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F. Prates/Grazzini, Data Assimilation Training Course March 2006 1 Error Tracking F. Prates/ F. Grazzini.

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Presentation on theme: "F. Prates/Grazzini, Data Assimilation Training Course March 2006 1 Error Tracking F. Prates/ F. Grazzini."— Presentation transcript:

1 F. Prates/Grazzini, Data Assimilation Training Course March 2006 1 Error Tracking F. Prates/ F. Grazzini

2 F. Prates/Grazzini, Data Assimilation Training Course March 2006 2 Monitoring of the forecasting system is carried out on daily basis by a meteorologist at ECMWF. The main reason of this activity is to investigate bad or very inconsistent forecast trying to detect deficiencies in the analysis and in the forecasting system. Investigations are covering all aspects of the system, often dealing with initial conditions (data availability) and data assimilation problems. INTRODUCTION ERROR TRACKING BY MEANS OF SYNOPTIC-DIAGNOSIS

3 F. Prates/Grazzini, Data Assimilation Training Course March 2006 3 Every day we summarize our findings in the MetOps Daily Report. The daily report is posted on our internal web site where can be accessed by people in RD and OD. Every four months there is a special meeting (OD/RD meeting) in which OD present a summary of the daily reports of the previous months. Daily Report

4 F. Prates/Grazzini, Data Assimilation Training Course March 2006 4 Investigations can be divided in the following main steps:  Spot the problem  Find out when the error enter in the system  Where did it happen  What caused the error TROUBLESHOOTING PROCEDURES

5 F. Prates/Grazzini, Data Assimilation Training Course March 2006 5 WHEN? Verification statistics would tell which forecast had a bad performance

6 F. Prates/Grazzini, Data Assimilation Training Course March 2006 6 ANALYSISECMWF D+6 FC WHEN ?

7 F. Prates/Grazzini, Data Assimilation Training Course March 2006 7 WHERE? Different techniques are used to identify the origin of forecast error 1) Error maps: A sequence of maps shows how initial errors will move downstream. Focus on the evolution of the most amplified error wave train. because … Error patterns become more complex as the forecast range increases. The energy associated to the wave train is transmitted by their group velocity which is different of phase speed of the individual perturbations.

8 F. Prates/Grazzini, Data Assimilation Training Course March 2006 8 +144

9 F. Prates/Grazzini, Data Assimilation Training Course March 2006 9 +120

10 F. Prates/Grazzini, Data Assimilation Training Course March 2006 10 +96

11 F. Prates/Grazzini, Data Assimilation Training Course March 2006 11 +84

12 F. Prates/Grazzini, Data Assimilation Training Course March 2006 12 +72

13 F. Prates/Grazzini, Data Assimilation Training Course March 2006 13 +60

14 F. Prates/Grazzini, Data Assimilation Training Course March 2006 14 +48

15 F. Prates/Grazzini, Data Assimilation Training Course March 2006 15 +36

16 F. Prates/Grazzini, Data Assimilation Training Course March 2006 16 +24

17 F. Prates/Grazzini, Data Assimilation Training Course March 2006 17 +12

18 F. Prates/Grazzini, Data Assimilation Training Course March 2006 18 Winter track Summer track The most likely areas for errors(energy) to rapidly amplify(release) are baroclinic regions and developing cyclones They provide the most efficient mechanism for the “spread of influence” in mid-latitude upper- tropospheric westerlies. Theoretical and observational studies indicate that the energy associated to the wave packets travel at 30˚/day in midlatitudes. ERROR PROPAGATION / DOWNSTREAM DEVELOPMENT (Anders Persson)

19 F. Prates/Grazzini, Data Assimilation Training Course March 2006 19 WHERE? 2) EPS perturbations: The perturbation fields computed by EPS can help to identify where the atmosphere is sensitive to possible errors growth. These perturbations are generated using singular vectors of a linear version of ECMWF, which maximize the total energy norm (phase space) over a 48- hour time interval with a energy peaking at around 700 hPa in regions of strong barotropic and baroclinic energy conversion, at initial time. Thus we expected that small errors in initial conditions will amplify most rapidly affecting the forecast.

20 F. Prates/Grazzini, Data Assimilation Training Course March 2006 20 WHERE?

21 F. Prates/Grazzini, Data Assimilation Training Course March 2006 21 Zoom on wind sensitivity: is suggesting a reshaping of the Jet streak west of Alaska SENSITIVITY MAPS: KEY ANALYSIS ERROR

22 F. Prates/Grazzini, Data Assimilation Training Course March 2006 22 COMPARISON WITH OTHERS MODELS ANALYSISD+6 ECMWF D+6 UKMOD+6 T255/UKMO ANA

23 F. Prates/Grazzini, Data Assimilation Training Course March 2006 23 ANALYSIS DIFFERENCE AT 500 hPa Z

24 F. Prates/Grazzini, Data Assimilation Training Course March 2006 24 WHERE? –Part 2

25 F. Prates/Grazzini, Data Assimilation Training Course March 2006 25 WHERE? 3) Extracting envelope of packets of synoptic waves (Rossby): Locate wave packets in atmospheric data (wave velocity group). This method is applied to the field difference between to successive forecast runs. We do expect … that local differences in the beginning would propagate in a form of Rossby waves packets in upper troposphere

26 F. Prates/Grazzini, Data Assimilation Training Course March 2006 26 DIFFERENCE TRACKING: streamfunction+envelope of the difference

27 F. Prates/Grazzini, Data Assimilation Training Course March 2006 27 DIFFERENCE TRACKING: streamfunction+envelope of the difference

28 F. Prates/Grazzini, Data Assimilation Training Course March 2006 28 DIFFERENCE TRACKING: streamfunction+envelope of the difference

29 F. Prates/Grazzini, Data Assimilation Training Course March 2006 29 DIFFERENCE TRACKING: streamfunction+envelope of the difference

30 F. Prates/Grazzini, Data Assimilation Training Course March 2006 30 DIFFERENCE TRACKING: streamfunction+envelope of the difference

31 F. Prates/Grazzini, Data Assimilation Training Course March 2006 31 DIFFERENCE TRACKING: streamfunction+envelope of the difference

32 F. Prates/Grazzini, Data Assimilation Training Course March 2006 32 DIFFERENCE TRACKING: streamfunction+envelope of the difference

33 F. Prates/Grazzini, Data Assimilation Training Course March 2006 33

34 F. Prates/Grazzini, Data Assimilation Training Course March 2006 34 ANALYSIS INCREMENTS : 20020416 00utc 850hPa

35 F. Prates/Grazzini, Data Assimilation Training Course March 2006 35 WHAT DATA? After “when” and “where” has been answered… ECMWF data base provides records and statistics of available observations in the area The cause/effect relation between increments and obs is not always trivial But we can… Assess the impact of different obs data in the analysis comparing the obs departures from the first-guess and analysis. With 4DVAR the increments no longer have a local interpretation- the errors may come from other regions as a consequence of the assimilation window. Other causes… If one or several observations are wrong → quality control is applied If the obs errors turn out to be systematic → blacklisting is produced

36 F. Prates/Grazzini, Data Assimilation Training Course March 2006 36 DROPSONDES and TEMPs at 850 hPa – 23-03utc 20040216 38 Dropsondes Analysis mass and wind increments at 850 hPa

37 F. Prates/Grazzini, Data Assimilation Training Course March 2006 37 OBSERVATIONS STATISTICS - 08082002 12UTC 3D view of the increments isosurfaces: 20040216 00utc Profile of mass increments at cursor location

38 F. Prates/Grazzini, Data Assimilation Training Course March 2006 38 DROPSONDES DEPARTURES from First-Guess and Analysis The analysis(solid) shows a significant reduction of the departure from Obs -> strong impact of the observation

39 F. Prates/Grazzini, Data Assimilation Training Course March 2006 39 AIRCRAFT DEPARTURES from First-Guess and Analysis Fairly strong impact also from aircraft

40 F. Prates/Grazzini, Data Assimilation Training Course March 2006 40 ATOVS RADIANCES DEPARTURES from the First-Guess Weak impact from polar satellite radiances due to very small departure from the First-Guess Not Used

41 F. Prates/Grazzini, Data Assimilation Training Course March 2006 41 QSCAT DEPARTURES from First-Guess and Analysis Moderate positive impact from Qscat

42 F. Prates/Grazzini, Data Assimilation Training Course March 2006 42 SUMMARY Synoptic diagnosis of NWP forecast is a necessary complement to the usual statistical verifications. Diagnostic tools allow to identify complex problems that often do not show up in objective scores. Through this type of monitoring we have been able to identify several problems successively taken under consideration by the RD department.

43 F. Prates/Grazzini, Data Assimilation Training Course March 2006 43 To find out more: http://www.ecmwf.int/products/forecasts/guide/Monitoring_of_the_data_assimilation_system.html Persson, A, 2000: Synoptic-dynamic diagnosis of medium range weather forecast systems, ECMWF Seminar on diagnosis of models and data assimilation systems, 6-10 September 1999.pp.123-137. Zimin A. V. et al., May 2003: Extracting Envelopes of Rossby Wave Packets, Monthly Weather Review, 131, pp. 1011-1017

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