mirror of https://github.com/Askill/claude.git
110 lines
5.0 KiB
Cython
110 lines
5.0 KiB
Cython
# claude_top_level_library
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import claude_low_level_library as low_level
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import numpy as np
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cimport numpy as np
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cimport cython
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ctypedef np.float64_t DTYPE_f
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# laplacian of scalar field a
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# def laplacian(a):
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# output = np.zeros_like(a)
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# if output.ndim == 2:
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# for i in np.arange(1,nlat-1):
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# for j in range(nlon):
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# output[i,j] = (scalar_gradient_x_2D(a,i,(j+1)%nlon) - scalar_gradient_x_2D(a,i,(j-1)%nlon))/dx[i] + (scalar_gradient_y_2D(a,i+1,j) - scalar_gradient_y_2D(a,i-1,j))/dy
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# return output
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# if output.ndim == 3:
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# for i in np.arange(1,nlat-1):
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# for j in range(nlon):
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# for k in range(nlevels-1):
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# output[i,j,k] = (scalar_gradient_x(a,i,(j+1)%nlon,k) - scalar_gradient_x(a,i,(j-1)%nlon,k))/dx[i] + (scalar_gradient_y(a,i+1,j,k) - scalar_gradient_y(a,i-1,j,k))/dy + (scalar_gradient_z(a,i,j,k+1)-scalar_gradient_z(a,i,j,k-1))/(2*dz[k])
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# return output
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# divergence of (a*u) where a is a scalar field and u is the atmospheric velocity field
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def divergence_with_scalar(a,u,v,dx,dy):
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output = np.zeros_like(a)
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nlat, nlon, nlevels = output.shape[:]
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au = a*u
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av = a*v
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for i in range(nlat):
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for j in range(nlon):
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for k in range(nlevels):
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output[i,j,k] = low_level.scalar_gradient_x(au,dx,nlon,i,j,k) + low_level.scalar_gradient_y(av,dy,nlat,i,j,k) #+ 0.1*scalar_gradient_z(a*w,i,j,k)
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return output
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def radiation_calculation(np.ndarray temperature_world, np.ndarray temperature_atmos, np.ndarray air_pressure, np.ndarray air_density, np.ndarray heat_capacity_earth, np.ndarray albedo, DTYPE_f insolation, np.ndarray lat, np.ndarray lon, np.ndarray heights, np.ndarray dz, np.int_t t, np.int_t dt, DTYPE_f day, DTYPE_f year, DTYPE_f axial_tilt):
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# calculate change in temperature of ground and atmosphere due to radiative imbalance
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cdef np.int_t nlat,nlon,nlevels,i,j
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cdef DTYPE_f fl = 0.1
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cdef np.ndarray upward_radiation,downward_radiation,optical_depth,Q, pressure_profile, density_profile
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nlat = temperature_atmos.shape[0]
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nlon = temperature_atmos.shape[1]
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nlevels = temperature_atmos.shape[2]
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upward_radiation = np.zeros(nlevels)
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downward_radiation = np.zeros(nlevels)
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optical_depth = np.zeros(nlevels)
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Q = np.zeros(nlevels)
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for i in range(nlat):
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for j in range(nlon):
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# calculate optical depth
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pressure_profile = air_pressure[i,j,:]
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density_profile = air_density[i,j,:]
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optical_depth = low_level.surface_optical_depth(lat[i])*(fl*(pressure_profile/pressure_profile[0]) + (1-fl)*(pressure_profile/pressure_profile[0])**4)
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# calculate upward longwave flux, bc is thermal radiation at surface
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upward_radiation[0] = low_level.thermal_radiation(temperature_world[i,j])
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for k in np.arange(1,nlevels):
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upward_radiation[k] = (upward_radiation[k-1] - (optical_depth[k]-optical_depth[k-1])*(low_level.thermal_radiation(temperature_atmos[i,j,k])))/(1+optical_depth[k-1]-optical_depth[k])
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# calculate downward longwave flux, bc is zero at TOA (in model)
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downward_radiation[-1] = 0
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for k in np.arange(0,nlevels-1)[::-1]:
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downward_radiation[k] = (downward_radiation[k+1] - low_level.thermal_radiation(temperature_atmos[i,j,k])*(optical_depth[k+1]-optical_depth[k]))/(1 + optical_depth[k] - optical_depth[k+1])
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# gradient of difference provides heating at each level
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for k in np.arange(nlevels):
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Q[k] = -low_level.scalar_gradient_z_1D(upward_radiation-downward_radiation,dz,0,0,k)/(1E3*density_profile[k])
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# make sure model does not have a higher top than 50km!!
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# approximate SW heating of ozone
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if heights[k] > 20E3:
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Q[k] += low_level.solar(5,lat[i],lon[j],t,day, year, axial_tilt)*((((heights[k]-20E3)/1E3)**2)/(30**2))/(24*60*60)
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temperature_atmos[i,j,:] += Q*dt
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# update surface temperature with shortwave radiation flux
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temperature_world[i,j] += dt*((1-albedo[i,j])*(low_level.solar(insolation,lat[i],lon[j],t, day, year, axial_tilt) + downward_radiation[0]) - upward_radiation[0])/heat_capacity_earth[i,j]
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return temperature_world, temperature_atmos
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def velocity_calculation(u,v,air_pressure,old_pressure,air_density,coriolis,gravity,dx,dy,dt):
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# introduce temporary arrays to update velocity in the atmosphere
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u_temp = np.zeros_like(u)
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v_temp = np.zeros_like(v)
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w_temp = np.zeros_like(u)
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nlat,nlon,nlevels = air_pressure.shape[:]
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# calculate acceleration of atmosphere using primitive equations on beta-plane
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for i in np.arange(1,nlat-1):
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for j in range(nlon):
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for k in range(nlevels):
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u_temp[i,j,k] += dt*( -u[i,j,k]*low_level.scalar_gradient_x(u,dx,nlon,i,j,k) - v[i,j,k]*low_level.scalar_gradient_y(u,dy,nlat,i,j,k) + coriolis[i]*v[i,j,k] - low_level.scalar_gradient_x(air_pressure,dx,nlon,i,j,k)/air_density[i,j,k] )
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v_temp[i,j,k] += dt*( -u[i,j,k]*low_level.scalar_gradient_x(v,dx,nlon,i,j,k) - v[i,j,k]*low_level.scalar_gradient_y(v,dy,nlat,i,j,k) - coriolis[i]*u[i,j,k] - low_level.scalar_gradient_y(air_pressure,dy,nlat,i,j,k)/air_density[i,j,k] )
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w_temp[i,j,k] += -(air_pressure[i,j,k]-old_pressure[i,j,k])/(dt*air_density[i,j,k]*gravity)
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u += u_temp
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v += v_temp
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w = w_temp
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# approximate friction
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u *= 0.95
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v *= 0.95
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return u,v,w |