Ensemble Forecasting: Thorpex-Tigge and use in Applications
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Transcript of Ensemble Forecasting: Thorpex-Tigge and use in Applications
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Ensemble Forecasting: Thorpex-Tigge and use in Applications
Tom Hopson
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OutlineI. Thorpex-Tigge data setII. Ensemble forecast examples:
a) Southwestern African flooding
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• 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
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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
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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
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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
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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
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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
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Early May 2011, floods in southwestern Africa
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Early May 2011, floods in southwestern Africa-- examine ens forecasts … ECMWF 24hr precip
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Early May 2011, floods in southwestern Africa-- examine ens forecasts … NCEP GEFS 24hr precip
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Early May 2011, floods in southwestern Africa-- examine ens forecasts … ECMWF 5-day precip
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Early May 2011, floods in southwestern Africa-- examine ens forecasts … NCEP GEFS 5day precip