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The National Renewable Energy Laboratory (NREL), in partnership with the Interstate Renewable Energy Council (IREC), has recently released a report which identifies a suite of best practices for producing trusted, validated hosting capacity analysis results reflecting real-world grid conditions.
Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecast of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (AQUA) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparison with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellites is examined via comparisons with GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm hr-1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.
Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products. 3
Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25deg terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.
Validation of models using observation data has been a central activity at the Community Coordinated Modeling Center (CCMC) over more than a decade. The Comprehensive Assessment of Models and Events using Library Tools (CAMEL) framework is a database-driven implementation of the interactive analysis of model-data comparisons that simultaneously provides a view across a multitude of locations and time periods. CAMEL thus extends the single-location or single-trajectory timeseries data comparison capabilities provided by the CCMC online visualization that formed the backbone of initial validation efforts. We demonstrate CAMEL capabilities for an example application to study Neutral Density in the upper atmosphere among comparisons in the heliosphere, the Earth’s radiation belt, magnetosphere, and ionosphere-thermosphere-mesosphere domains.
Residual stresses cause geometric distortion and affect mechanical performance of additively manufactured structures, yet they are notoriously difficult to assess and predict. Distortion (warpage) can drive parts outside dimensional tolerance limits, leading to part rejection or rework. For parts that meet tolerance, locked-in residual stress fields can affect structural integrity during operation, particularly subcritical cracking by fatigue, creep, or corrosion. This work develops benchmark data for a common additive manufacturing process (Wire Arc Additive Manufacturing) that can be applied for calibration and validation of physical process models that predict residual stress fields. The work includes design of two different samples of differing geometry, detailed manufacturing records for a set of physical samples, and an extensive set of residual stress measurement data developed using two diverse techniques (the contour method and neutron diffraction). An initial application of the work is also reported, where a modeling challenge was issued to secure residual stress model predictions from two independent laboratories that were blind to residual stress measurement data. These initial blind residual stress predictions show significant discrepancies relative to the measurement data, illustrating the potential value of the underlying validation data. An open repository for this work, including the sample designs, manufacturing process records, and the residual stress data, is also provided for future application in non-blind validation efforts.
Computational Unsteady Aerodynamics computer codes are being increasingly used. In order to validate their results they must be tested against valid experimental data. The present report aims at collecting reliable experimental data on unsteady aerodynamics and presenting them in a form which permits use for verification of codes. For ease of handling, the data are also presented in machine readable form (CD-ROM). Data on increasingly complex generic forms were selected and the following categories are covered: flutter, buffet, stability and control, dynamic stall, cavity flows, store separation. Computational solutions are included in order to permit evaluation of codes and analysis of solutions which differ from experimental data.
The quality of the quantitative spectral data collected by an imaging spectrometer instrument is critically dependent upon the accuracy of the spectral and radiometric calibration of the system. In order for the collected spectra to be scientifically useful, the calibration of the instrument must be precisely known not only prior to but during data collection. Thus, in addition to a rigorous in-lab calibration procedure, the airborne instruments designed and built by the NASA/JPL Imaging Spectroscopy Group incorporate an on board calibrator (OBC) system with the instrument to provide auxiliary in-use system calibration data. The output of the OBC source illuminates a target panel on the backside of the foreoptics shutter both before and after data collection. The OBC and in-lab calibration data sets are then used to validate and post-process the collected spectral image data. The resulting accuracy of the spectrometer output data is therefore integrally dependent upon the stability of the OBC source. In this paper we describe the design and application of the latest iteration of this novel device developed at NASA/JPL which integrates a halogen-cycle source with a precisely designed fiber coupling system and a fiber-based intensity monitoring feedback loop. The OBC source in this Airborne Testbed Spectrometer was run over a period of 15 hours while both the radiometric and spectral stabilities of the output were measured and demonstrated stability to within 1% of nominal.
This report investigates numerical modeling methods for thermal ratcheting analysis of packed-bed thermal energy storage (TES) tank and discusses the validation results via comparison with experimental data. The experimental data obtained from various design characteristics of packed-bed thermocline tanks, including the Thermal Energy Distribution System (TEDS) TES tank at Idaho National Laboratory (INL), were used to validate thermal and mechanical models developed in this study to evaluate the thermal ratcheting potential. The thermal model was shown to predict the transient thermal propagation through the packed-bed thermocline tanks generally well. However, a larger discrepancy was observed during the comparison with the data from TEDS, presumably due to the uncertainty of boundary conditions given from the experiment. Based on the comparative study between the thermal model predictions and experimental data of various packed-bed thermocline tanks, potential improvements were suggested for the future TEDS experiments for more precise validation study. For mechanical (thermally induced stress) analysis, two different modeling approaches were tested to evaluate hoop stress applied to the packed-bed TES tank wall, which is a major cause of thermal ratcheting process: (i) infinite rigidity model and (ii) Drucker-Prager (DP) model. The ‘model (i)’ is a conservative method with infinite rigidity assumption of granular filler inside a TES tank, whereas the ‘model (ii)’ is a method that takes into account more realistic processes such as thermal expansion of filler and tank wall as well as inter-particle interactions during the cyclic operation of a packed-bed TES tank. The validity of each modeling method was examined by comparing the numerical simulation with the experimental data obtained from the packed-bed TES tank for Solar One pilot plant. Then, the effects of various model parameters were discussed to evaluate the thermal ratcheting potential of the TEDS TES tank. The preliminary thermal ratcheting analysis implies that the TEDS TES tank will hold its structural integrity during the normal operation cycles.
This paper describes an AERONET-based Surface Reflectance Validation Network (ASRVN) and its dataset of spectral surface bidirectional reflectance and albedo based on MODIS TERRA and AQUA data. The ASRVN is an operational data collection and processing system. It receives 50x50 square kilometer subsets of MODIS L1B data from MODAPS and AERONET aerosol and water vapor information. Then it performs an accurate atmospheric correction for about 100 AERONET sites based on accurate radiative transfer theory with high quality control of the input data. The ASRVN processing software consists of L1B data gridding algorithm, a new cloud mask algorithm based on a time series analysis, and an atmospheric correction algorithm. The atmospheric correction is achieved by fitting the MODIS top of atmosphere measurements, accumulated for 16-day interval, with theoretical reflectance parameterized in terms of coefficients of the LSRT BRF model. The ASRVN takes several steps to ensure high quality of results: 1) cloud mask algorithm filters opaque clouds; 2) an aerosol filter has been developed to filter residual semi-transparent and sub-pixel clouds, as well as cases with high inhomogeneity of aerosols in the processing area; 3) imposing requirement of consistency of the new solution with previously retrieved BRF and albedo; 4) rapid adjustment of the 16-day retrieval to the surface changes using the last day of measurements; and 5) development of seasonal back-up spectral BRF database to increase data coverage. The ASRVN provides a gapless or near-gapless coverage for the processing area. The gaps, caused by clouds, are filled most naturally with the latest solution for a given pixels. The ASRVN products include three parameters of LSRT model (k(sup L), k(sup G), k(sup V)), surface albedo, NBRF (a normalized BRF computed for a standard viewing geometry, VZA=0 deg., SZA=45 deg.), and IBRF (instantaneous, or one angle, BRF value derived from the last day of MODIS measurement for specific viewing geometry) for MODIS 500m bands 1-7. The results are produced daily at resolution of 1 km in gridded format. We also provide cloud mask, quality flag and a browse bitmap image. The new dataset can be used for a wide range of applications including validation analysis and science research.
The purpose of the MISR experiment is to acquire systematic multi-angle imagery for global monitoring over a multi-year period of top-of-atmosphere and surface albedos and to measure the shortwave radiative properties of aerosols, clouds, and surface scenes.
Multispectral data requirements for Earth science applications are not always studied rigorously studied before a new remote sensing system is designed. A study of the spatial resolution, spectral bandpasses, and radiometric sensitivity requirements of real-world applications would focus the design onto providing maximum benefits to the end-user community. To support systematic studies of multispectral data requirements, the Applications Research Toolbox (ART) has been developed at NASA's Stennis Space Center. The ART software allows users to create and assess simulated datasets while varying a wide range of system parameters. The simulations are based on data acquired by existing multispectral and hyperspectral instruments. The produced datasets can be further evaluated for specific end-user applications. Spectral synthesis of multispectral images from hyperspectral data is a key part of the ART software. In this process, hyperspectral image cubes are transformed into multispectral imagery without changes in spatial sampling and resolution. The transformation algorithm takes into account spectral responses of both the synthesized, broad, multispectral bands and the utilized, narrow, hyperspectral bands. To validate the spectral synthesis algorithm, simulated multispectral images are compared with images collected near-coincidentally by the Landsat 7 ETM+ and the EO-1 ALI instruments. Hyperspectral images acquired with the airborne AVIRIS instrument and with the Hyperion instrument onboard the EO-1 satellite were used as input data to the presented simulations.
Since the Tropical Rainfall Measuring Mission (TRMM) satellite launch in November 1997, the TRMM Satellite Validation Office (TSVO) at NASA Goddard Space Flight Center (GSFC) has been performing quality control and estimating rainfall from the KPOL S-band radar at Kwajalein, Republic of the Marshall Islands. Over this period, KPOL has incurred many episodes of calibration and antenna pointing angle uncertainty. To address these issues, the TSVO has applied the Relative Calibration Adjustment (RCA) technique to eight years of KPOL radar data to produce Ground Validation (GV) Version 7 products. This application has significantly improved stability in KPOL reflectivity distributions needed for Probability Matching Method (PMM) rain rate estimation and for comparisons to the TRMM Precipitation Radar (PR). In years with significant calibration and angle corrections, the statistical improvement in PMM distributions is dramatic. The intent of this paper is to show improved stability in corrected KPOL reflectivity distributions by using the PR as a stable reference. Inter-month fluctuations in mean reflectivity differences between the PR and corrected KPOL are on the order of 1-2 dB, and inter-year mean reflectivity differences fluctuate by approximately 1 dB. This represents a marked improvement in stability with confidence comparable to the established calibration and uncertainty boundaries of the PR. The practical application of the RCA method has salvaged eight years of radar data that would have otherwise been unusable, and has made possible a high-quality database of tropical ocean-based reflectivity measurements and precipitation estimates for the research community.
We present comparisons of cloud droplet size distributions retrieved from the Research Scanning Polarimeter (RSP) data with correlative in situ measurements made during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). This field experiment was based at St. Johns airport, Newfoundland, Canada with the latest deployment in May - June 2016. RSP was onboard the NASA C-130 aircraft together with an array of in situ and other remote sensing instrumentation. The RSP is an along-track scanner measuring polarized and total reflectances in9 spectral channels. Its unique high angular resolution allows for characterization of liquid water droplet size using the rainbow structure observed in the polarized reflectances in the scattering angle range between 135 and 165 degrees. A parametric fitting algorithm applied to the polarized reflectances provides retrievals of the droplet effective radius and variance assuming a prescribed size distribution shape (gamma distribution). In addition to this, we use a non-parametric method, Rainbow Fourier Transform (RFT), which allows us to retrieve the droplet size distribution (DSD) itself. The latter is important in the case of clouds with complex structure, which results in multi-modal DSDs. During NAAMES the aircraft performed a number of flight patterns specifically designed for comparison of remote sensing retrievals and in situ measurements. These patterns consisted of two flight segments above the same straight ground track. One of these segments was flown above clouds allowing for remote sensing measurements, while the other was at the cloud top where cloud droplets were sampled. We compare the DSDs retrieved from the RSP data with in situ measurements made by the Cloud Droplet Probe (CDP). The comparisons show generally good agreement with deviations explainable by the position of the aircraft within cloud and by presence of additional cloud layers in RSP view that do not contribute to the in situ DSDs. In the latter case the distributions retrieved from the RSP data were consistent with the multi-layer cloud structures observed in the correlative High Spectral Resolution Lidar (HSRL) profiles. The comparison results provide a rare validation of polarimetric droplet size retrieval techniques, which can be used for analysis of satellite data on global scale.
The desire to predict the effort in developing or explaining the quality of software has led to the proposal of several metrics. As a step toward validating these metrics, the Software Engineering Laboratory (SEL) has analyzed the software science metrics, cyclomatic complexity, and various standard program measures for their relation to effort (including design through acceptance testing), development errors (both discrete and weighted according to the amount of time to locate and fix), and one another. The data investigated are collected from a project FORTRAN environment and examined across several projects at once, within individual projects and by reporting accuracy checks demonstrating the need to validate a database. When the data comes from individual programmers or certain validated projects, the metrics' correlations with actual effort seem to be strongest. For modules developed entirely by individual programmers, the validity ratios induce a statistically significant ordering of several of the metrics' correlations. When comparing the strongest correlations, neither software science's E metric cyclomatic complexity not source lines of code appears to relate convincingly better with effort than the others.
We present comparisons of cloud droplet size distributions (DSDs) retrieved from the research scanning polarimeter (RSP) data with correlative in situ measurements made during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). The airborne portion of this field experiment was based out of St. John's airport, Newfoundland, Canada with the focus of this paper being on the deployment in May - June 2016. RSP was onboard the NASA C-130 aircraft together with an array of in situ and other remote sensing instrumentation. The RSP is an along-track scanner measuring the polarized and total reflectance in 9 spectral channels. Its uniquely high angular resolution allows for characterization of liquid water droplet sizes using the rainbow structure observed in the polarized reflectance over the scattering angle range from 135 to 165.degrees The rainbow is dominated by single scattering of light by cloud droplets, so its structure is characteristic specifically of the droplet sizes at cloud top (within unit optical depth into the cloud, equivalent to approximately 50m). A parametric fitting algorithm applied to the polarized reflectance provides retrievals of the droplet effective radius and variance assuming a prescribed size distribution shape (gamma distribution). In addition to this, we use a non-parametric method, the Rainbow Fourier Transform (RFT), which allows us to retrieve the droplet size distribution itself. The latter is important in the case of clouds with complex microphysical structure, or multiple layers of cloud, which result in multi-modal DSDs. During NAAMES the aircraft performed a number of flight patterns specifically designed for comparisons between remote sensing retrievals and in situ measurements. These patterns consisted of two flight segments above the same straight ground track. One of these segments was flown above clouds allowing for remote sensing measurements, while the other was near the cloud top where cloud droplets were sampled. We compare the DSDs retrieved from the RSP data with in situ measurements made by the Cloud Droplet Probe (CDP). The comparisons generally show good agreement (better than 1 micron for effective radius and in most cases better than 0.02 for effective variance) with deviations explainable by the position of the aircraft within the cloud, or by the presence of additional cloud layers between the cloud being sampled by the in situ instrumentation and the altitude of the remote sensing segment. In the latter case, the multi-modal DSDs retrieved from the RSP data were consistent with the multi-layer cloud structures observed in the correlative High Spectral Resolution Lidar (HSRL) profiles. The results of these comparisons provide a rare validation of polarimetric droplet size retrieval techniques, demonstrating their accuracy and robustness and the potential of satellite data of this kind on a global scale.
The present paper summarizes the current status of the activities of the on-going WPEC subgroup 46 which was the last significant initiative of Massimo before he passed away. The goal of WPEC/SG46 is to define, test and document a methodology to provide unambiguous feedback to the nuclear data evaluator community, based on the joint use of integral experiments and data assimilation techniques. Part of this effort resulted in a renewed analysis of the original Target Accuracy Requirement (TAR) exercise of WPEC/SG26, by adding more diverse nuclear systems and parameters, including reaction channels correlations and a coarser energy group structure; and making use of the progress made in the most recent evaluations in terms of both nuclear data and covariance matrices. The preliminary outcomes of the updated TAR exercise, documented by various groups worldwide are clear already: uncertainty reductions are required for many nuclide-reaction pairs in a variety of energy range if the target uncertainty requirements set by the industry are to be met, especially for k{sub eff}. The inclusion of correlations between the various reaction channels had a major impact on the magnitude of the required uncertainty reduction. Those uncertainty reductions are unlikely to be met by differential measurements alone. However, the selection of relevant integral experiment and a subsequent adjustment procedure may help meet these requirements. (authors)
This document details the data collection, storage, and analysis plan and methodologies for conducting the field validation portion of DOE’s Cold Climate Heat Pump (CCHP) Challenge. The study is focused on validating the in-field heating performance of prototypical CCHPs that have successfully demonstrated that they meet or exceed the Challenge specification in a laboratory setting. The units that have passed laboratory testing will be installed in real homes along with sensors and loggers for monitoring performance over an entire heating season. Data collected through monitoring will be cleaned and stored in a secure database. The data will be analyzed for calculating the key metrics defined by the Challenge including heating capacities at low outdoor air temperatures (below 32 °F), efficiency in terms of the Coefficient of Performance (COP), switchover temperatures and auxiliary heat staging. Demand Response (DR) capabilities will also be tested using specially designed DR events. In most cases, shoulder season and cooling season performance will also be determined and reported.