Heikki JunttiElena Saltikoff, Harri Hohti, Seppo Pulkkinen,Finnish Mereorological Institute

Rudolf Kalteböck, Austrocontrol

Sevilla May 2017

Provision of probabilistic nowcastsPNOWWA* project

* = Probabilistic Nowcasting of Winter Weather for Airports

Content

1. What’s PNOWWA?

2. Need for probabilistic winter weather forecasts at airports

3. Weather radar based nowcast methods for precipitation forecasting.

4. Findings at PNOWWA after first scientific demo.

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Winter Weather at airports

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Winter weather in PNOWWA:• snow, • sleet, • freezing rain/drizzle, • frost

Winter weather influences at airports

Winter weather influences to airport activities:• Runway maintenance (during the cleaning time runway is closed)• De-icing need and duration of actions • Choose of anti-icing fluid and timing for de-icing• Tower (capacity of airport Low Visibility Procedures)• Luggage handling, fuelling, parking, passenger ground transform etc.Effects of winter weather to airport activities can be mitigated if weather is predicted well (unlike many other meteorological phenomena)Better quality of winter weather forecast will aid for timing of airport activities needed. -> increase the predictability of mission trajectory and so can improve the ATM capacity*-> reduce the environmental impacts of flights

* SES Strategic Performance Objectives (SESAR ATM Master Plan)

Winter Weather in SESAR

• In SESAR1 WP 11.2 Eumetnet developed (2012-2016):

• Nowcasting of runway conditions (deterministic)

• Nowcasting and forecasting of visibility during snowfall (deterministic)

• De-icing weather type index for de-icing managers (deterministic)

•TOPLINK Large Scale Demonstration (LSD 2016):

• Probability of Airport Winter Weather Conditions

• Winter Weather Condition Contour

• Winter Weather Condition Contour for General Aviation

•Deployment (2017-2021)

• Some winter weather products will be deployed to operative service

•Exploratory Research (2016-2018)

• PNOWWA = Probabilistic NOWcasting of Winter Weather for Airports

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PNOWWA Objectives

1. To develop method for probabilistic 0-3h snow forecasts

2. To understand impact of mountains and sea to snowfall

3. To identify and promote use of probability forecasts in variety of airport activities

PNOWWA 6

Partners

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Austrocontrol

Deutsches Zentrumfür Luft- und

Raumfahrt (DLR)

FinnishMeteorological

Institute

ResearchDemos

Probabilitydistributions

Terrain effectsUser needs

Project Goals

PNOWWA

Snowfall. Intensity. Visibility.

e.g. RunwayThroughput

De-icingCapacity

Balancing

Airport users opinions for probabilistic winter weather forecasts – potential benefits• Helps to make objective

decisions

• When cost-loss ratios areknown it can be used in decision support

• Positive attitude to probabilistic forecasts

• Need for lead time 3 and 12-24 hoursproducts

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Useful lead time for warning of critical weather for all responsens (PNOWWA survey)

Airport users opinions– highest negative impact affecting on airport operations

1. Heavy snowfall

2. (low visibility)

3. Freezing rain and drizzle

4. Moderate snowfall

5. Wind speed above

6. Sleet

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the type of winter weather affecting negatively to airport operation(PNOWWA survey)

Thresholds of winter weather to runway maintenance, de-icing and tower

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user weather thresholds

Maintenance

dry snow over 10 mm/15min 5-10 mm/15min 1-5 mm/h/15 min less than 1 mm/15 min

wet snow over 5 mm/15min 3-5 mm/15min 1-2 mm/15min less than 1 mm/15min

freezing RA occurence probability %

freezing of surfaces after air cooling to minus deg. occurence probability %

De-icingDe-icing weather type (based of duration of de-icing of a plane) DIW 3 DIW 2 DIW 1 DIV 0

Tower VIS in Snow less than 600 m 600-1500 m 1500-3000 m over 3000 m

Based on communication with users in different airports for relevant weather conditions affecting to their processes. Relevancy will be tested during demonstration.

SESAR 1 IR: Nowcasting with extrapolation of radar imagesExperiences of SESAR1:

• Deterministic De-icing weather type forecast (DIW) was developed in WP 11.02

• DIW was used as an enabler in V3 Validation Exercice VP-513: 06.06.02 De-Icing Step1

• -> DIW adds value for de-icing managers compared to use of TAF only

• ->DIW was felt to be useful, but users felt that one numerical value is insufficient for them (they wanted estimate how confident the DIW was by looking weather radar pictures themselves)

Radar echo extrapolation method used: Andersson method

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Nowcasting with extrapolation of radar images in PNOWWA

Comparing threeapproaches:

• Andersson

• RAVAKE

• STEPS

Common principle:

Time= distance/speed

Example:

storm 75 km away,

moving 50 km/h

arrives in 90 minutes

PNOWWA General presentation - Saltikoff

…..dry……..…… snow...maybe

Benchmark:Andersson & Ivarsson 1991

Used in SESAR1 demos

Motion assumed to besame as 850 hPa windfrom numericalweather predictionmodel

Uncertainty growing withtime, related to precipfield texture

Pixels in 6th sector = forecast for 90 min

PNOWWA General presentation - Saltikoff

Classic approach:RAVAKE

Movement analysed withAMV (atmospheric motionvectors) comparing recentradar images

Trajector field calculatedbackwards from 2d motion vector field

Uncertainty from Gaussianellipse around source area

Pixels in ellipse = forecastfor 90 min

PNOWWA General presentation - Saltikoff

Trajectory of

deterministic

nowcast

Analysed

movement

vectors from

radar images

1 h

2 h

3 h

Point nowcast

Deviation ellipses

due to uncertainties

of speed and

direction of

movement

Deterministic

trajectory

Content of ellipse gives the probability

distribution of rainfall intensity for

each time step

RAVAKE

Newest approach:Stochastic Ensembles

Motion field e.g. Fromatmospheric motionvectors can be changed

Uncertainty of motionassessed from a set of trajectories

Uncertainty due to growthand decay modeled by a stochastic random field

PNOWWA General presentation - Saltikoff

STEPS: Forecast Ensembles

and Probabilities+5 minutes +15 minutes +30 minutesNowcast

• 51 ensemble members are obtained by perturbing precipitation intensities and motion field.

• The ensemble mean represents the “most probable” precipitation intensity.

• The mean field becomes smoother when the forecast time increases: badly predictable

scales are filtered out.

• The ensembles also yield probability distributions of precipitation intensities.

Mem

bers

Ensem

ble

mean

PNOWWA Scientific demo 2017

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• On line service with automatic update• Tailored products to:

• Runway maintenance• De-icing agents• Tower

• Probabilities of the weather categories defined with users are used to individual users

• Forecasted parameters:• Accumulation of DRY snow• Accumulation of WET snow• Probability of freezing rain• Probability of freezing of wet runways• De-icing weather type (categories

dependent on the time of individual plane de-icing duration

• Decrease of visibility CAUSED BY SNOW (fog or mist outscored)

Runway maintenance demo

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De-icing demo

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De-icing time of individual airplane is directly dependent on the weather during stay of it on ground.

During weather conditions of high DIW de-icing time of aircraft is long.

DIW=3 -> ice or a lot of snow on the aircraft

DIW=2 -> some amount of snow on the aircraft

DIW=1 -> only frost on the aircraft

DIW=0 -> no de-icing need

Tower demo

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Only influence of snow precipitation is taken into account! No fog, mist or blowing snow

Perhaps exceedance probabilitieswould be the right tool after all

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45 min 60 min 75 min 90 min

> 10 mm 0 0 0 0

5-10 mm 70 60 50 30

1-5 mm 0 0 0 0

< 1 mm 30 40 50 70

45 min 60 min 75 min 90 min

> 10 mm 0 0 10 0

> 5mm 30 20 40 20

> 1mm 70 60 50 30

< 1 mm 30 40 50 70

Most probable class Exceedance

Next steps in PNOWWA

• Verification of results of previous winter – comparing extrapolation methods for finding optimum way to define probability forecast from radar information

• Discuss with users at airports for getting more feedback during next winter

• Preparing for second demo 11/2017 - 02/2018

• Second demo

• Analysis of results

• Conclusions and recommendations

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This project has received funding from the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 699221

The opinions expressed herein reflect the author’s view only. Under no circumstances shall the SESAR Joint Undertaking be responsible for any use that may be made of the information contained herein.

Questions and comments?Thank you very much for your attention!

PNOWWA Probabilistic Nowcasting ofWinter Weather for Airports