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WxData 2.3 Released

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@edrewitz edrewitz released this 10 Sep 22:57
WxData2.3
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WxData 2.3

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(C) Eric J. Drewitz 2025-2026

Documentation And Examples

WxData Documentation and Examples

What's New?

Several new end-to-end data clients to download and process model data from the Canadian Meteorological Centre (CMC)

  1. Canadian Global Deterministic Prediction System (GDPS)
  2. Canadian Regional Deterministic Prediction System (RDPS)
  3. Canadian High Resolution Deterministic Prediction System (HRDPS)
  4. Canadian Global Ensemble Prediction System (GEPS)
  5. Canadian Seasonal to Inter-annual Prediction System (CanSIPS) Forecasts
  6. Canadian Seasonal to Inter-annual Prediction System (CanSIPS) Hindcasts
from wxdata.model_data.cmc.gdps.gdps import gdps
from wxdata.model_data.cmc.rdps.rdps import rdps
from wxdata.model_data.cmc.hrdps.hrdps import hrdps
from wxdata.model_data.cmc.geps.geps import geps
from wxdata.model_data.cmc.cansips.forecast.cansips_forecast import cansips_forecast
from wxdata.model_data.cmc.cansips.hindcast.cansips_hindcast import cansips_hindcast

Important Note

The cansips_hindcast() client automates the methodology described here to create a 30-year climatology in order to compute forecast anomalies.

CanSIPS Hindcast Methodology TLDR

  1. Beginning at the current month, download the hindcast for all forecast steps from month 0 (current month) through month 11 (12 steps total) for each year from 1991-2020.

  2. Create an xarray.array for each year of hindcast data.

  3. Combine these datasets into one xarray.array.

  4. Create an xarray.array of the ensemble mean between all 40 ensemble members of the combined dataset.

Improvements

Nothing at this time.

Bug Fixes

Nothing at this time.

Assets 3
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