improving ensemble qpf in nmc dr. dai kan national meteorological center of china (nmc)...
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Improving Ensemble QPF in NMCImproving Ensemble QPF in NMC
Dr. Dai KanNational Meteorological Center of China (NMC)
International Training Course for Weather Forecasters
11/1, 2012, Kunming
OutlineOutline QPF operations in NMCQPF operations in NMC Improving QPF by ensembleImproving QPF by ensemble Improving PQPFImproving PQPF
WFO-- subdivision of NMC (National Meteorological
Center)
NMCNMC
Administrative Office
Personnel and Staff Education Division
Division of Operational & Reach Management and S. T. development
Integrative Office
Weather Forecasting Office
NWP Operating and Developing Division
Typhoon and Marine Met. Division
Applied Met. Services Division
NMC Agricultural Met. Center
Met. Service for Decision-making Office
Open Forecast System Laboratory
Retirees Office
Operations( 8)
Management( 5)
Severe Weather Prediction Center
QPE
QPF (no PQPF)
Early warning of heavy rain
Precipitation phase in Winter
Total process precipitation forecast
QPF’s duties
7-Day 24Hour Precipitation Forecast:
Day1-3: Updated Twice a day, at 00,12UTC
Day4-7: Updated Once a day, at 00UTC
00UTC
12UTC
Threshold: 0.1, 10, 25, 50, 100, 250mm
Various observation dataOperational
determinate model Ensemble model
Distinguish w
eather system
QP
F verification
Ensem
ble QP
FE
nsemble Q
PF
QPF Products
Multi-m
odel ensemble Q
PF
Multi-m
odel ensemble Q
PF
Point to point forecast
Synoptic situation forecast
Grid editing technique
QPF revise
Blending m
ethod QP
F
Historical data query
QP
F G
ridding
Key method
QPF technical support and operational process
Ensemble systemEnsemble system
T213-GEPS, 10 days, 15 mem.T213-GEPS, 10 days, 15 mem. WRF-REPS, 60 hours, 15 mem.WRF-REPS, 60 hours, 15 mem. ECMWF, NCEP GEPSECMWF, NCEP GEPS TIGGE dataset (not real-time, 3 TIGGE dataset (not real-time, 3
days-delay)days-delay)
Ensemble analysis and visualization systemEnsemble analysis and visualization system
Ensemble Predication Toolkits V0.3
probability spaghetti Stamp
Box-plot
OutlineOutline QPF operations in NMCQPF operations in NMC Improving current QPF by ensembleImproving current QPF by ensemble Improving PQPF Improving PQPF
Ensemble outputs as a single forecast
Mean and spread
Max, middle, min
%10, %25, %75, %90 quantile
Probability-matching ensemble mean (PM)Compared with deterministic forecast
Advantages and disadvantages of each product
How to improve current operational QPF by ensemble
Observations:
Longitude: 110~122E
Latitude: 28~38N
Covering Huaihe catchment
745 observation stations
~0.4 degree space
Forecasts:
ECMWF global EPS
2007~2012, summer
Verification
Verifications resultsModel forecast to stations, 1-day 24h rain rate ~ frequency
deterministic forecast, PM approximate to
ensemble member
Compared with observation curve:
— <33mm, over-forecast
— >33mm, under-forecast
Verifications results
Mean and middle forecast:
More under-forecasts for heavy rain
No improvement for light or moderate
rain
Verifications results
Max forecast:
More over-forecast
Close to observation for heavy rain (>150mm)
Min forecast:
More under-forecast
Close to observation for light rain (<10mm)
Verifications results
10% 25%
75% 90%Close to obs. for different precipitation amount
Except PM, no statistic products close to deterministic forecast
Each product has advantages and disadvantages
Can we construct a single product which fuse advantages of each product?
Fusing productFor each grid point, there are 51 member
forecast MF.
Set fusing value FP = :(1)If max(MF) >= 100mm, then
FP=max(MF);
(2)If %90(MF) >= 50mm, then FP=
%90(MF) ;
(3)If %75(MF) >= 25mm, then FP=
%75(MF) ;
(4)If middle(MF) >= 10mm, then FP=
middle(MF) ;
(5)Else FP= %10(MF)
Verifications results
FP approximate to observations
for different precipitation amount
Verifications results
FP has higher Threat score than
deterministic forecast for each
precipitation amount
Threat score
Fusing product( 1 ) Good for short-range (0~72h) QPF,
higher TS than deterministic forecast for
different amount rain.
( 2 ) Easily implemented in QPF
operations.
( 3 ) Risk of high false alarm ratio,
special for medium-range
( 4 ) Threshold decided roughly and
subjectively.
( 5 ) In future, use frequency match
algorithm to precisely calibrate frequency
error.
OutlineOutline QPF operations in NMCQPF operations in NMC Improving QPF by ensembleImproving QPF by ensemble Improving PQPF Improving PQPF
Verifications results2007~2012, Summer, 1day precipitation – station obs.
Under-dispersiveness:Under-dispersiveness:U shape of Talagrand histogram
Verifications results2007~2012, Summer, 1day precipitation – station obs.
Lack of reliability:Lack of reliability:
Reliability curve not on the diagonal
•0.1mm/1Day, Overforecasting (wet bias)
•25mm/1Day, Poor resolution (overconfident)
0.1mm/1Day 25mm/1Day
Verifications results2007~2012, Summer, 1day precipitation – station obs.
Low accuracy for high thresholds:Low accuracy for high thresholds:ROC area 0.74 < 0.8 for thresholds > 50mm/1Day
50mm/1Day
Relative operating characteristic
Post-processingTo provide reliable forecasts
Logistic regression approach
Choice of predictors x. Estimation of the b0 and b1 over a training period. Calibrated probabilities p for a threshold T directly addressed.
Post-processing
Logistic regression approach
Predictors: ensemble mean and spread with 1/3 power transformation
Training period: latest 30 days ; or 2007~2011 5 years summer history forecast (from TIGGE archive )
Forecast period: 2012 summer
Post-processing0.1mm/1dayOriginal
0.1mm/1dayCalibration(history forecast)
0.1mm/1dayCalibration(30 train days)
Post-processing10mm/1dayOriginal
10mm/1dayCalibration(30 train days)
10mm/1dayCalibration(history forecast)
Post-processing25mm/1dayOriginal
25mm/1dayCalibration(history forecast)
50mm/1dayOriginal
50mm/1dayCalibration(history forecast)
Logistic Regression PQPF( 1 ) Calibrate ensemble PQPF
effectively
( 2 ) More training samples, more better
results
( 3 ) History forecast errors may change
with model updating, which influence the
calibration.
( 4 ) Reforecast can offer a better way,
which we can not gain these dataset.
No product close to deterministic forecast Each product has advantages and
disadvantages
Can we get a statistic product which close to deterministic forecast or member forecast
Can we construct a product which fuse advantages of each product
Probability matching
1. Rank the gridded rainfall from all n QPFs from largest to smallest, the keep every nth
value starting with the n/2-th value.
2. Rank the gridded rainfall from the ensemble mean from largest to smallest.
3. Match the two histograms, mapping rain rates from (1) onto locations from (2).
(from Beth Ebert )
……………
… … … … …
…
…
…
…
1~51
52~102
Rank form largest to smallest
Ensemble mean
Ensemblemember
Verifications results
PM approximate to ensemble
member or deterministic forecast
QPF Products
Day1: 6-h QPF, updated 3 times a day at 00, 06, 12 UTC
Winter: Day1-3 24-h QPF updated twice a day
Including the snow, freezing rain, sleet.
24h 48h 72h
Precipitation phase forecast:
Total process precipitation (for the whole life of a synoptic system)