short-range ensemble prediction system at inm josé a. garcía-moya & carlos santos smnt – inm...

Short-Range Ensemble Prediction

System at INM

José A. García-Moya & Carlos SantosSMNT – INM

COSMO MeetingZurich, September 2005

September 2005 COSMO Meeting 2

Introduction

Surface parameters are the most important ones for weather forecast.

Forecast of extreme events (convective precip, gales,…) is probabilistic even for the short-range.

Short Range Ensemble prediction can help to forecast these events.

Forecast risk (Palmer, ECMWF Seminar 2002) is the goal for both Medium- and, also, “Short-Range Prediction” (quotation is mine).


Meteorological Framework

Main Weather Forecast issues are related with Short-Range extreme events.

Convective precipitation is the most dangerous weather event in Spain.

Western Mediterranean is a quasi-closed sea rounded by high mountains.

In autumn sea is warmer than air, then low troposphere is conditionally unstable most of the time.

Several cases of more than 200 mm/few hours every year.

Some fast cyclogenesis like “tropical cyclones” happen.


Ensemble for Short-Range

Extreme weather events have a low predictability even in the Short Range (less than 72 hours).

Convection is highly non-linear and it shows a chaotic behaviour.

Then a probabilistic approach may help to improve the prediction of such phenomena.


Errors in LAMs

Due to the model formulation Multimodel thecniques

Due to uncertainties in the initial state Singular vectors, breeding

Due to uncertainties at boundaries From different deterministic global models From a global ensemble

Due to the parameterization schemes Mutiphysics Stochastic physic techniques


Multi-model

Hirlam. HRM

from DWD. MM5 UM

Unified Model from UKMO.


Multi-Boundaries

From different global deterministic models: ECMWF UM

UKMO AVN

NCEP GME

DWD.


Ensemble

72 hours forecast four times a day (00, 06, 12 y 18 UTC).

Characteristics: 4 models. 4 boundary conditions. 4 last ensembles (HH, HH-6, HH-12, HH-18).

16 member ensemble every 6 hours Time-lagged Super-Ensemble of 64

members every 6 hours.


Actual Ensemble

72 hours forecast once a day (00 UTC). Characteristics:

4 models. 4 boundary conditions.

13 (of 16 expected) member ensemble every 24 hours


Actual Ensemble II

BCs / Model

AVN ECMWF

GME UM

Hirlam X X X X

Hrm X X X X

MM5 X X X X

UM O O O X


Road Map2003-2004

Research to find best ensemble for the Short Range

Jun 04 – Jun 05

Building Multimodel System

Jun 05-Dec 05

Mummubn/16 members

Daily run non-operational

Mar 06 Mummub 16/16

members

Full operations

Jun 06 Mummub+4lag64 members

First try


Post-processing

Integration areas 0.25 latxlon, 40 levels Interpolation to a common area

~ North Atlantic + Europe Grid 380x184, 0.25º

Software Enhanced PC + Linux ECMWF Metview + Local developments

Outputs Deterministic Ensemble probabilistic


Post-processing III


Post-processing II


Monitoring in real time

Intranet web server Deterministic outputs

Models X BCs tables Maps for each couple (model,BCs)

Ensemble probabilistic outputs Probability maps: 6h accumulated

precipitation, 10m wind speed, 24h 2m temperature trend

Ensemble mean & Spread maps EPSgrams (not fully-operational)

Verification


Monit 1: home


Monit 2: all models X bcs


Monit 3: one member Z500


Monit 4: one member 6h Acc Precip


Monit 5: All Prob 24h 2m T trend


Monit 6: Prob maps 24h 2m T trend


Monit 7: Spread - Emean maps


Monit 8: EPSgrams

EPSgrams Not fully operational


Case study: Aug, 20, 2005

Prob. Map & RADAR 12-18Z


Case study: Aug, 20, 2005

Prob. Map & RADAR 00-24Z


Validation ECMWF operational analysis as reference. Verification software

~ ECMWF Metview + Local developments Deterministic scores

Bias & Rms for each member Probabilistic ensemble scores

Talagrand ROC Spread vs Ensemble mean error

15 days of comparison (Aug, 17 to 31, 2005).


Talagrand Diagrams

Ensemble members ranked from smallest to greatest value.

Percent of cases which verifying analysis falls in an interval.

First interval, below smallest member.

Last one, above greatest member. Z500, T500, Msl Pressure

H+24, H+48


Spread vs

Ensemble Mean Error

Z500 H+00 to H+72

T500 H+00 to H+72

Msl Pressure H+00 to H+72


ROC Curves

10m Wind Speed Thresholds: 10m/s, 15m/s H+24, H+48

24h Accumulated Precipitation Thresholds: 1mm, 5mm, 10mm, 20mm H+24, H+48


Advantages: Better representation of model errors (SAMEX and

DEMETER). Consistent set of perturbations of initial state and

boundaries. Better results (SAMEX, DEMETER, Arribas et al., MWR

2005). Disadvantages:

Difficult to implement operationally (four different models should be maintained operationally).

Expensive in terms of human resources. No control experiment in the ensemble.

Conclusions for Multimodel


Future 16 members full-operational Bias removal Calibration: Bayessian Model

Averaging Verification against observations Time-lagged 64 members 4runs/day More Post processing software

(targeting clustering)


Team

García-Moya, J.A. Head, Pre-processing BCs, Hirlam

Callado, A. UM

Santos, C. Post-processing, Verification, Hirlam

Santos, D. MM5

Simarro, J. HRM, Pre-processing BCs


Thanks to…

MetOffice Ken Mylne, Jorge Bornemann

DWD Detlev Majewski, Michael Gertz

ECMWF Metview Team


Questions

?... [email protected] [email protected]

short-range ensemble prediction system at inm josé a. garcía-moya & carlos santos smnt – inm...

Documents

homecosmo meetingmonit

cosmo meetingensemble72

member z500cosmo meetingmonit

t trendcosmo meetingmonit

models x bcscosmo meetingmonit

h acc precipcosmo meetingmonit

expected member ensemble

shortrange extreme events