Systematic Errors in the ECMWF Forecasting System
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1 Systematic Errors in the ECMWF Forecasting System Thomas Jung ECMWF
2 Introduction Two principal sources of forecast error: Uncertainties in the initial conditions Model error How to identify model errors? Relaxation experiments (Klinker) Budget diagnosis (Klinker and Sardeshmukh) Adjoint technique (see lectures on Sensitivity) Sensitivity experiments Diagnosis of systematic errors
3 Concept of Systematic Error dˆ( se f t, o t ) d (ˆ f t ) d (ˆ o t ) Relatively stratightforward to compute (simple maths) BUT there are pitfalls: finite length (significance tests) apparent systematic error for short time series (loss of predicatibility) Observations might be biased
4 Scope of the Study Q: Do we still have significant systematic errors in the ECMWF forecasting system? If so, what are the main problems? How did systematic errors evolve over the years? How do systematic errors grow? How well do we simulate specific phenomena (e.g., blocking, extratropical cyclones)? How sensitive are systematic errors to horizontal resolution?
5 Data Systematic errors and their growth Operational forecasts (D+10) ERA-40 forecasts (D+10) EPS control forecasts (D+20) Monthly forecasts (D+30) Seasonal forecasts (beyond D+30) Evolution of systematic errors Operational forecasts + analyses Verification (ERA-40, satellite products)
6 Systematic Z500 Error Growth: Medium-Range (DJFM ) D+1 D+3 D+5 D+10
7 Asymptotic Z500 Errors (DJFM )
8 Asymptotic Z500 Errors (DJFM ) Cycle 26r1 Cycle 26r3
9 Two Different Aerosol Climatologies
10 Systematic Error Growth: EPSC Z500 (DJFM ) Forecast Range (hours)
11 Evolution of D+3 Systematic Z500 Errors
12 Reduction of Systematic Errors ( )
13 Systematic Errors: AMIP Models
14 Systematic D+3 Z500 Errors: 3 NWP Models ECMWF Meteo-France DWD
15 Blocking Methodology L L H H
16 Blocking Frequency Biases (23r4) ERA-40 D+1 D+4 D+7 D+10 DJFM
17 Asymptotic Systematic Errors cy26r1
18 Blocking Episodes (DJFM ) n >30 ERA Model 188 (26r1)
19 Blocking and Horizontal Resolution
20 Stochastic Physics: The Rationale Stochastic forcing from unresolved processes. Model tendencies due to parameterized physical processes have a certain coherence on the space and time scales associated, for example, with organized convection. A way to simulate this is to impose space-time correlation on the random numbers.
21 North Pacific Weather Regimes
22 Influence of Stochastic Physics on North Pacific Weather Regimes
23 Schematic of the MJO From Madden and Julian (1994)
24 The Madden and Julian Oscillation
25 Extratropical Cyclones: Questions How well do we simulate observed characteristics of extratropical cyclones? Cyclone tracking (do we learn something new?) How sensitive are the results to horizontal resolution?
26 Extratropical Cyclones: Experiments Four data sets: ERA-40 for verification T95L60 run (29R1) T159L60 run (29R1) T255L40 run (29R1) 6-hourly MSLP interpolated to a common 2.5x2.5 deg grid DJFM (forecasts start 1st October)
27 Tracking Software Strategy: Searching for and tracking local minima in MSLP fields High temporal resolution required (6-hourly data) Data have been interpolated to 1-hourly data for tracking The accuracy of the software is very high The software is fast
28 Extratropical Cyclone Tracks ( )
29 Number of Northern Hemisphere Cyclones
30 Number of Cyclones DJFM
31 Synoptic Activity Bias II T95 T159-T95 T159 T255 T255-T159
32 Conclusions (1) Main systematic errors: Atmospheric circulation over the North Pacific Synoptic activity Euro-Atlantic blocking Clouds Temperature in the stratosphere Specific humidity in the tropics KE of TE over the Northern Hemisphere Madden-and-Julian Oscillation
33 Conclusions (2) Did we improve in terms of systematic errors? There is no straightforward answer. It depends on the parameter/phenomenon, region, vertical level, season, and the forecast range being considered.
34 Conclusions (3) In general, though: Improvements for most parameters, particularly in the short-range and near medium-range. Neutral for some parameters/phenomena. Only a few deteriorations.
35 References Jung, T. and A.M. Tompkins, 2003: Systematic Errors in the ECMWF Forecasting System. ECMWF Technicial Memorandum Jung, T., A.M. Tompkins, and R.J. Rodwell, 2004: Systematic Errors in the ECMWF Forecasting System. ECMWF Newsletter, 100, Jung, A.M. Tompkins, and R.J. Rodwell, 2005: Some Aspects of Systematic Errors in the ECMWF Model. Atmos. Sci. Lett., 6, Jung, T., T.N. Palmer and G.J. Shutts, 2005: Influence of a stochastic parameterization on the frequency of occurrence of North Pacific weather regimes in the ECMWF model. Geophys. Res. Lett., 32, doi: /2005gl024248, L Jung, T., 2005: Systematic Errors of the Atmospheric Circulation in the ECMWF Model. Quart. J. Roy. Meteor. Soc., 131, Jung, T., S.K. Gulev, I. Rudeva and V. Soloviov, 2006: Sensitivity of extratropical cyclone characteristics to horizontal resolution in the ECMWF model. Quart. J. Roy. Meteor. Soc., in press. Gates and coauthors, 1999: An Overview of the Results of the Atmospheric Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80, Hoskins, B.J. and K.I. Hodges, 2002: New Perspectives on the Northern Winter Storm Tracks. J. Atmos. Sci., 59, Koehler, M., 2005: ECMWF Technicial Memorandum Palmer, T.N., G. Shutts, R. Hagedorn, F. Doblas-Reyes, T. Jung, and M. Leutbecher, 2005: Representing Model Uncertainty in Weather and Climate Prediction. Ann. Rev. Earth. Planet Sci., 33,
36 Impact of Stochastic Physics on Blocking (Dec-Mar ) ERA40 CNTL Stoch. Phys. 95% CL for Control
37 Number of Cases of Cyclo-Genesis DJFM
Influence of stochastic physics on the frequency of occurrence of North Pacific weather regimes in the ECMWF model
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