WRF model exercise Experimental Boundary layer meteorology Björn Claremar 2012

Outline Why use models? The WRF model – Physics schemes The computer lab

Use of models Observations are not always sufficient to find out meteorology between observational stations. Observations are not always sufficient to find out meteorology between observational stations. Meteorological re-analysis data are good to fill in gaps between observations (e.g. NCEP, ERA-40, ERA-Interim) Meteorological re-analysis data are good to fill in gaps between observations (e.g. NCEP, ERA-40, ERA-Interim) – Numerical weather model makes sub-sequent short-range forecast with observations (at ground and in free atmosphere) as initial and boundary forcing – too coarse resolution (>80 km) to catch the influences on weather from the complex topography.

Regional modelling gives more detail Simulated precipitation GCM 300 km GCM interp. to 50 km RCM 50 km

RCA3 with higher resolution Precipitation (DJF, ) as a function of resolution

EXAMPLE OF DYNAMICAL DOWNSCALING

Gridded dataset, partly based on: – Direct meteorological observations and satellite measurements – Prognostic atmospheric model – Assimilation of observations to physical consistent fields. – Precipitation fully prognostic Data available from 1990 and onwards. 7 Downscaling process ERA-Interim re-analysis

Developed at National Center for Atmospheric Research (NCAR) Equations: Fully compressible, Euler non-hydrostatic, conservative for scalar variables. Vertical Coordinate: Terrain-following. Physics schemes: interchangeable This study – Running WRF3 with ERA-Interim at the boundaries (3-D fields and near surface parameters) and below (SST/ice/snow) to 8 and 2.7 km grid and 27 vertical levels. 8 Downscaling process WRF3 model Advanced Research WRF ERA-Interim (6 h) WRF Terrestrial data 30’’ Preprocessing and initialization 200 m contour lines 2.7 km

Precipitation becomes more influenced by the terrain and increases more than 100% locally. Precipitation is much larger in WRF compared to ERA-Interim (too much? Better with polar physics?) 9 Preliminary results (standard WRF)- precipitation ERA-InterimWRF Mean yearly precipitation (mm)

Geostrophic wind (not influenced by surface friction) – Finer details 10-m – Finer details – Increase of wind at mountain tops and as channeling and corner effects (note southern tip) – Decrease of wind in lee areas 10 Results – wind case 10 m (m/s) ERA-Interim WRF Geostrophic (m/s) 1100 m (m/s) 10 m (m/s)

10 m-wind – Lee in valleys and maxima over the ice sheet – Distinct katabatic winds and channeling 11 Nordaustlandet

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14 Wind direction

What is WRF The Weather Research and Forecasting (WRF) is developed at National Center for Atmospheric Research (NCAR) Designed to serve both operational forecasting (NMM) and atmospheric research needs (ARW). Physics and dynamics schemes are interchangeable Features multiple dynamical cores, a 3-dimensional variational (3DVAR) data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. (Not used here) WRF is currently in operational use at NCEP, AFWA and other centers

Dynamical equations fully compressible Non-hydrostatic (with a runtime hydrostatic option). vertical coordinate is a terrain-following hydrostatic pressure coordinate. grid-spacing can vary with height Grid staggering is the Arakawa C-grid. – u components at the centers of the left and right grid faces, and the v components at the centers of the upper and lower grid faces Runge-Kutta 2nd and 3rd order time integration schemes, and 2nd to 6th order advection schemes in both horizontal and vertical. time-split small step for acoustic and gravity-wave modes. The dynamics conserves scalar variables. 1- or 2-way nesting available Moving nests (prescribed moves and vortex tracking) nudging

ARAKAWA grids

Application fields suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers. Idealized simulations (e.g. LES, convection, baroclinic waves) – Parameterization research – Data assimilation research – Forecast research – Real-time NWP – Hurricane research – Regional climate research (dynamical downscaling) of re-analysis fields or atmosphere-ocean coupled general circulation models (AOGCMs) – Coupled-model applications – Teaching

Descriptions and references at guide_chap5.htm#Phys guide_chap5.htm#Phys Long wave radiation physics Short wave radiation physics Cumulus physics (small scale convective clouds) (important only over sea in the Arctic) Land-surface model We concentrate on: Cloud physics (microphysics): simulates cloud and precipitation processes Boundary layer physics Surface layer physics 19 7 different physics types

20 Present land surface model (LSM) in WRF 4 soil layers (10, 30, 60, 100 cm) Runoff and infiltration Soil layers can turn into snow layers

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Polar WRF The key modifications for Polar WRF are in the land surface model (LSM) Noah : – Optimal surface energy balance and heat transfer over sea ice and permanent ice surfaces – Implementation of a variable sea ice thickness and snow thickness over sea ice – Implementation of seasonally-varing sea ice albedo Also specific physics suitable for Arctic conditions (in dark blur in following slides)

Microphysics (mp_physics) X-class: number of different “particles” Single-moment: Particle mixing ratio Double-moment: Particle mixing ratio + particel number concentration Some examples Single moment c. WRF Single-Moment 3-class scheme: A simple, efficient scheme with ice and snow processes suitable for mesoscale grid sizes (3). d. WRF Single-Moment 5-class scheme: A slightly more sophisticated version of (c) that allows for mixed-phase processes and super-cooled water (4). e. Eta microphysics: The operational microphysics in NCEP models. A simple efficient scheme with diagnostic mixed-phase processes. For fine resolutions (< 5km) use option (5) and for coarse resolutions use option (95). f. WRF Single-Moment 6-class scheme: A scheme with ice, snow and graupel processes suitable for high-resolution simulations (6). Double-moment h. New Thompson et al. scheme: A new scheme with ice, snow and graupel processes suitable for high-resolution simulations (8). This adds rain number concentration and updates the scheme from the one in Version 3.0. New in Version 3.1. j. Morrison double-moment scheme (10). Double-moment ice, snow, rain and graupel for cloud- resolving simulations. New in Version 3.0. k. WRF Double-Moment 5-class scheme (14). This scheme has double-moment rain. Cloud and CCN for warm processes, but is otherwise like WSM5. New in Version 3.1. l. WRF Double-Moment 6-class scheme (16). This scheme has double-moment rain. Cloud and CCN for warm processes, but is otherwise like WSM6. New in Version 3.1.

Surface Layer (sf_sfclay_physics) Scheme must be working with PBL physics a. MM5 similarity: Based on Monin-Obukhov – with Carslon-Boland viscous sub-layer – standard similarity functions from look-up tables (sf_sfclay_physics = 1). b. Eta similarity: Used in Eta model. Based on Monin-Obukhov – with Zilitinkevich thermal roughness length – standard similarity functions from look-up tables (2). d. QNSE surface layer. Quasi-Normal Scale Elimination PBL scheme’s surface layer option (4). – new theory for stably stratified regions e. MYNN surface layer. Nakanishi and Niino PBL’s surface layer scheme (5). Includes better interaction of the turbulence with microphysics and radiation in the PBL, compared to the MYJ2.5 scheme

PBL turbulence closure First order closure (K-theory): 2 nd order moments are parameterized 1.5 order closure – TKE and potential temperature variance budgets – K M = f(TKE,  ’ variance) Higher order closures – Only higher order moments are parameterized – Example: 2 nd order closure uses budgets for 2 nd order moments and parameterizes 3 rd order moments, dissipation terms and pressure gradient correlations

4. Planetary Boundary layer (bl_pbl_physics) a. Yonsei University scheme: Non-local-K scheme with explicit entrainment layer and parabolic K profile in unstable mixed layer (bl_pbl_physics = 1). b. Mellor-Yamada-Janjic scheme (MYJ2.5): Eta operational scheme. One-dimensional prognostic turbulent kinetic energy scheme with local vertical mixing (2). e. Quasi-Normal Scale Elimination PBL (4). A TKE-prediction option that uses a new theory for stably stratified regions. Daytime part uses eddy diffusivity mass-flux method with shallow convection (mfshconv = 1) which is added in Version 3.4. f. Mellor-Yamada Nakanishi and Niino (MYNN) Level 2.5 PBL (5). – Predicts sub-grid TKE terms. – Includes better interaction of the turbulence with microphysics and radiation in the PBL, compared to the MYJ2.5 scheme

QNSE scheme for PBL/surface layer (Quasi-Normal Scale Elimination) k-  model uses a new theory for stably stratified regions. accounts for anisotropic turbulence and gravity waves and is not based on the shortcomings of Reynolds stress models in very stable stratifications.

Computer lab ModelMicrophysics Turbulence/ surface layer Vertical resolution EvaPolar WRFMorrisonMYJ28 JakobPolar WRFWRF3MYJ28 ChristianPolar WRFMorrisonQNSE28 AdamPolar WRFMorrisonMYJ32 JennyStandard WRFMorrisonMYJ28 Run WRF model during 36 h with different physical parameterizations and vertical resolution. You are also welcome to extend the test if you have time!

Look at: time evolution of temperature at Nordenskiöldbreen in comparison to measurements. time evolution of wind speed at Nordenskiöldbreen in comparison to measurements. time evolution of temperature profile time evolution of wind profile

Running WRF You will use the KALKYL computer cluster at UPPMAX The environment is scientific Linux and you will reach the system via the program Putty and WinSCP In Putty you can run the programs WinSCP is better to use when downloading data and changing in text files. You will do your analyses in MATLAB (or excel).

UPPMAX Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) is Uppsala University's resource of high-performance computers, large-scale storage, and know-how of high-performance computing (HPC). The center is a member of the national metacenter Swedish National Infrastructure for Computing (SNIC). UPPMAX was founded in 2003, but builds on a much longer successful history of HPC activities at Uppsala University. UPPMAX is hosted by the Department of Information Technology, Faculty of Science and Technology at Uppsala University and is one of the six centra in the national metacenter Swedish National Infrastructure for Computing (SNIC).Department of Information TechnologyFaculty of Science and TechnologyUppsala University (SNIC) UPPMAX is part of the SweGrid national grid.SweGrid UPPMAX is funded by the Faculty and by SNIC.

The KALKYL cluster Technical Summary 348 Compute Nodes with 2784 CPU cores 9504 Gigabyte total RAM 113 Terabyte total disk interconnected with a 4:1 oversubscribed DDR Infiniband fabric 696 CPU's in 348 dual CPU, quad core, nodes HP SL170h G6 compute servers Quad-core Intel® Xeon 5520 (Nehalem 2.26 GHz, 8MB cache) processors – 316 nodes with 24 GB memory and 250 GB hard disk – 16 nodes with 48 GB memory and 250 GB hard disk – 16 nodes with 72 GB memory and 2 TB hard disk

This study – Running WRF with ERA-Interim at the boundaries (3-D fields and near surface parameters) and below (SST/ice/snow) to 24 and 8 km grid and 27 vertical levels. 33 Downscaling process WRF3 model Advanced Research WRF ERA-Interim (6 h) WRF Terrestrial data 30’’ Preprocessing and initialization 200 m contour lines 2.7 km Done!

Namelist &time_control run_days = 0, run_hours = 1, run_minutes = 0, run_seconds = 0, start_year = 0001, start_month = 01, start_day = 01, start_hour = 00, start_minute = 00, start_second = 00, end_year = 0001, end_month = 01, end_day = 01, end_hour = 01, end_minute = 00, end_second = 00, history_interval = 10,

Namelist &domains time_step = 3, time_step_fract_num = 0, time_step_fract_den = 1, max_dom = 1, s_we = 1, e_we = 202, s_sn = 1, e_sn = 3, s_vert = 1, e_vert = 81, dx = 250, dy = 250, p_top_requested = 5000, eta_levels = 1.000, 0.997, 0.993, 0.988, 0.982, 0.975, 0.965, 0.952, 0.937, 0.917, 0.891, 0.860, 0.817, 0.766, 0.707, 0.644, 0.576, 0.507, 0.444, 0.380, 0.324, 0.273, 0.228, 0.188, 0.152, 0.121, 0.093, 0.069, 0.048, 0.029, 0.014, 0.000,

Namelist &physics mp_physics = 2, ra_lw_physics = 0, ra_sw_physics = 0, radt = 0, sf_sfclay_physics = 0, sf_surface_physics = 0, bl_pbl_physics = 0, bldt = 0, cu_physics = 0, cudt = 0, isfflx = 0, ifsnow = 0, icloud = 0, num_soil_layers = 5, mp_zero_out = 0,