Page 1 – HAZARDS IN CANADA By Jacinthe LACROIX Meteorological Service of Canada.
High-resolution Mesoscale Modeling and Diagnosing of a Severe Fog Event 1 Meteorological Service of...
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High-resolution Mesoscale Modeling and Diagnosing of a Severe Fog Event 1 Meteorological Service of Canada, Burlington, Ontario, Canada 2 Meteorological Service of Canada, Downsview, Ontario, Canada 3 Canadian Meteorological Centre, Dorval, Quebec, Canada ZUOHAO CAO 1* , ISMAIL GULTEPE 2 , Amin Erfani 3 , and Patrick King 2 *Contact: Phone: 905-336-6006 E-mail: [email protected] Abstract Fog has significant impacts on Canadian society, especially on road transportation. On average, about 53 fatal vehicle accidents in which fog is a contributing factor occur each year on Canadian highways. Due to its small-scale nature, fog is a localized short-lived phenomenon and it is therefore difficult to detect and predict. To improve operational fog detection and prediction in Canada, a project has been implemented through the collaboration between scientific and operational community. As one of the studies of this project, we have investigated a severe fog event that occurred near Barrie, Ontario, on March 17, 2003. This event resulted in an accident involved more than 200 vehicles and two-dozen personal injuries. The dense fog event is simulated using the Canadian Operational Global Environmental Multiscale Model (GEM). The structure and evolution of the fog event is further resolved using the very high-resolution GEM Limited Area Model (GEM-LAM) by employing a nesting technique. The numerical simulations are verified and supported by both satellite observations and surface observations. Diagnoses are also performed to understand the physical processes responsible for the dense fog event. Introduction The fog event Experiment design Results -Fog impacts range from delays in aviation, marine, and surface transportation and deliveries to serious accidents caused in part by poor visibility. - Although efforts have been made on observational aspects of fog phenomena (e.g., Duynkerke 1991; Guedalia and Bergot 1994), using numerical weather prediction models (NWP) to forecast fog formation, evolution, and dispersion as well as its areal extent, vertical depth, and intensity is still one of the most challenges confronting with our meteorological community (Ballard et al. 1991; Bergot and Guedalia 1994; Pagowski et al. 2004). - The difficulties are mainly due to fog forecasting requirements of an adequate representation of the nocturnal boundary layer, a high-resolution vertical grid close to the surface, a parameterization of soil-atmosphere interactions, and small-scale surface heterogeneities. - In this study, we will employ the 3-D very high-resolution GEM Limited Area Model (GEM-LAM) to simulate a dense fog event, which caused two-dozen personal injuries and more than 200 vehicle damages, and to examine physical processes contributing to this severe fog event. (a). A fog-related accident on Highway 400 near Barrie, Ontario, Canada - March 17, 200 - “More than 60 separate pile-ups kept rescue worker busy along Highway 400 all day yesterday.” - “One man was flown to hospital in Toronto in critical condition and a paramedic said the fog-shrouded scene looked like a war zone.” - Over 200 vehicles, two dozen injuries - Control: ISBA surface scheme and Kong-Yau explicit microphysics scheme - Experiment 1: Force restore surface scheme and Kong-Yau explicit microphysics scheme to examine the effects of surface processes on liquid water content (LWC) - Experiment 2: ISBA surface scheme, Kong-Yau explicit microphysics scheme and Kain-Fritsch convection scheme to examine the effects of subgrid convection on LWC - Experiment 3: Force restore surface scheme, Kong-Yau explicit microphysics scheme and Kain-Fritsch convection scheme to examine the combined effects of surface processes and subgrid convection on LWC Fig. 1 Liquid water content (at an interval of 5x10 -5 kg/kg) on 10 UTC 17 March 2003 at a sigma level of 0.9606 obtained from (a) GEM and (b) GEM-LAM. a. GEM b. GEM-LAM Fig. 2 Same as Fig. 1 except GEM-LAM at a sigma level of 0.9151. -LWC is well simulated using the high-resolution GEM-LAM in the area of interest where LWC is not captured by the coarse- resolution GEM. - Convective process play important roles in fog formation. Results show that with inclusion of Kain-Fritsch convective parameterization scheme, the vertical depth of fog is thicker than the one without the convective parameterization scheme. - Fog development is sensitive to land surface process schemes. The simulation with ISBA scheme leads to more intensive fog than the one with force restore scheme, particularly during the late stage of fog development. Concluding remarks Bill Stanford, Toronto Star (a). Canadian operational global environmental multiscale model (GEM) - 15 km resolution in horizontal; 58 levels in vertical - initial conditions obtained from Canadian Meteorological Centre analyses - physical processes: land surface process ISBA; PBL; Kain-Fritsch convection; new Sundqvist explicit scheme; radiation scheme (b). GEM – Limited Area Model (GEM-LAM) - 2.5 km resolution in horizontal; 58 levels in vertical - initial and boundary conditions obtained from GEM outputs using a self-nesting technique - physical processes: ISBA; PBL; Kong-Yau explicit microphysics scheme Numerical models a. GEM-LAM Control b. GEM-LAM Control + Kain-Fritsch (b). Overview of the event - CMC analysis at 12 UTC 17 March 2003 - low wind speed (in blue arrow) - low dew-point depression (in red) - Surface map at 12 UTC 17 March 2003 - post ridge and weak front - fog - observations at Barrie, Ontario, Canada on 17 March 2003 - the GOES image at 13:15 UTC 17 March - green color indicating fog areas The numerical simulations were performed on the IBM supercomputer of Environment Canada. Acknowledgments a. GEM-LAM Control b. GEM-LAM + Force restore Fig. 3 Same as Fig. 2 except on 14 UTC 17 March 2003 at a sigma level of 0.8571. 0 2 4 6 8 10 12 14 16 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1617 18 19 20 21 22 23 LocalTime Visibility(km )
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Transcript of High-resolution Mesoscale Modeling and Diagnosing of a Severe Fog Event 1 Meteorological Service of...
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High-resolution Mesoscale Modeling and Diagnosing of a Severe Fog Event1Meteorological Service of Canada, Burlington, Ontario, Canada2Meteorological Service of Canada, Downsview, Ontario, Canada3Canadian Meteorological Centre, Dorval, Quebec, Canada ZUOHAO CAO1*, ISMAIL GULTEPE2, Amin Erfani3, and Patrick King2*Contact: Phone: 905-336-6006 E-mail: [email protected] has significant impacts on Canadian society, especially on road transportation. On average, about 53 fatal vehicle accidents in which fog is a contributing factor occur each year on Canadian highways. Due to its small-scale nature, fog is a localized short-lived phenomenon and it is therefore difficult to detect and predict. To improve operational fog detection and prediction in Canada, a project has been implemented through the collaboration between scientific and operational community. As one of the studies of this project, we have investigated a severe fog event that occurred near Barrie, Ontario, on March 17, 2003. This event resulted in an accident involved more than 200 vehicles and two-dozen personal injuries. The dense fog event is simulated using the Canadian Operational Global Environmental Multiscale Model (GEM). The structure and evolution of the fog event is further resolved using the very high-resolution GEM Limited Area Model (GEM-LAM) by employing a nesting technique. The numerical simulations are verified and supported by both satellite observations and surface observations. Diagnoses are also performed to understand the physical processes responsible for the dense fog event. IntroductionThe fog event Experiment designResultsFog impacts range from delays in aviation, marine, and surface transportation and deliveries to serious accidents caused in part by poor visibility. Although efforts have been made on observational aspects of fog phenomena (e.g., Duynkerke 1991; Guedalia and Bergot 1994), using numerical weather prediction models (NWP) to forecast fog formation, evolution, and dispersion as well as its areal extent, vertical depth, and intensity is still one of the most challenges confronting with our meteorological community (Ballard et al. 1991; Bergot and Guedalia 1994; Pagowski et al. 2004). The difficulties are mainly due to fog forecasting requirements of an adequate representation of the nocturnal boundary layer, a high-resolution vertical grid close to the surface, a parameterization of soil-atmosphere interactions, and small-scale surface heterogeneities. In this study, we will employ the 3-D very high-resolution GEM Limited Area Model (GEM-LAM) to simulate a dense fog event, which caused two-dozen personal injuries and more than 200 vehicle damages, and to examine physical processes contributing to this severe fog event. (a). A fog-related accident on Highway 400 near Barrie, Ontario, Canada- March 17, 200 More than 60 separate pile-ups kept rescue worker busy along Highway 400 all day yesterday. - One man was flown to hospital in Toronto in critical condition and a paramedic said the fog-shrouded scene looked like a war zone. - Over 200 vehicles, two dozen injuries- Control: ISBA surface scheme and Kong-Yau explicit microphysics scheme - Experiment 1: Force restore surface scheme and Kong-Yau explicit microphysics scheme to examine the effects of surface processes on liquid water content (LWC) Experiment 2: ISBA surface scheme, Kong-Yau explicit microphysics scheme and Kain-Fritsch convection scheme to examine the effects of subgrid convection on LWC Experiment 3: Force restore surface scheme, Kong-Yau explicit microphysics scheme and Kain-Fritsch convection scheme to examine the combined effects of surface processes and subgrid convection on LWC Fig. 1 Liquid water content (at an interval of 5x10-5 kg/kg) on 10 UTC 17 March 2003 at a sigma level of 0.9606 obtained from (a) GEM and (b) GEM-LAM. a. GEM b. GEM-LAM Fig. 2 Same as Fig. 1 except GEM-LAM at a sigma level of 0.9151. LWC is well simulated using the high-resolution GEM-LAM in the area of interest where LWC is not captured by the coarse-resolution GEM. Convective process play important roles in fog formation. Results show that with inclusion of Kain-Fritsch convective parameterization scheme, the vertical depth of fog is thicker than the one without the convective parameterization scheme. Fog development is sensitive to land surface process schemes. The simulation with ISBA scheme leads to more intensive fog than the one with force restore scheme, particularly during the late stage of fog development. Concluding remarksBill Stanford, Toronto Star (a). Canadian operational global environmental multiscale model (GEM) - 15 km resolution in horizontal; 58 levels in vertical - initial conditions obtained from Canadian Meteorological Centre analyses - physical processes: land surface process ISBA; PBL; Kain-Fritsch convection; new Sundqvist explicit scheme; radiation scheme (b). GEM Limited Area Model (GEM-LAM) - 2.5 km resolution in horizontal; 58 levels in vertical - initial and boundary conditions obtained from GEM outputs using a self-nesting technique - physical processes: ISBA; PBL; Kong-Yau explicit microphysics schemeNumerical modelsa. GEM-LAM Control b. GEM-LAM Control + Kain-Fritsch (b). Overview of the event CMC analysis at 12 UTC 17 March 2003 low wind speed (in blue arrow) low dew-point depression (in red) - Surface map at 12 UTC 17 March 2003- post ridge and weak front fog Hourly visibility observations at Barrie, Ontario, Canada on 17 March 2003 the GOES image at 13:15 UTC 17 March green color indicating fog areasThe numerical simulations were performed on the IBM supercomputer of Environment Canada. Acknowledgmentsa. GEM-LAM Control b. GEM-LAM + Force restore Fig. 3 Same as Fig. 2 except on 14 UTC 17 March 2003 at a sigma level of 0.8571.
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Hourly Data Report for March 17, 20030:0060:001.6666666667736.10:006.4
TTempDew Point TempRel HumWind DirWind SpdVisibilityStn PressHmdxWind ChillWeather1:0001:000774.91:006.4
iCC%10's degkm/hkmkPa2:0072:001.9444444444755.92:006.4
m3:0063:001.6666666667744.53:004
e4:0024:000.5555555556773.64:001.1
0:006.11.6731766.497.4NA5:0075:001.9444444444784.55:000
1:004.91.27706.497.41NA6:0096:002.5764.96:000
2:005.91.9751876.497.4NA7:00117:003.0555555556754.17:000
3:004.50.274196497.38NA8:0078:001.9444444444803.78:000
4:003.60771521.197.34NA9:0069:001.6666666667844.19:000
5:004.5178177097.32NA10:00610:001.6666666667805.710:006.4
6:004.91.176179097.31NA11:00011:0006511.211:0015
7:004.10752011097.34NA12:00012:0006610.712:004
8:003.70.580197097.39NA13:00413:001.11111111115814.213:0015
9:004.11.684176097.38NA14:00914:002.55814.214:0012.9
10:005.72.5801666.497.41NA15:001115:003.0555555556719.815:009.7
11:0011.24.86501597.38NA16:001116:003.0555555556787.816:003.2
12:0010.74.7660497.34NA17:00417:001.111111111180617:000
13:0014.26.1583341597.31NA18:00618:001.6666666667794.518:000
14:0014.265832912.997.3NA19:00219:000.5555555556783.519:000
15:009.84.87131119.797.35NA20:00720:001.944444444481320:000
16:007.84.27831113.297.37NA21:00721:001.9444444444782.121:000
17:0062.980324097.42NA22:00222:000.5555555556801.922:000
18:004.51.179336097.49NA23:00723:001.9444444444791.623:000
19:003.5078312097.56NA
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23:001.6-1.679327097.72NA
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