974 lines
37 KiB
Python
974 lines
37 KiB
Python
"""
|
||
weather_api_comparison.py
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||
=========================
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Compare daily precipitation from multiple free weather APIs across two locations:
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- Arnhem, Netherlands (51.985°N, 5.899°E) — European climate
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- Angata, Kenya ( 1.330°S, 34.738°E) — tropical / sugarcane context
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ARCHIVE providers (no API key required):
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1. Open-Meteo ERA5 — current SmartCane provider (0.25°, global)
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2. Open-Meteo ERA5-Land — higher resolution variant (0.10°, global)
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3. Open-Meteo CERRA — EU regional reanalysis (0.05°, EU only)
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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 — 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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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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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 datetime
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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.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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LOCATIONS = {
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"Arnhem_NL": {"lat": 51.985, "lon": 5.899},
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"Angata_KE": {"lat": -1.330, "lon": 34.738},
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}
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# Archive: last 12 months
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ARCHIVE_END = datetime.date.today() - datetime.timedelta(days=2) # ERA5 lags ~2 days
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ARCHIVE_START = ARCHIVE_END - datetime.timedelta(days=365)
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# Forecast: today + 7 days
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FORECAST_START = datetime.date.today()
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FORECAST_END = FORECAST_START + datetime.timedelta(days=7)
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# Optional API keys — leave "" to skip that provider
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OPENWEATHERMAP_KEY = "" # https://openweathermap.org/api
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WEATHERAPI_KEY = "" # https://www.weatherapi.com/
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OUTPUT_DIR = Path("weather_comparison_plots")
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OUTPUT_DIR.mkdir(exist_ok=True)
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USER_AGENT = "SmartCane-WeatherComparison/1.0 (research; contact via github)"
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# ============================================================
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# ARCHIVE FETCHERS
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# ============================================================
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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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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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f"?latitude={lat}&longitude={lon}"
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f"&daily=precipitation_sum"
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f"&start_date={start}&end_date={end}"
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f"{model_suffix}"
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f"&timezone=UTC"
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)
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r = requests.get(url, timeout=30)
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r.raise_for_status()
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body = r.json()
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df = pd.DataFrame({
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"date": pd.to_datetime(body["daily"]["time"]),
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"rain_mm": body["daily"]["precipitation_sum"],
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})
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df["rain_mm"] = pd.to_numeric(df["rain_mm"], errors="coerce").clip(lower=0).fillna(0)
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# ERA5-Land sometimes returns values in meters (Open-Meteo API quirk).
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# Auto-detect: if annual total < 50mm for any non-polar location, assume m → convert.
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if df["rain_mm"].sum() < 50 and len(df) > 30:
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df["rain_mm"] = df["rain_mm"] * 1000
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print(f" ⚠ Unit auto-converted m→mm (values were implausibly small)")
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return df
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def fetch_nasa_power(lat, lon, start, end):
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"""NASA POWER — daily PRECTOTCORR (precipitation corrected), 0.5° grid."""
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url = (
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"https://power.larc.nasa.gov/api/temporal/daily/point"
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f"?parameters=PRECTOTCORR"
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f"&community=AG"
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f"&longitude={lon}&latitude={lat}"
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f"&start={start.strftime('%Y%m%d')}&end={end.strftime('%Y%m%d')}"
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f"&format=JSON"
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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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raw = body["properties"]["parameter"]["PRECTOTCORR"]
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df = pd.DataFrame([
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{"date": pd.to_datetime(k, format="%Y%m%d"), "rain_mm": max(v, 0)}
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for k, v in raw.items()
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if v != -999 # NASA POWER fill value
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])
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return df.sort_values("date").reset_index(drop=True)
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# ============================================================
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# FORECAST FETCHERS
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# ============================================================
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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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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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f"&daily=precipitation_sum"
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f"&forecast_days={days + 1}"
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f"&timezone=UTC"
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)
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r = requests.get(url, timeout=30)
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r.raise_for_status()
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body = r.json()
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df = pd.DataFrame({
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"date": pd.to_datetime(body["daily"]["time"]),
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"rain_mm": body["daily"]["precipitation_sum"],
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})
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df["rain_mm"] = pd.to_numeric(df["rain_mm"], errors="coerce").fillna(0)
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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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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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r.raise_for_status()
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body = r.json()
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records = []
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for entry in body["properties"]["timeseries"]:
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ts = pd.to_datetime(entry["time"])
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data = entry["data"]
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precip = 0.0
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if "next_1_hours" in data:
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precip = data["next_1_hours"]["details"].get("precipitation_amount", 0.0)
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elif "next_6_hours" in data:
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precip = data["next_6_hours"]["details"].get("precipitation_amount", 0.0) / 6
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records.append({"datetime": ts, "precip_hour": precip})
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hourly = pd.DataFrame(records)
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hourly["date"] = hourly["datetime"].dt.date
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daily = (
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hourly.groupby("date")["precip_hour"]
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.sum()
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.reset_index()
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.rename(columns={"precip_hour": "rain_mm"})
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)
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daily["date"] = pd.to_datetime(daily["date"])
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return daily
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def fetch_openweathermap_forecast(lat, lon, api_key):
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"""OpenWeatherMap One Call 3.0 — daily forecast (needs paid/free key)."""
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url = (
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f"https://api.openweathermap.org/data/3.0/onecall"
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f"?lat={lat}&lon={lon}"
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f"&exclude=current,minutely,hourly,alerts"
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f"&appid={api_key}&units=metric"
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)
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r = requests.get(url, timeout=30)
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r.raise_for_status()
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body = r.json()
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records = []
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for day in body.get("daily", []):
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records.append({
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"date": pd.to_datetime(day["dt"], unit="s").normalize(),
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"rain_mm": day.get("rain", 0.0),
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})
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return pd.DataFrame(records)
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def fetch_weatherapi_forecast(lat, lon, api_key, days=7):
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"""WeatherAPI.com free forecast (up to 3 days on free tier, 14 on paid)."""
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url = (
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f"https://api.weatherapi.com/v1/forecast.json"
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f"?key={api_key}&q={lat},{lon}&days={days}&aqi=no&alerts=no"
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)
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r = requests.get(url, timeout=30)
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r.raise_for_status()
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body = r.json()
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records = []
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for day in body["forecast"]["forecastday"]:
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records.append({
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"date": pd.to_datetime(day["date"]),
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"rain_mm": day["day"].get("totalprecip_mm", 0.0),
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})
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return pd.DataFrame(records)
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# ============================================================
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# STATS
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# ============================================================
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def compare_stats(df, ref_col, other_col):
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"""Compute MAE, RMSE, bias, Pearson r between two columns."""
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valid = df[[ref_col, other_col]].dropna()
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if len(valid) < 5:
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return {"n": len(valid), "MAE": None, "RMSE": None, "Bias": None, "r": None}
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diff = valid[other_col] - valid[ref_col]
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mae = diff.abs().mean()
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rmse = (diff**2).mean()**0.5
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bias = diff.mean()
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r = valid[ref_col].corr(valid[other_col])
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return {"n": len(valid), "MAE": round(mae,2), "RMSE": round(rmse,2),
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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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||
|
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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({
|
||
"pair": f"{name_i}\nvs\n{name_j}",
|
||
"both_dry": round(both_dry, 1),
|
||
"both_wet": round(both_wet, 1),
|
||
"disagree": round(disagree, 1),
|
||
"n": len(valid),
|
||
})
|
||
return results
|
||
|
||
|
||
# ============================================================
|
||
# PLOTTING — ARCHIVE
|
||
# ============================================================
|
||
|
||
ARCHIVE_COLORS = {
|
||
"ERA5 (Open-Meteo)": "#1f77b4",
|
||
"ERA5-Land (Open-Meteo)": "#ff7f0e",
|
||
"CERRA (Open-Meteo)": "#2ca02c",
|
||
"NASA POWER": "#d62728",
|
||
}
|
||
|
||
|
||
def _build_spread_frame(data_dict):
|
||
"""Merge all valid archive providers onto a common date axis.
|
||
Returns a DataFrame with one column per provider + _mean and _std columns.
|
||
"""
|
||
valid = {name: df for name, df in data_dict.items()
|
||
if df is not None and len(df) > 0}
|
||
if not valid:
|
||
return None, []
|
||
|
||
merged = None
|
||
for name, df in valid.items():
|
||
tmp = df[["date", "rain_mm"]].rename(columns={"rain_mm": name})
|
||
merged = tmp if merged is None else merged.merge(tmp, on="date", how="outer")
|
||
|
||
cols = list(valid.keys())
|
||
merged[cols] = merged[cols].clip(lower=0)
|
||
merged["_mean"] = merged[cols].mean(axis=1)
|
||
merged["_std"] = merged[cols].std(axis=1)
|
||
return merged.sort_values("date").reset_index(drop=True), cols
|
||
|
||
|
||
def plot_archive_with_spread(data_dict, location_name, start, end, output_dir):
|
||
"""Three-panel archive plot:
|
||
Top: Individual provider lines + multi-source mean±std shading
|
||
Middle: 30-day rolling mean
|
||
Bottom: Inter-source std (agreement signal — used to flag uncertain periods)
|
||
"""
|
||
spread, _ = _build_spread_frame(data_dict)
|
||
if spread is None:
|
||
return
|
||
|
||
valid_providers = {name: df for name, df in data_dict.items()
|
||
if df is not None and len(df) > 0}
|
||
|
||
fig, axes = plt.subplots(3, 1, figsize=(14, 12), sharex=True)
|
||
|
||
# --- Top: daily raw + spread band ---
|
||
ax1 = axes[0]
|
||
ax1.fill_between(
|
||
spread["date"],
|
||
(spread["_mean"] - spread["_std"]).clip(lower=0),
|
||
spread["_mean"] + spread["_std"],
|
||
alpha=0.15, color="gray", label="Multi-source ±1 std"
|
||
)
|
||
ax1.plot(spread["date"], spread["_mean"], color="black", linewidth=1.0,
|
||
linestyle="--", alpha=0.6, label="Multi-source mean", zorder=5)
|
||
for name, df in valid_providers.items():
|
||
ax1.plot(df["date"], df["rain_mm"],
|
||
label=name, color=ARCHIVE_COLORS.get(name, "gray"),
|
||
linewidth=0.7, alpha=0.85)
|
||
ax1.set_ylabel("Precipitation (mm/day)")
|
||
ax1.set_title(
|
||
f"{location_name} — Daily Precipitation with Multi-Source Spread\n"
|
||
f"{start} → {end} | Grey band = ±1 std across all providers"
|
||
)
|
||
ax1.legend(fontsize=8, ncol=2)
|
||
ax1.grid(True, alpha=0.3)
|
||
|
||
# --- Middle: 30-day rolling mean ---
|
||
ax2 = axes[1]
|
||
roll_mean = spread.set_index("date")["_mean"].rolling(30, min_periods=15).mean()
|
||
roll_std = spread.set_index("date")["_std"].rolling(30, min_periods=15).mean()
|
||
ax2.fill_between(
|
||
roll_mean.index,
|
||
(roll_mean - roll_std).clip(lower=0),
|
||
roll_mean + roll_std,
|
||
alpha=0.15, color="gray"
|
||
)
|
||
for name, df in valid_providers.items():
|
||
rolled = df.set_index("date")["rain_mm"].rolling(30, min_periods=15).mean()
|
||
ax2.plot(rolled.index, rolled.values,
|
||
label=name, color=ARCHIVE_COLORS.get(name, "gray"), linewidth=1.5)
|
||
ax2.set_ylabel("30-day rolling mean (mm/day)")
|
||
ax2.legend(fontsize=8)
|
||
ax2.grid(True, alpha=0.3)
|
||
|
||
# --- Bottom: inter-source std (agreement signal) ---
|
||
ax3 = axes[2]
|
||
ax3.fill_between(
|
||
spread["date"], 0, spread["_std"],
|
||
color="purple", alpha=0.35,
|
||
label="Std across providers (higher = less agreement)"
|
||
)
|
||
median_std = spread["_std"].median()
|
||
ax3.axhline(median_std, color="purple", linestyle=":", linewidth=1,
|
||
label=f"Median std = {median_std:.2f} mm/day")
|
||
ax3.set_ylabel("Std dev across\nproviders (mm)")
|
||
ax3.set_xlabel("Date")
|
||
ax3.legend(fontsize=8)
|
||
ax3.grid(True, alpha=0.3)
|
||
ax3.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))
|
||
|
||
fig.autofmt_xdate()
|
||
plt.tight_layout()
|
||
path = output_dir / f"archive_{location_name}.png"
|
||
plt.savefig(path, dpi=150, bbox_inches="tight")
|
||
plt.close()
|
||
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))
|
||
|
||
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=ARCHIVE_COLORS.get(name, "gray"), linewidth=1.8)
|
||
|
||
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"
|
||
)
|
||
ax.legend(fontsize=9)
|
||
ax.grid(True, alpha=0.3)
|
||
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %Y"))
|
||
fig.autofmt_xdate()
|
||
plt.tight_layout()
|
||
|
||
path = output_dir / f"cumulative_{location_name}.png"
|
||
plt.savefig(path, dpi=150)
|
||
plt.close()
|
||
print(f" Saved: {path}")
|
||
|
||
|
||
def plot_vs_era5(data_dict, location_name, output_dir):
|
||
"""Each provider vs ERA5 reference: scatter + regression line.
|
||
|
||
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.
|
||
- Points above = provider wetter than ERA5 on that day.
|
||
- r = Pearson correlation (1 = perfect). MAE = mean absolute error in mm/day.
|
||
- Bias = provider minus ERA5 on average (positive = provider wetter).
|
||
"""
|
||
ref_name = "ERA5 (Open-Meteo)"
|
||
ref_df = data_dict.get(ref_name)
|
||
if ref_df is None:
|
||
return
|
||
|
||
others = [(n, df) for n, df in data_dict.items()
|
||
if n != ref_name and df is not None and len(df) > 0]
|
||
if not others:
|
||
return
|
||
|
||
n = len(others)
|
||
fig, axes = plt.subplots(1, n, figsize=(5 * n, 5), squeeze=False)
|
||
|
||
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 = ARCHIVE_COLORS.get(name, "steelblue")
|
||
ax.scatter(valid["rain_mm_ref"], valid["rain_mm_cmp"],
|
||
s=4, alpha=0.35, color=color)
|
||
|
||
lim = max(valid.max().max(), 1) * 1.05
|
||
ax.plot([0, lim], [0, lim], "r--", linewidth=1, label="Perfect agreement")
|
||
|
||
if len(valid) > 5:
|
||
coeffs = np.polyfit(valid["rain_mm_ref"], valid["rain_mm_cmp"], 1)
|
||
x_fit = np.linspace(0, lim, 100)
|
||
ax.plot(x_fit, np.polyval(coeffs, x_fit), "k-", linewidth=1,
|
||
alpha=0.6, label=f"Regression (slope={coeffs[0]:.2f})")
|
||
|
||
stats = compare_stats(merged, "rain_mm_ref", "rain_mm_cmp")
|
||
ax.set_xlim(0, lim); ax.set_ylim(0, lim)
|
||
ax.set_xlabel("ERA5 (Open-Meteo) mm/day", fontsize=9)
|
||
ax.set_ylabel(f"{name} mm/day", fontsize=9)
|
||
ax.set_title(
|
||
f"{name}\nr={stats['r']} MAE={stats['MAE']} mm Bias={stats['Bias']:+.2f} mm",
|
||
fontsize=9
|
||
)
|
||
ax.legend(fontsize=7)
|
||
ax.grid(True, alpha=0.3)
|
||
|
||
fig.suptitle(
|
||
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()
|
||
path = output_dir / f"vs_era5_{location_name}.png"
|
||
plt.savefig(path, dpi=150)
|
||
plt.close()
|
||
print(f" Saved: {path}")
|
||
|
||
|
||
def plot_pairwise_heatmap(data_dict, location_name, output_dir):
|
||
"""Two heatmaps side by side: pairwise Pearson r and pairwise RMSE.
|
||
|
||
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
|
||
|
||
n = len(names)
|
||
short = [nm.replace(" (Open-Meteo)", "\n(OM)") for nm in names]
|
||
|
||
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
|
||
)
|
||
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"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}")
|
||
|
||
|
||
# ============================================================
|
||
# MAIN
|
||
# ============================================================
|
||
|
||
def run_location(loc_name, lat, lon):
|
||
print(f"\n{'='*60}")
|
||
print(f" {loc_name} ({lat}°, {lon}°)")
|
||
print(f"{'='*60}")
|
||
|
||
# ---- ARCHIVE ----
|
||
print("\n[Archive]")
|
||
archive_data = {}
|
||
|
||
print(" Fetching Open-Meteo ERA5...")
|
||
try:
|
||
archive_data["ERA5 (Open-Meteo)"] = fetch_openmeteo_archive(
|
||
lat, lon, ARCHIVE_START, ARCHIVE_END, model="era5"
|
||
)
|
||
print(f" → {len(archive_data['ERA5 (Open-Meteo)'])} days")
|
||
except Exception as e:
|
||
print(f" ✗ ERA5 failed: {e}")
|
||
archive_data["ERA5 (Open-Meteo)"] = None
|
||
|
||
time.sleep(0.5)
|
||
print(" Fetching Open-Meteo ERA5-Land...")
|
||
try:
|
||
archive_data["ERA5-Land (Open-Meteo)"] = fetch_openmeteo_archive(
|
||
lat, lon, ARCHIVE_START, ARCHIVE_END, model="era5_land"
|
||
)
|
||
print(f" → {len(archive_data['ERA5-Land (Open-Meteo)'])} days")
|
||
except Exception as e:
|
||
print(f" ✗ ERA5-Land failed: {e}")
|
||
archive_data["ERA5-Land (Open-Meteo)"] = None
|
||
|
||
time.sleep(0.5)
|
||
# CERRA only covers Europe (roughly 20°W–45°E, 30°N–80°N)
|
||
if -20 <= lon <= 45 and 30 <= lat <= 80:
|
||
print(" Fetching Open-Meteo CERRA (EU only)...")
|
||
try:
|
||
archive_data["CERRA (Open-Meteo)"] = fetch_openmeteo_archive(
|
||
lat, lon, ARCHIVE_START, ARCHIVE_END, model="cerra"
|
||
)
|
||
print(f" → {len(archive_data['CERRA (Open-Meteo)'])} days")
|
||
except Exception as e:
|
||
print(f" ✗ CERRA failed: {e}")
|
||
archive_data["CERRA (Open-Meteo)"] = None
|
||
else:
|
||
print(" Skipping CERRA (outside EU coverage)")
|
||
archive_data["CERRA (Open-Meteo)"] = None
|
||
|
||
time.sleep(0.5)
|
||
print(" Fetching NASA POWER...")
|
||
try:
|
||
archive_data["NASA POWER"] = fetch_nasa_power(lat, lon, ARCHIVE_START, ARCHIVE_END)
|
||
print(f" → {len(archive_data['NASA POWER'])} days")
|
||
except Exception as e:
|
||
print(f" ✗ NASA POWER failed: {e}")
|
||
archive_data["NASA POWER"] = None
|
||
|
||
# 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_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 deterministic forecast...")
|
||
try:
|
||
forecast_data["Open-Meteo Forecast"] = fetch_openmeteo_forecast(lat, lon)
|
||
print(f" → {len(forecast_data['Open-Meteo Forecast'])} days")
|
||
except Exception as e:
|
||
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:
|
||
forecast_data["YR.no"] = fetch_yr_forecast(lat, lon)
|
||
print(f" → {len(forecast_data['YR.no'])} days")
|
||
except Exception as e:
|
||
print(f" ✗ YR.no failed: {e}")
|
||
forecast_data["YR.no"] = None
|
||
|
||
if OPENWEATHERMAP_KEY:
|
||
time.sleep(0.5)
|
||
print(" Fetching OpenWeatherMap forecast...")
|
||
try:
|
||
forecast_data["OpenWeatherMap"] = fetch_openweathermap_forecast(
|
||
lat, lon, OPENWEATHERMAP_KEY
|
||
)
|
||
print(f" → {len(forecast_data['OpenWeatherMap'])} days")
|
||
except Exception as e:
|
||
print(f" ✗ OpenWeatherMap failed: {e}")
|
||
forecast_data["OpenWeatherMap"] = None
|
||
|
||
if WEATHERAPI_KEY:
|
||
time.sleep(0.5)
|
||
print(" Fetching WeatherAPI.com forecast...")
|
||
try:
|
||
forecast_data["WeatherAPI.com"] = fetch_weatherapi_forecast(
|
||
lat, lon, WEATHERAPI_KEY
|
||
)
|
||
print(f" → {len(forecast_data['WeatherAPI.com'])} days")
|
||
except Exception as e:
|
||
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)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
print(f"Weather API Comparison — {datetime.date.today()}")
|
||
print(f"Archive: {ARCHIVE_START} → {ARCHIVE_END}")
|
||
print(f"Forecast: {FORECAST_START} → {FORECAST_END}")
|
||
print(f"Output: {OUTPUT_DIR.resolve()}")
|
||
|
||
for loc_name, coords in LOCATIONS.items():
|
||
run_location(loc_name, coords["lat"], coords["lon"])
|
||
time.sleep(1)
|
||
|
||
print(f"\nDone. Plots saved to: {OUTPUT_DIR.resolve()}")
|