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PP KENDA-O : Km-Scale Ensemble-Based Data Assimilation

This report evaluates a data assimilation system over five years, focusing on technical developments, observation usage, soil moisture analysis, and adaptation to high-resolution models.

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PP KENDA-O : Km-Scale Ensemble-Based Data Assimilation

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  1. Status Report for KENDA-O • PP KENDA-O : Km-Scale Ensemble-Based Data Assimilation • for the use of High-Resolution Observations • (Sept. 2015 – Aug. 2020) • Task 1: further development of LETKF scheme (conventional obs, operationalisation) •  understanding discrepancies betw. DWD and MeteoSwissKENDA results • Task 2: extended use of observations •  Mode-S operational at DWD • Task 3: lower boundary: soil moisture analysis using satellite soil moisture data •  new fellow Paride Ferrante familiarizing, SM-DA runs in parallel suite • Task 4: adaptation to ICON-LAM  schedule , • hybrid methods (also particle filters)  paper submitted

  2. Overview Task 1: Further development of KENDA DWD • (lots of) technical work (diagnostic datool; BACY-1; ‘Swiss experiment’) • bias correction for AMDAR temperature (mixed impact) • can AMDAR be omitted if Mode-S are used?  experiment: moderate negative impact, particularly on precipitation MeteoSwiss • parallel KENDA COSMO-E suite (up to +24h) with additive inflation since 1 Jan. 2018 (DACE V1.53; but still not with 24-bit coding of T_SO) • 2-year position on additive covariance inflation / regional B matrix: Claire Merker • NMC method, based on COSMO-E re-forecasts for 1 year • visit at DWD 30 – 31 Jan. (coord., learn about B matrix generation in DACE, …)

  3. Overview Task 1: Further development of KENDA ARPAE-SIMC • continued tests of KENDA initial conditions for the 2.2 km ensemble COMET • KENDA slightly worse than COMET-LETKF, continued investigation • implementation + test of KENDA at higher resolution (2.2 km) Stochastic Pattern Generator (Roshydromet) • work on a new Bayesian model Error Modeling Scheme (BEMS) • combines SPPT and an additive perturbation scheme • produces non-Gaussian perturbations consistent with model statistics, for meaningful humidity / cloud fields • the spatio-temporal aspect is simulated using the SPG

  4. Overview Task 2: Extended use of observations 3-D radar (radial velocity Vr , reflectivity Z) • Vr: Elisabeth Bauernschubert: • 5-week convective summer exp. (revised obs errors, elevations 0.5°, 1.5°, 3.5°): small positive impact on precipitation, reduced when Mode-S is also used; tiny benefit on (rs/sfc) wind; positive in radar verif up to +4h • neutral impact on low stratus; further winter test to be evaluated • Z: Axel Seifert, Alberto de Lozar: • positive impact from TKE cycling • issue: occasionally large spurious circles of reflectivity in 0-h forecasts, why? • plans (outside KENDA-O): investigate role of model errors in DA (rel. to LES), tune (micro)physics to better match obs (radar, satellite) • Uli Blahak: fixed position for radar, but fully busy with coordinating SINFONY  Christian Welzbacher ~ 50% ? • Z: Virginia Poli, Thomas Gastaldo (ARPAE-SIMC): DA with  1 Italian radars: • continued sensitivity exp. (using different obs errors for Z) • technical work

  5. Overview Task 2: Extended use of observations GNSS slant total delay (STD, Michael Bender) • experiments: positive impact on precip so far, mixed otherwise (negative at low levels) • obs positioned further aloft: negative impact • unexpected result finally identified to be due to a code bug in DACE/LETKF (related to vertical localisation set in namelistfor a specific obs type)  progress slowed down

  6. Overview Task 2: Extended use of observations SEVIRI radiances • direct use of all-sky (cloudy) SEVIRI WV + IR window radiances: Axel Hutt continued work on clear-skyWV radiances: • revised Jacobian / vertical localisation (no impact so far) • bias correction  small positive impact from clear-sky WV data for the first time • use of NWC-SAF cloud top height (CTH) product: currently no resources • (outside KENDA-O:) direct use of all-sky (cloudy) SEVIRI VIS/NIR radiances • first cycled DA test, working on vertical localisation, Albedo, etc.

  7. Overview Task 2: Extended use of observations Screen-level obs • RH2M (+ T2M) : preliminary tests at DWD • 10-m wind : test w. new criteria (dep on. roughness length + d2zs/(dx2dy2)) for station selection: ~ neutral impact Mode-S aircraft • winter tests, operational at DWD (see later) Ground-based remote sensing (T + qv prof.: Raman lidar, MW radiometer) • MCH: proof-of-concept assimilation trial with COSMO-7 by nudging: 0.5-hourly profiles are assimilated successfully AMSU, ATMS, IASI • nothing done

  8. Task 4.1 KENDA adaptation for ICON-LAM: (ambitious!) schedule (1) development of experimental KENDA-LETKF for ICON-LAM • 02/18: MEC-based LETKF for ICON-LAM (working) in BACY-1 basic cycle (technically,no snow/SST ana.)  short exp.1 – 4 days to find bugs, gain experience • 03/18: MEC-based LETKF for ICON-LAM in BACY-1 full DA cycle (with snow/SST analysis), technically with LHN (to do: use of obs only in inner part of (irregular) domain)  technical equivalence with MEC-based COSMO-KENDA cycle, diagnose • 04/18: ICON-LAM online-obs4-D LETKF in BACY-1 (implement obs operators in ICON)  technical equiv. with operational COSMO-KENDA, start running tests • 04 – 12/18: consolidate ICON-LAM-KENDA to get similar performance as with operational COSMO-KENDA (possibly with remaining ‘known issues’)

  9. Task 4.1 KENDA adaptation for ICON-LAM: (ambitious!) schedule (2) variational extension for deterministic analysis: • 02/18 – 12/18: ICON-LAM regional B matrix (in collab. w. MeteoSwiss) • 03/18 – 11/18: 3DVar for ICON-LAM, technical development (requires re-write of ‘COSMO’ obs operators, develop TL + adjoint, otherwise use global operator) • 07/18 – 03/19: EnVar for ICON-LAM, technical development (4-D EnVar would require substantial (> 3 months AR) implementation :  Grib input / output of several time slot and handling them in BACY)  extension of horiz. interpolation operators (2D  2D + time)) towards operational use • 07/18 – 12/18: ICON-LAM in NUMEX, thereafter extended experiments • 07/19: ICON-LAM parallel suite

  10. Mode-S aircraft: Introduction Mode-S aircraft • derived from radar data from air-traffic control, processed + provided by KNMI (de Haan, Geophys. Res., 2011; de Haan and Stoffelen, Wea. Forecast.., 2012) • wind vector + temperature, T derived from Mach number • every 4 sec • compared to AMDAR: • 4 x more data (after 40% thinning) • no humidity, larger T error from: Lange and Janjic, MWR 2016

  11. Mode-S aircraft: parallel trial with adjusted obs error 1 – 24 Sept. 2017 (trial in parallel suite) RH RH2M rad global T total cloud T2M wind speed ps mid-level cloud wind dir. 10-m wind dir. • slightly positive change in RMSE [%]

  12. Mode-S aircraft: winter test, upper-air / surface / radar verif. Dec 2016 (1 Dec. 2016 – 1 Jan. 2017) FSS (precip 0.1 mm/h , 30 km) RH RH2M 0 UTC runs REF Mode-S T T2M 12 UTC runs wind speed ps wind dir. 10-m wind dir. • Dec. 2016: positive impact in upper-air + synopverif. change in RMSE [%]

  13. Mode-S aircraft: winter test, low-level cloud (low stratus) REF Mode-S worse better hits / missed events / false alarms / undefined (observed mid-level, high, or fractional cloud)

  14. Mode-S aircraft: winter test, low-level cloud (low stratus) REF Mode-S better hits / missed events / false alarms / undefined (observed mid-level, high, or fractional cloud)

  15. Mode-S aircraft: winter test, low-level cloud (low stratus) REF Mode-S better hits / missed events / false alarms / undefined (observed mid-level, high, or fractional cloud)

  16. Mode-S aircraft: winter test, low-level cloud (low stratus) REF Mode-S better worse hits / missed events / false alarms / undefined (observed mid-level, high, or fractional cloud)

  17. Mode-S aircraft: winter test, low-level cloud (low stratus) REF Mode-S better worse hits / missed events / false alarms / undefined (observed mid-level, high, or fractional cloud)

  18. Mode-S aircraft: summary • impact tested in summer + winter, in BACY experiments (data cut-off time not taken into account) + in parallel suite • impact varies between neutral and clearly positive • no single parameter in any test period worse • impact depends on weather situation: clearly positive for • precipitation in convective situations with weak advection / large-scale forcing • (radiative) low stratus  Mode-S introduced operationally at DWD on 4 Oct. 2018

  19. Investigation of discrepancies between MeteoSwiss & DWD KENDA MeteoSwiss analysis verification DWD verification + 0 h COSMO-1  nudging COSMO-E  KENDA COSMO-7  nudging Winter 2016 Spring 2017 (without additive inflation)

  20. Investigation of discrepancies between MeteoSwiss & DWD KENDA  run experiments: comparison KENDA vs. Nudging for Dec. 2016 (winter, extended low stratus periods) MeteoSwiss • MCH COSMO-E setup (EC-LBC, domain, …) + MCH obs (BUFR radiosondes), but DWD settings of KENDA (additive inflation, 24-bit coding of T_SO, etc.) • still being set up DWD : ‘Swiss experiment’ • DWD setup (KENDA, ICON-LBC, obs(no Mode-S)), but on COSMO-E domain • standard verification: similar results as with COSMO-DE

  21. Investigation of discrepancies between MeteoSwiss & DWD KENDA temperature # obs RMSE MEC based on Swiss cdfin files  no LETKF first guess check 1 – 26 Dec 2016 RMSE # obs MEC based on DWD ekf files  with LETKF first guess check 1 – 27 Dec 2016 • LETKF f.g. check • rejects too many obs • (for verif & analysis)

  22. Investigation of discrepancies between MeteoSwiss & DWD KENDA temperature f.g.: < 100 1 – 25 Dec 2016 act + pass: ~ 48000 MEC based on Swiss cdfin files  no LETKF first guess check MEC based on DWD ekf files  with LETKF first guess check f.g.: ~ 1500 act + pass: ~ 32000 near LBC / above 300 hPa • LETKF f.g. check • rejects too many obs • (for verif & analysis)

  23. Investigation of discrepancies between MeteoSwiss & DWD KENDA relative humidity # obs RMSE MEC based on Swiss cdfin files  no LETKF first guess check 1 – 26 Dec 2016 RMSE # obs MEC based on DWD ekf files  with LETKF first guess check 1 – 27 Dec 2016 • LETKF f.g. check • rejects too many obs • (for verif & analysis)

  24. Investigation of discrepancies between MeteoSwiss & DWD KENDA wind speed # obs RMSE MEC based on Swiss cdfin files  no LETKF first guess check 1 – 26 Dec 2016 RMSE # obs MEC based on DWD ekf files  with LETKF first guess check 1 – 27 Dec 2016 • LETKF f.g. check • rejects too many obs • (for verif & analysis)

  25. Investigation of discrepancies between MeteoSwiss & DWD KENDA RH T wind speed wind dir. 1 – 27 Dec 2016 MEC based on Swiss cdfin files  no LETKF first guess check MEC based on DWD ekf files  with LETKF first guess check • MEC mode: no effect on wind scores • but affects T at low levels and RH

  26. Investigation of discrepancies between MeteoSwiss & DWD KENDA • performance differences between KENDA and nudging similar in ‘Swiss experiment’ as with COSMO-DE (e.g. low stratus, surface verif, standard radiosonde verif) • MeteoSwiss radiosonde (cdfin) obs input: about 50% more data (T, RH, wind) • COSMO (cdfin-based MEC) first guess check rejects almost no data • LETKF first guess check rejects about 5% for T, RH and about 2.5% for wind, particularly near inversions (and in stratosphere)  too many good obs are rejected • discrepancies in upper-air analysis scores at MCH and DWD are (apparently) mainly due to different quality control in verification, not due to difference in analysis and forecast performance of KENDA as a result of different model domains, ensemble LBC’s, data input, etc. • solution: refine first guess check in LETKF analysis (work started)

  27. Status Report for KENDA-O Christoph SchraffDeutscher Wetterdienst, Offenbach, Germany Contributions / input by: Hendrik Reich, Andreas Rhodin, Roland Potthast, Klaus Stephan, Ulrich Blahak, Michael Bender, Elisabeth Bauernschubert, Axel Hutt, … (DWD) Daniel Leuenberger, Alexander Haefele (MeteoSwiss); Sylvain Robert (ETH) Chiara Marsigli, Virginia Poli, Tiziana Paccagnella, Thomas Gastaldo (ARPA-SIM) Lucio Torrisi, Francesca Marcucci, Valerio Cardinali (COMET) Mikhail Tsyrulnikov, Dmitri Gayfulin (HMC)

  28. Mode-S aircraft: Introduction • bug fixes in COSMO V5.04d • experiment 26 May – 10 June 2016 (‘E19’) • best results with thinning (40 % active) • Mode-S dominate above 800 hPa number of active obs • a-posteriori Desroziers statistics from DA experiment for estimation of obs errors: unexpected large Mode-S wind errors • data processing corrected by KNMI since 15 May 2017, re-processed historical data on request • new exp. ‘E22’ with re-processed data: Mode-S wind obs errors similar to AMDAR (but specified obs error variance still large)

  29. Mode-S aircraft: BACY test for convective period with weak advection Mode-S aircraft : forecast verification (26 May – 10 June 2016) wind dir. RH wind dir. wind speed ps RH2M wind speed T T2M TD2M change in RMSE [%] • positive impact from Mode-S throughout

  30. Mode-S aircraft: BACY test for convective period with weak advection 0-UTC runs 26 May – 10 June 2016 12-UTC runs Ref Mode-S (E19) Mode-S (E22) 0.1 mm/h 1-hrly precip FSS ( 30 km) 1 mm/h • Mode-S: clear long-lasting positive impact

  31. Mode-S aircraft: parallel trial trial in parallel suite 1 – 28 Aug. 2017 1 – 20 Aug. 2017 , 0-UTC runs 0.1 mm/h RH ps RH2M T 1 mm/h T2M wind speed change in RMSE [%] • Aug. 2017: much smaller positive impact than in 2016 convective period ! Why? • weather? data quality, data cut-off time ? (the last 15 min of Mode-S obs not available in time)

  32. Mode-S aircraft: data availability / timeliness / cut-off time data availability at cut-off time = 15 min (!) of COSMO-DE AMDAR obs valid -29 to -15 min AMDAR obs valid -14 to -0 min Mode-S obs valid -29 to -15 min 97 % available 70 % available 95 % available • operational setup: only the last 15 min of Mode-S data are missing  cut-off time unlikely reason of minor impact in parallel suite Mode-S obs valid -14 to -0 min 0 % available

  33. Mode-S aircraft: new obs errors (specified / estimated) 1 – 24 Sept. 2017 (trial in parallel suite) • in comparison to AMDAR: Mode-S data quality at least as good as for re-processed data  data quality very unlikely reason of minor impact in parallel suite  forecast impact of Mode-S apparently dependent on weather regime

  34. Mode-S aircraft: winter test, cloud Percent Correct (-1, 1) Mode-S – REF low-level cloud total cloud

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