forecast quality and predictability of severe european cyclones

Forecast quality and predictability of severe European cyclonesJenny OwenPeter Knippertz, Tomasz Trzeciak.

University of Leeds, School of Earth and Environment, Leeds, UK

• Damaging weather• Important for Europe• Cause fatalities and

economic losses

Xynthia

LotharDaria Friedhelm

Motivation

• Select historic storms – Storm Severity Index: – Measures ‘unusualness’ of wind

speed – Cubed ~ power of the wind ~

damage• Track storms automatically

– Minima in mean sea level pressure (MSLP)

– Connect together at consecutive timesteps

Method IHow well are severe European windstorms forecast?

What factors affect forecast quality?

Daria

Selected Storms

• Daria• Nana• Vivian• Wiebke• Udine• Verena• Agnes• Urania• Silke• Lara

• Anatol• Franz• Lothar• Martin• Kerstin• Rebekka• Elke• Lukas• Pawel• Jennifer

• Frieda• Jeanette• Gero• Cyrus• Hanno• Kyrill• Emma• Klaus• Quinten• Xynthia

Method II

• Categorise storms:

1. Jet stream shape, relative to the track of the storm

2. Processes that govern deepening, by pressure tendency equation

Categorising Storms: Jet

• Based on jet stream (wind speed at 300hPa).• Meridional sections that move with the storm track.• Similar plots for θe showed no clear groupings.

KlausKyrill

Xynthia

Split Jet

Cross Early Cross Late

Jeanette

Edge

Categorising Storms: Jet

Split

Cross EarlyEdge

Cross Late

• Klaus• Vivian• Wiebke• Kyrill• Lothar• Martin• Emma• Jeanette• Daria• Agnes

• Anatol• Udine• Rebekka• Lara• Xynthia• Jennifer• Gero• Hanno• Silke• Elke

• Urania• Nana• Quinten• Verena• Kerstin• Pawel• Cyrus• Lukas• Franz• Frieda

Pressure Tendency Equation

• Fink et al. (2012, GRL) applied the Pressure Tendency Equation to mid-latitude cyclones

• 3o x 3o column • From surface to 100hPa • Box moves along storm track

and compares properties from one time step to the next

• Identify processes that add or remove mass from column and affect core pressure

Pressure Tendency Equation

horiz vert diab

Density tendency

Precip

Stratosphere

Categorising Storms: PTE

storm dphidt ep res horiz vert diabEmma 0.00 1.30 0.00 76.98 0.00 21.71Kyrill 1.44 1.45 0.33 66.83 0.00 29.82Daria 2.70 1.55 0.00 64.41 0.00 31.20Martin 0.26 2.16 0.00 59.53 0.00 38.05Jennifer 4.19 2.29 0.00 56.88 0.00 36.64Vivian 0.00 1.52 1.14 53.76 0.00 43.58Klaus 2.52 3.36 0.00 43.30 0.00 50.57Wiebke 18.11 1.92 0.22 41.66 0.00 38.07Xynthia 1.81 3.99 0.00 33.11 0.00 61.09Lothar 6.77 3.71 0.00 31.40 0.00 57.91

PTE terms’ contribution to deepening for ten of the storms

Baroclinicity Diabatic Processes

Categorising Storms: PTE

• Klaus• Vivian• Wiebke• Kyrill• Lothar• Martin• Emma• Jeanette• Daria• Agnes

• Anatol• Udine• Rebekka• Lara• Xynthia• Jennifer• Gero• Hanno• Silke• Elke

• Urania• Nana• Quinten• Verena• Kerstin• Pawel• Cyrus• Lukas• Franz• Frieda

HorizDiab

Linking Categories

Horiz DiabCross Early 7 1Edge 9 1Cross Late 6 3Split 2 2

• Storms that spend longer on the north side of the jet tend to be more baroclinic – stronger temperature gradients.

• Diabatic storms tend to spend more time on the south side of the jet – warmer and wetter, more potential for latent heat release.

Method III

• Run automatic tracker on ECMWF Ensemble Control Forecast– Spatial and temporal resolution– Initialisation time

• Match forecast tracks to analysis tracks– Quantify best match based on proximity of analysis

and forecast tracks at similar time– Quality control: reject if tracks > 20 degrees apart at

any matched point

Matched Tracks: Location

KlausKyrill

Xynthia

Split Jet

Cross Early

Edge

Cross Late

Jeanette

• Some storms are better forecast than others• Some tracks are not a good match

Matched Tracks: Pressure

KlausKyrill

XynthiaJeanette

• Storms not always weaker in forecast – but difficult to see big picture

Method IV

• Assess how forecast quality varies with lead time

• Correlations– Correlation coefficient, R– Test for significance of correlation, T

• Future Work: Perform more rigorous statistical tests

Results: Latitude & Longitude

0 20 40 60 80 100 120 140 160 180

-12

-8

-4

0

4

8

12

16

f(x) = 0.0105853701735742 x − 0.170403825717322R² = 0.0679506436132739

f(x) = 0.0259708200495926 x − 0.627258235919236R² = 0.0751527330139643

LongitudeLinear (Longitude)LatitudeLinear (Latitude)

Lead Time

Anal

ysis

- Fo

reca

st

• Storms move more slowly W-E in forecasts, than in analysis• Storms slightly further south in forecasts

r t Sig?Latitude 0.261 2.30 Longitude 0.274 2.53

Results: Core Pressure

0 20 40 60 80 100 120 140 160 180

-50

-40

-30

-20

-10

0

10

20

f(x) = − 0.109502733955367 x − 1.20664273232731R² = 0.175392067572964

Pressure

Lead time

Anal

ysis

- Fo

reca

st

• Storms have higher core pressure in forecast => storm less intense in forecast• Agrees with previous work e.g. Froude et al.

r t Sig?Pressure -0.419 4.10

Jet Stream Type: Pressure

0 20 40 60 80 100 120 140 160 180

-50

-40

-30

-20

-10

0

10

f(x) = − 0.055654958645443 x + 2.28705098314606R² = 0.0720957782387813

f(x) = − 0.084335804218684 x − 4.26868775664918R² = 0.101438967935236

f(x) = NaN x + NaNR² = 0f(x) = NaN x + NaNR² = 0

CELinear (CE)EdgeLinear (Edge)CLLinear (CL)

Anal

ysis

- Fo

reca

st

r n t Sig?Cross early -0.63 20 3.45 Edge -0.42 20 1.99 Cross late -0.32 27 1.68 (0.1)Split -0.27 13 0.92

• Core pressure underprediction stronger in some jet stream types than others

PTE Type: Core Pressure

0 20 40 60 80 100 120 140 160 180

-50

-40

-30

-20

-10

0

10

f(x) = − 0.102317916264443 x + 1.98644315454146R² = 0.179841782079172f(x) = − 0.108323254944458 x − 3.239246408431R² = 0.175891664489393

HorizLinear (Horiz)Diab

r n t Sig?Horiz -0.419 54 3.33 Diab -0.424 26 2.29

• Indication that core pressure underprediction stronger in storms where baroclinic processes dominate deepening, than in those where diabatic processes dominate.

• Needs further statistical testing

Resolution: Core Pressure

• Operational forecast, so forecast system upgraded regularly (dynamics and resolution).

• Some evidence of relationship between forecast quality and system evolution.

100 150 200 250 300 350 400

-35

-30

-25

-20

-15

-10

-5

0

5

10

f(x) = 0.0523009605263158 x − 23.1280243026316R² = 0.337452399082539

Forecast at 36 hours lead time

Resolution (TL)

Fore

cast

- An

alys

is

Summary I

• Selected 30 European windstorms.• Categorised by:

– Jet stream – Processes that dominate deepening (PTE)

• Assessed forecast quality:– Longitude & latitude– Core pressure (intensity)

Summary II

• Storms in forecast too slow.• Core pressure generally underforecast:

– Strength of relationship with lead time depends on jet stream type.

– Baroclinic storms may be more underforecast than diabatic ones.

• Tendency for improvements of forecast system to affect forecast quality.