[701] | 1 | # apparently we should initialize MPI before doing anything else |
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| 2 | from mpi4py import MPI |
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| 3 | comm = MPI.COMM_WORLD |
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| 4 | mpi_rank, mpi_size = comm.Get_rank(), comm.Get_size() |
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| 5 | print '%d/%d starting'%(mpi_rank,mpi_size) |
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| 6 | |
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| 7 | # now start doing something useful |
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[697] | 8 | from dynamico import unstructured as unst |
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| 9 | from dynamico import dyn |
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| 10 | from dynamico import time_step |
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| 11 | from dynamico import DCMIP |
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| 12 | from dynamico import meshes |
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[701] | 13 | #import dynamico.xios as xios |
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[697] | 14 | |
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| 15 | import math as math |
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| 16 | import matplotlib.pyplot as plt |
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| 17 | import numpy as np |
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| 18 | import time |
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[701] | 19 | import argparse |
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[697] | 20 | |
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| 21 | #------------------------ initial condition ------------------------- |
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| 22 | |
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| 23 | # Parameters for the simulation |
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| 24 | g = 9.80616 # gravitational acceleration in meters per second squared |
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| 25 | omega = 7.29211e-5 # Earth's angular velocity in radians per second |
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| 26 | f0 = 2.0*omega # Coriolis parameter |
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| 27 | u_0 = 20.0 # velocity in meters per second |
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| 28 | T_0 = 288.0 # temperature in Kelvin |
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| 29 | d2 = 1.5e6**2 # square of half width of Gaussian mountain profile in meters |
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| 30 | h_0 = 2.0e3 # mountain height in meters |
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| 31 | lon_c = np.pi/2.0 # mountain peak longitudinal location in radians |
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| 32 | lat_c = np.pi/6.0 # mountain peak latitudinal location in radians |
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| 33 | radius = 6.371229e6 # mean radius of the Earth in meters |
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| 34 | ref_press = 100145.6 # reference pressure (mean surface pressure) in Pascals |
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| 35 | ref_surf_press = 930.0e2 # South Pole surface pressure in Pascals |
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| 36 | Rd = 287.04 # ideal gas constant for dry air in joules per kilogram Kelvin |
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| 37 | Cpd = 1004.64 # specific heat at constant pressure in joules per kilogram Kelvin |
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| 38 | kappa = Rd/Cpd # kappa=R_d/c_p |
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| 39 | N_freq = np.sqrt(g**2/(Cpd*T_0)) # Brunt-Vaisala buoyancy frequency |
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| 40 | N2, g2kappa = N_freq**2, g*g*kappa |
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| 41 | |
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| 42 | def DCMIP2008c5(grid,llm): |
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| 43 | def Phis(lon,lat): |
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| 44 | rgrc = radius*np.arccos(np.sin(lat_c)*np.sin(lat)+np.cos(lat_c)*np.cos(lat)*np.cos(lon-lon_c)) |
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| 45 | return g*h_0*np.exp(-rgrc**2/d2) |
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| 46 | def ps(lon, lat, Phis): |
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| 47 | return ref_surf_press * np.exp( |
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| 48 | - radius*N2*u_0/(2.0*g2kappa)*(u_0/radius+f0)*(np.sin(lat)**2-1.) |
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| 49 | - N2/(g2kappa)*Phis ) |
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| 50 | def ulon(lat): return u_0*np.cos(lat) |
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| 51 | def ulat(lat): return 0.*lat |
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| 52 | def f(lon,lat): return f0*np.sin(lat) # Coriolis parameter |
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| 53 | |
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| 54 | nqdyn, preff, Treff = 1, 1e5, 300. |
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| 55 | thermo = dyn.Ideal_perfect(Cpd, Rd, preff, Treff) |
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| 56 | |
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| 57 | meshfile = meshes.MPAS_Format('grids/x1.%d.grid.nc'%grid) |
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| 58 | # mesh = meshes.Unstructured_Mesh(meshfile, llm, nqdyn, radius, f) |
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| 59 | pmesh = meshes.Unstructured_PMesh(comm,meshfile) |
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[760] | 60 | pmesh.partition_metis() |
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[697] | 61 | mesh = meshes.Local_Mesh(pmesh, llm, nqdyn, radius, f) |
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| 62 | mesh.pmesh=pmesh |
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| 63 | |
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| 64 | lev,levp1 = np.arange(llm),np.arange(llm+1) |
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| 65 | lon_i, lat_i, lon_e, lat_e = mesh.lon_i, mesh.lat_i, mesh.lon_e, mesh.lat_e |
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| 66 | lat_ik,k_i = np.meshgrid(mesh.lat_i,lev, indexing='ij') |
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| 67 | lon_ik,k_i = np.meshgrid(mesh.lon_i,lev, indexing='ij') |
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| 68 | lat_il,l_i = np.meshgrid(mesh.lat_i,levp1, indexing='ij') |
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| 69 | lon_il,l_i = np.meshgrid(mesh.lon_i,levp1, indexing='ij') |
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| 70 | lat_ek,k_e = np.meshgrid(mesh.lat_e,lev, indexing='ij') |
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| 71 | |
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| 72 | Phis_i = Phis(lon_i, lat_i) |
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| 73 | ps_i = ps(lon_i, lat_i, Phis_i) |
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| 74 | |
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| 75 | if llm==18: |
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| 76 | ap_l=[0.00251499, 0.00710361, 0.01904260, 0.04607560, 0.08181860, |
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| 77 | 0.07869805, 0.07463175, 0.06955308, 0.06339061, 0.05621774, 0.04815296, |
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| 78 | 0.03949230, 0.03058456, 0.02193336, 0.01403670, 0.007458598, 0.002646866, |
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| 79 | 0.0, 0.0 ] |
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| 80 | bp_l=[0.0, 0.0, 0.0, 0.0, 0.0, 0.03756984, 0.08652625, 0.1476709, 0.221864, |
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| 81 | 0.308222, 0.4053179, 0.509588, 0.6168328, 0.7209891, 0.816061, 0.8952581, |
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| 82 | 0.953189, 0.985056, 1.0 ] |
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| 83 | if llm==26: |
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| 84 | ap_l=[0.002194067, 0.004895209, 0.009882418, 0.01805201, 0.02983724, 0.04462334, 0.06160587, |
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| 85 | 0.07851243, 0.07731271, 0.07590131, 0.07424086, 0.07228744, 0.06998933, 0.06728574, 0.06410509, |
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| 86 | 0.06036322, 0.05596111, 0.05078225, 0.04468960, 0.03752191, 0.02908949, 0.02084739, 0.01334443, |
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| 87 | 0.00708499, 0.00252136, 0.0, 0.0 ] |
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| 88 | bp_l=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01505309, 0.03276228, 0.05359622, |
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| 89 | 0.07810627, 0.1069411, 0.1408637, 0.1807720, 0.2277220, 0.2829562, 0.3479364, 0.4243822, |
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| 90 | 0.5143168, 0.6201202, 0.7235355, 0.8176768, 0.8962153, 0.9534761, 0.9851122, 1.0 ] |
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[701] | 91 | if llm==49: |
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| 92 | ap_l=[0.002251865, 0.003983890, 0.006704364, 0.01073231, 0.01634233, 0.02367119, |
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| 93 | 0.03261456, 0.04274527, 0.05382610, 0.06512175, 0.07569850, 0.08454283, |
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| 94 | 0.08396310, 0.08334103, 0.08267352, 0.08195725, 0.08118866, 0.08036393, |
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| 95 | 0.07947895, 0.07852934, 0.07751036, 0.07641695, 0.07524368, 0.07398470, |
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| 96 | 0.07263375, 0.07118414, 0.06962863, 0.06795950, 0.06616846, 0.06424658, |
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| 97 | 0.06218433, 0.05997144, 0.05759690, 0.05504892, 0.05231483, 0.04938102, |
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| 98 | 0.04623292, 0.04285487, 0.03923006, 0.03534049, 0.03116681, 0.02668825, |
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| 99 | 0.02188257, 0.01676371, 0.01208171, 0.007959612, 0.004510297, 0.001831215, |
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| 100 | 0.0, 0.0 ] |
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| 101 | bp_l=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, |
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| 102 | 0.006755112, 0.01400364, 0.02178164, 0.03012778, 0.03908356, 0.04869352, |
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| 103 | 0.05900542, 0.07007056, 0.08194394, 0.09468459, 0.1083559, 0.1230258, |
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| 104 | 0.1387673, 0.1556586, 0.1737837, 0.1932327, 0.2141024, 0.2364965, |
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| 105 | 0.2605264, 0.2863115, 0.3139801, 0.3436697, 0.3755280, 0.4097133, |
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| 106 | 0.4463958, 0.4857576, 0.5279946, 0.5733168, 0.6219495, 0.6741346, |
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| 107 | 0.7301315, 0.7897776, 0.8443334, 0.8923650, 0.9325572, 0.9637744, |
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| 108 | 0.9851122, 1.0] |
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[697] | 109 | |
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| 110 | ap_l, bp_l = ref_press*np.asarray(ap_l[-1::-1]), bp_l[-1::-1] # reverse indices |
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| 111 | ptop = ap_l[-1] # pressure BC |
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| 112 | |
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[701] | 113 | if mpi_rank==0: print ptop, ap_l, bp_l |
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[697] | 114 | ps_il,ap_il = np.meshgrid(ps_i,ap_l, indexing='ij') |
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| 115 | ps_il,bp_il = np.meshgrid(ps_i,bp_l, indexing='ij') |
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| 116 | hybrid_coefs = meshes.mass_coefs_from_pressure_coefs(g, ap_il, bp_il) |
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| 117 | pb_il = ap_il + bp_il*ps_il |
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| 118 | mass_ik, pb_ik = mesh.field_mass(), mesh.field_mass() |
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| 119 | for l in range(llm): |
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| 120 | pb_ik[:,l]=.5*(pb_il[:,l]+pb_il[:,l+1]) |
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| 121 | mass_ik[:,l]=(pb_il[:,l]-pb_il[:,l+1])/g |
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| 122 | Tb_ik = T_0 + 0.*pb_ik |
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| 123 | gas = thermo.set_pT(pb_ik,Tb_ik) |
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| 124 | Sik = gas.s*mass_ik |
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| 125 | # start at hydrostatic geopotential |
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| 126 | Phi_il = mesh.field_w() |
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| 127 | Phi_il[:,0]=Phis_i |
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| 128 | for l in range(llm): |
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| 129 | Phi_il[:,l+1] = Phi_il[:,l] + g*mass_ik[:,l]*gas.v[:,l] |
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| 130 | |
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| 131 | ujk, Wil = mesh.ucov3D(ulon(lat_ek),ulat(lat_ek)), mesh.field_w() |
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| 132 | |
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[701] | 133 | if mpi_rank==0: |
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| 134 | print 'ztop (m) = ', Phi_il[0,-1]/g |
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| 135 | print 'ptop (Pa) = ', gas.p[0,-1], ptop |
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[697] | 136 | dx=mesh.de.min() |
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| 137 | params=dyn.Struct() |
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| 138 | params.g, params.ptop = g, ptop |
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| 139 | params.dx, params.dx_g0 = dx, dx/g |
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| 140 | params.pbot, params.rho_bot = 1e5+0.*mesh.lat_i, 1e6+0.*mesh.lat_i |
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| 141 | return thermo, mesh, hybrid_coefs, params, (mass_ik,Sik,ujk,Phi_il,Wil), gas |
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| 142 | |
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| 143 | #------------------------ main program ------------------------- |
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| 144 | |
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[701] | 145 | unst.setvar('dynamico_mpi_rank', mpi_rank) |
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| 146 | |
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| 147 | parser = argparse.ArgumentParser() |
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| 148 | parser.add_argument("-r", "--refinement", type=int, |
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| 149 | default=5, choices=[4, 5, 6, 7], |
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| 150 | help="grid refinement level") |
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| 151 | parser.add_argument("-l", "--llm", type=int, |
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| 152 | default=49, choices=[18, 26, 49], |
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| 153 | help="number of vertical levels") |
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| 154 | args = parser.parse_args() |
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| 155 | nqtot, llm, grid = 1, args.llm, 2+10*(4**args.refinement) |
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| 156 | #nqtot, llm, grid = 1,26,40962 |
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| 157 | |
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[697] | 158 | T, Nslice, courant = 14400, 24, 3.0 |
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| 159 | caldyn_thermo, caldyn_eta = unst.thermo_entropy, unst.eta_lag |
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| 160 | #caldyn_thermo, caldyn_eta = unst.thermo_entropy, unst.eta_mass |
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[701] | 161 | |
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[697] | 162 | thermo, mesh, hybrid_coefs, params, flow0, gas0 = DCMIP2008c5(grid,llm) |
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| 163 | llm, dx = mesh.llm, params.dx |
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[701] | 164 | if mpi_rank==0: |
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| 165 | print 'grid, llm, local_gridsize, dx =', grid, llm, mesh.Ai.size, dx |
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| 166 | if caldyn_eta == unst.eta_lag: |
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| 167 | print 'Lagrangian coordinate.' |
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| 168 | else: |
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| 169 | print 'Mass-based coordinate.' |
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[697] | 170 | |
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| 171 | unst.ker.dynamico_init_hybrid(*hybrid_coefs) |
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| 172 | |
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| 173 | dt = courant*.5*dx/np.sqrt(gas0.c2.max()) |
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| 174 | |
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[701] | 175 | if False: |
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| 176 | nt = int(math.ceil(T/dt)) |
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| 177 | dt = T/nt |
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| 178 | else: |
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| 179 | nt=100 |
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| 180 | if mpi_rank==0: print 'Time step : %g s' % dt |
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[697] | 181 | |
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| 182 | #mesh.plot_e(mesh.le/mesh.de) ; plt.title('le/de') |
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| 183 | #plt.savefig('fig_DCMIP2008c5/le_de.png'); plt.close() |
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| 184 | |
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| 185 | #mesh.plot_i(mesh.Ai) ; plt.title('Ai') |
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| 186 | #plt.savefig('fig_DCMIP2008c5/Ai.png'); plt.close() |
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| 187 | |
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| 188 | scheme = time_step.ARK2(None, dt, a32=0.7) |
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| 189 | caldyn_step = unst.caldyn_step_HPE(mesh,scheme,nt, caldyn_thermo,caldyn_eta, thermo,params,params.g) |
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[701] | 190 | |
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[697] | 191 | def next_flow(m,S,u): |
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| 192 | caldyn_step.mass[:,:], caldyn_step.theta_rhodz[:,:], caldyn_step.u[:,:] = m,S,u |
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[701] | 193 | # caldyn_step.remap() |
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[697] | 194 | caldyn_step.next() |
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| 195 | return (caldyn_step.mass.copy(), caldyn_step.theta_rhodz.copy(), |
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| 196 | caldyn_step.u.copy(), caldyn_step.geopot.copy()) |
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| 197 | |
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| 198 | def plots(it): |
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| 199 | s=S/m |
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| 200 | for l in range(llm): |
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| 201 | z[:,l]=.5*(Phi[:,l+1]+Phi[:,l])/params.g |
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| 202 | vol[:,l]=(Phi[:,l+1]-Phi[:,l])/params.g/m[:,l] # specific volume |
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| 203 | gas = thermo.set_vs(vol, s) |
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| 204 | s=.5*(s+abs(s)) |
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| 205 | t = (it*T)/3600 |
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| 206 | print( 'ptop, model top (m) :', unst.getvar('ptop'), Phi.max()/unst.getvar('g') ) |
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| 207 | mesh.plot_i(gas.T[:,llm/2]) |
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| 208 | plt.title('T at t=%dh'%(t)) |
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| 209 | plt.savefig('fig_DCMIP2008c5/T%02d.png'%it) |
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| 210 | plt.close() |
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| 211 | |
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| 212 | mesh.plot_i(m[:,llm/2]) |
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| 213 | plt.title('mass at t=%dh'%(t)) |
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| 214 | plt.savefig('fig_DCMIP2008c5/m%02d.png'%it) |
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| 215 | plt.close() |
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| 216 | |
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| 217 | mesh.plot_i(Phi[:,0]) |
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| 218 | plt.title('Surface geopotential at t=%dh'%(t)) |
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| 219 | plt.savefig('fig_DCMIP2008c5/Phis%02d.png'%it) |
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| 220 | plt.close() |
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| 221 | |
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| 222 | z, vol = mesh.field_mass(), mesh.field_mass() |
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| 223 | m,S,u,Phi,W = flow0 |
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| 224 | caldyn_step.geopot[:,0]=Phi[:,0] |
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| 225 | #plots(0) |
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| 226 | |
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[701] | 227 | #context=xios.XIOS_Context(mesh.pmesh,mesh,nqtot, T) |
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[697] | 228 | |
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| 229 | for it in range(Nslice): |
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[701] | 230 | # context.update_calendar(it) |
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[697] | 231 | unst.setvar('debug_hevi_solver',False) |
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| 232 | time1, elapsed1 =time.time(), unst.getvar('elapsed') |
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| 233 | m,S,u,Phi = next_flow(m,S,u) |
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| 234 | time2, elapsed2 = time.time(), unst.getvar('elapsed') |
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[701] | 235 | if mpi_rank==0: |
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| 236 | factor = 1000./nt |
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| 237 | print 'ms per full time step : ', factor*(time2-time1), factor*(elapsed2-elapsed1) |
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| 238 | factor = 1e9/(4*nt*m.size) |
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| 239 | print 'nanosec per gridpoint per call to caldyn_hevi : ', factor*(time2-time1), factor*(elapsed2-elapsed1) |
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[697] | 240 | |
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[701] | 241 | if False: |
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| 242 | s=S/m |
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| 243 | s=.5*(s+abs(s)) |
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| 244 | for l in range(llm): |
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| 245 | z[:,l]=.5*(Phi[:,l+1]+Phi[:,l])/params.g |
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| 246 | vol[:,l]=(Phi[:,l+1]-Phi[:,l])/params.g/m[:,l] # specific volume |
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| 247 | gas = thermo.set_vs(vol, s) |
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| 248 | ss = np.asarray(gas.T, dtype=np.double) |
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| 249 | # context.send_field_primal('theta', ss) |
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| 250 | #plots(it+1) |
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[697] | 251 | |
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[701] | 252 | unst.ker.dynamico_print_trace() |
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| 253 | |
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| 254 | #print 'xios.context_finalize()' |
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| 255 | #context.finalize() |
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| 256 | #print 'xios.finalize()' |
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| 257 | #xios.finalize() |
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| 258 | print 'MPI Rank %d Done'%mpi_rank |
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