the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mid-Atlantic Nocturnal Low-Level Jet Characteristics: A machine learning analysis of radar wind profiles
Abstract. This paper introduces a machine-learning-driven approach for automated Nocturnal Low-Level Jet (NLLJ) identification using observations of wind profiles from a Radar Wind Profiler (RWP). The work discussed here is an effort to lay the groundwork for a systematic study of the Mid-Atlantic NLLJ’s formation mechanisms and their influence on nocturnal and diurnal air quality in major urban regions by establishing a general framework of NLLJ features and characteristics with an identification algorithm. Leveraging a comprehensive wind profile dataset maintained by the Maryland Department of Environment’s RWP network, our methodology employs supervised machine learning techniques to isolate the features of the south-westerly NLLJ. This methodology was developed to illuminate spatiotemporal patterns and nuanced characteristics of NLLJ events, unveiling their significant role in shaping the planetary boundary layer. This paper discusses the construction of this methodology, its performance against known NLLJs in the current literature, intended usage, and a preliminary statistical analysis. First light results from this analysis have identified a total of 90 south-westerly NLLJs from May–September of 2017–2021 as captured by the RWP stationed in Beltsville, MD (39.05°, -76.87°, 135 m ASL). A composite of these 90 jets is presented to better illustrate many of the bulk parameters, such as core height, duration, and maximum wind speed, associated with the onset and decay of the Mid-Atlantic NLLJ. We hope our study equips researchers and policymakers with further means to monitor, predict, and address these nocturnal dynamics phenomena that frequently influence boundary layer composition and air quality in the U.S. Mid-Atlantic and Northeastern regions.
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RC1: 'Comment on amt-2024-37', Anonymous Referee #2, 16 May 2024
General comments:
The document addresses scientific issues that are relevant to AMT, provided that some elements are clarified.
The scientific methods and assumptions seems valid but not clearly outlined.
The results are not sufficient to support the interpretations and conclusions.
The description of experiments and calculations is not sufficiently complete and precise to allow their reproduction by fellow scientists .
The authors give not proper credit to related work but clearly indicate their own contribution.
The title clearly reflect the contents of the paper.
The abstract provides a concise and complete summary.
The overall presentation is structured and clear.
The language is fluent and precise.
Any parts of the paper (figures) should be clarified.
The number and quality of references are not appropriate.
This article deals with a technique for identifying NLLJs using machine learning. Has machine learning already been used in this type of identification or is it an innovation? This should be specified and references introduced if not.
Automatic identification of NLLJs has already been developed using physics-based algorithms, so why use machine learning when these algorithms have already proved their worth ? A paragraph could be added to discuss these aspects.
Many references do not appear in the bibliography (e.g. Delgado 2013, Weldegauber 2009, Caroll 2021, ...) while others present in the bibliography are not cited in the text (e.g. Bonner 1968, Dejong 2024, Doubler 2015, Hu 2013a & b, Karipot 2009, Liang 2018, Lima, 2018 & 2019, ...). Please, review the entire bibliography. As it stands, it is impossible to verify the veracity of everything written.
The context is well presented but is repeated many times in the text (for example : NLLJs are noctural events).
As far as the figures are concerned, each of the panels should be identified by a letter and the captions should be more detailed. The legends are not complete.
There is not enough detail on how the algorithm works to reproduce it.
Specific comments :
L.27 "noctural events" already introduced in L. 26.
L.37-39 Wind speed does not decrease as far as the free troposphere in Figure 2.
L.54 LLJs not defined
L.61 NLLJ already defined
L66. MD not defined
L.75 This paper is not based on a radar network but on a single radar forming part of a network.
L.78 identified by which radar?
Section 2 contains a single sub-section 2.1 and an excessively long introduction. Please, fix this section.
L.84 - 85 This paper is not based on a network of radars but on a single radar forming part of a network.
L.85 Is there a reference with the full characteristics of this RWP?
L 86. MD not defined
Figure 1: Is it necessary to indicate the location of the Cambrigde and Cumberland RWPs? If so, highlight the location of the Beltsville RWP.
L.94 What is the value of the wind speed identified by sonde?
L.95 NOAA NCEP not defined.
L.95 Unlikely date and please add time.
L.96 Wind speed measured by sonde not known. The measured wind profile could be added to figure 1 in order to clearly see the jet.
L.99-110 This part should be included in the previous one, some things are repeated. It is not about data or method.
L.112 Figure 5 is too far from this paragraph.
L.116 Why are some data not available?
L.117 Are we to understand that these are daily files? This is not specified.
L.120 Only one sub-section follows, not several.
Figure 2(B) SPD not defined, present the curves in the legend to Figure 2. The figure should be centred on the NLLJ event in order to show its development clearly (from 20:00 UTC on 19 May, for example).
L.123-124 Insert a reference.
L.130 What time does night begin?
L.133 -134 repetition of encountered
L.137 what does a file represent? The number of columns and rows is irrelevant.
L 138. Section 2.2 is missing.
Section 3 should be expanded to provide a better understanding of how the algorithm works by focusing on its more detailed relevant phases. For example, section 3.1 could be the subject of a section in its own right, giving step-by-step details of how the algorithm works. Presented like this, it is difficult to understand clearly how it works. Note that there are no references in this key paragraph. At the end of the paragraph, the test results are not clear enough to be properly understood.
L.147 Detail this analysis.
L.148 Why use radial speeds when wind strength and direction are already taken into account? Why use the signal-to-noise ratio, what does it provide?
Figure 3, some elements are illegible and some acronyms are not defined.
Section 3.2 already contains results and could be introduced in section 4. In addition, this section only focuses on 2 cases, which does not seem sufficient to properly qualify the algorithm's performance. A more detailed study would enable us to test it more thoroughly by comparing the NLLJs identified by the algorithm with all the NLLJs identified by the manual method in a year other than the one used for training (the test set already identified, for example). This would make it possible to better characterise the algorithm's shortcomings, by quantifying the number of false events not taken into account, the % of missing data on average per event, etc. Without this kind of statistic, no conclusions can be drawn.
Simple post-processing could be used directly to eliminate outliers if no neighbours are present and to include all the data between the ground and the jet.
Figure 4: Each sub-panel should be indicated by a letter. Perhaps this figure should be split into two separate figures focusing on each event. As with figure 2, the events should be centred to better see their development.
L.218-220 Without seeing the winter months, such a conclusion cannot be drawn.
Please show the missing months in Figure 5. In addition, in Figure 5, it can be seen that May contains the most events and not June, July or August.
L.222-224 include references.
L.226 The year 2017 contains more events than 2019.
L.238-239 Give examples of synergy and cite references.
L 246 Core time not defined
L.247 Replace all « m/s » with m.s-1
L.257-259 What percentage of events does this represent? Using simple post-processing, why not exclude this erroneous data?
Section 4.3 As NLLJs are nocturnal events and sunset times vary according to the season, the data should be standardised according to these times. Otherwise, the morphology of NLLJs could not be correctly presented.
L.276 EDT is local time? Mention this earlier in the article and add sunset and sunrise hours on all figures.
Section 5: Some conclusions may need to be modified in the light of the above changes.
L.333 I-95 corridor not defined
L.346 delete « also »
Citation: https://doi.org/10.5194/amt-2024-37-RC1 -
RC2: 'Comment on amt-2024-37', Anonymous Referee #1, 13 Jul 2024
The study described in the manuscript is the development of an ML-based algorithm for the identification of nocturnal low level jet events in Beltsville, MD from radar wind profiler data. The wind profiler data is used to identify the events and to characterize the wind characteristics and seasonality of events at this location. There are some major issues with the manuscript that I explain below related to the articulation of the need for the ML-based algorithm, the methods used for evaluation of the algorithm and finally with the claims made in the summary that are not based on findings of the study. Based on these issues I recommend publication of this manuscript only after the major revisions detailed below.
Major Issues:
1) Motivation of the need for a new NLLJ identification algorithm - The manuscript quite clearly cites the previous studies that have examined NLLJs in the Mid-Atlantic and explains how the events are identified. These events are used as part of the evaluation of the ML-based algorithm developed in the study. If there is a robust enough method to identify these events, robust enough to be used to evaluate the ML model(s), why is there a need for an ML-based identification method at all? Please articulate this, that is, what the benefit of an ML based algorithm is and why the present method is inadequate.2) The method for the identification of the events for training and then for evaluation is not well explained in the manuscript as written. Based on what is written, it appears that a year's worth of RWP data and a pre-defined set of NLLJ events were used as training, and then the evaluation was done based on a subjective "by eye" examination of a selection of events found in the literature. The algorithm is then put to use for the 2017-2021 period and the events' wind speed characterized. This is not a robust training and evaluation method and should be improved before the study is published.
3) The manuscript's introduction contains a description of LLJ events and their characteristics from the literature that include intertial oscillations, temperature profiles and wind characteristics, as well as the influence of these events on the local atmosphere. Both the introduction and the summary refer to the study as characterizing NLLJ events and helping to understand them, but the characterization here is limited to wind characteristics. I recommend the use of some auxilliary dataset (perhaps a reanalysis) to characterize the events properly once they are identified by the algorithm.
Line by line:Line 46 - "It is believed that the mid-Atlantic NLLJ is akin to the SGP NLLJ..."
Need a reference here or say that you will show this here.Line 96 - "...clear disagreement..." - what clear disagreement is being referred to? between what?
Line 98 - NARR is not an operational model - its an analysis (reanalysis).{Fig 1 - what is the shading? 900 mb wind speed from NARR? Also - manuscript says for the case of may 20, 2024 and the figure says "composite" - what is plotted?}
Line 114 - This section talks about Figure 5 (before any manuscript mention of figs 2-4) - please reorder the figures to be consistent with the order they are referred to in the manuscript
Lines 111-121 - should move to inside section 2.1.
Lines 127-129 - There is not enough explanation here about the "inflection points", what they are and why they are important. I assume this detail is included in Zhang et al 2006, but some more of the detail is needed here.
Line 154 - "...visually conceptualized in Figure 3." How/why is the data pre-processing included in the algorithm execution loop? is this done more than once?
Line 176 - Please explain what an f1 macro test is, what the scores mean, and how this was evaluated. Alternatively, remove this statement.
Line 186 - "...more than satisfactory..." is not quantifiable, particularly when the algorithm testing is by visual inspection (of 50 cases used for training or for all the cases identified in fig 5?).
Line 211 - Why does the present study not include the "ongoing model refinement"?line 315 - The connection to synoptic situation not established in study - connection to season, yes.
line 317 - "..understanding the atmospheric at play..." was not part of the study, and a connection to predictive capability was not established.322 - "critical characteristics"... also not established.
Citation: https://doi.org/10.5194/amt-2024-37-RC2
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