Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

Raoult, Nina M.; Jupp, Tim E.; Cox, Peter M.; Luke, Catherine M.

doi:10.5194/gmd-9-2833-2016

Articles | Volume 9, issue 8

https://doi.org/10.5194/gmd-9-2833-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-9-2833-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 9, issue 8

Development and technical paper

|

25 Aug 2016

Development and technical paper |

| 25 Aug 2016

Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

Nina M. Raoult, Tim E. Jupp, Peter M. Cox, and Catherine M. Luke

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Views and downloads (calculated since 20 Jan 2016)

Month	HTML	PDF	XML	Total
Jan 2016	99	50	3	152
Feb 2016	51	36	2	89
Mar 2016	38	14	0	52
Apr 2016	14	7	1	22
May 2016	14	3	0	17
Jun 2016	8	3	0	11
Jul 2016	6	8	0	14
Aug 2016	72	32	3	107
Sep 2016	63	26	9	98
Oct 2016	18	9	1	28
Nov 2016	20	17	3	40
Dec 2016	15	20	3	38
Jan 2017	28	35	9	72
Feb 2017	26	21	6	53
Mar 2017	35	49	9	93
Apr 2017	37	53	7	97
May 2017	38	29	17	84
Jun 2017	38	27	17	82
Jul 2017	25	22	2	49
Aug 2017	10	10	0	20
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Oct 2017	8	5	0	13
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Apr 2021	7	7	1	15
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Jan 2023	56	7	3	66
Feb 2023	48	18	1	67
Mar 2023	51	8	1	60
Apr 2023	37	9	0	46
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Jun 2023	73	9	2	84
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Oct 2023	51	14	1	66
Nov 2023	31	13	0	44
Dec 2023	49	11	2	62
Jan 2024	41	17	1	59
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Mar 2024	47	27	4	78
Apr 2024	57	8	6	71
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Jun 2024	35	8	3	46
Jul 2024	19	18	5	42
Aug 2024	19	7	6	32
Sep 2024	10	6	3	19
Oct 2024	14	12	1	27
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Dec 2024	9	7	2	18
Jan 2025	14	9	0	23
Feb 2025	31	12	2	45
Mar 2025	15	8	0	23
Apr 2025	15	20	0	35
May 2025	34	18	2	54
Jun 2025	38	16	0	54
Jul 2025	41	13	2	56
Aug 2025	39	20	2	61
Sep 2025	49	17	2	68
Oct 2025	35	32	4	71
Nov 2025	49	13	8	70

Cumulative views and downloads (calculated since 20 Jan 2016)

Month	HTML	PDF	XML	Total
Jan 2016	99	50	3	152
Feb 2016	51	36	2	89
Mar 2016	38	14	0	52
Apr 2016	14	7	1	22
May 2016	14	3	0	17
Jun 2016	8	3	0	11
Jul 2016	6	8	0	14
Aug 2016	72	32	3	107
Sep 2016	63	26	9	98
Oct 2016	18	9	1	28
Nov 2016	20	17	3	40
Dec 2016	15	20	3	38
Jan 2017	28	35	9	72
Feb 2017	26	21	6	53
Mar 2017	35	49	9	93
Apr 2017	37	53	7	97
May 2017	38	29	17	84
Jun 2017	38	27	17	82
Jul 2017	25	22	2	49
Aug 2017	10	10	0	20
Sep 2017	13	13	0	26
Oct 2017	8	5	0	13
Nov 2017	7	2	0	9
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Jan 2018	9	11	0	20
Feb 2018	11	5	0	16
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Apr 2018	10	5	0	15
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Jul 2018	13	12	1	26
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Jul 2022	29	5	1	35
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Oct 2022	40	3	0	43
Nov 2022	44	7	0	51
Dec 2022	47	9	1	57
Jan 2023	56	7	3	66
Feb 2023	48	18	1	67
Mar 2023	51	8	1	60
Apr 2023	37	9	0	46
May 2023	47	8	0	55
Jun 2023	73	9	2	84
Jul 2023	46	5	0	51
Aug 2023	44	10	1	55
Sep 2023	37	7	0	44
Oct 2023	51	14	1	66
Nov 2023	31	13	0	44
Dec 2023	49	11	2	62
Jan 2024	41	17	1	59
Feb 2024	46	11	0	57
Mar 2024	47	27	4	78
Apr 2024	57	8	6	71
May 2024	21	20	3	44
Jun 2024	35	8	3	46
Jul 2024	19	18	5	42
Aug 2024	19	7	6	32
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Oct 2024	14	12	1	27
Nov 2024	13	24	5	42
Dec 2024	9	7	2	18
Jan 2025	14	9	0	23
Feb 2025	31	12	2	45
Mar 2025	15	8	0	23
Apr 2025	15	20	0	35
May 2025	34	18	2	54
Jun 2025	38	16	0	54
Jul 2025	41	13	2	56
Aug 2025	39	20	2	61
Sep 2025	49	17	2	68
Oct 2025	35	32	4	71
Nov 2025	49	13	8	70

Latest update: 23 Nov 2025

Short summary

We present a set of "optimal" parameter values used to describe the influence of vegetation in a numerical climate model, and the software suite that we developed to find it. Observational data from ~ 100 locations were used, and the optimal parameters improve the fit in 90 % of the locations. The new parameter values will allow the climate model to give better predictions, and our software should prove useful in future calibrations.