MeteoIO 2.4.2: a preprocessing library for meteorological data

Bavay, M.; Egger, T.

doi:https://doi.org/10.5194/gmd-7-3135-2014

Articles | Volume 7, issue 6

https://doi.org/10.5194/gmd-7-3135-2014

© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-7-3135-2014

© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 7, issue 6

Model description paper

|

19 Dec 2014

Model description paper |

| 19 Dec 2014

MeteoIO 2.4.2: a preprocessing library for meteorological data

M. Bavay and T. Egger

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Oct 2014	27	19	2	48
Nov 2014	17	9	0	26
Dec 2014	86	63	5	154
Jan 2015	43	48	3	94
Feb 2015	31	23	5	59
Mar 2015	13	12	0	25
Apr 2015	59	17	11	87
May 2015	21	23	15	59
Jun 2015	31	20	7	58
Jul 2015	31	20	6	57
Aug 2015	13	16	6	35
Sep 2015	9	10	4	23
Oct 2015	20	14	2	36
Nov 2015	14	24	7	45
Dec 2015	4	26	1	31
Jan 2016	28	20	1	49
Feb 2016	20	17	4	41
Mar 2016	13	19	1	33
Apr 2016	7	11	2	20
May 2016	14	9	7	30
Jun 2016	9	6	2	17
Jul 2016	9	6	4	19
Aug 2016	16	5	1	22
Sep 2016	15	10	2	27
Oct 2016	11	10	2	23
Nov 2016	11	4	2	17
Dec 2016	5	5	1	11
Jan 2017	18	7	3	28
Feb 2017	24	9	3	36
Mar 2017	21	9	6	36
Apr 2017	35	14	8	57
May 2017	26	19	5	50
Jun 2017	35	11	1	47
Jul 2017	17	7	0	24
Aug 2017	5	7	2	14
Sep 2017	11	13	1	25
Oct 2017	4	6	1	11
Nov 2017	9	14	1	24
Dec 2017	10	9	2	21
Jan 2018	3	16	0	19
Feb 2018	10	7	1	18
Mar 2018	8	17	0	25
Apr 2018	9	17	0	26
May 2018	9	17	0	26
Jun 2018	4	15	2	21
Jul 2018	7	12	0	19
Aug 2018	14	9	1	24
Sep 2018	6	15	1	22
Oct 2018	14	24	2	40
Nov 2018	16	16	0	32
Dec 2018	10	9	2	21
Jan 2019	11	13	0	24
Feb 2019	8	10	1	19
Mar 2019	5	11	0	16
Apr 2019	6	11	3	20
May 2019	6	17	0	23
Jun 2019	4	10	1	15
Jul 2019	9	12	1	22
Aug 2019	11	8	0	19
Sep 2019	5	16	1	22
Oct 2019	10	10	1	21
Nov 2019	16	10	1	27
Dec 2019	4	15	1	20
Jan 2020	16	9	2	27
Feb 2020	14	7	0	21
Mar 2020	3	10	0	13
Apr 2020	4	11	0	15
May 2020	8	8	0	16
Jun 2020	17	22	0	39
Jul 2020	43	36	33	112
Aug 2020	12	9	1	22
Sep 2020	9	11	0	20
Oct 2020	8	18	0	26
Nov 2020	7	8	0	15
Dec 2020	8	9	0	17
Jan 2021	23	8	1	32
Feb 2021	10	9	0	19
Mar 2021	9	8	0	17
Apr 2021	10	7	0	17
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Jun 2021	15	9	0	24
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Jan 2022	19	22	0	41
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Apr 2022	31	6	0	37
May 2022	39	3	1	43
Jun 2022	46	3	2	51
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Nov 2022	60	18	0	78
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Jan 2023	49	23	0	72
Feb 2023	44	14	0	58
Mar 2023	71	30	1	102
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May 2023	40	14	1	55
Jun 2023	59	15	1	75
Jul 2023	61	10	0	71
Aug 2023	50	9	1	60
Sep 2023	40	8	2	50
Oct 2023	67	11	1	79
Nov 2023	35	6	0	41
Dec 2023	58	12	3	73
Jan 2024	56	8	1	65
Feb 2024	51	14	5	70
Mar 2024	71	15	8	94
Apr 2024	63	15	10	88
May 2024	43	20	7	70
Jun 2024	40	14	1	55
Jul 2024	45	18	4	67
Aug 2024	35	14	5	54
Sep 2024	32	11	6	49
Oct 2024	34	8	0	42
Nov 2024	24	5	3	32
Dec 2024	20	12	1	33
Jan 2025	37	16	2	55
Feb 2025	44	12	4	60
Mar 2025	42	12	4	58
Apr 2025	41	13	0	54
May 2025	37	16	0	53
Jun 2025	68	17	1	86
Jul 2025	37	24	0	61
Aug 2025	53	20	3	76
Sep 2025	46	26	2	74
Oct 2025	57	34	4	95
Nov 2025	26	5	2	33

Cumulative views and downloads (calculated since 03 Jun 2014)

Month	HTML	PDF	XML	Total
Jun 2014	57	33	3	93
Jul 2014	48	17	3	68
Aug 2014	13	18	1	32
Sep 2014	7	10	3	20
Oct 2014	27	19	2	48
Nov 2014	17	9	0	26
Dec 2014	86	63	5	154
Jan 2015	43	48	3	94
Feb 2015	31	23	5	59
Mar 2015	13	12	0	25
Apr 2015	59	17	11	87
May 2015	21	23	15	59
Jun 2015	31	20	7	58
Jul 2015	31	20	6	57
Aug 2015	13	16	6	35
Sep 2015	9	10	4	23
Oct 2015	20	14	2	36
Nov 2015	14	24	7	45
Dec 2015	4	26	1	31
Jan 2016	28	20	1	49
Feb 2016	20	17	4	41
Mar 2016	13	19	1	33
Apr 2016	7	11	2	20
May 2016	14	9	7	30
Jun 2016	9	6	2	17
Jul 2016	9	6	4	19
Aug 2016	16	5	1	22
Sep 2016	15	10	2	27
Oct 2016	11	10	2	23
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Dec 2016	5	5	1	11
Jan 2017	18	7	3	28
Feb 2017	24	9	3	36
Mar 2017	21	9	6	36
Apr 2017	35	14	8	57
May 2017	26	19	5	50
Jun 2017	35	11	1	47
Jul 2017	17	7	0	24
Aug 2017	5	7	2	14
Sep 2017	11	13	1	25
Oct 2017	4	6	1	11
Nov 2017	9	14	1	24
Dec 2017	10	9	2	21
Jan 2018	3	16	0	19
Feb 2018	10	7	1	18
Mar 2018	8	17	0	25
Apr 2018	9	17	0	26
May 2018	9	17	0	26
Jun 2018	4	15	2	21
Jul 2018	7	12	0	19
Aug 2018	14	9	1	24
Sep 2018	6	15	1	22
Oct 2018	14	24	2	40
Nov 2018	16	16	0	32
Dec 2018	10	9	2	21
Jan 2019	11	13	0	24
Feb 2019	8	10	1	19
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Apr 2019	6	11	3	20
May 2019	6	17	0	23
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May 2020	8	8	0	16
Jun 2020	17	22	0	39
Jul 2020	43	36	33	112
Aug 2020	12	9	1	22
Sep 2020	9	11	0	20
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Mar 2021	9	8	0	17
Apr 2021	10	7	0	17
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Jun 2021	15	9	0	24
Jul 2021	13	9	0	22
Aug 2021	11	15	0	26
Sep 2021	12	12	0	24
Oct 2021	12	27	0	39
Nov 2021	8	31	0	39
Dec 2021	13	10	0	23
Jan 2022	19	22	0	41
Feb 2022	18	13	0	31
Mar 2022	9	7	0	16
Apr 2022	31	6	0	37
May 2022	39	3	1	43
Jun 2022	46	3	2	51
Jul 2022	25	2	0	27
Aug 2022	52	6	0	58
Sep 2022	44	4	0	48
Oct 2022	48	8	2	58
Nov 2022	60	18	0	78
Dec 2022	47	7	1	55
Jan 2023	49	23	0	72
Feb 2023	44	14	0	58
Mar 2023	71	30	1	102
Apr 2023	44	23	0	67
May 2023	40	14	1	55
Jun 2023	59	15	1	75
Jul 2023	61	10	0	71
Aug 2023	50	9	1	60
Sep 2023	40	8	2	50
Oct 2023	67	11	1	79
Nov 2023	35	6	0	41
Dec 2023	58	12	3	73
Jan 2024	56	8	1	65
Feb 2024	51	14	5	70
Mar 2024	71	15	8	94
Apr 2024	63	15	10	88
May 2024	43	20	7	70
Jun 2024	40	14	1	55
Jul 2024	45	18	4	67
Aug 2024	35	14	5	54
Sep 2024	32	11	6	49
Oct 2024	34	8	0	42
Nov 2024	24	5	3	32
Dec 2024	20	12	1	33
Jan 2025	37	16	2	55
Feb 2025	44	12	4	60
Mar 2025	42	12	4	58
Apr 2025	41	13	0	54
May 2025	37	16	0	53
Jun 2025	68	17	1	86
Jul 2025	37	24	0	61
Aug 2025	53	20	3	76
Sep 2025	46	26	2	74
Oct 2025	57	34	4	95
Nov 2025	26	5	2	33

Latest update: 07 Nov 2025

Short summary

The open-source MeteoIO library has been designed to perform the data preprocessing required by numerical models using large meteorological data sets, with a strong emphasis on simplicity and modularity. It retrieves, filters and resamples the data if necessary as well as provides spatial interpolations and parameterizations. It presents a uniform interface to meteorological data in the models, hides the complexity of the preprocessing and guarantees a robust behaviour in case of data errors.