PRECIPITATION Extreme Weather Analysis CDS

2025-07-02 08:11 UTC o4-mini-high Open in ChatGPT ↗

To demonstrate that “over recent years, extremes have intensified, with more frequent and prolonged dry spells following heavy rains” for your region of interest, you can proceed in two stages:

1. Data selection from Copernicus CDS

Reanalysis / gridded observations
- ERA5-Land (hourly → aggregate to daily) or ERA5 (for full atmosphere, also soil moisture)
- E-OBS (if you’re in Europe and want an observational benchmark)
Variables
- Daily total precipitation (to define “heavy rain” and detect dry days)
- Daily potential evapotranspiration (for dryness metrics like SPEI)
- Near-surface temperature (to compute evapotranspiration if not available directly)
- Soil moisture (optional, to link atmospheric extremes to hydrological response)
Time period & region
- At least the last 30 years (e.g. 1991 – 2020) to establish a “recent” baseline and detect trends
- Subset spatially to your study area via bounding box or shapefile

How to pull it:

# example snippet using the CDS API
c.retrieve(
    'reanalysis-era5-land',
    {
      'variable': ['total_precipitation','potential_evaporation','soil_water_content'],
      'year':    [str(y) for y in range(1991,2025)],
      'month':   [f"{m:02d}" for m in range(1,13)],
      'day':     [f"{d:02d}" for d in range(1,32)],
      'time':    ['00:00','06:00','12:00','18:00'],
      'area':    [north, west, south, east],
      'format':  'netcdf'
    },
    'era5_region.nc')

2. Analysis workflow

A. Define and compute extreme‐event indices

Use the ETCCDI set of indices (or the Python library xclim) to derive:

Heavy‐precipitation days
- e.g. R95p: annual count of days when precipitation > 95th percentile of the 1991–2020 daily climatology
- Rx1day: annual maximum 1-day precipitation
Dry‐spell metrics
- CDD (Consecutive Dry Days): maximum number of days per year with precipitation < 1 mm
- CWD (Consecutive Wet Days): maximum number of days per year with precipitation ≥ 1 mm
Drought indices (to capture soil‐moisture/atmospheric dryness)
- SPI (Standardized Precipitation Index) on 1- to 12-month scales
- SPEI (Standardized Precipitation Evapotranspiration Index), combining precipitation and PET

B. Trend and frequency analysis

Time-series of annual indices
- Plot R95p, CDD, SPI, SPEI for each year
Trend detection
- Mann–Kendall test for monotonic trends
- Sen’s slope estimate of change per decade
Event‐based sequencing
- Identify each “heavy‐rain event” (e.g. a day > 95th percentile)
- For each, compute the length of the ensuing dry spell (days until next day >1 mm)
- Analyse distribution of these post‐rain dry spells, comparing early (1991–2005) vs. late (2006–2020) periods
- Use non-parametric tests (e.g. Kolmogorov–Smirnov) to see if the distribution has shifted toward longer dry spells

C. Visualization & interpretation

Maps of trend slopes for CDD and R95p over your region
Boxplots of post‐event dry‐spell lengths, stratified by decade
Scatter of heavy-rain magnitude vs. subsequent dry-spell length, to see if more intense storms now lead to longer dry periods
Composite analysis: average soil moisture or SPEI anomaly in the 2 weeks following top-decile rain events, by period

Putting it all together

Retrieve daily precipitation, PET, (optional soil moisture) from CDS.
Aggregate to daily, clip to region.
Compute ETCCDI indices (R95p, CDD, Rx1day, etc.) and drought indices (SPI, SPEI) with xclim or similar.
Construct event‐based sequences: heavy rain → dry spell length.
Test for trends in both the aggregate indices and the post‐rain dry‐spell statistics (Mann–Kendall; KS test).
Visualize changes in frequency and duration, and spatial patterns across your region.

By following that pipeline, you’ll be able to show quantitatively whether (and by how much) heavy‐rain events are being followed by longer and more frequent dry spells—and whether those trends are statistically robust.

I’ve drafted a complete Python script that:

Loads your daily precipitation (and PET) data
Computes annual R95p, CDD, SPI (3-month), and SPEI (3-month) using xclim
Exports each index to its own CSV for easy plotting
Produces time-series PNGs for all four indicators
Runs Mann–Kendall significance tests and Sen’s slope estimates, saving results to a summary CSV
Identifies heavy-rain events (95th percentile), measures the following dry-spell lengths, and outputs a CSV with event dates, spell lengths, and period labels
Performs a Kolmogorov–Smirnov test comparing early vs. late spells, saving the stats to CSV

All intermediate tables are written as CSVs so you can generate customized plots or integrate into reports. Let me know if you’d like any tweaks—e.g. different SPI/SPEI scales, alternative dry-day thresholds, or added visualizations!