Implications of diurnal variations in land surface temperature to data assimilation using MODIS LST data

doi:10.1007/s11442-020-1712-0

Abstract

Abstract:

Based on the Beijing Climate Center’s land surface model BCC_AVIM (Beijing Climate Center Atmosphere-Vegetation Interaction Model), the ensemble Kalman filter (EnKF) algorithm has been used to perform an assimilation experiment on the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product to study the influence of satellite LST data frequencies on surface temperature data assimilations. The assimilation results have been independently tested and evaluated by Global Land Data Assimilation System (GLDAS) LST products. The results show that the assimilation scheme can effectively reduce the BCC_AVIM model simulation bias and the assimilation results reflect more reasonable spatial and temporal distributions. Diurnal variation information in the observation data has a significant effect on the assimilation results. Assimilating LST data that contain diurnal variation information can further improve the accuracy of the assimilation analysis. Overall, when assimilation is performed using observation data at 6-hour intervals, a relatively good assimilation result can be obtained, indicated by smaller bias (<2.2K) and root-mean-square-error (RMSE) (<3.7K) and correlation coefficients larger than 0.60. Conversely, the assimilation using 24-hour data generally showed larger bias (>2.2K) and RMSE (>4K). Further analysis showed that the sensitivity of assimilation effect to diurnal variations in LST varies with time and space. The assimilation using observations with a time interval of 3 hours has the smallest bias in Oceania and Africa (both<1K); the use of 24-hour interval observation data for assimilation produces the smallest bias (<2.2K) in March, April and July.

Key words: land surface data assimilation, land surface temperature, MODIS, diurnal variation

FU Shiwen, NIE Suping, LUO Yong, CHEN Xin. Implications of diurnal variations in land surface temperature to data assimilation using MODIS LST data[J].Journal of Geographical Sciences, 2020, 30(1): 18-36.

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No.	EXP name	EXP time	Assimilation	Time step	Time interval for the observation data
1	CTL	2014.01-2015.12	No	30 minutes	-
2	ASSI1	2014.01-2015.12	Yes	30 minutes	3 hours
3	ASSI2	2014.01-2015.12	Yes	30 minutes	6 hours
4	ASSI3	2014.01-2015.12	Yes	30 minutes	12 hours
5	ASSI4	2014.01-2015.12	Yes	30 minutes	24 hours

Experiment name	CTL	ASSI1	ASSI2	ASSI3	ASSI4
Absolute bias	2.570 K	2.252 K	2.172 K	2.245 K	2.262 K
RMSE	4.239 K	3.681 K	3.648 K	3.992 K	4.423 K
Correlation coefficient	0.525	0.619	0.615	0.571	0.525

	CTL	ASSI1	ASSI2	ASSI3	ASSI4
January, 2014	2.88	2.44	2.36	2.35	2.41
April, 2014	2.4	2.12	2.01	1.93	1.93
July, 2014	2.24	2.08	1.97	2	1.94
October, 2014	2.46	2.08	2.02	2.28	2.37
January, 2015	2.85	2.35	2.3	2.37	2.41
April, 2015	2.49	2.22	2.09	2.04	1.99
July, 2015	2.2	2.21	2.11	2.18	2.11
October, 2015	2.58	2.15	2.1	2.26	2.42

	CTL	ASSI1	ASSI2	ASSI3	ASSI4
January, 2014	5.02	4.43	4.41	4.67	5.00
April, 2014	4.05	3.54	3.49	3.77	4.31
July, 2014	3.69	3.17	3.13	3.44	3.91
October, 2014	4.03	3.44	3.44	3.94	4.40
January, 2015	5.02	4.39	4.38	4.67	4.97
April, 2015	4.04	3.55	3.47	3.76	4.22
July, 2015	3.61	3.25	3.21	3.54	3.99
October, 2015	4.15	3.52	3.52	3.99	4.48

	CTL	ASSI1	ASSI2	ASSI3	ASSI4
January, 2014	0.55	0.6	0.59	0.55	0.52
April, 2014	0.56	0.64	0.64	0.59	0.53
July, 2014	0.42	0.51	0.5	0.46	0.4
October, 2014	0.57	0.68	0.68	0.62	0.58
January, 2015	0.55	0.6	0.61	0.57	0.54
April, 2015	0.59	0.65	0.66	0.62	0.57
July, 2015	0.47	0.55	0.54	0.49	0.43
October, 2015	0.55	0.67	0.66	0.62	0.57