Christoph Schraff Deutscher Wetterdienst, Offenbach, Germany 22.07.2018 KENDA operationalisation & status Christoph Schraff Deutscher 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)

KENDA operationalisation & status KENDA-O overview 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 (work towards operationalization: additive covariance inflation) limiter to soil moisture perturbations AMDAR bias correction, RTPS, AMDAR RH Task 2: extended use of observations Mode-S aircraft (afternoon or tomorrow) GPS slant total delay (afternoon) Task 3: lower boundary: soil moisture analysis using satellite soil moisture data Task 4: adaptation to ICON-LAM, hybrid methods / particle filters

operational use of KENDA MeteoSwiss: KENDA operational for EPS since 19 May 2016 (x = 2.2 km) KENDA for det. COSMO-1: 2018 (T,qv in PBL still worse, some problems  slides) DWD: KENDA operational for det. + EPS since 21 March 2017 (x = 2.8 km) ARPAE-SIMC: KENDA operational for determ. forecast (Italy) since May 2017, for EPS soon (2.2 km) COMET: KENDA code adapted to include (Italy) required capabilities of COMET system and run in a parallel suite (x = 10 km) (still slightly worse than COMET code)

KENDA-LETKF pre-operational setup KENDA at DWD: KENDA-LETKF pre-operational setup  KENDA: 4D-LETKF + LHN (latent heat nudging for assimilation of radar precip) benchmark: operational Nudging + LHN operational EPS (40 mem.)  K: Kalman Gain for ensemble mean (unperturbed) 1 hour 1 hour (pre-) operational settings ( Schraff et al. 2016, QJRMS) : adaptive horizontal localisation (keep # obs constant, 50 km ≤ s ≈ std dev ≤ 100 km) adaptive mutliplicative covariance inflation (obs-f.g. statistics) + RTPP (p = 0.75) explicit soil moisture perturbations conventional obs types only (radiosonde, aircraft, wind profiler, synop)

pre-operational KENDA suite at DWD in summer 2016: effect of soil moisture perturbations (SMP) 24 Aug. 2016 soil moisture layer 5, diff. betw. 2 mem. (1,8) T2m at noon, diff. betw. 2 mem. (1,8) 200 100 -100 10 5 -5 -10 standard soil moisture perturb.:  T2m deviations of individual ensemble members unrealistically large in some situations 100 50 -50 10 5 -5 -10 test: soil moisture perturbations reduced by 50 % :  T2m deviations realistic  implement limiter to spread of soil moisture index (SMI) and assess impact on LETKF (spread)

description of new setup with limiter for soil moisture perturbations current setup: soil moisture perturbations:  = 0.072 SMI / day modifications for parallel suite: no explicit SM perturbations added where spread(SMI) > 0.15  “soft limiter” for SMP depth weight of perturbations explicit soil temperature (T_SO) perturbations:  = 0.48 K / day

soft limiter of soil moisture perturbations: impact on a sunny day (23 June 2016) spread of soil moisture index (14 UTC) soil level 2 : 1 – 3 cm soil level 5 : 27 – 81 cm original (Exp 10396) with SMP limiter (Exp 10416) soft limiter: SMI spread limited to ~ 0.15 SMI

soft limiter of soil moisture perturbations: impact on a sunny day (23 June 2016) spread of soil temperature soil layer 1: 0 – 1 cm afternoon (14 UTC) night (0 UTC) original (Exp 10396) with SMP limiter (Exp 10416) T_SO spread decreased mostly at daytime

soft limiter of soil moisture perturbations: impact on a sunny day (23 June 2016) spread of 2-m temperature afternoon (14 UTC) mid-level cloud (12 UTC) Meteosat (12 UTC) original (Exp 10396) with SMP limiter (Exp 10416) T_2M spread decreased mainly in sunny areas

soft limiter of soil moisture perturbations: impact on spread of T2M, day by day spread of 2-m temperature, time series of 0-UTC runs 26 May – 27 June 2016 afternoon (14 UTC) sunny day – 25 % reduction 5 – 25 % 1 – 25 June 2017 many sunny days reduction 10 – 35 % T_2M spread reduction larger, due to sunny days, drier soil

soft limiter of soil moisture perturbations: impact on spread of T2M, day by day spread of 2-m temperature, time series of 0-UTC runs 26 May – 27 June 2016 night (2 UTC) reduction ≤ 10 % 1 – 25 June 2017 reduction 5 – 18 % T_2M spread reduction larger (as for soil temperature, effect from daytime?)

soft limiter of soil moisture perturbations: impact on spread, surface verification spread 0-UTC runs 26 May – 27 June 2016 original SMP limiter 1 – 25 June 2017 parallel suite: spread more strongly reduced, but still larger than in old period

why is the reduction of spread of T2M larger than in previous test? relative difference [%] of domain-averaged soil moisture in deterministic run (on 22 June 2017/16) (SM2017 – SM2016)/SM2016  soil is drier in 2017 period

/(SMS_new_2017 + SMS_new_2016)*2 why is the reduction of spread of T2M larger than in previous test? scaled differences [%] of domain-averaged spread of soil moisture (SMS) on 22 June 2017 (“2017” test) resp. 22 June 2016 (“2016” test) (SMS_2017 – SMS_2016) /(SMS_new_2017 + SMS_new_2016)*2 2017 2016 (SMS_old – SMS_new) /(SMS_new_2017 + SMS_new_2016)*2 SMS limiter results in larger spread reduction in 2017 “old” setting: operational “new” setting: with soft limiter  spread larger in 2017, particularly in old setting 14

reduction of [%] of rmse impact of SMP limiter on scores, deterministic: surface verification SMP limiter vs. original T 2M RH2M ps 26 May – 27 June 2016 reduction of [%] of rmse T 2M RH2M ps 1 – 25 June 2017 forecast lead time [h] parallel suite: still neutral (slightly better for RH2M in first 4 hours)

(averaged over lead times & initial times) impact of SMP limiter on scores, deterministic: radiosonde verification 26 May – 27 June 2016 T RH wind speed wind direct. 1 – 25 June 2017 pressure [hPa] T RH wind speed wind direct. reduction [%] of rmse (averaged over lead times & initial times) parallel suite: neutral

impact of SMP limiter on scores, deterministic: precipitation against radar 0-UTC runs 12-UTC runs original SMP limiter 26 May – 2 July 2016 1-hrly precip FSS ( 30 km ) 1 mm/h 1 – 12 June 2017 parallel suite: no improvement , neutral

impact of SMP limiter on scores, EPS: surface verification (1 – 25 June 2017) spread T 2M RH2M ps original SMP limiter RMSE CRPS reduction [%] of CRPS

impact of SMP limiter on scores, EPS: radiosonde verification (1 – 25 June 2017) reduction [%] of CRPS orig with SM limiter

impact of SMP limiter on scores, EPS: radiosonde verification (1 – 25 June 2017) reduction [%] of CRPS orig with SM limiter

impact of SMP limiter on scores, EPS: radiosonde verification (1 – 25 June 2017) reduction [%] of CRPS orig with SM limiter

Brier Score decomposition: impact of SMP limiter on scores, EPS: precip / radar verif. (26 May – 12 July 2016) 0-UTC runs slightly better reliability (except high threshold) + better resolution (not susceptible to calibration!) lead time [h] 1 mm/h 0.1 mm/h 5 mm/h the lower the better the higher the better reliability: average agreement betw. fcst & obs val.; related to (cond.) bias original SMP limiter Brier Score decomposition:  resolution  reliability 0.1 1 5 10 15 threshold [mm/h] resolution: ability of forecast to separate one type of outcome from another

impact of soft SMP limiter: summary impact of reduced soil moisture perturbations on scores: T_2M spread strongly reduced in sunny days as required T_2M + RH_2M spread moderately reduced in general in June 2017 more than in June 2016, because in 2017: more sunny days original soil moisture perturbations larger drier soil deterministic scores: neutral EPS: slightly (more) reduced (bias) errors (upper-air T, surface pressure), slightly improved resolution for precip hypothesis: due to introduction of additive covariance inflation (in February), reduced soil moisture perturbations can be afforded or are even beneficial  soft limiter to soil moisture perturbations operational since early July

Task 1, tests for refinements: bias correction for AMDAR global bias correction (BC) for AMDAR depends on: individual aircraft flight phase time: online bias correction (time scale ~ 30 days (?)) tests (2 weeks): use BC values from global system online BC in KENDA, starting from global values  impact similar to 1), due to large time scale (which should be even larger than for global system, due to less data) online BC in KENDA, starting from zero  little impact, due to large time scale online BC in KENDA would require very large time scale or could become statistically ‘unstable’, would require very long experiments caveat: AMDAR obs biased towards global ICON system

bias correction for AMDAR Task 1, tests: bias correction for AMDAR forecast verification (26 May – 10 June 2016) wind dir. RH wind dir. wind speed ps T wind speed RH2M T2M TD2M change in RMSE [%] positive impact from bias-correction

bias correction for AMDAR Task 1, tests: bias correction for AMDAR 26 May – 10 June 2016 BC-AMDAR vs. ref 0-UTC runs 6-UTC runs 1-hrly precip FSS (30 km) improvement 1 mm/h 12-UTC runs 18-UTC runs mean / 5% / 95% confidence interval (bootstrapping) AMDAR bias correction: mixed impact

Task 1, tests for refinements: AMDAR RH data : very small, neutral impact Relaxation To Prior Spread (RTPS): replacing: adaptive multiplicative covariance inflation (based on obs – fg statistics) Relaxation To Prior Perturbations (RTPP)

Task 1, tests: RTPS RH T forecast verification (26 May – 10 June 2016) wind dir. RH wind dir. wind speed ps T wind speed RH2M T2M TD2M change in RMSE [%] slightly negative / neutral impact

Task 1, tests: RTPS 0-UTC runs 12-UTC runs 0.1 mm/h 1 mm/h 26 May – 10 June 2016 0-UTC runs 12-UTC runs Ref RTPS 0.1 mm/h 1-hrly precip FSS ( 30 km ) 1 mm/h RTPS: clear long-lasting positive impact on 0- / 12-UTC runs

Task 1, tests: RTPS 6-UTC runs 18-UTC runs 0.1 mm/h 1 mm/h 26 May – 10 June 2016 6-UTC runs 18-UTC runs Ref RTPS 0.1 mm/h 1-hrly precip FSS ( 30 km ) 1 mm/h RTPS: negative impact on 6- / 18-UTC runs for 1 mm/h

Task 2: Extended use of observations: ongoing Progress in KENDA-O: Task 2: Extended use of observations: ongoing radar radial winds: DA exp., promising results radar reflectivity: DA sensitivity tests, results mostly preliminary:  ARPAE-SIMC (≤ 4 radars): 1h / 30’/ 15’ cycling, temporal thinning, RTPS, addit. infl.  DWD: warm bubbles, remapping of Z for Gaussianity, 1.1km, 2-moment microphys. GPS slant total delay: technical work incl. monitoring, in V5.04d, new DA exp. running (old exp: benefit on precip, elsewhere mixed (negative at low levels)) SEVIRI WV: many (mostly clear-sky) sensitivity DA tests, inconclusive, no benefit yet SEVIRI cloud top height: no resources T2M, RH2M: delayed (resources at MCH in 2018) Mode-S aircraft : DA exp., promising results Raman lidar (T-, q- profiles): quality of Payerne obs improved, DA being set up AMSU, ATMS, IASI clear-sky radiances: no resources 3 new projects at DWD ( WG1): SEVIRI VIS, lightning, (nowcast) objects

Task 2, extended use of observations: Mode-S aircraft derived from radar data from air- traffic control, processed + provided by KNMI wind vector + temperature, T derived from Mach number every 4 sec compared to AMDAR: >10 x more data no humidity, larger T error from: Lange and Janjic, MWR 2016

Task 2, extended use of observations: Mode-S aircraft 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)

Task 2, extended use of observations: Mode-S aircraft radiosonde verif. for DA cycle (26 May – 10 June 2016) positive impact from Mode-S, enhanced by corrected data

Task 2, extended use of observations: Mode-S aircraft Mode-S aircraft : forecast verification (26 May – 10 June 2016) wind dir. RH wind dir. wind speed ps T wind speed RH2M T2M TD2M change in RMSE [%] positive impact from Mode-S throughout

Task 2, extended use of observations: Mode-S aircraft 26 May – 10 June 2016 0-UTC runs 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

trial in parallel suite Task 2, extended use of observations: Mode-S aircraft 1 – 28 Aug. 2017 trial in parallel suite 1 – 20 Aug. 2017 , 0-UTC runs RH ps 0.1 mm/h T RH2M 1 mm/h wind speed T2M 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)

Task 2, extended use of observations: Mode-S aircraft trial in parallel suite data availability at cut-off time = 15 min ! AMDAR obs valid -29 to -15 min AMDAR obs valid -14 to -0 min 70 % available Mode-S obs valid -29 to -15 min 97 % available 95 % available Mode-S obs valid -14 to -0 min 0 % available

Task 4.1: KENDA for ICON-LAM (incl. EnVar) MEC-based LETKF for ICON-LAM: ready to be tested (e.g. grid pt. assignment) (with COSMO obs operators) except for (hydrostatic) balancing step(s) & exclusion of obs near lateral BC full 4-D LETKF for ICON-LAM: implementing part of DACE code, incl. COSMO + global obs operators in ICON,  first working version Nov. 2017 thereafter: complement global obs operators by functionality of COSMO operators  needed for EnVar (TL + adjoint of obs operators, incl. radar etc.) first tests LETKF with ICON-LAM planned for spring 2018 (Hollborn)

Task 4.2: Particle Filter (PF) methods (to account for non-Gaussianity) hybrid ETKPF (Sylvain Robert (ETH) et al., study finished): 1-week DA with PF for conv. obs in COSMO: f.g. (+1h) slightly improved over LETKF studying several algorithms for adaptive choice for weight of EnKF and PF in analysis (idea: use PF where useful, fall back on EnKF where Gaussian or PF does not work well) QJRMS: “A local ensemble transform Kalman particle filter for convective scale DA” EnVar PF-Var global TEMP verif, 2 – 24 May.2016 hybrid PF-Var: using PF ensemble in EnVar for deterministic analysis:  forecasts only slightly worse than operational EnVar with ensemble B from LETKF (promising!) Local adaptive PF (Roland Potthast et al., ongoing): work with global ICON (improved resampling to create new members) PF runs stably in 1-month tests scores still about 20% worse than LETKF

Task 2: Extended use of observations Progress in KENDA-O: 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) STD observation operator in COSMO V5.04d AutoAlert implemented (for automatic QC of each station & product, bías correction files, monitoring of station coord., visualisation, … ) plans: 2017 - 2018: refinement of DA, impact experiments, make STD technically ready for operational use, passive monitoring (converter ASCII (NetCDF) to BUFR  NetCDF : reader in COSMO) 2018: operational introduction of STD in KENDA

Task 2, extended use of observations: GNSS Slant Total Delay (STD) Monitoring of STD obs from GFZ with new ‘EPOS-8’ processing bias: (rather) small relative std. dev. (std.dev./mean-abs-value): does not increase with decreasing elevation STD data quality does not decrease with decreasing elevation (  7°)

Task 2, extended use of observations: GNSS Slant Total Delay (STD) Local Ensemble Transform KF  obs location & localisation of STD’s / ZTD’s

Task 2, extended use of observations: GNSS Slant Total Delay (STD) for specific humidity analysis increments: analysis increment for a single (STD / ZTD) obs  a number : c

Task 2, extended use of observations: GNSS Slant Total Delay (STD) analysis increment for temperature analysis increments: for a single (STD / ZTD) obs  a number : c

Task 2, extended use of observations: GNSS Slant Total Delay (STD) analysis increment for pressure analysis increments: for a single (STD / ZTD) obs  a number : c

Task 2, extended use of observations: GNSS Slant Total Delay (STD) analysis increments pressure temperature specific humidity approx. optimal localisation scale ? optimal to treat STD/ZTD obs as if located approx. here (?) possible implementation of individual obs re-location: HT available in COSMO: approximate roughly & store in ‘fof’ feedback file (‘plevel’, ‘plev_width’) Pf implicitly available in LETKF: use model columns above station from ensemble to compute variance (neglecting horizontal variability of variances)

Task 2: Extended use of observations

Task 2: Extended use of observations Screen-level obs RH2M (+ T2M) : tests at MeteoSwiss in 2018 10-m wind : new criteria (dep on. roughness length + d2zoro/(dx2dy2)) for station selection of 10-m wind in COSMO V5.04d Ground-based remote sensing (T + qv prof.: Raman lidar, MW radiometer) MeteoSwiss: improved quality of temperature obs from Payerne Raman lidar setting up DA of 0.5-hourly lidar qv + T as TEMP bulletin

Task 2: Extended use of observations SEVIRI radiances direct use of all-sky (cloudy) SEVIRI WV + IR window radiances (Axel Hutt) reproduce method of Harnisch et al. 2016, QJ, for cloud-dep. obs errors + bias correction technical work (upgrade to V5.04d, RTTOV-12, BACY, …) sensitivity experiments with use clear-sky WV channels: data selection (clear-sky; high orography), obs operator (effective radius related to qc, qi), obs error, bias correction, thinning, …  results mostly a bit inconclusive, bias correction is needed, overall impact of clear-sky obs neutral or negative LMU: new PostDoc Josef Schröttle 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 Lilo Bach at DWD since ~ July (work together with Leonard Scheck, LMU)