Meteorological Service of Canada Status Report

1. Meteorological Service of Canada Status Report Global Observation Data Exchange for NWP Lannion, France Alain Beaulne, Simon Pellerin & many colleagues Meteorological Service of Canada 16-19 May, 2017

2. Contents • Data Assimilation and NWP Status Report (Alain Beaulne) • Update on observation usage • Operational Forecasting Systems • Current observation assimilated • Future plans • Other forecasting systems • Canadian networks status report and various topics (Simon Pellerin) • HPC Renewal Architecture • Canadian networks BUFR migration • TAC2BUFR Data Assimilation Systems migration • Canadian Radar Renewal project • Canadian AMDAR Program • Network of Networks initiative

3. Update on observation usage Since last meeting (Sep. 2015) : • SNPP (CriS, ATMS, AMVs) • Himawari-8 and MeteoSat-8 (CSR, AMVs) • ISS (RapidScat :discontinued August 2016) • GB-GPS (Europe)

4. Operational Forecasting Systems Global Deterministic Forecasting System (GDPS) Assimilation component • 4D-EnVar, no outer-loop (70 inner-loop iterations), increments computed every hour • Analysis increment horizontal grid : 0.45ox0.45ogaussian • 6h assimilation window, 15 minutes intervals • Background error-covariances : Average of NMC method covariances and 4D ensemble covariances from 256 ensemble members (EnKF) every hour • Buehner et al. (2015)

5. Operational Forecasting Systems Global Deterministic Forecasting System (GDPS) Forecast component • 2x 1287x417 Yin-Yang horizontal grid 25km/17.2km • 80 staggered hybrid levels, model lid at 0.1hPa • Qaddouri et al. (2015)

6. Operational Forecasting Systems RDPS (Regional) Assimilation component • LAM domain (pilot is the GDPS). • 4D-EnVar, 50 inner-loop iterations Forecast component • lat-lon1108x1082 points • 10km. • Caron et al. (2016)

7. Current observation assimilated Radiances (instrument/satellites): • AMSU-A (NOAA15/18/19, AQUA, Metop-A/B • MHS (NOAA18/19, Metop-A/B) • ATMS (SNPP) • SSMIS (DMSP17/18) • CSR (GOES13/15, MeteoSat-8/10, Himawari-8) • AIRS (AQUA) • IASI (Metop-A/B) • CriS (SNPP) RARS (NOAA15/18/19, Metop-B) GEPS model (Ensembles) AMSU-A / MHS / ATMS

8. Current observation assimilated Radiances (channels used): • AMSU-A (ch. 4 to 14) • MHS (ch. 2 to 5) • ATMS (ch. 5 to 15 and 17 to 22) • SSMIS (ch 12 to 18) • CSR (6.25um; GOES = 6.55um) • AIRS (142 ch.) • IASI (142 ch.) • CriS (103 ch.)

9. Current observation assimilated Definition of Band and Region from Blumstein and al., 2004

10. Current observation assimilated Other satellite observations: • GPS-RO refractivities(COSMIC, GRACE, TerraSAR, TanDEM, Metop) • GB-GPS (E-GVAP) • AMVs (Geo, NOAA, Metop, SNPP, Aqua, Terra) • Scatterometer winds (Metop) Non-satellite observations • Aircrafts • Radiosondes • Surface GEPS model (Ensembles) All except GB-GPS

11. Current observation assimilated Introduction of inter-channel obs-error correlations • For all radiances (MW and IR) • Based on the method of Desroziers et al. (2005) • Heilliette et al. (2014) Data thinning for radiances • Done on a 150km x 150km grid

12. Evolution : observation assimilated

13. Future plans Satellite data assimilation : • MWHS-2 (FY-3C) (RARS : ch. 10 to 16) • CSR : All WV channels (MeteoSat: 2, Himawari-8: 3) • GOES-R • AMVs (hourly, dual-metop) • ADM-Aeolus • Radiances (MW and IR) : Use of non-homogeneous thinning (higher resolution in midlatitudes)

14. Other forecasting systems Caldas (Canadian Land Data Assimilation System) Operational • Only screen-level observations Next 18 months • Retrievals of surface skin temperatures (GOES) Research • MIRAS (SMOS) • SMAP (SMAP) • Bilodeau et al. (2016)

15. Other forecasting systems GIOPS (Canadian Global Ice Ocean Prediction System) • Ocean data assimilation system uses satellite observations of SLA and SST. • Ice analyses (SIA) produced using satellite observations • Smith et al. (2016)

16. Other forecasting systems Sea-surface temperature (SST) analysis Operational • AVHRR (NOAA18/19, Metop-A/B) Next 18 months • VIIRS (SNPP) • AMSR-2 (GCOM-W1) Research • SLSTR (Sentinel 3A) • AHI (Himawari-8) • ABI (GOES-R)

17. Other forecasting systems Sea-level anomaly (SLA) Operationnal AVISO product : • Jason 2/3 • Cryosat-2 • Sentinel-3A • Saral/Altika

18. Other forecasting systems Sea-ice analysis (SIA) Operational • SSMI (DMSP-15) • SSMIS (DMSP-16/17/18) • ASCAT (Metop-A/B) * • AMSR-2 (GCOM-W1) * • AVHRR (NOAA-19) RARS * Next 18 months • VIIRS (SNPP) • MIRAS (SMOS) • SAR (RADARSAT-2) * In RIOPS (Regional)

19. ECCC HPC Renewal Architecture • From December 2003: IBM Power5, more Power5, and Power7 • RFP issued in November 2014 and Contract Awarded to IBM on May 27 2016 • Initial system, + 2 performance upgrades every 30 months • Option for 3rd performance upgrade. • Computing • About 70,000 Intel Broadwell cores • Supercomputer and PPP combined • More than 40PB of disk storage • 2.5 PB scratch storage per supercomputer (one per data hall)* • 16 PB site store per data hall (and coherent!!) • 1.1 PB disk cache to the archive per data hall • 200+ TB of SSDs for homes • More than 190 petabytes of tape storage (tape only, total of the two copies)

20. Resulting Architecture

21. Canadian networks BUFR migration • Radiosondes: • Expecting full implementation of BUFR for by October 2017 • Date for the ceasing of TAC bulletins not set out yet • Limitation of providing hi res RS (limitations of appropriate communication for remote locations) • Renewal process undergoing; proof of performance step completed 2 weeks ago; solution includes pressure sensors • Provided resolusition will be revised after renewal contract award • Marine program • AVOS – No target date. some uncertainty around feasibility of creating BUFR from source • Moored buoys – BUFR is expected to be part of WBS migration which will occur in 3-5 years time

22. Canadian networks BUFR migration • SYNOP BUFR Migration • Initial experimental flows ran from Jan. 2015 to Nov 2016 • Main and Intermediate SYNOP migration complete as of Nov 2016 • Intermediate SYNOP produced only for auto-stations • No mobile or hourly SYNOP bulletins produced • No planned date yet for turn-down of TAC SYNOP • Climat Migration • Currently only TAC CLIMAT bulletins : Infrastructure needs to be updated to generate TDCF • First delivery in early 2018 • First round will include 112 or more of total 132 RBCN stations

23. Selected TAC or BUFR reports over North America Number of levels for TAC (red) and BURF (blue) reports are shown for 16 December 2015 at 00Z. Blue dots indicate that the BUFR report was selected where as the other colors indicate that the TAC report was selected in assimilation experiment.

24. Stations reporting high-resolution data over Europe …. Number of levels for TAC (red) and BUFR (blue) reports are shown for 16 December 2015 at 00Z. Blue dots indicate that the BUFR report was selected where as the other colors indicate that the TAC report was selected in the assimilation experiment.

25. TAC2BUFR DAS migration - SYNOP • MSC receives about 80% of SYNOP stations in BUFR reports; • Use of a metadata dictionary to decode the SYNOP data; • BUFR usage in the MSC GDPS : • Priority is given to TAC • 90% of reports in TAC are assimilated; • BUFR only reports that are included in the CMC metadata dictionary are assimilated (10%); • The CMC metadata dictionary needs to be updated in order to add more BUFR only reports that are not assimilated. GTS 20% 80% TAC only BUFR reports Selection scheme TAC 90% BUFR 10% Data Assimilation Systems

26. TAC2BUFR - BUOYS • MSC receives almost all of BUOY reports in BUFR; • BUFR usage in the MSC GDPS : • Priority is given to TAC; • 56% of reports in TAC and 44% of reports in BUFR are assimilated. GTS 1% 99% TAC only BUFR reports Selection scheme TAC 56% BUFR 44% Data Assimilation Systems

27. TAC2BUFR - Radiosondes • MSC receives about 70% of radiosonde stations in BUFR reports; • Roughly 42% of the BUFR reports are generated through conversion of TEMP – referred to as reformatted reports ; • About 28% of stations report native BUFR (presence of drift position); • BUFR usage in the MSC GDPS : • Priority is given to TAC; • 99,3% of reports in TAC are assimilated; • BUFR only reports (reports from the ASAP ships) are assimilated from networks that don't produce TAC anymore (0,7%); • Metadata from BUFR reports are used after a position check with the CMC metadata dictionary is performed. GTS 30% 42% 28% Native BUFR Drift positions Reformatted BUFR No drift positions TAC only Selection scheme TAC 99,3% BUFR 0.7% Data Assimilation Systems

28. TAC2BUFR – New data selection scheme for Radiosondes • A selection scheme is developed with the following criteria: • Completeness of the BUFR reports; • Quality control of native BUFR reports; • Calculation of a dry energy norm of short-range forecast departures for TAC and BUFR reports. • The BUFR report is selected only if the number of observations in the BUFR report is greater than in the TAC report, if less than 10% of the profile contains suspicious drift positions and if the dry energy norm is smaller. • With this new selection scheme, most of the native reports are selected. • New data selection is scheduled to be implemented in fall 2017. GTS 30% 42% 28% Native BUFR Drift positions Reformatted BUFR No drift positions TAC only New Selection scheme TAC 83% BUFR 17% Data Assimilation Systems

29. Radiosonde data selection before assimilation Radiosonde profile Schleswig, Germany 16 December 2015 00Z Radiosonde profiles are thinned in such a way that one set of observations (Temperature, wind and dewpoint) are selected per model levels (dashed green lines). This figure shows data selected for the Schleswig station, which reported a high-resolution BUFR profile (2468 levels) used in the experiment (red) and a TAC profile (80 levels) used in the control (blue). After thinning, 68 levels, corresponding to model levels up to 8 hPa, were selected in the experiment, while 29 levels for the temperature and 43 levels for the wind were selected in the control. The number of levels for temperature and wind are not the same for TAC because significant levels for temperature and wind are not the same.

30. O-B Verification Scores Experiment Control Verification scores for short-term forecast (Background) against radiosonde observations (O-B) for the wind components (U and V), temperature and Dew point depression over Europe for the period of 15 to 26 December 2015. The scores for the control are in blue and those for the experiment are in red. The dashed curves are the bias and the solid curves are the standard deviation. The scores for the experiment are much better because a greater number of higher quality radiosonde observation are used, and data on significant levels are used in the control for which the representativeness error is larger. This is due to the fact that significant levels are often located in sharp transitions that the model can not well represent. For instance, strong temperature inversions (e.g. tropopause) and strong wind shears.

31. Canadian Radars : existing network Non-Doppler range: 256 km Doppler range: 112.5 km

32. Canadian Radar Renewal project : 1st scenario C-band: Range 240 km, Dedicated Doppler range 150 km S-band range 240 km 2023 Two new S band Sites

33. Canadian Radar Renewal project : 2nd scenario S-band range 240 km 2023 Two new S band Sites

34. C-AMDAR : Current Status • Canadian AMDAR program is and will continue to decline • Jazz is replacing it’s CRJ-200 with DHC-8/Q-400, which do not have capability of meeting AMDAR temperature accuracy requirements • As of Jan/2017 – 14 AC Jazz aircraft reporting Project Decommissioning Timelines

35. Proposed Initial Engagement Priorities • Considerations • Current understanding of airline/aircraft coverage & destination pairs • Perceived positive impacts on MSC priority applications (NWP, forecasting) • Potential ease of implementation • Additional Strategies • Stabilization/Sustainability of CDN AMDAR program through enrollment of Major Airlines (access to US airlines into CDN hubs, momentum) • AMDAR expansion through enrollment of regional airlines • Technical challenges with regional airlines servicing the north

36. Network of Networks (NoN)Collaborative initiative to enhance access and interoperability of hydrometeorological data in Canada

38. References • B. Bilodeau, M. Carrera, A. Russell, X. Wang and S. Belair, "Impacts of SMAP data in EnvironmentCanada'sRegionalDeterministicPrediction System," 2016 IEEE International Geoscience and RemoteSensing Symposium (IGARSS), Beijing, 2016, pp. 5233-5236. doi: 10.1109/IGARSS.2016.7730363 • Buehner, M., McTaggart-Cowan R., Beaulne A., Charette C., Garand L., Heillette S., Lapalme E., Laroche S., Macpherson S.R., Morneau J, Zadra A., 2015: Implementation of DeterministicWeatherForecastingSystemsBased on Ensemble–Variational Data Assimilation at Environment Canada. Part I: The Global System. Mon. Wea. Rev., 144, 2532–2559. DOI: http://dx.doi.org/10.1175/MWR-D-14-00354.1 • Caron, J.-F., and A. Zadra, 2015: Changes to the RegionalDeterministicPrediction System (RDPS) from version 4.2.0 to version 5.0.0. Canadian Meteorological Centre Technical Note. [Available on requestfromEnvironment Canada, Centre Météorologique Canadien, division du développement, 2121 route Transcanadienne, 4e étage, Dorval, Québec, H9P1J3 or via the following web site : http://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/technote_rdps-500_20160907_e.pdf • Heilliette, S., L. Garand, and M. Buehner, 2014 : Correlatedinterchannelobservation errorstatistics for radiances : estimation and impact in a nearoperationalcontext at Environment Canada. 94th Amer. Meteorol. Soc. Conf., Atlanta, GA, US, 2-6 Feb. 2014 • Qaddouri, A., C. Girard, and L. Garand, 2015: Changes to the Global DeterministicPrediction System (GDPS) from version 4.0.1 to version 5.0.0 – Yin-Yang gridconfirguration. Canadian Meteorological Centre Technical Note. [Available on requestfromEnvironment Canada, Centre Météorologique Canadien, division du développement, 2121 route Transcanadienne, 4e étage, Dorval, Québec, H9P1J3 or via the following web site : http://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/lib/technote_gdps-500_20151215_e.pdf • Smith, G. C., Roy, F., Reszka, M., SurcelColan, D., He, Z., Deacu, D., Belanger, J.-M., Skachko, S., Liu, Y., Dupont, F., Lemieux, J.-F., Beaudoin, C., Tranchant, B., Drévillon, M., Garric, G., Testut, C.-E., Lellouche, J.-M., Pellerin, P., Ritchie, H., Lu, Y., Davidson, F., Buehner, M., Caya, A. and Lajoie, M. (2016), Seaiceforecastverification in the Canadian Global IceOceanPrediction System. Q.J.R. Meteorol. Soc., 142: 659–671. doi:10.1002/qj.2555