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At least 649 records · Page 36

Application of remote sensing to hydrology

Streamflow forecasting and hydrologic modelling are considered in a feasibility assessment of using the data produced by remote observation from space and/or aircraft to reduce the time and expense normally involved in achieving the ability to predict the hydrological behavior of an ungaged watershed. Existing watershed models are described, and both stochastic and parametric techniques are discussed towards the selection of a suitable simulation model. Technical progress and applications are reported and recommendations are made for additional research.

Ambaruch, R.↗

Intrusions into the lower stratospheric Arctic vortex during the winter of 1991-1992

Investigations of the kinematics of the lower stratospheric Arctic vortex during the winter of 1991-1992 using the contour advection with surgery technique reveal three distinct events in which there was substantial intrusion of midlatitude air into the vortex, in apparent contradiction of the view that the polar vortex constitutes an isolated air mass. Two of these events, in late January and mid-February, were well documented. They were predicted in high-resolution forecasts by the European Centre for Medium-Range Weather Forecasts, most clearly in experimental forecasts with reduced diffusion. Direct confirmation of the presence of the intrusions and of their calculated locations was provided by aerosol observations from the airborne differential absorption laser lidar aboard the NASA DC-8, taken as part of the second Airborne Arctic Stratospheric Expedition campaign; aerosol-rich air of midlatitude origin was seen in the expected position of the intrusions. The reality of the February event was also confirmed by in situ measurements from the NASA ER-2. Such events may be significant for the chemical processes taking place within the winter vortex. The intrusions were evidently related to the meteorology of the northern stratosphere during this winter and in particular to persistent tropospheric blocking over the northeastern Atlantic Ocean and western Europe and concomitant ridging into the lower stratospheric vortex in this region. Nevertheless, preliminary investigations have indicated that such events are not uncommon in other northern hemisphere winters, although no such events were found in the southern hemisphere during the Antarctic winter of 1987.

Plumb, R. A.↗

Consideration of Dynamical Balances

The quasi-balance of extra-tropical tropospheric dynamics is a fundamental aspect of nature. If an atmospheric analysis does not reflect such balance sufficiently well, the subsequent forecast will exhibit unrealistic behavior associated with spurious fast-propagating gravity waves. Even if these eventually damp, they can create poor background fields for a subsequent analysis or interact with moist physics to create spurious precipitation. The nature of this problem will be described along with the reasons for atmospheric balance and techniques for mitigating imbalances. Attention will be focused on fundamental issues rather than on recipes for various techniques.

NNMI↗

Consideration of Dynamical Balances

The quasi-balance of extra-tropical tropospheric dynamics is a fundamental aspect of nature. If an atmospheric analysis does not reflect such balance sufficiently well, the subsequent forecast will exhibit unrealistic behavior associated with spurious fast-propagating gravity waves. Even if these eventually damp, they can create poor background fields for a subsequent analysis or interact with moist physics to create spurious precipitation. The nature of this problem will be described along with the reasons for atmospheric balance and techniques for mitigating imbalances. Attention will be focused on fundamental issues rather than on recipes for various techniques.

Errico, Ronald M.↗

In-Flight Evaluation of the Traffic Aware Planner on the NASA HU-25A Guardian Aircraft

NASA’s Traffic Aware Planner (TAP) software is a research-prototype decision support tool that provides pilots with time- and fuel-saving route recommendations that optimize their current trajectory. The software runs on a first-of-a-kind system architecture onboard three aircraft in revenue service conducting operational evaluations with a major domestic airline. Therefore, significant NASA-internal testing is required prior to releasing the software to the partner airline. This paper describes a flight test plan that exercises the functionality of the TAP software in a representative operational environment, describes the system architecture developed and implemented for the NASA Langley HU-25A Guardian aircraft to support the test objectives, presents outcomes of the flight test campaign, and discusses use cases that demonstrate the value of flight testing for this activity.Research into flight path optimization of transport aircraft conducted by the National Aeronautics and SpaceAdministration (NASA) has produced an operational concept known as Traffic Aware Strategic Aircrew Requests(TASAR) [1, 2]. This near-term concept [3] provides the aircrew with a flight deck decision support tool known asthe Traffic Aware Planner (TAP). The TAP software leverages a growing number of information sources on the flightdeck to make time- and fuel-saving route optimization recommendations to the aircrew while en route. The aircrewcan then use the suggestions provided by the tool to make route change requests with a greater likelihood of acceptanceby air traffic control (ATC). Since TASAR is a concept intended for the current operational environment, it isintentionally designed to have no safety-critical impact or require any changes to current Federal AviationAdministration (FAA) rules and procedures [4, 5].The research prototype TAP system [6–8], explained further in Section III.C, continually incorporates up-to-dateaircraft state data from onboard avionics, as well as the latest position of surrounding traffic, the most recent windforecast, and the most recent convective weather forecast, in order to calculate candidate trajectory modifications thatimprove upon the current active route. These trajectories account for user-selectable objective functions [3] of reducedfuel burn, reduced flight time, or an airline-derived combination of factors known as trip cost. Previous analyses andsimulations have estimated substantial savings for airlines employing this technique within the U.S. National AirspaceSystem (NAS) [9–11]. Operational evaluations with Alaska Airlines seek to validate these projected benefits usingmeasured data while simultaneously providing benefits to the airline [12, 13].The TAP software has undergone a number of human-in-the-loop simulations [14] and flight test activities[15–17] in order to validate the operational concept, evaluate human factors considerations (e.g., workload, usability,distraction, etc.), and to assess the ability of the software to function in a representative operational environment (e.g.,connected to live avionics data, using in-flight internet connectivity, etc.). However, these simulations and flight testcampaigns did not account for the hardware architecture implemented on the three aircraft for Alaska Airlines’operational evaluations of the TAP software. Therefore, a need was identified to thoroughly test the functionality ofthe software in a similar hardware architecture to that of the partner airline’s aircraft. Information regarding testapparatus and environments used to evaluate TAP prior to testing on the HU-25A can be found in reference [18].A campaign of flight trials on a NASA aircraft, the HU-25A Guardian, was conducted to ensure that the researchprototype TAP system functions well in a configuration similar to the Alaska Airlines aircraft prior to deployment.This airborne, networked environment enables an assessment of the operational factors unique to the flight environment. Additionally, this activity evaluated the effectiveness and benefit of new TAP functionality andoperation in a relevant flight environment while allowing the rapid prototyping of new concepts and features.This paper is organized as follows: Section II discusses the details of the flight test plan, flight profiles, and theduties of personnel involved with conducting flight operations. Section III describes the test platform, avionicsequipage, and system architecture. Section IV presents a discussion of results, and Section V contains concludingremarks.

Underwood, Matthew C.↗

Using High-Resolution NSRDB Data to Evaluate Cloud Mask Forecast from WRF-Solar EPS

Validating spatiotemporal distributions of cloud forecasts using numerical weather prediction (NWP) models is difficult as this requires high-quality cloud-property at significantly high spatial and temporal resolution over extended periods of time. Observations of cloud properties, such as cloud mask, cloud optical thickness, and cloud type, are vital for assessing the capability of NWP models to forecast various types of clouds. Using the National Solar Radiation Database (NSRDB), this research evaluates ensemble cloud-mask predictions from the WRF-Solar ensemble prediction system (WRF-Solar EPS). From the WRF-Solar EPS, day-ahead solar forecasts for the contiguous United States (CONUS) for 2018 are simulated. Given the NSRDB data is accessible at a resolution of 2 km, we can calculate the cloud fraction across the 9-km grid of WRF-Solar EPS. This allows us to spatially assess the cloud-mask forecasts using two methods against the high-resolution NSRDB: (a) considering all 2-km NSRDB clouds in the forecast domain (EMAll), and (b) using a minimum cloud fraction threshold of 50% to designate a pixel as cloudy (EMP50). The low-resolution cloud masks from WRF-Solar EPS are evaluated directly against the cloud-resolving scale gridded observations from NSRDB using EMAll. With EMP50, we presume that scenes with less than 50% cloud cover from the 2-km NSRDB are clear. Thus, this assessment approach allows for a fair comparison with WRF-Solar EPS resolved to a 9-km grid. A method of point-by-point verification is used to evaluate dichotomous (yes/no) cloud mask predictions against the NSRDB. For each pixel of model extent, cloud frequency and traditional metrics (e.g., probability of detection, false alarm rate, and hit rate, etc.) are computed and compared with satellite-derived data sets. Mismatched cloud frequency (MCF) is computed to measure the present capability of WRF-Solar EPS in representing various types of clouds, which are categorized using three levels of cloud top height (CTH) and cloud optical depth (COD) across entire CONUS. Preliminary results show that the WRF-Solar EPS provides MCF values ranging from 9% to 46%, 16% to 33%, and 8% to 27% for low-level, middle-level, and high-level clouds, respectively, for three CTHs. The model produces MCFs ranging from 27% to 46%, 13% to 34%, and 8% to 19% for thin, medium-thickness, and thick clouds, respectively, for three CODs. The presentation will include a detailed description of the current outcomes as well as potential future extensions. The evaluation approach established in this study is readily extensible to the evaluation of cloud predictions from different ensemble NWP models. In addition, the findings of the suggested evaluation technique aid in identifying model weaknesses and will ultimately lead to advances in WRF-Solar EPS's skill in predicting clouds and solar irradiance.

cloud mask forecast↗

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗

Simulation of Continental Shallow Cumulus Populations Using an Observation-Constrained Cloud-System Resolving Model

Continental shallow cumulus (ShCu) clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign are simulated by using an observation-constrained cloud-system resolving model. On this day, ShCu forms over Oklahoma and southern Kansas and some of these clouds transition to deeper, precipitating convection during the afternoon. We apply a four-dimensional ensemble-variational (4DEnVar) hybrid technique in the Community Gridpoint Statistical Interpolation (GSI) system to assimilate operational data sets and unique boundary layer measurements including a Raman lidar, radar wind profilers, radiosondes, and surface stations collected by the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory into the Weather Research and Forecasting (WRF) model to ascertain how improved environmental conditions can influence forecasts of ShCu populations and the transition to deeper convection. Independent observations from aircraft, satellite, as well as ARM's remote sensors are used to evaluate model performance in different aspects. Several model experiments are conducted to identify the impact of data assimilation (DA) on the prediction of clouds evolution. The analyses indicate that ShCu populations are more accurately reproduced after DA in terms of cloud initiation time and cloud base height, which can be attributed to an improved representation of the ambient meteorological conditions and the convective boundary layer. Extending the assimilation to 18 UTC (local noon) also improved the simulation of shallow-to-deep transitions of convective clouds.

54 ENVIRONMENTAL SCIENCES↗

Scenario Creation and Power-Conditioning Strategies for Operating Power Grids with Two-Stage Stochastic Economic Dispatch

A significant difficulty associated with the use of stochastic programming to solve optimal power flow problems on a 5-minute timescale is the quality of renewable energy scenarios input by the user. This is especially true when considering power systems with high penetrations of renewable energy, e.g. wind power. This paper introduces the use of stochastic programming to solve the DC optimal power flow problem with scenarios drawn directly from high-fidelity data sets. Hence, the proposed method avoids the problem of lost physics by finding high-fidelity analogs that can describe future states of the system. Furthermore, this method can be simply extended to output multi-period scenarios to the stochastic program. We demonstrate the effectiveness of this technique by simulating dispatch operations on a synthetic test system over the course of a week.

94 GMLC - Grid Modernization Laboratory Consortium↗

PRIME

SAND2021-0565 O PRIME is a modeling framework designed for the real-time characterization and forecasting of partially observed epidemics. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The estimation problem is posed as one of Bayesian inference and solved using a Markov Chain Monte Carlo technique. The framework can accommodate multiple epidemic waves and can help identify different disease dynamics at the regional, state, and country levels. Examples are provided using publicly available COVID-19 data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Safta, Cosmin↗

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗

PRIME - A Software Toolkit for the Characterization of Partially Observed Epidemics in a Bayesian Framework

PRIME is a modeling framework designed for the “real-time’” characterization and forecasting of partially observed epidemics. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov Chain Monte Carlo technique. The framework can accommodate multiple epidemic waves and can help identify different disease dynamics at the regional, state, and country levels. We include examples using publicly available COVID-19 data.

97 MATHEMATICS AND COMPUTING↗

Modeling Nanoconfinement Effects Using Active Learning

Predicting the spatial configuration of gas in nanopores of is relevant in applications such as fluid flow forecasting and hydrocarbon reserves estimation. For example, shale reservoirs have suffered from computationally intractable multiscale problems, since fluid properties such as viscosity, density, and adsorption must be calculated by using expensive molecular dynamics (MD) simulations within each nanopore, whereas flow through these connected nanopores must be simulated at the micrometer scale. We utilize machine learning techniques to quickly and accurately model nanoscale confinement effects as an important step toward bridging the nano and micro scales. Our workflow is based on building and training physics-based deep-neural-networks models by learning from a database of MD calculations. The model accounts for the adsorption phenomenon by predicting the statistical distribution of gas inside nanopores. Because large databases of MD calculations are expensive to create, we investigate active learning (AL) as a data set construction strategy. In this workflow, new data are selected based on the model uncertainty via the query-by-committee approach. We show that our workflow obtains accurate models that generalize to real scanning electron microscopy geometries with 1/10th of the number of MD calculations required vs random data set generation. Our method enables the possibility of modeling nanoconfinement effects at the mesoscale, where complex connected sets of nanopores affect flow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Initial results from a mesoscale atmospheric simulation system and comparisons with the AVE-SESAME I data set

The development of a comprehensive mesoscale atmospheric simulation system (MASS) is described in detail. The modeling system is designed for both research and real-time forecast applications. The 14-level numerical model, which has a 48 km grid mesh, can be run over most of North America and the adjacent oceanic regions. The model employs sixth-order accurate numerics, generalized similarity theory boundary-layer physics, a sophisticated cumulus parameterization scheme, and state of the art analysis and initialization techniques. Examples of model output on the synoptic and subsynoptic scales are presented for the AVE-SESAME I field experiment on 10-11 April 1979. The model output is subjectively compared to the observational analysis and the LFM II output on the synoptic scale. Subsynoptic model output is compared to analyses generated from the AVE-SESAME I data set.

Kaplan, M. L.↗

Integrated Medical Model Verification, Validation, and Credibility

The Integrated Medical Model (IMM) was designed to forecast relative changes for a specified set of crew health and mission success risk metrics by using a probabilistic (stochastic process) model based on historical data, cohort data, and subject matter expert opinion. A probabilistic approach is taken since exact (deterministic) results would not appropriately reflect the uncertainty in the IMM inputs. Once the IMM was conceptualized, a plan was needed to rigorously assess input information, framework and code, and output results of the IMM, and ensure that end user requests and requirements were considered during all stages of model development and implementation. METHODS: In 2008, the IMM team developed a comprehensive verification and validation (VV) plan, which specified internal and external review criteria encompassing 1) verification of data and IMM structure to ensure proper implementation of the IMM, 2) several validation techniques to confirm that the simulation capability of the IMM appropriately represents occurrences and consequences of medical conditions during space missions, and 3) credibility processes to develop user confidence in the information derived from the IMM. When the NASA-STD-7009 (7009) was published, the IMM team updated their verification, validation, and credibility (VVC) project plan to meet 7009 requirements and include 7009 tools in reporting VVC status of the IMM. RESULTS: IMM VVC updates are compiled recurrently and include 7009 Compliance and Credibility matrices, IMM VV Plan status, and a synopsis of any changes or updates to the IMM during the reporting period. Reporting tools have evolved over the lifetime of the IMM project to better communicate VVC status. This has included refining original 7009 methodology with augmentation from the NASA-STD-7009 Guidance Document. End user requests and requirements are being satisfied as evidenced by ISS Program acceptance of IMM risk forecasts, transition to an operational model and simulation tool, and completion of service requests from a broad end user consortium including Operations, Science and Technology Planning, and Exploration Planning. CONCLUSIONS: The VVC approach established by the IMM project of combining the IMM VV Plan with 7009 requirements is comprehensive and includes the involvement of end users at every stage in IMM evolution. Methods and techniques used to quantify the VVC status of the IMM have not only received approval from the local NASA community but have also garnered recognition by other federal agencies seeking to develop similar guidelines in the medical modeling community.

Validation↗

Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS

With recent developments in computational techniques, Data-driven Machine Learning Models (DMLs) have shown great potential in simulating streamflow and capturing the rainfall-runoff relationship in given watersheds, which are traditionally fulfilled by Process-based Hydrologic Models (PHMs). There are debates on whether the DMLs can outperform and possibly replace the classical PHMs for streamflow simulation and river forecasting, but no clear conclusions have been made. This study aims to investigate whether the newer DMLs have any potential in further improving the simulation accuracy of classical PHMs, and vice versa. To do this, we compared a few popular PHMs and DMLs over four watersheds across the Continental US (CONUS) that are associated with different input, climate, and regional conditions. A total of five hydrologic models were chosen, including (1) two classical lumped models, i.e., the Sacramento Soil Moisture Accounting (SAC-SMA) and Xinanjiang (XAJ); (2) one modern distributed model, termed Coupled Routing and Excess Storage (CREST); (3) and two DMLs including an Artificial Neural Networks (ANN) and a deep learning model, termed Long Short Term Memory (LSTM). Our results demonstrated that the DMLs still significantly biased when using the baseline input scenario with the PHMs. However, the DMLs fed with delayed input scenarios had great potential and can reach high simulation accuracy. The DMLs, especially the ANN, outperformed other employed models under the rainfall-runoff relationship in which rainfall dominantly drives. Furthermore, the DMLs also showed better performance in the high-flow regime, while the PHMs had a better performance for the low-flow regime, implying both PHMs and DMLs have their own merits and are worthy of joint development. In general, our study indicated a great potential of using DMLs to simulate streamflow, but further studies are still needed to verify the transferability and scalability of DMLs in large-scale experiments, such as the Distributed Model Intercomparison Projects 1&2 conducted by National Weather Services but to compare modern DMLs and PHMs.

58 GEOSCIENCES↗

Some Recent Developments in Numerical Modelling at ECMWF

A new atmospheric model was introduced into operational forecasting at the European Center for Medium Range Weather Forecasts, on 21 April 1983. The principal differences between this model and the Center's first operational model were in the adiabatic formulation, which, in the new model, includes use of a spectral representation in the horizontal, a more general vertical coordinate, and a modified, more-efficient, time-stepping scheme. In addition, new programming techniques and standards were adopted to facilitate both the model's use as a research tool and its adaptation to make full use of the features of the recently-acquired CRAY X-MP computer. A number of revisions were also made in detailed aspects of the formulation of the parameterization schemes. The operational change to this new model was accompanied by a second important change, namely the use of a higher envelope orography in the lower boundary conditions of the model. Problems in the operational performance of the model and development preparatory to the future operational implementation of a high resolution version of the model on the CRAY X-MP are discussed.

Simmons, A. J.↗

Preface: C/NOFS Results and Equatorial Ionospheric Dynamics

The Communication/Navigation Outage Forecasting System (C/NOFS) satellite was launched into orbit in April 2008 as part of an ongoing effort to understand and identify plasma irregularities that adversely impact the propagation of radio waves in the upper atmosphere. Combined with recent improvements in radar, airglow, and ground-based studies, as well as state-of-the-art modeling techniques, the C/NOFS mission has led to new insights into equatorial ionospheric electrodynamics. In order to document these advances, the C/NOFS Results and Equatorial Dynamics Technical Interchange Meeting was held in Albuquerque, New Mexico from 12 to 14 March 2013. The meeting was a great success with 55 talks and 22 posters, and covered topics including the numerical simulations of plasma irregularities, the effects of atmospheric tides, stratospheric phenomena, and magnetic storms on the upper atmosphere, causes and predictions of scintillation-causing ionospheric irregularities, current and future instrumentation efforts in the equatorial region. The talks were broken into the following three topical sessions: A. Ambient Ionosphere and Thermosphere B. Transient Phenomena in the Low-Latitude Ionosphere C. New Missions, New Sensors, New Science and Engineering Issues. The following special issue was planned as a follow-up to the meeting. We would like to thank Mike Pinnock, the editors and staff of Copernicus, and our reviewers for their work in bringing this special issue to the scientific community. Our thanks also go to Patricia Doherty and the meeting organizing committee for arranging the C/NOFS Technical Interchange Meeting.

CNOFS↗