the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Jan 2024
Assessing rainfall radar errors with an inverse stochastic modelling framework
Abstract. Weather radar is a crucial tool in rainfall estimation, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and the drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods, imposing path-integrated attenuation effects, a stochastic drop-size distribution field, along with sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field, and the corrected ‘best guess’ rainfall field which would be obtained if they were observed in the real-world case. The structure of these errors is then investigated, with a focus on frequency and behaviour of ‘rainfall shadows’. Half of simulated weather radar images have at least 3 % of significant rainfall rates shadowed and 25 % had at least 45 km2 containing rainfall shadows, resulting in underestimation of potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well at generating realistic weather radar images visually, for a large range of event types.
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RC1: 'Comment on egusphere-2024-26', Anonymous Referee #1, 25 Mar 2024
The authors discuss weather radar measurements and attempt to quantify the frequency and extent to which important rainfall information is captured in these measurements. Clearly, such measurements are paramount for detailed recording of storms and precipitation. However, error sources like attenuation and ground clutter can obscure these rainfall measurements. The authors introduce a novel approach by using a stochastic model to simulate rainfall data, which is considered accurate. They then systematically introduce errors to this data to emulate the radar estimation process and study the error patterns. The model effectively generates these images for various event types, aiding in understanding and correcting radar rainfall estimation errors.
Honestly, there is not much to write about the paper. The paper is clear, well-structured, and crafted, supported by informative and high-quality figures and equations. Of course, there are several assumptions used but overall, the methods are robust. The examples provided for high bias, low and high variability are informative and indicative, alongside the individual image-based results. The work also acknowledges potential limitations and suggests possible extensions. I suggest the authors have a careful look for typos, e.g.: line 41 … is that…, line 53 … Section ??. Also try to provide some additional information about the methodological choices that are just mentioned, e.g., line 91. …a log-normal… indeed simple but is it the right distribution for this case? Or line 92 - how did you estimate the anisotropy? Concluding, I believe this is a high-quality technical study that achieves its intended goal and adds to the literature.
Citation: https://doi.org/10.5194/egusphere-2024-26-RC1 -
AC1: 'Reply on RC1', Amy Green, 23 Apr 2024
The authors would like to thank reviewer 1 for the positive comments and appreciate the time and effort made and insightful comments for improvements to our paper. We thank the reviewer for pointing out two typos in the manuscript (line 41 and line 53), which will be corrected in the revised manuscript.
We agree with the comments made about clarifying methodological assumptions on line 91 (log-normal assumptions) and line 92 (anisotropy), and propose to add the following clarification to the revised manuscript in Section 3.3:
“The random noise field is added to rainfall values to prevent numerical instabilities, with the marginal distribution from Fig. 2 converted to rainfall rates in Fig. 3. When considering the logarithm of weather radar noise (i.e. dry day images and values of dBZ corresponding to rainfall rates less than 0.1mmh-1), these are sufficiently Gaussian to satisfy the assumption of a Log-Normal marginal distribution for random noise effects. A Log-Normal marginal distribution allows for a simple and easy transformation when simulating the field using Gaussian random field theory. Empirical variograms of these values were estimated to identify an appropriate correlation structure, which has a very short correlation range of around 5km. The optimal spatial transformation for minimising least squares between the marginal variogram values of the two spatial dimensions is used to estimate field anisotropy from empirical variogram fields, with estimates suggesting that isotropy of random noise fields is a valid assumption in this case.”
Citation: https://doi.org/10.5194/egusphere-2024-26-AC1
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AC1: 'Reply on RC1', Amy Green, 23 Apr 2024
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RC2: 'Comment on egusphere-2024-26', Anonymous Referee #2, 25 Mar 2024
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AC2: 'Reply on RC2', Amy Green, 23 Apr 2024
The authors would like to thank reviewer 2 for the in depth comments, and appreciate the time and effort made and insightful comments for improvements to our paper. We thank the reviewer for pointing out the need for clarification on methodology and agree that key explanations (e.g. how the best guess rainfall field is estimated and how beam broadening/radar-sampling is taken into account) should be expanded upon and clarified and will be done in the revised manuscript. In the supplementary document attached, is a breakdown of author comments in response to the detailed major and minor points given by reviewer 2.
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AC3: 'Figure 16', Amy Green, 23 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-26/egusphere-2024-26-AC3-supplement.pdf
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AC4: 'Figure 17', Amy Green, 23 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-26/egusphere-2024-26-AC4-supplement.pdf
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AC2: 'Reply on RC2', Amy Green, 23 Apr 2024
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