Add P3 examples (#52)

Added flight tracks, humidity comparison, wband radar AXBTs and wsra.

Co-authored-by: Theresa Mieslinger <[email protected]>
Co-authored-by: Tobias Kölling <[email protected]>

.github/workflows/buildbook.yaml

@@ -4,25 +4,31 @@ on: [push, pull_request]
 
 jobs:
   build:
-
     runs-on: ubuntu-latest
     strategy:
       matrix:
         python-version: [3.8]
+    defaults:
+      run:
+        shell: bash -l {0}
 
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v2
       with:
         python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install -r requirements.txt
+    - name: Set up Python modules with conda
+      uses: conda-incubator/setup-miniconda@v2
+      with:
+        activate-environment: how_to_eurec4a
+        environment-file: environment.yml
+        python-version: ${{ matrix.python-version }}
+        auto-activate-base: false
     - name: build book
       run: |
-          jupyter-book build -W -n --keep-going  how_to_eurec4a
+        conda info
+        jupyter-book build -W -n --keep-going  how_to_eurec4a
     - name: Archive build artifacts
       if: always()
       uses: actions/upload-artifact@v2

environment.yml

@@ -0,0 +1,8 @@
+name: how_to_eurec4a
+dependencies:
+  - python>=3.7
+  - anaconda
+  - cartopy
+  - pip
+  - pip:
+    - -r file:requirements.txt

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

@@ -12,6 +12,13 @@
     - file: wales
     - file: hamp_comparison
     - file: cloudmasks
+- file: pthree
+  sections:
+    - file: p3_flight_tracks
+    - file: p3_humidity_comparison
+    - file: p3_wband_radar
+    - file: p3_AXBTs
+    - file: p3_wsra
 - file: meteor
   sections:
     - file: meteor_cloudradar

how_to_eurec4a/p3_AXBTs.md

@@ -0,0 +1,205 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.11.2
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
+# Ocean temperatures: AXBTs and SWIFT buoys
+
+During EUREC4A/ATOMIC the P-3 deployed 165 Airborne eXpendable BathyThermographs (AXBTs)
+to measure profiles of ocean temperature. (These are kind of the oceanic equivalent of
+dropsondes but they don't measure salinity.) Often these were dropped around
+other ocean temperature measurements - for example the autonomous
+Surface Wave Instrument Floats with Tracking (SWIFT) buoys deployed from the
+Ron Brown by Elizabeth Thompson of NOAA and her colleagues. The SWIFT deployment
+is described in {cite}`Quinn:2021`.
+
+Let's take a look at some of the AXBT measurements and how they compare to the
+SWIFTs.
+
+```{code-cell} ipython3
+import xarray as xr
+import numpy as np
+import datetime
+
+import matplotlib.pyplot as plt
+plt.style.use(["./mplstyle/book"])
+%matplotlib inline
+
+import eurec4a
+cat = eurec4a.get_intake_catalog()
+```
+
+Mapping takes quite some setup. Maybe we'll encapsulate this later but for now we repeat code
+in each notebook.
+
+```{code-cell} ipython3
+:tags: [hide-cell]
+
+import matplotlib as mpl
+import matplotlib.ticker as mticker
+
+import cartopy.crs as ccrs
+from   cartopy.feature import LAND
+from   cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
+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 set_up_map(plt, lon_w = -60.5, lon_e = -49, lat_s = 10, lat_n = 16.5):
+    ax = plt.axes(projection=ccrs.PlateCarree())
+    ax_to_map(ax, lon_w, lon_e, lat_s, lat_n)
+    return(ax)
+
+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'}
+```
+
+What days did the P-3 fly on? We can find out via the flight segmentation files.
+
+```{code-cell} ipython3
+# On what days did the P-3 fly? These are UTC date
+all_flight_segments = eurec4a.get_flight_segments()
+flight_dates = np.unique([np.datetime64(flight["takeoff"]).astype("datetime64[D]")
+                          for flight in all_flight_segments["P3"].values()])
+```
+
+Now set up colors to code each flight date during the experiment. One could choose
+a categorical palette so the colors were as different from each other as possible.
+Here we'll choose from a continuous set that spans the experiment so days that are
+close in time are also close in color.
+
+```{code-cell} ipython3
+:tags: [hide-cell]
+
+# Like mpl.colors.Normalize but works also with datetime64 objects
+def mk_norm(vmin, vmax):
+    def norm(values):
+        return (values - vmin) / (vmax - vmin)
+    return norm
+norm = mk_norm(np.datetime64("2020-01-15"),
+               np.datetime64("2020-02-15"))
+
+# color map for things coded by flight date
+#   Sample from a continuous color map running from start to end of experiment
+def color_of_day(day):
+    return plt.cm.viridis(norm(day), alpha=0.9)
+```
+
+The P-3 only deployed AXBTs on some flights, and the SWIFT buoys were only deployed
+on a subset of those dates.
+
+```{code-cell} ipython3
+axbts = cat.P3.AXBT.Level_3.to_dask()
+swifts = [cat[s].all.to_dask() for s in list(cat) if "SWIFT" in s]
+axbt_dates = np.intersect1d(np.unique(axbts.time.astype("datetime64[D]").values),
+                            flight_dates)
+
+swift_candidates = np.unique(np.concatenate([swift.time.astype('datetime64[D]').values
+                                             for swift in swifts]))
+# Dates with potential SWIFT/P-3 overlap
+swift_dates = np.intersect1d(swift_candidates, axbt_dates)
+```
+
+For plotting purposes it'll be handy to define a one-day time window and to convert between date/time formats
+
+```{code-cell} ipython3
+one_day = np.timedelta64(1, "D")
+
+def to_datetime(dt64):
+    epoch = np.datetime64("1970-01-01")
+    second = np.timedelta64(1, "s")
+    return datetime.datetime.utcfromtimestamp((dt64 - epoch) / second)
+```
+
+Now we can make a map that shows where the AXBTs were deployed and where the SWIFTs
+were on days there the two platforms overlapped
+
+```{code-cell} ipython3
+fig = plt.figure(figsize = (8.3, 9.4))
+ax  = set_up_map(plt)
+add_gridlines(ax)
+
+#
+# AXBT locations
+#
+for d in axbt_dates:
+    flight = axbts.sel(time=slice(d, d + one_day))
+    ax.scatter(flight.lon, flight.lat,
+               lw=2, alpha=0.5, color=color_of_day(d),
+               transform=ccrs.PlateCarree(), zorder=7,
+               label=f"{to_datetime(d):%m-%d}")
+
+#
+# SWIFT locations on selected dates (where there's overlap)
+#
+for d in swift_dates:
+    flight = axbts.sel(time=slice(d, d + one_day))
+    for swift in swifts:
+        drift = swift.sel(time = flight.time.mean(), method = "nearest")
+        ax.scatter(drift.lon, drift.lat,
+                   alpha=1, color=color_of_day(d),
+                   transform=ccrs.PlateCarree(), zorder=7, marker = "p")
+
+plt.legend(ncol=2,loc=(0.0,0.0),fontsize=12,framealpha=0.8,markerscale=1,
+           title="Flight date (MM-DD-2020)")
+```
+
+On 19 Jan and 3 Feb the AXBTs bracket the SWIFTs; on 23 Jan the SWIFTs are at
+the southern end of the AXBT pattern.
+
+The next plot will focus on 19 Jan.
+Let's look at the profile of ocean temperature in the first 150 m from the AXBTs
+and compare the near-surface temperatures to the SWIFTs they are surrounding.
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=[8.3, 9.4])
+d = np.datetime64("2020-01-19")
+axbt_1day   = axbts.sel(time=slice(d, d + one_day))
+# Swift data at mean of AXBT times
+swifts_1day = [s.sel(time = axbt_1day.time.mean(), method = "nearest") for s in swifts]
+
+axbt_1day.temperature.where(axbt_1day.depth < 150).plot.line(y="depth",
+                                                             add_legend=False, yincrease=False)
+ax.set_xlabel("Sea water temperature (K)")
+ax.set_ylabel("Depth (m)")
+
+#
+# Inset plot! https://matplotlib.org/3.1.1/gallery/subplots_axes_and_figures/zoom_inset_axes.html
+#
+axin = ax.inset_axes([0.06, 0.84, 0.6, 0.12])
+axin.scatter([s.sea_water_temperature.values + 273.15 for s in swifts_1day],
+             # SWIFTs 16 and 17 report water temperature at 0.5 m depth; SWIFTs 23-25 report at 0.3 m
+             # See the variable long_name or  Tables 8 and 9 of Quinn et al.
+             [0.5 if '0.5' in s.sea_water_temperature.long_name else 0.3 for s in swifts_1day],
+             color="0.25",
+             s = 1.5 * plt.rcParams['lines.markersize'] ** 2)
+axbt_1day.temperature.where(axbt_1day.depth < 3).plot.line(y="depth",
+                                                           add_legend=False,
+                                                           yincrease=False, ax = axin)
+axin.set_xlabel("Sea water temperature (K)")
+axin.set_ylabel("Depth (m)")
+ax.indicate_inset_zoom(axin)
+```