Download presentation
Presentation is loading. Please wait.
1
Tom Hopson, NCAR (among others) Satya Priya, World Bank
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
2
Outline 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
3
Satellite 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, Infrared 0.25 deg GSMAP JAXA Japan 0.1 deg CMORPH NOAA ~0.25 deg Our NCAR merged product is a simple average of the available satellite products
4
TIGGE Forecasts Forecasts are on 2 day delay from TIGGE (The International Grand Global Ensemble). Forecast Center Country / Region # of Ensemble Members Forecast Out to: Currently on Display ECMWF Europe 50 15 days Yes UKMO UK 11 7 days CMC Canada 20 16 days NCEP USA < Dec 2015 CMA China 14 No CPTEC Brazil MeteoFrance France 34 4.5 days JMA Japan 26 11 days BoM Australia 32 10 days KMA Korea 23 10.5 days Originally a project of THORPEX: a World Weather Research Programme project to accelerate the improvements in the accuracy of 1-day to 2-week high-impact weather forecasts.
5
Archive Status and Monitoring, Variability between providers
6
Outline 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
7
Quantile 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
8
Multi-modeling using Quantile Regression
9
5-Day Lead-Time Time-Series for Bagmati Station Khagaria 007-mgd4ptn
CMA 5-Day Lead-Time Time-Series for Bagmati Station Khagaria 007-mgd4ptn Multi-Model CMC-NCEP NCEP CMC Multi-Model All 5 Centers CPTEC ECMWF
10
5-Day Lead-Time Time-Series for Kosi Station Azmabad 029-mgd5ptn
CMA 5-Day Lead-Time Time-Series for Kosi Station Azmabad 029-mgd5ptn Multi-Model CMC-NCEP NCEP CMC Multi-Model All 5 Centers CPTEC ECMWF
11
Outline 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
12
Rank Histograms – Multi-Model All 5 Centers, 5-Day Lead-Time Forecasts
13
Skill 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 If needed, brief discussion of the ‘skill-score’ idea, since we’ll present skill scores in the remaining slides
14
Brier Skill-Score for Bagmati Station Khagaria 007-mgd4ptn
CMA Multi-Model CMC-NCEP NCEP CMC Multi-Model All 5 Centers CPTEC ECMWF multi-modeling improves best forecast (ECMWF) by roughly two (or more) days of forecast lead-time
15
Brier Skill-Score for Kosi Station Azmabad 029-mgd5ptn
CMA Multi-Model CMC-NCEP NCEP CMC Multi-Model All 5 Centers CPTEC ECMWF
16
RMSE Skill-Score for Bagmati Station Khagaria 007-mgd4ptn
CMA Multi-Model CMC-NCEP NCEP CMC Multi-Model All 5 Centers CPTEC ECMWF
17
RMSE Skill-Score for Kosi Station Azmabad 029-mgd5ptn
CMA Multi-Model CMC-NCEP NCEP CMC Multi-Model All 5 Centers CPTEC ECMWF
18
Regressor Usage in Quantile Regression Calibration
Bagmati 007-mgd4ptn Kosi 029-mgd5ptn All Basins CPTEC NCEP ECMWF CMC CMA ECMWF superior overall, but other centers significantly contribute Dependence on location (basin)
19
Summary 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 Multi-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
20
“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) “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.” Walter Orr Roberts
Similar presentations
