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5.5.20031 The development of statistical interpretation and adaptation of NWP at FMI Juha Kilpinen, Ahti Sarvi and Mikael Jokimäki Finnish Meteorological.

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Presentation on theme: "5.5.20031 The development of statistical interpretation and adaptation of NWP at FMI Juha Kilpinen, Ahti Sarvi and Mikael Jokimäki Finnish Meteorological."— Presentation transcript:

1 5.5.20031 The development of statistical interpretation and adaptation of NWP at FMI Juha Kilpinen, Ahti Sarvi and Mikael Jokimäki Finnish Meteorological Institute http://www.fmi.fi/ http://www.fmi.fi/ Past operational methods: –Perfect Prognosis (with multiple regression) –Kalman filtering –Decision threes Present pre-operational methods: –Fuzzy systems for points –Perfect Prognosis for grid points

2 5.5.20032 The development of statistical interpretation and adaptation of NWP at FMI Past operational methods: –Perfect Prognosis (with multiple regression) for three stations, several parameters –Kalman filtering temperature, min/max temperatute, off shore winds, PoP tests, for stations –Decision threes several parameters, for grid data

3 5.5.20033 The development of statistical interpretation and adaptation of NWP at FMI Present pre-operational methods: –Fuzzy systems for points ECMWF temperature –Perfect Prognosis for grid data temperature/ground temperature/Min-Max HIRLAM and ECMWF data to be used within the grid editing process

4 5.5.20034 and editing of Real time database: observations numerical forecasts final forecasts interpretations Climatological database Post processing: creates customer products from the data edited by forecasters; texts, graphics, etc. Customers: Observations: satellites weather radars surface observations soundings Numerical weather forecast models (by supercomputers) Forecasting process at FMI Visualisation and Editing by Forecaster (interpretations) Old Vax workstation Old manual process

5 5.5.20035 Observations (Global & Local) Customers: Public Web Business: Media Aviation Industry Security: General public Authorities Editing by forecasters (FMI) Post processing Production Servers (FMI) Real time Database (FMI) Post processing (e.g. statistical adaptation) HIRLAM model (CSC) Climate database (FMI) Forecasts Observations Boundaries Forecasts Graphics text forecasts etc. ECMWF Monitoring SMS(FMI) Forecasters: Manual products Forecasting process at FMI

6 5.5.20036 The Grid Editor Smart Tools: ability to make Scripts to perform more Complicated and often Repeated editing actions in A more easy manner (suitable Also for adaptation purposes) IF (N>5) T=T+3

7 5.5.20037 MAE of temperature forecasts (3 stations, 9 seasons, 0.5-5 days) Centralized editing on commercial side

8 5.5.20038 HIRLAM DMO and Obs (25.4.2001)HIRLAM PPM and Obs (25.4.2001) Error ~ 5-9 degrees max error 10 degrees Error ~ 10-15 degrees max error -20 degrees

9 5.5.20039 Perfect Prognosis method for temperature forecasting Juha Kilpinen 2100 grid points, HIRLAM and ECMWF models applies same models for both data sources (HIRLAM/ECMWF) developmental data from TEMP’s of Jokioinen (02935) and Sodankylä (02836), 20 years of data separate models for 00UTC, 03UTC, 06UTC, 09UTC, 12UTC, 15UTC, 18UTC and 21 UTC (see Fig.) over sea or lakes DMO is used data stratification for four seasons, overlap of seasons 1 month (see Fig.) TEMP data from surface up to 500 HPa used, also derived new predictors used multiple linear regression (Systat 10) forward selection of predictors, a new predictor should increase the reduction of variance of the model by at least 0.5%.

10 5.5.200310 Derived predictors for PPM FF850 = SQRT(ABS(V850*U850)) TYPE_PRHFF = (P_P0H-949)/15.6+(24-FF850)/3.93+(100-RH850)/28 CL_MAX = MAX(RH500*RH500/100,RH700*RH700/100,RH850*RH850/100) TYPE_PCLFF = (P_P0H-949)/15.6+(100-CL_MAX)/28.2+(24- FF850)/3.93 P_P0H2 = P_P0H-1013 Z8502 = Z850-1500 Z7002 = Z700-3000 Z5002 = Z500-5200 COSINUS = COS(2*3.1417*JUL/360) SINUS = SIN(2*3.1417*JUL/360)

11 5.5.200311 Estimation error of dependent PPM model UTC

12 5.5.200312 Estimation error of dependent PPM model UTC

13 5.5.200313 12 UTC TEMP 00 UTC TEMP SYNOP 09 12 15 18 21 00 03 06 UTC Connections of TEMP and SYNOP data in estimation

14 5.5.200314 Winter (5 months) Spring (3 months) Summer (5 months) Autumn (3 months) Winter (5 months) Data stratification and overlap of seasonal models overlap 1 month

15 5.5.200315 Perfect Prognosis for temperature forecasts: A typical model Data for the following results were selected according to: (SEASON_SF= 2) AND (HH= 12) 4 case(s) deleted due to missing data. Dep Var: T2M_P0H N: 548 Multiple R: 0.98533425 Squared multiple R: 0.97088359 Adjusted squared multiple R: 0.97050616 Standard error of estimate: 1.17789326 Effect Coefficient Std Error Std Coef Tolerance t P(2 Tail) CONSTANT -25.23580650 22.51961688 0.00000000. -1.12061 0.26295 Z500 0.00951429 0.00397318 0.21740651 0.0065415 2.39463 0.01698 T700 -0.14111429 0.05617324 -0.12111370 0.0231975 -2.51213 0.01229 RH700 -0.01421594 0.00192526 -0.06048159 0.8036540 -7.38390 0.00000 T850 -0.49766238 0.03697928 -0.43038524 0.0527208 -1.34E01 0.00000 COSINUS -1.75269612 0.19284168 -0.10759268 0.3847601 -9.08878 0.00000 Z8502 0.25777160 0.00698847 3.60146923 0.0056557 36.88525 0.00000 P_P0H2 -2.08679352 0.04590369 -3.40166499 0.0096300 -4.54E01 0.00000 Analysis of Variance Source Sum-of-Squares df Mean-Square F-ratio P Regression 2.49825E+04 7 3.56892E+03 2.57232E+03 0.00000000 Residual 7.49214E+02 540 1.38743253 ------------------------------------------------------------------------------- Durbin-Watson D Statistic 1.52355145 First Order Autocorrelation 0.23719694

16 5.5.200316 Perfect Prognosis for temperature forecasts: A typical model Data for the following results were selected according to: (SEASON_WS= 2) AND (HH= 00) 4 case(s) deleted due to missing data. Dep Var: T2M_P0H N: 3335 Multiple R: 0.949 Squared multiple R: 0.901 Adjusted squared multiple R: 0.901 Standard error of estimate: 1.404 Effect Coefficient Std Error Std Coef Tolerance t P(2 Tail) CONSTANT 22.426 0.529 0.000. 42.386 0.000 T850 0.063 0.023 0.065 0.054 2.753 0.006 COSINUS -2.238 0.129 -0.147 0.418 -17.372 0.000 Z8502 0.134 0.004 2.186 0.006 30.671 0.000 P_P0H2 -1.050 0.036 -1.966 0.007 -29.113 0.000 RH850 0.022 0.002 0.104 0.516 13.627 0.000 SINUS -1.114 0.058 -0.155 0.460 -19.281 0.000 TYPE_PNFFP0H -0.916 0.021 -0.365 0.433 -44.007 0.000 Analysis of Variance Source Sum-of-Squares df Mean-Square F-ratio P Regression 59581.994 7 8511.713 4320.200 0.000 Residual 6554.898 3327 1.970 ------------------------------------------------------------------------------- *** WARNING *** Case 11951 has large leverage (Leverage = 0.012) Durbin-Watson D Statistic 1.670 First Order Autocorrelation 0.165

17 5.5.200317 Perfect Prognosis for temperature forecasts: The models of Jokioinen (02935) used south of Jokioinen, the models of Sodankylä (02836) used north of Sodankylä and interpolation between these stations PPM calculated after every HIRLAM run (4 times a day) and for ECMWF data once a day to a grid Verification results available for stations (ME, MAE,...) Verification results available for grid (based on MESAN analysis) Timeseries of forecasts and observations for stations

18 5.5.200318 Location of Jokioinen and Sodankylä; ECWMF PPM and DMO

19 5.5.200319

20 5.5.200320 Verification results of PPM: Mean Error ME (bias) Mean Absolute Error MAE HIRLAM 00 UTC analysis ECMWF 12 UTC corresponding to the same valid time +06h +48h 18h

21 5.5.200321

22 5.5.200322

23 5.5.200323 ECMWF MAE Summer 2002

24 5.5.200324 Error of HIRLAM (and PPM) temperature forecasts (summer 2002 30 stations) Forecast length (hours)

25 5.5.200325 Error of ECMWF (and PPM) temperature forecasts (summer 2002 30 stations) Forecast length (hours)

26 5.5.200326 Dep Var: T2M_09UTC N: 3290 Multiple R: 0.962 Squared multiple R: 0.926 Adjusted squared multiple R: 0.926 Standard error of estimate: 1.354 Effect Coefficient Std Error Std Coef Tolerance t P(2 Tail) CONSTANT 25.947 0.238 0.000. 109.076 0.000 V700 -0.007 0.004 -0.011 0.766 -2.042 0.041 T850 -0.411 0.017 -0.377 0.089 -23.784 0.000 COSINUS -1.556 0.092 -0.091 0.771 -16.947 0.000 CL_MAX -0.085 0.015 -0.031 0.803 -5.861 0.000 Z8502 0.246 0.003 3.617 0.010 77.126 0.000 P_P0H2 -1.995 0.027 -3.249 0.012 -73.829 0.000 Dep Var: T2M_12UTC N: 3289 Multiple R: 0.983 Squared multiple R: 0.967 Adjusted squared multiple R: 0.967 Standard error of estimate: 0.945 Effect Coefficient Std Error Std Coef Tolerance t P(2 Tail) CONSTANT 30.611 0.166 0.000. 184.289 0.000 V700 -0.026 0.002 -0.039 0.767 -10.829 0.000 T850 -0.506 0.012 -0.445 0.089 -41.932 0.000 COSINUS -0.753 0.064 -0.042 0.771 -11.746 0.000 CL_MAX -0.317 0.010 -0.110 0.803 -31.163 0.000 Z8502 0.274 0.002 3.864 0.010 123.162 0.000 P_P0H2 -2.218 0.019 -3.459 0.012 -117.535 0.000 Jokioinen Summer 12 UTC TEMP PPM Models for 09 UTC and 12 UTC

27 5.5.200327 Error of HIRLAM (and PPM) temperature forecasts (autumn 2002 30 stations) Forecast length (hours)

28 5.5.200328 Temperature error of HIRLAM (and PPM) at Jokioinen (02935) summer 2002 Forecast length (hours)

29 5.5.200329 Temperature error of ECMWF (and PPM) at Jokioinen (02935) summer 2002 Forecast length (hours)

30 5.5.200330 Temperature error of HIRLAM (and PPM) at Sodankylä (02836) summer 2002 Forecast length (hours)

31 5.5.200331 Temperature error of ECMWF (and PPM) at Sodankylä (02836) summer 2002 Forecast length (hours)

32 5.5.200332 Error of ECMWF (and PPM) temperature forecasts (autumn 2002 30 stations) Forecast length (hours)

33 5.5.200333 Error of HIRLAM (and PPM) temperature forecasts (spring 2002 30 stations) Forecast length (hours)

34 5.5.200334 Error of ECMWF (and PPM) temperature forecasts (spring 2002 30 stations) Forecast length (hours)

35 5.5.200335 Error of HIRLAM (and PPM) temperature forecasts (winter 2002-2003 30 stations) Forecast length (hours)

36 5.5.200336 Error of ECMWF (and PPM) temperature forecasts (winter 2002-2003 30 stations) Forecast length (hours)

37 5.5.200337 Residuals versus Estimates Sodankylä PPM model in Winter (00 UTC)

38 5.5.200338 Error of ECMWF (and PPM) temperature forecasts (summer 2002 30 stations) Forecast length (hours)

39 5.5.200339 Error of ECMWF (and PPM) temperature forecasts (winter 2002-2003 30 stations) Forecast length (hours)

40 5.5.200340 Error of HIRLAM and ECMWF (& PPM) temperature forecasts in Finland (one year, 30 stations) Forecast length (hours)

41 5.5.200341 A Fuzzy system for adaptation of ECMWF T2m forecasts Ahti Sarvi Fuzzy system has been applied to correct the temperature (T 2m ) forecasts of ECMWF. These forecasts as well as HIRLAM forecasts have errors (systematic) typically in stable conditions (inversions). The objective of fuzzy system approach has been to utilize the information included in forecasts and corresponding observations by constructing a set of 2m temperature estimators based on the verifications of the most recent 27 successive 10 day forecasts. The set of estimates given by these estimators may involve missing values and outliers, but in fuzzy set approach these contradictions in the data do not cause problems provided that the amount of the information included in the set of estimates input to the system is sufficient.

42 5.5.200342 A Fuzzy system for adaptation of ECMWF T2m forecasts In an iterative solution process of fuzzy system a membership function, the values of which are normalized between zero and one, assigns the grade of membership for each estimate and zero for messy data and thus excludes the messy data from the solution and prevents it from corrupting the final estimate given by the system. The verification results for a short test period are presented

43 5.5.200343 Error of temperature forecasts (ECMWF/FUZZY_system 1-10 days mean) winter 2003 30 stations

44 5.5.200344 Concluding remarks PPM system needs some tuning After that it may be useful in editor environment –As a new SmartTool-script –As a method within the editor Fuzzy system has to be studied further but the preliminary results look promising; However, Fuzzy system needs a lot of work compared to other methods

45 5.5.200345 Reference Glahn, H.R.,1985: Statistical Weather Forecasting. Probability, Statistics and Decision Making in the Atmospheric Sciences, A.H. Murphy and R.W. Katz, Eds., Westview Press, 289-335. R

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