Ensemble Forecasting: Thorpex-Tigge and use in Applications Tom Hopson.

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Transcript of Ensemble Forecasting: Thorpex-Tigge and use in Applications Tom Hopson.

Ensemble Forecasting: Thorpex-Tigge and use in Applications

Tom Hopson

OutlineI. Thorpex-Tigge data setII. Ensemble forecast examples:

a) Southwestern African flooding

• TIGGE, the THORPEX Interactive Grand Global Ensemble

• component of the World Weather Research Programme

• TIGGE archive consists of ensemble forecast data from ten global NWP centers

• designed to accelerate the improvements in the accuracy of 1-day to 2 week high-impact weather forecasts for the benefit of humanity.

• starting from October 2006

• available for scientific research

• near-real time forecasts (some centers delayed)

THORPEX Interactive Grand Global Ensemble

Archive Status and Monitoring, Data Receipt

Archive Centre

Current Data Provider

NCAR NCEP

CMC

UKMO

ECMWFMeteoFrance

JMAKMA

CMA

BoMCPTEC

IDD/LDM

HTTP

FTP

Unidata IDD/LDM

Internet Data Distribution / Local Data Manager

Commodity internet application to send and receive data

NCDC

Archive Status and Monitoring, Variability between providers

N200N128

0.56x0.561.00x1.001.25x0.831.25x1.251.50x1.50

0 1 2 3 4

Spatial Resolution

ECMWF UKMO JMA NCEP CMA CMC BOM MF KMA CPTEC

Number of Data Providers

Mod

el R

esol

ution

ECMW

F

UKMOJM

ANCEP

CMACMC

BOM MFKMA

CPTEC

0

10

20

30

40

50

60

70

80 # fields, # ensemble members

Conforming parameters

Ensemble Members

ECMW

F

UKMO

JMA

NCEPCM

ACM

CBOM M

FKM

ACPTEC

02468

1012141618

Forecast Length, Initialization

Forecast Length (Days)

Forecasts/day

Archive Status and Monitoring, Archive Completeness

PL = Pressure Level, PT = 320K θ Level, PV = ± 2 Potential Vorticity Level, SL = Single/Surface Level

Variable LvL ECWF UKMO JMA NCEP CMA CMC BOM MetF KMA CPTC

Geopotential Z PL

Specific H PL

T PL

U-velocity PL

V-velocity PL

Potential Vor PT

Potential T PV

U-velocity PV

V-Velocity PV

U 10m SL

V 10m SL

CAPE SL

Conv. Inhib. SL

Land-sea SL

Mean SLP SL

Orog. SL

Skin T SL

Snow D. H20 SL

Snow F. H20 SL

Archive Status and Monitoring, Archive CompletenessVariable LvL ECWF UKMO JMA NCEP CMA CMC BOM MetF KMA CPTC

Soil Moist. SL

Soil T SL

Sunshine D. SL

Surf. DPT SL

Surf. ATmax SL

Surf. ATmin SL

Surf. AT SL

Surf. P SL

LW Rad. Out SL

LH flux SL

Net Rad SL

Net Therm. Rad SL

Sensible Rad. SL

Cloud Cov SL

Column Water SL

Precipitation SL

Wilt. Point SL

Field Cap. SL

PL = Pressure Level, PT = 320K θ Level, PV = ± 2 Potential Vorticity Level, SL = Single/Surface Level

OutlineI. Motivation for ensemble forecasting and post-

processinga) Introduce Quantile Regression (QR; Kroenker and

Bassett, 1978) post-processing procedureII. Ensemble forecast verificationIII. Thorpex-Tigge data setIV. Ensemble forecast examples:

a) Southwestern African floodingb) African meningitisc) US Army test range weather forecastingd) Bangladesh flood forecasting

Early May 2011, floods in southwestern Africa

Early May 2011, floods in southwestern Africa-- examine ens forecasts … ECMWF 24hr precip

Early May 2011, floods in southwestern Africa-- examine ens forecasts … NCEP GEFS 24hr precip

Early May 2011, floods in southwestern Africa-- examine ens forecasts … ECMWF 5-day precip

Early May 2011, floods in southwestern Africa-- examine ens forecasts … NCEP GEFS 5day precip