September 23rd, 2020 by Melinda Webster/University of Alaska Fairbanks
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.
August 13th, 2019 by Andy Hynous, Mission Operations Manager, NASA Balloon Program Office
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.
July 29th, 2019 by Rebecca Scholten/Vrije Universiteit Amsterdam
2019 is bound to become one of the largest fire years on record in the Arctic Circle, and especially in Siberia. How much carbon these fires release remains a challenging question. Very little ground data on fire emissions is available for Siberia and estimations are difficult since the main part of the emissions originates from organic soils, which is harder to retrieve from satellite imagery than emissions from aboveground biomass. Our research team from the Vrije Universiteit Amsterdam (the Netherlands), Woods Hole Research Center, Northern Arizona University (USA), Pyrenean Institute of Ecology (Spain), and the Siberian Branch of the Russian Academy of Sciences and Tomsk State University (Russia) are joining forces to better understand fire dynamics in Siberia.
After an adventurous three-hour drive, our field crew gathered with the local collaborators at Kajbasovo Research Station near Tomsk, in Russia. We aimed at finding old pine trees in burned and unburned sites, which we then core with tree borers to build tree-ring chronologies. Wildfires in this western part of the Siberian boreal forest usually don’t burn with high intensity allowing some resilient trees to survive multiple fire cycles. Thus, we aimed at using the chronologies to reconstruct the fire history of the area and to assess the response and recovery times of the ecosystem after fire events and other disturbances.
Little did we know that we would ourselves witness the severity of this year’s fire season. Except for the first day, we did not see a clear sky. From then on, the sun would only appear as a bright orange or blood red ball behind lots of smoke originating from wildfires in the Krasnoyarsk region hundreds of kilometers away. One good thing about this is that it dampened the heat, since we were already quite warmly dressed in our tick- and mosquito-proof clothing.
Mosquitoes and heat, however, were only small obstacles, as we set out with our borers to find trees older than 100 years. We really wanted trees from that age so that we can build sufficiently long chronologies. Even at the most remote places we were surprised to often see signs of human activity such as past logging, resin extraction or littering. One day we even saved a duckling out of a fisher net set up a good 4 hours bumpy drive away from the next village. Or sometimes we would simply not find old trees because of natural disturbances or growth restrictions. Eventually, we did manage to sample 12 sites with old trees with different fire severities and hydrologic characteristics. These will now be analysed further in the lab to extract and crossdate the tree rings.
Being in the field and having only very little time to sample can be an intense working experience, but there were many special little moments too. Our driver overcame every obstacle on the way to bring us to very remote places, and our cook took great care of us with plenty of delicious borscht, buckwheat and blinis (type of pancakes) and provided large amounts of water and kompot (sweet fruit beverage). And our evenings were spent at camp fires diving into local culture and connecting the people.
After ten exciting days in Tomsk we are now resting and recovering in Yakutsk for the weekend. We are using the time for some team building activities, and we are enjoying some solid hours of sleep. We went shopping for supplies for the second part of our field campaign, which will lead us to even more remote areas around the little villages of Ert and Batamay in the next four weeks. There, we will visit recently burned forests and measure the carbon losses due to fire events. In addition, we will take more tree chronologies to estimate the stand age, and count seedlings to see how forests recover after fires of different severities.
This field campaign is part of the ‘Fires pushing trees North’ project funded by the Netherlands Organisation for Scientific Research (NWO) and affiliated with NASA ABoVE. The Tomsk part of the campaign was funded by INTERACT.
This blog post was written by Rebecca Scholten, PhD student at Vrije Universiteit Amsterdam, researching arctic-boreal fire dynamics.
I have been working with a project focused on drought in Kenya for months using NASA satellite data and was excited to get a ground-based perspective of the country and meet fellow Earth Scientists in Nairobi, Kenya. My colleague Eric Anderson and I attended a week long course on the Quality Index Insurance Certification (also known as QUIIC), which provides methods to evaluate the quality of satellite-based indices for use in agriculture/pastoralist insurance.
I’ve been working with SERVIR since November 2018 to support the development of a lower-latency vegetation index, inspired by Kenya Livestock Insurance Program needs. A lower-latency product can enable programs like these to provide relief sooner, potentially before total losses. The Normalized Difference Vegetation Index (NDVI) is currently used in this insurance program and provides a satellite-based measure of vegetation health, which can show how much forage is available for livestock consumption. When conditions are bad, the program is intended to help people through the season without experiencing devastating losses.
Indices, such as vegetation health, can be monitored using satellites and provide a low cost way to detect things like drought, especially where field data is scarce and pastoralists may otherwise be uninsurable under traditional contracts. Index insurance programs are meant to promote farmer and pastoralist resilience, but if they are designed poorly they can actually leave people worse off. For example, if conditions are bad one year but the index fails to trigger payouts, farmers would be worse off had they purchased insurance and not received a payout. The information-rich course, led by economics experts from UC Davis (Michael Carter and Elinor Belami), helped us understand a different side of applying remote sensing to real world problems. We learned about the economics of insurance and evaluated the quality of using different indices compared to traditional insurance for a focus region.
It was a great experience to be able to see a different culture and meet so many people from different backgrounds. Our next mission is to bring what we learned in the course back to the SERVIR hubs and explore ways to apply it. Using methods to measure quality of an insurance index we can decide if index based insurance is appropriate for different regions.