--- 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:t}`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 import pathlib plt.style.use(pathlib.Path("./mplstyle/book")) %matplotlib inline import eurec4a cat = eurec4a.get_intake_catalog(use_ipfs="QmahMN2wgPauHYkkiTGoG2TpPBmj3p5FoYJAq9uE9iXT9N") ``` 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") timestamp = (dt64 - epoch) / second return datetime.datetime.fromtimestamp(timestamp, datetime.UTC) ``` 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.sel(depth=slice(None,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.sel(depth=slice(None,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); ```