17:45 UTC

67. 67 Algorithm Output daytime IFR probabilities for 1/10/2014 at 17:45 UTC

68. 68 Algorithm Output Surface Observations: VFR MVFR IFR LIFR daytime IFR probabilities for 1/10/2014 at 17:45 UTC MAKE IMAGE WITH SFC OBS

69. 69 Algorithm Output Full disk terminator IFR probabilities for 1/10/2014 at 13:15 UTC

70. 70 Algorithm Output Zoom in Full disk terminator IFR probabilities for 1/10/2014 at 13:15 UTC

71. 71 Algorithm Output IFR probabilities smoothly transition through the terminator region with the use of temporal data terminator IFR probabilities for 1/10/2014 at 13:15 UTC

72. 72 Algorithm Output Surface Observations: VFR MVFR IFR LIFR MAKE IMAGE WITH SFC OBS terminator IFR probabilities for 1/10/2014 at 13:15 UTC

73. 73 Retrieval Strategy (fog depth)   The fog depth retrieval is performed on all pixels not identified as ice by the cloud type algorithm   During the day, the fog depth is computed using the retrieved liquid water path and an assumed liquid water content consistent with in-situ measurements of fog.   At night, a regression relationship, conceptually similar to the one developed by Ellrod, is used.   Fog depth retrievals are not possible in the day/night terminator.

74. 74 Retrieval Strategy (Fog depth)   The enterprise GS fog/low cloud depth retrieval can be broken into 1 step. 1. 1.Apply the fog depth retrieval to all pixels not identified as ice clouds. · ·The fog depth retrieval methodology differs between day and night.

75. 75 Physical Description Daytime approach: Use the retrieved liquid water path (LWP) and an assumed liquid water content (LWC) to calculate the cloud geometrical thickness (e.g. fog depth). The fog depth (  Z) is given by: The LWP is produced by the cloud team. The LWC is assumed to be 0.06 g/m 3 (Hess et. al, 1998). The LWP is only available for solar zenith angles < 70 o.

76. 76 Physical Description Nighttime approach: Use a regression relationship between the ems(3.9  m) and fog depth determined from ground based instruments to estimate fog depth. The fog depth (  Z) is given by the following regression equation: This approach is analogous to the Ellrod method, except ems(3.9  m) is used in lieu of the 3.9 - 11  m brightness temperature difference. The following analysis will illustrate the benefits of using ems(3.9  m). A and B are regression constants.

77. 77 Physical Description Nighttime approach: Use a regression relationship between the ems(3.9  m) and fog depth determined from ground based instruments to estimate fog depth. BTDems(3.9)

78. 78 Physical Description Nighttime approach: Use a regression relationship between the ems(3.9  m) and fog depth determined from ground based instruments to estimate fog depth. BTDems(3.9) Correlation: -0.72Correlation: -0.06

79. 79 Physical Description Terminator approach: Despite the ABI’s advanced capabilities, fog depth estimation is not possible in the day/night terminator. The fog depth (  Z) is set equal to missing in the day/night terminator.

80. 80 Algorithm Output Daytime fog depth retrieval Fog depth not available in the terminator region

81. 81 Algorithm Output Nighttime fog depth retrieval

82. 82 Algorithm Output   Algorithm runs the same on conus, mesoscale, and full-disk scans.   Processing currently limited to viewing angles < 70 degrees (F&PS specifies 70 degrees).   Algorithm will output the fog mask, fog depth, and quality flags.

83. 83 Practical Considerations: Exception Handling  During the day, the algorithm performs best when the 0.65, 3.9 and 11  m channels provide valid data.  At night, the algorithm performs best when the 3.9 and 11  m channels provide valid data.  If none of the above channel combinations are available, the quality flags will indicate that the fog mask and fog depth could not be determined and the fog mask will be set to “No fog”  If clear sky radiance calculations (based on NWP data) are unavailable, the algorithm output will also be set to unknown. However, the algorithm can tolerate any NWP 0 - 24 hour forecast data valid for the ABI image time.

84. 84 Assumptions and Limitations   Algorithm performance demands accurate NWP and computationally efficient and accurate clear- sky RTM calculations comparable to or better than what is available today.   The processing system allows for multiple scan lines of data (at least 200), preferably with overlap between scan segments.   Unknown spectral shifts in the ABI channels used in the fog/low cloud algorithm will cause biases in the clear sky radiance calculations which will adversely impact algorithm performance.

85. 85 Performance Estimates  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 few slides.

86. 86 Performance Estimates (fog/low stratus detection) Accuracy statistics as a function of probability threshold

87. 87 Performance Estimates (fog/low stratus detection) All Pixels Liquid Pixels Only Daytime IFR 91.3% (48%) 95.7% (52%) Nighttime IFR 87.4% (48%) 90.4% (56%) # of validation pts ~650,000~390,000 The validation data set was comprised of 12 (1 day from each month) 24-hr periods of GOES-13 data collocated with sfc obs The accuracy of the Enterprise GS fog/low stratus algorithm exceeds the 70% F&PS accuracy specification during both day and night. Max. Algorithm Accuracy (threshold)

88. 88 Performance Estimates (fog/low stratus detection) Observed YES Observed NO Total Forecasted YES 20852904229894 Forecasted NO 46255557142603397 Total67107566184633291 Daytime IFR Accuracy (48% threshold)

89. 89 Performance Estimates (fog/low stratus detection) Nighttime Reliability tables IFRLIFR MVFR

90. 90 Performance Estimates (fog/low stratus detection) Daytime Reliability tables IFRLIFR MVFR

91. 91 Performance Estimates 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. 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.

92. 92 Performance Estimates The combination of SODAR and ceilometer data can be used to validate the fog depth product. fog depth

93. 93 Performance Estimates (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. DaytimeNighttime

94. 94 Performance Estimates: Summary  The validation of the fog/low stratus products show that: »The fog depth algorithm should easily meet the 500 m accuracy requirement. »The IFR probability product should easily meet the 70% accuracy requirement

95. 95 References Bendix, J., B. Thies, T. Nauss, and J. Cermak, 2006: A Feasibility Study of Daytime Fog and Low Stratus Detection with TERRA/AQUA-MODIS Over Land. Meteor. Appl., 13, 111-125. Bendix, J., B. Thies, J. Cermak, and T. Nauss, 2005: Ground Fog Detection from Space Based on MODIS Daytime Data - A Feasibility Study. Weather and Forecasting. 20, 989-1005. Bendix, J., J. Cermak, and B. Thies, 2004: New Perspectives in Remote Sensing of Fog and Low Stratus - TERRA/AQUA-MODIS and MSG. Proceedings 3rd Int. Conf. on Fog, Fog Collection and Dew, 11-15 Oct. 2004, Cape Town, South Africa., G2.1-G2.4. Bendix, J., 2002: A Satellite-Based Climatology of Fog and Low-Level Stratus in Germany and Adjacent Areas. Atmos. Res., 64, 3-18. Cermak, J., and J. Bendix, 2008: A Novel Approach to Fog/Low Stratus Using Meteosat 8 Data. Atm. Res., 87, 279-292. Cermak, J., and J. Bendix, 2007: Dynamical Nighttime Fog/Low Stratus Detection Based on Meteosat SEVIRI Data: A Feasibility Study. Pure appl. geophys., 164, 1179-1192. Cermak, J., and J. Bendix, 2006: Satellite-Based Nowcasting of Fog and Low Stratus. Proceedings of the WWRP Symposium on Nowcasting and Very Short Range Forecasting, Toulouse, France. Cermak, J., B. Thies, and J. Bendix, 2004: A New Approach to Fog Detection Using SEVIRI and MODIS Data. Proceeedings 2004 EUMETSAT Met. Sat. Conf. Prague, Czech Rep., 130-136. Ellrod, G.P., 2002: Estimation of Low Cloud Base Heights at Night from Satellite Infrared and Surface Temperature Data. National Weather Digest., 26, 39- 44. Ellrod, G.P., 1995: Advances in the Detection and Analysis of Fog at Night Using GOES Multispectral Infrared Imagery. Weather and Forecasting., 10, 606- 619. Eyre, J.R., J.L. Brownscomb, and R.J. Allam, 1984: Detection of Fog at Night Using Advanced Very High Resolution Radiometer (AVHRR) Imagery. Meteor. Mag., 113, 266-275. Hess, M., P. Koepke and I. Schult, 1998: Optical Properties of Aerosols and Clouds. Bull. Amer. Meteor. Soc., 79, 831-844. Lee, T.F., F.J. Turk, and K. Richardson, 1997: Stratus and Fog Products Using GOES-8-9 3.9-µm Data. Weather and Forecasting., 12, 664-677. Underwood, S.J., G.P. Ellrod, A.L. Kuhnert, 2004: A Multiple-Case Analysis of Nocturnal Radiation-Fog Development in the Central Valley of California Utilizing the GOES Nighttime Fog Product. J. Appl. Meteor. 43, 297-311.

96. 96 Backup Material