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Institute for Atmospheric Physics – University of Mainz. Name of method: SAL - Structure, Ampliutde, Location. Investigators: Marcus Paulat , Heini Wernli – Institute for Atmospheric Physics, University of Mainz

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Presentation Transcript
slide1

Institute for Atmospheric Physics – University of Mainz

Name of method:

SAL - Structure, Ampliutde, Location

Investigators:

Marcus Paulat, Heini Wernli– Institute for Atmospheric Physics, University of Mainz

Christoph Frei– Bundesamt für Meteorologie und Klimatologie, MeteoSwiss Zürich

Martin Hagen- Institut für Physik der Atmosphäre, DLR Oberpfaffenhofen

Literature / References: none

- presentations at workshops in Boulder (2006) and ECMWF (2007)

- manuscript in preparation (Wernli et al., MWR)

16. February 2007

slide2

Marcus Paulat Christoph Frei Martin Hagen Heini Wernli

What kind of data?

  • only gridded data
  • fields: so far only precipitation, in principle also applicable to other fields with well-structures signatures (e.g. wind gusts)
  • presently used FC data: ECMWF and COSMO-LM precipitation forecasts (accumulated over 1 or 24 h) over Germany on rotated lon/lat grid with 7 km horizontal resolution
  • presently used OBS data: gridded rain gauge data set on same grid as models, based upon ~4000 stations with 24 h values; 1-h disaggregated fields through combination of rain gauge and radar data
slide3

Marcus Paulat Christoph Frei Martin Hagen Heini Wernli

Basic strategy (I): General concept

  • consider precipitation in pre-specified area (e.g. river catchment)
  • SAL consists of three independent components
  • components address quality of structure (S), amplitude (A) and location (L) of QPF in that area
  • according to SAL a forecast is perfect if S = A = L = 0
  • S requires the definition of precipitation objects, using a threshold value
  • but: no attribution between precipitation objects in forecast and observations!
slide4

Marcus Paulat Christoph Frei Martin Hagen Heini Wernli

Basic strategy (II): Definition of the components

A = (D(Rmod) - D(Robs)) / 0.5*(D(Rmod) + D(Robs))

D(…) denotes the area-mean value (e.g. catchment)

normalized amplitude error in considered area

A  [-2, …, 0, …, +2]

L = |r(Rmod) - r(Robs)| / distmax

r(…) denotes the centre of gravity of the precipitation field in the area

normalized location error in considered area

L  [0, …, 1]

S = (V(Rmod*) - V(Robs*)) / 0.5*(V(Rmod*) + V(Robs*))

V(…) denotes the weighted volume average of all scaled precipitation objects in considered area

normalized structure error in considered area

S  [-2, …, 0, …, +2]

slide5

Marcus Paulat Christoph Frei Martin Hagen Heini Wernli

Basic strategy (III): The S component

scaling for every object: R* = R / Rmax; R*  [Rthresh/Rmax, …, 1]

R

R*

Rmax

1

Rthresh

Rthresh/Rmax

x

x

V(R*)

V(R)

circular precipitation object

slide6

Marcus Paulat Christoph Frei Martin Hagen Heini Wernli

Basic strategy (IV): Example 1

R

R*

Rmax

1

OBS

V(R*)

x

x

R

R*

1

MOD

Rmax

V(R*)

x

x

S = 0

A < 0

slide7

Marcus Paulat Christoph Frei Martin Hagen Heini Wernli

Basic strategy (V): Example 2

R

R*

Rmax

1

OBS

V(R*)

x

x

R

R*

1

MOD

Rmax

V(R*)

x

x

S > 0

A = 0

slide8

S A L - statistics: 24h accumulated

summer seasons 2001-2004 for catchment Rhine

aLMo

ECMWF

2

1

A-component

0

-1

-2

-2

-1

0

1

2

-2

-1

0

1

2

S-component

S-component

Marcus Paulat Christoph Frei Martin Hagen Heini Wernli

slide9

Marcus Paulat Christoph Frei Martin Hagen Heini Wernli

Kind of verification information / Verification questions

  • 3 independent components to quantify quality of structure, amplitude and location of QPF
  • information is valid for a pre-specified area (e.g. river catchment, state, field campaign area, …)
  • questions:
  • - is the domain averaged precipitation amount correctly forecast? -> A
  • - is the centre of the precipitation distribution in the domain correctly forecast? -> L
  • - does the forecast capture the typical structure of the precipitation field
  • (e.g. large broad vs. small peaked objects)? -> S
slide10

Marcus Paulat Christoph Frei Martin Hagen Heini Wernli

Strength

  • physically meaningful information about amplitude, location and structure of QPF
  • relatively intuitive
  • SAL approach is close to “subjective human judgement” (claim)
  • no attribution between objects is required (difficult for small objects)
  • computationally simple
slide11

Marcus Paulat Christoph Frei Martin Hagen Heini Wernli

Weaknesses and limitations

  • non-perfect QPFs can yield S = A = L = 0 (slight modification of L component is underway)
  • no consideration of orientation of objects
  • currently very simple definition of objects (threshold value)
  • S value is sensitive to choice of threshold used for object definition (tests are underway to quantify sensitivity)
  • so far only preliminary results for Germany have been computed