def check_overlap(wrf_path, ts_now):
"""
Check if the WRF file <wrf_path> timstamps contain <ts_now>.
"""
wrfout = WRFModelData(wrf_path)
outts = wrfout['GMT']
if ts_now in outts:
return True
else:
print("INFO: previous forecast [%s - %s] exists, running DA till %s" %
(str(outts[0]), str(outts[-1]), str(ts_now)))
return False
}
# cfg = { 'station_info_dir' : '../real_data/witch_creek',
# 'station_obs_dir' : '../real_data/witch_creek',
# 'station_list_file' : '../real_data/witch_creek/station_list',
# 'wrf_data_file' : '../real_data/witch_creek/wrf20071021_witchcreek_all.nc',
# 'assimilation_window' : 3600,
# 'output_dir' : 'results_wc',
# 'max_dist' : 200.0,
# 'bin_width' : 20,
# 'standardize' : False
# }
# load the smallest domain
wrf_data = WRFModelData(cfg['wrf_data_file'], ['T2', 'PSFC', 'Q2', 'HGT'])
# read in vars
lon, lat = wrf_data.get_lons(), wrf_data.get_lats()
hgt = wrf_data['HGT']
tm = wrf_data.get_gmt_times()
Nt = len(tm)
print("Loaded %d timestamps from WRF." % Nt)
# load station data from files
with open(cfg['station_list_file'], 'r') as f:
si_list = f.read().split('\n')
si_list = filter(lambda x: len(x) > 0 and x[0] != '#', map(string.strip, si_list))
def run_module():
# read in configuration file to execute run
print("Reading configuration from [%s]" % sys.argv[1])
with open(sys.argv[1]) as f:
cfg = eval(f.read())
# ensure output path exists
if not os.path.isdir(cfg['output_dir']):
os.mkdir(cfg['output_dir'])
# configure diagnostics
init_diagnostics(os.path.join(cfg['output_dir'], 'moisture_model_v1_diagnostics.txt'))
# Error covariance matrix condition number in kriging
diagnostics().configure_tag("skdm_cov_cond", False, True, True)
# Assimilation parameters
diagnostics().configure_tag("assim_K0", False, True, True)
diagnostics().configure_tag("assim_K1", True, True, True)
diagnostics().configure_tag("assim_data", False, False, True)
diagnostics().configure_tag("obs_residual_var", True, True, True)
diagnostics().configure_tag("fm10_model_residual_var", True, True, True)
diagnostics().configure_tag("fm10_model_var", False, True, True)
diagnostics().configure_tag("fm10_kriging_var", False, True, True)
### Load and preprocess WRF model data
# load WRF data
wrf_data = WRFModelData(cfg['input_file'], tz_name = 'US/Mountain')
# read in spatial and temporal extent of WRF variables
lat, lon = wrf_data.get_lats(), wrf_data.get_lons()
tm = wrf_data.get_gmt_times()
Nt = cfg['Nt'] if cfg['Nt'] is not None else len(tm)
dom_shape = lat.shape
# retrieve the rain variable
rain = wrf_data['RAIN']
# moisture equilibria are now computed from averaged Q,P,T at beginning and end of period
Ed, Ew = wrf_data.get_moisture_equilibria()
### Load observation data from the stations
# load station data from files
with open(os.path.join(cfg['station_data_dir'], cfg['station_list_file']), 'r') as f:
si_list = f.read().split('\n')
si_list = filter(lambda x: len(x) > 0, map(string.strip, si_list))
# for each station id, load the station
stations = []
for sinfo in si_list:
code = sinfo.split(',')[0]
mws = MesoWestStation(sinfo, wrf_data)
for suffix in [ '_1', '_2', '_3', '_4', '_5', '_6', '_7' ]:
mws.load_station_data(os.path.join(cfg['station_data_dir'], '%s%s.xls' % (code, suffix)))
stations.append(mws)
print('Loaded %d stations.' % len(stations))
# check stations for nans
stations = filter(MesoWestStation.data_ok, stations)
print('Have %d stations with complete data.' % len(stations))
# set the measurement variance of the stations
for s in stations:
s.set_measurement_variance('fm10', cfg['fm10_meas_var'])
# build the observation data
obs_data_fm10 = build_observation_data(stations, 'fm10', wrf_data, tm)
### Initialize model and visualization
# find maximum moisture overall to set up visualization
maxE = 0.5
# construct initial conditions from timestep 1 (because Ed/Ew at zero are zero)
E = 0.5 * (Ed[1,:,:] + Ew[1,:,:])
# set up parameters
Q = np.eye(9) * cfg['Q']
P0 = np.eye(9) * cfg['P0']
dt = (tm[1] - tm[0]).seconds
print("INFO: Computed timestep from WRF is is %g seconds." % dt)
K = np.zeros_like(E)
V = np.zeros_like(E)
mV = np.zeros_like(E)
predicted_field = np.zeros_like(E)
mresV = np.zeros_like(E)
Kf_fn = np.zeros_like(E)
Vf_fn = np.zeros_like(E)
mid = np.zeros_like(E)
Kg = np.zeros((dom_shape[0], dom_shape[1], 9))
cV12 = np.zeros_like(E)
# moisture state and observation residual variance estimators
mod_re = OnlineVarianceEstimator(np.zeros_like(E), np.ones_like(E) * 0.05, 1)
obs_re = OnlineVarianceEstimator(np.zeros((len(stations),)), np.ones(len(stations),) * 0.05, 1)
# initialize the mean field model (default fit is 1.0 of equilibrium before new information comes in)
mfm = MeanFieldModel(cfg['lock_gamma'])
# construct model grid using standard fuel parameters
Tk = np.array([1.0, 10.0, 100.0]) * 3600
models = np.zeros(dom_shape, dtype = np.object)
models_na = np.zeros_like(models)
for p in np.ndindex(dom_shape):
models[p] = CellMoistureModel((lat[p], lon[p]), 3, E[p], Tk, P0 = P0)
models_na[p] = CellMoistureModel((lat[p], lon[p]), 3, E[p], Tk, P0 = P0)
m = None
plt.figure(figsize = (12, 8))
### Run model for each WRF timestep and assimilate data when available
for t in range(1, Nt):
model_time = tm[t]
print("INFO: time: %s, step: %d" % (str(model_time), t))
# run the model update
for p in np.ndindex(dom_shape):
i, j = p
models[p].advance_model(Ed[t-1, i, j], Ew[t-1, i, j], rain[t-1, i, j], dt, Q)
models_na[p].advance_model(Ed[t-1, i, j], Ew[t-1, i, j], rain[t-1, i, j], dt, Q)
# prepare visualization data
f = np.zeros((dom_shape[0], dom_shape[1], 3))
f_na = np.zeros((dom_shape[0], dom_shape[1], 3))
for p in np.ndindex(dom_shape):
f[p[0], p[1], :] = models[p].get_state()[:3]
f_na[p[0], p[1], :] = models_na[p].get_state()[:3]
P = models[p].get_state_covar()
cV12[p] = P[0,1]
mV[p] = P[1,1]
mid[p] = models[p].get_model_ids()[1]
diagnostics().push("fm10_model_var", (t, np.mean(mV)))
# run Kriging on each observed fuel type
Kf = []
Vf = []
fn = []
for obs_data, fuel_ndx in [ (obs_data_fm10, 1) ]:
# run the kriging subsystem and the Kalman update only if we have observations
if model_time in obs_data:
# retrieve observations for current time
obs_t = obs_data[model_time]
# fit the current estimation of the moisture field to the data
base_field = f[:,:,fuel_ndx]
mfm.fit_to_data(base_field, obs_data[model_time])
# find differences (residuals) between observed measurements and nearest grid points
# use this to update observation residual standard deviation
obs_vals = np.array([o.get_value() for o in obs_data[model_time]])
mod_vals = np.array([base_field[o.get_nearest_grid_point()] for o in obs_data[model_time]])
mod_na_vals = np.array([f_na[:,:,fuel_ndx][o.get_nearest_grid_point()] for o in obs_data[model_time]])
obs_re.update_with(obs_vals - mod_vals)
diagnostics().push("obs_residual_var", (t, np.mean(obs_re.get_variance())))
# predict the moisture field using observed fuel type
predicted_field = mfm.predict_field(base_field)
# update the model residual estimator and get current best estimate of variance
mod_re.update_with(f[:,:,fuel_ndx] - predicted_field)
mresV = mod_re.get_variance()
diagnostics().push("fm10_model_residual_var", (t, np.mean(mresV)))
# krige observations to grid points
Kf_fn, Vf_fn = trend_surface_model_kriging(obs_data[model_time], wrf_data, predicted_field)
krig_vals = np.array([Kf_fn[o.get_nearest_grid_point()] for o in obs_data[model_time]])
diagnostics().push("assim_data", (t, fuel_ndx, obs_vals, krig_vals, mod_vals, mod_na_vals))
plot_model_snapshot(cfg, tm, t, fuel_ndx, obs_vals, krig_vals, mod_vals, mod_na_vals)
diagnostics().push("fm10_kriging_var", (t, np.mean(Vf_fn)))
# append to storage for kriged fields in this time instant
Kf.append(Kf_fn)
Vf.append(Vf_fn)
fn.append(fuel_ndx)
# if there were any observations, run the kalman update step
if len(fn) > 0:
Nobs = len(fn)
# run the kalman update in each model independently
# gather the standard deviations of the moisture fuel after the Kalman update
for p in np.ndindex(dom_shape):
O = np.zeros((Nobs,))
V = np.zeros((Nobs, Nobs))
# construct observations for this position
for i in range(Nobs):
O[i] = Kf[i][p]
V[i,i] = Vf[i][p]
# execute the Kalman update
Kp = models[p].kalman_update(O, V, fn)
Kg[p[0], p[1], :] = Kp[:, 0]
# push new diagnostic outputs
diagnostics().push("assim_K0", (t, np.mean(Kg[:,:,0])))
diagnostics().push("assim_K1", (t, np.mean(Kg[:,:,1])))
# prepare visualization data
f = np.zeros((dom_shape[0], dom_shape[1], 3))
for p in np.ndindex(dom_shape):
f[p[0], p[1], :] = models[p].get_state()[:3]
plt.clf()
plt.subplot(3,3,1)
render_spatial_field_fast(m, lon, lat, f[:,:,0], '1-hr fuel')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,2)
render_spatial_field_fast(m, lon, lat, f[:,:,1], '10-hr fuel')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,3)
render_spatial_field_fast(m, lon, lat, f_na[:,:,1], '10hr fuel - no assim')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,4)
render_spatial_field_fast(m, lon, lat, Kg[:,:,0], 'Kalman gain for 1-hr fuel')
plt.clim([0.0, 3.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,5)
render_spatial_field_fast(m, lon, lat, Kg[:,:,1], 'Kalman gain for 10-hr fuel')
plt.clim([0.0, 1.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,6)
render_spatial_field_fast(m, lon, lat, Kf_fn, 'Kriging field')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,7)
render_spatial_field_fast(m, lon, lat, mid, 'Model ids')
plt.clim([0.0, 5.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,8)
render_spatial_field_fast(m, lon, lat, Vf_fn, 'Kriging variance')
plt.clim([0.0, np.max(Vf_fn)])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,9)
render_spatial_field_fast(m, lon, lat, mresV, 'Model res. variance')
plt.clim([0.0, np.max(mresV)])
plt.axis('off')
plt.colorbar()
plt.savefig(os.path.join(cfg['output_dir'], 'moisture_model_t%03d.png' % t))
# store the diagnostics in a binary file
diagnostics().dump_store(os.path.join(cfg['output_dir'], 'diagnostics.bin'))
# make a plot of gammas
plt.figure()
plt.plot(diagnostics().pull('mfm_gamma'), 'bo-')
plt.title('Mean field model - gamma')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_gamma.png'))
plt.figure()
plt.plot(diagnostics().pull('skdm_cov_cond'))
plt.title('Condition number of covariance matrix')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_sigma_cond.png'))
# make a plot for each substation
plt.figure()
D = diagnostics().pull("assim_data")
for i in range(len(stations)):
plt.clf()
# get data for the i-th station
t_i = [ o[0] for o in D]
obs_i = [ o[2][i] for o in D]
krig_i = [ o[3][i] for o in D]
mod_i = [ o[4][i] for o in D]
mod_na_i = [ o[5][i] for o in D]
mx = max(max(obs_i), max(mod_i), max(krig_i), max(mod_i))
plt.plot(t_i, obs_i, 'ro')
plt.plot(t_i, krig_i, 'bo-')
plt.plot(t_i, mod_i, 'kx-', linewidth = 1.5)
plt.plot(t_i, mod_na_i, 'mx-')
plt.ylim([0.0, 1.1 * mx])
plt.legend(['Obs.', 'Kriged', 'Model', 'NoAssim'])
plt.title('Station observations fit to model and kriging field')
plt.savefig(os.path.join(cfg['output_dir'], 'station%02d.png' % (i+1)))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_K1")],
[d[1] for d in diagnostics().pull("assim_K1")], 'ro-')
plt.title('Average Kalman gain')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kalman_gain_10hr.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_K0")],
[d[1] for d in diagnostics().pull("assim_K0")], 'ro-')
plt.title('Average Kalman gain')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kalman_gain_1hr.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("fm10_model_var")],
[d[1] for d in diagnostics().pull("fm10_model_var")], 'ro-')
plt.title('Average fm10 model variance')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_fm10_model_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("fm10_model_residual_var")],
[d[1] for d in diagnostics().pull("fm10_model_residual_var")], 'ro-')
plt.title('Average fm10 model residual variance')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_fm10_model_residual_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("fm10_kriging_var")],
[d[1] for d in diagnostics().pull("fm10_kriging_var")], 'ro-')
plt.title('Kriging field variance')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kriging_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("obs_residual_var")],
[d[1] for d in diagnostics().pull("obs_residual_var")], 'ro-')
plt.title('Observation residual variance')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_observation_residual_variance.png'))
plt.figure()
plt.plot(diagnostics().pull("mfm_mape"), 'ro-', linewidth = 2)
plt.title('Mean absolute prediction error of station data')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_station_mape.png'))
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
def run_module():
# read in configuration file to execute run
print("Reading configuration from [%s]" % sys.argv[1])
with open(sys.argv[1]) as f:
cfg = eval(f.read())
# ensure output path exists
if not os.path.isdir(cfg['output_dir']):
os.mkdir(cfg['output_dir'])
# configure diagnostics
init_diagnostics(
os.path.join(cfg['output_dir'], 'moisture_model_v1_diagnostics.txt'))
# Trend surface model diagnostics
diagnostics().configure_tag("kriging_cov_cond", True, True, True)
diagnostics().configure_tag("s2_eta_hat", True, True, True)
diagnostics().configure_tag("kriging_rmse", True, True, True)
diagnostics().configure_tag("kriging_beta", True, True, True)
diagnostics().configure_tag("kriging_iters", False, True, True)
diagnostics().configure_tag("kriging_subzero_s2_estimates", False, True,
True)
diagnostics().configure_tag("fm10_kriging_var", True, True, True)
diagnostics().configure_tag("f0_summary", True, True, True)
diagnostics().configure_tag("f1_summary", True, True, True)
diagnostics().configure_tag("f2_summary", True, True, True)
diagnostics().configure_tag("f3_summary", True, True, True)
# Assimilation parameters
diagnostics().configure_tag("K0_summary", True, True, True)
diagnostics().configure_tag("K1_summary", True, True, True)
diagnostics().configure_tag("K2_summary", True, True, True)
diagnostics().configure_tag("K3_summary", True, True, True)
diagnostics().configure_tag("assim_info", False, False, True)
# Model forecast, analysis and non-assimilated model: state, covariance, errors
diagnostics().configure_tag("fm10f_rmse", True, True, True)
diagnostics().configure_tag("fm10na_rmse", True, True, True)
# all simulation times and all assimilation times (subset)
diagnostics().configure_tag("mta", False, True, True)
diagnostics().configure_tag("mt", False, True, True)
# observation values and their nearest grid points
diagnostics().configure_tag("obs_vals", False, True, True)
diagnostics().configure_tag("obs_ngp", False, True, True)
# in test mode, we will emit observations at the target station
# our predictions, the nearest grid point and the test station id
diagnostics().configure_tag("test_obs", True, True, True)
diagnostics().configure_tag("test_pred", True, True, True)
diagnostics().configure_tag("test_ngp", True, True, True)
diagnostics().configure_tag("test_station_id", True, True, True)
### Load and preprocess WRF model data
# load WRF data
wrf_data = WRFModelData(cfg['wrf_output'],
['T2', 'Q2', 'PSFC', 'RAINNC', 'RAINC', 'HGT'])
wrf_data.slice_field('HGT')
# read in spatial and temporal extent of WRF variables
lat, lon = wrf_data.get_lats(), wrf_data.get_lons()
hgt = wrf_data['HGT']
tm = wrf_data.get_gmt_times()
Nt = cfg['Nt'] if cfg.has_key('Nt') and cfg['Nt'] is not None else len(tm)
dom_shape = lat.shape
print('INFO: domain size is %d x %d grid points.' % dom_shape)
print('INFO: domain extent is lats (%g to %g) lons (%g to %g).' %
(np.amin(lat), np.amax(lat), np.amin(lon), np.amax(lon)))
# if writing is requested, open output file and set up dimensions
if cfg['write_fields'] not in ['all', 'fmc_gc', 'none']:
error('FATAL: write_fields must be one of all, fmc_gc or none.')
if cfg['write_fields'] == 'none':
cfg['write_fields'] = False
out_file = None
ncfmc_gc, ncfm10a, ncfm10aV, ncfm10f, cnfm10fV, ncfm10na = None, None, None, None, None, None
nctsmV, ncKg = None, None
if cfg['write_fields']:
out_file = netCDF4.Dataset(cfg['output_dir'] + '/fields.nc', 'w')
out_file.createDimension('Time', None)
out_file.createDimension('fuel_moisture_classes_stag', 5)
out_file.createDimension('south_north', dom_shape[0])
out_file.createDimension('west_east', dom_shape[1])
ncfmc_gc = out_file.createVariable(
'FMC_GC', 'f4',
('Time', 'fuel_moisture_classes_stag', 'south_north', 'west_east'))
if cfg['write_fields'] == 'all':
ncfm10a = out_file.createVariable(
'fm10a', 'f4', ('Time', 'south_north', 'west_east'))
ncfm10aV = out_file.createVariable(
'fm10a_var', 'f4', ('Time', 'south_north', 'west_east'))
ncfm10na = out_file.createVariable(
'fm10na', 'f4', ('Time', 'south_north', 'west_east'))
ncfm10f = out_file.createVariable(
'fm10f', 'f4', ('Time', 'south_north', 'west_east'))
ncfm10fV = out_file.createVariable(
'fm10f_var', 'f4', ('Time', 'south_north', 'west_east'))
nctsmV = out_file.createVariable(
'tsm_var', 'f4', ('Time', 'south_north', 'west_east'))
ncKg = out_file.createVariable(
'kalman_gain', 'f4', ('Time', 'south_north', 'west_east'))
print('INFO: opened fields.nc for writing ALL output fields.')
else:
print('INFO: opened field.nc for writing FMC_GC only.')
test_mode = (cfg['run_mode'] == 'test')
tgt_station = None
if cfg['run_mode'] == 'test':
print('INFO: running in TEST mode! Will perform leave-one-out tesing.')
tgt_station_id = cfg['target_station_id']
diagnostics().push('test_station_id', tgt_station_id)
elif cfg['run_mode'] == 'production':
print(
'INFO: running in PRODUCTION mode! Using all observation stations.'
)
else:
error('FATAL: invalid run mode! Must be "test" or "production".')
# determine simulation times
tm_start = parse_datetime(
cfg['start_time']) if cfg['start_time'] is not None else tm[0]
tm_end = parse_datetime(
cfg['end_time']) if cfg['end_time'] is not None else tm[-1]
# if the required start time or end time are outside the simulation domain, exit with an error
if tm_start < tm[0] or tm_end > tm[-1]:
print('FATAL: invalid time range, required [%s-%s], availble [%s-%s]' %
(str(tm_start), str(tm_end), str(tm[0]), str(tm[-1])))
sys.exit(2)
print('INFO: time limits are %s to %s\nINFO: simulation is from %s to %s' %
(str(tm_start), str(tm_end), str(tm[0]), str(tm[-1])))
# retrieve dynamic covariates and remove mean at each time point for T2 and PSFC
T2 = wrf_data['T2']
#T2 -= np.mean(np.mean(T2,axis=0),axis=0)[np.newaxis,np.newaxis,:]
PSFC = wrf_data['PSFC']
#PSFC -= np.mean(np.mean(PSFC,axis=0),axis=0)[np.newaxis,np.newaxis,:]
# numerical fix - if it rains at an intensity of less than 0.001 per hour, set rain to zero
# also, use log(rain + 1) to prevent wild trend surface model predictions when stations see little rain
# but elsewhere there is too much rain
# without this, numerical errors in trend surface model may pop up
rain = wrf_data['RAIN']
#rain[rain < 0.01] = 0.0
rain = np.log(rain + 1.0)
# moisture equilibria are now computed from averaged Q,P,T at beginning and end of period
Ed, Ew = wrf_data.get_moisture_equilibria()
### Load observation data from the stations
# compute the diagonal distance between grid points
grid_dist_km = great_circle_distance(lon[0, 0], lat[0, 0], lon[1, 1],
lat[1, 1])
print('INFO: diagonal distance in grid is %g' % grid_dist_km)
# load station data from files
with open(cfg['station_list_file'], 'r') as f:
si_list = f.read().split('\n')
si_list = filter(lambda x: len(x) > 0 and x[0] != '#',
map(string.strip, si_list))
# for each station id, load the station
stations = []
for code in si_list:
mws = MesoWestStation(code)
mws.load_station_info(
os.path.join(cfg["station_info_dir"], "%s.info" % code))
mws.register_to_grid(wrf_data)
if mws.get_dist_to_grid() < grid_dist_km / 2.0:
print(
'Station %s: lat %g lon %g nearest grid pt %s lat %g lon %g dist_to_grid %g'
% (code, mws.lat, mws.lon, str(mws.grid_pt), lat[mws.grid_pt],
lon[mws.grid_pt], mws.dist_grid_pt))
mws.load_station_data(
os.path.join(cfg["station_data_dir"], "%s.obs" % code))
if test_mode and mws.get_id() == tgt_station_id:
tgt_station = mws
print(
'INFO: in test mode, targeting station %s (removed from data pool).'
% tgt_station_id)
diagnostics().push("test_ngp", mws.get_nearest_grid_point())
else:
stations.append(mws)
print('Loaded %d stations (discarded %d stations, too far from grid).' %
(len(stations), len(si_list) - len(stations)))
if test_mode and tgt_station is None:
error(
'FATAL: in test mode, a station was removed that was not among accepted stations.'
)
# build the observation data
obs_data_fm10 = build_observation_data(stations, 'FM')
# build target data if in test mode
tgt_obs_fm10 = None
test_ngp = None
if test_mode:
test_ngp = tgt_station.get_nearest_grid_point()
tgt_obs_fm10 = build_observation_data([tgt_station], 'FM')
### Initialize model and visualization
# construct initial conditions from timestep 0
E = 0.5 * (Ed[0, :, :] + Ew[0, :, :])
# set up parameters
Nk = 4 # we simulate 4 types of fuel
Q = np.diag(cfg['Q'])
P0 = np.diag(cfg['P0'])
Tk = np.array([1.0, 10.0, 100.0, 1000.0]) * 3600
dt = (tm[1] - tm[0]).seconds
print("INFO: Computed timestep from WRF is is %g seconds." % dt)
mresV = np.zeros_like(E)
mid = np.zeros_like(E)
Kg = np.zeros((dom_shape[0], dom_shape[1], len(Tk) + 2))
# preprocess all static covariates
cov_ids = cfg['covariates']
Xd3 = len(cov_ids) + 1
X = np.zeros((dom_shape[0], dom_shape[1], Xd3))
Xr = np.zeros((dom_shape[0], dom_shape[1], Xd3))
static_covar_map = {
"lon": lon - np.mean(lon),
"lat": lat - np.mean(lat),
"elevation": hgt - np.mean(hgt),
"constant": np.ones(dom_shape)
}
dynamic_covar_map = {"temperature": T2, "pressure": PSFC, "rain": rain}
for i in range(1, Xd3):
cov_id = cov_ids[i - 1]
if cov_id in static_covar_map:
print('INFO: found static covariate %s' % cov_id)
Xr[:, :, i] = static_covar_map[cov_id]
elif cov_id in dynamic_covar_map:
print('INFO: found dynamic covariate %s' % cov_id)
else:
print('FATAL: unknown covariate %s encountered' % cov_id)
sys.exit(2)
print("INFO: there are %d covariates (including model state)" % Xd3)
# retrieve assimilation time window
assim_time_win = cfg['assimilation_time_window']
print('GMM init: equilibrium (%g,%g,%g) and at 86,205 %g' %
(np.amin(E), np.mean(E), np.amax(E), E[86, 205]))
models = GridMoistureModel(
E[:, :, np.newaxis][:, :, np.zeros((4, ), dtype=np.int)], Tk, P0)
models_na = GridMoistureModel(
E[:, :, np.newaxis][:, :, np.zeros((4, ), dtype=np.int)], Tk, P0)
### Run model for each WRF timestep and assimilate data when available
t_start, t_end = 1, len(tm) - 1
while tm_start > tm[t_start]:
t_start += 1
while tm_end < tm[t_end]:
t_end -= 1
# the first FMC_GC value gets filled out with equilibria
if cfg['write_fields']:
for i in range(Nk):
ncfmc_gc[0, i, :, :] = E
print('INFO: running simulation from %s (%d) to %s (%d).' %
(str(tm[t_start]), t_start, str(tm[t_end]), t_end))
for t in range(t_start, t_end + 1):
model_time = tm[t]
print("INFO: time: %s, step: %d" % (str(model_time), t))
diagnostics().push("mt", model_time)
models_na.advance_model(Ed[t - 1, :, :], Ew[t - 1, :, :],
rain[t - 1, :, :], dt, Q)
models.advance_model(Ed[t - 1, :, :], Ew[t - 1, :, :],
rain[t - 1, :, :], dt, Q)
# extract fuel moisture contents [make a fresh copy every iteration!]
f = models.get_state().copy()
f_na = models_na.get_state().copy()
# push 10-hr fuel state & variance of forecast
if cfg['write_fields'] == 'all':
ncfm10f[t, :, :] = models.get_state()[:, :, 1]
ncfm10fV[t, :, :] = models.P[:, :, 1, 1]
ncfm10na[t, :, :] = models_na.get_state()[:, :, 1]
# examine the assimilated fields (if assimilation is activated)
for i in range(4):
diagnostics().push("f%d_summary" % i, (t, np.amin(
f[:, :, i]), np.mean(f[:, :, i]), np.amax(f[:, :, i])))
if np.any(f[:, :, i] < 0.0):
print(
"WARN: in field %d there were %d negative moisture values !"
% (i, np.count_nonzero(f[:, :, i] < 0.0)))
ind = np.unravel_index(np.argmin(f[:, :, i]), f.shape[:2])
print(models.P[ind[0], ind[1], :, :])
print("Full model state at position %d,%d:" % (ind[0], ind[1]))
print(models.m_ext[ind[0], ind[1], :])
if np.any(f[:, :, i] > 2.5):
print(
"WARN: in field %d there were %d moisture values above 2.5!"
% (i, np.count_nonzero(f[:, :, i] > 2.5)))
ind = np.unravel_index(np.argmax(f[:, :, i]), f.shape[:2])
print(models.P[ind[0], ind[1], :, :])
print("Full model state at position %d,%d:" % (ind[0], ind[1]))
print(models.m_ext[ind[0], ind[1], :])
if cfg['assimilate']:
# run Kriging on each observed fuel type
Kfs, Vfs, fns = [], [], []
for obs_data, fuel_ndx in [(obs_data_fm10, 1)]:
# run the kriging subsystem and the Kalman update only if have valid observations
valid_times = [
z for z in obs_data.keys()
if abs(total_seconds(z - model_time)) < assim_time_win /
2.0
]
print(
'INFO: there are %d valid times at model time %s for fuel index %d'
% (len(valid_times), str(model_time), fuel_ndx))
if len(valid_times) > 0:
# add model time as time when assimilation occurred
diagnostics().push("mta", model_time)
# retrieve observations for current time
obs_valid_now = []
for z in valid_times:
obs_valid_now.extend(obs_data[z])
print(
'INFO: model time %s, assimilating %d observations.' %
(str(model_time), len(obs_valid_now)))
# construct covariates for this time instant
X[:, :, 0] = f[:, :, fuel_ndx]
for i in range(1, Xd3):
cov_id = cov_ids[i - 1]
if cov_id in static_covar_map:
X[:, :, i] = Xr[:, :, i]
elif cov_id in dynamic_covar_map:
F = dynamic_covar_map[cov_id]
X[:, :, i] = F[t, :, :]
else:
error('FATAL: found unknown covariate %s' % cov_id)
# find differences (residuals) between observed measurements and nearest grid points
obs_vals = [o.get_value() for o in obs_valid_now]
obs_ngp = [
o.get_nearest_grid_point() for o in obs_valid_now
]
diagnostics().push("obs_vals", obs_vals)
diagnostics().push("obs_ngp", obs_ngp)
mod_vals = np.array(
[f[i, j, fuel_ndx] for i, j in obs_ngp])
mod_na_vals = np.array(
[f_na[i, j, fuel_ndx] for i, j in obs_ngp])
diagnostics().push("fm10f_rmse",
np.mean((obs_vals - mod_vals)**2)**0.5)
diagnostics().push(
"fm10na_rmse",
np.mean((obs_vals - mod_na_vals)**2)**0.5)
# krige observations to grid points
Kf_fn, Vf_fn = fit_tsm(obs_valid_now, X)
if np.count_nonzero(Kf_fn > 2.5) > 0:
rain_t = dynamic_covar_map['rain'][t, :, :]
print(
'WARN: in TSM found %d values over 2.5, %d of those had rain, clamped to 2.5'
% (np.count_nonzero(Kf_fn > 2.5),
np.count_nonzero(
np.logical_and(Kf_fn > 2.5, rain_t > 0.0))))
Kf_fn[Kf_fn > 2.5] = 2.5
if np.count_nonzero(Kf_fn < 0.0) > 0:
print(
'WARN: in TSM found %d values under 0.0, clamped to 0.0'
% np.count_nonzero(Kf_fn < 0.0))
Kf_fn[Kf_fn < 0.0] = 0.0
krig_vals = np.array([Kf_fn[ngp] for ngp in obs_ngp])
diagnostics().push("assim_info",
(t, fuel_ndx, obs_vals, krig_vals,
mod_vals, mod_na_vals))
diagnostics().push("fm10_kriging_var", (t, np.mean(Vf_fn)))
if cfg['write_fields'] == 'all':
nctsmV[t, :, :] = Vf_fn
# append to storage for kriged fields in this time instant
Kfs.append(Kf_fn)
Vfs.append(Vf_fn)
fns.append(fuel_ndx)
# if there were any observations, run the kalman update step
if len(fns) > 0:
NobsClasses = len(fns)
O = np.zeros((dom_shape[0], dom_shape[1], NobsClasses))
V = np.zeros(
(dom_shape[0], dom_shape[1], NobsClasses, NobsClasses))
for i in range(NobsClasses):
O[:, :, i] = Kfs[i]
V[:, :, i, i] = Vfs[i]
# execute the Kalman update
if len(fns) == 1:
models.kalman_update_single2(O, V, fns[0], Kg)
else:
models.kalman_update(O, V, fns, Kg)
# push new diagnostic outputs
if cfg['write_fields'] == 'all':
ncKg[t, :, :] = Kg[:, :, 1]
for i in range(4):
diagnostics().push(
"K%d_summary" % i,
(t, np.amin(Kg[:, :, i]), np.mean(
Kg[:, :, i]), np.amax(Kg[:, :, i])))
if np.any(models.get_state()[:, :, i] < 0.0):
print(
"WARN: in field %d there were %d negative moisture values !"
% (i,
np.count_nonzero(
models.get_state()[:, :, i] < 0.0)))
ind = np.unravel_index(
np.argmin(models.get_state()[:, :, i]),
models.get_state().shape[:2])
print(models.P[ind[0], ind[1], :, :])
print(
"TSM input at given position: value %g variance %g"
% (O[ind[0], ind[1]], V[ind[0], ind[1]]))
print("Model state at given position:")
print(models.m_ext[ind[0], ind[1], :])
# store post-assimilation (or forecast depending on whether observations were available) FM-10 state and variance
if cfg['write_fields'] == 'all':
ncfm10a[t, :, :] = models.get_state()[:, :, 1]
ncfm10aV[t, :, :] = models.P[:, :, 1, 1]
# we don't care if we assimilated or not, we always check our error on target station if in test mode
if test_mode:
valid_times = [
z for z in tgt_obs_fm10.keys()
if abs(total_seconds(z - model_time)) < assim_time_win /
2.0
]
tgt_i, tgt_j = test_ngp
diagnostics().push("test_pred", f[tgt_i, tgt_j, 1])
if len(valid_times) > 0:
# this is our target observation [FIXME: this disregards multiple observations if multiple happen to be valid]
tgt_obs = tgt_obs_fm10[valid_times[0]][0]
obs = tgt_obs.get_value()
diagnostics().push("test_obs", obs)
else:
diagnostics().push("test_obs", np.nan)
# store data in wrf_file variable FMC_G
if cfg['write_fields']:
ncfmc_gc[t, :Nk, :, :] = np.transpose(
models.get_state()[:, :, :Nk], axes=[2, 0, 1])
# store the diagnostics in a binary file when done
diagnostics().dump_store(os.path.join(cfg['output_dir'],
'diagnostics.bin'))
# close the netCDF file (relevant if we did write into FMC_GC)
if out_file is not None:
out_file.close()
def run_module():
# read in configuration file to execute run
print("Reading configuration from [%s]" % sys.argv[1])
with open(sys.argv[1]) as f:
cfg = eval(f.read())
# ensure output path exists
if not os.path.isdir(cfg['output_dir']):
os.mkdir(cfg['output_dir'])
# configure diagnostics
init_diagnostics(os.path.join(cfg['output_dir'], 'moisture_model_v1_diagnostics.txt'))
# Trend surface model diagnostics
diagnostics().configure_tag("kriging_cov_cond", True, True, True)
diagnostics().configure_tag("s2_eta_hat", True, True, True)
diagnostics().configure_tag("kriging_rmse", True, True, True)
diagnostics().configure_tag("kriging_beta", True, True, True)
diagnostics().configure_tag("kriging_iters", False, True, True)
diagnostics().configure_tag("kriging_subzero_s2_estimates", False, True, True)
diagnostics().configure_tag("fm10_kriging_var", True, True, True)
diagnostics().configure_tag("f0_summary", True, True, True)
diagnostics().configure_tag("f1_summary", True, True, True)
diagnostics().configure_tag("f2_summary", True, True, True)
diagnostics().configure_tag("f3_summary", True, True, True)
# Assimilation parameters
diagnostics().configure_tag("K0_summary", True, True, True)
diagnostics().configure_tag("K1_summary", True, True, True)
diagnostics().configure_tag("K2_summary", True, True, True)
diagnostics().configure_tag("K3_summary", True, True, True)
diagnostics().configure_tag("assim_info", False, False, True)
# Model forecast, analysis and non-assimilated model: state, covariance, errors
diagnostics().configure_tag("fm10f_rmse", True, True, True)
diagnostics().configure_tag("fm10na_rmse", True, True, True)
# all simulation times and all assimilation times (subset)
diagnostics().configure_tag("mta", False, True, True)
diagnostics().configure_tag("mt", False, True, True)
# observation values and their nearest grid points
diagnostics().configure_tag("obs_vals", False, True, True)
diagnostics().configure_tag("obs_ngp", False, True, True)
# in test mode, we will emit observations at the target station
# our predictions, the nearest grid point and the test station id
diagnostics().configure_tag("test_obs", True, True, True)
diagnostics().configure_tag("test_pred", True, True, True)
diagnostics().configure_tag("test_ngp", True, True, True)
diagnostics().configure_tag("test_station_id", True, True, True)
### Load and preprocess WRF model data
# load WRF data
wrf_data = WRFModelData(cfg['wrf_output'], ['T2', 'Q2', 'PSFC', 'RAINNC', 'RAINC', 'HGT'])
wrf_data.slice_field('HGT')
# read in spatial and temporal extent of WRF variables
lat, lon = wrf_data.get_lats(), wrf_data.get_lons()
hgt = wrf_data['HGT']
tm = wrf_data.get_gmt_times()
Nt = cfg['Nt'] if cfg.has_key('Nt') and cfg['Nt'] is not None else len(tm)
dom_shape = lat.shape
print('INFO: domain size is %d x %d grid points.' % dom_shape)
print('INFO: domain extent is lats (%g to %g) lons (%g to %g).' % (np.amin(lat),np.amax(lat),np.amin(lon),np.amax(lon)))
# if writing is requested, open output file and set up dimensions
if cfg['write_fields'] not in [ 'all', 'fmc_gc', 'none']:
error('FATAL: write_fields must be one of all, fmc_gc or none.')
if cfg['write_fields'] == 'none':
cfg['write_fields'] = False
out_file = None
ncfmc_gc, ncfm10a, ncfm10aV, ncfm10f, cnfm10fV, ncfm10na = None, None, None, None, None, None
nctsmV, ncKg = None, None
if cfg['write_fields']:
out_file = netCDF4.Dataset(cfg['output_dir'] + '/fields.nc', 'w')
out_file.createDimension('Time', None)
out_file.createDimension('fuel_moisture_classes_stag', 5)
out_file.createDimension('south_north', dom_shape[0])
out_file.createDimension('west_east', dom_shape[1])
ncfmc_gc = out_file.createVariable('FMC_GC', 'f4', ('Time', 'fuel_moisture_classes_stag', 'south_north', 'west_east'))
if cfg['write_fields'] == 'all':
ncfm10a = out_file.createVariable('fm10a', 'f4', ('Time', 'south_north', 'west_east'))
ncfm10aV = out_file.createVariable('fm10a_var', 'f4', ('Time', 'south_north', 'west_east'))
ncfm10na = out_file.createVariable('fm10na', 'f4', ('Time', 'south_north', 'west_east'))
ncfm10f = out_file.createVariable('fm10f', 'f4', ('Time', 'south_north', 'west_east'))
ncfm10fV = out_file.createVariable('fm10f_var', 'f4', ('Time', 'south_north', 'west_east'))
nctsmV = out_file.createVariable('tsm_var', 'f4', ('Time', 'south_north', 'west_east'))
ncKg = out_file.createVariable('kalman_gain', 'f4', ('Time', 'south_north', 'west_east'))
print('INFO: opened fields.nc for writing ALL output fields.')
else:
print('INFO: opened field.nc for writing FMC_GC only.')
test_mode = (cfg['run_mode'] == 'test')
tgt_station = None
if cfg['run_mode'] == 'test':
print('INFO: running in TEST mode! Will perform leave-one-out tesing.')
tgt_station_id = cfg['target_station_id']
diagnostics().push('test_station_id', tgt_station_id)
elif cfg['run_mode'] == 'production':
print('INFO: running in PRODUCTION mode! Using all observation stations.')
else:
error('FATAL: invalid run mode! Must be "test" or "production".')
# determine simulation times
tm_start = parse_datetime(cfg['start_time']) if cfg['start_time'] is not None else tm[0]
tm_end = parse_datetime(cfg['end_time']) if cfg['end_time'] is not None else tm[-1]
# if the required start time or end time are outside the simulation domain, exit with an error
if tm_start < tm[0] or tm_end > tm[-1]:
print('FATAL: invalid time range, required [%s-%s], availble [%s-%s]' %
(str(tm_start), str(tm_end), str(tm[0]), str(tm[-1])))
sys.exit(2)
print('INFO: time limits are %s to %s\nINFO: simulation is from %s to %s' %
(str(tm_start), str(tm_end), str(tm[0]), str(tm[-1])))
# retrieve dynamic covariates and remove mean at each time point for T2 and PSFC
T2 = wrf_data['T2']
#T2 -= np.mean(np.mean(T2,axis=0),axis=0)[np.newaxis,np.newaxis,:]
PSFC = wrf_data['PSFC']
#PSFC -= np.mean(np.mean(PSFC,axis=0),axis=0)[np.newaxis,np.newaxis,:]
# numerical fix - if it rains at an intensity of less than 0.001 per hour, set rain to zero
# also, use log(rain + 1) to prevent wild trend surface model predictions when stations see little rain
# but elsewhere there is too much rain
# without this, numerical errors in trend surface model may pop up
rain = wrf_data['RAIN']
#rain[rain < 0.01] = 0.0
rain = np.log(rain + 1.0)
# moisture equilibria are now computed from averaged Q,P,T at beginning and end of period
Ed, Ew = wrf_data.get_moisture_equilibria()
### Load observation data from the stations
# compute the diagonal distance between grid points
grid_dist_km = great_circle_distance(lon[0,0], lat[0,0], lon[1,1], lat[1,1])
print('INFO: diagonal distance in grid is %g' % grid_dist_km)
# load station data from files
with open(cfg['station_list_file'], 'r') as f:
si_list = f.read().split('\n')
si_list = filter(lambda x: len(x) > 0 and x[0] != '#', map(string.strip, si_list))
# for each station id, load the station
stations = []
for code in si_list:
mws = MesoWestStation(code)
mws.load_station_info(os.path.join(cfg["station_info_dir"], "%s.info" % code))
mws.register_to_grid(wrf_data)
if mws.get_dist_to_grid() < grid_dist_km / 2.0:
print('Station %s: lat %g lon %g nearest grid pt %s lat %g lon %g dist_to_grid %g' %
(code, mws.lat, mws.lon, str(mws.grid_pt), lat[mws.grid_pt], lon[mws.grid_pt], mws.dist_grid_pt))
mws.load_station_data(os.path.join(cfg["station_data_dir"], "%s.obs" % code))
if test_mode and mws.get_id() == tgt_station_id:
tgt_station = mws
print('INFO: in test mode, targeting station %s (removed from data pool).' % tgt_station_id)
diagnostics().push("test_ngp", mws.get_nearest_grid_point())
else:
stations.append(mws)
print('Loaded %d stations (discarded %d stations, too far from grid).' % (len(stations), len(si_list) - len(stations)))
if test_mode and tgt_station is None:
error('FATAL: in test mode, a station was removed that was not among accepted stations.')
# build the observation data
obs_data_fm10 = build_observation_data(stations, 'FM')
# build target data if in test mode
tgt_obs_fm10 = None
test_ngp = None
if test_mode:
test_ngp = tgt_station.get_nearest_grid_point()
tgt_obs_fm10 = build_observation_data([tgt_station], 'FM')
### Initialize model and visualization
# construct initial conditions from timestep 0
E = 0.5 * (Ed[0,:,:] + Ew[0,:,:])
# set up parameters
Nk = 4 # we simulate 4 types of fuel
Q = np.diag(cfg['Q'])
P0 = np.diag(cfg['P0'])
Tk = np.array([1.0, 10.0, 100.0, 1000.0]) * 3600
dt = (tm[1] - tm[0]).seconds
print("INFO: Computed timestep from WRF is is %g seconds." % dt)
mresV = np.zeros_like(E)
mid = np.zeros_like(E)
Kg = np.zeros((dom_shape[0], dom_shape[1], len(Tk)+2))
# preprocess all static covariates
cov_ids = cfg['covariates']
Xd3 = len(cov_ids) + 1
X = np.zeros((dom_shape[0], dom_shape[1], Xd3))
Xr = np.zeros((dom_shape[0], dom_shape[1], Xd3))
static_covar_map = { "lon" : lon - np.mean(lon), "lat" : lat - np.mean(lat), "elevation" : hgt - np.mean(hgt), "constant" : np.ones(dom_shape) }
dynamic_covar_map = { "temperature" : T2, "pressure" : PSFC, "rain" : rain }
for i in range(1, Xd3):
cov_id = cov_ids[i-1]
if cov_id in static_covar_map:
print('INFO: found static covariate %s' % cov_id)
Xr[:, :, i] = static_covar_map[cov_id]
elif cov_id in dynamic_covar_map:
print('INFO: found dynamic covariate %s' % cov_id)
else:
print('FATAL: unknown covariate %s encountered' % cov_id)
sys.exit(2)
print("INFO: there are %d covariates (including model state)" % Xd3)
# retrieve assimilation time window
assim_time_win = cfg['assimilation_time_window']
print('GMM init: equilibrium (%g,%g,%g) and at 86,205 %g' % (np.amin(E),np.mean(E),np.amax(E),E[86,205]))
models = GridMoistureModel(E[:,:,np.newaxis][:,:,np.zeros((4,),dtype=np.int)], Tk, P0)
models_na = GridMoistureModel(E[:,:,np.newaxis][:,:,np.zeros((4,),dtype=np.int)], Tk, P0)
### Run model for each WRF timestep and assimilate data when available
t_start, t_end = 1, len(tm)-1
while tm_start > tm[t_start]:
t_start+=1
while tm_end < tm[t_end]:
t_end-=1
# the first FMC_GC value gets filled out with equilibria
if cfg['write_fields']:
for i in range(Nk):
ncfmc_gc[0, i, :, :] = E
print('INFO: running simulation from %s (%d) to %s (%d).' % (str(tm[t_start]), t_start, str(tm[t_end]), t_end))
for t in range(t_start, t_end+1):
model_time = tm[t]
print("INFO: time: %s, step: %d" % (str(model_time), t))
diagnostics().push("mt", model_time)
models_na.advance_model(Ed[t-1,:,:], Ew[t-1,:,:], rain[t-1,:,:], dt, Q)
models.advance_model(Ed[t-1,:,:], Ew[t-1,:,:], rain[t-1,:,:], dt, Q)
# extract fuel moisture contents [make a fresh copy every iteration!]
f = models.get_state().copy()
f_na = models_na.get_state().copy()
# push 10-hr fuel state & variance of forecast
if cfg['write_fields'] == 'all':
ncfm10f[t,:,:] = models.get_state()[:,:,1]
ncfm10fV[t,:,:] = models.P[:,:,1,1]
ncfm10na[t,:,:] = models_na.get_state()[:,:,1]
# examine the assimilated fields (if assimilation is activated)
for i in range(4):
diagnostics().push("f%d_summary" % i, (t, np.amin(f[:,:,i]), np.mean(f[:,:,i]), np.amax(f[:,:,i])))
if np.any(f[:,:,i] < 0.0):
print("WARN: in field %d there were %d negative moisture values !" % (i, np.count_nonzero(f[:,:,i] < 0.0)))
ind = np.unravel_index(np.argmin(f[:,:,i]), f.shape[:2])
print(models.P[ind[0],ind[1],:,:])
print("Full model state at position %d,%d:" % (ind[0],ind[1]))
print(models.m_ext[ind[0],ind[1],:])
if np.any(f[:,:,i] > 2.5):
print("WARN: in field %d there were %d moisture values above 2.5!" % (i, np.count_nonzero(f[:,:,i] > 2.5)))
ind = np.unravel_index(np.argmax(f[:,:,i]), f.shape[:2])
print(models.P[ind[0],ind[1],:,:])
print("Full model state at position %d,%d:" % (ind[0],ind[1]))
print(models.m_ext[ind[0],ind[1],:])
if cfg['assimilate']:
# run Kriging on each observed fuel type
Kfs, Vfs, fns = [], [], []
for obs_data, fuel_ndx in [ (obs_data_fm10, 1) ]:
# run the kriging subsystem and the Kalman update only if have valid observations
valid_times = [z for z in obs_data.keys() if abs(total_seconds(z - model_time)) < assim_time_win/2.0]
print('INFO: there are %d valid times at model time %s for fuel index %d' % (len(valid_times), str(model_time), fuel_ndx))
if len(valid_times) > 0:
# add model time as time when assimilation occurred
diagnostics().push("mta", model_time)
# retrieve observations for current time
obs_valid_now = []
for z in valid_times:
obs_valid_now.extend(obs_data[z])
print('INFO: model time %s, assimilating %d observations.' % (str(model_time), len(obs_valid_now)))
# construct covariates for this time instant
X[:,:,0] = f[:,:,fuel_ndx]
for i in range(1, Xd3):
cov_id = cov_ids[i-1]
if cov_id in static_covar_map:
X[:, :, i] = Xr[:, :, i]
elif cov_id in dynamic_covar_map:
F = dynamic_covar_map[cov_id]
X[:, :, i] = F[t, :, :]
else:
error('FATAL: found unknown covariate %s' % cov_id)
# find differences (residuals) between observed measurements and nearest grid points
obs_vals = [o.get_value() for o in obs_valid_now]
obs_ngp = [o.get_nearest_grid_point() for o in obs_valid_now]
diagnostics().push("obs_vals", obs_vals)
diagnostics().push("obs_ngp", obs_ngp)
mod_vals = np.array([f[i,j,fuel_ndx] for i,j in obs_ngp])
mod_na_vals = np.array([f_na[i,j,fuel_ndx] for i,j in obs_ngp])
diagnostics().push("fm10f_rmse", np.mean((obs_vals - mod_vals)**2)**0.5)
diagnostics().push("fm10na_rmse", np.mean((obs_vals - mod_na_vals)**2)**0.5)
# krige observations to grid points
Kf_fn, Vf_fn = fit_tsm(obs_valid_now, X)
if np.count_nonzero(Kf_fn > 2.5) > 0:
rain_t = dynamic_covar_map['rain'][t,:,:]
print('WARN: in TSM found %d values over 2.5, %d of those had rain, clamped to 2.5' %
(np.count_nonzero(Kf_fn > 2.5),
np.count_nonzero(np.logical_and(Kf_fn > 2.5, rain_t > 0.0))))
Kf_fn[Kf_fn > 2.5] = 2.5
if np.count_nonzero(Kf_fn < 0.0) > 0:
print('WARN: in TSM found %d values under 0.0, clamped to 0.0' % np.count_nonzero(Kf_fn < 0.0))
Kf_fn[Kf_fn < 0.0] = 0.0
krig_vals = np.array([Kf_fn[ngp] for ngp in obs_ngp])
diagnostics().push("assim_info", (t, fuel_ndx, obs_vals, krig_vals, mod_vals, mod_na_vals))
diagnostics().push("fm10_kriging_var", (t, np.mean(Vf_fn)))
if cfg['write_fields'] == 'all':
nctsmV[t,:,:] = Vf_fn
# append to storage for kriged fields in this time instant
Kfs.append(Kf_fn)
Vfs.append(Vf_fn)
fns.append(fuel_ndx)
# if there were any observations, run the kalman update step
if len(fns) > 0:
NobsClasses = len(fns)
O = np.zeros((dom_shape[0], dom_shape[1], NobsClasses))
V = np.zeros((dom_shape[0], dom_shape[1], NobsClasses, NobsClasses))
for i in range(NobsClasses):
O[:,:,i] = Kfs[i]
V[:,:,i,i] = Vfs[i]
# execute the Kalman update
if len(fns) == 1:
models.kalman_update_single2(O, V, fns[0], Kg)
else:
models.kalman_update(O, V, fns, Kg)
# push new diagnostic outputs
if cfg['write_fields'] == 'all':
ncKg[t,:,:] = Kg[:,:,1]
for i in range(4):
diagnostics().push("K%d_summary" % i, (t, np.amin(Kg[:,:,i]), np.mean(Kg[:,:,i]), np.amax(Kg[:,:,i])))
if np.any(models.get_state()[:,:,i] < 0.0):
print("WARN: in field %d there were %d negative moisture values !" % (i, np.count_nonzero(models.get_state()[:,:,i] < 0.0)))
ind = np.unravel_index(np.argmin(models.get_state()[:,:,i]), models.get_state().shape[:2])
print(models.P[ind[0],ind[1],:,:])
print("TSM input at given position: value %g variance %g" % (O[ind[0],ind[1]], V[ind[0],ind[1]]))
print("Model state at given position:")
print(models.m_ext[ind[0],ind[1],:])
# store post-assimilation (or forecast depending on whether observations were available) FM-10 state and variance
if cfg['write_fields'] == 'all':
ncfm10a[t,:,:] = models.get_state()[:,:,1]
ncfm10aV[t,:,:] = models.P[:,:,1,1]
# we don't care if we assimilated or not, we always check our error on target station if in test mode
if test_mode:
valid_times = [z for z in tgt_obs_fm10.keys() if abs(total_seconds(z - model_time)) < assim_time_win/2.0]
tgt_i, tgt_j = test_ngp
diagnostics().push("test_pred", f[tgt_i, tgt_j,1])
if len(valid_times) > 0:
# this is our target observation [FIXME: this disregards multiple observations if multiple happen to be valid]
tgt_obs = tgt_obs_fm10[valid_times[0]][0]
obs = tgt_obs.get_value()
diagnostics().push("test_obs", obs)
else:
diagnostics().push("test_obs", np.nan)
# store data in wrf_file variable FMC_G
if cfg['write_fields']:
ncfmc_gc[t,:Nk,:,:] = np.transpose(models.get_state()[:,:,:Nk],axes=[2,0,1])
# store the diagnostics in a binary file when done
diagnostics().dump_store(os.path.join(cfg['output_dir'], 'diagnostics.bin'))
# close the netCDF file (relevant if we did write into FMC_GC)
if out_file is not None:
out_file.close()
def run_module():
# read in configuration file to execute run
print("Reading configuration from [%s]" % sys.argv[1])
with open(sys.argv[1]) as f:
cfg = eval(f.read())
# ensure output path exists
if not os.path.isdir(cfg['output_dir']):
os.mkdir(cfg['output_dir'])
# configure diagnostics
init_diagnostics(
os.path.join(cfg['output_dir'], 'moisture_model_v1_diagnostics.txt'))
# Error covariance matrix condition number in kriging
diagnostics().configure_tag("skdm_cov_cond", False, True, True)
# Assimilation parameters
diagnostics().configure_tag("assim_K0", False, True, True)
diagnostics().configure_tag("assim_K1", True, True, True)
diagnostics().configure_tag("assim_data", False, False, True)
diagnostics().configure_tag("fm10_model_var", False, True, True)
diagnostics().configure_tag("fm10_kriging_var", False, True, True)
### Load and preprocess WRF model data
# load WRF data
wrf_data = WRFModelData(cfg['input_file'], tz_name='US/Mountain')
# read in spatial and temporal extent of WRF variables
lat, lon = wrf_data.get_lats(), wrf_data.get_lons()
tm = wrf_data.get_gmt_times()
Nt = cfg['Nt'] if cfg.has_key('Nt') and cfg['Nt'] is not None else len(tm)
dom_shape = lat.shape
# retrieve the rain variable
rain = wrf_data['RAIN']
# moisture equilibria are now computed from averaged Q,P,T at beginning and end of period
Ed, Ew = wrf_data.get_moisture_equilibria()
### Load observation data from the stations
# load station data from files
with open(os.path.join(cfg['station_data_dir'], cfg['station_list_file']),
'r') as f:
si_list = f.read().split('\n')
si_list = filter(lambda x: len(x) > 0 and x[0] != '#',
map(string.strip, si_list))
# for each station id, load the station
stations = []
for code in si_list:
mws = MesoWestStation(code)
mws.load_station_info(
os.path.join(cfg["station_data_dir"], "%s.info" % code))
mws.register_to_grid(wrf_data)
mws.load_station_data(
os.path.join(cfg["station_data_dir"], "%s.obs" % code))
stations.append(mws)
print('Loaded %d stations.' % len(stations))
# check stations for nans
stations = filter(MesoWestStation.data_ok, stations)
print('Have %d stations with complete data.' % len(stations))
# build the observation data
# obs_data_fm10 = build_observation_data(stations, 'FM')
obs_data_fm10 = {}
### Initialize model and visualization
# find maximum moisture overall to set up visualization
maxE = 0.5
# construct initial conditions from timestep 1 (because Ed/Ew at zero are zero)
E = 0.5 * (Ed[1, :, :] + Ew[1, :, :])
# set up parameters
Q = np.eye(9) * cfg['Q']
P0 = np.eye(9) * cfg['P0']
dt = (tm[1] - tm[0]).seconds
print("INFO: Computed timestep from WRF is is %g seconds." % dt)
K = np.zeros_like(E)
V = np.zeros_like(E)
mV = np.zeros_like(E)
predicted_field = np.zeros_like(E)
mresV = np.zeros_like(E)
Kf_fn = np.zeros_like(E)
Vf_fn = np.zeros_like(E)
mid = np.zeros_like(E)
Kg = np.zeros((dom_shape[0], dom_shape[1], 9))
cV12 = np.zeros_like(E)
# initialize the mean field model (default fit is 1.0 of equilibrium before new information comes in)
mfm = MeanFieldModel(cfg['lock_gamma'])
# construct model grid using standard fuel parameters
Tk = np.array([1.0, 10.0, 100.0]) * 3600
models = np.zeros(dom_shape, dtype=np.object)
models_na = np.zeros_like(models)
for p in np.ndindex(dom_shape):
models[p] = CellMoistureModel((lat[p], lon[p]), 3, E[p], Tk, P0=P0)
models_na[p] = CellMoistureModel((lat[p], lon[p]), 3, E[p], Tk, P0=P0)
m = None
plt.figure(figsize=(12, 8))
### Run model for each WRF timestep and assimilate data when available
for t in range(1, Nt):
model_time = tm[t]
print("INFO: time: %s, step: %d" % (str(model_time), t))
# run the model update
for p in np.ndindex(dom_shape):
i, j = p
models[p].advance_model(Ed[t - 1, i, j], Ew[t - 1, i, j],
rain[t - 1, i, j], dt, Q)
models_na[p].advance_model(Ed[t - 1, i, j], Ew[t - 1, i, j],
rain[t - 1, i, j], dt, Q)
# prepare visualization data
f = np.zeros((dom_shape[0], dom_shape[1], 3))
f_na = np.zeros((dom_shape[0], dom_shape[1], 3))
for p in np.ndindex(dom_shape):
f[p[0], p[1], :] = models[p].get_state()[:3]
f_na[p[0], p[1], :] = models_na[p].get_state()[:3]
P = models[p].get_state_covar()
cV12[p] = P[0, 1]
mV[p] = P[1, 1]
mid[p] = models[p].get_model_ids()[1]
diagnostics().push("fm10_model_var", (t, np.mean(mV)))
# run Kriging on each observed fuel type
Kf = []
Vf = []
fn = []
for obs_data, fuel_ndx in [(obs_data_fm10, 1)]:
# run the kriging subsystem and the Kalman update only if we have observations
if model_time in obs_data:
# retrieve observations for current time
obs_t = obs_data[model_time]
# fit the current estimation of the moisture field to the data
base_field = f[:, :, fuel_ndx]
mfm.fit_to_data(base_field, obs_data[model_time])
# find differences (residuals) between observed measurements and nearest grid points
# use this to update observation residual standard deviation
obs_vals = np.array(
[o.get_value() for o in obs_data[model_time]])
mod_vals = np.array([
base_field[o.get_nearest_grid_point()]
for o in obs_data[model_time]
])
mod_na_vals = np.array([
f_na[:, :, fuel_ndx][o.get_nearest_grid_point()]
for o in obs_data[model_time]
])
# predict the moisture field using observed fuel type
predicted_field = mfm.predict_field(base_field)
# krige observations to grid points
Kf_fn, Vf_fn = trend_surface_model_kriging(
obs_data[model_time], wrf_data, predicted_field)
krig_vals = np.array([
Kf_fn[o.get_nearest_grid_point()]
for o in obs_data[model_time]
])
diagnostics().push(
"assim_data",
(t, fuel_ndx, obs_vals, krig_vals, mod_vals, mod_na_vals))
plot_model_snapshot(cfg, tm, t, fuel_ndx, obs_vals, krig_vals,
mod_vals, mod_na_vals)
diagnostics().push("fm10_kriging_var", (t, np.mean(Vf_fn)))
# append to storage for kriged fields in this time instant
Kf.append(Kf_fn)
Vf.append(Vf_fn)
fn.append(fuel_ndx)
# if there were any observations, run the kalman update step
if len(fn) > 0:
Nobs = len(fn)
# run the kalman update in each model independently
# gather the standard deviations of the moisture fuel after the Kalman update
for p in np.ndindex(dom_shape):
O = np.zeros((Nobs, ))
V = np.zeros((Nobs, Nobs))
# construct observations for this position
for i in range(Nobs):
O[i] = Kf[i][p]
V[i, i] = Vf[i][p]
# execute the Kalman update
Kp = models[p].kalman_update(O, V, fn)
Kg[p[0], p[1], :] = Kp[:, 0]
# push new diagnostic outputs
diagnostics().push("assim_K0", (t, np.mean(Kg[:, :, 0])))
diagnostics().push("assim_K1", (t, np.mean(Kg[:, :, 1])))
# prepare visualization data
f = np.zeros((dom_shape[0], dom_shape[1], 3))
for p in np.ndindex(dom_shape):
f[p[0], p[1], :] = models[p].get_state()[:3]
plt.clf()
plt.subplot(3, 3, 1)
render_spatial_field_fast(m, lon, lat, f[:, :, 0], '1-hr fuel')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 2)
render_spatial_field_fast(m, lon, lat, f[:, :, 1], '10-hr fuel')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 3)
render_spatial_field_fast(m, lon, lat, f_na[:, :, 1],
'10hr fuel - no assim')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 4)
render_spatial_field_fast(m, lon, lat, Kg[:, :, 0],
'Kalman gain for 1-hr fuel')
plt.clim([0.0, 3.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 5)
render_spatial_field_fast(m, lon, lat, Kg[:, :, 1],
'Kalman gain for 10-hr fuel')
plt.clim([0.0, 1.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 6)
render_spatial_field_fast(m, lon, lat, Kf_fn, 'Kriging field')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 7)
render_spatial_field_fast(m, lon, lat, mid, 'Model ids')
plt.clim([0.0, 5.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 8)
render_spatial_field_fast(m, lon, lat, Vf_fn, 'Kriging variance')
plt.clim([0.0, np.max(Vf_fn)])
plt.axis('off')
plt.colorbar()
# plt.subplot(3,3,9)
# render_spatial_field_fast(m, lon, lat, mresV, 'Model variance')
# plt.clim([0.0, np.max(mresV)])
# plt.axis('off')
# plt.colorbar()
plt.savefig(
os.path.join(cfg['output_dir'], 'moisture_model_t%03d.png' % t))
# store the diagnostics in a binary file
diagnostics().dump_store(os.path.join(cfg['output_dir'],
'diagnostics.bin'))
# make a plot of gammas
plt.figure()
plt.plot(diagnostics().pull('mfm_gamma'), 'bo-')
plt.title('Mean field model - gamma')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_gamma.png'))
plt.figure()
plt.plot(diagnostics().pull('skdm_cov_cond'))
plt.title('Condition number of covariance matrix')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_sigma_cond.png'))
# make a plot for each substation
plt.figure()
D = diagnostics().pull("assim_data")
for i in range(len(stations)):
plt.clf()
# get data for the i-th station
t_i = [o[0] for o in D]
obs_i = [o[2][i] for o in D]
krig_i = [o[3][i] for o in D]
mod_i = [o[4][i] for o in D]
mod_na_i = [o[5][i] for o in D]
mx = max(max(obs_i), max(mod_i), max(krig_i), max(mod_i))
plt.plot(t_i, obs_i, 'ro')
plt.plot(t_i, krig_i, 'bo-')
plt.plot(t_i, mod_i, 'kx-', linewidth=1.5)
plt.plot(t_i, mod_na_i, 'mx-')
plt.ylim([0.0, 1.1 * mx])
plt.legend(['Obs.', 'Kriged', 'Model', 'NoAssim'])
plt.title('Station observations fit to model and kriging field')
plt.savefig(
os.path.join(cfg['output_dir'], 'station%02d.png' % (i + 1)))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_K1")],
[d[1] for d in diagnostics().pull("assim_K1")], 'ro-')
plt.title('Average Kalman gain')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kalman_gain_10hr.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_K0")],
[d[1] for d in diagnostics().pull("assim_K0")], 'ro-')
plt.title('Average Kalman gain')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kalman_gain_1hr.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("fm10_model_var")],
[d[1] for d in diagnostics().pull("fm10_model_var")], 'ro-')
plt.title('Average fm10 model variance')
plt.savefig(os.path.join(cfg['output_dir'],
'plot_fm10_model_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("fm10_model_residual_var")],
[d[1] for d in diagnostics().pull("fm10_model_residual_var")],
'ro-')
plt.title('Average fm10 model residual variance')
plt.savefig(
os.path.join(cfg['output_dir'],
'plot_fm10_model_residual_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("fm10_kriging_var")],
[d[1] for d in diagnostics().pull("fm10_kriging_var")], 'ro-')
plt.title('Kriging field variance')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kriging_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("obs_residual_var")],
[d[1] for d in diagnostics().pull("obs_residual_var")], 'ro-')
plt.title('Observation residual variance')
plt.savefig(
os.path.join(cfg['output_dir'],
'plot_observation_residual_variance.png'))
plt.figure()
plt.plot(diagnostics().pull("mfm_mape"), 'ro-', linewidth=2)
plt.title('Mean absolute prediction error of station data')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_station_mape.png'))
'standardize': True
}
# cfg = { 'station_info_dir' : '../real_data/witch_creek',
# 'station_obs_dir' : '../real_data/witch_creek',
# 'station_list_file' : '../real_data/witch_creek/station_list',
# 'wrf_data_file' : '../real_data/witch_creek/wrf20071021_witchcreek_all.nc',
# 'assimilation_window' : 3600,
# 'output_dir' : 'results_wc',
# 'max_dist' : 200.0,
# 'bin_width' : 20,
# 'standardize' : False
# }
# load the smallest domain
wrf_data = WRFModelData(cfg['wrf_data_file'], ['T2', 'PSFC', 'Q2', 'HGT'])
# read in vars
lon, lat = wrf_data.get_lons(), wrf_data.get_lats()
hgt = wrf_data['HGT']
tm = wrf_data.get_gmt_times()
Nt = len(tm)
print("Loaded %d timestamps from WRF." % Nt)
# load station data from files
with open(cfg['station_list_file'], 'r') as f:
si_list = f.read().split('\n')
si_list = filter(lambda x: len(x) > 0 and x[0] != '#',
map(string.strip, si_list))
def run_module():
# read in configuration file to execute run
print("Reading configuration from [%s]" % sys.argv[1])
with open(sys.argv[1]) as f:
cfg = eval(f.read())
# ensure output path exists
if not os.path.isdir(cfg['output_dir']):
os.mkdir(cfg['output_dir'])
# configure diagnostics
init_diagnostics(os.path.join(cfg['output_dir'], 'moisture_model_v1_diagnostics.txt'))
diagnostics().configure_tag("skdm_obs_res", False, True, True)
diagnostics().configure_tag("skdm_cov_cond", False, True, True)
diagnostics().configure_tag("assim_mV", False, False, True)
diagnostics().configure_tag("assim_K0", False, False, True)
diagnostics().configure_tag("assim_K1", False, False, True)
diagnostics().configure_tag("assim_data", False, False, True)
diagnostics().configure_tag("assim_mresV", False, False, True)
diagnostics().configure_tag("kriging_variance", False, False, True)
diagnostics().configure_tag("kriging_obs_res_var", False, False, True)
print("INFO: input file is [%s]." % cfg['input_file'])
wrf_data = WRFModelData(cfg['input_file'], tz_name = 'US/Pacific')
# read in vars
lat, lon = wrf_data.get_lats(), wrf_data.get_lons()
tm = wrf_data.get_local_times()
rain = wrf_data['RAIN']
Ed, Ew = wrf_data.get_moisture_equilibria()
# find maximum moisture overall to set up visualization
# maxE = max(np.max(Ed), np.max(Ew)) * 1.2
maxE = 0.3
# obtain sizes
Nt = rain.shape[0]
dom_shape = lat.shape
# load station data from files
tz = pytz.timezone('US/Pacific')
stations = [StationAdam() for s in station_list]
for (s,sname) in zip(stations, station_list):
s.load_station_data(os.path.join(station_data_dir, sname), tz)
s.register_to_grid(wrf_data)
s.set_measurement_variance('fm10', 0.05)
# build the observation data structure indexed by time
obs_data_fm10 = build_observation_data(stations, 'fm10', wrf_data, tm)
# construct initial conditions
E = 0.5 * (Ed[1,:,:] + Ew[1,:,:])
# set up parameters
Q = np.eye(9) * 0.001
P0 = np.eye(9) * 0.01
dt = 10.0 * 60
K = np.zeros_like(E)
V = np.zeros_like(E)
mV = np.zeros_like(E)
predicted_field = np.zeros_like(E)
mresV = np.zeros_like(E)
Kf_fn = np.zeros_like(E)
Vf_fn = np.zeros_like(E)
mid = np.zeros_like(E)
Kg = np.zeros((dom_shape[0], dom_shape[1], 9))
cV12 = np.zeros_like(E)
# initialize the mean field model (default fit is 1.0 of equilibrium before new information comes in)
mfm = MeanFieldModel(cfg['lock_gamma'])
# construct model grid using standard fuel parameters
Tk = np.array([1.0, 10.0, 100.0]) * 3600
models = np.zeros(dom_shape, dtype = np.object)
models_na = np.zeros_like(models)
for pos in np.ndindex(dom_shape):
models[pos] = CellMoistureModel((lat[pos], lon[pos]), 3, E[pos], Tk, P0 = P0)
models_na[pos] = CellMoistureModel((lat[pos], lon[pos]), 3, E[pos], Tk, P0 = P0)
m = None
plt.figure(figsize = (12, 8))
# run model
for t in range(1, Nt):
model_time = tm[t]
print("Time: %s, step: %d" % (str(model_time), t))
# pre-compute equilibrium moisture to save a lot of time
E = 0.5 * (Ed[t,:,:] + Ew[t,:,:])
# run the model update
for pos in np.ndindex(dom_shape):
i, j = pos
models[pos].advance_model(Ed[t, i, j], Ew[t, i, j], rain[t, i, j], dt, Q)
models_na[pos].advance_model(Ed[t, i, j], Ew[t, i, j], rain[t, i, j], dt, Q)
# prepare visualization data
f = np.zeros((dom_shape[0], dom_shape[1], 3))
f_na = np.zeros((dom_shape[0], dom_shape[1], 3))
for p in np.ndindex(dom_shape):
f[p[0], p[1], :] = models[p].get_state()[:3]
f_na[p[0], p[1], :] = models_na[p].get_state()[:3]
mV[pos] = models[p].get_state_covar()[1,1]
cV12[pos] = models[p].get_state_covar()[0,1]
mid[p] = models[p].get_model_ids()[1]
# run Kriging on each observed fuel type
Kf = []
Vf = []
fn = []
for obs_data, fuel_ndx in [ (obs_data_fm10, 1) ]:
if model_time in obs_data:
# fit the current estimation of the moisture field to the data
base_field = f[:,:,fuel_ndx]
mfm.fit_to_data(base_field, obs_data[model_time])
# find differences (residuals) between observed measurements and nearest grid points
# use this to update observation residual standard deviation
obs_vals = np.array([o.get_value() for o in obs_data[model_time]])
mod_vals = np.array([f[:,:,fuel_ndx][o.get_nearest_grid_point()] for o in obs_data[model_time]])
mod_na_vals = np.array([f_na[:,:,fuel_ndx][o.get_nearest_grid_point()] for o in obs_data[model_time]])
obs_re.update_with(obs_vals - mod_vals)
diagnostics().push("kriging_obs_res_var", (t, np.mean(obs_re.get_variance())))
# retrieve the variance of the model field
mresV = mod_re.get_variance()
# krige data to observations
if cfg['kriging_strategy'] == 'uk':
Kf_fn, Vf_fn, gamma, mape = universal_kriging_data_to_model(obs_data[model_time],
obs_re.get_variance() ** 0.5,
base_field,
wrf_data,
mresV ** 0.5, t)
# replace the stored gamma with the uk computed gamma
diagnostics().pull("mfm_gamma")[-1] = gamma
diagnostics().pull("mfm_mape")[-1] = mape
print("uk: replaced mfm_gamma %g, mfm_mape %g" % (gamma, mape))
# update the residuals estimator with the current
mod_re.update_with(gamma * f[:,:,fuel_ndx] - Kf_fn)
elif cfg['kriging_strategy'] == 'tsm':
# predict the moisture field using observed fuel type
predicted_field = mfm.predict_field(base_field)
# run the tsm kriging estimator
Kf_fn, Vf_fn = trend_surface_model_kriging(obs_data[model_time], wrf_data, predicted_field)
# update the model residual estimator and get current best estimate of variance
mod_re.update_with(f[:,:,fuel_ndx] - predicted_field)
else:
raise ValueError('Invalid kriging strategy [%s] in configuration.' % cfg['kriiging_strategy'])
krig_vals = np.array([Kf_fn[o.get_nearest_grid_point()] for o in obs_data[model_time]])
diagnostics().push("assim_data", (t, fuel_ndx, obs_vals, krig_vals, mod_vals, mod_na_vals))
plot_model_snapshot(cfg, tm, t, fuel_ndx, obs_vals, krig_vals, mod_vals, mod_na_vals)
# append to storage for kriged fields in this time instant
Kf.append(Kf_fn)
Vf.append(Vf_fn)
fn.append(fuel_ndx)
# if there were any observations, run the kalman update step
if len(fn) > 0:
Nobs = len(fn)
# run the kalman update in each model independently
# gather the standard deviations of the moisture fuel after the Kalman update
for pos in np.ndindex(dom_shape):
O = np.zeros((Nobs,))
V = np.zeros((Nobs, Nobs))
# construct observations for this position
for i in range(Nobs):
O[i] = Kf[i][pos]
V[i,i] = Vf[i][pos]
# execute the Kalman update
Kij = models[pos].kalman_update(O, V, fn)
Kg[pos[0], pos[1], :] = Kij[:, 0]
# prepare visualization data
f = np.zeros((dom_shape[0], dom_shape[1], 3))
for p in np.ndindex(dom_shape):
f[p[0], p[1], :] = models[p].get_state()[:3]
plt.clf()
plt.subplot(3,3,1)
render_spatial_field_fast(m, lon, lat, f[:,:,0], '1-hr fuel')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,2)
render_spatial_field_fast(m, lon, lat, f[:,:,1], '10-hr fuel')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,3)
render_spatial_field_fast(m, lon, lat, f_na[:,:,1], '10hr fuel - no assim')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,4)
render_spatial_field_fast(m, lon, lat, Kg[:,:,0], 'Kalman gain, fm1')
plt.clim([0.0, 3.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,5)
render_spatial_field_fast(m, lon, lat, Kg[:,:,1], 'Kalman gain, fm10')
plt.clim([0.0, 1.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,6)
render_spatial_field_fast(m, lon, lat, Kf_fn, 'Kriging field')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,7)
render_spatial_field_fast(m, lon, lat, mid, 'Model ids')
plt.clim([0.0, 5.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,8)
render_spatial_field_fast(m, lon, lat, Vf_fn, 'Kriging var')
plt.clim([0.0, np.max(Vf_fn)])
plt.axis('off')
plt.colorbar()
plt.subplot(3,3,9)
render_spatial_field_fast(m, lon, lat, mresV, 'fm10 model var')
plt.clim([0.0, np.max(mresV)])
plt.axis('off')
plt.colorbar()
plt.savefig(os.path.join(cfg['output_dir'], 'moisture_model_t%03d.png' % t))
# push new diagnostic outputs
diagnostics().push("assim_K0", (t, np.mean(Kg[:,:,0])))
diagnostics().push("assim_K1", (t, np.mean(Kg[:,:,1])))
diagnostics().push("assim_mV", (t, np.mean(mV)))
diagnostics().push("assim_mresV", (t, np.mean(mresV)))
diagnostics().push("kriging_variance", (t, np.mean(Vf_fn)))
# store the gamma coefficients
with open(os.path.join(cfg['output_dir'], 'gamma.txt'), 'w') as f:
f.write(str(diagnostics().pull('mfm_gamma')))
# make a plot of gammas
plt.figure()
plt.plot(diagnostics().pull('mfm_gamma'))
plt.title('Mean field model - gamma')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_gamma.png'))
plt.figure()
plt.plot(diagnostics().pull('skdm_cov_cond'))
plt.title('Condition number of covariance matrix')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_sigma_cond.png'))
# make
# make a plot for each substation
plt.figure()
D = diagnostics().pull("assim_data")
for i in range(len(stations)):
plt.clf()
# get data for the i-th station
t_i = [ o[0] for o in D]
obs_i = [ o[2][i] for o in D]
krig_i = [ o[3][i] for o in D]
mod_i = [ o[4][i] for o in D]
mod_na_i = [ o[5][i] for o in D]
mx = max(max(obs_i), max(mod_i), max(krig_i), max(mod_i))
plt.plot(t_i, obs_i, 'ro')
plt.plot(t_i, krig_i, 'bo-')
plt.plot(t_i, mod_i, 'kx-', linewidth = 1.5)
plt.plot(t_i, mod_na_i, 'mx-')
plt.ylim([0.0, 1.1 * mx])
plt.legend(['Obs.', 'Kriged', 'Model', 'NoAssim'])
plt.title('Station observations fit to model and kriging field')
plt.savefig(os.path.join(cfg['output_dir'], 'station%02d.png' % (i+1)))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_K1")],
[d[1] for d in diagnostics().pull("assim_K1")], 'ro-')
plt.title('Average Kalman gain')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kalman_gain_10hr.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_K0")],
[d[1] for d in diagnostics().pull("assim_K0")], 'ro-')
plt.title('Average Kalman gain')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kalman_gain_1hr.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_mV")],
[d[1] for d in diagnostics().pull("assim_mV")], 'ro-')
plt.title('Average model variance')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_fm10_model_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_mresV")],
[d[1] for d in diagnostics().pull("assim_mresV")], 'ro-')
plt.title('Average fm10 residual variance')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_fm10_model_residual_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("kriging_variance")],
[d[1] for d in diagnostics().pull("kriging_variance")], 'ro-')
plt.title('Kriging field variance')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kriging_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("kriging_obs_res_var")],
[d[1] for d in diagnostics().pull("kriging_obs_res_var")], 'ro-')
plt.title('Observation residual variance')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_observation_residual_variance.png'))
plt.figure()
plt.plot(diagnostics().pull("mfm_mape"), 'ro-', linewidth = 2)
plt.title('Mean absolute prediction error of station data')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_station_mape.png'))
diagnostics().dump_store(os.path.join(cfg['output_dir'], 'diagnostics.bin'))
# as a last step encode all the frames as video
os.system("cd %s; avconv -qscale 1 -r 20 -b 9600 -i moisture_model_t%%03d.png video.mp4" % cfg['output_dir'])
if __name__ == '__main__':
init_diagnostics('results/examine_mesowest_station_data.log')
# load stations and match then to grid points
# load station data from files
with open(os.path.join(station_data_dir, 'clean_stations'), 'r') as f:
si_list = f.read().split('\n')
si_list = filter(lambda x: len(x) > 0, map(string.strip, si_list))
# load wrf model data
wrf_data = WRFModelData(os.path.join(station_data_dir, wrf_data_file),
fields = ['T2', 'Q2', 'PSFC', fm_var_name, 'FMC_EQUI'],
tz_name = 'GMT')
lon, lat, tm = wrf_data.get_lons(), wrf_data.get_lats(), wrf_data.get_times()
efms, wrf_equi = wrf_data[fm_var_name], wrf_data['FMC_EQUI']
T2 = wrf_data['T2']
print('Loaded %d times from WRF output (%s - %s).' % (len(tm), str(tm[0]), str(tm[-1])))
# for each station id, load the station
stations = []
for sinfo in si_list:
code = sinfo.split(',')[0]
mws = MesoWestStation(sinfo, wrf_data)
for suffix in [ '_1', '_2', '_3', '_4', '_5', '_6', '_7' ]:
mws.load_station_data(os.path.join(station_data_dir, '%s%s.xls' % (code, suffix)))
stations.append(mws)
# load required variables
load_lst = list(varlst)
if 'RAIN' in load_lst:
load_lst.remove('RAIN')
load_lst.extend(['RAINC', 'RAINNC' ])
if 'RH' in load_lst:
load_lst.remove('RH')
load_lst.extend(['T2', 'PSFC', 'Q2'])
load_lst = list(set(load_lst))
print("INFO: loading the following variables %s" % str(load_lst))
print("INFO: loading wrf model output data ...")
w = WRFModelData(args.wrfout_file, load_lst)
ts = w['GMT']
N = len(ts)
if 'RH' in varlst:
w.compute_relative_humidity()
print("INFO: processing variables %s" % str(varlst))
print("INFO: loading county shapes ...")
lats, lons = w.get_lats(), w.get_lons()
m = setup_basemap_proj(lats, lons)
counties = load_colorado_shapes(m)
print("INFO: found %d times in wrfout." % len(ts))
def run_module():
# read in configuration file to execute run
print("Reading configuration from [%s]" % sys.argv[1])
with open(sys.argv[1]) as f:
cfg = eval(f.read())
# init diagnostics
init_diagnostics(
os.path.join(cfg['output_dir'], 'moisture_model_v1_diagnostics.txt'))
diagnostics().configure_tag("s2_eta_hat", True, True, True)
diagnostics().configure_tag("kriging_rmse", True, True, True)
diagnostics().configure_tag("kriging_beta", True, True, True)
diagnostics().configure_tag("kriging_iters", False, True, True)
diagnostics().configure_tag("kriging_subzero_s2_estimates", False, True,
True)
# load the wrfinput file
wrfin = WRFModelData(cfg['wrf_input'],
['T2', 'Q2', 'PSFC', 'HGT', 'FMC_GC', 'FMEP'])
lat, lon = wrfin.get_lats(), wrfin.get_lons()
ts_now = wrfin['GMT'][0]
dom_shape = lat.shape
print('INFO: domain size is %d x %d grid points, wrfinput timestamp %s' %
(dom_shape[0], dom_shape[1], str(ts_now)))
print('INFO: domain extent is lats (%g to %g) lons (%g to %g).' %
(np.amin(lat), np.amax(lat), np.amin(lon), np.amax(lon)))
# compute the diagonal distance between grid points
grid_dist_km = great_circle_distance(lon[0, 0], lat[0, 0], lon[1, 1],
lat[1, 1])
print('INFO: diagonal distance in grid is %g' % grid_dist_km)
# load observations but discard those too far away from the grid nodes
obss = load_raws_observations(cfg['observations'], lat, lon, grid_dist_km)
fm10 = build_observation_data(obss)
print('INFO: %d different time instances found in observations' %
len(fm10))
# if a previous cycle is available (i.e. the wrfoutput is a valid file)
if os.path.exists(cfg['wrf_output_prev']) and check_overlap(
cfg['wrf_output_prev'], ts_now):
# load the model as a wrfout with all default variables
wrfout = WRFModelData(cfg['wrf_output_prev'])
outts = wrfout['GMT']
print("INFO: previous forecast [%s - %s] exists, running DA till %s" %
(str(outts[0]), str(outts[-1]), str(ts_now)))
# run from the start until now (retrieve fuel moisture, extended parameters, covariance matrix)
model = run_data_assimilation(wrfout, fm10, ts_now, cfg)
# store this for the current time instance (fm, ep in the wrfinput, P next to it)
d = netCDF4.Dataset(cfg['wrf_input'], 'r+')
d.variables['FMC_GC'] = fm
d.variables['FMEP'] = ep
d.close()
# store the covariance matrix alongside the wrfinput file
dir = os.path.dirname(wrfin)
store_covariance_matrix(P, os.path.join(dir, 'P.nc'))
else:
print(
"INFO: no previous forecast found, running DA from equilibrium at %s"
% (str(ts_now)))
# initialize from weather equilibrium and perform one DA step
model = init_from_equilibrium(wrfin, fm10, ts_now, cfg)
# store result in wrfinput dataset
d = netCDF4.Dataset(cfg['wrf_input'], 'r+')
fmcep = model.get_state()
d.variables['FMC_GC'][0, :3, :, :] = fmcep[:, :, :3].transpose(
(2, 0, 1))
d.variables['FMEP'][0, :, :, :] = fmcep[:, :, 3:5].transpose((2, 0, 1))
d.close()
store_covariance_matrix(
model.get_state_covar(),
os.path.join(os.path.dirname(cfg['wrf_input']), 'P.nc'))
return 0
def run_module():
# read in configuration file to execute run
print("Reading configuration from [%s]" % sys.argv[1])
with open(sys.argv[1]) as f:
cfg = eval(f.read())
# init diagnostics
init_diagnostics(os.path.join(cfg['output_dir'], 'moisture_model_v1_diagnostics.txt'))
diagnostics().configure_tag("s2_eta_hat", True, True, True)
diagnostics().configure_tag("kriging_rmse", True, True, True)
diagnostics().configure_tag("kriging_beta", True, True, True)
diagnostics().configure_tag("kriging_iters", False, True, True)
diagnostics().configure_tag("kriging_subzero_s2_estimates", False, True, True)
# load the wrfinput file
wrfin = WRFModelData(cfg['wrf_input'], ['T2', 'Q2', 'PSFC', 'HGT', 'FMC_GC', 'FMEP'])
lat, lon = wrfin.get_lats(), wrfin.get_lons()
ts_now = wrfin['GMT'][0]
dom_shape = lat.shape
print('INFO: domain size is %d x %d grid points, wrfinput timestamp %s' % (dom_shape[0], dom_shape[1], str(ts_now)))
print('INFO: domain extent is lats (%g to %g) lons (%g to %g).' % (np.amin(lat),np.amax(lat),np.amin(lon),np.amax(lon)))
# compute the diagonal distance between grid points
grid_dist_km = great_circle_distance(lon[0,0], lat[0,0], lon[1,1], lat[1,1])
print('INFO: diagonal distance in grid is %g' % grid_dist_km)
# load observations but discard those too far away from the grid nodes
obss = load_raws_observations(cfg['observations'], lat, lon, grid_dist_km)
fm10 = build_observation_data(obss)
print('INFO: %d different time instances found in observations' % len(fm10))
# if a previous cycle is available (i.e. the wrfoutput is a valid file)
if os.path.exists(cfg['wrf_output_prev']) and check_overlap(cfg['wrf_output_prev'],ts_now):
# load the model as a wrfout with all default variables
wrfout = WRFModelData(cfg['wrf_output_prev'])
outts = wrfout['GMT']
print("INFO: previous forecast [%s - %s] exists, running DA till %s" % (str(outts[0]),str(outts[-1]),str(ts_now)))
# run from the start until now (retrieve fuel moisture, extended parameters, covariance matrix)
model = run_data_assimilation(wrfout, fm10, ts_now, cfg)
# store this for the current time instance (fm, ep in the wrfinput, P next to it)
d = netCDF4.Dataset(cfg['wrf_input'], 'r+')
d.variables['FMC_GC'] = fm
d.variables['FMEP'] = ep
d.close()
# store the covariance matrix alongside the wrfinput file
dir = os.path.dirname(wrfin)
store_covariance_matrix(P, os.path.join(dir, 'P.nc'))
else:
print("INFO: no previous forecast found, running DA from equilibrium at %s" % (str(ts_now)))
# initialize from weather equilibrium and perform one DA step
model = init_from_equilibrium(wrfin, fm10, ts_now, cfg)
# store result in wrfinput dataset
d = netCDF4.Dataset(cfg['wrf_input'], 'r+')
fmcep = model.get_state()
d.variables['FMC_GC'][0,:3,:,:] = fmcep[:,:,:3].transpose((2,0,1))
d.variables['FMEP'][0,:,:,:] = fmcep[:,:,3:5].transpose((2,0,1))
d.close()
store_covariance_matrix(model.get_state_covar(), os.path.join(os.path.dirname(cfg['wrf_input']), 'P.nc'))
return 0
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
def run_module():
# read in configuration file to execute run
print("Reading configuration from [%s]" % sys.argv[1])
with open(sys.argv[1]) as f:
cfg = eval(f.read())
# ensure output path exists
if not os.path.isdir(cfg['output_dir']):
os.mkdir(cfg['output_dir'])
# configure diagnostics
init_diagnostics(
os.path.join(cfg['output_dir'], 'moisture_model_v1_diagnostics.txt'))
diagnostics().configure_tag("skdm_obs_res", False, True, True)
diagnostics().configure_tag("skdm_cov_cond", False, True, True)
diagnostics().configure_tag("assim_mV", False, False, True)
diagnostics().configure_tag("assim_K0", False, False, True)
diagnostics().configure_tag("assim_K1", False, False, True)
diagnostics().configure_tag("assim_data", False, False, True)
diagnostics().configure_tag("assim_mresV", False, False, True)
diagnostics().configure_tag("kriging_variance", False, False, True)
diagnostics().configure_tag("kriging_obs_res_var", False, False, True)
print("INFO: input file is [%s]." % cfg['input_file'])
wrf_data = WRFModelData(cfg['input_file'], tz_name='US/Pacific')
# read in vars
lat, lon = wrf_data.get_lats(), wrf_data.get_lons()
tm = wrf_data.get_local_times()
rain = wrf_data['RAIN']
Ed, Ew = wrf_data.get_moisture_equilibria()
# find maximum moisture overall to set up visualization
# maxE = max(np.max(Ed), np.max(Ew)) * 1.2
maxE = 0.3
# obtain sizes
Nt = rain.shape[0]
dom_shape = lat.shape
# load station data from files
tz = pytz.timezone('US/Pacific')
stations = [StationAdam() for s in station_list]
for (s, sname) in zip(stations, station_list):
s.load_station_data(os.path.join(station_data_dir, sname), tz)
s.register_to_grid(wrf_data)
s.set_measurement_variance('fm10', 0.05)
# build the observation data structure indexed by time
obs_data_fm10 = build_observation_data(stations, 'fm10', wrf_data, tm)
# construct initial conditions
E = 0.5 * (Ed[1, :, :] + Ew[1, :, :])
# set up parameters
Q = np.eye(9) * 0.001
P0 = np.eye(9) * 0.01
dt = 10.0 * 60
K = np.zeros_like(E)
V = np.zeros_like(E)
mV = np.zeros_like(E)
predicted_field = np.zeros_like(E)
mresV = np.zeros_like(E)
Kf_fn = np.zeros_like(E)
Vf_fn = np.zeros_like(E)
mid = np.zeros_like(E)
Kg = np.zeros((dom_shape[0], dom_shape[1], 9))
cV12 = np.zeros_like(E)
# initialize the mean field model (default fit is 1.0 of equilibrium before new information comes in)
mfm = MeanFieldModel(cfg['lock_gamma'])
# construct model grid using standard fuel parameters
Tk = np.array([1.0, 10.0, 100.0]) * 3600
models = np.zeros(dom_shape, dtype=np.object)
models_na = np.zeros_like(models)
for pos in np.ndindex(dom_shape):
models[pos] = CellMoistureModel((lat[pos], lon[pos]),
3,
E[pos],
Tk,
P0=P0)
models_na[pos] = CellMoistureModel((lat[pos], lon[pos]),
3,
E[pos],
Tk,
P0=P0)
m = None
plt.figure(figsize=(12, 8))
# run model
for t in range(1, Nt):
model_time = tm[t]
print("Time: %s, step: %d" % (str(model_time), t))
# pre-compute equilibrium moisture to save a lot of time
E = 0.5 * (Ed[t, :, :] + Ew[t, :, :])
# run the model update
for pos in np.ndindex(dom_shape):
i, j = pos
models[pos].advance_model(Ed[t, i, j], Ew[t, i, j], rain[t, i, j],
dt, Q)
models_na[pos].advance_model(Ed[t, i, j], Ew[t, i, j],
rain[t, i, j], dt, Q)
# prepare visualization data
f = np.zeros((dom_shape[0], dom_shape[1], 3))
f_na = np.zeros((dom_shape[0], dom_shape[1], 3))
for p in np.ndindex(dom_shape):
f[p[0], p[1], :] = models[p].get_state()[:3]
f_na[p[0], p[1], :] = models_na[p].get_state()[:3]
mV[pos] = models[p].get_state_covar()[1, 1]
cV12[pos] = models[p].get_state_covar()[0, 1]
mid[p] = models[p].get_model_ids()[1]
# run Kriging on each observed fuel type
Kf = []
Vf = []
fn = []
for obs_data, fuel_ndx in [(obs_data_fm10, 1)]:
if model_time in obs_data:
# fit the current estimation of the moisture field to the data
base_field = f[:, :, fuel_ndx]
mfm.fit_to_data(base_field, obs_data[model_time])
# find differences (residuals) between observed measurements and nearest grid points
# use this to update observation residual standard deviation
obs_vals = np.array(
[o.get_value() for o in obs_data[model_time]])
mod_vals = np.array([
f[:, :, fuel_ndx][o.get_nearest_grid_point()]
for o in obs_data[model_time]
])
mod_na_vals = np.array([
f_na[:, :, fuel_ndx][o.get_nearest_grid_point()]
for o in obs_data[model_time]
])
obs_re.update_with(obs_vals - mod_vals)
diagnostics().push("kriging_obs_res_var",
(t, np.mean(obs_re.get_variance())))
# retrieve the variance of the model field
mresV = mod_re.get_variance()
# krige data to observations
if cfg['kriging_strategy'] == 'uk':
Kf_fn, Vf_fn, gamma, mape = universal_kriging_data_to_model(
obs_data[model_time],
obs_re.get_variance()**0.5, base_field, wrf_data,
mresV**0.5, t)
# replace the stored gamma with the uk computed gamma
diagnostics().pull("mfm_gamma")[-1] = gamma
diagnostics().pull("mfm_mape")[-1] = mape
print("uk: replaced mfm_gamma %g, mfm_mape %g" %
(gamma, mape))
# update the residuals estimator with the current
mod_re.update_with(gamma * f[:, :, fuel_ndx] - Kf_fn)
elif cfg['kriging_strategy'] == 'tsm':
# predict the moisture field using observed fuel type
predicted_field = mfm.predict_field(base_field)
# run the tsm kriging estimator
Kf_fn, Vf_fn = trend_surface_model_kriging(
obs_data[model_time], wrf_data, predicted_field)
# update the model residual estimator and get current best estimate of variance
mod_re.update_with(f[:, :, fuel_ndx] - predicted_field)
else:
raise ValueError(
'Invalid kriging strategy [%s] in configuration.' %
cfg['kriiging_strategy'])
krig_vals = np.array([
Kf_fn[o.get_nearest_grid_point()]
for o in obs_data[model_time]
])
diagnostics().push(
"assim_data",
(t, fuel_ndx, obs_vals, krig_vals, mod_vals, mod_na_vals))
plot_model_snapshot(cfg, tm, t, fuel_ndx, obs_vals, krig_vals,
mod_vals, mod_na_vals)
# append to storage for kriged fields in this time instant
Kf.append(Kf_fn)
Vf.append(Vf_fn)
fn.append(fuel_ndx)
# if there were any observations, run the kalman update step
if len(fn) > 0:
Nobs = len(fn)
# run the kalman update in each model independently
# gather the standard deviations of the moisture fuel after the Kalman update
for pos in np.ndindex(dom_shape):
O = np.zeros((Nobs, ))
V = np.zeros((Nobs, Nobs))
# construct observations for this position
for i in range(Nobs):
O[i] = Kf[i][pos]
V[i, i] = Vf[i][pos]
# execute the Kalman update
Kij = models[pos].kalman_update(O, V, fn)
Kg[pos[0], pos[1], :] = Kij[:, 0]
# prepare visualization data
f = np.zeros((dom_shape[0], dom_shape[1], 3))
for p in np.ndindex(dom_shape):
f[p[0], p[1], :] = models[p].get_state()[:3]
plt.clf()
plt.subplot(3, 3, 1)
render_spatial_field_fast(m, lon, lat, f[:, :, 0], '1-hr fuel')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 2)
render_spatial_field_fast(m, lon, lat, f[:, :, 1], '10-hr fuel')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 3)
render_spatial_field_fast(m, lon, lat, f_na[:, :, 1],
'10hr fuel - no assim')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 4)
render_spatial_field_fast(m, lon, lat, Kg[:, :, 0], 'Kalman gain, fm1')
plt.clim([0.0, 3.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 5)
render_spatial_field_fast(m, lon, lat, Kg[:, :, 1],
'Kalman gain, fm10')
plt.clim([0.0, 1.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 6)
render_spatial_field_fast(m, lon, lat, Kf_fn, 'Kriging field')
plt.clim([0.0, maxE])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 7)
render_spatial_field_fast(m, lon, lat, mid, 'Model ids')
plt.clim([0.0, 5.0])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 8)
render_spatial_field_fast(m, lon, lat, Vf_fn, 'Kriging var')
plt.clim([0.0, np.max(Vf_fn)])
plt.axis('off')
plt.colorbar()
plt.subplot(3, 3, 9)
render_spatial_field_fast(m, lon, lat, mresV, 'fm10 model var')
plt.clim([0.0, np.max(mresV)])
plt.axis('off')
plt.colorbar()
plt.savefig(
os.path.join(cfg['output_dir'], 'moisture_model_t%03d.png' % t))
# push new diagnostic outputs
diagnostics().push("assim_K0", (t, np.mean(Kg[:, :, 0])))
diagnostics().push("assim_K1", (t, np.mean(Kg[:, :, 1])))
diagnostics().push("assim_mV", (t, np.mean(mV)))
diagnostics().push("assim_mresV", (t, np.mean(mresV)))
diagnostics().push("kriging_variance", (t, np.mean(Vf_fn)))
# store the gamma coefficients
with open(os.path.join(cfg['output_dir'], 'gamma.txt'), 'w') as f:
f.write(str(diagnostics().pull('mfm_gamma')))
# make a plot of gammas
plt.figure()
plt.plot(diagnostics().pull('mfm_gamma'))
plt.title('Mean field model - gamma')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_gamma.png'))
plt.figure()
plt.plot(diagnostics().pull('skdm_cov_cond'))
plt.title('Condition number of covariance matrix')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_sigma_cond.png'))
# make
# make a plot for each substation
plt.figure()
D = diagnostics().pull("assim_data")
for i in range(len(stations)):
plt.clf()
# get data for the i-th station
t_i = [o[0] for o in D]
obs_i = [o[2][i] for o in D]
krig_i = [o[3][i] for o in D]
mod_i = [o[4][i] for o in D]
mod_na_i = [o[5][i] for o in D]
mx = max(max(obs_i), max(mod_i), max(krig_i), max(mod_i))
plt.plot(t_i, obs_i, 'ro')
plt.plot(t_i, krig_i, 'bo-')
plt.plot(t_i, mod_i, 'kx-', linewidth=1.5)
plt.plot(t_i, mod_na_i, 'mx-')
plt.ylim([0.0, 1.1 * mx])
plt.legend(['Obs.', 'Kriged', 'Model', 'NoAssim'])
plt.title('Station observations fit to model and kriging field')
plt.savefig(
os.path.join(cfg['output_dir'], 'station%02d.png' % (i + 1)))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_K1")],
[d[1] for d in diagnostics().pull("assim_K1")], 'ro-')
plt.title('Average Kalman gain')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kalman_gain_10hr.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_K0")],
[d[1] for d in diagnostics().pull("assim_K0")], 'ro-')
plt.title('Average Kalman gain')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kalman_gain_1hr.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_mV")],
[d[1] for d in diagnostics().pull("assim_mV")], 'ro-')
plt.title('Average model variance')
plt.savefig(os.path.join(cfg['output_dir'],
'plot_fm10_model_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("assim_mresV")],
[d[1] for d in diagnostics().pull("assim_mresV")], 'ro-')
plt.title('Average fm10 residual variance')
plt.savefig(
os.path.join(cfg['output_dir'],
'plot_fm10_model_residual_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("kriging_variance")],
[d[1] for d in diagnostics().pull("kriging_variance")], 'ro-')
plt.title('Kriging field variance')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_kriging_variance.png'))
plt.figure()
plt.plot([d[0] for d in diagnostics().pull("kriging_obs_res_var")],
[d[1] for d in diagnostics().pull("kriging_obs_res_var")], 'ro-')
plt.title('Observation residual variance')
plt.savefig(
os.path.join(cfg['output_dir'],
'plot_observation_residual_variance.png'))
plt.figure()
plt.plot(diagnostics().pull("mfm_mape"), 'ro-', linewidth=2)
plt.title('Mean absolute prediction error of station data')
plt.savefig(os.path.join(cfg['output_dir'], 'plot_station_mape.png'))
diagnostics().dump_store(os.path.join(cfg['output_dir'],
'diagnostics.bin'))
# as a last step encode all the frames as video
os.system(
"cd %s; avconv -qscale 1 -r 20 -b 9600 -i moisture_model_t%%03d.png video.mp4"
% cfg['output_dir'])