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Ensemble Forecasting: Thorpex-Tigge and use in Applications. Tom Hopson. Outline. Thorpex -Tigge data set Ensemble forecast examples: a)Southwestern African flooding. THORPEX Interactive Grand Global Ensemble . TIGGE, the THORPEX Interactive Grand Global Ensemble - PowerPoint PPT Presentation
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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/LDMInternet Data Distribution / Local Data ManagerCommodity 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
ECMWF
UKMOJM
ANCEP
CMACMC
BOM MFKMA
CPTEC
0
10
20
30
40
50
60
70
80 # fields, # ensemble members
Conforming parameters
Ensemble Members
ECMWFUKMO
JMA
NCEPCMA
CMCBOM MF
KMACPTEC
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 CPTCSoil 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