Data and Tools

NASA Worldview is the best starting point for users new to satellite data and is freely available online. Worldview provides the capability to interactively browse global, full-resolution satellite imagery and then download the underlying data. Most of the 400+ available products are updated within three hours of observation, essentially showing the entire Earth as it looks “right now.” This supports time-critical application areas such as wildfire management, air quality measurements, and flood monitoring.

View current natural hazards and events using the Events tab which reveals a list of natural events, including wildfires, tropical storms, and volcanic eruptions. Animate the imagery over time. Arctic and Antarctic views of several products are also available for a “full globe” perspective. Worldview and Giovanni together will answer the basic needs for most HAQAST applications.

You can view a NASA webinar here, and a short video tutorial here, created by the HAQAST Communications Team.

And here’s a written tutorial (you can find a downloadable pdf here).

NASA’s Fire Information for Resource Management System (FIRMS) distributes Near Real-Time (NRT) active fire data within 3 hours of satellite overpass from both the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). FIRMS includes a web-based Fire Mapper and a wide range of downloadable data, from fire maps to shapefiles. FIRMS was developed to provide near real-time active fire locations to natural resource managers that faced challenges obtaining timely satellite-derived fire information. A detailed set of FAQs can be found here.

The Infusing satellite Data into Environmental Applications (IDEA) project is a collaboration between NASA, EPA, and NOAA focused on improving AQ assessment, management, and prediction. It should be of great interest to the HAQAST community. IDEA provides detailed maps of various pollutants (PM_2.5, AOD, etc.) over the continental US from different satellites. Detailed tutorials for using IDEA data can be found here.

Along with Worldview, members of the health and air quality community will find Giovanni extremely helpful. Giovanni is a web-based interface that allows users to interactively analyze gridded data online without having to download anything. It is a flexible platform that allows a user to average data over time, create a range of plot types and formats, compare variables, and graphically display information. You can also download plot source files in netCDF format.

Here’s a tutorial (you can find a downloadable pdf here), and a short video tutorial here, created by the HAQAST Communications Team.

GMAO uses coupled Earth-System models and analyses, along with a broad range of satellite observations, to study and predict phenomena that evolve on seasonal to decadal timescales. The current computing capacity enables GMAO to simulate the entire globe at spatial resolutions previously only possible with regional models. These “global mesoscale model” simulations serve for forefront evaluations of model performance and form the basis for Observing System Simulation Experiments. You can check out NASA GMAO here.

The Air Quality Data Analysis Tool allows users to view and compare data layers, use analysis tools to get statistics (min, max, standard deviation, trend, correlation) on data values, download data, and animate layers over time. Available data layers include TROPOMI, MODIS, and VIIR/SNPP Deep Blue.

Immersive Science via Virtual Reality is a project in development to leverage VR technology to step into simulations and experience scientific concepts in unprecedented ways and to provide real-time ML-based air quality prediction. View the project preview here.

The Dashboard’s ozone metric shows the average daily maximum 8-hour concentration of ozone in the air (in ppb) over the course of a month. The City Health Dashboard uses data from EPA Air Quality System and NOAAA National Air Quality Forecast Capability from the period of January 2018 to December 2022. To access City Health Dashboard data, click here.

This interactive map provides access to a broad suite of information related to wildland fires in Alaska. Access the interactive Alaska Wildland Fire dashboard here.

These data represent the GEOS-FP product of surface PM2.5 forecast generated by NASA/GMAO (Global Modeling and Assimilation Office) using the most recent validated GEOS system with input of Terra & Aqua Satellite AOD. To access the dashboard’s complimentary dataset, click here.

Python tutorials designed for undergraduate and graduate students with an interest in air quality and the atmospheric sciences (no or minimal prior coding experience). Python is a programming language that is easy to learn and is capable of complex, rigorous computations. It is becoming the popular coding language for scientific researchers in many disciplines. Learn more and access the tutorials here.

Earth Observatory specializes in extremely high quality photographs, graphs, charts, and other visual material focused on planet earth. Well known for the Image of the Day, Earth Observatory also provides animated and static global maps, as well as high-quality datasets.

A comprehensive website dedicated to the current capabilities of observing air pollution from space and using the data for health, air quality, and food security applications. You’ll be able to download maps and images, browse through free data and visualization resources, download various publications, and sample a wide variety of NASA’s air-quality media. This is a great general resource intended for health and air quality managers as well as others who are looking for less-technical NASA resources.

The tri-agency Dashboard is a concerted effort between the European Space Agency (ESA), Japan Aerospace Exploration Agency (JAXA), and National Aeronautics and Space Administration (NASA). The dashboard combines the resources, technical knowledge and expertise of the three partner agencies to strengthen our global understanding of the environmental and economic effects of the COVID-19 pandemic.

The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. MERRA-2 is a NASA atmospheric reanalysis for the satellite era using the Goddard Earth Observing System Model, Version 5 (GEOS-5) with its Atmospheric Data Assimilation System (ADAS), version 5.12.4. The MERRA project focuses on historical climate analyses for a broad range of weather and climate time scales and places the NASA EOS suite of observations in a climate context. MERRA-2 is the first long-term global reanalysis to assimilate space-based observations of aerosols and represent their interactions with other physical processes in the climate system.

MERRA-2 data are available here; the HAQAST community may be particularly interested in the Atmospheric Chemistry data sets. A comprehensive list of datasets can be found here. NASA is in the midst of continuing to develop MERRA-2 and related tools, and more will become available in the near future. You can stay abreast of new developments by signing up for the newsletter.

On the Earthdata Forum, data users can ask questions and have discussions with subject matter experts from NASA Distributed Active Archive Centers (DAACs). Users can sort questions by keyword discipline, DAAC, major project or services/usage. They read announcements about new releases and updates as well as engage with other users through leaving comments and asking questions. Access the Earthdata Forum here.

If you’re looking for TROPOMI data, there is a nice brief overview of the various products here. Links to download, validate, and beginning plotting the data can be found here.

Tropomino2.us displays NO2 tropospheric vertical column amounts observed by TROPOMI over the continental USA, southern Canada and northern Mexico. It includes daily and seasonal TROPOMI NO2 data, as well as trends over time.

The surface NO2 dataset provides an estimate of ground-level NO2 for 2019 and annual means for 2005-2019. Satellite data from TROPOMI is paired with information from the GEOS-Chem chemical transport model, ground-based monitoring and OMI satellite data. Estimates are provided at ~1 km resolution. For more information on the surface NO2 dataset, see here. To access the datasets themselves, see here (2005-2019) and here (2019).

TROPOMI also now includes two additional datasets for Nitrogen dioxide monitoring: CONUS Monthly NO2 and Global Monthly NO2. While both resources offer monthly and annual temporal resolution, CONUS Monthly NO2 includes seasonal data. Stay tuned for more updates on Global Monthly NO2, as development is still in progress.

The Ozone Monitoring Instrument (OMI) can distinguish between aerosol types, such as smoke, dust and sulfates, and measures cloud pressure and coverage, which provides data to derive tropospheric ozone. OMI measurements are highly synergistic with the other instruments on the Aura platform.

The Urban Air Quality (AQ) Explorer is a website that offers users city-averaged annual exposure and health burden estimates of NO2, O3, and PM2.5 between 2000 and 2019 using OMI and MODIS satellite data. City averaged CO2 emissions are also included. To access the Urban AQ Explorer, click here.

The Nitrogen Dioxide Surface-Level Annual Average Concentrations Product contains estimated global NO2 surface values derived using a Land Use Regression (LUR) model, which is based on 5220 NO2 monitors in 58 countries and land use variables. Annual Global surface level NO2 concentration levels (ppb) for the years 1990, 1995, 2000, and annualy from 2005 through 2020 are provided at high (~1km2) spatial resolution. The dataset is a result of combining multiple products such as OMI, a land use regression model (Larkin et al. 2017), and other data-sets in order to adjust and correct for satellite pass time and cloud coverage. Access the dataset here.

NASA makes available a great deal of data free to download. There are various ways to search for the data products that you need, and various ways to download it. This site is a comprehensive resource that should get you oriented and downloading quickly.

AERONET is a large federation of ground-based remote-sensing networks that all focus on aerosols. The program provides a long-term, continuous, and readily accessible public-domain database of aerosol optical, microphysical, and radiative properties. The AERONET web site provides data analysis and dissemination tools. You can also download data. Members of the AQ community will be particularly interested in Aerosol Optical Depth Data Display, the AOD Download Tool, and Data Synergy Tool. Check back soon for a Data Synergy Tool how-to.

Global and regional PM2.5 concentrations are estimated using information from satellite-, simulation- and monitor-based sources. Aerosol optical depth from multiple satellites (MODIS, VIIRS, MISR, SeaWiFS, and VIIRS) and their respective retrievals (Dark Target, Deep Blue, MAIAC) is combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations to produce geophysical estimates that explain most of the variance in ground-based PM2.5 measurements. A subsequent statistical fusion incorporates additional information from PM2.5 measurements. To access the dataset that ran from 1998 to 2022, click here.

AeroStat is an online environment for the direct statistical intercomparison of global aerosol parameters in which data provenance and data quality can be readily accessed by scientists. Users can easily visualize and analyze statistical properties of atmospheric aerosol events, including data collected from multiple sensors and quality assurance (QA) properties of these data. AeroStat also provides a “Bias Adjustment” option to allow users to adjust the satellite data relative to an AERONET baseline. For more on AeroStat, click here. And for a detailed how-to, visit this site.

The Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC) seeks to provide public access to a range of data collections, including:

• • All levels of both NASA Terra and Aqua MODIS-derived science data products.
  • All levels of NASA standard versions of the NPP Suomi VIIRS-derived science products in the near future.
  • European Space Agency’s Envisat, Medium Resolution Imaging Spectrometer (MERIS), Sentinel-3, Sea and Land Surface Temperature Radiometer (SLSTR) and Ocean and Land Colour Instrument (OLCI).
  • MODIS Airborne Simulator (MAS) and Autonomous Modular Sensor (AMS) airborne collections.
  • Certain ancillary data collections.

The HAQAST community will be especially interested in the Atmosphere Products, the various data available for download, and the tools and services. The LAADS DAAC is an extensive site, and it’s worth spending some time exploring it.

ASDC tools and services including the ability to subset data products by date, time, and geographically so that you only have to download the data you need instead of the entire data set. Broader searches are also possible, the Explore Collections tool is available to search through all ASDC publicly available data and the Earthdata Search allows user to browse and access NASA earth science data. Subseting tools include the ASDC CALIPSO Subsetter, ASDC CERES Subsetter, ASDC MOPITT Subsetter, and ASDC TES Subsetter. Explore all tools and services here.

ASDC’s SOOT (Sub-Orbital Order Tool) is an easy way to access and download sub-orbital and airborne data. It is designed to promote suborbital research and analysis. Here you can discover and access the airborne and field campaign data archived at the Atmospheric Science Data Center (ASDC). The SOOT Power User Interface is intended for experienced airborne data users and airborne science teams. Data is sorted by campaign and deployment including ACTIVATE, AJAX, CAMP2EX, LISTOS, NAAMES, and ORACLES. You can find the full ASDC SOOT Power User Interface here.

This strategy allows you to specify the specific places and time ranges for which you’d like data. This is helpful if you if you do not want an enormous worldwide dataset.

1. Go to https://disc.gsfc.nasa.gov/.
2. Enter your search term (e.g.: NO₂).
3. Many different results will appear; make sure you find OMI L3 data to get interpreted & gridded data.
   1. Note: L3 signifies that the data has been gridded. Click here for more on data processing levels.
4. Click on the icon subset/get data. This will allow you to subset your data temporally and spatially

5. Click Refine Date Range to select the range of dates for which you’d like data.
6. Click Refine Spatial Region to focus on the area for which you’d like data.
   1. Note: Refining the spatial region automatically populates the spatial subset and vice versa.
7. Click the Variables dropdown and choose the variable you’d like to download.
8. Click File Format dropdown to choose the file you’d like.
9. Click Get Data to begin downloading your data.

10. Select Download links list. It will save as a text file.
11. In order to retrieve your data, click Instructions for downloading and follow the instructions that best suit your software.

Earthdata also allows you to subset data temporally—but not spatially—as well as to look at the data in an interactive manner.

1. Go to: https://earthdata.nasa.gov/.
2. Enter your search parameter (e.g.: NO₂, ozone) AND OMI L3 (e.g.: Search “OMI L3 ozone”)
   1. Note: L3 signifies that the data has been gridded. Click here for more on data processing levels.
3. There will be 3 boxes at the top of the screen; in the case of searching for OMI L3 ozone, select OMI/Aura…Daily L3…at GES DISC

4. You will be taken to a screen with the data collection you selected. This collection will include all the data available for your product—in this case, daily column totals going back to 10/1/2004.
5. To temporally subset your data, click Back to Collections.

6. Add this collection to your project by clicking the green + sign.

7. To select the specific temporal granules you want, click your data product.
8. Choose the time range for which you want data by:
   1. Clicking the time selection tool
   2. Entering Start and End times
   3. Clicking Apply Filter

9. Earthdata can visualize your selected data, during the time period you chose, for specific regions. To do so, find your location by dragging the map and zooming in or out.

10. Select the area for which you want data by:
    1. Clicking the area selection tool
    2. Choosing which polygon or point you’ll use for your selection
    3. Selecting the area on the map for which you want data.

11. To download your data, click download data.
12. To access your data, go to https://urs.earthdata.nasa.gov/documentation/for_users/welcome and follow the instructions that best suit your software.

Simple Subset Wizard allows you to easily subset your data by space and time. It has no interactive features.

1. Go to: https://disc.gsfc.nasa.gov/SSW/.
2. You can search by keyword (eg.: OMI) or data set (searching by data set assumes that you know exactly which set you want). Search for OMI NO2.
3. Enter a date range.
4. Click the map icon to select the area for which you want data.

5. Click Search for Data Sets.
6. Hover over each data product for a brief description, or click the product to read the details of the set.
7. Expand each subset to see individual data products.
8. Select the specific product you want.
9. Choose your preferred file format.

10. Click Subset Selected Data Sets.
11. Click View Subset Results.
12. Click Downloading Instructions and follow the steps to retrieve your data.

The Neighborhood Emission Mapping Operation (NEMO) is a high-resolution (1km) dataset of anthropogenic air pollutant emissions over the Contiguous United States (CONUS). NEMO includes air pollutants such as volatile organic compounds (VOCs), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ammonia (NH3), and particulate matters (PM, both PM2.5 and PM10). This downloadable resource was made possible through the work of the air quality research team at George Mason University, and the National Emission Inventories from the U.S. EPA.

The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and volcanic eruptions. This dataset also provides the forecast of surface PM_2.5 (PM25_TOT), PM_2.5 organic carbon (PM25_OC), and PM_2.5 black carbon (PM25_BC) every 3 hours. Both regional and global models from multiple agencies are used to create the ensemble, including the NASA’s Goddard Earth Observing System and the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory. Access HAQES here.

This dataset contains an updated version of spatially interpolated daily PM2.5 concentrations observed by monitors in the Environmental Protection Agency’s Air Quality System and non-smoke seasonal background PM2.5 estimates for 2006-2022. Click here to access the EPA’s Air Quality System Data Mart (an internet database). This database was developed by the Pierce Group, whose links to datasets can be found here.

The hourly near-surface air temperature dataset provides users with monthly averaged diurnal products at 70 meters over multiple metropolitan areas. Using ECOSTRESS satellite data from 2020-2023, this resource features both hourly and monthly temporal resolutions. Stay tuned for a link to access this dataset.

Similarly, the land surface temperature (LST) dataset offers a long-term mean composite with improved spatial representation. This tool utilizes MODIS satellite data from 2003-2022, and supports a monthly mean (both day and night) of LST in New Mexico at 1km spacial resolution. The LST dataset was recently delivered to stakeholders.

Both the near-surface air and LST datasets were supported in development by HAQAST PI Chris Uejio.

Hourly and daily mean PM2.5 mass concentration for 2000 to 2020 based on Machine Learning model. The models were trained for the Continental United States (CONUS), Alaska, Puerto Rico, and Hawaii. This dataset was developed through the use of the Goddard Earth Observing System Model (GEOS) data assimilation system and the Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol model. The resulting dataset incorporates MERRA-2 satellite data at a spatial resolution of 0.5 x 0.625 degrees. Check out the global daily PM2.5 dataset here.

An updated version of this dataset has newly been released: the MERRA-2 CNN_HAQAST Bias Corrected Global Hourly Surface Total PM2.5 Mass Concentration V1 product. This resource was created specifically for HAQAST, specifically the “Machine Learning Derived MERRA-2 PM2.5” project partially led by member Pawan Gupta. The data this tool provides was derived using a convolutional neural network (CNN) machine learning method and ran from January 2000 to June 2024. Access this revolutionary new dataset here and here.

This summary dataset provides raw and log-transformed light at night (LAN) at different geographic resolutions (raw, census tract, count), and for different time periods (yearly, 2012-2020). The resource contains the annual average of ALAN for each year onto US counties and tracts, and derived summary measures (e.g., mean, median, standard deviation, inter-quartile range) of ALAN for each county and census tract in the U.S. using data from all grids located within the borderline of a county or tract. Access the annual ALAN summary dataset here.

This is a Python interface for the Remote Sensing Information Gateway (RSIG) WebAPI developed by Barron Henderson (EPA). RSIG allows for easy access to subsets of environmental datasets, including satellite, modeled and sensor data. The pyrsig package aids in downloading data to work with analyses in Python. This page includes Jupyter notebook examples on downloading TROPOMI NO2, AQS ozone, and PurpleAir PM2.5  https://barronh.github.io/pyrsig/.

This tool is a simple way to process satellite data for the EPA’s Community Multiscale Air Quality Modeling System (CMAQ), to help make satellite data comparable to CMAQ. You can find a poster with documentation here, and read more about the Python tool here.

Air Quality Jupyter Notebook is a Python tool. The demo walks through six cases to demonstrate the analysis and visualization abilities: 2021 Alisal Wildfire, 2021 California Wildfires, 2018 Carr Wildfire, Los Angeles ports backlog Fall 2021, Fireworks during 4th of July 2022 in Los Angeles county, Air Pollution in the Yellow Sea, 2022 Fire Season in Southeast Asia.

If  you’re looking for OMI NO2 for the CONUS on a 12km x 12km grid, Tracey Holloway’s team at UW provides monthly files through their  Wisconsin Horizontal Interpolation Program for Satellites (WHIPS) program. This open-source program interpolates level 2 satellite retrievals onto a user-defined fixed grid, in effect creating custom-gridded level 3 satellite product.

Output is flexible in resolution and projection, and can accommodate both equirectangular and lambert conic conformal projections. Currently, WHIPS can process TROPOMI, OMI, MOPITT and MODIS data products. Access WHIPS here and visit this page for more information.

McIDAS-V is a free, open source, visualization and data analysis package developed at the Space Science and Engineering Center (SSEC) at the University of Wisconsin-Madison.  The software displays data from a variety of Geostationary (e.g. GOES-16, Himawari) and Low Earth Orbit (e.g. JPSS, MODIS, TROPOMI) satellites/instruments.  McIDAS-V also supports radar, point observation, gridded (e.g. netCDF, grib), and other geophysical data in 2- and 3-dimensions.  The software can analyze and manipulate data with its powerful mathematical functions.

See the Download McIDAS-V webpage for installers on Linux, macOS, and Windows.  The McIDAS-V Documentation webpage includes links to the User’s Guide, as well as a variety of individual tutorials for different data types supported in McIDAS-V.  Please post any questions or comments about the software to the McIDAS-V Support Forums.

If you are looking to start using Phyton which is a programming language that is easy to learn and is capable of complex, rigorous computations consider using this tutorial series for researchers in atmospheric and geophysical sciences.

These tutorials are designed for undergraduate and graduate students with no or minimal prior coding experience. They do not assume any knowledge about computer programming or coding, but they are targeted toward students with an interest in air quality and the atmospheric sciences.

Through these tutorials, you will learn to:

• Read, write, and understand Python syntax
• Handle multiple file formats including csv, Excel, and netCDF
• Visualize data in different ways

If you are interested using ArcGIS for analysis in air quality and the atmospheric sciences consider using the following tutorial.

The Holloway group created this tutorial for mapping gridded (Level-3) TROPOMI NO2 with ArcGIS Pro, both as the gridded product and as allocated to census tracts. Users will gain experience with common tools that can be useful in GIS-based air quality analysis such as clipping, filtering, and allocating data from one format to another.

These user friendly tutorials use Google Earth Engine to plot the ratio of formaldehyde to nitrogen dioxide that can be used to assess ozone sensitivity.