{"title":"为旱地农业合并天气雷达和雨量计","authors":"Peter Weir, Peter Dahlhaus","doi":"10.1071/es23023","DOIUrl":null,"url":null,"abstract":"<p>The areal extent of rainfall remains one of the most challenging meteorological variables to model accurately due to its high spatial and temporal variability. Weather radar is a remote sensing instrument that is increasingly used to estimate rainfall by providing unique observations of precipitation events at fine spatial and temporal resolutions, which are difficult to obtain using conventional rain gauge networks. Dense rain gauge networks combined with operational weather radars are widely considered as the most reliable source of rainfall depth estimates. This paper compares the various sources of rainfall data available and explores the benefits of merging radar data with rain gauge data by reviewing the outcomes of a case study of a major agricultural cropping and pasture region. Comparison is made of rainfall measurements obtained from a dense rain gauge network covered by the output from a weather radar installation. We conclude that merging radar data with rain gauge data provides improved resolution of the spatial variability of rainfall, resulting in a significantly improved data source for agricultural water management and hydrological modelling. However, the use of weather radar merged with rain gauge data is generally underrated as a management tool.</p>","PeriodicalId":55419,"journal":{"name":"Journal of Southern Hemisphere Earth Systems Science","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Merging weather radar and rain gauges for dryland agriculture\",\"authors\":\"Peter Weir, Peter Dahlhaus\",\"doi\":\"10.1071/es23023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The areal extent of rainfall remains one of the most challenging meteorological variables to model accurately due to its high spatial and temporal variability. Weather radar is a remote sensing instrument that is increasingly used to estimate rainfall by providing unique observations of precipitation events at fine spatial and temporal resolutions, which are difficult to obtain using conventional rain gauge networks. Dense rain gauge networks combined with operational weather radars are widely considered as the most reliable source of rainfall depth estimates. This paper compares the various sources of rainfall data available and explores the benefits of merging radar data with rain gauge data by reviewing the outcomes of a case study of a major agricultural cropping and pasture region. Comparison is made of rainfall measurements obtained from a dense rain gauge network covered by the output from a weather radar installation. We conclude that merging radar data with rain gauge data provides improved resolution of the spatial variability of rainfall, resulting in a significantly improved data source for agricultural water management and hydrological modelling. However, the use of weather radar merged with rain gauge data is generally underrated as a management tool.</p>\",\"PeriodicalId\":55419,\"journal\":{\"name\":\"Journal of Southern Hemisphere Earth Systems Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Southern Hemisphere Earth Systems Science\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1071/es23023\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Earth and Planetary Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Southern Hemisphere Earth Systems Science","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1071/es23023","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Earth and Planetary Sciences","Score":null,"Total":0}
Merging weather radar and rain gauges for dryland agriculture
The areal extent of rainfall remains one of the most challenging meteorological variables to model accurately due to its high spatial and temporal variability. Weather radar is a remote sensing instrument that is increasingly used to estimate rainfall by providing unique observations of precipitation events at fine spatial and temporal resolutions, which are difficult to obtain using conventional rain gauge networks. Dense rain gauge networks combined with operational weather radars are widely considered as the most reliable source of rainfall depth estimates. This paper compares the various sources of rainfall data available and explores the benefits of merging radar data with rain gauge data by reviewing the outcomes of a case study of a major agricultural cropping and pasture region. Comparison is made of rainfall measurements obtained from a dense rain gauge network covered by the output from a weather radar installation. We conclude that merging radar data with rain gauge data provides improved resolution of the spatial variability of rainfall, resulting in a significantly improved data source for agricultural water management and hydrological modelling. However, the use of weather radar merged with rain gauge data is generally underrated as a management tool.
期刊介绍：
The Journal of Southern Hemisphere Earth Systems Science (JSHESS) publishes broad areas of research with a distinct emphasis on the Southern Hemisphere. The scope of the Journal encompasses the study of the mean state, variability and change of the atmosphere, oceans, and land surface, including the cryosphere, from hemispheric to regional scales.
general circulation of the atmosphere and oceans,
climate change and variability ,
climate impacts,
climate modelling ,
past change in the climate system including palaeoclimate variability,
atmospheric dynamics,
synoptic meteorology,
mesoscale meteorology and severe weather,
tropical meteorology,
observation systems,
remote sensing of atmospheric, oceanic and land surface processes,
weather, climate and ocean prediction,
atmospheric and oceanic composition and chemistry,
physical oceanography,
air‐sea interactions,
coastal zone processes,
hydrology,
cryosphere‐atmosphere interactions,
land surface‐atmosphere interactions,
space weather, including impacts and mitigation on technology,
ionospheric, magnetospheric, auroral and space physics,
data assimilation applied to the above subject areas .
Authors are encouraged to contact the Editor for specific advice on whether the subject matter of a proposed submission is appropriate for the Journal of Southern Hemisphere Earth Systems Science.
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