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NASA NTRS2020

Building a standardized Observing System Simulation Experiment (OSSE) framework for Mars

We advocate that the Decadal Survey recommends the NASA Science Mission Directorate to develop a rigorous Observing System Simulation Experiment (OSSE) framework for Mars, to optimize future atmospheric observations. Atmospheric conditions on Mars are a potential hazard source for landing missions. Errors in the estimates of atmospheric density profiles, inadequate knowledge of wind vertical structure and dust concentration as a function of height are likely causes of uncertainty at the landing site on the order of kilometers. An operational real-time weather forecasting capability for Mars would reduce such uncertainties, carrying enormous benefits to future robotic missions, and would be an invaluable prerequisite for human missions.A real-time forecasting capability relies upon three fundamental components: a critical mass of observing systems, a data assimilation system (DAS), and a global forecast model. The DAS allows the model to ingest the data effectively, optimizing the observational information content,and transforming them into a gridded representation of the atmosphere at a given time, called an ‘analysis’. The analysis is the best estimate of the atmospheric state for that time, and also represents a set of ‘initial conditions’ from which a global model can be initialized, to predict a future state of the atmosphere. The connection between analysis and forecast represents the foundation of modern weather forecasting. However, from the point of view of a forecast system,not all observations are equally impactful, partially because of the problem of “observational error correlation”, one important research topic in data assimilation development. For the Earth, partly due to the spontaneous and deregulated development of observations and forecast capabilities worldwide for more than half a century,the use of observations in contemporary operational forecast systems is suboptimal, with many potentially useful data being underutilized. On the contrary, Mars atmospheric scientists are in the unique situation of designing the next-generation observing systems by learning from the experience gathered on the Earth, so as to assure that the future instruments are specifically optimized to give the maximum benefit to a future weather forecast capability.An immensely powerful tool that has been firmly established by atmospheric scientists on the Earth is represented by a properly designed OSSE framework. A realistic OSSE framework cannot only quantify the benefit of future data types, be them surface based or space borne, but can also help design and optimize an entire observational network. Furthermore, OSSEs can provide deep insights into an atmosphere’s behavior, by addressing conceptual problems of its intrinsic predictability and delineating the regions or features of the atmosphere which are more sensitive to additional data and would benefit from a denser sampling. The difficulties posed by OSSEs are fundamentally different for Earth and Mars. For Earth, the enormous data volume imposes a tremendous constraint on any innovation in the observing systems: it is very hard for a single sensor to impact the skill. For Mars, the problem is the opposite: almost any additional instrument will exert some impact. However, OSSEs can help to evaluate the cost/benefit for every sensor and suggest optimal data configuration and density.The purpose of this white paper is to provide an introduction to a rigorously designed OSSE framework, explain the underlying problems and challenges, and engage the Mars community to collaborate with Earth Atmospheric scientists in order to develop a joint-OSSE framework for Mars with the largest consensual basis possible. An OSSE infrastructure would increase the understanding of the Martian atmosphere, would help NASA to optimize instrument specifications and orbit choice, providing the maximium benefit for a given expenditure of resources, and could even help establishing a roadmap for a future real-time weather forecasting capability.

Oreste Reale↗
NASA NTRS2017

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

NeMO=NET↗
NASA NTRS2018

A 3D Citizen Science Video Game for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network aimed at accurately assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. We present here the active learning component of the project, which consists of an interactive video game prototype for tablet and mobile devices where players are able to intuitively label morphology classifications over mm-scale 3D coral reef imagery. Active learning applications present a novel methodology for engaging the public while efficiently providing large-scale training and test data for increasingly complex and data-intensive machine learning algorithms. NeMO-Net trains players on domain-specific knowledge through interactive tutorials and periodically checks players' input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide personalized classification training. Players can rate the classifications of other players, unlock rewards and join a global community as they explore and classify coral reefs and other shallow marine environments.

Citizen Science↗
NASA NTRS2007

Eye Safe, Visible Wavelength Lidar Systems: Design and Operational Advances, Results and Potential

In the early nineties the first of the eye safe visible wavelength lidar systems known now as Micro Pulse Lidar (MPL) became operational. The important advance of the design was a system that, unlike most existing lidar, operated at eye safe energy densities and could thus operate unattended for full time monitoring. Since that time there have been many dozens of these systems produced and applied for full time profiling of atmospheric cloud and aerosol structure. There is currently an observational network of MPL sites to support global climate research. In thc course of application of these instruments there have been significant improvements in the, design and performance of the systems. In the last half decade particularly there has been significant application and technical development of MPL systems. In this paper we review progress. The current MPL systems in use are all single wavelength systems designed for cloud and aerosol applications. For the cloud and aerosol applications, both lidar depolarization and multi wavelength measurements have significant applications. These can be accomplished with the MPL, approach. The main current challenge for the lidar network activity are in the area of the reliability, repeatability and efficiency of data processing. The network makes use of internet data downloads and automated processing. The heights of all cloud and aerosol layers are needed. The recent emphasis has been in operationally deriving aerosol extinction cross section. Future emphasis will include adding cirrus optical parameters. For operational effectiveness, improvements to simplify routine data signal calibration are being researched. Overall the MPL systems have proven very effective. A large data base of results from globally distributed sites can be easily accessed through the internet. Applications have included atmospheric model development. Validation of current global satellite observations of aerosol and clouds, including now orbital lidar observations, was a primary goal for NASA. Although sampling issues require careful consideration, results have proven useful.

Spinhirne, James↗
NASA NTRS2020

Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley

Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We conduct an in-depth examination of the spatial and temporal characteristics of the AOD-PM2.5 relationship by leveraging data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaign where eight NASA Aerosol Robotic Network (AERONET) sites were co-located with EPA Air Quality System (AQS) monitoring sites in California’s Central Valley from November 2012 to April 2013. With this spatiotemporally rich data we found that linear calibration models (R(exp 2) = 0.35, RMSE = 10.38 µg/cu.m) were significantly improved when spatial (R(exp 2) = 0.45, RMSE = 9.54 µg/cu.m), temporal (R(exp 2) = 0.62, RMSE = 8.30 µg/cu.m), and spatiotemporal (R(exp 2) = 0.65, RMSE = 7.58 µg/cu.m) functions were included. As a use-case we applied the best spatiotemporal model to convert space-borne MultiAngle Imaging Spectroradiometer (MISR) AOD observations to predict PM2.5 over the region (R(exp 2) = 0.60, RMSE = 8.42 µg/cu.m). Our results imply that simple AERONET AOD-PM2.5 calibrations are robust and can be reliably applied to space-borne AOD observations, resulting in PM2.5 prediction surfaces for use in downstream applications.

Meytar Sorek-Hamer↗
NASA NTRS2010

Sources of Uncertainty in Predicting Land Surface Fluxes Using Diverse Data and Models

In the domain of predicting land surface fluxes, models are used to bring data from large observation networks and satellite remote sensing together to make predictions about present and future states of the Earth. Characterizing the uncertainty about such predictions is a complex process and one that is not yet fully understood. Uncertainty exists about initialization, measurement and interpolation of input variables; model parameters; model structure; and mixed spatial and temporal supports. Multiple models or structures often exist to describe the same processes. Uncertainty about structure is currently addressed by running an ensemble of different models and examining the distribution of model outputs. To illustrate structural uncertainty, a multi-model ensemble experiment we have been conducting using the Terrestrial Observation and Prediction System (TOPS) will be discussed. TOPS uses public versions of process-based ecosystem models that use satellite-derived inputs along with surface climate data and land surface characterization to produce predictions of ecosystem fluxes including gross and net primary production and net ecosystem exchange. Using the TOPS framework, we have explored the uncertainty arising from the application of models with different assumptions, structures, parameters, and variable definitions. With a small number of models, this only begins to capture the range of possible spatial fields of ecosystem fluxes. Few attempts have been made to systematically address the components of uncertainty in such a framework. We discuss the characterization of uncertainty for this approach including both quantifiable and poorly known aspects.

Dungan, Jennifer L.↗
NASA NTRS1996

Temporal Evolution of SL-9 Impact Sites on Jupiter and Global Maps of Jupiter from Multi-Observatory Visible and Infrared Images

The objective of this research was to investigate the temporal behavior of the impact features on Jupiter created by the fragments of the Shoemaker Levy-9 comet that collided with the planet in July 1994. The primary observations used in the study were ground based images of Jupiter acquired from the Swedish Solar Vacuum Tube on the island of La Palma in the Canary Islands. The measurement of position of the impact features in images acquired immediately after the impact over a period of a few days revealed that the apparent drift rates were too high and that a repetitive pattern could be seen in the longitude position on successive rotations. This could be explained only by the fact that the measured longitudes of the impact sites were being affected by parallax due to a significant elevation of the impact debris above the nominal cloud top altitude value used for image navigation. Once the apparent positions are analyzed as a function of the meridian angle, the parallax equation can be used to infer the height of the impact features above the cloud deck, once the true impact position (longitude) for the feature is known. Due to their inherent high spatial resolution, the HST measurements of the impact site locations have been accepted widely. However, these suffer from the parallax themselves since few of them were obtained at central meridian. Ground based imaging have the potential to improve this knowledge as they do observe most of the impact sites on either side of the central meridian, except for the degraded resolution. Measurements over a large number of images enables us to minimize the position error through regression and thus estimate both the actual impact site location devoid of parallax bias, and also of the altitude level of the impact debris above the cloud deck. With rapid imaging there is the potential to examine the time evolution of the altitude level. Several hundred ground based images were processed, navigated and subjected to the impact site location measurements. HST images were also acquired and used to calibrate the results and to improve the sample. The resources available enabled an in-depth study only of impact site A, however, many more images have since become available through the global network observations through Lowell Observatory.

Limaye, Sanjay S.↗
NASA NTRS2019

Exploring Aerosols Near Clouds With High‐Spatial‐Resolution Aircraft Remote Sensing During SEAC(4)RS

Since aerosols are important to our climate system, we seek to observe the variability of aerosol properties within cloud systems. When applied to the satellite‐borne Moderate‐resolution Imaging Spectroradiometer (MODIS), the Dark Target retrieval algorithm provides global aerosol optical depth (AOD; at 0.55 μm) in cloud‐free scenes. Since MODIS' resolution (500‐m pixels, 3‐ or 10‐km product) is too coarse for studying near‐cloud aerosol, we ported the Dark Target algorithm to the high‐resolution (~50‐m pixels) enhanced‐MODIS Airborne Simulator (eMAS), which flew on the high‐altitude ER‐2 during the Studies of Emissions, Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys Airborne Science Campaign over the United States in 2013. We find that even with aggressive cloud screening, the ~0.5‐km eMAS retrievals show enhanced AOD, especially within 6 km of a detected cloud. To determine the cause of the enhanced AOD, we analyze additional eMAS products (cloud retrievals and degraded‐resolution AOD), coregistered Cloud Physics Lidar profiles, MODIS aerosol retrievals, and ground‐based Aerosol Robotic Network observations. We also define spatial metrics to indicate local cloud distributions near each retrieval and then separate into near‐cloud and far‐from‐cloud environments. The comparisons show that low cloud masking is robust, and unscreened thin cirrus would have only a small impact on retrieved AOD. Some of the enhancement is consistent with clear‐cloud transition zone microphysics such as aerosol swelling. However, 3‐D radiation interaction between clouds and the surrounding clear air appears to be the primary cause of the high AOD near clouds.

aerosol retrieval algorithm↗
NASA NTRS2003

On the Measurement Errors of the Joss-Waldvogel Disdrometer

The Joss-Waldvogel (JW) disdrometer is considered to be a reference instrument for drop size distribution measurements. It has been widely used in many field campaigns as part of validation efforts of radar rainfall estimation. It has also been incorporated in radar rain gauge rainfall observation networks at several ground validation sites for NASA s Tropical Rainfall Measuring Mission (TRMM). It is anticipated that the Joss-Waldvogel disdrometer will be one of the key instruments for ground validation for the upcoming Global Precipitation Measurement (GPM) mission. The JW is an impact type disdrometer and has several shortcomings. One such shortcoming is that it underestimates the number of small drops in heavy rain due to the disdrometer dead time. The detection of smaller drops is also suppressed in the presence of background noise. Further, drops larger than 5.0 to 5.5 mm diameter cannot be distinguished by the disdrometer. The JW assumes that all raindrops fall at their terminal fall speed. Ignoring the influence of vertical air motion on raindrop fall speed results in errors in determining the raindrop size. Also, the bulk descriptors of rainfall that requires the fall speed of the drops will be overestimated or underestimated due to errors in measured size and assumed fall velocity. Long-term observations from a two-dimensional video disdrometer are employed to simulate the JW disdrometer and assess how it s shortcomings affect radar rainfall estimation. Data collected from collocated JW disdrometers were also incorporated in this study.

Tokay, Ali↗
NASA NTRSDate not supplied

Characterization of OCO-2 and ACOS-GOSAT biases and errors for flux estimates

We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~1.1 ppm, while having 2-3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5-10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

Susan S Kulawik↗
NASA NTRS1971

Interhemispheric comparison of atmospheric circulation features as evaluated from NIMBUS satellite data

Findings are presented for IRIS data from NIMBUS 3 in mapping the global ozone distribution. The seasonal and regional variations of ozone, especially in the Southern Hemisphere, reveal features that were not evident from the sparse ground-based ozone observation network in this hemisphere. A regression analysis was undertaken for temperature and height fields on radiance data. Spectrum analyses of upper wind data from the North American section and Australia were completed.

Reiter, E. R.↗
NASA NTRS1980

Halley's comet 1985-86: space exploration

A coordinated program to explore Halley's comet in 1985 to 86 is proposed. The program employs a variety of observational systems for remote observations and utilizes spacecraft encounters with the comet to obtain in-situ measurements. Included in the observational network are groundbased observatories, the Space Telescope, a Spacelab cometary observatory, small astronomical satellites, and experiments carried on airborne observatories and sounding rockets. It is assumed that a ballistic flythrough technique will be used to carry out the spacecraft encounters. The proposed strategy calls for the simultaneous launch of two spacecraft towards an intercept with Halley in March 1986. Following the Halley encounter one spacecraft is retargeted to intercept comet Borrelly in January 1988, while the other spacecraft proceeds to an encounter with comet Tempel 2 in September 1988.

Farquhar, R. W.↗
NASA NTRS1980

A description of the NSSL cases used for a simulated VAS retrieval study

A documentation of eight National Severe Storm Laboratory severe storm cases, which serve as a basis for a simulated VISSR Atmospheric Sounder retrieval study, is presented in this paper. Six of the selected cases provide a control data set to complete the statistical information needed for retrieval techniques based upon the use of regression matrices. The other two cases are to be used in the actual retrieval experiments. The selection was based upon the presence of moisture gradients in the analysis region, the availability of satellite images at the selected time periods, and the extent of cloud cover within the observing network.

Mostek, A.↗
NASA NTRS1983

The MST radar technique: Requirements for operational weather forecasting

There is a feeling that the accuracy of mesoscale forecasts for spatial scales of less than 1000 km and time scales of less than 12 hours can be improved significantly if resources are applied to the problem in an intensive effort over the next decade. Since the most dangerous and damaging types of weather occur at these scales, there are major advantages to be gained if such a program is successful. The interest in improving short term forecasting is evident. The technology at the present time is sufficiently developed, both in terms of new observing systems and the computing power to handle the observations, to warrant an intensive effort to improve stormscale forecasting. An assessment of the extent to which the so-called MST radar technique fulfills the requirements for an operational mesoscale observing network is reviewed and the extent to which improvements in various types of forecasting could be expected if such a network is put into operation are delineated.

Larsen, M. F.↗
NASA NTRS1985

The Structure and Dynamics of an Observed Moist Front

The structure and dynamics of the moist cold front of 25 and 26 April 1979, the third observing day of the Severe Environmental Storms and Mesoscale experiment (SESAME), are investigated through the use of a three-dimensional mesoscale numerical model. This work is one of the first studies in which model results are compared, in a one-to-one manner, with a detailed observational analysis, namely that of Ogura and Portis (1982) as taken from the SESAME observations. In addition, frontogenetical effects, both adiabatic and diabatic, are studied on a vertical cross-section through the front; similarities and differences with the adiabatic analysis of Ogura and Portis are discussed. Many similarities exist between the modeled and analyzed fields, although the analysis tends to have weaker horizontal gradients due to the coarseness of the observational network. Vorticity and convergence near the surface were found to have the same magnitude in both the model solution and the analysis, in contrast to idealized frontogenesis models which predict vorticity to be much larger than convergence.

Orlanski, I.↗
NASA NTRS1985

Gravity Waves

In recent years it has been widely recognized that vertically propagating gravity waves play an important role in the dynamics of the middle atmosphere in partly determining the large scale temperature and wind structure through their energy and momentum transport. In particular, the meridional temperature distribution and the mean wind profile in the upper mesosphere are considered to be largely affected by the turbulence diffusion and friction that gravity waves induce through their breaking. For an understanding of the middle atmosphere circulation, it is therefore very important to know the global distribution and temporal variation of gravity waves in a climatological sense. The results of the statistics for gravity wave activity in the stratosphere and mesosphere are presented as a function of latitude and month, with the aid of meteorological rocket network observations.

Hirota, I.↗