Quick Start

The easiest way to start using the package is to install it using pip install rainfallqc.

python

To use RainfallQC in a project, a polars dataframe can be directly input to the QC checks like:

import polars as pl
import rainfallqc.gauge_checks

data = pl.read_csv("path/to/your/rain_gauge_data.csv")
flags = rainfallqc.gauge_checks.check_intermittency(data, target_gauge_col="rain_mm")

All quality control checks in the RainfallQC package begin with check_

Content overview

RainfallQC contains six modules:

1. gauge_checks - For detecting abnormalities in summary and descriptive statistics.

2. comparison_checks - For detecting abnormalities by comparing to benchmark data.

3. timeseries_checks - For detecting abnormalities in patterns of the data record.

4. neighbourhood_checks - For detecting abnormalities based on measurements in neighbouring gauges.

5. subhourlyqc_checks - An extension for IntenseQC to be applied to sub-hourly rainfall data.

6. pypwsqc_filters - For applying quality assurance protocols and filters for rainfall data.

You can find a jupyter notebook with an easy-to-follow example here

Which checks are suitable for my data’s temporal resolution?

As you can imagine, not all quality control checks are suitable for all temporal resolutions. Therefore, we have created a table that shows which checks are suitable for which temporal resolutions, and which can be applied after aggregating data (“agg”) to a coarser temporal resolution.

Which checks are suitable for my data’s time-resolution

Short name	Long name	<15-min	15-min	hourly	daily	monthly
QC1	Percentiles	✓	✓	✓	✓	✓
QC2	K-largest	✓	✓	✓	✓	✓
QC3	Days of week	✓	✓	✓	✓	☓
QC4	Hours of day	✓	✓	✓	☓	☓
QC5	Intermittency	✓	✓	✓	✓	✓
QC6	Breakpoints	✓	✓	✓	✓	☓
QC7	Minimum value change	✓	✓	✓	✓	✓
QC8	R99p	✓	✓	✓	✓	☓
QC9	PRCPTOT	✓	✓	✓	✓	☓
QC10	World Record	✓	✓	✓	✓	☓
QC11	Rx1day	✓	✓	✓	☓	☓
QC12	CDD (Dry spells)	✓	✓	✓	✓	☓
QC13	Daily accumulations	agg	agg	✓	✓	☓
QC14	Monthly accumulations	✓	✓	✓	✓	☓
QC15	Streaks	agg	agg	✓	✓	☓
QC16	Daily neighbours (wet)	✓	✓	✓	✓	☓
QC17	Hourly neighbours (wet)	✓	✓	✓	☓	☓
QC18	Daily neighbours (dry)	✓	✓	✓	✓	☓
QC19	Hourly neighbours (dry)	✓	✓	✓	☓	☓
QC20	Monthly neighbours	agg	agg	agg	agg	✓
QC21	Timing offset	✓	✓	✓	✓	☓
QC22	Pre-QC affinity index	✓	✓	✓	✓	✓
QC23	Pre-QC pearson correlation	✓	✓	✓	✓	✓
QC24	Daily factor	agg	agg	agg	✓	☓
QC25	Monthly factor	agg	agg	agg	agg	✓
HQC_UK1hr	Check exceedance of UK 1h record	✓	✓	☓	☓	☓
HQC_UK24hr	Check exceedance of UK 24h record	✓	✓	☓	☓	☓
HQC_UK24hr_rolling	Check 24h-sum exceedance of UK 24h record	✓	✓	☓	☓	☓
HQC_streaks_20mm	Check streaks (20 mm min)	✓	✓	☓	☓	☓
SHQC_freqResChecker	Check data has sub-hourly frequency	✓	✓	☓	☓	☓
SHQC_subH_checkr	Check sub-hourly rainfall thresholds	✓	✓	☓	☓	☓
FZ	Faulty Zeros	✓	✓	✓	✓	☓
SO	Station Outliers	✓	✓	✓	☓	☓