NWP at NOAA s Earth System Research Laboratory, Global Systems Division (ESRL/GSD): developments and applications for physics parameterizations

Transcription

1 NWP at NOAA s Earth System Research Laboratory, Global Systems Division (ESRL/GSD): developments and applications for physics parameterizations Georg Grell, Joe Olson, Shan Sun, Ben Green, Li Zhang, Ravan Ahmadov, Isidora Jankov, Stan Benjamin, Ligia Bernardet, many others

2 Overview Developments of ESRL s operational storm scale and regional scale modeling physics suite (currently using WRF) Overview of storm scale and regional scale physics developments MYNN-EDMF-Shallow convection Stochastic physics Some aspects of global modeling: Recent work with the Grell-Freitas convective parameterization inline chemistry, seasonal forecasting experiments with a couple atmosphere/ocean/chemistry model Future modeling plans at ESRL/GSD

3 RAPid Refresh (RAP), and High Resolution Rapid Refresh (HRRR) domains Expanded (new) RAP domain (13 km) Hourly update cycle for RAP and HRRR operational Additional experimental runs 750m nest experimental RAP also with full chemistry (twice a day experimental) HRRR with Smoke and other anthropogenic emissions twice a day for 36 hr forecasts - experimental 3

4 Current Status - NOAA Hourly Updated Models RAP RAP - Rapid Refresh (Benjamin et al., MWR, 2016) 13km NOAA situational awareness model for high-impact weather New 18-hour forecast each hour NOAA/NCEP operational 1 May 2012 RAPv2 implementation 25 Feb 2014 Hourly use by National Weather Service, SPC/AWC/WPC, FAA, private sector HRRR HRRR High-Resolution Rapid Refresh - 3km - Storm/energy/aviation guidance - Real-time operational NCEP, and experimental- ESRL supercomputer - NCEP implementation HRRRv1-30 Sept HRRRv2/RAPv3 -NCEP implementation- Aug

5 RAP/HRRR Physical Processes & Parameterizations Model Component Non-local Turbulent transport Clouds - microphysics Currently under development in RAP/HRRR MYNN Mass-flux Chaboureau-Bechtold Thompson aerosol-aware Will be in WRFV3.9 Aspects of ongoing developments EDMF multi plume approach (Neggers et al), momentum transport inclusion, scale aware Use of wildfires, dust, sea salt, other emissions for Thompson aerosol aware microphysics, prognostic application of Chaboureau-Bechtold, tuning of radiation coupling Stochastic approaches in progress Stochastic entrainment Stochastic SPP component for cloud fractions Non resolved deep convection Multi plume approach Grell-Freitas parameterization Will be in WRFV3.9 Implementation and evaluation in HWRF, FIM, and GFS Stochastic SPP and SPPT in progress Land Surface and coupling to PBL RUC LSM/ MYNN Sfc Layer Real-time green fraction, alternatives to M-O for surface layer Stochastic SPP, SPPT in progress 5

6 Development of a scale-aware parameterization of subgrid cloudiness feedback to radiation. Joseph Olson 1,2, Jaymes Kenyon 1,2, Georg Grell 1, John Brown 1, Wayne Angevine 1,2, Stan Benjamin 1, Kay Suselj 3 1 NOAA s Earth System Research Laboratory, Boulder, CO 2 Cooperative Institute for Research in Environmental Science 3 NASA s Jet Propulsion Laboratory, Pasadena, CA FY16-17

7 Subgrid clouds in the MYNN-EDMF Scheme Stratus component from partial-condensation scheme within the eddy diffusivity component. Shallow-cumulus component from mass-flux component. Scale-Aware Requirements for a Turbulent Mixing Scheme 1)Reduction of parameterized mixing as dx -> 0. 2)Change in the behavior of the scheme as dx -> 0. Mass-flux (shallow-cu) scheme represent smaller plumes as dx -> 0. Eddy Diffusivity scheme transforms to 3D mixing as dx -> 0. Boundary Layer-Cloud Physics Development 7

8 MYNN Boundary Layer Scheme Modifications 1. Mass-flux component (MYNN-EDMF) Dynamic Multi-Plume: dynamic number/sizes of plumes. Adapts to different mode grid spacing Adapts to growth of PBL. Options to transport momentum, TKE, and chemical species. Option to activate stochastic lateral entrainment rates (Suselj et al. 2013). Total mixing (mass-flux transport & eddy diffusivity) is solved simultaneously and implicitly (Suselj et al. 2013). 2. Subgrid-scale clouds Chaboureau and Bechtold (2002 & 2005) convective & stratus components. Diagnostic-decay method implemented. Coupled to the radiation schemes. Boundary Layer-Cloud Physics Development 8 8

9 The image part with relationship ID rid4 was not found in the file. Dynamic Multi-Plume Model LCL Model grid column Boundary Layer-Cloud Physics Development 9

10 Dynamic Multi-Plume (DMP) B) Number of plumes (N) is further limited by the PBLH. For example, at dx = 1000 meters, a maximum of 7 plumes are available, but the number used grows as the PBLH grows: A) The maximum number of plumes available (Nmax) is determined by the model grid spacing. Max plume width = 0.75*dx Boundary Layer-Cloud Physics Development (#) (m) 10 (#) (m) 10

11 Scale-Aware Tapering of Mass-Flux Scheme Taken from Honnert et al. (2011, JAS, their figure 5): ShCu: TKE in the entrainment layer PBL: TKE in boundary layer Boundary Layer-Cloud Physics Development 11

12 Comparison of Original and New Physics Shortwave up at TOA Δx = 16 km Δx = 8 km Δx = 4 km Original; shallow-cumulus scheme activated Δx = 2 km Δx = 1 km New MYNN-EDMF scheme with subgrid clouds Above figure taken from Field et al (2013) 12 UTC 31 Jan

13 RAP/HRRR Physics Aerosol aware microphysics and radiation need aerosols: Should we really use an aerosol climatology in the presence of strong aerosol sources? Strong sources such as wildfires or dust can decrease SW radiation drastically as well as change CCN by orders of magnitudes

14 HRRR-Smoke: 3km horizontal resolution, used for aerosol aware microphysics HRRR-Smoke: VIIRS Fire Radiative Power, 3 prognostic aerosols Sept HRRR-Smoke will include FRP data from VIIRS and MODIS, Thompson aerosol-aware microphysics (water friendly and ice friendly aerosols), including anthropogenic emissions Direct and indirect effect: only small additional computer resources needed

15 Plumerise in HRRR: The 1-d in-line cloud model: governing equations aaa W equation U equation 1 st law of thermodynamic water vapor conservation cloud water conservation rain/ice conservation equation for radius size Example of injection height with heat flux of 30 and 80 kw/m 2 Injection layer Freitas et al., GRL 2006, ACP 2007, 2010 aaa

16 HRRR-Smoke simulated vertically integrated aerosol concentrations and aerosol optical depth from VIIRS for August 27, 2015 Modeled vertically integrated aerosol concentrations VIIRS AOD VIIRS data also very useful for independent verification!

17 Quantitative evaluation with retro runs: comparison of two HRRRsmoke retro periods ( 10 days) with and without feedback: RAOB verification over HRRR domain Temperature BIAS climatology real emissions difference

18 SFC TEMPBIAS Surface temperature verification over HRRR domain TSS Skill Score Ceiling < 3000 ft verification over HRRR domain climatology real emissions difference

19 Example of HRRR-Smoke forecast during 2016 fire season

20 Short wave radiation differences for one particular time in comparison to integrated smoke AUG 19, 00Z

21 Summary and future plans for aerosols and microphysics 1. With a double moment aerosol aware microphyics scheme only 2 additional variables are used, including smoke in an operational version of the HRRR with cycling does not degrade the forecast indications are it might improve forecasts 2. Need an extended testing period (1 year) to validate (1) 3. Dust and sea salt parameterization should be included 4. Add more fire satellite detection data (MODIS, GOES-R) and smoke boundary conditions in future 5. Radiative impact versus microphysics impact

22 Some early results for using stochastic physics Focus on MYNN PBL Parameters Mixing length 30% Aerodynamic roughness length 30% Thermal/moisture roughness length 30% Mass fluxes 20% Prandtl number limit 2.5 +/- 1 (only for stable conditions) Cloud fraction 20% Temporal and spatial lengths 150km and 6hr 300km and 12hr 600km and 24hr Combination of MYNN PBL SPP with SPPT and SKEB 8-members 4 cases initialized at 06Z Green positive correlation Red negative correlation Figure presents Spread/Skill for SPP, SPP+SPPT and SPP+SPPT+SKEB

23 Overview Developments of ESRL s operational storm scale and regional scale modeling physics suite (currently using WRF) Overview of storm scale and regional scale physics developments MYNN-EDMF-Shallow convection Stochastic physics Some aspects of global modeling: Recent work with the Grell-Freitas convective parameterization inline chemistry, seasonal forecasting experiments with a couple atmosphere/ocean/chemistry model Global modeling is changing at ESRL: Switch from ESRL model to NGGPS is starting, but results shown here are still with ESRL s model

24 FIM: Flow-following- finite-volume Icosahedral Model IHYCOM: Icosahedral Hybrid Coordinate Ocean Model Inline Chemistry from WRF-Chem Seasalt, dust, dms emissions modules from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model Anthropogenic emissions from the Hemispheric Transport of Air Pollution (HTAP) project FIM To be replaced with new NGGPS core, once available! Currently three different chem suites available: Icosahedral horizontal grid Isentropic-sigma hybrid vertical coordinate adaptive in vertical concentrates 1. GOCART around frontal zones, tropopause 2. GOCART + gas-phase chemistry 3. Complex Biomass burning are Satellite derived (MODIS), Different injection coupling height calculated appproach: online inline, the two with one-dimensional plumerise model aerosols models share the same horizontal grid. Simple sulfate and aerosol chemistry from GOCART (more complex available) Wet deposition for resolved and non resolved Aerosol optical properties are calculated online with MIE calculations for short wave and longwave RRTMG radiation parameterization aerosols+gas-phase + secondary organic

25 Recent new implementations into GF scheme Momentum transport (as in SAS and/or ECMWF) Additional closure for deep convection: Diurnal cycle effect (Bechtold) Changed cloud water detrainment treatment Mass conserving tracer transport Additional closures for shallow convection (Boundary Layer Equilibrium (BLQE, Raymond 1995; W *, Grant 2001, Heat Engine, Renno and Ingersoll, JAS 1996) PDF approach for normalized mass flux profiles was implemented Originally to fit LES modeling for shallow convection allows easy application of mass conserving stochastic perturbation of vertical heating and moistening profiles Provides smooth vertical profiles Latest implements: memory and third type of cloud (mid-level convection) Stochastic part in WRF now coupled to Stochastic Parameter Perturbation (SPP), and Stochastic Kinetic Energy Backscatter (SKEBS) approach (J. Berner ) 25

26 Momentum transport Effect of cloud scale horizontal pressure gradients (Gregory et al. 1997, Zhang and Wu, 2000) is to adjust the in-cloud winds towards those of the large scale flow. For the ECMWF approach (follows Gregory et al., 1997), the entrainment rate is simply adjusted E(u,v) up =E up +λd up D(u,v) up =D up +λd up Where E(u,v) and D(u,v) are simply the entrainment/detrainment rates. For SAS approach equations follow directly Zhang and Wu, 2003 The pressure gradient force across the updraft is proportional to the product of mass flux and vertical shear of the mean wind, Proportionality constant is -.55 for Zhang and Wu, Gregory at al at first assumed the constant to be -.7 Both are very simple to implement. Proportionality constant was tested for Stochastic Parameter Perturbation (SPP)

27 Heat source from momentum transport: dissipation if kinetic energy As in ECMWF, we also include an additional heat source representing dissipation of kinetic energy (Steinheimer et al 2007)

28 Changing the vertical mass flux PDF s Large changes in vertical redistribution of heat and moisture Mass conserving for stochastic approaches significant impact on HAC s, Increases spread for ensemble data assimilation PDF1 PDF2 1d version of GF only

29 Impact of momentum transport and diurnal cycle implementation Changing momentum transport constants: large impact on comparison of global wind speed biases Improving wind bias has significant impact on HAC s but does not necessarily improve HAC s 30 retro FIM runs, about 30km resolution, 120hr forecasts Diurnal Cycle implementation, 120 hour forecasts: precipitation averaged over Amazon basin is improved HAC s little impacted

30 FIM/IHYCOM sub-seasonal hindcast experiments: 600 one month runs (Green et al. 2017) Experiment name FIM-AGF FIM-CGF FIM-SAS CFSv2 Dynamic core FIM FIM FIM GFS Atmospheric model Horizontal grid (structure, resolution) (Icosahedral, G7 ~60 km) (Icosahedral, G7 ~60 km) (Icosahedral, G7 ~60 km) (Spectral, T126 ~100 km) Vertical grid 64 hybrid σ-θ layers 64 hybrid σ-θ layers 64 hybrid σ-θ layers 64 hybrid σ-p layers Deep conv. scheme Revised GF Revised GF SAS (2015 GFS) SAS (Saha et al. 2010) All other physics 2015 GFS 2015 GFS 2015 GFS Saha et al. (2014) Ocean model Dynamic core None ihycom ihycom MOM4 Horizontal grid (structure, resolution) N/A (Icosahedral, G7 ~60 km) (Icosahedral, G7 ~60 km) Variable (Saha et al. 2010, pp ) Vertical grid N/A 32 hybrid σ-ρ layers 32 hybrid σ-ρ layers 40 stretched height layers *Uncoupled atmosphere-only setup; monthly SSTs from Hadley Centre interpolated to daily. 30

31 Figure 1: Single-model skill and spread a c b d Top: Bivariate correlation Bottom: RMSE and spread Left: RMM; Right: VPM Interesting points: FIM-AGF much worse than FIM-CGF (and other coupled runs); no surprise, and no more FIM-AGF results will be shown Higher correlations (more skill) but also higher RMSE (error magnitudes) for RMM than for VPM FIM-CGF and CFSv2 are comparable in skill and RMSE; FIM-SAS much worse across the board 31

32 IN GFS: Comparisons of surface precipitation rate (24 h avg mm/day) SAS (operational), SAS (imfdepcnv=2), GF (v3a), v3b (tuning experiment) Global average Average over the Tropics (20S 20N) schemes Total Convective Convective (land+ocean) Land (Conv) Ocean (conv) SAS (op) SAS (2) GF (v3a) GF (v3b, imid=0) GF (v3b, imid=1) Frac (%) (Conv/tot) V3a So far best performance Experimental example

33 First run of GF scheme in GFS, no tuning or data assimilation yet 10 day forecasts over 3 month period 1-day T RMSE 10-day T RMSE SAS typically better than GFS-GF early in forecast, but GFS-GF better later. Seen in T, RH at surface and upper air

34 Final changes (not including tuning) over last month Latest implementations: memory and third type of cloud (mid-level convection) Splitting the module into three parts: Driver (may be different for various physics suites) Module for deep convection (independent of dynamic core or physics suite) Module for shallow convection (also independent) General clean up of unused arrays, and adding comments Evaluation happening in regional as well as global models on timescales from storm-scale to sub-seasonal 34

35 Experimental: aerosol awareness Change 1: Change constant autoconversion rate to aerosol (CCN) dependent Berry conversion Change 2: Modified evaporation of raindrops (Jiang and Feingold) based on empirical relationship Change 2 introduces a proportionality between precipitation efficiency (PE) and total normalized condensate (I 1 ), requiring determination of the proportionality constant C pr

36 Evaluating aerosols impacts on Numerical Weather Prediction Saulo Freitas, Arlindo Silva, Angela Benedetti, Georg Grell, Oriol Jorba, Morad Mokhtari, Samuel Remy and many other WGNE Members Participants Many questions left to ask: 1. How simple/complex does the chemistry need to be to predict aerosols with enough accuracy 2. How does (1) impact NWP for short, medium, and long range applications 3. Impact versus improvement 4. With NOAA s Next Generation Global Prediction System (NGGPS) program this was the ideal time to start asking the question of what should be part of a stateof-the-art NGGPS modeling system 5. What can we afford with respect to computational requirements?

37 Latest aerosol work, regional and global scales WGNE comparisons Full chemistry run (with feedbacks minus meteorology only run Double moment microphysics Average over 20 runs, 3 days, 12Z T2m differences, Low AOD: Most of this warming caused by constant droplet number assumption in meteorology only run

38 Averaging in areas with significant convection, dx= 1.7km PM2.5 (μg/m 3 ) Box averaged vertical profile of CLW+ICE Lat = -4.5 to -6.5 Lon -68 to -72 T2M, 18Z, Sep 10 1.E6*kg/kg RNW appeared unpredictable: Convection has different strength For high resolution run: CLW and ICE appear to have a signal

39 T2M difference fields, September 10, 1200UTC- mid-morning. Positive (red) is warmer compared to MET simulation with convective parameterization 1 run only! Will have to retune GF and run all 20 cases! Using convective parameterization with and without aerosol awareness Direct effect only DIR +IND DX=5km Average over 20 runs! Full chemistry and physics, aerosol indirect explicitly included 39

40 Using chemistry and aerosol suites with different complexity: An NGGPS project that started before the dynamic core was known Use ESRL s Flow following finite volume Icosahedral Model (FIM) as dynamic core place holder GFS physics package, except for Grell-Freitas convective parameterization (GF has capability of wet scavenging, aqueous phase chemistry and aerosol interactions) Chemistry suites: Simple: bulk aerosols (GOCART) with sectional dust and sea salt 17 additional prognostic 3d variables Not so simple: GOCART coupled with gas-phase chemistry (RACM) 66 additional prognostic variables Much more complex: RACM and modal aerosols with Secondary Organic Aerosols using Volatility Basis System (VBS) > 100 additional prognostic 3d variable Almost non-existent: ice friendly, water friendly aerosols, total pm2.5 3 additional prognostic 3d variables (in the works)

41 Can we even predict aerosols with some confidence: Evaluation of chemical composition with ATom The Atmospheric Tomography Mission (ATom) will study the impact of human-produced air pollution on greenhouse gases and on chemically reactive gases in the atmosphere. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. 8/15/16 South Atlantic, Punta Arenas to Ascension Is. 8/17/16 Equatorial towards North Atlantic, Ascension Is. to Azores

42 Preliminary data: comparisons of Aerosol and Gas Tracers between FIM-Chem and ATom EC CO Preliminary data Preliminary data 8/15/2016 and 8/17/2016 The model shows good performance in reproducing the height-latitude profiles of EC and CO at the low altitude, especially capturing the biomass burning plumes. Discrepancies between model predictions and measurements are mainly over the altitude above 4~5km.

43 AOD evaluation over longer timeperiods: using AFWA version of GOCART Scheme Dust Evaluation with data from AERONET Similar evaluation near biomass burning

44 Is there an impact of aerosols on NWP? Only direct/semidirect impact is considered here! 00 Z Surface temperature differences 00 Precipitation differences Z (convective) 12 Z mm/day Domain averaged precip and surface temperatures are very slightly lower 0 C

45 mm/day Is there an impact of the gas-phase chemistry on NWP? Surface T differences 0 C Convective Precipitation differences

46 Future work in global modeling, collaboration with ESRL/CSD, ESRL/PSD, EMC, ARL, and EPA HRRR-WRF-ARW for regional storm-scale model working with NCEP, NCAR, other labs, switch to FV3 will be tested Switch to NGGPS core, FV3. Test of aerosol awareness in GF scheme Tuning of GF within GFS physics Sub-seasonal/seasonal impact of wildfires and aerosols with coupled atmos/chem/ocean model More detailed look at 5 to 10 day height anomaly correlations and WGNE case for South America Feedback to global NWP also with microphysics: In addition to GFS physics, this will also run with Thompson aerosol aware microphysics Evaluate different dust and sea salt modules

47 Credits also go to: Jian-Wen Bao, Sara A. Michelson, Evelyn Grell, Cécile Penland, Stefan Tulich, Phil Pegion Ongoing Research on NWP Model Physics Parameterizations at NOAA/ESRL/PSD 1. Microphysical Consistency between Grid-Resolved and subgrid Cloud Parameterizations at Gray-Zone Resolution 2. Coherent 3-D TKE-based subgrid mixing: development of a scale-adaptive TKE-based subgrid mixing scheme in the WRF model 3. Stochastic parameterizations based on observations and high-resolution simulations

48 Thank you for your attention! Questions?