Probabilities from COSMO-2 derived with the neighborhood method

Federal Department of Home Affairs FDHAFederal Office of Meteorology and Climatology MeteoSwiss

Probabilities from COSMO-2 derived with the neighborhood

method

COSMO General Meeting

18 – 21 September 2007

Athens, Greece

Pirmin Kaufmann, MeteoSwiss

2 COSMO General Meeting 2007, Athens, Greece

[email protected] (presented by [email protected])

Neighborhood method

• Probabilistic forecast from deterministic model• Represents probability related to local-scale spatial and

temporal model uncertainty and predictability• Important especially on small scales (e.g. thunderstorms)

• Method does not represent uncertainty in synoptic forcing (complement to, not substitute for EPS)



Original shape of the neighborhood

• Ellipsoidal • Original shape of

Theis et al. (2005)• Spatial radius

decreases with increasing distance in time: Space-time dependency

x

y

t



Shape of the neighborhood

• Cylindrical• Assuming spatial and

temporal uncertainty are independent

• True in weak ambient winds, not true for strong ambient winds

• Equal weights for all grid poins

x

y

t



0

0.5

1

-30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30

spatial radius

wei

gh

t

Linearly fading weights

• Problem: Circles around singular high model values • Idea: Smooth edges• Introduce linear fading of weights (similar to relaxation)• Adds sponge layer around cylindrical neighborhood

Large, medium, small neighborhood

Federal Department of Home Affairs FDHAFederal Office of Meteorology and Climatology MeteoSwiss

Cases from „Windbank“ Simulations1995 – 1999

Threshold 35 mm / 12 h



1995-07-12 (Case A) Temporal radius

rt=6rt=3

rt=1obs



1995-07-12 (Case A) Spatial radius

rxy=15rxy=10

rxy=5obs



1995-09-13 (Case E) Temporal radius

rt=6rt=3

rt=1obs



1995-09-13 (Case E) Spatial radius

rxy=15rxy=10

rxy=5obs



1999-10-25 (Case L)Temporal radius

rt=3

obs rt=1

rt=6



1999-10-25 (Case L)Spatial radius

obs rxy=5

rxy=15rxy=10



Recent case: 8/9 August 2007 Flood



Objective probabilistic verification

• Verification needs to be done, but can it be done?• High density rain gauge network: Only 24 h resolution• Network with 10 min rainfall data: Only coarse spatial

resolution• Optimal radius: Quality measure (e.g. BSS) increases with

increasing radius, „an optimal neighborhood size cannot be found at all“ (experience of S. Theis; Theis 2005) • Result for 7 km COSMO resolution -> still valid at 2 km ?

• Sparseness of data: Extreme precipitation • occurs only in few cases• is often limited in spatial extent



Neighborhood method

• Main influence is spatial radius rxy and fading zone rf.

• Only minor changes with temporal radius rt.

• Consequence of accumulated precipitation values• Should we treat the time dimension differently

altogether? -> Maximum over time• Current settings at MeteoSwiss (precipitation):

• COSMO-2: rxy=15, rf=15, rt=3

• COSMO-7: rxy= 5, rf= 5, rt=3

• Need of high resolution data for verification: Resolution of rain gauges insufficient, either spatial or temporal

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Probabilities from COSMO-2 derived with the neighborhood method