def render_plot_frame(x):
(m, lon, lat, rect, field, ttl, x, y, c, clim, plot_ndx) = x
import matplotlib.pyplot as plt
plt.figure(figsize=(12,10))
render_spatial_field(m, lon, lat, field, ttl)
m.drawparallels(np.arange(rect[0], rect[1], 1.0))
m.drawmeridians(np.arange(rect[2], rect[3],1.0))
x, y = m(x, y)
plt.scatter(x, y, c=c)
if clim is not None:
plt.clim(clim)
plt.colorbar()
plt.savefig('results/field_%04d.png' % plot_ndx)
mws.load_station_info(os.path.join(cfg["station_info_dir"], "%s.info" % code))
mws.register_to_grid(wrf_data)
mws.load_station_data(os.path.join(cfg["station_obs_dir"], "%s.obs" % code))
stations.append(mws)
print('Loaded %d stations.' % len(stations))
# construct basemap for rendering
domain_rng = wrf_data.get_domain_extent()
m = Basemap(llcrnrlon=domain_rng[0],llcrnrlat=domain_rng[1],
urcrnrlon=domain_rng[2],urcrnrlat=domain_rng[3],
projection = 'mill')
# show the equilibrium field and render position of stations on top
plt.figure(figsize = (12,9))
render_spatial_field(m, lon, lat, hgt[0,:,:], 'Station positions over topography')
for s in stations:
slon, slat = s.get_position()
x, y = m(slon, slat)
plt.plot(x, y, 'o', markersize = 4, markerfacecolor = 'magenta')
plt.text(x, y, s.get_id(), color = 'black')
# compute station means and standard deviations
station_stats = {}
for s in stations:
v = [o.get_value() for o in s.get_observations("FM")]
stdev = np.std(v)
if np.isnan(stdev) or abs(stdev) < 1e-6:
stdev = 1.0
station_stats[s.get_id()] = (np.mean(v), stdev)
def run_module():
# configure diagnostics
init_diagnostics("results/kriging_test_diagnostics.txt")
diagnostics().configure_tag("skdm_obs_res", True, True, True)
diagnostics().configure_tag("skdm_obs_res_mean", True, True, True)
wrf_data = WRFModelData(
'../real_data/witch_creek/realfire03_d04_20071022.nc')
# read in vars
lat, lon = wrf_data.get_lats(), wrf_data.get_lons()
tm = wrf_data.get_times()
rain = wrf_data['RAINNC']
Ed, Ew = wrf_data.get_moisture_equilibria()
# obtain sizes
Nt = rain.shape[0]
dom_shape = lat.shape
locs = np.prod(dom_shape)
# load station data, match to grid points and build observation records
# load station data from files
tz = pytz.timezone('US/Pacific')
stations = [
Station(os.path.join(station_data_dir, s), tz, wrf_data)
for s in station_list
]
obs_data = build_observation_data(stations, 'fuel_moisture', wrf_data)
# construct initial vector
mfm = MeanFieldModel()
# set up parameters
mod_res_std = np.ones_like(Ed[0, :, :]) * 0.05
obs_res_std = np.ones((len(stations), )) * 0.1
# construct a basemap representation of the area
lat_rng = (np.min(lat), np.max(lat))
lon_rng = (np.min(lon), np.max(lon))
m = Basemap(llcrnrlon=lon_rng[0],
llcrnrlat=lat_rng[0],
urcrnrlon=lon_rng[1],
urcrnrlat=lat_rng[1],
projection='mill')
plt.figure(figsize=(10, 6))
# run model
ndx = 1
for t in range(1, Nt):
model_time = wrf_data.get_times()[t]
E = 0.5 * (Ed[t, :, :] + Ew[t, :, :])
# if we have an observation somewhere in time, run kriging
if model_time in obs_data:
print("Time: %s, step: %d" % (str(model_time), t))
mfm.fit_to_data(E, obs_data[model_time])
Efit = mfm.predict_field(E)
# krige data to observations
K, V = simple_kriging_data_to_model(obs_data[model_time],
obs_res_std, Efit, wrf_data,
mod_res_std, t)
plt.clf()
plt.subplot(2, 2, 1)
render_spatial_field(m, lon, lat, Efit, 'Equilibrium')
plt.clim([0.0, 0.2])
plt.colorbar()
plt.subplot(2, 2, 2)
render_spatial_field(m, lon, lat, K, 'Kriging field')
plt.clim([0.0, 0.2])
plt.colorbar()
plt.subplot(2, 2, 3)
render_spatial_field(m, lon, lat, V, 'Kriging variance')
plt.clim([0.0, np.max(V)])
plt.colorbar()
plt.subplot(2, 2, 4)
render_spatial_field(m, lon, lat, K - Efit,
'Kriging vs. mean field residuals')
# plt.clim([0.0, np.max()])
plt.colorbar()
plt.savefig('model_outputs/kriging_test_t%03d.png' % (ndx))
ndx += 1
llcrnrlon=lon_rng[0], llcrnrlat=lat_rng[0], urcrnrlon=lon_rng[1], urcrnrlat=lat_rng[1], projection="mill"
)
# compute the equilibrium moisture on grid points
Ed, Ew = equilibrium_moisture(v["PSFC"][:, :, :], Q2, v["T2"][:, :, :])
pb.ion()
pb.figure(figsize=(14, 7))
for i in range(Ed.shape[0]):
# if i > 0 and np.all(rain[i,:,:] == 0):
# continue
pb.clf()
pb.subplot(221)
render_spatial_field(m, lon, lat, Ed[i, :, :], "Drying equilibrium [%d]" % i)
pb.clim([np.min(Ed), np.max(Ed)])
pb.colorbar()
pb.subplot(222)
render_spatial_field(m, lon, lat, Ew[i, :, :], "Wetting equilibrium")
pb.clim([np.min(Ew), np.max(Ew)])
pb.colorbar()
pb.subplot(223)
render_spatial_field(m, lon, lat, rain[i, :, :], "Rain")
pb.clim([np.min(rain), np.max(rain)])
pb.colorbar()
pb.subplot(224)
render_spatial_field(m, lon, lat, Q2[i, :, :], "Water vapor ratio")
pb.clim([np.min(Q2), np.max(Q2)])
pb.colorbar()
pb.draw()
os.path.join(cfg["station_obs_dir"], "%s.obs" % code))
stations.append(mws)
print('Loaded %d stations.' % len(stations))
# construct basemap for rendering
domain_rng = wrf_data.get_domain_extent()
m = Basemap(llcrnrlon=domain_rng[0],
llcrnrlat=domain_rng[1],
urcrnrlon=domain_rng[2],
urcrnrlat=domain_rng[3],
projection='mill')
# show the equilibrium field and render position of stations on top
plt.figure(figsize=(12, 9))
render_spatial_field(m, lon, lat, hgt[0, :, :],
'Station positions over topography')
for s in stations:
slon, slat = s.get_position()
x, y = m(slon, slat)
plt.plot(x, y, 'o', markersize=4, markerfacecolor='magenta')
plt.text(x, y, s.get_id(), color='black')
# compute station means and standard deviations
station_stats = {}
for s in stations:
v = [o.get_value() for o in s.get_observations("FM")]
stdev = np.std(v)
if np.isnan(stdev) or abs(stdev) < 1e-6:
stdev = 1.0
station_stats[s.get_id()] = (np.mean(v), stdev)
mfm = MeanFieldModel()
# construct basemap for rendering
llcrnrlon = min(map(lambda x: x.lon, stations))
llcrnrlat = min(map(lambda x: x.lat, stations))
urcrnrlon = max(map(lambda x: x.lon, stations))
urcrnrlat = max(map(lambda x: x.lat, stations))
m = Basemap(llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat,
urcrnrlon=urcrnrlon,
urcrnrlat=urcrnrlat,
projection = 'mill')
# show the equilibrium field and render position of stations on top
render_spatial_field(m, lon, lat, E[0,:,:], 'Equilibrium moisture')
plt.figure()
m.drawparallels(np.arange(llcrnrlat,urcrnrlat,1.0))
m.drawmeridians(np.arange(llcrnrlon,urcrnrlon,1.0))
for s in stations:
slon, slat = s.get_position()
x, y = m(slon, slat)
plt.plot(x, y, 'o', markersize = 8, markerfacecolor = 'magenta')
plt.text(x, y, s.get_name(), color = 'white')
x, y = m(lon.ravel(), lat.ravel())
plt.plot(x, y, 'k+', markersize = 4)
plt.savefig('results/station_localization.png')
# find common observation times for the station and for the WRF model
tms = stations[0].get_obs_times()
urcrnrlon=lon_rng[1],urcrnrlat=lat_rng[1],
projection = 'mill')
# compute the equilibrium moisture on grid points
Ed, Ew = equilibrium_moisture(v['PSFC'][:,:,:], Q2, v['T2'][:,:,:])
pb.ion()
pb.figure(figsize = (14, 7))
for i in range(Ed.shape[0]):
# if i > 0 and np.all(rain[i,:,:] == 0):
# continue
pb.clf()
pb.subplot(221)
render_spatial_field(m, lon, lat, Ed[i,:,:], 'Drying equilibrium [%d]' % i)
pb.clim([np.min(Ed), np.max(Ed)])
pb.colorbar()
pb.subplot(222)
render_spatial_field(m, lon, lat, Ew[i,:,:], 'Wetting equilibrium')
pb.clim([np.min(Ew), np.max(Ew)])
pb.colorbar()
pb.subplot(223)
render_spatial_field(m, lon, lat, rain[i,:,:], 'Rain')
pb.clim([np.min(rain), np.max(rain)])
pb.colorbar()
pb.subplot(224)
render_spatial_field(m, lon, lat, Q2[i,:,:], 'Water vapor ratio')
pb.clim([np.min(Q2), np.max(Q2)])
pb.colorbar()
pb.draw()
def run_module():
# configure diagnostics
init_diagnostics("results/kriging_test_diagnostics.txt")
diagnostics().configure_tag("skdm_obs_res", True, True, True)
diagnostics().configure_tag("skdm_obs_res_mean", True, True, True)
wrf_data = WRFModelData('../real_data/witch_creek/realfire03_d04_20071022.nc')
# read in vars
lat, lon = wrf_data.get_lats(), wrf_data.get_lons()
tm = wrf_data.get_times()
rain = wrf_data['RAINNC']
Ed, Ew = wrf_data.get_moisture_equilibria()
# obtain sizes
Nt = rain.shape[0]
dom_shape = lat.shape
locs = np.prod(dom_shape)
# load station data, match to grid points and build observation records
# load station data from files
tz = pytz.timezone('US/Pacific')
stations = [Station(os.path.join(station_data_dir, s), tz, wrf_data) for s in station_list]
obs_data = build_observation_data(stations, 'fuel_moisture', wrf_data)
# construct initial vector
mfm = MeanFieldModel()
# set up parameters
mod_res_std = np.ones_like(Ed[0,:,:]) * 0.05
obs_res_std = np.ones((len(stations),)) * 0.1
# construct a basemap representation of the area
lat_rng = (np.min(lat), np.max(lat))
lon_rng = (np.min(lon), np.max(lon))
m = Basemap(llcrnrlon=lon_rng[0],llcrnrlat=lat_rng[0],
urcrnrlon=lon_rng[1],urcrnrlat=lat_rng[1],
projection = 'mill')
plt.figure(figsize = (10, 6))
# run model
ndx = 1
for t in range(1, Nt):
model_time = wrf_data.get_times()[t]
E = 0.5 * (Ed[t,:,:] + Ew[t,:,:])
# if we have an observation somewhere in time, run kriging
if model_time in obs_data:
print("Time: %s, step: %d" % (str(model_time), t))
mfm.fit_to_data(E, obs_data[model_time])
Efit = mfm.predict_field(E)
# krige data to observations
K, V = simple_kriging_data_to_model(obs_data[model_time], obs_res_std, Efit, wrf_data, mod_res_std, t)
plt.clf()
plt.subplot(2,2,1)
render_spatial_field(m, lon, lat, Efit, 'Equilibrium')
plt.clim([0.0, 0.2])
plt.colorbar()
plt.subplot(2,2,2)
render_spatial_field(m, lon, lat, K, 'Kriging field')
plt.clim([0.0, 0.2])
plt.colorbar()
plt.subplot(2,2,3)
render_spatial_field(m, lon, lat, V, 'Kriging variance')
plt.clim([0.0, np.max(V)])
plt.colorbar()
plt.subplot(2,2,4)
render_spatial_field(m, lon, lat, K - Efit, 'Kriging vs. mean field residuals')
# plt.clim([0.0, np.max()])
plt.colorbar()
plt.savefig('model_outputs/kriging_test_t%03d.png' % (ndx))
ndx += 1
