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In numpy, we also have more options for quickly (and without much code) examining the contents of an array. One of the most helpful tools for this is np.where. np.where uses a conditional statement on the array and returns an array that contains indices of all the values that were true for the conditional statement. We can then call the original array and use the new array to get all the values that were true for the conditional statement.
There are also functions like max and min that will give the maximum and minimum, respectively. | # Defining starting and ending values of the array, as well as the number of elements in the array.
start = 0
stop = 100
n_elements = 201
x = np.linspace(start, stop, n_elements)
print(x) | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | VandyAstroML/Vanderbilt_Computational_Bootcamp | mit |
And we can select only those values that are divisible by 5: | # This function returns the indices that match the criteria of `x % 5 == 0`:
x_5 = np.where(x%5 == 0)
print(x_5)
# And one can use those indices to *only* select those values:
print(x[x_5]) | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | VandyAstroML/Vanderbilt_Computational_Bootcamp | mit |
Or similarly: | x[x%5 == 0] | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | VandyAstroML/Vanderbilt_Computational_Bootcamp | mit |
And you can find the max and min values of the array: | print('The minimum of `x` is `{0}`'.format(x.min()))
print('The maximum of `x` is `{0}`'.format(x.max())) | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | VandyAstroML/Vanderbilt_Computational_Bootcamp | mit |
Numpy also provides some tools for loading and saving data, loadtxt and savetxt. Here I'm using a function called transpose so that instead of each array being a row, they each get treated as a column.
When we load the information again, it's now a 2D array. We can select parts of those arrays just as we could for 1D arrays. | start = 0
stop = 100
n_elem = 501
x = np.linspace(start, stop, n_elem)
# We can now create another array from `x`:
y = (.1*x)**2 - (5*x) + 3
# And finally, we can dump `x` and `y` to a file:
np.savetxt('myfile.txt', np.transpose([x,y]))
# We can also load the data from `myfile.txt` and display it:
data = np.loadtxt('myfile.txt')
print('2D-array from file `myfile.txt`:\n\n', data, '\n')
# You can also select certain elements of the 2D-array
print('Selecting certain elements from `data`:\n\n', data[:3,:], '\n') | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | VandyAstroML/Vanderbilt_Computational_Bootcamp | mit |
Resources
Scientific Lectures on Python - Numpy: iPython Notebook
Data Science iPython Notebooks - Numpy: iPython Notebook
Matplotlib
Matplotlib is a Python 2D plotting library which
* produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms
* Quick way to visualize data from Python
* Main plotting utility in Python
From their website (http://matplotlib.org/):
Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.
A great starting point to figuring out how to make a particular figure is to start from the Matplotlib gallery and look for what you want to make. | ## Importing modules
%matplotlib inline
# Importing LaTeX
from matplotlib import rc
rc('text', usetex=True)
# Importing matplotlib and other modules
import matplotlib.pyplot as plt
import numpy as np | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | VandyAstroML/Vanderbilt_Computational_Bootcamp | mit |
We can now load in the data from myfile.txt | data = np.loadtxt('myfile.txt') | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | VandyAstroML/Vanderbilt_Computational_Bootcamp | mit |
The simplest figure is to simply make a plot. We can have multiple figures, but for now, just one. The plt.plot function will connect the points, but if we want a scatter plot, then plt.scatter will work. | plt.figure(1, figsize=(8,8))
plt.plot(data[:,0],data[:,1])
plt.show() | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | VandyAstroML/Vanderbilt_Computational_Bootcamp | mit |
You can also pass the *data.T value instead: | plt.figure(1, figsize=(8,8))
plt.plot(*data.T)
plt.show() | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | VandyAstroML/Vanderbilt_Computational_Bootcamp | mit |
We can take that same figure and add on the needed labels and titles. | # Creating figure
plt.figure(figsize=(8,8))
plt.plot(*data.T)
plt.title(r'$y = 0.2x^{2} - 5x + 3$', fontsize=20)
plt.xlabel('x value', fontsize=20)
plt.ylabel('y value', fontsize=20)
plt.show() | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | VandyAstroML/Vanderbilt_Computational_Bootcamp | mit |
There's a large number of options available for plotting, so try using the initial code below, combined with the information here to try out a few of the following things: changing the line width, changing the line color | plt.figure(figsize=(8,8))
plt.plot(data[:,0],data[:,1])
plt.title(r'$y = 0.2x^{2} - 5x + 3$', fontsize=20)
plt.xlabel('x value', fontsize=20)
plt.ylabel('y value', fontsize=20)
plt.show() | notebooks/Week_05/05_Numpy_Matplotlib.ipynb | VandyAstroML/Vanderbilt_Computational_Bootcamp | mit |
<table class="ee-notebook-buttons" align="left"><td>
<a target="_blank" href="http://colab.research.google.com/github/google/earthengine-api/blob/master/python/examples/ipynb/UNET_regression_demo.ipynb">
<img src="https://www.tensorflow.org/images/colab_logo_32px.png" /> Run in Google Colab</a>
</td><td>
<a target="_blank" href="https://github.com/google/earthengine-api/blob/master/python/examples/ipynb/UNET_regression_demo.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td></table>
Introduction
This is an Earth Engine <> TensorFlow demonstration notebook. Suppose you want to predict a continuous output (regression) from a stack of continuous inputs. In this example, the output is impervious surface area from NLCD and the input is a Landsat 8 composite. The model is a fully convolutional neural network (FCNN), specifically U-net. This notebook shows:
Exporting training/testing patches from Earth Engine, suitable for training an FCNN model.
Preprocessing.
Training and validating an FCNN model.
Making predictions with the trained model and importing them to Earth Engine.
Setup software libraries
Authenticate and import as necessary. | # Cloud authentication.
from google.colab import auth
auth.authenticate_user()
# Import, authenticate and initialize the Earth Engine library.
import ee
ee.Authenticate()
ee.Initialize()
# Tensorflow setup.
import tensorflow as tf
print(tf.__version__)
# Folium setup.
import folium
print(folium.__version__) | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Variables
Declare the variables that will be in use throughout the notebook.
Specify your Cloud Storage Bucket
You must have write access to a bucket to run this demo! To run it read-only, use the demo bucket below, but note that writes to this bucket will not work. | # INSERT YOUR BUCKET HERE:
BUCKET = 'your-bucket-name' | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Set other global variables | # Specify names locations for outputs in Cloud Storage.
FOLDER = 'fcnn-demo'
TRAINING_BASE = 'training_patches'
EVAL_BASE = 'eval_patches'
# Specify inputs (Landsat bands) to the model and the response variable.
opticalBands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7']
thermalBands = ['B10', 'B11']
BANDS = opticalBands + thermalBands
RESPONSE = 'impervious'
FEATURES = BANDS + [RESPONSE]
# Specify the size and shape of patches expected by the model.
KERNEL_SIZE = 256
KERNEL_SHAPE = [KERNEL_SIZE, KERNEL_SIZE]
COLUMNS = [
tf.io.FixedLenFeature(shape=KERNEL_SHAPE, dtype=tf.float32) for k in FEATURES
]
FEATURES_DICT = dict(zip(FEATURES, COLUMNS))
# Sizes of the training and evaluation datasets.
TRAIN_SIZE = 16000
EVAL_SIZE = 8000
# Specify model training parameters.
BATCH_SIZE = 16
EPOCHS = 10
BUFFER_SIZE = 2000
OPTIMIZER = 'SGD'
LOSS = 'MeanSquaredError'
METRICS = ['RootMeanSquaredError'] | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Imagery
Gather and setup the imagery to use for inputs (predictors). This is a three-year, cloud-free, Landsat 8 composite. Display it in the notebook for a sanity check. | # Use Landsat 8 surface reflectance data.
l8sr = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
# Cloud masking function.
def maskL8sr(image):
cloudShadowBitMask = ee.Number(2).pow(3).int()
cloudsBitMask = ee.Number(2).pow(5).int()
qa = image.select('pixel_qa')
mask1 = qa.bitwiseAnd(cloudShadowBitMask).eq(0).And(
qa.bitwiseAnd(cloudsBitMask).eq(0))
mask2 = image.mask().reduce('min')
mask3 = image.select(opticalBands).gt(0).And(
image.select(opticalBands).lt(10000)).reduce('min')
mask = mask1.And(mask2).And(mask3)
return image.select(opticalBands).divide(10000).addBands(
image.select(thermalBands).divide(10).clamp(273.15, 373.15)
.subtract(273.15).divide(100)).updateMask(mask)
# The image input data is a cloud-masked median composite.
image = l8sr.filterDate('2015-01-01', '2017-12-31').map(maskL8sr).median()
# Use folium to visualize the imagery.
mapid = image.getMapId({'bands': ['B4', 'B3', 'B2'], 'min': 0, 'max': 0.3})
map = folium.Map(location=[38., -122.5])
folium.TileLayer(
tiles=mapid['tile_fetcher'].url_format,
attr='Map Data © <a href="https://earthengine.google.com/">Google Earth Engine</a>',
overlay=True,
name='median composite',
).add_to(map)
mapid = image.getMapId({'bands': ['B10'], 'min': 0, 'max': 0.5})
folium.TileLayer(
tiles=mapid['tile_fetcher'].url_format,
attr='Map Data © <a href="https://earthengine.google.com/">Google Earth Engine</a>',
overlay=True,
name='thermal',
).add_to(map)
map.add_child(folium.LayerControl())
map | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Prepare the response (what we want to predict). This is impervious surface area (in fraction of a pixel) from the 2016 NLCD dataset. Display to check. | nlcd = ee.Image('USGS/NLCD/NLCD2016').select('impervious')
nlcd = nlcd.divide(100).float()
mapid = nlcd.getMapId({'min': 0, 'max': 1})
map = folium.Map(location=[38., -122.5])
folium.TileLayer(
tiles=mapid['tile_fetcher'].url_format,
attr='Map Data © <a href="https://earthengine.google.com/">Google Earth Engine</a>',
overlay=True,
name='nlcd impervious',
).add_to(map)
map.add_child(folium.LayerControl())
map | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Stack the 2D images (Landsat composite and NLCD impervious surface) to create a single image from which samples can be taken. Convert the image into an array image in which each pixel stores 256x256 patches of pixels for each band. This is a key step that bears emphasis: to export training patches, convert a multi-band image to an array image using neighborhoodToArray(), then sample the image at points. | featureStack = ee.Image.cat([
image.select(BANDS),
nlcd.select(RESPONSE)
]).float()
list = ee.List.repeat(1, KERNEL_SIZE)
lists = ee.List.repeat(list, KERNEL_SIZE)
kernel = ee.Kernel.fixed(KERNEL_SIZE, KERNEL_SIZE, lists)
arrays = featureStack.neighborhoodToArray(kernel) | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Use some pre-made geometries to sample the stack in strategic locations. Specifically, these are hand-made polygons in which to take the 256x256 samples. Display the sampling polygons on a map, red for training polygons, blue for evaluation. | trainingPolys = ee.FeatureCollection('projects/google/DemoTrainingGeometries')
evalPolys = ee.FeatureCollection('projects/google/DemoEvalGeometries')
polyImage = ee.Image(0).byte().paint(trainingPolys, 1).paint(evalPolys, 2)
polyImage = polyImage.updateMask(polyImage)
mapid = polyImage.getMapId({'min': 1, 'max': 2, 'palette': ['red', 'blue']})
map = folium.Map(location=[38., -100.], zoom_start=5)
folium.TileLayer(
tiles=mapid['tile_fetcher'].url_format,
attr='Map Data © <a href="https://earthengine.google.com/">Google Earth Engine</a>',
overlay=True,
name='training polygons',
).add_to(map)
map.add_child(folium.LayerControl())
map | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Sampling
The mapped data look reasonable so take a sample from each polygon and merge the results into a single export. The key step is sampling the array image at points, to get all the pixels in a 256x256 neighborhood at each point. It's worth noting that to build the training and testing data for the FCNN, you export a single TFRecord file that contains patches of pixel values in each record. You do NOT need to export each training/testing patch to a different image. Since each record potentially contains a lot of data (especially with big patches or many input bands), some manual sharding of the computation is necessary to avoid the computed value too large error. Specifically, the following code takes multiple (smaller) samples within each geometry, merging the results to get a single export. | # Convert the feature collections to lists for iteration.
trainingPolysList = trainingPolys.toList(trainingPolys.size())
evalPolysList = evalPolys.toList(evalPolys.size())
# These numbers determined experimentally.
n = 200 # Number of shards in each polygon.
N = 2000 # Total sample size in each polygon.
# Export all the training data (in many pieces), with one task
# per geometry.
for g in range(trainingPolys.size().getInfo()):
geomSample = ee.FeatureCollection([])
for i in range(n):
sample = arrays.sample(
region = ee.Feature(trainingPolysList.get(g)).geometry(),
scale = 30,
numPixels = N / n, # Size of the shard.
seed = i,
tileScale = 8
)
geomSample = geomSample.merge(sample)
desc = TRAINING_BASE + '_g' + str(g)
task = ee.batch.Export.table.toCloudStorage(
collection = geomSample,
description = desc,
bucket = BUCKET,
fileNamePrefix = FOLDER + '/' + desc,
fileFormat = 'TFRecord',
selectors = BANDS + [RESPONSE]
)
task.start()
# Export all the evaluation data.
for g in range(evalPolys.size().getInfo()):
geomSample = ee.FeatureCollection([])
for i in range(n):
sample = arrays.sample(
region = ee.Feature(evalPolysList.get(g)).geometry(),
scale = 30,
numPixels = N / n,
seed = i,
tileScale = 8
)
geomSample = geomSample.merge(sample)
desc = EVAL_BASE + '_g' + str(g)
task = ee.batch.Export.table.toCloudStorage(
collection = geomSample,
description = desc,
bucket = BUCKET,
fileNamePrefix = FOLDER + '/' + desc,
fileFormat = 'TFRecord',
selectors = BANDS + [RESPONSE]
)
task.start() | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Training data
Load the data exported from Earth Engine into a tf.data.Dataset. The following are helper functions for that. | def parse_tfrecord(example_proto):
"""The parsing function.
Read a serialized example into the structure defined by FEATURES_DICT.
Args:
example_proto: a serialized Example.
Returns:
A dictionary of tensors, keyed by feature name.
"""
return tf.io.parse_single_example(example_proto, FEATURES_DICT)
def to_tuple(inputs):
"""Function to convert a dictionary of tensors to a tuple of (inputs, outputs).
Turn the tensors returned by parse_tfrecord into a stack in HWC shape.
Args:
inputs: A dictionary of tensors, keyed by feature name.
Returns:
A tuple of (inputs, outputs).
"""
inputsList = [inputs.get(key) for key in FEATURES]
stacked = tf.stack(inputsList, axis=0)
# Convert from CHW to HWC
stacked = tf.transpose(stacked, [1, 2, 0])
return stacked[:,:,:len(BANDS)], stacked[:,:,len(BANDS):]
def get_dataset(pattern):
"""Function to read, parse and format to tuple a set of input tfrecord files.
Get all the files matching the pattern, parse and convert to tuple.
Args:
pattern: A file pattern to match in a Cloud Storage bucket.
Returns:
A tf.data.Dataset
"""
glob = tf.io.gfile.glob(pattern)
dataset = tf.data.TFRecordDataset(glob, compression_type='GZIP')
dataset = dataset.map(parse_tfrecord, num_parallel_calls=5)
dataset = dataset.map(to_tuple, num_parallel_calls=5)
return dataset | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Use the helpers to read in the training dataset. Print the first record to check. | def get_training_dataset():
"""Get the preprocessed training dataset
Returns:
A tf.data.Dataset of training data.
"""
glob = 'gs://' + BUCKET + '/' + FOLDER + '/' + TRAINING_BASE + '*'
dataset = get_dataset(glob)
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).repeat()
return dataset
training = get_training_dataset()
print(iter(training.take(1)).next()) | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Evaluation data
Now do the same thing to get an evaluation dataset. Note that unlike the training dataset, the evaluation dataset has a batch size of 1, is not repeated and is not shuffled. | def get_eval_dataset():
"""Get the preprocessed evaluation dataset
Returns:
A tf.data.Dataset of evaluation data.
"""
glob = 'gs://' + BUCKET + '/' + FOLDER + '/' + EVAL_BASE + '*'
dataset = get_dataset(glob)
dataset = dataset.batch(1).repeat()
return dataset
evaluation = get_eval_dataset() | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Model
Here we use the Keras implementation of the U-Net model. The U-Net model takes 256x256 pixel patches as input and outputs per-pixel class probability, label or a continuous output. We can implement the model essentially unmodified, but will use mean squared error loss on the sigmoidal output since we are treating this as a regression problem, rather than a classification problem. Since impervious surface fraction is constrained to [0,1], with many values close to zero or one, a saturating activation function is suitable here. | from tensorflow.python.keras import layers
from tensorflow.python.keras import losses
from tensorflow.python.keras import models
from tensorflow.python.keras import metrics
from tensorflow.python.keras import optimizers
def conv_block(input_tensor, num_filters):
encoder = layers.Conv2D(num_filters, (3, 3), padding='same')(input_tensor)
encoder = layers.BatchNormalization()(encoder)
encoder = layers.Activation('relu')(encoder)
encoder = layers.Conv2D(num_filters, (3, 3), padding='same')(encoder)
encoder = layers.BatchNormalization()(encoder)
encoder = layers.Activation('relu')(encoder)
return encoder
def encoder_block(input_tensor, num_filters):
encoder = conv_block(input_tensor, num_filters)
encoder_pool = layers.MaxPooling2D((2, 2), strides=(2, 2))(encoder)
return encoder_pool, encoder
def decoder_block(input_tensor, concat_tensor, num_filters):
decoder = layers.Conv2DTranspose(num_filters, (2, 2), strides=(2, 2), padding='same')(input_tensor)
decoder = layers.concatenate([concat_tensor, decoder], axis=-1)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Activation('relu')(decoder)
decoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Activation('relu')(decoder)
decoder = layers.Conv2D(num_filters, (3, 3), padding='same')(decoder)
decoder = layers.BatchNormalization()(decoder)
decoder = layers.Activation('relu')(decoder)
return decoder
def get_model():
inputs = layers.Input(shape=[None, None, len(BANDS)]) # 256
encoder0_pool, encoder0 = encoder_block(inputs, 32) # 128
encoder1_pool, encoder1 = encoder_block(encoder0_pool, 64) # 64
encoder2_pool, encoder2 = encoder_block(encoder1_pool, 128) # 32
encoder3_pool, encoder3 = encoder_block(encoder2_pool, 256) # 16
encoder4_pool, encoder4 = encoder_block(encoder3_pool, 512) # 8
center = conv_block(encoder4_pool, 1024) # center
decoder4 = decoder_block(center, encoder4, 512) # 16
decoder3 = decoder_block(decoder4, encoder3, 256) # 32
decoder2 = decoder_block(decoder3, encoder2, 128) # 64
decoder1 = decoder_block(decoder2, encoder1, 64) # 128
decoder0 = decoder_block(decoder1, encoder0, 32) # 256
outputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(decoder0)
model = models.Model(inputs=[inputs], outputs=[outputs])
model.compile(
optimizer=optimizers.get(OPTIMIZER),
loss=losses.get(LOSS),
metrics=[metrics.get(metric) for metric in METRICS])
return model | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Training the model
You train a Keras model by calling .fit() on it. Here we're going to train for 10 epochs, which is suitable for demonstration purposes. For production use, you probably want to optimize this parameter, for example through hyperparamter tuning. | m = get_model()
m.fit(
x=training,
epochs=EPOCHS,
steps_per_epoch=int(TRAIN_SIZE / BATCH_SIZE),
validation_data=evaluation,
validation_steps=EVAL_SIZE) | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Note that the notebook VM is sometimes not heavy-duty enough to get through a whole training job, especially if you have a large buffer size or a large number of epochs. You can still use this notebook for training, but may need to set up an alternative VM (learn more) for production use. Alternatively, you can package your code for running large training jobs on Google's AI Platform as described here. The following code loads a pre-trained model, which you can use for predictions right away. | # Load a trained model. 50 epochs. 25 hours. Final RMSE ~0.08.
MODEL_DIR = 'gs://ee-docs-demos/fcnn-demo/trainer/model'
m = tf.keras.models.load_model(MODEL_DIR)
m.summary() | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Prediction
The prediction pipeline is:
Export imagery on which to do predictions from Earth Engine in TFRecord format to a Cloud Storge bucket.
Use the trained model to make the predictions.
Write the predictions to a TFRecord file in a Cloud Storage.
Upload the predictions TFRecord file to Earth Engine.
The following functions handle this process. It's useful to separate the export from the predictions so that you can experiment with different models without running the export every time. | def doExport(out_image_base, kernel_buffer, region):
"""Run the image export task. Block until complete.
"""
task = ee.batch.Export.image.toCloudStorage(
image = image.select(BANDS),
description = out_image_base,
bucket = BUCKET,
fileNamePrefix = FOLDER + '/' + out_image_base,
region = region.getInfo()['coordinates'],
scale = 30,
fileFormat = 'TFRecord',
maxPixels = 1e10,
formatOptions = {
'patchDimensions': KERNEL_SHAPE,
'kernelSize': kernel_buffer,
'compressed': True,
'maxFileSize': 104857600
}
)
task.start()
# Block until the task completes.
print('Running image export to Cloud Storage...')
import time
while task.active():
time.sleep(30)
# Error condition
if task.status()['state'] != 'COMPLETED':
print('Error with image export.')
else:
print('Image export completed.')
def doPrediction(out_image_base, user_folder, kernel_buffer, region):
"""Perform inference on exported imagery, upload to Earth Engine.
"""
print('Looking for TFRecord files...')
# Get a list of all the files in the output bucket.
filesList = !gsutil ls 'gs://'{BUCKET}'/'{FOLDER}
# Get only the files generated by the image export.
exportFilesList = [s for s in filesList if out_image_base in s]
# Get the list of image files and the JSON mixer file.
imageFilesList = []
jsonFile = None
for f in exportFilesList:
if f.endswith('.tfrecord.gz'):
imageFilesList.append(f)
elif f.endswith('.json'):
jsonFile = f
# Make sure the files are in the right order.
imageFilesList.sort()
from pprint import pprint
pprint(imageFilesList)
print(jsonFile)
import json
# Load the contents of the mixer file to a JSON object.
jsonText = !gsutil cat {jsonFile}
# Get a single string w/ newlines from the IPython.utils.text.SList
mixer = json.loads(jsonText.nlstr)
pprint(mixer)
patches = mixer['totalPatches']
# Get set up for prediction.
x_buffer = int(kernel_buffer[0] / 2)
y_buffer = int(kernel_buffer[1] / 2)
buffered_shape = [
KERNEL_SHAPE[0] + kernel_buffer[0],
KERNEL_SHAPE[1] + kernel_buffer[1]]
imageColumns = [
tf.io.FixedLenFeature(shape=buffered_shape, dtype=tf.float32)
for k in BANDS
]
imageFeaturesDict = dict(zip(BANDS, imageColumns))
def parse_image(example_proto):
return tf.io.parse_single_example(example_proto, imageFeaturesDict)
def toTupleImage(inputs):
inputsList = [inputs.get(key) for key in BANDS]
stacked = tf.stack(inputsList, axis=0)
stacked = tf.transpose(stacked, [1, 2, 0])
return stacked
# Create a dataset from the TFRecord file(s) in Cloud Storage.
imageDataset = tf.data.TFRecordDataset(imageFilesList, compression_type='GZIP')
imageDataset = imageDataset.map(parse_image, num_parallel_calls=5)
imageDataset = imageDataset.map(toTupleImage).batch(1)
# Perform inference.
print('Running predictions...')
predictions = m.predict(imageDataset, steps=patches, verbose=1)
# print(predictions[0])
print('Writing predictions...')
out_image_file = 'gs://' + BUCKET + '/' + FOLDER + '/' + out_image_base + '.TFRecord'
writer = tf.io.TFRecordWriter(out_image_file)
patches = 0
for predictionPatch in predictions:
print('Writing patch ' + str(patches) + '...')
predictionPatch = predictionPatch[
x_buffer:x_buffer+KERNEL_SIZE, y_buffer:y_buffer+KERNEL_SIZE]
# Create an example.
example = tf.train.Example(
features=tf.train.Features(
feature={
'impervious': tf.train.Feature(
float_list=tf.train.FloatList(
value=predictionPatch.flatten()))
}
)
)
# Write the example.
writer.write(example.SerializeToString())
patches += 1
writer.close()
# Start the upload.
out_image_asset = user_folder + '/' + out_image_base
!earthengine upload image --asset_id={out_image_asset} {out_image_file} {jsonFile} | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Now there's all the code needed to run the prediction pipeline, all that remains is to specify the output region in which to do the prediction, the names of the output files, where to put them, and the shape of the outputs. In terms of the shape, the model is trained on 256x256 patches, but can work (in theory) on any patch that's big enough with even dimensions (reference). Because of tile boundary artifacts, give the model slightly larger patches for prediction, then clip out the middle 256x256 patch. This is controlled with a kernel buffer, half the size of which will extend beyond the kernel buffer. For example, specifying a 128x128 kernel will append 64 pixels on each side of the patch, to ensure that the pixels in the output are taken from inputs completely covered by the kernel. | # Output assets folder: YOUR FOLDER
user_folder = 'users/username' # INSERT YOUR FOLDER HERE.
# Base file name to use for TFRecord files and assets.
bj_image_base = 'FCNN_demo_beijing_384_'
# Half this will extend on the sides of each patch.
bj_kernel_buffer = [128, 128]
# Beijing
bj_region = ee.Geometry.Polygon(
[[[115.9662455210937, 40.121362012835235],
[115.9662455210937, 39.64293313749715],
[117.01818643906245, 39.64293313749715],
[117.01818643906245, 40.121362012835235]]], None, False)
# Run the export.
doExport(bj_image_base, bj_kernel_buffer, bj_region)
# Run the prediction.
doPrediction(bj_image_base, user_folder, bj_kernel_buffer, bj_region) | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Display the output
One the data has been exported, the model has made predictions and the predictions have been written to a file, and the image imported to Earth Engine, it's possible to display the resultant Earth Engine asset. Here, display the impervious area predictions over Beijing, China. | out_image = ee.Image(user_folder + '/' + bj_image_base)
mapid = out_image.getMapId({'min': 0, 'max': 1})
map = folium.Map(location=[39.898, 116.5097])
folium.TileLayer(
tiles=mapid['tile_fetcher'].url_format,
attr='Map Data © <a href="https://earthengine.google.com/">Google Earth Engine</a>',
overlay=True,
name='predicted impervious',
).add_to(map)
map.add_child(folium.LayerControl())
map | python/examples/ipynb/UNET_regression_demo.ipynb | google/earthengine-api | apache-2.0 |
Document Table of Contents
1. Key Properties --> Overview
2. Key Properties --> Resolution
3. Key Properties --> Timestepping
4. Key Properties --> Orography
5. Grid --> Discretisation
6. Grid --> Discretisation --> Horizontal
7. Grid --> Discretisation --> Vertical
8. Dynamical Core
9. Dynamical Core --> Top Boundary
10. Dynamical Core --> Lateral Boundary
11. Dynamical Core --> Diffusion Horizontal
12. Dynamical Core --> Advection Tracers
13. Dynamical Core --> Advection Momentum
14. Radiation
15. Radiation --> Shortwave Radiation
16. Radiation --> Shortwave GHG
17. Radiation --> Shortwave Cloud Ice
18. Radiation --> Shortwave Cloud Liquid
19. Radiation --> Shortwave Cloud Inhomogeneity
20. Radiation --> Shortwave Aerosols
21. Radiation --> Shortwave Gases
22. Radiation --> Longwave Radiation
23. Radiation --> Longwave GHG
24. Radiation --> Longwave Cloud Ice
25. Radiation --> Longwave Cloud Liquid
26. Radiation --> Longwave Cloud Inhomogeneity
27. Radiation --> Longwave Aerosols
28. Radiation --> Longwave Gases
29. Turbulence Convection
30. Turbulence Convection --> Boundary Layer Turbulence
31. Turbulence Convection --> Deep Convection
32. Turbulence Convection --> Shallow Convection
33. Microphysics Precipitation
34. Microphysics Precipitation --> Large Scale Precipitation
35. Microphysics Precipitation --> Large Scale Cloud Microphysics
36. Cloud Scheme
37. Cloud Scheme --> Optical Cloud Properties
38. Cloud Scheme --> Sub Grid Scale Water Distribution
39. Cloud Scheme --> Sub Grid Scale Ice Distribution
40. Observation Simulation
41. Observation Simulation --> Isscp Attributes
42. Observation Simulation --> Cosp Attributes
43. Observation Simulation --> Radar Inputs
44. Observation Simulation --> Lidar Inputs
45. Gravity Waves
46. Gravity Waves --> Orographic Gravity Waves
47. Gravity Waves --> Non Orographic Gravity Waves
48. Solar
49. Solar --> Solar Pathways
50. Solar --> Solar Constant
51. Solar --> Orbital Parameters
52. Solar --> Insolation Ozone
53. Volcanos
54. Volcanos --> Volcanoes Treatment
1. Key Properties --> Overview
Top level key properties
1.1. Model Overview
Is Required: TRUE Type: STRING Cardinality: 1.1
Overview of atmosphere model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.overview.model_overview')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.2. Model Name
Is Required: TRUE Type: STRING Cardinality: 1.1
Name of atmosphere model code (CAM 4.0, ARPEGE 3.2,...) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.overview.model_name')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.3. Model Family
Is Required: TRUE Type: ENUM Cardinality: 1.1
Type of atmospheric model. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.overview.model_family')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "AGCM"
# "ARCM"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
1.4. Basic Approximations
Is Required: TRUE Type: ENUM Cardinality: 1.N
Basic approximations made in the atmosphere. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.overview.basic_approximations')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "primitive equations"
# "non-hydrostatic"
# "anelastic"
# "Boussinesq"
# "hydrostatic"
# "quasi-hydrostatic"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
2. Key Properties --> Resolution
Characteristics of the model resolution
2.1. Horizontal Resolution Name
Is Required: TRUE Type: STRING Cardinality: 1.1
This is a string usually used by the modelling group to describe the resolution of the model grid, e.g. T42, N48. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.resolution.horizontal_resolution_name')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
2.2. Canonical Horizontal Resolution
Is Required: TRUE Type: STRING Cardinality: 1.1
Expression quoted for gross comparisons of resolution, e.g. 2.5 x 3.75 degrees lat-lon. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.resolution.canonical_horizontal_resolution')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
2.3. Range Horizontal Resolution
Is Required: TRUE Type: STRING Cardinality: 1.1
Range of horizontal resolution with spatial details, eg. 1 deg (Equator) - 0.5 deg | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.resolution.range_horizontal_resolution')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
2.4. Number Of Vertical Levels
Is Required: TRUE Type: INTEGER Cardinality: 1.1
Number of vertical levels resolved on the computational grid. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.resolution.number_of_vertical_levels')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
2.5. High Top
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Does the atmosphere have a high-top? High-Top atmospheres have a fully resolved stratosphere with a model top above the stratopause. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.resolution.high_top')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
3. Key Properties --> Timestepping
Characteristics of the atmosphere model time stepping
3.1. Timestep Dynamics
Is Required: TRUE Type: STRING Cardinality: 1.1
Timestep for the dynamics, e.g. 30 min. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_dynamics')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
3.2. Timestep Shortwave Radiative Transfer
Is Required: FALSE Type: STRING Cardinality: 0.1
Timestep for the shortwave radiative transfer, e.g. 1.5 hours. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_shortwave_radiative_transfer')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
3.3. Timestep Longwave Radiative Transfer
Is Required: FALSE Type: STRING Cardinality: 0.1
Timestep for the longwave radiative transfer, e.g. 3 hours. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_longwave_radiative_transfer')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
4. Key Properties --> Orography
Characteristics of the model orography
4.1. Type
Is Required: TRUE Type: ENUM Cardinality: 1.1
Time adaptation of the orography. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.orography.type')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "present day"
# "modified"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
4.2. Changes
Is Required: TRUE Type: ENUM Cardinality: 1.N
If the orography type is modified describe the time adaptation changes. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.key_properties.orography.changes')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "related to ice sheets"
# "related to tectonics"
# "modified mean"
# "modified variance if taken into account in model (cf gravity waves)"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
5. Grid --> Discretisation
Atmosphere grid discretisation
5.1. Overview
Is Required: TRUE Type: STRING Cardinality: 1.1
Overview description of grid discretisation in the atmosphere | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.grid.discretisation.overview')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6. Grid --> Discretisation --> Horizontal
Atmosphere discretisation in the horizontal
6.1. Scheme Type
Is Required: TRUE Type: ENUM Cardinality: 1.1
Horizontal discretisation type | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_type')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "spectral"
# "fixed grid"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6.2. Scheme Method
Is Required: TRUE Type: ENUM Cardinality: 1.1
Horizontal discretisation method | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_method')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "finite elements"
# "finite volumes"
# "finite difference"
# "centered finite difference"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6.3. Scheme Order
Is Required: TRUE Type: ENUM Cardinality: 1.1
Horizontal discretisation function order | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_order')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "second"
# "third"
# "fourth"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6.4. Horizontal Pole
Is Required: FALSE Type: ENUM Cardinality: 0.1
Horizontal discretisation pole singularity treatment | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.horizontal_pole')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "filter"
# "pole rotation"
# "artificial island"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6.5. Grid Type
Is Required: TRUE Type: ENUM Cardinality: 1.1
Horizontal grid type | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.grid_type')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Gaussian"
# "Latitude-Longitude"
# "Cubed-Sphere"
# "Icosahedral"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7. Grid --> Discretisation --> Vertical
Atmosphere discretisation in the vertical
7.1. Coordinate Type
Is Required: TRUE Type: ENUM Cardinality: 1.N
Type of vertical coordinate system | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.grid.discretisation.vertical.coordinate_type')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "isobaric"
# "sigma"
# "hybrid sigma-pressure"
# "hybrid pressure"
# "vertically lagrangian"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8. Dynamical Core
Characteristics of the dynamical core
8.1. Overview
Is Required: TRUE Type: STRING Cardinality: 1.1
Overview description of atmosphere dynamical core | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.overview')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.2. Name
Is Required: FALSE Type: STRING Cardinality: 0.1
Commonly used name for the dynamical core of the model. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.name')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.3. Timestepping Type
Is Required: TRUE Type: ENUM Cardinality: 1.1
Timestepping framework type | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.timestepping_type')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Adams-Bashforth"
# "explicit"
# "implicit"
# "semi-implicit"
# "leap frog"
# "multi-step"
# "Runge Kutta fifth order"
# "Runge Kutta second order"
# "Runge Kutta third order"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.4. Prognostic Variables
Is Required: TRUE Type: ENUM Cardinality: 1.N
List of the model prognostic variables | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.prognostic_variables')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "surface pressure"
# "wind components"
# "divergence/curl"
# "temperature"
# "potential temperature"
# "total water"
# "water vapour"
# "water liquid"
# "water ice"
# "total water moments"
# "clouds"
# "radiation"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9. Dynamical Core --> Top Boundary
Type of boundary layer at the top of the model
9.1. Top Boundary Condition
Is Required: TRUE Type: ENUM Cardinality: 1.1
Top boundary condition | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_boundary_condition')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "sponge layer"
# "radiation boundary condition"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.2. Top Heat
Is Required: TRUE Type: STRING Cardinality: 1.1
Top boundary heat treatment | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_heat')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9.3. Top Wind
Is Required: TRUE Type: STRING Cardinality: 1.1
Top boundary wind treatment | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_wind')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10. Dynamical Core --> Lateral Boundary
Type of lateral boundary condition (if the model is a regional model)
10.1. Condition
Is Required: FALSE Type: ENUM Cardinality: 0.1
Type of lateral boundary condition | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.lateral_boundary.condition')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "sponge layer"
# "radiation boundary condition"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11. Dynamical Core --> Diffusion Horizontal
Horizontal diffusion scheme
11.1. Scheme Name
Is Required: FALSE Type: STRING Cardinality: 0.1
Horizontal diffusion scheme name | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_name')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11.2. Scheme Method
Is Required: TRUE Type: ENUM Cardinality: 1.1
Horizontal diffusion scheme method | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_method')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "iterated Laplacian"
# "bi-harmonic"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12. Dynamical Core --> Advection Tracers
Tracer advection scheme
12.1. Scheme Name
Is Required: FALSE Type: ENUM Cardinality: 0.1
Tracer advection scheme name | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_name')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Heun"
# "Roe and VanLeer"
# "Roe and Superbee"
# "Prather"
# "UTOPIA"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12.2. Scheme Characteristics
Is Required: TRUE Type: ENUM Cardinality: 1.N
Tracer advection scheme characteristics | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_characteristics')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Eulerian"
# "modified Euler"
# "Lagrangian"
# "semi-Lagrangian"
# "cubic semi-Lagrangian"
# "quintic semi-Lagrangian"
# "mass-conserving"
# "finite volume"
# "flux-corrected"
# "linear"
# "quadratic"
# "quartic"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12.3. Conserved Quantities
Is Required: TRUE Type: ENUM Cardinality: 1.N
Tracer advection scheme conserved quantities | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conserved_quantities')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "dry mass"
# "tracer mass"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12.4. Conservation Method
Is Required: TRUE Type: ENUM Cardinality: 1.1
Tracer advection scheme conservation method | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conservation_method')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "conservation fixer"
# "Priestley algorithm"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13. Dynamical Core --> Advection Momentum
Momentum advection scheme
13.1. Scheme Name
Is Required: FALSE Type: ENUM Cardinality: 0.1
Momentum advection schemes name | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_name')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "VanLeer"
# "Janjic"
# "SUPG (Streamline Upwind Petrov-Galerkin)"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.2. Scheme Characteristics
Is Required: TRUE Type: ENUM Cardinality: 1.N
Momentum advection scheme characteristics | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_characteristics')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "2nd order"
# "4th order"
# "cell-centred"
# "staggered grid"
# "semi-staggered grid"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.3. Scheme Staggering Type
Is Required: TRUE Type: ENUM Cardinality: 1.1
Momentum advection scheme staggering type | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_staggering_type')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Arakawa B-grid"
# "Arakawa C-grid"
# "Arakawa D-grid"
# "Arakawa E-grid"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.4. Conserved Quantities
Is Required: TRUE Type: ENUM Cardinality: 1.N
Momentum advection scheme conserved quantities | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conserved_quantities')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Angular momentum"
# "Horizontal momentum"
# "Enstrophy"
# "Mass"
# "Total energy"
# "Vorticity"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.5. Conservation Method
Is Required: TRUE Type: ENUM Cardinality: 1.1
Momentum advection scheme conservation method | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conservation_method')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "conservation fixer"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14. Radiation
Characteristics of the atmosphere radiation process
14.1. Aerosols
Is Required: TRUE Type: ENUM Cardinality: 1.N
Aerosols whose radiative effect is taken into account in the atmosphere model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.aerosols')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "sulphate"
# "nitrate"
# "sea salt"
# "dust"
# "ice"
# "organic"
# "BC (black carbon / soot)"
# "SOA (secondary organic aerosols)"
# "POM (particulate organic matter)"
# "polar stratospheric ice"
# "NAT (nitric acid trihydrate)"
# "NAD (nitric acid dihydrate)"
# "STS (supercooled ternary solution aerosol particle)"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15. Radiation --> Shortwave Radiation
Properties of the shortwave radiation scheme
15.1. Overview
Is Required: TRUE Type: STRING Cardinality: 1.1
Overview description of shortwave radiation in the atmosphere | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.overview')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.2. Name
Is Required: FALSE Type: STRING Cardinality: 0.1
Commonly used name for the shortwave radiation scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.name')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.3. Spectral Integration
Is Required: TRUE Type: ENUM Cardinality: 1.1
Shortwave radiation scheme spectral integration | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_integration')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "wide-band model"
# "correlated-k"
# "exponential sum fitting"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.4. Transport Calculation
Is Required: TRUE Type: ENUM Cardinality: 1.N
Shortwave radiation transport calculation methods | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.transport_calculation')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "two-stream"
# "layer interaction"
# "bulk"
# "adaptive"
# "multi-stream"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.5. Spectral Intervals
Is Required: TRUE Type: INTEGER Cardinality: 1.1
Shortwave radiation scheme number of spectral intervals | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_intervals')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
16. Radiation --> Shortwave GHG
Representation of greenhouse gases in the shortwave radiation scheme
16.1. Greenhouse Gas Complexity
Is Required: TRUE Type: ENUM Cardinality: 1.N
Complexity of greenhouse gases whose shortwave radiative effects are taken into account in the atmosphere model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.greenhouse_gas_complexity')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "CO2"
# "CH4"
# "N2O"
# "CFC-11 eq"
# "CFC-12 eq"
# "HFC-134a eq"
# "Explicit ODSs"
# "Explicit other fluorinated gases"
# "O3"
# "H2O"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
16.2. ODS
Is Required: FALSE Type: ENUM Cardinality: 0.N
Ozone depleting substances whose shortwave radiative effects are explicitly taken into account in the atmosphere model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.ODS')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "CFC-12"
# "CFC-11"
# "CFC-113"
# "CFC-114"
# "CFC-115"
# "HCFC-22"
# "HCFC-141b"
# "HCFC-142b"
# "Halon-1211"
# "Halon-1301"
# "Halon-2402"
# "methyl chloroform"
# "carbon tetrachloride"
# "methyl chloride"
# "methylene chloride"
# "chloroform"
# "methyl bromide"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
16.3. Other Flourinated Gases
Is Required: FALSE Type: ENUM Cardinality: 0.N
Other flourinated gases whose shortwave radiative effects are explicitly taken into account in the atmosphere model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.other_flourinated_gases')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "HFC-134a"
# "HFC-23"
# "HFC-32"
# "HFC-125"
# "HFC-143a"
# "HFC-152a"
# "HFC-227ea"
# "HFC-236fa"
# "HFC-245fa"
# "HFC-365mfc"
# "HFC-43-10mee"
# "CF4"
# "C2F6"
# "C3F8"
# "C4F10"
# "C5F12"
# "C6F14"
# "C7F16"
# "C8F18"
# "c-C4F8"
# "NF3"
# "SF6"
# "SO2F2"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
17. Radiation --> Shortwave Cloud Ice
Shortwave radiative properties of ice crystals in clouds
17.1. General Interactions
Is Required: TRUE Type: ENUM Cardinality: 1.N
General shortwave radiative interactions with cloud ice crystals | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.general_interactions')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "scattering"
# "emission/absorption"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
17.2. Physical Representation
Is Required: TRUE Type: ENUM Cardinality: 1.N
Physical representation of cloud ice crystals in the shortwave radiation scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.physical_representation')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "bi-modal size distribution"
# "ensemble of ice crystals"
# "mean projected area"
# "ice water path"
# "crystal asymmetry"
# "crystal aspect ratio"
# "effective crystal radius"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
17.3. Optical Methods
Is Required: TRUE Type: ENUM Cardinality: 1.N
Optical methods applicable to cloud ice crystals in the shortwave radiation scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.optical_methods')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "T-matrix"
# "geometric optics"
# "finite difference time domain (FDTD)"
# "Mie theory"
# "anomalous diffraction approximation"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
18. Radiation --> Shortwave Cloud Liquid
Shortwave radiative properties of liquid droplets in clouds
18.1. General Interactions
Is Required: TRUE Type: ENUM Cardinality: 1.N
General shortwave radiative interactions with cloud liquid droplets | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.general_interactions')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "scattering"
# "emission/absorption"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
18.2. Physical Representation
Is Required: TRUE Type: ENUM Cardinality: 1.N
Physical representation of cloud liquid droplets in the shortwave radiation scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.physical_representation')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "cloud droplet number concentration"
# "effective cloud droplet radii"
# "droplet size distribution"
# "liquid water path"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
18.3. Optical Methods
Is Required: TRUE Type: ENUM Cardinality: 1.N
Optical methods applicable to cloud liquid droplets in the shortwave radiation scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.optical_methods')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "geometric optics"
# "Mie theory"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
19. Radiation --> Shortwave Cloud Inhomogeneity
Cloud inhomogeneity in the shortwave radiation scheme
19.1. Cloud Inhomogeneity
Is Required: TRUE Type: ENUM Cardinality: 1.1
Method for taking into account horizontal cloud inhomogeneity | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_inhomogeneity.cloud_inhomogeneity')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Monte Carlo Independent Column Approximation"
# "Triplecloud"
# "analytic"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
20. Radiation --> Shortwave Aerosols
Shortwave radiative properties of aerosols
20.1. General Interactions
Is Required: TRUE Type: ENUM Cardinality: 1.N
General shortwave radiative interactions with aerosols | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.general_interactions')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "scattering"
# "emission/absorption"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
20.2. Physical Representation
Is Required: TRUE Type: ENUM Cardinality: 1.N
Physical representation of aerosols in the shortwave radiation scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.physical_representation')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "number concentration"
# "effective radii"
# "size distribution"
# "asymmetry"
# "aspect ratio"
# "mixing state"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
20.3. Optical Methods
Is Required: TRUE Type: ENUM Cardinality: 1.N
Optical methods applicable to aerosols in the shortwave radiation scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.optical_methods')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "T-matrix"
# "geometric optics"
# "finite difference time domain (FDTD)"
# "Mie theory"
# "anomalous diffraction approximation"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
21. Radiation --> Shortwave Gases
Shortwave radiative properties of gases
21.1. General Interactions
Is Required: TRUE Type: ENUM Cardinality: 1.N
General shortwave radiative interactions with gases | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.shortwave_gases.general_interactions')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "scattering"
# "emission/absorption"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22. Radiation --> Longwave Radiation
Properties of the longwave radiation scheme
22.1. Overview
Is Required: TRUE Type: STRING Cardinality: 1.1
Overview description of longwave radiation in the atmosphere | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_radiation.overview')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22.2. Name
Is Required: FALSE Type: STRING Cardinality: 0.1
Commonly used name for the longwave radiation scheme. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_radiation.name')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22.3. Spectral Integration
Is Required: TRUE Type: ENUM Cardinality: 1.1
Longwave radiation scheme spectral integration | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_integration')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "wide-band model"
# "correlated-k"
# "exponential sum fitting"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22.4. Transport Calculation
Is Required: TRUE Type: ENUM Cardinality: 1.N
Longwave radiation transport calculation methods | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_radiation.transport_calculation')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "two-stream"
# "layer interaction"
# "bulk"
# "adaptive"
# "multi-stream"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22.5. Spectral Intervals
Is Required: TRUE Type: INTEGER Cardinality: 1.1
Longwave radiation scheme number of spectral intervals | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_intervals')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
23. Radiation --> Longwave GHG
Representation of greenhouse gases in the longwave radiation scheme
23.1. Greenhouse Gas Complexity
Is Required: TRUE Type: ENUM Cardinality: 1.N
Complexity of greenhouse gases whose longwave radiative effects are taken into account in the atmosphere model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_GHG.greenhouse_gas_complexity')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "CO2"
# "CH4"
# "N2O"
# "CFC-11 eq"
# "CFC-12 eq"
# "HFC-134a eq"
# "Explicit ODSs"
# "Explicit other fluorinated gases"
# "O3"
# "H2O"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
23.2. ODS
Is Required: FALSE Type: ENUM Cardinality: 0.N
Ozone depleting substances whose longwave radiative effects are explicitly taken into account in the atmosphere model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_GHG.ODS')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "CFC-12"
# "CFC-11"
# "CFC-113"
# "CFC-114"
# "CFC-115"
# "HCFC-22"
# "HCFC-141b"
# "HCFC-142b"
# "Halon-1211"
# "Halon-1301"
# "Halon-2402"
# "methyl chloroform"
# "carbon tetrachloride"
# "methyl chloride"
# "methylene chloride"
# "chloroform"
# "methyl bromide"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
23.3. Other Flourinated Gases
Is Required: FALSE Type: ENUM Cardinality: 0.N
Other flourinated gases whose longwave radiative effects are explicitly taken into account in the atmosphere model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_GHG.other_flourinated_gases')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "HFC-134a"
# "HFC-23"
# "HFC-32"
# "HFC-125"
# "HFC-143a"
# "HFC-152a"
# "HFC-227ea"
# "HFC-236fa"
# "HFC-245fa"
# "HFC-365mfc"
# "HFC-43-10mee"
# "CF4"
# "C2F6"
# "C3F8"
# "C4F10"
# "C5F12"
# "C6F14"
# "C7F16"
# "C8F18"
# "c-C4F8"
# "NF3"
# "SF6"
# "SO2F2"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
24. Radiation --> Longwave Cloud Ice
Longwave radiative properties of ice crystals in clouds
24.1. General Interactions
Is Required: TRUE Type: ENUM Cardinality: 1.N
General longwave radiative interactions with cloud ice crystals | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.general_interactions')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "scattering"
# "emission/absorption"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
24.2. Physical Reprenstation
Is Required: TRUE Type: ENUM Cardinality: 1.N
Physical representation of cloud ice crystals in the longwave radiation scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.physical_reprenstation')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "bi-modal size distribution"
# "ensemble of ice crystals"
# "mean projected area"
# "ice water path"
# "crystal asymmetry"
# "crystal aspect ratio"
# "effective crystal radius"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
24.3. Optical Methods
Is Required: TRUE Type: ENUM Cardinality: 1.N
Optical methods applicable to cloud ice crystals in the longwave radiation scheme | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.optical_methods')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "T-matrix"
# "geometric optics"
# "finite difference time domain (FDTD)"
# "Mie theory"
# "anomalous diffraction approximation"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
25. Radiation --> Longwave Cloud Liquid
Longwave radiative properties of liquid droplets in clouds
25.1. General Interactions
Is Required: TRUE Type: ENUM Cardinality: 1.N
General longwave radiative interactions with cloud liquid droplets | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.general_interactions')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "scattering"
# "emission/absorption"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
Subsets and Splits