Presentation is loading. Please wait.

Presentation is loading. Please wait.

F. Prates Data Assimilation Training Course April 2008 1 Error Tracking F. Prates.

Similar presentations


Presentation on theme: "F. Prates Data Assimilation Training Course April 2008 1 Error Tracking F. Prates."— Presentation transcript:

1 F. Prates Data Assimilation Training Course April Error Tracking F. Prates

2 F. Prates Data Assimilation Training Course April 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 by detecting 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 Data Assimilation Training Course April 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 Data Assimilation Training Course April Investigations can be divided in the following main steps: When did occur (Verification Scores) Where did it happen (Error maps, EPS and Increment charts) What caused the error (Departures from different obs syst) TROUBLESHOOTING PROCEDURES

5 F. Prates Data Assimilation Training Course April WHEN? Verification statistics should tell which forecast had a bad performance

6 F. Prates Data Assimilation Training Course April WHEN? Verification statistics should tell which forecast had a bad performance

7 F. Prates Data Assimilation Training Course April WHEN ? FC 24 th 0Z FC 25 th 0Z AN 29 th 0Z AN 30 th 0Z

8 F. Prates Data Assimilation Training Course April WHEN ? AN 30Jul 0Z Fc+120 Fc+144 Fc+168

9 F. Prates Data Assimilation Training Course April WHEN ?(comparison with other models) NCEP D+5 MONTL D+5 AN 30 th 0Z BRAKL D+5 Best forecast

10 F. Prates Data Assimilation Training Course April WHEN ?(Inconsistency between successive fcs) FC 16 th 12Z FC 16 th 0Z a priori evaluation

11 F. Prates Data Assimilation Training Course April WHERE? Different techniques are used to identify the origin of forecast error 1) Error maps: A sequence of maps shows how initial errors will propagate 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.

12 F. Prates Data Assimilation Training Course April Winter track Summer track The most likely areas for errors (energy) to amplify rapidly (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)

13 F. Prates Data Assimilation Training Course April

14 F. Prates Data Assimilation Training Course April

15 F. Prates Data Assimilation Training Course April

16 F. Prates Data Assimilation Training Course April

17 F. Prates Data Assimilation Training Course April Wave train of errors

18 F. Prates Data Assimilation Training Course April Wave train of errors ?

19 F. Prates Data Assimilation Training Course April But most of the cases the error map is quite confusing !

20 F. Prates Data Assimilation Training Course April hPa geopot. (556 gpdam) fcst 10 Sep. 12 UTC - 13 Sep. 00 UTC Std. dev. 500 hPa geopot. of 51 ensemble members Influence Area: Extropical Transition Typhoon Maemi (2003) [Doris Anwender et al]

21 F. Prates Data Assimilation Training Course April hPa geopot. (556 gpdam) fcst 10 Sep. 12 UTC - 14 Sep. 12 UTC Std. dev. 500 hPa geopot. of 51 ensemble members Influence Area: Extropical Transition Typhoon Maemi (2003) [Doris Anwender et al]

22 F. Prates Data Assimilation Training Course April Influence Area: Extropical Transition Typhoon Maemi (2003) [Doris Anwender et al] 500 hPa geopot. (556 gpdam) fcst 10 Sep. 12 UTC - 15 Sep. 12 UTC Std. dev. 500 hPa geopot. of 51 ensemble members

23 F. Prates Data Assimilation Training Course April hPa geopot. (556 gpdam) fcst 10 Sep. 12 UTC - 20 Sep. 12 UTC Std. dev. 500 hPa geopot. of 51 ensemble members Influence Area: Extropical Transition Typhoon Maemi (2003) [Doris Anwender et al]

24 F. Prates Data Assimilation Training Course April 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.

25 WHERE? SW & W regions of Hudson Bay can be sensitive to possible error growth

26 F. Prates Data Assimilation Training Course April ANALYSIS INCREMENTS : UTC 700-hPa

27 F. Prates Data Assimilation Training Course April ANALYSIS INCREMENTS : UTC 700-hPa

28 F. Prates Data Assimilation Training Course April WHAT DATA? ECMWF data base provides records and statistics of available observations in the area (300 million obs values per day, 99% is from satellite) The cause/effect relation between obs and increments 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 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

29 F. Prates Data Assimilation Training Course April M Temp ANAL VT: 22Jul to 27Jul Z 30C< Orange < 35C35C< Red < 40C 25Jul 24Jul 23Jul22Jul 26Jul27Jul

30 F. Prates Data Assimilation Training Course April CAPE VT: 22Jul to 27Jul Z Orange <> CAPE > 5000 J/kg 25Jul 24Jul 23Jul 22Jul 26Jul27Jul

31 F. Prates Data Assimilation Training Course April NOAA Surface AN AN 24 12Z AN 25 12Z

32 F. Prates Data Assimilation Training Course April Anomalous warm conditions in NW USA and Canada during several days … and very high convective potential instability reaching a peak on 24 th & 25 th across the region … … preceded an advancing southward cold frontal system into the region

33 F. Prates Data Assimilation Training Course April

34 F. Prates Data Assimilation Training Course April WHAT DATA?

35 F. Prates Data Assimilation Training Course April WHAT DATA? A set of Temp obs was not used during several days because of the very anomalous warm layer (temperature observations were considered suspicious by quality control check) at lower levels … obs humidity was assumed suspicious by this quality check

36 F. Prates Data Assimilation Training Course April Daily Rep. of 25 th April: on 19 April 06UTC (2007) winds from 7 dropsondes were used even though the location information (lat/lon) was completely incorrect. The lat/lon was probably not reported and for some reason and they ended up as dropsondes from 0N; 0E. [..] although the wind departures were very large, they were not rejected by VarQC and therefore used by 4DVar. Decision: blacklist rule to reject all data with lat/lon=0/0 Other causes…:missing coordinates wrong observations

37 F. Prates Data Assimilation Training Course April Other causes…:missing coordinates wrong observations

38 F. Prates Data Assimilation Training Course April 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.

39 F. Prates Data Assimilation Training Course April To find out more: 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


Download ppt "F. Prates Data Assimilation Training Course April 2008 1 Error Tracking F. Prates."

Similar presentations


Ads by Google