Download - FRAM, Montreal, Que 15 June 2005 Analysis of Hazardous Fog and Low Clouds Using Meteorological Satellite Data Gary P. Ellrod NOAA/NESDIS, Camp Springs,

FRAM, Montreal, Que15 June 2005

Analysis of Hazardous Fog and Low Clouds Using

Meteorological Satellite Data

Gary P. EllrodNOAA/NESDIS, Camp Springs, MD

([email protected])


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


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?


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


Radiative Properties of Clouds


Nighttime Fog Detection Using GOES Multi-spectral Image Data


Features Observed in Nighttime Fog Images

Yellow = T4 – T2 > 2C


Fog-related Highway AccidentWindsor, Ont., 3 Sep 1999 (Pagowski et al 2004)


Spread of Lake Fog – Time Lapse


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


Fog Clearing on 3 Sep 1999


Snow vs Fog Using Visible and Shortwave IR

MODIS m CH6MODIS Visible CH1


Snow vs Fog Using Visible and Shortwave IR

MODIS 3.9m CH6MODIS Visible CH1


RGB Depiction of Fog Over Snow-Covered Ground (MODIS)

Red = VisibleGreen= 1.6mBlue= 11m IR

Fog is yellowSnow is redBare surface is green


Daytime Fog DiscriminationUsing Visible and IR Data


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


Low Visibility Determination


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


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)


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


Fog Depth Verification


Fog Depth Product – 3 Sep 99


Fog Depth Estimation• Application of fog depth to forecast burnoff time

GOES Fog Depth, 1045 UTC


Results for 3 Sep 99 Case

GOES Fog Depth, 1045 UTC GOES visible, 1415 UTC


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


Depth Threshold for GOES Detection270 m

~160 m

~100 m ?


Technology Upgrades

Needed for Better Fog Detection from GOES


1. Optimal SWIR wavelengths


2. Improved ResolutionBased on AVHRR IR (3.7 m and 11.0 m)


3. Improved Signal to Noise

MODIS Fog Depth GOES Fog Depth


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


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.


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.