\nDivision of Atmospheric Sciences and Geophysics, Department of Physics, University of Helsinki, Helsinki, Finland; Finnish Meteorological Institute, Helsinki, Finland; Centre for Atmospheric Science, School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Manchester, United Kingdom<\/em><\/p>\n
(Submitted 24 January 2010; in final form 26 March 2010)<\/p>\n
ABSTRACT<\/p>\n
Case studies are a staple of meteorological work, yet few meteorologists do it well.\u00a0 This article presents 16 principles that can help authors improve their case studies, at both the research and writing stages.\u00a0 These principles involve study design, organization and approaches to the research, writing the manuscript, and figure design.\u00a0 Other topics covered include citations and reference lists, terminology, and formatting.<\/p>\n
Corresponding author address<\/em>: David M. Schultz, Finnish Meteorological Institute, P.O. Box 503, Erik Palm\u00e9nin Aukio 1, FI-00101, Helsinki, Finland, E-mail:\u00a0david.schultz@fmi.fi<\/a><\/p>\n Case studies are indispensable to meteorology. By describing how the weather evolved during a specific event, case studies can be integral to improving weather forecasts and can reveal mesoscale phenomena that were previously undiscovered.\u00a0 Each of us can recall classic case studies that have inspired us by their interesting choice of topic, novel approach, easy-to-read text, clear figures, and concrete conclusions.<\/p>\n No one group of scientists is better suited to producing some of the best case studies than operational meteorologists.\u00a0\u00a0 Every day they go to work and do battle with the weather, trying to understand and predict it.\u00a0 When we think of the authors of the best meteorological writing, those who we admire for their ability to address interesting problems and make sense of them, many forecasters from different generations come to mind: Joseph Galway (Lewis 1996), Leonard Snellman (MacDonald 2000;\u00a0www.nwas.org\/members\/snellman.php<\/a>), Robert Johns (Lewis 2007), Bradley Colman, and Matthew Bunkers.<\/p>\n With role models such as these, writing a case study should be relatively straightforward. After all, how difficult can it be to describe to other meteorologists what happened and how it happened?\u00a0 Instead, my experience with reading others\u2019 case studies as an editor, a reviewer, or an interested reader indicates that few authors do it well.\u00a0 Facts seem to be thrown at the reader for no purpose.\u00a0 Statements are made, seemingly without evidence.\u00a0 Readers are shown dozens of unlabeled figure panels and expected to make sense of them.\u00a0 These are just a few of the problems with many case studies, whether written by forecasters, students, researchers, or even tenured professors.<\/p>\n The purpose of this article is to help to raise the quality of case studies by providing some guidance for those who research and write them.\u00a0 I offer 16 principles or rules that represent the standard approaches, best practices, and simple guidelines to help authors to communicate their research.\u00a0 My word can hardly be considered definitive on these matters, yet I have seen what works, what survives the peer-review process, and what is effective science. Indeed, the best scientific articles do adhere to many of these principles.\u00a0 Despite this evidence of success for those who follow these 16 principles, the principles are not intended to straightjacket authors into a rigid framework, where individuality and creativity get trumped. Instead, to quote Strunk and White\u2019s (2000, 66)\u00a0<\/span>The Elements of Style<\/em>:<\/span> I would like to hear your opinions.\u00a0 I hope you will submit your comments on this article, your experiences, and your suggestions on conducting and writing excellent case studies to the\u00a0EJSSM<\/em>\u00a0Scientific Discussions forum.<\/p>\n To motivate this article, I begin by considering the purpose of a case study in section 2 and why an author should write effective case studies in section 3.\u00a0 Then, section 4 narrows the huge potential scope of this article by discussing what will\u00a0not<\/em>\u00a0be covered.\u00a0 The 16 principles for producing a high-quality case study follow, organized into sections by study design (section 5), organization and approaches (section 6), writing the manuscript (section 7), numerical modeling (section 8), and figure design and production (section 9).\u00a0 Section 10 discusses references, and section 11 provides miscellaneous other advice.\u00a0 Finally, section 12 concludes this article.<\/p>\n Often, case studies describe extreme events that may only happen once in a lifetime.\u00a0 The value of studying a single extreme event is potentially limited by its representativeness.\u00a0 What value is documenting an event that never may be seen again?\u00a0 This is a legitimate question, one that is further discussed in section 5.<\/p>\n By contrast, case studies can have a greater purpose than just documentation.\u00a0 For example, National Weather Service Science and Operations Officer Jon Zeitler has a list of three criteria he looks for in case studies: (i) a unique or rare occurrence of a weather event, (ii) a demonstration of how new or unusual observations can be used to identify, analyze, or forecast an event, and (iii) a demonstration of how theory can be applied, especially for unusual cases.\u00a0 A meaningful case study meets two or all three criteria.<\/p>\n The first criterion might also be stated in the words of retired National Weather Service forecaster Jim Johnson, \u201cWhat the hell was that?\u201d\u00a0 Some phenomena in the atmosphere may not be understood yet, and a high-quality case study brings it to the attention of other meteorologists who might help.\u00a0 Such an observational case study also might lay the groundwork for a field program to collect more detailed observations of the phenomenon or a model simulation in a future article.\u00a0 Examples include Bosart (1983), McNulty (1991), and Schultz and Knox (2007).<\/p>\n Articles do not have to be limited to a single case.\u00a0 Indeed, comparing and contrasting two or more cases allow the author to show what may regulate the observed differences.\u00a0 Because describing two or more events risks producing a lengthy account, such manuscripts need to be focused to avoid unnecessary details.\u00a0 Examples of manuscripts where each one compares two different events include Pagnotti and Bosart (1984), Rogers and Bosart (1991), Schultz (2004b), and Doswell and Haugland (2007).<\/p>\n Finally, published case studies can be helpful to authors performing meta-analyses on a given topic by allowing the collection of all cases in the literature that meet certain criteria.\u00a0 Such an analysis can be a powerful means to challenge orthodoxy.\u00a0 Examples include the meta-analysis of occluded fronts by Schultz and Mass (1993, their Appendix) that demonstrated that cold-type occlusions, if they existed, were rare, and the meta-analysis by Bryan and Fritsch (2000, their Table 2) that showed the existence of moist absolutely unstable layers.<\/p>\n Despite their importance to meteorology, case studies have gotten a bad reputation\u2014I believe partly because many studies violate many of the 16 principles later in this article. Why should an author care about the quality of case studies, or scientific articles in general?<\/p>\n For these reasons, we all have a responsibility to improve the quality of case studies.\u00a0 Effective communication through properly written case studies can be a means to improve our forecasts, educate our colleagues, and advance our science.<\/p>\n It would be an overstatement to say that a single article could contain all the information that an author would need to know to write an effective case study.\u00a0 As such, I want to make it clear what I will\u00a0not\u00a0<\/em>focus on in this article.<\/p>\n I assume that you are interested in scientific exploration and understand the scientific method. I assume that you know how to construct a hypothesis, evaluate its validity, eliminate competing hypotheses, and support your arguments.\u00a0 If not, I recommend reading Valiela (2001), Booth et al. (2003), and Weston (2009).<\/p>\n I assume that you know the basic structure of a scientific paper:\u00a0 abstract, introduction, data and methods, results, discussion, and conclusions.\u00a0 If not, then read chapters 9\u201313 of Day and Gastel (2006), chapter 4 of Schultz (2009), and\u00a0A Guide to Science Writing\u00a0<\/em>by the\u00a0Journal of Young Investigators\u00a0<\/em>(www.jyi.org<\/a>).<\/em><\/p>\n Finally, I assume that you can produce coherent, clear, and precise scientific writing.\u00a0 Writing should flow logically from one idea to the next, rather than being a disconnected series of sentences. Most people can improve their writing with a little guidance, regardless of their ability. To improve scientific writing, if not writing in general, I recommend Gopen and Swan (1990), Strunk and White (2000), and Williams (2006).<\/p>\n Sixteen principles for excellent scientific case studies follow (highlighted in blue).<\/p>\n #1:\u00a0 Have a well-defined purpose.<\/strong><\/p>\n Before considering writing\u2014indeed, at the start of the research\u2014ask the question, \u201cWhat is my motivation in studying this case?\u201d\u00a0 Many times research projects start with a busted forecast, damaging storm, or an interesting weather observation.\u00a0 Alternatively, the case may be typical of a weather phenomenon that has plagued forecasters for a long time, but never has been documented.\u00a0 Whatever the reason, a research project with a strong motivation has a greater chance of success.<\/p>\n Beyond your motivation is the question of what results you hope to share with others.\u00a0 The best case studies go beyond just a description of the event and feature something new:\u00a0 some evolution of a weather phenomenon that has never been observed before, some study revealing the physical process responsible for an unusual structure, or some case study as part of climatology to understand the frequency of occurrence of a particular weather phenomenon.<\/p>\n Avoid giving a\u00a0weather briefing<\/em>, which is just a description of what happened. Mere documentation of a case generally results in the reader not having a take-away message. What was the reader supposed to learn from this event?\u00a0 How will the reader employ the lessons learned from this case?\u00a0 \u00a0Instead of a weather briefing, deliver a\u00a0map discussion<\/em>, a focused, stimulating investigation of some aspect of the case that critically and scientifically evaluates questions aimed at a deeper understanding.<\/p>\n Without a purpose to focus the case study, the author runs the risk of aimless blathering\u2014more weather briefing than map discussion. An explicit purpose statement near the end of the introduction to the paper is powerful.\u00a0 A well-stated purpose also is more likely to interest the audience and give them a statement to evaluate the success of the paper on its own terms.<\/p>\n Examples of clear and concise purpose statements include the following three examples.<\/p>\n Avoid multipart papers\u2014titled \u201cPart 1\u201d and \u201cPart 2\u201d\u2014which rarely are received favorably by reviewers (Schultz 2010).\u00a0 Reviewers often find that such multipart papers usually only have one article\u2019s worth of material in it anyway.\u00a0 If you have sufficient material for two or more papers on a similar topic, the best strategy is to publish independent manuscripts that can each stand on its own.\u00a0 Section 3.3 in Schultz (2009) discusses further why I discourage authors from writing multipart manuscripts.<\/p>\n #2:\u00a0 Write a clear, concise, informative, and accurate title.<\/strong><\/p>\n Being the first thing the audience reads of your paper, the title can either attract the audience or repel them. A good title consists of \u201cthe fewest possible words that adequately describe the contents of the paper (Day and Gastel 2006, 39).\u201d\u00a0 The title should also be clearly worded, concise, informative, and accurate (Lipton 1998). An attention-commanding title doesn\u2019t hurt either.<\/p>\n The title should reflect the purpose of the manuscript.\u00a0 A title such as \u201cAn Investigation of the North Carolina Cold-Air Damming Episode of 4 April 2003\u201d is not sufficiently descriptive.\u00a0 \u201cEnhancement of Cold-Air Damming in North Carolina by the Evaporation of Rain on 4 April 2003\u201d is more descriptive and conveys information about the principal findings, although it is a bit long.\u00a0 If the date or location of the event is not significant to the audience, then it could be deleted. Avoid obvious and unnecessary words like \u201cstudy\u201d or \u201cinvestigation\u201d in the title.\u00a0 Chapter 3 of Schultz (2009) discusses writing titles in more detail, and an excerpt appears at\u00a0www.eloquentscience.com\/2009\/08\/excerpt-chapter-3-writing-an-effective-title<\/a>.<\/p>\n #3:\u00a0 Discuss the frequency of occurrence of the event.<\/strong><\/p>\n After having read the purpose of the study, the reader next will want to know the likelihood of a similar event happening again.\u00a0 Is that a once-in-a-lifetime flash flood or a yearly occurrence?\u00a0 If the event is unprecedented, say which records were broken and by how much.\u00a0 If the event occurs more frequently, a small climatology, composite analysis, search for analogs, or list of other similar events easily can give some indication of the representativeness of the event.\u00a0 Examples of such a combined case study\u2013climatology article include Colman and Dierking (1992), Colle and Mass (1995), Dean and Bosart (1996), Novak et al. (2004), and Schultz et al. (2004).\u00a0 Examples of a case study\u2013analog article include Gyakum and Roebber (2001) and McTaggart-Cowan et al. (2006).\u00a0 For many events, the author may not know the event frequency.\u00a0 Thus, a simple statement to that effect can alert researchers to more work needed.<\/p>\n #4:\u00a0 Use appropriate datasets and methods.<\/strong><\/p>\n Most authors would benefit from more planning before conducting a research project.\u00a0 When brainstorming about the manuscript and outlining its body, consider the datasets, methods, analysis techniques, and graphics to use. Have you employed an appropriate dataset that resolves the features to discuss?\u00a0 Are there other available datasets that could help to justify some conclusions or shed light on unconsidered aspects of the case? Are your methods appropriate for the questions asked?\u00a0 Below are two examples.<\/p>\n Example 1.<\/u>\u00a0 Many automated frontal analyses and some manual frontal analyses use equivalent potential temperature\u00a0q<\/em>e or wet-bulb potential temperature\u00a0q<\/em>w.\u00a0 Strictly speaking, fronts should be defined using air temperature (if the surface is relatively flat), potential temperature\u00a0q<\/em>, or virtual potential temperature\u00a0q<\/em>v (e.g., Sanders and Doswell 1995; Sanders 1999).<\/p>\n Example 2.<\/u>\u00a0 Occasionally an author will use the quasigeostrophic omega equation (e.g, section 6.4 in Holton 2004) as a diagnostic tool to diagnose surface cyclone development.\u00a0 The omega equation is the wrong tool. The quasigeostrophic height-tendency equation (e.g, section 6.3.1 in Holton 2004) is more appropriate in that context.<\/p>\n #5:\u00a0 Where possible, present your results using an ingredients-based approach.<\/strong><\/p>\n There are nearly an infinite number of maps, charts, and graphics that can be shown to illustrate a case.\u00a0 Some authors try to fit them all into one manuscript!\u00a0 Knowing what subset of maps to present, however, may not be obvious.\u00a0 How do you know what to include?<\/p>\n Many authors focus their research presentations using an ingredients-based approach.\u00a0 Ingredients are those items necessary and sufficient for an event to occur. For example, McNulty (1978) and Johns and Doswell (1992) have articulated the ingredients for deep, moist convection: lift, instability and moisture.\u00a0 Doswell et al. (1996) have described the ingredients for flash flooding, and Schultz et al. (2002) have discussed the ingredients for winter precipitation.<\/p>\n Authors not employing an ingredients-based approach often do not possess a framework to organize their research and present their data.\u00a0 Without organization, the manuscript often lacks focus, with the authors presenting maps just for the sake of presenting them, rather than for being the best choice to illustrate their case. An ingredients-based approach limits the discussion and the number of figures, keeping the presentation more focused and compact.<\/p>\n #6:\u00a0 Structure your presentation by following the forecast funnel from largest to smallest scales.<\/strong><\/p>\n Once the ingredients are known, the case study is usually best organized in a manner consistent with how forecasters think.\u00a0 Snellman (1982) describes the\u00a0forecast funnel<\/em>\u00a0process for approaching a weather forecast, starting with developing an understanding of the largest scales first, then understanding progressively smaller-scale phenomena (Fig. 1).<\/p>\n Such an approach also can be useful for writing a case study.\u00a0 For example, by placing that convective storm within a large-scale context first, the reader can appreciate better the storm\u2019s environment and the large-scale processes controlling the environment.<\/p>\n #7:\u00a0 Limit the number of figures to the most essential.<\/strong><\/p>\n A benefit of using an ingredients-based approach and following the forecast funnel is that the figures remain focused on the purpose of the case study. Limit the number of figures to the bare minimum to tell the story and to convince the audience of your argument.\u00a0 Poorly written case studies often contain too many figures that are unnecessary or too tangential.\u00a0 Although the addition of one or two interesting tidbits about the case that do not contribute to the primary purpose of the study can add some color to the manuscript, too many such tidbits distract and tire readers.\u00a0 Readers may even start to forget the purpose of the case study, lost in a forest of too many irrelevant facts and figures.<\/p>\n <\/p>\n To keep readers focused, the author needs focus. One approach to maintaining focus happens before writing starts.\u00a0 Find a large conference table and lay out all the proposed figures in the order they likely will appear in the manuscript, or arrange them on your computer screen.\u00a0 Do these figures tell a story in the proposed order? Will rearranging them improve the story? Are any figures superfluous or missing? Employ ingredients-based and forecast-funnel approaches for the best story.<\/p>\n I am hesitant about offering guidelines on the number of figures in a manuscript because each study will require a different total.\u00a0 Nevertheless, I think a reasonable guideline is to aim for no more than 20 figures containing 50 panels.\u00a0 More than 25 figures or 100 panels will test even the most patient reader.<\/p>\n Finally, authors have a selfish reason for limiting the number of figures in a manuscript.\u00a0 Well-designed figures can be quite time consuming to produce, and poorly constructed figures are often common irritations for reviewers.\u00a0 Reducing these potential targets will save time and potentially make your manuscript more appealing to reviewers.<\/p>\n #8:\u00a0 Provide evidence for all claims.<\/strong><\/p>\n Forecasters\u2014faced with often insufficient data, conflicting numerical models, a chaotic atmosphere, and forecast deadlines\u2014necessarily must rely upon intuition in their daily jobs (e.g., Doswell 2004; section 2 in Steenburgh et al. 2010). The intuition that serves them well in forecasting, however, can be anathema to a research article, which requires a compelling argument based on evidence presented in the manuscript.<\/p>\n Similarly, observational case studies, especially those using the operational data stream, often lack all the data needed to draw definitive conclusions. This lack of necessary information should not stop good science from being done, but the author needs to be clear to the readers what conclusions can safely be drawn and which are reasoned speculation.<\/p>\n Consider the following example. If you believe that the vertical circulations associated with horizontal convective rolls in the boundary layer were responsible for organizing deep convection on a given day, then you must present observational evidence: of the rolls (e.g., radar imagery showing the mature circulations), that the rolls caused the circulations (i.e., the conditions for horizontal convective rolls were met beforehand), that the circulations preceded convection, and that the rolls produced ascent where the convection eventually occurred.\u00a0 Without evidence for all of these steps, your argument correspondingly weakens.<\/p>\n If the evidence is inadequate, then it may be appropriate to speculate on the causes, if prefaced with \u201cI\/We speculate that\u2026.\u201d The primary point of your manuscript, however, cannot rely on speculation. Use speculation sparingly, so as to ensure the legitimacy of your arguments in the manuscript.<\/p>\n Oftentimes authors may say that the goal of their case study is to improve forecasts.\u00a0 How a single case study can do so may not necessarily be obvious to the reader. For example, a signature in the satellite or radar imagery preceding a severe weather event may be\u00a0associated with<\/em>\u00a0the event, but may not necessarily\u00a0cause<\/em>\u00a0the event or precede all severe weather events.\u00a0 Furthermore, a single case is not sufficient to determine if the signature is characteristic of all cases that do produce severe weather and is absent in all cases that do not.\u00a0 Only a more comprehensive study such as a climatology can provide such evidence.\u00a0 Therefore, be careful when claiming a silver bullet for forecasting a particular phenomenon based on a single case study.<\/p>\n #9:\u00a0\u00a0Avoid map-room jargon, imprecise wording, and incorrect scientific concepts.<\/strong><\/p>\n Map-room jargon likely arose as meteorologists developed shorthand, colorful, or humorous terminology to describe more complicated aspects of the science. Although some jargon is essential for scientific communication, other jargon is simply inappropriate for legitimate scientific discourse.\u00a0 Below are five different types of jargon to omit from your writing and your speaking.<\/p>\n Appendix B, \u201cCommonly Misused Scientific Words and Expressions\u201d from Schultz (2009), has further examples of how to make language more precise and is available from\u00a0www.eloquentscience.com\/category\/excerpts<\/a>.<\/p>\n #10:\u00a0 Clearly define the geography.<\/strong><\/p>\n Because science (and the weather) is global, research results from the United States may be relevant to forecasters and researchers around the world, and vice versa.\u00a0 Just as most American scientists would not necessarily be able to identify the provinces in China, neither should Americans expect Chinese readers to know the U.S. states and their locations.\u00a0 Some region-specific terminology (e.g., Capital District, Golden Triangle) or local geographical locations (e.g., rivers, lesser mountain ranges) may not be known to readers, even within your own country.<\/p>\n You can do several things to help readers to understand the geographical setting of an article.\u00a0 The first would be to minimize the use of geographical terms that may not be widely known, or define the terms when first used.\u00a0 The second would be to include a figure whose purpose is to locate geographical places described in the text (Fig. 2).\u00a0 Even better would be to label the geography directly on each figure (Fig. 3), if such annotations do not cause clutter.\u00a0 Every map also should have a horizontal length scale and an indication of which direction is north (if omitting the north arrow otherwise would be ambiguous or if including it would benefit the figure).<\/p>\n #11: Model simulations should do more than just attempt to replicate the observations.<\/strong><\/p>\n With the advent of cheap computing power and freely available mesoscale numerical models\u2014WRF (Weather Research and Forecasting model), MM5 (Penn State\u2013National Center for Atmospheric Research Mesoscale Model, Version 5), or the workstation Eta\u2014modeling a weather event is currently in the hands of nearly anyone.\u00a0 Simulating an event to see if the model can reproduce it, however, is not sufficient cause for a published case study.<\/p>\n A model simulation that successfully reproduces the event in question is a powerful tool for understanding the relevant physical processes involved.\u00a0 That simulation, however, is only the first part of producing a scientific publication\u2014proper diagnosis is the second component. The model output is a four-dimensional, dynamically consistent dataset, and this output can often be a substitute for the lack of observational data (assuming, of course, that a realistic simulation is achieved).<\/p>\n For example, computing diagnostic quantities from the model output\u2014such as moist potential vorticity (e.g., Novak et al. 2006; Schultz and Knox 2007), frontogenesis (e.g., Schultz 2004b; Novak et al. 2006), or the terms in the momentum equation (e.g., Colle and Mass 1995)\u2014can provide insights into the relevant physical processes.\u00a0 Alternatively, rerunning the model after altering the topography (e.g., Onton and Steenburgh 2001), water temperature (e.g., Onton and Steenburgh 2001), the transparency of the clouds to solar radiation (e.g., Roebber et al. 2002), or the assimilation of certain data points (e.g., Zhang et al. 2002) may demonstrate the importance of some physical processes and their sensitivity to the forecast of the event, as well.<\/p>\n #12:\u00a0 Thoroughly critique model output.<\/strong><\/p>\n The next task is to verify that the model output \u201cfaithfully\u201d represents the observations.\u00a0 Faithfully is in quotation marks to indicate that different people will have different opinions about how well the model represents the observations.\u00a0 Some may be quite concerned about the ability of the model to predict details of precipitation or wind, although others may not, preferring instead to focus on a satisfactory large-scale forecast.\u00a0 Some may want a near-perfect correspondence between the model output and data, whereas others may be comfortable with a lesser correspondence.<\/p>\n Consider Fig. 3.\u00a0 At first glance, the WRF model produced precipitation in more-or-less the right areas: northern Nebraska and southern South Dakota, orographic precipitation in central Colorado, and northwest\u2013southeast-oriented bands over eastern Colorado. Thus, an author might write, \u201cthe model simulation reproduced the observed precipitation features.\u201d\u00a0 If no more was said, however, the author would risk losing credibility with the readers.<\/p>\n Instead, many differences are apparent.\u00a0 The modeled bands in eastern Colorado are much narrower than their observed counterparts.\u00a0 The interesting precipitation structures in northern Nebraska and southern South Dakota are modeled as a broad region of precipitation.\u00a0 The modeled precipitation maxima are less than those observed, and the orographic precipitation is less widespread in the simulation.<\/p>\n Despite these important differences, the model simulation may be of sufficient veracity to diagnose this event and understand the reasons why the precipitation bands in eastern Colorado happened, as in the Schumacher et al. (2010) simulation. Looking at the model output with a\u00a0 critical eye demonstrates to the readers that, yes, the model was not perfect, but it may be acceptable for the purposes of the manuscript.<\/p>\n When doing research, we often create\u00a0working figures<\/em>, rough-draft versions created with the default settings in the software and not tailored to the specifics of the manuscript.\u00a0 These figures should not be thrown into the manuscript without careful editing and redesign.\u00a0\u00a0Publication-quality figures<\/em>\u00a0clean up these working figures by making them more clear, understandable, and aesthetic. The creation of publication-quality figures may take considerable time and effort that usually are repaid by kinder comments from the reviewers.<\/p>\n #13: Design the figures to be readable when printed at the reduced size in the journal.<\/strong><\/p>\n Design the figures to the size that they will appear in the journal.\u00a0 Make sure that all the font sizes and wind barbs are large enough to read.\u00a0 Beware of dotted lines, which can disappear when the figure is reduced for the journal.\u00a0 Use sans serif fonts (e.g., Helvetica, Arial) because they survive reproduction better than serif fonts (e.g., Times New Roman).<\/p>\n #14: Make similar figures consistent with each other.<\/strong><\/p>\n In many manuscripts of case studies, a common figure style may be repeated.\u00a0 For example, the surface map or sounding diagram may be presented at three different times in different locations throughout the manuscript.\u00a0 Each figure should be designed to be as consistent with the others as much as possible.\u00a0 If possible, use the same map background, line widths, color schemes, etc. Variable names, symbols, units, and contour intervals, should be consistent between similar figures, between the text and the figures, and with convention.\u00a0 For example, surface pressure fields should be contoured every 4 hPa (or every 1 or 2 hPa for mesoscale analyses), not 2.5 or 5 hPa.\u00a0 Similar figures should be plotted at the same size for easy comparison.<\/p>\n Alternatively, designing a figure with multiple panels (e.g., Fig. 3) can help with comparisons between similar fields. Label the panels (a), (b), (c), etc.<\/p>\n #15: Annotate figures to highlight important features for the reader.<\/strong><\/p>\n Once figures are designed, it may be useful to include annotations to guide the reader to important regions (e.g., highlighting a featured vorticity maximum, locating the rear-inflow notch on radar imagery, drawing a 1:1 line on a scatterplot, including error bars, labeling geographical locations).\u00a0 Also, computer-generated figures may produce a default title legend needing replacement by something more sensible.\u00a0 Rather than using the default legend for a line graph, try labeling the lines directly.<\/p>\n Avoid what Tufte (2001, chapter 5) called\u00a0chartjunk:<\/em>\u00a0extraneous grid lines, annotations, three-dimensional effects on two-dimensional graphs, and unnecessary graphical flourishes that detract from, compete with, or obscure the data rather than supplement it or enhance it.\u00a0 The data on the figure is of the highest importance and every effort should be made to have the data stand out from the rest of the figure. For example, graphs from Microsoft\u00aeExcel may need to be overhauled severely (Su 2008) before publication.<\/p>\n Other tips for figures include the following.<\/p>\n For more information about the effective design of specific types of figures (e.g., line graphs, scatterplots, bar charts, horizontal maps), read section 11.7 in Schultz (2009).<\/p>\n #16:\u00a0 Fully describe and cite all figures and figure panels, in the text and in the captions.<\/strong><\/p>\n Simply put, if you show a figure or a figure panel, cite it and explain it within the text. If figure panels are not explained in the text but are included \u201cfor completeness,\u201d they should be eliminated.\u00a0 Cite figures in the text frequently so that the reader knows what specific figure is mentioned.<\/p>\n When describing the figure, discuss the most obvious aspects first, even if these are not the features of primary interest.\u00a0 Doing so gives the readers confidence that they are interpreting the figure correctly and prepares the readers for a more detailed look.<\/p>\n Do not repeat information in the text that belongs in the caption.\u00a0 For example, \u201cFig. 5 shows the 500-hPa heights and relative vorticity, indicating that the short-wave trough moved over Iowa at 1200 UTC.\u201d This sentence can be written more simply as \u201cThe short-wave trough moved over Iowa at 1200 UTC (Fig. 5).\u201d\u00a0 The caption should include a description of all figure elements.\u00a0 The meaning of all lines should be discussed and the units and contour intervals should be included. All shadings or color schemes should be described and have a legend, if necessary.<\/p>\n So many submitted manuscripts are lacking in basic etiquette for citations and reference lists.\u00a0 Include these points in your final checklist before submission.<\/p>\n Although following these 16 principles (collected in Table 1) will not guarantee that your paper will be accepted for publication, these principles will help to avoid the common pitfalls that have trapped others.\u00a0 By presenting a clear, well organized, and scientifically justifiable case study, you will begin to develop a reputation as a clear thinker and presenter. You may even begin to be invited to give presentations at conferences and training courses. Table 1:<\/u>\u00a0The 16 principles to writing an effective case study.<\/p>\n This article grew out of writing my book\u00a0<\/span>Eloquent Science: A Practical Guide to Becoming a Better Writer, Speaker, and Atmospheric Scientist\u00a0<\/em>(Schultz 2009)\u2014the result of too much good advice on writing effective case studies to cram into one chapter.\u00a0 I thank my past advisors and mentors who taught me how to perform research and write it up:\u00a0 Clifford Mass, Lance Bosart, Daniel Keyser, and Charles Doswell.\u00a0 I especially thank Charles Doswell for his inspiration for Appendix B in Schultz (2009).\u00a0 I also thank my two closest collaborators for their years of inspiration, challenges, and friendship when cowriting journal articles:\u00a0 Paul Roebber and Jim Steenburgh.\u00a0 I thank the three formal reviewers (John Lewis, Paul Markowski, and Greg Mann), editors Roger Edwards and Ryan McCammon, as well as the following colleagues who provided comments on an earlier version of this manuscript: Thomas Andretta, Peter Banacos, Matthew Bunkers, Jay Charney, Stephen Corfidi, Chris Davis, Nikolai Dotzek, Jim Johnson, Paul Roebber, and Jon Zeitler.\u00a0 Partial funding comes from Vaisala Oyj.<\/span><\/p>\n Banacos, P. C., and D. M. Schultz, 2005: The use of moisture flux convergence in forecasting convective initiation: Historical and operational perspectives.\u00a0Wea. Forecasting,<\/em>\u00a020,<\/strong>\u00a0351\u2013366.<\/p>\n Batchelor, G. K., 1981: Preoccupations of a journal editor.\u00a0J. Fluid Mech.,<\/em>\u00a0106,<\/strong>\u00a01\u201325.<\/p>\n Booth, W. C., G. G. Colomb, and J. M. Williams, 2003:\u00a0The Craft of Research<\/em>. 2nd ed. University of Chicago Press, 329 pp.<\/p>\n Bosart, L. F., 1983: Analysis of a California Catalina eddy event.\u00a0Mon. Wea. Rev.<\/em>,\u00a0111<\/strong>, 1619\u20131633.<\/p>\n Bryan, G. H., and J. M. Fritsch, 2000: Moist absolute instability: The sixth static stability state.\u00a0Bull. Amer. Meteor. Soc.,<\/em>\u00a081,<\/strong>\u00a01207\u20131230.<\/p>\n Colle, B. A., and C. F. Mass, 1995: The structure and evolution of cold surges east of the Rocky Mountains.\u00a0Mon. Wea. Rev.<\/em>,\u00a0123<\/strong>, 2577\u20132610.<\/p>\n Colman, B. R., and C. F. Dierking, 1992: The Taku wind of southeast Alaska: Its identification and prediction.\u00a0Wea. Forecasting<\/em>,\u00a07<\/strong>, 49\u201364.<\/p>\n Day, R. A., and B. Gastel, 2006:\u00a0How to Write and Publish a Scientific Paper<\/em>. 6th ed. Cambridge University Press, 302 pp.<\/p>\n Dean, D. B., and L. F. Bosart, 1996: Northern Hemisphere 500-hPa trough merger and fracture: A climatology and case study.\u00a0Mon. Wea. Rev.<\/em>,\u00a0124<\/strong>, 2644\u20132671.<\/p>\n Doswell, C. A. III, 2004: Weather forecasting by humans\u2014Heuristics and decision making.\u00a0Wea. Forecasting,\u00a0<\/em>19<\/strong>, 1115\u20131126.<\/p>\n \u2014\u2014, and M. J. Haugland, 2007: A comparison of two cold fronts\u2014Effects of the planetary boundary layer on the mesoscale.\u00a0Electronic J. Severe Storms Meteor.,<\/em>\u00a02<\/strong>\u00a0(4),<\/strong>\u00a01\u201312.<\/p>\n1.\u00a0 Introduction<\/h2>\n
\u2026these cautionary remarks, these subtly dangerous hints, are presented in the form of rules, but they are, in essence, mere gentle reminders; they state what most of us know and at times forget.<\/span><\/p>\n2.\u00a0 Why write a case study?<\/h2>\n
3.\u00a0 Why do it well?<\/h2>\n
\n
4.\u00a0 Three assumptions<\/h2>\n
a. How to do science<\/h3>\n
b. The structure of a scientific paper<\/h3>\n
c. Scientific writing<\/h3>\n
5. Designing the study<\/h2>\n
\n
6. Organization and approaches<\/h2>\n
![\"\"](\"https:\/\/ejssm.org\/archives\/wp-content\/uploads\/2021\/09\/5-2-fig1-133x300.jpg\")
<\/strong>Figure 1<\/u>: The forecast funnel that
\nauthors generally should follow
\nwhen presenting a case study.
\n[Adapted from Snellman (1982).]<\/p>\n7. Writing the manuscript<\/h2>\n
\n
\n![\"\"](\"https:\/\/ejssm.org\/archives\/wp-content\/uploads\/2021\/09\/5-2-fig-2.jpg\")
Figure 2<\/u>: A figure used to define the geographical locations described in a manuscript\u2019s text body.\u00a0 Elevation above mean sea level (m) is shaded according to the scale. Plus signs indicate locations\u00a0 of available MesoWest stations. Bold roman text labels the special observing systems (e.g., mobile laboratories, radars). Regular roman text labels observing stations, ski resorts, and cities. Bold italic text labels geographic and political areas. Thin solid lines represent county and lake boundaries.\u00a0 The gray circle around DOW2 represents the 30-km range ring. The dashed box represents the location of an inset figure not shown here. [Figure and caption adapted from Fig. 1a in Schultz and Trapp (2003).]<\/li>\n<\/ol>\n\n
8. \u00a0Special guidelines for numerical modeling studies<\/h2>\n
![\"\"](\"https:\/\/ejssm.org\/archives\/wp-content\/uploads\/2021\/09\/5-2-fig-3.jpg\")
Figure 3<\/u>: Six-hour precipitation (mm, color) ending
\n0000 UTC 17 February 2007 from a) Stage IV
\nproduct (merged rain gauge and radar-derived
\nprecipitation) and b) WRF model output.
\n[Adapted from Schumacher et al. (2010).
\nFigure panels courtesy of Russ Schumacher.]<\/p>\n9. Figures<\/h2>\n
\n
10. \u00a0References<\/h2>\n
\n
11. Other formatting and terminology issues<\/h2>\n
\n
12. Conclusion<\/h2>\n
\nWriting a case study and doing it well can be a substantial effort, so be prepared to invest the time to do it right.\u00a0 Do not rush a manuscript out the door.\u00a0 Seek the guidance of others during the research and writing phases, especially those with experience and reputations as good scientists and communicators. Give a presentation at your forecast office, university, laboratory, or at a regional weather conference. These opportunities can bring feedback to improve the manuscript and to develop your scientific skills.\u00a0 Furthermore, they may result in future research collaborations.\u00a0 A little effort can go a long way, and the rewards are potentially quite great, indeed.<\/p>\n\n\n
\n #1:\u00a0Have a well-defined purpose.<\/td>\n<\/tr>\n \n #2: Write a clear, concise, informative, and accurate title.<\/td>\n<\/tr>\n \n #3:\u00a0Discuss thefrequency of occurrence of the event.<\/td>\n<\/tr>\n \n #4:\u00a0Use appropriatedatasets and methods.<\/td>\n<\/tr>\n \n #5: Where possible,present your results using an ingredients based approach.<\/td>\n<\/tr>\n \n #6:\u00a0Structure yourpresentation by following the forecast funnel from largest to smallest scales.<\/td>\n<\/tr>\n \n #7:\u00a0Limit the numberof figures to the most essential.<\/td>\n<\/tr>\n \n #8: Provideevidence for all claims.<\/td>\n<\/tr>\n \n #9:\u00a0Avoid map-roomjargon, imprecise wording, and incorrect scientific concepts.<\/td>\n<\/tr>\n \n #10: Clearly define the geography.<\/td>\n<\/tr>\n \n #11: Model simulations should do more than just attempt to replicate the observations.<\/td>\n<\/tr>\n \n #12: Thoroughly critique model output.<\/td>\n<\/tr>\n \n #13: Design the figures to be readable when printed at the reduced size in the journal.<\/td>\n<\/tr>\n \n #14: Make similar figures consistent with each other.<\/td>\n<\/tr>\n \n #15: Annotate figures to highlight important features for the reader.<\/td>\n<\/tr>\n \n #16: Fully describe and cite all figures and figure panels, in the text and in the captions.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Acknowledgments<\/h2>\n
REFERENCES<\/h2>\n