Giovanni as a Tool for Teaching Remote Sensing Applications 2012 Gregory G. Leptoukh Giovanni Workshop Rich Kleidman Science Systems and Applications, Inc ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences

NASA and Earth Science Applied Sciences Program Applications to Decision Making: Eight Thematic Areas Agricultural Efficiency Air Quality Climate Disaster Management Ecological Forecasting Public Health Water Resources First I need to tell you a little bit about ARSET. We are a program funded by NASA Applied Sciences to provide professional training and education corresponding to the eight application areas.   The program has been in existence for four years and was originally tasked with providing training in air quality applications. This past year we have also added water resources. Eventually we hope to expand ARSET to cover all eight application areas shown in this slide. In this presentation I will be dealing exclusively with the air quality part of the program Weather (Aviation)

Who are we training ? Air Quality Managers and Regulators EPA, state and local regulatory agencies, US Forest Service Scientists/Technical: Meteorologists, air quality forecasters and modelers, health scientists, AQ researchers Other/public: project managers, reps. from health agencies, World Bank Our primary task is to provide training to professionals working as applied end users in air quality. Space permitting we also allow participation from academic institutions both staff and students.

Expertise ANY Audience can span a large range in expertise: - No background in remote sensing and little science background - No background in remote sensing and some science background - Introductory expertise with satellite data - Moderate expertise with satellite data As you can see we cater to those with little to moderate experience using remote sensing products. We offer both webinar and in-person courses. The ease of use of makes it an ideal platform for our training activities and target audience.

NASA Satellite Products for Air Quality Applications Particulate Pollution (dust, haze, smoke) - Qualitative: Visual imagery - Quantitative*: Column Products and vertical extinction profiles Fire Products: Fire locations or ‘hot spots’ Trace Gases - Quantitative*: Column Products - Vertical profiles: mostly mid-troposphere - Some layer products These are the kinds of products that we focus on in our trainings.

Giovanni Instances Used in ARSET-AQ Training Terra and Aqua MODIS Daily Aura OMI Level 2G Aqua/AIRS Global Daily Air Quality A-Train Along CloudSat Track MISR Daily To teach about the types of products shown in the last slide we frequently make use of the Giovanni instances shown in this slide. They are listed according to the frequency of use.

Our principal uses for Giovanni - conceptual organization First exposure to hands on use of satellite data. Illustrating proper use of data Quick access to data for exploratory analysis Comparison of coincident data sets

Introductory Activity Used to: Provide a first exposure to satellite data products. Begin the process of educating our users about how to evaluate the quality of satellite remote sensing products and proper and improper uses of data and tools. Illustrate the strengths and limitations of Giovanni. The document which provides instructions on how to produce the plots used in the activity as well as the presentation used to guide the follow up discussion can be found on our website. http://Airquality.gsfc.nasa.gov/Tool The activity is called “Giovanni Beginning Exploration” The slides that follow are taken from a presentation and activity that we use on the first day of almost all of our in person courses. I will only be illustrating one of our activities that is centered around Giovanni. We have several others which we use on a regular basis.

Evaluating Remote Sensing Data Or How to Avoid Making Great Discoveries by Misinterpreting Data Richard Kleidman ARSET-AQ Applied Remote Sensing Education and Training A project of NASA Applied Sciences

It may take a few seconds to really get the idea behind this comic strip. Interpreting data can be a tricky business.

AOD at 550 nm Area Average Plot Giovanni MODIS Daily Instance June 15 - 30, 2008 We first provide instructions on how to generate these plots for a specific set of dates and locations. These are carefully pre-selected to show a large difference between Terra and Aqua. We want this to be a good jumping off point for a thoughtful evaluation of satellite data and what the Giovanni Tool does and does not do. Aqua Daily Overpass ~ 1:30 PM local time Terra Daily Overpass ~ 10:30 AM local time

Real or Not Real ? You Decide! A Potential Discovery! Terra Aqua We ask users to first describe the differences produced by the two MODIS sensors and then to spend some time speculating on the potential causes for these differences. Aqua Daily Overpass ~ 1:30 PM local time Terra Daily Overpass ~ 10:30 AM local time

AOD at 550 nm Area Average Plot Giovanni MODIS Daily Instance June 15 - 30, 2008 Hypothesis 1: The differences in aerosol concentration represent a Diurnal Cycle with different amounts of aerosol being produced or transported afternoon and morning. Hypothesis 2: The differences in aerosol concentration are either not real or not significant and could be caused by Sensor error Algorithm error Sampling error ? Since our users are not very experienced at this point we then suggest some possibilities and go through a discussion and evaluation of each one.

Possible ways to interpret these differences Real world differences Leads to a conclusion(s) about aerosols Can lead to further research about aerosols Differences due to other factors Can lead to false conclusion about aerosols Need to be explored and understood to avoid similar problems in the future - Can lead to advances in remote sensing capabilities! These are two very broad categories. Note from the last point that even discoveries that don’t reflect real world phenomena can be useful and should not be ignored.

Important Factors to Understand Analysis Tool Giovanni – provides data in 1 degree resolution Data Products– Aqua and Terra Level 2 Products are from a single overpass 10 KM resolution Aqua and Terra Level 3 Products are global composites in 1 Degree resolution Here we introduce the idea that the Giovanni tool itself might be a factor in the results.

Important Factors to Understand Mid – latitude 1° x 1° is about 85 Km x 110 Km ~ 90 10 Km MODIS retrievals possible ~ 45 10 Km MODIS retrievals possible Swath Nadir Edge Now we can use the results of the Giovanni activity as a springboard for learning some things about the product itself.

Important Factors to Understand One MODIS 10 Km Retrieval Begins life as 400 .5 km (at nadir) pixels And ends as a product composed of 12 – 120 pixels

Information Necessary to Understand the Results Data Products– Aqua and Terra Level 2 Products are in 10 KM resolution Aqua and Terra Level 3 Products are in 1 Degree resolution Giovanni provides Level 3 Data in 1 Degree resolution. Sensor Characteristics – Aqua and Terra are identical designs There are some small differences in sensor performance We begin to review and summarize the factors that could contribute to the differences we saw in the results from the two sensors. We intend that our users will generalize these ideas to all data sets they will encounter. Algorithm Details– Aqua and Terra use the same algorithm.

AOD at 550 nm Area Average Plot June 15 - 22, 2008 Examining other features in this data set and using our knowledge of the sensor and products can help us to understand the cause of the differences in mean aerosol. A blank (white) square has no retrievals for the entire time period. The ease of use of Giovanni allows us to have the users go back and plot the results for the same area but a shorter time period. Now we begin to see gaps in the data for Terra and not Aqua. We use this to lead a discussion about the effects of data sampling. In fact, the reason for the differences in the results between the two sensors is sampling. There are some high aerosol events that Aqua was able to observe but that Terra was not. Aqua Terra

Evaluating Data Understand the sensor characteristics Understand the product details Understand the data visualization tools and outputs. These are the final points we emphasize. Good advice for experienced scientists as well as novices.

Giovanni helps us turn indiscriminate consumers of satellite data products into connoisseurs