Case Studies of Stratus Forecasts:
Stratus Formation and Burn off
Erica, your spelling, grammar, and organiztion make it hard for me to read this. Please
see me.
Doug Sinton
by
Erica J. Silva
Litereature Review
Prepared For:
Prof. D.M. Sinton
Prof. R.D. Bornstein
19 October 2006
Summary
Predicting stratus formation and dissipation is a challnge in meteorology.
Specificly SP studying San Francisco International Airport location one is able to
conclude that the Pacific Ocean and San Francisco Bay work together in enhancing haze
and decreasing visibility for pilots. Fog physics and dynamics are utilized to examine a
case by case situation, which are the numerical simualtion of a fog event: 1 boundary
layer model, influence of terrain on fog development, and numerical evaluation of
radiation fog variables. Stratus physics and dynamics describe stratus surge prediction,
formation and dissipation of west coast stratus, forecast of coastal stratus using satellite
retrievals, numerical experiment in predicting stratus clouds, gulf stream front cause of
stratus lower Atlantic coast, summertime stratus over offshore waters of California, and
breakup of marine stratus. Forecast techniques forecast Mesoscale Phenomena, National
Weathe Service Spaceflight Meteorology Group, Expert System Approach, and future
forecast applications. Case studies will be described on the US west coast and the Hudson
Valley fog variables.
Being able to easilty predict stratus formation and burnoff will be a great
improvement in meteorological research and studies. Computer modles used to predict
stratus visibility at 2 miles or less are those such as nested grid model (NGM) which sorts
and plots 48 hour 4 panel plots. The physics and dynamics behind stratus burnoff and
formation where inward and outward mixing are concerned are critical in predicting
stratus.
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Outline
I. Fog physics and Dynamics
a. Fog on U.S. West Coast
b. Hudson Valley Fog Environment
c. Numerical Simulation of a Fog Event: 1-D Boundary Layer Model
d. Influence of Terrain on Fog Development
e. Numerical Evaluation of Radiation Fog Variables
II. Stratus Physics and Dynamics
a. Stratus Surge Prediction
b. Formation and Dissipation of West Coast Stratus
c. Forecasts of Coastal Stratus Using Satellite Retrievals
d. Numerical Experiment in Predicting Stratus Clouds
e. Gulf Stream Front casue of stratus lower Atlantic Coast
f. Summertime Stratus over offshore Waters of California
g.Breakup of Marine Stratus
III. Forecast Techniques
a. Hourly Generated Soundings to Forecast Mesoscale Phenomena
b. National Weather Service Spaceflight Meteorology Group
c. Expert System Approach for Prediction of Maritime Visibility Obscuration
d. Future Forecast Applications
e. Warm Season Hourly Probabilistic Forecasts of Low Ceiling
f. Warm Season Burnoff of Low Clouds
IV. Case Studies
a. Non West Coast
1. Hudson Valley Fog
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b. West Coast
1. Fog un US Wets Coast
V. Fog and Stratus
a. Inward Mixing in Dissipation of Fog and Stratus
b. Numerical Prediction of Fog and Stratus
c. Transition of Stratus into Fog
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Sites have been selected for hourly model guidance of forecast fields as a vertical
profile provided by National Centers for Environmental Predictions (NCEP). The
previous resolution availability was not as good as the present. Predicting fog relates to
predicting low-level moisture. Because not all models predict surface levels, one must
chooose surface. Relative humidity occurenece of 100% will usually result in fog
formation (Hart et al. 1998). It then is relvant to investigate forecast for relative humidity
charaterized by low-level subsaturation. Fog prediction for hourly sounding were
analyzed to find out its ability to make fog predictions as shown in Table 1.
Nested Grid Model (NGM) forecasted visibility of two miles or less. As an
illustration, from September 1995 to March 1996 at Pittsburgh and State College
Pennyslvannia. Hourly, the forecast was correct on the 66th percentile. Table 1 shows that
the best visibility of less than or equal to 2 mi and best forecast accuracy is NGM. NGM
showed that fog was likely to develop form or burnoff in a couple of hours with relative
humidity in low-level. Because of radiation, NGM was not good at forecasting local
ground fog.
Forecast products have advantages and disadvantages as shown in Table 2. A
process takes place in their forecast soundings. They are first placed in ASCII (American
Standard Code for Information Interchange) tabular format. The generation of forecast
products consequently takes place utilizing Grid Analysis and Display System
(GrADS).The forecast is the retrieved. Table 3 displays plot type, display fields, foreast
applications, advantages and disadvantages for profile graphical display products.
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A high Richardson number means weak shear (unstable) while a low richardsoon
number indicates strong vertical shear (stable). In Table 3 wherever there is an x this
means there was no report by the pilot concerning turbulence. When the richardson
number dropped below 1 you have strong vertical shear theefore forecasting was
prominent. The results of a richardson number of 0.25 or less was 64% for the 614
turbulence events reported by pilots. Of these events 74% resulted in a richardson number
of less than 0.5 and 89% were reported with less than one.
A field project was carried out offshore of central Oregon during August 1999 to
evaluate mesoscale model simulations of coastal stratiform cloud layers. Procedures for
mapping cloud physical parameters such as cloud optical depth, droplet effective radius,
and liquid water path retrieved from Geostationary Operational Environmental Satellite
(GOES) Imager multichannel data were developed and implemented. Aircraft
measurements by the University of Wyoming provided in situ verification for the satellite
retrieval parameters and for the forecast model simulations of the US Navy nonhydrodtatic mesoscale prediction system, the Coupled Ocean/Atmosphere Mesoscale Prediction
System (COAMPS) (Wetzel et al. 2001).
Case studies show that the satellite retrieval methods are valid within the range of
uncertainty associated with aircraft measurements of the microphysical parameters and
demonstrate how the gridded cloud parameters retrieved from satellite data can be
utilized for mesoscale model verification. Satellite-derived products with applications to
forecasting, such as temporal trends and composites of droplet size and liquid water path,
are also discussed.
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A 1-D higher-order closure model is used to test fog formation along the
California Coast. The Lagrangian or totla derivative is used to as a justifier for this study
Fog forms by a long period of marine kayer preconditioning, radiative cooling, and
subsidence (Kora in et al. 2001). The San Frincisco International Air Port is near the San
Franciso Bay and Pacific Ocean, which explains why the air port itself is effected by the
stratus (Hilliker et al. 1999). Burnoff happens when the stratus ceiling is above 2000 ft.
Using statistics, specificly speaking linear regression (LR) and a neural network, one is
able tot tackle the forecasting problem of stratus burnoff (Dean et al. 2002). A tough job
forecasters have is predicting the onset of stratus after having clear skies on California
coast (Felsch et al. 1993).Diurnal variation and inversion height depends on intensity of
sea breeze to forecast the formation and dissipation of stratus (Neiburger 1944).
To detect and forecast fog after sunset cerntain GOES channels are used. Other
satellite images are used to figure out temperature on top of the clouds (Brody et al.
1997). It should be mentioned that fog and stratus have different seasons and thicknesses
(Leipper 1994). The best way for an aircraft to be maneuvered when flying to the
destination and has no visibility is by ground-based radar (Peak et al. 1989).A numerical
scimulation can be used to forecast stratus formation like souther Texas for example
(LeBlanc et al. 1969). Without the help of microcomputers in 1981 (Klein 1976).
Something that has not been studied thorougly is the way stratus and rasiation fog
dissipate inward to out ward or vice versa or all together at the same time (Gurka eta la.
1978).
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Radiation Fog can not be properly predicted without favorable conditions such as
wind structure for example (Fitzjarrald et al. 1989). With the right meteoroligical
conditions fog is definite to occur with saturation near the surface of earth or lower part
of the atmosphere (Musson-Genon et al. 1987). Lacking predictors for analzing low level
cloud and not observing important characteristics from a statistical standpoint was
thought to be a failure because fog and stratus dissipation is a challenge (Fisher et al.
1963). It is crticial to know well numerical prediction models predict fog formation;
without this knowledge air line industry may get into trouble (Golding 1993). One of the
many ways to locate stratus is by looking at the weather chart for winter (Carson 1950).
The Geostationary satellite takes infrared and visible images every 30 minutes near the
California coast while stratus forms in the marine layer (Simon 1977). Stratus breakup is
brought up by the dry air inversion (Tag et al. 1987). A model specificly for radiaton fog
exists to test certain mechanisms within it to make forecasts of radiation fog (Lala et al.
1975).
Fog and stratus formation and dissipation are one of the challenges in
meteorology. Numerical models have been handy when observing patterns during a
season. Visible images of stratus clouds are shown in Fig. 1. This is helpful when
comparing a season over the other. In addition, unders- tanding how stratus dissipates and
forms is critical because this will help one know this piece of information and this will be
able to help reserachers in figuring out the forecast of stratus. Stratus and fog dissipation
predition depends on human wisdom and their ability to use scientific instruments to the
best of their potential and the best of the potentila of the equipment. Science without
resources like models, computers, infrared imagery, and equatons will make the task of
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forecasting hence predicting stratus formation and burnoff. Stratus is found in abundence
during the summer time in the San Friacisco Bay Area. The leading casue is the Bay
itself and the Pacific Ocean surrounding San Francisco. This is not the best time of the
year for airplane landing at the San Francisco airport because of the increased amout of
haze;hence, the decreased amount of visibiliy casusing airplane traffic and a landing
which requires skill and practice. Hudson Valley fog lasts longer than thirty minutes
which means the layer is quite thick. The thicker the layer, the harder the visibility and
the harder the visibility the leat likely to travel.
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References
Brody, F. C., R. A. Lafosse, D. G. Bellue, and T. Oram, 1997: Operations of the National
Weather Service Spaceflight Meteorology Group. Wea. Forecasting, 12, 526-544.
Carson, R. B., 1950: The Gulf Stream Front: A Cause of Stratus on the lower Atlantic
Coast. Mon. Wea. Rev., 78, 91-101.
Dean, A. R., and B. H. Fiedler, 2002: Forecasting Warm-Season Burnoff of Low Clouds
at the San Francisco International Airport Using Linear Regression and a Neural
Network. J. Appl. Meteor., 41, 629–639.
Felsch, P., and W. Whitlatch, 1993: Stratus Surge Prediction along the Central California
Coast. Wea. Forecasting, 8, 204-213.
Fisher, E. L., and P. Caplan, 1963: An Experiment in Numerical Prediction of Fog and
Stratus. J. Atmos. Sci., 20, 425-437.
Fitzjarrald, D. R., and G. G. Lala, 1989: Hudson Valley Fog Environments. J. Appl.
Meteor., 28, 1303-1328.
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Golding, B. W., 1993: A Sudy of the Influence of Terrain on Fog Development. Mon.
Wea. Rev., 121, 2529-2541.
Gurka, J. J., 1978: The Role of Inward Mixing in the Dissipation of Fog and Stratus.
Mon. Wea. Rev., 106, 1633-1635.
Hart, R. E., G. S. Forbes, and R. H. Grumm, 1998: Forecasting Techniques The Use of
Hourly Model-Generated Soundings to Forecast Mesoscale Phenomena. Part I: Initial
Assessment in Forecasting Warm-Season Phenomena. Wea. Forecasting., 13, 1165–
1185.
Hilliker, J. L., and J. M. Fritsch, 1999: An Observations-Based Statistical System for
Warm-Season Hourly Probabilistic Forecasts of Low Ceiling at the San Francisco
International Airport. J. Appl. Meteor., 38, 1692–1705.
Klein, W. H., 1976: The AFOS Program and Future Forecast Applications. Mon. Wea.
Rev., 104, 1494-1504.
Kora in, D., J. Lewis, W. T. Thompson, C. E. Dorman, and J. A. Businger, 2001: Transition of Stratus into Fog along the California Coast: Observations and Modeling. J.
Atmos. Sci., 58, 1714–1731.
Lala, G. G., E. Mandel, and J. E. Jiusto, 1975: A Numerical Evaluation of Radiation Fog
Variables. J. Atmos. Sci., 32, 720-728.
LeBlanc, L. L., S. AFB III., and K. C. Brundidge, 1969: A Numerical Experiment in
Predicting Stratus Clouds. J. Appl. Meteor., 8, 177-189.
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Leipper, D. F., 1994: Fog on the U.S. West Coast: A Review. Bull. Amer. Meteor. Soc.,
75, 229-240.
Musson-Genon, L., 1987: Numerical Simulation of a Fog Event with a One-Dimensional
Boundary Layer Model. Mon. Wea. Rev., 115, 592-607.
Neiburger, M., 1944: Temperature Changes During Formation and Dissipation of West
Coast Stratus. J. Atmos. Sci., 1, 29-41.
Peak, J. E., and P. M. Tag, 1989: An Expert System Approach for Presiction of Maritime
Visibility Obscuration. Mon. Wea. Rev., 117, 2641-2653.
Simon, R. L., 1977: The Summertime Stratus over the Offshore Waters of California.
Mon. Wea. Rev., 105, 1310-1314.
Tag, P. M., and S. W. Payne, 1987: An Examination of the Breakup of Marine Stratus: a
Three-Dimensional Numerical Investigation. J. Atmos. Sci., 44, 208-223.
Wetzel, M. A., S. K. Chai, M. J. Szumowski, W. T. Thompson, T. Haack, G. Vali, and R.
Kelly, 2001: Evaluation of COAMPS Forecasts of Coastal Stratus Using Satellite
Microphysical Retrievals and Aircraft Measurements. Wea. Forecasting, 16, 588–
599.
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Table 1. Optimal threshold low-level relative humidity values for prediction of fog
(visibility less than 2 mi) for each of the three models. The forecast accuracy at each of
these threshold values is also presented (Hart 1998).
Table 2. Summary of types of forecast products generated from the hourly model
soundings. Forecast applications of each of the products as well as each product’s
advantages and disadvantages are presented (Hart 1998).
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Table 3. Results from the analysis of Eta and MESO forecasting ability for pilot-reported
turbulence. The period of analysis spans 2 weeks at three major airports—New York
City’s LaGuardia (LGA), Chicago’s O’Hare (ORD), and Pittsburgh (PIT) (Hart 1998).
+
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Fig. 1. Visible images of the stratus clouds along the California coast on 13 Apr 1999,
taken from the GOES-10 weather satellite (subsatellite longitude of 135°W). Latitude
lines are 35° and 40°N; longitude lines are 115°, 120°, and 125°W (Kora in 2001).
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