ATSC 301: syllabus

ATSC 301: syllabus#

By the end of this course you should have a good understanding of

how radiation is emitted, absorbed, transmitted and reflected by surfaces, clouds, and the atmosphere (the “forward problem”)
how radiometers, radars and lidars are used to infer temperature and surface and cloud properties (the “inverse problem”)
how orbiting radar and climate sensors (MODIS, Cloudsat, Calypso, GOES ABI) are combined with global forecast models to study the atmosphere, ocean and land surface
how to map spatial remote sensing data the earth’s surface using Python’s geographic information software
how to write clear, documented and tested code that can ingest, manipulate and display data, and how to turn equations into computer algorithms in Python

Assessment	weight
Bi-Weekly Assignments/Quizzes	45%
Mid-term	15%
Final (including takehome part)	40%

Week	Topic
Week 1 1/6 - 1/10	Introduction, course outline, Beer’s law, flux

Week 2 1/13 - 1/17	Jupyter introduction, satellite data
Assignment 1: Brightness temperatures
Week 3 1/20 - 1/24	Reading geotifs
Week 4 1/27 - 1/31	Geographic coordinate systems, Schwartzchild equation
Assignment 2: Stull Chapter 2 problems
Week 5 2/3 - 2/7	Schwartzchild eqn with absorption and emission
	Cartopy mapping and image resampling
Assignment 3: Flux from radiance
Week 6 2/10 - 2/14	Pandas, Weighting functions for temperature retrieval
Assignment: mid-term review

Midterm: Monday Feb. 24


Week 7 2/24 - 2/28	xarray, geotiffs, Landsat and Sentinel data

Week 8 3/3 - 3/7	Reading cloud optimized geotiffs, Landsat channels
Assignment 4: NDVI
Week 9 3/10 - 3/14	Analyzing Cloudsat radar data
Week 10 3/17 - 3/21	Comparing Cloudsat with the ECMWF model
Assignment 5: Cloudsat and Landsat data
Week 11 3/24 - 3/28	Heating rates for climate modeling, false color images
Assignment 6: Radar equation, Hurricane case study
Week 12 3/31 - 4/4	GIS processing review, final exam guide
Assignment 7: Raster/vector overlays and true color
Week 13 4/7	Catch-up, review