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All sources NASA NTRS DOE OSTI

DOE OSTI2016

wsacrblrhiaux (a0)

ARM's scanning cloud radars are fully coherent dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (WSACR; 1.7 kW peak power) or X-band (XSACR; 20 kW peak power). Beamwidths at Ka and W bands are roughly matched at 0.3 degrees. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies that are unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the Ka-SACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the W-SACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ration.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

wsacrppiaux (a0)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

wsacrcfrqc (b1)

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements

The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.

David M. Giles↗

NASA NTRS2007

Performance of the Lidar Design and Data Algorithms for the GLAS Global Cloud and Aerosol Measurements

The Geoscience Laser Altimeter System (GLAS) launched in early 2003 is the first polar orbiting satellite lidar. The instrument design includes high performance observations of the distribution and optical scattering cross sections of atmospheric clouds and aerosol. The backscatter lidar operates at two wavelengths, 532 and 1064 nm. For the atmospheric cloud and aerosol measurements, the 532 nm channel was designed for ultra high efficiency with solid state photon counting detectors and etalon filtering. Data processing algorithms were developed to calibrate and normalize the signals and produce global scale data products of the height distribution of cloud and aerosol layers and their optical depths and particulate scattering cross sections up to the limit of optical attenuation. The paper will concentrate on the effectiveness and limitations of the lidar channel design and data product algorithms. Both atmospheric receiver channels meet and exceed their design goals. Geiger Mode Avalanche Photodiode modules are used for the 532 nm signal. The operational experience is that some signal artifacts and non-linearity require correction in data processing. As with all photon counting detectors, a pulse-pile-up calibration is an important aspect of the measurement. Additional signal corrections were found to be necessary relating to correction of a saturation signal-run-on effect and also for daytime data, a small range dependent variation in the responsivity. It was possible to correct for these signal errors in data processing and achieve the requirement to accurately profile aerosol and cloud cross section down to 10-7 llm-sr. The analysis procedure employs a precise calibration against molecular scattering in the mid-stratosphere. The 1064 nm channel detection employs a high-speed analog APD for surface and atmospheric measurements where the detection sensitivity is limited by detector noise and is over an order of magnitude less than at 532 nm. A unique feature of the GLAS is a full acquisition of the surface return pulse, which has important application to the atmospheric transmission retrieval.

Spinhirne, James D.↗

NASA NTRS2018

Retrievals of Cloud Droplet Size from the Research Scanning Polarimeter Data: Validation Using In Situ Measurements

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.

Remote Sensing↗

NASA NTRS2017

CloudSat at 11— Now What?

The CloudSat mission recently completed eleven years of on-orbit operations, providing unique radar profiles of the vertical structure of clouds. CloudSat is a member of the A-Train, an international constellation of Earth-science satellites at 705 km altitude with an ascending node at 1:30 PM local time. Five years into the mission, the CloudSat spacecraft survived a near-death experience when its battery developed a current-limiting impedance restriction. Dramatic changes were made to the operations of the spacecraft, allowing the mission to continue providing unique weather- and climate-related data on clouds. While several more years of operations are possible, a number of challenges still exist. We discuss the science, the history, and options for the future of CloudSat.

Vane, Deborah G.↗

DOE OSTI2026

Daily, 30 m Resolution NDSI Data for the East River Watershed, CO for 2000-2020

This dataset contains daily Normalized Difference Snow Index (NDSI) values at 30 m spatial resolution for the East River watershed in Colorado, USA. The temporal range of these data includes water years 2001-2020. These data were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). This model fuses low spatial and high temporal resolution data from MODIS (500 m, daily) with high spatial and low temporal resolution data from Landsat (30 m, 16 days) to create a 30m synthetic daily snow product. This product allows for the analysis of historical snow covered area trends in the East River Watershed at fine spatiotemporal resolutions where it was not available previously. This research was performed as a part of the Department of Energy’s Subsurface Biogeochemical Research Program with the primary intent of better understanding the timing and spatial patterns of water delivery to the Critical Zone in mountain watersheds. Each .zip file contains one "water year" of data (October 1 - September 30; i.e., water year 2010 starts October 1, 2010 and ends September 30, 2011). Each zip file contains the following: STARFM daily Normalized Difference Snow Index (NDSI) fusion data files in GeoTiff format with one layer for each day between Landsat data acquisition dates (i.e., for dates of Landsat acquisition, the Landsat image is included for that date). The study area is located in an area of Landsat path overlap, so Landsat dates acquisitions are every 7-9 days. Landsat NDSI files containing the high spatial (30m), low temporal (7-9 days due to Landsat path overlap) resolution data used as input to STARFM in GeoTiff format with one layer for each day. Dates for which no Landsat data were obtained are included as NoData layers. MODIS NDSI files containing the high temporal (daily), low spatial (500m) resolution data used as input to STARFM in GeoTiff format with one layer for each day. Please note the MODIS data were resampled to 30m pixels for input into the STARFM model. The data have a scale factor of 10,000 and a no data value of -32767. The projection of all datasets is WGS 84 (EPSG: 4326), which has a latitude/longitude based degree resolution of 0.0002694946 X 0.0002694946, and approximates to the 30 m spatial resolution mentioned above. The Layer Index files in .csv format. They contain information for each layer in the above GeoTiff files regarding the corresponding date for each layer, the fraction of pixels in the image that contain valid data (missing data is due to either cloud cover or poor data quality; these values are not percent snow cover). Dates of Landsat overpass are indicated in these files. If no Landsat data were able to be obtained due to cloud cover or lack of Landsat Tier 1 data available on Google Earth Engine, this is also noted.

EARTH SCIENCE > CRYOSPHERE > SNOW/ICE↗

NASA NTRS2012

Trends in Surface Temperature at High Latitudes

The earliest signal of a climate change is expected to be found in the polar regions where warming is expected to be amplified on account of ice-albedo feedbacks associated with the high reflectivity of snow and ice. Because of general inaccessibility, there is a general paucity of in situ data and hence the need to use satellite data to observe the large-scale variability and trends in surface temperature in the region. Among the most important sensors for monitoring surface temperature has been the Advanced Very High Resolution Radiometer (AVHRR) which was first launched in 1978 and has provided continuous thermal infrared data since 1981. The top of the atmosphere data are converted to surface temperature data through various schemes that accounts for the unique atmospheric and surface conditions in the polar regions. Among the highest source of error in the data is cloud masking which is made more difficult in the polar region because of similar Signatures of clouds and snow lice covered areas. The availability of many more channels in the Moderate Resolution Imaging Spectroradiometer (MODIS) launched on board Terra satellite in December 1999 and on board Aqua in May 2002 (e.g., 36 visible and infrared channels compared to 5 for AVHRR) made it possible to minimize the error. Further capabilities were introduced with the Advanced Microwave Scanning Radiometer (AMSR) which has the appropriate frequency channels for the retrieval of sea surface temperature (SST). The results of analysis of the data show an amplified warming in the Arctic region, compared with global warming. The spatial distribution of warming is, however, not uniform and during the last 3 decades, positive temperature anomalies have been most pronounced in North America, Greenland and the Arctic basin. Some regions of the Arctic such as Siberia and the Bering Sea surprisingly show moderate cooling but this may be because these regions were anomalously warm in the 1980s when the satellite record started. Also, the SST in the Arctic basin is observed to be anomalously high in 2007 when the perennial ice cover declined dramatically to its lowest extent. In the Antarctic, surface temperature trends are much more moderate with the most positive trends occurring in the Antarctic Peninsula and parts of Western Antarctica while some cooling are observed in the Antarctic Plateau and the Ross Sea. The trends in SST in the region is similar to global averages but precipitation from more evaporation may have a key role in the spatial distribution of surface temperature in the ice covered region

Comiso, Josefino C.↗

NASA NTRS1982

Effect of subpixel-sized cloud on target discrimination from satellite data

The paper reports results of a study of the effects of subpixel-sized clouds on remotely sensed radiance; the NOAA-7 Advanced Very High Resolution Radiometer was used in the study. It is noted that it is quite possible that clouds could exist with dimensions of less than 1 x 1 km, which will not be resolvable as discrete pixels filled only by cloud, but which will be recorded as pixels with radiance values not characteristic of the ground target. It is shown that for a clear atmosphere the presence of even 20% cloud produces a fivefold decrease or worse in vegetative index at all scan angles for both pure and mixed targets. These effects are large and show that undetected cloud can produce drastic changes in combinations of recorded radiance used for vegetative assessment.

Duggin, M. J.↗

NASA NTRS1981

Homogeneous condensation - Freezing nucleation rate measurements for small water droplets in an expansion cloud chamber

Experimental data on ice nucleation, presented in an earlier paper, are analyzed to yield information about the homogeneous nucleation rate of ice from supercooled liquid and the heights of energy barriers to that nucleation. The experiment consisted of using an expansion cloud chamber to nucleate from the vapor a cloud of supercooled pure water drops and the observation of the fraction of drops which subsequently froze. The analysis employed standard classical homogeneous nucleation theory. The data are used to extract the first experimental measurement (albeit indirect) of the activation energy for the transfer of a water molecule across the liquid-ice interface at temperatures near -40 C. The results provide further evidence that the local liquid structure becomes more icelike as the temperature is lowered.

Hagen, D. E.↗

NASA NTRS2012

CloudSat Anomaly Recovery and Operational Lessons Learned

In April 2011, NASA's pioneering cloud profiling radar satellite, CloudSat, experienced a battery anomaly that placed it into emergency mode and rendered it operations incapable. All initial attempts to recover the spacecraft failed as the resultant power limitations could not support even the lowest power mode. Originally part of a six-satellite constellation known as the "A-Train", CloudSat was unable to stay within its assigned control box, posing a threat to other A-Train satellites. CloudSat needed to exit the constellation, but with the tenuous power profile, conducting maneuvers was very risky. The team was able to execute a complex sequence of operations which recovered control, conducted an orbit lower maneuver, and returned the satellite to safe mode, within one 65 minute sunlit period. During the course of the anomaly recovery, the team developed several bold, innovative operational strategies. Details of the investigation into the root-cause and the multiple approaches to revive CloudSat are examined. Satellite communication and commanding during the anomaly are presented. A radical new system of "Daylight Only Operations" (DO-OP) was developed, which cycles the payload and subsystem components off in tune with earth eclipse entry and exit in order to maintain positive power and thermal profiles. The scientific methodology and operational results behind the graduated testing and ramp-up to DO-OP are analyzed. In November 2011, the CloudSat team successfully restored the vehicle to consistent operational collection of cloud radar data during sunlit portions of the orbit. Lessons learned throughout the six-month return-to-operations recovery effort are discussed and offered for application to other R&D satellites, in the context of on-orbit anomaly resolution efforts.

cloud profiling radar satellite↗

NASA NTRS2014

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

Data Analytics↗

DOE OSTI2017

Diagnosing Cloud Biases in the GFDL AM3 Model With Atmospheric Classification

In this paper, we define a set of 21 atmospheric states, or recurring weather patterns, for a region surrounding the Atmospheric Radiation Measurement Program's Southern Great Plains site using an iterative clustering technique. The states are defined using dynamic and thermodynamic variables from reanalysis, tested for statistical significance with cloud radar data from the Southern Great Plains site, and are determined every 6 h for 14 years, creating a time series of atmospheric state. The states represent the various stages of the progression of synoptic systems through the region (e.g., warm fronts, warm sectors, cold fronts, cold northerly advection, and high-pressure anticyclones) with a subset of states representing summertime conditions with varying degrees of convective activity. We use the states to classify output from the NOAA/Geophysical Fluid Dynamics Laboratory AM3 model to test the model's simulation of the frequency of occurrence of the states and of the cloud occurrence during each state. The model roughly simulates the frequency of occurrence of the states but exhibits systematic cloud occurrence biases. Comparison of observed and model-simulated International Satellite Cloud Climatology Project histograms of cloud top pressure and optical thickness shows that the model lacks high thin cloud under all conditions, but biases in thick cloud are state-dependent. Frontal conditions in the model do not produce enough thick cloud, while fair-weather conditions produce too much. Finally, we find that increasing the horizontal resolution of the model improves the representation of thick clouds under all conditions but has little effect on high thin clouds. However, increasing resolution also changes the distribution of states, causing an increase in total cloud occurrence bias.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2002

A New Look Into the Treatment of Large Drops in Radiative Transfer

One of the weakest links in conventional cloud radiation models is the way a size distribution of cloud particles is mathematically handled: one averages measured drop concentrations over space, evaluates the extinction and scattering cross sections using the mean drop size distribution function and solves the radiative transfer equation with average characteristics. This technique assumes that all drop sizes are well represented in ary given interval along the direction of photon travel. But the concentration of large drops can be so law that this assumption is significantly violated. This is clearly seen if one examines of how the appearance of drops changes with the scale. In the poster we demonstrate the results of our analysis of FSSP data acquired during FIRE'87 and both FSSP and DC1 data from cloud IOP in Spring 2000. The analysis shows that, in general, the average number of drops observed in an interval along the direction of photon travel is proportional to the interval's length (or scale) powered to a drop scaling exponent. For small droplets the scaling exponent is equal to 1, as predicted by a conventional radiative transfer model. However, for large drops, the scaling exponent can fall distinctly below 1. Since the solution of radiative transfer equation depends on the drop scaling exponent, its deviation from unity can lead to a systematic bias in estimation of cloud radiative properties. We discuss the importance of the scaling exponent for the characteristics of the small-scale drop size variability for large and small droplets. Most of the existing cloud radiation models, however, are insensitive to this parameter.

Marshak, Alexander↗

NASA NTRS1995

FIR line profiles as probes of warm gas dynamics

Measurements of the shapes, velocities, and intensities of FIR lines all help to probe the dynamics, physical associations, and excitation conditions of warm gas in molecular clouds. With this in mind, we have observed the J=9-8, 12-11,14-13, and 16-15 lines of (12)CO and the 158 micron line of C II in a number of positions in 4 selected clouds. The data were obtained with a laser heterodyne spectrometer aboard NASA's Kuiper Airborne Observatory. Line measurements at 0.6 km/s resolution allow us to resolve the profiles completely, and thereby to distinguish between UV-and shock-heating mechanisms for the high-excitation gas. For CO, the high-J linewidths lie in the range of 4-20 km/s (FWHM), similar to those observed for low-J (J less than 4) transitions in these sources. This correspondence suggests that the hotter gas (T = 200-600 K) is dynamically linked to the quiescent gas component, perhaps by association with the UV-heated peripheries of the numerous cloud clumps. Much of the C II emission is thought to emanate from these cloud peripheries, but the line profiles generally do not match those seen in CO. None of the observed sources show any evidence in high-J (12)CO emission for shock-excitation (i.e., linewidths greater than 30 km/s).

Betz, A. L.↗

DOE OSTI2015

REVEALING THE PHYSICAL PROPERTIES OF MOLECULAR GAS IN ORION WITH A LARGE-SCALE SURVEY IN J = 2-1 LINES OF {sup 12}CO, {sup 13}CO, AND C{sup 18}O

We present fully sampled ∼3' resolution images of {sup 12}CO(J = 2-1), {sup 13}CO(J = 2-1), and C{sup 18}O(J = 2-1) emission taken with the newly developed 1.85 m millimeter-submillimeter telescope over the entire area of the Orion A and B giant molecular clouds. The data were compared with J = 1-0 of the {sup 12}CO, {sup 13}CO, and C{sup 18}O data taken with the Nagoya 4 m telescope and the NANTEN telescope at the same angular resolution to derive the spatial distributions of the physical properties of the molecular gas. We explore the large velocity gradient formalism to determine the gas density and temperature using line combinations of {sup 12}CO(J = 2-1), {sup 13}CO(J = 2-1), and {sup 13}CO(J = 1-0) assuming a uniform velocity gradient and abundance ratio of CO. The derived gas density is in the range of 500 to 5000 cm{sup –3}, and the derived gas temperature is mostly in the range of 20 to 50 K along the cloud ridge with a temperature gradient depending on the distance from the star forming region. We found that the high-temperature region at the cloud edge faces the H II region, indicating that the molecular gas is interacting with the stellar wind and radiation from the massive stars. In addition, we compared the derived gas properties with the young stellar objects distribution obtained with the Spitzer telescope to investigate the relationship between the gas properties and the star formation activity therein. We found that the gas density and star formation efficiency are positively well correlated, indicating that stars form effectively in the dense gas region.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Innovative Data Management System in Support of Remedial Actions for Legacy Radioactive Contamination - 17280

The Port Hope Area Initiative (PHAI) is a project mandated and funded by the Government of Canada to remove contamination from properties with legacy low-level radioactive waste (LLRW) contamination arising from a crown-owned radium and, later, uranium refinery in the Town of Port Hope, Ontario. From 1976 to 1981, a community clean-up project was undertaken, with the specific purpose to decrease the level of historic LLRW contamination within Port Hope. Since then, residual LLRW has been managed by the Low Level Radioactive Waste Management Office (LLRWMO) (and its predecessors). The PHAI and the Port Hope Project (PHP) were initiated in 2001 with a formal agreement between affected municipalities and the federal government to develop a safe, long-term management solution for the historic LLRW remaining within Port Hope. An important activity of the PHP is the survey of about 4,800 'small scale sites' (SSS) throughout the community to confirm the presence or absence of LLRW. This involves surveys of properties based on the measurement of exterior gamma, contaminated objects and surfaces, soil concentrations and indoor radon and classification to Port Hope project specific criteria. A very large amount of information is involved in the project including; scheduling individual field visits to the properties, capture of field data and comments, laboratory sample tracking, QA/QC, report generation and internal and client project reporting at regular schedules. Field data collection of exterior gamma radiation surveys includes direct (real time) and interactive loading of data into the Information Management (IM) system to support initial QA/QC and reporting back to the field on radiation levels. Screening soil measurements including X-ray fluorescence (XRF) and gamma radiation are collected from all 20 cm increments in each borehole in addition to down-hole gamma radiation measurements and logging of soil characteristics which are used in documentation and interpretation of the property condition. The IM system outlined in this paper was developed to respond to the requirements for radiological surveys in Port Hope. The IM system continues to evolve as a part of continuing review and improvement. The IM system incorporates Web-enabled field tools and cloud hosted data and applications, provides increased field efficiency, timeliness in reporting, real time QA/QC analysis, and consistency in tracking project advances and changes made to the data for decision-making and reporting on an ongoing basis. Examples of specific applications are provided. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗