Abstract
Satellites offer a way of estimating rainfall away from rain gauges which can be utilised to overcome the limitations imposed by gauge density on traditional rain gauge analyses. In this study, Australian station data along with the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and the Bureau of Meteorology’s (BOM) Australian Gridded Climate Dataset (AGCD) rainfall analysis are combined to develop an improved satellite-gauge rainfall analysis over Australia that uses the strengths of the respective data sources. We investigated a variety of correction and blending methods with the aim of identifying the optimal blended dataset. The correction methods investigated were linear corrections to totals and anomalies, in addition to quantile-to-quantile matching. The blending methods tested used weights based on the error variance to MSWEP (Multi-Source Weighted Ensemble Product), distance to the closest gauge, and the error from a triple collocation analysis to ERA5 and Soil Moisture to Rain. A trade-off between away-from- and at-station performances was found, meaning there was a complementary nature between specific correction and blending methods. The most high-performance dataset was one corrected linearly to totals and subsequently blended to AGCD using an inverse error variance technique. This dataset demonstrated improved accuracy over its previous version, largely rectifying erroneous patches of excessive rainfall. Its modular use of individual datasets leads to potential applicability in other regions of the world.
Highlights
Rainfall is a fundamental part of the water cycle that brings freshwater to Earth’s surface
Gauges are considered the most accurate of rainfall estimates [6], they are still subject to their own biases, such as those from wind, evaporation, wetting and splashing effects, as well as those induced from the instrument and observer [7]. Another crucial limitation of rain gauges is that they offer a point-based measurement, whereas a gridded product is valuable for climate monitoring over large scales as well as for use in scientific models [8]
This was followed by the linear correction-to-anomaly methods, with the Empirical Bayesian Kriging (EBK) and Empirical Bayesian Kriging Regression Prediction (EBKRP) having a larger edge over Ordinary Kriging (OK) than what was obtained with a correction to totals
Summary
Rainfall is a fundamental part of the water cycle that brings freshwater to Earth’s surface. Gauges are considered the most accurate of rainfall estimates [6], they are still subject to their own biases, such as those from wind, evaporation, wetting and splashing effects, as well as those induced from the instrument and observer [7] Another crucial limitation of rain gauges is that they offer a point-based measurement, whereas a gridded product is valuable for climate monitoring over large scales as well as for use in scientific models [8]. Point-based observations can be converted into a grid via objective analysis methods but there can be significant deficiencies where rain gauge density is low [9] and when short time scales are concerned [10] This is because rainfall is a variable that can exhibit high spatiotemporal variation.
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