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Update LPS_training_exercise2.ipynb #3

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8 changes: 4 additions & 4 deletions LPS_training_exercise2.ipynb
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Expand Up @@ -248,7 +248,7 @@
"id": "tsWrx8i9W235"
},
"source": [
"Uncertainty variables have an assocaited error-correlation structure -- since we didn't define this for `u_noise`, it is assumed to be random (i.e., errors are uncorrelated between pixels).\n",
"Uncertainty variables have an associated error-correlation structure -- since we didn't define this for `u_noise`, it is assumed to be random (i.e., errors are uncorrelated between pixels).\n",
"\n",
"Next let's add a calibration uncertainty component, `u_bt_cal`, with a more complicated error-correlation structure using the `err_corr` attribute. This uses the [error-correlation parameterisations](https://comet-toolkit.github.io/unc_website/specification/draft-v0.1/unc_specification.html#appendix-a-error-correlation-parameterisations) defined by the draft UNC Specfication (it is also possible to add custom error-correlation parameterisations).\n",
"\n",
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"\n",
"Thermal Infrared multi-spectral data, like our example AVHRR dataset, is used to develop Level 2 (L2) Climate Data Records (CDRs) such as Sea or Land Surface Temperature (SST or LST). SST/LST retriavals account for the atmosphere to evaluate the surface temperature from the top of atmosphere L1 brightness temperature.\n",
"\n",
"A widely approach for this is called the \"split window\" method. A simplified form of this algorithm could be represented as,\n",
"A widely used approach for this is called the \"split window\" method. A simplified form of this algorithm could be represented as,\n",
"\n",
"$SST = a T_{11} - b T_{12}$\n",
"\n",
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"\n",
"**punpy**'s [`MeasurementFunction`](https://punpy.readthedocs.io/en/latest/content/punpy_digital_effects_table.html#measurement-function) class enables a much simpler method for propagating the uncertainties of measurement datasets defined using **obsarray**. It is an alternative interface to the **punpy** propagation functions we used in Step 3.\n",
"\n",
"For this approach instead defining the measurement function as a python function, we define a measurement function class which should be a subclass of the **punpy** `MeasurementFunction` class. We can then use the class's [`propagate_ds`](https://punpy.readthedocs.io/en/latest/content/punpy_digital_effects_table.html#functions-for-propagating-uncertainties) method to propagate all dataset uncertainties in one go!"
"For this approach instead of defining the measurement function as a python function, we define a measurement function class which should be a subclass of the **punpy** `MeasurementFunction` class. We can then use the class's [`propagate_ds`](https://punpy.readthedocs.io/en/latest/content/punpy_digital_effects_table.html#functions-for-propagating-uncertainties) method to propagate all dataset uncertainties in one go!"
]
},
{
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"cell_type": "markdown",
"metadata": {},
"source": [
"# **link to Next Exercise**\n",
"# **Link to next exercise**\n",
"\n",
"In [Exercise 3](https://colab.research.google.com/github/comet-toolkit/comet_training/blob/main/LPS_training_exercise3.ipynb) you will either try to apply these tools to propagate uncertainties for your own usecase, or alternatively try to implement uncertainty propagation for a usecase we have provided. "
]
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