Getting Started

This guide provides a quick introduction to using bsrn for quality control and clear-sky modeling.

BSRNDataset (recommended)

The simplest way to work with a BSRN archive file is through BSRNDataset, which reads the file, resolves station metadata, and exposes chainable pipeline methods:

import bsrn

ds = bsrn.BSRNDataset.from_file("data/QIQ/qiq0125.dat.gz")

ds.solpos()            # solar position + extraterrestrial
ds.clear_sky()         # clear-sky irradiance (Ineichen)
ds.qc_test()           # 6-level quality control flags
ds.qc_mask()           # optional: NaN failed irradiance, drop flags

Functional API

All pipeline steps are also available as standalone functions, useful when working with non-BSRN data or custom DataFrames:

from bsrn.physics.geometry import add_solpos_columns
from bsrn.modeling.clear_sky import add_clearsky_columns
from bsrn.qc.wrapper import run_qc

df = add_solpos_columns(df, lat=47.80, lon=124.49, elev=170)
df = add_clearsky_columns(df, lat=47.80, lon=124.49, elev=170)
df = run_qc(df, lat=47.80, lon=124.49, elev=170)

Key Features

• Automated Quality Control: 6 levels of QC based on BSRN standards.

• Clear-sky Modeling: Support for Ineichen, McClear, and REST2.

• Clear-sky Detection: Multiple algorithms (Reno, Ineichen, etc.).

• Cloud Enhancement detection: Killinger, Yang, Gueymard.

• Irradiance Separation: Erbs, BRL, Engerer2, Yang4.