| 05 Feb 2026
Design and trial implementation of a continental-scale, kilometre-resolution hourly precipitation analysis for Australia using satellite, radar and gauges
Abstract. High-resolution precipitation information is essential for hydrometeorological applications such as extreme weather monitoring, flood forecasting, and disaster risk management. Despite substantial advances in satellite, radar, and gauge observations, producing kilometre-resolution sub-daily precipitation analyses over continental domains remains challenging due to heterogeneous data availability, scale mismatches, and computational constraints. This study presents the design and trial implementation of BRAIN (blended rainfall), a continental-scale, kilometre-resolution hourly precipitation analysis for Australia. In this initial implementation, BRAIN integrates three key data sources from the Australian Bureau of Meteorology: geostationary satellite rainfall estimates from Himawari (2 km, 10 min), radar rainfall estimates (1 km, 5 min), and sub-daily rain gauge observations. The trialled system incorporates quality control, spatiotemporal aggregation, bias correction, and a simplified statistical interpolation configuration designed to balance performance with scalability at continental scale. Source contributions are weighted according to their spatial and temporal error characteristics, allowing each data type to influence the analysis where it is most informative. The trial implementation produces hourly rainfall fields at 2-km resolution across the Australian continent. Evaluation for the trial period 2022–2023 indicates that the blended analysis improves upon satellite-only, radar-only, and satellite–gauge products, outperforms the gauge-based interpolation approach currently used in flood operations, and provides more spatially coherent and detailed rainfall structures than the current daily operational product. These results demonstrate the feasibility and utility of the proposed design and trial implementation in the Australian context, with potential extension to long-term historical reconstruction and near–real-time applications. The system design is flexible and scalable, enabling future upgrades such as finer spatial and temporal resolutions and the incorporation of additional data sources. Beyond the Australian context, this study provides an additional reference for large-scale multi-source precipitation analysis at kilometre and hourly resolutions.
Overview
This manuscript describes the development of a new operational hourly, km scale precipitation analysis for the entire Australian continent, titled 'BRAIN'. The authors adapt an existing method - Optimal Interpolation - to blend gauged precipitation, a satellite precipiation estimate and precipitation estimates from radar. The use of a robust existing method in OI is in my view a sensible choice for an operational system, and the authors make well-judged simplications to enable its deployment at continental scale (e.g. the use of single radar grid cells to estimate error characteristics). The authors use stringent verification measures, including strict cross-validation and comparisons against a range of existing products that are in wide use in Australia. These are high hurdles to clear, yet the authors are able to demonstrate that BRAIN clearly outperforms existing alternatives. In addition, the manuscript is concise and clear, and in general a pleasure to read. No continentally consistent precipitation product at this temporal or spatial resolution currently exists for Australia, and when BRAIN is operationalised it promises to be a game-changing innovation for a wide range of scientific, industrial and environmental applications. I essentially recommend that the manuscript be published as is, subject to technical corrections.
I leave it to the authors to determine which of my recommendations they wish to act on, of which I will note only one here: I thought too much emphasis was put on the performance of BRAIN shown in Figure 7, which shows only two isolated events. I prefer the authors to refer to their robust assessments of performance in FIgures 5, 6 and 8 (and in the very useful Supplementary materials) than on Figure 7.
I congratulate the authors on what I believe will be a highly impactful product.
Specific comments
Introduction is lucid, economical and comprehensive. It clearly outlines 1) the promise of this product, 2) the challenges in establishing a sub-daily rainfall product in Australia 3) the range of methods available 4) a clear justification for the authors' use of optimal interpolation for their BRAIN product and 5) how the authors will adapt OI for their product.
242 "the "true value"" suggest "the "true value", T,"
L243 "the "true value"" - it's not clear from the equations below or the prose how the true values (T_k or T_i) are defined; accordingly the equations for errors are not explicit. Please describe how the 'true value' is estimated or defined (in prose is fine). I appreciate that OI methods are well known, but for those of us (like me) who are not very familiar with them, it would be nice to have this spelled out.
L251 "in normalised form" should this be "in standardised form"? Normalisation can imply a normalising transformation.
L255-267 "This system of linear equations is solved to determine the optimal weights 𝑤_𝑖, which are used to obtain the best precipitation estimate at each target grid cell by optimally combining satellite, radar, and gauge data." The equations above use a single term (O) to describe observations from gauges or radar, and weights vary with i (location), not data source. What happens when both radar and gauges are available? I assume these have quite different error characeristics, so I couldn't follow how they can be all thrown in together. Oh - never mind, this is explained below (Lines 271-317). It might be good to either avoid the statement "are used to obtain the best precipitation estimate at each target grid cell by optimally combining satellite, radar, and gauge data" at this point, or foreshadow the coming explanation of how you actually combine different observational data sources below, or both. The authors can add a version of this statement at the end of Section 3.2 if they wish.
L272 "while background-to-observation error correlations are assumed to be zero, as different sensor systems are considered independent" no change here, merely a comment: this is interesting, as it's possible to envisage cases where these are not independent (though I think the use of satellite as background vs gauges/radar means the authors are reasonably safe in this assumption).
L285 "only the nearest radar grid cell to each target grid cell is retained as a supplementary observation" Might be good to (very briefly) note how 'nearest' is defined here: is it possible to have more than one 'nearest' cell? Never mind, this is also covered below!
L321 "except for gauges located outside the radar range where no radar information is available" I think this is saying that gauges outside the radar range are used to correct satellite data. Is this correct? (And if not, why not?)
L335 "For gauged locations, this task is relatively straightforward, as gauge observations can be used as ground truth to estimate the error characteristics of the background and observational inputs" Excepting that gauges are point data and satellite/radar are areal averages, which will be smoother in time and space. How did the authors account for areal averaging to compute errors against gauges?
L352 "Specifically, the Australian domain was divided into tropical, subtropical, and temperate subregions." It would be nice to see these regions on a map; could simkply be added to Figure 3a or similar.
Evaluation strategies: outpeforming both IDW and AGCD is a high bar to clear; I commend the authors for this kind of rigour.
L393 "The spatial match between the gridded data and gauge locations was established using nearest interpolation." I'm not sure what was being interpolated here - please clarify.
L407 "direct comparison with global precipitation products would be neither fully fair nor particularly informative." Fair enough - I assume the authors are suggesting BRAIN would substantially outperform these global products. As the verification in this study is already very rigorous, and you can't do everything, this is fine. I think it would nonetheless be interesting to compare BRAIN to these global products in a future study if possible, as it would reinforce confidence in BRAIN for Australian users.
L547 "BR-SRG clearly outperforms AGCD and IDW in representing fine-scale spaital structure and intensity". I understand the need to show example predictions to illustrate how BRAIN functions in comparison to other products. I do not think it's appropriate to use these figures to describe relative performance, in particular because e.g. BRAIN does not outperform AGCD in out-of-radar regions on average according to Figure 6. Suggest this sentence be reworded to "BR-SRG shows fine-scale structure and intensities not present in AGCD and IDW."
L550 Figure 7. A few things to improve about this figure: 1) it would be nice to put the dates for the two rows in the y-axis label of the left-most panel. 2) I could not see the 'x' symbols described in the text; please choose a symbol that is legible. 3) I do not think it's good practice to present additional results on rainfall totals at gauges in the caption. This information could be presented in small text boxes within each panel; alternatively an additional panel could be added to each row with a bar chart showing the values for each product.
L561 "underestimates peaks"; as per the previous comment, this does not reflect e.g. bias statistics shown in Figures 5 & 6. It's my view that readers are drawn to examples like these, and they may use them to infer that these results are generally true, rather than refer to the much more robust statistics presented in Figs 5 & 6. I think that all that can be said is that BRAIN shows much greater spatial detail than the other products. This detail is supported by the verification metrics shown in Figures 5 & 6.
L565 "AGCD (Fig. 7e) fails to capture the peak at Cape Tribulation". 1) International readers may not know where Cape Tribuation is and 2) The totals by AGCD on and off the coast around Cape Tribulation are higher than either BR-SRG or IDW, so I do not think this comment accurate reflects what's shown in the figure. Again, I suggest avoiding making inferences about performance from figure 7. Figure 7 is useful in showing (1) the additional spatial detail shown by BRAIN over both AGCD and IDW and (2) that the spatial detail is consistent across the continent, while for IDW it is not. In my view conclusions on performance should be drawn only from the robust assessments shown in Figs 5 & 6.
L616 "Future development of BRAIN will focus on improving input data quality, refining error characterisation, and enhancing spatial and temporal resolution." (1) It would be nice to give an indication of how computationally demanding a re-estimation of the OI is when new predictor products are used or existing predictor products change. (2) Will the use of e.g. additional satellite products violate the assumption of error independence between products that is crucial to the simplifications pre
Typos etc.
L39 "Satellites-based" should be "Satellite-based"
L50 "collocation-based approach provides" should be "co-location-based approaches provide"
L51 Suggest a sentence break before 'However', and suggest "However, both performance-based and co-location-based approaches typically..."
L58 Suggest a sentence break before 'While', and suggest 'While offering high flexibility, ML-based approaches have...'
L59 "In addition, all above" should be "In addition, all the above"
L75 "data source used for background field" should be "data source used for the background field"
L251 "By expanding and expressing the above equation" suggest "By expanding and expressing Equation 4"
L264 "is adjusted weight accounting" should be "is the adjusted weight accounting"
L337 suggest deleting "due to the absence of gauge observations" - it's redundant.
L394 "For the trialled implementation" suggest "For the trial implementation"
L414 "mean bias error" suggest deleting "error" as it's redundant
L604 "providing a consistent and flexible rainfall analysis capability" Suggest deleting "capability"
L607 "for gridded radar source" suggest deleting "source"