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Meteorological Service of Canada Status Report

This status report provides updates on the usage of observation data for global Numerical Weather Prediction (NWP) and presents future plans for data assimilation systems. It covers topics such as EnKF and EnVar for global and regional deterministic and ensemble prediction systems, usage of BUFR RS, SYNOP, and Buoys, sea surface temperature analysis, ice ocean prediction systems, Canadian land data assimilation system, and radar data assimilation plans.

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Meteorological Service of Canada Status Report

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  1. Meteorological Service of Canada Status Report 2nd Global Observation Data Exchange for NWP New Delhi, India Simon Pellerin & many colleagues Meteorological Service of Canada 27-30 November, 2018

  2. Contents • Update on observation usage for NWP : Major data assimilation implementation in September 2018) • EnKF for Global Ensemble Prediction System (GEPS; 39 km) • EnVar for Global Deterministic Prediction System (GDPS; 25 km) • EnVar for Regional Deterministic Prediction System (RDPS; 10 km) • Usage of BUFR RS, SYNOP and Buoys • Future plans for NWP-DAS • Other DA Systems • Sea Surface Temperature analysis • Global/Regional Ice Ocean Prediction System (G/RIOPS) • Regional and Global Ice analyses • Canadian Land Data Assimilation System (Caldas) • Plan for Radar data assimilation

  3. Operational MSC Numerical Weather Prediction Systems (June 2018) Observations Main systems (with assimilation) Sub-systems Products/Forecasts Emergency Response Global Systems Hurricane Forecasts • GDPS-NEMO (**) (EnVar) • GEPS (EnKF) • CanSIPS (Nudging) UMOS-SCRIBE WAM-Global GDWPS ATModel (2) Global Ice Ocean Forecasts SIP (La Nina/El Nino) (3) NAEFS Global Wave Forecasts Weekly to Multi-seasonal Probabilistic Outlooks RAQDPS FireWork UMOS-AQ Emergency Response Regional Wave Forecasts RMPS-GSL (4) RDPS-CGSL Regional Systems UMOS-SCRIBE System dependencies • RDPS (EnVar) • RIOPS_A (*) (3DVar, PA (4)) Polar Forecasts (YOPP) Severe-Weather Forecasts WAM-Reg RDWPS REWPS HRDPS-Natl CAPS (*) REPS Regional Ice Ocean Forecasts Public, Maritime, Aviation Forecasts ATModel(2) RIOPS_F(*) SHOP(*) Other Analyses Air Quality & AQHI Forecasts Water Cycle Forecasts Storm Surge Forecasts • GIOPS_A (SEEK(1)) • CaPA (OI), SST (OI) • RDAQA (OI) • Ice Analysis (3DVar) • Land surface (***) aTAGS NowCast RDSPS RESPS(*) WCPS-GLS (*) Legend (1): SEEK: Singular Evolutive Extended Kalman (2):ATModel: Atmospheric Transport Model (3): SIP: Seasonal Inter-annual Prediction (4): Pseudo-Analysis (*): Experimental systems (**): Coupled systems (***): Soil, snow, humidity, and temperature Contact: Implementation & Operational Services (CMOI-CMC) at ec.production-info.ec@canada.ca

  4. Changes to global EnKF (GEPS) • Analysis increments and forecast: • 800 x 400 Gaussian grid at 50 km -> Yin-Yang grid at 39 km • Initialisation of first-guess: digital filter -> incremental analysis update • Recycles some key dynamics and physics variables: • virtual temperature, horizontally staggered winds, cloud condensate, boundary layer variables • model top raised from 2 hPa to 0.1 hPa (L74 to L81) • Infrared observations : AIRS, IASI and CrIS added (14 channels)

  5. Changes to global EnKF (Cont.) • Quality control with a Huber norm for all observations • Further (with respect to the EnVar) thinning is applied for satellite wind, radiance, aircraft and Scatterometer observations • Many changes in the processing of the observations common to EnVar

  6. Changes to radiosonde observations • Use of high-precision of temperature and humidity observations • Much higher vertical resolution with the reporting of the accurate time and position at each level • Sampling every 2 seconds allows up to 3600 levels for a 2 hour ascent • Revised saturation vapor pressure formula (AERK) • Revised humidity limits in the analysis 19 July 2018, 00 UTC

  7. New rejection criteria for humidity observations • Previously all radiosonde humidity observations where assimilated from surface to 70 hPa • New rejection criteria for humidity (based on ECMWF implementation): • Rejected if above 100 hPa for Vaisala 92 and 41 series • Rejected if above 300 hPa for all other radiosonde types • Rejected if T < -60 C for Vaisala 92 et 41 series • Rejected if T < -40 C for all other radiosonde types

  8. Radiosonde data Selection for DA Since the end of 2014, CMC’s NWP systems ingest BUFR reports The selection scheme BUFR selected if : • Verifies the completeness of BUFR profiles • Nbr of obs. in the BUFR profile > TAC profiles • Comparison between the native drift and the estimated drift (based on the native time displacement and the averaged wind between 2 pressure levels.) • less than 10% of the profile contains suspicious drift positions • dry energy norm of short-range forecast departures (O-B) is calculated for both TAC and BUFR profiles. • dry energy norm of BUFR profile < 1.3 X norm TAC profile GTS ~96% 35% 40% Native BUFR Drift positions Reformatted BUFR No drift positions TAC Selection scheme TAC 67% BUFR 33% Data Assimilation Systems

  9. 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 : • 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

  10. 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

  11. Changes to AMV observations • New data selection using QI1/2 (incl. GOES) • New background check scheme • Situation dependent observation errors, (tracking and height assignment errors) (Forsythe and Saunders,2008) • Winds rejected above 200 hPa (160 hPa over the Tropics) instead of 50 hPa. • Assimilation of dual-Metop data (40-60N/S latitude bands) • Zenith angle for GEO extended to 68 degrees to fill the gaps in high latitudes • Assimilation of hourly GOES and Meteosat data (GOES16 included since 7 June 2018) • Revised QC criteria (QI1 > 85 for HIMAWARI data • Background check is now applied to the square vector difference • Revised blacklisting based on the one implemented at the UK Met-Office 3 August 2018, 0600 UTC

  12. Pre-screening of AMV Data According to Quality Flags OPE GOES-16 GOES-13 OPE GOES-16 GOES-13 RFF/EE RFF/EE

  13. Mean Number of AMVs per Analysis (Overlap Period: 15 Dec – 05 Jan 2018)

  14. Assimilation of additional CSR WV Channels • Assimilation of all CSR water vapor (WV) channels available • ( Previous / New ) • Sensitivity peaking levels (US Standard Atmosphere) • 6,25mm = 350hPa, 6,95mm = 450hPa, 7,35mm = 600hPa • Same operational QC criteria applied to avoid cloud contamination • (lower peaking channel = more sensitive to cloud = less data assimilated ) • In the data selection process, priority is given to profiles having the greatest number of channels • GOES-16 has 3 CSR WV channels. Provisional maturity data currently tested. Full maturity product will be available in may 2019;

  15. Changes to GB-GPS data processing Observation Error • Old: ZTD observation errors (OER) are set dynamically based on the observed ZWD (PW) using coefficients obtained from linear regression of GPS site monthly ZTD Std(O-P) with site monthly mean ZWD. • New: ZTD OER as a function of observed ZWD are estimated using the Desroziers (2005) method. This new approach gives ZTD OER that are significantly lower than before. Backround Check • Old: Abs(O-P) < 4∙ SQRT(OER2+FGE2) • where FGE is the background ZTD error. This method gave unrealistically large values for FGE in rare cases such that very large O-P values could pass the O-P check. • New: Abs(O-P) < 4∙ Std(O-P) • where Std(O-P) is determined by using linear regression between archived O-P and ZWD (PW).

  16. Implementation of RTTOV-12 • Operational version 10.2 no longer supported by EUMETSAT NWP SAF since version 12 release on February 2017 • New version allows to use newer coefficient files: • Better vertical resolution (from 44 to 54 levels). Particularly beneficial for high peaking MW channels such as AMSU-A 13 and 14 (and ATMS 14 and 15) • No extrapolation of optical properties at the surface below 1013.25 hPa • Improved consistency for water vapor between microwave and IR instruments • Updated spectroscopy, trace gases concentration, and for some instruments spectral response functions

  17. Change in the number of observations assimilated per day in the GDPS Average number of observations assimilated (in million) during the winter 2017 period

  18. Changes to 4D-Ensemble-Variational assimilation (both GDPS and RDPS) • Code was significantly modified to improve efficiency on new XC-40 computer (now ~10 minutes on 27 nodes, with higher resolution): • Reorganize calculations and array shapes to improve cache reuse • Use MPI derived types to avoid need to recopy data during MPI data transposes in spectral transform • Use RAM disk to improve efficiency/stability when reading ensemble • Avoid reading same file on all MPI tasks (e.g. RTTOV coefficient files) • Increased horizontal resolution of the analysis increment from ~50km to ~37km using a grid that fits well on XC-40 nodes • Use ensemble covariances over all vertical levels (except top few) and increase weight of ensemble covariances in hybrid mix: • Was (0.5Bnmc + 0.5Bens), now (0.375Bnmc + 0.75Bens) • Flow dependant B matrix raised from 3hPa to 0.1 hPa

  19. 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 (GOES15, MeteoSat-8/11, Himawari-8) • AIRS (AQUA) • IASI (Metop-A/B) • CriS (SNPP) RARS (NOAA15/18/19, Metop-B) GEPS model (Ensembles) AMSU-A / MHS / ATMS

  20. 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; 7.35; 6.95; GOES-15 = 6.55um) • AIRS (142 ch.) • IASI (142 ch.) • CriS (103 ch.)

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

  22. 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, dual-Metop) • Scatterometer winds (Metop) Non-satellite observations • Aircrafts • Radiosondes • Surface GEPS model (Ensembles) All except GB-GPS

  23. Current observation assimilated 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

  24. Changes to the Regional Deterministic Prediction System (RDPS) - LAM • Intermittent analysis cycle  Continuous cycle • Digital filter initialization  Incremental analysis update Catchups w/r to GDPS • Coldstart  Warmstart (recycling of key physics variables) Joint changes with GDPS • 50% Bens / 50% Bnmc 75% Bens / 37,5% Bnmc • 50 iterations  70 iterations + Hessien cycling Change specific to RDPS • 39km EnKF; Obs proc.; RTTOV 12; EnVar ch. • Additional surface observations • Shared ops suite with GDPS

  25. Additional surface observations (RDPS) SYNOP + METAR + MSC partner networks reports valid at 00, 06, 12 and 18 UTC SYNOP reports valid at 00, 06, 12 and 18 UTC Previous Sept. 2018 implementation

  26. Future plans for NWP-DAS Satellite data assimilation : • MWHS-2 (FY-3C) (RARS & Global: ch. 10 to 16) • CSR : All WV channels (GOES-16/17) • AMVs (GOES-17 and NOAA-20) • CrIS FSR from NOAA-20 & NPP • Suominet (UCAR) GB-GPS • FY-3C GPS-RO • ADM-Aeolus (L2B from ECMWF) • Radiances (MW and IR) : Use of non-homogeneous thinning (higher resolution in midlatitudes)

  27. Sea Surface Temperature analysis • SST analysis refers to a depth temperature (foundation SST) without diurnal variability • Global 0.1º resolution, latitude/longitude grid • Assimilates satellite data (AVHRR, AMSR2, VIIRS), in situ data (moored and drifting buoys, ships) • Methodology: the statistical interpolation method is applied to the analysis problem, the observation quality control, and to the satellite bias correction • Length scales of the background error correlations - isotropic and symmetric about the equator • Uses ice information: proxy SST data are inserted at locations where ice is present – ice concentration greater than 0.6 • Special treatment of cold wakes due to tropical storms

  28. Sea Surface Temperature analysis

  29. GIOPS – Global Ice Ocean Prediction System • Produces weekly and daily analyses • Numerical model NEMO-CICE • Tri-polar ORCA grid 0.25° resolution, less than 15km in the Arctic, 50 vertical levels • Based on SAM2 (Système d’Assimilation Mercator v2) • Analysis method : reduced-order Kalman filter using a SEEK formulation • Background error covariances based on the statistics of a collection of 3D ocean state anomalies derived from a multi-year hindcast simulation • Data assimilated: • Sea surface temperature (from satellite and in situ observations), • Subsurface temperature and salinity (from Argo, XBT, moorings, etc.) • Quality-controlled observations provided by CLS (Collecte Localisation Satellites) France • Sea level anomalies from satellite altimeters

  30. GIOPS analysis

  31. Regional and Global Ice analyses • The ice analyses run 4 times per day • Data assimilated: Satellite data (AVHRR,ASCAT,SSMI,SSMIS,AMSR2), CIS charts • Assimilation method : 3D Var

  32. Regional and Global Ice analysis

  33. In the future • GIOPS • SLA from Sentinel3b, Swot, Jason-CS • SST • VIIRS data from NOAA20 • SLSTR data from Sentinel3 • Ice • Ice thickness from Cryosat 2, IceSat 2, SMOS, SMAP • Ice concentration from VIIRS (S-NPP, NOAA20)

  34. Canadian Land Data Assimilation System(Caldas) Operational • Only screen-level observations Research • MIRAS (SMOS: Passive L-band brightness temperatures) • SMAP (SMAP :  Passive L-band brightness temperatures) • GOES : Skin Temperature Retrievals (North America) • AIRS/IASI : Skin TemperatureRetrievals (Global) • CRIS : Skin TemperatureRetrievals (Global) • IMS (Interactive Multisensor Snow and Ice Mapping System) :  Pseudo-observations for snow (Northern Hemisphere)

  35. Plan for Radar data assimilation Assimilation of 2D reflectivity mosaics through Latent Heat Nudging Research @ 10 km completed Research @ 2.5 km ongoing Operational @2.5 km ~2020 Assimilation of Doppler velocities Monitoring of observations Monitoring of observations & assimilation research 2019 Operational ~2021 Direct assimilation of radar Reflectivity Later than 2022

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