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Environment Canada

Environment Environnement Canada Canada. Environment Canada. “ CMC Monitoring of GLFE TAMDAR Data”. Gilles Verner, Yulia Zaitseva, R éal Sarrazin / Gilles Fournier Canadian Meteorological Centre / AMWSD. Outline. Background on CMC models and monitoring

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Environment Canada

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  1. Environment Environnement Canada Canada Environment Canada “CMC Monitoring of GLFE TAMDAR Data” Gilles Verner, Yulia Zaitseva, Réal Sarrazin / Gilles Fournier Canadian Meteorological Centre / AMWSD EC GLFE-TAMDAR Presentation

  2. Outline • Background on CMC models and monitoring • Status of Canadian AMDAR Program Development • Plans for the Future • Monitoring of AMDAR data at CMC • Monitoring of GLFE-TAMDAR at CMC • Conclusion and Discussion EC GLFE-TAMDAR Presentation

  3. Background on CMC models and monitoring EC GLFE-TAMDAR Presentation

  4. CMC Operational Models GEM model used for both global and regional versions 4D-var assimilation for global system as of March 15, 2005 Regional system still using 3D-Var Global Model Regional Model • Variable resolution grid • Resolution of .1375º (~15 km) • 58 eta levels • Kain-Fritsch scheme • Sundqvist stratiform scheme • ISBA surface module with • soil moisture pseudo-analysis • (error feedback, no data) • 48-hour forecasts (00Z -12Z) • Cut-off of T+1h40 • Uniform grid • Resolution of .9º (~100 km) • 28 eta levels • Kuo convection scheme • Sundqvist stratiform scheme • Force-restore surface module • with climatogical soil moisture • 10 day forecasts at 00Z • and 6 day forecasts at 12Z. • Cut-off of T+3h00 EC GLFE-TAMDAR Presentation

  5. Distribution maps of aircraft observations assimilated in 6-hour period: 3D-VAR analysis CMC EC GLFE-TAMDAR Presentation

  6. Distribution maps of aircraft observations assimilated in 6-hour period: 4D-VAR analysis The amount of aircraft data assimilated in 4D-VAR has tripled compared to 3D-VAR! CMC EC GLFE-TAMDAR Presentation

  7. Data assimilated in 3D-VAR Analysis Global - Monthly mean number of assimilated observations per 24 hours over 6h- assimilation window: 3D-VAR analysis (green color –aircraft observations). CMC EC GLFE-TAMDAR Presentation

  8. Data assimilated in 4D-VAR Analysis Global - Monthly mean number of assimilated observations per 24 hours over 6h- assimilation window: 4D-VAR analysis (green color –aircraft observations). CMC EC GLFE-TAMDAR Presentation

  9. Status of Canadian AMDAR Program Development EC GLFE-TAMDAR Presentation

  10. Development with AC Jazz • Largest Regional with 67 DHC-8s and increasing fleet of CRJs to 73 by March 2006 • AMDAR started with 21 AC Jazz DHC-8 100 in June 2002 but T bias issue • 24 AC Jazz CRJs reporting good T and wind data on GTS (73 by March 06) • 24 AC Jazz upgraded DHC-8 reporting good T and wind data (67 Nov 05) • Canadian AMDAR data: assimilated at CMC • distributed on GTS since 4 Jan 05 • displayed on FSL web since 12 Jan 2005 • C&C operational since 3 Feb 2005 EC GLFE-TAMDAR Presentation

  11. Sabre C&C System EC GLFE-TAMDAR Presentation

  12. FSL web-Canadian data, 24 hrs, 5 Apr 05 EC GLFE-TAMDAR Presentation

  13. Aircraft Profiles in Real-Time EC GLFE-TAMDAR Presentation

  14. Development with First Air • First Proof Of Concept system about to be tested on a test B727 • If test is positive and funds available, proceed with implementation on 8 aircraft by 31 March 06 and 6 in FY06/07 EC GLFE-TAMDAR Presentation

  15. Historical Background • 4-phase development contract began in mid-2002: • Phase 1 – feasibility analysis (completed by 31 March 2003): • not economically and technically possible to upgrade each aircraft • TAMDAR selected due to its promise to be easily adaptable to various aircraft configurations and requiring minimal certification • Phase 2 – development of POC ISAT/TAMDAR/Internet (completed in Fall 2003) • Phase 3 – POC system testing on a B727 (most of the delays - hope to be completed by 30 June 2005): • certification by FAA and then Transport Canada generated significant delays • lots of unexpected technical problems (GPS, data rates, calibration, First Air/Skytrack/AirDat priorities) • capital procurement funding returned twice due to these delays • Phase 4 – deployment on 15 aircraft (8 in FY05/06; 6 in FY06/07): • if test is positive and funding available • test to be difficult as, contrarily to GLFE, not much data in the north • calibration request heavier than anticipated • wind quality in the north is a big unknown • a lot of the QC moved to the ground processing centre EC GLFE-TAMDAR Presentation

  16. Projected Weekly Ascents/Descents Notes: 1. Includes expected CRJ and DHC-8 operated by Jazz and 15 aircraft operated by First Air 2. Does not cover WestJet and Air Canada 3. Canadian North would add 30% more data in North 4. AFIRS/UpTime would be deployed to fill holes EC GLFE-TAMDAR Presentation

  17. Development with AFIRS/UpTime • AFIRS = Automated Flight Information and Reporting System • Independent datalink system for small airlines that cannot afford ACARS • Per flight hour data fees – No upfront costs to clients • Partnership with TC’s Flight Data Monitoring (FDM) program • AMDAR capability was developed and tested on 3 HawkAir DHC-8s operating in BC (T-bias issue) • AMDAR system based on AMS AFIRS expected to be on all 5 B737 aircraft from Canadian North by 30 June 2005 • A dedicated T/RH sensor integrated to AFIRS is being investigated EC GLFE-TAMDAR Presentation

  18. Plans for the Future EC GLFE-TAMDAR Presentation

  19. Plans for the Future • Impact studies of Canadian AMDAR data by CMC and Canadian operational forecasters(?) in FY05/06 • On-going activities: • Internal development (CMC…) • AC Jazz comms • First Air comms; TAMDAR LCM • AMS AFIRS/UpTime comms (Canadian North, HawkAir, etc.) • WestJet, Air Canada… comms • LCM for required non aircraft critical systems • Remaining development activities: • Implement on 15 First Air aircraft • Expand coverage through AFIRS/UpTime • AMDAR development on WestJet (B737 aircraft) • AMDAR development on Air Canada Embraers ERJs • Business Case to EC for the measurement of humidity • Aviation-related (icing, turbulence) – BC to NAV CANADA EC GLFE-TAMDAR Presentation

  20. BC to NAV CANADA Objectives: contribution of NAV CANADA sought on development and operation of: • AMDAR turbulence reporting capacity; • AMDAR icing reporting capacity; • AMDAR Program on-going communication costs associated with expanding AMDAR coverage EC GLFE-TAMDAR Presentation

  21. AMDAR VS GEOSS • AMDAR meets all global GEOSS requirements: • Affordable • Expandable • Sustainable • Global coverage • International standards • Can target observations • Best global in-situ tropospheric data for satellite calibration • Air Quality Sensing Load? • Aircraft mesoscale network filling hi-res plume dispersion model in case of a NCB attack? EC GLFE-TAMDAR Presentation

  22. Monitoring of AMDAR data at CMC EC GLFE-TAMDAR Presentation

  23. Aircraft Sensor Monitoring • Meteorological Centres such as CMC that run Numerical Weather Prediction (NWP) models can monitor the performance of aircraft sensors used in AMDAR on a continuous and real-time basis • Monitoring based on observed minus first guess values (innovations), as well as data rejection statistics, extracted from operational data assimilation system • Monitoring is performed for individual aircrafts as well as by AMDAR programs (e.g. E-AMDAR, GLFE, etc). • Time evolution of innovations, as well as their statistical distribution are extremely useful tools EC GLFE-TAMDAR Presentation

  24. QC techniques at CMC • In CMC 3D and 4D-Var, data QC based on 2 checks: • A simple background check (comparison with first guess, data are rejected if departure from first guess is larger than pre-specified limits (4-5 times the normalised std deviations). This is used to identify large (or gross) errors • A more sophisticated variational quality control which is applied during the minimisation process, taking into account the consistency of the observations with other observations as well as the first guess and the final analysis. QC decisions can (and do) change during the minimisation process EC GLFE-TAMDAR Presentation

  25. Ex. T Bias on DHC-8 Aircraft Aug 02 Unacceptable mean T-bias over 2C from DHC-8 using original OEM temperature probe CMC EC GLFE-TAMDAR Presentation

  26. Ex. T Bias on DHC-8 Aircraft Aug 02 Significant change in TT/UV biases: probes changes by Jazz in Dec 04! CMC EC GLFE-TAMDAR Presentation

  27. Ex. T Bias on DHC-8 Aircraft Density plot of innovations of temperature, all data for month of October 2004. Note the known temperature bias of the DHC-8 CMC EC GLFE-TAMDAR Presentation

  28. Ex. T Bias on DHC-8 Aircraft Density plot of innovations of temperature, all data for month of January 2005. Note that the temperature bias of the DHC-8 is gone! CMC EC GLFE-TAMDAR Presentation

  29. Ex. DHC-8 Aircraft Wind Monitoring Scatter plot for wind, all data for month of March 2005. Note a few bad values when forecasting light winds! CMC EC GLFE-TAMDAR Presentation

  30. AC Jazz Data assimilated at CMC Impact of 4D-Var CMC EC GLFE-TAMDAR Presentation

  31. Monitoring of GLFE-TAMDAR at CMC EC GLFE-TAMDAR Presentation

  32. Monitoring of GLFE TAMDAR at CMC • Data in BUFR format obtained from AIRDAT ftp server and processed like other AMDAR • Special care was taken to properly interpret quality flags which are present in the BUFR files: TAMDAR data flagged as SUSPECT or BAD were NOT included in the monitoring, but are available in the database. Counts on how many data are flagged. • Monitoring done for all data as well as for individual aircraft. • Tables of “suspect” data generated on a monthly basis using the standard WMO criteria • Results available on a monitoring web site (intranet) • Test restricted to temperature and wind EC GLFE-TAMDAR Presentation

  33. GLFE Data Received at CMC GLFE observations decoded by CMC. About 4700 observations from all levels, over a 6-hour window centered at 18 UTC on 04 April 2005. CMC EC GLFE-TAMDAR Presentation

  34. GLFE Data Received at CMC Time series of the amount of data received, 25 day period. CMC EC GLFE-TAMDAR Presentation

  35. Monitoring of GLFE TAMDAR Data at CMC Innovations of MVD and speed bias, all data with good flag only. Note speed bias. CMC EC GLFE-TAMDAR Presentation

  36. Monitoring of GLFE TAMDAR Data at CMC Innovation of temperature, all data with good flag only. Note occasional larger deviations, but biases remain small. CMC EC GLFE-TAMDAR Presentation

  37. Monitoring of GLFE TAMDAR Data at CMC Innovation of temperature, all data with good flag only, month of February. Note more frequent larger deviations, but biases remain small. CMC EC GLFE-TAMDAR Presentation

  38. Monitoring of GLFE TAMDAR Data at CMC Bad data Density plot of innovations of temperature, all data with good flag only, month of February. Note some very bad data (large deviations) but with good flag. These bad data are flagged by CMC background check. CMC EC GLFE-TAMDAR Presentation

  39. Monitoring of GLFE TAMDAR Data at CMC Scatter plot for wind, all data for month of March 2005. Note some bad wind data (larger deviations) but with good flag. These bad data are affecting the overall statistics. Bad data CMC EC GLFE-TAMDAR Presentation

  40. Single Aircraft: GLFE-0217 Density plot of innovations of temperature for aircraft GLFE-0217, all data with good flag only, month of March. Note some very bad data (large deviations) but with good flag CMC EC GLFE-TAMDAR Presentation

  41. Single Aircraft: GLFE-0205 Density plot of innovations of temperature for aircraft GLFE-0205, all data with good flag only, month of March. TAMDAR TT data is generally of very good quality. CMC EC GLFE-TAMDAR Presentation

  42. Single Aircraft: GLFE-0205 Scatter plot for wind for aircraft GLFE-0205, all data with good flag only, month of March. TAMDAR wind data is generally of very good quality. Some positive speed bias a small concern. CMC EC GLFE-TAMDAR Presentation

  43. Monitoring criteria for Suspect Aircraft March 2005 TAMDAR Pressure Categories (hPa) LOW PRESS: 701 - SFC MID PRESS: 301 - 700 HIGH PRESS: 300 - 100 ID: is the aircraft tail number NA: is the total number of available observations NE: is the total number of erroneous observations NR: is the number of rejected observations NG: is the number of gross observations excluding erroneous data NC: is the number of exactly calm winds excluding erroneous data TBIAS: is the temperature bias for non-gross temperatures and non-erroneous data TRMS: is the RMS temperature difference excluding gross errors and erroneous data SBIAS: is the speed bias for non-gross winds and non-erroneous data WRMS: is the RMS wind difference excluding gross errors and erroneous data Selection criteria : num obs >= LOW:20, MID:50, HIGH:50 SUSPECT CRITERIA Temperature Bias: LOW 3.0; MID 2.0; HIGH 2.0 Temperature RMS: LOW 4.0; MID 3.0; HIGH 3.0 Wind Speed Bias: LOW 3.0; MID 2.5; HIGH 2.5 Wind RMS: LOW 10.0; MID 8.0; HIGH 10.0 More than 2% of observations are gross EC GLFE-TAMDAR Presentation

  44. Results as a table BUFR FORMAT TEMPERATURE OBSERVATIONS SUSPECT TEMPERATURES ID ELEM LEVEL NA NE NG NR TRMS TBIAS GLFE0238 TEMP 301-700 156 6 32 36 1.9 -1.1 GLFE0283 TEMP 301-700 338 106 61 120 1.1 -0.8 GLFE0217 TEMP 301-700 2392 163 140 196 1.5 -0.6 GLFE0251 TEMP 301-700 2034 110 82 142 1.2 -0.4 GLFE0238 TEMP 701-SFC 213 92 56 109 1.9 -0.2 GLFE0283 TEMP 701-SFC 509 119 113 201 4.1 0.6 GLFE0217 TEMP 701-SFC 3757 195 297 585 3.7 0.6 GLFE0242 TEMP 301-700 616 9 0 13 3.3 3.0 NON-SUSPECT TEMPERATURES ID ELEM LEVEL NA NE NG NR TRMS TBIAS GLFE0247 TEMP 701-SFC 647 456 0 342 1.9 -1.2 GLFE0262 TEMP 301-700 2131 63 0 16 1.4 -1.0 GLFE0244 TEMP 301-700 2788 1961 0 1946 1.4 -0.9 GLFE0203 TEMP 301-700 1643 221 0 33 1.5 -0.9 GLFE0266 TEMP 301-700 1630 69 0 16 1.4 -0.9 ETC… EC GLFE-TAMDAR Presentation

  45. Conclusion and Discussion EC GLFE-TAMDAR Presentation

  46. Conclusion and Discussion • Summary of monitoring results: • TAMDAR data generally of good quality. Some concern about a small positive wind bias • Some obviously bad data are making it to the BUFR files and are corrupting the overall statistics. This is affecting a few aircraft (for February, GLFE 217,225, 240,244,248,249,253,255,270,271,275,279 and 287. • These bad data are transmitted with a good quality flag (data with the bad flags are not used). • A more stringent QC at the source should be considered to remove these bad data • These bad data are usually identified by the NWP QC processes. This would prevent their assimilation. • Monitoring by NWP process important and useful to identify issues with data EC GLFE-TAMDAR Presentation

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