Datasets:

jotone
/

arpa-osmer-weatherstations

code large_stringclasses 62 values	date date32	rr float64 0 1.08k ⌀	t180_min float64 -98 29.3 ⌀	t180_max float64 -22.6 62.3 ⌀	t180_med float64 -25.6 45.9 ⌀	rh_med float64 0 100 ⌀	v10_med float64 0 84 ⌀	v10_max float64 0 440 ⌀	dd10_max float64 0 360 ⌀	bf float64 0 456 ⌀	rg float64 0 62k ⌀	press_med float64 0 1.05k ⌀
103400	2019-01-01	0	-3.9	6.1	-1.3	92	1	8	360	null	5,425	null
103400	2019-01-02	0	-4.5	9.2	1.9	52	6	33	302	null	5,161	null
103400	2019-01-03	0	-1.4	7	1.9	26	6	50	226	null	6,725	null
103400	2019-01-04	0	-5.8	2.9	-2.1	70	3	17	58	null	4,574	null
103400	2019-01-05	0	-6.7	3	-3.6	83	2	10	360	null	5,249	null
103400	2019-01-06	0	-5.5	10.8	0.4	69	3	16	224	null	6,986	null
103400	2019-01-07	0	-4.4	6.4	-1.3	85	2	19	360	null	6,694	null
103400	2019-01-08	0	-3.7	2	-1.1	94	1	9	360	null	1,251	null
103400	2019-01-09	0.2	-4.2	5.2	-0.5	81	2	15	72	null	6,507	null
103400	2019-01-10	0	-4.8	8.4	-0.9	63	3	19	51	null	6,424	null
103400	2019-01-11	0	-6.8	4.9	-2.5	75	1	17	53	null	7,506	null
103400	2019-01-12	0	-5.1	2.9	-1.9	76	1	6	131	null	3,940	null
103400	2019-01-13	0	-5.4	7.8	-0.4	76	2	13	360	null	7,709	null
103400	2019-01-14	0	-2.1	13.4	4.2	61	5	36	257	null	7,942	null
103400	2019-01-15	0	-3.3	9.4	2.7	46	4	34	242	null	7,740	null
103400	2019-01-16	0	-5.3	8.2	0.4	77	1	15	360	null	8,604	null
103400	2019-01-17	8	0.6	2.7	1.3	91	1	8	167	null	null	null
103400	2019-01-18	5	0.4	5.3	2.1	96	2	16	360	null	2,489	null
103400	2019-01-19	0	-1	4	0.5	86	2	17	360	null	4,165	null
103400	2019-01-20	0	-4.3	6.4	-0.4	88	1	12	360	null	8,460	null
103400	2019-01-21	0	-4	7.3	-0.2	84	1	8	176	null	6,876	null
103400	2019-01-22	0.2	-5.3	6.6	-0.9	82	1	12	360	null	9,290	null
103400	2019-01-23	3	-2.1	1.4	-0.7	96	1	19	45	null	1,863	null
103400	2019-01-24	1.2	-4.6	5.8	-1.2	92	2	8	243	null	7,279	null
103400	2019-01-25	0.2	-6.5	6.3	-2.6	85	2	9	64	null	9,641	null
103400	2019-01-26	0.2	-7	5.2	-2.5	83	2	14	58	null	8,402	null
103400	2019-01-27	0	-5.6	2.6	-1.7	91	1	17	252	null	2,765	null
103400	2019-01-28	6.4	-2.7	3.9	0.7	95	2	13	299	null	1,744	null
103400	2019-01-29	4.4	-5.6	4.3	-1.9	92	1	8	40	null	6,791	null
103400	2019-01-30	2.2	-3.6	2.8	-0.9	91	1	6	360	null	3,321	null
103400	2019-01-31	5.2	-2	7.4	0.7	84	2	17	52	null	4,815	null
103400	2019-02-01	89.8	-0.2	1.5	0.6	99	1	12	255	null	null	null
103400	2019-02-02	104	1.6	4.9	3.2	null	3	14	360	null	909	null
103400	2019-02-03	22	0	3.7	1.7	null	1	11	47	null	2,005	null
103400	2019-02-04	0	-2.6	9.6	1.3	91	2	9	58	null	10,557	null
103400	2019-02-05	0.2	-4.1	10.4	0.5	84	2	9	231	null	11,353	null
103400	2019-02-06	0.2	-4.4	11.6	1	84	1	8	135	null	11,804	null
103400	2019-02-07	0.2	-3.7	10.5	1.9	83	1	8	149	null	10,505	null
103400	2019-02-08	0	0.2	13.8	4.7	79	3	23	62	null	10,292	null
103400	2019-02-09	0	-1.7	11	2.6	87	3	28	360	null	9,749	null
103400	2019-02-10	12.4	0.1	3.5	1.9	99	2	11	360	null	821	null
103400	2019-02-11	0.6	-1.1	9.1	3.6	75	4	30	210	null	5,256	null
103400	2019-02-12	0	-3.1	10.4	1.9	75	4	28	65	null	12,499	null
103400	2019-02-13	0.2	-3.6	11.1	1.5	83	3	26	59	null	13,189	null
103400	2019-02-14	0	-2.6	12.9	2.1	84	2	9	250	null	10,098	null
103400	2019-02-15	0.2	-2.8	15.7	3.5	80	2	25	79	null	13,255	null
103400	2019-02-16	0	-2.4	15.8	4	79	2	10	95	null	13,788	null
103400	2019-02-17	0.2	-2.6	17.3	4.2	78	2	9	138	null	14,407	null
103400	2019-02-18	0	-2.5	15.9	4	80	2	26	58	null	14,233	null
103400	2019-02-19	0.2	-2.3	11.5	2.6	90	3	25	63	null	12,523	null
103400	2019-02-20	0	-1.2	12.1	3.1	87	2	21	56	null	10,978	null
103400	2019-02-21	0	-1.8	15.7	5.2	77	3	26	360	null	13,646	null
103400	2019-02-22	0	-0.7	19.1	7.2	64	3	31	52	null	14,659	null
103400	2019-02-23	0	-2.6	7.7	2.8	61	6	29	54	null	15,037	null
103400	2019-02-24	0	-4.3	9.2	1.3	70	3	21	54	null	10,987	null
103400	2019-02-25	0	-2	19.6	5.9	67	3	17	79	null	14,872	null
103400	2019-02-26	0	0.9	18.1	7	66	2	10	182	null	8,888	null
103400	2019-02-27	0	0.6	21.7	8.7	65	3	26	52	null	15,824	null
103400	2019-02-28	0	-0.7	16	6.2	74	4	26	60	null	14,936	null
103400	2019-03-01	0	-0.8	12.3	4.8	80	2	21	54	null	6,925	null
103400	2019-03-02	0	-0.5	15.1	5.7	76	4	30	66	null	15,664	null
103400	2019-03-03	0.2	-1	14.5	6	75	4	26	59	null	14,935	null
103400	2019-03-04	9.2	0.2	8.8	4.4	93	5	28	360	null	5,053	null
103400	2019-03-05	0	1.3	14.4	5.8	85	4	25	45	null	15,482	null
103400	2019-03-06	0	1.6	12.8	6.8	83	4	27	75	null	7,751	null
103400	2019-03-07	2.8	4.7	10.8	7.9	92	7	31	60	null	1,465	null
103400	2019-03-08	4	5.6	13.2	9.1	89	5	24	360	null	9,544	null
103400	2019-03-09	0	4.8	13.8	8.5	85	4	23	75	null	13,480	null
103400	2019-03-10	0	5.7	9	7.5	91	2	12	46	null	2,178	null
103400	2019-03-11	0	2.6	17.1	7.8	49	7	42	251	null	14,096	null
103400	2019-03-12	0	-1.1	14.2	6.1	39	6	25	242	null	19,791	null
103400	2019-03-13	0	-1.5	11.6	3.8	71	4	26	54	null	11,345	null
103400	2019-03-14	0	-0.9	9.9	4.2	81	5	28	54	null	13,473	null
103400	2019-03-15	0	-0.3	13	6.2	74	5	32	57	null	17,203	null
103400	2019-03-16	0	0.1	13.8	7.3	79	4	25	63	null	12,785	null
103400	2019-03-17	1.8	6.2	10.6	8.4	90	3	20	360	null	2,210	null
103400	2019-03-18	20	-0.6	9	4.7	89	5	26	294	null	4,140	null
103400	2019-03-19	0.2	-0.6	11.8	4.3	74	3	19	143	null	13,113	null
103400	2019-03-20	0	-2.3	12.9	4.3	67	4	22	59	null	21,652	null
103400	2019-03-21	0	-2.6	16.1	5.7	63	4	26	61	null	21,139	null
103400	2019-03-22	0	-1.8	19.2	8.2	56	5	32	360	null	21,260	null
103400	2019-03-23	0	-0.1	22	10.2	59	4	30	56	null	21,593	null
103400	2019-03-24	0	1.9	21	11.2	62	4	29	61	null	null	null
103400	2019-03-25	5.2	3.2	18.4	9.8	67	6	31	47	null	15,226	null
103400	2019-03-26	0	0.7	16.1	8	47	5	25	59	null	22,642	null
103400	2019-03-27	0	-1.4	13.6	5.5	58	4	27	66	null	19,136	null
103400	2019-03-28	0	-1.4	13.7	6.3	63	5	30	47	null	22,291	null
103400	2019-03-29	0	-0.1	16.9	8.1	63	4	25	42	null	22,322	null
103400	2019-03-30	0	-0.1	18.6	9	63	5	28	50	null	21,987	null
103400	2019-03-31	0	0.3	21.8	10.5	53	4	27	47	null	23,121	null
103400	2019-04-01	0	1.2	22.2	11.4	54	3	21	61	null	20,385	null
103400	2019-04-02	0	3.3	19.7	12.4	58	5	23	57	null	15,043	null
103400	2019-04-03	13	7.1	12.3	9.3	88	4	27	59	null	4,594	null
103400	2019-04-04	212.6	6.1	9.8	7.9	99	7	62	360	null	806	null
103400	2019-04-05	40.6	6.8	9.9	8.3	99	8	47	51	null	5,281	null
103400	2019-04-06	0.2	6.7	11.2	8.7	87	3	21	360	null	7,421	null
103400	2019-04-07	0	4.2	15.1	9.2	77	4	26	65	null	11,966	null
103400	2019-04-08	1.4	5.9	13.1	8.9	92	2	12	288	null	4,561	null
103400	2019-04-09	10.4	6.5	18.9	10.3	88	3	22	57	null	11,275	null
103400	2019-04-10	30.8	8	17.8	10.3	90	3	27	64	null	10,325	null

End of preview. Expand in Data Studio

OSMER ARPA FVG — Validated Weather Observations

Validated meteorological observations from the OSMER network of ARPA FVG (Regional Environmental Protection Agency of Friuli-Venezia Giulia, NE Italy): temperature, humidity, precipitation, wind (mean/vector/gust + direction), global radiation, pressure, leaf wetness and soil temperatures. Two resolutions: hourly point observations and daily aggregates (min/max/mean), packaged as Parquet for analysis and ML.

This is a self-archived snapshot of the public OSMER feeds at https://www.meteo.fvg.it/ — the ARPA-FVG door to the same physical station network as the Protezione Civile FVG monitoring system.

Attribution

Source: ARPA FVG - OSMER e GRN — http://www.meteo.fvg.it/

License: Creative Commons Attribution–ShareAlike 3.0 Italy (CC BY-SA 3.0 IT) — http://creativecommons.org/licenses/by-sa/3.0/it/

You must credit "ARPA FVG - OSMER e GRN" and the URL above, indicate if changes were made, and — because this is ShareAlike — distribute any derivative under the same CC BY-SA 3.0 (IT) license.

Real-time embargo. OSMER prohibits republishing real-time data before 24 hours from the datum's reference time. This dataset therefore excludes observations from the last 24 hours; the export pipeline drops them before upload.

Dataset structure

Config	File(s)	Description
`hourly` (default)	`hourly/{CODE}/date=YYYY-MM-DD/observations.parquet`	One row per hourly observation, day-partitioned per station.
`daily`	`daily/{CODE}/aggregates.parquet`	One row per station-day (min/max/mean aggregates).
`stations`	`stations.parquet`	One row per station (code, name, WGS84 lat/lon, altitude, first year, active flag).

`hourly` columns

code (station), dt (timestamp[us, UTC]), rr (mm), prec_type, t180 (°C, 2 m), v10 (km/h mean), ff10 (km/h vector), vmax (km/h gust), dd10/dd10max (°N), rg (KJ/m²), cloudiness, rh (%), press (hPa), bf (min, leaf wetness), t50 (°C, 50 cm), tm10 (°C, −10 cm). Missing sensors → null.

`daily` columns

code, date, rr, t180_min/max/med, rh_med, v10_med/max, dd10_max, bf, rg, press_med.

`stations` columns

code, name, lat, lon, alt, start_year, active, valid_to.

Times are UTC. Units follow the OSMER feed (um attribute); see column notes above.

Usage

from datasets import load_dataset

hourly  = load_dataset("<user>/osmer-arpa-fvg-weather", split="train")            # default config
daily   = load_dataset("<user>/osmer-arpa-fvg-weather", "daily", split="train")
stations = load_dataset("<user>/osmer-arpa-fvg-weather", "stations", split="train")

Or read the Parquet directly with polars / pandas / duckdb. Join code → stations.code for station name and WGS84 location.

Coverage & provenance

Region: Friuli-Venezia Giulia, Italy. ~63 stations, validated network.
Hourly: self-archived from dev.meteo.fvg.it/xml/stazioni/<CODE>.xml (one observation per poll); coverage grows from when collection started.
Daily: from the rolling 7-day _week.xml feeds (and, where available, the OSMER archive).
History: OSMER's validated archive reaches back to ~1991; deep backfill, where included, is sourced from https://www.osmer.fvg.it/archivio.php.

Limitations

Real-time feeds are near-real-time and may be revised; daily aggregates use keep-last on revision.
The 24-hour embargo means the very latest day is intentionally absent.

Citation

@misc{osmer_arpafvg_weather,
  title  = {OSMER ARPA FVG — Validated Weather Observations},
  author = {ARPA FVG - OSMER e GRN},
  url    = {http://www.meteo.fvg.it/},
  note   = {Redistributed under CC BY-SA 3.0 IT},
}