Scientific computing in natural sciences 4010-ONPa

A detailed description of the subject is provided at the level of a given teaching cycle in the Description section.

Term 2025L:

Lecture Topics
1. General Information about the Atmosphere
- Atmospheric Composition and Trace Gases
- Vertical Profiles of Temperature, Pressure, and Density
- Division of the Atmosphere into Layers: Troposphere, Stratosphere, Mesosphere, Thermosphere
- Standard Atmosphere
- Atmospheric Circulation Barometric Systems Atmospheric Fronts
2. Basics of Atmospheric Measurements
- Types of Measurements: In Situ and Remote Sensing
- Measurement Networks and Their Importance
- Meteorological Messages and Measurement Campaigns
- Atmospheric Soundings (Weather Balloons, Lidars and Ceilometers, SDNE Mode-S from Commercial Aircraft)
3. Satellite and Radar Measurements
- Geostationary and Polar Satellites
- Principle of Operation of Satellite Instruments
- Types of Satellite Data, Their Availability and Resolution
- Principle of Operation of Radars Radar Networks
- Availability and Interpretation of Radar Data
4. Numerical Fluid Modeling on the Example of the Earth's Atmosphere
- From the "ab" Approach initio” for global circulation models
- Equation discretization (finite difference and finite element methods)
- Stability of finite difference methods
- Boundary and initial value problems
5. Introduction to climatology, data reanalysis
- Definition of climate and weather
- Spatial and temporal scales of climate
- Climate variability and change
- Reanalysis of measurement data
6. Data assimilation
- Concept and goals of observational data assimilation
- Sources of observational and model errors
- Basic data assimilation methods (nudging, variational methods, Kalman filters)
- Impact of data assimilation on numerical forecast quality
7. Verification of numerical weather forecast models
- Deterministic and probabilistic verification,
- Forecast quality measures (bias, RMSE, ACC, ETS),
- Comparison of forecasts with observations and reanalyses
8. Atmospheric modeling using AI/ML models
- The role of AI/ML methods in Meteorology
- Areas of AI/ML applications in atmospheric modeling (weather forecasting, statistical downscaling, physical model emulation, detection and classification of extreme events)
- Source data for model training and processing methods
- Machine learning methods used in meteorology
- Comparison of physical and data-driven approaches
- Limitations, interpretability, and challenges

The order of topics covered and the level of detail may change slightly

Term 2026Z:

The order of topics covered and the level of detail may change slightly.

Course coordinators

Marta Kopeć
Karolina Szafranek

Type of course

obligatory courses

Mode

Blended learning

Prerequisites (description)

(in Polish) Brak.

Learning outcomes

Field-specific learning outcomes referenced against the characteristics of the Polish Qualifications Framework (PQF) at level 7:

the student knows and understands:
K_W09 - in-depth, selected methods and tools for modeling the course of selected natural phenomena and processes,

the student is able to:
K_U05 - plan and conduct computer simulations, analyze their results, and formulate conclusions relevant to a selected field of science,
K_U09 - analyze selected problems and identify algorithms and computational methods suitable for solving them,

and course-specific learning outcomes:
The student knows and understands:
W1 - Knows and thoroughly understands selected methods and tools for modeling the course of selected phenomena and processes used in fluid modeling and atmospheric physics [K_W09]

The student is able to:
U1 - Plan and conduct computer simulations, analyze their results, and draw conclusions regarding the modeling of atmospheric processes [K_U05]
U2 - Analyze selected problems related to atmospheric physics measurement data and identify algorithms and computational methods useful for solving them [K_U09]

Assessment criteria

1. Completion of the course ends with one common grade for all classes (lecture + laboratory) carried out as part of the course.
2. A sick leave does not exempt students from knowledge of the material. It only entitles them to an individualized form of assessment.
3. Students who have received approval for an individualized course of study are required to contact the course coordinator to determine how to achieve all the learning outcomes assigned to the course. If the above-mentioned outcomes cannot be achieved, the coordinator may refuse to grant credit for the course.
4. Attendance is mandatory. In cases of justified absences, the student is required to contact the course coordinator immediately.
5. Regarding the use of artificial intelligence (AI) tools in the preparation of coursework, the rules set out in the "Regulations on the Use of Artificial Intelligence Tools in the Educational Process for the Computational Engineering Degree Programme" apply.
6. Detailed grading criteria are provided at the level of the given academic cycle in the "Notes" section.

Practical placement

Not applicable.

Bibliography

The subject literature has been posted at the level of a given teaching cycle in the Bibliography section.

Term 2025L:

1. Holton J.R., An Introduction to Dynamic Meteorology
2. Salby M.L., Physics of the Atmosphere and Climate
3. Press W.H., Teukolsky S.A., Vetterling W.T., Flannery B.P., Numerical Recipes: The Art of Scientific Computing, Third Edition, Cambridge University Press 2007; – free access online https://numerical.recipes/book.html
4. T. Peng, An Introduction to Computational Physics, Cambridge Univ.

Term 2026Z:

1. Holton J.R., An Introduction to Dynamic Meteorology
2. Salby M.L., Physics of the Atmosphere and Climate
3. Press W.H., Teukolsky S.A., Vetterling W.T., Flannery B.P., Numerical Recipes: The Art of Scientific Computing, Third Edition, Cambridge University Press 2007; – książkę tę można bezpłatnie czytać online pod adresem https://numerical.recipes/book.html
4. T. Peng, An Introduction to Computational Physics, Cambridge Univ.

Notes

Term 2026Z:

Students receive credit for classes based on:
1. class attendance,
2. independent completion of three practical exercises during the semester, submitted no later than the end of classes in a given semester (verification of learning outcomes U1 and U2),
3. a written exam, to which the student is admitted after meeting the conditions described in points 1 and 2 (verification of learning outcome W1).

The final grade for the course is determined based on the arithmetic mean of the three components above.