docs: updated README and documentation

Author: martibosch

README.md

@@ -9,9 +9,9 @@
 
 # Meteora
 
-Pythonic interface to access data from meteorological stations. Key features:
+Pythonic interface to access observations from meteorological stations. Key features:
 
-- easily stream data [from multiple providers (e.g., Automated Surface/Weather Observing Systems (ASOS/AWOS), MetOffice...)](https://meteora.readthedocs.io/en/latest/supported-providers.html) into pandas data frames.
+- easily stream meteorological observations [from multiple providers, from global (e.g., Global Historical Climatology Network hourly (GHCNh)) and regional (e.g., MetOffice) networks to citizen weather stations (e.g., Netatmo)](https://meteora.readthedocs.io/en/latest/supported-providers.html) into pandas data frames.
 - user-friendly arguments to filter data by region, variables or date ranges.
 - request caching with [requests-cache](https://github.com/requests-cache/requests-cache) to avoid re-downloading data and help bypassing API limits.
 
@@ -20,74 +20,71 @@ Pythonic interface to access data from meteorological stations. Key features:
 Meteora provides a set of provider-specific clients to get observations from meteorological stations. For instance, it can be used to stream [the one-minute ASOS data](https://madis.ncep.noaa.gov/madis_OMO.shtml) from the [Iowa Environmental Mesonet](https://mesonet.agron.iastate.edu/request/asos/1min.phtml) into a pandas data frame:
 
 ```python
-from meteora.clients import ASOSOneMinIEMClient
+from meteora.clients import GHCNHourlyClient
 
-region = "Oregon"
-variables = ["temperature", "pressure", "precipitation", "surface_wind_speed"]
-start = "2021-08-13"
-end = "2021-08-16"
+region = "Davos, Switzerland"
+variables = ["temperature", "precipitation", "surface_wind_speed"]
+start = "12-11-2021"
+end = "12-12-2021"
 
-client = ASOSOneMinIEMClient(region=region)
-ts_df = client.get_ts_df(variables, start=start, end=end)
+client = GHCNHourlyClient(region)
+ts_df = client.get_ts_df(variables, start, end)
 ts_df.head()
 ```
 
+```
+[########################################] | 100% Completed | 16.94 s
+```
+
 <div>
 <table border="1" class="dataframe">
   <thead>
     <tr style="text-align: right;">
       <th></th>
       <th></th>
       <th>temperature</th>
-      <th>pressure</th>
       <th>precipitation</th>
       <th>surface_wind_speed</th>
     </tr>
     <tr>
-      <th>station</th>
-      <th>valid(UTC)</th>
-      <th></th>
+      <th>Station_ID</th>
+      <th>time</th>
       <th></th>
       <th></th>
       <th></th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th rowspan="5" valign="top">AST</th>
-      <th>2021-08-13 00:00:00</th>
-      <td>68.0</td>
-      <td>29.942</td>
+      <th rowspan="5" valign="top">SZM00006784</th>
+      <th>2021-12-11 00:00:00</th>
+      <td>-4.7</td>
       <td>0.0</td>
-      <td>10.0</td>
+      <td>5.1</td>
     </tr>
     <tr>
-      <th>2021-08-13 00:01:00</th>
-      <td>67.0</td>
-      <td>29.942</td>
+      <th>2021-12-11 01:00:00</th>
+      <td>-4.8</td>
       <td>0.0</td>
-      <td>10.0</td>
+      <td>5.1</td>
     </tr>
     <tr>
-      <th>2021-08-13 00:02:00</th>
-      <td>67.0</td>
-      <td>29.942</td>
+      <th>2021-12-11 02:00:00</th>
+      <td>-4.8</td>
       <td>0.0</td>
-      <td>10.0</td>
+      <td>3.6</td>
     </tr>
     <tr>
-      <th>2021-08-13 00:03:00</th>
-      <td>67.0</td>
-      <td>29.942</td>
+      <th>2021-12-11 03:00:00</th>
+      <td>-4.8</td>
       <td>0.0</td>
-      <td>9.0</td>
+      <td>3.6</td>
     </tr>
     <tr>
-      <th>2021-08-13 00:04:00</th>
-      <td>68.0</td>
-      <td>29.942</td>
+      <th>2021-12-11 04:00:00</th>
+      <td>-4.7</td>
       <td>0.0</td>
-      <td>8.0</td>
+      <td>3.1</td>
     </tr>
   </tbody>
 </table>
@@ -99,10 +96,12 @@ We can also get the station locations using the `stations_gdf` property:
 import contextily as cx
 
 ax = client.stations_gdf.plot()
-cx.add_basemap(ax, crs=client.stations_gdf.crs)
+cx.add_basemap(ax, crs=client.stations_gdf.crs, attribution=False)
 ```
 
-![oregon-stations](https://github.com/martibosch/meteora/raw/main/docs/figures/oregon-stations.png)
+![davos-stations](https://github.com/martibosch/meteora/raw/main/docs/figures/davos-stations.png)
+
+*(C) OpenStreetMap contributors, Tiles style by Humanitarian OpenStreetMap Team hosted by OpenStreetMap France*
 
 See [the user guide](https://meteora.readthedocs.io/en/latest/user-guide.html) for more details about the features of Meteora as well as the [list of supported providers](https://meteora.readthedocs.io/en/latest/supported-providers.html).
 

docs/figures/davos-stations.png

@@ -20,12 +20,18 @@ following table.
 | ASOS/METAR | |:earth_africa:|  | |:white_check_mark:| | |:white_check_mark:| |
 | (IEM)      | Global            | Yes                  | None                 |
 +------------+-------------------+----------------------+----------------------+
+| GHCNh      | |:earth_africa:|  | |:white_check_mark:| | |:white_check_mark:  |
+| (NOAA)     | Global            | Yes                  | None                 |
++------------+-------------------+----------------------+----------------------+
 | Meteocat   | Catalonia         | |:white_check_mark:| | |:key:| API key      |
 |            |                   | Yes                  | (free)               |
 +------------+-------------------+----------------------+----------------------+
 | MetOffice  | |:great_britain:| | |:clock1:| Latest    | |:key:| API key      |
 |            | United Kingdom    | 24h only             | (free)               |
 +------------+-------------------+----------------------+----------------------+
+| Netatmo    | |:earth_africa:|  | |:white_check_mark:| | |:key:| API key      |
+|            | Global            | Yes                  | (free)               |
++------------+-------------------+----------------------+----------------------+
 
 See more details about how to use each provider in the respective
 sections below as well as in the `API
@@ -48,6 +54,11 @@ Agrometeo
 service that provides weather data for agriculture, which can be
 accessed for free and without authentication.
 
+Global Historical Climatology Network hourly (GHCNh)
+----------------------------------------------------
+
+The `Global Historical Climatology Network hourly (GHCNh) <https://www.ncei.noaa.gov/products/global-historical-climatology-network-hourly>`__ is a dataset of hourly surface weather observations from fixed, land-based stations from numerous sources around the world. The GHCNh is managed by the National Oceanic and Atmospheric Administration (NOAA) and can be accessed for free and without authentication. Note that the same dataset is also provided at the daily and monthly resolutions, however Meteora currently only supports the hourly dataset (since daily and monthly aggregations can be easily computed from the hourly records).
+
 Iowa Environmental Mesonet (IEM)
 --------------------------------
 

docs/figures/oregon-stations.png

@@ -10,10 +10,12 @@ hidden:
 maxdepth: 1
 ---
 
-user-guide/asos-example
+user-guide/overview
+user-guide/data-structures
 ```
 
-- [ASOS example](https://meteora.readthedocs.io/en/latest/user-guide/asos-example.html)
+- [Overview of key features](https://meteora.readthedocs.io/en/latest/user-guide/overview.html)
+- [Data structures](https://meteora.readthedocs.io/en/latest/user-guide/data-structures.html)
 
 ## Selecting a region