1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 | """
AMSIMP Preprocessing Class. For information about this class is
described below.
Copyright (C) 2021 AMSIMP
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see https://www.gnu.org/licenses/.
"""
# ------------------------------------------------------------------------------#
# Importing Dependencies
import os
import socket
import requests
import tensorflow as tf
from tensorflow.keras.mixed_precision import experimental as mixed_precision
from astropy import units
from astropy.units.quantity import Quantity
from tqdm import tqdm
import iris
import numpy as np
# ------------------------------------------------------------------------------#
class Preprocessing:
"""
This is the preprocessing class for AMSIMP.
"""
def __init__(
self, forecast_length=120, amsimp_ic=True, initialisation_conditions=None
):
"""
The parameter, forecast_length, defines the length of the
weather forecast (defined in hours). Defaults to a value of 120.
It is currently not recommended to generate a climate forecast
using this software, as it has not been tested for this purpose.
This may change at some point in the future.
The parameter, amsimp_ic, is a boolean which states whether the software will
utilise the initialisation conditions provided by AMSIMP. The initialisation
conditions provided are from the Global Forecasting System. They are
currently stored on AMSIMP GitHub repository. It is updated on the 1st, 7th,
13th, and 18th hour. Currently, this option does not provide support for
an ensemble prediction system. This, however, will be added in a future version
of the software.
The parameter, initialisation_conditions, defines the state of the atmosphere
in the past thirty days in two-hour intervals up to the present
moment. The following parameters must be defined: 2-metre
temperature (2m_temperature), 850 hPa temperature (air_temperature),
850 hPa geopotential (geopotential), and total precipitation
(total_precipitation). The expected input parameter is
a file name. Each file must have the same grid points as
all of the other cubes. The grid must be 2 dimensional, and have
ha spatial resolution of 1 degree, which is approximately 100
kilometres. Introplation will be invoked if this is not the case,
which may have a negative impact on the performance of the software and
by extension the forecast produced. The latitude points must range from
-90 to 90, and the longitude points must range from 0 to 360.
"""
# Suppress Tensorflow warnings.
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
# Make the aforementioned variables available else where in the class.
self.amsimp_ic = amsimp_ic
if type(forecast_length) != Quantity:
forecast_length *= units.hr
self.forecast_length = forecast_length.to(units.hr)
self.initialisation_conditions = initialisation_conditions
# Ensure self.amsimp_ic is a boolean value.
if not isinstance(self.amsimp_ic, bool):
raise ValueError("The parameter, amsimp_ic, must be a boolean value.")
# Ensure self.forecast_length is greater than, or equal to 1.
if self.forecast_length.value <= 0:
raise ValueError(
"The parameter, forecast_length, must be a positive number greater than, or equal to 1. "
+ "The value of forecast_length was: {}".format(self.forecast_length)
)
# Ensure self.forecast_length is a factor of 4.
if self.forecast_length.value % 4 != 0:
raise ValueError(
"The parameter, forecast_length, must be evenly divisible by four."
)
# Ensure the parameter, self.initialisation_conditions, is not defined when the
# parameter self.amsimp_ic is defined to be true.
if self.amsimp_ic:
if self.initialisation_conditions != None:
raise Exception(
"The parameter, initialisation_conditions, must not be defined when amsimp_ic is defined to be true."
)
# Error checking if self.initialisation_conditions is defined.
if not self.initialisation_conditions == None:
# Ensure self.initialisation_conditions is a string value.
if not isinstance(self.initialisation_conditions, str):
raise ValueError(
"The parameter, initialisation_conditions, must be a string value."
)
# Check if file provided to the software exists.
if not os.path.exists(self.initialisation_conditions):
raise FileNotFoundError(
"The file provided to the software could not be located."
)
# Output warning if file is not of the format, NetCDF.
if not ".nc" in self.initialisation_conditions:
raise Warning(
"Currently AMSIMP only officially supports the NetCDF file format."
)
# Function to check for an internet connection.
def is_connected():
try:
host = socket.gethostbyname("www.github.com")
s = socket.create_connection((host, 80), 2)
s.close()
return True
except OSError:
pass
return False
# Check for an internet connection.
if not is_connected() and amsimp_ic:
raise Exception(
"You must connect to the internet in order to utilise AMSIMP."
+ " Apologies for any inconvenience caused."
)
# ------------------------------------------------------------------------------#
def __download_file(self, url, desc):
r"""Generates and downloads a cube with the required parameter.
Parameters
----------
url : `str`
The url of the required parameter to download
desc : `str`
The name of the required parameter to download
Returns
-------
`iris.cube.Cube`
Cube of the downloaded parameter
Notes
-----
This method is activated when the parameter, amsimp_ic, is defined to
be true. The data is downloaded from the AMSIMP Initial Conditions
repository. This is intended as a private method, and may not function
correctly if used.
See Also
--------
load_dataset
"""
# Create download request.
response = requests.get(url, stream=True)
# Size of file.
total_size_in_bytes = int(response.headers.get("content-length", 0))
# Size of block.
block_size = 1024 # 1 Kibibyte
# Define download progress bar.
progress_bar = tqdm(
total=total_size_in_bytes,
unit="iB",
unit_scale=True,
desc="Downloading {} initialisation condition".format(desc),
)
# Download file
with open("temp.nc", "wb") as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
# Close progress bar.
progress_bar.close()
# Determine if the file was downloaded in its entirety.
if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
raise Exception(
"An unknown error occurred, which resulted in the download failing."
)
# Load parameter from dataset.
parameter = iris.load("temp.nc")[0]
parameter.data
# Remove temporary file.
os.remove("temp.nc")
return parameter
def load_dataset(self):
r"""Generates a cube list with the required dataset loaded, either the
file provided is loaded or the files from the AMSIMP Initial Conditions
repository are downloaded and saved into a single file.
Returns
-------
`iris.cube.CubeList`
Cube list with the required dataset loaded
Notes
-----
The AMSIMP Initial Conditions repository is update four times daily, at
1 am, 7 am, 1 pm, and 7 pm. The near real-time initialisation conditions
are provided by the National Oceanic and Atmospheric Adminstrations'
Global Data Assimilation System (GDAS).
"""
# Load dataset based on whether the user defined the initialisation
# conditions, or not.
if self.amsimp_ic:
# Download file from the GitHub repository if necessary.
if not os.path.exists("initialisation_conditions.nc"):
# 2 metre temperature.
t2m = self.__download_file(
"https://github.com/amsimp/initial-conditions/raw/main/initialisation_conditions/2m_temperature.nc",
"2 metre temperature",
)
# Total precipitation.
tp = self.__download_file(
"https://github.com/amsimp/initial-conditions/raw/main/initialisation_conditions/total_precipitation.nc",
"total precipitation",
)
# Air temperature at 850 hPa.
t850 = self.__download_file(
"https://github.com/amsimp/initial-conditions/raw/main/initialisation_conditions/air_temperature.nc",
"850 hPa air temperature",
)
# Geopotential at 500 hPa.
z500 = self.__download_file(
"https://github.com/amsimp/initial-conditions/raw/main/initialisation_conditions/geopotential.nc",
"500 hPa geopotential",
)
# Define dataset.
dataset = iris.cube.CubeList([t2m, tp, t850, z500])
# Save dataset.
iris.save(dataset, "initialisation_conditions.nc")
# Load dataset.
dataset = iris.load("initialisation_conditions.nc")
else:
# Load dataset provided by the user.
dataset = iris.load(self.initialisation_conditions)
return dataset
# ------------------------------------------------------------------------------#
def lat(self):
r"""Generates an array of latitude lines in accordance with the shape
expected by the operational model of the AMSIMP Global Forecast Model.
Returns
-------
`numpy.ndarray`
Latitude lines
Notes
-----
The resolution of this model is approximately 100 kilometres (1 degree).
See Also
--------
lon
"""
lat = np.linspace(90, -90, 721)[4:-3:4]
return lat
def lon(self):
r"""Generates an array of longitude lines in accordance with the shape
expected by the operational model of the AMSIMP Global Forecast Model.
Returns
-------
`numpy.ndarray`
Longitude lines
Notes
-----
The resolution of this model is approximately 100 kilometres (1 degree).
See Also
--------
lon
"""
lon = np.linspace(0, 359.75, 1440)[::4]
return lon
# ------------------------------------------------------------------------------#
def parameter_extraction(self):
r"""Generates a cube list with the expected parameters extracted in the
expected order for interplolation and normalisation.
Returns
-------
`iris.cube.CubeList`
Cube list with expected parameters extracted
Notes
-----
The parameters, in this order, are: air temperature at 2 metres above
the surface, air temperature at a pressure surface of 850 hectopascals,
and geopotential at a pressure surface of 500 hectopascals.
See Also
--------
load_dataset, interpolate_dataset, normalise_dataset
"""
# Extract the relevant parameters from the dataset.
parameters = ["t2m", "t", "z"]
dataset = self.load_dataset().extract(parameters)
# Ensure all parameters are present.
if len(dataset) != 3:
raise Exception(
"All of the expected parameters were not present in the dataset."
)
# Ensure the pressure surface defined is correct if it is relevant for
# a given parameter.
# Air temperature at 850 hPa.
try:
# Retrieve DimCoord from cube.
t850_p = dataset.extract("air_temperature")[0].coord("pressure")
# Ensure units are in hectopascals.
t850_p.convert_units("hPa")
# Check if the pressure surface is at 850 hPa.
if not int(t850_p.points[0]) == 850:
raise Exception(
"The air temperature values provided are not on the correct pressure surface, which is 850 hPa."
)
except:
pass
# Geopotential at 500 hPa.
try:
# Retrieve DimCoord from cube.
z500_p = dataset.extract("geopotential")[0].coord("pressure")
# Ensure units are in hectopascals.
z500_p.convert_units("hPa")
# Check if the pressure surface is at 500 hPa.
if not int(z500_p.points[0]) == 500:
raise Exception(
"The geopotential values provided are not on the correct pressure surface, which is 500 hPa."
)
except:
pass
return dataset
def interpolate_dataset(self):
r"""Generates a cube list with the expected parameters, interpolated
if necessary onto the grid required for input into the operational AMSIMP
Global Forecast Model.
Returns
-------
`iris.cube.CubeList`
Cube list with interpolated grid for operational model
Notes
-----
This method also ensures the expected number of time steps are
included, and raises an error when an insufficient number is present.
The number of time steps required is 6.
See Also
--------
load_dataset, parameter_extraction, normalise_dataset
"""
# Define dataset.
dataset = self.parameter_extraction()
# Define expected coordinates.
# Latitude.
lat = self.lat()
# Longitude
lon = self.lon()
# Check if the expected number of time steps are present.
# If an insufficient number is present.
if dataset[0].shape[0] < 6:
raise ValueError(
"Six timesteps are required in order to generate a forecast."
)
# If an excess number is present.
elif dataset[0].shape[0] > 6:
dataset = dataset[-6:]
# Check if longitude values contain a negative number.
if np.min(dataset[0].coord("longitude").points) < 0:
raise ValueError(
"The longitude coordinate system provided is not supported. Longitude values must range from 0 to 360."
)
# Define grid points for interpolation.
grid_points = [("latitude", lat), ("longitude", lon)]
# Loop through dataset.
for i in range(len(dataset)):
# Interpolate dataset to the required coordinates.
dataset[i] = dataset[i].interpolate(grid_points, iris.analysis.Linear())
return dataset
def normalise_dataset(self):
r"""Generates a NumPy array with the expected parameters, normalised,
processed onto the grid required for input into the operational AMSIMP
Global Forecast Model.
Returns
-------
`numpy.ndarray`
Normalised and preprocessed dataset for forecast model input
Notes
-----
This method also converts the parameters into the correct units of
measurement if it is necessary to do so.
See Also
--------
load_dataset, parameter_extraction, interpolate_dataset
"""
# Define dataset.
dataset = self.interpolate_dataset()
# Define directory.
import amsimp.preprocessing
directory = os.path.dirname(amsimp.preprocessing.__file__)
# Load normalisation variables.
# Mean.
mean = np.load(directory + "/model/mean.npy")
# Standard deviation.
std = np.load(directory + "/model/std.npy")
# Convert cube list to NumPy array.
dataset_numpy = np.zeros(
(
len(dataset),
dataset[0].shape[0],
dataset[0].shape[1],
dataset[0].shape[2],
)
)
# Ensure units are correct.
# 2 metre temperature (K).
dataset[0].convert_units("K")
# Air temperature at 850 hPa (K).
dataset[1].convert_units("K")
# Geopotential at 500 hPa (m2 s-2).
dataset[2].convert_units("m2 s-2")
# Loop through dataset.
for i in tqdm(range(len(dataset)), desc="Interpolating dataset"):
# Add to NumPy array.
dataset_numpy[i] = dataset[i].data
# Transpose.
dataset_numpy = np.transpose(dataset_numpy, (1, 2, 3, 0))
# Normalise.
dataset_numpy = (dataset_numpy - mean) / std
# Reshape for model input.
dataset = dataset_numpy.reshape(
1,
dataset[0].shape[0],
dataset[0].shape[1],
dataset[0].shape[2],
len(dataset),
)
return dataset
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AMSIMP Operational Model Class. For information about this
class is described below.
Copyright (C) 2021 AMSIMP
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
# -----------------------------------------------------------------------------------------#
# Importing Dependencies.
import os
from tqdm import tqdm
import iris
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import ConvLSTM2D, Dropout, Dense
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.optimizers import Adam
from amsimp.preprocessing import Preprocessing
# -----------------------------------------------------------------------------------------#
class OperationalModel(Preprocessing):
"""
This is the operational model class for AMSIMP.
"""
def model_architecture(self):
r"""Generates the operational AMSIMP Global Forecast Model architecture.
Returns
-------
`tf.keras.Sequential`
Operational AMSIMP Global Forecast Model architecture.
Notes
-----
This architecture is currently based on the ConvLSTM layer, which has
been pretrained on the dataset from the year 2009 to the year 2016.
A major drawback of LSTMs in its handling of spatiotemporal data is due
to its usage of full connections in input-to-state and state-to-state
transitions in which no spatial information is encoded. To overcome
this problem, a distinguishing feature of a ConvLSTM cell is that all
the inputs and gates of the ConvLSTM layer are 3D tensors whose last
two dimensions are spatial dimensions.
"""
# Create, and train models.
# Optimiser.
opt = Adam(lr=1e-3, decay=1e-5)
# Create model.
model = Sequential()
# First layer.
model.add(
ConvLSTM2D(
filters=64,
kernel_size=(7, 7),
input_shape=(6, 179, 360, 3),
padding="same",
return_sequences=True,
activation="tanh",
recurrent_activation="hard_sigmoid",
kernel_initializer="glorot_uniform",
unit_forget_bias=True,
dropout=0.3,
recurrent_dropout=0.3,
go_backwards=True,
)
)
# Batch normalisation.
model.add(BatchNormalization())
# Dropout.
model.add(Dropout(0.1))
# Second layer.
model.add(
ConvLSTM2D(
filters=32,
kernel_size=(7, 7),
padding="same",
return_sequences=True,
activation="tanh",
recurrent_activation="hard_sigmoid",
kernel_initializer="glorot_uniform",
unit_forget_bias=True,
dropout=0.4,
recurrent_dropout=0.3,
go_backwards=True,
)
)
# Batch normalisation.
model.add(BatchNormalization())
# Third layer.
model.add(
ConvLSTM2D(
filters=32,
kernel_size=(7, 7),
padding="same",
return_sequences=True,
activation="tanh",
recurrent_activation="hard_sigmoid",
kernel_initializer="glorot_uniform",
unit_forget_bias=True,
dropout=0.4,
recurrent_dropout=0.3,
go_backwards=True,
)
)
# Batch normalisation.
model.add(BatchNormalization())
# Dropout.
model.add(Dropout(0.1))
# Final layer.
model.add(
ConvLSTM2D(
filters=32,
kernel_size=(7, 7),
padding="same",
return_sequences=True,
activation="tanh",
recurrent_activation="hard_sigmoid",
kernel_initializer="glorot_uniform",
unit_forget_bias=True,
dropout=0.5,
recurrent_dropout=0.3,
go_backwards=True,
)
)
# Batch normalisation.
model.add(BatchNormalization())
# Add dense layer.
model.add(Dense(3))
# Compile model.
model.compile(optimizer=opt, loss="mse", metrics=["mean_absolute_error"])
return model
def generate_forecast(self):
r"""Generates a forecast with the current AMSIMP Global Forecast Model
architecture.
Returns
-------
`iris.cube.CubeList`
The forecast generated with the operational model
Notes
-----
This model has been pretrained on the dataset from the year 2009 to the
year 2016. The architecture of the current operational model is
currently based on the ConvLSTM layer. The prognostic variables are: air
temperature at 2 metres above the surface, air temperature at a pressure
surface of 850 hectopascals, and geopotential at a pressure surface of
500 hectopascals.
See Also
--------
model_architecture
"""
# Define model input.
model_input = self.normalise_dataset()
# Define directory.
import amsimp.preprocessing
directory = os.path.dirname(amsimp.preprocessing.__file__)
# Define model and load weights.
model = self.model_architecture()
model.load_weights(directory + "/model/global_forecast_model.h5")
# Define forecast output array.
model_output = np.zeros(
(
int((self.forecast_length.value / 2) + 1),
model_input.shape[2],
model_input.shape[3],
model_input.shape[4],
)
)
# Add current conditions to output array.
model_output[0] = model_input[0, -1]
# Create iterative predictions.
it = 1
pbar = tqdm(
total=int(self.forecast_length.value / 12), desc="Generating forecast"
)
while it < (self.forecast_length.value / 2):
# Generate predictions based on current model input.
predictions = model.predict(model_input)
# Add predictions to output array.
model_output[it : it + 6] = predictions[0]
# Define as new model input.
model_input = predictions
# Increment iteration.
it += 6
pbar.update()
# Close progress bar.
pbar.close()
# Define progress bar.
pbar = tqdm(total=3, desc="Outputting forecast")
# Inverse of normalisation.
# Load normalisation variables.
# Mean.
mean = np.load(directory + "/model/mean.npy")
# Standard deviation.
std = np.load(directory + "/model/std.npy")
# Determine inverse.
model_output = (model_output * std) + mean
# Define forecast for each parameter.
# 2 metre temperature.
t2m = model_output[:, :, :, 0]
# 850 hPa temperature.
t = model_output[:, :, :, 1]
# 500 hPa geopotential.
z = model_output[:, :, :, 2]
# Define time coordinate.
time = self.interpolate_dataset()[0].coord("time")
# Define unit of measurement.
time_unit = time.units
# Define values.
time = (
np.linspace(0, self.forecast_length.value, model_output.shape[0])
+ time.points[-1]
)
# Define the coordinates for the cubes.
# Time.
time = iris.coords.DimCoord(time, standard_name="time", units=time_unit)
# Latitude.
lat = iris.coords.DimCoord(
self.lat(), standard_name="latitude", units="degrees"
)
# Longitude
lon = iris.coords.DimCoord(
self.lon(), standard_name="longitude", units="degrees"
)
# Aux coords.
# 850 hPa for temperature.
p850 = iris.coords.AuxCoord(
np.array([850]), standard_name="air_pressure", units="hPa"
)
# 500 hPa for geopotential.
p500 = iris.coords.AuxCoord(
np.array([500]), standard_name="air_pressure", units="hPa"
)
# Define cubes.
# 2 metre temperature.
t2m = iris.cube.Cube(
t2m,
long_name="2m_temperature",
var_name="t2m",
units="K",
dim_coords_and_dims=[(time, 0), (lat, 1), (lon, 2)],
attributes={
"source": "AMSIMP Global Forecast Model",
},
)
pbar.update()
# 850 hPa temperature.
t = iris.cube.Cube(
t,
standard_name="air_temperature",
var_name="t",
units="K",
dim_coords_and_dims=[(time, 0), (lat, 1), (lon, 2)],
attributes={
"source": "AMSIMP Global Forecast Model",
},
)
t.add_aux_coord(p850)
pbar.update()
# 500 hPa geopotential.
z = iris.cube.Cube(
z,
standard_name="geopotential",
var_name="z",
units="m2 s-2",
dim_coords_and_dims=[(time, 0), (lat, 1), (lon, 2)],
attributes={
"source": "AMSIMP Global Forecast Model",
},
)
z.add_aux_coord(p500)
pbar.update()
# Finish progress bar.
pbar.close()
# Define output forecast.
forecast = iris.cube.CubeList([t2m, t, z])
return forecast
|