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Update lab 6
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52 changes: 31 additions & 21 deletions book/labs/lab_06.ipynb
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"* Reproject, clip, mask, and resample raster datasets.\n",
"* Export processed data to various formats such as NetCDF and GeoTIFF.\n",
"\n",
"## Exercise 1: Exploring a New Dataset\n",
"## Exercise 1: Exploring the Sea Surface Temperature Dataset\n",
"\n",
"1. Load the Xarray tutorial dataset `rasm`.\n",
"2. Inspect the `Dataset` object and list all the variables and dimensions.\n",
"3. Select the `Tair` variable (air temperature).\n",
"4. Print the attributes, dimensions, and coordinates of `Tair`."
"1. **Load the sea surface temperature dataset** from the NetCDF file ([`sea_surface_temperature.nc`](https://github.com/opengeos/datasets/releases/download/netcdf/sea_surface_temperature.nc)).\n",
"2. **Inspect the `Dataset` object** and list all the available variables and dimensions in the dataset.\n",
"3. **Select the `sst` variable** (sea surface temperature).\n",
"4. **Print the attributes, dimensions, and coordinates** of the `sst` variable to understand the metadata.\n",
"\n",
"This exercise allows you to practice selecting specific subsets of data and visualizing SST patterns over a specified period."
]
},
{
Expand All @@ -40,9 +42,11 @@
"source": [
"## Exercise 2: Data Selection and Indexing\n",
"\n",
"1. Select a subset of the `Tair` data for the date `1980-07-01` and latitude `70.0`.\n",
"2. Create a time slice for the entire latitude range between January and March of 1980.\n",
"3. Plot the selected time slice as a line plot."
"1. **Select a subset of the `sst` data** for a specific time (`2010-07-01`) and latitude (`0.0`), which represents the Equator.\n",
"2. **Create a time slice** for the SST data between January and March 2010 for all latitudes and longitudes.\n",
"3. **Plot the time slice** as a line plot, showing the latitude-averaged SST over time.\n",
"\n",
"This exercise allows you to practice selecting specific subsets of data and visualizing SST patterns over a specified period."
]
},
{
Expand All @@ -58,9 +62,11 @@
"source": [
"## Exercise 3: Performing Arithmetic Operations\n",
"\n",
"1. Compute the mean of the `Tair` data over the `time` dimension.\n",
"2. Subtract the computed mean from the original `Tair` dataset to get the temperature anomalies.\n",
"3. Plot the mean temperature and the anomalies on separate plots."
"1. **Compute the mean SST** over the entire time range (2010-2015) to obtain the average sea surface temperature for each spatial location.\n",
"2. **Calculate the temperature anomalies** by subtracting the computed mean from the original SST values. This helps understand how SST deviates from the mean during the time period.\n",
"3. **Plot both the mean SST and the anomalies** on separate plots to visualize spatial temperature patterns and deviations. You can select a specific time to display the anomalies.\n",
"\n",
"This exercise introduces arithmetic operations on the dataset, focusing on the concept of temperature anomalies."
]
},
{
Expand All @@ -76,9 +82,11 @@
"source": [
"## Exercise 4: GroupBy and Resampling\n",
"\n",
"1. Use `groupby` to calculate the seasonal mean temperature (`Tair`).\n",
"2. Use `resample` to calculate the monthly mean temperature for 1980.\n",
"3. Plot the seasonal mean for each season and the monthly mean."
"1. **Use `groupby` to calculate the seasonal mean SST**. Group the data by season (`DJF`, `MAM`, `JJA`, and `SON`) and compute the average SST for each season.\n",
"2. **Resample the dataset to compute the monthly mean SST**. This aggregates the data on a monthly basis.\n",
"3. **Plot the seasonal mean SST and the monthly mean SST** to visualize how sea surface temperature varies by season and by month.\n",
"\n",
"This exercise demonstrates how to group and resample time-series data, commonly used in climate data analysis."
]
},
{
Expand All @@ -92,12 +100,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 5: Writing Data to netCDF\n",
"## Exercise 5: Writing Data to NetCDF\n",
"\n",
"1. **Select the SST anomalies** calculated in Exercise 3 for further analysis and export.\n",
"2. **Convert the `sst` variable to `float32`** to optimize file size before writing the data to a NetCDF file.\n",
"3. **Write the anomalies data** to a new NetCDF file named `sst_anomalies.nc` for storage and future use.\n",
"4. **Load the saved NetCDF file** back into memory and print its contents to verify the saved data.\n",
"\n",
"1. Select the temperature anomalies calculated in Exercise 3.\n",
"2. Convert the `Tair` variable to `float32` to optimize file size.\n",
"3. Write the anomalies data to a new netCDF file named `tair_anomalies.nc`.\n",
"4. Load the data back from the file and print its contents."
"This exercise teaches how to export processed geospatial data to NetCDF, a widely-used file format in climate data analysis."
]
},
{
Expand Down Expand Up @@ -168,7 +178,7 @@
"## Exercise 9: Mask the Raster Using a Vector Dataset\n",
"\n",
"1. Load the GeoJSON file at https://github.com/opengeos/datasets/releases/download/raster/Derna_Libya.geojson using `geopandas`.\n",
"2. Use the GeoJSON to mask the raster dataset, keeping only the data within the GeoJSON boundaries.\n",
"2. Use the GeoJSON to mask the reprojected raster dataset, keeping only the data within the GeoJSON boundaries.\n",
"3. Plot the masked raster data."
]
},
Expand Down Expand Up @@ -214,7 +224,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
"version": "3.11.8"
}
},
"nbformat": 4,
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50 changes: 30 additions & 20 deletions book/labs/lab_06.md
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Expand Up @@ -26,53 +26,63 @@ By the end of this lab, you will have a better understanding of how to:
* Reproject, clip, mask, and resample raster datasets.
* Export processed data to various formats such as NetCDF and GeoTIFF.

## Exercise 1: Exploring a New Dataset
## Exercise 1: Exploring the Sea Surface Temperature Dataset

1. Load the Xarray tutorial dataset `rasm`.
2. Inspect the `Dataset` object and list all the variables and dimensions.
3. Select the `Tair` variable (air temperature).
4. Print the attributes, dimensions, and coordinates of `Tair`.
1. **Load the sea surface temperature dataset** from the NetCDF file ([`sea_surface_temperature.nc`](https://github.com/opengeos/datasets/releases/download/netcdf/sea_surface_temperature.nc)).
2. **Inspect the `Dataset` object** and list all the available variables and dimensions in the dataset.
3. **Select the `sst` variable** (sea surface temperature).
4. **Print the attributes, dimensions, and coordinates** of the `sst` variable to understand the metadata.

This exercise allows you to practice selecting specific subsets of data and visualizing SST patterns over a specified period.

```{code-cell} ipython3
```

## Exercise 2: Data Selection and Indexing

1. Select a subset of the `Tair` data for the date `1980-07-01` and latitude `70.0`.
2. Create a time slice for the entire latitude range between January and March of 1980.
3. Plot the selected time slice as a line plot.
1. **Select a subset of the `sst` data** for a specific time (`2010-07-01`) and latitude (`0.0`), which represents the Equator.
2. **Create a time slice** for the SST data between January and March 2010 for all latitudes and longitudes.
3. **Plot the time slice** as a line plot, showing the latitude-averaged SST over time.

This exercise allows you to practice selecting specific subsets of data and visualizing SST patterns over a specified period.

```{code-cell} ipython3
```

## Exercise 3: Performing Arithmetic Operations

1. Compute the mean of the `Tair` data over the `time` dimension.
2. Subtract the computed mean from the original `Tair` dataset to get the temperature anomalies.
3. Plot the mean temperature and the anomalies on separate plots.
1. **Compute the mean SST** over the entire time range (2010-2015) to obtain the average sea surface temperature for each spatial location.
2. **Calculate the temperature anomalies** by subtracting the computed mean from the original SST values. This helps understand how SST deviates from the mean during the time period.
3. **Plot both the mean SST and the anomalies** on separate plots to visualize spatial temperature patterns and deviations. You can select a specific time to display the anomalies.

This exercise introduces arithmetic operations on the dataset, focusing on the concept of temperature anomalies.

```{code-cell} ipython3
```

## Exercise 4: GroupBy and Resampling

1. Use `groupby` to calculate the seasonal mean temperature (`Tair`).
2. Use `resample` to calculate the monthly mean temperature for 1980.
3. Plot the seasonal mean for each season and the monthly mean.
1. **Use `groupby` to calculate the seasonal mean SST**. Group the data by season (`DJF`, `MAM`, `JJA`, and `SON`) and compute the average SST for each season.
2. **Resample the dataset to compute the monthly mean SST**. This aggregates the data on a monthly basis.
3. **Plot the seasonal mean SST and the monthly mean SST** to visualize how sea surface temperature varies by season and by month.

This exercise demonstrates how to group and resample time-series data, commonly used in climate data analysis.

```{code-cell} ipython3
```

## Exercise 5: Writing Data to netCDF
## Exercise 5: Writing Data to NetCDF

1. **Select the SST anomalies** calculated in Exercise 3 for further analysis and export.
2. **Convert the `sst` variable to `float32`** to optimize file size before writing the data to a NetCDF file.
3. **Write the anomalies data** to a new NetCDF file named `sst_anomalies.nc` for storage and future use.
4. **Load the saved NetCDF file** back into memory and print its contents to verify the saved data.

1. Select the temperature anomalies calculated in Exercise 3.
2. Convert the `Tair` variable to `float32` to optimize file size.
3. Write the anomalies data to a new netCDF file named `tair_anomalies.nc`.
4. Load the data back from the file and print its contents.
This exercise teaches how to export processed geospatial data to NetCDF, a widely-used file format in climate data analysis.

```{code-cell} ipython3
Expand Down Expand Up @@ -111,7 +121,7 @@ By the end of this lab, you will have a better understanding of how to:
## Exercise 9: Mask the Raster Using a Vector Dataset

1. Load the GeoJSON file at https://github.com/opengeos/datasets/releases/download/raster/Derna_Libya.geojson using `geopandas`.
2. Use the GeoJSON to mask the raster dataset, keeping only the data within the GeoJSON boundaries.
2. Use the GeoJSON to mask the reprojected raster dataset, keeping only the data within the GeoJSON boundaries.
3. Plot the masked raster data.

```{code-cell} ipython3
Expand Down

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