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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

The North Pacific Summer Jet and Climate Extremes over North America: Mechanisms and Model Biases

The North Pacific summer jet (NPSJ) plays a critical role as a waveguide for weather systems and other sub-seasonal Rossby waves entering North America and therefore has a controlling influence on the warm season weather and climate extremes over much of the continent. In particular, much of the warm season precipitation that occurs over the central United States depends on subseasonal transients that are able to tap moisture from the Gulf of Mexico as they propagate across the continent. The GEOS-5 atmospheric general circulation model (AGCM), like many AGCMs, is deficient in the simulation of the NPSJ. It is shown that the deficiency is composed of: 1) a stunted jet in which the strongest winds are confined to the Asian continent, failing to extend across the North Pacific into the Gulf of Alaska as observed, and 2) a zonally symmetric poleward shift in the jet. These biases combine to impede the eastward propagation of the weather systems into the continent (the stunted jet), and deprive those systems that do enter the continent access to the moisture from the Gulf (the northward shift), leading to a dry bias over the central US. It is shown that the stunted jet bias is the result of too strong heating that occurs just south of the jet core over and near Tibet. Furthermore, it is shown that the poleward shift of the NPSJ can be corrected in the current GEOS-5 AGCM by increasing the vertical resolution. The implications of these results for improving warm season forecasts of extreme events will be discussed.

NPS↗
NASA NTRS1974

Calibration and evaluation of Skylab altimetry for geodetic determination of the geoid

The author has identified the following significant results. The analysis was based on a time series intrinsic relationship between the satellite ephemeris, altimeter measured ranges, and the corresponding a priori values of subsatellite geoidal heights. Using, least squares processing with parameter weighting, the objective was to recover: (1) the absolute geoidal heights of the subsatellite points; and (2) the associated altimeter calibration constants. Preliminary results from Skylab mission SL-2 are given, using various combinations from two sets of orbit ephemeris and altimeter ranges. It is shown that correctly scaled geoidal heights cannot be deduced by merely subtracting the altimeter range from the geodetic height of the satellite unless the satellite ephemeris and the altimeter have no unknown significant systematic errors or biases and drifts. It is emphasized that the primary objective of the Skylab altimeter is to determine the instrument feasibility. Any additional applications of the data such as for geodesy, geophysics, and oceanography are desirable. Although accurate orbit is required for such applications, it is not a prerequisite for determining the instrument feasibility.

Mourad, A. G.↗
NASA NTRS1991

Development of a calibration workstation for SIR-C

The paper describes the status of a comprehensive synthetic aperture radar (SAR) software package developed specifically to aid the calibration of a multifrequency and multipolarization radar like the Shuttle Imaging Radar, SIR-C. The primary data types processed are engineering telemetry, point targets (corner reflectors and polarimetric active radar calibrators), distributed targets (clutter), tone-generators, and ground receivers. The parameters derived to aid the calibration of the production processor include measurements of relative radiometric channel to channel imbalance, absolute calibration constants, antenna pattern corrections, noise levels, polarimetric cross-talk, and geometric offsets. The software reports standard performance measurements such as resolution, peak and integrated sidelobes, radiometric response accuracy, and geometrical distortions such as scale errors, position bias and standard deviation, and image skew. Furthermore, the inclusion of software to allow updates of the shuttle-state vector (position and velocity) is considered.

Madsen, S. N.↗
NASA NTRS1998

Real Time Land-Surface Hydrologic Modeling Over Continental US

The land surface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. Spatially and temporally variable rainfall and available energy, combined with land surface heterogeneity cause complex variations in all processes related to surface hydrology. The characterization of the spatial and temporal variability of water and energy cycles are critical to improve our understanding of land surface-atmosphere interaction and the impact of land surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated land surface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes. This has motivated the NWP to impose ad hoc corrections to the land surface states to prevent this drift. A proposed methodology is to develop Land Data Assimilation schemes (LDAS), which are uncoupled models forced with observations, and not affected by NWP forcing biases. The proposed research is being implemented as a real time operation using an existing Surface Vegetation Atmosphere Transfer Scheme (SVATS) model at a 40 km degree resolution across the United States to evaluate these critical science questions. The model will be forced with real time output from numerical prediction models, satellite data, and radar precipitation measurements. Model parameters will be derived from the existing GIS vegetation and soil coverages. The model results will be aggregated to various scales to assess water and energy balances and these will be validated with various in-situ observations.

Houser, Paul R.↗
NASA NTRS2010

Special Component Designs for Differential-Amplifier MMICs

Special designs of two types of electronic components transistors and transmission lines have been conceived to optimize the performances of these components as parts of waveguide-embedded differential-amplifier monolithic microwave integrated circuits (MMICs) of the type described in the immediately preceding article. These designs address the following two issues, the combination of which is unique to these particular MMICs: Each MMIC includes a differential double-strip transmission line that typically has an impedance between 60 and 100 W. However, for purposes of matching of impedances, transmission lines having lower impedances are also needed. The transistors in each MMIC are, more specifically, one or more pair(s) of InP-based high-electron-mobility transistors (HEMTs). Heretofore, it has been common practice to fabricate each such pair as a single device configured in the side-to-side electrode sequence source/gate/drain/gate/source. This configuration enables low-inductance source grounding from the sides of the device. However, this configuration is not suitable for differential operation, in which it is necessary to drive the gates differentially and to feed the output from the drain electrodes differentially. The special transmission-line design provides for three conductors, instead of two, in places where lower impedance is needed. The third conductor is a metal strip placed underneath the differential double-strip transmission line. The third conductor increases the capacitance per unit length of the transmission line by such an amount as to reduce the impedance to between 5 and 15 W. In the special HEMT-pair design, the side-to-side electrode sequence is changed to drain/gate/source/gate/ drain. In addition, the size of the source is reduced significantly, relative to corresponding sizes in prior designs. This reduction is justified by the fact that, by virtue of the differential configuration, the device has an internal virtual ground, and therefore there is no need for a low-resistance contact between the source and the radio-frequency circuitry. The source contact is needed only for DC biasing. These designs were implemented in a single-stage-amplifier MMIC. In a test at a frequency of 305 GHz, the amplifier embedded in a waveguide exhibited a gain of 0 dB; after correcting for the loss in the waveguide, the amplifier was found to afford a gain of 0.9 dB. In a test at 220 GHz, the overall gain of the amplifier- and-waveguide assembly was found to be 3.5 dB.

Kangaslahti, Pekka↗
NASA NTRS2012

NASA Tech Briefs, April 2012

Topics include: Computational Ghost Imaging for Remote Sensing; Digital Architecture for a Trace Gas Sensor Platform; Dispersed Fringe Sensing Analysis - DFSA; Indium Tin Oxide Resistor-Based Nitric Oxide Microsensors; Gas Composition Sensing Using Carbon Nanotube Arrays; Sensor for Boundary Shear Stress in Fluid Flow; Model-Based Method for Sensor Validation; Qualification of Engineering Camera for Long-Duration Deep Space Missions; Remotely Powered Reconfigurable Receiver for Extreme Environment Sensing Platforms; Bump Bonding Using Metal-Coated Carbon Nanotubes; In Situ Mosaic Brightness Correction; Simplex GPS and InSAR Inversion Software; Virtual Machine Language 2.1; Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction; Pandora Operation and Analysis Software; Fabrication of a Cryogenic Bias Filter for Ultrasensitive Focal Plane; Processing of Nanosensors Using a Sacrificial Template Approach; High-Temperature Shape Memory Polymers; Modular Flooring System; Non-Toxic, Low-Freezing, Drop-In Replacement Heat Transfer Fluids; Materials That Enhance Efficiency and Radiation Resistance of Solar Cells; Low-Cost, Rugged High-Vacuum System; Static Gas-Charging Plug; Floating Oil-Spill Containment Device; Stemless Ball Valve; Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs; Oxygen-Methane Thruster; Lunar Navigation Determination System - LaNDS; Launch Method for Kites in Low-Wind or No-Wind Conditions; Supercritical CO2 Cleaning System for Planetary Protection and Contamination Control Applications; Design and Performance of a Wideband Radio Telescope; Finite Element Models for Electron Beam Freeform Fabrication Process Autonomous Information Unit for Fine-Grain Data Access Control and Information Protection in a Net-Centric System; Vehicle Detection for RCTA/ANS (Autonomous Navigation System); Image Mapping and Visual Attention on the Sensory Ego-Sphere; HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis; and IMAGESEER - IMAGEs for Education and Research.

Source record↗
NASA NTRS2013

A Fast and Sensitive New Satellite SO2 Retrieval Algorithm based on Principal Component Analysis: Application to the Ozone Monitoring Instrument

We describe a new algorithm to retrieve SO2 from satellite-measured hyperspectral radiances. We employ the principal component analysis technique in regions with no significant SO2 to capture radiance variability caused by both physical processes (e.g., Rayleigh and Raman scattering and ozone absorption) and measurement artifacts. We use the resulting principal components and SO2 Jacobians calculated with a radiative transfer model to directly estimate SO2 vertical column density in one step. Application to the Ozone Monitoring Instrument (OMI) radiance spectra in 310.5-340 nm demonstrates that this approach can greatly reduce biases in the operational OMI product and decrease the noise by a factor of 2, providing greater sensitivity to anthropogenic emissions. The new algorithm is fast, eliminates the need for instrument-specific radiance correction schemes, and can be easily adapted to other sensors. These attributes make it a promising technique for producing longterm, consistent SO2 records for air quality and climate research.

Principal component analysis↗
NASA NTRS2003

Improved Measurement of Coherence in Presence of Instrument Noise

A method for correcting measured coherence spectra for the effect of incoherent instrument noise has been developed and demonstrated. Coherence measurements are widely used in engineering and science to determine the extent to which two signals are alike. The signals may come from two different sources or from the same source at different times. The coherence of time-lagged signals from a single source is an excellent indication of the effective lifetime of the signal components as a function of their frequency. Unfortunately, incoherent instrument noise will bias the measurement to lower values and may lead the user of the data to false conclusions about the longevity of significant features. The new method may be used whenever both the signal and noise power spectra are known and the noise is incoherent both with the signal and with itself at the applicable time delays. It provides a corrected coherence spectrum given the measured coherence and power spectra. For powerlaw signal spectra and instrumental white noise, the correction formula takes a particularly simple and explicit form. Since many geophysical signals exhibit powerlaw behavior and most instrument noise spectra approach white noise, the simplified form should be widely applicable in meteorology, oceanography, geology, and planetary geophysics.

Merceret, Francis J.↗
NASA NTRS2010

Development and Evaluation of a Cloud-Gap-Filled MODIS Daily Snow-Cover Product

The utility of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover products is limited by cloud cover which causes gaps in the daily snow-cover map products. We describe a cloud-gap-filled (CGF) daily snowcover map using a simple algorithm to track cloud persistence, to account for the uncertainty created by the age of the snow observation. Developed from the 0.050 resolution climate-modeling grid daily snow-cover product, MOD10C1, each grid cell of the CGF map provides a cloud-persistence count (CPC) that tells whether the current or a prior day was used to make the snow decision. Percentage of grid cells "observable" is shown to increase dramatically when prior days are considered. The effectiveness of the CGF product is evaluated by conducting a suite of data assimilation experiments using the community Noah land surface model in the NASA Land Information System (LIS) framework. The Noah model forecasts of snow conditions, such as snow-water equivalent (SWE), are updated based on the observations of snow cover which are obtained either from the MOD1 OC1 standard product or the new CGF product. The assimilation integrations using the CGF maps provide domain averaged bias improvement of -11 %, whereas such improvement using the standard MOD1 OC1 maps is -3%. These improvements suggest that the Noah model underestimates SWE and snow depth fields, and that the assimilation integrations contribute to correcting this systematic error. We conclude that the gap-filling strategy is an effective approach for increasing cloud-free observations of snow cover.

Hall, Dorothy K.↗
NASA NTRS2021

Accounting for the photochemical variation in stratospheric NO2 in the SAGE III/ISS solar occultation retrieval

The Stratospheric Aerosol and Gas Experiment (SAGE) III has been operating on the International Space Station (ISS) since mid-2017. Nitrogen dioxide (NO2) number density profiles are routinely retrieved from SAGE III/ISS solar occultation measurements in the middle atmosphere. Although NO2 density varies throughout the day due to photochemistry, the standard SAGE NO2 retrieval algorithm neglects these variations along the instrument’s line of sight by assuming that the number density has a constant gradient within a given vertical layer of the atmosphere. This assumption will result in a retrieval bias for a species like NO2 that changes rapidly across the terminator. In this work we account for diurnal variations in retrievals of NO2 from the SAGE III/ISS measurements, and we determine the impact of this algorithm improvement on the resulting NO2 number densities. The first step in applying the diurnal correction is to use publicly available SAGE III/ISS products to convert the retrieved number density profiles to optical depth profiles. The retrieval is then re-performed with a new matrix that applies photochemical scale factors for each point along the line of sight according to the changing solar zenith angle. In general NO2 that is retrieved by accounting for these diurnal variations is more than 10% lower than the standard algorithm below 30 km. This effect is greatest in winter at high latitudes and generally greater for sunrise occultations than sunset. Comparisons with coincident profiles from the Optical Spectrograph and InfraRed Imager System (OSIRIS) show that NO2 from SAGE III/ISS is generally biased high; however the agreement improves by up to 20% in the mid-stratosphere when diurnal variations are accounted for in the retrieval. We conclude that diurnal variations along the SAGE III/ISS line of sight are an important term to consider for NO2 analyses at altitudes below 30 km.

Stratospheric NO2↗
NASA NTRS2021

A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks

Retrieval of aquatic biogeochemical variables, such as the near-surface concentration of chlorophyll-a (Chla) in inland and coastal waters via remote observations, has long been regarded as a challenging task. This manuscript applies Mixture Density Networks (MDN) that use the visible spectral bands available by the Operational Land Imager (OLI) aboard Landsat-8 to estimate Chla. We utilize a database of co-located in situ radiometric and Chla measurements (N = 4,354), referred to as Type A data, to train and test an MDN model (MDN(A)). This algorithm’s performance, having been proven for other satellite missions, is further evaluated against other widely used machine learning models (e.g., support vector machines), as well as other domain-specific solutions (OC3), and shown to offer significant advancements in the field. Our performance assessment using a held-out test data set suggests that a 49% (median) accuracy with near-zero bias can be achieved via the MDN(A) model, offering improvements of 20 to 100% in retrievals with respect to other models. The sensitivity of the MDN(A) model and benchmarking methods to uncertainties from atmospheric correction (AC) methods, is further quantified through a semi-global matchup dataset (N = 3,337), referred to as Type B data. To tackle the increased uncertainties, alternative MDN models (MDN(B)) are developed through various features of the Type B data (e.g., Rayleigh-corrected reflectance spectra ρ(s)). Using held-out data, along with spatial and temporal analyses, we demonstrate that these alternative models show promise in enhancing the retrieval accuracy adversely influenced by the AC process. Results lend support for the adoption of MDN(B) models for regional and potentially global processing of OLI imagery, until a more robust AC method is developed. Index Terms—Chlorophyll-a, coastal water, inland water, Landsat-8, machine learning, ocean color, aquatic remote sensing.

Brandon Smith↗
NASA NTRS2020

Improved SIFTER v2 algorithm for long-term GOME-2A satellite retrievals of fluorescence with a correction for instrument degradation

Solar-induced fluorescence (SIF) data from satellites are increasingly used as a proxy for photosynthetic activity by vegetation, and as a constraint on gross primary production. Here we report on improvements in the algorithm to retrieve mid-morning (09:30 hrs local time) SIF estimates on the global scale from GOME-2 sensor on the Metop-A satellite (GOME-2A) for the period 2007-2019. Our new SIFTER (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval) v2 algorithm improves over a previous version by using a narrower spectral window that avoids strong oxygen absorption and is less sensitive to water vapour absorption, by constructing stable reference spectra from a 6-year period (2007-2012) of atmospheric spectra over the Sahara, and by applying a latitude-dependent zero-level adjustment that accounts for biases in the data product. We generated stable, good-quality SIF retrievals between January 2007 and June 2013, when GOME-2A degradation in the near infrared was still limited. After the narrowing of the GOME-2A swath in July 2013, we characterized the throughput degradation of the level-1 data in order to derive reflectance corrections and apply these for the SIF retrievals between July 2013 and December 2018. SIFTER v2 data compares well with the independent NASA v2.8 data product. Especially in the evergreen tropics, SIFTER v2 no longer shows the underestimates against other satellite products that were seen in SIFTER v1. The new data product includes uncertainty estimates for individual observations, and is best used for mostly clear-sky scenes, and when spectral residuals remain below a certain spectral autocorrelation threshold. Our results support the use of SIFTER v2 data to be used as an independent constraint on photosynthetic activity on regional to global scales.

Solar-induced fluorescence (SIF)↗
NASA NTRS1986

The Shane-Wirtanen counts - Systematics and two-point correlation function

Residual errors in the Selder et al. (SSGP) map which caused a break in both the correlation factor (CF) and the filamentary appearance of the Shane-Wirtanen map are examined. These errors, causing a residual rms fluctuation of 11 percent in the SSGP-corrected counts and a systematic rms offset of 8 percent in the mean count per plate, can be attributed to counting pattern and plate vignetting. Techniques for CF reconstruction in catalogs affected by plate-related systematic biases are examined, and it is concluded that accurate restoration may not be possible. Surveys designed to measure the CF at the depth of the SW counts on a scale of 2.5 deg, must have systematic errors of less than or about 0.04 mag.

De Lapparent, V.↗
NASA NTRS1986

Preparation of satellite soundings for insertion in a variational objective analysis model

The performance of a variational analysis model being developed at the Illinois State Water Survey, which allows blending of satellite and conventional soundings in a way which preserves the information content of both data sources, requires preparation of the input data before insertion into the model. Using operationally retrieved Tiros-N TOVS soundings and rawinsonde data, four procedures to prepare satellite soundings for model insertion are demonstrated: (1) determination and removal of biases, (2) determination of standard errors of the satellite temperatures to assist in the assignment of weights in the model, (3) conversion of layer mean temperatures to level temperatures, and (4) determination of the action necessary for correcting for the nonsynoptic nature of the satellite data.

Kidder, Stanley Q.↗
NASA NTRS2019

Extreme Case of Spectral Band Difference Correction Between the OSIRIS-REX-NAVCAM2 and DSCOVR-EPIC Imagers

Earth-viewed images acquired during a recent asteroid intercept mission present a unique opportunity for radiometric calibration of visible imagers onboard a space exploration probe. Measurements from the CERES consistent DSCOVR-EPIC imager act as a reference in providing spatially, temporally, and angularly matched radiance values for deriving OSIRIS-REx-NavCam sensor calibration gains. The calibration is accomplished using an optimized all-sky tropical ocean ray-matching technique, which employs complex pixel remapping, navigation correction, and angular geometry consideration. Of critical consideration in this specific inter-calibration event is the extreme difference in spectral response function (SRF) width between the NavCam and EPIC imagers, which could cause a rather large bias. The NASA-LaRC SCIAMACHY based online spectral band adjustment factor (SBAF) calculation tool provides an empirical solution to such potential spectral-difference-induced biases through a high spectral- resolution hyper spectral convolution approach. The adjustments produced from this tool can effectively reduce the calibration gain bias of NavCam2 by nearly 6%, thereby adjusting the NavCam2 sensor to within 3.2% of its prelaunch calibration. These results highlight the capability of the SBAF tool to account for exceptionally disparate SRFs.

Scarino, Benjamin↗
NASA NTRS2019

Differences Between OCO‐2 and GOME‐2 SIF Products From a Model‐Data Fusion Perspective

Space-borne retrievals of solar-induced chlorophyll fluorescence (SIF) over land surfaces have recently become a resource for studying and quantifying the broad scale dynamics of gross carbon uptake (gross primary productivity—GPP) across ecosystems. To prepare for the assimilation of SIF data in terrestrial biosphere models, we examine how differences between SIF products (due to differences in acquisition characteristics and processing chain) may affect the optimization of model parameters and the resultant GPP estimate. We compare recent daily mean SIF products (one from the Orbiting Carbon Observatory-2 [OCO-2] and two from the Global Ozone Monitoring Experiment–2 [GOME-2], GlobFluo [GF] and NASA-v28 [N28], missions), averaged at 0.5° × 0.5° spatial resolution and 16-day temporal resolution, at the biome level. Phase differences between these products are relatively small. A first-order correction of the difference in spectral sampling between the two instruments shows that OCO-2 and N28 are consistent in terms of magnitude and amplitude, while GF is twice as large as the others. Using a bias-blind toy data assimilation framework, we analyze how biases between SIF products, and between model and products, can be partially alleviated by optimizing the slope and intercept parameters of a linear GPP-SIF operator. As observation biases can transfer to biases in other optimized process-based parameters and to modeled carbon fluxes— thereby resulting in unidentified inaccurate parameter values—we argue that potential SIF biases should be treated cautiously in real-world experiments in order to achieve realistic and reliable future simulations.

Gross primary production↗
NASA NTRS2006

Self-Adaptive System based on Field Programmable Gate Array for Extreme Temperature Electronics

In this work, we report the implementation of a self-adaptive system using a field programmable gate array (FPGA) and data converters. The self-adaptive system can autonomously recover the lost functionality of a reconfigurable analog array (RAA) integrated circuit (IC) [3]. Both the RAA IC and the self-adaptive system are operating in extreme temperatures (from 120 C down to -180 C). The RAA IC consists of reconfigurable analog blocks interconnected by several switches and programmable by bias voltages. It implements filters/amplifiers with bandwidth up to 20 MHz. The self-adaptive system controls the RAA IC and is realized on Commercial-Off-The-Shelf (COTS) parts. It implements a basic compensation algorithm that corrects a RAA IC in less than a few milliseconds. Experimental results for the cold temperature environment (down to -180 C) demonstrate the feasibility of this approach.

adaptive hardwares↗
NASA NTRSDate not supplied

A Deep Neural Network for Achieving Spectrally Consistent and Seamless Infrared Radiance Measurements Across Geostationary Satellite Domains

The NASA Clouds and the Earth's Radiant Energy System (CERES) project provides the scientific community with observed top-of-atmosphere (TOA) shortwave and longwave fluxes for climate monitoring and climate model validation. To achieve this goal, CERES relies on TOA broadband fluxes derived from geostationary satellite (GEO) imagery to account for the diurnal flux variations between the CERES observation intervals. Consistent global flux derivation depends on accurate and consistent cloud retrievals. Scene-dependent spectral measurement inconsistency of the instruments that make up the contiguous ring of GEO observations (GEO-Ring), as well as limb darkening effects, can cause discontinuities in derived cloud properties and radiative fluxes at the boundaries of adjacent imager domains. Although the algorithms utilize radiative transfer models to account for instrument-band-dependent atmospheric correction and viewing zenith angle (VZA) dependency, small discontinuities may persist due to uncertainties inherent to the multiple imager-specific algorithms. Furthermore, while hyperspectral-instrument-based spectral band adjustment factors may effectively account for spectrally induced bias, they are less effective at reducing variance owed to the specific composition of the viewed scene, which is challenging to robustly characterize. As such, this article highlights the use of a deep neural network (DNN) to resolve spectral-and VZA-induced biases between GEO-Ring imagers. The DNN uses available infrared (IR) channels from the GEO instruments, along with viewing and solar illumination geometry, to estimate homogenized, VIIRS-like IR radiances for use in the GEO cloud algorithm. This approach is effective at mitigating scene-dependent spectral variance and VZA dependency, resulting in consistent radiance measurements across the GEO-Ring, thereby leading toward a more seamless global cloud assessment.

deep learning↗