Spring 2027

Boundary Layer Meteorology

Boundary Layer Meteorology covers observations and theories for boundary layers, turbulence, spectra, plumes, dust devils, convection, and terrain effects in the lowest 2 km of the atmosphere.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 3

Recent recorded grades — Spring 2019: 3.60 GPA, 80.0% A/AB (n=10 letter grades); Spring 2022: 3.39 GPA, 55.6% A/AB (n=9 letter grades); Spring 2026: 3.78 GPA, 100.0% A/AB (n=9 letter grades).

What you’ll be able to do

  • Describe key features, governing equations, and parameterization of turbulence for the fluid atmosphere and ocean, including Reynolds averaging, similarity theory, and large-eddy simulation.

  • Apply turbulence theories and surface energy balance to explain the evolution and development of the surface layer, entrainment zone, and neutral, convective, and stable atmospheric boundary-layers.

  • Interpret how boundary-layer processes impact lower atmosphere phenomena such as flux measurement, air pollution dispersion, urban heat islands, sea breezes, terrain-induced flow.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
ATMOCN 773

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

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SectionModeEnrolled / capacityWaitlist

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records

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Student experience

the class

Recent recorded grades — Spring 2019: 3.60 GPA, 80.0% A/AB (n=10 letter grades); Spring 2022: 3.39 GPA, 55.6% A/AB (n=9 letter grades); Spring 2026: 3.78 GPA, 100.0% A/AB (n=9 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Boundary layers, turbulence, spectra, plumes, dust devils, convection, and terrain effects in the lowest 2 km of the atmosphere.
  • Turbulence parameterization, Reynolds averaging, similarity theory, and large-eddy simulation for the fluid atmosphere and ocean.
  • Surface energy balance and the evolution of surface layers, entrainment zones, and neutral, convective, and stable atmospheric boundary layers.
  • Impacts of boundary-layer processes on flux measurement, air pollution dispersion, urban heat islands, sea breezes, and terrain-induced flow.

Skills

  • Describing key features, governing equations, and parameterization of turbulence using Reynolds averaging, similarity theory, and large-eddy simulation.
  • Applying turbulence theories and surface energy balance to explain the evolution of surface layers, entrainment zones, and neutral, convective, and stable boundary layers.
  • Interpreting how boundary-layer processes impact phenomena like flux measurement, air pollution dispersion, urban heat islands, sea breezes, and terrain-induced flow.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "0e64da5f5865101b10f7c425b5da73fcc27c366299515a6c4022879098cc627b",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}