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Precision Assessment of the HPLC Phytoplankton Pigment Dataset Analyzed by NASA to Quantify Global Variability in Support of Ocean Color Remote Sensing
Get PDFThe ability to generate chlorophyll a (Chl a) assessments from ocean color orbital sensors, such as VIIRS and MODIS, that satisfy the requirements to be climate-quality data record (CDR) quality is contingent in part on the quality of the in situ ground or sea truth observations that serve as datasets for vicarious calibration and algorithm validation activities. NASA has a mandate to collect, analyze, and distribute in situ data of the highest possible quality with documented uncertainties and in keeping with established performance metrics. Using a dataset of over 18,000 HPLC phytoplankton pigment samples representing water collected in all major ocean basins analyzed a central laboratory (Field Support Group (FSG) of the Ocean Ecology Laboratory (OEL) at NASA Goddard Space Flight Center (GSFC)), we performed an assessment of the global precision among sample replicates of Chl a as well as major accessory pigments. We investigated the impacts of filtration volume, water basin, collection technique, pigment concentration, and different filtration volumes for replicate filters on replicate filter precision, as well as investigating any pigment-specific differences. Our results quantify sample variability with the goal of understanding any systemic biases or biogeographic influences
A Physical Model of Moulin Evolution on the Greenland Ice Sheet
Get PDFNearly all proglacial water discharge from the Greenland Ice Sheet is routed englacially via moulins. Identification of these moulins in high-resolution imagery is a frequent topic of study, but the processes controlling how and where moulins form remain poorly understood. Because moulins may reasonably compose approximately 10-15% of the englacial-subglacial hydrologic system, the evolution and shape of moulins can alter both the timing and variability of meltwater inputs to the bed. This evolution can impact both the form of the subglacial hydrologic system and associated response of ice motion. Here, we develop a physical model of moulin formation and evolution to constrain the role of englacial processes in shaping the form and structure of the subglacial hydrologic system. Within this model, moulin geometry is controlled by a balance of viscous and elastic deformation and is dependent on that deformation, refreezing, and the dissipation of turbulent and sensible heat energy. All of which are dependent on the characteristics of the available supraglacial meltwater and the surrounding ice. We find moulin geometry is responsive to changes in these parameters over the course of hours to days, indicating that diurnal and multi-day variations in melt can substantially alter the geometry of a moulin and, consequently, the pressure-discharge relationship at the bed of the ice sheet. Therefore, there is no single moulin shape that can appropriately represent englacial storage across the Greenland Ice Sheet
Monitoring for Flight Path Management: Inputs to Pilot Monitoring Training
Get PDFThe commercial aviation industry world-wide has identified a need for improved pilot monitoring (e.g., ICAO, 2016). More specifically, aviation safety data indicate that failures in pilots' monitoring for flight path management (FPM) have contributed to a range of undesired outcomes: accidents, major upsets, and non-compliance with ATC guidance. The FAA has further stated that these types of FPM failures are likely to worsen with the increasingly complex air traffic control systems and FPM concepts proposed for NextGen (https://www.faa.gov/nextgen/what_is_nextgen/) operations (e.g., see Hah et al., 2017). An important element of this additional complexity will be the introduction of new automation or artificial intelligence that is intended to work with the flight crew but can add additional monitoring burdens. One potential mitigation for this situation is to enhance pilot training for effective monitoring. NASA Ames Research Center was asked to identify and evaluate training approaches that have the potential to enhance pilots' ability to effectively monitor for FPM (with the result of improved awareness). The focus of this work is to identify, develop or validate training guidance to improve pilot monitoring/awareness regarding FPM and mitigate the recent trend of accidents and incidents, especially Loss of Control (LOC) events. The result of this work should be guidance for FAA training and standards organizationssuch as the Air Carrier Training Aviation Rulemaking Committee (ACT ARC)to aid in reducing the risk of incidents and accidents due to inadequate pilot monitoring/awareness
SPACE-CENT: Physiological Monitioring of Fluid Shifts During Orthostatic Tilt
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Assimilating All-Sky Microwave Radiance Data to Improve NASA GEOS Forecasts and Analysis
Get PDFThe NASA Global Modeling and Assimilation Office (GMAO) has been pursuing efforts to utilize all-sky (clear+cloudy+precipitating) MW radiance data and has developed a system to assimilate all-sky GPM Microwave Imager (GMI) radiance data in the Goddard Earth Observing System (GEOS) during the last PMM funding period. The system provides additional constraints on the analysis process near the storm regions and adjusts the geophysical parameters such as precipitation, cloud, moisture, surface pressure, and wind by combining information from GMI radiance measurements and model forecasts in an optimal manner. The system proved that assimilating the GMI all-sky radiance data improve the GEOS atmospheric analyses and forecasts. This all-sky data framework has been included in the GEOS Forward Processing (FP) system since July 11, 2018 and assimilates all-sky GMI data in real-time for GEOS global analysis and forecast production at the GMAO. We are currently extending this all-sky GMI radiance data assimilation system to assimilate more all-sky MW radiance data from other sensors such as the Microwave Humidity Sounder (MHS), the Advanced Technology Microwave Sounder (ATMS), the Special Sensor Microwave Imager/Sounder (SSMIS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometery (SAPHIR) onboard the GPM constellation spacecrafts. Preliminary results from this extended all-sky system show increased benefit from cloud- and precipitation-affected MW radiances with much larger spatial and temporal coverages compared to the all-sky system assimilating GMI alone and improved GEOS forecast skills especially for lower tropospheric humidity fields
Task 51 - Cloud-Optimized Format Study
Get PDFThe cloud infrastructure provides a number of capabilities that can dramatically improve access and use of Earth Observation data. However, in many cases, data may need to be reorganized and/or reformatted in order to make them tractable to support cloud-native analysis/access patterns. The purpose of this study is to examine the pros and cons of different formats for storing data on the cloud. The evaluation will focus on both enabling high-performance data access and usage as well as to meet the existing scientific data stewardship needs of EOSDIS
High Resolution Global Coupled Chemistry-Meteorology Simulations Using the NASA GEOS Composition Forecast System, GEOS-CF
No full textWe will give an overview of the NASA Global Earth Observing System Composition Forecast system (GEOS-CF), a high-resolution (0.25 degree) global composition model developed by the NASA Global Modeling and Assimilation Office (GMAO). This system combines the GEOS weather and aerosol model with the GEOS-Chem chemistry module (version 12) to provide a holistic view of atmospheric composition that captures a wide range of air pollutants such as ozone, nitrogen oxides, volatile organic compounds, and fine particulate matter. The spatial resolution of 0.25 degrees (approx. 25 km) is fine enough to resolve local features such as nighttime ozone titration previously resolved only by urban or regional models. Furthermore, since there are no boundary conditions for a global model, the GEOS-CF captures large-scale processes such as long-range transport of air pollutants from forest fires. Comparisons against surface observations highlight the models overall capability to reproduce the diurnal variability of air pollutants under a variety of meteorological conditions. In addition, we show how machine learning techniques can be used to correct for sub-grid variability, which further improves model estimates at a given surface observation site. The GEOS-CF system offers a new tool for scientists and the public health community alike and is being developed jointly with several government and non-profit partners. As an example, we will show the use of GEOS-CF during the Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE). The campaign, conducted in collaboration between NASA and the Bureau of Ocean Energy Management (BOEM), aims to investigate the response of onshore air quality to Outer Continental Shelf (OCS) oil and gas exploration, development and production. Detailed gas-phase chemistry, as provided by GEOS-CF, is critical to understand the formation of air pollution related to hydrocarbon emissions from offshore oil and gas activities. The accuracy of GEOS-CF can be further improved by incorporating detailed offshore emissions compiled by BOEM
Prediction Skill of the 2012 U.S. Great Plains Flash Drought in Subseasonal Experiment (SubX) Models
Get PDFFlash droughts refer to droughts that develop much more rapidly than normal (i.e., on the order of weeks to a few months). Such droughts can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict flash droughts in advance would greatly enhance our preparation for them and potentially mitigate their impacts. We investigated the prediction skill of U.S. flash droughts at subseasonal lead times in global forecast systems participating in the Subseasonal Experiment (SubX) project. An additional comprehensive set of hindcasts with NASA?s GEOSv2.1, a model with relatively high prediction skill, was performed to investigate the separate contributions of atmospheric and land initial conditions to flash drought prediction skill. Here we focus on results for the 2012 Great Plains flash drought, noting that the findings based on this event are generally applicable to other U.S. flash droughts. The prediction skill of the SubX models is quite variable. While the skill is limited to less than 2 weeks in most models, it is considerably higher (3-4 weeks or more) for certain models and initialization dates. The enhanced prediction skill is found to originate from two robust sources: 1) accurate soil moisture initialization, and 2) the satisfactory representation of quasi-stationary cross-North Pacific Rossby wave trains that lead to the rapid intensification of flash droughts. Our results corroborate earlier findings that accurate soil moisture initialization is important for skillful subseasonal forecasts and highlight the need for additional research on the sources and predictability of drought-inducing quasi-stationary Rossby waves
Dynamical Forecasts of Tropical Terrestrial Carbon Fluxes with the NASA S2S Retrospective Forecast System
Get PDFRecent advances in the ability to predict climate anomalies at sub-seasonal to seasonal (S2S) timescales allow us to explore the possibility of forecasting carbon flux anomalies. Although carbon flux forecasting is a relatively new concept, it is potentially beneficial as it can help us better understand global and regional land-atmosphere carbon feedbacks associated with climate variations and can provide guidance for future field mission design. Here we evaluate the skill of forecasted terrestrial carbon anomalies generated from meteorological anomalies produced with the NASA Global Modeling and Assimilation Office (GMAO) S2S forecast system. We focus here on three representative time periods (the most recent 2015-2016 El Nino, 2011 La Nina, and 2014 as a neutral year), with each corresponding 9-month forecast comprising four ensemble members initialized in the preceding December. The meteorological variables produced by the GMAO forecast system were bias-corrected using a climatology derived from the Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) before being used to drive a suite of offline simulations with the NASA Catchment-CN terrestrial biosphere model, a model that computes water-energy-carbon dynamics. Forecasts are evaluated by comparing against satellite-driven estimates of gross primary production (GPP) and inverse model estimates of net carbon flux that incorporate satellite carbon dioxide measurements. We find that the restrospectively predicted carbon fluxes in the tropics reasonably reproduce the signs and magnitudes of the observed anomalies between the 2015-2016 El Nino and the 2011 La Nina for both net flux and GPP. For instance, for the El Nino period, the magnitude of the forecasted negative GPP anomaly in the South American tropics (which undergoes anomalously warm and dry conditions) agrees with the observed GPP anomaly at leads of up to three or four months. Overall, this study demonstrates potential skill in the forecast of biospheric carbon fluxes a few months in advance, a capability that could contribute to attribution studies focusing on carbon flux variations and support innovative observation strategies in the future
CubeSat Measures World's First Ice Cloud Map to Support Climate Research
Get PDFVirginia Diodes, Inc. received NASA SBIR Awards to fund research and development for a lesser developed region of the electromagnetic spectrumterahertz waves. Their work led to funding from NASA ESTO, and the resulting CubeSat (named IceCube) captured the worlds first ice cloud map, which will contribute to our understanding of Earths climat