Convert 360_day calendars by choosing random dates to drop or add

doc/whats-new.rst

@@ -22,6 +22,8 @@ v2024.04.0 (unreleased)
 
 New Features
 ~~~~~~~~~~~~
+- New "random" method for converting to and from 360_day calendars (:pull:`8603`).
+  By `Pascal Bourgault <https://github.com/aulemahal>`_.
 
 
 Breaking changes

xarray/coding/calendar_ops.py

@@ -64,7 +64,7 @@ def convert_calendar(
       The target calendar name.
     dim : str
       Name of the time coordinate in the input DataArray or Dataset.
-    align_on : {None, 'date', 'year'}
+    align_on : {None, 'date', 'year', 'random'}
       Must be specified when either the source or target is a `"360_day"`
       calendar; ignored otherwise. See Notes.
     missing : any, optional
@@ -143,6 +143,16 @@ def convert_calendar(
       will be dropped as there are no equivalent dates in a standard calendar.
 
       This option is best used with data on a frequency coarser than daily.
+
+    "random"
+      Similar to "year", each day of year of the source is mapped to another day of year
+      of the target. However, instead of having always the same missing days according
+      the source and target years, here 5 days are chosen randomly, one for each fifth
+      of the year. However, February 29th is always missing when converting to a leap year,
+      or its value is dropped when converting from a leap year. This is similar to the method
+      used in the LOCA dataset (see Pierce, Cayan, and Thrasher (2014). doi:10.1175/JHM-D-14-0082.1).
+
+      This option is best used on daily data.
     """
     from xarray.core.dataarray import DataArray
 
@@ -174,14 +184,20 @@ def convert_calendar(
 
     out = obj.copy()
 
-    if align_on == "year":
+    if align_on in ["year", "random"]:
         # Special case for conversion involving 360_day calendar
-        # Instead of translating dates directly, this tries to keep the position within a year similar.
-
-        new_doy = time.groupby(f"{dim}.year").map(
-            _interpolate_day_of_year, target_calendar=calendar, use_cftime=use_cftime
-        )
-
+        if align_on == "year":
+            # Instead of translating dates directly, this tries to keep the position within a year similar.
+            new_doy = time.groupby(f"{dim}.year").map(
+                _interpolate_day_of_year,
+                target_calendar=calendar,
+                use_cftime=use_cftime,
+            )
+        elif align_on == "random":
+            # The 5 days to remove are randomly chosen, one for each of the five 72-days periods of the year.
+            new_doy = time.groupby(f"{dim}.year").map(
+                _random_day_of_year, target_calendar=calendar, use_cftime=use_cftime
+            )
         # Convert the source datetimes, but override the day of year with our new day of years.
         out[dim] = DataArray(
             [
@@ -229,6 +245,27 @@ def _interpolate_day_of_year(time, target_calendar, use_cftime):
     ).astype(int)
 
 
+def _random_day_of_year(time, target_calendar, use_cftime):
+    """Return a day of year in the new calendar.
+
+    Removes Feb 29th and five other days chosen randomly within five sections of 72 days.
+    """
+    year = int(time.dt.year[0])
+    source_calendar = time.dt.calendar
+    new_doy = np.arange(360) + 1
+    rm_idx = np.random.default_rng().integers(0, 72, 5) + 72 * np.arange(5)
+    if source_calendar == "360_day":
+        for idx in rm_idx:
+            new_doy[idx + 1 :] = new_doy[idx + 1 :] + 1
+        if _days_in_year(year, target_calendar, use_cftime) == 366:
+            new_doy[new_doy >= 60] = new_doy[new_doy >= 60] + 1
+    elif target_calendar == "360_day":
+        new_doy = np.insert(new_doy, rm_idx - np.arange(5), -1)
+        if _days_in_year(year, source_calendar, use_cftime) == 366:
+            new_doy = np.insert(new_doy, 60, -1)
+    return new_doy[time.dt.dayofyear - 1]
+
+
 def _convert_to_new_calendar_with_new_day_of_year(
     date, day_of_year, calendar, use_cftime
 ):

xarray/tests/test_calendar_ops.py

@@ -106,6 +106,45 @@ def test_convert_calendar_360_days(source, target, freq, align_on):
         assert conv.size == 359 if freq == "D" else 359 * 4
 
 
+def test_convert_calendar_360_days_random():
+    da_std = DataArray(
+        np.linspace(0, 1, 366),
+        dims=("time",),
+        coords={
+            "time": date_range(
+                "2004-01-01",
+                "2004-12-31",
+                freq="D",
+                calendar="standard",
+                use_cftime=False,
+            )
+        },
+    )
+    da_360 = DataArray(
+        np.linspace(0, 1, 360),
+        dims=("time",),
+        coords={
+            "time": date_range("2004-01-01", "2004-12-30", freq="D", calendar="360_day")
+        },
+    )
+
+    conv = convert_calendar(da_std, "360_day", align_on="random")
+    conv2 = convert_calendar(da_std, "360_day", align_on="random")
+    assert (conv != conv2).any()
+
+    conv = convert_calendar(da_360, "standard", use_cftime=False, align_on="random")
+    assert np.datetime64("2004-02-29") not in conv.time
+    conv2 = convert_calendar(da_360, "standard", use_cftime=False, align_on="random")
+    assert (conv2 != conv).any()
+
+    # Ensure that added days are evenly distributed in the 5 fifths of each year
+    conv = convert_calendar(da_360, "noleap", align_on="random", missing=np.NaN)
+    conv = conv.where(conv.isnull(), drop=True)
+    nandoys = conv.time.dt.dayofyear[:366]
+    assert all(nandoys < np.array([74, 147, 220, 293, 366]))
+    assert all(nandoys > np.array([0, 73, 146, 219, 292]))
+
+
 @requires_cftime
 @pytest.mark.parametrize(
     "source,target,freq",