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FRAM, Montreal, Que15 June 2005
Analysis of Hazardous Fog and Low Clouds Using
Meteorological Satellite Data
Gary P. EllrodNOAA/NESDIS, Camp Springs, MD
FRAM, Montreal, Que15 June 2005
Outline• Benefits/limitations of remote sensing• Detection of low clouds
– Night: Longwave – Shortwave IR– Day: Visible and Shortwave IR
• Determination of low ceilings• Fog depth estimates• Technology upgrades needed• Summary
FRAM, Montreal, Que15 June 2005
Nighttime GOES Infrared Fog
Detection Capabilities• Advantages:
– High frequency (15-30 min)– Good spatial coverage, resolution (4km)
• Limitations– Obscuration by higher clouds– Some fog too narrow, thin to detect– False signatures (sandy soils)– Is it fog or stratus?
FRAM, Montreal, Que15 June 2005
Remote Sensing of Fog
• Radiative studies (Hunt 1973)
• Experience with AVHRR in U.K. (Eyre et al 1984)
• GOES investigations– Gurka 1978, 1980– Ellrod 1991, 1994– Lee (NRL) et al 1997
• METEOSAT– Cermak, Bendix
Nighttime fog product from GOESSounder, June 1987
FRAM, Montreal, Que15 June 2005
Radiative Properties of Clouds
FRAM, Montreal, Que15 June 2005
Nighttime Fog Detection Using GOES Multi-spectral Image Data
FRAM, Montreal, Que15 June 2005
Features Observed in Nighttime Fog Images
Yellow = T4 – T2 > 2C
FRAM, Montreal, Que15 June 2005
Fog-related Highway AccidentWindsor, Ont., 3 Sep 1999 (Pagowski et al 2004)
FRAM, Montreal, Que15 June 2005
Spread of Lake Fog – Time Lapse
FRAM, Montreal, Que15 June 2005
Daytime Fog Detection
• Visible images– Smooth texture, sharply defined borders,
moderate brightness
• 3.9 m IR (or 1.6m AVHRR)– Fog droplets are good reflectors at 3.9m
• Result is relatively warm Tb
– Snow is poor reflector at 3.9m– Result: Good contrast with snow or cold
ground
FRAM, Montreal, Que15 June 2005
Fog Clearing on 3 Sep 1999
FRAM, Montreal, Que15 June 2005
Snow vs Fog Using Visible and Shortwave IR
MODIS m CH6MODIS Visible CH1
FRAM, Montreal, Que15 June 2005
Snow vs Fog Using Visible and Shortwave IR
MODIS 3.9m CH6MODIS Visible CH1
FRAM, Montreal, Que15 June 2005
RGB Depiction of Fog Over Snow-Covered Ground (MODIS)
Red = VisibleGreen= 1.6mBlue= 11m IR
Fog is yellowSnow is redBare surface is green
FRAM, Montreal, Que15 June 2005
Daytime Fog DiscriminationUsing Visible and IR Data
FRAM, Montreal, Que15 June 2005
Estimation of Low Cloud Base Category from GOES
• When GOES IR cloud top is <4º K from surface temperature, low clouds (<1000 ft) likely
Brown 1987Ellrod 2003
FRAM, Montreal, Que15 June 2005
Low Visibility Determination
FRAM, Montreal, Que15 June 2005
GOES Low Cloud Base Product
Available for all regions of the U. S. and parts of southern Canada
at: http://www.orbit.nesdis.noaa.gov/smcd/opdb/fog.html
Verification of LCB Product *Overall verification for low clouds detected but not
covered by cirrus clouds (N = 2381): • POD = 72 %• FAR = 11 %
Regional Statistics
* Completed in 2001-2002
FRAM, Montreal, Que15 June 2005
San Francisco Fog Project (Terabeam Inc, 2001)GOES Ceiling Categories
Categories created to compare satellite data with ceilometer data.
Brightness values plotted against ceilometer ceiling heights. Top-left and bottom-right quadrants (separated by dashed lines) show category 1 and 2 agreement, respectively. Top-right shows false alarms, bottom-left shows under-detection.
San Francisco Fog Project (Terabeam)
FRAM, Montreal, Que15 June 2005
Estimation of Fog Depth
• Based on BTD for 3.9m and 10.7m IR
• Developed using cloud top heights from aircraft pilot reports (PIREPs)
Brightness count difference (GOES-7 Sounder) vsfog depth estimated from PIREPs
FRAM, Montreal, Que15 June 2005
Fog Depth Verification
FRAM, Montreal, Que15 June 2005
Fog Depth Product – 3 Sep 99
FRAM, Montreal, Que15 June 2005
Fog Depth Estimation• Application of fog depth to forecast burnoff time
GOES Fog Depth, 1045 UTC
FRAM, Montreal, Que15 June 2005
Results for 3 Sep 99 Case
GOES Fog Depth, 1045 UTC GOES visible, 1415 UTC
FRAM, Montreal, Que15 June 2005
Visible Brightness DifferencesFog vs Cloud-Free to Estimate Clearing Time
• Requires visible (CH1) imagery >1.5 hours after sunrise (Gurka 1974)– Uses following data:
• Digital brightness count difference (fog vs clear region)
• Obtain incoming solar radiation
– Larger brightness difference = longer clearing time after sunrise
FRAM, Montreal, Que15 June 2005
Depth Threshold for GOES Detection270 m
~160 m
~100 m ?
FRAM, Montreal, Que15 June 2005
Technology Upgrades
Needed for Better Fog Detection from GOES
FRAM, Montreal, Que15 June 2005
1. Optimal SWIR wavelengths
FRAM, Montreal, Que15 June 2005
2. Improved ResolutionBased on AVHRR IR (3.7 m and 11.0 m)
FRAM, Montreal, Que15 June 2005
3. Improved Signal to Noise
MODIS Fog Depth GOES Fog Depth
FRAM, Montreal, Que15 June 2005
Summary and Conclusions• GOES can effectively detect fog/low
clouds and show areal extent– Problems with small scale, shallow fog
• Able to estimate fog depth, ceilings– Good correlation with SFO visibility data
• GOES needs to be complemented by surface data to be most effective
• GOES-R will have major upgradeshttp://www.orbit.nesdis.noaa.gov/smcd/opdb/fog.html
FRAM, Montreal, Que15 June 2005
References
• Hunt, G. E., 1973: Radiative properties of terrestrial clouds at visible and IR thermal window wavelengths. QJRMS, 99, 346-369.
• Eyre, J. R., J. L. Brownscombe, and R. J. Allam, 1984: Detection of fog at night using AVHRR imagery. Meteor. Mag., 113, 266-271.
• Ellrod, G. P., 1994: Advances in the detectio of fog at night using GOES multispectral IR imagery, Wea. Forecasting, 10, 606-619.
• Pagowski, M., I. Gultepe, and P. King, 2004: Analysis and modeling of an extremely dense fog event in Southern Ontario. J. Appl. Meteor., 43, 3-16.
FRAM, Montreal, Que15 June 2005
References• Brown, R., 1987: Observations of the structure of a
deep fog. Meteorological Magazine, 116, 329-338.• Ellrod, G. P., 2002: Estimation of low cloud base
heights at night from satellite infrared and surface temperature data. Nat. Wea. Digest, 26, 39-44.
• Fischer, K. et al, 2003: Validation of GOES Imager experimental low cloud data products for terrestrial free space optical telecommunications. 12th AMS Conference on Satellite Meteor. and Oceanography, Long Beach, California, 9-13 Feb 2003.
• Gurka, J., 1974: Using satellite data for forecasting fog and stratus dissipation. Preprints, 5th Conf. on Weather Forecasting and Analysis, March 4-7, 1974, St. Louis, MO, AMS, Boston, 54-57.