ThorpexTigge and use in Applications Tom Hopson Outline Thorpex Tigge data set Ensemble forecast examples a Southwestern African flooding TIGGE the THORPEX Interactive Grand Global Ensemble ID: 232860
Download Presentation The PPT/PDF document "Ensemble Forecasting:" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Ensemble Forecasting:
Thorpex-Tigge and use in Applications
Tom HopsonSlide2
Outline
Thorpex
-Tigge
data set
Ensemble forecast examples:
a) Southwestern African
floodingSlide3
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 Slide4
Archive Status and Monitoring, Data Receipt
Archive Centre
Current Data Provider
NCAR
NCEP
CMC
UKMO
ECMWF
MeteoFrance
JMA
KMA
CMA
BoM
CPTEC
IDD/LDM
HTTP
FTP
Unidata
IDD
/LDM
I
nternet
D
ata
D
istribution /
L
ocal
D
ata
M
anager
Commodity internet application to send and receive data
NCDCSlide5
Archive Status and Monitoring, Variability between providersSlide6
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
KMACPTC
Geopotential ZPL
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
SLSlide7
Archive Status and Monitoring, Archive Completeness
Variable
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 LevelSlide8
Outline
Motivation for ensemble forecasting and post-processing
Introduce
Quantile
Regression (QR;
Kroenker
and Bassett, 1978)
p
ost
-processing procedure
Ensemble forecast verification
Thorpex-Tigge data set
Ensemble forecast examples:a) Southwestern African floodingb) African meningitisc) US Army test range weather forecastingd) Bangladesh flood forecastingSlide9
Early May 2011, floods in southwestern Africa Slide10
Early May 2011, floods in southwestern Africa
-- examine ens forecasts … ECMWF 24hr precip Slide11
Early May 2011, floods in southwestern Africa
-- examine ens forecasts … NCEP GEFS 24hr precip Slide12
Early May 2011, floods in southwestern Africa
-- examine ens forecasts … ECMWF 5-day precip Slide13
Early May 2011, floods in southwestern Africa
-- examine ens forecasts … NCEP GEFS 5day precip