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NASA NTRS2015

Evaluation of a Model-Based Groundwater Drought Indicator in the Conterminous U.S.

Monitoring groundwater drought using land surface models is a valuable alternative given the current lack of systematic in situ measurements at continental and global scales and the low resolution of current remote sensing based groundwater data. However, uncertainties inherent to land surface models may impede drought detection, and thus should be assessed using independent data sources. In this study, we evaluated a groundwater drought index (GWI) derived from monthly groundwater storage output from the Catchment Land Surface Model (CLSM) using a GWI similarly derived from in situ groundwater observations. Groundwater observations were obtained from unconfined or semi-confined aquifers in eight regions of the central and northeastern U.S. Regional average GWI derived from CLSM exhibited strong correlation with that from observation wells, with correlation coefficients between 0.43 and 0.92. GWI from both in situ data and CLSM was generally better correlated with the Standard Precipitation Index (SPI) at 12 and 24 month timescales than at shorter timescales, but it varied depending on climate conditions. The correlation between CLSM derived GWI and SPI generally decreases with increasing depth to the water table, which in turn depends on both bedrock depth (a CLSM parameter) and mean annual precipitation. The persistence of CLSM derived GWI is spatially varied and again shows a strong influence of depth to groundwater. CLSM derived GWI generally persists longer than GWI derived from in situ data, due at least in part to the inability of coarse model inputs to capture high frequency meteorological variability at local scales. The study also showed that groundwater can have a significant impact on soil moisture persistence where the water table is shallow. Soil moisture persistence was estimated to be longer in the eastern U.S. than in the west, in contrast to previous findings that were based on models that did not represent groundwater. Assimilation of terrestrial water storage data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission improved the correlation between CLSM based regional average GWI and that based on in situ data in six of the eight regions. Practical issues regarding the application of GRACE assimilated groundwater storage for drought detection are discussed. An important conclusion of this study is that model parameters that control the depth to the water table, including bedrock depth, strongly influence the evolution and persistence of simulated groundwater and require careful configuration for drought monitoring.

Evaluation↗

NASA NTRS2012

Study of Movement and Seepage Along Levees Using DINSAR and the Airborne UAVSAR Instrument

We have studied the utility of high resolution SAR (synthetic aperture radar) for levee monitoring using UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) data collected along the dikes and levees in California's Sacramento-San Joaquin Delta and along the lower Mississippi River. Our study has focused on detecting and tracking changes that are indicative of potential problem spots, namely deformation of the levees, subsidence along the levee toe, and seepage through the levees, making use of polarimetric and interferometric SAR techniques. Here was present some results of those studies, which show that high resolution, low noise SAR imaging could supplement more traditional ground-based monitoring methods by providing early indicators of seepage and deformation.

levee health↗

NASA NTRS2012

Parametric 3D Atmospheric Reconstruction in Highly Variable Terrain with Recycled Monte Carlo Paths and an Adapted Bayesian Inference Engine

We describe a method for accelerating a 3D Monte Carlo forward radiative transfer model to the point where it can be used in a new kind of Bayesian retrieval framework. The remote sensing challenge is to detect and quantify a chemical effluent of a known absorbing gas produced by an industrial facility in a deep valley. The available data is a single low resolution noisy image of the scene in the near IR at an absorbing wavelength for the gas of interest. The detected sunlight has been multiply reflected by the variable terrain and/or scattered by an aerosol that is assumed partially known and partially unknown. We thus introduce a new class of remote sensing algorithms best described as "multi-pixel" techniques that call necessarily for a 3D radaitive transfer model (but demonstrated here in 2D); they can be added to conventional ones that exploit typically multi- or hyper-spectral data, sometimes with multi-angle capability, with or without information about polarization. The novel Bayesian inference methodology uses adaptively, with efficiency in mind, the fact that a Monte Carlo forward model has a known and controllable uncertainty depending on the number of sun-to-detector paths used.

Monte Carlo↗

NASA NTRS2011

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

image processing↗

NASA NTRS2013

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

Hurricanes↗

NASA NTRS2014

Evaluation of a Model-Based Groundwater Drought Indicator in the Conterminous U.S.

hydrology↗

NASA NTRS2014

Gemini NorthNIRI Spectra of Pluto and Charon: Simultaneous Analysis of the Surface and Atmosphere

94035We report on our analysis of blended Pluto and Charon spectra over the wavelength range 1.4 to 2.5 m as obtained by the NIRI instrument on Gemini North on June 25-28, 2004. The data have a resolving power () around 1500 and a SNR around 200 per pixel. The observed blended spectra are compared to models that combine absorption from the solid ice on the surface using Hapke theory, and absorption from the gaseous atmosphere. We assume the spectrum is a combination of several spatially separate spectral units: a CH4-rich ice unit, a volatile unit (an intimate mixture of N2, CH4 and CO), and a Charon unit (H2O, ammonia hydrate and kaolinite). We test for the presence of hydrocarbons (i.e. C2H6) and nitriles (i.e. HCN) and examine cases where additional ices are present as either pure separate spatial units, mixed with the CH4-rich unit or part of the volatile unit. We conclude that 2-4 of Plutos surface is covered with pure-C2H6 and our identification of C2H6 is significantly strengthened when absorption due to gaseous CH4 is included. The inclusion of Plutos atmosphere demonstrates that low-resolution, high-SNR observations are capable of detecting Plutos atmosphere during a time when Plutos atmosphere may have been undergoing rapid changes (1988-2002) and no high-resolution spectra were obtained. In particular, we identify features at 1.665 and 2.317 m as the Q-branch of the 23 and 3+4 bands of gaseous CH4, respectively. The later band is also evident in many previously published spectra of Pluto. Our analysis finds it is unnecessary to include 13CO to explain the depth of the 2.405 m, which has been previously suggested to be a spectral blended with C2H6, but we cannot definitively rule out its presence. Funding for this work (Cook) has been provided by a NASA-PATM grant.

Charon↗

NASA NTRS2016

Horizon Based Orientation Estimation for Planetary Surface Navigation

Planetary rovers navigate in extreme environments for which a Global Positioning System (GPS) is unavailable, maps are restricted to relatively low resolution provided by orbital imagery, and compass information is often lacking due to weak or not existent magnetic fields. However, an accurate rover localization is particularly important to achieve the mission success by reaching the science targets, avoiding negative obstacles visible only in orbital maps, and maintaining good communication connections with ground. This paper describes a horizon solution for precise rover orientation estimation. The detected horizon in imagery provided by the on board navigation cameras is matched with the horizon rendered over the existing terrain model. The set of rotation parameters (roll, pitch yaw) that minimize the cost function between the two horizon curves corresponds to the rover estimated pose.

localization↗

NASA NTRS2016

Marginalizing Instrument Systematics in HST WFC3 Transit Light Curves

Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3) infrared observations at 1.1-1.7 microns probe primarily the H2O absorption band at 1.4 microns, and have provided low-resolution transmission spectra for a wide range of exoplanets. We present the application of marginalization based on Gibson to analyze exoplanet transit light curves obtained from HST WFC3 to better determine important transit parameters such as "ramp" probability (R (sub p)) divided by "ramp" total (R (sub asterisk)), which are important for accurate detections of H2O. We approximate the evidence, often referred to as the marginal likelihood, for a grid of systematic models using the Akaike Information Criterion. We then calculate the evidence-based weight assigned to each systematic model and use the information from all tested models to calculate the final marginalized transit parameters for both the band-integrated and spectroscopic light curves to construct the transmission spectrum. We find that a majority of the highest weight models contain a correction for a linear trend in time as well as corrections related to HST orbital phase. We additionally test the dependence on the shift in spectral wavelength position over the course of the observations and find that spectroscopic wavelength shifts delta (sub lambda) times lambda) best describe the associated systematic in the spectroscopic light curves for most targets while fast scan rate observations of bright targets require an additional level of processing to produce a robust transmission spectrum. The use of marginalization allows for transparent interpretation and understanding of the instrument and the impact of each systematic evaluated statistically for each data set, expanding the ability to make true and comprehensive comparisons between exoplanet atmospheres.

Wakeford, H. R.↗

NASA NTRS2015

A Photometric Machine-Learning Method to Infer Stellar Metallicity

Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' < or = 18 mag), with 4500 K < or = Teff < or = 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of approx.0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

machine learning↗

NASA NTRS2016

Observed Trend in Surface Wind Speed Over the Conterminous USA and CMIP5 Simulations

There has been no spatial surface wind map even over the conterminous USA due to the difficulty of spatial interpolation of wind field. As a result, the reanalysis data were often used to analyze the statistics of spatial pattern in surface wind speed. Unfortunately, no consistent trend in wind field was found among the available reanalysis data, and that obstructed the further analysis or projection of spatial pattern of wind speed. In this study, we developed the methodology to interpolate the observed wind speed data at weather stations using random forest algorithm. We produced the 1-km daily climate variables over the conterminous USA from 1979 to 2015. The validation using Ameriflux daily data showed that R2 is 0.59. Existing studies have found the negative trend over the Eastern US, and our study also showed same results. However, our new datasets also revealed the significant increasing trend over the southwest US especially from April to June. The trend in the southwestern US represented change or seasonal shift in North American Monsoon. Global analysis of CMIP5 data projected the decrease trend in mid-latitude, while increase trend in tropical region over the land. Most likely because of the low resolution in GCM, CMIP5 data failed to simulate the increase trend in the southwest US, even though it was qualitatively predicted that pole ward shift of anticyclone help the North American Monsoon.

simulations↗

NASA NTRS2016

Spectroscopic Characterisation of Microlensing Events: Towards a New Interpretation of OGLE-2011-BLG-0417

The microlensing event OGLE-2011-BLG-0417 is an exceptionally bright lens binary that was predicted to present radial velocity variation at the level of several km s1. Pioneer radial velocity follow-up observations with the UVES spectrograph at the ESOVLT of this system clearly ruled out the large radial velocity variation, leaving a discrepancy between the observation and the prediction. In this paper, we further characterise the microlensing system by analysing its spectral energy distribution (SED) derived using the UVES spectrum and new observations with the ARCoIRIS (CTIO) near-infrared spectrograph and the Keck adaptive optics instrumentNIRC2 in the J, H, and Ks-bands. We determine the mass and distance of the stars independently from the microlensing modelling. We find that the SED is compatible with a giant star in the Galactic bulge and a foreground star with a mass of 0.94 +/- 0.09 M solar mass at a distance of 1.07 +/- 0.24 kpc. We find that this foreground star is likely the lens. Its parameters are not compatible with the onespreviously reported in the literature (0.52 +/- 0.04 M solar mass at 0.95 +/- 0.06 kpc), based on the microlensing light curve. A thoughtful reanalysis of the microlensing event is mandatory to fully understand the reason of this new discrepancy. More importantly, this paper demonstrates that spectroscopic follow-up observations of microlensing events are possible and provide independent constraints on the parameters of the lens and source stars, hence breaking some degeneracies in the analysis. UV-to-NIR low-resolution spectrographs like X-shooter (ESOVLT) could substantially contribute to this follow-up efforts, with magnitude limits above all microlensing events detected so far.

microlensing event OGLE-2011-BLG-0417↗

NASA NTRS2013

The Discovery of Pulsating Hot Subdwarfs in NGC 2808

We present preliminary results of a Hubble Space Telescope program to search for pulsating hot subdwarfs in the core of NGC 2808. These observations, obtained in March of 2013, were motivated by the recent discovery of such stars in the outskirts of Omega Cen. Both Omega Cen and NGC 2808 are massive globular clusters exhibiting complex stellar populations and large numbers of extreme horizontal branch stars. Our far-UV photometric monitoring of over 100 UV-bright stars has revealed at least six pulsating subdwarfs with periods ranging from 100 to 150 seconds. In the UV color-magnitude diagram of NGC 2808, all six of these stars lie immediately below the canonical horizontal branch, a region populated by the subluminous blue hook stars.Three of these six pulsators also have low-resolution far-UV spectroscopy that is sufficient to broadly constrain their atmospheric abundances and effective temperatures. Curiously, the spectroscopic and photometric data do not exhibit the uniformity one might expect from a well-defined instability strip.

Hubble Space Telescope↗

NASA NTRS2019

Cellular Statistical Models of Broken Cloud Fields. Part IV: Effects of Pixel Size on Idealized Satellite Observations

In the fourth part of our “Cellular Statistical Models of Broken Cloud Fields” series we use the binary Markov processes framework for quantitative investigation of the effects of low resolution of idealized satellite observations on the statistics of the retrieved cloud masks. We assume that the cloud fields are Markovian and are characterized by the “actual” cloud fraction (CF) and scale length. We use two different models of observations: a simple discrete-point sampling and a more realistic “pixel” protocol. The latter is characterized by a state attribution function (SAF) which has the meaning of the probability that the pixel with a certain CF is declared cloudy in the observed cloud mask. The stochasticity of the SAF means that the cloud/clear attribution is not ideal and can be affected by external or unknown factors. We show that the observed cloud masks can be accurately described as Markov chains of pixels and use the master-matrix formalism (introduced in Part III of the series) for analytical computation of their parameters: the “observed” CF and scale length. This procedure allows us to establish a quantitative relationship (which is pixel-size dependent) between the actual and the observed cloud-field statistics. The feasibility of restoring the former from the latter is considered. The adequacy of our analytical approach to idealized observations is evaluated using numerical simulations. Comparison of the observed parameters of the simulated datasets with their theoretical expectations showed an agreement within 0.005 for the CF, while for the scale length it is within 1% in the sampling case and within 4% in the pixel case.

Mikhail D. Alexandrov↗

NASA NTRS2018

Polarization Ray Tracing and Polarization Aberration Compensation in Reflective Astronomical Telescopes

Assessing and optimizing polarization performance in the context of ray-based optical design can be challenging. We describe an approach to this problem that decouples polarization effects from optical system geometry for reflective systems. Each surface's polarization properties are parameterized in terms of their impact on retardance and diattenuation in the small angle-of-incidence limit, separating polarization assessment from the task of coating design. A low-resolution ray trace of the system is adequate to determine ray geometry at each interface, which can then be interpolated to rapidly evaluate net Jones Matrix pupil functions. Coating behavior can be easily varied using the ellipsometric parameters to investigate impacts and compensation. Desired values of these parameters can then be specified as constraints in coating design. Investigation with candidate telescope optical designs for LUVOIR show baseline root-mean-square wavefront errors in the nm range for the on-diagonal Jones matrix component, and throughputs of tens of parts per million. Promising possibilities for compensation using a purpose-designed coating on the secondary mirror are discussed, which reduce the on-diagonal wavefront error by a factor 20, with accompanying but more modest reductions in coupling into off-diagonal terms.

Sabatke, Derek↗

NASA NTRS2018

WFIRST CGI Integral Field Spectrograph Performance and Post-Processing in the OS6 Observing Scenario

The WFIRST coronagraph instrument (CGI) will have an integral field spectrograph (IFS) backend to disperse the entire field of view at once and obtain spatially-resolved, low-resolution spectra of the speckles and science scene. The IFS will be key to understanding the spectral nature of the speckles, obtain science spectra of planets and disks, and will be used for broadband wavefront control. In order to characterize, predict, and optimize the performance of the instrument, we present a detailed model of the IFS in the context of the new OS6 observing scenario. The simulation includes spatial, spectral, and temporal variations of the speckle field on the IFS detector plane, which allows us to explore several post-processing methods and assess what gains can be expected. The simulator includes the latest models of the detector behavior when operating in photon-counting mode.

WFIRST coronagraph instrument (CGI)↗

NASA NTRS2018

SCExAO/CHARIS Near-Infrared Direct Imaging, Spectroscopy, and Forward-Modeling of κ and b: A Likely Young, Low-Gravity Superjovian Companion

We present SCExAO/CHARIS high-contrast imaging/JHK integral ﬁeld spectroscopy of κ And b, a directly imaged low-mass companion orbiting a nearby B9V star. We detect κ And b at a high signal-to-noise ratio and extract high-precision spectrophotometry using a new forward-modeling algorithm for (A-)LOCI complementary to KLIP-FM developed by Pueyo et al. κ And b’s spectrum best resembles that of a low-gravity L0–L1 dwarf (L0–L1γ). Its spectrum and luminosity are very well matched by 2MASS J0141-4633 and several other 12.5–15 M(sub J) free-ﬂoating members of the 40 Myr old Tuc–Hor Association, consistent with a system age derived from recent interferometric results for the primary, a companion mass at/near the deuterium-burning limit (13(sup +12, sub -2) M(sub J)), and a companion-to-primary mass ratio characteristic of other directly imaged planets (q ∼ 0.005(sup +0.005, sub -0.001)). We did not unambiguously identify additional, more closely orbiting companions brighter and more massive than κ And b down to ρ ∼ 0".3 (15 au). SCExAO/CHARIS and complementary Keck/NIRC2 astrometric points reveal clockwise orbital motion. Modeling points toward a likely eccentric orbit: a subset of acceptable orbits include those that are aligned with the star’s rotation axis. However, κ And b’s semimajor axis is plausibly larger than 55 au and in a region where disk instability could form massive companions. Deeper high-contrast imaging of κ And and low-resolution spectroscopy from extreme adaptive optics systems such as SCExAO/CHARIS and higher-resolution spectroscopy from Keck/OSIRIS or, later, IRIS on the Thirty Meter Telescope could help to clarify κ And b’s chemistry and whether its spectrum provides an insight into its formation environment.

Currie, Thayne↗

NASA NTRS2018

Spatial Representativeness Error in the Ground-Level Observation Networks for Black Carbon Radiation Absorption

There is high uncertainty in the direct radiative forcing of black carbon (BC), an aerosol that strongly absorbs solar radiation. The observation-constrained estimate, which is several times larger than the bottom-up estimate, is influenced by the spatial representativeness error due to the mesoscale inhomogeneity of the aerosol fields and the relatively low resolution of global chemistry-transport models. Here we evaluated the spatial representativeness error for two widely used observational networks (AErosol RObotic NETwork and Global Atmosphere Watch) by downscaling the geospatial grid in a global model of BC aerosol absorption optical depth to 0.1° × 0.1°. Comparing the models at a spatial resolution of 2° × 2° with BC aerosol absorption at AErosol RObotic NETwork sites (which are commonly located near emission hot spots) tends to cause a global spatial representativeness error of 30%, as a positive bias for the current top-down estimate of global BC direct radiative forcing. By contrast, the global spatial representativeness error will be 7% for the Global Atmosphere Watch network, because the sites are located in such a way that there are almost an equal number of sites with positive or negative representativeness error.

Wang, Rong↗