NEO News

Visualizing Changes in Nitrogen Dioxide Levels During the COVID-19 Pandemic

On March 11, 2020, COVID-19 was classified as a global pandemic by the World Health Organization. That same month, all New York City non-essential businesses were ordered to close by the governor’s office and several residents fled the city to get away from the rapidly spreading virus. There is typically a significant amount of nitrogen dioxide (NO2) in the air from the burning of fossil fuels during mass transportation, especially in larger cities like New York City. But, because all of the non-essential businesses were closed, along with many transportation lines, there was a significant decrease in NO2 in March 2020 compared to previous years.

The data probe function in the NEO analysis tool shows a significant decrease in NO2 levels in March 2020 compared to March 2018 & 2019.

By adding the Nitrogen Dioxide dataset to the analysis tool for March 2018, 2019 & 2020, we can compare NO2 levels over one geographic coordinate using the data probe function or over a distance using the plot transect function. For more information on how this is done, check out our post on NEO Analysis in 10 Easy Steps. According to these New York City snapshots, NO2 levels decreased by roughly half in comparison to the previous 2018 and 2019 average NO2 levels when city operations were normal.

A quick draw of a transect line using the plot transect function shows a decrease in NO2 levels in March 2020 compared to March 2018 & 2019 over New York State.

The Governor of Sao Paulo, Brazil, Joao Dorio, also ordered a shutdown of the state for two weeks at the end of March 2020 to help slow the spread of the virus. The NO2 levels in April 2020 in comparison to the previous two years also decreased by nearly half.

Here is a snapshot of South America with the data probe floating over Sao Paulo, Brazil to compare NO2 levels in April 2018, 2019 & 2020.

Global human behavior changed rapidly as COVID-19 spread across the globe and the change can be detected from satellites in space. NASA scientists are monitoring several atmospheric indicators globally, including NO2, to read a global pulse on how our atmosphere is responding. Although NEO datasets are heavily processed for visualization and should not be used for scientific analysis, we can still qualitatively see changes on a global scale.

Global snapshot of NO2 levels in March 2020.

How to Visualize NEO Imagery in Excel

Did you know you can use Excel to visualize raster datasets? If not, follow this short tutorial and find out how.

Let’s use the cloud fraction imagery NEO provides for this example.

Step 1. Go to the cloud fraction imagery page and choose the CSV for Excel download option from the drop-down at 1.0-degree resolution for a month and year of your choice. I am going to download the latest monthly image for August 2020.

This is the cloud fraction imagery page with green arrows pointing to the selections you need to make when you download the CSV file.

Step 2. Open the CSV in Excel and select all data except for the latitude and longitude row and column (which are the first row and first column).

All of the cells except for the latitude and longitude row and column should be selected in this step. Here is an example of what the Excel sheet will look like.

Step 3. Find and replace all 9999 values with an empty cell. I pressed the space bar a couple of times in the Replace with: cell. Once you click the Replace All button, an alert message will come up, and you will notice the cells that previously had 9999 are now empty.

This is an example of where to find and replace the 9999 values.
This alert will come up once you hit the Replace All button.

Step 4. From the Excel home tab: Select conditional formatting, color scales, and choose one of the 2-color scheme options available or select More Rules… and choose a different minimum and maximum value color. I am going to choose blue for the minimum color and white for the maximum color to create a look similar to what is available on the cloud fraction page.

Here is where you need to be for step 4.
For the new formatting rule, I selected blue for the minimum color and white for the maximum color. The maximum value corresponds to the highest cloud fraction while the minimum value represents low to no cloud cover.

Step 5. Zoom out using the slider on the bottom right side of the excel window and you will notice the global imagery taking shape.

Voila! There is your image coming to life in Excel. Now it is time to zoom in and investigate the cloud fraction values at different latitudes and longitudes. You may also want to try repeating the process at a higher resolution.

I remember learning the difference between raster and vector data in my entry-level GIS courses. Vector data is all of the point, line, and polygon data while raster data is made of cells or pixels. I wish my professor would have shown me how to visualize raster data in Excel at the time to really grasp cell values that make up the imagery we see as a whole. It certainly would have been easier to process!

Please share what you process in the comments below. We would love to hear any feedback or suggestions you may have.

Questions asked frequently at NEO

After many years of serving the public with global visualizations of Earth’s system processes, we gathered the most frequent questions visitors of our site ask and answered them for you. The FAQs page can be found on the home page and lives here: https://neo.sci.gsfc.nasa.gov/faq/. Please look these questions over and see if they answer questions you may have had already or discoveries that will help understand our site better.

If you read through the page and still have your own unanswered questions we did not cover, please send us an email using the contact form below and we will make sure you are heard.

NEO FTP Service No Longer Available

Effective June 13, 2019, the NEO File Transfer Protocol (FTP) service is no longer available. It has been replaced with access via HTTPS.

NEO FTP Service is Retiring

NASA is in the process of deprecating the use of FTP protocol for file access across the agency. As a result, downloading files from NEO via FTP may no longer be available after April 15, 2019. NEO will support bulk downloading options via HTTPS, but FTP client software applications will no longer be able to access NEO holdings. Similarly, if users are using FTP command line utilities or scripts to download from the archive, those will need to be converted to using HTTPS-access methods.

The directories containing the bulk files can be found at https://neo.sci.gsfc.nasa.gov/archive/ Additionally, you may also use our Web Mapping Service (WMS).

For users who have scripts or command line experience, we recommend using either wget or curl to facilitate downloading from the bulk archive. There is quite a bit of documentation and examples that can be found simply by searching, or even just looking at the wget man page, but here are a couple wget examples:

If you want to maintain an up-to-date mirror of a specific directory, retrieving only the PNG files:

wget --no-directories --no-host-directories --no-parent --recursive --mirror --accept "*.PNG" -l1 https://neo.sci.gsfc.nasa.gov/archive/rgb/MOD_LSTD_M/

Same as above, but get only the images from 2007 (for example):

wget --no-directories --no-host-directories --no-parent --recursive --mirror --accept "*2007*.PNG" -l1 https://neo.sci.gsfc.nasa.gov/archive/rgb/MOD_LSTD_M/

If you are not comfortable using command line utilities to download NEO imagery, there is a growing number of graphical interfaces to facilitate downloading over HTTPS. Some are stand-alone applications, some are browser plugins. You can find these applications by searching on “download multiple files from website.”

Analysis: Pacific life – how is it related to ocean temperature?

Note that these examples are intended for curious people looking for hands-on Earth data exploration. Primary scientific research will require additional analyses through other methods. For the basics on how to use the NEO tool, see ‘Analysis tool in 10 easy steps’.

Here we explore phytoplankton blooms and their relationship to sea surface temperatures, with background information featured in ClimateBits: Phytoplankton.

Recent studies link warmer waters off the U.S. west coast to more frequent toxic algae blooms, negatively impacting the marine food web and the economy. In 2014-16, the waters off the west coast were unusually warm and were famously dubbed the ‘warm blob’ by the press. The warmer ocean impacted weather on the west coast and was linked to lower fish catches and stressed sea life.

A toxic algae bloom in 2015 extended from California to Alaska resulting in the closure of the Dungeness crab fishery and an economic decline of $100 million, according to the Fisheries of the U.S. Report, 2015. Sea lion strandings increased, including a starving baby sea lion that seated itself at a San Diego restaurant in early 2016, weighing half of what it should for its age according to the Sea World rescue team.

Following the strong El Niño of 2015-16, ocean temperatures off the west coast returned to ‘normal’. Here we use NEO to explore these reports. How do the satellite sea surface temperature records compare before, during, and after the warm anomaly?

Figure 1. North Pacific Sea Surface Temperatures during February 2013 (red), February 2015 (green), February 2017 (blue). Transect values from NW to SE along the U.S. west coast.

A NEO comparison of ocean surface temperatures for the month of February before the warm anomaly in 2013 (red), during the warm anomaly in 2015 (green), and after the warm anomaly in 2017 (blue). Along the entire west coast – from Alaska to the Baja Peninsula – temperatures during the warm blob (February 2015) were roughly 3 degrees C (or 5 degrees F) warmer compared to before (February 2013) and after (February 2017).

Temperatures off of Alaska (Distance ~ 0km along the transect) were around 7C in February 2013 and 2017, but around 10C in 2015. Off of southern California (Distance ~ 2000km), temperatures were around 13C in February 2013 but 16C during the warm blob in 2015. West of the Baja Peninsula (Distance ~ 3500km), temperatures were around 21C in 2013 and 2017, but 25C in 2015.

How do the temperature changes relate to ocean biology measured by satellites?

Figure 2. North Pacific chlorophyll concentrations during February 2013 (red), February 2015 (green), and February 2017 (blue) plotted in a histogram for the area west of California outlined in white.

Chlorophyll concentrations indicate the amount of phytoplankton blooming. More phytoplankton means more food for fish and the rest of the marine food web. In the chlorophyll histogram in Figure 2, chlorophyll during the warm blob in February 2015 (green) had lower values (around 0.1 mg/m3) more frequently than the other two years. The waters were almost 10 times more productive (approaching 0.9 mg/m3) in February 2013 (red) compared to the other two years. Recall that February 2013 had the coolest water.

Usually, cooler surface water means that the water has recently been at depth — below the sunlit surface. Deep water containing unused nutrients can support new phytoplankton blooms. Thus, cooler water is generally associated with higher chlorophyll concentrations. How do the two data sets compare along the west coast before, during, and after the warm blob?

Here we compare sea surface temperature and chlorophyll along a transect from NW to SE off the coast of California for February 2013, 2015, 2017.

Figure 3. Sea surface temperature (red) and chlorophyll (green) plotted along the white transect line in the large panel, from northwest to southeast for February 2013 (left), February 2015 (middle), February 2017 (right) – before, during, and after the warm blob, respectively.

In all of the plots in Figures 3, sea surface temperature and chlorophyll demonstrate their inverse relationship. Cooler, more productive water to the north is contrasted with warmer, less productive water toward the south. The peaks in the chlorophyll (green line) correspond to phytoplankton filaments typically associated with nutrient entrainment along the boundaries of circulation features, such as in the California Current system. Note that over the 2000km transect from northwest to southeast, temperatures changed about 10C and chlorophyll concentrations changed more than an order of magnitude (10x). Also notice that February 2013 (Figure 3, left) had chlorophyll peaks reaching concentrations around 5 mg/m3. During the warm anomaly in 2015, chlorophyll concentrations were never above 0.9 mg/m3. After the demise of the warm blob, sea surface temperatures cooled in 2017 (Figure 3, right) compared to 2015 (Figure 3, middle), chlorophyll concentrations remained low (< 0.9 mg/m3) and were certainly much lower than in 2013.

Diving into the 2017 data a bit more through scatter plots, we can highlight the geographical distributions of different data combinations.

Where are the highest chlorophyll concentrations?

Figure 4. Scatter plot of sea surface temperature (bottom axis) versus chlorophyll (left axis) during February 2017 for the region within the white line. The highest chlorophyll values (magenta box on the scatter plot) are highlighted in magenta on the map. Note that the values at the very top of the plot (74mg/m3) are outliers or artifacts.

Where are the warmest waters within the area outlined in white?

Figure 5. Same plot as Figure 4, with the magenta area highlighting a different distribution of temperature (16-21C) and chlorophyll values (0.05-0.2 mg/m3).

Where are the coolest waters within the area outlined in white?

Figure 6. Same plot as Figure 4 and 5, with the magenta area highlighting a different distribution of temperature (7-10C) and chlorophyll values (0.2-0.8 mg/m3).

Not surprisingly, the coolest waters are in the north; the warmest waters are in the south and the most productive waters with the highest chlorophyll values are next to the coast where nutrients were plentiful. Recall that January and February 2017 was a time of plentiful rain and snow on the west coast (a.k.a. atmospheric rivers that led to much run-off from land).

Note: This blog was written in response to a request for an analysis comparing sea surface temperature and chlorophyll. If there is an analysis you would like to see in this blog, please let us know! 

Analysis: Hot in the city

As the northern hemisphere approaches summer, we explore land surface temperatures that are featured in ClimateBits: Urban Heat Islands.

Note that these examples are intended for curious people looking for hands-on Earth data exploration. Primary scientific research will require additional analyses through other methods. For the basics on how to use the NEO tool, see ‘Analysis tool in 10 easy steps’.

Urban Heat Islands are places on land where buildings, roads, and other impervious surfaces trap more heat than the surrounding rural area. During summer, an urban place like New York City can be 4°C (7°F) or more warmer than surrounding rural areas. Vegetation plays a cooling role through transpiration. Cities such as Minneapolis, Chicago and St. Louis — where most trees were cleared to make way for pavement and development — are urban heat islands surrounded by cooler forests.

Demonstrate seasonal changes

Load March, June and September, 2016 for land surface temperature [day]. These are found under the ‘Land’ category. Note the difference between ‘land surface temperature’ and ‘average land surface temperature’ data sets, the latter being climatology. We use the former in this example. These are MODIS/Terra observations collected since February, 2000 at daily, 8 day and monthly temporal resolution. Here we compare [day] temperatures.

The warmest land is colored yellow; coolest land is colored light blue. Hottest places are in the tropics and during summer in areas where the land is driest. Coldest places are covered in snow and ice. Black areas are missing data — over the ocean or due to cloud cover or lack of sunlight. The values along the white transect on the large map are plotted for March (red), June (green), September (blue).

The white line drawn from south of Lake Michigan east to New York City shows that the transect was about 10°C cooler in March compared to June and September in 2016. As the month of maximum sunlight, June would be expected to be warmest, yet September temperatures were not much cooler due to the thermal inertia of the land.

Compare day/night seasonal changes

Now load March, June and September, 2016 for land surface temperature [night]. Night temperatures are also coldest for places covered in snow and ice, but have important differences from daytime temperatures for warm areas.

The same line drawn from south of Lake Michigan east to New York City corresponds to the plot of nighttime temperatures for March (red), June (green), September (blue). September temperatures were again very close to those in June, especially for the urban areas at either end of the transect (near Chicago and New York City).

Compare urban and rural day/night temperatures

Looking at a weekly map from the end of June, we can compare day and night temperatures with a focus on urban versus rural New York.

Land surface temperature [day] in red and [night] in green for the week of June 26-July 4, 2016. Histograms show temperature distributions around urban New York City (left) compared to rural upstate New York (right).

The first thing to notice is the higher daytime temperatures around New York City (maximum 37°C) compared to upstate New York (maximum 28°C). Second, are the higher nighttime temperatures around New York City (most of values are much greater than 19°C) compared to upstate New York (most of the values are less than 19°C). Notice especially that there is more overlap between daytime and nighttime temperature distributions for New York City. This is the urban heat island effect.

Related Reading

Analysis: Reflections on the Blue Planet

To better engage you on critical Earth science topics, NEO launched a new web-based analysis tool. This Analysis Blog explores NEO data sets used in ClimateBits: Albedo. Albedo is the fraction of incoming solar energy that is immediately reflected back to space.

Note that these examples are intended for curious people looking for hands-on Earth data exploration. Primary scientific research will require additional analyses through other methods. For the basics on how to use the NEO tool, see ‘Analysis tool in 10 easy steps’.

Reflected shortwave radiation

Categorized under ‘Energy’, maps of reflected shortwave radiation show the amount of solar or shortwave energy (in Watts per square meter) reflected by the Earth. These are CERES observations combined with MODIS measurements, available since July, 2006. Brighter colors indicate more reflection while dark blue indicates the least reflection. The brightest, most reflective regions are associated with clouds, snow and ice. Because clouds move quickly, they are best observed in daily maps. The 8 day and monthly composites mute transient weather patterns. More persistent features, such as polar ice caps, can be observed and compared at longer time increments. The least reflective regions are dark surfaces without cloud cover, such as forests and the ocean. The poles are dark during their winters because of the absence of sunlight then.

Reflected Shortwave Radiation (in Watts per square meter). The pale green to white regions show where more sunlight is reflected; dark blue regions are where the least sunlight is reflected.

Land albedo

Categorized under ‘Energy’ as well as ‘Land’, maps of albedo show how reflective land surfaces are from 0, meaning no reflection, to 0.9, indicating nearly all incoming solar energy is reflected. These maps are derived from MODIS measurements, available since February, 2000 at 16 day and monthly composites. Dark blue indicates the least reflection and white indicates the most. Black areas are missing data – over the ocean or due to cloud cover or lack of sunlight.

Land albedo scales from 0 (dark blue) meaning no incoming sunlight reflected to 0.9 (white) meaning almost all sunlight reflected (1 would be all). Black areas mean “no data,” either over ocean or because persistent cloudiness prevented a view of the land surface. Notice the highest albedos are due to ice caps, glaciers and snow-cover.

Comparison: different surfaces

Africa is a continent with the Sahara Desert north of savannah grasslands and then forests with thick vegetation. To see how different land cover impacts albedo and reflected radiation, we compare them during January, 2017. We limit our analysis to the area delineated by the yellow box (below, left). Use Data Probe and Plot transect to explore the whole geographic area, comparing and contrasting values of albedo and reflected radiation.

Left: Map of the region selected as the yellow box. Right: a comparison of albedo and reflected radiation from north to south along the transect (white line).

Notice that albedo and reflected radiation are highest over the Sahara Desert, except for the dark spot associated with the Tibesti mountains in northern Chad. Albedo and reflected radiation decline over the savannah grasslands, which are darker. Farther south, over the tropical rain forest, however, reflected radiation starts to rise while albedo continues to decline – likely due to evapotranspiration that promotes cloud formation.

Left: region selected (white box). Right: scatter plot of albedo versus reflected radiation within that region.

A scatter plot of the transition zone between desert and savannah demonstrates the direct relationship between albedo and reflected radiation.

 

NEO Analysis in 10 Easy Steps

Would you like to explore satellite data yourself? The new NEO analysis tool provides an easy way to compare imagery online and this new blog series highlights different Earth science concepts by pairing an introductory video with an investigation of relevant satellite imagery found here. After you’ve learned the steps, you can try these examples for yourself:

ANALYSIS: PACIFIC LIFE – HOW IS IT RELATED TO OCEAN TEMPERATURE?

ANALYSIS: HOT IN THE CITY

ANALYSIS: REFLECTIONS ON THE BLUE PLANET

Here are the basic steps to follow. Try it now!

Read more

Analysis Tool Development Update

I get plenty of email from users who are still trying to wrestle with the Java version of the analysis tool on the NEO website. (If you are one of those please consult our instructions, but also read on.) I get an equal number asking about the status of a replacement for the Java version.

So, an update is due: We are currently in the process of developing a replacement for the Java analysis tool. It will be 100% HTML/Javascript and will not require any plugins or other software aside from your modern browser. As of now I think I can safely say that it will be ready and deployed on the site in the early part of 2017. Once that is completed, the Java-based tool will be retired permanently.

Thank you all for your continued patience. This has been a long time coming.

Update, February 13, 2017: The new version analysis tool is in place on the site now and ready for testing. I have noticed a few quirks so we are working to address those, but in the meantime, please feel free to test it and let me know how it works for you.

Contact Us

Need to get a hold of someone at NEO? Just fill out the form below.





Trouble with this form? Submit your comment here.