1 Teaching a Weather Forecasting Class in the 2020s
2
3 Lars van Galen1, Oscar Hartogensis1, Imme Benedict1, Gert-Jan Steeneveld1
4
5 1:Wageningen University, Meteorology and Air Quality Section, PO box 47, 6700 AA Wa-
6 geningen, The Netherlands.
7
8 Corresponding author address:
9 Gert-Jan Steeneveld
11 Tel 0031317483839
1
1
Early Online Release: This preliminary version has been accepted for publication in Bulletin of the American Meteorological Society, may be fully cited, and has been assigned DOI 10.1175/BAMS-D-20-0107.1. The final typeset copyedited article will replace the EOR at the above DOI when it is published.
© 2021 American Meteorological Society Unauthenticated | Downloaded 09/28/21 11:35 PM UTC 12 Abstract
13 We report on renewing the undergraduate course about synoptic meteorology and weather
14 forecasting at Wageningen University (The Netherlands) to meet the current-day requirements
15 of operational forecasters. Weather strongly affects human activities through its impact on
16 transportation, energy demand planning and personal safety, especially in the case of weather
17 extremes. Numerical weather prediction models (NWP) have developed rapidly in recent dec-
18 ades, with reasonably high scores, even on the regional scale. The amount of available NWP
19 model output has sharply increased. Hence, the role and value of the operational weather fore-
20 caster has evolved into the role of information selector, data quality manager, storyteller, and
21 product developer for specific customers. To support this evolution, we need new academic
22 training methods and tools at the bachelor’s level. Here, we present a renewed education strat-
23 egy for our weather forecasting class, called Atmospheric Practical, including redefined learn-
24 ing outcomes, student activities, and assessments. In addition to teaching the interpretation of
st 25 weather maps, we underline the need for 21 century skills like dealing with open data, data
26 handling, and data analysis. These skills are taught using Jupyter Python notebooks as the
27 leading analysis tool. Moreover, we introduce assignments about communication skills and
28 forecast product development as we aim to benefit from the internationalization of the class-
29 room. Finally, we share the teaching material presented in this paper for the benefit of the
30 community.
31 Capsule
32 The role of operational weather forecasters has changed in recent decades, which means that
33 the new academic training in operational synoptic meteorology given at the BSc level must
34 address new topics, skills and infrastructure.
35 Key words: teaching, education, weather forecasting, synoptic meteorology
2
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 36 1. Introduction
37 Weather forecasting is critical not only for social activities for the general public but also for
38 transportation, energy supply, water management, agriculture, and many other crucial infra-
39 structures and business decisions. Weather forecasts have become increasingly more accurate
40 in recent decades due to improved numerical weather prediction (NWP) systems as a result of
41 advances in understanding physical processes, data assimilation techniques and computing ca-
42 pacity (Bauer et al. 2015).
43 With these advances, the role of the operational forecaster has changed. Nowadays, the
44 output of multiple NWP models is freely available at a high temporal frequency, and they
45 cover the continental-scale dynamics for the medium range (3-7 days), and the mesoscale dy-
46 namics for the short range (up to 48 h). Also, observations from satellites, radar systems and
47 routine and crowdsourced near-surface weather stations are readily available. In addition, the
48 userbase of weather forecasts has diversified, requiring tailor-made forecasts for a wide range
49 of applications. As a result, the forecaster’s tasks have increasingly shifted from adapting the
50 NWP results for local conditions, towards data (model and observations) treatment, critical
51 data selection and storytelling for stakeholders. Educating the upcoming generation of
52 weather forecasters should consider the evolution occurring in the field, which motivated us
53 to revise the Atmospheric Practical course at Wageningen University (WU). Also, student
54 mobility and the diversity in the academic education landscape has strengthened, resulting in
55 students with variable prior knowledge. Previously, most of our students were Dutch and all
56 had a uniform prior knowledge from a common study program. Nowadays, the students who
57 enroll in our program have diverse geographical, cultural and educational backgrounds. Alt-
58 hough it may initially pose some challenges, this diversity also offers an opportunity for deep-
59 ening the course (Apple et al., 2014).
60 The Atmospheric Practical course teaches the fundament and practice of operational
3
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 61 weather forecasting and synoptic meteorology, introducing innovations that reflect the devel-
62 opment of the field. We mainly address the introduction of an intake questionnaire, the inter-
63 national classroom, a new student activity to set up a forecast product, the implementation of
64 a modern scientific program language for data analysis, and the deeper attention needed for
65 communicating a weather forecast.
66
67 2. Positioning of the Atmospheric Practical course in the curriculum
68 The Atmospheric Practical is an optional course offered in the 3-year BSc program Soil, Wa-
69 ter, Atmosphere, which combines courses in the three disciplines with special attention to in-
70 terfaces at the land surface and vegetation. Students taking the course need two 6-ECTS at-
71 mospheric introduction courses (each with a workload of 168 hours): “Introduction Atmos-
72 phere” that uses an in-house made course reader and “Meteorology and Climate” based on
73 Wallace and Hobbs (2006). These courses deal with basic atmospheric physics and chemistry
74 covering thermodynamics, radiation, atmospheric dynamics, and boundary layers. Both
75 courses discuss basic weather forecasting, such as interpretation of synoptic observations and
76 radio soundings, and data-assimilation, as well as the concept of deterministic chaos and its
77 consequences. Didactically, the courses combine classroom lectures with pen-and-paper exer-
78 cises and computer-based assignments, mostly addressing the lower cognitive levels of
79 Bloom’s taxonomy (Anderson and Bloom 2001) regarding understanding, recognizing and in-
80 terpreting atmospheric processes. Finally, both courses include a number of weather briefings
81 by a meteorologist from DTN Weather (Data Transmission Network, formerly MeteoGroup).
82 After two years, students specialize in one discipline offered in the BSc program. Typi-
83 cally, 25-40 students follow the Atmospheric Practical for their specialization. The course in-
84 troduces the forecasting cycle, which includes the process of obtaining observed weather data
85 and model outcomes to compile a forecast for different end-users (section 3).
4
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 86 3. Course history, content, structure, learning outcomes and student assessments
87 First, we summarize the course history and then go on to compare the forecasting cycle be-
88 tween the 1980s and the 2020s. Finally, we address the renewed course learning outcomes,
89 structure and student assessment methods.
90 In the previous version of the course, the interpretation of surface and upper air observa-
91 tions and the NWP model output were key. The selected case studies had a national or West-
92 ern European focus, and the exercises involved a lot of manual work (often paper exercises on
93 printed weather maps) or were performed with a variety of outdated software packages. Also,
94 the NWP model datasets were limited to coarse resolutions of >25 km. Some exercises fo-
95 cused on spatiotemporal scales exceeding the characteristic time scales of synoptic meteorol-
96 ogy, e.g., exercises about the physical climatology of the whole globe. For student assess-
97 ments, there was limited discrimination between students, and a number of crucial subjects
98 such as communicating weather forecasts were absent. Nevertheless, the original set up
99 served its purpose for 15 years and was highly appreciated by students who graded the course
100 with a 4.3 out of 5 for the past six years.
101
102 a. Course content: Forecasting cycle
103 The forecasting cycle, which is the backbone of the course, has changed substantially over
104 the years. The forecasting cycle contains the following steps (Inness and Dorling 2013):
105 1. Collecting observations.
106 2. Using collected observations to specify the initial conditions for the forecast.
107 3. Using a model to extrapolate the state of the atmosphere in the future.
108 4. Experienced forecasters assessing the output of the model.
109 5. Producing forecasts for customers.
110 Figure 1a depicts a typical forecast cycle in the 1980s, when the short-term forecast was 5
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 111 made using synoptic observations. Only a limited number of global models (ECMWF, GFS,
112 JMA) were available and they were characterized by relatively coarse grid spacings (~50 km)
113 and lacked information on the mesoscale. Mesoscale meteorological models such as MM5
114 (Dudhia et al. 2002), WRF (Powers et al. 2017), HARMONIE (Bengtsson et al. 2017) and
115 COSMO (Doms and Baldauf 2013) did not exist. After a consistency check of the short-range
116 NWP model forecast with observations, operational meteorologists actively modified the fore-
117 cast by “interpolating” the NWP output to local scales to account for the effects of unresolved
118 lakes, mountains, soils and land use. The medium-range forecasts were formulated mostly
119 qualitative. Finally, the forecast was verified against observations and lessons were learnt for
120 further model development.
121 Nowadays, the spatiotemporal detail of observations has increased enormously, which bet-
122 ter facilitates regional analysis, and even local (~1 km) nowcasting is possible (Figure 1b).
123 Additionally, dense networks of personal weather stations are available via websites such as
124 www.wunderground.com, www.netatmo.com/weathermap, https://pressurenet.io/ and
125 https://wow.metoffice.gov.uk/. Despite their relatively low accuracy and unknown siting,
126 these personal weather stations can offer local weather information about areas where official
127 observations are relatively scarce (Napoly et al. 2018, Hintz et al. 2019, De Vos et al. 2020,
128 Mandement and Caumont 2020). Also, a wealth of output from global and regional NWP
129 models is available with output up to 1 hour temporal and ~1 km horizontal spatial resolution.
130 Hence, the value that an operational forecaster can add to the forecast by accounting for local
131 conditions has decreased. Simultaneously, ensemble forecasting has matured and quantifies
132 the model uncertainty. This development now allows for local and regional weather forecasts
133 and warnings, while these were issued on the national level in the past. This advancement pro-
134 vides the meteorologist with the new task of assessing NWP uncertainty and translating it into
135 a meaningful forecast for end users.
6
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 136 The end user perspective to forecasts has changed as well. In the past, forecast communica-
137 tion occurred in relatively general statements that fit with the spatiotemporal scale that could
138 be resolved. Nowadays, many customer services, like precision agriculture, wind energy com-
139 panies, festival organizers as well as road and rail management agencies require targeted fore-
140 casts for their fields. Thus, forecast communication has become more important than in the
141 past (Pandya et al. 2009, Eden 2011). Customers want to know what consequences the fore-
142 casted weather could have for their operations and services. We will explain how these evolv-
143 ing aspects in the forecasting cycle are addressed in the course learning outcomes and student
144 assessments. Thereby we want to emphasize that the changes we made to the course are im-
145 plemented in consultation with representatives of the private meteorology sector, including
146 the senior forecaster from DTN. With their advice we could adjust the course to fit the needs
147 asked for graduates in meteorology.
148 b. Learning outcomes
149 Learning outcomes are crucial for the course delineation since they help to determine the cog-
150 nitive level as well as the student activities and assessments. In this third-year course, the
151 learning outcomes address the higher cognitive levels according to Bloom’s taxonomy (An-
152 derson and Bloom 2001), such as analyze and evaluate, along with the lower cognitive levels
153 on identifying and explaining. Taking into account the forecasting cycle, after successful com-
154 pletion of the Atmospheric Practical course students are expected to be able to:
155 Identify and recognize the different types of meteorological data and how frequently
156 these are available;
157 Modify, analyze and interpret different types of meteorological data;
158 Explain and discuss the relation between several atmospheric quantities varying in
159 space and time;
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 160 Monitor, observe and analyze the weather situation, and perform a professional fore-
161 cast of weather parameters using real-time or historical data;
162 Communicate a weather forecast for different customer groups and media;
163 Discriminate and evaluate the research performed at the various research facilities of-
164 fering meteorological services, education and data abroad;
165 Appraise career perspectives, research activities, meteorological disseminated data and
166 services provided by operational and research institutes abroad.
167 Table 1 summarizes the learning outcomes and the student activities that lead to the fulfilment
168 of the learning outcomes. Obviously, the students are exposed to a variety of teaching meth-
169 ods and activities, which will be elaborated on in the next section.
170
171 c. Course structure
172 For scheduling reasons, the course entails three consecutive weeks of full-day classes consist-
173 ing of 36 practical exercises (called modules, Table 2) and subsequently, one week where stu-
174 dents participate in an excursion abroad. Each week has a specific theme and the course
175 evolves from a relatively basic level towards higher learning outcomes.
176 Each module starts with a bit of theory from the course reader consisting of selected texts,
177 internet pages or book chapters that should take students about an hour to read. The course
178 reader is included in the supplementary material (S3). The course emphasizes the practical ap-
179 proach which enables students to digest the course material, since many individuals learn
180 most effectively by using concrete examples (Roebber 2005). As such, each module continues
181 with a practical assignment in which students actively diagnose and interpret weather obser-
182 vations or NWP model output using Python Notebooks (see section 4vii, and supplementary
183 material S4) or the MIDAS meteorological workstation (introduced below).
184 The first week’s theme concerns “Weather observations and models”. The course begins
8
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 185 with (re)introducing the basic synoptic plotting system, which is subsequently used to make a
186 coherent surface weather picture at the national and European scale, including front detection.
187 Students learn about the routinely available meteorological observations (surface and aerolog-
188 ical observations), their time frequency, and how they are exchanged between meteorological
189 services. Successful integration of observations with models and theory assists students to bet-
190 ter understand physical processes in the atmosphere (Etherton et al. 2011). Also, students
191 learn to visualize observations and model output in the MIDAS meteorological workstation
192 provided by DTN. MIDAS was chosen because of the long-lasting collaboration with DTN,
193 that started as a spin-off company of WU in the 1980s. In addition, students work with NWP
194 model output to learn about spatial resolution and the related consequences for a weather fore-
195 cast. This first week’s theme is finalized with a practical test (more details below under As-
196 sessment).
197 The second week focuses on the theme “Weather analysis”, which includes the interpreta-
198 tion of the spatiotemporal observations into a coherent understanding of synoptic and
199 mesoscale weather. First, radio sounding observations are studied to investigate convective
200 indices such as CAPE, CIN, and the Boyden index. Second, weather radar observations and
201 their limitations are discussed. Subsequently, the course treats satellite observations in the IR
202 and VIS ranges, and the RGB enhancement of images from METEOSAT/SEVIRI and
203 MetOp/AVHRR. The theme is completed with atmospheric dynamic concepts such as diver-
204 gence, vorticity, thickness and advection to improve the students’ understanding of the day-
205 to-day weather.
206 The third week focuses on “Weather forecasting”, where students practice making weather
207 forecasts (building upon preceding knowledge and tools) and present weather briefings for a
208 geographical area experiencing interesting weather (section 4vi). Moreover, students develop
209 meteorological products for specific customer groups based on scientific literature and from
9
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 210 direct NWP output (section 4v). For example, students are asked to develop a forecast for
211 clear air turbulence based on relevant model parameters from NWP output. As such, students
212 learn that the job of a forecaster has broadened and now includes forecast product develop-
213 ment. Yarger et al. (2000) reported that such forecasting exercises may lead to pronounced
214 improvement of long-term knowledge retention. Finally, the last day of the course is focused
215 on communicating weather forecasts (more details below under innovations).
216
217 d. Student Assessment
218 Student assessment consists of five different aspects. The first theme “Weather observa-
219 tions and models” is assessed with an individual closed book practical test that counts for 20%
220 of the final grade. The second assessment consists of a weather briefing preparation. Students
221 are divided into groups of two and given four hours to prepare a PowerPoint file with 10
222 slides. In this file, groups have to forecast the current weather for the Netherlands and the
223 outlook for the coming three days, applying the knowledge gained throughout the course. The
224 PowerPoint is evaluated via a rubric (Table S1) and counts for 20% of the final grade.
225 In theme “Weather forecasting”, students need to present a weather briefing (section 4vii).
226 The same rubric (Table S1) is used as in week 2, but now the students need to show their
227 presentation skills. Overall, this element counts for 30% of the final grade.
228 The fourth assessment element is the forecast product development (section 4v) and
229 counts for 10% of the final grade. Students are graded based on a rubric (Table S2) that sum-
230 marizes the requirements of the forecast product. The final assessment item is a student report
231 about the excursion abroad (20%), which we will not elaborate on here.
232
233 4. Innovations
234 Both the changing requirements for operational meteorologists and the diversity of student
10
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 235 backgrounds (geographically, but also diversity in prior knowledge) and interests have in-
236 spired us to introduce several course innovations. These innovations concern education
237 tools/infrastructure (sections 4i and ii), didactics and content (sections 4iii and further), and
238 are presented here along with motivation and examples.
239
240 i. Feedback
241 Prompt and accurate feedback is crucial for successful teaching (Chickering and Gamson
242 1987). In the earlier course setup, all student assignments were corrected and graded manu-
243 ally, which was labor intensive, and students would not receive feedback until days/weeks
244 later. Now, students submit their answers to the assignments via the online teaching platform
245 Brightspace and promptly receive the correct answers electronically. This allows students to
246 immediately check their work and talk with the lecturers about any questions that arise. More-
247 over, the reduced workload for the lecturers means that there is more time to help students
248 with specific issues. This approach makes the student responsible for their study progress ra-
249 ther than the lecturers. Students’ ability to engage in learning and their ability to reflect on
250 their own progress can lead to increased self-efficacy and self-confidence as students learn
251 that they can reach expected goals. This approach also allows students to reflect on their
252 learning strategies and gain study ownership. College readiness is enhanced when students
253 demonstrate this behavior. Teaching methods that lack sufficient attention to student motiva-
254 tion, engagement, goals, self-efficacy, and persistence are less likely to result in student learn-
255 ing gains (Conley and French 2014).
256
257 ii. Programming environment
258 To encourage an active learning environment, students handle meteorological model out-
259 put and observations in Python Jupyter Notebooks. Unifying the programming language to
11
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 260 Python prevents different data types from being investigated with different software tools. Py-
261 thon is a fast growing, open-source programming language and is frequently used amongst
262 meteorologists and climatologists. As such, programming skills in Python are beneficial for
263 future studies and working environments. Python notebooks allow for treating, plotting and
264 interpreting datasets together in an internet-based browser environment. As these notebooks
265 can also contain textual instructions and exercises, the notebooks combine the exercise in-
266 structions and programming tasks into one single document. Finally, the collection of Note-
267 books enables students to download and process open-source data by themselves in later
268 stages of their program.
269 Each notebook is set up in a similar way. First, the class specific learning outcomes are in-
270 dicated, together with the student activity (Figure 2). Thereafter, required libraries and the da-
271 taset(s) are loaded, and the spatiotemporal dimensions of the dataset can be explored. Subse-
272 quently, the notebook provides learning information and a clear question that needs to be an-
273 swered by editing the Python code. By executing the code block, the resulting figure appears
274 which in this case is a satellite image of the near infrared in MSG channel 4. Most Python
275 skills learnt in the modules should be applied in the ‘forecast product development’ module
276 (section 4v), where students build code to develop and visualize a forecast product.
277
278 iii. Intake
279 The university’s strategic education plan promotes personalized learning paths, where stu-
280 dents themselves choose which courses or modules they wish to take based on their prior
281 knowledge, interests and skills. Insight into the diversity of prior knowledge, skills and inter-
282 ests is collected through an online intake questionnaire four weeks prior to the start of the
283 course. For example, the questionnaire provides information about the students' geographical
284 backgrounds and knowledge of local weather systems, which facilitates the international
12
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 285 classroom (see section 4iv). In addition, the questionnaire collects information about attended
286 meteorology courses (BSc or MSc program, home university or foreign university) and com-
287 puting skills (see Supplementary material). The questionnaire helps to discover a student’s in-
288 terest in the different meteorological scales and the sub-disciplines within meteorology. With
289 the answers to the questionnaire, lecturers learn about the strengths and weaknesses of the stu-
290 dent cohort, enabling them to timely adapt the course content. For example, the lecturers have
291 prepared a collection of modules at either preparatory or deeper levels which allows them to
292 be somewhat flexible in offering certain modules depending on the course edition.
293 Concretely, one year the intake revealed that a substantial number of students were al-
294 ready working for weather companies in their spare time, e.g., as assistant meteorologists for
295 written or online media, or as a nighttime forecaster for road slipperiness. This prior back-
296 ground knowledge helps to shape the course content for these more advanced students, and
297 their work experience can be shared with the other students via short presentations.
298 The questionnaire contains a short self-assessment about student knowledge concerning
299 weather phenomena and analysis tools, e.g. about thermodynamic diagrams, synoptic obser-
300 vations and identification of fronts (Figure 3). Despite being taught in preceding courses, the
301 modest self-assessment scores underline the need for repeating these topics in the current
302 course.
303 iv. Internationalization
304 Nowadays, many meteorological work environments require a global understanding of
305 weather systems with customers and stakeholders all over the world, not to mention the multi-
306 plicity of nationalities and cultures amongst colleagues in the international labor market. As
307 such, the course should contain international aspects, which is achieved by a so-called interna-
308 tional classroom. Internationalization is the incorporation of international, intercultural and/or
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 309 global dimensions into the course content and the learning outcomes, assessment tasks, teach-
310 ing methods and support services (Leask 2015). Basically, international classroom comprises
311 three key components: i) English as the language of instruction, which is the case for our
312 whole BSc program, ii) Intercultural competence and iii) International framework. Intercul-
313 tural competence starts when reflecting on norms and values in different countries and the re-
314 alization that not all students will have had this reflection, and the appreciation that there are
315 many ways to be a good student. Students should develop new ways of learning that are help-
316 ful in their new learning context.
317 Students in well-functioning international classes, will be well prepared for an international
318 labor market and society. Students from different cultural backgrounds work together in an
319 international class and learn about new habits and ways of doing things that are different from
320 what they learned in their own countries. Students also learn to express themselves in a lan-
321 guage that is not their mother tongue (e.g. Apple et al. 2014). They also come across new
322 knowledge and other methods of teaching and assessment. These are all experiences that con-
323 tribute to successful functioning in an international environment. In part, these benefits also
324 apply to lecturers. They too have to deal with communicating in a different language with stu-
325 dents from other cultures. International students act as a mirror that shows how teaching
326 methods are shaped by beliefs and culturally determined norms.
327 We achieve an international classroom by inviting our foreign students to give presenta-
328 tions about the weather and climate of their home country and the forecasting challenges that
329 are posed. This is an example of so-called place-based teaching that is a fruitful manner that
330 supports inclusiveness (Apple et al. 2014). This covers both the physical and dynamic aspects
331 and the societal relevance of the phenomena. For instance, a Zimbabwean student presented
332 monsoon circulation in his country, how it had evolved during the recent decades in terms of
333 timing, intensity, and its impact on food production and the government’s food policy (Figure
14
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 334 4). Also, he reported about the relatively limited observational infrastructure in Zimbabwe,
335 and how seasonal forecasts are downscaled with the COSMO and WRF models. For native
336 students, such a short contribution is very meaningful since the curriculum lacks a course in
337 tropical meteorology.
338 Internationalization is also achieved through a graded weather briefing in which interconti-
339 nental actual weather phenomena are used (section 4vii). Based on the ECMWF extreme fore-
340 cast index, the lecturers select regions where interesting weather phenomena are forecast in
341 the coming 72 hours. Consequently, students are asked to make a weather forecast for this re-
342 gion, which implies that they need to invest time in understanding the driving physical and
343 dynamical mechanisms as well as the local vulnerability and stakeholders.
344 For example, students could be tasked with making a visibility forecast for dust storms in
345 northern Africa. These storms occur during relatively southerly positioned jet streams, com-
346 bined with high near surface wind speeds and relatively dry soils. Since dust is not a direct
347 model output, students have to explore the physical processes and empirical methods to trans-
348 late direct model output for that region to visibility. As a second example, so called Nor’east-
349 ers are macro-scale extratropical storms that impact the north Atlantic areas of the U.S.A.
350 Nor'easters are usually accompanied by heavy rain or snow, and can cause severe coastal
351 flooding, coastal erosion, hurricane-force winds, or blizzard conditions. Student forecasts
352 should address the case-specific societal impacts and assess their likelihood and occurrence,
353 for example, by examining soundings to quantify the possibility of snow.
354
355 v. Forecast product development
356 More and more forecaster jobs require skills to develop and implement forecast products for
357 specific applications and customers. This entails the capacity to digest and manipulate routine
15
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 358 NWP output (data science approach) and the presentation of a hands-on visualization and in-
359 terpretation for customers (Garbanzo-Salas and Jimenez-Robles, 2020). Hence, we introduced
360 an 8-hour assignment about processing direct model output (e.g. ECMWF or GFS) into a
361 powerful targeted forecast product (e.g. wind energy, clear air turbulence, road weather fore-
362 casts, rule-based fog forecasting). In practice, students are provided some peer-reviewed pa-
363 pers that present methods to forecast meteorological phenomena based on direct model out-
364 put. By reading these papers, students come to realize that scientific research has been the ba-
365 sis of the forecast method. Subsequently, students design a flowchart of the required input and
366 manipulation steps that need to be taken to calculate the indices. This flowchart also helps stu-
367 dents to build the structure of their Python code to calculate and plot their forecast in a Jupyter
368 notebook. As a by-product, students learn to think about a strategy for data management, pro-
369 gramming structure and data visualization, which are skills that are required in jobs in data
370 analytics and in academic research. Finally, students evaluate their own forecast against ob-
371 servations. With this assignment, students develop and evaluate their own forecast product,
372 and thereby, address the highest cognitive level of Bloom’s taxonomy (Anderson and Bloom
373 2001).
374 As an example, one product development assignment deals with clear air turbulence
375 (CAT). CAT forecasts are based on indices of contrasting complexity that account for hori-
376 zontal and vertical wind shear (|v/z|), convergence (Cvg), deformation (D), and atmospheric
377 stability, e.g., the Ellrod's turbulence index (T1) and the Ellrod and Knapp (1992) (T2) index.
378 T1 is defined as
휕푣 379 푇1 = 퐷 | |, 휕푧
380 Alternatively, T2 builds upon T1 and is defined as
휕푣 381 푇2 = (퐷 + 퐶 ) | |, 푣푔 휕푧
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. -7 -2 -7 -2 382 CAT is likely to occur if T1> 7.10 s or T2 > of 5.10 s (Overeem 2002, Lee 2013).
383 As an illustration of student work, Figure 5 shows the flowchart for the CAT forecast
384 based on T1 and T2. First, wind components are loaded from open-source GFS forecast files,
385 and subsequently the deformation, vertical wind shear and convergence are estimated, and fi-
386 nally combined in the metric of interest. Figure 6 shows the forecast which indicates the areas
387 with high risk of CAT at the 300 hPa level in yellow. Pilot reports of CAT taken in the hour
388 around the valid forecast time are plotted over the U.S.A. as forecast verification.
389
390 vi. Communication
391 Successful communication of weather forecasts to customer groups is as important as creating
392 a weather forecast from NWP model results (Eden 2011). Nowadays, the forecaster's focus
393 has evolved towards the role of storyteller, i.e., what does the weather forecast mean for a par-
394 ticular customer? This communication aspect was absent before the course revision and has
395 been introduced by means of an excursion to the weather room of DTN and a subsequent as-
396 signment. Here, a senior communication meteorologist presents the prerequisites for success-
397 ful forecast communication, illustrated with striking examples of how accurate and timely
398 communication prevented damage and casualties.
399 Initially, the chief meteorologist briefs the students about the weather expected in the
400 upcoming three days. The students are then asked to set up a communication strategy for dif-
401 ferent customers and different media based on the presented weather forecast. Importantly,
402 the focus is not on the meteorological aspects of the forecast itself, but on the strategy of how
403 to communicate the briefed forecast to the customers. The customer groups are the general au-
404 dience, aviation, road maintenance, agriculture, offshore, the energy sector, and organizers of
405 an outdoor festival. For each topic, two groups of two students get about one hour to prepare a
406 5-min presentation about their communication strategy. Subsequently, the strategies laid out
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 407 in the two presentations are commented on by fellow students and by the senior communica-
408 tion meteorologist. Thus, the students learn from each other in an active learning environ-
409 ment. This is where foreign students can bring in their experience on how communication is
410 different in their home country, facilitating the international classroom. Finally, during this
411 excursion, students learn how a dynamic and broadly oriented weather consultant agency op-
412 erates nowadays.
413 Figure 7 presents a sample communication strategy for flower farmers. First, crucial
414 weather variables for flower preservation are listed such as high wind speeds, hail and high
415 temperatures. Based on the briefed forecast, high chances of precipitation and hail storms with
416 some wind gusts were expected. Finally, the communication strategy included direct consulta-
417 tion with flower farmers and TV presentations for agriculture websites.
418
419 vii. Weather briefing by students
420 Some key activities of a forecaster include analyzing meteorological observations and model
421 data, selecting the most relevant information, synthesizing this information into a physically
422 consistent story, and communicating the forecast to customers and colleagues. This process
423 occurs under time pressure. Students follow these steps when creating and presenting a
424 weather briefing.
425 The weather briefing assignment focuses on analyzing the NWP output and its interpreta-
426 tion, while section vi focuses on proper forecast communication. A senior meteorologist from
427 DTN starts the assignment with a presentation about tips and tricks to structure a weather
428 briefing. Weather briefing assignments for selected place-based weather phenomena foreseen
429 in the coming 72 hours are distributed to student teams which are made up of two students.
430 Some phenomena might be new for them and require reading textbooks or online articles to
431 understand the physical and dynamic processes at hand. In the 4 hours of preparation, students
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 432 create a 12-min weather briefing aimed at a professional audience, which includes a Power-
433 Point file and an oral presentation.
434 After the briefing, presenting students interact with their peers, the lecturers and the senior
435 meteorologist from DTN. Students have to defend their forecasts in a short debate. The lectur-
436 ers underline the need to understand the physical processes behind the weather phenomenon
437 at hand and the awareness of the uncertainty of the presented forecast. In addition, they en-
438 courage students to use multiple data sources presented earlier in the course. After reflection
439 and interactions with more knowledgeable forecasters, students are able to connect processes
440 to underlying theory (Pandya et al. 2012).
441
442 viii. Model interpretation and model verification
443 Nowadays, critically assessing model results and their value is a crucial skill of a meteorol-
444 ogist in view of the large data volume available in the forecasting cycle. Hence, this skill must
445 be properly trained in our course to achieve the following educational goals for students
446 (Schultz et al. 2015):
447 learning how such models are constructed and run and working with model output;
448 recognizing that all models are wrong but some are still useful;
449 being able to identify those weather and air‐quality phenomena that can be well fore-
450 cast from those that cannot, whether it be due to resolution or unaccounted for physi-
451 cal or chemical processes;
452 understanding that a model simulation may have specific variables that it forecasts
453 well and variables that it forecasts less well.
454 The Atmospheric Practical covers model interpretation in several ways. In week 1, students
455 learn about temporal and spatial resolutions of both weather and climate/re-analysis models,
456 and how this affects different meteorological variables. Furthermore, after the student weather
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 457 forecast in week 2, students can directly verify their forecasts against one presented by a pro-
458 fessional meteorologist from DTN. As such, students can determine themselves in a rather
459 safe environment whether they structured the presentation similarly, and whether all crucial
460 weather elements were included in their own product. This collaborative instruction and expe-
461 riential education strengthen the operational–academic relationships and students’ apprecia-
462 tion of the intricacies of forecasting (Cohen et al. 2018). Also, in a module on Model Verifica-
463 tion students learn about different model verification techniques, such as bias, association, ac-
464 curacy, skill, reliability and discrimination. They also learn about skill scores for infrequently
465 occurring weather events using contingency tables and metrics such as hit rate, false alarm
466 rate, Heidke skill scores, ROC diagrams, etc. Students estimate these scores using archived
467 long-term forecast data and observations.
468 In addition, we familiarize students with “jumpiness” or “inconsistency” of model fore-
469 casts (Zsoter et al. 2009) through the so-called ‘flipflop index’ (Griffith et al. 2019), and stu-
470 dents practice with this index based on a recent set of forecasts for the same valid date. Model
471 jumpiness occurs when consecutive runs of a model provide substantially different solutions,
472 e.g., alternating cold or warm solutions in a situation when frontal zones are nearby but with
473 an uncertain timing. Students should learn that with substantial jumpiness, the latest model
474 run is not necessarily the best one to use for the actual forecast. Finally, during the forecast
475 product development module (see section 4v), students have to develop and verify a forecast
476 against observations. As such, students are forced to critically assess their own forecast, i.e.,
477 why they are successful or not.
478
479 5. Discussion
480 This section discusses educational innovations with respect to the requirements of operational
481 meteorologists and brings the presented course into perspective with regards to educational
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 482 efforts at other universities and meteorological institutes.
483 Scheduling reasons limit us to three weeks of full-time practicals. Given the short amount
484 of time available and given that some students have limited prior knowledge and practical ex-
485 perience, students are not fully prepared to run operations alone in a weather room after com-
486 pletion of the course. Nevertheless, the course provides students with a thorough introduction
487 to operational meteorology and training in basic skills to facilitate development towards be-
488 coming a skilled meteorologist. The course makes students aware of the multi-faceted world
489 of modern operational weather services and will assist students in making a choice for their
490 future (meteorological) career. Further training is provided in the MSc program, or by the hir-
491 ing public or private weather agency. WU offers courses in Atmospheric Dynamics, Numeri-
492 cal Techniques, and Atmospheric Modelling (Steeneveld and Vilà 2019).
493 WMO has formulated requirements in their BIP-M for certification of meteorologists
494 (WMO 2012, 2019) in the form of learning outcomes. Students enrolled in the Atmospheric
495 Practical gain a firm introduction to many aspects of these requirements. The learning out-
496 comes and the related modules from the course are presented in Table 1. In general, the
497 course treats many topics listed in the BIP-M at the correct cognitive level, but time con-
498 straints prevent us from including more practical training that would allow students to fulfil
499 the BIP-M requirements after completion of the course. For example, the Dutch National
500 Weather Service offers a 4-month training program that allows students to truly comply with
501 the BIP-M requirements.
502 Ideally, a deeper discussion of the forecast with stakeholders and customers in situations
503 that are critical for society would ideally be part of the course but cannot be trained in depth
504 due to the time constraints. For example, students in a simulation environment may be faced
505 with different roles and responsibilities during severe weather events occurring at an open-air
506 music festival. Apart from the role as the operational forecaster, roles such as mayor, festival
21
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 507 director, or head of the police department may help to allow the student to experience the pro-
508 cess of decision making in crisis situations. Alternatively, Eumetrain offers a set of meaning-
509 ful simulation environments in which a student is put into a training situation. For example,
510 students can be tasked to prepare a forecast for a planned flight where the crew may have to
511 handle fog (http://www.eumetrain.org/simulators/FogSimulator/sim.html) or deep convection
512 (http://www.eumetrain.org/simulators/ConvectionInTheCarpathianBasin/index.html).
513 Other universities also offer practical training in synoptic meteorology. Grundstein et al.
514 (2011) describes a severe weather laboratory exercise for an Introductory Weather and Cli-
515 mate class for freshmen that uses inquiry-based learning techniques. In the lab, students play
516 the role of meteorologists making forecasts for severe weather by utilizing collaborative
517 learning in teams. The study shows that important content knowledge is retained better in
518 comparison to a traditional lab approach and that students found the new lab more engaging.
519 While our course intends to serve students at the BSc level, we have chosen relatively high
520 cognitive educational learning objectives and since students should fulfil the learning objec-
521 tives individually, we support the laboratory approach.
522 Bond and Mass (2009) show that many students’ methods evolve over time in their course.
523 They report that only after the first few weeks do most students develop a consistent, com-
524 plete, and efficient procedure for examining a large amount of observational and NWP model
525 data and then effectively distil this information into a reasonable prediction. This indicates
526 that a 3-week course is a rather short amount of time to treat the basics of the forecasting cy-
527 cle.
528 Further course development is still needed to match expectations, wishes and time con-
529 straints. For example, we aim to revise the online intake interviews. Our first experience with
530 the intake questionnaire is that it helps students to open their minds to the course, learn about
22
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 531 the expectations of the lecturers and their relative freedom. At the same time, the question-
532 naire raises expectations that we so far cannot always fulfil. In addition, the questionnaire
533 confirmed that student interests in themes, weather phenomena and sub-disciplines are broad,
534 which is challenging to accommodate. An advanced intake questionnaire should sense more
535 effectively a student’s intrinsic motivation to enroll in the course, and their viewpoint on their
536 future career as an operational meteorologist, all of which can be useful for further course de-
537 velopment.
538 The personal learning paths that the course introduces offer students the opportunity to fo-
539 cus on certain meteorological phenomena, especially in the forecast product development
540 module. Personal learning paths can be strengthened throughout the course. Ideally, we would
541 like well-prepared students to skip certain modules if they can prove sufficient prior
542 knowledge. Hence, these students can concentrate on more advanced topics than those that are
543 commonly available . In practice, however, it is challenging to organize and supervise stu-
544 dents with different tracks running in parallel. Moreover, requirements for the examination
545 and the student assessment may be diluted and become less transparent, which should be
546 avoided.
547
548 6. Conclusion
549 Recent innovations in the Atmospheric Practical course at Wageningen University (The Neth-
550 erlands), which prepares students to become a weather forecaster, are presented. Innovations
551 introduced in the course are: 1) the online student intake questionnaire; 2) the strong focus on
552 the role of a forecaster as a communicator, i.e. what is in the forecast for me as a stakeholder?;
553 3) the international classroom approach in which students from abroad bring in their
554 knowledge about weather phenomena in their home country; 4) the development of a forecast
555 product for specific weather phenomena for dedicated target groups (e.g. clear air turbulence
23
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 556 indices in aviation, rule-based forecasts predicting fog) from direct model output illustrating
557 that forecast products have a scientific background and at the same time teaching students to
558 be critical towards these methods and assess their uncertainty; 5) the introduction of a unified
559 modern software tool (Python, with data analysis and visualization in Jupyter notebooks) that
560 enables students to download and analyze weather observations or model fields during as well
561 as after the course. The course facilitates an active approach where all aspects are trained and
562 assessed as a practical activity.
563
564 Acknowledgements
565 We thank Wim van den Berg (DTN) and Reinout van den Born (www.weer.nl), Heleen ter
566 Pelkwijk (KNMI), Leo Kroon (formerly at WU), Ellen Torfs and Peter Kalverla (WU). We
567 acknowledge funding from a WU education innovation grant C18_20. We acknowledge
568 Robin Palmer for language editing of the manuscript.
569
570 Supplementary Material
571 The supplementary material consists of the rubric for the weather briefing (Table S1) and the
572 rubric for the forecast product development (Table S2). Furthermore, we include the reader of
573 the course (S3). Last, all python notebooks for the course are provided, together with the data
574 and code that these notebooks are based on (S4).
575
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 674 Table captions:
675 Table 1: Consistency table with course learning outcomes and student tasks
676 Table 2: Course outline and time schedule organized in three themes. Elements of the student
677 assessments have been marked in red.
678 Table 3: Learning outcomes in synoptic and mesoscale meteorology formulated by the WMO
679 in their BIP-M certification of meteorologists (WMO, 2012, 2019), and how these outcomes
680 are addressed in the Atmospheric Practical course.
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 681 Figure captions:
682 Figure 1: Illustration of the forecasting cycle in the past and current day. Start reading the fig-
683 ure from the middle top.
684
685 Figure 2: Examples of a Jupyter Python notebook as a tool to teach the Atmospheric Practical. 686 All notebooks are provided as supplementary material.
687
688 Figure 3: Example results of student (n=23) self-assessment of their prior knowledge as inves-
689 tigated by the online intake. Students were asked to assess their familiarity and understanding
690 of radio soundings (blue), SYNOP plotting system (orange) and weather front properties
691 (grey). Score of 1 indicates ‘I do not know the concept’ and score of 5 indicates ‘I have mas-
692 tered the concept’.
693
694 Figure 4: Illustration of international classroom example. Slides by a student from Zimbabwe
695 explaining the meteorology of his home country and a recent intense precipitation event.
696 Courtesy: student Sinclair Chinyoka.
697
698 Figure 5: Flow chart illustrating the steps from direct model output to indices used for a fore-
699 cast product for clear air turbulence that students design as part of the forecast product devel-
700 opment assignment. Courtesy: student Brian Verhoeven.
701
702 Figure 6: Forecast TI2 index at the 300 hPa level over the United States for November 27,
-7 -2 703 2018 00UTC as an example of a forecast product. Blue: TI2 below threshold of 5.10 s , Yel-
704 low: TI2 above threshold, red dots: PIREP observations. Courtesy: student Brian Verhoeven.
705
30
Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 706 Figure 7: Example of a communication strategy for a weather forecast in spring aimed at
707 flower farmers.
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709 710 Figure 1: Illustration of the forecasting cycle in the past and current day. Start reading the fig- 711 ure from the middle top. 712
713
32
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715
716
717 Figure 2: Examples of a Jupyter Python notebook as a tool to teach the Atmospheric Practical. 718 All notebooks are provided as supplementary material.
33
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40%
35%
30%
25%
20%
15%
10% Percentage of student's Percentage of student's responses
5%
0% 1 2 3 4 5 Score (1 (low) -5 (high)) 719
720 Figure 3: Example results of student (n=23) self-assessment of their prior knowledge as inves- 721 tigated by the online intake. Students were asked to assess their familiarity and understanding 722 of radio soundings (blue), SYNOP plotting system (orange) weather front properties (grey). 723 Score of 1 indicates ‘I do not know the concept’ and score of 5 indicates ‘I have mastered the 724 concept’.
725
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727
728 Figure 4: Illustration of international classroom example. Slides by a student from Zimbabwe 729 explaining the meteorology of his home country and a recent intense precipitation event. 730 Courtesy: Sinclair Chinyoka.
35
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732 Figure 5: Flow chart illustrating the steps from direct model output to indices (Ellrod's turbu-
733 lence index (T1) and the Ellrod and Knapp (1992) index (T2)) used for a forecast product for
734 clear air turbulence that students design as part of the forecast product development assign-
735 ment. Courtesy to student Brian Verhoeven.
36
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737 Figure 6: Forecast TI2 index at the 300 hPa level over the United States for November 27,
-7 -2 738 2018 00 UTC as an example of a forecast product. Blue: TI2 below threshold of 5.10 s ,
739 Yellow: TI2 above threshold, red dots: PIREP observations. Courtesy: student Brian
740 Verhoeven.
37
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742 Figure 7: Example of a communication strategy for a weather forecast in spring aimed at
743 flower farmers.
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 744 Table 1: Consistency table with course learning outcomes and student tasks
Plot and in- Plot and Perform Prepare Develop Visit DTN Prepare and present terpret rou- interpret practical weather forecast communication tine synop NWP test/exam briefing product strategy for cus- observa- model out- from raw tomer group tions put NWP out- put Identify and recognize the different types of x x X x x meteorological data and how often these are available;
Modify, analyze and interpret different types x x X x x of meteorological data; Monitor, observe and analyze the weather sit- x X x x uation and perform a professional forecast us- ing real-time or historical data Communicate a weather forecast for different x x x customer groups and media Explain and discuss the relation between sev- X x x x eral atmospheric quantities varying in space and time; Discriminate and evaluate the research per- x formed at the various research facilities offer- ing meteorological services, education and data abroad (Germany); Appraise the career perspective, research ac- x tivities, meteorological disseminated data and services provided by EUMETSAT, or similar institutes in Germany. 745
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 747 Table 2: Course outline and time schedule organized in three themes. Elements of the student
748 assessments have been marked in red.
Theme 1: Weather observations and models Morning Afternoon 01. Translating weather observations into an over- 02. Preanalysis large weather map Mon view 03. Front detection from surface observations 02. Preanalysis large weather map 04. Frontal passage characteristics Tue 06. Analyzing a series of weather maps 05. Frontal analysis large weather map Wed 07. Introduction to Python 08. Weather model data interpretation 09. Introduction to meteorological database program Thu MIDAS 11. Detecting fronts with theta-w analysis 10. Upper air weather and coupling to surface Fri Exam
Theme 2: Weather analysis Morning Afternoon Mon 12. Soundings introduction 13. Soundings and convection Tue 14. Introduction to satellites 15. Satellite enhancements Wed 16. Weather RADAR 17. Thickness and advection Thu 18. Prepare weather briefing 19. Vertical cross-sections Fri 20. Divergence and vorticity
Theme 3: Weather forecasting Morning Afternoon Mon 24. Forecast product development 24. Forecast product development Tue 21. Analysis of ECMWF forecasts 22. Model verification 23. Weather Briefing (Group 1) 23. Weather Briefing (Group 1) Wed 26. Forecast uncertainty quantification via flipflop 26. Forecast uncertainty quantification via flipflop index (Group 2) index (Group 2) 23. Weather Briefing (Group 2) 23. Weather Briefing (Group 2) Thu 26. Forecast uncertainty quantification via flipflop 26. Forecast uncertainty quantification via flipflop index (Group 1) index (Group 2) Fri 25. Excursion to DTN 25. Excursion to DTN
Theme 4: Excursion to Germany
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1. 749 Table 3: Learning outcomes in synoptic and mesoscale meteorology formulated by the WMO 750 in their BIP-M certification of meteorologist (WMO, 2012,2019), and how they are addressed 751 in the Atmospheric Practical course.
Learning outcomes WMO (WMO,2012, 2019) Addressed in Atmospheric Practical course I. Use physical and dynamical reasoning to describe and explain Prior knowledge from previous courses the formation, evolution and characteristics (including extreme Module 3, 4, 5 & 6 (Front detection and characteris- or hazardous weather conditions) of synoptic-scale weather sys- tics) tems in (a) mid-latitude and polar regions and (b) tropical re- Module 11 (Potential wet-bulb to detect fronts) gions, and assess the limitations of theories and conceptual Module 17 & 20 (thickness, advection, divergence and models concerning these weather systems; vorticity) No tropical meteorology II. Use physical and dynamical reasoning to describe and ex- Module 12 & 13 (Soundings and convection) plain the formation, evolution and characteristics (including ex- Weather briefing on extreme events (section 4vii) treme or hazardous weather conditions) of convective and mesoscale phenomena, and assess the limitations of theories and conceptual models about these phenomena; III. Monitor and observe the weather situation, and use real-time Module 1, 2 & 10 (weather observations) or historic data, including satellite and radar data, to prepare Module 15 & 16 (satellite data & enhancement) analyses and basic forecasts; Module 2, 6 & 8 (model data interpretation, and fore- cast analysis) Weather briefings IV. Describe service delivery in terms of the nature, use and Forecast Product Development (section v) benefits of the key products and services, including warnings Communication of weather product (section vi) and assessment of weather-related risks. 752
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Unauthenticated | Downloaded 09/28/21 11:35 PM UTC Accepted for publication in Bulletin of the American Meteorological Society. DOI 10.1175/BAMS-D-20-0107.1.