Climate Examples

Heat Events

This workflow cleans station observations and produces an analysis-ready heat-events dataset.

Inputs

climate_observations_raw.csv:

station_id	date	tmax_c	prcp_mm	qc_flag
ST001	2025-01-12	39.4	0.0	A
ST001	2025-01-13	41.2	1.2	A
ST002	2025-01-13	not_available	0.0	A
ST003	2025-01-14	36.8		A
ST004	2025-01-14	42.7	0.0	B
ST005	2025-01-14	40.1	5.3	A

climate_stations.csv:

station_id	station_name	country	elevation_m
ST001	Nairobi Central	KE	1661
ST002	Garissa	KE	151
ST003	Mombasa	KE	50
ST005	Kisumu	KE	1131

Pipeline

wowdata: 0
pipeline:
  start:
    uri: climate_observations_raw.csv
    type: csv
  steps:
    - transform:
        op: cast
        params:
          types:
            tmax_c: number
            prcp_mm: number
          on_error: "null"
    - transform:
        op: filter
        params:
          where: "qc_flag == 'A'"
    - transform:
        op: filter
        params:
          where: "tmax_c >= 40"
    - transform:
        op: join
        params:
          right: climate_stations.csv
          on: [station_id]
          how: left
    - transform:
        op: derive
        params:
          new: is_extreme
          expr: "tmax_c >= 42"
    - sink:
        uri: climate_heat_events.csv
        type: csv

Run:

wow run climate_heat_events.yaml

Expected Output

climate_heat_events.csv:

station_id	date	tmax_c	prcp_mm	qc_flag	station_name	country	elevation_m	is_extreme
ST001	2025-01-13	41.2	1.2	A	Nairobi Central	KE	1661	False
ST005	2025-01-14	40.1	5.3	A	Kisumu	KE	1131	False

Rainfall Deficit Alerts

This workflow compares observed rainfall to station normals and flags large deficits.

Inputs

climate_rainfall_obs.csv:

station_id	date	rain_mm	qc_flag
ST101	2025-03-01	2.5	A
ST102	2025-03-01	trace	A
ST103	2025-03-01	18.0	A
ST104	2025-03-01	0.0	B
ST101	2025-03-02	1.0	A
ST103	2025-03-02	22.0	A

climate_rainfall_normals.csv:

station_id	normal_mm
ST101	25.0
ST102	12.0
ST103	20.0
ST104	8.0

Pipeline

wowdata: 0
pipeline:
  start:
    uri: climate_rainfall_obs.csv
    type: csv
  steps:
    - transform:
        op: cast
        params:
          types:
            rain_mm: number
          on_error: "null"
    - transform:
        op: filter
        params:
          where: "qc_flag == 'A'"
    - transform:
        op: join
        params:
          right: climate_rainfall_normals.csv
          on: [station_id]
          how: left
    - transform:
        op: derive
        params:
          new: deficit_mm
          expr: "normal_mm - rain_mm"
    - transform:
        op: derive
        params:
          new: is_deficit_alert
          expr: "deficit_mm >= 20"
    - sink:
        uri: climate_rainfall_alerts.csv
        type: csv

Run:

wow run climate_rainfall_alerts.yaml

Expected Output

climate_rainfall_alerts.csv:

station_id	date	rain_mm	qc_flag	normal_mm	deficit_mm	is_deficit_alert
ST101	2025-03-01	2.5	A	25.0	22.5	True
ST102	2025-03-01		A	12.0		False
ST103	2025-03-01	18.0	A	20.0	2.0	False
ST101	2025-03-02	1.0	A	25.0	24.0	True
ST103	2025-03-02	22.0	A	20.0	-2.0	False