NWP at NOAAs Earth System Research Laboratory, Global Systems - PowerPoint PPT Presentation

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

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

RAPid Refresh (RAP), and High Resolution Rapid Refresh (HRRR) domains • Hourly update cycle for RAP Expanded (new) RAP domain (13 km) 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

Current Status - NOAA Hourly Updated Models RAP - Rapid Refresh (Benjamin et al., MWR, RAP 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 – High-Resolution Rapid Refresh - 3km - Storm/energy/aviation guidance - Real-time operational – NCEP, and experimental - ESRL supercomputer - NCEP implementation HRRRv1 - 30 Sept 2014 - HRRRv2/RAPv3 -NCEP implementation- Aug 2016 HRRR 4

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

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

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

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

Dynamic Multi-Plume Model The image part with relationship ID rId4 was not found in the file. LCL Model grid column Boundary Layer-Cloud Physics Development 9

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: 1 2 3 4 5 6 7 (#) A) The maximum number of plumes 100 200 300 400 500 600 700 (m) available (N max ) is determined by the model grid spacing. Max plume width = 0.75*dx 1 2 3 4 5 6 7 8 9 10 (#) 100 200 300 400 500 600 700 800 900 1000 (m) Boundary Layer-Cloud Physics Development 10

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

Comparison of Original and New Physics Shortw ave up at TOA Δx = 16 km Δx = 8 km Δx = 4 km Δx = 2 km Δx = 1 km Original ; shallow-cumulus scheme activated New MYNN-EDMF scheme with subgrid clouds Above figure taken from Field et al (2013) – 12 UTC 31 Jan 2010. 12

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

HRRR-Smoke : 3km horizontal resolution, used for aerosol aware microphysics HRRR-Smoke: VIIRS Fire Radiative Pow er, 3 prognostic aerosols 2016- 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 28-29 Sept 2016

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

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!

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

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

Example of HRRR-Smoke forecast during 2016 fire season

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

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

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

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