Semi automating forecasts for canadian airports in the great lakes area
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Semi Automating Forecasts for Canadian Airports in the Great Lakes Area. by George A. Isaac 1 , With contributions from Monika Bailey, Faisal S. Boudala, Stewart G. Cober, Robert W. Crawford, Bjarne Hansen, Ivan Heckman, Laura X. Huang, Alister Ling, and Janti Reid

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Semi Automating Forecasts for Canadian Airports in the Great Lakes Area

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Semi Automating Forecastsfor Canadian Airports in theGreat Lakes Area


George A. Isaac1,

With contributions from

Monika Bailey, Faisal S. Boudala, Stewart G. Cober,

Robert W. Crawford, Bjarne Hansen, Ivan Heckman,

Laura X. Huang, Alister Ling, and Janti Reid

Cloud Physics and Severe Weather Research Section, and

Environment Canada

Great Lakes Operational Meteorology Workshop 2013

Webinar – May 14, 2013


Funds from

Transport Canada

Search and Rescue New Initiatives Fund


Environment Canada

Also operations and research colleagues at CMC/RPN, others at CMAC-East (e.g. Stephen Kerr, Gilles Simard) and CMAC-West (e.g. Tim Guezen, Bruno Larochelle) and others within our Section (e.g. Bill Burrows)

Canadian Airport Nowcasting (CAN-Now)

  • To improve short term forecasts (0-6 hour) or Nowcasts of airport severe weather.

  • Develop a forecast system which will include routinely gathered information (radar, satellite, surface based data, pilot reports), numerical weather prediction model outputs, and a limited suite of specialized sensors placed at the airport.

  • Forecast/Nowcast products to be issued with 1-15 min resolution for most variables.

  • Test this system, and its associated information delivery system, within an operational airport environment (e.g. Toronto and Vancouver International Airports ).

Isaac, G.A., Bailey, M., Boudala, F.S., Cober, S.G., Crawford, R.W., Donaldson, N., Gultepe, I., Hansen, B., Heckman, I., Huang, L.X., Ling, A., Mailhot, J., Milbrandt, J.A., Reid, J., and Fournier, M. (2012), The Canadian airport nowcasting system(CAN-Now). Accepted to Meteorological Applications.

Algorithm Development

Visibility/Fog … RVR


Blowing Snow




Precipitation Type

Precipitation Intensity

Lightning/Convective Storm

Real Time Verification

Main equipment at Pearson at the old Test and Evaluation site near the existing Met compound


Observation Building









NAV Canada 78D


















Pearson Instrument Site

  • 21 instrument bases with power and data feeds.

  • 10m apart; rows 15m apart

  • Present Weather Sensor (Vaisala FD12P)

  • Spare

  • Camera

  • # Power distribution box

  • 4.Present Weather Sensor (Parsivel)

  • 3D Ultrasonic Wind Sensor (removed)

  • 6.Microwave Profiling Radiometer (Radiometrics)

  • Precipitation Occurrence Sensor (POSS)

  • Icing detector (Rosemount)

  • Precipitation gauge (Belfort) with Nipher Shield

  • Ultrasonic snow depth

  • 10.Hotplate (Yankee – removed)

  • 11.Tipping Bucket rain gauge TB3

  • 12.Precipitation Switch

  • 13.Spinning arm, liquid/total water content probe --proposed

  • 14.10 m Tower, 2D ultrasonic wind sensor

  • Ceilometer (Vaisala CT25K)

  • Vertically Pointing 3 cm Radar (McGill)

  • Hotplate Precipitation Meter (Yankee)

  • Temp, humidity, pressure, solar radiation

  • Precipitation gauge (Geonor) with Nipher Shield

  • 20.Spare

  • 10m Tower Spare

  • (Proposed or removed equipment)


CAN-Now Situation Chart

Thresholds as applied on Situation Chart


Dry RWY (precipitation rate ≤ 0.2 mm/h and visibility ≥ 1 SM):

x-wind (knots) < 15:GREEN

15 ≤ x-wind (knots) < 20:YELLOW

20 ≤ x-wind (knots) < 25:ORANGE

x-wind (knots) ≥ 25:RED(NOT PERMITTED)

Wet RWY (precipitation rate > 0.2 mm/h or visibility < 1 SM):

x-wind (knots) < 5:GREEN

5 ≤ x-wind (knots) < 10:YELLOW

10 ≤ x-wind (knots) < 15:ORANGE

x-wind (knots) ≥ 15:RED(NOT PERMITTED)



vis (SM) ≥ 6:GREEN(VFR)

3 ≤ vis (SM) < 6:BLUE(MVFR)

½ ≤ vis (SM) < 3:YELLOW(IFR)


vis (SM) < ¼:RED (BLO LANDING)



ceiling (ft) ≥ 2500:GREEN(VFR)

1000 ≤ ceiling (ft) < 2500:BLUE(MVFR)

400 ≤ ceiling (ft) < 1000:YELLOW(IFR)

150 ≤ ceiling (ft) < 400:ORANGE (BLO ALTERNATE)

ceiling (ft) < 150:RED (BLO LANDING)


Shear & Turbulence:

momentum flux FQ (Pa) < 0.75:GREEN(LGT)

0.75 ≤ mom. flux FQ (Pa) < 1.5:YELLOW(MOD)

mom flux FQ (Pa) ≥ 1.5:RED (SEV)

eddy dissipation rate (m2/3/s) < 0.3:GREEN(LGT)

0.3 ≤ EDR (m2/3/s) < 0.5:YELLOW(MOD)

EDR (m2/3/s) ≥ 0.5:RED (SEV)

eddy dissipation rate (m2/3/s) < 0.3:GREEN(LGT)

0.3 ≤ EDR (m2/3/s) < 0.5:YELLOW(MOD)

EDR (m2/3/s) ≥ 0.5:RED (SEV)

If the windspeed (relative to surface wind direction) exceeds, any of the following:

level[2] (~125m/410ft) - level[0] >= 25 kts

level[4] (~325m/1060ft) - level[0] >= 40 kts:RED

level[5] (~440m/1440ft) - level[0] >= 50 kts



rate (mm/h) > 7.5:RED (HEAVY)

2.5 < rate (mm/h) ≤ 7.5:ORANGE (MODERATE)

0.2 < rate (mm/h) ≤ 2.5:YELLOW(LIGHT)

0 < rate (mm/h) ≤ 0.2: GREEN(TRACE)

rate (mm/h) = 0 : GREEN(NO PRECIP)



Lightning Distance ≤ 6 SM RED (TS)

Lightning Distance ≤ 10 SM ORANGE (VCTS)

Lightning Distance ≤ 30 SM YELLOW(LTNG DIST)

Lightning within area (> 30 SM) YELLOW

Lightning forecast map received GREEN(NO LTNG FCST)



TWC < 0.1 g/m3 or TT ≥ 0°C GREEN

TWC ≥ 0.1 g/m3 where TT < 0°C YELLOW (POTENTIAL ICING)


RVR (ft) < 600 RED(NOT PERMITTED) 600 ≤ RVR (ft) < 1200 -or- ceiling (ft) < 100: RED(CAT IIIa)1200 ≤ RVR (ft) < 2600 -or- 100 ≤ ceiling (ft) < 200: ORANGE (CAT II)2600 ft ≤ RVR < 3 SM -or- 200 ≤ ceiling (ft) < 1000: YELLOW (CAT I) 3 ≤ RVR (SM) < 6 -or- 1000 ≤ ceiling (ft) < 2500: BLUE (MVFR) RVR (SM) ≥ 6 -and- ceiling (ft) ≥ 2500: GREEN (VFR)--------------------------------------------------------------------------------------

RWY Condition:

precipitation rate (mm/h) > 0.2: ORANGE (Possible WET rwy)precipitation rate (mm/h) ≤ 0.2: YELLOW (Possible DRY rwy)--------------------------------------------------------------------------------------

Wx Only AAR:Cell colour is based on meteorological conditions – same as CAT-level

Meteorologically-limited theoretical maximum AAR determined from

look-up table of documented AAR values based on runway configuration and meteorological conditions (CAT-level).

Runway configuration determined solely from crosswind thresholds for maximum potential capacity.

Thanks to Bill Burrows

Web Site

A Web site has been created at:

The site is accessible externally only with a user name and password. The site is currently active in a research mode to obtain feedback..




The mean absolute error for continuous variables for CYYZ. CLI refers to the error if a climate average were used as the predictor.

Mean absolute error wind direction at CYYZ calculated with all the data and then when wind speeds less than 5 knots are removed.

The main idea behind Nowcasting is that extrapolation of observations, by simple or sophisticated means, shows better skill than numerical forecast models in the short term. For precipitation, Nowcasting techniques are usually better for 6 hours or more.


NWP Models

Theoretical Limit

From Golding (1998) Meteorol. Appl., 5, 1-16

Adaptive Blending of Observations and Models (ABOM)

Forecast at

lead time p

Change predicted

by model



Change predicted

by obs trend

Nowcasting Techniques Which Combine Model(s) and Observations

INTWINTW combines predictions from several NWP models by weighting them based on past performance (6 hours) and doing a bias correction using the most recent observation. (SMOW-V10 used GEM 1, 2.5 and 15 km)

Related Papers

  • Isaac, G.A., P. Joe, J. Mailhot, M. Bailey, S. Bélair, F.S. Boudala, M. Brugman, E. Campos, R.L.Carpenter Jr., R.W.Crawford, S.G. Cober, B. Denis, C. Doyle, H.D. Reeves, I.Gultepe, T. Haiden, I. Heckman, L.X. Huang, J.A. Milbrandt, R. Mo, R.M. Rasmussen, T. Smith, R.E. Stewart, D. Wang and L.J. Wilson, 2012b: Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-10): A World Weather Research Programme project. Pure and Applied Geophysics. (DOI: 10.1007/s00024-012-0579-0).

  • Bailey, M.E., G.A. Isaac, I. Gultepe, I. Heckman and J. Reid, 2012: Adaptive Blending of Model and Observations for Automated Short Range Forecasting: Examples from the Vancouver 2010 Olympic and Paralympic Winter Games. Pure and Applied Geophysics. DOI 10.1007/s00024-012-0553-x.

  • Huang, L.X., G. A. Isaac, and G. Sheng, 2012: Integrating NWP Forecasts and Observation Data to Improve Nowcasting Accuracy. Weather and Forecasting, 27, 938-953.

  • Huang, Laura X, George A. Isaac, and Grant Sheng, 2012: A New Integrated Weighted Model in SNOW-V10: Verification of Continuous Variables. Pure and Applied Geophysics. DOI 10.1007/s00024-012-0548-7.

  • Huang, Laura X, George A. Isaac, and Grant Sheng, 2012: A New Integrated Weighted Model in SNOW-V10: Verification of Categorical Variables. Pure and Applied Geophysics. DOI 10.1007/s00024-012-0549-6.

NWP Model with Minimum MAE in CAN-Now for

Winter Dec 1/09 – Mar 31/10 and

Summer June 1/10 to Aug 31/10 Periods

Based on First 6 Hours of Forecast

  • Winter period – Dec. 1, 2009 to Mar. 31, 2010

  • Summer period - June 1 to August 31, 2010

Time (h) for Model to Beat Persistence



Huang, L.X., G.A. Isaac and G. Sheng, 2012: Integrating NWP Forecasts and Observation Data to Improve Nowcasting Accuracy, Weather and Forecasting, 27, 938-953.

Shows the mean absolute error (MAE) in temperature and RH at CYYZ for the winter of 2009/10 as a function of forecast lead time averaged over the whole season. Temperature and relative humidity ABOM REG and ABOM LAM are compared to the raw model output and persistence.

Categories Being Used in CAN-Now Analysis



Heidke Skill Score: Multi-Categories


Observed category




The HSS and ACC scores for the relaxed set of criteria.


Progress is being made to forecast aviation related variables using numerical model output and nowcast schemes. We already have a system which uses climatology (WIND III).

RH predictions are poor, barely beating climatology. (Impacts visibility forecasts)

Visibility forecasts are poor from statistical point of view. (also require snow and rain rates)

Cloud base forecasts, although showing some skill, could be improved with better model resolution in boundary layer.

Wind direction either poorly forecast or measured.

There are many difficulties in measuring parameters, especially precipitation amount and type.

Overall statistical scores do not show complete story.Need emphasis on high impact events.

Selection of model point to best represent site is a critical process.

Summary (continued)

Weather changes rapidly, especially in complex terrain, and it is necessary to get good measurements at time resolutions of at least 1 -15 min. CAN-Now and SNOW-V10 attempted to get measurements at 1 min resolution where possible.

Because of the rapidly changing nature of the weather, weather forecasts also must be given at high time resolution.

Verification of mesoscale forecasts, and nowcasts, must be done with appropriate data (time and space). Data collected on hourly basis are not sufficient.

Nowcast schemes which blend NWP models and observations at a site, outperform individual NWP models and persistence after 1-2 hours.

Summary (continued)

Currently using products to develop a First Guess TAF (FGT).

The FGT system is being tested at the Aviation Weather Centres (CMAC-East and West) and is showing considerable promise, especially for VFR conditions.

A recent IRP (last week) suggested many things that need addressing, including the verification of FGT and comparison with what forecasters are now producing. The algorithms definitely need some improvement (e.g. Low cloud is often predicted in Arctic under cold conditions when skies are clear, and there are issues with precipitation type)


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