Semi Automating Forecasts for Canadian Airports in the Great Lakes Area

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Semi Automating Forecastsfor Canadian Airports
in theGreat Lakes Area

• by
•  
• 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
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Acknowledgements

• Funds from
• Transport Canada
• Search and Rescue New Initiatives Fund
• NAV CANADA
• 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)

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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 ).

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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.
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Algorithm Development

• Visibility/Fog RVR
• Ceiling
• Blowing Snow
• Turbulence
• Winds/Gusts/Shear
• Icing
• Precipitation Type
• Precipitation Intensity
• Lightning/Convective Storm
• Real Time Verification

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

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CAN-Now Situation Chart
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Thresholds as applied on Situation Chart
Crosswinds Dry RWY (precipitation rate 0.2
mm/h and visibility 1 SM) x-wind
(knots) lt 15 GREEN 15 x-wind (knots) lt
20 YELLOW 20 x-wind (knots) lt 25 ORANGE
x-wind (knots) 25 RED (NOT PERMITTED) Wet
RWY (precipitation rate gt 0.2 mm/h or visibility
lt 1 SM) x-wind (knots) lt 5 GREEN 5
x-wind (knots) lt 10 YELLOW 10 x-wind (knots)
lt 15 ORANGE x-wind (knots)
15 RED (NOT PERMITTED) ------------------------
--------------------------------------------------
------------ Visibility vis (SM)
6 GREEN (VFR) 3 vis (SM) lt
6 BLUE (MVFR) ½ vis (SM) lt
3 YELLOW (IFR) ¼ vis (SM) lt ½ ORANGE
(BLO ALTERNATE) vis (SM) lt ¼ RED (BLO
LANDING) -----------------------------------------
---------------------------------------------
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Ceiling ceiling (ft)
2500 GREEN (VFR) 1000 ceiling (ft) lt
2500 BLUE (MVFR) 400 ceiling (ft) lt
1000 YELLOW (IFR) 150 ceiling (ft) lt
400 ORANGE (BLO ALTERNATE) ceiling
(ft) lt 150 RED (BLO LANDING) ---------------
--------------------------------------------------
--------------------- Shear Turbulence
momentum flux FQ (Pa) lt 0.75 GREEN (LGT) 0.75
mom. flux FQ (Pa) lt 1.5 YELLOW (MOD)
mom flux FQ (Pa) 1.5 RED (SEV)
eddy dissipation rate (m2/3/s) lt
0.3 GREEN (LGT) 0.3 EDR
(m2/3/s) lt 0.5 YELLOW (MOD)
EDR (m2/3/s) 0.5 RED (SEV) eddy
dissipation rate (m2/3/s) lt 0.3 GREEN (LGT)
0.3 EDR (m2/3/s) lt
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 level2 (125m/410ft)
- level0 gt 25 kts level4
(325m/1060ft) - level0 gt 40 kts RED
level5 (440m/1440ft) - level0 gt 50 kts
-------------------------------------------------
-------------------------------------
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Precipitation rate (mm/h) gt 7.5 RED
(HEAVY) 2.5 lt rate (mm/h) 7.5 ORANGE
(MODERATE) 0.2 lt rate (mm/h)
2.5 YELLOW (LIGHT) 0 lt rate (mm/h) 0.2
GREEN (TRACE) rate (mm/h) 0
GREEN (NO PRECIP) ---------------
--------------------------------------------------
--------------------- TSTM LTNG Lightning
Distance 6 SM RED (TS) Lightning Distance
10 SM ORANGE (VCTS) Lightning Distance 30
SM YELLOW (LTNG DIST) Lightning within
area (gt 30 SM) YELLOW Lightning forecast map
received GREEN (NO LTNG FCST) -----------------
--------------------------------------------------
------------------- ICING TWC lt 0.1 g/m3 or TT
0C GREEN TWC 0.1 g/m3 where TT lt 0C
YELLOW (POTENTIAL ICING)
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CAT-level RVR (ft) lt 600
RED (NOT PERMITTED) 600 RVR (ft) lt 1200 -or-
ceiling (ft) lt 100 RED (CAT
IIIa)1200 RVR (ft) lt 2600 -or- 100 ceiling
(ft) lt 200 ORANGE (CAT II)2600 ft RVR lt
3 SM -or- 200 ceiling (ft) lt 1000 YELLOW
(CAT I) 3 RVR (SM) lt 6 -or- 1000
ceiling (ft) lt 2500 BLUE (MVFR)
RVR (SM) 6 -and- ceiling (ft)
2500 GREEN (VFR)-------------------------
--------------------------------------------------
----------- RWY Condition precipitation rate
(mm/h) gt 0.2 ORANGE (Possible WET
rwy)precipitation rate (mm/h) 0.2 YELLOW
(Possible DRY rwy)-----------------------------
--------------------------------------------------
------- Wx Only AARCell 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.
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Thanks to Bill Burrows
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Web Site

• A Web site has been created at
  http//saguenay-1.ontario.int.ec.gc.ca/cannow/cyyz
  /wx/index_e.php?airport1
• The site is accessible externally only with a
  user name and password. The site is currently
  active in a research mode to obtain feedback..

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Conditions Change Rapidly
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The mean absolute error for continuous variables
for CYYZ. CLI refers to the error if a climate
average were used as the predictor.
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Mean absolute error wind direction at CYYZ
calculated with all the data and then when wind
speeds less than 5 knots are removed.
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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.
Nowcasting
NWP Models
Theoretical Limit
From Golding (1998) Meteorol. Appl., 5, 1-16
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Adaptive Blending of Observations and Models
(ABOM)
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)
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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.

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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
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Winter period Dec. 1, 2009 to Mar. 31,
2010 Summer period - June 1 to August 31, 2010
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Variable LAM LAM REG REG RUC RUC INTW INTW
Variable CYYZ CYVR CYYZ CYVR CYYZ CYVR CYYZ CYVR
TEMP 6 3 4 3.5 4.5 5 2.5 0.5
RH 6 6 no 6 no no 3.5 3
WS 2.5 3.5 4.5 3.5 3 no 1 2.5
GUST no no no 5 3.5 no 1.5 1.5
Time (h) for Model to Beat Persistence
Winter
Variable LAM LAM REG REG RUC RUC INTW INTW
Variable CYYZ CYVR CYYZ CYVR CYYZ CYVR CYYZ CYVR
TEMP 2.5 2.5 2.2 2.5 1.5 no 0.5 0.5
RH 3 3 3.2 4.5 3 no 1 1
WS 3 5 3.5 5 2.2 no 1.5 2.5
GUST no no 5.5 no 2.2 no 0.5 4
Summer
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.
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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.
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Categories Being Used in CAN-Now Analysis
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Heidke Skill Score Multi-Categories
Using
Observed category
1 2 3 . . . . . K total
1 N(F1)
2 N(F2)
3 N(F3)
. . . .
K N(Fk)
total N(O1) N(O2) N(O3) N(Ok) N
Forecast Category
Calculate
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The HSS and ACC scores for the relaxed set of
criteria.
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Summary

• 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.

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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.

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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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Questions?