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THE FOG REMOTE SENSING AND MODELING (FRAM) FIELD PROJECT AND PRELIMINARY RESULTS I. Gultepe1, S .G. Cober2, G. Pearson3, J. A. Milbrandt4, B. Hansen2 G. A.Isaac2, S. Platnick5, P. Taylor6, M. Gordon6, and J.P. Oakley7,

Submitted to Bulletin of AMS on May 1 2007 Revised November 2007 ABSTRACT The main purpose of this work is to describe a major field project on fog and summarize the preliminary results. The three field phases of Fog Remote Sensing and Modeling (FRAM) project were conducted over two regions of Canada: 1) Center for Atmospheric Research Experiments (CARE), Toronto, Ontario (FRAM-C), and 2) Lunenburg, Nova Scotia (FRAM-L1;L2). The FRAM-C component representing continental fog, and FRAM-L, representing the marine fog environment, took place from November 2005 to April 2006, and during June of 2006 and 2007, respectively. The main objectives of the project were 1) better description of fog environments, 2) development of microphysical parameterizations for model applications, 3) development of remote sensing methods for fog nowcasting/forecasting, 4) understanding of issues related to instrument capabilities and improvement of the analysis, and 5) integration of model data with observations to predict and detect fog areas and particle phase. During the project phases, various measurements at the surface, including droplet spectra and concentration, aerosol concentrations, visibility, 3D turbulent wind components, radiative fluxes, precipitation as well as liquid water content (LWC) profiles, ceiling, and satellite measurements were collected. These observations will be studied to better forecast/nowcast fog events in association with results obtained from numerical forecast models, namely, Canadian Global Environmental Multiscale (GEM) model and GEM-LAM (Limited Area Model). 1 Corresponding author: Ismail Gultepe, Cloud Physics and Severe Weather Research Section, Science and Technology Branch, MRD, Environment Canada, Toronto, Ontario, M3H 5T4, Canada 2

Cloud Physics and Severe Weather Research Section,Science and Technology Branch, MRD, Environment Canada Toronto, ON, M3H 5T4, Canada 3

National Laboratory for Marine and Coastal Meteorology, Environment Canada, Dartmouth, NS

4

Numerical Weather Prediction Research Section, Meteorological Research Division, Environment Canada, Dorval, QC

5

Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, MD, USA

6

Department of Earth and Space Science and Engineering York University, Toronto, ON. M3J 1P3 Canada

7

School of Electrical and Electronic Engineering, University of Manchester UK

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It is suggested that improved scientific and observational understanding of fog will lead to better forecasting/nowcasting skills benefiting the aviation, transportation, and shipping communities.

Detailed observations of various fog types, representing land and ocean environments with different thermodynamic conditions suggest that better scientific understanding of the fog related processes can significantly reduce the total economic losses.

The total economic loss associated with the impact of fog on aviation, marine and land transportation can be comparable to those of tornadoes or, in some cases, winter storms. For example, in the pre-Christmas period of December 20-23, 2006, the British Airport Authority (BAA) reported that a blanket of fog and freezing fog over the UK forced 175000 passengers to miss flights from its seven British airports, with Heathrow the worst affected (Milmo, 2007). Early estimates suggested this disruption to air travel cost British Airways at least £25 million (Gadher and Baird, 2007). The costs to stranded passengers in terms of money and inconvenience may be impossible to calculate. Previous studies have also shown that human and financial losses due to accidents related to fog episodes were very common. In Canada, approximately 50 people per year die due to fog related motor vehicle accidents (Gultepe et al., 2007a). In describing ground transportation in Illinois, USA, Westcott (2007) stated that approximately 4000 accidents and 30 deaths occur annually under foggy conditions in Illinois, excluding the city of Chicago. In Europe, a major fog project called COST-722 (COoperation in Field of Scientific and Technical Research) with objectives of reducing economic losses and loss

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of human life was also performed to develop advanced methods for very short-range forecasts of fog and low clouds (Jacobs et al., 2007).

An earlier work by Petterssen (1956) suggested that fog classification based on temperature (T) can be divided into three types: 1) liquid fog (T>-10°C), 2) mixed phase fog (-10°C>T>-30°C), and 3) ice fog (T0.05 g m-2 >0.1 mm h-1

respectively. Vaisala WRG101 Vaisala Ceilometer CT25K POSS Climatronic aerosol profiler ClearView video unit Young Sonic anemometer [81000] Vaisala DST111 Vaisala DSC111 Eppley IR; SW Radiometers Buoys Wind profiler RASS Campbell Scientific HMP45C

>0.2 mm -

±1C; 5%

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Table 2: Fog occurrence summary based on FD12P observations during FRAM-C. The Vis1000, ∆t, Vis500, PR, and T (sd) represent the Vis over 1000 m, fog duration, Vis over 500 m, precipitation rate per hour, and mean temperature (standard deviation), respectively. Lines with light grey color are not considered; PR=0-0.1 mm h-1 for fog (green); PR=0.1-1 mm h-1 for drizzle (yellow); PR> 1 mm h-1 for rain (red); ice fog cases (light blue), and snow (darker blue). The PR for rain is not included in mean PR calculation. The terms in last column are defined as: WRM FNT: Warm Front; CFNT: Cold Front; FNTL: Frontal; CFP: Cold Frontal Passage; APP FNT: Approaching Front; NLY, SLY: Northerly, Southerly; FLW: Flow (synoptic); SHWRS: Showers; LOW: Low pressure system; TROF: Trough of low pressure; R-, R, R+: Rain - light, moderate, heavy; S-, S, S+: Snow - light, moderate, heavy; SCT/BKN CLD: Scattered/Broken Cloud; INCRSG: Increasing. Case 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Day Dec. 21 Dec. 25 Dec. 26 Jan. 02 Jan. 04 Jan. 05 Jan. 21 Jan. 23 Jan. 30 Feb. 03 Feb. 11 Feb. 12 Feb. 18 Feb. 26 Mar. 09 Mar. 10 Mar. 13 Mar. 25 Apr. 15 Averages

Fog

Vis1000 [m]

∆t [min]

Vis500 [m]

∆t [min]

PR [mm/h]

763 674 896 328 571 477 767 189 695 680 429 317 696 689 621 277 575 934 689 593

15 347 23 475 137 308 153 240 62 182 239 44 93 79 14 255 113 18 55 150

428 419 204 431 367 453 137 450 464 265 224 435 429 484 199 341 308 318

2 90 352 52 198 9 219 7 19 140 35 28 14 3 215 47 12 76

0.23 0.47 0.35 0.33 0.86 0.67 0.56 1.10 1.05 0.35 0.19 0.29 5.38 0.36

T (sd ) [°C] -7.37(3.17) 3.91(0.36) 2.66(0.22) 5.24(0.36) 5.91(0.16) -0.20(0.18) 3.74(0.26) -4.80(1.10) 9.10(0.63) 0.47(0.31) -13.00(1.50) -17.11(0.76) -12.55(2.25) -6.38(0.45) 3.72(0.10) 6.05(0.54) 5.95(3.41) -0.02(0.39) 10.09(0.18) -0.70(0.88)

Synoptic condition WRM FNT RWRM FNT R+ C FNT RWRM FNT RWRM FNT RWRM FNT RWRM FNT R NLY FLW WRM FNT WRM FNT R NLY FLW NLY FLW C FNT SNLY FLW SWRM FNT RWRM FNT RWRM FNT RNLY FLW RWRM FNT R+

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Table 3: Same as Table 2 except for fog occurrence summary based on FD12P observations during FRAM-L1 which took during June of 2006. Case 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Day June 2 June 3 June 4 June 5 June 6 June 7 June 8 June 9 June 10 June 11 June 12 June 13 June 14 June 15 June 16 June 17 June 18 June 19 June 20 June 21 June 22 June 23 June 24 June 25 June 26 June 27 June 28 June 29 June 30 July 01 July 02 July 03 July 04 July 05 July 06 Averages

Fog

-

-

-

77%

Vis1000 [m]

∆t [min]

Vis500 [m]

∆t [min]

PR [mm/h]

711 728 563 347 771 302 415 300 281 370 269 294 407 376 406 456 292 392 320 532 368 358 399 329 337 620 301 721 415

16 61 390 284 7 153 450 1026 36 532 428 406 329 620 198 128 114 52 149 61 765 772 695 774 375 55 432 107 348

450 457 399 304 254 267 200 203 277 252 219 219 228 225 338 256 333 260 375 279 283 343 284 253 363 227 354 293

4 5 211 251 137 309 840 31 413 411 346 207 418 126 84 106 44 130 28 605 633 572 689 308 20 363 20 271

0.25 0.55 3.77 0.02 2.69 0.40 1.7 2.79 0.00 0.05 0.04 0.10 3.12 1.0 11.37 0.45 0.00 0.01 0.00 0.01 0.63 0 21.5 2.29 0 0.19

T (sd )

Synoptic condition

[°C] 13.33(0.73) 13.75(0.23) 9.92(0.47) 11.36(1.49)

FNTL SHWRS FNTL SHWRS FNTL SHWRS FNTL CLD

10.91(0.17) 10.84(0.68) 12.03(0.51) 12.11(0.89)

BKN CLD FNTL-R LOW TROF FNTL SHWRS

14.46(0.97) 13.39(1.02) 11.05(0.62)

LOW SHWRS SLY FLOW LOW R-

11.78(0.52) 14.24(0.43) 13.11(0.85) 14.16(0.48) 13.87(0.35) 14.02(0.48) 14.86(0.39) 14.52(0.53) 14.96(0.46) 15.91(0.73) 16.26(0.77) 16.93(0.37 16.41(1.36) 13.96(0.56) 14.69(0.34) 15.17(0.73) 14.01(0.34) 13.64(0.62)

SLY FLOW BKN CLD BKN CLD CFP SLY FLOW BKN CLD FNTL SHWRS FEW SHWRS FEW SHWRS SCT CLD SLY FLOW SLY FLOW APP FNT SWLY FLOW

FEW SHWRS FNTL SHWRS INCRSG CLD

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Table 4: Same as Table 2 except for the fog occurrence summary based on FD12P observations from FRAM-L2 which took during June of 2007. Case

Day

Fog

Vis1000 [m]

∆t [min]

Vis500 [m]

∆t [min]

PR1 [mm/h]

922 406 242 453 732 502 484

5 525 654 52 96 366 36

309 179 350 -448 382 349

403 583 34 3 219 24

0.00 0.58 1.31 0.31 0.03 1.78

[°C] 10.38(0.93) 7.46(0.25) 9.57(1.73) 11.72(1.04) 11.72(1.04) 10.15(0.69) 10.87(1.40) 13.82(1.70)

T (sd )

1 2 3 4 5 6 7 8 9

June 2 June 3 June 4 June 5 June 6 June 7 June 8 June 9 June 10

10 11

June 11 June 12

-

790

7

-

0

31.22

11.87(1.40) 12.40(0.68)

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

June 13 June 14 June 15 June 16 June 17 June 18 June 19 June 20 June 21 June 22 June 23 June 24 June 25 June 26 June 27 June 28 June 29 Averages

-

862 882 328 559 -

84 15 156 28 201

476 266 325 308

1 132 12 -

0.62 0.01 0.80 0 8.36 -

11.14(0.73) 10.90(0.77) 11.69(0.28) 13.47(0.62) 12.61(0.81) 11.19(1.12)

-

-

-

33%

545

141

0.34

Synoptic condition FNTL SHWRS FNTL SHWRS FNTL SHWRS SLY FLOW LOW TROF ELY FLOW LOW TROF SHWRS LOW TROF SHWRS

ELY FLOW ELY FLOW

FNTL SHWRS FNTL CLD

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Table 4: Vis versus RHw relationships based on various field programs and from the RUC model.

Relationship

Conditions

Reference

Vis RUC = 60 exp(−2.5 * ( RH w − 15) / 80)

Set to 5 km at RHw=95%

RUC model,

Vis FRAM −C = −41.5 ln( RH w ) + 192.3

For RHw