markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
values |
|---|---|---|---|---|
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... | # 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 a... | 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.load... | 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 vi... | ## 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... | # 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 = opticalBan... | 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... | 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 ... | 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-ba... | 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, '... | 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 exp... | # 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 ... | 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)
... | 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 = ... | 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 treati... | 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='s... | 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 packa... | # 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 followin... | 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... | 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... | # 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(
... | 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=T... | 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. Dynam... | # 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"
# ... | 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 mo... | 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 - pleas... | 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 val... | 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... | 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"
#... | 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"
# "Run... | 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"
# ... | 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 spe... | 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-Lagran... | 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 - ... | 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... | 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: [Pl... | 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"
#... | 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 ... | 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 ... | 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... | 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 t... | # 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... | 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-14... | 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-2... | 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... | 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"
# "anom... | 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 distributio... | 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: [Plea... | 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... | 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"
# "anoma... | 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 -... | 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:... | 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 ... | # 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 ... | 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-142... | 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-22... | 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 p... | 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"
# "anoma... | 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
No community queries yet
The top public SQL queries from the community will appear here once available.