the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Radar based high resolution ensemble precipitation analyses over the French Alps
Abstract. Reliable estimation of precipitation fields at high resolution is a key issue for snow cover modelling in mountainous areas, where the density of precipitation networks is far too low to capture their complex variability with topography. Adequate quantification of the remaining uncertainty in precipitation estimates is also necessary for further assimilation of complementary snow observations in snow models. Radar observations provide spatialised estimates of precipitation with high spatial and temporal resolution, and are often combined with rain gauge observations to improve the accuracy of the estimate. However, radar measurements suffer from significant shortcomings in mountainous areas (in particular, unrealistic spatial patterns due to ground clutter). Precipitation fields simulated by high-resolution numerical weather prediction (NWP) models provide an alternative estimate, but suffer from systematic biases and positioning errors. Even though these uncertainties can be partially described by ensemble NWP systems and systematic errors can be reduced by statistical post-processing, NWP precipitation estimates are still not reliable enough for the requirements of high resolution snow cover modelling.
In this study, better precipitation estimates are obtained through a specific analysis based on a combination of all these available products. First, a pre-processing step is proposed to mitigate the main deficiencies of radar and gauges precipitation estimation products, focusing on reducing unrealistic spatial patterns. This method also provides a spatialised estimate of the associated error in mountainous areas, based on a climatological analysis of both radar and NWP-estimated precipitation. Three ensemble daily precipitation analysis methods are then proposed, first using only the modified precipitation estimates and associated errors, then combining them with ensemble NWP simulations based on the Particle Filter and Ensemble Kalman Filter data assimilation algorithms. The performance of the different precipitation analysis methods is evaluated at a local scale using independent ski resort precipitation observations. The evaluation of the pre-processing step shows its ability to remove the main spatial artefacts coming from the radar measurements and to improve the precipitation estimates at the local scale. The local scale evaluations of the ensemble analyses do not demonstrate an additional benefit of ensemble NWP forecasts, but their contrasted spatial patterns are challenging to evaluate with the available data.
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RC1: 'Comment on egusphere-2024-668', Anonymous Referee #1, 13 May 2024
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General comments
In this study, the authors have undertaken a considerable effort to refine precipitation analysis in mountainous regions, a challenging yet crucial area of research. A high-resolution ensemble precipitation analysis is developed by combining data from rain gauges, radar observations and a numerical weather prediction model (NWP). Initially, the authors address artifacts inherent in a combined gauge and radar-derived precipitation product (ANTILOPE), with the aim of mitigating issues like ground cluttering in a preprocessing phase, enhancing the accuracy of the precipitation fields in complex terrain. Subsequently, they employ three distinct approaches, two leveraging data from an ensemble NWP system, to generate an ensemble precipitation analysis using the preprocessed precipitation fields. In summary, the authors strive to improve precipitation products in mountainous areas, aiming also to correctly characterizing the uncertainty in the estimates. Through these developments, improved precipitation datasets can significantly enhance the accuracy of high-resolution snow simulations. Therefore, this work holds importance for various applications including water resource management, natural hazard mitigation, and climate impact assessments.
While the study demonstrates significant strengths, particularly in the quality of results and highly information illustrations of radar measurement issues, certain aspects warrant refinement before an eventual publication. In particular, the methods for characterizing the errors in the ANTILOPE dataset and the approach for correcting the spatial artifacts in that particular product requires a much clearer explanation. The authors tend to provide somewhat loosely coupled information about the methods in a few bullet points, albeit somewhat similar to a puzzle with a few missing pieces. To enhance clarity, the authors could employ hypothetical numerical examples illustrating key concepts. Additionally, the authors should also outline the assumption underlying their methods (especially those described in section 3.1.1 to 3.1.4). Finally, the authors may also want to discuss how undercatch by precipitation gauges will impact snow simulations using the precipitation datasets developed in this study, and discuss potential mitigation strategies.
Overall, the authors present a highly relevant study that forms an important contribution to the snow modelling community in particular and cryospheric sciences at large. The choices of methods appear sound, and the results are interesting and promising. To increase the value of the study further, please consider improving the description of the methods in particular, and also complement the results with more information. Please also see the detailed comments outlined below.
Specific comments
Line 2: What does “their” in “to capture their complexity” refer to?
Lines 6-9: The combination of the two sentences seems to suggest that the radar product is unbiased, in contrast to the precipitation fields from the biased NWP products. Is this correct?
Lines 13-14: Please rephrase “radar and gauges precipitation estimation products” to “precipitation estimates by radar and gauges” or similar.
Line 19: Does “ski resort” provide any relevant information here?
Line 24: Maybe change “human activities” to “practical applications”?
Lines 45-48: Please also mention that the precipitation measurements are affected by systematic undercatch in case of solid precipitation.
Lines 58-78: These two paragraphs lack a clear statement about the research gap of current studies, and what research is needed for filling this gap.
Lines 80-83: Perhaps move these lines to the end of the paragraph.
Lines 90-91: Consider moving the sentence “Evaluation data is only available for the period from 1 December 2021 to 30 April 2022” to section 2.4 describing the evaluation data.
Line 90-94: Consider adding information here stating that 24 h sums of all precipitation products up to 08:00 CET were analyzed in this study (if this is the case).
Section 2.4 and 2.5: The authors have chosen to first present grid-based products based on observations (section 2.1 to 2.3), followed by the point evaluation measurements (section 2.4), and finally the gridded data from the NWP (section 2.5). To me, it seems more logical to first present all gridded products (currently sections 2.1-2.3 and 2.5), followed by the evaluation data (currently section 2.4). Thus, consider swapping order of section 2.4 and 2.5.
Figure 2: What is the blueish horizontal bar in the left panel?
Figure 2: The text in the figure is very small. Please try to enlarge where possible.
Figure 2, caption: Consider changing the color of the line in Figure 1 showing the radar beam from Pic Blanc to Grandes Rousses massif from gray to a more prominent color.
Heading 2.2: Consider changing to “radar-rain gauge combination” which seems to be a more common terminology (Ochoa-Rodriguez et al., 2019).
Line 130: I wonder whether the usage of “1x1 km^2” is correct. Maybe “1 by 1 km” is better. Or even “…is available at a 1 km resolution…”. If adapting another convention, please change throughout the paper.
Line 139: Rephrase from “the errors’ magnitude” to “the magnitude of the errors”.
Section 2.4: Please provide the number of stations including their minimum, mean and maximum altitude.
Lines 159-162: Add a reference to a study about undercatch issues with precipitation gauges such as Rasmussen et al. (2012).
Line 160: Change from “under-catchment” to “undercatch”.
Lines 167-170: Why were precipitation sums calculated as a 24 h sum up to 06:00 UTC and not up to 08:00 CET as with the other products? What influence does this have on the analysis presented in the study?
Line 177: What region does “metropolitan France” refer to?
Line 188: Change from “half those” to “half of those”.
Line 188: Change from “in between” to “between the two peaks”.
Lines 188-190: Please rephrase this sentence since it currently does not read well. Maybe even spilt into two sentences.
Lines 205-206: Please use either “true” or “real” precipitation, and align this with the notation used for the variables. Likely, “true” is a better terminology than “real” precipitation.
Equation 1: Consider renaming the variable representing accumulated precipitation (RR) to an acronym that cannot be confused with the variable representing the ratios (R). The current usage of variable names is confusing.
Line 215: Change from “gauges locations” to “location of gauges”.
Line 222: Change from “far from pixel i of a distance d_ik” to “with a distance d_ik separating point i and k”.
Line 225: What does this ratio express? Please provide a meaning to the reader for easier understanding of the methods.
Section 3.1.1 to 3.1.4: The explanation of these methods needs to be improved. Potentially a hypothetical numerical example will help the reader to understand the methods better illustrating the core concept of the method using (a) one location including a gauge observation, (b) one location where the ANTILOPE is free of artifacts, and (c) a final location where ANTILOPE is affected by ground cluttering.
Lines 291-306: What do the authors mean with a spatial observation error and a dynamic error? Is the first constant in space while the second varies in space?
Lines 321-322: Reformulate. Likely the reference does suggest to reduce the number of assimilated observations in general, but not to reduce the number of particles to 16 and the number of observations to 1 in particular, as I understand the current text.
Line 353: What does “improved predictive added value” mean?
Line 355: Consider removing “local” from the sentence.
Line 361: What is the difference between systematic errors and biases?
Lines 367-368: Change from “the estimated and observed 24 h precipitation values time series” to “the estimated and observed time series of 24 h precipitation records” or similar.
Line 386: Repetition of “when only solid precipitation is considered”.
Line 387-390: Consider swapping order of these two sentences.
Lines 396-398: Where are these results?
Line 402: The first sentence on this line should probably be combined with the previous sentence.
Section 4: For the reader, it is likely easier if single sections presents results with a focus on individual figures. As an example, results from Figure 8 and 9 are presented in section 4.2.1 on lines 407-409, and again in section 4.2.2 on lines 411-417. In this example, the heading of the first section could be changed to “Impact of pre-processing procedure on spatial precipitation patterns” and the second section to “Skill of the precipitation estimates” (or similar) to allow for a focus of individual figures in single sections. Of course, cross references are allowed, but the current presentation of the results is confusing to me.
Figure 8: Please find a more descriptive label for the vertical axis.
Figure 9, caption: Rephrase to “Ratio between estimated and observed accumulated precipitation for the different methods assessed in this study” or similar.
Lines 420-421: Change from “(along the black line Hopson, 2014)” to “(along the one-to-one line) as described by Hopson et al. (2014)” or similar.
Line 424: What does “quite comparable” mean? Please avoid fuzzy terms.
Line 426: “To a lesser extent” than what?
Figure 10, caption: Please split the last sentence into two sentences to improve readability.
Line 448: Remove “estimation” in the part “precipitation estimation products”.
Line 449: Consider changing from “more competitive” to “provide better results”.
Line 465-467: Please provide the number of stations above 2000 m.a.s.l.
Line 473: Consider changing from “suffers from some” to “has”.
Line 483: Consider changing from “It will compare” to “In such a study, we will”.
Lines 475-493: In my opinion, the authors overuse bullet list making the text difficult to read. Please consider reducing the use of bullet points throughout the paper.
Line 504: “More spatially homogeneous errors” than what and why?
Line 519-520: Note that a precipitation product with shortcomings was assimilated and not direct observations. Please discuss the implications of this approach for the final results.
Line 531: What does “despite major shortcomings” refer to?
Line 535: What does “the advanced” mean in this context?
Line 537: Consider changing from “local 24 h precipitation” to “24 h precipitation sums” or similar.
Line 540: Is the ARMOE system “the most advanced high-resolution numerical weather prediction model” of all NWPs in the world?
Line 543: Consider changing from “The authors” to “In this study, we”.
Line 546: Reformulate. Change from “They also applied a correction algorithm” to “A correction algorithm was applied” or similar.
Line 549: “Local” seems to refer to station observations. Please clarify.
Line 550: Please give one or two examples of possible refinements in the conclusion.
Technical comments
Line 37: A misplaced comma.
Line 44: Rephrase to “precipitation inputs provided”.
Line 59-61: There seems to be an error with the usage of parenthesis here.
Line 80: Remove “preliminary”.
Line 148: Change from “Fig. 1” to “Figure 1”.
Line 379: Wrong units: “g m^-2”.
Line 380: “This Figure”. Misplace capital letter.
Line 383: Inconsistent usage of units: “kg/m2/24h”. Please also correct many of the figure titles.
Line 387: Likely refers to appendix B and not A.
Line 393: A white space is missing.
Line 395: Error in figure reference.
Line 401: Likely wrong reference to section 5.
Line 416: Change from “Figure 9” to “Figure 9b”.
Line 440: Consider changing from “Figure 11” to “Figure 11e and f”.
Line 460: Wrong reference.
Line 510: Wrong reference format.
Line 583: Remove “has” from the sentence.
References
Ochoa-Rodriguez, S., Wang, L. P., Willems, P., & Onof, C. (2019). A Review of Radar-Rain Gauge Data Merging Methods and Their Potential for Urban Hydrological Applications. Water Resources Research, 55(8), 6356-6391. https://doi.org/10.1029/2018wr023332
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer, A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., Smith, C., Nitu, R., Hall, M., Ikeda, K., & Gutmann, E. (2012). HOW WELL ARE WE MEASURING SNOW? The NOAA/FAA/NCAR Winter Precipitation Test Bed. Bulletin of the American Meteorological Society, 93(6), 811-829. https://doi.org/10.1175/Bams-D-11-00052.1
Citation: https://doi.org/10.5194/egusphere-2024-668-RC1
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