Alex Haughton is a graduate student in the Astrophysical and Planetary Sciences department at University of Colorado Boulder studying ultraviolet instrumentation with sounding rockets. His team has traveled to Equatorial Launch Australia’s Arnhem Space Center near Nhulunbuy, Australia to launch the Dual-channel Extreme Ultraviolet Continuum Experiment (DEUCE) Sounding Rocket and observe the stars Alpha Centauri A & B in extreme ultraviolet wavelengths.
It takes a village to send something to space.
Currently, the Suborbital Imaging Spectrograph for Transition region Irradiance from Nearby Exoplanet host stars (SISTINE) rocket payload is sitting on the launch pad ready to go. As we wait for high winds to die down, I am reflecting on how many people it takes to get here.
SISTINE has a science team of four people in East Arnhem Land, but many others were left back at the lab in Colorado – mechanical and electrical engineers, technicians, graduate and undergraduate students. The payload itself consists of a telescope made by Nu-Tek in Maryland, special ultraviolet reflective coatings applied by NASA Goddard, a diffraction grating made by Horiba JY in France, and a microchannel plate detector made by Sensor Sciences in Berkely. If all those things sound like advanced technology that take a lot of people, work, and know how to make, it’s because they are.
And that’s just the science payload. There’s the minor matter of launching it into outer space. There are over 70 people here from NASROC (NASA Rockets), White Sands Missile Range, and NASA. These are people who can set up and operate the motors of a sounding rocket (the part that goes boom), people who can set up antennas to communicate with the rocket as it flies to and from space and monitor where it is, people who can point the rocket in the right direction when it gets there, people to pick up the rocket and bring it back when it comes back down, and people to manage all these other people. This blog post is facilitated by a NASA communications team sent to cover the launch (you can find some of their excellent work here).
Then there’s the local infrastructure. The launch site itself was built with a partnership between Equatorial Launch Australia and the Gumatj Corporation, the Indigenous owned and run corporation that manages the land on which the launch site was built. East Arnhem Land is beautiful, but densely populated it is not, and the influx of people requires places to stay, food to eat, vehicles to drive, and other resources that stretch the capabilities of the nearby town of Nhulunbuy (the area has responded admirably).
I feel like there’s a tendency to put a single face on scientific discovery or technical achievement. The Nobel Prizes certainly tend in this direction. Theoreticians of the past may have had fewer people supporting them, but modern scientific discoveries are built on the culmination of years, if not decades, of hard work by large teams of people, all of whom are critical to success.
Sometime in next few decades the astronomy community will begin detecting and analyzing atmospheric data from potentially habitable exoplanets. There’s a chance life signatures could be discovered. When this happens, the next two launches, along with a multitude of other projects, will have helped paved the way for that discovery. And these two launches, small potatoes compared to what it takes for even moderately sized space telescopes, take a village to succeed.
On November 23, the Royal Thai Government’s Pollution Control Department (PCD) and SERVIR-Mekong launched the Mekong Air Quality Explorer (AQE) tool at a press event in Bangkok, Thailand. Due to ongoing COVID restrictions, my colleagues from the SERVIR Science Coordination Office (SCO) and I participated in the event remotely. In working with the SERVIR-Mekong hub, I learned that poor air quality in Southeast Asia is a recurring problem that has lingered for over a decade. In contrast to smaller numbers of ground monitoring stations, Earth observations have proven essential to provide consistent and accurate air quality information. Co-developed by SERVIR-Mekong, PCD, and the Geo-Informatics and Space Technology Development Agency (GISTDA), the AQE uses Earth observation inputs in a web-based platform that forecasts and monitors air quality in the Lower Mekong region.
The press event took place at the Ministry of Natural Resources and Environment in Bangkok, and included remarks, a tool demo, and a Q&A session—and I got to watch it all live online. With about 40 participants, the event featured speakers such as: Mr. Athapol Charoenshunsa, PCD Director General and Chairman of the event; Dr. Steven G. Olive, Mission Director of USAID’s Regional Development Mission for Asia (RDMA); Dr. Lawrence Friedl, Director of NASA Earth Science Division’s Applied Science Program (through a pre-recorded video); and Mr. Aslam Perwaiz, Deputy Executive Director of the Asian Disaster Preparedness Center (ADPC). Additionally, GISTDA, the National Research Council of Thailand, the Prime Minister’s Delivery Unit, the Thai Health Promotion Foundation, the Department of Health, and about 20 local media channels and newspapers were at the event.
Collaboration with Local and Regional Partners
As some background, SERVIR, a joint NASA and USAID program, works with leading regional organizations to help countries worldwide use Earth observations and geospatial technologies to address environmental challenges. Led by the ADPC, SERVIR-Mekong works with organizations in countries across the Lower Mekong, including Thailand. I wasn’t working with the Mekong hub at the time, but in April of 2019, SERVIR-Mekong initiated a collaboration with local authorities to improve air quality monitoring and forecasting. The release event builds on nearly two years of research, trainings, and collaborative development in the area of air quality monitoring and forecasting. As Aekkapol Aekakkararungroj, a Remote Sensing and GIS Specialist from ADPC and my co-worker, stated: “Tackling air pollution needs to be done right now. This requires cooperation from grassroots to policy makers. Earth observation technology from space is one of the most important tools to bridge the gap—to help them better communicate, and collaboratively manage the situation on the ground.”
Research and Development for Air Quality Monitoring and Forecasting
I learned a lot about Air Quality monitoring from Dr. Gupta, the lead scientist of the AQE. He told me that in the past two decades, satellite observations of atmospheric aerosols and trace gases have been used to address surface air pollution issues. NASA has invested significant resources in researching and developing data products ready to be used in applications. The products used for the AQE are created through a research and analysis (R&A) project of NASA’s Science of Terra, Aqua and Suomi-NPP program (PI – Dr. Pawan Gupta). The R&A project focuses on air quality research and data product development for the Indian-Subcontinent, which has been expanded to include Thailand in collaboration with SERVIR. “AQE is an excellent example where NASA’s R&A program collaborated with NASA’s Applied Science program to use science for real-life application,” said Dr. Gupta.
The AQE uses aerosol and meteorological forecasts from NASA’s existing advanced climate model called the Goddard Earth Observing System (GEOS). GEOS assimilates millions of daily Earth observations and provides global forecasts up to 10 days in advance under its forward processing (FP) stream. Forecasts using the GEOS system are experimental and are produced for research purposes only. Therefore, the AQE uses NASA’s global aerosol forecasts informed by satellite observations, real-time ground monitoring data from the PCD, and an advanced machine learning algorithm to provide three hourly air quality forecasts for the next three days. The machine learning algorithm helps calibrate global forecasts with local conditions and provide better accuracy. This all sounds super cool, right?
In addition, AQE has real-time satellite imagery, fire detection, and aerosol retrievals from NASA’s MODIS and VIIRS sensors. These near real-time products can help human forecasters evaluate model forecasted fields for improved decision making on final forecast outputs. AQE also has gridded historical (past two decades) data on aerosols and fires developed under the R&A project (Gupta et al., 2020). The historical datasets can evaluate change over time and help understand the impact of any significant policy changes on emissions in the region.
Capacity Building Through Training at Partner Institutions
In addition to app development, the SERVIR-Mekong team has supported training and youth outreach efforts. Dr. Gupta conducted two training sessions on the Remote Sensing of Air Quality: the first for the ADPC in July 2019, and the second in August 2020 for the PCD and GISTDA, in which I had the chance to participate. The latter was supported by NASA Applied Remote Sensing Training and the Committee of Earth Observation Satellites. These trainings built technical capabilities within Thai institutions on satellite remote sensing basics, the use of satellite data for air quality applications, and advantages and limitations of satellite datasets. These trainings were also used to introduce and get feedback on AQE while still under development.
To increase youth engagement in the work, in February of 2020, SERVIR-Mekong, PCD, USAID, and the Department of State’s Young Southeast Asian Leaders Initiative hosted Smogathon Thailand 2020. The event brought together young professionals, students, and technical experts to tackle air pollution using satellite data.
Development of an Online Visualization System – An Air Quality Explorer
The AQE started as a simple visualization tool for historical aerosols and fire data to support a R&A project. Around the same time, SERVIR-Mekong showed an interest in air quality applications in the Lower Mekong region, which motivated the SERVIR team to further develop this visualization tool and include other data sets. The collaboration with PCD allowed us to use their ground monitoring data, which then combined with GEOS reanalysis (MERRA2) to develop machine learning (ML) models. These ML models were evaluated against independent datasets using a 10-fold validation strategy. Finally, an ensemble model is used to calculate surface PM2.5 for the entire region. These ML models were implemented in automatic data processing, which generate three hourly air quality index maps for the next three days following Thai air quality standards. In addition to historical data and forecasting, near real-time satellite data layers from NASA’s Land, Atmosphere Near real-time Capabilities for EO (LANCE) were incorporated in the tool. The AQE had been through testing and improvement for almost a year with all the partners, including PCD, GISTDA, and SERVIR-Mekong, before it was adopted by PCD and became part of the Thai air quality management system.
The adoption of AQE by the PCD is an excellent example of how NASA’s science, research, and data are being applied around the world to address real-life problems. The AQE is also a first step by PCD in adopting Earth observations to complement and fill gaps in ground-based air quality monitoring systems. The AQE also addresses air quality forecasting, a gap in their air quality management program.
Since the adoption and release, PCD has already made several improvement requests for the AQE tool to serve their air quality needs. This includes improved spatial resolution, expanding the regional coverage to include neighboring countries to understand transboundary pollution, and including data on more air pollutants such as ozone, SO2, and NO2. There is also an opportunity to explore air quality observations by geostationary satellites by JAXA (i.e., Himawari-8/9) and KARI (i.e., GEMS) for the region. SERVIR-Mekong and SCO will continue to work on these aspects in close collaboration with PCD and GISTDA. I’m really excited to contribute to this effort and see the direction the AQE will take in the future!
Sea ice geophysicist Melinda Webster is blogging from the RV Polarstern, an icebreaker ship locked in Arctic sea ice for the MOSAiC expedition. Webster will use MOSAiC data as a blueprint to evaluate and extend the seasonal capability of data from NASA’s ICESat-2 satellite for sea ice research.
On August 17, the Polarstern reached the North Pole during its search for a new ice floe for the MOSAiC observatory, the research icebreaker Polarstern. We made surprisingly quick progress through the sea-ice pack owing to thin, warm ice conditions and a fragmented sea-ice cover. It took us less than seven days to cover 597 nautical miles (676 miles/1,087 kilometers) in a straight-line distance. The ice at the North Pole was level, seasonal ice extensively covered in dark melt ponds. I can only imagine how different it must have looked 30 years ago when thick, hummocky multiyear sea ice was more normal.
From the North Pole, we established a new ice camp at 87.717N, 104.313E on about 1.3-meter thick, first-year sea ice with numerous interconnected melt ponds. About half of the melt ponds were completely melted through to the ocean and the floe itself contained a network of old, melted cracks. These conditions required us to wear survival suits for much of our field work. After bouts of rain, snow, freezing, and melting, we were lucky enough to experience freeze-up, a time when the sea-ice cover begins to grow continuously from autumn to spring.
Two days ago, we completed our work at the snowy, frozen ice camp and began our journey to Bremerhaven, Germany. As we travel through the ice pack, we’re conducting “ice stations”–measurements collected at certain locations to capture the spatial variability in snow depth, melt pond depth, and sea ice thickness, which are important for understanding Arctic sea ice characteristics during the autumn transition. Yesterday, we observed snow cover that was about 8 centimeters deep, refrozen melt ponds, and “fields” of frost flowers on refrozen leads. Today, we’re traveling through wet first-year ice with open melt ponds covering about 25 percent of the area–a stark contrast from conditions yesterday due to a recent storm that passed through with above-freezing temperatures and rain.
As the Polarstern passes through changing ice conditions on our way southward, I can’t help but wonder what the sea-ice cover looked like 30 years ago and what it will look like 30 years from now.
CYGNSS was launched on December 15, 2016 at 13:27:21 UTC and today marks the completion of its third year on orbit. Much has happened in that time and the future looks very promising, with all eight microsatellites healthy and operating continuously in their nominal science data-taking mode. Year 1 was spent on engineering commissioning and initial ocean wind measurements. In year 2, the ocean wind retrievals were refined and characterized and investigations of observations over land began in earnest. This past year has seen progress on several fronts. An important refinement to the engineering calibration has been developed. Ocean wind measurements in tropical cyclones have been successfully introduced into numerical hurricane forecast models. And the ability to image inland waterbodies with high resolution has been leveraged to demonstrate two new measurement capabilities.
CYGNSS measures the strength of GPS signals reflected by the Earth surface, from which properties of the surface are derived. To do this accurately requires knowledge of the signal strength transmitted by the GPS satellite. The original approach to calibration assumed each GPS satellite had a unique transmit power level and that it did not vary in time. The unique power levels were estimated using measurements averaged over several months. Subsequent analysis has revealed that GPS transmit power can vary significantly, depending on the particular satellite and on its position in orbit. In order to compensate for the changes, the direct signal GPS receivers on all eight CYGNSS satellites were reprogrammed in 2018 to act as power meters which can monitor the variations in transmit power [Wang et al., 2019. A Real-Time EIRP Level 1 Calibration Algorithm for the CYGNSS Mission using the Zenith Measurements. Proc. IGARSS 2019]. The impact of this change is illustrated in the following figure. The top panel shows the geographical distribution of the average error in CYGNSS wind speed for a single GPS transmitter (SVN 63) assuming its transmit power is constant. The center panel shows the actual GPS transmit power as measured by the new on-board monitoring system. Clear localized differences in the measured power are evident which correlate with the errors in wind speed. The bottom panel shows the distribution of average wind speed error after the monitored power is incorporated into the calibration. The discrepancies have been largely removed.
Over the past year, CYGNSS ocean wind measurements have been added to the HWRF numerical forecast model used by the National Hurricane Center and compared to the standard operational forecast, which does not yet use CYGNSS. To forecast a hurricane, HWRF is first initialized with satellite, airborne, and ground based measurements. The storm is then allowed to develop in software, guided by underlying physical principles of thermodynamics, radiation and mass and energy conservation. An example of this is shown in the following figure, which presents three versions of the horizontal wind speed at 3 km altitude for Hurricane Harvey on 25 Aug 2018 at 06Z. The left panel is the 24 hr forecast predicted on 24 Aug by the operational version of HWRF without CYGNSS data. The center panel is the HWRF prediction with CYGNSS winds included. Note the azimuthal shift in predicted peak winds into the northeast quadrant when CYGNSS winds are included. The right panel shows measurements made by NOAA’s airborne doppler radar of the actual winds at 3 km. The predicted azimuthal shift is confirmed, suggesting that the storm’s development is being more accurately modeled [Cui et al, 2019. A Preliminary Impact Study of CYGNSS Ocean Surface Wind Speeds on Numerical Simulations of Hurricanes. Geophys. Res. Ltrs.].
More accurate modeling of storm development should help forecast the intensity of its surface winds, and this has been demonstrated by another study using HWRF [Annane et al., 2018. Impact of CYGNSS Data on Tropical Cyclone Analysis and Forecasts Using the Operational HWRF. 33rd AMS Conf. Hurricanes Trop. Meteo.]. In this case, a 120 hr forecast of minimum sea level pressure (MSLP) in the eye of Hurricane Michael was conducted on 8 Oct 2018 at 00Z without and with CYGNSS winds included as one of the inputs. The left panel in the following figure shows the true (best track) MSLP in black and the operational forecast (without CYGNSS) in red. The rapid drop in MSLP that occurred between ~30-60 hr is not well forecast. The right panel shows the HWRF forecast with CYGNSS winds included and can be seen to more accurately predict the rapid intensification of the storm.
CYGNSS measurements over land are able to provide high resolution tracks across inland waterbodies when the surface is calm enough to support coherent specular scattering. A new land/water mask has been developed which leverages this capability as well as the ability of CYGNSS to penetrate through clouds, rain and vegetation canopies. As a result, dynamic changes in waterbodies (e.g. due to flooding, seasons or human development) can be resolved better than existing water masks that rely on optical satellite imagers. [Gerlein-Safdi and Ruf, 2019. A CYGNSS-Based Algorithm for the Detection of Inland Waterbodies, Geophys. Res. Ltrs.]. An example of this new capability is shown in the seasonal watermasks of the Okavango Delta in Botswana derived from CYGNSS overpasses in 2018. The left panel, for Dec/Jan/Feb, shows the typical river boundaries that are present during the dry season. The mask in the right panel, for Jun/Jul/Aug, captures the widespread flooding which occurs during the rainy season.
CYGNSS overpasses of rivers also produce high resolution tracks that can be used to measure a river’s width and infer its streamflow rate. This has been successfully demonstrated using overpasses of the Pascagoula River in Mississippi during a major flood event in April 2019 [Warnock and Ruf, 2019. Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator, Remote Sens.]. Streamflow measurements by a USGS river gauge made before, during and after the event are shown in the upper panel below, together with markers at the times of each of five overpasses by CYGNSS. The lower panel shows the Associated GNSS-R Width (AGW) of the river derived from each CYGNSS overpass, together with the Pascagoula’s streamflow rate at the time.
The two are highly correlated, suggesting that another new type of CYGNSS data product may be possible over inland waterbodies.
Did you know that NASA has a balloon program? At NASA, we not only launch balloons, we launch balloons that can carry almost 8,000 pounds to over 120,000 feet. That’s like taking two Ford Mustangs, fully loaded, and flying three times higher than a Boeing 777. When our balloons fly, they carry world class telescopes, cosmic ray detectors, and Earth science instruments. Many of NASA’s most successful satellites actually started out as balloon missions. I am always amazed when I realize how capable these platforms are.
The NASA balloon program conducts launches from the four corners of the world. Right now I’m in Ft. Sumner, New Mexico, the Land of Enchantment, where we’re gearing up for another campaign. Next, we’ll be taking flight over Antarctica. We also fly regularly from Wanaka, New Zealand and Esrange, Sweden. Keep checking back here for more updates! I’m going to put updates on our science missions, updates on life in the field, and more information on how we conduct our launches!
Until next time, I’ve attached a picture from last year’s Ft. Sumner Campaign, taken by yours truly, to help give a sense of scale for our balloons.