mirror of https://github.com/Askill/claude.git
273 lines
10 KiB
Python
273 lines
10 KiB
Python
# toy model for use on stream
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# Please give me your Twitch prime sub!
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# CLimate Analysis using Digital Estimations (CLAuDE)
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import numpy as np
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import matplotlib.pyplot as plt
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import time, sys, pickle
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import claude_low_level_library as low_level
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import claude_top_level_library as top_level
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######## CONTROL ########
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day = 60*60*24 # define length of day (used for calculating Coriolis as well) (s)
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resolution = 3 # how many degrees between latitude and longitude gridpoints
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nlevels = 10 # how many vertical layers in the atmosphere
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top = 50E3 # top of atmosphere (m)
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planet_radius = 6.4E6 # define the planet's radius (m)
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insolation = 1370 # TOA radiation from star (W m^-2)
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gravity = 9.81 # define surface gravity for planet (m s^-2)
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axial_tilt = -23.5 # tilt of rotational axis w.r.t. solar plane
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year = 365*day # length of year (s)
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dt_spinup = 60*137
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dt_main = 60*9
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spinup_length = 0*day
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###
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advection = True # if you want to include advection set this to be True
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advection_boundary = 8 # how many gridpoints away from poles to apply advection
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save = False # save current state to file?
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load = False # load initial state from file?
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plot = True # display plots of output?
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level_plots = True # display plots of output on vertical levels?
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nplots = 5 # how many levels you want to see plots of (evenly distributed through column)
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###########################
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# define coordinate arrays
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lat = np.arange(-90,91,resolution)
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lon = np.arange(0,360,resolution)
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nlat = len(lat)
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nlon = len(lon)
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lon_plot, lat_plot = np.meshgrid(lon, lat)
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heights = np.arange(0,top,top/nlevels)
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heights_plot, lat_z_plot = np.meshgrid(lat,heights)
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# initialise arrays for various physical fields
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temperature_world = np.zeros((nlat,nlon)) + 290
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temperature_atmos = np.zeros((nlat,nlon,nlevels))
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air_pressure = np.zeros_like(temperature_atmos)
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u = np.zeros_like(temperature_atmos)
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v = np.zeros_like(temperature_atmos)
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w = np.zeros_like(temperature_atmos)
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air_density = np.zeros_like(temperature_atmos)
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# #######################
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# read temperature and density in from standard atmosphere
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f = open("standard_atmosphere.txt", "r")
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standard_height = []
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standard_temp = []
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standard_density = []
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for x in f:
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h, t, r = x.split()
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standard_height.append(float(h))
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standard_temp.append(float(t))
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standard_density.append(float(r))
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f.close()
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density_profile = np.interp(x=heights/1E3,xp=standard_height,fp=standard_density)
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temp_profile = np.interp(x=heights/1E3,xp=standard_height,fp=standard_temp)
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for k in range(nlevels):
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air_density[:,:,k] = density_profile[k]
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temperature_atmos[:,:,k] = temp_profile[k]
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###########################
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# weight_above = np.interp(x=heights/1E3,xp=standard_height,fp=standard_density)
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# top_index = np.argmax(np.array(standard_height) >= top/1E3)
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# if standard_height[top_index] == top/1E3:
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# weight_above = np.trapz(np.interp(x=standard_height[top_index:],xp=standard_height,fp=standard_density),standard_height[top_index:])*gravity*1E3
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# else:
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# weight_above = np.trapz(np.interp(x=np.insert(standard_height[top_index:], 0, top/1E3),xp=standard_height,fp=standard_density),np.insert(standard_height[top_index:], 0, top/1E3))*gravity*1E3
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###########################
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albedo = np.zeros_like(temperature_world) + 0.2
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heat_capacity_earth = np.zeros_like(temperature_world) + 1E6
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albedo_variance = 0.001
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for i in range(nlat):
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for j in range(nlon):
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albedo[i,j] += np.random.uniform(-albedo_variance,albedo_variance)
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specific_gas = 287
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thermal_diffusivity_roc = 1.5E-6
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sigma = 5.67E-8
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air_pressure = air_density*specific_gas*temperature_atmos
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# define planet size and various geometric constants
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circumference = 2*np.pi*planet_radius
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circle = np.pi*planet_radius**2
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sphere = 4*np.pi*planet_radius**2
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# define how far apart the gridpoints are: note that we use central difference derivatives, and so these distances are actually twice the distance between gridboxes
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dy = circumference/nlat
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dx = np.zeros(nlat)
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coriolis = np.zeros(nlat) # also define the coriolis parameter here
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angular_speed = 2*np.pi/day
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for i in range(nlat):
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dx[i] = dy*np.cos(lat[i]*np.pi/180)
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coriolis[i] = angular_speed*np.sin(lat[i]*np.pi/180)
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dz = np.zeros(nlevels)
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for k in range(nlevels-1): dz[k] = heights[k+1] - heights[k]
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dz[-1] = dz[-2]
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#################### SHOW TIME ####################
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if plot:
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# set up plot
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f, ax = plt.subplots(2,figsize=(9,9))
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f.canvas.set_window_title('CLAuDE')
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ax[0].contourf(lon_plot, lat_plot, temperature_world, cmap='seismic')
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ax[1].contourf(lon_plot, lat_plot, temperature_atmos[:,:,0], cmap='seismic')
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plt.subplots_adjust(left=0.1, right=0.75)
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ax[0].set_title('Ground temperature')
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ax[1].set_title('Atmosphere temperature')
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cbar_ax = f.add_axes([0.85, 0.15, 0.05, 0.7])
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# allow for live updating as calculations take place
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if level_plots:
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level_divisions = int(np.floor(nlevels/nplots))
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level_plots_levels = range(nlevels)[::level_divisions]
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g, bx = plt.subplots(nplots,figsize=(9,8),sharex=True)
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g.canvas.set_window_title('CLAuDE atmospheric levels')
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for k, z in zip(range(nplots), level_plots_levels):
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z += 1
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bx[k].contourf(lon_plot, lat_plot, temperature_atmos[:,:,z], cmap='seismic')
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bx[k].set_title(str(heights[z])+' km')
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bx[k].set_ylabel('Latitude')
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bx[-1].set_xlabel('Longitude')
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plt.ion()
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plt.show()
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# INITIATE TIME
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t = 0
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if load:
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# load in previous save file
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temperature_atmos,temperature_world,u,v,w,t,air_density,albedo = pickle.load(open("save_file.p","rb"))
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while True:
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initial_time = time.time()
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if t < spinup_length:
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dt = dt_spinup
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velocity = False
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else:
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dt = dt_main
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velocity = True
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# print current time in simulation to command line
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print("+++ t = " + str(round(t/day,2)) + " days +++", end='\r')
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print('T: ',round(temperature_world.max()-273.15,1),' - ',round(temperature_world.min()-273.15,1),' C')
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print('U: ',round(u.max(),2),' - ',round(u.min(),2),' V: ',round(v.max(),2),' - ',round(v.min(),2),' W: ',round(w.max(),2),' - ',round(w.min(),2))
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# print(profile(air_density))
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# print(profile(air_pressure)/100)
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before_radiation = time.time()
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temperature_world, temperature_atmos = top_level.radiation_calculation(temperature_world, temperature_atmos, air_pressure, air_density, heat_capacity_earth, albedo, insolation, lat, lon, heights, dz, t, dt, day, year, axial_tilt)
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time_taken = float(round(time.time() - before_radiation,3))
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# print('Radiation: ',str(time_taken),'s')
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# update air pressure
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old_pressure = np.copy(air_pressure)
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air_pressure = air_density*specific_gas*temperature_atmos
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if velocity:
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before_velocity = time.time()
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u,v,w = top_level.velocity_calculation(u,v,air_pressure,old_pressure,air_density,coriolis,gravity,dx,dy,dt)
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time_taken = float(round(time.time() - before_velocity,3))
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# print('Velocity: ',str(time_taken),'s')
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before_advection = time.time()
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if advection:
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# allow for thermal advection in the atmosphere, and heat diffusion in the atmosphere and the ground
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# atmosp_addition = dt*(thermal_diffusivity_air*laplacian(temperature_atmos))
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atmosp_addition = dt*top_level.divergence_with_scalar(temperature_atmos,u,v,dx,dy)
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temperature_atmos[advection_boundary:-advection_boundary,:,:] -= atmosp_addition[advection_boundary:-advection_boundary,:,:]
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temperature_atmos[advection_boundary-1,:,:] -= 0.5*atmosp_addition[advection_boundary-1,:,:]
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temperature_atmos[-advection_boundary,:,:] -= 0.5*atmosp_addition[-advection_boundary,:,:]
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# as density is now variable, allow for mass advection
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# density_addition = dt*divergence_with_scalar(air_density)
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# air_density[advection_boundary:-advection_boundary,:,:] -= density_addition[advection_boundary:-advection_boundary,:,:]
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# air_density[(advection_boundary-1),:,:] -= 0.5*density_addition[advection_boundary-1,:,:]
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# air_density[-advection_boundary,:,:] -= 0.5*density_addition[-advection_boundary,:,:]
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# temperature_world += dt*(thermal_diffusivity_roc*laplacian(temperature_world))
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time_taken = float(round(time.time() - before_advection,3))
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# print('Advection: ',str(time_taken),'s')
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before_plot = time.time()
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if plot:
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# update plot
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test = ax[0].contourf(lon_plot, lat_plot, temperature_world, cmap='seismic')
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ax[0].streamplot(lon_plot, lat_plot, u[:,:,0], v[:,:,0], color='white',density=1)
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ax[0].set_title('$\it{Ground} \quad \it{temperature}$')
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ax[0].set_xlim((lon.min(),lon.max()))
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ax[0].set_ylim((lat.min(),lat.max()))
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ax[0].set_ylabel('Latitude')
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ax[0].axhline(y=0,color='black',alpha=0.3)
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ax[0].set_xlabel('Longitude')
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ax[1].contourf(heights_plot, lat_z_plot, np.transpose(np.mean(temperature_atmos,axis=1)), cmap='seismic')
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ax[1].contour(heights_plot,lat_z_plot, np.transpose(np.mean(u,axis=1)), colors='white',levels=20,linewidths=1,alpha=0.8)
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ax[1].streamplot(heights_plot, lat_z_plot, np.transpose(np.mean(v,axis=1)),np.transpose(np.mean(10*w,axis=1)),color='black',density=0.75)
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ax[1].set_title('$\it{Atmospheric} \quad \it{temperature}$')
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ax[1].set_xlim((-90,90))
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ax[1].set_ylim((0,heights.max()))
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ax[1].set_ylabel('Height (m)')
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ax[1].set_xlabel('Latitude')
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f.colorbar(test, cax=cbar_ax)
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cbar_ax.set_title('Temperature (K)')
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f.suptitle( 'Time ' + str(round(24*t/day,2)) + ' hours' )
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if level_plots:
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for k, z in zip(range(nplots), level_plots_levels):
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z += 1
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bx[k].contourf(lon_plot, lat_plot, temperature_atmos[:,:,z], cmap='seismic')
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bx[k].streamplot(lon_plot, lat_plot, u[:,:,z], v[:,:,z], color='white',density=1)
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bx[k].set_title(str(round(heights[z]/1E3))+' km')
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bx[k].set_ylabel('Latitude')
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bx[k].set_xlim((lon.min(),lon.max()))
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bx[k].set_ylim((lat.min(),lat.max()))
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bx[-1].set_xlabel('Longitude')
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plt.pause(0.01)
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ax[0].cla()
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ax[1].cla()
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if level_plots:
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for k in range(nplots):
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bx[k].cla()
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time_taken = float(round(time.time() - before_plot,3))
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# print('Plotting: ',str(time_taken),'s')
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# advance time by one timestep
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t += dt
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time_taken = float(round(time.time() - initial_time,3))
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print('Time: ',str(time_taken),'s')
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if save:
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pickle.dump((temperature_atmos,temperature_world,u,v,w,t,air_density,albedo), open("save_file.p","wb"))
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# sys.exit() |