Wrap-up of SPPT Tests & Introduction to iSPPT Kevin Lupo Research Update 4 Apr 2017

Brief SPPT Review Stochastically Perturbed Parameterization Tendencies Tunable, red noise field evolving in time and space over the forecast domain Account for sub-grid scale parameterization uncertainties Multiply net tendencies by noise field Improve ensemble dispersion (e.g., Romine et al. 2014; ECMWF)

Last Time… 1st suite of ensembles for TS Lee/remnants case

Last Time… 1st suite of ensembles for TS Lee/remnants case Increase decorrelation time scale Increase cutoff threshold Increase length scale Increased ensemble spread

Updates Moving away from exploratory tests of SPPT New: Next: Rerun ensembles using Thompson microphysics scheme Introduction to iSPPT Next: Run ensembles for Taiwan case Tests of iSPPT

Corrections after exploratory experiments New WSM3 RRTM Dudhia Monin-Obukhov Noah YSU KF, None WSM3  Thompson 30 min  15 min Δx = 15 km Δx = 3 km Six 5-member ensembles Member 000 – nens 1 Member 001 – nens 88 Member 002 – nens 237 Member 003 – nens 492*** Member 004 – nens 623

Ensemble Design Ensemble 000: R14 Ensemble 001: R14 (change t) l = 150 km; t = 3600 s; σ = 0.35 Ensemble 001: R14 (change t) l = 150 km; t = 900 s; σ = 0.35 Ensemble 002: R14 (change l) l = 15 km; t = 3600 s; σ = 0.35 Ensemble 003: R14 (change σ) l = 150 km; t = 3600 s; σ = 0.5 Ensemble 004: R14 (change t) l = 150 km; t = 21600 s; σ = 0.35 Ensemble 005: ECMWF (Leutbecher et al. 2016) l = 500 km; t = 21600 s; σ = 0.52

Romine et al. 2014 SPPT Settings WSM3 gridpt_stddev_sppt = 0.35, 0.35, lengthscale_sppt = 150000.0, 150000.0, timescale_sppt = 3600.0, 3600.0,

Romine et al. 2014 SPPT Settings Thompson gridpt_stddev_sppt = 0.35, 0.35, lengthscale_sppt = 150000.0, 150000.0, timescale_sppt = 3600.0, 3600.0,

Ensemble Design Ensemble 000: R14 Ensemble 001: R14 (change t) l = 150 km; t = 3600 s; σ = 0.35 Ensemble 001: R14 (change t) l = 150 km; t = 900 s; σ = 0.35 Ensemble 002: R14 (change l) l = 15 km; t = 3600 s; σ = 0.35 Ensemble 003: R14 (change σ) l = 150 km; t = 3600 s; σ = 0.5 Ensemble 004: R14 (change t) l = 150 km; t = 21600 s; σ = 0.35 Ensemble 005: ECMWF (Leutbecher et al. 2016) l = 500 km; t = 21600 s; σ = 0.52

R14, t=900s WSM3 gridpt_stddev_sppt = 0.35, 0.35, lengthscale_sppt = 0.35, 0.35, lengthscale_sppt = 150000.0, 150000.0, timescale_sppt = 900.0, 900.0,

R14, t=900s Thompson gridpt_stddev_sppt = 0.35, 0.35, 0.35, 0.35, lengthscale_sppt = 150000.0, 150000.0, timescale_sppt = 900.0, 900.0,

Ensemble Design Ensemble 000: R14 Ensemble 001: R14 (change t) l = 150 km; t = 3600 s; σ = 0.35 Ensemble 001: R14 (change t) l = 150 km; t = 900 s; σ = 0.35 Ensemble 002: R14 (change l) l = 15 km; t = 3600 s; σ = 0.35 Ensemble 003: R14 (change σ) l = 150 km; t = 3600 s; σ = 0.5 Ensemble 004: R14 (change t) l = 150 km; t = 21600 s; σ = 0.35 Ensemble 005: ECMWF (Leutbecher et al. 2016) l = 500 km; t = 21600 s; σ = 0.52

R14, l=15km WSM3 gridpt_stddev_sppt = 0.35, 0.35, lengthscale_sppt = 0.35, 0.35, lengthscale_sppt = 15000.0, 15000.0, timescale_sppt = 3600.0, 3600.0,

R14, l=15km Thompson gridpt_stddev_sppt = 0.35, 0.35, 0.35, 0.35, lengthscale_sppt = 15000.0, 15000.0, timescale_sppt = 3600.0, 3600.0,

Ensemble Design Ensemble 000: R14 Ensemble 001: R14 (change t) l = 150 km; t = 3600 s; σ = 0.35 Ensemble 001: R14 (change t) l = 150 km; t = 900 s; σ = 0.35 Ensemble 002: R14 (change l) l = 15 km; t = 3600 s; σ = 0.35 Ensemble 003: R14 (change σ) l = 150 km; t = 3600 s; σ = 0.5 Ensemble 004: R14 (change t) l = 150 km; t = 21600 s; σ = 0.35 Ensemble 005: ECMWF (Leutbecher et al. 2016) l = 500 km; t = 21600 s; σ = 0.52

R14, σ=0.5 WSM3 gridpt_stddev_sppt = 0.5, 0.5, lengthscale_sppt = 0.5, 0.5, lengthscale_sppt = 150000.0, 150000.0, timescale_sppt = 3600.0, 3600.0,

R14, σ=0.5 Thompson gridpt_stddev_sppt = 0.5, 0.5, lengthscale_sppt = 0.5, 0.5, lengthscale_sppt = 150000.0, 150000.0, timescale_sppt = 3600.0, 3600.0,

Ensemble Design Ensemble 000: R14 Ensemble 001: R14 (change t) l = 150 km; t = 3600 s; σ = 0.35 Ensemble 001: R14 (change t) l = 150 km; t = 900 s; σ = 0.35 Ensemble 002: R14 (change l) l = 15 km; t = 3600 s; σ = 0.35 Ensemble 003: R14 (change σ) l = 150 km; t = 3600 s; σ = 0.5 Ensemble 004: R14 (change t) l = 150 km; t = 21600 s; σ = 0.35 Ensemble 005: ECMWF (Leutbecher et al. 2016) l = 500 km; t = 21600 s; σ = 0.52

R14, t=21600s WSM3 gridpt_stddev_sppt = 0.35, 0.35, lengthscale_sppt = 0.35, 0.35, lengthscale_sppt = 150000.0, 150000.0, timescale_sppt = 21600.0, 21600.0,

R14, t=21600s Thompson gridpt_stddev_sppt = 0.35, 0.35, 0.35, 0.35, lengthscale_sppt = 150000.0, 150000.0, timescale_sppt = 21600.0, 21600.0,

Ensemble Design Ensemble 000: R14 Ensemble 001: R14 (change t) l = 150 km; t = 3600 s; σ = 0.35 Ensemble 001: R14 (change t) l = 150 km; t = 900 s; σ = 0.35 Ensemble 002: R14 (change l) l = 15 km; t = 3600 s; σ = 0.35 Ensemble 003: R14 (change σ) l = 150 km; t = 3600 s; σ = 0.5 Ensemble 004: R14 (change t) l = 150 km; t = 21600 s; σ = 0.35 Ensemble 005: ECMWF (Leutbecher et al. 2016) l = 500 km; t = 21600 s; σ = 0.52

ECMWF Operational WSM3 gridpt_stddev_sppt = 0.52, 0.52, 0.52, 0.52, lengthscale_sppt = 500000.0, 500000.0, timescale_sppt = 21600.0, 21600.0,

ECMWF Operational Thompson gridpt_stddev_sppt = 0.52, 0.52, 0.52, 0.52, lengthscale_sppt = 500000.0, 500000.0, timescale_sppt = 21600.0, 21600.0,

WSM3

Thompson

Next Step: iSPPT SPPT: Perturbs sum of individual physics tendencies Assumes model errors in each parameterization scheme are correlated with each other iSPPT: Independent SPPT Perturb each physics scheme with an independent random pattern Does not assume errors are correlated Potentially over-dispersive (ECMWF 2016) http://www.ecmwf.int/sites/default/files/special_projects/2016/spgbtpps-2016-report1.pdf

Namelist parameters for SPPT

Namelist parameters for iSPPT

Next Step: iSPPT Test – Experiment Design Consistency test with SPPT One member of WRF ensemble Run new scheme with only SPPT turned on, iSPPT off Run new scheme with SPPT off, iSPPT on for radiation, boundary layer tendencies Both tests of the new scheme should result in the same perturbation & model output as the original ensemble member

End

Project Motivation (PIRE) US – Taiwan partnership Improve forecasts of major precipitation events Interdisciplinary project Climate Ensemble/DA Microphysics Impacts/Social Science Climate Event characteristics (extremes, forcing, location, season…) & Composites Regional climate modeling Ensemble/Data Assimilation Microphysics Best scheme for given regime Relative importance Role in extreme precipitation events Impacts/Social Science Risk perception Communication with Emergency Managers Political impacts/cross-cultural analysis http://www.albany.edu/news/62787.php

PIRE – Ensemble Forecasting (1) Improve ensemble forecasts of heavy precipitation events using SPPT scheme Address underdispersion, optimization for high-resolution convection-permitting ensemble forecast systems (Romine et al. 2014).

PIRE – Ensemble Forecasting (1) Improve ensemble forecasts of heavy precipitation events using SPPT scheme Random field evolving with time Tunable (lengthscale, timescale, amplitude) Multiply total physics tendency by perturbation at each time step (ra, bl, cu, shcu, ifire) Ensemble 000 Member 000 Member 001 Member 002 Member 003 Member 004 iseed = 1 iseed = 88 iseed = 237 iseed = 492 iseed = 623

Ensemble 000 – Member 000 Ensemble 000 – Member 001 sppt = 1, 1, gridpt_stddev_sppt = 0.35, 0.35, stddev_cutoff_sppt = 2.0, 2.0, lengthscale_sppt = 150000.0, 150000.0, timescale_sppt = 3600.0, 3600.0, sppt_vertstruc = 0, ISEED_SPPT = 88, / sppt = 1, 1, gridpt_stddev_sppt = 0.35, 0.35, stddev_cutoff_sppt = 2.0, 2.0, lengthscale_sppt = 150000.0, 150000.0, timescale_sppt = 3600.0, 3600.0, sppt_vertstruc = 0, ISEED_SPPT = 1, / 12 hr 24 hr 36 hr 48 hr

ECMWF Technical Notes The ensemble forecasts from the ECMWF Integrated Forecasting System (IFS) include a representation of model uncertainty via the Stochastically Perturbed Parametrisation Tendencies (SPPT) scheme (Buizza et al., 1999; Palmer et al., 2009; Shutts et al., 2011). SPPT is designed to account for uncertainties associated with the sub-grid physics parametrisations — radiation, cloud and convection, diffusion and gravity wave drag schemes. At each timestep, all of the physical parametrisations change the prognostic model variables (winds, temperature and humidity) by a certain amount, that is generally defined as “tendency”. The SPPT scheme randomly perturbs the net of these tendencies with multiplicative noise. Therefore, the scheme attributes the greatest uncertainty to the largest net tendencies while preserving the relative balances between the tendencies of different physical processes. Despite its relative simplicity, the SPPT scheme has yielded positive results. Analyses of the performance of the IFS ensembles — both from the ECMWF ensemble (ENS) for medium-range to sub-seasonal forecasts and System 4 (S4) for seasonal forecasts — demonstrate clear positive impacts due to the inclusion of SPPT (Shutts et al., 2011; Weisheimer et al., 2014). http://www.ecmwf.int/sites/default/files/elibrary/2016/16682-towards-process-level-representation-model-uncertainties-stochastically-perturbed.pdf

ECMWF Technical Notes The simplicity of the SPPT scheme comes at a cost. Applying multiplicative noise to the net physics tendencies can enhance or diminish the effect of the represented physical processes, but it is unable (in a single timestep) to trigger a new state, e.g. generation of a cloud layer, thereby, not directly capturing the large uncertainty associated with the timing and location of convection. Related, the dominant uncertainty in the radiation scheme arises from the presence (or lack) of clouds. By contrast, the radiative transfer process in clear-skies is well described by the radiation scheme (Pincus et al., 2003). In SPPT, tendencies due to both clear and cloudy skies radiative processes are perturbed alike. Indeed, in recognition of this shortcoming of SPPT, an exception is applied in the stratosphere: a tapering function reduces the SPPT perturbations to zero above 50 hPa, where the dominant contribution to the net physics tendencies is from clear-sky radiation. Another pragmatic choice is to apply a tapering function such that SPPT does not perturb the tendencies in the lowest 300 m, in this instance, in order to avoid numerical instabilities. An additional concern with SPPT relates to the inconsistency that arises between the perturbed physics tendencies and fluxes that are computed from the unperturbed tendencies: no correction is made to the top-of-the-atmosphere or surface fluxes after perturbing the atmospheric tendencies, so an energy imbalance is introduced into the system and individual ensemble members no longer conserve energy. http://www.ecmwf.int/sites/default/files/elibrary/2016/16682-towards-process-level-representation-model-uncertainties-stochastically-perturbed.pdf

http://www.ecmwf.int/sites/default/files/special_projects/2016/spgbtpps-2016-report1.pdf