Add P3 WSRA example, update copyright year

how_to_eurec4a/_config.yml

@@ -5,9 +5,9 @@
 
 #######################################################################################
 # Book settings
-title                       : How to EUREC4A  # The title of the book. Will be placed in the left navbar.
-author                      : EUREC4A community  # The author of the book
-copyright                   : "2020"  # Copyright year to be placed in the footer
+title                       : How to EUREC4A      # The title of the book. Will be placed in the left navbar.
+author                      : EUREC4A community   # The author of the book
+copyright                   : "2021"              # Copyright year to be placed in the footer
 logo                        : logo_pyeurec4a.png  # A path to the book logo
 repository:
   url                       : https://github.com/eurec4a/how_to_eurec4a

how_to_eurec4a/_toc.yml

@@ -17,6 +17,7 @@
     - file: p3_humidity_comparison
     - file: p3_wband_radar
     - file: p3_AXBTs
+    - file: p3_wsra
 - file: meteor
   sections:
     - file: meteor_cloudradar

how_to_eurec4a/p3_wsra.md

@@ -0,0 +1,127 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.12
+    jupytext_version: 1.7.1
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
+# WSRA example: surface wave state
+
+During EUREC4A/ATOMIC the P-3 flew with a Wide Swath Radar Altimeter (WSRA),
+a digital beam-forming radar altimeter operating at 16 GHz in the Ku band. It
+generates 80 narrow beams spread over 30 deg to produce a topographic map of the
+sea surface waves and their backscattered power. These measurements allow for
+continuous reporting of directional ocean wave spectra and quantities derived from
+this including significant wave height, sea surface mean square slope, and the
+height, wavelength, and direction of propagation of primary and secondary wave fields.
+WSRA measurements are processed by the private company [ProSensing](https://www.prosensing.com),
+which designed and built the instrument.
+
+The WSRA also produces rainfall rate estimates from path-integrated attenuation but
+we won't look at those here.
+
+The data are available through the EUREC4A intake catalog.
+
+```{code-cell} ipython3
+import xarray as xr
+import matplotlib.pyplot as plt
+import seaborn as sns
+import colorcet as cc
+%pylab inline
+
+import eurec4a
+cat = eurec4a.get_intake_catalog()
+```
+
+Mapping takes quite some setup. Maybe this should become part of the `eurec4a` Python module.
+
+```{code-cell} ipython3
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mticker
+from   matplotlib.offsetbox import AnchoredText
+
+import cartopy as cp
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+from   cartopy.feature import LAND
+from   cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
+
+from   mpl_toolkits.axes_grid1.inset_locator import inset_axes
+from   mpl_toolkits.axes_grid1 import make_axes_locatable
+def set_up_map(plt, lon_w = -60.5, lon_e = -49, lat_s = 10, lat_n = 16.5):
+    ax  = plt.axes(projection=ccrs.PlateCarree())
+    # Defining boundaries of the plot
+    ax.set_extent([lon_w,lon_e,lat_s,lat_n]) # lon west, lon east, lat south, lat north
+    ax.coastlines(resolution='10m',linewidth=1.5,zorder=1);
+    ax.add_feature(LAND,facecolor='0.9')
+    return(ax)
+
+def ax_to_map(ax, lon_w = -60.5, lon_e = -49, lat_s = 10, lat_n = 16.5):
+    # Defining boundaries of the plot
+    ax.set_extent([lon_w,lon_e,lat_s,lat_n]) # lon west, lon east, lat south, lat north
+    ax.coastlines(resolution='10m',linewidth=1.5,zorder=1);
+    ax.add_feature(LAND,facecolor='0.9')
+
+def add_gridlines(ax):
+    # Assigning axes ticks
+    xticks = np.arange(-65,0,2.5)
+    yticks = np.arange(0,25,2.5)
+    gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,linewidth=1, color='black', alpha=0.5, linestyle='dotted')
+    gl.xlocator = mticker.FixedLocator(xticks)
+    gl.ylocator = mticker.FixedLocator(yticks)
+    gl.xformatter = LONGITUDE_FORMATTER
+    gl.yformatter = LATITUDE_FORMATTER
+    gl.xlabel_style = {'size': 10, 'color': 'k'}
+    gl.ylabel_style = {'size': 10, 'color': 'k'}
+    gl.right_labels = False
+    gl.bottom_labels = False
+    gl.xlabel = {'Latitude'}
+```
+
+We'll select an hour's worth of observations from a single flight day. WSRA data
+are stored as "trajectories" - discrete times with associated positions and
+observations.
+
+```{code-cell} ipython3
+wsra_example = cat.P3.wsra["P3-0119"].to_dask().sel(trajectory=slice(0,293))
+```
+Now it's interesting to see how the wave slope (top panel) and the wave height (bottom)
+vary spatially on a given day.
+
+```{code-cell} ipython3
+fig, (ax1, ax2) = plt.subplots(nrows=2, sharex = True, figsize = (12,8), subplot_kw={'projection': ccrs.PlateCarree()})
+
+#
+# Mean square slope
+#
+ax_to_map(ax1, lon_e=-50, lon_w=-54.5, lat_s = 13, lat_n = 16)
+add_gridlines(ax1)
+pts = ax1.scatter(wsra_example.longitude,wsra_example.latitude,
+           c=wsra_example.sea_surface_mean_square_slope_median,
+           vmin = 0.02, vmax=0.04,
+           cmap=cc.cm.fire,
+           alpha=0.5,
+           transform=ccrs.PlateCarree(),zorder=7)
+fig.colorbar(pts, ax=ax1, shrink=0.75, aspect=10, label="Mean Square Slope (rad$^2$)")
+#
+# Significant wave height
+#
+ax_to_map(ax2, lon_e=-50, lon_w=-54.5, lat_s = 13, lat_n = 16)
+add_gridlines(ax2)
+pts = ax2.scatter(wsra_example.longitude,wsra_example.latitude,
+           c=wsra_example.sea_surface_wave_significant_height,
+           vmin = 1, vmax=5,
+           cmap=cc.cm.bgy,
+           alpha=0.5,
+           transform=ccrs.PlateCarree(),zorder=7)
+fig.colorbar(pts, ax=ax2, shrink=0.75, aspect=10, label="Significant Wave Height (m)")
+
+plt.tight_layout()
+```