Finding point method and re-optimized method for the brightness
temperature simulation from Fengyun 4A AGRI in infrared channels

Mingwei Zhu; Shuanggen Jin; Shuanggen Jin; Shuanggen Jin; Di Di; Di Di

doi:10.1364/AO.472382

Applied Optics
Vol. 61,
Issue 34,
pp. 10159-10170
(2022)
•https://doi.org/10.1364/AO.472382

Finding point method and re-optimized method for the brightness temperature simulation from Fengyun 4A AGRI in infrared channels

Mingwei Zhu, Shuanggen Jin, and Di Di

Not Accessible

Your library or personal account may give you access

Get PDF
Email
Share
Get Citation
Copy Citation Text
Mingwei Zhu, Shuanggen Jin, and Di Di, "Finding point method and re-optimized method for the brightness temperature simulation from Fengyun 4A AGRI in infrared channels," Appl. Opt. 61, 10159-10170 (2022)

Export Citation
- BibTex
- Endnote (RIS)
- HTML
- Plain Text
Citation alert
Save article

Check for updates

Related Topics
Table of Contents Category
- Atmospheric and Oceanic Optics
Optics & Photonics Topics
?

The topics in this list come from the Optics and Photonics Topics applied to this article.

About this Article
History
- Original Manuscript: August 5, 2022
- Revised Manuscript: October 17, 2022
- Manuscript Accepted: October 31, 2022
- Published: November 30, 2022

Abstract

The correlated $k$-distribution (CKD) is a fast radiative transfer model and is often used in atmospheric absorption simulation. In the paper, we apply two automatic CKD methods to satellite brightness temperature simulations from the Fengyun 4A Advanced Geostationary Radiation Imager (AGRI) in infrared channels, namely, the finding point method (FPM) and the re-optimized method (ROM). In the calculation, we used Radiative Transfer for the Television Observation Satellite Operational Vertical Sounder (RTTOV) as the comparison, and we use line-by-line (LBL) integration as the reference. Compared with LBL in the brightness temperature simulation of real profiles, the errors of FPM in 7.1 µm and 13.5 µm channels are 0.22 K, $- 0.13 \;{\rm K}$ for mean error and 0.3128 K, 0.2184 K for root mean square error (RMSE), respectively, which are larger than that of RTTOV, with 0.16 K, 0.02 K, 0.2144 K, and 0.1226 K, respectively. In the other channels, the results show that of ROM has the highest accuracy and RTTOV has the lowest accuracy. In general, FPM and ROM can achieve very good accuracy in satellite infrared remote sensing.

Full Article | PDF Article

More Like This

Efficient radiative transfer model for thermal infrared brightness temperature simulation in cloudy atmospheres

Wenwen Li, Feng Zhang, Yi-Ning Shi, Hironobu Iwabuchi, Mingwei Zhu, Jiangnan Li, Wei Han, Husi Letu, and Hiroshi Ishimoto
Opt. Express 28(18) 25730-25749 (2020)

Principal component-based radiative transfer model for hyperspectral sensors: theoretical concept

Xu Liu, William L. Smith, Daniel K. Zhou, and Allen Larar
Appl. Opt. 45(1) 201-209 (2006)

Sensor-based clear and cloud radiance calculations in the community radiative transfer model

Quanhua Liu, Y. Xue, and C. Li
Appl. Opt. 52(20) 4981-4990 (2013)

Previous Article Next Article

Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (11)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (7)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (10)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Channel	Type	Central Wavelength	Spatial Resolution	Main Purpose
1	Visible and near-infrared	0.47 µm	1 km	Aerosol
2		0.65 µm	0.5–1 km	Fog, cloud
3		0.825 µm	1 km	Vegetation
4	Short-wave Infrared	1.375 µm	2 km	Cirrus
5		1.61 µm	2 km	Cloud, snow
6		2.25 µm	2–4 km	Cirrus, aerosol
7	Mid-wave infrared	3.75 µm	2 km	Fire
8	Mid-wave infrared	3.75 µm	4 km	Land surface
9	Water vapor	6.25 µm	4 km	High level water vapor
10	Water vapor	7.1 µm	4 km	Middle level water vapor
11	Long-wave infrared	8.5 µm	4 km	Water vapor, cloud
12		10.7 µm	4 km	Surface temperature
13		12.0 µm	4 km	Surface temperature
14		13.5 µm	4 km	Cloud thickness

Radius
	7.1 µm			12 µm
NP^a	1	2	3	1	2	3
1	97.5256	97.5256	97.5256	154.7020	154.7020	154.7020
2	79.4161	79.4161	79.4161	17.6616	34.4463	17.6616
3	7.2099	11.6883	7.2099	6.4087	14.5144	6.4087
4	4.4004	7.3327	4.4004	4.5836	10.0388	4.5836
5	3.1598	4.1100	2.9634	2.1061	2.0127	2.2027
6	2.7341	1.2368	2.4509	1.6124	1.5355	1.6002

	Me (K)			RMSE (K)
Channel	RTTOV	FPM	ROM	RTTOV	FPM	ROM
6.25 µm	−1.0245	0.0032	0.0027	1.0879	0.1103	0.0641
7.10 µm	−0.0066	−0.0320	0.0004	0.1120	0.2128	0.0682
8.50 µm	0.1968	−0.0081	−0.0541	0.2204	0.0459	0.0650
10.8 µm	−0.1095	0.0205	0.0115	0.1567	0.0266	0.0168
12.0 µm	−0.0673	0.0098	−0.0114	0.1164	0.0328	0.0206
13.5 µm	−0.2514	0.0362	0.0334	0.3256	0.1742	0.1398

Channel	RTTOV	FPM	ROM
6.25 µm	0.0052	0.0140	0.0140
7.10 µm	0.0051	0.0070	0.0070
8.50 µm	0.0046	0.0042	0.0042
10.8 µm	0.0047	0.0018	0.0019
12.0 µm	0.0037	0.0009	0.0009
13.5 µm	0.0036	0.0079	0.0079

Input Data	Source	URL
Atmospheric profiles	ERA-Interim	https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-interim
Land surface temperature
Land surface emission
Sea surface temperature data	NSMC	http://satellite.nsmc.org.cn/PortalSite/Data/Satellite.aspx
Cosine of viewing zenith angles
Cloud detection data

Channel	RTTOV	FPM	ROM
6.25 µm	0.0125	0.0091	0.0091
7.10 µm	0.0055	0.0042	0.0042
8.50 µm	0.0024	0.0033	0.0033
10.8 µm	0.0065	0.0009	0.0009
12.0 µm	0.0044	0.0013	0.0013
13.5 µm	0.0044	0.0063	0.0063

	ME^a (K)			RMSE^b (K)
Channel	RTTOV	FPM	ROM	RTTOV	FPM	ROM
6.25 µm	−0.77	−0.10	−0.05	0.8167	0.1383	0.0837
7.10 µm	0.16	0.22	0.04	0.2144	0.3128	0.0850
8.50 µm	0.28	0.07	0.02	0.2873	0.0752	0.0380
10.8 µm	−0.17	0.01	0.00	0.1951	0.0228	0.0227
12.0 µm	−0.10	−0.02	−0.04	0.1321	0.0283	0.0420
13.5 µm	0.02	−0.13	−0.11	0.1226	0.2184	0.2004

	ME^a (K)			RMSE^b (K)
Channel	RTTOV	FPM	ROM	RTTOV	FPM	ROM
6.25 µm	−0.77	−0.10	−0.05	0.8167	0.1383	0.0837
7.10 µm	0.16	0.22	0.04	0.2144	0.3128	0.0850
8.50 µm	0.28	0.07	0.02	0.2873	0.0752	0.0380
10.8 µm	−0.17	0.01	0.00	0.1951	0.0228	0.0227
12.0 µm	−0.10	−0.02	−0.04	0.1321	0.0283	0.0420
13.5 µm	0.02	−0.13	−0.11	0.1226	0.2184	0.2004

Abstract

Data availability

Cited By

Figures (11)

Tables (7)

Equations (10)

Applied Optics