cpm_projection.qmd

---
title: "UKCP 2.2 Temporal Interpolation"
format:
  html:
    code-fold: true
execute:
  cache: true
jupyter: python3
---

To align climate projections with measured records, we interpolate five or six extra time points---standard or leap years respectively---per year.

```{python}
#| label: cpm-time-series

from typing import Final
from pathlib import Path
from pprint import pprint

from xarray.core.types import T_Dataset
from matplotlib import pyplot as plt
from matplotlib.figure import Figure
from matplotlib.axes import Axes

from clim_recal.utils.data import VariableOptions, RunOptions
from clim_recal.utils.xarray import (
    annual_group_xr_time_series,
    plot_xarray,
)
from clim_recal.utils.docs import CPMSummaryTimeSeries, gap_360_days, plot_axvlines

vars: tuple[str] = ('tasmax', 'tasmin', 'pr')
runs: tuple[str] = ('01', '05', '06', '07', '08')

cpm_summary_ts = CPMSummaryTimeSeries(runs=runs, variables=vars)

cpm_medians: dict[str, dict[tuple[str, str], T_Dataset]] = cpm_summary_ts.get_local_xarrays_dict()

cal_360_day_4_years: Final[int] = 360*4
cal_standard_4_years: Final[int] = 365*3 + 366

check_days: Final[int] = 6
```

With the CPM annual timeseries loaded, we can now plot projections from 1 December 1980 to 30 November 2080:

```{python}
#| label: fig-tasmax-raw-time-series
#| fig-cap: "Raw CPM projection 360 day years"

fig, axs = plt.subplots(3, sharex=True)
for run_id, cpm_raw_median in cpm_medians['raw'].items():
    var, run = run_id
    cpm_raw_median[var][:cal_360_day_4_years*2].plot(
        label=f"run-{run}",
        ax=axs[vars.index(var)],
    )

plt.legend()
plt.show()
```

```{python}
#| label: fig-convert-time-series
#| fig-cap: "Converted CPM to standard years"

fig, axs = plt.subplots(3, sharex=True)
for run_id, cpm_convert_linear_median in cpm_medians['linear'].items():
    var, run = run_id
    cpm_convert_linear_median[var][:cal_standard_4_years*2].plot(
        label=f"run-{run}",
        ax=axs[vars.index(var)],
    )

plt.legend()
plt.show()
```

```{python}
#| label: fig-tasmax-run-01-december-overlayed
#| fig-cap: "Compare CPM tasmax run 01 360 vs projected standard leap year December"

var = 'tasmax'
run = '01'
run_id = (var, run)
cpm_convert_linear_median = cpm_medians['linear'][run_id]
trimmed_raw_to_standard = (
    cpm_medians['raw'][run_id][var][:check_days].convert_calendar('standard', align_on="year")
)

trimmed_raw_to_standard.plot(label="raw", marker=".")
cpm_convert_linear_median[var][:check_days + 1].plot(label="convert", marker=".")

plt.legend()
plt.show()
```

```{python}
#| label: fig-december-overlayed
#| fig-cap: "Compare CPM 360 vs projected standard leap year December"

check_days: Final[int] = 6

fig, axs = plt.subplots(3, 5, sharex=True, sharey=True)
for run_id, cpm_convert_linear_median in cpm_medians['linear'].items():
    var, run = run_id
    trimmed_raw_to_standard = (
        cpm_medians['raw'][run_id][var][:check_days].convert_calendar(
            'standard', align_on="year"
        )
    )
    cpm_convert_linear_median[var][:check_days + 1].plot(
        label="convert",
        marker=".",
        ax=axs[vars.index(var), runs.index(run)]
      )
    trimmed_raw_to_standard.plot(
        label="raw",
        marker=".",
        ax=axs[vars.index(var), runs.index(run)],
      )

plt.legend()
plt.show()
```

Average annual projections.

```{python}
#| label: fig-tasmax-annual-means
#| fig-cap: "Annual average of means of raw tasmax CPM 360 day years"



annual_raw_medians: T_Dataset = annual_group_xr_time_series(
  cpm_medians['raw']['tasmax', '01'].tasmax,
  variable_name=VariableOptions.TASMAX,
  plot_path=None,
  time_stamp=None)

annual_convert_linear_medians: T_Dataset = annual_group_xr_time_series(
  cpm_medians['linear']['tasmax', '01'].tasmax,
  variable_name=VariableOptions.TASMAX,
  plot_path=None,
  time_stamp=None)

annual_convert_nearest_medians: T_Dataset = annual_group_xr_time_series(
  cpm_medians['nearest']['tasmax', '01'].tasmax,
  variable_name=VariableOptions.TASMAX,
  plot_path=None,
  time_stamp=None)

annual_raw_medians.plot(label='raw', linewidth=5)
annual_convert_linear_medians.plot(label='convert', linewidth=3)
annual_convert_nearest_medians.plot(label='nearest', linewidth=1)

plot_axvlines(plt, gap_360_days(is_leap_year=False))

plt.title("Medians of taxmax for each day of the year 1980-01-01 to 2080-11-30")
plt.legend()
plt.show()
```

For more detailed analysis comparing `nearest` and `linear` interpolation see:

- [`cpm_projection_diff_plots.ipynb`](../notebooks/cpm_projection_diff_plots.html)
- [`cpm_projection_diff_plots_linear_nearest.ipynb`](../notebooks/cpm_projection_diff_plots_linear_nearest.html)

<!-- Leap year projection. -->
<!-- ```{python} -->
<!-- #| label: fig-tasmax-leap-means -->
<!-- #| fig-cap: "Leap year average of means of raw tasmax CPM 360 day years" -->
<!---->
<!-- leap_annual_means_raw: T_Dataset = cpm_medians['raw']['tasmax', '01'].where( -->
<!--   cpm_medians['raw']['tasmax', '01'].time.dt.year % 4 == 0 -->
<!-- ) -->
<!---->
<!-- leap_annual_means_raw.tasmax.plot(label="raw") -->
<!---->
<!-- plt.legend() -->
<!-- plt.show() -->
<!-- ``` -->