Precipitation Retrieval Using IR Channels Brightness Temperature from SEVIRI

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Gheiby A.; Khwarazmi S.; RAHNAMA M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: IRAN-WATER RESOURCES RESEARCH Year:2021 | Volume:17 | Issue:1 Page(s): 102-115

Download Full-Text

Persian Verion

View:

179

Download:

Cites:

Information Journal Paper

Title

Precipitation Retrieval Using IR Channels Brightness Temperature from SEVIRI

Author(s)

Gheiby A. | Khwarazmi S. | RAHNAMA M. | Issue Writer Certificate

Keywords

Precipitation

Infrared Brightness Temperature

SEVIRI

Regression Model

Artificial Neural Network

Abstract

This study is performed to retrieve Precipitation amount using Spinning Enhanced Visible and InfraRed Imager (SEVIRI) from Meteosat Second Generation (MSG). According to the relationship between the infrared channels brightness temperature and the microphysical and optical properties of the clouds (such as cloud top temperature, cloud top height, optical thickness, particle size, and particle phase), and the influence of changes in these properties on the determination of Precipitation amount and intensity, the relationship between brightness temperature and Precipitation have been studied for two stations in Hormozgan province. The performance of an Artificial Neural Network and also several Regression Models to estimate Precipitation has been evaluated. The results showed that the exponential Gaussian process Regression Model with performing principal component analysis by RMSE of 0. 44, POD of 0. 96 and the HSS of 0. 67 for Precipitation threshold 0. 1 mm for the less than 10 mm Precipitation data set have the best performance. The Artificial Neural Network also presented a RMSE of 1. 27 which indicates weaker performance in comparison with the Regression Model but showed good performance in distinguishing Precipitation conditions from non-Precipitation conditions (POD of 0. 85 and HSS of 0. 48). By comparing the correlation between Precipitation and brightness temperature of infrared channels (average 0. 36), and the correlation between observed and retreived Precipitation (0. 91 in the Regression Model and 0. 43 in the neural network), it can be concluded that the Precipitation products which extracted in this study, show a good correlation between observed and retrieved Precipitation.

Cites

No record.

References

No record.

Cite

APA: Copy

Gheiby, A., Khwarazmi, S., & RAHNAMA, M.. (2021). Precipitation Retrieval Using IR Channels Brightness Temperature from SEVIRI. IRAN-WATER RESOURCES RESEARCH, 17(1 ), 102-115. SID. https://sid.ir/paper/955044/en

Vancouver: Copy

Gheiby A., Khwarazmi S., RAHNAMA M.. Precipitation Retrieval Using IR Channels Brightness Temperature from SEVIRI. IRAN-WATER RESOURCES RESEARCH[Internet]. 2021;17(1 ):102-115. Available from: https://sid.ir/paper/955044/en

IEEE: Copy

A. Gheiby, S. Khwarazmi, and M. RAHNAMA, “Precipitation Retrieval Using IR Channels Brightness Temperature from SEVIRI,” IRAN-WATER RESOURCES RESEARCH, vol. 17, no. 1 , pp. 102–115, 2021, [Online]. Available: https://sid.ir/paper/955044/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops