from dynamico import unstructured as unst from dynamico import dyn from dynamico import time_step from dynamico import DCMIP from dynamico import meshes from dynamico import xios from dynamico import precision as prec from dynamico.meshes import Cartesian_mesh as Mesh import math as math import numpy as np import time import argparse import logging from numpy import pi, log, exp, sin, cos # Baroclinic instability test based on Ullrich et al. 2015, QJRMS parser = argparse.ArgumentParser() parser.add_argument("--mpi_ni", type=int, default=1, help="number of x processors") parser.add_argument("--mpi_nj", type=int, default=1, help="number of y processors") parser.add_argument("--T", type=float, default=3600., help="Length of time slice in seconds") parser.add_argument("--N", type=int, default=24, help="Number of time slices") parser.add_argument("--log",type=str,default='DEBUG',choices=('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'), help="Set verbosity level of logging") parser.add_argument("--Davies_N1", type=int, default=3) parser.add_argument("--Davies_N2", type=int, default=3) args = parser.parse_args() def baroclinic_3D(Lx,nx,Ly,ny,llm,ztop=25000.): Rd = 287.0 # Gas constant for dryy air (j kg^-1 K^-1) T0 = 288.0 # Reference temperature (K) lap = 0.005 # Lapse rate (K m^-1) b = 2. # Non dimensional vertical width parameter u0 = 35. # Reference zonal wind speed (m s^-1) a = 6.371229e6 # Radius of the Earth (m) ptop = 2000. y0 = Ly*0.5 Cpd = 1004.5 p0 = 1e5 up = 1. # amplitude (m/s) xc,yc,lp = 0.,Ly/2.,600000. omega = 7.292e-5 # Angular velocity of the Earth (s^-1) phi0 = 90.*np.pi/180.0 # Reference latitude North pi/4 (deg) f0 = 2*omega*np.sin(phi0) beta0 = 2*omega*np.cos(phi0)/a fb = 2*omega*np.sin(phi0) - y0*2*omega*np.cos(phi0)/a def Phi_xy(y): fc = y*y - (Ly*y/pi)*sin(2*pi*y/Ly) fd = Ly*Ly/(2*pi*pi)*cos(2*pi*y/Ly) return .5*u0*( fb*(y-y0-Ly/(2*pi)*sin(2*pi*y/Ly)) + .5*beta0*(fc-fd-(Ly*Ly/3.)- Ly*Ly/(2*pi*pi)) ) def Phi_xyeta(y,eta): return T0*g/lap*(1-eta**(Rd*lap/g)) + Phi_xy(y)*log(eta)*exp(-((log(eta)/b)**2)) def ulon(x,y,eta): u = -u0*(sin(pi*y/Ly)**2)*log(eta)*(eta**(-log(eta)/(b*b))) # u = u + up*exp(-(((x-xc)**2+(y-yc)**2)/lp**2)) return u def tmean(eta) : return T0*eta**(Rd*lap/g) def T(y,eta) : return tmean(eta)+(Phi_xy(y)/Rd)*(((2/(b*b))*(log(eta))**2)-1)*exp(-((0.5*log(eta))**2)) def p(eta): return p0*eta # eta = p/p_s def eta(alpha) : return (1-(lap*ztop*alpha/(T0)))**(g/(Rd*lap)) # roughly equispaced levels def coriolis(x,y): return f0+beta0*y filename = 'cart_%03d_%03d.nc'%(nx,ny) logging.info('Reading Cartesian mesh ...') meshfile = meshes.DYNAMICO_Format(filename) pmesh = meshes.Unstructured_PMesh(comm,meshfile) pmesh.partition_curvilinear(args.mpi_ni,args.mpi_nj) nqdyn, radius = 1, None mesh = meshes.Local_Mesh(pmesh, llm, nqdyn, radius, coriolis) alpha_k = (np.arange(llm) +.5)/llm alpha_l = (np.arange(llm+1)+ 0.)/llm x_ik, alpha_ik = np.meshgrid(mesh.lon_i, alpha_k, indexing='ij') y_ik, alpha_ik = np.meshgrid(mesh.lat_i+.5*Ly, alpha_k, indexing='ij') x_il, alpha_il = np.meshgrid(mesh.lon_i, alpha_l, indexing='ij') y_il, alpha_il = np.meshgrid(mesh.lat_i+.5*Ly, alpha_l, indexing='ij') x_ek, alpha_ek = np.meshgrid(mesh.lon_e, alpha_k, indexing='ij') y_ek, alpha_ek = np.meshgrid(mesh.lat_e+.5*Ly, alpha_k, indexing='ij') print('ztop(ptop) according to Eq. 7:', T0/lap*(1.-(ptop/p0)**(Rd*lap/g))) print(np.shape(alpha_k),np.shape(alpha_l)) thermo = dyn.Ideal_perfect(Cpd, Rd, p0, T0) eta_il = eta(alpha_il) eta_ik = eta(alpha_ik) eta_ek = eta(alpha_ek) print('min max eta_il', np.min(eta_il),np.max(eta_il)) Phi_il = Phi_xyeta(y_il, eta_il) Phi_ik = Phi_xyeta(y_ik, eta_ik) p_ik = p(eta_ik) T_ik = T(y_ik, eta_ik) #ik full level(40), il(41) gas = thermo.set_pT(p_ik,T_ik) mass_ik = mesh.field_mass() for l in range(llm): mass_ik[:,l]=(Phi_il[:,l+1]-Phi_il[:,l])/(g*gas.v[:,l]) Sik, Wil = gas.s*mass_ik, mesh.field_w() u_ek = mesh.ucov3D(ulon(x_ek, y_ek, eta_ek), 0.*eta_ek) print('ztop (m) = ', Phi_il[0,-1]/g, ztop) ptop = p(eta(1.)) print( 'ptop (Pa) = ', gas.p[0,-1], ptop) params=dyn.Struct() params.ptop=ptop params.dx=dx params.dx_g0=dx/g params.g = g # define parameters for lower BC pbot = p(eta_il[:,0]) print( 'min p, T :', pbot.min(), tmean(pbot/p0)) gas_bot = thermo.set_pT(pbot, tmean(pbot/p0)) params.pbot = gas_bot.p params.rho_bot = 1e6/gas_bot.v return thermo, mesh, params, prec.asnum([mass_ik,Sik,u_ek,Phi_il,Wil]), gas def diagnose(Phi,S,m,W): s=S/m ; s=.5*(s+abs(s)) for l in range(llm): v[:,l]=(Phi[:,l+1]-Phi[:,l])/(g*m[:,l]) w[:,l]=.5*params.g*(W[:,l+1]+W[:,l])/m[:,l] z[:,l]=.5*(Phi[:,l+1]+Phi[:,l])/params.g gas = thermo.set_vs(v,s) return gas, w, z class Davies: def __init__(self,N1,N2,x_i,y_i,x_e,y_e): self.N1, self.N2 = N1, N2 self.beta_i = self.mask(x_i,y_i) self.beta_e = self.mask(x_e,y_e) self.x_e,self.y_e = x_e,y_e def mask0(self,c,c0,delta): # 1D building block for Davies relaxation N1, N2 = self.N1, self.N2 m = np.zeros(c.size) c1,c2 = c0-N1*delta, c0-(N1+N2)*delta for i in range(c.size): ci=c[i] m[i] = (1.+np.cos((ci-c2)*np.pi/(N2*delta)))/2.0 if ci < c2 : m[i]=1. if ci > c1 : m[i]=0. return m def relax(self, llm, step, flow): beta_i, beta_e = self.beta_i, self.beta_e m,S,u,Phi,W=flow for l in range(llm): step.mass[:,l] = beta_i*step.mass[:,l] + (1.-beta_i)*m[:,l] step.theta_rhodz[:,l] = beta_i*step.theta_rhodz[:,l] + (1.-beta_i)*S[:,l] step.u[:,l] = beta_e*step.u[:,l] + (1.-beta_e)*u[:,l] for l in range(llm+1): step.geopot[:,l] = beta_i*step.geopot[:,l] + (1.-beta_i)*Phi[:,l] step.W[:,l] = beta_i*step.W[:,l] + (1.-beta_i)*W[:,l] class myDavies(Davies): def mask(self,x,y): logging.debug('davies dy = %f' % dy) return self.mask0(y,.5*Ly,dy)*self.mask0(-y,.5*Ly,dy) with xios.Client() as client: # setup XIOS which creates the DYNAMICO communicator comm = client.comm mpi_rank, mpi_size = comm.Get_rank(), comm.Get_size() #-------------Logging verbosity configuration--------------------------- myloglevel = args.log myloglevel = getattr(logging, myloglevel.upper()) logging.basicConfig(filename='out.log',filemode='w',level=myloglevel, format='%(processName)s:%(name)s:%(filename)s:%(module)s:%(funcName)s:%(lineno)d:%(levelname)s:%(message)s' ) logging.debug('%d/%d starting'%(mpi_rank,mpi_size)) g, Lx, Ly = 9.81, 4e7, 6e6 nx, ny, llm = 200, 30, 25 dx,dy = Lx/nx,Ly/ny unst.setvar('g',g) thermo, mesh, params, flow0, gas0 = baroclinic_3D(Lx,nx,Ly,ny,llm) mass_bl,mass_dak,mass_dbk = meshes.compute_hybrid_coefs(flow0[0]) print( 'Type of mass_bl, mass_dak, mass_dbk : ', [x.dtype for x in mass_bl, mass_dak, mass_dbk]) unst.ker.dynamico_init_hybrid(mass_bl,mass_dak,mass_dbk) T = args.T dt = 360. dz = flow0[3].max()/(params.g*llm) nt = int(math.ceil(T/dt)) dt = T/nt logging.info( 'Time step : %d x %g = %g s' % (nt,dt,nt*dt)) caldyn_thermo, caldyn_eta = unst.thermo_entropy, unst.eta_mass if False: # time stepping in Python caldyn = unst.Caldyn_NH(caldyn_thermo,caldyn_eta, mesh,thermo,params,params.g) scheme = time_step.ARK2(caldyn.bwd_fast_slow, dt) def next_flow(m,S,u,Phi,W): return scheme.advance((m,S,u,Phi,W),nt) else: # time stepping in Fortran scheme = time_step.ARK2(None, dt) caldyn_step = unst.caldyn_step_NH(mesh,scheme,1, caldyn_thermo,caldyn_eta,thermo,params,params.g) davies = myDavies(args.Davies_N1, args.Davies_N2, mesh.lon_i, mesh.lat_i, mesh.lon_e,mesh.lat_e) print('mask min/max :', davies.beta_i.min(), davies.beta_i.max() ) logging.debug('mask min/max :') logging.debug(davies.beta_i.min()) logging.debug(davies.beta_i.max()) def next_flow(m,S,u,Phi,W): # junk,fast,slow = caldyn.bwd_fast_slow(flow, 0.) caldyn_step.mass[:,:], caldyn_step.theta_rhodz[:,:], caldyn_step.u[:,:] = m,S,u caldyn_step.geopot[:,:], caldyn_step.W[:,:] = Phi,W for i in range(nt): caldyn_step.next() davies.relax(llm, caldyn_step, flow0) return (caldyn_step.mass.copy(), caldyn_step.theta_rhodz.copy(), caldyn_step.u.copy(), caldyn_step.geopot.copy(), caldyn_step.W.copy()) m,S,u,Phi,W = flow0 if caldyn_thermo == unst.thermo_theta: s=S/m theta = thermo.T0*np.exp(s/thermo.Cpd) S=m*theta title_format = 'Potential temperature at t=%g s (K)' else: title_format = 'Specific entropy at t=%g s (J/K/kg)' w=mesh.field_mass() z=mesh.field_mass() #T, nslice, dt = 3600., 1, 3600. Nslice = args.N with xios.Context_Curvilinear(mesh,1, 24*3600) as context: # now XIOS knows about the mesh and we can write to disk v = mesh.field_mass() # specific volume (diagnosed) for i in range(Nslice): context.update_calendar(i) # Diagnose quantities of interest from prognostic variables m,S,u,Phi,W gas, w, z = diagnose(Phi,S,m,W) # write to disk context.send_field_primal('ps', davies.beta_i) context.send_field_primal('temp', gas0.T) context.send_field_primal('p', gas.p) context.send_field_primal('theta', gas.s) context.send_field_primal('uz', w) print( 'ptop, model top (m) :', unst.getvar('ptop'), Phi.max()/unst.getvar('g')) time1, elapsed1 =time.time(), unst.getvar('elapsed') m,S,u,Phi,W = next_flow(m,S,u,Phi,W) time2, elapsed2 =time.time(), unst.getvar('elapsed') factor = 1000./nt print( 'ms per full time step : ', factor*(time2-time1), factor*(elapsed2-elapsed1)) factor = 1e9/(4*nt*nx*ny*llm) print( 'nanosec per gridpoint per full time step : ', factor*(time2-time1), factor*(elapsed2-elapsed1)) context.update_calendar(Nslice+1) # make sure XIOS writes last iteration logging.info('************DONE************')