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Article
Teaching a Weather Forecasting Class in the
2020s
Lars van Galen, Oscar Hartogensis, Imme Benedict, and Gert-Jan Steeneveld
ABSTRACT: We report on redesigning the undergraduate course in synoptic meteorology and
weather forecasting at Wageningen University (the Netherlands) to meet the current-day require-
ments for operational forecasters. Weather strongly affects human activities through its impact on
transportation, energy demand planning, and personal safety, especially in the case of weather
extremes. Numerical weather prediction (NWP) models have developed rapidly in recent decades,
with reasonably high scores, even on the regional scale. The amount of available NWP model output
has sharply increased. Hence, the role and value of the operational weather forecaster has evolved
into the role of information selector, data quality manager, storyteller, and product developer for
specific customers. To support this evolution, we need new academic training methods and tools
at the bachelor’s level. Here, we present a renewed education strategy for our weather forecast-
ing class, called Atmospheric Practical, including redefined learning outcomes, student activities,
and assessments. In addition to teaching the interpretation of weather maps, we underline the
need for twenty-first-century skills like dealing with open data, data handling, and data analysis.
These skills are taught using Jupyter Python Notebooks as the leading analysis tool. Moreover, we
introduce assignments about communication skills and forecast product development as we aim
to benefit from the internationalization of the classroom. Finally, we share the teaching material
presented in this paper for the benefit of the community.
KEYWORDS: Forecasting; Forecasting techniques; Numerical weather prediction/forecasting;
Operational forecasting; Education
https://doi.org/10.1175/BAMS-D-20-0107.1
Corresponding author: Gert-Jan Steeneveld, gert-jan.steeneveld@wur.nl
Supplemental material: https://doi.org/10.1175/BAMS-D-20-0107.2
In final form 8 June 2021
©2022 American Meteorological Society
For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.
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AFFILIATIONS: van Galen, Hartogensis, Benedict, and Steeneveld—Meteorology and Air Quality Section,
Wageningen University, Wageningen, Netherlands
W
eather forecasting is critical not only for social activities for the general public but
also for transportation, energy supply, water management, agriculture, and many
other crucial infrastructures and business decisions. Weather forecasts have become
increasingly more accurate in recent decades due to improved numerical weather prediction
(NWP) systems as a result of advances in understanding physical processes, data assimilation
techniques, and computing capacity (Bauer et al. 2015).
With these advances, the role of the operational forecaster has changed. Nowadays, the
output of multiple NWP models is freely available at a high temporal frequency, and they
cover the continental-scale dynamics for the medium range (3–7 days) and the mesoscale
dynamics for the short range (up to 48 h). Also, observations from satellites, radar systems,
and routine and crowdsourced near-surface weather stations are readily available. In addition,
the userbase of weather forecasts has diversified, requiring tailor-made forecasts for a wide
range of applications. As a result, the forecaster’s tasks have increasingly shifted from adapt-
ing the NWP results for local conditions, toward data (model and observations) treatment,
critical data selection, and storytelling for stakeholders. Educating the upcoming generation
of weather forecasters should consider the evolution occurring in the field, which motivated
us to revise the Atmospheric Practical course at Wageningen University (WU). Also, student
mobility and the diversity in the academic education landscape has strengthened, resulting
in students with variable prior knowledge. Previously, most of our students were Dutch and
all had a uniform prior knowledge from a common study program. Nowadays, the students
who enroll in our program have diverse geographical, cultural, and educational backgrounds.
Although it may initially pose some challenges, this diversity also offers an opportunity for
deepening the course (Apple et al. 2014).
The Atmospheric Practical course teaches the fundament and practice of operational
weather forecasting and synoptic meteorology, introducing innovations that reflect the de-
velopment of the field. We mainly address the introduction of an intake questionnaire, the
international classroom, a new student activity to set up a forecast product, the implementation
of a modern scientific program language for data analysis, and the deeper attention needed
for communicating a weather forecast.
Position of the Atmospheric Practical course in the curriculum
The Atmospheric Practical is an optional course offered in the 3-yr B.S. program in Soil,
Water, Atmosphere, which combines courses in the three disciplines with special atten-
tion to interfaces at the land surface and vegetation. Students taking the course need two
6-ECTS atmospheric introduction courses (each with a workload of 168 h): “Introduction
Atmosphere” that uses an in-house-made course reader and “Meteorology and Climate”
based on Wallace and Hobbs (2006). These courses deal with basic atmospheric physics
and chemistry covering thermodynamics, radiation, atmospheric dynamics, and boundary
layers. Both courses discuss basic weather forecasting, such as interpretation of synoptic
observations and radio soundings, and data assimilation, as well as the concept of deter-
ministic chaos and its consequences. Didactically, the courses combine classroom lectures
with pen-and-paper exercises and computer-based assignments, mostly addressing the lower
cognitive levels of Bloom’s taxonomy (Anderson and Bloom 2001) regarding understand-
ing, recognizing, and interpreting atmospheric processes. Finally, both courses include
a number of weather briefings by a meteorologist from Data Transmission Network (DTN
Weather, formerly MeteoGroup).
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After 2 years, students specialize in one discipline offered in the B.S. program. Typically,
25–40 students take the Atmospheric Practical for their specialization. The course introduces
the forecasting cycle, which includes the process of obtaining observed weather data and
model outcomes to compile a forecast for different end users.
Course history, content, structure, learning outcomes, and student assessments
First, we summarize the course history and then go on to compare the forecasting cycle be-
tween the 1980s and the 2020s. Finally, we address the renewed course learning outcomes,
structure, and student assessment methods.
In the previous version of the course, the interpretation of surface and upper-air observa-
tions and the NWP model output were key. The selected case studies had a national or west-
ern European focus, and the exercises involved a lot of manual work (often paper exercises
on printed weather maps) or were performed with a variety of outdated software packages.
Also, the NWP model datasets were limited to coarse resolutions of >25 km. Some exercises
focused on spatiotemporal scales exceeding the characteristic time scales of synoptic meteo-
rology, e.g., exercises about the physical climatology of the whole globe. Concerning student
assessments, there was limited discrimination between students, and a number of crucial
subjects such as communicating weather forecasts were absent. Nevertheless, the original
set up served its purpose for 15 years and was highly appreciated by students who graded
the course with a 4.3 out of 5 for the past 6 years.
Course content: Forecasting cycle. The forecasting cycle, which is the backbone of the course,
has changed substantially over the years. The forecasting cycle contains the following steps
(Inness and Dorling 2013):
1) Collecting observations.
2) Using collected observations to specify the initial conditions for the forecast.
3) Using a model to extrapolate the state of the atmosphere in the future.
4) Experienced forecasters assessing the output of the model.
5) Producing forecasts for customers.
Figure 1a depicts a typical forecast cycle in the 1980s, when the short-term forecast was
made using synoptic observations. Only a limited number of global models (ECMWF, GFS,
JMA) were available and they were characterized by relatively coarse grid spacings (~50 km)
and lacked information on the mesoscale. Mesoscale meteorological models such as MM5
(Dudhia et al. 2000), WRF (Powers et al. 2017), HARMONIE (Bengtsson et al. 2017), and
COSMO (Doms and Baldauf 2013) did not exist. After a consistency check of the short-range
NWP model forecast with observations, operational meteorologists actively modified the
forecast by “interpolating” the NWP output to local scales to account for the effects of unre-
solved lakes, mountains, soils, and land use. The medium-range forecasts were formulated
mostly qualitative. Finally, the forecast was verified against observations and lessons were
learned for further model development.
Nowadays, the spatiotemporal detail of observations has increased enormously, which
better facilitates regional analysis, and even local (~1 km) nowcasting is possible (Fig. 1b).
Additionally, dense networks of personal weather stations are available via websites
(such as www.wunderground.com, www.netatmo.com/weathermap, https://pressurenet.io/, and
https://wow.metoffice.gov.uk/). Despite their relatively low accuracy and unknown siting, these
personal weather stations can offer local weather information about areas where official
observations are relatively scarce (Napoly et al. 2018; Hintz et al. 2019; de Vos et al. 2020;
Mandement and Caumont 2020). Also, a wealth of output from global and regional NWP
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Fig. 1. Illustration of the forecasting cycle in the past and current day. Start reading the figure
from “State of the Atmosphere”.
models is available with output up to 1-h temporal and ~1-km horizontal spatial resolution.
Hence, the value that an operational forecaster can add to the forecast by accounting for local
conditions has decreased. Simultaneously, ensemble forecasting has matured and quantifies
the model uncertainty. This development now allows for local and regional weather forecasts
and warnings, while these were issued on the national level in the past. This advancement
provides the meteorologist with the new task of assessing NWP uncertainty and translating
it into a meaningful forecast for end users.
The end user perspective to forecasts has changed as well. In the past, forecast commu-
nication occurred in relatively general statements that fit with the spatiotemporal scale that
could be resolved. Nowadays, many customer services, like precision agriculture, wind energy
companies, festival organizers as well as road and rail management agencies require targeted
forecasts for their fields. Thus, forecast communication has become more important than in
the past (Pandya et al. 2009; Eden 2011). Customers want to know what consequences the
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forecasted weather could have for their operations and services. We will explain how these
evolving aspects in the forecasting cycle are addressed in the course learning outcomes and
student assessments. Thereby we want to emphasize that the changes we made to the course
are implemented in consultation with representatives of the private meteorology sector, in-
cluding the senior forecaster from DTN. With their advice we could adjust the course to fit the
needs asked for graduates in meteorology.
Learning outcomes. Learning outcomes are crucial for the course delineation since they help
to determine the cognitive level as well as the student activities and assessments. In this third-
year course, the learning outcomes address the higher cognitive levels according to Bloom’s
taxonomy (Anderson and Bloom 2001), such as analyze and evaluate, along with the lower
cognitive levels on identifying and explaining. Taking into account the forecasting cycle, after
successful completion of the Atmospheric Practical course students are expected to be able to
• identify and recognize the different types of meteorological data and how frequently these
are available;
• modify, analyze, and interpret different types of meteorological data;
• explain and discuss the relation between several atmospheric quantities varying in space
and time;
• monitor, observe, and analyze the weather situation, and perform a professional forecast
of weather parameters using real-time or historical data;
• communicate a weather forecast for different customer groups and media;
• discriminate and evaluate the research performed at the various research facilities offering
meteorological services, education, and data abroad; and
• appraise career perspectives, research activities, meteorological disseminated data, and
services provided by operational and research institutes abroad.
Table 1 summarizes the learning outcomes and the student activities that lead to the fulfil-
ment of the learning outcomes. Obviously, the students are exposed to a variety of teaching
methods and activities, which will be elaborated on in the next section.
Course structure. For scheduling reasons, the course entails three consecutive weeks
of full-day classes consisting of 36 practical exercises (called modules, Table 2) and
subsequently, one week where students participate in an excursion abroad. Each week
has a specific theme and the course evolves from a relatively basic level toward higher
learning outcomes.
Each module starts with a bit of theory from the course reader consisting of selected texts,
internet pages, or book chapters that should take students about an hour to read. The course
reader is included in the online supplemental material (Table S3; https://doi.org/10.1175/BAMS-
D-20-0107.2). The course emphasizes the practical approach which enables students to digest
the course material, since many individuals learn most effectively by using concrete examples
(Roebber 2005). As such, each module continues with a practical assignment in which students
actively diagnose and interpret weather observations or NWP model output using Python Note-
books (see “Weather briefing by students” section and supplemental material Table S4) or the
Meteorological Information Display and Analysis System (MIDAS) meteorological workstation
(introduced below).
The first week’s theme concerns “weather observations and models.” The course begins
with (re)introducing the basic synoptic plotting system, which is subsequently used to make
a coherent surface weather picture at the national and European scale, including front detec-
tion. Students learn about the routinely available meteorological observations (surface and
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Table 1. Consistency table with course learning outcomes and student tasks.
Plot and Prepare
interpret and present
routine Plot and Perform Prepare Develop forecast communication
synoptic interpret NWP practical weather product from raw strategy for
observations model output test/exam briefing NWP output Visit DTN customer group
Identify and recognize × × × × ×
the different types of
meteorological data and how
often these are available
Modify, analyze, and × × × × ×
interpret different types
of meteorological data
Monitor, observe, and × × × ×
analyze the weather situation
and perform a professional
forecast using real-time or
historical data
Communicate a weather × × ×
forecast for different
customer groups and media
Explain and discuss the × × × ×
relation between several
atmospheric quantities
varying in space and time
Discriminate and evaluate ×
the research performed at
the various research facilities
offering meteorological
services, education, and data
abroad
Appraise career perspectives, ×
research activities,
meteorological disseminated
data and services provided
by operational and research
institutes abroad
aerological observations), their time frequency, and how they are exchanged between
meteorological services. Successful integration of observations with models and theory assists
students to better understand physical processes in the atmosphere (Etherton et al. 2011).
Also, students learn to visualize observations and model output in the MIDAS meteorological
workstation provided by DTN. MIDAS was chosen because of the long-lasting collaboration
with DTN, that started as a spin-off company of WU in the 1980s. In addition, students work
with NWP model output to learn about spatial resolution and the related consequences for a
weather forecast. This first week’s theme is finalized with a practical test (more details below
under the “Student assessment” section).
The second week focuses on the theme “weather analysis,” which includes the interpre-
tation of the spatiotemporal observations into a coherent understanding of synoptic and
mesoscale weather. First, radio sounding observations are studied to investigate convective
indices such as CAPE, CIN, and the Boyden index. Second, weather radar observations and
their limitations are discussed. Subsequently, the course treats satellite observations in the
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Unauthenticated | Downloaded 03/12/22 08:28 AM UTCTable 2. Course outline and time schedule organized in three themes. Elements of the student
assessments have been italicized.
Theme 1: Weather observations and models
Morning Afternoon
01. Translating weather observations into an overview 02. Preanalysis large weather map
Mon
02. Preanalysis large weather map 03. Front detection from surface observations
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 MIDAS
Thu 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
26. Forecast uncertainty quantification via flip-flop index (group 2)
flip-flop index (group 2)
23. Weather briefing (group 2) 23. Weather briefing (group 2)
Thu 26. Forecast uncertainty quantification via
26. Forecast uncertainty quantification via flip-flop index (group 1)
flip-flop index (group 2)
Fri 25. Excursion to DTN 25. Excursion to DTN
Theme 4: Excursion to Germany
infrared (IR) and visible (VIS) ranges, and the red–green–blue (RGB) enhancement of images
from Meteosat/SEVIRI and MetOp/AVHRR. The theme is completed with atmospheric dynamic
concepts such as divergence, vorticity, thickness, and advection to improve the students’
understanding of the day-to-day weather.
The third week focuses on “weather forecasting,” where students practice making weather
forecasts (building upon preceding knowledge and tools) and present weather briefings for a
geographical area experiencing interesting weather (“Communication” section). Moreover, stu-
dents develop meteorological products for specific customer groups based on scientific literature
and from direct NWP output (“Forecast product development” section). For example, students
are asked to develop a forecast for clear-air turbulence based on relevant model parameters from
NWP output. As such, students learn that the job of a forecaster has broadened and now includes
forecast product development. Yarger et al. (2000) reported that such forecasting exercises may
lead to pronounced improvement of long-term knowledge retention. Finally, the last day of the
course is focused on communicating weather forecasts (more details below under innovations).
Student assessment. Student assessment consists of five different aspects. The first theme,
“weather observations and models,” is assessed with an individual closed-book practical test
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preparation. Students are divided into groups of two and are given 4 h to prepare a PowerPoint
file with 10 slides. In this file, groups have to forecast the current weather for the Netherlands
and the outlook for the coming 3 days, applying the knowledge gained throughout the course.
The PowerPoint is evaluated via a rubric (Table S1) and counts for 20% of the final grade.
In the theme “weather forecasting,” students need to present a weather briefing (“Weather brief-
ing by students” section). The same rubric (Table S1) is used as in week 2, but now the students
need to show their presentation skills. Overall, this element counts for 30% of the final grade.
The fourth assessment element is the forecast product development (“Forecast product
development” section) and counts for 10% of the final grade. Students are graded based
on a rubric (Table S2) that summarizes the requirements of the forecast product. The final
assessment item is a student report about the excursion abroad (20%), which we will not
elaborate on here.
Innovations
Both the changing requirements for operational meteorologists and the diversity of student
backgrounds (geographically, but also diversity in prior knowledge) and interests have in-
spired us to introduce several course innovations. These innovations concern education tools/
infrastructure (“Feedback” and “Programming environment” sections), didactics, and content
(“Intake” section and further) and are presented here along with motivation and examples.
Feedback. Prompt and accurate feedback is crucial for successful teaching (Chickering
and Gamson 1987). In the earlier course setup, all student assignments were corrected and
graded manually, which was labor intensive, and students would not receive feedback until
days or weeks later. Now, students submit their answers to the assignments via the online
teaching platform Brightspace and promptly receive the correct answers electronically.
This allows students to immediately check their work and talk with the lecturers about any
questions that arise. Moreover, the reduced workload for the lecturers means that there is more
time to help students with specific issues. This approach makes the student responsible for
their study progress rather than the lecturers. Students’ ability to engage in learning and their
ability to reflect on their own progress can lead to increased self-efficacy and self-confidence
as students learn that they can reach expected goals. This approach also allows students to
reflect on their learning strategies and gain study ownership. College readiness is enhanced
when students demonstrate this behavior. Teaching methods that lack sufficient attention to
student motivation, engagement, goals, self-efficacy, and persistence are less likely to result
in student learning gains (Conley and French 2014).
Programming environment. To encourage an active learning environment, students handle
meteorological model output and observations in Python Jupyter Notebooks. Unifying the
programming language to Python prevents different data types from being investigated with
different software tools. Python is a fast growing, open-source programming language and
is frequently used among meteorologists and climatologists. As such, programming skills in
Python are beneficial for future studies and working environments. Python Notebooks allow
for treating, plotting, and interpreting datasets together in an internet-based browser environ-
ment. As these notebooks can also contain textual instructions and exercises, the notebooks
combine the exercise instructions and programming tasks into one single document. Finally,
the collection of notebooks enables students to download and process open-source data by
themselves in later stages of their program.
Each notebook is set up in a similar way. First, the class specific learning outcomes are
indicated, together with the student activity (Fig. 2). Thereafter, required libraries and the
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sions of the dataset can be explored. Subsequently, the
notebook provides learning information and a clear
question that needs to be answered by editing the Py-
thon code. By executing the code block, the resulting
figure appears which in this case is a satellite image
of the near-infrared in Meteosat Second Generation
(MSG) channel 4. Most Python skills learned in the
modules should be applied in the “forecast product
development” module (“Forecast product develop-
ment” section), where students build code to develop
and visualize a forecast product.
Intake. The university’s strategic education plan pro-
motes personalized learning paths, where students
themselves choose which courses or modules they wish
to take based on their prior knowledge, interests, and
skills. Insight into the diversity of prior knowledge, skills,
and interests is collected through an online intake ques-
tionnaire four weeks prior to the start of the course. For
example, the questionnaire provides information about
the students’ geographical backgrounds and knowledge
of local weather systems, which facilitates the interna-
tional classroom (see “Internationalization” section). In
addition, the questionnaire collects information about
attended meteorology courses (B.S. or M.S. program,
home university or foreign university) and computing
skills (see supplemental material). The questionnaire
helps to discover a student’s interest in the different
meteorological scales and the subdisciplines within
meteorology. With the answers to the questionnaire,
lecturers learn about the strengths and weaknesses of
the student cohort, enabling them to timely adapt the
course content. For example, the lecturers have prepared
a collection of modules at either preparatory or deeper
levels which allows them to be somewhat flexible in of-
fering certain modules depending on the course edition.
Concretely, one year the intake revealed that a
substantial number of students were already working
for weather companies in their spare time, e.g., as as-
sistant meteorologists for written or online media or Fig. 2. Example of a Jupyter Python Notebook as a tool
as a nighttime forecaster for road slipperiness. This to teach the Atmospheric Practical. All notebooks are
prior background knowledge helps to shape the course provided as supplemental material.
content for these more advanced students, and their
work experience can be shared with the other students
via short presentations.
The questionnaire contains a short self-assessment about student knowledge concerning
weather phenomena and analysis tools, e.g., about thermodynamic diagrams, synoptic observa-
tions, and identification of fronts (Fig. 3). Despite being taught in preceding courses, the modest
self-assessment scores underline the need for repeating these topics in the current course.
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Unauthenticated | Downloaded 03/12/22 08:28 AM UTCInternationalization. Nowadays, many
meteorological work environments require
a global understanding of weather systems
with customers and stakeholders all over
the world, not to mention the multiplicity of
nationalities and cultures among colleagues
in the international labor market. As such,
the course should contain international
aspects, which is achieved by a so-called
international classroom. Internationalization
is the incorporation of international, inter-
cultural and/or global dimensions into the
course content and the learning outcomes, Fig. 3. Example results of student (n = 23) self-assessment
of their prior knowledge as investigated by the online
assessment tasks, teaching methods, and
intake. Students were asked to assess their familiarity and
support services (Leask 2015). Basically,
understanding of radio soundings (blue), SYNOP plotting
international classroom comprises three key system (orange), and weather front properties (gray). Score
components: (i) English as the language of of 1 indicates “I do not know the concept” and score of 5
instruction, which is the case for our whole indicates “I have mastered the concept.”
B.S. program; (ii) intercultural competence;
and (iii) international framework. Intercul-
tural competence starts when reflecting on norms and values in different countries and the
realization that not all students will have had this reflection and the appreciation that there
are many ways to be a good student. Students should develop new ways of learning that are
helpful in their new learning context.
Students in well-functioning international classes, will be well prepared for an international
labor market and society. Students from different cultural backgrounds work together in an
international class and learn about new habits and ways of doing things that are different
from what they learned in their own countries. Students also learn to express themselves in a
language that is not their mother tongue (e.g., Apple et al. 2014). They also come across new
knowledge and other methods of teaching and assessment. These are all experiences that
contribute to successful functioning in an international environment. In part, these benefits
also apply to lecturers. They too have to deal with communicating in a different language with
students from other cultures. International students act as a mirror that shows how teaching
methods are shaped by beliefs and culturally determined norms.
We achieve an international classroom by inviting our foreign students to give presenta-
tions about the weather and climate of their home country and the forecasting challenges
that are posed. This is an example of so-called place-based teaching that is a fruitful manner
that supports inclusiveness (Apple et al. 2014). This covers both the physical and dynamic
aspects and the societal relevance of the phenomena. For instance, a Zimbabwean student
presented the monsoon circulation in his country, how it had evolved during the recent de-
cades in terms of timing, intensity, and its impact on food production and the government’s
food policy (Fig. 4). Also, he reported about the relatively limited observational infrastructure
in Zimbabwe, and how seasonal forecasts are downscaled with the COSMO and WRF models.
For native students, such a short contribution is very meaningful since the curriculum lacks
a course in tropical meteorology.
Internationalization is also achieved through a graded weather briefing in which inter-
continental actual weather phenomena are used (“Weather briefing by students” section).
Based on the ECMWF extreme forecast index, the lecturers select regions where interest-
ing weather phenomena are forecast in the coming 72 h. Consequently, students are asked
to make a weather forecast for this region, which implies that they need to invest time in
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Unauthenticated | Downloaded 03/12/22 08:28 AM UTCFig. 4. Illustration of international classroom example. Slides by a student from Zimbabwe explaining the meteorology
of his home country and a recent intense precipitation event. Courtesy: student Sinclair Chinyoka.
understanding the driving physical and dynamical mechanisms as well as the local vulner-
ability and stakeholders.
For example, students could be tasked with making a visibility forecast for dust storms
in northern Africa. These storms occur during relatively southerly positioned jet streams,
combined with high near surface wind speeds and relatively dry soils. Since dust is not a
direct model output, students have to explore the physical processes and empirical methods
to translate direct model output for that region to visibility. As a second example, so-called
nor’easters are macroscale extratropical storms that impact the North Atlantic areas of the
United States. Nor’easters are usually accompanied by heavy rain or snow, and can cause
severe coastal flooding, coastal erosion, hurricane-force winds, or blizzard conditions. Stu-
dent forecasts should address the case-specific societal impacts and assess their likelihood
and occurrence, for example, by examining soundings to quantify the possibility of snow.
Forecast product development. More and more forecaster jobs require skills to develop
and implement forecast products for specific applications and customers. This entails
the capacity to digest and manipulate routine NWP output (data science approach)
and the presentation of a hands-on visualization and interpretation for customers
(Garbanzo-Salas and Jimenez-Robles 2020). Hence, we introduced an 8-h assignment about
processing direct model output (e.g., ECMWF or GFS) into a powerful targeted forecast prod-
uct (e.g., wind energy, clear-air turbulence, road weather forecasts, rule-based fog forecast-
ing). In practice, students are provided some peer-reviewed papers that present methods to
forecast meteorological phenomena based on direct model output. By reading these papers,
students come to realize that scientific research has been the basis of the forecast method.
Subsequently, students design a flowchart of the required input and manipulation steps
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students totobuild
build the
the
structure
structure of
ofof their
their Python
Python code
code to
toto calculate
calculate andand plot
plot their
their forecast
forecast in
inin a Jupyter Notebook. As
structure
structure
structure ofof their
their
their Python
Python
Python code
code
code toto calculate
calculate
calculate andandand plot
plot
plot their
their
their forecast
forecast
forecast inin aaaJupyter
Jupyter
aJupyter
Jupyter Notebook.
Notebook.
Notebook.
Notebook. As
As
As As
aaby-product,
by-product,students
aaaby-product,
by-product,
by-product, studentslearn
students
students
students
learntoto
learn
learn
learn
tothink
totothink
thinkabout
think
think
aboutaaastrategy
about
about
about
strategyfor
strategy
aastrategy
strategy
fordata
for
for
for
datamanagement,
data
data
data
management,programming
management,
management,
management,
programming
programming
programming
programming
structure,and
structure,
structure, anddata
and datavisualization,
data visualization,which
visualization, whichare
which areskills
are skillsthat
skills thatare
that arerequired
are requiredinin
required injobs
jobsinin
jobs indata
dataanalytics
data analytics
analytics
structure,
structure, and
and data
data visualization,
visualization, which
which are
are skills
skills that
that are
are required
required ininjobs
jobs inindata
data analytics
analytics
and
and
and in
inin academic
academic
academic research.
research.
research. Finally,
Finally,
Finally, students
students
students evaluate
evaluate
evaluate their
their
their ownown
own forecast
forecast
forecast against
against
against observations.
observations.
observations.
and
and inin academic
academic research.
research. Finally,
Finally, students
students evaluate
evaluate their
their own
own forecast
forecast against
against observations.
observations.
With
With
With this
this
this assignment,
assignment,
assignment, students
students
students develop
develop
develop and
and
and evaluate
evaluate
evaluate their
their
their own
own
own forecast
forecast
forecast product,
product,
product, andthereby,
and
and thereby,
thereby,
With
With this
this assignment,
assignment, students
students develop
develop and
and evaluate
evaluate their
their own
own forecast
forecast product,
product, and
and thereby,
thereby,
addressthe
address
address thehighest
the highestcognitive
highest cognitivelevel
cognitive levelofof
level ofBloom’s
Bloom’staxonomy
Bloom’s taxonomy(Anderson
taxonomy (Andersonand
(Anderson andBloom
and Bloom2001).
Bloom 2001).
2001).
address
address the
the highest
highest cognitive
cognitive level
level ofofBloom’s
Bloom’s taxonomy
taxonomy (Anderson
(Anderson and
and Bloom
Bloom 2001).
2001).
AsAs
As anan
an example,
example,
example, one
one
one product
product
product development
development
development assignment
assignment
assignment deals
deals
deals with
with
with clear
clear
clear air
air
air turbulence
turbulence
turbulence (CAT).
(CAT).
(CAT).
As
As anan example,
example, one
one product
product development
development assignment
assignment deals
deals with
with clear
clear air
air turbulence
turbulence (CAT).
(CAT).
CATforecasts
CAT
CAT forecastsare
forecasts arebased
are basedon
based onindices
on indicesofof
indices ofcontrasting
contrastingcomplexity
contrasting complexitythat
complexity thataccount
that accountfor
account forhorizontal
for horizontaland
horizontal and
and
CAT
CAT forecasts
forecasts are
are based
based on
on indices
indices ofofcontrasting
contrasting complexity
complexity that
that account
account for
for horizontal
horizontal and
and
vertical
vertical
vertical wind
wind
wind shear
shear
shear (|∂υ/∂z|),
(|∂υ/∂z|),
(|∂υ/∂z|), convergence
convergence
convergence (C
(C(Cug ), deformation
),gdeformation
deformation
),deformation
deformation (D),
(D),
(D), and
and
and atmospheric
atmospheric
atmospheric stability,
stability,
stability, e.g.,
e.g.,
e.g.,
vertical
vertical wind
wind shear
shear (|∂υ/∂z|),
(|∂υ/∂z|), convergence
convergence (C(C gu),
u),
ug
(D),
(D), and
and atmospheric
atmospheric stability,
stability, e.g.,
e.g.,
the
the
the Ellrod’s
Ellrod’s
Ellrod’s turbulence
turbulence
turbulence index
index
index (T1)
(T1)
(T1) and
and
and the
the
the
ugugEllrod and Knapp (1992) (T2) index. T1 is defined as
Ellrod
Ellrod and
and Knapp
Knapp (1992)
(1992) (T2)
(T2) index.
index. T1
T1 isisdefined
defined as
as
the
the Ellrod’s
Ellrod’s turbulence
turbulence index
index (T1)
(T1) and
and the
the Ellrod
Ellrod and
and Knapp
Knapp (1992)
(1992) (T2)
(T2) index.
index. T1
T1 isis defined
defined as
as
sυ
ssυυ .. .
T1==DD
T1
T1 = D ssz.z
sz
Alternatively,
Alternatively,
Alternatively, T2
T2
T2 builds
builds
builds upon
upon
upon T1
T1
T1 and
and
and is
isis defined
defined
defined as
as
as
Alternatively,
Alternatively, T2
T2 builds
builds upon
upon T1
T1 and
and isis defined
defined as
as
sυ
T2 T2== DD++CC υgssυυ . .
T2 = D + C υg ssz.z
υg
sz
CATisis
CAT islikely
likelytoto tooccur
occurifififT1T1>>>77
7×××1010 s−2oror
−7 −2
s orT2T2>>>ofof
of555 ××10
10 ss−2
−7 −2
(Overeem2002; 2002;Lee Lee2013).
2013).
CAT likely occur T1 10 s T2>>ofof55×××10
T2 10 s−2 (Overeem
(Overeem 2002; Lee 2013).
−7−7 −2 −7−7 −2
CATisislikely
CAT likelytotooccuroccurififT1 T1>>77××10 10 ss or
−7 −2
−7 −2
orT2 10−7−7 s
s−2 (Overeem
(Overeem 2002;
2002; Lee
Lee 2013).
2013).
Asan
As
As anillustration
an illustrationofof
illustration ofstudent
studentwork,
student work,Fig.
work, Fig.55
Fig. 5shows
showsthe
shows theflowchart
the flowchartfor
flowchart forthe
for theCAT
the CATforecast
CAT forecastbased
forecast based
based
As
As an
an illustration
illustration ofofstudent
student work,
work, Fig.
Fig. 55shows
shows the
the flowchart
flowchart for
for the
the CAT
CAT forecast
forecast based
based
on
on
on T1
T1
T1 and
and
and T2.
T2.
T2. First,
First,
First, wind
wind
wind components
components
components are
are
are loaded
loaded
loaded from
from
from open-source
open-source
open-source GFS
GFS
GFS forecast
forecast
forecast files,
files,
files, and
and
and
on
on T1
T1 and
and T2.
T2. First,
First, wind
wind components
components are
are loaded
loaded from
from open-source
open-source GFS
GFS forecast
forecast files,
files, and
and
subsequentlythe
subsequently
subsequently thedeformation,
the deformation,vertical
deformation, verticalwind
vertical windshear,
wind shear,and
shear, andconvergence
and convergenceare
convergence areestimated,
are estimated,and
estimated, andfinally
and finally
finally
subsequently
subsequently the
the deformation,
deformation, vertical
vertical wind
wind shear,
shear, and
and convergence
convergence are
are estimated,
estimated, and
and finally
finally
combinedinin
combined
combined inthe
themetric
the metricofof
metric ofinterest.
interest.Figure
interest. Figure66
Figure 6shows
showsthe
shows theforecast
the forecastwhich
forecast whichindicates
which indicatesthe
indicates theareas
the areaswith
areas with
with
combined
combined ininthe
the metric
metric ofofinterest.
interest. Figure
Figure 66shows
shows the
the forecast
forecast which
which indicates
indicates the
the areas
areas with
with
high
high
high risk
risk
risk of
ofof CAT
CAT
CAT at
atat the
the
the 300-hPa
300-hPa
300-hPa level
level
level in
inin yellow.
yellow.
yellow. Pilot
Pilot
Pilot reports
reports
reports of
ofof CAT
CAT
CAT taken
taken
taken in
inin the
the
the hour
hour
hour around
around
around
high
high risk
risk ofof CAT
CAT atat the
the 300-hPa
300-hPa level
level inin yellow.
yellow. Pilot
Pilot reports
reports ofof CAT
CAT taken
taken inin the
the hour
hour around
around
the
the
the valid
valid
valid forecast
forecast
forecast time
time
time are
are
are plotted
plotted
plotted over
over
over the
the
the United
United
United States
States
States as
as
as forecast
forecast
forecast verification.
verification.
verification.
the
the valid
valid forecast
forecast time
time are
are plotted
plotted over
over the
the United
United States
States as
as forecast
forecast verification.
verification.
Communication.Successful
Communication.
Communication. Successfulcommunication
Successful communicationofof
communication ofweather
weatherforecasts
weather forecaststoto
forecasts tocustomer
customergroups
customer groupsisis
groups isas as
as
Communication.
Communication. Successful
Successful communication
communication ofofweather
weather forecasts
forecasts totocustomer
customer groups
groups isisas as
importantas
important
important ascreating
as creatingaaaweather
creating weatherforecast
weather forecastfrom
forecast fromNWP
from NWPmodel
NWP modelresults
model results(Eden
results (Eden2011).
(Eden 2011).Nowadays,
2011). Nowadays,
Nowadays,
important
important as as creating
creating aaweather
weather forecast
forecast from
from NWP
NWP model
model results
results (Eden
(Eden 2011).
2011). Nowadays,
Nowadays,
the
the
the forecaster’s
forecaster’s
forecaster’s focus
focus
focus has
has
has evolved
evolved
evolved toward
toward
toward the
the
the role
role
role of
ofof storyteller,
storyteller,
storyteller, i.e.,
i.e.,
i.e., what
what
what does
does
does the
the
the weather
weather
weather
the
the forecaster’s
forecaster’s focus
focus has
has evolved
evolved toward
toward the
the role
role ofof storyteller,
storyteller, i.e.,
i.e., what
what does
does the
the weather
weather
forecastmean
forecast
forecast meanfor
mean foraaaparticular
for particularcustomer?
particular customer?This
customer? Thiscommunication
This communicationaspect
communication aspectwas
aspect wasabsent
was absentbefore
absent beforethe
before the
the
forecast
forecast mean
mean for
for aaparticular
particular customer?
customer? This
This communication
communication aspect
aspect was
was absent
absent before
before the
the
courserevision
course
course revisionand
revision andhas
and hasbeen
has beenintroduced
been introducedby
introduced bymeans
by meansofof
means ofananexcursion
an excursiontoto
excursion tothetheweather
the weatherroom
weather roomofof
room of
course
course revision
revision and
and has
has been
been introduced
introduced byby means
means ofofanan excursion
excursion totothe
the weather
weather room
room ofof
DTN
DTN and
and a subsequent assignment. Here, a senior communication meteorologist presents
DTN
DTN
DTN and
and
and aaasubsequent
subsequent
asubsequent
subsequent assignment.
assignment.
assignment.
assignment. Here,
Here,
Here,
Here, aaasenior
senior
asenior
senior communication
communication
communication
communication meteorologist
meteorologist
meteorologist
meteorologist presents
presents
presents
presents
the
the
the prerequisites
prerequisites
prerequisites for
for
for successful
successful
successful forecast
forecast
forecast communication,
communication,
communication, illustrated
illustrated
illustrated with
with
with striking
striking
striking examples
examples
examples
the
the prerequisites
prerequisites for
for successful
successful forecast
forecast communication,
communication, illustrated
illustrated with
with striking
striking examples
examples
ofofhow
of howaccurate
how accurateand
accurate andtimely
and timelycommunication
timely communicationprevented
communication preventeddamage
prevented damageand
damage andcasualties.
and casualties.
casualties.
ofofhow
howaccurate
accurateand andtimely
timelycommunication
communicationprevented preventeddamage damageand andcasualties.
casualties.
Initially,the
Initially,
Initially, thechief
the chiefmeteorologist
chief meteorologistbriefs
meteorologist briefsthe
briefs thestudents
the studentsabout
students aboutthe
about theweather
the weatherexpected
weather expectedinin
expected inthe the
the
Initially,the
Initially, thechief
chiefmeteorologist
meteorologistbriefs briefsthe thestudents
studentsabout aboutthe theweather
weather expected
expected ininthe the
upcomingthree
upcoming
upcoming threedays.
three days.The
days. Thestudents
The studentsare
students arethen
are thenasked
then askedtoto
asked tosetsetup
set upaaacommunication
up communicationstrategy
communication strategyfor
strategy for
for
upcoming
upcoming three
three days.
days. The
The students
students are
are then
then asked
asked totosetset upup aacommunication
communication strategy
strategy for
for
different
different
different customers
customers
customers and
and
and different
different
different media
media
media based
based
based onon
on the
the
the presented
presented
presented weather
weather
weather forecast.
forecast.
forecast. Importantly,
Importantly,
Importantly,
different
different customers
customers and
and different
different media
media based
based onon the
the presented
presented weather
weather forecast.
forecast. Importantly,
Importantly,
the
the
the focus
focus
focus is
isis not
not
not onon
on the
the
the meteorological
meteorological
meteorological aspects
aspects
aspects of
ofof the
the
the forecast
forecast
forecast itself
itself
itself but
but
but on
on
on the
the
the strategyofof
strategy
strategy of
the
the focus
focus isis not
not on
on the
the meteorological
meteorological aspects
aspects ofof the
the forecast
forecast itself
itself but
but onon the
the strategy
strategy ofof
howtoto
how
how tocommunicate
communicatethe
communicate thebriefed
the briefedforecast
briefed forecasttoto
forecast tothe thecustomers.
the customers.The
customers. Thecustomer
The customergroups
customer groupsare
groups arethe
are the
the
how
how totocommunicate
communicate the
the briefed
briefed forecast
forecast totothe the customers.
customers. The
The customer
customer groups
groups are
are the
the
general
general
general audience,
audience,
audience, aviation,
aviation,
aviation, road
road
road maintenance,
maintenance,
maintenance, agriculture,
agriculture,
agriculture, offshore,
offshore,
offshore, the
the
the energy
energy
energy sector,
sector,
sector, and
and
and
general
general audience,
audience, aviation,
aviation, road
road maintenance,
maintenance, agriculture,
agriculture, offshore,
offshore, the
the energy
energy sector,
sector, and
and
organizers
organizers
organizers of
ofof an
an
an outdoor
outdoor
outdoor festival.
festival.
festival. For
For
For each
each
each topic,
topic,
topic, two
two
two groups
groups
groups of
ofof two
two
two students
students
students get
get
get about
about
about one
one
one
organizers
organizers ofof an
an outdoor
outdoor festival.
festival. For
For each
each topic,
topic, two
two groups
groups ofof two
two students
students get
get about
about one
one
hour
hour to
toto prepare
prepare a 5-min presentation about their communication strategy. Subsequently, the
hour
hour
hour toto prepare
prepare
prepare aaa5-min
5-min
a5-min
5-min presentation
presentation
presentation
presentation about
about
about
about their
their
their
their communication
communication
communication
communication strategy.
strategy.
strategy.
strategy. Subsequently,
Subsequently,
Subsequently,
Subsequently, the
the
the
the
strategieslaid
strategies
strategies laidout
laid outinin
out inthethetwo
the twopresentations
two presentationsare
presentations arecommented
are commentedon
commented onby
on byfellow
by fellowstudents
fellow studentsand
students andby
and bythe
by the
the
strategies
strategies laid
laid out
out ininthe
the two
two presentations
presentations are
are commented
commented on
on by
by fellow
fellow students
students and
and byby the
the
seniorcommunication
senior
senior communicationmeteorologist.
communication meteorologist.Thus,
meteorologist. Thus,the
Thus, thestudents
the studentslearn
students learnfrom
learn fromeach
from eachother
each otherinin
other inananactive
an active
active
senior
senior communication
communication meteorologist.
meteorologist. Thus,
Thus, the
the students
students learn
learn from
from each
each other
other ininan an active
active
learning
learning
learning environment.
environment.
environment. This
This
This is
isis where
where
where foreign
foreign
foreign students
students
students can
can
can bring
bring
bring in
inin their
their
their experience
experience
experience on
on
on how
how
how
learning
learning environment.
environment. This
This isis where
where foreign
foreign students
students can
can bring
bring inin their
their experience
experience on
on how
how
communicationisis
communication
communication isdifferent
differentinin
different intheir
theirhome
their homecountry,
home country,facilitating
country, facilitatingthe
facilitating theinternational
the internationalclassroom.
international classroom.
classroom.
communication
communication isisdifferent
different inintheir
their home
home country,
country, facilitating
facilitating the
the international
international classroom.
classroom.
Finally,during
Finally,
Finally, duringthis
during thisexcursion,
this excursion,students
excursion, studentslearn
students learnhow
learn howaaadynamic
how dynamicand
dynamic andbroadly
and broadlyoriented
broadly orientedweather
oriented weather
weather
Finally,
Finally, during
during this
this excursion,
excursion, students
students learn
learn how
how aadynamic
dynamic and
and broadly
broadly oriented
oriented weather
weather
consultant
consultant
consultant agency
agency
agency operates
operates
operates nowadays.
nowadays.
nowadays.
consultant
consultant agency
agency operates
operates nowadays.
nowadays.
Figure
Figure 77 7 presents
presents a sample communicationstrategy strategyfor forflower
flowerfarmers. farmers.First, First,crucial
crucial
Figure
Figure
Figure 77 presents
presents
presents aaaa sample
sample
sample
sample communication
communication
communication
communication strategy
strategy
strategy for
for
for flower
flower
flower farmers.
farmers.
farmers. First,
First,
First, crucial
crucial
crucial
weather
weather
weather variables
variables
variables for
for
for flower
flower
flower preservation
preservation
preservation areare
are listed
listed
listed such
such
such asas
as high
high
high wind
wind
wind speeds,
speeds,
speeds, hail,
hail,
hail, and
and
and high
high
high
weather
weather variables
variables for
for flower
flower preservation
preservation are
are listed
listed such
such as
as high
high wind
wind speeds,
speeds, hail,
hail, and
and high
high
AAAMM
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03/12/22temperatures. Based
on the briefed forecast,
high chances of precipi-
tation and hailstorms
with some wind gusts
were expected. Final-
ly, the communication
strategy included di-
rect consultation with
flower farmers and TV
presentations for agri-
culture websites.
Weather briefing by
students. Some key ac-
tivities of a forecaster
include analyzing mete-
orological observations
and model data, select-
ing the most relevant
information, synthesiz-
ing this information into
a physically consistent
story, and communicat-
ing the forecast to cus-
tomers and colleagues.
T his process occurs
under time pressure.
Students follow these
steps when creating and
presenting a weather
briefing.
The weather brief-
ing assignment focuses
Fig. 5. Flowchart illustrating the steps from direct model output to indices [Ellrod’s
on analyzing the NWP
turbulence index (T1) and the Ellrod and Knapp (1992) index (T2)] used for a
output and its interpre-
forecast product for clear-air turbulence that students design as part of the
tation, while the “Com- forecast product development assignment. Courtesy: student Brian Verhoeven.
munication” section
focuses on proper fore-
cast communication. A senior meteorologist from DTN starts the assignment with a presen-
tation about tips and tricks to structure a weather briefing. Weather briefing assignments
for selected place-based weather phenomena foreseen in the coming 72 h are distributed to
student teams which are made up of two students. Some phenomena might be new for them
and require reading textbooks or online articles to understand the physical and dynamic
processes at hand. In the 4 h of preparation, students create a 12-min weather briefing aimed
at a professional audience, which includes a PowerPoint file and an oral presentation.
After the briefing, presenting students interact with their peers, the lecturers, and the
senior meteorologist from DTN. Students have to defend their forecasts in a short debate.
The lecturers underline the need to understand the physical processes behind the weather
phenomenon at hand and the awareness of the uncertainty of the presented forecast. In
AMERICAN METEOROLOGICAL SOCIETY F E B R UA RY 2 0 2 2 E260
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