Improving our Ability to Nowcast WinterWeather in Complex Terrain

byGeorge Isaac1, Paul Joe1, Jocelyn Mailhot2, Monika Bailey1,

Stephane Bélair2, Faisal Boudala1, Mindy Brugman3, Edwin Campos4,Richard Carpenter5, Stewart Cober1, Bertrand Denis3, Chris Doyle3,

D. Forsyth6, I. Gultepe 1, Thomas Haiden7, Laura Huang1, Jason Milbrandt2,Ruping Mo3, Roy Rasmussen8, Trevor Smith3, Ron Stewart 9,

and Donghai Wang10

1 Cloud Physics and Severe Weather Research Section, Environment Canada2 Recherche en prévision numérique, Environment Canada3 Meteorological Service of Canada, Environment Canada4 Argonne National Laboratory, Illinois, USA5Weather Decision Technologies, Oklahoma, USA6National Severe Storms Laboratory, Norman, Oklahoma, USA7Central Institute for Meteorology and Geodynamics (ZAMG), Austria8 National Center for Atmospheric Research, Colorado, USA9 Department of Environment and Geography, University of Manitoba10Chinese Academy of Meteorological Science, China

Main Goals ofScience of Nowcasting Olympic Weather

for Vancouver 2010 (SNOW-V10)

• To improve our understanding and ability toforecast/nowcast low cloud, and visibility;

• To improve our understanding and ability toforecast precipitation amount and type;

• To improve forecasts of wind speed, gusts anddirection;

• To develop better forecast system productionsystem(s).

• Assess and evaluate value to end users;• To increase the capacity of WMO member

states (Training component).

Participating GroupsObserving Systems and Associated Nowcast Systems

EC Region OAN Sites EC Research Sites UBC (McKendry) and U. Manitoba (Stewart) NCAR WSDDM System + (Rasmussen) BOM STEPS System (Seed) NSSL Radar (Forsyth) FMI (Koistinen)

Modeling Systems EC Research Models WDT (Carpenter) China (Donghai Wang) Austria INCA (Haiden) Germany (Bott) Switzerland (Mueller)

Verification EC Team NCAR (Brown)

Others:

CSU (Chandra & Vaisala)

U. Washington (House)

Approved World WeatherResearch Programme Projectof WMO

The Winter Olympic Challenge

Steep topography, highly variable weather elements in space and time

5 km

Village Creekside

Summary of Instruments• Callaghan scanning C-Band Radar located near Whistler.

• NOAA/OU X-Band Dual- Polarization Radar located at Canada/U.S. Border south of Vancouver.

• Vertically Pointing X-Band Radars were located at TFT, RND and WWA.

• A 915 MHz Boundary Layer Wind Profiler located at Squamish Airport (WSK).

• A profiling microwave radiometer located at Timing Flats (TFT)

• A rawindsonde system located at Nesters (VOC).

• Ceilometers (6) located at YVR, WWA, TFT, WSK, WGP, and VOG,

• Recording visibility meters (6) located at VOA, VOL, RND, VOT, VOG and YVR.

• Hot Plates located at (6) YVR, VOG, RND, VOA, VOL and TFT

• Present weather sensors,(10) either the Vaisala FD12P, Parsivel or POSS located at VOA, VOL,VOD, WWA, WSK, YVR, TFT, VOT, RND, and VOG,

• A 3D anemometer located at the ski jump (VOW).

• Temperature, relative humidity and GPS sensors were installed on the Whistler Village toRoundhouse Gondola, and the Whistler to Blackcomb gondola.

• The Roundhouse site, near the helipad, was instrumented with a significant list of equipment.as an extension of the Fog Remote Sensing and Modeling Project (FRAM) (Gultepe et al., 2009).

• Special surface sites measuring temperature and humidity were installed by UBC up WhistlerMountain

• Snow Density measurements were made at some selected sites, mainly VOA, VOL RND andVOC after significant snowstorms.

• Snow Photographs were made continuously at RND as a special project during some intensiveperiods and then irregularly outside those periods.

•

Model Name Organization CountrySpatial

Resolution

TemporalResolutionAvailable

Times of DayRun (UTC)

Length ofForecst(hours)

General Description

ABOMLAM1kmEnvironment

CanadaCanada 1 Km 15 min Every 15 min Max 6 h

Adaptive Blending ofObservation and Models

using GEM LAM1k

ABOMREGEnvironment

CanadaCanada 15 km 15 min Every 15 min Max 6 h

Adaptive Blending ofObservation and Models

using GEM Regional

INTWEnvironment

CanadaCanada

1 and 15km

15 min Every 15 min Max 6 hINTegrated Weighted

Model using LAM1k, GEMRegional and Observations

LAM1kEnvironment

CanadaCanada 1 km

30 s (Model),15 min(Tables)

11 and 20 UTC 19 hLimited-Area version of

GEM model

LAM2.5kEnvironment

CanadaCanada 2.5 km

1 min(Model), 15min (Tables)

06 and 15 UTC 33 hLimited-Area version of

GEM model

REGEnvironment

CanadaCanada 15 km

7.5 min(Model), 15min (Tables)

00, 06, 12, 18UTC

48 hRegional version of GEM(Global Environmental

Multiscale) model

Canadian Models Used in SNOW-V10

Model Name Organization CountrySpatial

Resolution

TemporalResolutionAvailable

Times of Day Run(UTC)

Length ofForecst (hours)

General Description

CMAChinese

MeteorologicalAdministration

China15 km & 3

km1 hour 00 and 12 UTC 48 h & 24 h CMA GRAPES-Meso NWP model

WDTUSL

WeatherDecision

Technologiesand

NanoWeather

USApointwise or100 m grid

02, 08, 14, 20 UTC 48 hSurface layer model nested in

NAM. Works particularly well inquiescent condistions..

WSDDMNational Centerfor AtmosphericResearch (NCAR)

USARadar

Resolution

10 min(based on

radarupdate)

Every 10 min 2 hours

Nowcast based on storm trackingof radar echo using crosscorrelation and real-timecalibration with surface

precipitation gauges.

ZAMGINCA

Central Institutefor Meteorology

andGeodynamics

(ZAMG)

Austria 1 km 1 hour Every hour 18 hours

The Integrated NowcastingThrough Comprehensive Analysis(INCA) system uses downscaledECMWF forecasts as a first guess

and applies corrections accordingto the latest observation.

ZAMGINCARR

Central Institutefor Meteorology

andGeodynamics

(ZAMG)

Austria 1 km 15 min Every 15 min 18 hours

The precipitation module of INCAcombines raingauge and radar

data, taking into account intensity-dependant elevation effects. The

forecasting mode is based ondisplacement by INCA motion

vectors, merging into the ECMWFmodel through prescribed

weighting.

Other Countries Models Used in SNOW-V10

Products During Olympics• Each group produced a Table showing 24 hour

forecast of significant variables for main venuesites (hourly intervals and 10 to 15 min intervals infirst two hours). Similar to what forecastersproduce

• A Research Support Desk was run duringOlympics and Paralympics (virtual and on-site)providing real time support to forecasters.

• A SNOW-V10 Web site was created with many ofthe products (time series for sites, remote sensingproducts, area displays, soundings (gondola andothers), and a very successful Blog.

VANOCChris Doyle

Central Forecast OfficeTrevor Smith et al.

Venue ForecastersCypress FreestyleCallaghan Nordic

Callaghan Ski JumpWhistler AlpineWhistler Sliding

SNOW-V10Research Support Desk (RSD)

On-Site (3 scientists)WebEx (at least once per day)

SNOW-V10 Web SiteSNOW-V10 Blog

1 min Data from Research SitesWhistler Radar

Microwave RadarMRR

Wind ProfilerGondola Profiles

Model and Data ChartsForecast Tables

Nowcastsetc

Pacfic Weather Centre Data15 Min DataSoundings

MeteogramsSatellite Data

Other Model Data

Harvey’sCloud

Equipment onWhistler mountainprovided good datafor forecasters andhelp inunderstandingweather processes

Most of SNOW-V10 data from instrumentsare now being displayed on Web site(http://www.snow-v10.ca). Model data isalso being shown.

RemoteSensors

Roy Rasmussen et al (NCAR)Weather Support to Deicing Decision Making (WSDDM)

2 km

GEM LAM forpoints along Sea toSky Highwayshowing flowreversal with daytime heating. Note23 Z is 15 LocalTime.

Aerials’jump

Spectatorgrandstand

Preparation of field-of-play 2 hours before training…

Freestyle Skiing Ladies Aerials FinalFebruary 24, 2010

Notes by Mindy Brugman as posted onSNOW-V10 Blog

The event….Last night at the Cypress aerial site itbegan with drizzle and was quite foggy… Wewalked to the bright lights of the venue. There wasno precipitation at the venue the entirecompetition. The competition started at 1930PST. The first pictures (not shown here) are takenat the start of the competition and you can see thatthe fog was much worse at the start. I stoppedtaking pictures since I could not seeanything. Then it began to clear just near the endof the competiton – and I clicked a few pictures.You can detect a flying bug above the lights. Thatsreally the gold medal winner, Lydia Lassila, ofAustralia!Based on the event last night – I suspect ladiesaerials may qualify as a new paralympic sport forthe visually impaired.

Forecast < -25 -25 ≤ x < -20 -20 ≤ x < -4 -4 ≤ x < -2 -2 ≤ x < 0 0 ≤ x < 2 2 ≤ x < 4 > 4 Total

< -25 0 0 0 0 0 0 0 0 0

-25 ≤ x < -20 0 0 0 0 0 0 0 0 0

-20 ≤ x < -4 0 0 590 220 214 37 12 0 1073

-4 ≤ x < -2 0 0 78 348 359 259 65 1 1110

-2 ≤ x < 0 0 0 0 121 828 321 92 2 1364

0 ≤ x < 2 0 0 0 4 397 742 186 76 1405

2 ≤ x < 4 0 0 0 0 10 57 218 118 403

> 4 0 0 0 0 0 0 55 134 189

Total 0 0 668 693 1808 1416 628 331 5544

lam1k Temperature (°) at VOL HSS=0.392

Observed

Forecast < 30 30 ≤ x < 65 65 ≤ x < 90 90 ≤ x < 94 94 ≤ x < 98 > 98 Total

< 30 3 12 0 0 0 0 15

30 ≤ x < 65 21 854 227 19 14 5 1140

65 ≤ x < 90 0 525 729 143 253 752 2402

90 ≤ x < 94 0 1 85 53 72 253 464

94 ≤ x < 98 0 12 51 44 181 539 827

> 98 0 2 23 26 131 514 696

Total 24 1406 1115 285 651 2063 5544

lam1k Relative Humidity (%) at VOL HSS=0.269

Observed

Adaptive Blending of Observationsand Models (ABOM)

ABOM reduces to:• Observation persistence when r = s = 0, w = 1• Pure observation trend when r = 0, s = 1, w = 0• Model + obs trend + persistence for all other r, sand w

Change predictedby model

Forecast atlead time p

CurrentObservation

Change predictedby obs trend

• Coefficients s and r are proportional to the performance overrecent history, relative to obs persistence (w) and to

• Local tuning parameters (not yet optimized at all SNOWV10 sites)

• Updated using new obs data every 15 minutes

Temperature Mean Absolute Error:Averaged by Nowcast Lead Time to 6 Hours

0 1 2 3 4 5 60

0.5

1

1.5

2

2.5

3

3.5

4

Forecast Lead Time (Hours)

Me

an

Ab

so

lute

Err

ors

VOC TEMPERATURE MAE

ABOM REG

REG

Obs Pers

LAM 2.5

ABOM LAM 2.5

LAM 1

ABOM LAM1

0 1 2 3 4 5 60.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

Forecast Lead Time (Hours)M

ea

nA

bso

lute

Err

ors

RND TEMPERATURE MAE

ABOM REG

REG

Obs Pers

LAM 1

ABOM LAM1

LAM 2.5

ABOM LAM2.5

1) Nowcast - Model crossover lead-time2) Improvement over persistence

Background of INTW•• INTW refers to integrated weighted modelINTW refers to integrated weighted model

• LAM 1k, LAM 2.5k and REG 15k were

selected to generate INTW

•• Major steps of INTW generationMajor steps of INTW generation– Data pre-checking - defining the available NWP models and

observations

– Extracting the available data for specific variable and location

– Calculating statistics from NWP model data, e.g. MAE, RMSE

– Deriving weights from model variables based on modelperformance

– Defining and performing dynamic and variational biascorrection

– Generating Integrated Model forecasts (INTW)

NWP Model with Minimum MAELAM 1k – red, LAM 2.5k – dark yellow, REG - blue

Site Temp RH WS Gust

Vancouver (YVR) 1.15 6.85 1.44 2.09

Whistler Creek side (VOB) 1.19 8.62 0.6 1.84

Timing Flats (VOT) 1.39 8.2 0.6 1.75

Whistler Mt. Mid Lvl. (VOL) 1.5 8.61 0.67 1.64

West Vancouver (WWA) 1.32 10.13 0.71 1.29

Squamish Airport (WSK) 1.53 10.19 0.7 1.19

Cypress Bowl North (VOE) 1.17 6.7 0.5 1.43

Callaghan SJ Bottom (VOX) 1.31 9.09 0.74 1.62

Callaghan Ski Jump (VOW) 1.66 9.56 0.57 1.62

Callaghan Valley (VOD) 1.31 8.92 0.54 1.45

Nesters (VOC) 1.39 7.83 0.67 1.45

Blackcomb (VOI) 1.13 8.24 0.52 1.32

Cypress Bowl (VOG) 1.57 0.69 1.43

Whistler (VOA) 1.19 7.73 1.46 1.93

Pemberton Airport (WGP) 2.05 10.75 0.56 0.75

Model with Minimum MAE for INTW and NWP modelsINTW – green, LAM 1k – red, LAM 2.5k – dark yellow, REG - blue

Site Temp RH WS Gust

Vancouver (YVR) 0.82 5.2 1.3 1.6

Whistler Creek side 0.81 5.19 0.39 0.83

Timing Flats (VOT) 0.97 5.84 0.48 0.94

Whistler Mt. Mid Lvl. 0.84 5.37 0.58 0.99

West Vancouver 0.9 6.95 0.67 1.02

Squamish Airport 1.02 6.23 0.66 1

Cypress Bowl North 0.89 4.74 0.5 1.04

Callaghan SJ Bottom 1.02 6.71 0.47 1.01

Callaghan Ski Jump 0.9 5.67 0.49 0.98

Callaghan Valley 1.07 7.09 0.48 0.91

Nesters (VOC) 1.03 5.82 0.51 1.04

Blackcomb (VOI) 0.85 5.36 0.47 0.95

Cypress Bowl (VOG) 0.96 0.69 1.16

Whistler (VOA) 0.9 5.55 1.19 1.61

Pemberton Airport 1.15 6.8 0.56 0.72

Preliminary Comparison of SportForecast with Canadian Modelsfor Top of Downhill (VOL), Mid-Station (VOL) and Timing Flats(VOT)

Relative Humidity

Preliminary Comparison of SportForecast with Canadian Modelsfor Top of Downhill (VOL), Mid-Station (VOL) and Timing Flats(VOT)

Wind Direction

Discussion of Areas NeedingImprovements

• 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, couldeasily be improved with better model resolution in boundarylayer.

• Wind direction either poorly forecast or measured.• Model (Cdn) has a bias against high wind speeds.• Overall statistical scores do not show complete story. Need

emphasis on high impact events.• Model graupel not observed and not well defined.• Snow Rate and Precipitation Type measurements over short

time scales need improvement.

Summary• Many physical processes were well documented during

SNOW-V10 such as: diabatic effects due to melting snow,wind flow around and over terrain, diurnal flow reversal invalleys associated with daytime heating, precipitationreductions and increases due to local terrain influences, etc

• Weather changes rapidly in complex terrain and it isnecessary to get good measurements at time resolutions ofat least 1 -15 min. SNOW-V10 attempted to getmeasurements 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.

• There are many difficulties in measuring parameters,especially precipitation amount and type in this environment.Special scanning strategies were necessary for the scanningradar placed at Whistler and significant differences wereobserved with many different types of surface precipitationsensors placed at several locations.

Summary• SNOW-V10 has made progress in forecasting “non traditional”

variables like ceiling, visibility, wind gusts, etc.• Nowcast schemes are showing skill.• There is value in presenting real-time obs and several models

to forecasters doing Nowcasting.• The techniques tested are not yet good enough to issue

standalone nowcasts. Significant errors (or conflicts) exist inhigh impact events that require forecaster intervention.

• Weather Forecasts, both long and short term, were critical inthe conduct of these games.

.

Forecast and Research in theOlympic Sochi Testbed 2014

FROST-2014

Questions?