Introduction of KMA statistic model and ensemble system. Korea Meteorological Administration Numerical Weather Prediction Division Joo-Hyung Son. PPM (Perfect Prognostic Method) Daily Max/Min and midnight temperature Probability of Precipitation MOS (Model Output Statistics)
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Introduction of KMA statistic model and ensemble system
Korea Meteorological Administration
Numerical Weather Prediction Division
Joo-Hyung Son
PPM (Perfect Prognostic Method)
Daily Max/Min and midnight temperature
Probability of Precipitation
MOS (Model Output Statistics)
Digital Forecast
KF(Kalman Filtering)/DLM(Dynamic Linear Model)
Daily Max/Min Temperature
3 hourly temperature
Daily Max/Min Temperature of 10 days
Statistical models
Max/Min Temp
PoP
PPM
PPM
RDAPS
KF
Max/Min Temp
KF
DLM
3hr Temp
RDLM
GDAPS
Max/Min Temp
DLM
GDLM
Predictant
00 UTC : +1(00UTC, Max/Min)
12UTC : +1(Max), +2(00UTC, Min)
Forecast regions
70 sites in Korea
Model development
May 1, 1988 – Feb 28, 1992 (4 years)
Regional reanalysis of JMA
Climate data of 70 weather sites
PPM Model structure for Max/Min Temp
Forecast equation
Temp (t) = A + B*obs(0) + {Ci*model predictori(t)}
A, B, Ci (i=1,2,…,n): fixed coefficients
predictor
predictor
1000, 850, 700,500,400,300hPa
Wind speed, direction, Temperature
Dewpoint temp,
Height et al. from RDAPS
Observation, climate
Forecast eqs for
each season, sights
predictant
Max/Min and 00LST temperature
of 70 sights
Predictant
PPM Predictors
PPM Model structure for PoP
Forecast equation
Temp (t) = A + B*obs(0) + {Ci*modeli(t)}
A, B, Ci (i=1,2,…,n): fixed coefficients
predictor
predictor
1000, 850, 700,500,400,300hPa
Wind speed, direction, Temperature
Dewpoint temp,
Height et al. from RDAPS
Observation, climate
Forecast eqs for
Each region according
to warm and cold season
predictant
PoP of 18 regions
Predictant
PPM Predictors
Predictant
00 UTC : +1(Min/Max), +2(Min)
12 UTC : +1(Max), +2(Min/Max)
Forecast regions
40 in Korea,
32 in North Korea, China, Japan
Kalman Filter algorithm
vt~N(0,Vt): observation noise
wt~N(0,Wt) : process noise
Gt = 1
V0 = 2
4/365 0 0
W0= 0 1/365 0
0 0 1/365
1
Ft = RDAPS
Latest Obs temp
DLM(Dynamic Linear Model)
DLM
DLM(Dynamic Linear Model)
vt~N(0,Vt)
wt~N(0,Wt)
RDLM(Regional DLM)
3hourly forecast up to 48hr
RDAPS
38 sites
GDLM(Global DLM)
Max/Min temp for 10 days
GDAPS
38 sites
DLM(Dynamic Linear Model)
Ensemble Prediction System
Breeding
Pert. run
AnalysisD+Perturbation
normalization
Control run
AnalysisD
AnalysisD+1
Schematic diagram
Breeding + Rotation
AnalysisD+Perturbation
Pert. run
normalization
Control run
AnalysisD+1
AnalysisD
Rotation
Rotation
Schematic diagram
D+1 day
D day
D day + 12hr
D+1 day +12hr
2005. 6. 11
old(cray-before)
NEW (cray-frot)
2005. 6. 11
old(cray-before)
NEW (cray-frot)
Mean and Spread
Spaghetti
Spaghetti ( with ensemble spread)
5520m
5640m
• display the global model, mean and standard deviation and spaghetti as well as each member.
12-hour precipitation > given thresholds
: 1, 5, 10mm for winter season
: 1, 10, 50mm for other seasons
The probability
These probability maps are used for the early warning guidance of severe weather.
Surface maximum wind > 10m/s, 14m/s
The probability
These probability maps are used for the early warning guidance of severe weather.
Precipitation
Sfc Max Wind
12hr accumul >=1mm
12hr accumul >= 10mm
12hr accumul >= 50mm
sfc wind >= 10m/s
sfc wind >= 14m/s
Seoul, Daegu, Daejeon
Busan et al.
Largest value
Upper quartile
Median
Lower quartile
Smallest value
Interpretation
of boxplots
Image
of PDF
Time series of primary cities
Ensemble Plumes
Time series of 8-day forecast at cities
The dispersion of members with forecast evolution
Variable : Pmsl, 500H, 850 T
Hwangsa (yellow sand) trajectory
Typhoon Strike Probability Map by EPS
Thank you
Factor analysis