calibration and multimodeling Development of Flood Forecasting for the Ganges and the Brahmaputra Basins using satellite based precipitation ensemble weather forecasts and remotelysensed river widths and height ID: 808031
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Slide1
Flood forecasting precipitation products
calibration and multi-modeling:Development of Flood Forecasting for the Ganges and the Brahmaputra Basins using satellite based precipitation, ensemble weather forecasts, and remotely-sensed river widths and height
Tom Hopson, NCAR (among others)
Satya
Priya
, World Bank
Slide2Outline
Review of precipitation products
Observations – satellite and rain gauge
Thorpex
Tigge
ensemble forecasts
Calibration and multi-modeling using
quantile
regression (QR)
Review QR
Bagmati
and
Kosi
gauge locations
Time-series results
Verification
Rank histograms
Skill score (SS) concept
Brier SS
RMSE SS
Regressor
usage
Slide3Satellite Products
Satellite products are available as soon as each 24-hour accumulation period is completed.
Product
Name
Institution
Country
Sensor Types
Resolution
TRMM
NASA
USA
Passive microwave,Infrared0.25 degGSMAPJAXAJapanPassive microwave,Infrared0.1 degCMORPHNOAAUSAPassive microwave,Infrared~0.25 deg
Our NCAR merged product is a simple average of the available satellite products
Slide4TIGGE Forecasts
Forecasts are on 2 day delay from TIGGE (
T
he
I
nternational
G
rand
G
lobal
Ensemble).
Forecast CenterCountry /Region# of Ensemble MembersForecast Out to:Currentlyon DisplayECMWFEurope5015 daysYesUKMOUK117 daysYesCMCCanada2016 daysYesNCEPUSA2016 days<
Dec 2015CMA
China
14
15 days
No
CPTECBrazil1415 daysNoMeteoFranceFrance344.5 daysNoJMAJapan2611 daysNoBoMAustralia3210 daysNoKMAKorea2310.5 daysNo
Slide5Archive Status and Monitoring, Variability between providers
Slide6Outline
Review of precipitation products
Observations – satellite and rain gauge
Thorpex
Tigge
ensemble forecasts
Calibration and multi-modeling using
quantile
regression (QR)
Review QR
Bagmati and Kosi gauge locationsTime-series resultsVerificationRank histogramsSkill score (SS) conceptBrier SSRMSE SSRegressor usage
Slide7Quantile
Regression (QR)
Our application
Combining rainfall forecasts from 5 centers: CMA, CMC, CPTECH, ECMWF, NCEP conditioned
on
:
Ensemble mean of each center
Ranked forecast
ensemble
Slide8Multi-modeling using
Quantile
Regression
Slide95-Day Lead-Time Time-Series for
Bagmati
Station
Khagaria
007-mgd4ptn
CMA
CMC
ECMWF
NCEP
CPTEC
Multi-Model CMC-NCEP
Multi-Model All 5 Centers
Slide10CMA
CMCECMWFNCEP
CPTEC
Multi-Model CMC-NCEP
Multi-Model All 5 Centers
5-Day Lead-Time Time-Series for
Kosi
Station
Azmabad
029-
mgd5ptn
Slide11Outline
Review of precipitation products
Observations – satellite and rain gauge
Thorpex
Tigge
ensemble forecasts
Calibration and multi-modeling using
quantile
regression (QR)
Review QR
Bagmati and Kosi gauge locationsTime-series resultsVerificationRank histogramsSkill score (SS) conceptBrier SSRMSE SSRegressor usage
Slide12Rank Histograms – Multi-Model All 5 Centers, 5
-Day Lead-Time Forecasts
Slide13Skill Scores
Single value to summarize performance.
Reference forecast - best naive guess; persistence, climatology
A perfect forecast implies that the object can be perfectly observed
Positively oriented – Positive is good
Slide14Brier Skill-Score for
Bagmati Station Khagaria 007-mgd4ptn
CMA
CMC
ECMWF
NCEP
CPTEC
Multi-Model CMC-NCEP
Multi-Model All 5 Centers
multi-modeling improves best forecast (ECMWF) by
roughly two (or more)
days of forecast lead-time
Slide15Brier Skill-Score for
Kosi Station Azmabad 029-mgd5ptnCMACMC
ECMWF
NCEP
CPTEC
Multi-Model CMC-NCEP
Multi-Model All 5 Centers
Slide16RMSE Skill-Score for
Bagmati Station Khagaria 007-mgd4ptn CMACMC
ECMWF
NCEP
CPTEC
Multi-Model CMC-NCEP
Multi-Model All 5 Centers
Slide17RMSE Skill-Score for
Kosi Station Azmabad 029-mgd5ptnCMACMC
ECMWF
NCEP
CPTEC
Multi-Model CMC-NCEP
Multi-Model All 5 Centers
Slide18Regressor Usage in Quantile
Regression Calibration
Bagmati
007-mgd4ptn
Kosi
029-mgd5ptn
All Basins
ECMWF superior overall, but other centers significantly contribute
Dependence on location (basin)
CPTEC
NCEP
ECMWFCMCCMA
Slide19Summary
ECMWF generally outperforms other centers after
postprocessing
for a variety of metrics
However, combination of NCEP and CMC (Canada) can reach similar combined skill to ECMWF for our two example basin
M
ulti
-modeling roughly gains two days of forecast lead-time as a rule-of-
thumb
In general, the center with the best forecast skill is strongly location/catchment-
dependent
Slide20“
I have a very strong feeling that science exists to serve human welfare. It
’
s wonderful to have the opportunity given us by society to do basic research, but in return, we have a very important moral responsibility to apply that research to benefiting humanity.
”
Dr. Walter Orr Roberts (NCAR founder)
Slide21Slide22Slide23Slide24