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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Distributionally Robust Decentralized Volt-Var Control With Network Reconfiguration

Here, this paper presents a decentralized volt-var optimization (VVO) and network reconfiguration strategy to address the challenges arising from the growing integration of distributed energy resources, particularly photovoltaic (PV) generation units, in active distribution networks. To reconcile control measures with different time resolutions and empower local control centers to handle intermittency locally, the proposed approach leverages a two-stage distributionally robust optimization; decisions on slow-responding control measures and set points that link neighboring subnetworks are made in advance while considering all plausible distributions of uncertain PV outputs. We present a decomposition algorithm with an acceleration scheme for solving the proposed model. Numerical experiments on the IEEE 123 bus distribution system are given to demonstrate its outstanding out-of-sample performance and computational efficiency, which suggests that the proposed method can effectively localize uncertainty via risk-informed proactive timely decisions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2007

Using Flow Regime Lightning and Sounding Climatologies to Initialize Gridded Lightning Threat Forecasts for East Central Florida

Each morning, the forecasters at the National Weather Service in Melbourne, FL (NWS MLB) produce an experimental cloud-to-ground (CG) lightning threat index map for their county warning area (CWA) that is posted to their web site (httl://www.srh.weather.gov/mlb/ghwo/lightning.shtml) . Given the hazardous nature of lightning in East Central Florida, especially during the warm season months of May September, these maps help users factor the threat of lightning, relative to their location, into their daily plans. The maps are color-coded in five levels from Very Low to Extreme, with threat level definitions based on the probability of lightning occurrence and the expected amount of CG activity. On a day in which thunderstorms are expected, there are typically two or more threat levels depicted spatially across the CWA. The locations of relative lightning threat maxima and minima often depend on the position and orientation of the low-level ridge axis, forecast propagation and interaction of sea/lake/outflow boundaries, expected evolution of moisture and stability fields, and other factors that can influence the spatial distribution of thunderstorms over the CWA. The lightning threat index maps are issued for the 24-hour period beginning at 1200 UTC each day with a grid resolution of 5 km x 5 km. Product preparation is performed on the AWIPS Graphical Forecast Editor (GFE), which is the standard NWS platform for graphical editing. Until recently, the forecasters created each map manually, starting with a blank map. To improve efficiency of the forecast process, NWS MLB requested that the Applied Meteorology Unit (AMU) create gridded warm season lightning climatologies that could be used as first-guess inputs to initialize lightning threat index maps. The gridded values requested included CG strike densities and frequency of occurrence stratified by synoptic-scale flow regime. The intent was to improve consistency between forecasters while allowing them to focus on the mesoscale detail of the forecast. Several studies took place at the Florida State University (FSU) and NWS Tallahassee (TAE) in which they created daily flow regimes using Florida 1200 UTC synoptic soundings and CG strike densities, or number of strikes per specified area. The soundings used to determine the flow regimes were taken at Miami (MIA), Tampa (TBW), and Jacksonville (JAX), FL, and the lightning data for the strike densities came from the National Lightning Detection Network (NLDN). The densities were created on a 2.5 km x 2.5 km grid for every hour of every day during the warm seasons in the years 1989-2004. The grids encompass an area that includes the entire state of Florida and adjacent Atlantic and Gulf of Mexico waters. Personnel at FSU and NWS TAE provided this data and supporting software for the work performed by the AMU.

Lambert, Winifred↗
DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Connect the Dots: In Situ 4-D Seismic Monitoring of CO 2 Storage With Spatio-Temporal CNNs

4-D seismic imaging has been widely used in CO 2 sequestration projects to monitor the fluid flow in the volumetric subsurface region that is not sampled by wells. Ideally, real-time monitoring and near-future forecasting would provide site operators with great insights to understand the dynamics of the subsurface reservoir and assess any potential risks. However, due to obstacles such as high deployment cost, availability of acquisition equipment, exclusion zones around surface structures, only very sparse seismic imaging data can be obtained during monitoring. That leads to an unavoidable and growing knowledge gap over time. The operator needs to understand the fluid flow throughout the project lifetime and the seismic data are only available at a limited number of times. This is insufficient for understanding reservoir behavior. To overcome those challenges, we have developed spatio-temporal neural-network-based models that can produce high-fidelity interpolated or extrapolated images effectively and efficiently. Specifically, our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized by optical flow. We validate the performance of our models using real 4-D post-stack seismic imaging data acquired at the Sleipner CO 2 sequestration field. We employ two different strategies in evaluating our models. Numerically, we compare our models with different baseline approaches using classic pixel-based metrics. We also conduct a blind survey and collect a total of 20 responses from domain experts to evaluate the quality of data generated by our models. Finally, via both numerical and expert evaluation, we conclude that our models can produce high-quality 2-D/3-D seismic imaging data at a reasonable cost, offering the possibility of real-time monitoring or even near-future forecasting of the CO 2 storage reservoir.

4-D seismic imaging↗
DOE OSTI2014

Aerosol indirect effect on the grid-scale clouds in the two-way coupled WRF–CMAQ: model description, development, evaluation and regional analysis

This study implemented first, second and glaciation aerosol indirect effects (AIE) on resolved clouds in the two-way coupled Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) modeling system by including parameterizations for both cloud drop and ice number concentrations on the basis of CMAQ-predicted aerosol distributions and WRF meteorological conditions. The performance of the newly developed WRF–CMAQ model, with alternate Community Atmospheric Model (CAM) and Rapid Radiative Transfer Model for GCMs (RRTMG) radiation schemes, was evaluated with observations from the Clouds and the See http://ceres.larc.nasa.gov/ . Earth's Radiant Energy System (CERES) satellite and surface monitoring networks (AQS, IMPROVE, CASTNET, STN, and PRISM) over the continental US (CONUS) (12 km resolution) and eastern Texas (4 km resolution) during August and September of 2006. The results at the Air Quality System (AQS) surface sites show that in August, the normalized mean bias (NMB) values for PM 2.5 over the eastern US (EUS) and the western US (WUS) are 5.3% (-0.1%) and 0.4% (-5.2%) for WRF–CMAQ/CAM (WRF–CMAQ/RRTMG), respectively. The evaluation of PM 2.5 chemical composition reveals that in August, WRF–CMAQ/CAM (WRF–CMAQ/RRTMG) consistently underestimated the observed SO 4 2- by -23.0% (-27.7%), -12.5% (-18.9%) and -7.9% (-14.8%) over the EUS at the Clean Air Status Trends Network (CASTNET), Interagency Monitoring of Protected Visual Environments (IMPROVE) and Speciated Trends Network (STN) sites, respectively. Both configurations (WRF–CMAQ/CAM, WRF–CMAQ/RRTMG) overestimated the observed mean organic carbon (OC), elemental carbon (EC) and and total carbon (TC) concentrations over the EUS in August at the IMPROVE sites. Both configurations generally underestimated the cloud field (shortwave cloud forcing, SWCF) over the CONUS in August due to the fact that the AIE on the subgrid convective clouds was not considered when the model simulations were run at the 12 km resolution. This is in agreement with the fact that both configurations captured SWCF and longwave cloud forcing (LWCF) very well for the 4 km simulation over eastern Texas, when all clouds were resolved by the finer resolution domain. The simulations of WRF–CMAQ/CAM and WRF–CMAQ/RRTMG show dramatic improvements for SWCF, LWCF, cloud optical depth (COD), cloud fractions and precipitation over the ocean relative to those of WRF default cases in August. The model performance in September is similar to that in August, except for a greater overestimation of PM 2.5 due to the overestimations of SO 4 2- , NH 4 + , NO 3 - , and TC over the EUS, less underestimation of clouds (SWCF) over the land areas due to the lower SWCF values, and fewer convective clouds in September. Finally, this work shows that inclusion of indirect aerosol effect treatments in WRF–CMAQ represents a significant advancement and milestone in air quality modeling and the development of integrated emissions control strategies for air quality management and climate change mitigation.

indirect aerosol forcing↗
NASA NTRS2006

Using Flow Regime Lightning and Sounding Climatologies to Initialize Gridded Lightning Threat Forecasts for East Central Florida

Each morning, the forecasters at the National Weather Service in Melbourne, FL (NWS MLB) produce an experimental cloud-to-ground (CG) lightning threat index map for their county warning area (CWA) that is posted to their web site (http://www.srh.weather.gov/mlb/ghwo/lightning.shtml) . Given the hazardous nature of lightning in East Central Florida, especially during the warm season months of May September, these maps help users factor the threat of lightning, relative to their location, into their daily plans. The maps are color-coded in five levels from Very Low to Extreme, with threat level definitions based on the probability of lightning occurrence and the expected amount of CG activity. On a day in which thunderstorms are expected, there are typically two or more threat levels depicted spatially across the CWA. The locations of relative lightning threat maxima and minima often depend on the position and orientation of the low-level ridge axis, forecast propagation and interaction of sea/lake/outflow boundaries, expected evolution of moisture and stability fields, and other factors that can influence the spatial distribution of thunderstorms over the CWA. The lightning threat index maps are issued for the 24-hour period beginning at 1200 UTC each day with a grid resolution of 5 km x 5 km. Product preparation is performed on the AWIPS Graphical Forecast Editor (GFE), which is the standard NWS platform for graphical editing. Currently, the forecasters create each map manually, starting with a blank map. To improve efficiency of the forecast process, NWS MLB requested that the Applied Meteorology Unit (AMU) create gridded warm season lightning climatologies that could be used as first-guess inputs to initialize lightning threat index maps. The gridded values requested included CG strike densities and frequency of occurrence stratified by synoptic-scale flow regime. The intent is to improve consistency between forecasters while allowing them to focus on the mesoscale detail of the forecast, ultimately benefiting the end-users of the product. Several studies took place at the Florida State University (FSU) and NWS Tallahassee (TAE) in which they created daily flow regimes using Florida 1200 UTC synoptic soundings and CG strike densities, or number of strikes per specified area. The soundings used to determine the flow regimes were taken at Miami (MIA), Tampa (TBW), and Jacksonville (JAX), FL, and the lightning data for the strike densities came from the National Lightning Detection Network (NLDN). The densities were created on a 2.5 km x 2.5 km grid for every hour of every day during the warm seasons in the years 1989-2004. The grids encompass an area that includes the entire state of Florida and adjacent Atlantic and Gulf of Mexico waters. Personnel at FSU and NWS TAE provided this data and supporting software for the work performed by the AMU.

Lambert, Winifred↗
DOE OSTI2023

Efficient Super-Resolution of Near-Surface Climate Modeling Using the Fourier Neural Operator

Downscaling methods are critical in efficiently generating high-resolution atmospheric data. However, state-of-the-art statistical or dynamical downscaling techniques either suffer from the high computational cost of running a physical model or require high-resolution data to develop a downscaling tool. Here, we demonstrate a recently proposed zero-shot super-resolution method, the Fourier neural operator (FNO), to efficiently perform downscaling without the need for high-resolution data. Because the FNO learns dynamics in Fourier space, FNO is a resolution-invariant emulator; it can be trained at a coarse resolution and produces emulation at any high resolution. We applied FNO to downscale a 4-km resolution Weather Research and Forecasting (WRF) Model simulation of near-surface heat-related variables over the Great Lakes region. The FNO is driven by the atmospheric forcings, and topographic features used in the WRF model at the same resolution. We incorporated a physics-constrained loss in FNO by using the Clausius-Clapeyron relation to better constrain the relations among the emulated states. Trained on merely 600 WRF snapshots at 4-km resolution, the FNO shows comparable performance with a widely used convolutional network, U-Net, achieving averaged modified Kling-Gupta Efficiency of 0.88 and 0.94 on the test dataset for temperature and pressure, respectively. We then employed the FNO to produce 1-km emulations to reproduce the fine climate features. Further, by taking the WRF simulation as ground truth, we show consistent performances at the two resolutions, suggesting the reliability of FNO in producing high-resolution dynamics. Our study demonstrates the potential of using FNO for zero-shot super-resolution in generating first-order estimation on atmospheric modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

MPC4CLR (Model-Predictive-Control-for-Critical-Load-Restoration-in-Power-Distribution-Systems) [SWR-22-24]

Model predictive control (MPC) is a system or process control technique for making decisions under uncertainty via rolling look-ahead optimizations at each control step where only the current step decisions are applied, and the rest are discarded. In this work, we developed an MPC for a critical load restoration (CLR) in power distribution systems to recover system service (electricity delivery) following an extreme event-triggered substation outage. The method considers the problem of controlling distributed energy resources (DERs) of the distribution system with the objective of achieving maximum load pick up while satisfying distribution network flow and voltage constraints. A linearized optimal power flow (OPF) model is employed to represent the physics of the network. The problem formulation is augmented with a ramping (up) reserve product for the DERs to ensure improved and upward monotonic load restoration as time evolves. Simulation analysis and performance tests are performed using a modified IEEE 13-bus test feeder integrated with wind, solar, microturbine, and energy storage battery. The software is developed using various software packages in Julia and Python. The MPC model is implemented using the JuMP optimization language in Julia while the data analytics including renewable generation and load demand forecasts, running the MPC simulation and visualizations is performed in Python.

Eseye, Abinet Tesfaye↗
NASA NTRS2011

The GOES-R Series Geostationary Lightning Mapper (GLM)

The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. Superior spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), which will have just completed Critical Design Review and move forward into the construction phase of instrument development. The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development (an engineering development unit and 4 flight models), a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms, cal/val performance monitoring tools, and new applications. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms

Goodman, Steven J.↗
DOE OSTI2024

A comparison of model validation approaches for echo state networks using climate model replicates

As global temperatures continue to rise, climate mitigation strategies such as stratospheric aerosol injections (SAI) are increasingly discussed, but the downstream effects of these strategies are not well understood. As such, there is interest in developing statistical methods to quantify the evolution of climate variable relationships during the time period surrounding an SAI. Feature importance applied to echo state network (ESN) models has been proposed as a way to understand the effects of SAI using a data-driven model. This approach depends on the ESN fitting the data well. If not, the feature importance may place importance on features that are not representative of the underlying relationships. Typically, time series prediction models such as ESNs are assessed using out-of-sample performance metrics that divide the times series into separate training and testing sets. However, this model assessment approach is geared towards forecasting applications and not scenarios such as the motivating SAI example where the objective is using a data driven model to capture variable relationships. Here, in this paper, we demonstrate a novel use of climate model replicates to investigate the applicability of the commonly used repeated hold-out model assessment approach for the SAI application. Simulations of an SAI are generated using a simplified climate model, and different initialization conditions are used to provide independent training and testing sets containing the same SAI event. The climate model replicates enable out-of-sample measures of model performance, which are compared to the single time series hold-out validation approach. For our case study, it is found that the repeated hold-out sample performance is comparable, but conservative, to the replicate out-of-sample performance when the training set contains enough time after the aerosol injection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

On the effectiveness of neural operators at zero-shot weather downscaling

Machine-learning (ML) methods have shown great potential for weather downscaling. These data-driven approaches provide a more efficient alternative for producing high-resolution weather datasets and forecasts compared to physics-based numerical simulations. Neural operators, which learn solution operators for a family of partial differential equations, have shown great success in scientific ML applications involving physics-driven datasets. Neural operators are grid-resolution-invariant and are often evaluated on higher grid resolutions than they are trained on, i.e., zero-shot super-resolution. Given their promising zero-shot super-resolution performance on dynamical systems emulation, we present a critical investigation of their zero-shot weather downscaling capabilities, which is when models are tasked with producing high-resolution outputs using higher upsampling factors than are seen during training. To this end, we create two realistic downscaling experiments with challenging upsampling factors (e.g., 8x and 15x) across data from different simulations: the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit. While neural operator-based downscaling models perform better than interpolation and a simple convolutional baseline, we show the surprising performance of an approach that combines a powerful transformer-based model with parameter-free interpolation at zero-shot weather downscaling. We find that this Swin-Transformer-based approach mostly outperforms models with neural operator layers in terms of average error metrics, whereas an Enhanced Super-Resolution Generative Adversarial Network-based approach is better than most models in terms of capturing the physics of the ground truth data. We suggest their use in future work as strong baselines.

17 WIND ENERGY↗
NASA NTRS2008

Systematic Independent Validation of Inner Heliospheric Models

This presentation is the first in a series which will provide independent validation of community models of the outer corona and inner heliosphere. In this work we establish a set of measures to be used in validating this group of models. We use these procedures to generate a comprehensive set of results from the Wang- Sheeley-Arge (WSA) model which will be used as a baseline, or reference, against which to compare all other models. We also run a test of the validation procedures by applying them to a small set of results produced by the ENLIL Magnetohydrodynamic (MHD) model. In future presentations we will validate other models currently hosted by the Community Coordinated Modeling Center(CCMC), including a comprehensive validation of the ENLIL model. The Wang-Sheeley-Arge (WSA) model is widely used to model the Solar wind, and is used by a number of agencies to predict Solar wind conditions at Earth as much as four days into the future. Because it is so important to both the research and space weather forecasting communities, it is essential that its performance be measured systematically, and independently. In this paper we offer just such an independent and systematic validation. We report skill scores for the model's predictions of wind speed and IMF polarity for a large set of Carrington rotations. The model was run in all its routinely used configurations. It ingests line of sight magnetograms. For this study we generated model results for monthly magnetograms from the National Solar Observatory (SOLIS), Mount Wilson Observatory and the GONG network, spanning the Carrington rotation range from 1650 to 2068. We compare the influence of the different magnetogram sources, performance at quiet and active times, and estimate the effect of different empirical wind speed tunings. We also consider the ability of the WSA model to identify sharp transitions in wind speed from slow to fast wind. These results will serve as a baseline against which to compare future versions of the model

MacNeice, P. J.↗
NASA NTRS1990

ISLE: Intelligent Selection of Loop Electronics. A CLIPS/C++/INGRES integrated application

The Intelligent Selection of Loop Electronics (ISLE) system is an integrated knowledge-based system that is used to configure, evaluate, and rank possible network carrier equipment known as Digital Loop Carrier (DLC), which will be used to meet the demands of forecasted telephone services. Determining the best carrier systems and carrier architectures, while minimizing the cost, meeting corporate policies and addressing area service demands, has become a formidable task. Network planners and engineers use the ISLE system to assist them in this task of selecting and configuring the appropriate loop electronics equipment for future telephone services. The ISLE application is an integrated system consisting of a knowledge base, implemented in CLIPS (a planner application), C++, and an object database created from existing INGRES database information. The embedibility, performance, and portability of CLIPS provided us with a tool with which to capture, clarify, and refine corporate knowledge and distribute this knowledge within a larger functional system to network planners and engineers throughout U S WEST.

Fischer, Lynn↗
DOE OSTI2022

Device Classification for Industrial Control Systems Using Predicted Traffic Features

To achieve a secure interconnected Industrial Control System (ICS) architecture, security practitioners depend on accurate identification of network host behavior. However, accurate machine learning based host identification methods depends on the availability of significant quantities of network traffic data, which can be difficult to obtain due to system constraints such as network security, data confidentiality, and physical location. In this work, we propose a network traffic feature prediction method based on a generative model, which achieves high host identification accuracy. Furthermore, we develop a joint training algorithm to improve host identification performance compared to separate training of the generative model and the classifier responsible for host identification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Microstructure and Performance of Nano-WC Particle-Strengthened Ni Coatings by Electro-brush Plating

Nano-WC particle-strengthened Ni composite coatings were prepared by electro-brush plating. A series of artificial neural network models was developed for the analysis and prediction of the coating performance. The microstructure and phase structure were studied with SEM, XRD and EDS. Nano-WC particles as the hard phase were distributed evenly and dispersed in the coatings, which was conducive to the hardness enhancement of the coatings. Additionally, nano-WC particles as microballs inserted in the coatings could reduce the friction when wear of the coating surface occurred. Furthermore, the addition of nano-WC particles delayed the time of crack initiation and propagation in the coatings, which increased the service lifetime when they worked in a thermal shock environment.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1998

COOP 3D ARPA Experiment 109 National Center for Atmospheric Research

Coupled atmospheric and hydrodynamic forecast models were executed on the supercomputing resources of the National Center for Atmospheric Research (NCAR) in Boulder, Colorado and the Ohio Supercomputing Center (OSC)in Columbus, Ohio. respectively. The interoperation of the forecast models on these geographically diverse, high performance Cray platforms required the transfer of large three dimensional data sets at very high information rates. High capacity, terrestrial fiber optic transmission system technologies were integrated with those of an experimental high speed communications satellite in Geosynchronous Earth Orbit (GEO) to test the integration of the two systems. Operation over a spacecraft in GEO orbit required modification of the standard configuration of legacy data communications protocols to facilitate their ability to perform efficiently in the changing environment characteristic of a hybrid network. The success of this performance tuning enabled the use of such an architecture to facilitate high data rate, fiber optic quality data communications between high performance systems not accessible to standard terrestrial fiber transmission systems. Thus obviating the performance degradation often found in contemporary earth/satellite hybrids.

Source record↗
NASA NTRS2010

An Objective Verification of the North American Mesoscale Model for Kennedy Space Center and Cape Canaveral Air Force Station

The 45th Weather Squadron (45 WS) Launch Weather Officers use the 12-km resolution North American Mesoscale (NAM) model (MesoNAM) text and graphical product forecasts extensively to support launch weather operations. However, the actual performance of the model at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) has not been measured objectively. In order to have tangible evidence of model performance, the 45 WS tasked the Applied Meteorology Unit to conduct a detailed statistical analysis of model output compared to observed values. The model products are provided to the 45 WS by ACTA, Inc. and include hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The objective analysis compared the MesoNAM forecast winds, temperature and dew point, as well as the changes in these parameters over time, to the observed values from the sensors in the KSC/CCAFS wind tower network. Objective statistics will give the forecasters knowledge of the model's strength and weaknesses, which will result in improved forecasts for operations.

Bauman, William H., III↗
DOE OSTI2014

SU-E-J-115: Using Markov Chain Modeling to Elucidate Patterns in Breast Cancer Metastasis Over Time and Space

Purpose: Traditionally, breast cancer metastasis is described as a process wherein cancer cells spread from the breast to multiple organ systems via hematogenous and lymphatic routes. Mapping organ specific patterns of cancer spread over time is essential to understanding metastatic progression. In order to better predict sites of metastases, here we demonstrate modeling of the patterned migration of metastasis. Methods: We reviewed the clinical history of 453 breast cancer patients from Memorial Sloan Kettering Cancer Center who were non-metastatic at diagnosis but developed metastasis over time. We used the variables of organ site of metastases as well as time to create a Markov chain model of metastasis. We illustrate the probabilities of metastasis occurring at a given anatomic site together with the probability of spread to additional sites. Results: Based on the clinical histories of 453 breast cancer patients who developed metastasis, we have learned (i) how to create the Markov transition matrix governing the probabilities of cancer progression from site to site; (ii) how to create a systemic network diagram governing disease progression modeled as a random walk on a directed graph; (iii) how to classify metastatic sites as ‘sponges’ that tend to only receive cancer cells or ‘spreaders’ that receive and release them; (iv) how to model the time-scales of disease progression as a Weibull probability distribution function; (v) how to perform Monte Carlo simulations of disease progression; and (vi) how to interpret disease progression as an entropy-increasing stochastic process. Conclusion: Based on our modeling, metastatic spread may follow predictable pathways. Mapping metastasis not simply by organ site, but by function as either a ‘spreader’ or ‘sponge’ fundamentally reframes our understanding of metastatic processes. This model serves as a novel platform from which we may integrate the evolving genomic landscape that drives cancer metastasis. PS-OC Trans-Network Project Grant Award for “Data Assimilation and ensemble statistical forecasting methods applied to the MSKCC longitudinal metastatic breast cancer cohort.”.

60 APPLIED LIFE SCIENCES↗