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At least 1,333 records · Page 74

Improved Rainfall Data in the Philippines through Concurrent Use of GPM IMERG and Ground-Based Measurements

The availability of accurate and reliable rainfall data that are applicable to various phenomenological, climatological, and modeling studies is important, especially in the Philippines, which is considered to be highly vulnerable to natural hazards and a changing climate. The presented strategy involved constructing a dataset consisting of synoptic data, automatic rain gauge (ARG) measurements, and satellite data that are co-registered, consistent, and formatted in the same manner. Although sparse in number, the synoptic stations provide the most accurate rainfall information and were used as the baseline for creating the dataset. The ARGs that are within a distance of 1 km to the synoptic stations were used to determine the correction factors needed to make the synoptic and ARG data consistent. Subsequently, the corrected ARGs were used to make the satellite IMERG data consistent with both ARG and synoptic data. In case of the latter, only IMERG pixels with at least 10 ARGs within the relatively large footprint of the satellite sensor were used in estimating the required correction parameters derived from a combination of a power transform and linear regression correction techniques. The final results show good agreement of synoptic and corrected ARG data with correlation coefficients of 0.94 and 0.97 for the 10 day and monthly data, respectively, and improvement in the linear regression slope from 0.67 to 0.90 for 10 day data, and 0.70 to 0.94 for monthly data. In addition, the corrected ARG data agree well with the corrected IMERG data, with correlation coefficients of 0.88 and 0.93 for the 10 day and monthly data, respectively, and an improvement in slope from 0.66 to 0.87 for 10 day data, and 0.74 to 0.99 for monthly data. The merit of using a combined dataset is illustrated through comparative analyses of the IMERG data and spatially interpolated synoptic and ARG data. The results show general agreements in spatial patterns of rainfall across the datasets, especially in areas where in situ measurements are recorded. The observed discrepancy when ground data is limited emphasizes the need for satellite IMERG data to obtain the true spatial patterns of rainfall distribution.

rainfall↗

The Development Of Enabling Technologies For Submillimeter-Wave Remote Sensing of Ice Clouds From Space

This paper presents the chronological development of technologies and techniques that have led to a satellite mission concept aimed at quantifying the temporal and spatial distributions of upper tropospheric ice clouds. The Submillimeter-wave and Infrared Ice Cloud Experiment (SIRICE) is an Earth System Science Pathfinder mission concept designed to improve our understanding of the upper tropospheric water cycle and its coupling to the Earth s radiation budget. Ice outflow from convective storm systems is known to play an important role in regional energy budgets; however, ice generation and subsequent precipitation and sublimation are poorly quantified. SIRICE will provide measurements of ice cloud distributions and microphysical properties which are needed for understanding the crucial link between the hydrologic and energy cycles. The SIRICE measurement platform is comprised of two integrated instruments, the Submillimeter/millimeter-wave radiometer (SM4) and the Infrared Cloud Ice Radiometer (IRCIR). The primary instrument is the SM4, a conical scanner that provides a 1600 km swath of the Earth's surface at 53 degree incidence. The SM4 has 6 linearly polarized receivers measuring 12 spectral bands centered at 183 GHz, 325 GHz, 448 GHz, 643 GHz and 874 GHz; two receivers at 643 GHz measure horizontal and vertical polarizations. Submillimeter-wavelengths are well suited to the remote sensing of ice clouds due to the relative size of the wavelengths to particle sizes. Upwelling emission from lower tropospheric water vapor is scattered by the ice clouds thus causing a brightness temperature depression at submillimeter wavelengths. The IRCIR is a push broom imager with approximately 1500 km swath and spectral channels at 11 and 12 micrometers. This combination of coincident infrared and submillimeter-wavelength measurements were chosen because of its ability to provide retrieval of ice water path and median particle size for a wide range of ice clouds from thin cirrus to thick anvil structures. Over the past decade there has been a parallel development of submillimeter-wave technologies, demonstration instruments, and remote sensing techniques that have led to the present SIRICE mission concept. Mapping of these developmental paths reveals the origins, rational and maturity of features of the SIRICE payload such as its channel selection, compact design, and multipoint calibration. This presentation traces the evolution of the SIRICE mission concept from the early 1990's to its present status.

Racette, Paul↗

Symmetry in polarimetric remote sensing

Relationships among polarimetric backscattering coefficients are derived from the viewpoint of symmetry groups. For both reciprocal and non-reciprocal media, symmetry encountered in remote sensing due to reflection, rotation, azimuthal, and centrical symmetry groups is considered. The derived properties are general and valid to all scattering mechanisms, including volume and surface scatterings and their interactions, in a given symmetrical configuration. The scattering coefficients calculated from theoretical models for layer random media and rough surfaces are shown to obey the symmetry relations. Use of symmetry properties in remote sensing of structural and environmental responses of scattering media is also discussed. Orientations of spheroidal scatterers described by spherical, uniform, planophile, plagiothile, erectophile, and extremophile distributions are considered to derive their polarimetric backscattering characteristics. These distributions can be identified from the observed scattering coefficients by comparison with theoretical symmetry calculations. A new parameter is then defined to study scattering structures in geophysical media. Observations from polarimetric data acquired by the Jet Propulsion Laboratory airborne synthetic aperture radar over forests, sea ice, and sea surface are presented. Experimental evidences of the symmetry relationships are shown and their use in polarimetric remote sensing is illustrated. For forests, the coniferous forest in Mt. Shasta area (California) and mixed forest near Presque Isle (Maine) exhibit characteristics of the centrical symmetry at C-band. For sea ice in the Beaufort Sea, multi-year sea ice has a cross-polarized ratio e close to e(sub 0), calculated from symmetry, due to the randomness in the scattering structure. First-year sea ice has e much smaller than e(sub 0) due to the preferential alignment of the columnar structure of the ice. From polarimetric data of a sea surface in the Bering Sea, it is observed that e and e(sub 0) are increasing with incident angle and e is greater than e(sub 0) at L-band because of the directional feature of sea surface waves. Symmetry properties of geophysical media can also be used to calibrate polarimetric radars.

Nghiem, S. V.↗

NASA's SnowEx Campaign and Measuring Global Snow from Space

Snow blankets 30% of Earth's land surface (60% of northern hemisphere land) in midwinter, dramatically changing our planet's land surface and affecting our weather for months. Seasonal snow is critically important to society for the management of water resources, natural hazards, water security, and in many economic sectors. The only practical way to estimate the quantity of snow on a global scale is through satellites. Despite 4 decades of satellite observations, the highly variable nature of snow still presents significant challenges toward achieving this goal. For example, current space-based techniques underestimate snow water equivalent (SWE) by as much as 50%, and model-based estimates can differ greatly versus estimates based on remotely-sensed observations. Snow community consensus is that a multi-sensor approach is needed to adequately address global snow, combined with modeling and data assimilation to fill the gaps in space and time. What remains, then, is how best to combine and use the various sensors under different types of snow conditions and confounding factors. NASA's multi-year SnowEx airborne campaign is designed to collect measurements needed to enable algorithm development and to guide those mission trade studies. Year 1 (2017) focused on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. This paper will discuss the various remote sensing options for snow, the challenging factors, and describe the recently-completed first year of SnowEx in Colorado, USA. Ground-based remote sensing and in situ data collection involved nearly 100 participants over three weeks. The airborne campaign included nine sensors on five aircraft. We will conclude by discussing options for a future snow satellite mission.

campaign↗

The Suomi National Polar-Orbiting Partnership (SNPP): Continuing NASA Research and Applications

The Suomi National Polar-orbiting Partnership (SNPP) satellite was successfully launched into a polar orbit on October 28, 2011 carrying 5 remote sensing instruments designed to provide data to improve weather forecasts and to increase understanding of long-term climate change. SNPP provides operational continuity of satellite-based observations for NOAA's Polar-orbiting Operational Environmental Satellites (POES) and continues the long-term record of climate quality observations established by NASA's Earth Observing System (EOS) satellites. In the 2003 to 2011 pre-launch timeframe, NASA's SNPP Science Team assessed the adequacy of the operational Raw Data Records (RDRs), Sensor Data Records (SDRs), and Environmental Data Records (EDRs) from the SNPP instruments for use in NASA Earth Science research, examined the operational algorithms used to produce those data records, and proposed a path forward for the production of climate quality products from SNPP. In order to perform these tasks, a distributed data system, the NASA Science Data Segment (SDS), ingested RDRs, SDRs, and EDRs from the NOAA Archive and Distribution and Interface Data Processing Segments, ADS and IDPS, respectively. The SDS also obtained operational algorithms for evaluation purposes from the NOAA Government Resource for Algorithm Verification, Independent Testing and Evaluation (GRAVITE). Within the NASA SDS, five Product Evaluation and Test Elements (PEATEs) received, ingested, and stored data and performed NASA's data processing, evaluation, and analysis activities. The distributed nature of this data distribution system was established by physically housing each PEATE within one of five Climate Analysis Research Systems (CARS) located at either at a NASA or a university institution. The CARS were organized around 5 key EDRs directly in support of the following NASA Earth Science focus areas: atmospheric sounding, ocean, land, ozone, and atmospheric composition products. The PEATES provided the system level interface with members of the NASA SNPP Science Team and other science investigators within each CARS. A sixth Earth Radiation Budget CARS was established at NASA Langley Research Center (NASA LaRC) to support instrument performance, data evaluation, and analysis for the SNPP Clouds and the Earth's Radiant Budget Energy System (CERES) instrument. Following the 2011 launch of SNPP, spacecraft commissioning, and instrument activation, the NASA SNPP Science Team evaluated the operational RDRs, SDRs, and EDRs produced by the NOAA ADS and IDPS. A key part in that evaluation was the NASA Science Team's independent processing of operational RDRs and SDRs to EDRs using the latest NASA science algorithms. The NASA science evaluation was completed in the December 2012 to April 2014 timeframe with the release of a series of NASA Science Team Discipline Reports. In summary, these reports indicated that the RDRs produced by the SNPP instruments were of sufficiently high quality to be used to create data products suitable for NASA Earth System science and applications. However, the quality of the SDRs and EDRs were found to vary greatly when considering suitability for NASA science. The need for improvements in operational algorithms, adoption of different algorithmic approaches, greater monitoring of on-orbit instrument calibration, greater attention to data product validation, and data reprocessing were prominent findings in the reports. In response to these findings, NASA, in late 2013, directed the NASA SNPP Science Team to use SNPP instrument data to develop data products of sufficiently high quality to enable the continuation of EOS time series data records and to develop innovative, practical applications of SNPP data. This direction necessitated a transition of the SDS data system from its pre-launch assessment mode to one of full data processing and production. To do this, the PEATES, which served as NASA's data product testing environment during the prelaunch and early on-orbit periods, were transitioned to Science Investigator-led Processing Systems (SIPS). The distributed data architecture was maintained in this new system by locating the SIPS at the same institutions at which the CARS and PEATES were located. The SIPS acquire raw SNPP instrument Level 0 (i.e. RDR) data over the full SNPP mission from the NOAA ADS and IDPS through the NASA SDS Data Distribution and Depository Element (SD3E). The SIPS process those data into NASA Level 1, Level 2, and global, gridded Level 3 standard products using peer-reviewed algorithms provided by members of the NASA Science Team. The SIPS work with the NASA SNPP Science Team in obtaining enhanced, refined, or alternate real-time algorithms to support the capabilities of the Direct Readout Laboratory (DRL). All data products, algorithm source codes, coefficients, and auxiliary data used in product generation are archived in an assigned NASA Distributed Active Archive Center (DAAC).

remote sensing↗

Primary igneous rocks on Mars: Composition and distribution

The present knowledge of the crustal composition of Mars is synthesized and implications discussed for in-situ resource utilization. Sources of information include remote sensing observations, Viking XRF chemical measurements, and characteristics of the SNC meteorites (which most researchers now believe originated on Mars). There are a number of lines of evidence that abundant ferrous-iron rich igneous crustal rocks (and derivative soils) are available at or very near the current Martian surface at many locations on the planet. Most of these exposures show spectroscopic evidence for abundant pyroxene, consistent with basaltic compositions. The SNC meteorites, which have basaltic compositions, were also studied extensively. Interpretations of Mars crustal chemistry and mineralogy (petrology) based on these various sources are reviewed, and their consistencies and differences are discussed.

Singer, Robert B.↗

Marked point process models of raindrop-size distributions

The principal process considered in this paper is the flux of raindrops through a volume of the atmosphere. This process is of fundamental importance for a wide variety of engineering and environmental problems, notably remote sensing of precipitation, infiltration of rainfall, soil erosion, atmospheric deposition of pollutants, and design of microwave communication systems. A marked point process model is developed in which the point process represents the arrival times of drops at the upper surface of a sample volume and the mark associated with a drop is its diameter. In the model, both the rate of occurrence of raindrops and the distribution of drop diameters vary randomly over time. Results that relate the drop-size distribution within the sample volume to the probability law of the drop-arrival process are presented. These results allow straightforward comparisons between temporal characterizations of drop-size distributions and spatial characterizations. Representations for derived processes such as rainfall rate and reflectivity are shown to be quite accurate using raindrop data from North Carolina.

Smith, James A.↗

Quantifying uncertainty in Pareto estimates of global lake area

Abstract Size is a critical factor determining the rate and occurrence of specific lake processes such as carbon sequestration and greenhouse gas emissions and emerging evidence suggests that small lakes in particular have particularly large CO 2 flux rates. Because we do not have a complete census of all lakes, upscaling estimates of such processes to small lakes at broad spatial scales requires the use of lake size‐abundance distributions rather than empirical measurements of area. Existing lake census efforts are incomplete such that as lakes become smaller, they are more likely to be omitted either because they are too small to be resolved from remote sensing products or because of limited ground surveying effort (i.e., “censoring” of small lakes relative to large lakes). The present study explores one potential shortcoming of prior approaches estimating global lake area using lake size‐abundance distributions. Namely, that these prior approaches rely on frequentist curve fitting techniques combined with an ad‐hoc cutoff determination strategy (visual inspection to determine a likely censoring point). This yields an over‐exact lake area estimate that is typically reported with no uncertainty bounds. I show how these shortcomings can be addressed with a Bayesian model that produces larger estimates of lake area uncertainty relative to the typical approach. When used as part of a sensitivity analysis, such an approach has the potential to enable more robust intercomparisons among studies of aquatic processes upscaling.

54 ENVIRONMENTAL SCIENCES↗

Sensors with Intelligent Measurement Platform and Low-cost Equipment (SIMPLE) – Performance Characterization

Design and performance of a digital signal processing platform for enhancing off-the-shelf voltage and current sensors are presented. The platform was developed for enabling advanced distribution grid applications with existing low-cost sensors. This digital sensor system leverages a variety of off-the-shelf voltage and current sensing technologies, analog-to-digital conversion, and sensor correction algorithms to yield more accurate and reliable digital measurements that support a multitude of applications and use cases. Sample performance and accuracy results are provided, including measurements at power frequency and higher harmonics. The system extends the native performance of the medium voltage sensors used in the system, for example, broadening measurement bandwidth beyond a few kHz, appropriate for measuring switching surges and harmonics on medium voltage distribution systems. The digital sensor system is installed and characterized in the field with a high-bandwidth optical calibration system to determine site-dependent calibration and performance issues.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Non-Lambertian effects on remote sensing of surface reflectance and vegetation index

This paper discusses the effects of non-Lambertian reflection from a homogeneous surface on remote sensing of the surface reflectance and vegetation index from a satellite. Remote measurement of the surface characteristics is perturbed by atmospheric scattering of sun light. This scattering tends to smooth the angular dependence of non-Lambertian surface reflectances, an effect that is not present in the case of Lambertian surfaces. This effect is calculated to test the validity of a Lambertian assumption used in remote sensing. For the three types of vegetations considered in this study, the assumption of Lambertian surface can be used satisfactorily in the derivation of surface reflectance from remotely measured radiance for a view angle outside the backscattering region. Within the backscattering region, however, the use of the assumption can result in a considerable error in the derived surface reflectance. Accuracy also deteriorates with increasing solar zenith angle. The angular distribution of the surface reflectance derived from remote measurements is smoother than that at the surface. The effect of surface non-Lambertianity on remote sensing of vegetation index is very weak. Since the effect is similiar in the visible and near infrared part of the solar spectrum for the vegetations treated in this study, it is canceled in deriving the vegetation index. The effect of the diffuse skylight on surface reflectance measurements at ground level is also discussed.

Lee, T. Y.↗

SRF-Based Quantum Transduction for Quantum Computing and Sensing

The next groundbreaking frontier in Quantum Information Science (QIS) is the development of low-noise interconnections over fiber optics between superconducting radio-frequency (SRF) quantum devices. High-efficiency microwave-optical transduction serves as a pivotal enabler for distributed quantum computing and sensor networks. This presentation will provide a comprehensive overview of quantum transduction achieved by coupling SRF cavities to electro-optic optical resonators, enabling efficient conversion between microwave and optical photons. This research, supported by the DOE Early Career Research Program, advances technologies for quantum computing and sensing, and complements the mission of the Superconducting Quantum Materials and Systems (SQMS) Center at Fermilab.

Zorzetti, Silvia [Fermilab] (ORCID:000000023208338↗

Thin film lithium niobate electric field sensors

We present our results for using thin film lithium niobate devices for electric field sensing applications. Micro-ring modulator and Mach–Zehnder modulator-based electric field sensors are demonstrated. Micro-ring resonator sensors can be used for low frequency (up to several GHz) electric field sensing applications and achieve a high sensitivity of 80 mV/(m Hz 1/2 ) with a very compact size of 300 μm, as limited by the intensity and phase noise of the used distributed feedback laser. A measurement bandwidth of 2.5 GHz is measured for these sensors and is limited by the detector bandwidth. Alternatively, Mach–Zehnder modulators allow for perfect phase matching between the radio frequency signals and optical signals, and they can be used for electric field sensing up to several THz. A sensitivity of 2.2 V/(m Hz 1/2 ) was obtained using our Mach–Zehnder electric field sensor with an interaction length of 600 μm. The Mach–Zehnder sensor can sense electric fields with frequencies reaching 0.6 THz based on the calculated results.

47 OTHER INSTRUMENTATION↗

Remote sensing of submerged aquatic vegetation in the lower Chesapeake Bay

An experimental water penetration film and black and white near infrared film were used to study the distribution of submerged aquatic vegetation in the lower Chesapeake Bay. Detailed description of the grass beds was obtained by flying at an altitude of 5,000 feet, at low tide when wind conditions were minimal. Results show that there was a 36% reduction in the amount of submerged aquatic vegetation in the lower Chesapeake Bay from 1971 to 1974, the greatest losses occurring in the York, Piankatank and Rappahannock rivers (tabulated data is given). Recovery of some grass beds occurs primarily through seedling recruitment and subsequent vegetative growth. Cownose rays are suspected as a main factor for the decimation of some of the grass beds. Maps and photographs of the areas studied are given.

Orth, R. J.↗

Remote sensing of optical properties in continuously stratified waters

The radiative transfer equation is solved by Monte Carlo methods for natural waters in which the optical properties are distributed with depth. It is demonstrated that interpreting the reflectance of a continuously stratified ocean in terms of an equivalent homogeneous ocean yields the average of a particular combination of the water's optical properties over the dimensionless penetration depth. Although in general the dimensionless penetration depth cannot be remotely measured, a method is presented for estimating the actual penetration depth from the remote observations if the medium's absorption coefficient is known, independent of depth, and sufficiently large. The application of this to the remote measurement of the vertical distribution of suspended sediments is discussed in detail.

Gordon, H. R.↗

Irrigation management with remote sensing

Two visible/near IR hand held radiometers and a hand held thermoradiometer were used along with soil moisture and lysimetric measurements in a study of soil moisture distribution in afalfa fields on the Navajo Indian Irrigation Project near farmington, New Mexico. Radiances from irrigated plots were measured and converted to reflectances. Surface soil water contents (o cm to 4 cm) were determined gravimetrically on samples collected at the same time as the spectral measurements. The relationship between the spectral measurements and the crop coefficient were evaluated to demonstrate potential for using spectral measurement to estimate crop coefficient.

Harlan, C.↗

Use of remote sensing for land use policy formulation

Multispectral scanning, infrared imagery, thematic mapping, and spectroradiometry from LANDSAT, GOES, and ground based instruments are being used to determine conifer distribution, maximum and minimum temperatures, topography, and crop diseases in Michigan's lower Peninsula. Image interpretation and automatic digital processing information from LANDSAT data are employed to classify and map the coniferous forests. Radiant temperature data from GOES were compared to temperature readings from the climatological station network. Digital data from LANDSAT is being used to develop techniques for detecting, monitoring, and modeling land surface change. Improved reflectance signatures through spectroradiometry aided in the detection of viral diseases in blueberry fields and vineyards. Soil survey maps from aerial reconnaissance are included as well as information on education, conferences, and awards.

Source record↗

Quantifying carbon budget, crop yields and their responses to environmental variability using the ecosys model for U.S. Midwestern agroecosystems

As one of the major agricultural production areas in the world, the United States (U.S.) Midwest plays a vital role in the global food supply and agricultural ecosystem services. Although significant efforts have been made in modeling the carbon cycle dynamics over this area, large uncertainty still exists in the previous simulations in terms of reproducing individual components of the carbon cycle and their responses to environmental variability. Here we evaluated the performance of an advanced agroecosystem model, ecosys, in simulating carbon budgets over the U.S. Midwest, considering both the magnitude of carbon flux/yield and its response to environmental (climate and soil) variability. We conducted model simulations and evaluations at 7 cropland eddy-covariance sites as well as over 293 counties of Illinois, Indiana, and Iowa in the U.S. Midwest. The site-level simulations showed that ecosys captured both the magnitude and seasonal patterns of carbon fluxes (i.e., net ecosystem carbon exchange (NEE), ecosystem gross primary production (GPP), and ecosystem respiration (Reco)), leaf area index (LAI), and dynamic plant carbon allocation processes, with R2 equal to 0.92, 0.87, 0.87, and 0.78 for GPP, NEE, Reco, and LAI, respectively across all the sites compared with the observations. For regional scale simulations, ecosys reproduced the spatial distribution and interannual variability of corn and soybean yields with the constraints of observed yields and a new remotely sensed GPP product, with R2 of multi-year averaged simulated and observed yield equal 0.83 and 0.80 for corn and soybean, respectively. The simulated responses of carbon cycle dynamics to environmental variability were consistent with that from the empirical observations at both site and regional scales. Furthermore, our results demonstrated the applicability of ecosys in simulating the carbon cycle and soil carbon dynamics of the U.S. Midwestern agroecosystems under different climate and soil conditions.

54 ENVIRONMENTAL SCIENCES↗

Monte Carlo study of the atmospheric spread function

Monte Carlo radiative transfer simulations are used to study the atmospheric spread function appropriate to satellite-based sensing of the earth's surface. The parameters which are explored include the nadir angle of view, the size distribution of the atmospheric aerosol, and the aerosol vertical profile.

Pearce, W. A.↗