Mapping Australia’s precipitation: harnessing the synergies of multi-satellite remote sensing and gauge network data

Coverage, resolution, and accuracy in the spatial temporal estimates of remotely sensed precipitation from space satellites, along with number instruments deployed to deliver these observations, are increasing. Of key interest this study is unsurpassed opportunity offered by their broad continuous coverage complement sparse, but more accurate, situ rain gauge measurements for building climate resilience at local, regional, global scales. For many parts globe, remains untapped. Australia no exception provides a unique challenge, given small fraction continent that has gauges, highly diverse due its large size, apparent worsening extreme weather events both frequency intensity. Notwithstanding great impetus, continent-wide record multi-satellite-gauge fused data lacking. This missing asset prerequisite understanding emerging spatiotemporal dynamics precipitation, without which reliable forecasts would be difficult if not impossible. seeks address need. Here, we develop method can synergistically fuse different sources. More specifically, aim Precipitation Profiler-Observation Fusion Estimation (PPrOFusE), tool high-quality multi-satellite data. We test apply Australia, it means limited scope region. By design, PPrOFusE built-in capability assess strengths weaknesses each platform. In case study, period 22 years (2000–2022) using network Australian Bureau Meteorology (BOM) together satellite Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping (GSMaP) National Oceanic Atmospheric Administration’s (NOAA) Climate Prediction Center Morphing technique (CMORPH). Our proposed fusion consists two steps. Step 1, relationship among three sources modeled multiple linear regression location, returning least squares associated coefficient vector. 2, such vector all locations fitted autoregression model, whereafter vectors those void gauges predicted interpolation. Key findings twofold. First, CMORPH accurate most regions than GSMaP. Second, clustering analysis over last 20 suggests trends on Australia’s changing climate, relative BOM’s six major zones, previous century: (a) increased variability north, consistent meteorological expectations, amid southwards expansion wet summer dominant zones across continent; (b) edge arid region shifts pushes out Mediterranean winter rainfall southern Australia.