fog forecasting for the southern region

8/10/2019 Fog Forecasting for the Southern Region

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SEPTEMBER 1997 545C R O F T E T A L .

1997 American Meteorological Society

Fog Forecasting for the Southern Region: A Conceptual Model Approach

PAUL J. CROFT

Jackson State University, Jackson, Mississippi

RUSSELLL. PFOST

National Weather Service, Jackson, Mississippi

JEFFREYM. MEDLIN

National Weather Service, Mobile, Alabama

G. ALAN JOHNSON

National Weather Service, Slidell, Louisiana(Manuscript received 1 February 1996, in final form 21 April 1997)

ABSTRACT

The prediction of fog occurrence, extent, duration, and intensity remains difficult despite improvements innumerical guidance and modeling of the fog phenomenon. This is because of the dependency of fog on micro-physical and mesoscale processes that act within the boundary layer and that, in turn, are forced by the prevailingsynoptic regime. Given existing and new technologies and techniques already available to the operationalforecaster, fog prediction may be improved by the development and application of a simple conceptual model.A preliminary attempt at such a model is presented for the southern region of the United States (gulf coastalstates) and requires information regarding cloud condensation nuclei, moisture availability (or saturation), anddynamic forcing. Each of these factors are assessed with regard to their extent and evolution with time. Anillustration, and potential application, of how the model could be used is detailed as no extensive operationaltesting has yet been completed. Instead, the model is applied in hindcast to verify its application. Successfuluse of the model will require an operational forecaster to assimilate all available tools including climatology,numerical guidance, sounding analysis, model diagnostic software, and satellite imagery. These must be used

to characterize and quantify the nature of the local and regional boundary layer in the forecast region accordingto macroscale forcing and moisture availability, the initial local settings and boundary layer, qualitative assessmentof cloud condensation nuclei, and the interaction of these in time and space. Once identified, the evolution ofthe boundary layer may be forecast with regard to the overall environment for fog occurrence, its likely extent,intensity, and duration.

1. Introduction

The impact and significance of fog to personal safetyand local economies has been documented by manyauthors (e.g., Croft et al. 1995; Johnson and Graschel1992; Martin and Suckling 1987; George 1960). Theseimpacts range from delays in aviation, marine, and sur-

face transportation and deliveries to serious accidentscaused in part by poor visibility. Regional airport andmarine port operations are often slowed or delayed ata cost of several thousands of dollars a day (Garmon etal. 1996). There have also been deadly consequences

Corresponding author address: Dr. Paul J. Croft, Department ofPhysics and Atmospheric Sciences, Jackson State University, 1400J. R. Lynch Street, P.O. Box 17660, Jackson, MS 39217-0460.E-mail: [email protected]

from fog, such as the Amtrak disaster just north of Mo-bile Bay, Alabama, in 1993 and the Mobile Baywaychain-reaction highway crash of 1995.

In the day to day forecast operations of the National

Weather Service the decision of whether or not to fore-cast significant fog (i.e., fog that leads to economic andor life-threatening impacts) is a complex process. Theforecast decision is based on the assimilation of data

and the use of numerical and mesoscale tools by fore-casters and the forecasters own experience. As severaldifferent approaches may be taken when forecasting fog,

results can vary greatly for any given situation. Forexample, one forecaster may rely on persistence andclimatology while another may rely solely on the anal-ysis of raw data in real time. In either case a forecaster

is considering the processes involved in fog develop-

ment.


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546 VOLUME 12W E A T H E R A N D F O R E C A S T I N G

a. Fog

Fog is often defined as simply a cloud on the ground[e.g., the textbook by Lutgens and Tarbuck (1995)] thathas formed through a cooling or humidification process.In some cases it has been imprecisely defined as a mist

or as suspended water droplets that have formed as airreached or approached saturation (Huschke 1959). Amore precise definition of fog indicates that it occurswhen water droplets form and are suspended in air thatis within 10% of saturation (Houghton 1985). However,these definitions do not consider the observed dropletconcentration of fog that determines a fogs thickness(intensity or opacity) and its microphysical and dynamiccharacter.

Cloud drops typically range from 10 to 40 m indiameter (Young 1993) and mist (or drizzle) from 100to 500 m (e.g., Neuberger 1957; Wayne 1993). Thedrop size distribution of fog may range from 2 to 65m, with the distribution in some marine fogs reported

to be as low as 1 to 10 m (Houghton 1985; Jiusto1981). These differences are a function of the cloudcondensation nuclei available and their characteristics.More recent study indicates that marine environmentsproduce low droplet concentrations with large droplets,whereas continental environments produce high dropletconcentrations with smaller droplets (Twohy et al.1995). Although high concentrations of cloud conden-sation nuclei limit droplet sizes, it is the droplet con-centration that determines fog opacity.

The prediction of fog demands that a forecaster men-tally integrate various scale interactions that can lead tofog. This natural atmospheric integration is, however,an inverse problem. The forecaster, despite knowledgeof the conditions associated with fog, must anticipateand predict fog occurrence a priori. Therefore fore-casters often use fog types as a guide [e.g., radiative,advective, and combinatorial; George (1960)]. Fogtypes are further classified according to the synopticregime that produces them, the geographic region inwhich they form, or the evolutionary processes that af-fect their formation and spread. For example, Stull(1988) cites two kinds of radiation fog based upon theircharacter. One is most dense near the ground and morediffuse with height. The other has a sharp top, similarto stratocumulus, and forms within a well-mixed stablelayer. These differences clearly result from variationsbetween stable and thermal (convective) internal bound-ary layer evolutions.

b. The fog forecast problem

The forecasts of fog occurrence, its extent, duration,and intensity are difficult operationally as fog is aboundary layer phenomenon that often displays greatvariability in time and space. Further, as the boundarylayer is driven and initially set up by the synoptic-scale circulation, the forecast of fog is, to a first ap-

proximation, determined by the macroscale. However,fog occurrence often displays mesoscale features as de-termined by regional characteristics of, and contribu-tions from, the boundary layer. The diagnosis and pre-diction of these interactions is not readily accomplishedwith current operational models. Further, the interac-tions may be complicated by microphysical processeswithin and outside of fog masses or when decouplingof the boundary layer occurs.

Numerous local and regional studies and modelingattempts have been made concerning fog and fog fore-casting. Many of these have led to the development offog forecasting techniques based on climatology andchecklists (e.g., Garmon et al. 1996; M. Sutton 1994,unpublished manuscript; Johnson and Graschel 1992)and empirical and statistical relationships (e.g., Bacin-schi and Filip 1976; Gimmestad 1993). Others havefocused on the use of satellite imagery (e.g., Gurka1978; Ellrod et al. 1989), numerical modeling (e.g., Bar-

ker 1977; Forkel and Zdunkowski 1986; Bergot andGuedalia 1994), operational models (e.g., Burroughsand Alpert 1993), and the development (or proposeddevelopment) of operational conceptual models (e.g.,Leipper 1994; Croft et al. 1995). A few have consideredthe microphysical processes involved in fog occurrence(e.g., Meyer and Lala 1990; Ackerman et al. 1995) inan operational setting. Unfortunately, many numericalmodels are either incapable of recreating the fog processor suffer from poor parameterizations, gross assump-tions, or the lack of certain physical data. As such, spe-cific and highly specialized conceptual model approach-es are more likely to provide the operational forecasterwith the tools necessary to predict fog [e.g., the LIBS

approach; Leipper (1995)] according to an overall un-derstanding of the fog process.

2. Traditional fog forecasting

As fog occurs within the boundary layer, a forecastermust focus on the evolution of weather across all scalesthat may lead to saturation of all or some portion of theboundary layer. However, the application of the com-monly used forecast funnel approach (UCAR 1991)will be impractical unless an assessment is made of theboundary layers ability to support fog dynamically andthermodynamically. One typical approach is to studypersistence and climatology to determine synoptic re-

gimes that lead to boundary layer saturation on a largescale. The same approach could be used to detail thethermodynamic environment necessary for fog. Thus,to a first approximation, a forecaster may predict fogbased on regional and site-specific climatologies.

a. Climatology

For portions of Mississippi, Louisiana, and Alabama,the greatest average number of dense fog days (visibilityless than mi) occurs near the coastline (Fig. 1) and


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FIG. 1. Isopleths of the average number of dense fog days (visibilityless than mi) yr1 for portions of Alabama, Florida, Louisiana, andMississippi.

FIG. 2. Same as in Fig. 1 except for the cool season (OctoberMarch).

FIG. 3. Relative frequency of fog restriction (i.e., fog reported attime of observation) by time of day for JAN, MOB, and MSY. Basedon data for the period 194890.

during the cool season (Fig. 2). The frequency is highestduring the early morning hours (Fig. 3). Thus on a re-gional basis (and subsynoptic scale), fog occurrence isstrongly dependent upon radiative flux and other coolingmechanisms that lead to saturation of the boundary lay-er. This flux may also be accomplished, and/or en-hanced, through advective and boundary layer modifi-cation processes.

However, this approach fails to fully assess the dy-namic of the fog occurrence and reveals little aboutmesoscale variations of the boundary layer over time.For example, a coastal zone with steep topographic vari-ation can lead to nocturnal drainage flows that interactwith ambient synoptic flows (e.g., Golding 1993). In a

second case, during the fall when river water temper-atures are relatively high (compared to the air above)and strong radiational cooling occurs through a deeplayer of dry air, fog forms immediately above the watersurface. The fogs extent is then a function of the den-dritic pattern of the river and local saturation of theboundary layer. Unfortunately, the operational assess-ment and quantification of such local climatic charac-teristics is difficult and often relies heavily on forecasterexperience.

b. Numerical guidance

One means by which forecasters may attempt to es-

timate or quantify mesoscale variations operationally isthrough the use of model output statistics, or MOS guid-ance. Through statistical regression, MOS equation so-lutions offer insight to mesoscale variations (and mi-croscale climates) and thus potentially fog formationand persistence. The 24-h MOS cool season fog forecastequations for Jackson (JAN), Mississippi; Mobile(MOB), Alabama; and New Orleans Moisant Airport(MSY), Louisiana; were examined for their statisticalprediction of dense fog (visibility less than mi). Foreach location the best statistical predictor (defined ac-

cording to partial correlation coefficients) is the grid

binary 1000-mb relative humidity, an indicator of suf-ficient boundary layer saturation. This predictor indi-cates that when a critical value of relative humidity isreached or exceeded, fog is expected. Other leading pre-dictors include stability and mixing ratios, and monthlyrelative frequencies of visibilities less than 3 mi de-pending on which site is examined. The effective cor-relation coefficients (one indicating perfect correlation)for these predictors, when combined in separate re-gression equations for JAN, MOB, and MSY, are 0.158,0.195, and 0.108, respectively.

An examination of 6-h MOS fog forecast equationswas also made. In this case, for each location the leadpredictor for dense fog is the latest observed obstruction

to visibility at the station at model initial time. Otherleading predictors include dewpoint depression, ceilingheight, and visibility, which are often site specific. Cor-relation coefficients for each 6-h equation ranged from0.267 at JAN to 0.427 at MOB. While performance is


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TABLE 1. Selected dense fog days (visibility less than mi) at JAN for the period 199395. Summary information includes date ofoccurrence, the principle fog type (i.e., radiative or advective), temperature and dewpoint at time of dense fog, season of occurrence, whetherprecipitation was observed during the 24 h preceding the observation of dense fog, the depth of the boundary layer, the lowest visibilityobserved, and the vertical wind shear (i.e., backing, veering, or neutral).

Date Type

Sfc T/Td

(F) Season

Pcpn last

24 h?

Depth of moist

layer (m)

Lowestvisibility

(mi) Winds

24 Mar 9314 May 9326 Oct 9316 Nov 9324 Nov 9317 Dec 93

2 Jan 9412 Jan 94

6 Feb 9412 Feb 94

RadiationRadiationRadiationAdvectionAdvectionRadiationAdvectionRadiationRadiationAdvection

44/4454/5456/5661/6144/4438/3846/4643/4253/5335/35

SpringSpringFallFallFallWinterWinterWinterWinterWinter

NoYesNoYesNoNoYesYesYesYes

6040

475100

55500895525455505

BackNeut.BackVeerNeut.BackVeerBackBackVeer

22 Apr 9425 Jun 9430 Jun 94

6 Aug 944 Oct 94

23 Oct 9431 Oct 94

6 Nov 949 Nov 949 Dec 94

RadiationRadiationRadiationRadiationRadiationRadiationAdvectionRadiationAdvectionAdvection

60/6069/6972/7171/7059/5861/6060/5948/4862/6261/61

SpringSummerSummerSummerFallFallFallFallFallFall

NoYesYesYesNoNoNoYesNoNo

45010

480985

44411450

501920

960

Neut.VeerNeut.Neut.BackBackVeerBackVeerVeer

17 Dec 9418 Dec 9426 Dec 9431 Dec 9420 Feb 9521 Mar 95

2 Jun 95

RadiationRadiationRadiationAdvectionRadiationRadiationRadiation

55/5536/3527/2653/5344/4456/5663/63

FallFallWinterWinterWinterSpringSpring

YesNoNoYesYesNoYes

142280

1041883

5550

465

BackBackBackVeerNeut.BackNeut.

improved, the predictors identified as statistically sig-

nificant fall within the realm of persistence nowcastingand are traditionally used by operational forecasters.Clearly the correlation coefficients illustrate a basic in-ability of current operational numerical model statisticalguidance to provide adequate information for reliableprediction of fog 624 h in advance.

c. Sounding analysis

Therefore fog forecasting requires a direct exami-nation of the boundary layer environment in whichdense fog occurs. To illustrate, rawinsonde data for JANwere obtained and analyzed for the period 199395 fordays on which dense fog (visibility less than mi, 0.8

km) was observed. A total of 53 days were identified.In order to isolate and remove inconsistencies in thedata, those days in which precipitation was occurringat the time of the dense fog were removed. Also, ifsurface observational data showed that the fog was tran-sient (i.e., lasting less than an hour) or very localized(e.g., ground fog with vertical extent less than a fewmeters), these occurrences were also removed. Data for27 days remained and each event was categorized as anadvective (8) or radiative (19) case. The associated tem-perature, dewpoint, season of occurrence, whether or

not precipitation occurred within the previous 24 h, the

depth of the moist layer [inferred from sounding infor-mation using SHARP: the Skew-THodograph and Re-search Program, by Hart and Korotky (1991)], the low-est visibility observed, and surface to 700-mb windcharacteristics (veering or backing) were determined(Table 1).

Temperatures during dense fog events varied from3 to 22C (2772F) and most occurred during thecool season. The fall of precipitation within the previous24 h did not appear to be necessary for dense fog asthis condition was met in only half of the cases ex-amined. The average depth of the moist layer (as definedby the saturated layer of the sounding) for the advectivecases was 846 m, while the average depth for radiative

cases was 350 m [and this is consistent with Jiusto(1981)]. Dense radiation fog occurred under weak back-ing (cold air advection) or neutral wind pattern (Fig.4a), whereas dense advection fog occurred under a veer-ing (warm air advection) wind pattern (Fig. 4b). Verydry air aloft, often with more than 20C of dewpointdepression, was evident in all cases and typically pro-duced a goalpost when temperature and dewpointwere plotted on a skew-Tdiagram [and is similar to thethreshold criteria determined by Leipper (1995)]. Thisgoalpost represents an open atmospheric window for


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FIG. 4. Typical soundings associated with dense fog (visibility lessthan mi) at JAN for (a) radiative (backing) and (b) advective(veering) cases.

FIG. 5. Conceptual model for fog forecasting. Each base surfacerepresents the character of cloud condensation nuclei present in theboundary layer and a variety of atmospheric combinations of moistureavailability (or saturation) and dynamic forcing supportive of fogoccurrence, extent, and intensity. Movement on and between basesurfaces represents transition processes.

longwave radiative cooling, which can strengthen anear-surface inversion layer.

3. Conceptual model

As climatology and numerical guidance provide bothsynoptic-scale and site-specific information, and assounding analysis is strictly site specific, none of thesetechniques alone are appropriate for mesoscale predic-tion of fog. In combination, and with a sufficient degreeof specificity in time and space, they can allow mesos-cale fog predictions to be made. To achieve this, a frame-work for a simple operational conceptual model ap-proach to local and regional fog forecasting, similar tothat by Leipper (1995), is presented for the southern

region of the United States (gulf coast states). The con-ceptual model is based on macroscale forcing and mois-ture availability (or saturation), the character of theboundary layer, a qualitative assessment of the signif-icance of fog microphysics (according to cloud con-densation nuclei), and the interaction and change ofthese in time and space. The model (Fig. 5) is intendedto ultimately serve as a collection of surfaces in spaceand time that represent varying atmospheric conditionsthat result in varying extents, intensities, and durationsof fog.

a. Description

The model surfaces (Fig. 5) represent the characterof boundary layer air in a forecast region in terms ofthe unique distribution of cloud condensation nuclei.These base surfaces range from maritime to continentaland allow a forecaster to assess the nature, and initialconcentrations of, local (or imported) cloud condensa-tion nuclei and their associated drop sizes. This providesinformation as to the intensity (opacity or thickness) offog and its duration. If there is a local variation, or aforced synoptic change, the forecaster simply movesfrom one base surface to another. In the process, a fore-

caster assesses the impact of changes in cloud conden-sation nuclei and drop size as the change takes placeand at any boundaries between different air masses.

Once an initial forecast time base surface is estab-lished, a forecaster may move laterally on the faceof that surface to quantify moisture availability (or sat-uration) and dynamic forcing. Moisture availability isassessed according to moisture present and moisture thatcan be realized through condensational cooling. Dy-namic forcing (primarily with regard to lifting and cool-ing mechanisms and moisture pooling and transport) isassessed according to the predominance of boundarylayer effects (e.g., base-state flow, local circulations, andsurface parcel thermodynamics) or synoptic flows pe-

culiar to the forecast region. This must be determinedthrough the application of basic meteorological prin-ciples for any location to separate boundary layergrowth from boundary layersynoptic interaction andfrom synoptically forced boundary layer growth.

For both moisture and dynamics, a mixed zone ortransition is also possible. In the case of moisture avail-ability the forecaster can determine the extent of fog(i.e., patchy versus widespread) as well as intensity (incombination with cloud condensation nuclei informa-tion). From dynamic forcing the forecaster can assess


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the duration (e.g., all-night radiative event) and extentof fog occurrence (given saturation). The approach issimilar to Leipper (1995) in that synoptically producedinitial conditions are quantified according to the devel-opment of a surface boundary layer inversion. The evo-lution of the boundary layer is then considered withregard to airmass modification (and associated conti-nental and marine interactions) and fog formation.

b. Illustration

To illustrate the models use, consider fog occurrenceacross the gulf coastal states following the arrival of acooler and drier air mass, its modification, and the ap-proach of a frontal system. Initially the forecaster de-termines that a continental-base surface is present andthat moisture availability across the region is low withsynoptic-scale forcing. This places the forecaster in thelower-left portion of the bottom surface (Fig. 5) and

makes fog unlikely (or very localized and very limitedin extent and duration). However, if synoptic forcingweakens and boundary layer processes become domi-nant, the forecaster moves to the upper-left portion ofthe surface. In this case, radiational cooling is maxi-mized overnight and other areas of fog form in the vi-cinity of local moisture/saturation maxima (e.g., patchyfog). A forecaster might therefore anticipate fog of vary-ing intensity, and of longer duration, in a number oflocations in a forecast region.

Within 2448 h, the air mass in place modifies ac-cording to local factors through energy exchange pro-cesses (e.g., evaporative flux) and as related to phys-iographic features and location (i.e., over land, water,

or coastal interface). Differential heating and cooling,local cloud condensation nuclei, and moisture sourcescontribute to this modification. Therefore the forecasteris now on a mixed-base surface (i.e., the middle surfacein Fig. 5) with greater moisture availability. If anothernight of radiative cooling takes place, fog formation maybe widespread, more intense, and of longer duration. Atthe same time, local differences in the base surface con-ditions may exist and create sharp boundaries (e.g., asrelated to landsea-breeze circulations or valley winds).For example, moisture availability will be greatest nearthe coast, and cloud condensation nuclei will more likelybe dominated by maritime sources rather than conti-nental and local sources. Such local variations within

the local boundary layer can be inferred from isentropicsurfaces and the resulting sharp boundaries (and vari-ations in intensity) between fog and no-fog areas wellknown in satellite imagery.

Following this airmass modification period, a coldfrontal system approaches the gulf coastal states. Theincreased pressure gradient often leads to synoptic-scaleforcing and results in the dominance of advective pro-cesses that act to homogenize the air across the forecastregion (in many cases). In response, the initial basesurface evolves from one that is mixed to one that is

more maritime with greater moisture availability andmaritime cloud condensation nuclei. This puts the fore-caster closer to, or on, the maritime base surface on thelower-right corner. In this situation, if fog occurs, it ismore likely to behave as a synoptic-scale mass withembedded mesoscale features. Fog would be expectedto be widespread and of long duration with much vari-ation in intensity due to local mixing. Any coolingthrough advective lift (e.g., upslope flow), or by passageover a cold ground surface, would enhance fog for-mation.

4. Operational application

The preceding illustration of the conceptual model(section 3) was intended to provide the forecaster guid-ance on how to apply the model. The model is purposelysimplistic to allow for its ultimate application to, andmodification for, any operational forecast scenario or

location. The model does not provide specific thresholdvalues for any specific operational situation as thresh-olds will vary significantly from location to location(e.g., see Leipper 1995), are not readily known, or havenot as yet been thoroughly nor completely investigated.When known (e.g., Leipper 1995), forecasts of fog oc-currence, extent, intensity, and duration may be quan-tified and verified. The conceptual model here presentsa framework in which forecasters develop an integratedassessment of how microphysical and thermodynamicconditions and dynamic processes, in association withlocal and mesoscale effects and interactions, result invariations in fog occurrence, extent, intensity, and du-ration.

To further illustrate the potential utility of this simpleoperational conceptual model, as no operational testinghas been completed, two specific fog situations are ex-amined in hindcast. The first is a radiational event thatoccurred over northern Mobile Bay in Alabama. Thisevent was notable because it resulted in the worst mul-tiple-vehicle accident in U.S. history despite the intensefogs rather limited extent and duration. The second sit-uation considers advective sea fog over the Gulf of Mex-ico. In each case the operational tools used in fog fore-casting are highlighted and the initial conceptual modelapplied.

a. Mobile bayway disaster

The chain reaction collision that occurred during sun-rise on 20 March 1995 on the Mobile I-10 Bayway wasthe largest ever in U. S. history. Nearly 200 vehicleswere involved and over 100 persons were injured. In-surance losses have been estimated in the hundreds ofmillions of dollars. Miraculously, only one person waskilled. Despite the collisions notoriety, the fog occur-rence itself was not particularly noteworthy or unusual.Indeed, based on availableGOES-8 channel-differencedsatellite imagery (Fig. 6), the areal extent of the fog and


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FIG. 6. Satellite imagery sequence (channel differenced) for Mobile Bay and vicinity. Hourly images for 0145 through 0445 EST (upperleft, upper right, lower left, lower right) show the initial local development of dense fog. The last image shows the increased intensity andextent of fog prior to the multiple-vehicle accident described in the text.

stratus was initially rather limited and represented a fair-ly typical radiational event for the given time of year.Eyewitness reports indicated that the top of the fog layerwas ragged. Several days prior a synoptic-scale eventprovided a 2-in. basin average rainfall associated withextensive runoff. This was followed by cooler and drierboundary layer air that quickly modified.

An examination of the initial hour (0000 UTC 20March 1995) 80-km early eta gridded model output anda composite of multipressure level synoptic-scale fea-tures was completed (not shown). Immediately prior tothe fog event strong anticyclonic flow existed through

a deep tropospheric layer and contributed to regionalsubsidence and the drying out of midlevel air parcelsas they descended dry adiabatically. This would placethe forecaster between the continental-and mixed-basesurfaces (Fig. 5) following initial airmass modification.The surface pressure gradient was weak and supportednearly calm boundary layer winds over the region witha 4-mb pressure difference between west Florida andeast Texas. Soundings upstream of Mobile from JANand SIL (Figs. 7a, b) indicated very dry air extendingfrom the surface to approximately 400 mb with a goal-

post appearance just prior to the fog event. At somemiddle-tropospheric levels dewpoint depressions werenearly 20C. Surface isodrosotherms revealed dew-points of 16C (60F) over far southwestern Alabama,indicating limited moisture availability and thus extentof fog formation.

The depth of the boundary layer was assumed to beno more than 25 mb deep (or less than 50 m) based oneyewitness accounts and modified sounding analyses(not shown). This information was used to select theappropriate isentropic surface (295 K) to determinewhether any acceleration or mixing of air in the bound-

ary layer was possible. Figure 8 depicts the pressuredistribution and wind flow pattern on the 295-K isen-tropic surface about 3 h prior to the development of thestrong low-level thermal inversion. Figure 9 depictswind vectors, mixing ratio (dashed), and hand-analyzedstreamlines found on the 295-K isentropic surface forthe same time. The col in the streamline pattern nearMobile Bay contributed to the light wind conditionsthroughout the entire lower troposphere. Any airmassmodification (moisture and temperature) over MobileBay was therefore related to the bay water temperature


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FIG. 7. SHARP soundings for (a) JAN and (b) SIL for 0000 UTC20 March 1995.

FIG. 8. Initial (0000 UTC) 80-km early Eta Model pressure distribution (mb) and wind (ms1) on the 295-K isentropic surface on 20 March 1995. Each half wind barb equals 2.5 ms1.

at the time (which was unavailable). There was also aneutral pressure advection pattern coincident with therelative maximum of low-level moisture over the bay,which persisted through 1200 UTC. This prevented tur-

bulent boundary layer mixing and the resultant verticalexchange of high-momentum air into the boundary lay-er.

Applying and summarizing the conceptual model ap-proach, a maritime base surface was located over MobileBay and experienced maximum boundary layer forcing(i.e., radiative cooling). This culminated in the prefer-ential formation of fog in those regions with radiativecooling rates sufficient to achieve saturation where highmoisture was present. The final fog plume (Fig. 6)was found along and to the east of the Mobile Rivervalley, and thus fog extent was determined in part bylocal physiography. In addition, the close proximity ofpaper mills (located in the vicinity of the accident site)and saltwater source (Mobile Bay) likely provided forlocalized modification and pooling of the aerosol dis-tribution. A resulting wide drop size distribution ofcloud condensation nuclei (related to local industryemissions) may have contributed to the fogs intensity

(or optical thickness) by increasing droplet concentra-tions. Accident victims indicated that the fog appearedas a solid wall with near-zero visibility when they droveinto it. Radiative effects, related to sunrise, as well aschemical effects, may have also played a role in thefogs intensity.

b. Sea fog assessment

Sea fog and stratus can affect extensive areas of thenorthern Gulf of Mexico as well as the immediate coast-al plain. Although there is an abundance of moistureavailable over the gulf, sea fog development, if it occurs,and its onshore motion is a function of coastal zone


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FIG. 9. Initial (0000 UTC) 80-km early Eta Model wind vectors (m s1), mixing ratio (gkg1), and hand-analyzed streamlines.

FIG. 10. Locations of supplementary aviation weather reportingstations over the northern Gulf of Mexico.

processes (e.g., convergence and friction) and gradientsin sea surface temperatures. This is particularly trueduring the winter and early spring months (DecemberMarch) when cold dry air pushes south to the gulf coastand overlies the warm gulf waters. In addition, coldwaters develop near the coastline due to drainage ofcold river basin waters to the coast and the presence ofcold air masses over the shallow coastal waters. Theseconditions often lead to an unstable layer of near-surfaceevaporative, or cold advection, fog near the coast. When

the flow is reversed (i.e., a return flow event), warmmoist air is forced inland over colder air and groundsurfaces resulting in a cooling of the warm air. Thecooling can lead to stratus and warm advection fog overtime as a deeper and more stable boundary layer de-velops.

Despite the existence of an operational advective seafog forecast model for the northeast coast (Burroughs

and Alpert 1993), none currently exists for any portionof the Gulf of Mexico. As a result, other methods mustbe used to assess the possibility of fog occurrence inthis region, especially given the lack of adequate andaccurate model initialization. Therefore efforts have fo-cused on developing graphs, based on empirical evi-dence, to predict sea fog occurrence and intensity. Thesehave focused on the relationship between atmospherictemperature, dewpoint, and wind with sea surface tem-peratures. For example, climatological data for 10 sup-

plementary aviation weather reporting stations acrossthe northern Gulf of Mexico (Fig. 10) were collectedfor the winter and early spring months (Johnson andGraschel 1992). Due to limited data availability, thesedata were obtained for the years 198586 and 198890(and have since been discarded).

Upon examination of this dataset, distinctions weremade between those conditions associated with the oc-currence or nonoccurrence of fog. Fog intensity (basedon visibility thresholds) for both warm advection (Figs.11ac) and cold advection (Fig. 11d) cases were alsoconsidered. Each graph suggests parameter relationshipsfor observing advective sea fog based on empirical ev-

idence that have been applied successfully in operationsby the Slidell National Weather Service Office for thelast several years. They allow a forecaster to physicallyassess the importance of the magnitude of the air andsea temperature difference, the dewpoint depression,and wind. The air and sea temperature difference, anddewpoint depressions, offer the forecaster a surrogatemeasure of moisture availability and air mass, boundarylayer, modification. The wind provides information re-garding synoptic versus boundary layer forcing. Theabove information is obtained operationally by the Sli-


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FIG. 11. Advective sea fog occurrence, and restriction to visibility, as a function of (a) windspeed vs dewpoint depression, (b) dewpoint depression vs airsea temperature difference, (c)dewpoint depression vs dewpointsea temperature difference for warm advection sea fogs, andas a function of (d) relative humidity vs airsea temperature difference for cold advection seafogs.

dell National Weather Service Office using observationsand model gridded data.

Applying and summarizing the conceptual model ap-proach, a maritime-base surface is present when air andsea temperature differences are minimal and winds light.Moisture availability (or saturation) is maximized, as areboundary layer processes under clear skies (i.e., radiationalcooling) and sea fog results. The presence of these con-ditions over a large area can lead to widespread, thick,and long-duration fog. In the event advection is added,sea fog may begin to move toward the coast. As the fogmass encounters the coastal zone, it undergoes a funda-mental transformation due to turbulent mixing. In addition,the fog mass passes over a changing surface, which willforce changes in the nuclei and drop size distributions.These are often manifest in the observed weather (e.g.,

fog and drizzle at the coast versus denser widespread foginland) as a fog mass moves (and/or redevelops) onshore.Thus the base nuclei surface will become mixed, moistureavailability (or saturation) will decrease (unless coolingby the land, or through lift over the land occurs), andadvective flow diminish.

c. Forecasting tools

The use of the conceptual model presented requiresforecasters to acquire basic information about cloud

condensation nuclei, moisture availability, and dynamic

forcing. An assessment of the local aerosol field, and

its evolution in time and space, can be based on a localknowledge of nuclei sources and sinks. The makeup and

character of these could be determined by direct mea-

surement (e.g., using satellite data, although presently

not possible in the operational environment) or quali-

tatively inferred. The resulting intensity of fog may then

be considered (pragmatically) to be a function of the

concentration of water droplets suspended in the air.

This concentration is dependent upon the drop size dis-

tribution. However, as the distribution of aerosols

(amount and size distribution) is to a large extent de-

termined by advection of these into a region, aside from

the local origination of aerosols, variations in intensity

over time must be considered. The use of MOS guidance

and gridded model data (including trajectories) can as-

sist the forecaster in identifying relevant flow pattern

variations that will affect the local aerosol composition.

Once the aerosol field is evaluated, an assessment of

stability and moisture according to the synoptic regime

(e.g., airmass type) and boundary layer contributions

require diagnosis of cooling and/or lifting mechanisms

that could result in fog formation. This may be accom-

plished by application of model diagnostic software pro-

grams (e.g., isentropic analysis with personal computer


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based Interactive Display and Diagnostic System,PCGRIDDS, and mesoeta analysis fields) and by con-sideration of local physiographic effects. A forecastermay use the SHARP program in conjunction with grid-ded data from the rapid update cycle (or RUC) model(or the mesoeta) to diagnose the boundary layer. Indeed,model predictions of temperature and skin temperaturehave been used to forecast fog for space shuttle landingoperations (Garner and Batson 1995).

A quantification of boundary layer characteristics re-gionally may be aided by remote sensing techniques,including satellite and radar (or profilers). Satellite data,particularly from the GOES-8platform, offer high-res-olution imagery in both visible and infrared channels.Channel differencing techniques (e.g., Ellrod et al.1989; Ellrod 1995) provide forecasters with definitiveregional temperature and moisture distributions relevantto fog extent. Although these techniques are presentlymost useful in identifying areas of fog after their for-

mation, their operational promise is great. For example,precipitable water imagery can be used to diagnosereturn flow events (Johnson and Rabin 1993), andstatistical clustering techniques applied to satellite in-formation allow for the determination of low-level hu-midity gradients (Fuelberg et al. 1995).

Radar information may also be considered for its abil-ity to detect density gradients and thus inversions. Theclear-air mode capability of the WSR-88D provides in-formation regarding gradients of moisture and temper-ature that often appear within the ground clutter pattern.A careful study of typical ground clutter patterns in timelapse during super refractive conditions with regard toits average time of appearance, its reflective intensity,

its horizontal size, and its rate of change can help tocharacterize the local rate of thermal change in theboundary layer. Radar-derived products, such as stormtotal precipitation, may also be useful in identifyingareas that may have higher levels of moisture in theboundary layer.

5. Summary

To forecast fog more precisely, in terms of fog oc-currence, extent, intensity, and duration, operational me-teorologists must understand and diagnose the ther-modynamics, kinematics, and microphysics of the fogprocess. Presently the thermodynamic and kinematic

components are readily quantified, or inferred, whereasthe microphysics and boundary layer processes are not.Therefore, as numerical models have so far been in-adequate in this regard, it is important that an opera-tional forecaster develop and use a conceptual fog fore-cast model. The successful use of such a model willdepend on the forecasters ability to assimilate the ap-propriate tools and techniques available in real time.

Prior to this however, a forecaster must ingest andproperly conceptualize the various elements of the mod-el in order to provide quantification of each for the given

forecast zone. This can be achieved through the appli-cation of the latest mesoscale tools and observations inhindcast as well as climatic study. In the process, quan-tification of fog forecasting may be accomplished andcritical thresholds determined for the base surfacesof the conceptual model presented here. Until improve-ments in operational models (and in particular opera-tional cloud physics) can be made, a forecaster mustmentally solve the fog inverse problem to truly forecastfog rather than simply respond to its formation. Onlyin this way will the regional prediction of fog in spaceand time improve.

Acknowledgments. The authors wish to thank FredSettelmaier of the Techniques Development Laboratoryfor printouts of MOS equation predictors and JamesPurdom for satellite imagery of the central gulf coastalstates. Thanks also go to Jeff Garmon for his assistancewith the technical aspects of the study and to Robert E.

(Gene) Merritt for his personal comments regarding theMobile Bayway disaster. Thanks also to Southern Re-gion, as well as Monesa Watts, for help in the draftingof figures. We are very grateful for the assistance pro-vided by the reviewers and editor in completing thispaper.

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