Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite data
Abstract Rainfall estimation over large areas is important for a thorough understanding of water availability, influencing societal decision-making, as well as being an input for scientific models. Traditionally, Australia utilizes a gauge-based analysis for rainfall estimation, but its performance...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-11-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-25255-6 |
| _version_ | 1811315037410689024 |
|---|---|
| author | Zhi-Weng Chua Alex Evans Yuriy Kuleshov Andrew Watkins Suelynn Choy Chayn Sun |
| author_facet | Zhi-Weng Chua Alex Evans Yuriy Kuleshov Andrew Watkins Suelynn Choy Chayn Sun |
| author_sort | Zhi-Weng Chua |
| collection | DOAJ |
| description | Abstract Rainfall estimation over large areas is important for a thorough understanding of water availability, influencing societal decision-making, as well as being an input for scientific models. Traditionally, Australia utilizes a gauge-based analysis for rainfall estimation, but its performance can be severely limited over regions with low gauge density such as central parts of the continent. At the Australian Bureau of Meteorology, the current operational monthly rainfall component of the Australian Gridded Climate Dataset (AGCD) makes use of statistical interpolation (SI), also known as optimal interpolation (OI) to form an analysis from a background field of station climatology. In this study, satellite observations of rainfall were used as the background field instead of station climatology to produce improved monthly rainfall analyses. The performance of these monthly datasets was evaluated over the Australian domain from 2001 to 2020. Evaluated over the entire national domain, the satellite-based SI datasets had similar to slightly better performance than the station climatology-based SI datasets with some individual months being more realistically represented by the satellite-SI datasets. However, over gauge-sparse regions, there was a clear increase in performance. For a representative sub-domain, the Kling-Gupta Efficiency (KGE) value increased by + 8% (+ 12%) during the dry (wet) season. This study is an important step in enhancing operational rainfall analysis over Australia. |
| first_indexed | 2024-04-13T11:23:05Z |
| format | Article |
| id | doaj.art-71bb0e7025e34002b8819b3aa9db824d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T11:23:05Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-71bb0e7025e34002b8819b3aa9db824d2022-12-22T02:48:46ZengNature PortfolioScientific Reports2045-23222022-11-0112111510.1038/s41598-022-25255-6Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite dataZhi-Weng Chua0Alex Evans1Yuriy Kuleshov2Andrew Watkins3Suelynn Choy4Chayn Sun5Bureau of MeteorologyBureau of MeteorologyBureau of MeteorologyBureau of MeteorologyRoyal Melbourne Institute of TechnologyRoyal Melbourne Institute of TechnologyAbstract Rainfall estimation over large areas is important for a thorough understanding of water availability, influencing societal decision-making, as well as being an input for scientific models. Traditionally, Australia utilizes a gauge-based analysis for rainfall estimation, but its performance can be severely limited over regions with low gauge density such as central parts of the continent. At the Australian Bureau of Meteorology, the current operational monthly rainfall component of the Australian Gridded Climate Dataset (AGCD) makes use of statistical interpolation (SI), also known as optimal interpolation (OI) to form an analysis from a background field of station climatology. In this study, satellite observations of rainfall were used as the background field instead of station climatology to produce improved monthly rainfall analyses. The performance of these monthly datasets was evaluated over the Australian domain from 2001 to 2020. Evaluated over the entire national domain, the satellite-based SI datasets had similar to slightly better performance than the station climatology-based SI datasets with some individual months being more realistically represented by the satellite-SI datasets. However, over gauge-sparse regions, there was a clear increase in performance. For a representative sub-domain, the Kling-Gupta Efficiency (KGE) value increased by + 8% (+ 12%) during the dry (wet) season. This study is an important step in enhancing operational rainfall analysis over Australia.https://doi.org/10.1038/s41598-022-25255-6 |
| spellingShingle | Zhi-Weng Chua Alex Evans Yuriy Kuleshov Andrew Watkins Suelynn Choy Chayn Sun Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite data Scientific Reports |
| title | Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite data |
| title_full | Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite data |
| title_fullStr | Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite data |
| title_full_unstemmed | Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite data |
| title_short | Enhancing the Australian Gridded Climate Dataset rainfall analysis using satellite data |
| title_sort | enhancing the australian gridded climate dataset rainfall analysis using satellite data |
| url | https://doi.org/10.1038/s41598-022-25255-6 |
| work_keys_str_mv | AT zhiwengchua enhancingtheaustraliangriddedclimatedatasetrainfallanalysisusingsatellitedata AT alexevans enhancingtheaustraliangriddedclimatedatasetrainfallanalysisusingsatellitedata AT yuriykuleshov enhancingtheaustraliangriddedclimatedatasetrainfallanalysisusingsatellitedata AT andrewwatkins enhancingtheaustraliangriddedclimatedatasetrainfallanalysisusingsatellitedata AT suelynnchoy enhancingtheaustraliangriddedclimatedatasetrainfallanalysisusingsatellitedata AT chaynsun enhancingtheaustraliangriddedclimatedatasetrainfallanalysisusingsatellitedata |