Shaunna Donaher MPO 531 Final Project April 22, 2008

To design a lesson plan describing the AErosol RObotic NETwork, the instrumentation used and the science involved and create a class project to use actual data to connect to meteorology.

Lesson Introduction to AERONET Instrumentation Background science AERONET locations Class Project CRYSTAL-FACE Data Access Instructions Assignment/Answers

The AERONET (AErosol RObotic NETwork) program is a federation of ground-based remote sensing aerosol networks collaborated on by national agencies, institutes, universities, individual scientists, and partners. The program provides a long-term, continuous and readily accessible public domain database of aerosol optical depth, microphysical and radiative properties for aerosol research and characterization, validation of satellite retrievals, and synergism with other databases.

Suspended particles in the atmosphere Ranging in size from a few molecules to tens of micrometers Our main interest is aerosols with 0.1<r<20 μm 1.0 μm is an approximate separation between “fine” and “course” aerosols

The integral of the light extinction by aerosols within an atmospheric column Related to aerosol size distribution A larger optical depth is related to a thicker layer/cloud, i.e. more extinction (and vice versa) (AOD or τ)

AERONET collaboration provides globally distributed observations of spectral aerosol optical depth (AOD) inversions precipitable water The network imposes standardization of instruments, calibration, processing and distribution. Aerosol optical depth data are computed for three data quality levels: Level 1.0 (unscreened) Level 1.5 (cloud-screened) Level 2.0 (cloud screened and quality-assured)

Which of the 3 levels would you use to have the highest confidence in your data set? Sun photometer system

The Cimel Electronique 318A spectral radiometer is a solar-powered, weather-hardy, robotically-pointed sun and sky spectral sun photometer. A sensor head points the sensor head at the sun according to a preprogrammed routine. The Cimel controller, batteries, and Vitel satellite transmission equipment are usually deployed in a weatherproof plastic case. Cimel Spectral Radiometer

The radiometer makes two basic measurements-either direct sun or sky-both within several programmed sequences. The direct sun measurements are made in eight spectral bands requiring approximately 10 seconds at wavelengths of 340, 380, 440, 500, 670, 870, 940 and 1020 nm the 940 nm channel is used for column water abundance determination A preprogrammed sequence of measurements is taken by these instruments from 7 am-7pm daily.

Solar radiation

Absorption is the process by which incident radiant energy is retained by a substance (i.e. the atmosphere) In the solar spectrum some wavelengths have high transmittance and others have high absorption. These wavelengths are taken advantage of when designing instrumentation. Instruments transmit at a certain wavelength knowing how much will be absorbed by a normal atmosphere.

Optical depth is calculated from spectral extinction of direct beam radiation at each wavelength based on the Beer-Lambert-Bouger Law. Attenuation due to Rayleigh scatter, and absorption by ozone, and gaseous pollutants is estimated and removed to isolate the aerosol optical depth (AOD). A sequence of three such measurements are taken 30 seconds apart creating a triplet observation per wavelength.

Relates the absorption of light to the material the light is traveling through. The law states that there is a logarithmic dependence between the transmission (or transmissivity) of light through a substance and the product of the absorption coefficient of the substance, and the distance the light travels through the material (i.e. the path length). *Also known as Beer-Lambert-Bouger Law, Beer-Lambert Law, Beer-Bouger Law

transmissivity (T) absorption coefficient (α) path length (ι) molar absorptivity of the absorber (ε) concentration of absorbing species in the material (c) absorption cross section (σ) density of absorbers (N) For gases T= I 1 /I 0 = 10 -αι = 10 -ειc Absorbance A=-log 10 (I 1 /I 0 )

The scattering of light by particles much smaller than the wavelength hitting them. Small size parameter approximation: x=2π/λ Rayleigh scattering occurs when x << 1

Although the concentration of the ozone in the ozone layer is very small, it is vitally important to life because it absorbs biologically harmful ultraviolet (UV) radiation emitted from the Sun. UV radiation is divided into three categories, based on its wavelength: UV-A, UV-B, and UV-C UV-C, which would be very harmful to humans, is entirely screened out by ozone at around 35 km altitude. The ozone layer is very effective at screening out UV-B; for radiation with a wavelength of 290 nm, the intensity at Earth's surface is 350 billion times weaker than at the top of the atmosphere.

In addition to the direct solar irradiance measurements that are made with a field of view of 1.2 degrees, these instruments measure the sky radiance in four spectral bands (440, 670, 870 and 1020 nm) along the solar principal plane (i.e., at constant azimuth angle, with varied scattering angles) up to nine times a day and along the solar almucantar (i.e., at constant elevation angle, with varied azimuth angles) up to six times a day. The approach is to acquire aureole and sky radiances observations through a large range of scattering angles from the sun through a constant aerosol profile to retrieve size distribution, phase function and aerosol optical depth.

During the large air mass periods direct sun measurements are made at 0.25 air mass intervals, while at smaller air masses the interval between measurements is typically 15 minutes. The time variation of clouds is usually greater than that of aerosols causing an observable variation in the triplets that can be used to screen clouds in many cases. Additionally the 15-minute interval allows a longer temporal frequency check for cloud contamination.

Sun Photometers absorb direct sunlight energy with an LED light and convert the intensity into a quantified voltage to measure aerosols in the atmosphere. The intensity of sunlight at the top of the earth's atmosphere is constant. While the sunlight travels through the atmosphere, though, aerosols can dissipate the energy by scattering (Rayleigh) and absorbing the light. More aerosols in the atmosphere cause more scattering and less energy transmitted to the surface. Knowing the sunlight's energy at the top of the atmosphere, the thickness of the atmosphere, and the amount of sunlight transmitted to the earth's surface and can allows us to determine the amount of scattering, and thus, the amount of aerosols.

sim/sunphotometer.html Click on LAUNCH You can vary sun angle, atmospheric pressure, haziness, and particle size to see impact on green channel and red channel sun voltage, and aerosol OD.

Atmospheric Conditions - Level of haze due to aerosols. Sun Angle (Degrees) - (90 degrees is straight overhead) Decreased sun angle early and late in the day increases the amount of atmosphere that the light from the sun passes through before reaching sun photometer. Air pressure - Changes in air pressure (due to altitude or weather systems) change the density of the air. A decrease in air pressure (decrease in air density) causes less light to be scattered by air molecules and increases the amount of light reaching the sun photometer. Particle size - The relative scattering of red vs. green light by aerosol particles is dependent on the particle size.

The changes in output voltage caused by aerosols can sometimes be very small. So, it is important to calibrate sun photometers carefully. Basically, this involves inferring what the sun photometer would see if there were no atmosphere between its detector and the sun. From the ground, this calibration is done by looking at the sun through varying amounts of atmosphere and using a mathematical model to predict what would happen to the output voltage if the atmosphere suddenly disappeared. This is time consuming and expensive!!

Satellite Data are transmitted hourly or half hourly from the memory of the sun photometer microprocessor via the Data Collection Systems (DCS) to either of three geosynchronous satellites GOES, METEOSAT or GMS and then retransmitted to the appropriate ground receiving station. Internet Data may be downloaded automatically from the Cimel sun photometer and stored on the local computer. This computer can run software to automatically transfer K7 files to the AERONET processing system through the Internet. Alternatively, users may download K7 files manually from the instruments using a PC or laptop and manually submit these files to the processing system.

Once collected, AERONET data is post-processed with 4 algorithms Aerosol Optical Depth (AOD) retrieval AOD cloud screening Seaprism processing Sky Radiance data (Almucantars and Principal Planes) inversion Then it is stored online and access is available to anyone

Aerosol Optical Depth (AOD) Sun photometer measurements of the direct solar radiation provide information to calculate the columnar aerosol optical depth (AOD). AOD can be used to compute columnar water vapor (Precipitable Water) and estimate the aerosol size. Aerosol Inversions Inverting sun photometer radiance measurements produces aerosol optical properties such as size distribution, single scattering albedo, phase functions, and the complex index of refraction. Solar Flux High frequency solar flux calculations. A companion to AERONET. Ocean Color A new network component called AERONET – Ocean Color (AERONET- OC). Provides the additional capability of measuring the radiance emerging from the sea (i.e., water-leaving radiance) with modified sun- photometers installed on offshore platforms like lighthouses, oceanographic and oil towers. AERONET-OC is instrumental in satellite ocean color validation activities through standardized measurements a) performed at different sites with a single measuring system and protocol, b) calibrated with an identical reference source and method, and c) processed with the same code.

Year(s): Project : Smoke/Sulfates, Clouds And Radiation (SCAR) 1996: Tropospheric Aerosol Radiative Observational eXperiment (TAROX) 1997: The Zambian International Biomass Burning Emissions Experiment (ZIBBEE) 1997: North Atlantic Regional Aerosol Characterization Experiment (ACE-2) : Indian Ocean Experiment (INDOEX) 2000: SAFARI : Argentine Commission on Space Activities (CONAE)-SAC-C 2001 : Asian-Pacific Regional Aerosol Characterization Experiment (ACE-Asia) July-August 2001: Chesapeake Lighthouse & Aircraft Measurement for Satellites (CLAMS) 2003 – Present: Base Asia - Thailand 2004-Present : African Monsoon Multidisciplinary Analysis (AMMA) 2004: United Arab Emirates Unified Aerosol Experiment (UAE 2 ) 2005-Present : Atmospheric Brown Cloud (ABC) 2006: Megacity Aerosol Experiment in Mexico City (MAX-MEX) 2007: CALIPSO and Twilight Zone (CATZ) - Ground-based Validation :TIGERZ (India)

Using AOD data from CRYSTAL-FACE What is CF? Data Download Process Data Analysis Tips Assignment Possible answers to assignment

Field experiment designed to investigate tropical cirrus cloud physical properties and formation processes. The primary target region is southern Florida and the surrounding waters where deep convection is known to occur frequently in July. Instrumented sites that include multi-frequency millimeter radar, lidar, and radiometry will be located at two locations. A site situated on the southwest coast will allow sampling of the cirrus outflow from diurnally forced convection. Also, a site will be located in southeastern Florida where cirrus from maritime disturbances and local convection will be observed.

We will be using data from July 2002 from the CRYSTAL-FACE East Site In left column, click on Download Tool under Aerosol Optical Depth Click on map a couple of times to zoom in on South Florida Then click on CRYSTAL_FACE (25N,80W) under the map

Version 2 Direct Sun Algorithm Start Date 1 Jan 2002 End Date 31 Dec 2002 Choose Level 2.0 for AOD w/ Precipitable Water Choose Single File for Instrument Information Choose Level 2.0 for Total Optical Depth Choose Level 1.5 for AOD Modes Choose All Points for Data Format You should be on a page that looks like this

Click Download Accept Conditions Save.zip file as _021231_CRYSTAL_FACE

Use a file extractor to unzip files In Windows, simply right click on zipped folder, choose Extract All, and go through the Extraction Wizard. This will create a new folder with the 4 unzipped files. (If you still need help, check out the Data File Help Page at Once files have been unzipped, open a spreadsheet program (i.e. MS Excel). Go to File and Open. Choose the LEV20 file (you may have to change type to all files to see them). Choose Delimited as your data type, then Comma (or tab if columns don’t show up when you scroll down) as your delimiter. Make sure the first column is in Date Format (DMY)- this will work with Tot20 file, not other 2)

Repeat with each data file (you should have 3 in total: Lev20, Oneil_15, and Tot20)- don’t worry about Solar_Info Save each file as an Excel Workbook For help with column header meaning and units, visit

Part 1: Familiarizing yourself with the data Using the Tot20 file, you will see that there are several variables listed for each wavelength. What are they? Total Aerosol Optical Depth Aerosol Optical Depth Aerosol Optical Depth due to Rayleigh scattering Aerosol Optical Depth due to O3 Aerosol Optical Depth due to NO2 Aerosol Optical Depth due to CO2 Aerosol Optical Depth due to CH4 Aerosol Optical Depth due to Water Vapor Now choose all of the variables for the 1020 nm wavelength except for the total aerosol optical depth. Create a new column in your worksheet summing them up. What variable does your new column equal? What does this tell you about optical depth? (that total optical depth is the sum of each individual optical depth) Also you will see that some wavelengths are not applicable for this site. What are they? 1640, 667, 555, 551, 532, 531, 490, 443, 412 (all in nm)

Now open the Lev20 file. You may have noticed when saving it that the date function did not convert properly (things don’t always go smoothly with data processing and analysis!). Also, it is challenging to combine dd-mm-yy with hh-mm-ss format for graphing purposes (especially if your data set covers more than 100 years and is not in yyyy format). For that reason, we often use Julian Day (technically day of year since Jan. 1 st at 12:00 am). This is really useful when dealing with data taken within one year (or one month as in our case). Convert JD to dd-mm-hh-mm-ss. Assume that it is not a leap year. Show work Convert (Apr 23 at 7:28:30 AM) to Julian Day. Assume that it is not a leap year. Show work

(Jan)-28 (Feb)-31 (Mar)-30 (Apr)-31 (May)-30 (June)=14 (July 14 th ) *86400 seconds= seconds /60= minutes /60= hours (12 hours) *60= (30 minutes).74976*60= (45 seconds)

31 (Jan)+28 (Feb)+31 (Mar)+23 (Apr)=113 Convert 7 hrs into seconds: 7*60*60= *60= = /86400 seconds in a day=

Part 2: Data Analysis Using the Tot20 file, plot the total AOD for each wavelength by JD all on the same plot. What do you notice? (the smaller wavelengths see higher optical depths). Why do you think this is? (Aerosol optical depths typically decrease with increasing wavelength)

Continuing with the Tot20 file, plot the total AOD for 1020 nm vs. JD. Do you notice any trend over the month? (Not really, maybe a slight decrease) Now plot the AOD due to Rayleigh scattering for the same wavelength vs. JD. Is there a trend in this plot? If so, what is it and why do you think it is occurring? Hint: You may want to plot the time trends of other variables. (Yes, it is increasing with time. You could suggest that this is due to changes in relative humidity, but water vapor AOD plot does not confirm this. It actually winds up mimicking the trend in pressure. The AMS glossary states that Rayleigh optical depth is directly proportional to surface pressure).

Now let’s look at the Lev20 file. Plot JD vs. water AOD. Find the lowest water AOD. What is it and what day does it occur? cm at = 7/27/2002. Now find the highest water AOD? What is it and what day does it occur? cm at =7/16/2002

We are going to look at back trajectories of air masses to try to determine what caused the difference in water AOD. Go back to the Aeronet Web Page Click on Data Synergy Tool at the top Click Access Data Synergy Tool On map at left select Back Trajectory Zoom in on S. FL. Then click on Dry_Tortugas below map On the new page, under Surface Data check Aeronet and Under Model Output click Back Trajectory Put in the two 2 dates at the calendar in the lower left

Using the Lev20 file still, plot JD and Solar Zenith Angle. What does this suggest to you about the hours the sun photometer was running? (mostly started around noon (90 degrees) and ran until later in the day. This is confirmed by looking closely at JD.)

Now we are going to use the ONeill_15 file for λ=500 nm. Make plots of JD vs. total, fine and coarse aerosol OD JD vs. standard deviation of total aerosol OD Std of fine aerosols vs. std of coarse aerosol OD Looking at the plot of total, fine and coarse aerosol optical depths, what kind of statement(s) can you make about the dominating aerosols? (There isn’t a big difference between numbers of the two types most of the time. But there are six cases where the coarse aerosol OD dominates the total AOD. These could be from strong sea breezes bringing salt aerosols inland. Also in the middle of the period, there is a slight increase fine aerosol OD.) Do you think this plot represents an influence of African dust at any point? Why or why not? (Possibly. The fine aerosols in the middle of July could be from Saharan dust. Any answer is OK here as long as it is backed up.)

Now look at the plots of standard deviation. Does this support your conclusion about African dust above? (Could suggest that the increase in variation in the middle of the month may be due to an influence of African dust bringing in more fine particles than normal.) The plot comparing the two standard deviations has an interesting structure. What does this suggest to you about the air masses that were affecting S. FL. during July 2002? (The linear plot suggests that the air masses typically brought in the same relationship of fine and coarse particles to S. FL. all month long. As the variance of AOD for fine particles increased, so did the variance of AOD for coarse particles and vice versa. Even the outliers follow the linear pattern).

This lecture and tutorial are designed to expose undergraduate students to remote sensing of aerosol properties. During the lecture, they will learn about sun photometers and see a brief explanation of the physics behind light absorption in the atmosphere. The assignment should familiarize students with field experiments and data management techniques. Students will have to read literature to determine which variables they are being asked to use. They will have to connect the physical data to meteorological processes to come up with a real-world understanding of what the data represents. Unfortunately, the goal of including back trajectory analysis does not work for this site. If the assignment is modified for another site, than slide 50 could be potentially be used and that question included in the assignment.

Obviously, some of this material is advanced and is best presented towards the end of the semester. Certain slides (i.e. slides 19 and 20) pertain more towards Physical Meteorology topics and you may choose to remove them for an instrumentation class where the focus is on data collection and manipulation. The instructions for data access are very explicit, but beyond that students should be able to figure out which variables they need to use and which column those variables are located in. They will also need to figure out how to plot in Excel and hand in professional, labeled figures. Some of the assignment questions have exact answers, while for others there will be a wide range of acceptable answers. For the latter credit should be given as long as students show intelligent thought processes backed with scientific reasoning.

Vis/spectrum.htm Vis/spectrum.htm m m Levy, R. C., and R. T. Pinker, 2007: Remote sensing of spectral aerosol properties: A classroom experience. Bull. Amer. Met. Soc.,