11-12 April 2005EC GLFE-TAMDAR Presentation1 Environment Canada “CMC Monitoring of GLFE TAMDAR Data” Environment Environnement Canada Gilles Verner, Yulia Zaitseva, Réal Sarrazin / Gilles Fournier Canadian Meteorological Centre / AMWSD

11-12 April 2005EC GLFE-TAMDAR Presentation2 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

11-12 April 2005EC GLFE-TAMDAR Presentation3 Background on CMC models and monitoring

11-12 April 2005EC GLFE-TAMDAR Presentation4 CMC Operational Models Global ModelRegional Model 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 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 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

11-12 April 2005EC GLFE-TAMDAR Presentation5 Distribution maps of aircraft observations assimilated in 6-hour period: 3D-VAR analysis CMC

11-12 April 2005EC GLFE-TAMDAR Presentation6 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

11-12 April 2005EC GLFE-TAMDAR Presentation7 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

11-12 April 2005EC GLFE-TAMDAR Presentation8 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

11-12 April 2005EC GLFE-TAMDAR Presentation9 Status of Canadian AMDAR Program Development

11-12 April 2005EC GLFE-TAMDAR Presentation10 Largest Regional with 67 DHC-8s and increasing fleet of CRJs to 73 by March 2006 AMDAR started with 21 AC Jazz DHC 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) Development with AC Jazz 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

11-12 April 2005EC GLFE-TAMDAR Presentation11 Sabre C&C System

11-12 April 2005EC GLFE-TAMDAR Presentation12 FSL web-Canadian data, 24 hrs, 5 Apr 05

11-12 April 2005EC GLFE-TAMDAR Presentation13 Aircraft Profiles in Real-Time

11-12 April 2005EC GLFE-TAMDAR Presentation14 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

11-12 April 2005EC GLFE-TAMDAR Presentation15 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

11-12 April 2005EC GLFE-TAMDAR Presentation16 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

11-12 April 2005EC GLFE-TAMDAR Presentation17 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

11-12 April 2005EC GLFE-TAMDAR Presentation18 Plans for the Future

11-12 April 2005EC GLFE-TAMDAR Presentation19 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

11-12 April 2005EC GLFE-TAMDAR Presentation20 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

11-12 April 2005EC GLFE-TAMDAR Presentation21 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?

11-12 April 2005EC GLFE-TAMDAR Presentation22 Monitoring of AMDAR data at CMC

11-12 April 2005EC GLFE-TAMDAR Presentation23 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

11-12 April 2005EC GLFE-TAMDAR Presentation24 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

11-12 April 2005EC GLFE-TAMDAR Presentation25 Ex. T Bias on DHC-8 Aircraft Unacceptable mean T-bias over 2C from DHC-8 using original OEM temperature probe CMC Aug 02

11-12 April 2005EC GLFE-TAMDAR Presentation26 Ex. T Bias on DHC-8 Aircraft Significant change in TT/UV biases: probes changes by Jazz in Dec 04! CMC Aug 02

11-12 April 2005EC GLFE-TAMDAR Presentation27 Ex. T Bias on DHC-8 Aircraft Density plot of innovations of temperature, all data for month of October Note the known temperature bias of the DHC-8 CMC

11-12 April 2005EC GLFE-TAMDAR Presentation28 Ex. T Bias on DHC-8 Aircraft Density plot of innovations of temperature, all data for month of January Note that the temperature bias of the DHC-8 is gone! CMC

11-12 April 2005EC GLFE-TAMDAR Presentation29 Ex. DHC-8 Aircraft Wind Monitoring Scatter plot for wind, all data for month of March Note a few bad values when forecasting light winds! CMC

11-12 April 2005EC GLFE-TAMDAR Presentation30 AC Jazz Data assimilated at CMC CMC Impact of 4D-Var

11-12 April 2005EC GLFE-TAMDAR Presentation31 Monitoring of GLFE-TAMDAR at CMC

11-12 April 2005EC GLFE-TAMDAR Presentation32 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

11-12 April 2005EC GLFE-TAMDAR Presentation33 GLFE Data Received at CMC 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.

11-12 April 2005EC GLFE-TAMDAR Presentation34 GLFE Data Received at CMC CMC Time series of the amount of data received, 25 day period.

11-12 April 2005EC GLFE-TAMDAR Presentation35 Monitoring of GLFE TAMDAR Data at CMC CMC Innovations of MVD and speed bias, all data with good flag only. Note speed bias.

11-12 April 2005EC GLFE-TAMDAR Presentation36 Monitoring of GLFE TAMDAR Data at CMC CMC Innovation of temperature, all data with good flag only. Note occasional larger deviations, but biases remain small.

11-12 April 2005EC GLFE-TAMDAR Presentation37 Monitoring of GLFE TAMDAR Data at CMC CMC Innovation of temperature, all data with good flag only, month of February. Note more frequent larger deviations, but biases remain small.

11-12 April 2005EC GLFE-TAMDAR Presentation38 Monitoring of GLFE TAMDAR Data at CMC CMC 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. Bad data

11-12 April 2005EC GLFE-TAMDAR Presentation39 Monitoring of GLFE TAMDAR Data at CMC CMC Scatter plot for wind, all data for month of March Note some bad wind data (larger deviations) but with good flag. These bad data are affecting the overall statistics. Bad data

11-12 April 2005EC GLFE-TAMDAR Presentation40 Single Aircraft: GLFE-0217 CMC 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

11-12 April 2005EC GLFE-TAMDAR Presentation41 Single Aircraft: GLFE-0205 CMC 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.

11-12 April 2005EC GLFE-TAMDAR Presentation42 Single Aircraft: GLFE-0205 CMC 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.

11-12 April 2005EC GLFE-TAMDAR Presentation43 Monitoring criteria for Suspect Aircraft March 2005 TAMDAR Pressure Categories (hPa) LOW PRESS: SFC MID PRESS: HIGH PRESS: 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

11-12 April 2005EC GLFE-TAMDAR Presentation44 Results as a table BUFR FORMAT TEMPERATURE OBSERVATIONS SUSPECT TEMPERATURES ID ELEM LEVEL NA NE NG NR TRMS TBIAS GLFE0238 TEMP GLFE0283 TEMP GLFE0217 TEMP GLFE0251 TEMP GLFE0238 TEMP 701-SFC GLFE0283 TEMP 701-SFC GLFE0217 TEMP 701-SFC GLFE0242 TEMP NON-SUSPECT TEMPERATURES ID ELEM LEVEL NA NE NG NR TRMS TBIAS GLFE0247 TEMP 701-SFC GLFE0262 TEMP GLFE0244 TEMP GLFE0203 TEMP GLFE0266 TEMP ETC…

11-12 April 2005EC GLFE-TAMDAR Presentation45 Conclusion and Discussion

11-12 April 2005EC GLFE-TAMDAR Presentation46 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