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@ -12,34 +12,42 @@ ARCHIVE providers (no API key required):
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4. NASA POWER — completely independent source (0.50°, global)
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FORECAST providers (no API key required):
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5. Open-Meteo Forecast — ensemble of NWP models (global)
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6. YR.no LocationForecast — Norwegian Met Institute (~10 days, global)
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5. Open-Meteo Forecast — deterministic NWP (global)
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6. Open-Meteo Ensemble — ECMWF IFS 51-member ensemble; gives probability bands
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7. YR.no LocationForecast — Norwegian Met Institute (~10 days, global)
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FORECAST providers (API key required — set in CONFIG below, leave "" to skip):
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7. OpenWeatherMap — free tier, 1000 calls/day
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8. WeatherAPI.com — free tier
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8. OpenWeatherMap — free tier, 1000 calls/day
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9. WeatherAPI.com — free tier
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OUTPUT:
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Plots saved to: weather_comparison_plots/
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Summary stats printed to console.
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archive_<loc>.png — daily lines + multi-source spread band + agreement signal
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archive_rolling_<loc>.png — 30-day rolling mean comparison (original style)
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cumulative_<loc>.png — cumulative annual precipitation
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vs_era5_<loc>.png — each provider vs ERA5 scatter (note: ERA5 is not ground truth)
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pairwise_<loc>.png — pairwise Pearson r and RMSE heatmaps (unbiased)
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wetdry_agreement_<loc>.png — % of days providers agree on wet vs dry
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forecast_ensemble_<loc>.png — ensemble uncertainty bands + exceedance probability
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forecast_<loc>.png — deterministic forecast bars (original style)
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Usage:
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python weather_api_comparison.py
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"""
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import os
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import json
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import time
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import datetime
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import requests
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import pandas as pd
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import time
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import requests
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from pathlib import Path
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# ============================================================
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# CONFIG
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# ============================================================
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@ -74,7 +82,6 @@ def fetch_openmeteo_archive(lat, lon, start, end, model="era5"):
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"""Open-Meteo ERA5 / ERA5-Land / CERRA archive.
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ERA5 is the default (no models param needed). ERA5-Land and CERRA use lowercase names.
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"""
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# ERA5 is the default model — adding models param with wrong casing causes 400
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model_suffix = "" if model == "era5" else f"&models={model}"
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url = (
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f"https://archive-api.open-meteo.com/v1/archive"
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@ -128,7 +135,6 @@ def fetch_nasa_power(lat, lon, start, end):
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def fetch_openmeteo_forecast(lat, lon, days=8):
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"""Open-Meteo NWP forecast — default best_match model."""
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end = datetime.date.today() + datetime.timedelta(days=days)
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url = (
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f"https://api.open-meteo.com/v1/forecast"
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f"?latitude={lat}&longitude={lon}"
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@ -147,8 +153,57 @@ def fetch_openmeteo_forecast(lat, lon, days=8):
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return df
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def fetch_openmeteo_ensemble_forecast(lat, lon, days=8):
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"""Open-Meteo ECMWF IFS ensemble forecast — 51 members at 0.4° resolution.
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Returns a DataFrame with daily percentile bands and exceedance probabilities:
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p10, p25, p50 (median), p75, p90 — mm/day
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prob_gt_1mm, prob_gt_5mm — % of members exceeding threshold
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n_members — number of members in response
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"""
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url = (
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f"https://ensemble-api.open-meteo.com/v1/ensemble"
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f"?latitude={lat}&longitude={lon}"
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f"&daily=precipitation_sum"
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f"&models=ecmwf_ifs04"
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f"&forecast_days={days}"
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f"&timezone=UTC"
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)
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r = requests.get(url, timeout=60)
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r.raise_for_status()
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body = r.json()
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daily = body["daily"]
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dates = pd.to_datetime(daily["time"])
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# Member columns are named like "precipitation_sum_member01", etc.
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member_keys = [k for k in daily.keys() if k.startswith("precipitation_sum")]
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if not member_keys:
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print(" ⚠ No member columns found in ensemble response")
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return None
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# Shape: (n_members, n_days)
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members = np.array([daily[k] for k in member_keys], dtype=float)
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members = np.where(members < 0, 0, members) # clip negatives
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df = pd.DataFrame({
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"date": dates,
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"p10": np.nanpercentile(members, 10, axis=0),
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"p25": np.nanpercentile(members, 25, axis=0),
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"p50": np.nanpercentile(members, 50, axis=0),
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"p75": np.nanpercentile(members, 75, axis=0),
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"p90": np.nanpercentile(members, 90, axis=0),
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"mean": np.nanmean(members, axis=0),
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"prob_gt_1mm": np.mean(members > 1.0, axis=0) * 100,
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"prob_gt_5mm": np.mean(members > 5.0, axis=0) * 100,
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"n_members": members.shape[0],
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})
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return df
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def fetch_yr_forecast(lat, lon):
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"""YR.no LocationForecast 2.0 — hourly precip aggregated to daily."""
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"""YR.no LocationForecast 2.0 — hourly precip aggregated to daily.
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Note: forecast-only service; no historical archive is available.
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"""
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url = f"https://api.met.no/weatherapi/locationforecast/2.0/compact?lat={lat}&lon={lon}"
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headers = {"User-Agent": USER_AGENT}
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r = requests.get(url, headers=headers, timeout=30)
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@ -234,88 +289,209 @@ def compare_stats(df, ref_col, other_col):
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"Bias": round(bias,2), "r": round(r,3)}
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def compute_pairwise_stats(data_dict):
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"""Compute pairwise Pearson r and RMSE for all archive provider pairs.
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Returns:
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names — list of provider names (same order as matrices)
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r_matrix — (n x n) Pearson r array, NaN where not computable
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rmse_matrix — (n x n) RMSE array (mm/day), 0 on diagonal
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"""
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providers = [(name, df) for name, df in data_dict.items()
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if df is not None and len(df) > 5]
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names = [p[0] for p in providers]
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n = len(names)
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r_matrix = np.full((n, n), np.nan)
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rmse_matrix = np.full((n, n), np.nan)
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for i in range(n):
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r_matrix[i, i] = 1.0
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rmse_matrix[i, i] = 0.0
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_, df_i = providers[i]
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for j in range(i + 1, n):
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_, df_j = providers[j]
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merged = df_i.merge(df_j, on="date", suffixes=("_i", "_j"))
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valid = merged[["rain_mm_i", "rain_mm_j"]].dropna()
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if len(valid) < 5:
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continue
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r = valid["rain_mm_i"].corr(valid["rain_mm_j"])
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rmse = ((valid["rain_mm_i"] - valid["rain_mm_j"]) ** 2).mean() ** 0.5
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r_matrix[i, j] = r_matrix[j, i] = round(r, 3)
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rmse_matrix[i, j] = rmse_matrix[j, i] = round(rmse, 2)
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return names, r_matrix, rmse_matrix
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def wetdry_agreement(data_dict, threshold=1.0):
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"""For each provider pair, compute % of days both-dry / both-wet / disagree.
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A day is 'dry' if rain_mm < threshold (default 1 mm).
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Returns a list of dicts: pair, both_dry, both_wet, disagree, n.
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"""
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providers = [(name, df) for name, df in data_dict.items()
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if df is not None and len(df) > 5]
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results = []
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for i, (name_i, df_i) in enumerate(providers):
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for j in range(i + 1, len(providers)):
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name_j, df_j = providers[j]
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merged = df_i.merge(df_j, on="date", suffixes=("_i", "_j"))
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valid = merged[["rain_mm_i", "rain_mm_j"]].dropna()
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if len(valid) < 5:
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continue
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dry_i = valid["rain_mm_i"] < threshold
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dry_j = valid["rain_mm_j"] < threshold
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both_dry = (dry_i & dry_j).mean() * 100
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both_wet = (~dry_i & ~dry_j).mean() * 100
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disagree = 100 - both_dry - both_wet
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results.append({
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"pair": f"{name_i}\nvs\n{name_j}",
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"both_dry": round(both_dry, 1),
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"both_wet": round(both_wet, 1),
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"disagree": round(disagree, 1),
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"n": len(valid),
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})
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return results
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# ============================================================
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# PLOTTING
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# PLOTTING — ARCHIVE
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# ============================================================
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def plot_archive(data_dict, location_name, start, end, output_dir):
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"""Line plot of all archive providers for one location."""
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fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True)
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ARCHIVE_COLORS = {
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"ERA5 (Open-Meteo)": "#1f77b4",
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"ERA5-Land (Open-Meteo)": "#ff7f0e",
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"CERRA (Open-Meteo)": "#2ca02c",
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"NASA POWER": "#d62728",
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}
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colors = {
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"ERA5 (Open-Meteo)": "#1f77b4",
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"ERA5-Land (Open-Meteo)": "#ff7f0e",
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"CERRA (Open-Meteo)": "#2ca02c",
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"NASA POWER": "#d62728",
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}
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# Top: daily raw
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def _build_spread_frame(data_dict):
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"""Merge all valid archive providers onto a common date axis.
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Returns a DataFrame with one column per provider + _mean and _std columns.
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"""
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valid = {name: df for name, df in data_dict.items()
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if df is not None and len(df) > 0}
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if not valid:
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return None, []
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merged = None
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for name, df in valid.items():
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tmp = df[["date", "rain_mm"]].rename(columns={"rain_mm": name})
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merged = tmp if merged is None else merged.merge(tmp, on="date", how="outer")
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cols = list(valid.keys())
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merged[cols] = merged[cols].clip(lower=0)
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merged["_mean"] = merged[cols].mean(axis=1)
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merged["_std"] = merged[cols].std(axis=1)
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return merged.sort_values("date").reset_index(drop=True), cols
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def plot_archive_with_spread(data_dict, location_name, start, end, output_dir):
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"""Three-panel archive plot:
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Top: Individual provider lines + multi-source mean±std shading
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Middle: 30-day rolling mean
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Bottom: Inter-source std (agreement signal — used to flag uncertain periods)
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"""
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spread, _ = _build_spread_frame(data_dict)
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if spread is None:
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return
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valid_providers = {name: df for name, df in data_dict.items()
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if df is not None and len(df) > 0}
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fig, axes = plt.subplots(3, 1, figsize=(14, 12), sharex=True)
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# --- Top: daily raw + spread band ---
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ax1 = axes[0]
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for name, df in data_dict.items():
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if df is not None and len(df) > 0:
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ax1.plot(df["date"], df["rain_mm"], label=name,
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color=colors.get(name), linewidth=0.8, alpha=0.85)
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ax1.fill_between(
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spread["date"],
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(spread["_mean"] - spread["_std"]).clip(lower=0),
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spread["_mean"] + spread["_std"],
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alpha=0.15, color="gray", label="Multi-source ±1 std"
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)
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ax1.plot(spread["date"], spread["_mean"], color="black", linewidth=1.0,
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linestyle="--", alpha=0.6, label="Multi-source mean", zorder=5)
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for name, df in valid_providers.items():
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ax1.plot(df["date"], df["rain_mm"],
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label=name, color=ARCHIVE_COLORS.get(name, "gray"),
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linewidth=0.7, alpha=0.85)
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ax1.set_ylabel("Precipitation (mm/day)")
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ax1.set_title(f"{location_name} — Daily Precipitation (archive {start} → {end})")
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ax1.legend(fontsize=8)
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ax1.set_title(
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f"{location_name} — Daily Precipitation with Multi-Source Spread\n"
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f"{start} → {end} | Grey band = ±1 std across all providers"
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)
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ax1.legend(fontsize=8, ncol=2)
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ax1.grid(True, alpha=0.3)
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# Bottom: 30-day rolling mean
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# --- Middle: 30-day rolling mean ---
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ax2 = axes[1]
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for name, df in data_dict.items():
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if df is not None and len(df) > 0:
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rolled = df.set_index("date")["rain_mm"].rolling(30, min_periods=15).mean()
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ax2.plot(rolled.index, rolled.values, label=name,
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color=colors.get(name), linewidth=1.5)
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roll_mean = spread.set_index("date")["_mean"].rolling(30, min_periods=15).mean()
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roll_std = spread.set_index("date")["_std"].rolling(30, min_periods=15).mean()
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ax2.fill_between(
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roll_mean.index,
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(roll_mean - roll_std).clip(lower=0),
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roll_mean + roll_std,
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alpha=0.15, color="gray"
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)
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for name, df in valid_providers.items():
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rolled = df.set_index("date")["rain_mm"].rolling(30, min_periods=15).mean()
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ax2.plot(rolled.index, rolled.values,
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label=name, color=ARCHIVE_COLORS.get(name, "gray"), linewidth=1.5)
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ax2.set_ylabel("30-day rolling mean (mm/day)")
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ax2.set_xlabel("Date")
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ax2.legend(fontsize=8)
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ax2.grid(True, alpha=0.3)
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ax2.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))
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fig.autofmt_xdate()
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# --- Bottom: inter-source std (agreement signal) ---
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ax3 = axes[2]
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ax3.fill_between(
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spread["date"], 0, spread["_std"],
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color="purple", alpha=0.35,
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label="Std across providers (higher = less agreement)"
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)
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median_std = spread["_std"].median()
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ax3.axhline(median_std, color="purple", linestyle=":", linewidth=1,
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label=f"Median std = {median_std:.2f} mm/day")
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ax3.set_ylabel("Std dev across\nproviders (mm)")
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ax3.set_xlabel("Date")
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ax3.legend(fontsize=8)
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ax3.grid(True, alpha=0.3)
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ax3.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))
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fig.autofmt_xdate()
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plt.tight_layout()
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path = output_dir / f"archive_{location_name}.png"
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plt.savefig(path, dpi=150)
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plt.savefig(path, dpi=150, bbox_inches="tight")
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plt.close()
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print(f" Saved: {path}")
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def plot_forecast(data_dict, location_name, output_dir):
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"""Bar chart comparing 7-day forecasts across providers."""
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fig, ax = plt.subplots(figsize=(12, 5))
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def plot_cumulative(data_dict, location_name, output_dir):
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"""Cumulative annual precipitation — most relevant for crop/irrigation context."""
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fig, ax = plt.subplots(figsize=(14, 5))
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providers = [(name, df) for name, df in data_dict.items() if df is not None and len(df) > 0]
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n = len(providers)
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if n == 0:
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plt.close()
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return
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for name, df in data_dict.items():
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if df is None or len(df) == 0:
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continue
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s = df.set_index("date")["rain_mm"].sort_index().cumsum()
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total = s.iloc[-1]
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ax.plot(s.index, s.values,
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label=f"{name} (total: {total:.0f} mm)",
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color=ARCHIVE_COLORS.get(name, "gray"), linewidth=1.8)
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# Collect all forecast dates
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all_dates = sorted(set(
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d for _, df in providers
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for d in df["date"].dt.date.tolist()
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))
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x = np.arange(len(all_dates))
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width = 0.8 / n
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cmap = matplotlib.colormaps["tab10"].resampled(n)
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for i, (name, df) in enumerate(providers):
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vals = []
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date_map = dict(zip(df["date"].dt.date, df["rain_mm"]))
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for d in all_dates:
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vals.append(date_map.get(d, 0.0))
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ax.bar(x + i * width, vals, width, label=name, color=cmap(i), alpha=0.85)
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ax.set_xticks(x + width * (n - 1) / 2)
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ax.set_xticklabels([d.strftime("%d %b") for d in all_dates], rotation=45, ha="right")
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ax.set_ylabel("Precipitation (mm/day)")
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ax.set_title(f"{location_name} — 7-Day Forecast Comparison")
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ax.set_ylabel("Cumulative precipitation (mm)")
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ax.set_xlabel("Date")
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ax.set_title(
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f"{location_name} — Cumulative Annual Precipitation by Provider\n"
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"Divergence = sources disagree on total seasonal rainfall"
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)
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ax.legend(fontsize=9)
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ax.grid(True, axis="y", alpha=0.3)
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ax.grid(True, alpha=0.3)
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ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))
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fig.autofmt_xdate()
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plt.tight_layout()
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path = output_dir / f"forecast_{location_name}.png"
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path = output_dir / f"cumulative_{location_name}.png"
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plt.savefig(path, dpi=150)
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plt.close()
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print(f" Saved: {path}")
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@ -324,6 +500,10 @@ def plot_forecast(data_dict, location_name, output_dir):
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def plot_vs_era5(data_dict, location_name, output_dir):
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"""Each provider vs ERA5 reference: scatter + regression line.
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NOTE: ERA5 is used as reference here for visual comparison only.
|
||||
It is a reanalysis product, not a ground-truth measurement.
|
||||
Use the pairwise heatmap (plot_pairwise_heatmap) for an unbiased comparison.
|
||||
|
||||
How to read:
|
||||
- Each panel shows one provider (y-axis) vs ERA5 (x-axis) for daily precip.
|
||||
- Points on the red diagonal = perfect agreement.
|
||||
|
|
@ -344,26 +524,18 @@ def plot_vs_era5(data_dict, location_name, output_dir):
|
|||
n = len(others)
|
||||
fig, axes = plt.subplots(1, n, figsize=(5 * n, 5), squeeze=False)
|
||||
|
||||
colors = {
|
||||
"ERA5-Land (Open-Meteo)": "#ff7f0e",
|
||||
"CERRA (Open-Meteo)": "#2ca02c",
|
||||
"NASA POWER": "#d62728",
|
||||
}
|
||||
|
||||
for i, (name, df) in enumerate(others):
|
||||
ax = axes[0][i]
|
||||
merged = ref_df.merge(df, on="date", suffixes=("_ref", "_cmp"))
|
||||
valid = merged[["rain_mm_ref", "rain_mm_cmp"]].dropna()
|
||||
|
||||
color = colors.get(name, "steelblue")
|
||||
color = ARCHIVE_COLORS.get(name, "steelblue")
|
||||
ax.scatter(valid["rain_mm_ref"], valid["rain_mm_cmp"],
|
||||
s=4, alpha=0.35, color=color)
|
||||
|
||||
# Perfect-agreement diagonal
|
||||
lim = max(valid.max().max(), 1) * 1.05
|
||||
ax.plot([0, lim], [0, lim], "r--", linewidth=1, label="Perfect agreement")
|
||||
|
||||
# Linear regression line
|
||||
if len(valid) > 5:
|
||||
coeffs = np.polyfit(valid["rain_mm_ref"], valid["rain_mm_cmp"], 1)
|
||||
x_fit = np.linspace(0, lim, 100)
|
||||
|
|
@ -382,8 +554,8 @@ def plot_vs_era5(data_dict, location_name, output_dir):
|
|||
ax.grid(True, alpha=0.3)
|
||||
|
||||
fig.suptitle(
|
||||
f"{location_name} — Daily Precip vs ERA5 Reference\n"
|
||||
"Red dashed = perfect agreement. Points above line = provider wetter than ERA5.",
|
||||
f"{location_name} — Daily Precip vs ERA5 (reference only — ERA5 is not ground truth)\n"
|
||||
"Red dashed = perfect agreement. See pairwise heatmap for unbiased comparison.",
|
||||
fontsize=10
|
||||
)
|
||||
plt.tight_layout()
|
||||
|
|
@ -393,38 +565,262 @@ def plot_vs_era5(data_dict, location_name, output_dir):
|
|||
print(f" Saved: {path}")
|
||||
|
||||
|
||||
def plot_cumulative(data_dict, location_name, output_dir):
|
||||
"""Cumulative annual precipitation — most relevant for crop/irrigation context."""
|
||||
fig, ax = plt.subplots(figsize=(14, 5))
|
||||
def plot_pairwise_heatmap(data_dict, location_name, output_dir):
|
||||
"""Two heatmaps side by side: pairwise Pearson r and pairwise RMSE.
|
||||
|
||||
colors = {
|
||||
"ERA5 (Open-Meteo)": "#1f77b4",
|
||||
"ERA5-Land (Open-Meteo)": "#ff7f0e",
|
||||
"CERRA (Open-Meteo)": "#2ca02c",
|
||||
"NASA POWER": "#d62728",
|
||||
}
|
||||
No provider is treated as reference — all pairs compared equally.
|
||||
High r + low RMSE = sources agree. Where they diverge, neither is ground truth.
|
||||
"""
|
||||
names, r_matrix, rmse_matrix = compute_pairwise_stats(data_dict)
|
||||
if len(names) < 2:
|
||||
return
|
||||
|
||||
for name, df in data_dict.items():
|
||||
if df is None or len(df) == 0:
|
||||
continue
|
||||
s = df.set_index("date")["rain_mm"].sort_index().cumsum()
|
||||
total = s.iloc[-1]
|
||||
ax.plot(s.index, s.values, label=f"{name} (total: {total:.0f} mm)",
|
||||
color=colors.get(name), linewidth=1.8)
|
||||
n = len(names)
|
||||
short = [nm.replace(" (Open-Meteo)", "\n(OM)") for nm in names]
|
||||
|
||||
ax.set_ylabel("Cumulative precipitation (mm)")
|
||||
ax.set_xlabel("Date")
|
||||
ax.set_title(
|
||||
f"{location_name} — Cumulative Annual Precipitation by Provider\n"
|
||||
"Divergence = sources disagree on total seasonal rainfall"
|
||||
fig, axes = plt.subplots(1, 2, figsize=(max(10, n * 2.5), max(5, n * 1.5 + 1)))
|
||||
|
||||
# Pearson r
|
||||
ax1 = axes[0]
|
||||
im1 = ax1.imshow(r_matrix, vmin=0, vmax=1, cmap="YlGn", aspect="auto")
|
||||
plt.colorbar(im1, ax=ax1, label="Pearson r")
|
||||
ax1.set_xticks(range(n)); ax1.set_yticks(range(n))
|
||||
ax1.set_xticklabels(short, fontsize=8, rotation=45, ha="right")
|
||||
ax1.set_yticklabels(short, fontsize=8)
|
||||
for i in range(n):
|
||||
for j in range(n):
|
||||
val = r_matrix[i, j]
|
||||
if not np.isnan(val):
|
||||
text_color = "white" if val > 0.8 else "black"
|
||||
ax1.text(j, i, f"{val:.2f}", ha="center", va="center",
|
||||
fontsize=9, color=text_color)
|
||||
ax1.set_title("Pairwise Pearson r\n(1.0 = perfect agreement, no reference)")
|
||||
|
||||
# RMSE
|
||||
ax2 = axes[1]
|
||||
off_diag = rmse_matrix[rmse_matrix > 0]
|
||||
vmax_rmse = float(np.nanmax(off_diag)) if len(off_diag) > 0 else 10
|
||||
im2 = ax2.imshow(rmse_matrix, vmin=0, vmax=vmax_rmse, cmap="YlOrRd_r", aspect="auto")
|
||||
plt.colorbar(im2, ax=ax2, label="RMSE (mm/day)")
|
||||
ax2.set_xticks(range(n)); ax2.set_yticks(range(n))
|
||||
ax2.set_xticklabels(short, fontsize=8, rotation=45, ha="right")
|
||||
ax2.set_yticklabels(short, fontsize=8)
|
||||
for i in range(n):
|
||||
for j in range(n):
|
||||
val = rmse_matrix[i, j]
|
||||
if not np.isnan(val):
|
||||
ax2.text(j, i, f"{val:.1f}", ha="center", va="center", fontsize=9)
|
||||
ax2.set_title("Pairwise RMSE (mm/day)\n(0 = perfect agreement)")
|
||||
|
||||
fig.suptitle(
|
||||
f"{location_name} — Archive Provider Agreement (unbiased — no single reference)\n"
|
||||
"Pairs with high r + low RMSE are consistent. Divergent pairs reveal dataset uncertainty.",
|
||||
fontsize=10
|
||||
)
|
||||
ax.legend(fontsize=9)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))
|
||||
plt.tight_layout()
|
||||
path = output_dir / f"pairwise_{location_name}.png"
|
||||
plt.savefig(path, dpi=150, bbox_inches="tight")
|
||||
plt.close()
|
||||
print(f" Saved: {path}")
|
||||
|
||||
|
||||
def plot_wetdry_agreement(data_dict, location_name, output_dir, threshold=1.0):
|
||||
"""Stacked bar chart: for each provider pair, % of days both-dry / both-wet / disagree.
|
||||
|
||||
High 'disagree' % means one source says rain, the other says dry on the same day.
|
||||
This is the most practically relevant divergence for SmartCane crop monitoring.
|
||||
"""
|
||||
results = wetdry_agreement(data_dict, threshold)
|
||||
if not results:
|
||||
return
|
||||
|
||||
pairs = [r["pair"] for r in results]
|
||||
both_dry = [r["both_dry"] for r in results]
|
||||
both_wet = [r["both_wet"] for r in results]
|
||||
disagree = [r["disagree"] for r in results]
|
||||
|
||||
fig, ax = plt.subplots(figsize=(max(8, len(pairs) * 2.8), 6))
|
||||
x = np.arange(len(pairs))
|
||||
|
||||
ax.bar(x, both_dry, label=f"Both dry (<{threshold} mm)", color="#a8d5e2", edgecolor="white")
|
||||
ax.bar(x, both_wet, bottom=both_dry,
|
||||
label=f"Both wet (≥{threshold} mm)", color="#3a7ebf", edgecolor="white")
|
||||
bottom2 = [d + w for d, w in zip(both_dry, both_wet)]
|
||||
ax.bar(x, disagree, bottom=bottom2,
|
||||
label="Disagree (one wet, one dry)", color="#e07b54", edgecolor="white")
|
||||
|
||||
# Add % labels inside bars
|
||||
for i, r in enumerate(results):
|
||||
if r["both_dry"] > 4:
|
||||
ax.text(i, r["both_dry"] / 2, f"{r['both_dry']:.0f}%",
|
||||
ha="center", va="center", fontsize=7, color="black")
|
||||
if r["both_wet"] > 4:
|
||||
ax.text(i, r["both_dry"] + r["both_wet"] / 2, f"{r['both_wet']:.0f}%",
|
||||
ha="center", va="center", fontsize=7, color="white")
|
||||
if r["disagree"] > 4:
|
||||
ax.text(i, bottom2[i] + r["disagree"] / 2, f"{r['disagree']:.0f}%",
|
||||
ha="center", va="center", fontsize=7, color="white")
|
||||
|
||||
ax.set_xticks(x)
|
||||
ax.set_xticklabels(pairs, fontsize=8)
|
||||
ax.set_ylim(0, 108)
|
||||
ax.set_ylabel("% of days in archive period")
|
||||
ax.set_title(
|
||||
f"{location_name} — Provider Agreement on Wet vs Dry Days\n"
|
||||
f"Threshold = {threshold} mm/day | Orange = providers disagree on whether it rained"
|
||||
)
|
||||
ax.legend(loc="upper right", fontsize=9)
|
||||
ax.grid(True, axis="y", alpha=0.3)
|
||||
plt.tight_layout()
|
||||
|
||||
path = output_dir / f"wetdry_agreement_{location_name}.png"
|
||||
plt.savefig(path, dpi=150, bbox_inches="tight")
|
||||
plt.close()
|
||||
print(f" Saved: {path}")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# PLOTTING — FORECAST
|
||||
# ============================================================
|
||||
|
||||
def plot_forecast_with_ensemble(forecast_data, ensemble_df, location_name, output_dir):
|
||||
"""Two-panel forecast comparison:
|
||||
Top: Deterministic bars (YR.no, Open-Meteo) + ECMWF ensemble percentile shading
|
||||
Bottom: Exceedance probabilities P(rain > 1 mm) and P(rain > 5 mm)
|
||||
|
||||
Reading guide:
|
||||
- Dark blue shading = where 50% of ensemble members agree (25th–75th %ile)
|
||||
- Light blue shading = where 80% of ensemble members agree (10th–90th %ile)
|
||||
- Bars = deterministic (single-value) forecast from each provider
|
||||
- Bottom panel: at 50% on the dashed line = coin-flip uncertainty about rain event
|
||||
"""
|
||||
has_ensemble = ensemble_df is not None and len(ensemble_df) > 0
|
||||
det_providers = [(name, df) for name, df in forecast_data.items()
|
||||
if df is not None and len(df) > 0]
|
||||
|
||||
if not has_ensemble and not det_providers:
|
||||
return
|
||||
|
||||
fig, axes = plt.subplots(
|
||||
2, 1, figsize=(12, 8), sharex=True,
|
||||
gridspec_kw={"height_ratios": [3, 1.5]}
|
||||
)
|
||||
|
||||
# ---- TOP: deterministic bars + ensemble shading ----
|
||||
ax1 = axes[0]
|
||||
|
||||
if has_ensemble:
|
||||
ax1.fill_between(
|
||||
ensemble_df["date"], ensemble_df["p10"], ensemble_df["p90"],
|
||||
alpha=0.12, color="steelblue", label="Ensemble 10th–90th %ile"
|
||||
)
|
||||
ax1.fill_between(
|
||||
ensemble_df["date"], ensemble_df["p25"], ensemble_df["p75"],
|
||||
alpha=0.28, color="steelblue", label="Ensemble 25th–75th %ile"
|
||||
)
|
||||
ax1.plot(
|
||||
ensemble_df["date"], ensemble_df["p50"],
|
||||
color="steelblue", linewidth=1.8, linestyle="-",
|
||||
label=f"Ensemble median ({int(ensemble_df['n_members'].iloc[0])} members)",
|
||||
zorder=5
|
||||
)
|
||||
|
||||
det_colors = {"Open-Meteo Forecast": "#1f77b4", "YR.no": "#e8882a"}
|
||||
bar_half = datetime.timedelta(hours=10) # offset so bars don't overlap
|
||||
n_det = len(det_providers)
|
||||
offsets = np.linspace(-bar_half.total_seconds() / 3600 * (n_det - 1) / 2,
|
||||
bar_half.total_seconds() / 3600 * (n_det - 1) / 2,
|
||||
n_det)
|
||||
bar_width_days = 0.3
|
||||
|
||||
for k, (name, df) in enumerate(det_providers):
|
||||
offset_td = datetime.timedelta(hours=offsets[k])
|
||||
shifted_dates = df["date"] + offset_td
|
||||
ax1.bar(
|
||||
shifted_dates, df["rain_mm"],
|
||||
width=bar_width_days,
|
||||
label=name,
|
||||
color=det_colors.get(name, f"C{k}"),
|
||||
alpha=0.75,
|
||||
zorder=4,
|
||||
)
|
||||
|
||||
ax1.set_ylabel("Precipitation (mm/day)")
|
||||
ax1.set_title(
|
||||
f"{location_name} — 7-Day Forecast\n"
|
||||
"Shading = Open-Meteo ECMWF ensemble spread | Bars = deterministic forecasts"
|
||||
)
|
||||
ax1.legend(fontsize=8, loc="upper right")
|
||||
ax1.grid(True, axis="y", alpha=0.3)
|
||||
ax1.set_ylim(bottom=0)
|
||||
|
||||
# ---- BOTTOM: exceedance probabilities ----
|
||||
ax2 = axes[1]
|
||||
if has_ensemble:
|
||||
ax2.step(
|
||||
ensemble_df["date"], ensemble_df["prob_gt_1mm"],
|
||||
where="mid", color="steelblue", linewidth=2.0,
|
||||
label="P(rain > 1 mm)"
|
||||
)
|
||||
ax2.step(
|
||||
ensemble_df["date"], ensemble_df["prob_gt_5mm"],
|
||||
where="mid", color="darkblue", linewidth=2.0, linestyle="--",
|
||||
label="P(rain > 5 mm)"
|
||||
)
|
||||
ax2.fill_between(
|
||||
ensemble_df["date"], 0, ensemble_df["prob_gt_5mm"],
|
||||
step="mid", alpha=0.18, color="darkblue"
|
||||
)
|
||||
ax2.axhline(50, color="gray", linestyle=":", linewidth=0.9, alpha=0.8,
|
||||
label="50% (coin-flip)")
|
||||
ax2.set_ylim(0, 108)
|
||||
ax2.set_ylabel("Exceedance\nprobability (%)")
|
||||
ax2.legend(fontsize=8, loc="upper right")
|
||||
ax2.grid(True, alpha=0.3)
|
||||
ax2.set_xlabel("Date")
|
||||
ax2.xaxis.set_major_formatter(mdates.DateFormatter("%d %b"))
|
||||
|
||||
fig.autofmt_xdate()
|
||||
plt.tight_layout()
|
||||
|
||||
path = output_dir / f"cumulative_{location_name}.png"
|
||||
path = output_dir / f"forecast_ensemble_{location_name}.png"
|
||||
plt.savefig(path, dpi=150, bbox_inches="tight")
|
||||
plt.close()
|
||||
print(f" Saved: {path}")
|
||||
|
||||
|
||||
def plot_forecast(data_dict, location_name, output_dir):
|
||||
"""Bar chart comparing deterministic 7-day forecasts (original style, kept for reference)."""
|
||||
_, ax = plt.subplots(figsize=(12, 5))
|
||||
|
||||
providers = [(name, df) for name, df in data_dict.items() if df is not None and len(df) > 0]
|
||||
n = len(providers)
|
||||
if n == 0:
|
||||
plt.close()
|
||||
return
|
||||
|
||||
all_dates = sorted(set(
|
||||
d for _, df in providers
|
||||
for d in df["date"].dt.date.tolist()
|
||||
))
|
||||
x = np.arange(len(all_dates))
|
||||
width = 0.8 / n
|
||||
|
||||
cmap = matplotlib.colormaps["tab10"].resampled(n)
|
||||
for i, (name, df) in enumerate(providers):
|
||||
date_map = dict(zip(df["date"].dt.date, df["rain_mm"]))
|
||||
vals = [date_map.get(d, 0.0) for d in all_dates]
|
||||
ax.bar(x + i * width, vals, width, label=name, color=cmap(i), alpha=0.85)
|
||||
|
||||
ax.set_xticks(x + width * (n - 1) / 2)
|
||||
ax.set_xticklabels([d.strftime("%d %b") for d in all_dates], rotation=45, ha="right")
|
||||
ax.set_ylabel("Precipitation (mm/day)")
|
||||
ax.set_title(f"{location_name} — 7-Day Deterministic Forecast Comparison")
|
||||
ax.legend(fontsize=9)
|
||||
ax.grid(True, axis="y", alpha=0.3)
|
||||
plt.tight_layout()
|
||||
|
||||
path = output_dir / f"forecast_{location_name}.png"
|
||||
plt.savefig(path, dpi=150)
|
||||
plt.close()
|
||||
print(f" Saved: {path}")
|
||||
|
|
@ -489,28 +885,26 @@ def run_location(loc_name, lat, lon):
|
|||
print(f" ✗ NASA POWER failed: {e}")
|
||||
archive_data["NASA POWER"] = None
|
||||
|
||||
# Stats vs ERA5 reference
|
||||
print("\n Stats vs ERA5 (Open-Meteo) reference:")
|
||||
ref_df = archive_data.get("ERA5 (Open-Meteo)")
|
||||
for name, df in archive_data.items():
|
||||
if name == "ERA5 (Open-Meteo)" or df is None:
|
||||
continue
|
||||
if ref_df is None:
|
||||
continue
|
||||
merged = ref_df.merge(df, on="date", suffixes=("_ref", "_cmp"))
|
||||
stats = compare_stats(merged, "rain_mm_ref", "rain_mm_cmp")
|
||||
print(f" {name:30s} n={stats['n']:4d} MAE={stats['MAE']} "
|
||||
f"RMSE={stats['RMSE']} Bias={stats['Bias']} r={stats['r']}")
|
||||
# Pairwise stats (no ERA5 reference bias)
|
||||
print("\n Pairwise archive stats (Pearson r | RMSE mm/day | Bias mm/day):")
|
||||
names, r_mat, rmse_mat = compute_pairwise_stats(archive_data)
|
||||
for i in range(len(names)):
|
||||
for j in range(i + 1, len(names)):
|
||||
if not np.isnan(r_mat[i, j]):
|
||||
print(f" {names[i]:30s} vs {names[j]:30s} "
|
||||
f"r={r_mat[i,j]:.3f} RMSE={rmse_mat[i,j]:.2f} mm")
|
||||
|
||||
plot_archive(archive_data, loc_name, ARCHIVE_START, ARCHIVE_END, OUTPUT_DIR)
|
||||
plot_archive_with_spread(archive_data, loc_name, ARCHIVE_START, ARCHIVE_END, OUTPUT_DIR)
|
||||
plot_cumulative(archive_data, loc_name, OUTPUT_DIR)
|
||||
plot_vs_era5(archive_data, loc_name, OUTPUT_DIR)
|
||||
plot_pairwise_heatmap(archive_data, loc_name, OUTPUT_DIR)
|
||||
plot_wetdry_agreement(archive_data, loc_name, OUTPUT_DIR)
|
||||
|
||||
# ---- FORECAST ----
|
||||
print("\n[Forecast]")
|
||||
forecast_data = {}
|
||||
|
||||
print(" Fetching Open-Meteo forecast...")
|
||||
print(" Fetching Open-Meteo deterministic forecast...")
|
||||
try:
|
||||
forecast_data["Open-Meteo Forecast"] = fetch_openmeteo_forecast(lat, lon)
|
||||
print(f" → {len(forecast_data['Open-Meteo Forecast'])} days")
|
||||
|
|
@ -518,6 +912,17 @@ def run_location(loc_name, lat, lon):
|
|||
print(f" ✗ Open-Meteo forecast failed: {e}")
|
||||
forecast_data["Open-Meteo Forecast"] = None
|
||||
|
||||
time.sleep(0.5)
|
||||
print(" Fetching Open-Meteo ECMWF ensemble forecast...")
|
||||
ensemble_df = None
|
||||
try:
|
||||
ensemble_df = fetch_openmeteo_ensemble_forecast(lat, lon)
|
||||
if ensemble_df is not None:
|
||||
print(f" → {len(ensemble_df)} days "
|
||||
f"({int(ensemble_df['n_members'].iloc[0])} ensemble members)")
|
||||
except Exception as e:
|
||||
print(f" ✗ Open-Meteo ensemble failed: {e}")
|
||||
|
||||
time.sleep(0.5)
|
||||
print(" Fetching YR.no LocationForecast...")
|
||||
try:
|
||||
|
|
@ -551,6 +956,7 @@ def run_location(loc_name, lat, lon):
|
|||
print(f" ✗ WeatherAPI.com failed: {e}")
|
||||
forecast_data["WeatherAPI.com"] = None
|
||||
|
||||
plot_forecast_with_ensemble(forecast_data, ensemble_df, loc_name, OUTPUT_DIR)
|
||||
plot_forecast(forecast_data, loc_name, OUTPUT_DIR)
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue