Development and Implementation of an Expert System for Forecasting Fog on US Highway 30 in Cedar Rapids, lowa

Final Report

September 1996

IDOT Research Project HR-357

lowa Department of Transportation

Eugene S. Takle, Principal Investigator Professor of Atmospheric Science

301 0 Agronomy Hall lowa State University

Ames, IA 5001 1

Iowa Department of Transportation Research Project HR-357

Development and implementation of an expert system for forecasting fog on US Highway 30 in Cedar Rapids

Eugene S. TaMe, Principal Investigator Professor of Atmospheric Science

3010 Agronomy Hall Iowa State University

Ames, LA 5001 1 Tel: (515) 294-9871 Fax: (515) 294-3163

Final Report September 1996

Abstract:

Several accidents, some involving fatalities, have occurred on U. S. Highway 30 near the Archer Daniels Midland Company (ASM) Corn Sweeteners plant in Cedar Rapids, Iowa. A contributing factor to many of these accidents has been the large amounts of water (vapor and liquid) emitted from multiple sources at ADM's facility located along the south side of the highway. Weather and road-closure data acquired from D O T have been used to develop a database of meteorological conditions preceding and accompanying closure of Highway 30 in Cedar Rapids. An expert system and a FORTRAN program were developed as aids in decision-making with regard to closure of Highway 30 near the plant. The computer programs were delivered to Freese-Notis Associates in Des Moines, Mr. James Phinney, Residence Maintenance Engineer in Cedar Rapids, and Surface Systems, Inc, in St. Louis for testing, evaluation, and final deployment. Reports from D O T personnel and D O T contract meteorologists indicate the decision tools have been successfully implemented and were judged to be helpful in forecasting road closures and in reducing costs and personnel time in monitoring the roadway.

ADM Pknt Setting and Emissions

The ADM corn sweeteners plant is on the southern edge of Cedar Rapid and is located along the south side of US Highway 30, an elevated, divided, 4-lane roadway of width 35 m and elevation 9 m above the plant. The plant consists of several sets of cooling towers, grain-dryer stacks, and water-treatment ponds. More details on the plant and its operation can be found in Thomson (1995). The cooling-tower complex closest to the roadway (about 150 m south) is a 7-cell linear mechanical-draft crossflow-type alcohol cooling tower #4 that had been observed by IDOT personnel to contribute most to the reduced visibility on the roadway. When this study began in December 1992, this cooling tower complex was operated frequently, if not continuously, during the fall, winter, and spring when cool temperatures and high ambient humidities could potentially combine with tower effluent to produce copious amounts of fog. By December 1993, however, the 7-cell unit #4 was being used only sporadically during the cold season, reducing (but not eliminating) the need to close the road at this time of year. ADM personnel advised us that use of unit #4 would depend on demand and could be put back in full operation. Also, even though occurrences of fog on the roadway were reduced, IDOT personnel were obligated to continue a high volume of roadway monitoring during the winter season.

Fog Formation

Fog originates when the ambient temperature and dewpoint temperature become identical (or nearly so), provided that sufficient condensation nuclei are available. Cooling tower fogs occur when a moisture vlume from a cooling tower is advected to ground level. While natural fogs " - - generally require small dewpoint depressions (temperature minus dewpoint temperature), cooling-tower fogs can occur with relatively large dewpoint depressions of more than 15' F. ow ever, roadway visibility does not become abroblkm if ambient conditions allow the copious amounts of cooling tower fog to rise and dissipate. Although some cooling-tower plumes can be large at these larger dewpoint depressions, observations indicated that a very small dewpoint depression is required to cause the ground fogging along US Highway 30 near the ADM plant.

Data Collection

IDOT began recording conditions of potential low visibility on Highway 30 in Cedar Rapids during the winter of 1989-90. A Surface Systems, Inc. (SSI) weather station at the site provided temperature, relative humidity, wind speed, and wind direction data. Visual estimates were made of the position of the vapor plumes relative to the roadway and its impact on driving visibility. Roadway closurelre-opening times and current weather conditions also were recorded with the plume observations.

Hourly surface weather observations from Cedar Rapids, Waterloo, Des Moines, Fort Dodge, Omaha, Sioux City, Ottumwa, and Mason City were archived at ISU to provide supplementary data for determining general weather conditions throughout the state. The Cedar Rapids airport is located south of the ADM plant about 4.8 km (3 mi). Comparison of portions (300 h) of the

two primary data sets (Cedar Rapids airport and SSYIDOT) revealed no significant difference in the observed temperature, dewpoint, or winds except that the SSI temperature and dewpoint sensors tended to give values about 1 degree F warmer than the airport. Additional airport data such as values and trends of cloud ceiling and measured visibility eventually were determined to be good indicators of potential fog formation.

IDOT data from October 1989 through March 1994 contain 2153 hours of observations including 27 road-closure events. Of the 27 closure events only 25 events were used due to questionable and missing IDOT data surrounding two events. A summary of the closure and monitoring information is shown in Table 1. Due to missing data, roughly 30% of the monitoring periods after November 1991 are without surface weather observations. All the available data that corresponded to periods of monitoring or road closure by IDOT personnel were used to further define the atmospheric conditions at the ADM site.

Table 1. Closure and monitoring events recorded by IDOT.

1989-1990 1990-1991 1991-1992 1992-1993 1993-1994 Totals

Road Closure 3 Ciosures 7 Closures 12 Closures 4 Closures 1 Closure 27 Closures Events 31 h 77 h 126 h 46 h 4 h 284 h

Monitoring 25 Times 39 Times 48 Times 26 Times 17 Times 155 Times Events 216h 616h 740 h 459 h 122 h 2153 h

More details on the analysis and interpretation of these data are given in the masters thesis of Paul Thompson (Thomson, 1995)

Revised criteria for fog formatio~z

An initial study of the cooling-tower fogs reducing visibility along U.S. Highway 30 was prepared by Radian Corporation in August of 1989. However, forecasts of cooling-tower fog based on criteria from the Radian report are too conservative and can lead to an over-prediction of fogging and a large number of 'false alarms'. This required excessive monitoring of the roadway by IDOT personnel. Analysis of data from 1989 through 1994 allowed us to revise the criteria for weather conditions accompanying and preceding the need for closure. According to on-site IDOT personnel, the revised criteria, which were forwarded to IDOT's contract meteorologists for their operational forecasts, contributed to improved forecasts from the meteorological consultants.

The revised criteria for closure are as follows: * Air temperature: >200Fand<500F * Dewpoint depression: <2" F * Wind direction: 1200 to 2700 * Wind speed: 23 knots * General visibility: < 2 mi * Cloud ceiling: < 1000 ft

Software Developrnelzt

Expert systems are computer programs developed to solve real-world problems using knowledge gained from human experts. The system seeks to capture enough of the human specialist's knowledge so it too will solve problems expertly. Specifically, an expert system solves problems traditionally requiring a human expert and does so using a model of human reasoning to reach the same conclusion as a human expert.

The final version of the expert system developed for the project separates the four meteorological inputs (temperature, dewpoint, wind speed, and wind direction) to produce a probability of each variable to cause fog along U.S. Highway 30 (see Appendix C for complete program listing). These are then multiplied together, along with a correction for dewpoint depression, to give an overall probability that the cooling tower plume will trigger a road closure. The system decides (yeslno), based on this overall probability, if the roadway may need to be closed. The actual rules and probabilities were developed using discriminate analysis techniques and trial-and-error methods to develop the best combination of accuracy (false alarm rate vs. missed closure events).

For the evaluation dataset, the system predicted 90% of the road closures while giving a false alarm rate of 15% during the same period. This evaluation assumes a perfect forecast, since the actual observed conditions were used to test the system. In daily operations, it is likely the system would provide more false alarms and a slightly lower success rate at predicting road closures. Because the actual conditions for a closure are very specific and must be forecast very precisely, any errors in the forecast could have significant impact on the accuracy of the expert system to forecast road closures.

The Expert System was delivered to personnel at Freese-Notis and IDOT in Cedar Rapids in December 1993. However, starting about this same time, ADM discontinued use of the alcohol #4 cooling tower that was the main source of water vapor along U.S. Highway 30. This caused the expert system to over-predict closure events and lowered the confidence of the forecasters using it. For this reason, we developed a supplemental procedure for fog prediction.

The development of a FORTRAN model to forecast plume behavior was started during the fall of 1994. The purpose of the program was to forecast plume behavior at least 24 hours in advance with little or no human intervention. In addition, the reduction of false alarms and improved accuracy over the Expert System in predicting closure events was a high priority since the reduction of roadway monitoring is a priority cost-reduction goal of IDOT.

The logic in the FORTRAN program (see Appendix D for a listing of the source code) is similar to the expert system in that individual probabilities for each factor are determined and then combined to achieve an overall probability of fog potential (P,, = P,, * P, * P,,). However, the FORTRAN version does not require human input for any of the atmospheric variables. Instead, the Model Output Statistics data set from a National Weather Service computer model is used to provide a forecast of surface conditions every 3 hours out to 60 hours after the initialization data are reported. Four categories were developed to provide guidelines for using the probability forecast. The categories used are a high, medium, low, and zero probability of reduced visibility along U.S. Highway 30. The categories are very conservative in nature, so during a forecast period with a category zero there is basically no chance of fog causing problems on U.S. Highway 30.

Observations from past road closures were used to determine the threshold for the high probability category. Using a probability of 80% as the threshold between medium and high probability, the model predicted a high category for the majority of the observed closures. When high probabilities are forecast, there is a very significant probability of low visibility along U.S. Highway 30 near the ADM plant that will require the roadway to be closed. The medium category is included to account for small errors in the forecast surface conditions and variations in the actual conditions at closure. If a medium category is forecast, users should be alert to possible reductions in visibility on the roadway. The low category indicates a small chance for visibility being reduced below ambient levels along U.S. Highway 30, so monitoring of the roadway would generally not be required.

During the winter 1994-1995, forecasts were generated every 12 hours (at approximately 1100 and 2300 LST) and sent electronically to IDOT personnel in Cedar Rapids and forecasters at Freese-Notis in Des Moines. The model produces plume forecasts out to 36 hours and lists the expected weather conditions as well as the fog category for each 3-hour forecast period (0000, 0300,0600, and 0900 for forecasts made at 2300 LST, or 1200,1500, 1800, and 2100 for forecasts generated at 1100 LST). Beginning 1 January 1995 the forecasts were sent via e-mail to IDOT in Cedar Rapids every 12 hours, except for sporadic events when the National Weather Service data were not received at ISU. An example of the forecast sent to D O T is shown in Appendix A. In addition to forecast plume behavior, the forecast form contains space for IDOT personnel to record observations of the plume's behavior. These are then returned to ISU to assist in verification of the model.

Evaluation of the FORTRAN model shows that during the fvst four forecast periods (out to 15 hours) the mean error is about 1°F for the temperature and about 0.5"F for the dewpoint. Wind direction and speed are also generally good during the short-term forecast periods. The trend is similar in the standard deviation of the errors for the early forecast periods, but longer forecast periods show a steady increase in standard deviation. A file containing the average bias for each variable at each period was produced to improve the accuracy of the forecast. This file is then used to remove some of the error occurring in each forecast. Initial results show this procedure works very well for correcting small errors in wind direction and wind speeds, temperature and dewpoint.

Results

The forecasts for the period from 1 January though 28 February 1995 were used to test the procedure. These forecasts were returned to ISU from IDOT personnel with plume and monitoring records for the period. The data include 85 separate forecasts sent to IDOT similar to the one shown in Appendix A. There were no road closures along U.S. Highway 30 during this period, but the road was monitored a total of 114 hours. Of these 114 hours, only 24 hours correspond to periods where the model was forecasting a high or medium fog probability, leaving 90 hours of monitoring when the model indicated there should be no problems with cooling-tower plumes affecting visibility along the road. During these 90 hours, only 6 hours corresponded to events missed by the Fortran program, periods where a low or zero category was forecast which also had an observed plume above the roadway at any elevation.

The model did tend to over predict fog problems during some forecast periods. However, when the plume forecasts are separated into 2 sections using the first four periods as a forecast of plume behavior and the remaining seven as an outlook, the model shows promise to significantly reduce the amount of monitoring required along U.S. Highway 30. This procedure could have cut the amount of monitoring from 114 hours to 60 hours, nearly a 50% reduction in the hours during only a single month. This assumes monitoring is required when a high or medium category is forecast which may not he the case. The decision to monitor the roadway is solely the responsibility of IDOT personnel in Cedar Rapids and the forecasts of plume behavior are only guidance to help in making those decisions. Different levels of probability could be used in determining when and if monitoring of the roadway should be started.

The forecasts produced by the FORTRAN program for the period from 1 January at 00 UTC through 28 February at 00 UTC were verified by IDOT and returned to ISU. During this period there were 21 hours when a plume was observed over the roadway, while 60 hours were predicted to have fog potential by the FORTRAN model, and over 100 hours were suggested by human forecasters to he problematic. Statistical analysis of data for all periods showed the model does have useful skill in forecasting plume behavior at the ADM facility. It should be noted that this only covers a very short period and overall results may he different if a larger data set were available. The threshold of 70% can be adjusted to fit the requirements of IDOT for the number of false alarms and missed forecasts they can tolerate.

The FORTRAN model has a very low false alarm rate while hit rates during the period approach 70%. Achieving the low false alarm rate was a goal of the project; with continued refinement during a full operational season, the hit rate should improve. The 70% hit rate is approximately equal to human forecasters, but the large reduction in false alarms means the model has better accuracy than the forecasters.

The FORTRAN code was delivered to D O T contract meteorologists at SSI In October 1995 and was used on a daily basis for preparing their forecasts of plume behavior at the Cedar Rapids site.

During the winter 1995-96 a test was conducted to see if the FORTRAN forecast system could be ported to another city. This test was stimulated by comments by the Highway Research Board that it might be useful to have a procedure that was portable and could be used at other sites. In response to a suggestion from Mr. Royce Fichtner of Marshalltown, we redeployed the system for application to Main Street in Marshalltown due to the IES plant to the south. The transformation of FORTRAN code for application to Marshalltown required interpolation of meteorological data, since Marshalltown has no local observation site comparable to that in Cedar Rapids. However, the Marshalltown site had no road-closure database comparable with the data from Cedar Rapids, so the system could only provide an advisory based on conditions leading to road closure in Cedar Rapids. The system issued forecasts for Marshalltown on 107 days during the 1995-96 winter period (November 1995 -April 1996). During this time, the system forecast high probability of fog on 2 days, medium probability on 8 days, low probability on 23 days and zero probability on 76 days. There were no reports of road closures during the winter period. From these results, we conclude that the system can easily be ported to another location, but that without local observations to correlate meteorological conditions to roadway closures, the system only can be expected to provide a fog advisory that conditions are conducive to fog.

Summary and Corzclusiorzs

All of the road-closure events on U.S. Highway 30 near the ADM plant are the result of pre- existing fog being enhanced by moisture sources at the ADM facility. The cooling towers and grain-dryer stacks do not, by themselves, produce plumes with enough horizontal extent or optical density to warrant closure of U.S. Highway 30. However, when the ambient air is moisture laden and visibility in the area is less than 1 mile, the added moisture from the plant can form ground fogs that reduce visibility to nearly zero.

The previous addition of new 'plume abatement' cooling towers and changes in winter operating procedures have improved conditions near the plant. The addition of more 'plume abatement' towers (combined with the removal of the existing crossflow towers) and the increase of the exit height of the grain-dryer stacks would further reduce the potential for hazardous visibility along U.S. Highway 30. Similarly, relocation of the existing cooling towers to locations well south from the roadway would also reduce the probability of those towers causing ground fog along the highway.

The fog problem is worse during the winter months since cold air can only hold relatively small amounts of water vapor, forcing the rest of form visible clouds. Most of the closure events occur during only the two coldest months and thentypically during periods that are unseasonably warm. However, events may occur whenever the required large-scale weather conditions are met. Therefore, an unseasonably cold event in the fall or spring may require the roadway to be closed.

To improve short and medium range (6 to 36 hours) forecasts of potential road closure events, an Expert System and Fortran program were developed. Both show good potential for reducing the amount of monitoring along the roadway while still maintaining a high degree of safety. The systems were developed using past closure events to produce rules and the probability of closure for each atmospheric variable. During initial testing, both systems predicted over 85% of the closure events and could have reduced roadway monitoring by over 50%.

A research paper summarizing the results and evaluation of the fog forecasting problem has been published by Takle and Thomson (1996). This paper evaluates the bias and threshold of forecast values for the fog problem and compares these with a previous study of the occurrence and forecasting of roadway and bridge frost (see Appendix B for copy of this paper).

References

Takle, E. S., and P. C. Thomson, 1996: Use of expert systems for roadway weather maintenance decisions. 1996 Semisesquicentennial Transportation Conference Proceedings. Center for Transportation Research and Education. Ames, IA. 183-187.

Thomson, P. C., 1995: Development of software to forecast periods of cooling-tower fog along U. S. Highway 30 in Cedar Rapids, Iowa, MS Thesis, Iowa State University. 112 pp.

APPENDIX E. EXAMPLE PLUME FORECAST

This file created on Tue Feb 21 09:58:46 CST 1995 Reading input file ngm.mos.00.22 Model bias file not found.

Forecast created using NGM MOS data from 21 FEB 95 taken at 1800 CST

DAY /FEB 22 /FEB 23 HOUR 00 0 3 0 6 09 12 15 18 21 00 03 06

TEMP 35. 36. 36. 39. 48. 51. 45. 39. 36. 31. 29. DWPT 29. 31: 32. 33. 36. 36. 35. 33. 29. 25. 22. WOIR 180. 216. 241. 276. 287. 285. 291. 309. 308. 315. 317. WSPD 8. 6. 4. 3. 5. 8. 8. 16. 24. 26. 25.

Plume Forecast for US Highway 30 near the ADM plant

PROB%53. 66. 66. 47. 27. 48. 27. 27. 29. 29. 29.. CAT 0 L L 0 0 0 0 0 0 0 0

Observations of plume behavior

ON ROAD - - - - - - - - - - - ABOVE ROAD

COOLING UNITS USED - - - - - - - - - - - The fog categories are based upon the predicted probability for a cooling tower fog effecting the visibility along Highway 30 near the Archer Daniels Midland plant.

The criteria for each category is:

High Probability Greater than or equal to 80

Medium Probability between 70 and 80

Low Probability between 60 and 70

Zero Probability less than 60

These categories provide only a general outlook of the cooling tower plume behavior based on a national computer model which forecasts weather conditions for the area. This forecast is experimental and users should be alert to possible large errors in these forecasts of plume behavior and weather conditions.

Version 0.94 26 JANUARY 1995

By: E.S. Takle and P.C. Thomson email: gstakle@iastate.edu pthomson@iastate.edu

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e (at Ihc

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rcc

as

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ae F

orc

ca

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ullo

okH

it Rale

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alse Ala

m Rat:

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54% 60%

66% 72%

78% 84%

90%

Probability Threshold

T&

Dd

lh

m

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ain

ing

r h

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ing

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br b

um

an

~rrc

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rsIo

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3.lb

se

rriulll show

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an lom

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la11 biu

cd o

wu

d

m.~

n~dnin

8.

lor rdx-.sm

"tc.

.nd ihlt

0. SW

I cornpa.

or riigh

lly lovcr b

n ihe

rxprn

3pum..W

e rmv

hu

iaih

si

mi, m

mp

u>

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iv U

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vlr #,,u

rn

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fog pu

blern

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au

n h

ld a h

rl r8v

01 10 mn

ro

~

RE

CO

MM

FND

AT

ION

S FO

R U

SE

OF

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PE

RT

SY

ST

FM

S

&r

cip

ticn

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dore

logng ih

rx syllcm

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dons aro

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rpn,y*,.m,

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be." d

crd

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uh

uu

ug

hi".

Ih.l,".h."m,y,.

sm, ur m

ore likly

to b

,"Ccc..r*, i( VI.7

srrd..i*".d lo rr-

<=a *r

pfir

r*eaC

.g..h

osro

n.

btidgr, rslher ihu, morr gen.

era

cond1tionsR.g.-rnnc.011111

weaU

rr).%hi.

is bcs-

,he number o

< N

IL, needrd io

diro"mina<

c K

cu

mn

cc

1mm

n

on

oa

vm

m ir llirh

llmilrd

IlBv

l M lo

r ouriy$

mm

sl. Venfi-

cation slsimp

lcr 3yslm

l il m

vrh rn

on m

an8g<

*lc last.

AP

PE

ND

IX C

. E

XI'

ER

T S

YST

Ehl

SO

UR

CE

CO

DE

The

following is t

he sou

rce listing for

rhe

expe

n sy

scer

n delivered to [

DO

T and Freere-Notis

pers

onne

l during

Dec

embe

r 19

93.

DO

NI\

IN :: C

OOVTWR3

ROOT P

WE

:: COOLTWR

Global KB

da

ta

--=-..ss----==-s=--.s-

CW

E STRUCTURE ::

ea

inm

ec

er groups :: ICOOLTWR-PLRMSI

groups : :

IVINDVEL-IULES DEWPOINT-RULES WINODIR-RULES

TC

XP

-RU

L~

COOLTWR-RULES HETI\-RULES

I H

um

ber of rules ::

46

Nu

mb

*< Of ineEa-rule,

:: 1

hi

:: iDOt4AINI

TEXTRGS : :

11

Frame :: COOLTWR

IDENTIFIER :: COOLTWR3

TRIWSLRTION :: la 3y3cem

Lo

predict

Che

foima2ion of cooling

tow

er

foq an

HU

Y 30 in c

ed

ar ~ap~dn,

IA baled on sLatisrlsal data.

I w

as

::

IFOGYES C~RRECTEDI

PROHPTEVER :

: l~his is a

PROTOTYPE ~xpecc sysrern to deeernune the

forma. cidn bf fog covcrinq ~uy

30 in C

edar ~apid, near the

arch

& oanie~s "idland

I rnow I

corn sweeteners p

lant. =he

Syscem

w6s

developed by E. 5. Takle and P. C. Thomen. The

developers are not responsible for a

ccidents, damaqc;or

1n1u

cy r

esulting from the use at Lhia sysrern. =his System

was

developed for forecarring fog nround the WH pl~nr in

Cedar Rapid.,

Iowa and may

no

t Ds accurate

ac

ocher

locacronl. :line 2 '*.

Thls

Sy

ste

m i

s s

~pyrighred and

shall not be copicd

or used In a

ny

form

without vriiten

perrmasron from E.

5. ?axle '-.

:line 2 [if you need help

at

any prompnpr,

press :ATTR (YELLOW BLINK1 PI :fiTTR IWHITEl

key! :line 2

Pioro=ype Y

ers

lon

3.0s.

14 oc

r 1333

I D

TS

PII

IIR

ES

IIL

TS

:: iTEnP DEWPOINT UlNDDlR YTNDVEL C

ORRECTED TOGYES1

RULCGROVPS ::

ICOOLTWR-RULES TEMP-RULES IIINODIR-RULES DEWPOINT-RULES

WINDVEL-RULES

I COOLTWR-PLRNS

:: ICORR CORRECTED DEWPOINT POGPROB FOGYES PB

-DEW

POII

IT

PB-TEM

P PB

-WIN

D PB-WINOIIIR

EB-WINOVEL TENP TEST

YILIDDIR WlNDVEL

I COOLTYR-RULES

:: IRULE026 RULE031 RULE038 RULE039 RULE040 RULE042

RULE043 RU

LE04

'I R

ULEO<S RULEOeB

I TEMP-RULES

:: IRULEOlS RULED19 RULE020 RULE021 RULE022 RULE023 RULE024

n,r

,eA

,q

, ---

- ,

WINDDIR-RULES

:: IRVLEOOP RULE010 RULE011 RULE012 RULE013 RULEOX*

RULE015 RULE016 RULE017 RULB036

I DEVPOlNT-RULES

:: IRVLEOOI RULE002 RULE003 RVLPO04 RULE005 RULE006

RULE001

RU

LE

008

I WINDVEL-RULES

:: (RULE029 RULE030 R

ULE031 RULE032 RVLS033 RULEO3e

RULE035 I

.-. .~

--

-=

TRIWSL~TION :: lcorreciion faccorl

TYPE :: S

INGLEVaLVED

EXPECT :: MIMBER

UPDATfD-B1 :: (RULE031 RULT039 RULE040 RULE0381

USED-BY :: (RULE045 RULE0461

CONTfiINED-IN :: tRVLEO46I

WG

E :: I0 4.1

CORRECTED

-==-

=.**-

TWSLRTION :: ICOcleCLed prDbahillty of fog1

TYPE i: SINGLEVIILUED

EXPECT :: POSITIVE-NUHBER

UPDATCD-BI :: lRVLEO45 RULE0461

USED-8Y :: IRULEOO R

ULE0421

WG

E :: I0 2.1

DE

VP

DL

M

..==..-* TWWSL~TION :: ithe forecast devpolnr

at

~u

y

301

eRoneT :: iznter me formcarr daupolnt

at

~u

y

30 in cedar ~apld.1

TYPE :: SINGLEVULUED

EXPECT :: N

UWER

USED-BY :: IRULEOOI RULE002 RULE003 R

ULE004 RULE005 RULE006 RVLE007

RV

LE

OO

B i

~.

HELP :: (Enter the d

ewolnf forecast in

degree* 1-

20

co lor)

.I

CONTfiINED-IN :: ISREFWK RULEOI4I

WC

E :

: 1-20 701

TWSLRTION ::

IProbdbllLiy of foq on

Hu

y 30 lo.

Lo 1.

))

TYPE :: SINGLEYnLUED

UP

OIIIE

O-B

Y

:: ISREFWUIK RULE0261

USED-BY

:: ISREFWUIK RULE0261

USED-81-THE-WX?

:: INRULEOOlI

CO

NT

IIINE

D-IN

:: (RULE037 RULE039 RULE040 RULE0381

TO

GY

ES

=

-==

=-

TWSLATiON :: ~~here

will be a cooling

rov

er

fog on nry 30

ne

ar

the

m

~ plant

v~

rh

the focecasc c

onditions.

1 TYPE ::

YE

SIN

0

UPDhTED-BY

:: ,RULE043 RULE0421

USED-BY-IHE-W&Y

:: lHRULE001)

- -=-====ss-=

TwIU\T~~~

:: (the p

rob.+bi~z~~

for fog d

ev

La

a qiven derpaincl

TY

PE

:: SINGLEVALUED

EXPECT :: NUMBER

"PORTED-BY

:: IRULEOOI RULE002 RULE003 RULE000 RULE005 RULE006 RULE007

RU

LE

00

8

1 CONTRINED-IN ::

IS

RE

FW

K RULE0261

WC

E :: 10

1.1

PB-TEMP

-*=-=

==

TWSU\TION

:: (<he probability of fag due to the temp.)

TYPE :: S

INGLEYRLUEO

EXPECT : : N

UM

BE

R

up

om

En

-8y

::

inL!LEO18 RULE019 RULE021 RULE022 RL~LEOZ~

RULE024 RULE025

RULE020

> CONTIII>IED-Ill : :

ISREWK RULE0261

WG

E :: 10 1.1

PB WINDDXR

- -==,,--==== T~SLATION

:: [pi~bablli~y

of f

og due

La

th

e vind direccionl

TYPE :: SINGLEYRLUED

EXPECT ::

NUMBER

VPDI\TED-BY :: lRULEO1O RULE011 RULE012 RULE013 RULE014 RULE015 RULE016

RULE017 RULE036 RULE009

I CONTAINED-I>! :: ISREPWK RULE0261

WG

E :: I0

1.1

78

WINDYEL

- -=

==

-==

==

- TRRNSVVTIOS ::

lprab of f

ag due to vrnd velocity1

TIDE :: SINGLEYaLUED

...-

EXPECT :: NUMBER

IIPDIITEII-81 :: IRULE029 R

ULEOlO &"LEO31

RULE032 RULE033 RULE034 RULE0351

WG

E :: I0

1.1 CONTAINED-IN :: ISRETWUIX RULE0261

92

TWSLATION ::

ithe

foreca3i a

ir temperature

ar

Huy 301

PR

ON

PT

:: l~n~er

the forecast air temperature

ac

nuy 30 in Cedac ~apids

TYPE :: SINGLEVALUED

EXPECT :: N

UMBER

VS

ED

-BY

:: IRVLEOlO RULE019 RULE021 RULE022 RVLEO23 RULE024 RULE025

RVLE020

1 HELP :: lEntei the foicca=i airiemp in degieca 1-30 to 70rt

.1 CONTAINED-IN ::

ISRE-K RULE0441

WG

E :: 1-30

70)

TE

ST

=

=-=

. . -. . . . . .

zm..-=m

=

T-SU

\TIO

N

:: ctne farecaa~ vind direction

at

~w

y

301

PROMPT ::

IEnLer the

fore

cs

ac

vind direction at Huy 301

TYPE : : SINGLEVALVED

EXPECT : : M

BER

USED-8K :: (RULE010 RULE011 RULE012 RULE013 RULE014 RULEOIS RULE016

RULE017 RULE036 RULE009

I HELP ::

(Enter th

e e

xpected rind direction

in

degrees 1120

Lo

2801

.I W

GE

:: 1120 2801

WINDVEL

=--=

==

=

TWSLATION

:: lene

toresa

sr vind velocity

at

nuy 301

PROMPT :: IEnrer the forecast vlnd speed iknoCs1 Huy 301

TYPE :: SINGLEVALUED

EXPECT :: I

NTEGER

USED-BY :: ~IRULEO~?

RULE030 RULE031 RULE032 RULE033 RULE034 RULE0351

XELP :: [Enter the expected wind speed in knots I0

to 4OkCS)

.I RANGE ::

10 401

RULE026

*=-=

--=

SUBJECT ::

CO

OL

WR

-RU

LE

S

If Probabiliry of foq an Hvy 30 0. to 1.

Is not known.

Then it is definite IlOOIl that Probsbiliry

oC fop on Huy 30 0

, to 1.

is

11 m

lnu

. 11111 rn

~n

u* I

the probsblli~y of fog due

to th

e temp. divided by 1.511

Limes 11

minus Ithe probablllry

for

fog dew re

s given derpoint divided by

1.0 Ill

rimes 11 m

rnv

a lprababillry of foq due

to The vlnd direction d

ivided

by 2.

111 fines I1

minus Ipcab

ot

fog due to wind vetoclcy divided by

1.71111.

> " A c < 0 0 0 .* . 4

: fL m 4

m u m 0 w v m * . k

m a c a" m 0

I O U m L.

O D " m e c " m .xu* .0 z e " 0 s " " " e 0 0" C "

C a * v 1 V

5 U

rn m r " u

.4 0 " e U "

w u m Y) a m , " " w , 5 E

w * a u U C : ?".2 _1 " Y

u > - 3 m 8 -0

e - G corn , m m = .4 0

F - Z E I A i r n - 3

F - " : o n " z JN 0 -

0 - " U 0 - u

u U " " 8 - " " .. ad ., * " .. .. V .." "

..un.,s .. ., .. c .*

a"", W 0" e e l 0 " O Y U E e s g : ,,,,,

- I ? U P P U - I I W " . D l m m " """mu- 4 l n O . u E 2 " 0 "

s Y = I

* " S 2 : ': .. - 5 C

" 0 v r . u G w

N O r U Li

u 2: Y " Q

.I * m Y n o I Y Y rn " n n o 4 4

A 0 0 " m 3"

4

$ $2 0 ".r: # m u " " a u 2 2 5 " " " m * " " U V " * 2 25

M U U W * u - 4 s

2 .5 ,: g , m e. 'S E" u W n n Y *.I n u

V " 4 .." Y C .. I LI .* " 8 0 :

z j g : V " " 0 . m u 253 Y . 3 4 , Y I - _ " 3 1 " N d u ,

c . . " W "I I : . - N C Q

?.,* z ..

n n 3

' C " i z " 7 Y I I 1 P

0 c . m C mno "I.'. D N

* " I * *:a .." * * .. 2 w .. * " C -1

D I " r 2 - " rn

C , o - 3 o l l c - 3 P

- 2 : " " e :, " G " " c, 5 " * " m a : 1IT

c u , a C C C " m * a 0 " I. 0 .." 0 " Y) * a" c 9 0 m

" s 0

" " 5 0

0 II

" w $ ' - P " . o

.a - %; P'" m 2 u a 2 . , N r t C "- - m v c .

C I W * " W " m * o U 5 1 5 G l N o o n n n u

PI. ..2 n o 0 r m ".. 3 ; ;.. n * n.? * " Y " - n " l

..a m , a,. ,.P a n " C e

- s :GI f

" " " w e

0 " ,- r i E P

rn " " 0 LI a " m

m * z v I ' T T C C w u 0 - a T

P 3. O Y " .. r Q .." "

o u e 4" * a n n c 5 " ., * , " " 5 0 - w .. 2 ". 5 a

a * w r . 5 C.. m n . .a 2 " m _ l C N .. * C c- - m * v

c t m n m s o

Y 5 , 5irYN " a "".N

0 2.. m.3 I. 0 ,, 2 . . 3 " - 0 t ' ' n w c.4 O Y U r n - n "l v w v * 3 0.. * m on n c P -0 ""

m a m " 5 4 3 7P P e n D " " M * m " " " 5 s L: 0 " " m D L1 " W * 0 " 0

P, = r z z " T T ,- * w W

" a a C

0 - Y .. ,- Y) .." " o m D

4 Y II

Q C n c 5 " * w 2 " " 5 O W - *

m 2 ". 5 :

U m C I I C - o ' " , , c -

O C * " c m m ~ o 7".IT,ir "I" I W ,. 81 0 " " .. @ .. .. O .. * z u-"" " N Z m 0 z - z

i ? a c ? c. " rn " m 2

4 * - 5 2- 2 .. w

, P ".. z 0 w : s

0 c . n r n n m m . 0 "5 Z C 0 L . W

D r.TD.0 %, "%: 2: Em.. 1 * " .. .. :S $ " = o * u - I - " " N Z

Ln 0 - 3 - 2 0" O 7 e n 2 : - "5 5 S 5 .. - 0 : z " 0 0 * r P " " n r r V I I * 0 "

n l 1:

?4 s .nu u

0 0

2- " 0 " v C r Y1 0 s "

Y 0 P " 1 r X " F " s r 4 . 6 e 0

r 0 .. 0

u

0 ..

t 2 :: $ 5

! 2 :z25'~%",,. " O C l n r n n m m . 0 r.*55m'.(I0 U 01.*n,m Ot""%: . " O D * , , W * " " YZ I * ( . . . . . Y . . " " . . . " W * O

" U U - F I ""*I - m m

wan-: O D "

z4.a 7 - 0 0

$-* II " 3 1 C 9"" r m C . W b, " " ? Z Z P rr

" " W 0 00

4 .. *. L T U " r r r Y 1 W " " " U .

' " * , " " 0 : 4" 3 " " ir

0 a * * p ;l t : n r? 0 C

* r

i * * $ 2-

111 r.2 r . C n - -OM",.

O C a m i ? s:i?z:: 0

D " * " " N

n n ? . % S n o 0 " r " 7 . a . . : . .. 0 " a 2 " . 0 84 - " : z z gs s " $ 0 2 *a X 5' -

< < z " 0 7- z g * 0 " 8 " - w m n n 'tr 'E

O i ( l II " " ?.= = F z a" r

4 " W 0. 0 0

c r + m a

" W 4 D O n

U n S Y , * * $ " n < a C

C.. o w : 0 -

0

Y" " e ?

% 0 r

APP

EN

DIX

D.

FOR

TR

AN

SO

UIl

CE

CO

DE

1 hc followtng is

!he F

onra

n S

ourL

C c

ode lor !he lllcrl (J~nu3ry 1995) r

eri

lon

ofthe model u

sed

IJ forecsrl caoltng-tower pidine bellarlor i

n C

cdar

Rxptdr

1 hl

r vc

rrlo

n jrcluder ine abtlny la

curreel obrened model bmr when aua8lable

INTEGER I,J,TSTISI,TEUPI5,191,DWPT1S.191,DIR15,19~,YELlS.191,

+HN

, 00, X

Y. HH,DOl,DD2,m12,m13

c

V-.90

original

Be

ca

version. 16 ~

ec

en

h=

. 94

-Us

ed

szmple average. of acH variables

c

c

V-.91

19 Decembci 9~

E -added

a c

res

sm

n scheme on W

H variables

5

-added BRL

a3

4th

da

ta $Ate

c

5

Vn.92

20 December 94

c

-breaX wind into componcnrs

c

-so

rro

sx

ed

122 oupour pcoblems

c -corrected end of m

anrh

problams in ourpvc

c

-fixed preblcmwhen no

dsra found

C

c W.93

30 Dectmbe.

94

G

-added

are

a for W

T people

to verlty lorecaats

c -changed outpur tomar slightly - (

PBTOG-~OO~

c -fixed d

ata

<c

ad

problem ot large negacivc tempss

E (this W

as also added

Co

0.921

c -fixed d

ac

e

ourp

ur

problem (added to 0.921

C

c

V-.94

26 January 95

c

-added model blaa

co

rr

cc

~ ourput

C

E ....

........

........

........

....

........

......

c read

tex

t tile tor

cu

cra

nc

nqm tile

an

d time

........

........

........

........

........

......

OPEN (UNIT-99, FILE-'.name. tilo', STATUS-'QLD.I

REUD 199.101

INPUTrlLC

OP

CN

(UNIT-98, FILE-'.Lime.file',

BTRTUSr'OLD'I

RCUD 198,101

CURRENT

"zed

Lo

select a

specific ngn tiie

..........

...........

c

INPUT?ILEP'ngm.rno1.00.02'

........

........

........

........

........

........

........

. PClnL'.

'This file c

rea

ted

on

.,CURRENT

PrinC-:Reading inpvc file

',IN

PU

TF

ILE

10

TO

RN

I\T

IR601

CLOSE (UNIT-99)

OPEN IUNIT=lOO. FILE=lNPUTFILE, STRTUS='OLD'I

- c read the temp.

ec

c.

from ngm frie

c ..............................................

TSTIII-0

14

RED llOO,llrEND-19) ST&TION.CREST

IE ISTA(Il.NE.STIITIONi

WT

O 14

E

1%

PRINT',5TRlI1,'

da

ta found .

RLm 1100. lZ.EIID-19.ERR-18l

DRY

REUD ~lOO.12.END-1~,ERR-181 HOUR

READ IlO0.13.CND-19,ERR-18>

THelli

RE-

1100,16~EIID~191 TMP1ll.TEMP~I,li,TfWP~I,21,TENPl1,3~,

+TEHPlI,4IrTEHP~I,SI,TEMP~I,61,TEHPII,7l,TEHP~I,81,TEHPII,9l,

+TEMP~I,1O1~TEHP~l,111.TEHP11,12l,TEHP1I,131,TEMPII,14I,

+TENPII,lSI,TEHPII,161,TENPlll 17~,TEMPll.18l,TEnPl1.191

If ITHP(ll.NE.'TEMe

'1

WT

O 18

REUD 1100.13.END-191 TMP411

$8;;:;;?8 *,"-"-;" 21- E : ? ; " - - - -* 2 " - , , a .

.L) -̂ I,,," " . - e g 5;:;t; e R-

???Z e w e - - . . "

r e - ;;;; = * O f - w - p ..--* - . a >*: ' A " = - - o m . o m = .

N + ? - g : "- rn -. : ? - m r - " - -

3%%:5;s*z0?j:g*ga o z z - ~ - * - , < - f - " " x e a

. 3 r l * Z " Z O * - * * z " m '". .. 0-4 0 - " " " " 3 N . . O " a r e - - 0 n - 3 - ."..-,. w < " - - c a > - C X * r r r z m m - a . e z x 7 rF;za;

8 - n . - r u w " Z""" " ' a - - .

: 0 ? Z 4 0 :: -2z??F c m - >

2,. " ' A "

* .3 * w x

= - x*",--. r " * " m , P,

e r z - m - r ! s : ? : N - 3 .

" 0 . - 2 g ='k 2 0 3 ; m E ? G " " " 0 - - I

H a * .. .

< w -

c apply

the

average blaa

to m

e forecasc

C..............-.

............*.

................

W 69 I=I.I9

00

68 J-1.4

BIAS IJ.11-0.0

68 CONTINUE

69 CONTINUE

OPEN iUN11-96, ERR-72, TILE-"BIAS". STATUS-'0LD.l

70 CONTINUE

COHENT-*R~~~~~~

model bias dara'

GOTO 13

11 COHENT-"Error icadlng modal blas flle.

blns

GW

O 13

72 COHENT-.'H~~~I

bu

s tila

no

r found."

13 PRIM., COHENT

find the lndlvidual probablilitzer

c i

$a

n=

as

adm-2.L new as

et 18 DEC 941

6.................*.**.................--......

RE

WIN

D i1GZl

READ 6102.361

wn,Do,rr

READ 6102,371 HH

36 FORMAT ilX, 12.2% 12.2~. 121

31 FORWIT 61x.121

HH-HH-6

IF INH.LT.11

THEN

HN-HHt24

00-DD-1

END IF

IF IDD.LT.1, THEN

HN-wn-1

IF (MH.LT.11 THEN

m=m+12

YY-YY-1

EN

D IF

ODl-DOIl

,842-m IF (HHZ.EQ.Z.RN0.DOl.GT.281 THEN

HH2-3

001-1

END IE

IF IHH2.EQ.4.OR.~2.EQ.6.ORR~2.EQ.9.0~!HH2.~9.1l.

rlUIO.DDl.W.301 THEN

m2-m2+1

DDl-1

END IF

TT IHH2.EQ.1.1WD.I)01.GTT311 T

HE

N

m2=HN+1

Doll1

END

IF

IF IEM2.EQ.3.lUID.DO1.GT.311 THEN

m2-m.1

001-1

.-. ~

EN

D IT

IF (m2.EQ.lO.IWD.ODt.GT.31I

TH

EN

m2-m+1

DD1-1

END IF

IF 1~2.EQ.12.WD.DD1.GT.311

THEN

HHz-m+1

DD1-1

END IF

IF (m3.EQ.2.IUIO.DD2.GT.281

TH

EN

m3-3

DDZ-1

END IT

IF IHH3.EQ.P.0R.~3.~Q.6.0R.~3.EQ.3.0RRHH3.EQ~11.

rAND.DD2.GT.301 THEN

m3Im3rl

00211

END IF

IT (m3.EQ.1.RNO.DD2.GT.3ll

THEN

109

M13-bWtl

DOZ=l

END IF

Im3.EQ.3.IWD.DD2.GTT3i, THEN

m3-mr1

DD2-l END IF

If ~~~~~Q.IO.RNO.DD~.GT.~I~

THEN

m3nm+i

052-1

END

IF

IF (MJ.EQ.~~.wD.DDz.GT.~II TH

EN

m3-m+1

002-1

END IT

3s

POR

MA

T ('Fprecd=t created using NGM MOS

da

ta from a,I2,. .,

fA3.' ',12.

taken a

t 0'.11,

.oo CST.>

39 fORHI\T

1 'rere

ca

st c

reated usrng

NG

N NOS

da

Ca

cram

., 12, +

., *A3.

',72: ta

ke

n

ac

',12. .no

CST*~

IF ITIHE.EO.'lZOO'I

THEN

PRINT 38,OD.HONTH,YY. H

H

ELSE II ITIHE.EQ.'OOOO'I

THEN

PRINT 39. DD, HONTH. YY, HH

' END

IT

IF ITIHE.EQ.'O000'1

THEN

PRINT 41,HONTH. DDl.HONTH3.002

ELSE IF lTlNE.EQ.'1200'~ THEN

PRINT 4Z.NONTH.DO.MONTH2.DDl

END IF

31 CORMAT lii5.11IfS.0.1XIl

32 FORHRT I'YDIR '.lllTS.O.lXII

33 mRmT IA5.3x.111A1.5x1b

34 PORHRT

35 FORHRT

41 F

OWT

42 FORM0.T

SO FORHRT

si S

OWT

IF ITIHE.EQ.'0000'1

THEN

PRINT 35,'00'.'03';06',

'09'. .12., .IS', '18.. '21'.

'00'.

+'0

1'.

'05

' ~

.

, ..

ELSE IF ITINE.EQ.'1200'1

THEN

PRINT 35, '12'. '15'. '18'.

'21'.

'00'. '03'.

'06'. .09',

'12'.

+'IS8, '18,

EN0 IF

PRINT-. '

' PRINT 31,'TEMP ~.AT~HP<ll,ATEM?l2I,RTEHel3l,RTENPl4l,RT~~PlS~,

+IITEHP161,RTEHPl71.ATEHPla1,ATEHPl91,ATEHPllO1,ATEHPlll~

PRINT 31,'tlWPf

'~~PTI1I,RDHPTI2l,RDWPTl3l,RDWPTl4I,RDWPTlS~,

+RDWPTl6IrRDWPTl7I,RDYPTI8l.RDWPTI91,RDWPTl1OI,ROllPTlllI

PRINT 32,RDIRl11,~IR121,~lRl3l,RDIRI4~,I\DIRlSI,~IR16l,

+RDlRl7l,RDI~l~l,RDIRl9l,RDIRllO~,RDlRl1ll

PRINT 31,'WSPD ',AVELI1I,~VCLl2~.AVELl3~,AVELl~l,AYELlSl,

+AVELI61,RVELl7l,AV~LI8I.AVELl91,AVELIIOI,AVELl111

PRINT','

' PRINT*.

' PRINT-,'

Plume Foresa=r f

~r

US Hxghuay 30 n

ear ~hc

RDH plant'

PRINT-. ' .

PRINT 34,'PROB l',PB~GI1I,PR€OG121,PBFOGI3I,PBFOGl4l,PBFOGl5l,

+PBFOG(61.PBSOG17l.PSFOGl8l,PBFOG191rPBFOGIIOIrPR€OGI111

PRINT 33..CAT

~,C~TI11,U\Tl21,U\T131,U\T141,CATl51,CATl61,

IUITI~),U\T~~I,~T~~I,~T~~O~,CRT~~~I

PR

TM

V..

7

a

PRINT.;

' PRINT ', '

ABOVE'

PRIWT $0, 'ROW','-'

' . .

. 1 . 8

. .

. I . ,

, -, - 9 - <

-,

-,

-,

*,*_

.,*

I

a .

-.

."R

,,,T

. . 8

-

. . .- . . . ,

PRINT 51, 'COOLING'

PllNT .. '

UNITS'

PRINT S0,'WSEDq, '-'

' .

. .

1

. I

,

,-

I_

,_

_.

+

I

I

13

,

.

.. ,

-.

-

-,

PE

E&

,

* -,

-, -

pcini.,'Ths

fog caiegories are based span the predicted

'p'obabilizy

for a

' pcinc.,'coolinq

tow

er

fog e

etec~ing the Yl3ibiliiy along

+Highway 30 nea

r'

prlnt.:ehe

archer Daniels Hldiand plant.'

print..

' '

pi

c,

The criteria for each category is:

PEI"L-,'

' print..'

High

Piobabllicy giearer chan oi equal

to 80'

P'i"f.,'

' p

rin

~'.

' Medium

Probabrliiy

be

twe

en

70

an

d 80,

PCi"L.,'

- pzinc'.'

Lou

Probabilrry between 60 and 70.

p'inL..-

' pcinr.,'

ze

io

ProbabiliLy less than 60

- P'i"L'.

' '

prlnc-.'These

sategoiies provlde only a

general

ou

iLo

ok

of

+th

e cooling

Co

we

r .

print-.'plume behavmr based

on

a

naiisnal compucer lnodel

+which forecasrs

print*,'ueather conditions far she

are

a.

his fozeCart is

+experimental and

- print.,'u=eis

should be aleic to posalble large

err

oc

s in

+these forecast.

ot

print..'plume

be

ha

vla

r and wearher condr~io~~.,

52

PRINT..

' PRINT.,

' '

PRINT.,

' PRINT-,

' PRINT'.

' PRINT*, 'emarl

PRINT.,'

' CLOSE 11001

CLOSE 1991

CLOSE 1981

END

YIr

ala

n

',Y

ER

SIW

By

: E.S.

Takls

and

9~LlikleBlarca~e.c.u

. . . . . . . ,

PRINT-, ' '

PRINT.,'

OblorvaCions ol plume behavior'

PRIM'.

. ' PRINT ', .

OH'

PRINT .jo.'ROI\D','-',I

' '

' '

. I '

4 '

. '

. .

- <

-,

-,

-,-

, -, -

+

_,-

( '

'