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The Populations of Carina. I. Decoding the Color–Magnitude Diagram

We investigate the color–magnitude diagram (CMD) of the Carina dwarf spheroidal galaxy using data of Stetson et al. and synthetic CMDs based on isochrones of Dotter et al., in terms of the parameters [Fe/H], age, and [ α /Fe], for the cases when (i) [ α /Fe] is held constant and (ii) [ α /Fe] is varied. The data are well described by four basic epochs of star formation, having [Fe/H] = −1.85, −1.5, −1.2, and ∼−1.15 and ages ∼13, 7, ∼3.5, and ∼1.5 Gyr, respectively (for [ α /Fe] = 0.1, constant [ α /Fe], and [ α /Fe] = 0.2, 0.1, −0.2, −0.2, variable [ α /Fe]), with small spreads in [Fe/H] and age of order 0.1 dex and 1–3 Gyr. Within an elliptical radius 13.′1, the mass fractions of the populations, at their times of formation, were (in decreasing age order) 0.34, 0.39, 0.23, and 0.04. This formalism reproduces five observed CMD features (two distinct subgiant branches of old and intermediate-age populations, two younger, main-sequence components, and the small color dispersion on the red giant branch (RGB). The parameters of the youngest population are less certain than those of the others, and given it is less centrally concentrated, it may not be directly related to them. High-resolution spectroscopically analyzed RGB samples appear statistically incomplete compared with those selected using radial velocity, which contain bluer stars comprising ∼5%–10% of the samples. We conjecture these objects may, at least in part, be members of the youngest population. We use the CMD simulations to obtain insight into the population structure of Carina's upper RGB.

79 ASTRONOMY AND ASTROPHYSICS↗

Digital storage and analysis of color Doppler echocardiograms

Color Doppler flow mapping has played an important role in clinical echocardiography. Most of the clinical work, however, has been primarily qualitative. Although qualitative information is very valuable, there is considerable quantitative information stored within the velocity map that has not been extensively exploited so far. Recently, many researchers have shown interest in using the encoded velocities to address the clinical problems such as quantification of valvular regurgitation, calculation of cardiac output, and characterization of ventricular filling. In this article, we review some basic physics and engineering aspects of color Doppler echocardiography, as well as drawbacks of trying to retrieve velocities from video tape data. Digital storage, which plays a critical role in performing quantitative analysis, is discussed in some detail with special attention to velocity encoding in DICOM 3.0 (medical image storage standard) and the use of digital compression. Lossy compression can considerably reduce file size with minimal loss of information (mostly redundant); this is critical for digital storage because of the enormous amount of data generated (a 10 minute study could require 18 Gigabytes of storage capacity). Lossy JPEG compression and its impact on quantitative analysis has been studied, showing that images compressed at 27:1 using the JPEG algorithm compares favorably with directly digitized video images, the current goldstandard. Some potential applications of these velocities in analyzing the proximal convergence zones, mitral inflow, and some areas of future development are also discussed in the article.

NASA Discipline Cardiopulmonary↗

Ocean Color Measurements with the Operational Land Imager on Landsat-8: Implementation and Evaluation in SeaDAS

The Operational Land Imager (OLI) is a multispectral radiometer hosted on the recently launched Landsat8 satellite. OLI includes a suite of relatively narrow spectral bands at 30 m spatial resolution in the visible to shortwave infrared, which makes it a potential tool for ocean color radiometry: measurement of the reflected spectral radiance upwelling from beneath the ocean surface that carries information on the biogeochemical constituents of the upper ocean euphotic zone. To evaluate the potential of OLI to measure ocean color, processing support was implemented in Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), which is an open-source software package distributed by NASA for processing, analysis, and display of ocean remote sensing measurements from a variety of spaceborne multispectral radiometers. Here we describe the implementation of OLI processing capabilities within SeaDAS, including support for various methods of atmospheric correction to remove the effects of atmospheric scattering and absorption and retrieve the spectral remote sensing reflectance (Rrs; sr−1). The quality of the retrieved Rrs imagery will be assessed, as will the derived water column constituents, such as the concentration of the phytoplankton pigment chlorophyll a.

Measurements↗

A Semianalytical Ocean Color Inversion Algorithm with Explicit Water Column Depth and Substrate Reflectance Parameterization

A semianalytical ocean color inversion algorithm was developed for improving retrievals of inherent optical properties (IOPs) in optically shallow waters. In clear, geometrically shallow waters, light reflected off the seafloor can contribute to the water-leaving radiance signal. This can have a confounding effect on ocean color algorithms developed for optically deep waters, leading to an overestimation of IOPs. The algorithm described here, the Shallow Water Inversion Model (SWIM), uses pre-existing knowledge of bathymetry and benthic substrate brightness to account for optically shallow effects. SWIM was incorporated into the NASA Ocean Biology Processing Group's L2GEN code and tested in waters of the Great Barrier Reef, Australia, using the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua time series (2002-2013). SWIM-derived values of the total non-water absorption coefficient at 443 nm, at(443), the particulate backscattering coefficient at 443 nm, bbp(443), and the diffuse attenuation coefficient at 488 nm, Kd(488), were compared with values derived using the Generalized Inherent Optical Properties algorithm (GIOP) and the Quasi-Analytical Algorithm (QAA). The results indicated that in clear, optically shallow waters SWIM-derived values of at(443), bbp(443), and Kd(443) were realistically lower than values derived using GIOP and QAA, in agreement with radiative transfer modeling. This signified that the benthic reflectance correction was performing as expected. However, in more optically complex waters, SWIM had difficulty converging to a solution, a likely consequence of internal IOP parameterizations. Whilst a comprehensive study of the SWIM algorithm's behavior was conducted, further work is needed to validate the algorithm using in situ data.

Great Barrier Reef↗

Ocean Color Measurements from Landsat-8 OLI using SeaDAS

The Operational Land Imager (OLI) is a multi-spectral radiometer hosted on the recently launched Landsat-8 satellite. OLI includes a suite of relatively narrow spectral bands at 30-meter spatial resolution in the visible to shortwave infrared that make it a potential tool for ocean color radiometry: measurement of the reflected spectral radiance upwelling from beneath the ocean surface that carries information on the biogeochemical constituents of the upper ocean euphotic zone. To evaluate the potential of OLI to measure ocean color, processing support was implemented in SeaDAS, which is an open-source software package distributed by NASA for processing, analysis, and display of ocean remote sensing measurements from a variety of satellite-based multi-spectral radiometers. Here we describe the implementation of OLI processing capabilities within SeaDAS, including support for various methods of atmospheric correction to remove the effects of atmospheric scattering and absorption and retrieve the spectral remote-sensing reflectance (Rrs; sr exp 1). The quality of the retrieved Rrs imagery will be assessed, as will the derived water column constituents such as the concentration of the phytoplankton pigment chlorophyll a.

ocean color↗

Atmospheric Correction for Satellite Ocean Color Radiometry

This tutorial is an introduction to atmospheric correction in general and also documentation of the atmospheric correction algorithms currently implemented by the NASA Ocean Biology Processing Group (OBPG) for processing ocean color data from satellite-borne sensors such as MODIS and VIIRS. The intended audience is graduate students or others who are encountering this topic for the first time. The tutorial is in two parts. Part I discusses the generic atmospheric correction problem. The magnitude and nature of the problem are first illustrated with numerical results generated by a coupled ocean-atmosphere radiative transfer model. That code allow the various contributions (Rayleigh and aerosol path radiance, surface reflectance, water-leaving radiance, etc.) to the topof- the-atmosphere (TOA) radiance to be separated out. Particular attention is then paid to the definition, calculation, and interpretation of the so-called "exact normalized water-leaving radiance" and its equivalent reflectance. Part I ends with chapters on the calculation of direct and diffuse atmospheric transmittances, and on how vicarious calibration is performed. Part II then describes one by one the particular algorithms currently used by the OBPG to effect the various steps of the atmospheric correction process, viz. the corrections for absorption and scattering by gases and aerosols, Sun and sky reflectance by the sea surface and whitecaps, and finally corrections for sensor out-of-band response and polarization effects. One goal of the tutorial-guided by teaching needs- is to distill the results of dozens of papers published over several decades of research in atmospheric correction for ocean color remote sensing.

MODIS↗

Chlorophyll Algorithms for Ocean Color Sensors - OC4, OC5 and OC6

A high degree of consistency and comparability among chlorophyll algorithms is necessary to meet the goals of merging data from concurrent overlapping ocean color missions for increased coverage of the global ocean and to extend existing time series to encompass data from recently launched missions and those planned for the near future, such as PACE, OLCI, HawkEye, EnMAP and SABIA-MAR. To accomplish these goals, we developed 65 empirical ocean color (OC) chlorophyll algorithms for 25 satellite instruments using the largest available and most globally representative database of coincident in situ chlorophyll a and remote sensing reflectances. Excellent internal consistency was achieved across these OC ‘Version -7’ algorithms, as demonstrated by a median regression slope and coefficient of determination (R (sup 2)) of 0.985 and 0.859, respectively, among 903 pairwise comparisons of OC-modeled chlorophyll. SeaWiFS and MODIS-Aqua satellite-to-in situ match-up results indicated equivalent, and sometimes superior, performance to current heritage chlorophyll algorithms.

ocean color↗

Comparing Level-2 and Level-3 Satellite Ocean Color Retrieval Validation Methodologies

Many ocean color data applications leverage global spatially composited Level-3(L3) satellite data, because of their regular Earth-grid frame of reference. However, ocean color satellite retrieval performance is routinely evaluated on Level-2 (L2) data at the native satellite swath resolution and geometries. This study assesses how accurately binned and gridded L3 data represent L2 satellite data products via satellite-to-in situ match-up activities.L2 and L3 satellite data retrievals of the photosynthetic pigment chlorophyll-a are compared with a common in situ data set, revealing similar L2 and L3 satellite-to-in situ performance for both MODIS-Aqua and VIIRS-SNPP. This agreement implies that L2 validation results are generally applicable to L3 data. However, uncertainties are introduced during the generation of L3 data from L2 data. L3 data comparisons introduce a wider temporal window between the time of in situ measurement and the time of the satellite observation, which can unintentionally reflect on the quality of the satellite retrieval or algorithm performance. The choice of L3 map projection may introduce additional uncertainty by spatially distorting the true location of the satellite retrievals. Each manipulation of satellite data beyond theinstrument's native spatio temporal reference (L2) reduces the applicability of L2 validation results to higher data processing levels.

Ocean Color↗

150 Shades of Green: Using the Full Spectrum of Remote Sensing Reflectance to Elucidate Color Shifts in the Ocean

This article proposes a simple and intuitive classification system by which to define full spectral remote sensing reflectance (Rrs(λ)) data with a quantitative output that enables a more manageable handling of spectral information for aquatic science applications. The weighted harmonic mean of the Rrs(λ) wavelengths outputs an Apparent Visible Wavelength (in units of nanometers), representing a one-dimensional geophysical metric of color that is inherently correlated to spectral shape. This dimensionality reduction of spectral information combined with the output along a continuum of wavelength values offers a robust and user-friendly means to describe and analyze spectral Rrs(λ) in terms of spatial and temporal trends and variability. The uncertainty in the algorithm's estimation of spectral shape is demonstrated on a global scale, in addition to the utility of the algorithm to discern spectral-spatial-temporal trends in the ocean, on a per-pixel basis for the entire 22 year continuous ocean color (SeaWiFS and MODIS-Aqua) time-series. This technique can be applied to datasets of varying multi- and hyper-spectral resolutions, providing continuity between heritage and future satellite sensors, and further enabling an effective means of elucidating similarities or differences in complex spectral signatures within the constraints of two dimensions. This straightforward means of conceptualizing multi-dimensional variability can help maximize the potential of the spectral information embedded in remote sensing data.

ocean color↗

Retrieving Aerosol Characteristics From the PACE Mission, Part 1: Ocean Color Instrument

NASA’s Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) satellite mission is scheduled to launch in 2022, with the Ocean Color Instrument (OCI) on board. For the first time reflected sunlight from the Earth across a broad spectrum from the ultraviolet (UV: 350 nm) to the short wave infrared (SWIR: 2260 nm) will be measured from a single instrument at 1 km spatial resolution. While seven discrete bands will represent the SWIR, the spectrum from 350 to 890 nm will be continuously covered with a spectral resolution of 5 nm. OCI will thus combine in a single instrument (and at an enhanced spatial resolution for the UV) the heritage capabilities of the Moderate resolution Imaging Spectroradiometer (MODIS) and the Ozone Monitoring Instrument (OMI), while covering the oxygen A-band (O2A). Designed for ocean color and ocean biology retrievals, OCI also enables continuation of heritage satellite aerosol products and the development of new aerosol characterization from space. In particular the combination of MODIS and OMI characteristics allows deriving aerosol height, absorption and optical depth along with a measure of particle size distribution. This is achieved by using the traditional MODIS visible-to-SWIR wavelengths to constrain spectral aerosol optical depth and particle size. Extrapolating this information to the UV channels allows retrieval of aerosol absorption and layer height. A more direct method to derive aerosol layer height makes use of O2A absorption methods, despite the relative coarseness of the nominal 5 nm spectral resolution of OCI. Altogether the PACE mission with OCI will be an unprecedented opportunity for aerosol characterization that will continue climate data records from the past decades and propel aerosol science forward toward new opportunities.

PACE↗

Estimating pixel-level uncertainty in ocean color retrievals from MODIS

The spectral distribution of marine remote sensing reflectance, R(rs), is the fundamental measurement of ocean color science, from which a host of bio-optical and biogeochemical properties of the water column can be derived. Estimation of uncertainty in these derived properties is thus dependent on knowledge of the uncertainty in satellite-retrieved R(rs) (u(c)(R(rs))) at each pixel. Uncertainty in R(rs), in turn, is dependent on the propagation of various uncertainty sources through the R(rs) retrieval process, namely the atmospheric correction (AC). A derivative-based method for uncertainty propagation is established here to calculate the pixel-level uncertainty in R(rs), as retrieved using NASA’s multiple-scattering epsilon (MSEPS) AC algorithm and verified using Monte Carlo (MC) analysis. The approach is then applied to measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, with uncertainty sources including instrument random noise, instrument systematic uncertainty, and forward model uncertainty. The uc(Rrs) is verified by comparison with statistical analysis of coincident retrievals from MODIS and in situ Rrs measurements, and our approach performs well in most cases. Based on analysis of an example 8-day global products, we also show that relative uncertainty in R(rs) at blue bands has a similar spatial pattern to the derived concentration of the phytoplankton pigment chlorophyll-a (chl-a), and around 7.3%, 17.0%, and 35.2% of all clear water pixels (chl-a ≤ 0.1 mg/cu.m) with valid u(c)(R(rs)) have a relative uncertainty ≤ 5% at bands 412 nm, 443 nm, and 488 nm respectively, which is a common goal of ocean color retrievals for clear waters. While the analysis shows that u(c)(R(rs)) calculated from our derivative-based method is reasonable, some issues need further investigation, including improved knowledge of forward model uncertainty and systematic uncertainty in instrument calibration.

Pixel-level uncertainty↗

The Impact and Estimation of Uncertainty Correlation for Multi-Angle Polarimetric Remote Sensing of Aerosols and Ocean Color

Multi-angle polarimetric (MAP) measurements contain rich information for characterization of aerosol microphysical and optical properties that can be used to improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere-ocean system, although uncertainty correlation among measurements is generally ignored due to lack of knowledge on its strength and characterization. In this work, we provide a practical framework to evaluate the impact of the angular uncertainty correlation from retrieval results and a method to estimate correlation strength from retrieval fitting residuals. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on neural network forward models, is used to conduct the retrievals and uncertainty quantification. In addition, we also discuss a flexible approach to include a correlated uncertainty model in the retrieval algorithm. The impact of angular correlation on retrieval uncertainties is discussed based on synthetic AirHARP and HARP2 measurements using a Monte Carlo uncertainty estimation method. Correlation properties are estimated using auto-correlation functions based on the fitting residuals from both synthetic AirHARP and HARP2 data and real AirHARP measurement, with the resulting angular correlation parameters found to be larger than 0.9 and 0.8 for reflectance and DoLP, respectively, which correspond to correlation angles of 10° and 5°. Although this study focuses on angular correlation from HARP instruments, the methodology to study and quantify uncertainty correlation is also applicable to other instruments with angular, spectral, or spatial correlations, and can help inform laboratory calibration and characterization of the instrument uncertainty structure.

PACE↗

NASA’s PACE Ocean Color Instrument Thermal Design Evolution: from Goddard’s Instrument Design Lab through Flight Development

NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, set to launch in 2024, seeks to provide data continuity for the ocean color, aerosol and cloud measurements acquired by NASA’s on-orbit Earth Science observatories since the 1990s. It will accomplish this through its Ocean Color Instrument (OCI), an optical spectrometer being developed for hyper-spectral measurements in the ultraviolet-to-near-infrared band between 340 nm and 2260 nm. Although OCI’s instrument architecture will provide greater insight and resolution than its predecessors in this wavelength range, the engineering required to achieve this also poses a greater challenge. In thermal engineering, this translates to a more complex thermal control approach to address high heat dissipations, stringent stabilities, the volume of heat that requires transport, and changing thermal environments due to tilting of the entire instrument ±20° twice per orbit. This current work explores how the PACE OCI instrument design has evolved from its initial conception in NASA Goddard’s Instrument Design Laboratory (IDL) to the current iteration of its flight design. The IDL studies explored three separate instrument configurations and two spatial resolutions per configuration, which were then down selected to a single instrument type and spatial resolution for flight instrument development. OCI subsequently went through major project milestones, including Preliminary Design Review (PDR), Critical Design Review (CDR), Pre-Environmental Review (PER) and Pre-Ship Review (PSR), with significant design updates along the way. This paper aims to provide a comprehensive account of OCI’s thermal control architecture evolution and the engineering drivers that have shaped it, with the goal of identifying trends spanning the full instrument development timeline to inform and advance future instrument thermal designs.

PACE↗

NASA’s PACE Ocean Color Instrument Thermal Design Evolution: from Goddard’s Instrument Design Lab through Flight Development

NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, set to launch in 2024, seeks to provide data continuity for the ocean color, aerosol and cloud measurements acquired by NASA’s on-orbit Earth Science observatories since the 1990s. It will accomplish this through its Ocean Color Instrument (OCI), an optical spectrometer being developed for hyper-spectral measurements in the ultraviolet-to-near-infrared band between 340 nm and 2260 nm. Although OCI’s instrument architecture will provide greater insight and resolution than its predecessors in this wavelength range, the engineering required to achieve this also poses a greater challenge. In thermal engineering, this translates to a more complex thermal control approach to address high heat dissipations, stringent stabilities, the volume of heat that requires transport, and changing thermal environments due to tilting of the entire instrument ±20° twice per orbit. This current work explores how the PACE OCI instrument design has evolved from its initial conception in NASA Goddard’s Instrument Design Laboratory (IDL) to the current iteration of its flight design. The IDL studies explored three separate instrument configurations and two spatial resolutions per configuration, which were then down selected to a single instrument type and spatial resolution for flight instrument development. OCI subsequently went through major project milestones, including Preliminary Design Review (PDR), Critical Design Review (CDR), Pre-Environmental Review (PER) and Pre-Ship Review (PSR), with significant design updates along the way. This paper aims to provide a comprehensive account of OCI’s thermal control architecture evolution and the engineering drivers that have shaped it, with the goal of identifying trends spanning the full instrument development timeline to inform and advance future instrument thermal designs.

PACE↗

Overview of NASA's Ocean Color Instrument Solar Calibration Architecture, Pre-Launch Tests and Preliminary On-Orbit Results

Launched in February 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer known as the Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. Like its predecessors, OCI will provide two day global coverage of TOA radiances. Unlike its predecessors, OCI will cover a spectral range from 340nm to 2260nm. Below 900nm, OCI will include two spectrometers that continuously span the ultraviolet to 600nm and 600nm to near-infrared spectral regions to provide hyperspectral radiances sampled every 2.5 nm, with a bandwidth of 5 nm for each channel. Wavelengths above 900nm are measured in seven discrete multispectral bands of varying bandwidths, six of which are at similar wavelengths to those on heritage missions to support both atmospheric and ocean color applications. Nominal spatial resolution is similar to the SeaWiFS instrument with 1050 m at nadir. As for SeaWiFS, the pixel size increases due to a ~20 degree tilt and as a function of scan angle. Variations in the radiometric sensitivity of each OCI channel over time will be monitored by solar diffuser measurements for short term instrument gain adjustments and independent lunar measurements for trend adjustments of long time periods, similar to the approach employed for the VIIRS instrument [4]. The OCI flight-unit was built at NASA’s Goddard Space Flight Center. At the time of this writing, OCI has completed on-orbit commissioning activities and normal science operations have begun. A key aspect of the OCI architecture is the capability to trend absolute and relative calibration changes over the course of mission life with solar calibration. Every 24 hours, the PACE spacecraft performs an inertial hold as the ground track nears the North Pole which orients a Quasi-Volume Diffuser (QVD) mounted on OCI towards the sun. By knowing the irradiance of the sun and the reflectivity of the target, the absolute radiance at the input to the OCI aperture can be computed as OCI scans the target. The allowable absolute uncertainty budget for each solar calibration measurement is 1.6% 1-sigma below 900nm at beginning of life (BOL) and the allowable relative uncertainty budget is ~0.26% 1-sigma. The Solar Calibration Assembly (SCA) consists of three targets selectable via a single mechanism which also opens a door. The targets consist of a Daily Bright Target (DBT), Monthly Bright Target (MBT), and Daily Dim Target (DDT). The bright targets are quartz QVDs with the monthly target being used to track the degradation of the daily target. The dim target is used to track CCD linearity using Progressive Time-Delay Integration (PTDI). A composite baffle is attached to the SCA housing aperture to block Earth shine and stray light from the spacecraft. The SCA assembly is mounted to a view port ~90° from OCI nadir. This paper provides an overview of driving solar calibration requirements, error-budgets and early trade studies which drove the solar calibration assembly (SCA) architecture and on-orbit maneuver. Measurements of the diffuser Bidirectional Reflectance Distribution Function (BRDF) at TNO, Netherlands and GSFC are briefly described. Optical modelling and test results at the sub-system and instrument level are included. Finally, preliminary measurements on-orbit are compared to pre-launch predictions.

ocean color↗

Measurement of color flow in $\mathbf{t\bar{t}}$ events from $\mathbf{p\bar{p}}$ collisions at $\mathbf{\sqrt{s}=1.96}$ TeV

We present the first measurement of the color representation of the hadronically decaying W boson in t{bar t} events, from 5.3 fb{sup -1} of integrated luminosity collected with the D0 experiment. A novel calorimeter-based vectorial variable, 'jet pull,' is used, sensitive to the color-flow structure of the final state. We find that the fraction of uncolored W bosons is 0.56 {+-} 0.42(stat+syst), in agreement with the standard model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

The Effects of Roof Membrane Color on Moisture Accumulation in Low-slope Commercial Roof Systems

The use of highly reflective roof membrane systems is being promoted and in some cases required in energy codes and green building codes and standards. Highly reflective membranes, which typically are light in color, have demonstrated reduced overall energy consumption in cooling dominated climate. These membranes also are theorized to reduce the heat island effect. Concern has been expressed about using highly reflective roof membrane systems in cool to cold climate zones because they potentially increase moisture accumulation in roof systems. Roof membranes are vapor retarders. The theory is that highly reflective membranes reflect the heat that could enter the roof assembly, potentially providing a condensing surface on the cold side of the roof assembly during winter months. The other concern is that roof systems using highly reflective membranes will not get hot enough during the summer months to dry out moisture that may have condensed or otherwise entered the roof assembly. This study focuses on mechanically attached, highly reflective, single-ply roof systems installed on low-slope (less than 2:12) structures in cool to cold climate zones. Three sources of data are considered when determining the moisture accumulation potential of these systems. 1.Test roof cuts taken during the winter months 2.Modeling data from a building envelope model specifically designed to evaluate moisture accumulation 3.Data from previous studies to determine the effects of roof membrane color on the drying rate of low-slope roof assemblies

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗