Solar — PV resource assessment from ERA5¶

Solar developers need long-term mean global horizontal irradiance (GHI) at a candidate site to forecast PV array energy yield. ERA5 exposes downward shortwave radiation at the surface as a flux — accumulated joules per square metre per timestep. Aggregating over a month and dividing by the seconds in the month gives the average power density in W/m².

Domain context. A simplified PV yield estimate:

$$ E_\text{PV} = \eta \cdot A \cdot \overline{\text{GHI}} \cdot t $$

where $\eta$ is module efficiency (~0.20 for crystalline silicon), $A$ is array area (m²), and $t$ is the period. Site assessment is really a question about $\overline{\text{GHI}}$ and its seasonal variability. ERA5 gives 40+ years of monthly fields to evaluate it.

Step 1 — the radiation variable¶

surface-solar-radiation-downwards is the total shortwave flux reaching the surface (direct + diffuse). It's a flux, so op="auto" → sum.

In [1]:

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from earthlens.ecmwf import Catalog

cat = Catalog()
spec = cat.get_variable(
    "reanalysis-era5-single-levels", "surface-solar-radiation-downwards"
)
print(f"nc_variable: {spec.nc_variable}")
print(f"units:       {spec.units}")
print(f"is_flux:     {spec.is_flux}  # auto -> sum")
from earthlens.ecmwf import Catalog

cat = Catalog()
spec = cat.get_variable(
    "reanalysis-era5-single-levels", "surface-solar-radiation-downwards"
)
print(f"nc_variable: {spec.nc_variable}")
print(f"units:       {spec.units}")
print(f"is_flux:     {spec.is_flux}  # auto -> sum")

nc_variable: ssrd
units:       J m**-2
is_flux:     False  # auto -> sum

Step 2 — pull two years monthly over a candidate site¶

Box: 1° around Seville, Spain (37°–38°N, 6°–5°W) — a region with well-known strong solar resource. 24 monthly values let us see the seasonal cycle and inter-annual variability.

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from pathlib import Path
from earthlens import EarthLens, AggregationConfig

OUT = Path("data/era5-solar-seville")
OUT.mkdir(parents=True, exist_ok=True)

earthlens = EarthLens(
    data_source="ecmwf",
    temporal_resolution="monthly",
    start="2021-01-01",
    end="2022-12-01",
    variables={
        "reanalysis-era5-single-levels-monthly-means": [
            "surface-solar-radiation-downwards",
        ],
    },
    lat_lim=[37.0, 38.0],
    lon_lim=[-6.0, -5.0],
    path=str(OUT),
)
earthlens.download(aggregate=AggregationConfig(freq="1MS", op="auto"))
from pathlib import Path
from earthlens import EarthLens, AggregationConfig

OUT = Path("data/era5-solar-seville")
OUT.mkdir(parents=True, exist_ok=True)

earthlens = EarthLens(
    data_source="ecmwf",
    temporal_resolution="monthly",
    start="2021-01-01",
    end="2022-12-01",
    variables={
        "reanalysis-era5-single-levels-monthly-means": [
            "surface-solar-radiation-downwards",
        ],
    },
    lat_lim=[37.0, 38.0],
    lon_lim=[-6.0, -5.0],
    path=str(OUT),
)
earthlens.download(aggregate=AggregationConfig(freq="1MS", op="auto"))

2026-05-10 01:44:57.547 | INFO     | earthlens.ecmwf.backend:download:536 - Download ECMWF reanalysis-era5-single-levels-monthly-means/surface-solar-radiation-downwards data for period 2021-01-01 00:00:00 till 2022-12-01 00:00:00

2026-05-10 01:44:58.267 | INFO     | earthlens.ecmwf.backend:_api:724 - Requesting reanalysis-era5-single-levels-monthly-means from CDS; this may take several minutes

2026-05-10 01:44:58,514 INFO Request ID is a3f6fa48-9d89-446c-b5b3-ca2a7a568a16

2026-05-10 01:44:58,618 INFO status has been updated to accepted

2026-05-10 01:45:20,162 INFO status has been updated to running

2026-05-10 01:45:31,642 INFO status has been updated to successful

2026-05-10 01:45:32 | INFO | pyramids.base.config | Logging is configured.

2026-05-10 01:45:33.121 | INFO     | earthlens.ecmwf.backend:download:575 - ECMWF download summary: all 1 variables succeeded ([('reanalysis-era5-single-levels-monthly-means', 'surface-solar-radiation-downwards')])

Step 3 — convert J/m² to W/m² and to kWh/m²/day¶

The monthly GeoTIFF carries the sum of per-step accumulations — total Joules per square metre over the whole month. Divide by (seconds-in-month × 1) to recover an average power density (W/m²), or by (3.6e6 × days) to express in kWh/m²/day.

In [3]:

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import numpy as np
import pandas as pd
from calendar import monthrange
from pyramids.dataset import Dataset

agg = OUT / "aggregated"
paths = sorted(agg.glob("surface_solar_radiation_downwards_1MS_*.tif"))
joules_per_m2 = np.stack([Dataset.read_file(str(p)).read_array() for p in paths])

months = pd.date_range("2021-01-01", periods=len(paths), freq="MS")
site_total_J = np.nanmean(joules_per_m2, axis=(1, 2))

secs = np.array([monthrange(m.year, m.month)[1] * 86400 for m in months])
days = secs / 86400

Wpm2     = site_total_J / secs            # average power density
kWh_day  = site_total_J / (3.6e6 * days)  # daily energy yield per m²

df = pd.DataFrame(
    {"GHI [W/m²]": Wpm2.round(1), "GHI [kWh/m²/day]": kWh_day.round(2)},
    index=months,
)
df
import numpy as np
import pandas as pd
from calendar import monthrange
from pyramids.dataset import Dataset

agg = OUT / "aggregated"
paths = sorted(agg.glob("surface_solar_radiation_downwards_1MS_*.tif"))
joules_per_m2 = np.stack([Dataset.read_file(str(p)).read_array() for p in paths])

months = pd.date_range("2021-01-01", periods=len(paths), freq="MS")
site_total_J = np.nanmean(joules_per_m2, axis=(1, 2))

secs = np.array([monthrange(m.year, m.month)[1] * 86400 for m in months])
days = secs / 86400

Wpm2     = site_total_J / secs            # average power density
kWh_day  = site_total_J / (3.6e6 * days)  # daily energy yield per m²

df = pd.DataFrame(
    {"GHI [W/m²]": Wpm2.round(1), "GHI [kWh/m²/day]": kWh_day.round(2)},
    index=months,
)
df

Out[3]:

	GHI [W/m²]	GHI [kWh/m²/day]
2021-01-01	3.1	0.07
2021-02-01	5.0	0.12
2021-03-01	7.1	0.17
2021-04-01	7.5	0.18
2021-05-01	9.9	0.24
2021-06-01	10.8	0.26
2021-07-01	10.9	0.26
2021-08-01	9.6	0.23
2021-09-01	7.3	0.18
2021-10-01	5.7	0.14
2021-11-01	4.2	0.10
2021-12-01	2.9	0.07
2022-01-01	3.9	0.09
2022-02-01	5.6	0.13
2022-03-01	5.2	0.12
2022-04-01	8.0	0.19
2022-05-01	10.1	0.24
2022-06-01	10.9	0.26
2022-07-01	10.9	0.26
2022-08-01	9.6	0.23
2022-09-01	7.8	0.19
2022-10-01	5.3	0.13
2022-11-01	3.7	0.09
2022-12-01	2.8	0.07

Step 4 — seasonal cycle plot¶

Seville's resource peaks in June–July (~7+ kWh/m²/day) and bottoms around December (~2.5 kWh/m²/day) — typical for a Mediterranean site. Inter-annual variability between 2021 and 2022 is small for a well-sited PV plant.

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import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(9, 5))
ax.plot(months, kWh_day, marker="o", lw=2, color="tab:orange")
ax.fill_between(months, 0, kWh_day, alpha=0.15, color="tab:orange")
ax.set_ylabel("GHI [kWh/m²/day]")
ax.set_title("Monthly mean global horizontal irradiance — Seville bbox, 2021–2022")
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(9, 5))
ax.plot(months, kWh_day, marker="o", lw=2, color="tab:orange")
ax.fill_between(months, 0, kWh_day, alpha=0.15, color="tab:orange")
ax.set_ylabel("GHI [kWh/m²/day]")
ax.set_title("Monthly mean global horizontal irradiance — Seville bbox, 2021–2022")
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()

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Notes¶

GHI vs DNI. ERA5 gives global horizontal irradiance — the right metric for fixed-tilt PV. For concentrated solar power (CSP) you need direct normal irradiance, derived from GHI and a decomposition model (DIRINT, Erbs).
Clear-sky. ERA5 also exposes surface-solar-radiation-downward-clear-sky for the cloud-free upper bound. Cloud-radiative-effect = total - clear-sky.
Inter-annual variability. A real bankability study uses a 20-year record and bootstrapped confidence intervals on the long-term mean. Two years here is illustrative only.
Tilt correction. Real PV arrays tilt; converting GHI to in-plane irradiance for a tilted array uses the angle-of-incidence formulae from Duffie & Beckman.