OPTION 2:

Study a retrieval algorithm of some sort and present it, along with examples of data from its use.

Trace Gas Examples:
1. Retrieval of tropospheric ozone by exploiting UV and VIS spectra obtained by satellite using data from SCIAMACHY. (only about 10% of ozone is in the troposphere.)

2. Retrieval of tropospheric ozone by combing AURA OMI and microwave limb sounder (MLS) measurements. (only about 10% of ozone is in the troposphere.)

3. TES retrieval of carbon dioxide for carbon cycle study.

4. TES retrieval of carbon monoxide for tropospheric air pollution applications.

5. Ozone increase in remote areas of China due to industrialization. You could understand the results of the paper, and update the analysis for years after 2007.

6. AURA SATELLITE INSTRUMENT PAPERS:

a. Overview of OMI, TES, and MLS on AURA.

b. OMI ozone using differential optical absorption spectroscopy (DOAS).
c. OMI calibration method.
d. OMI NO2 retrieval using DOAS.
e. OMI SO2 retrieval.
f. OMI cloud top pressure using rotational raman spectra and the Ring effect.
g. OMI conversion of data from level 0 to level 1.
h. OMI science objectives.
i. OMI data used to get surface UV radiation estimate.

j. TES Instrument Description: FTIR spectrometer on the satellite.
k. TES level 1 data and calibration mechanism.
l. TES retrieval of carbon dioxide.
l. TES retrieval of carbon monoxide.

m. Microwave limb sounder (MLS) clear sky model.
n. MLS ice cloud model.
o. MLS oxygen Zeeman splitting model.

Aerosol Examples:
1. Solar aureole measurements and inversion to obtain aerosol size distribution and optical depth. Second paper (Ben). Here is some data to start with from 2 September 2012. It is important to work the 'science' part of the problem in detail first and then to translate the results to an App for the iPhone and/or Android.

2. Glacial dust from Alaska. Paper from 2006; recent observation. (Emily and dust group).

Cloud Examples:
1. Cloud satellite cloud profiling radar from space, 94 GHz nadir looking backscatter.

2. Retrieval algorithm description used to produce MODIS cloud products like effective diameter, cloud phase, optical depth.

3. MODIS cloud mask product description in brief. Non technical description. Technical paper describing the cloud mask product.

4. Cloud microphysics retrieved from MODIS data: Description.

Meteorological Examples:

Surface Measurement Examples:
1. Sea ice extent in the arctic, seasonal changes.

2. Remote sensing of glacier snow grain size and albedo. Here is a paper that leads into the type of analysis that can be done with satellite remote sensing of snow microphysics. This paper gives a great description of in situ measurements used to verify results.

3. Retrieval of meteorological parameters from MODIS data: Description.

STUDENT PRESENTATIONS

OPTION 1:

Equation 1 in the MODIS retrieval description paper is used to calculate the surface reflectance at 470 nm and 660 nm. Processed MODIS radiance measurements provide the top of the atmosphere reflectance at these wavelengths. The requisite reflectances at wavelengths (470 nm, 660 nm, and 2130 nm) for this problem are available as MOD02 and MOD03 (MYD02 and MYD03) for the MODIS instrument on Terra (Aqua) satellites. The first two waelengths are available at 250 m resolution while the last one is at 500 m resolution. We will need to work with clear sky data, so will pick days/times and locations that we know are cloud free by careful manual inspection. (Here are some screen shots that show you how to get the data from LAADSWEB for this problem. Shot 1, shot 2, shot 3.)

Here is the MOD09 user manual. Here is a brief example (images made with HDFlook).

Pick a place in the world from the following list, and a time in summer months:
1. Rainforest of Brazil, equatorial region.
2. Western Europe.
3. Siberia.
4. Western US Great Basin.
5. Lake Tahoe and surrounding area (even though this problem is primarily associated with the land retrieval algorithm).
6. Barrow Alaska.
7. Other (discuss/explain your preference.)
Obtain reflectance granules and calculate the difference between the TOA and surface reflectances at 470 nm and 660 nm (using LAADSWEB). Interpret these results.
These two references will help you understand how to interpret the results.
Operational remote sensing of tropospheric aerosol over land from MODIS.
Correlation of 2300 nm and visible wavelength MODIS surface reflectances.

Prepare your findings in a powerpoint presentation to be shared with the rest of the class.

OPTION 2:

Prepare a MODIS aerosol retrieval case study (using for example the methods we covered in class. Here is a list of tools that may be helpful for case study development.). Find a problem that interests you: perhaps the aerosol coming off the Asian continent; aerosol emitted from the mining of oil sands near Fort Sherman Canada; aerosol coming off of the Sahara desert; smoke from any number of fires around the world. Prepare your case study as a powerpoint presentation to be shared with the rest of the class.

For example, this homework assignment could offer significant exotic foreign travel. Here is what you might do.
a. Go to the AERONET map of the world and find an AERONET location near where you would like to visit. Note the latitude and longitude of your area.

b. Go to Google Earth and put in your coordinates from step a. Set it up so you can get a nice image of the location. Screen dump this image into your presentation as a starting point.

c. Gather all of the AERONET data you can for this site; level 1.5 or level 2. Look at its behavior over time. Reflect on the seasonal and yearly dependence you observe with relation to geographical, political, life style, and economic activities and relationships.

d. Gather weather data to help understand the nature of your location.

e. Use GIOVANNI to look at aerosol optical depth and Angstrom exponents for appropriately time averaged periods that are suggested by the nature of the Aeronet data.

f. Use GIOVANNI to gather a time series for the MODIS retrieved AOD for a region as close to your point of interest as possible. Compare your GIOVANNI MODIS AOD with the AERONET AOD as time series and time aligned scatter plot.

For example, it would be an excellent idea to look at the Railroad Valley AERONET site in Nevada, and to compare it with MODIS retrievals (see HW1.)

1. Define and discuss precipitable water (pw). Include in your discussion the equation used to calculate pw, the role of pw in atmospheric dynamics and precipitation, and the impact of pw on infrared radiation balance in the atmosphere (hint: review this homework problem). (Here are the notes from our class discussion on Thursday the 30th of August.)

2. Grab the pw from the ground-based remote sensor (UNR Aeronet station) for the months of June, July, and August 2012, being sure to use the level 1.5 data (cloud screened). Grab the pw from the National Weather Service balloon soundings taken twice a day from the station near DRI (probably best to use the University of Wyoming site for this.) Make an overlay time series of pw from both sources. Comment on the qualitative assessment of the agreement of the pw obtained from direct measurement using a hygrometer on a balloon versus that obtained by ground based remote sensing using sun photometry, being careful to consider the elevation difference between the UNR sensor and the NWS sensor (you should calculate/estimate how much pw is in the atmosphere in the 143 meters elevation difference between these sensors). Make a scatter plot of the average pw from the UNR Aeronet site for time from 0z to 1z, every day, against the 0z NWS sounding data and comment on these measures of pw when compared this way. Note: you can compare pw also with that obtained from the GPS Occultation Network (here is a striking example of that network in action, Hurricane Katrina.) You can also compare Aeronet and NWS PW with mesoscale model output.