Counting and ranking the dates based on the number of grid cells when daily rainfall is greater than a threshold in a netcdf file using Python - python-3.x

Question

I have daily gridded rainfall data with dimensions (time: 14245, lon: 40, lat: 20) . I've calculated 2,3,5 and 7-days accumulated rainfall and their respective 90th percentiles at every grid points in my data domain. I've set my condition using DataArray.where(condition, drop=True) to know when daily rainfall amount exceed the threshold as shown in the code below. My current working code is here:
import numpy as np
import pandas as pd
import xarray as xr
#=== reading in the data ===
data_path = '/home/wilson/Documents/PH_D/GPCC/GPCC/GPCC_daily_1982-2020.nc'
data = xr.open_dataset(data_path)
#=== computing 2, 3, 5 and 7-days acummulated rainfall amount ===
data[['precip_2d']] = np.around(data.precip.rolling(time=2).sum(),decimals=2)
data[['precip_3d']] = np.around(data.precip.rolling(time=3).sum(),decimals=2)
data[['precip_5d']] = np.around(data.precip.rolling(time=5).sum(),decimals=2)
data[['precip_7d']] = np.around(data.precip.rolling(time=7).sum(),decimals=2)
#=== Computing 10% largest at each grid point (per grid cel) this is 90th percentile ===
data[['accum_2d_90p']] = np.around(data.precip_2d.quantile(0.9, dim='time'), decimals=2)
data[['accum_3d_90p']] = np.around(data.precip_3d.quantile(0.9, dim='time'), decimals=2)
data[['accum_5d_90p']] = np.around(data.precip_5d.quantile(0.9, dim='time'), decimals=2)
data[['accum_7d_90p']] = np.around(data.precip_7d.quantile(0.9, dim='time'), decimals=2)
#=== locating extreme events, i.e., when daily precip greater than 90th percentile of each of the accumulated rainfall amount ===
data[['extreme_2d']] = data['precip'].where(data['precip'] > data['accum_2d_90p'], drop=True)
data[['extreme_3d']] = data['precip'].where(data['precip'] > data['accum_2d_90p'], drop=True)
data[['extreme_5d']] = data['precip'].where(data['precip'] > data['accum_2d_90p'], drop=True)
data[['extreme_7d']] = data['precip'].where(data['precip'] > data['accum_2d_90p'], drop=True)
My problem now is how to count the number of grid cells/points within my domain where the condition is true on a particular date and using the result of the count to rank the date in descending order.
Expected result should look like a table that can be saved as txt file. For example: cells_count is a variable that contain desired result, when print(cells_count) gives
Date
Number of grid cells/point
1992-07-01
432
1983-09-23
407
2009-08-12
388

Answer 1

ok based on your comments it sounds like what you'd like is to get a list of dates in your time coordinate based on a global summary statistic of 3D (time, lat, lon) data. I'll use the condition (data['precip'] > data['accum_2d_90p']) as an example.
I'd definitely recommend reducing the dimensionality of your condition first, before working with the dates, because working with ragged 3D datetime arrays is a real pain. So since you mention wanting the count of pixels satisfying the criteria, you can simply do this:
global_pixel_count = (
(data['precip'] > data['accum_2d_90p']).sum(dim=("lat", "lon"))
)
Now, you have a 1-D array, global_pixel_count, indexed by time only. You can get the ranked order of dates from this using xr.DataArray.argsort:
sorted_date_positions = global_pixel_count.argsort()
sorted_dates = global_pixel_count.time.isel(
time=sorted_date_positions.values
)
This will return a DataArray of dates which sort the array in ascending order. You could reverse this to get the dates in descending order with sorted_date_positions.values[::-1] and you could select all the pixel counts in descending order with:
global_pixel_count.isel(time=sorted_date_positions.values[::-1])
you could also index your entire array with this indexer:
data.isel(time=sorted_date_positions.values[::-1])