Quantitative Precipitation Estimation (QPE) Rainfall from Meteorology Radar over Chi Basin

Areerachakul, N; Prongnuch, S; Longsomboon, P; Kandasamy, J

Quantitative Precipitation Estimation (QPE) Rainfall from Meteorology Radar over Chi Basin

Areerachakul, N Prongnuch, S Longsomboon, P Kandasamy, J

Permalink

Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Hydrology, 2022, 9, (10), pp. 178
Issue Date:: 2022-10-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (3.52 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Areerachakul, N
dc.contributor.author	Prongnuch, S
dc.contributor.author	Longsomboon, P
dc.contributor.author	Kandasamy, J https://orcid.org/0000-0003-0160-3519
dc.date.accessioned	2023-04-18T04:24:58Z
dc.date.available	2023-04-18T04:24:58Z
dc.date.issued	2022-10-01
dc.identifier.citation	Hydrology, 2022, 9, (10), pp. 178
dc.identifier.issn	2306-5338
dc.identifier.issn	2306-5338
dc.identifier.uri	http://hdl.handle.net/10453/169945
dc.description.abstract	This study of the Quantitative Estimation Precipitation (QEP) of rainfall, detected by two Meteorology Radars over Chi Basin, North-east Thailand, used data from the Thai Meteorological Department (TMD). The rainfall data from 129 rain gauge stations in the Chi Basin area, covering a period of two years, was also used. The study methodology consists of: firstly, deriving the QPE between radar and rainfall based on meteorological observations using the Marshall Palmer Stratiform, the Summer Deep Convection, and Regression Model and calibrating with rain gauge station data; secondly, Bias Correction using statistical method; thirdly, determining spatial variation using three methods, namely Kriging, Inverse Distance Weight (IDW), and the Minimum Curvature Method. The results of the study demonstrated the accuracy of estimating precipitation using meteorological radar. Estimated precipitation compared against an equivalent of 2 years of rain station measurement had a probability of detection (POD) of 0.927, where a value of 1 indicated perfect agreement, demonstrating the effectiveness of the method used to calibrate the radar data. The bias correction method gave high accuracy compared with measured rainfall. Furthermore, of the spatial estimation of rainfall methods, the Kriging methodology showed the best fit between estimation of rainfall distribution and measured rainfall distribution. Therefore, the results of this study showed that the rainfall estimation, using data from a meteorology radar, has good accuracy and can be useful, especially in areas where it is not possible to install and operate rainfall measurement stations, such as in heavily forested areas and/or in steep terrain. Additionally, good accuracy rainfall data derived from radar data can be integrated with other data used for water management and natural disasters for applications to reduce economic losses, as well as losses of life and property.
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Hydrology
dc.relation.isbasedon	10.3390/hydrology9100178
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0502 Environmental Science and Management, 0503 Soil Sciences
dc.title	Quantitative Precipitation Estimation (QPE) Rainfall from Meteorology Radar over Chi Basin
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0502 Environmental Science and Management
utslib.for	0503 Soil Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	open_access	*
dc.date.updated	2023-04-18T04:24:57Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	10

Abstract:

This study of the Quantitative Estimation Precipitation (QEP) of rainfall, detected by two Meteorology Radars over Chi Basin, North-east Thailand, used data from the Thai Meteorological Department (TMD). The rainfall data from 129 rain gauge stations in the Chi Basin area, covering a period of two years, was also used. The study methodology consists of: firstly, deriving the QPE between radar and rainfall based on meteorological observations using the Marshall Palmer Stratiform, the Summer Deep Convection, and Regression Model and calibrating with rain gauge station data; secondly, Bias Correction using statistical method; thirdly, determining spatial variation using three methods, namely Kriging, Inverse Distance Weight (IDW), and the Minimum Curvature Method. The results of the study demonstrated the accuracy of estimating precipitation using meteorological radar. Estimated precipitation compared against an equivalent of 2 years of rain station measurement had a probability of detection (POD) of 0.927, where a value of 1 indicated perfect agreement, demonstrating the effectiveness of the method used to calibrate the radar data. The bias correction method gave high accuracy compared with measured rainfall. Furthermore, of the spatial estimation of rainfall methods, the Kriging methodology showed the best fit between estimation of rainfall distribution and measured rainfall distribution. Therefore, the results of this study showed that the rainfall estimation, using data from a meteorology radar, has good accuracy and can be useful, especially in areas where it is not possible to install and operate rainfall measurement stations, such as in heavily forested areas and/or in steep terrain. Additionally, good accuracy rainfall data derived from radar data can be integrated with other data used for water management and natural disasters for applications to reduce economic losses, as well as losses of life and property.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/169945