Modification and validation of a new method to improve the accuracy of MODIS-derived dew point temperature over mainland China

Figures & data

Figure 1. Spatial distribution of 2153 meteorological stations and elevation (a), climate zones (b), annual average surface pressure (c), and the lowest vertical pressure level (d) in mainland China.

Figure 2. Flowchart of the methods we adopted for $T_{d,int}$ estimation.

Table 1. Accuracy of MODIS atmospheric profile products for $T_{d,int}$ estimation.

Product	Method	Year	n	R^2	RMSE (°C)	B (°C)
MOD07_L2	LVP	2016	247454	0.83	6.48	−1.57
		2017	243691	0.80	6.52	−0.90
		2016–2017	491145	0.81	6.50	−1.24
	ALR	2016	247454	0.85	5.70	0.59
		2017	243691	0.83	5.94	1.18
		2016–2017	491145	0.84	5.82	0.88
MYD07_L2	LVP	2016	272995	0.77	7.20	0.61
		2017	261234	0.72	7.65	1.23
		2016–2017	534229	0.75	7.42	0.91
	ALR	2016	272995	0.77	7.45	2.36
		2017	261234	0.72	7.95	2.91
		2016–2017	534229	0.74	7.70	2.63

Figure 3. Accuracy of $T_{d,int}$ retrieved from MOD07_L2 product at site scale and their scatterplots. (a) – (c) are R², RMSE and B achieved by the LVP method, (d) – (f) are R², RMSE and B achieved by the ALR method, and (g) – (i) are the scatterplots of R², RMSE and B achieved by these two methods.

Figure 4. Logarithmic regression models between ${\alpha }_T$ and AI for $T_{d,mean}$ estimation. (a) and (c) represent models calibrated by $T_{d,mean}$ and $T_{amin}$ observations, (b) represents model calibrated by $T_{d,mean}$ observations and MOD11A1 nighttime $T_s$, and (d) represents model calibrated by $T_{d,mean}$ observations and MYD11A1 nighttime $T_s$.

Figure 5. Accuracy of $T_{d,mean}$ estimation achieved by different methods. (a) and (d) are the accuracy of $T_{amin}$ as a direct proxy for $T_{d,mean}$, (b) and (e) are the accuracy of the correction method calibrated by $T_{amin}$ observations, and (c) and (f) are the accuracy of the correction method calibrated by MOD11A1 and MYD11A1 $T_s$, respectively.

Table 2. Accuracy of daily mean dew pint temperature estimates achieved by different methods. The unit of RMSE and B is °C.

Product	Method	Metrics	All n = 2153	Arid n = 122	Semi-arid n = 684	Dry sub-humid n = 301	Humid n = 1046
MOD11A1	I	R²	0.88	0.80	0.87	0.91	0.94
		RMSE	5.63	8.46	6.72	4.76	3.07
		B	3.47	6.32	4.76	2.63	1.56
	II	R²	0.90	0.80	0.87	0.91	0.94
		RMSE	4.18	5.53	4.88	3.96	2.68
		B	0.59	−0.14	1.28	0.07	0.17
	III	R²	0.88	0.79	0.86	0.88	0.88
		RMSE	4.62	5.91	5.05	4.48	3.66
		B	0.69	0.01	1.33	0.28	0.27
MYD11A1	I	R²	0.88	0.80	0.87	0.91	0.94
		RMSE	5.59	8.40	6.62	4.70	3.06
		B	3.41	6.26	4.63	2.50	1.54
	II	R²	0.90	0.80	0.87	0.91	0.94
		RMSE	4.19	5.50	4.86	3.97	2.68
		B	0.60	−0.07	1.25	0.03	0.21
	III	R²	0.90	0.81	0.87	0.89	0.89
		RMSE	4.42	5.39	4.77	4.38	3.62
		B	0.63	0.15	1.16	0.06	0.34

Figure 6. Logarithmic regression models between ${\alpha }_T$ and AI for $T_{d,int}$ estimation. (a) - (d) are models for $T_{d,int}$ estimation at Terra overpass time, and (e) - (h) are models for $T_{d,int}$ estimation at Aqua overpass time.

Table 3. Accuracy of instantaneous dew pint temperature estimates achieved by different methods. For simplicity, the $T_{d,int}$ estimated from $T_{d,int}$ and $T_{amin}$ observations, from $T_{d,int}$ observations and $T_s$, from $T_{d,int}^{LVP}$ and $T_s$, and from $T_{d,int}^{ALR}$ and $T_s$ was renamed as$T_{d,int}-T_{amin}$, $T_{d,int}-T_s$, $T_{d,int}^{LVP}-T_s$, and$T_{d,int}^{ALR}-T_s$, respectively. The unit of RMSE and B is °C.

Product	Metrics	$T_{d,int}^{LVP}$	$T_{d,int}^{ALR}$	$T_{d,int}-T_{amin}$	$T_{d,int}-T_s$	$T_{d,int}^{LVP}-T_s$	$T_{d,int}^{ALR}-T_s$
Terra MODIS	n	104260	104260	104260	104260	104260	104260
	R^2	0.77	0.81	0.86	0.87	0.87	0.87
	RMSE	6.89	6.20	5.22	5.22	5.23	5.33
	B	−0.84	1.37	0.62	0.70	−0.79	1.24
Aqua MODIS	n	110609	110609	110609	110609	110609	110609
	R^2	0.69	0.70	0.86	0.87	0.87	0.87
	RMSE	8.07	8.35	5.47	5.47	5.59	6.21
	B	1.46	3.28	0.79	0.82	1.43	3.07

Table 4. Accuracy of instantaneous dew pint temperature estimates achieved by different methods for the year 2017. $T_{d,int}^{LVP}-T_s$ and $T_{d,int}^{ALR}-T_s$ represent the correction method developed from $T_{d,int}^{LVP}$ and $T_s$ and from $T_{d,int}^{ALR}$ and $T_s$, respectively. The unit of RMSE and B is °C.

Method	Metrics	All n = 2153	Arid n = 122	Semi-arid n = 684	Dry sub-humid n = 301	Humid n = 1046
LVP	R²	0.74	0.74	0.72	0.74	0.70
	RMSE	6.89	6.27	7.27	7.27	6.25
	B	−0.84	−2.46	0.18	−1.61	−1.43
$T_{d,int}^{LVP}-T_s$	R²	0.85	0.74	0.83	0.85	0.87
	RMSE	5.22	6.91	5.53	5.14	4.02
	B	−0.65	−1.97	−0.01	−1.28	−0.86
ALR	R²	0.79	0.74	0.77	0.79	0.76
	RMSE	6.20	5.63	6.90	5.99	5.31
	B	1.37	−0.17	2.45	1.05	0.42
$T_{d,int}^{ALR}-T_s$	R²	0.85	0.74	0.83	0.85	0.87
	RMSE	5.35	6.63	5.91	5.02	4.01
	B	1.36	0.63	2.09	0.66	0.89

Figure 7. Accuracy of $T_{d,int}$ estimates achieved by correction methods at site scale and their scatterplots. (a) – (c) are R², RMSE and B achieved by the $T_{d,int}^{LVP}$-based correction method, (d) – (f) are R², RMSE and B achieved by the $T_{d,int}^{ALR}$-based correction method, and (g) – (i) are the scatterplots of R², RMSE and B achieved by these two methods.

Figure 8. Accuracy of MOD11A1 (a) and MYD11A1 (b) nighttime $T_s$ as the proxy for $T_{amin}$.

Figure 9. Relationships of the RMSE achieved by different estimates with each other and their variations with AI. (a) and (b) are the scatterplots of the RMSE achieved for $T_{d,mean}$ and $T_{d,int}$ estimation with the RMSE achieved for $T_{amin}$ estimation, respectively. (c) is the scatterplots of the RMSE achieved for $T_{d,int}$ estimation with the RMSE achieved by $T_{d,int}^{ALR}$. (d) – (f) are the variations of the RMSE achieved by $T_{d,mean}$, $T_{d,int}$ and $T_{d,int}^{ALR}$ estimation with AI, respectively.

Figure 10. Spatial distribution of annual average $T_d$ estimation and RMSE as well as their accuracy across all sites. (a) and (b) are the spatial distribution of annual average $T_{d,mean}$ and $T_{d,int}$ estimation, (c) and (d) are the accuracy of annual average $T_{d,mean}$ and $T_{d,int}$ estimation across all sites, and (e) and (f) are the spatial distribution of the average RMSE retrieved from the logarithmic regression models in Figure 9(d,e).

Figure 11. Seasonal variations of the RMSE achieved by the ALR and correction methods.

Data availability statement

MODIS products were obtained from https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61, meteorological observations were obtained from the China Meteorological Data Service Center at http://data.cma.cn/en/?r=data/detail&dataCode=J.0019.0010.S002, and aridity index dataset was obtained from https://cgiarcsi.community/data/global-aridity-and-pet-database/.