Assessing the Role of Seafloor Weathering in Global Geochemical Cycling

NASA Astrophysics Data System (ADS)

Farahat, N. X.; Abbot, D. S.; Archer, D. E.

2015-12-01

Low-temperature alteration of the basaltic upper oceanic crust, known as seafloor weathering, has been proposed as a mechanism for long-term climate regulation similar to the continental climate-weathering negative feedback. Despite this potentially far-reaching impact of seafloor weathering on habitable planet evolution, existing modeling frameworks do not include the full scope of alteration reactions or recent findings of convective flow dynamics. We present a coupled fluid dynamic and geochemical numerical model of low-temperature, off-axis hydrothermal activity. This model is designed to explore the the seafloor weathering flux of carbon to the oceanic crust and its responsiveness to climate fluctuations. The model's ability to reproduce the seafloor weathering environment is evaluated by constructing numerical simulations for comparison with two low-temperature hydrothermal systems: A transect east of the Juan de Fuca Ridge and the southern Costa Rica Rift flank. We explore the sensitivity of carbon uptake by seafloor weathering on climate and geology by varying deep ocean temperature, seawater dissolved inorganic carbon, continental weathering inputs, and basaltic host rock in a suite of numerical experiments.

Atlas : A library for numerical weather prediction and climate modelling

NASA Astrophysics Data System (ADS)

Deconinck, Willem; Bauer, Peter; Diamantakis, Michail; Hamrud, Mats; Kühnlein, Christian; Maciel, Pedro; Mengaldo, Gianmarco; Quintino, Tiago; Raoult, Baudouin; Smolarkiewicz, Piotr K.; Wedi, Nils P.

2017-11-01

The algorithms underlying numerical weather prediction (NWP) and climate models that have been developed in the past few decades face an increasing challenge caused by the paradigm shift imposed by hardware vendors towards more energy-efficient devices. In order to provide a sustainable path to exascale High Performance Computing (HPC), applications become increasingly restricted by energy consumption. As a result, the emerging diverse and complex hardware solutions have a large impact on the programming models traditionally used in NWP software, triggering a rethink of design choices for future massively parallel software frameworks. In this paper, we present Atlas, a new software library that is currently being developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), with the scope of handling data structures required for NWP applications in a flexible and massively parallel way. Atlas provides a versatile framework for the future development of efficient NWP and climate applications on emerging HPC architectures. The applications range from full Earth system models, to specific tools required for post-processing weather forecast products. The Atlas library thus constitutes a step towards affordable exascale high-performance simulations by providing the necessary abstractions that facilitate the application in heterogeneous HPC environments by promoting the co-design of NWP algorithms with the underlying hardware.

A Framework to Understand Extreme Space Weather Event Probability.

PubMed

Jonas, Seth; Fronczyk, Kassandra; Pratt, Lucas M

2018-03-12

An extreme space weather event has the potential to disrupt or damage infrastructure systems and technologies that many societies rely on for economic and social well-being. Space weather events occur regularly, but extreme events are less frequent, with a small number of historical examples over the last 160 years. During the past decade, published works have (1) examined the physical characteristics of the extreme historical events and (2) discussed the probability or return rate of select extreme geomagnetic disturbances, including the 1859 Carrington event. Here we present initial findings on a unified framework approach to visualize space weather event probability, using a Bayesian model average, in the context of historical extreme events. We present disturbance storm time (Dst) probability (a proxy for geomagnetic disturbance intensity) across multiple return periods and discuss parameters of interest to policymakers and planners in the context of past extreme space weather events. We discuss the current state of these analyses, their utility to policymakers and planners, the current limitations when compared to other hazards, and several gaps that need to be filled to enhance space weather risk assessments. © 2018 Society for Risk Analysis.

The DACCIWA model evaluation project: representation of the meteorology of southern West Africa in state-of-the-art weather, seasonal and climate prediction models

NASA Astrophysics Data System (ADS)

Kniffka, Anke; Benedetti, Angela; Knippertz, Peter; Stanelle, Tanja; Brooks, Malcolm; Deetz, Konrad; Maranan, Marlon; Rosenberg, Philip; Pante, Gregor; Allan, Richard; Hill, Peter; Adler, Bianca; Fink, Andreas; Kalthoff, Norbert; Chiu, Christine; Vogel, Bernhard; Field, Paul; Marsham, John

2017-04-01

DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) is an EU-funded project that aims to determine the influence of anthropogenic and natural emissions on the atmospheric composition, air quality, weather and climate over southern West Africa. DACCIWA organised a major international field campaign in June-July 2016 and involves a wide range of modelling activities. Here we report about the coordinated model evaluation performed in the framework of DACCIWA focusing on meteorological fields. This activity consists of two elements: (a) the quality of numerical weather prediction during the field campaign, (b) the ability of seasonal and climate models to represent the mean state and its variability. For the first element, the extensive observations from the main field campaign in West Africa in June-July 2016 (ground supersites, radiosondes, aircraft measurements) will be combined with conventional data (synoptic stations, satellites data from various sensors) to evaluate models against. The forecasts include operational products from centres such as the ECMWF, UK MetOffice and the German Weather Service and runs specifically conducted for the planning and the post-analysis of the field campaign using higher resolutions (e.g., WRF, COSMO). The forecast and the observations are analysed in a concerted way to assess the ability of the models to represent the southern West African weather systems and secondly to provide a comprehensive synoptic overview of the state of the atmosphere. In a second step the process will be extended to long-term modelling periods. This includes both seasonal and climate models, respectively. In this case, the observational dataset contains long-term satellite observations and station data, some of which were digitised from written records in the framework of DACCIWA. Parameter choice and spatial averaging will build directly on the weather forecasting evaluation to allow an assessment of the impact of short-term errors on long-term simulations.

Topographic and fire weather controls of fire refugia in forested ecosystems of northwestern North America

USGS Publications Warehouse

Krawchuk, Meg A.; Haire, Sandra L.; Coop, Jonathan D.; Parisien, Marc-Andre; Whitman, Ellen; Chong, Geneva W.; Miller, Carol

2016-01-01

for seven study fires that burned in conifer-dominated forested landscapes of the Western Cordillera of Canada between 2001 and 2014. We fit nine models, each for distinct levels of fire weather and terrain ruggedness. Our framework revealed that the predictability and abundance of fire refugia varied among these environmental settings. We observed highest predictability under moderate fire weather conditions and moderate terrain ruggedness (ROC-AUC = 0.77), and lowest predictability in flatter landscapes and under high fire weather conditions (ROC-AUC = 0.63–0.68). Catchment slope, local aspect, relative position, topographic wetness, topographic convergence, and local slope all contributed to discriminating where refugia occur but the relative importance of these topographic controls differed among environments. Our framework allows us to characterize the predictability of contemporary fire refugia across multiple environmental settings and provides important insights for ecosystem resilience, wildfire management, conservation planning, and climate change adaptation.

Integration of RAM-SCB into the Space Weather Modeling Framework

DOE PAGES

Welling, Daniel; Toth, Gabor; Jordanova, Vania Koleva; ...

2018-02-07

We present that numerical simulations of the ring current are a challenging endeavor. They require a large set of inputs, including electric and magnetic fields and plasma sheet fluxes. Because the ring current broadly affects the magnetosphere-ionosphere system, the input set is dependent on the ring current region itself. This makes obtaining a set of inputs that are self-consistent with the ring current difficult. To overcome this challenge, researchers have begun coupling ring current models to global models of the magnetosphere-ionosphere system. This paper describes the coupling between the Ring current Atmosphere interaction Model with Self-Consistent Magnetic field (RAM-SCB) tomore » the models within the Space Weather Modeling Framework. Full details on both previously introduced and new coupling mechanisms are defined. Finally, the impact of self-consistently including the ring current on the magnetosphere-ionosphere system is illustrated via a set of example simulations.« less

Integration of RAM-SCB into the Space Weather Modeling Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Welling, Daniel; Toth, Gabor; Jordanova, Vania Koleva

We present that numerical simulations of the ring current are a challenging endeavor. They require a large set of inputs, including electric and magnetic fields and plasma sheet fluxes. Because the ring current broadly affects the magnetosphere-ionosphere system, the input set is dependent on the ring current region itself. This makes obtaining a set of inputs that are self-consistent with the ring current difficult. To overcome this challenge, researchers have begun coupling ring current models to global models of the magnetosphere-ionosphere system. This paper describes the coupling between the Ring current Atmosphere interaction Model with Self-Consistent Magnetic field (RAM-SCB) tomore » the models within the Space Weather Modeling Framework. Full details on both previously introduced and new coupling mechanisms are defined. Finally, the impact of self-consistently including the ring current on the magnetosphere-ionosphere system is illustrated via a set of example simulations.« less

Rainfall extremes, weather and climatic characterization over complex terrain: A data-driven approach based on signal enhancement methods and extreme value modeling

NASA Astrophysics Data System (ADS)

Pineda, Luis E.; Willems, Patrick

2017-04-01

Weather and climatic characterization of rainfall extremes is both of scientific and societal value for hydrometeorogical risk management, yet discrimination of local and large-scale forcing remains challenging in data-scarce and complex terrain environments. Here, we present an analysis framework that separate weather (seasonal) regimes and climate (inter-annual) influences using data-driven process identification. The approach is based on signal-to-noise separation methods and extreme value (EV) modeling of multisite rainfall extremes. The EV models use a semi-automatic parameter learning [1] for model identification across temporal scales. At weather scale, the EV models are combined with a state-based hidden Markov model [2] to represent the spatio-temporal structure of rainfall as persistent weather states. At climatic scale, the EV models are used to decode the drivers leading to the shift of weather patterns. The decoding is performed into a climate-to-weather signal subspace, built via dimension reduction of climate model proxies (e.g. sea surface temperature and atmospheric circulation) We apply the framework to the Western Andean Ridge (WAR) in Ecuador and Peru (0-6°S) using ground data from the second half of the 20th century. We find that the meridional component of winds is what matters for the in-year and inter-annual variability of high rainfall intensities alongside the northern WAR (0-2.5°S). There, low-level southerly winds are found as advection drivers for oceanic moist of the normal-rainy season and weak/moderate the El Niño (EN) type; but, the strong EN type and its unique moisture surplus is locally advected at lowlands in the central WAR. Moreover, the coastal ridges, south of 3°S dampen meridional airflows, leaving local hygrothermal gradients to control the in-year distribution of rainfall extremes and their anomalies. Overall, we show that the framework, which does not make any prior assumption on the explanatory power of the weather and climate drivers, allows identification of well-known features of the regional climate in a purely data-driven fashion. Thus, this approach shows potential for characterization of precipitation extremes in data-scarce and orographically complex regions in which model reconstructions are the only climate proxies References [1] Mínguez, R., F.J. Méndez, C. Izaguirre, M. Menéndez, and I.J. Losada (2010), Pseudooptimal parameter selection of non-stationary generalized extreme value models for environmental variables, Environ. Modell. Softw. 25, 1592-1607. [2] Pineda, L., P. Willems (2016), Multisite Downscaling of Seasonal Predictions to Daily Rainfall Characteristics over Pacific-Andean River Basins in Ecuador and Peru using a non-homogenous hidden Markov model, J. Hydrometeor, 17(2), 481-498, doi:10.1175/JHM-D-15-0040.1, http://journals.ametsoc.org/doi/full/10.1175/JHM-D-15-0040.1

An Ensemble-Based Forecasting Framework to Optimize Reservoir Releases

NASA Astrophysics Data System (ADS)

Ramaswamy, V.; Saleh, F.

2017-12-01

Increasing frequency of extreme precipitation events are stressing the need to manage water resources on shorter timescales. Short-term management of water resources becomes proactive when inflow forecasts are available and this information can be effectively used in the control strategy. This work investigates the utility of short term hydrological ensemble forecasts for operational decision making during extreme weather events. An advanced automated hydrologic prediction framework integrating a regional scale hydrologic model, GIS datasets and the meteorological ensemble predictions from the European Center for Medium Range Weather Forecasting (ECMWF) was coupled to an implicit multi-objective dynamic programming model to optimize releases from a water supply reservoir. The proposed methodology was evaluated by retrospectively forecasting the inflows to the Oradell reservoir in the Hackensack River basin in New Jersey during the extreme hydrologic event, Hurricane Irene. Additionally, the flexibility of the forecasting framework was investigated by forecasting the inflows from a moderate rainfall event to provide important perspectives on using the framework to assist reservoir operations during moderate events. The proposed forecasting framework seeks to provide a flexible, assistive tool to alleviate the complexity of operational decision-making.

Climate change & extreme weather vulnerability assessment framework.

DOT National Transportation Integrated Search

2012-12-01

The Federal Highway Administrations (FHWAs) Climate Change and Extreme Weather Vulnerability : Assessment Framework is a guide for transportation agencies interested in assessing their vulnerability : to climate change and extreme weather event...

Towards the Next Generation of Space Environment Prediction Capabilities.

NASA Astrophysics Data System (ADS)

Kuznetsova, M. M.

2015-12-01

Since its establishment more than 15 years ago, the Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) is serving as an assess point to expanding collection of state-of-the-art space environment models and frameworks as well as a hub for collaborative development of next generation space weather forecasting systems. In partnership with model developers and international research and operational communities the CCMC integrates new data streams and models from diverse sources into end-to-end space weather impacts predictive systems, identifies week links in data-model & model-model coupling and leads community efforts to fill those gaps. The presentation will highlight latest developments, progress in CCMC-led community-wide projects on testing, prototyping, and validation of models, forecasting techniques and procedures and outline ideas on accelerating implementation of new capabilities in space weather operations.

The Space Weather Modeling Framework (SWMF): Models and Validation

NASA Astrophysics Data System (ADS)

Gombosi, Tamas; Toth, Gabor; Sokolov, Igor; de Zeeuw, Darren; van der Holst, Bart; Ridley, Aaron; Manchester, Ward, IV

In the last decade our group at the Center for Space Environment Modeling (CSEM) has developed the Space Weather Modeling Framework (SWMF) that efficiently couples together different models describing the interacting regions of the space environment. Many of these domain models (such as the global solar corona, the inner heliosphere or the global magneto-sphere) are based on MHD and are represented by our multiphysics code, BATS-R-US. SWMF is a powerful tool for coupling regional models describing the space environment from the solar photosphere to the bottom of the ionosphere. Presently, SWMF contains over a dozen components: the solar corona (SC), eruptive event generator (EE), inner heliosphere (IE), outer heliosphere (OH), solar energetic particles (SE), global magnetosphere (GM), inner magnetosphere (IM), radiation belts (RB), plasmasphere (PS), ionospheric electrodynamics (IE), polar wind (PW), upper atmosphere (UA) and lower atmosphere (LA). This talk will present an overview of SWMF, new results obtained with improved physics as well as some validation studies.

A new framework to increase the efficiency of large-scale solar power plants.

NASA Astrophysics Data System (ADS)

Alimohammadi, Shahrouz; Kleissl, Jan P.

2015-11-01

A new framework to estimate the spatio-temporal behavior of solar power is introduced, which predicts the statistical behavior of power output at utility scale Photo-Voltaic (PV) power plants. The framework is based on spatio-temporal Gaussian Processes Regression (Kriging) models, which incorporates satellite data with the UCSD version of the Weather and Research Forecasting model. This framework is designed to improve the efficiency of the large-scale solar power plants. The results are also validated from measurements of the local pyranometer sensors, and some improvements in different scenarios are observed. Solar energy.

The scientific challenges to forecasting the propagation of space weather through the heliosphere (Invited)

NASA Astrophysics Data System (ADS)

van der Holst, B.; Manchester, W.; Sokolov, I.; Toth, G.; Gombosi, T. I.

2013-12-01

Coronal mass ejections (CMEs) are a major source of potentially destructive space weather conditions. Understanding and forecasting these events are of utmost importance. In this presentation we discuss the progress towards a physics-based predictive capability within the Space Weather Modeling Framework (SWMF). We demonstrate our latest development in the AWSoM (Alfven Wave Solar Model) global model of the solar corona and inner heliosphere. This model accounts for the coupled thermodynamics of the electrons and protons via single fluid magnetohydrodynamics. The coronal heating and solar wind acceleration are addressed with Alfvén wave turbulence. The realistic 3D magnetic field is simulated using data from the photospheric magnetic field measurements. The AWSoM model serves as a workhorse for modeling CMEs from initial eruption to prediction at 1AU. With selected events we will demonstrate the complexity and challenges associated with CME propagation.

Trajectory-Based Performance Assessment for Aviation Weather Information

NASA Technical Reports Server (NTRS)

Vigeant-Langlois, Laurence; Hansman, R. John, Jr.

2003-01-01

Based on an analysis of aviation decision-makers' time-related weather information needs, an abstraction of the aviation weather decision task was developed, that involves 4-D intersection testing between aircraft trajectory hypertubes and hazardous weather hypervolumes. The framework builds on the hypothesis that hazardous meteorological fields can be simplified using discrete boundaries of surrogate threat attributes. The abstractions developed in the framework may be useful in studying how to improve the performance of weather forecasts from the trajectory-centric perspective, as well as for developing useful visualization techniques of weather information.

An Integrated Ensemble-Based Operational Framework to Predict Urban Flooding: A Case Study of Hurricane Sandy in the Passaic and Hackensack River Basins

NASA Astrophysics Data System (ADS)

Saleh, F.; Ramaswamy, V.; Georgas, N.; Blumberg, A. F.; Wang, Y.

2016-12-01

Advances in computational resources and modeling techniques are opening the path to effectively integrate existing complex models. In the context of flood prediction, recent extreme events have demonstrated the importance of integrating components of the hydrosystem to better represent the interactions amongst different physical processes and phenomena. As such, there is a pressing need to develop holistic and cross-disciplinary modeling frameworks that effectively integrate existing models and better represent the operative dynamics. This work presents a novel Hydrologic-Hydraulic-Hydrodynamic Ensemble (H3E) flood prediction framework that operationally integrates existing predictive models representing coastal (New York Harbor Observing and Prediction System, NYHOPS), hydrologic (US Army Corps of Engineers Hydrologic Modeling System, HEC-HMS) and hydraulic (2-dimensional River Analysis System, HEC-RAS) components. The state-of-the-art framework is forced with 125 ensemble meteorological inputs from numerical weather prediction models including the Global Ensemble Forecast System, the European Centre for Medium-Range Weather Forecasts (ECMWF), the Canadian Meteorological Centre (CMC), the Short Range Ensemble Forecast (SREF) and the North American Mesoscale Forecast System (NAM). The framework produces, within a 96-hour forecast horizon, on-the-fly Google Earth flood maps that provide critical information for decision makers and emergency preparedness managers. The utility of the framework was demonstrated by retrospectively forecasting an extreme flood event, hurricane Sandy in the Passaic and Hackensack watersheds (New Jersey, USA). Hurricane Sandy caused significant damage to a number of critical facilities in this area including the New Jersey Transit's main storage and maintenance facility. The results of this work demonstrate that ensemble based frameworks provide improved flood predictions and useful information about associated uncertainties, thus improving the assessment of risks as when compared to a deterministic forecast. The work offers perspectives for short-term flood forecasts, flood mitigation strategies and best management practices for climate change scenarios.

The Ensemble Space Weather Modeling System (eSWMS): Status, Capabilities and Challenges

NASA Astrophysics Data System (ADS)

Fry, C. D.; Eccles, J. V.; Reich, J. P.

2010-12-01

Marking a milestone in space weather forecasting, the Space Weather Modeling System (SWMS) successfully completed validation testing in advance of operational testing at Air Force Weather Agency’s primary space weather production center. This is the first coupling of stand-alone, physics-based space weather models that are currently in operations at AFWA supporting the warfighter. Significant development effort went into ensuring the component models were portable and scalable while maintaining consistent results across diverse high performance computing platforms. Coupling was accomplished under the Earth System Modeling Framework (ESMF). The coupled space weather models are the Hakamada-Akasofu-Fry version 2 (HAFv2) solar wind model and GAIM1, the ionospheric forecast component of the Global Assimilation of Ionospheric Measurements (GAIM) model. The SWMS was developed by team members from AFWA, Explorations Physics International, Inc. (EXPI) and Space Environment Corporation (SEC). The successful development of the SWMS provides new capabilities beyond enabling extended lead-time, data-driven ionospheric forecasts. These include ingesting diverse data sets at higher resolution, incorporating denser computational grids at finer time steps, and performing probability-based ensemble forecasts. Work of the SWMS development team now focuses on implementing the ensemble-based probability forecast capability by feeding multiple scenarios of 5 days of solar wind forecasts to the GAIM1 model based on the variation of the input fields to the HAFv2 model. The ensemble SWMS (eSWMS) will provide the most-likely space weather scenario with uncertainty estimates for important forecast fields. The eSWMS will allow DoD mission planners to consider the effects of space weather on their systems with more advance warning than is currently possible. The payoff is enhanced, tailored support to the warfighter with improved capabilities, such as point-to-point HF propagation forecasts, single-frequency GPS error corrections, and high cadence, high-resolution Space Situational Awareness (SSA) products. We present the current status of eSWMS, its capabilities, limitations and path of transition to operational use.

Emulation for probabilistic weather forecasting

NASA Astrophysics Data System (ADS)

Cornford, Dan; Barillec, Remi

2010-05-01

Numerical weather prediction models are typically very expensive to run due to their complexity and resolution. Characterising the sensitivity of the model to its initial condition and/or to its parameters requires numerous runs of the model, which is impractical for all but the simplest models. To produce probabilistic forecasts requires knowledge of the distribution of the model outputs, given the distribution over the inputs, where the inputs include the initial conditions, boundary conditions and model parameters. Such uncertainty analysis for complex weather prediction models seems a long way off, given current computing power, with ensembles providing only a partial answer. One possible way forward that we develop in this work is the use of statistical emulators. Emulators provide an efficient statistical approximation to the model (or simulator) while quantifying the uncertainty introduced. In the emulator framework, a Gaussian process is fitted to the simulator response as a function of the simulator inputs using some training data. The emulator is essentially an interpolator of the simulator output and the response in unobserved areas is dictated by the choice of covariance structure and parameters in the Gaussian process. Suitable parameters are inferred from the data in a maximum likelihood, or Bayesian framework. Once trained, the emulator allows operations such as sensitivity analysis or uncertainty analysis to be performed at a much lower computational cost. The efficiency of emulators can be further improved by exploiting the redundancy in the simulator output through appropriate dimension reduction techniques. We demonstrate this using both Principal Component Analysis on the model output and a new reduced-rank emulator in which an optimal linear projection operator is estimated jointly with other parameters, in the context of simple low order models, such as the Lorenz 40D system. We present the application of emulators to probabilistic weather forecasting, where the construction of the emulator training set replaces the traditional ensemble model runs. Thus the actual forecast distributions are computed using the emulator conditioned on the ‘ensemble runs' which are chosen to explore the plausible input space using relatively crude experimental design methods. One benefit here is that the ensemble does not need to be a sample from the true distribution of the input space, rather it should cover that input space in some sense. The probabilistic forecasts are computed using Monte Carlo methods sampling from the input distribution and using the emulator to produce the output distribution. Finally we discuss the limitations of this approach and briefly mention how we might use similar methods to learn the model error within a framework that incorporates a data assimilation like aspect, using emulators and learning complex model error representations. We suggest future directions for research in the area that will be necessary to apply the method to more realistic numerical weather prediction models.

A framework for standardized calculation of weather indices in Germany

NASA Astrophysics Data System (ADS)

Möller, Markus; Doms, Juliane; Gerstmann, Henning; Feike, Til

2018-05-01

Climate change has been recognized as a main driver in the increasing occurrence of extreme weather. Weather indices (WIs) are used to assess extreme weather conditions regarding its impact on crop yields. Designing WIs is challenging, since complex and dynamic crop-climate relationships have to be considered. As a consequence, geodata for WI calculations have to represent both the spatio-temporal dynamic of crop development and corresponding weather conditions. In this study, we introduce a WI design framework for Germany, which is based on public and open raster data of long-term spatio-temporal availability. The operational process chain enables the dynamic and automatic definition of relevant phenological phases for the main cultivated crops in Germany. Within the temporal bounds, WIs can be calculated for any year and test site in Germany in a reproducible and transparent manner. The workflow is demonstrated on the example of a simple cumulative rainfall index for the phenological phase shooting of winter wheat using 16 test sites and the period between 1994 and 2014. Compared to station-based approaches, the major advantage of our approach is the possibility to design spatial WIs based on raster data characterized by accuracy metrics. Raster data and WIs, which fulfill data quality standards, can contribute to an increased acceptance and farmers' trust in WI products for crop yield modeling or weather index-based insurances (WIIs).

Space Weather Forecasting at NOAA with Michigan's Geospace Model: Results from the First Year in Real-Time Operations

NASA Astrophysics Data System (ADS)

Cash, M. D.; Singer, H. J.; Millward, G. H.; Balch, C. C.; Toth, G.; Welling, D. T.

2017-12-01

In October 2016, the first version of the Geospace model was transitioned into real-time operations at NOAA Space Weather Prediction Center (SWPC). The Geospace model is a part of the Space Weather Modeling Framework (SWMF) developed at the University of Michigan, and the model simulates the full time-dependent 3D Geospace environment (Earth's magnetosphere, ring current and ionosphere) and predicts global space weather parameters such as induced magnetic perturbations in space and on Earth's surface. The current version of the Geospace model uses three coupled components of SWMF: the BATS-R-US global magnetosphere model, the Rice Convection Model (RCM) of the inner magnetosphere, and the Ridley Ionosphere electrodynamics Model (RIM). In the operational mode, SWMF/Geospace runs continually in real-time as long as there is new solar wind data arriving from a satellite at L1, either DSCOVR or ACE. We present an analysis of the overall performance of the Geospace model during the first year of real-time operations. Evaluation metrics include Kp, Dst, as well as regional magnetometer stations. We will also present initial results from new products, such as the AE index, available with the recent upgrade to the Geospace model.

The dependence of magnetosphere-ionosphere system on the Earth's magnetic dipole moment

NASA Astrophysics Data System (ADS)

Ngwira, C. M.; Pulkkinen, A. A.; Sibeck, D. G.; Rastaetter, L.

2017-12-01

Space weather is increasingly recognized as an international problem affecting several different man-made technologies. The ability to understand, monitor and forecast Earth-directed space weather is of paramount importance for our highly technology-dependent society and for the current rapid developments in awareness and exploration within the heliosphere. It is well known that the strength of the Earth's magnetic field changes over long time scales. We use physics-based simulations with the University of Michigan Space Weather Modeling Framework (SWMF) to examine how the magnetosphere, ionosphere, and ground geomagnetic field perturbations respond as the geomagnetic dipole moment changes. We discuss the implication of these results for our community and the end-users of space weather information.

Satellite Data and Machine Learning for Weather Risk Management and Food Security.

PubMed

Biffis, Enrico; Chavez, Erik

2017-08-01

The increase in frequency and severity of extreme weather events poses challenges for the agricultural sector in developing economies and for food security globally. In this article, we demonstrate how machine learning can be used to mine satellite data and identify pixel-level optimal weather indices that can be used to inform the design of risk transfers and the quantification of the benefits of resilient production technology adoption. We implement the model to study maize production in Mozambique, and show how the approach can be used to produce countrywide risk profiles resulting from the aggregation of local, heterogeneous exposures to rainfall precipitation and excess temperature. We then develop a framework to quantify the economic gains from technology adoption by using insurance costs as the relevant metric, where insurance is broadly understood as the transfer of weather-driven crop losses to a dedicated facility. We consider the case of irrigation in detail, estimating a reduction in insurance costs of at least 30%, which is robust to different configurations of the model. The approach offers a robust framework to understand the costs versus benefits of investment in irrigation infrastructure, but could clearly be used to explore in detail the benefits of more advanced input packages, allowing, for example, for different crop varieties, sowing dates, or fertilizers. © 2017 Society for Risk Analysis.

A proposed case-control framework to probabilistically classify individual deaths as expected or excess during extreme hot weather events.

PubMed

Henderson, Sarah B; Gauld, Jillian S; Rauch, Stephen A; McLean, Kathleen E; Krstic, Nikolas; Hondula, David M; Kosatsky, Tom

2016-11-15

Most excess deaths that occur during extreme hot weather events do not have natural heat recorded as an underlying or contributing cause. This study aims to identify the specific individuals who died because of hot weather using only secondary data. A novel approach was developed in which the expected number of deaths was repeatedly sampled from all deaths that occurred during a hot weather event, and compared with deaths during a control period. The deaths were compared with respect to five factors known to be associated with hot weather mortality. Individuals were ranked by their presence in significant models over 100 trials of 10,000 repetitions. Those with the highest rankings were identified as probable excess deaths. Sensitivity analyses were performed on a range of model combinations. These methods were applied to a 2009 hot weather event in greater Vancouver, Canada. The excess deaths identified were sensitive to differences in model combinations, particularly between univariate and multivariate approaches. One multivariate and one univariate combination were chosen as the best models for further analyses. The individuals identified by multiple combinations suggest that marginalized populations in greater Vancouver are at higher risk of death during hot weather. This study proposes novel methods for classifying specific deaths as expected or excess during a hot weather event. Further work is needed to evaluate performance of the methods in simulation studies and against clinically identified cases. If confirmed, these methods could be applied to a wide range of populations and events of interest.

A Weather Radar Simulator for the Evaluation of Polarimetric Phased Array Performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Byrd, Andrew D.; Ivic, Igor R.; Palmer, Robert D.

A radar simulator capable of generating time series data for a polarimetric phased array weather radar has been designed and implemented. The received signals are composed from a high-resolution numerical prediction weather model. Thousands of scattering centers, each with an independent randomly generated Doppler spectrum, populate the field of view of the radar. The moments of the scattering center spectra are derived from the numerical weather model, and the scattering center positions are updated based on the three-dimensional wind field. In order to accurately emulate the effects of the system-induced cross-polar contamination, the array is modeled using a complete setmore » of dual-polarization radiation patterns. The simulator offers reconfigurable element patterns and positions as well as access to independent time series data for each element, resulting in easy implementation of any beamforming method. It also allows for arbitrary waveform designs and is able to model the effects of quantization on waveform performance. Simultaneous, alternating, quasi-simultaneous, and pulse-to-pulse phase coded modes of polarimetric signal transmission have been implemented. This framework allows for realistic emulation of the effects of cross-polar fields on weather observations, as well as the evaluation of possible techniques for the mitigation of those effects.« less

Performance of the operational high-resolution numerical weather predictions of the Daphne project

NASA Astrophysics Data System (ADS)

Tegoulias, Ioannis; Pytharoulis, Ioannis; Karacostas, Theodore; Kartsios, Stergios; Kotsopoulos, Stelios; Bampzelis, Dimitrios

2015-04-01

In the framework of the DAPHNE project, the Department of Meteorology and Climatology (http://meteo.geo.auth.gr) of the Aristotle University of Thessaloniki, Greece, utilizes the nonhydrostatic Weather Research and Forecasting model with the Advanced Research dynamic solver (WRF-ARW) in order to produce high-resolution weather forecasts over Thessaly in central Greece. The aim of the DAPHNE project is to tackle the problem of drought in this area by means of Weather Modification. Cloud seeding assists the convective clouds to produce rain more efficiently or reduce hailstone size in favour of raindrops. The most favourable conditions for such a weather modification program in Thessaly occur in the period from March to October when convective clouds are triggered more frequently. Three model domains, using 2-way telescoping nesting, cover: i) Europe, the Mediterranean sea and northern Africa (D01), ii) Greece (D02) and iii) the wider region of Thessaly (D03; at selected periods) at horizontal grid-spacings of 15km, 5km and 1km, respectively. This research work intents to describe the atmospheric model setup and analyse its performance during a selected period of the operational phase of the project. The statistical evaluation of the high-resolution operational forecasts is performed using surface observations, gridded fields and radar data. Well established point verification methods combined with novel object based upon these methods, provide in depth analysis of the model skill. Spatial characteristics are adequately captured but a variable time lag between forecast and observation is noted. Acknowledgments: This research work has been co-financed by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" (contract number 11SYN_8_1088 - DAPHNE) in the framework of the operational programme "Competitiveness and Entrepreneurship" and Regions in Transition (OPC II, NSRF 2007-2013)

A Robust, Scalable Framework for Conducting Climate Change Susceptibility Analyses

DTIC Science & Technology

2014-05-01

for identifying areas of heightened risk from varying forms of climate forcings is needed. Based on global climate model projections, deviations from...framework provides an opportunity to easily combine multiple data sources — that are often freely available from many federal, state, and global ...Climate change and extreme weather events: implications for food production, plant diseases, and pests. Global Change and Human Health 2:90–104. ERDC/EL

Sol-Terra - AN Operational Space Weather Forecasting Model Framework

NASA Astrophysics Data System (ADS)

Bisi, M. M.; Lawrence, G.; Pidgeon, A.; Reid, S.; Hapgood, M. A.; Bogdanova, Y.; Byrne, J.; Marsh, M. S.; Jackson, D.; Gibbs, M.

2015-12-01

The SOL-TERRA project is a collaboration between RHEA Tech, the Met Office, and RAL Space funded by the UK Space Agency. The goal of the SOL-TERRA project is to produce a Roadmap for a future coupled Sun-to-Earth operational space weather forecasting system covering domains from the Sun down to the magnetosphere-ionosphere-thermosphere and neutral atmosphere. The first stage of SOL-TERRA is underway and involves reviewing current models that could potentially contribute to such a system. Within a given domain, the various space weather models will be assessed how they could contribute to such a coupled system. This will be done both by reviewing peer reviewed papers, and via direct input from the model developers to provide further insight. Once the models have been reviewed then the optimal set of models for use in support of forecast-based SWE modelling will be selected, and a Roadmap for the implementation of an operational forecast-based SWE modelling framework will be prepared. The Roadmap will address the current modelling capability, knowledge gaps and further work required, and also the implementation and maintenance of the overall architecture and environment that the models will operate within. The SOL-TERRA project will engage with external stakeholders in order to ensure independently that the project remains on track to meet its original objectives. A group of key external stakeholders have been invited to provide their domain-specific expertise in reviewing the SOL-TERRA project at critical stages of Roadmap preparation; namely at the Mid-Term Review, and prior to submission of the Final Report. This stakeholder input will ensure that the SOL-TERRA Roadmap will be enhanced directly through the input of modellers and end-users. The overall goal of the SOL-TERRA project is to develop a Roadmap for an operational forecast-based SWE modelling framework with can be implemented within a larger subsequent activity. The SOL-TERRA project is supported within the UK Space Agency's National Space Technology Programme under contract number RP10G0348A03.

Decision Modeling Framework to Minimize Arrival Delays from Ground Delay Programs

NASA Astrophysics Data System (ADS)

Mohleji, Nandita

Convective weather and other constraints create uncertainty in air transportation, leading to costly delays. A Ground Delay Program (GDP) is a strategy to mitigate these effects. Systematic decision support can increase GDP efficacy, reduce delays, and minimize direct operating costs. In this study, a decision analysis (DA) model is constructed by combining a decision tree and Bayesian belief network. Through a study of three New York region airports, the DA model demonstrates that larger GDP scopes that include more flights in the program, along with longer lead times that provide stakeholders greater notice of a pending program, trigger the fewest average arrival delays. These findings are demonstrated to result in a savings of up to $1,850 per flight. Furthermore, when convective weather is predicted, forecast weather confidences remain the same level or greater at least 70% of the time, supporting more strategic decision making. The DA model thus enables quantification of uncertainties and insights on causal relationships, providing support for future GDP decisions.

Solar Atmosphere to Earth's Surface: Long Lead Time dB/dt Predictions with the Space Weather Modeling Framework

NASA Astrophysics Data System (ADS)

Welling, D. T.; Manchester, W.; Savani, N.; Sokolov, I.; van der Holst, B.; Jin, M.; Toth, G.; Liemohn, M. W.; Gombosi, T. I.

2017-12-01

The future of space weather prediction depends on the community's ability to predict L1 values from observations of the solar atmosphere, which can yield hours of lead time. While both empirical and physics-based L1 forecast methods exist, it is not yet known if this nascent capability can translate to skilled dB/dt forecasts at the Earth's surface. This paper shows results for the first forecast-quality, solar-atmosphere-to-Earth's-surface dB/dt predictions. Two methods are used to predict solar wind and IMF conditions at L1 for several real-world coronal mass ejection events. The first method is an empirical and observationally based system to estimate the plasma characteristics. The magnetic field predictions are based on the Bz4Cast system which assumes that the CME has a cylindrical flux rope geometry locally around Earth's trajectory. The remaining plasma parameters of density, temperature and velocity are estimated from white-light coronagraphs via a variety of triangulation methods and forward based modelling. The second is a first-principles-based approach that combines the Eruptive Event Generator using Gibson-Low configuration (EEGGL) model with the Alfven Wave Solar Model (AWSoM). EEGGL specifies parameters for the Gibson-Low flux rope such that it erupts, driving a CME in the coronal model that reproduces coronagraph observations and propagates to 1AU. The resulting solar wind predictions are used to drive the operational Space Weather Modeling Framework (SWMF) for geospace. Following the configuration used by NOAA's Space Weather Prediction Center, this setup couples the BATS-R-US global magnetohydromagnetic model to the Rice Convection Model (RCM) ring current model and a height-integrated ionosphere electrodynamics model. The long lead time predictions of dB/dt are compared to model results that are driven by L1 solar wind observations. Both are compared to real-world observations from surface magnetometers at a variety of geomagnetic latitudes. Metrics are calculated to examine how the simulated solar wind drivers impact forecast skill. These results illustrate the current state of long-lead-time forecasting and the promise of this technology for operational use.

Evaluation of regional climate simulations for air quality modelling purposes

NASA Astrophysics Data System (ADS)

Menut, Laurent; Tripathi, Om P.; Colette, Augustin; Vautard, Robert; Flaounas, Emmanouil; Bessagnet, Bertrand

2013-05-01

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional "climate modeling" source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.

IMPACT: Integrated Modeling of Perturbations in Atmospheres for Conjunction Tracking

NASA Astrophysics Data System (ADS)

Koller, J.; Brennan, S.; Godinez, H. C.; Higdon, D. M.; Klimenko, A.; Larsen, B.; Lawrence, E.; Linares, R.; McLaughlin, C. A.; Mehta, P. M.; Palmer, D.; Ridley, A. J.; Shoemaker, M.; Sutton, E.; Thompson, D.; Walker, A.; Wohlberg, B.

2013-12-01

Low-Earth orbiting satellites suffer from atmospheric drag due to thermospheric density which changes on the order of several magnitudes especially during space weather events. Solar flares, precipitating particles and ionospheric currents cause the upper atmosphere to heat up, redistribute, and cool again. These processes are intrinsically included in empirical models, e.g. MSIS and Jacchia-Bowman type models. However, sensitivity analysis has shown that atmospheric drag has the highest influence on satellite conjunction analysis and empirical model still do not adequately represent a desired accuracy. Space debris and collision avoidance have become an increasingly operational reality. It is paramount to accurately predict satellite orbits and include drag effect driven by space weather. The IMPACT project (Integrated Modeling of Perturbations in Atmospheres for Conjunction Tracking), funded with over $5 Million by the Los Alamos Laboratory Directed Research and Development office, has the goal to develop an integrated system of atmospheric drag modeling, orbit propagation, and conjunction analysis with detailed uncertainty quantification to address the space debris and collision avoidance problem. Now with over two years into the project, we have developed an integrated solution combining physics-based density modeling of the upper atmosphere between 120-700 km altitude, satellite drag forecasting for quiet and disturbed geomagnetic conditions, and conjunction analysis with non-Gaussian uncertainty quantification. We are employing several novel approaches including a unique observational sensor developed at Los Alamos; machine learning with a support-vector machine approach of the coupling between solar drivers of the upper atmosphere and satellite drag; rigorous data assimilative modeling using a physics-based approach instead of empirical modeling of the thermosphere; and a computed-tomography method for extracting temporal maps of thermospheric densities using ground based observations. The developed IMPACT framework is an open research framework enabling the exchange and testing of a variety of atmospheric density models, orbital propagators, drag coefficient models, ground based observations, etc. and study their effect on conjunctions and uncertainty predictions. The framework is based on a modern service-oriented architecture controlled by a web interface and providing 3D visualizations. The goal of this project is to revolutionize the ability to monitor and track space objects during highly disturbed space weather conditions, provide suitable forecasts for satellite drag conditions and conjunction analysis, and enable the exchange of models, codes, and data in an open research environment. We will present capabilities and results of the IMPACT framework including a demo of the control interface and visualizations.

FLYSAFE, nowcasting of in flight icing supporting aircrew decision making process

NASA Astrophysics Data System (ADS)

Drouin, A.; Le Bot, C.

2009-09-01

FLYSAFE is an Integrated Project of the 6th framework of the European Commission with the aim to improve flight safety through the development of a Next Generation Integrated Surveillance System (NGISS). The NGISS provides information to the flight crew on the three major external hazards for aviation: weather, air traffic and terrain. The NGISS has the capability of displaying data about all three hazards on a single display screen, facilitating rapid pilot appreciation of the situation by the flight crew. Weather Information Management Systems (WIMS) were developed to provide the NGISS and the flight crew with weather related information on in-flight icing, thunderstorms, wake-vortex and clear-air turbulence. These products are generated on the ground from observations and model forecasts. WIMS supply relevant information on three different scales: global, regional and local (over airport Terminal Manoeuvring Area). Within the flysafe program, around 120 hours of flight trials were performed during February 2008 and August 2008. Two aircraft were involved each with separate objectives : - to assess FLYSAFE's innovative solutions for the data-link, on-board data fusion, data-display, and data-updates during flight; - to evaluate the new weather information management systems (in flight icing and thunderstorms) using in-situ measurements recorded on board the test aircraft. In this presentation we will focus on the in-flight icing nowcasting system developed at Météo France in the framework of FLYSAFE: the local ICE WIMS. The local ICE WIMS is based on data fusion. The most relevant information for icing detection is extracted from the numerical weather prediction model, the infra-red and visible satellite imagery and the ground weather radar reflectivities. After a presentation of the local ICE WIMS, we detail the evaluation of the local ICE WIMS performed using the winter and summer flight trial data.

From minerals to hillslopes: Towards an integrated framework for interpreting chemical and physical erosion

NASA Astrophysics Data System (ADS)

Hahm, W.; Riebe, C. S.; Ferrier, K.; Kirchner, J. W.

2011-12-01

Traditional frameworks for conceptualizing hillslope denudation distinguish between the movement of mass in solution (chemical erosion) and mass moved via mechanical processes (physical erosion). At the hillslope scale, physical and chemical erosion rates can be quantified by combining measurements of regolith chemistry with cosmogenic nuclide concentrations in bedrock and sediment, while basin-scale rates are often inferred from riverine solute and sediment loads. These techniques integrate the effects of numerous weathering and erosion mechanisms and do not provide prima facie information about the precise nature and scale of those mechanisms. For insight into erosional process, physical erosion has been considered in terms of two limiting regimes. When physical erosion outpaces weathering front advance, regolith is mobilized downslope as soon as it is sufficiently loosened by weathering, and physical erosion rates are limited by rates of mobile regolith production. This is commonly termed weathering-limited erosion. Conversely, when weathering front advance outpaces erosion, the mobile regolith layer grows thicker over time, and physical erosion rates are limited by the efficiency of downslope transport processes. This is termed transport-limited erosion. This terminology brings the description of hillslope evolution closer to the realm of essential realism, to the extent that measurable quantities from the field can be cast in a process-based framework. An analogous process-limitation framework describes chemical erosion. In supply-limited chemical erosion, chemical weathering depletes regolith of its reactive phases during residence on a hillslope, and chemical erosion rates are limited by the supply of fresh minerals to the weathering zone. Alternatively, hillslopes may exhibit kinetic-limited chemical erosion, where physical erosion transports regolith downslope before weatherable phases are completely removed by chemical erosion. We show how supply- and kinetic-limited chemical erosion can be distinguished from one another using data from a global compilation of physical and chemical erosion rates. As a step towards understanding these rates at the level of essential realism, we explore how the hillslope-scale regimes of supply- and kinetic-limited chemical erosion relate to existing conceptual frameworks that interpret weathering rates in terms of transport- and kinetic-limitation at the mineral scale.

The OSSE Framework at the NASA Global Modeling and Assimilation Office (GMAO)

NASA Astrophysics Data System (ADS)

Moradi, I.; Prive, N.; McCarty, W.; Errico, R. M.; Gelaro, R.

2017-12-01

This abstract summarizes the OSSE framework developed at the Global Modeling and Assimilation Office at the National Aeronautics and Space Administration (NASA/GMAO). Some of the OSSE techniques developed at GMAO including simulation of realistic observations, e.g., adding errors to simulated observations, are now widely used by the community to evaluate the impact of new observations on the weather forecasts. This talk presents some of the recent progresses and challenges in simulating realistic observations, radiative transfer modeling support for the GMAO OSSE activities, assimilation of OSSE observations into data assimilation systems, and evaluating the impact of simulated observations on the forecast skills.

The OSSE Framework at the NASA Global Modeling and Assimilation Office (GMAO)

NASA Technical Reports Server (NTRS)

Moradi, Isaac; Prive, Nikki; McCarty, Will; Errico, Ronald M.; Gelaro, Ron

2017-01-01

This abstract summarizes the OSSE framework developed at the Global Modeling and Assimilation Office at the National Aeronautics and Space Administration (NASA/GMAO). Some of the OSSE techniques developed at GMAO including simulation of realistic observations, e.g., adding errors to simulated observations, are now widely used by the community to evaluate the impact of new observations on the weather forecasts. This talk presents some of the recent progresses and challenges in simulating realistic observations, radiative transfer modeling support for the GMAO OSSE activities, assimilation of OSSE observations into data assimilation systems, and evaluating the impact of simulated observations on the forecast skills.

Tempest: Tools for Addressing the Needs of Next-Generation Climate Models

NASA Astrophysics Data System (ADS)

Ullrich, P. A.; Guerra, J. E.; Pinheiro, M. C.; Fong, J.

2015-12-01

Tempest is a comprehensive simulation-to-science infrastructure that tackles the needs of next-generation, high-resolution, data intensive climate modeling activities. This project incorporates three key components: TempestDynamics, a global modeling framework for experimental numerical methods and high-performance computing; TempestRemap, a toolset for arbitrary-order conservative and consistent remapping between unstructured grids; and TempestExtremes, a suite of detection and characterization tools for identifying weather extremes in large climate datasets. In this presentation, the latest advances with the implementation of this framework will be discussed, and a number of projects now utilizing these tools will be featured.

Dynamic Routing of Aircraft in the Presence of Adverse Weather Using a POMDP Framework

NASA Technical Reports Server (NTRS)

Balaban, Edward; Roychoudhury, Indranil; Spirkovska, Lilly; Sankararaman, Shankar; Kulkarni, Chetan; Arnon, Tomer

2017-01-01

Each year weather-related airline delays result in hundreds of millions of dollars in additional fuel burn, maintenance, and lost revenue, not to mention passenger inconvenience. The current approaches for aircraft route planning in the presence of adverse weather still mainly rely on deterministic methods. In contrast, this work aims to deal with the problem using a Partially Observable Markov Decision Processes (POMDPs) framework, which allows for reasoning over uncertainty (including uncertainty in weather evolution over time) and results in solutions that are more robust to disruptions. The POMDP-based decision support system is demonstrated on several scenarios involving convective weather cells and is benchmarked against a deterministic planning system with functionality similar to those currently in use or under development.

A Dynamic Hydrology-Critical Zone Framework for Rainfall-triggered Landslide Hazard Prediction

NASA Astrophysics Data System (ADS)

Dialynas, Y. G.; Foufoula-Georgiou, E.; Dietrich, W. E.; Bras, R. L.

2017-12-01

Watershed-scale coupled hydrologic-stability models are still in their early stages, and are characterized by important limitations: (a) either they assume steady-state or quasi-dynamic watershed hydrology, or (b) they simulate landslide occurrence based on a simple one-dimensional stability criterion. Here we develop a three-dimensional landslide prediction framework, based on a coupled hydrologic-slope stability model and incorporation of the influence of deep critical zone processes (i.e., flow through weathered bedrock and exfiltration to the colluvium) for more accurate prediction of the timing, location, and extent of landslides. Specifically, a watershed-scale slope stability model that systematically accounts for the contribution of driving and resisting forces in three-dimensional hillslope segments was coupled with a spatially-explicit and physically-based hydrologic model. The landslide prediction framework considers critical zone processes and structure, and explicitly accounts for the spatial heterogeneity of surface and subsurface properties that control slope stability, including soil and weathered bedrock hydrological and mechanical characteristics, vegetation, and slope morphology. To test performance, the model was applied in landslide-prone sites in the US, the hydrology of which has been extensively studied. Results showed that both rainfall infiltration in the soil and groundwater exfiltration exert a strong control on the timing and magnitude of landslide occurrence. We demonstrate the extent to which three-dimensional slope destabilizing factors, which are modulated by dynamic hydrologic conditions in the soil-bedrock column, control landslide initiation at the watershed scale.

Development and Evaluation of an Integrated Hydrological Modeling Framework for Monitoring and Understanding Floods and Droughts

NASA Astrophysics Data System (ADS)

Yang, Z. L.; Wu, W. Y.; Lin, P.; Maidment, D. R.

2017-12-01

Extreme water events such as catastrophic floods and severe droughts have increased in recent decades. Mitigating the risk to lives, food security, infrastructure, energy supplies, as well as numerous other industries posed by these extreme events requires informed decision-making and planning based on sound science. We are developing a global water modeling capability by building models that will provide total operational water predictions (evapotranspiration, soil moisture, groundwater, channel flow, inundation, snow) at unprecedented spatial resolutions and updated frequencies. Toward this goal, this talk presents an integrated global hydrological modeling framework that takes advantage of gridded meteorological forcing, land surface modeling, channeled flow modeling, ground observations, and satellite remote sensing. Launched in August 2016, the National Water Model successfully incorporates weather forecasts to predict river flows for more than 2.7 million rivers across the continental United States, which transfers a "synoptic weather map" to a "synoptic river flow map" operationally. In this study, we apply a similar framework to a high-resolution global river network database, which is developed from a hierarchical Dominant River Tracing (DRT) algorithm, and runoff output from the Global Land Data Assimilation System (GLDAS) to a vector-based river routing model (The Routing Application for Parallel Computation of Discharge, RAPID) to produce river flows from 2001 to 2016 using Message Passing Interface (MPI) on Texas Advanced Computer Center's Stampede system. In this simulation, global river discharges for more than 177,000 rivers are computed every 30 minutes. The modeling framework's performance is evaluated with various observations including river flows at more than 400 gauge stations globally. Overall, the model exhibits a reasonably good performance in simulating the averaged patterns of terrestrial water storage, evapotranspiration and runoff. The system is appropriate for monitoring and studying floods and droughts. Directions for future research will be outlined and discussed.

One-month validation of the Space Weather Modeling Framework geospace model

NASA Astrophysics Data System (ADS)

Haiducek, J. D.; Welling, D. T.; Ganushkina, N. Y.; Morley, S.; Ozturk, D. S.

2017-12-01

The Space Weather Modeling Framework (SWMF) geospace model consists of a magnetohydrodynamic (MHD) simulation coupled to an inner magnetosphere model and an ionosphere model. This provides a predictive capability for magnetopsheric dynamics, including ground-based and space-based magnetic fields, geomagnetic indices, currents and densities throughout the magnetosphere, cross-polar cap potential, and magnetopause and bow shock locations. The only inputs are solar wind parameters and F10.7 radio flux. We have conducted a rigorous validation effort consisting of a continuous simulation covering the month of January, 2005 using three different model configurations. This provides a relatively large dataset for assessment of the model's predictive capabilities. We find that the model does an excellent job of predicting the Sym-H index, and performs well at predicting Kp and CPCP during active times. Dayside magnetopause and bow shock positions are also well predicted. The model tends to over-predict Kp and CPCP during quiet times and under-predicts the magnitude of AL during disturbances. The model under-predicts the magnitude of night-side geosynchronous Bz, and over-predicts the radial distance to the flank magnetopause and bow shock. This suggests that the model over-predicts stretching of the magnetotail and the overall size of the magnetotail. With the exception of the AL index and the nightside geosynchronous magnetic field, we find the results to be insensitive to grid resolution.

GFDL's unified regional-global weather-climate modeling system with variable resolution capability for severe weather predictions and regional climate simulations

NASA Astrophysics Data System (ADS)

Lin, S. J.

2015-12-01

The NOAA/Geophysical Fluid Dynamics Laboratory has been developing a unified regional-global modeling system with variable resolution capabilities that can be used for severe weather predictions (e.g., tornado outbreak events and cat-5 hurricanes) and ultra-high-resolution (1-km) regional climate simulations within a consistent global modeling framework. The fundation of this flexible regional-global modeling system is the non-hydrostatic extension of the vertically Lagrangian dynamical core (Lin 2004, Monthly Weather Review) known in the community as FV3 (finite-volume on the cubed-sphere). Because of its flexability and computational efficiency, the FV3 is one of the final candidates of NOAA's Next Generation Global Prediction System (NGGPS). We have built into the modeling system a stretched (single) grid capability, a two-way (regional-global) multiple nested grid capability, and the combination of the stretched and two-way nests, so as to make convection-resolving regional climate simulation within a consistent global modeling system feasible using today's High Performance Computing System. One of our main scientific goals is to enable simulations of high impact weather phenomena (such as tornadoes, thunderstorms, category-5 hurricanes) within an IPCC-class climate modeling system previously regarded as impossible. In this presentation I will demonstrate that it is computationally feasible to simulate not only super-cell thunderstorms, but also the subsequent genesis of tornadoes using a global model that was originally designed for century long climate simulations. As a unified weather-climate modeling system, we evaluated the performance of the model with horizontal resolution ranging from 1 km to as low as 200 km. In particular, for downscaling studies, we have developed various tests to ensure that the large-scale circulation within the global varaible resolution system is well simulated while at the same time the small-scale can be accurately captured within the targeted high resolution region.

AFFECTS - Advanced Forecast For Ensuring Communications Through Space

NASA Astrophysics Data System (ADS)

Bothmer, Volker

2013-04-01

Through the AFFECTS project funded by the European Union's 7th Framework Programme, European and US scientists develop an advanced proto-type space weather warning system to safeguard the operation of telecommunication and navigation systems on Earth to the threat of solar storms. The project is led by the University of Göttingen's Institute for Astrophysics and comprises worldwide leading research and academic institutions and industrial enterprises from Germany, Belgium, Ukraine, Norway and the United States. The key objectives of the AFFECTS project are: State-of-the-art analysis and modelling of the Sun-Earth chain of effects on the Earth's ionosphere and their subsequent impacts on communication systems based on multipoint space observations and complementary ground-based data. Development of a prototype space weather early warning system and reliable space weather forecasts, with specific emphasis on ionospheric applications. Dissemination of new space weather products and services to end users, the scientific community and general public. The presentation summarizes the project highlights, with special emphasis on the developed space weather forecast tools.

Update of the DTM thermosphere model in the framework of the H2020 project `SWAMI'

NASA Astrophysics Data System (ADS)

Bruinsma, S.; Jackson, D.; Stolle, C.; Negrin, S.

2017-12-01

In the framework of the H2020 project SWAMI (Space Weather Atmosphere Model and Indices), which is expected to start in January 2018, the CIRA thermosphere specification model DTM2013 will be improved through the combination of assimilating more density data to drive down remaining biases and a new high cadence kp geomagnetic index in order to improve storm-time performance. Five more years of GRACE high-resolution densities from 2012-2016, densities from the last year of the GOCE mission, Swarm mean densities, and mean densities from 2010-2017 inferred from the geodetic satellites at about 800 km are available now. The DTM2013 model will be compared with the new density data in order to detect possible systematic errors or other kinds of deficiencies and a first analysis will be presented. Also, a more detailed analysis of model performance under storm conditions will be provided, which will then be the benchmark to quantify model improvement expected with the higher cadence kp indices. In the SWAMI project, the DTM model will be coupled in the 120-160 km altitude region to the Met Office Unified Model in order to create a whole atmosphere model. It can be used for launch operations, re-entry computations, orbit prediction, and aeronomy and space weather studies. The project objectives and time line will be given.

Evaluation and economic value of winter weather forecasts

NASA Astrophysics Data System (ADS)

Snyder, Derrick W.

State and local highway agencies spend millions of dollars each year to deploy winter operation teams to plow snow and de-ice roadways. Accurate and timely weather forecast information is critical for effective decision making. Students from Purdue University partnered with the Indiana Department of Transportation to create an experimental winter weather forecast service for the 2012-2013 winter season in Indiana to assist in achieving these goals. One forecast product, an hourly timeline of winter weather hazards produced daily, was evaluated for quality and economic value. Verification of the forecasts was performed with data from the Rapid Refresh numerical weather model. Two objective verification criteria were developed to evaluate the performance of the timeline forecasts. Using both criteria, the timeline forecasts had issues with reliability and discrimination, systematically over-forecasting the amount of winter weather that was observed while also missing significant winter weather events. Despite these quality issues, the forecasts still showed significant, but varied, economic value compared to climatology. Economic value of the forecasts was estimated to be 29.5 million or 4.1 million, depending on the verification criteria used. Limitations of this valuation system are discussed and a framework is developed for more thorough studies in the future.

A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

NASA Astrophysics Data System (ADS)

Saleh, Firas; Ramaswamy, Venkatsundar; Georgas, Nickitas; Blumberg, Alan F.; Pullen, Julie

2016-07-01

This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ˜ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

The Road Weather Bulletin : Road Weather Management Publications and Training Materials

DOT National Transportation Integrated Search

2011-01-01

This document summarizes results from the Road Weather Policy Forum held November 8-9, 2010 in Washington, D.C. The agenda outlines a research framework, broad research needs, and the various roles and responsibilities of several stakeholder sectors.

From the surface to the deep critical zone: Linking soil carbon, fluid saturation and weathering rate

NASA Astrophysics Data System (ADS)

Druhan, Jennifer; Lawrence, Corey; Oster, Jessica; Rempe, Daniella; Dietrich, William

2017-04-01

Shallow soils from a wide range of ecosystems demonstrate a clear and consistent relationship between effective fluid saturation and the rate at which organic carbon is converted to CO2. While the underlying mechanisms contributing to this dependence are diverse, a consistent pattern of maximum CO2 production at intermediate soil moisture supports a generalized functional relationship, which may be incorporated into a quantitative reactive transport framework. A key result of this model development is a prediction of the extent to which the inorganic carbon content of water in biologically active soils varies as a function of hydrologic parameters (i.e. moisture content and residence time), and in turn influences weathering reactions. Deeper in the CZ, the consistency of this relationship and the influence of hydrologically - regulated CO2 production on the rates of water - rock interaction are largely unknown. Here, we use a novel reactive transport model incorporating this functional relationship to consider how variations in the reactive potential of water entering the vadose zone influences subsurface weathering rates. We leverage two examples of variably saturated natural systems to consider (1) CO2 production and associated weathering potential regulated by seasonal hydrologic shifts and (2) the preservation of soil carbon signatures in the deep CZ over millennial timescales. First, at the Eel River CZ Observatory in Northern California, USA, a novel Vadose Zone Monitoring System (VMS) installed in a 14 - 20 m thick unsaturated section offers an unprecedented view into the physical, chemical and biological behavior of the depth profile separating soils from groundwater. Based on soil moisture, gas and fluid phase samples, we demonstrate a predictive relationship between seasonal hydrologic variations and the location and magnitude of geochemical weathering rates. Second, an environmental monitoring project in the Blue Springs Cave, Sparta, TN, USA, provides chemical and isotopic signatures of both soil and cave drip water, allowing constraint of a model for the evolution of fluid with depth through a karst system. The carbon isotope signatures of these speleothems have been suggested as a record of long term variations in CZ vegetation, soil respiration and carbon stability. Using our modeling approach, we offer a prediction of the extent to which hydrologically - driven variations in carbon respiration are converted to weathering rates in karst systems and ultimately preserved within the speleothem record. By combining this novel modeling approach with these two examples, we illustrate a quantitative framework for (1) the influence of hydro-biological coupling in shallow soils on deep weathering regimes in the Critical Zone, and (2) the preservation of these signals in the geologic record.

Kameleon Live: An Interactive Cloud Based Analysis and Visualization Platform for Space Weather Researchers

NASA Astrophysics Data System (ADS)

Pembroke, A. D.; Colbert, J. A.

2015-12-01

The Community Coordinated Modeling Center (CCMC) provides hosting for many of the simulations used by the space weather community of scientists, educators, and forecasters. CCMC users may submit model runs through the Runs on Request system, which produces static visualizations of model output in the browser, while further analysis may be performed off-line via Kameleon, CCMC's cross-language access and interpolation library. Off-line analysis may be suitable for power-users, but storage and coding requirements present a barrier to entry for non-experts. Moreover, a lack of a consistent framework for analysis hinders reproducibility of scientific findings. To that end, we have developed Kameleon Live, a cloud based interactive analysis and visualization platform. Kameleon Live allows users to create scientific studies built around selected runs from the Runs on Request database, perform analysis on those runs, collaborate with other users, and disseminate their findings among the space weather community. In addition to showcasing these novel collaborative analysis features, we invite feedback from CCMC users as we seek to advance and improve on the new platform.

Resolving the Multi-scale Behavior of Geochemical Weathering in the Critical Zone Using High Resolution Hydro-geochemical Models

NASA Astrophysics Data System (ADS)

Pandey, S.; Rajaram, H.

2015-12-01

This work investigates hydrologic and geochemical interactions in the Critical Zone (CZ) using high-resolution reactive transport modeling. Reactive transport models can be used to predict the response of geochemical weathering and solute fluxes in the CZ to changes in a dynamic environment, such as those pertaining to human activities and climate change in recent years. The scales of hydrology and geochemistry in the CZ range from days to eons in time and centimeters to kilometers in space. Here, we present results of a multi-dimensional, multi-scale hydro-geochemical model to investigate the role of subsurface heterogeneity on the formation of mineral weathering fronts in the CZ, which requires consideration of many of these spatio-temporal scales. The model is implemented using the reactive transport code PFLOTRAN, an open source subsurface flow and reactive transport code that utilizes parallelization over multiple processing nodes and provides a strong framework for simulating weathering in the CZ. The model is set up to simulate weathering dynamics in the mountainous catchments representative of the Colorado Front Range. Model parameters were constrained based on hydrologic, geochemical, and geophysical observations from the Boulder Creek Critical Zone Observatory (BcCZO). Simulations were performed in fractured rock systems and compared with systems of heterogeneous and homogeneous permeability fields. Tracer simulations revealed that the mean residence time of solutes was drastically accelerated as fracture density increased. In simulations that include mineral reactions, distinct signatures of transport limitations on weathering arose when discrete flow paths were included. This transport limitation was related to both advective and diffusive processes in the highly heterogeneous systems (i.e. fractured media and correlated random permeability fields with σlnk > 3). The well-known time-dependence of mineral weathering rates was found to be the most pronounced in the fractured systems, with a departure from the maximum system-averaged dissolution rate occurring after ~100 kyr followed by a gradual decrease in the reaction rate with time that persists beyond 104 kyr.

Teacher challenges, perceptions, and use of science models in middle school classrooms about climate, weather, and energy concepts

NASA Astrophysics Data System (ADS)

Yarker, Morgan Brown

Research suggests that scientific models and modeling should be topics covered in K-12 classrooms as part of a comprehensive science curriculum. It is especially important when talking about topics in weather and climate, where computer and forecast models are the center of attention. There are several approaches to model based inquiry, but it can be argued, theoretically, that science models can be effectively implemented into any approach to inquiry if they are utilized appropriately. Yet, it remains to be explored how science models are actually implemented in classrooms. This study qualitatively looks at three middle school science teachers' use of science models with various approaches to inquiry during their weather and climate units. Results indicate that the teacher who used the most elements of inquiry used models in a way that aligned best with the theoretical framework than the teachers who used fewer elements of inquiry. The theoretical framework compares an approach to argument-based inquiry to model-based inquiry, which argues that the approaches are essentially identical, so teachers who use inquiry should be able to apply model-based inquiry using the same approach. However, none of the teachers in this study had a complete understanding of the role models play in authentic science inquiry, therefore students were not explicitly exposed to the ideas that models can be used to make predictions about, and are representations of, a natural phenomenon. Rather, models were explicitly used to explain concepts to students or have students explain concepts to the teacher or to each other. Additionally, models were used as a focal point for conversation between students, usually as they were creating, modifying, or using models. Teachers were not observed asking students to evaluate models. Since science models are an important aspect of understanding science, it is important that teachers not only know how to implement models into an inquiry environment, but also understand the characteristics of science models so that they can explicitly teach the concept of modeling to students. This study suggests that better pre-service and in-service teacher education is needed to prepare students to teach about science models effectively.

Prediction of CMEs and Type II Bursts from Sun to Earth

NASA Astrophysics Data System (ADS)

Cairns, I. H.; Schmidt, J. M.; Gopalswamy, N.; van der Holst, B.

2017-12-01

Most major space weather events are due to fast CMEs and their shocks interacting with Earth's magnetosphere. SImilarly, type II solar radio bursts are well-known signatures of CMEs and their shocks moving through the corona and solar wind. The properties of the space weather events and the type II radio bursts depend sensitively on the CME velocity, shape, and evolution as functions of position and time, as well as on the magnetic field vector in the coronal and solar wind plasma, downstream of the CME shock, and inside the CME. We report simulations of CMEs and type II bursts from the Sun to Earth with the Space Weather Modelling Framework (2015 and 2016 versions), set up carefully using relevant data, and a kinetic radio emission theory. Excellent agreement between observations, simulations, and theory are found for the coronal (metric) type II burst of 7 September 2014 and associated CME, including the lack of radio emission in the solar wind beyond about 10 solar radii. Similarly, simulation of a CME and type II burst from the Sun to 1 AU over the period 29 November - 1 December 2013 yield excellent agreement for the radio burst from 10 MHz to 30 kHz for STEREO A and B and Wind, arrival of the CME at STEREO A within 1 hour reported time, deceleration of the CME in agreement with the Gopalswamy et al. [2011] observational analyses, and Bz rotations at STEREO A from upstream of the CME shock to within the CME. These results provide strong support for the type II theory and also that the Space WeatherModeling Framework can accurately predict the properties and evolution of CMEs and the interplanetary magnetic field and plasma from the Sun to 1 AU when sufficiently carefully initialized.

Decision Making and Risk Evaluation Frameworks for Extreme Space Weather Events

NASA Astrophysics Data System (ADS)

Uritskaya, O.; Robinson, R. M.; Pulkkinen, A. A.

2017-12-01

Extreme Space Weather events (ESWE) are in the spotlight nowadays because they can produce a significant impact not only due to their intensity and broad geographical scope, but also because of the widespread levels and the multiple sectors of the economy that could be involved. In the task of evaluation of the ESWE consequences, the most problematic and vulnerable aspect is the determination and calculation of the probability of statistically infrequent events and the subsequent assessment of the economic risks. In this work, we conduct a detailed analysis of the available frameworks of the general Decision-Making Theory in the presence of uncertainty, in the context of their applicability for the numerical estimation of the risks and losses associated with ESWE. The results of our study demonstrate that, unlike the Multiple-criteria decision analysis or Minimax approach to modeling of the possible scenarios for the ESWE effects, which prevail in the literature, the most suitable concept is the Games Against Nature (GAN). It enables an evaluation of every economically relevant aspect of space weather conditions and obtain more detailed results. Choosing the appropriate methods for solving GAN models, i.e. determining the most optimal strategy with a given level of uncertainty, requires estimating the conditional probabilities of Space Weather events for each outcome of possible scenarios of this natural disaster. Due to the specifics of complex natural and economic systems, with which we are dealing in this case, this problem remains unsolved, mainly because of inevitable loss of information at every stage of the decision-making process. The analysis is illustrated by deregulated electricity markets of the USA and Canada, whose power grid systems are known to be perceptive to ESWE. The GAN model is more appropriate in identifying potential risks in economic systems. The proposed approach, when applied to the existing database of Space Weather observations and numerical simulations, can provide more accurate forecasts of possible losses and allow for a more precise evaluation of the potential risks of the consequences of the ESWE for the vulnerable industries, such as electric power distribution systems, which have been shown to experience some of the most significant losses caused by ESWE.

The Role of Model and Initial Condition Error in Numerical Weather Forecasting Investigated with an Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Prive, Nikki C.; Errico, Ronald M.

2013-01-01

A series of experiments that explore the roles of model and initial condition error in numerical weather prediction are performed using an observing system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The use of an OSSE allows the analysis and forecast errors to be explicitly calculated, and different hypothetical observing networks can be tested with ease. In these experiments, both a full global OSSE framework and an 'identical twin' OSSE setup are utilized to compare the behavior of the data assimilation system and evolution of forecast skill with and without model error. The initial condition error is manipulated by varying the distribution and quality of the observing network and the magnitude of observation errors. The results show that model error has a strong impact on both the quality of the analysis field and the evolution of forecast skill, including both systematic and unsystematic model error components. With a realistic observing network, the analysis state retains a significant quantity of error due to systematic model error. If errors of the analysis state are minimized, model error acts to rapidly degrade forecast skill during the first 24-48 hours of forward integration. In the presence of model error, the impact of observation errors on forecast skill is small, but in the absence of model error, observation errors cause a substantial degradation of the skill of medium range forecasts.

Weather forecasting based on hybrid neural model

NASA Astrophysics Data System (ADS)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Fixed points, stable manifolds, weather regimes, and their predictability.

PubMed

Deremble, Bruno; D'Andrea, Fabio; Ghil, Michael

2009-12-01

In a simple, one-layer atmospheric model, we study the links between low-frequency variability and the model's fixed points in phase space. The model dynamics is characterized by the coexistence of multiple "weather regimes." To investigate the transitions from one regime to another, we focus on the identification of stable manifolds associated with fixed points. We show that these manifolds act as separatrices between regimes. We track each manifold by making use of two local predictability measures arising from the meteorological applications of nonlinear dynamics, namely, "bred vectors" and singular vectors. These results are then verified in the framework of ensemble forecasts issued from "clouds" (ensembles) of initial states. The divergence of the trajectories allows us to establish the connections between zones of low predictability, the geometry of the stable manifolds, and transitions between regimes.

Real-time, rapidly updating severe weather products for virtual globes

NASA Astrophysics Data System (ADS)

Smith, Travis M.; Lakshmanan, Valliappa

2011-01-01

It is critical that weather forecasters are able to put severe weather information from a variety of observational and modeling platforms into a geographic context so that warning information can be effectively conveyed to the public, emergency managers, and disaster response teams. The availability of standards for the specification and transport of virtual globe data products has made it possible to generate spatially precise, geo-referenced images and to distribute these centrally created products via a web server to a wide audience. In this paper, we describe the data and methods for enabling severe weather threat analysis information inside a KML framework. The method of creating severe weather diagnosis products that are generated and translating them to KML and image files is described. We illustrate some of the practical applications of these data when they are integrated into a virtual globe display. The availability of standards for interoperable virtual globe clients has not completely alleviated the need for custom solutions. We conclude by pointing out several of the limitations of the general-purpose virtual globe clients currently available.

A conditional stochastic weather generator for seasonal to multi-decadal simulations

NASA Astrophysics Data System (ADS)

Verdin, Andrew; Rajagopalan, Balaji; Kleiber, William; Podestá, Guillermo; Bert, Federico

2018-01-01

We present the application of a parametric stochastic weather generator within a nonstationary context, enabling simulations of weather sequences conditioned on interannual and multi-decadal trends. The generalized linear model framework of the weather generator allows any number of covariates to be included, such as large-scale climate indices, local climate information, seasonal precipitation and temperature, among others. Here we focus on the Salado A basin of the Argentine Pampas as a case study, but the methodology is portable to any region. We include domain-averaged (e.g., areal) seasonal total precipitation and mean maximum and minimum temperatures as covariates for conditional simulation. Areal covariates are motivated by a principal component analysis that indicates the seasonal spatial average is the dominant mode of variability across the domain. We find this modification to be effective in capturing the nonstationarity prevalent in interseasonal precipitation and temperature data. We further illustrate the ability of this weather generator to act as a spatiotemporal downscaler of seasonal forecasts and multidecadal projections, both of which are generally of coarse resolution.

Status of the seamless coupled modelling system ICON-ART

NASA Astrophysics Data System (ADS)

Vogel, Bernhard; Rieger, Daniel; Schroeter, Jenniffer; Bischoff-Gauss, Inge; Deetz, Konrad; Eckstein, Johannes; Foerstner, Jochen; Gasch, Philipp; Ruhnke, Roland; Vogel, Heike; Walter, Carolin; Weimer, Michael

2016-04-01

The integrated modelling framework ICON-ART [1] (ICOsahedral Nonhydrostatic - Aerosols and Reactive Trace gases) extends the numerical weather prediction modelling system ICON by modules for gas phase chemistry, aerosol dynamics and related feedback processes. The nonhydrostatic global modelling system ICON [2] is a joint development of German Weather Service (DWD) and Max Planck Institute for Meteorology (MPI-M) with local grid refinement down to grid sizes of a few kilometers. It will be used for numerical weather prediction, climate projections and for research purposes. Since January 2016 ICON runs operationally at DWD for weather forecast on the global scale with a grid size of 13 km. Analogous to its predecessor COSMO-ART [3], ICON-ART is designed to account for feedback processes between meteorological variables and atmospheric trace substances. Up to now, ICON-ART contains the dispersion of volcanic ash, radioactive tracers, sea salt aerosol, as well as ozone-depleting stratospheric trace substances [1]. Recently, we have extended ICON-ART by a mineral dust emission scheme with global applicability and nucleation parameterizations which allow the cloud microphysics to explicitly account for prognostic aerosol distributions. Also very recently an emission scheme for volatile organic compounds was included. We present first results of the impact of natural aerosol (i.e. sea salt aerosol and mineral dust) on cloud properties and precipitation as well as the interaction of primary emitted particles with radiation. Ongoing developments are the coupling with a radiation scheme to calculate the photolysis frequencies, a coupling with the RADMKA (1) chemistry and first steps to include isotopologues of water. Examples showing the capabilities of the model system will be presented. This includes a simulation of the transport of ozone depleting short-lived trace gases from the surface into the stratosphere as well as of long-lived tracers. [1] Rieger, D., et al. (2015), ICON-ART - A new online-coupled model system from the global to regional scale, Geosci. Model Dev., doi:10.5194/gmd-8-1659-2015. [2] Zängl, G., et al. (2014), The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD MPI-M: Description of the non-hydrostatic dynamical core. Q.J.R. Meteorol. Soc., doi: 10.1002/qj.2378 [3] Vogel, B., et al. (2009), The comprehensive model system COSMO-ART - Radiative impact of aerosol on the state of the atmosphere on the regional scale, Atmos. Chem. Phys., 9, 8661-8680

A New Framework to Compare Mass-Flux Schemes Within the AROME Numerical Weather Prediction Model

NASA Astrophysics Data System (ADS)

Riette, Sébastien; Lac, Christine

2016-08-01

In the Application of Research to Operations at Mesoscale (AROME) numerical weather forecast model used in operations at Météo-France, five mass-flux schemes are available to parametrize shallow convection at kilometre resolution. All but one are based on the eddy-diffusivity-mass-flux approach, and differ in entrainment/detrainment, the updraft vertical velocity equation and the closure assumption. The fifth is based on a more classical mass-flux approach. Screen-level scores obtained with these schemes show few discrepancies and are not sufficient to highlight behaviour differences. Here, we describe and use a new experimental framework, able to compare and discriminate among different schemes. For a year, daily forecast experiments were conducted over small domains centred on the five French metropolitan radio-sounding locations. Cloud base, planetary boundary-layer height and normalized vertical profiles of specific humidity, potential temperature, wind speed and cloud condensate were compared with observations, and with each other. The framework allowed the behaviour of the different schemes in and above the boundary layer to be characterized. In particular, the impact of the entrainment/detrainment formulation, closure assumption and cloud scheme were clearly visible. Differences mainly concerned the transport intensity thus allowing schemes to be separated into two groups, with stronger or weaker updrafts. In the AROME model (with all interactions and the possible existence of compensating errors), evaluation diagnostics gave the advantage to the first group.

Development and validation of a regional coupled forecasting system for S2S forecasts

NASA Astrophysics Data System (ADS)

Sun, R.; Subramanian, A. C.; Hoteit, I.; Miller, A. J.; Ralph, M.; Cornuelle, B. D.

2017-12-01

Accurate and efficient forecasting of oceanic and atmospheric circulation is essential for a wide variety of high-impact societal needs, including: weather extremes; environmental protection and coastal management; management of fisheries, marine conservation; water resources; and renewable energy. Effective forecasting relies on high model fidelity and accurate initialization of the models with observed state of the ocean-atmosphere-land coupled system. A regional coupled ocean-atmosphere model with the Weather Research and Forecasting (WRF) model and the MITGCM ocean model coupled using the ESMF (Earth System Modeling Framework) coupling framework is developed to resolve mesoscale air-sea feedbacks. The regional coupled model allows oceanic mixed layer heat and momentum to interact with the atmospheric boundary layer dynamics at the mesoscale and submesoscale spatiotemporal regimes, thus leading to feedbacks which are otherwise not resolved in coarse resolution global coupled forecasting systems or regional uncoupled forecasting systems. The model is tested in two scenarios in the mesoscale eddy rich Red Sea and Western Indian Ocean region as well as mesoscale eddies and fronts of the California Current System. Recent studies show evidence for air-sea interactions involving the oceanic mesoscale in these two regions which can enhance predictability on sub seasonal timescale. We will present results from this newly developed regional coupled ocean-atmosphere model for forecasts over the Red Sea region as well as the California Current region. The forecasts will be validated against insitu observations in the region as well as reanalysis fields.

Integrated modeling for assessment of energy-water system resilience under changing climate

NASA Astrophysics Data System (ADS)

Yan, E.; Veselka, T.; Zhou, Z.; Koritarov, V.; Mahalik, M.; Qiu, F.; Mahat, V.; Betrie, G.; Clark, C.

2016-12-01

Energy and water systems are intrinsically interconnected. Due to an increase in climate variability and extreme weather events, interdependency between these two systems has been recently intensified resulting significant impacts on both systems and energy output. To address this challenge, an Integrated Water-Energy Systems Assessment Framework (IWESAF) is being developed to integrate multiple existing or developed models from various sectors. The IWESAF currently includes an extreme climate event generator to predict future extreme weather events, hydrologic and reservoir models, riverine temperature model, power plant water use simulator, and power grid operation and cost optimization model. The IWESAF can facilitate the interaction among the modeling systems and provide insights of the sustainability and resilience of the energy-water system under extreme climate events and economic consequence. The regional case demonstration in the Midwest region will be presented. The detailed information on some of individual modeling components will also be presented in several other abstracts submitted to AGU this year.

GCSS/WGNE Pacific Cross-section Intercomparison: Tropical and Subtropical Cloud Transitions

NASA Astrophysics Data System (ADS)

Teixeira, J.

2008-12-01

In this presentation I will discuss the role of the GEWEX Cloud Systems Study (GCSS) working groups in paving the way for substantial improvements in cloud parameterization in weather and climate models. The GCSS/WGNE Pacific Cross-section Intercomparison (GPCI) is an extension of GCSS and is a different type of model evaluation where climate models are analyzed along a Pacific Ocean transect from California to the equator. This approach aims at complementing the more traditional efforts in GCSS by providing a simple framework for the evaluation of models that encompasses several fundamental cloud regimes such as stratocumulus, shallow cumulus and deep cumulus, as well as the transitions between them. Currently twenty four climate and weather prediction models are participating in GPCI. We will present results of the comparison between models and recent satellite data. In particular, we will explore in detail the potential of the Atmospheric Infrared Sounder (AIRS) and CloudSat data for the evaluation of the representation of clouds and convection in climate models.

Establishing a Numerical Modeling Framework for Hydrologic Engineering Analyses of Extreme Storm Events

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Xiaodong; Hossain, Faisal; Leung, L. Ruby

In this study a numerical modeling framework for simulating extreme storm events was established using the Weather Research and Forecasting (WRF) model. Such a framework is necessary for the derivation of engineering parameters such as probable maximum precipitation that are the cornerstone of large water management infrastructure design. Here this framework was built based on a heavy storm that occurred in Nashville (USA) in 2010, and verified using two other extreme storms. To achieve the optimal setup, several combinations of model resolutions, initial/boundary conditions (IC/BC), cloud microphysics and cumulus parameterization schemes were evaluated using multiple metrics of precipitation characteristics. Themore » evaluation suggests that WRF is most sensitive to IC/BC option. Simulation generally benefits from finer resolutions up to 5 km. At the 15km level, NCEP2 IC/BC produces better results, while NAM IC/BC performs best at the 5km level. Recommended model configuration from this study is: NAM or NCEP2 IC/BC (depending on data availability), 15km or 15km-5km nested grids, Morrison microphysics and Kain-Fritsch cumulus schemes. Validation of the optimal framework suggests that these options are good starting choices for modeling extreme events similar to the test cases. This optimal framework is proposed in response to emerging engineering demands of extreme storm events forecasting and analyses for design, operations and risk assessment of large water infrastructures.« less

An Overview of Atmospheric Composition OSSE Activities at NASA's Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

daSilva, Arlinda

2012-01-01

A model-based Observing System Simulation Experiment (OSSE) is a framework for numerical experimentation in which observables are simulated from fields generated by an earth system model, including a parameterized description of observational error characteristics. Simulated observations can be used for sampling studies, quantifying errors in analysis or retrieval algorithms, and ultimately being a planning tool for designing new observing missions. While this framework has traditionally been used to assess the impact of observations on numerical weather prediction, it has a much broader applicability, in particular to aerosols and chemical constituents. In this talk we will give a general overview of Observing System Simulation Experiments (OSSE) activities at NASA's Global Modeling and Assimilation Office, with focus on its emerging atmospheric composition component.

Statistical Modeling of Daily Stream Temperature for Mitigating Fish Mortality

NASA Astrophysics Data System (ADS)

Caldwell, R. J.; Rajagopalan, B.

2011-12-01

Water allocations in the Central Valley Project (CVP) of California require the consideration of short- and long-term needs of many socioeconomic factors including, but not limited to, agriculture, urban use, flood mitigation/control, and environmental concerns. The Endangered Species Act (ESA) ensures that the decision-making process provides sufficient water to limit the impact on protected species, such as salmon, in the Sacramento River Valley. Current decision support tools in the CVP were deemed inadequate by the National Marine Fisheries Service due to the limited temporal resolution of forecasts for monthly stream temperature and fish mortality. Finer scale temporal resolution is necessary to account for the stream temperature variations critical to salmon survival and reproduction. In addition, complementary, long-range tools are needed for monthly and seasonal management of water resources. We will present a Generalized Linear Model (GLM) framework of maximum daily stream temperatures and related attributes, such as: daily stream temperature range, exceedance/non-exceedance of critical threshold temperatures, and the number of hours of exceedance. A suite of predictors that impact stream temperatures are included in the models, including current and prior day values of streamflow, water temperatures of upstream releases from Shasta Dam, air temperature, and precipitation. Monthly models are developed for each stream temperature attribute at the Balls Ferry gauge, an EPA compliance point for meeting temperature criteria. The statistical framework is also coupled with seasonal climate forecasts using a stochastic weather generator to provide ensembles of stream temperature scenarios that can be used for seasonal scale water allocation planning and decisions. Short-term weather forecasts can also be used in the framework to provide near-term scenarios useful for making water release decisions on a daily basis. The framework can be easily translated to other locations and is intended to be a complement to the physical stream temperature modeling efforts that are underway on the river.

Multi-physics simulations of space weather

NASA Astrophysics Data System (ADS)

Gombosi, Tamas; Toth, Gabor; Sokolov, Igor; de Zeeuw, Darren; van der Holst, Bart; Cohen, Ofer; Glocer, Alex; Manchester, Ward, IV; Ridley, Aaron

Presently magnetohydrodynamic (MHD) models represent the "workhorse" technology for simulating the space environment from the solar corona to the ionosphere. While these models are very successful in describing many important phenomena, they are based on a low-order moment approximation of the phase-space distribution function. In the last decade our group at the Center for Space Environment Modeling (CSEM) has developed the Space Weather Modeling Framework (SWMF) that efficiently couples together different models describing the interacting regions of the space environment. Many of these domain models (such as the global solar corona, the inner heliosphere or the global magnetosphere) are based on MHD and are represented by our multiphysics code, BATS-R-US. BATS-R-US can solve the equations of "standard" ideal MHD, but it can also go beyond this first approximation. It can solve resistive MHD, Hall MHD, semi-relativistic MHD (that keeps the displacement current), multispecies (different ion species have different continuity equations) and multifluid (all ion species have separate continuity, momentum and energy equations) MHD. Recently we added two-fluid Hall MHD (solving the electron and ion energy equations separately) and are working on extended magnetohydrodynamics with anisotropic pressures. This talk will show the effects of added physics and compare space weather simulation results to "standard" ideal MHD.

Scenario-neutral Food Security Risk Assessment: A livestock Heat Stress Case Study

NASA Astrophysics Data System (ADS)

Broman, D.; Rajagopalan, B.; Hopson, T. M.

2015-12-01

Food security risk assessments can provide decision-makers with actionable information to identify critical system limitations, and alternatives to mitigate the impacts of future conditions. The majority of current risk assessments have been scenario-led and results are limited by the scenarios - selected future states of the world's climate system and socioeconomic factors. A generic scenario-neutral framework for food security risk assessments is presented here that uses plausible states of the world without initially assigning likelihoods. Measures of system vulnerabilities are identified and system risk is assessed for these states. This framework has benefited greatly by research in the water and natural resource fields to adapt their planning to provide better risk assessments. To illustrate the utility of this framework we develop a case study using livestock heat stress risk within the pastoral system of West Africa. Heat stress can have a major impact not only on livestock owners, but on the greater food production system, decreasing livestock growth, milk production, and reproduction, and in severe cases, death. A heat stress index calculated from daily weather is used as a vulnerability measure and is computed from historic daily weather data at several locations in the study region. To generate plausible states, a stochastic weather generator is developed to generate synthetic weather sequences at each location, consistent with the seasonal climate. A spatial model of monthly and seasonal heat stress provide projections of current and future livestock heat stress measures across the study region, and can incorporate in seasonal climate and other external covariates. These models, when linked with empirical thresholds of heat stress risk for specific breeds offer decision-makers with actionable information for use in near-term warning systems as well as for future planning. Future assessment can indicate under which states livestock are at greatest risk of heat stress; when coupled with assessments of additional measures (e.g. water and fodder availability) can inform on alternatives that provide satisfactory performance under a wide range of states (e.g. optimal cattle breed, supplemental feed, increased water access).

Atmosphere-Wave-Ocean Coupling from Regional to Global Earth System Models for High-Impact Extreme Weather Prediction

NASA Astrophysics Data System (ADS)

Chen, S. S.; Curcic, M.

2017-12-01

The need for acurrate and integrated impact forecasts of extreme wind, rain, waves, and storm surge is growing as coastal population and built environment expand worldwide. A key limiting factor in forecasting impacts of extreme weather events associated with tropical cycle and winter storms is fully coupled atmosphere-wave-ocean model interface with explicit momentum and energy exchange. It is not only critical for accurate prediction of storm intensity, but also provides coherent wind, rian, ocean waves and currents forecasts for forcing for storm surge. The Unified Wave INterface (UWIN) has been developed for coupling of the atmosphere-wave-ocean models. UWIN couples the atmosphere, wave, and ocean models using the Earth System Modeling Framework (ESMF). It is a physically based and computationally efficient coupling sytem that is flexible to use in a multi-model system and portable for transition to the next generation global Earth system prediction mdoels. This standardized coupling framework allows researchers to develop and test air-sea coupling parameterizations and coupled data assimilation, and to better facilitate research-to-operation activities. It has been used and extensively tested and verified in regional coupled model forecasts of tropical cycles and winter storms (Chen and Curcic 2016, Curcic et al. 2016, and Judt et al. 2016). We will present 1) an overview of UWIN and its applications in fully coupled atmosphere-wave-ocean model predictions of hurricanes and coastal winter storms, and 2) implenmentation of UWIN in the NASA GMAO GEOS-5.

Evaluation of CMAQ and CAMx Ensemble Air Quality Forecasts during the 2015 MAPS-Seoul Field Campaign

NASA Astrophysics Data System (ADS)

Kim, E.; Kim, S.; Bae, C.; Kim, H. C.; Kim, B. U.

2015-12-01

The performance of Air quality forecasts during the 2015 MAPS-Seoul Field Campaign was evaluated. An forecast system has been operated to support the campaign's daily aircraft route decisions for airborne measurements to observe long-range transporting plume. We utilized two real-time ensemble systems based on the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Comprehensive Air quality Model with extensions (CAMx) modeling framework and WRF-SMOKE- Community Multi_scale Air Quality (CMAQ) framework over northeastern Asia to simulate PM10 concentrations. Global Forecast System (GFS) from National Centers for Environmental Prediction (NCEP) was used to provide meteorological inputs for the forecasts. For an additional set of retrospective simulations, ERA Interim Reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF) was also utilized to access forecast uncertainties from the meteorological data used. Model Inter-Comparison Study for Asia (MICS-Asia) and National Institute of Environment Research (NIER) Clean Air Policy Support System (CAPSS) emission inventories are used for foreign and domestic emissions, respectively. In the study, we evaluate the CMAQ and CAMx model performance during the campaign by comparing the results to the airborne and surface measurements. Contributions of foreign and domestic emissions are estimated using a brute force method. Analyses on model performance and emissions will be utilized to improve air quality forecasts for the upcoming KORUS-AQ field campaign planned in 2016.

Integration of the radiation belt environment model into the space weather modeling framework

NASA Astrophysics Data System (ADS)

Glocer, A.; Toth, G.; Fok, M.; Gombosi, T.; Liemohn, M.

2009-11-01

We have integrated the Fok radiation belt environment (RBE) model into the space weather modeling framework (SWMF). RBE is coupled to the global magnetohydrodynamics component (represented by the Block-Adaptive-Tree Solar-wind Roe-type Upwind Scheme, BATS-R-US, code) and the Ionosphere Electrodynamics component of the SWMF, following initial results using the Weimer empirical model for the ionospheric potential. The radiation belt (RB) model solves the convection-diffusion equation of the plasma in the energy range of 10 keV to a few MeV. In stand-alone mode RBE uses Tsyganenko's empirical models for the magnetic field, and Weimer's empirical model for the ionospheric potential. In the SWMF the BATS-R-US model provides the time dependent magnetic field by efficiently tracing the closed magnetic field-lines and passing the geometrical and field strength information to RBE at a regular cadence. The ionosphere electrodynamics component uses a two-dimensional vertical potential solver to provide new potential maps to the RBE model at regular intervals. We discuss the coupling algorithm and show some preliminary results with the coupled code. We run our newly coupled model for periods of steady solar wind conditions and compare our results to the RB model using an empirical magnetic field and potential model. We also simulate the RB for an active time period and find that there are substantial differences in the RB model results when changing either the magnetic field or the electric field, including the creation of an outer belt enhancement via rapid inward transport on the time scale of tens of minutes.

Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

NASA Astrophysics Data System (ADS)

Gong, W.; Duan, Q.; Huo, X.

2017-12-01

Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

A Unified Model of Geostrophic Adjustment and Frontogenesis

NASA Astrophysics Data System (ADS)

Taylor, John; Shakespeare, Callum

2013-11-01

Fronts, or regions with strong horizontal density gradients, are ubiquitous and dynamically important features of the ocean and atmosphere. In the ocean, fronts are associated with enhanced air-sea fluxes, turbulence, and biological productivity, while atmospheric fronts are associated with some of the most extreme weather events. Here, we describe a new mathematical framework for describing the formation of fronts, or frontogenesis. This framework unifies two classical problems in geophysical fluid dynamics, geostrophic adjustment and strain-driven frontogenesis, and provides a number of important extensions beyond previous efforts. The model solutions closely match numerical simulations during the early stages of frontogenesis, and provide a means to describe the development of turbulence at mature fronts.

Extreme Weather Risk Assessment: The Case of Jiquilisco, El Salvador

NASA Astrophysics Data System (ADS)

Melendez, Karla; Ceppi, Claudia; Molero, Juanjo; Rios Insua, David

2014-05-01

All major climate models predict increases in both global and regional mean temperatures throughout this century, under different scenarios concerning future trends in population growth or economic and technological development. This consistency of results across models has strengthened the evidence about global warming. Despite the convincing facts and findings of climate researchers, there is still a great deal of skepticism around climate change. There is somewhat less consensus about some of the consequences of climate change, for example in reference to extreme weather changes, in particular as regards more local scales. However, such changes seem to have already considerable impact in many regions across the world in terms of lives, economic losses, and required changes in lifestyles. This may demand appropriate policy responses both at national and local levels. Our work provides a framework for extreme weather multithreat risk management, based on probabilistic risk assessment (PRA). This may be useful in comparing the effectiveness of different actions to manage risks and inform judgment concerning the appropriate resource allocation to mitigate the risks. The methodology has been applied to the case study of the "El Marillo II" community, located in the municipality of Jiquilisco in El Salvador. There, the main problem related with extreme weather conditions are the frequent floods caused by rainfall, hurricanes , and water increases in the Lempa river nearby located. However, droughts are also very relevant. Based on several sources like SNET, newspapers, field visits to the region and interviews, we have built a detailed database that comprises extreme weather daily data from January 1971 until December 2011. Forecasting models for floods and droughts were built suggesting the need to properly manage the risks. We subsequently obtained the optimal portfolio of countermeasures, given the budget constraints. KEYWORDS: CLIMATE CHANGE, EXTREME WEATHER, RISK ANALYSIS, DECISION ANALYSIS, EL SALVADOR.

Data-Model Comparisons of the October, 2002 Event Using the Space Weather Modeling Framework

NASA Astrophysics Data System (ADS)

Welling, D. T.; Chappell, C. R.; Schunk, R. W.; Barakat, A. R.; Eccles, V.; Glocer, A.; Kistler, L. M.; Haaland, S.; Moore, T. E.

2014-12-01

The September 27 - October 4, 2002 time period has been selected by the Geospace Environment Modeling Ionospheric Outflow focus group for community collaborative study because of its high magnetospheric activity and extensive data coverage. The FAST, Polar, and Cluster missions, as well as others, all made key observations during this period, creating a prime event for data-model comparisons. The GEM community has come together to simulate this period using many different methods in order to evaluate models, compare results, and expand our knowledge of ionospheric outflow and its effects on global dynamics. This paper presents Space Weather Modeling Framework (SWMF) simulations of this important period compared against observations from the Polar TIDE, Cluster CODIF and EFW instruments. Density and velocity of oxygen and hydrogen throughout the lobes, plasmasheet, and inner magnetosphere will be the focus of these comparisons. For these simulations, the SWMF couples the multifluid version of BATS-R-US MHD to a variety of ionospheric outflow models of varying complexity. The simplest is outflow arising from constant MHD inner boundary conditions. Two first-principles-based models are also leveraged: the Polar Wind Outflow Model (PWOM), a fluid treatment of outflow dynamics, and the Generalized Polar Wind (GPW) model, which combines fluid and particle-in-cell approaches. Each model is capable of capturing a different set of energization mechanisms, yielding different outflow results. The data-model comparisons will illustrate how well each approach captures reality and which energization mechanisms are most important. This work will also assess our current capability to reproduce ionosphere-magnetosphere mass coupling.

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

NASA Technical Reports Server (NTRS)

Kim, E.; Tedesco, M.; Reichle, R.; Choudhury, B.; Peters-Lidard C.; Foster, J.; Hall, D.; Riggs, G.

2006-01-01

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

Explicit simulation of ice particle habits in a Numerical Weather Prediction Model

NASA Astrophysics Data System (ADS)

Hashino, Tempei

2007-05-01

This study developed a scheme for explicit simulation of ice particle habits in Numerical Weather Prediction (NWP) Models. The scheme is called Spectral Ice Habit Prediction System (SHIPS), and the goal is to retain growth history of ice particles in the Eulerian dynamics framework. It diagnoses characteristics of ice particles based on a series of particle property variables (PPVs) that reflect history of microphysieal processes and the transport between mass bins and air parcels in space. Therefore, categorization of ice particles typically used in bulk microphysical parameterization and traditional bin models is not necessary, so that errors that stem from the categorization can be avoided. SHIPS predicts polycrystals as well as hexagonal monocrystals based on empirically derived habit frequency and growth rate, and simulates the habit-dependent aggregation and riming processes by use of the stochastic collection equation with predicted PPVs. Idealized two dimensional simulations were performed with SHIPS in a NWP model. The predicted spatial distribution of ice particle habits and types, and evolution of particle size distributions showed good quantitative agreement with observation This comprehensive model of ice particle properties, distributions, and evolution in clouds can be used to better understand problems facing wide range of research disciplines, including microphysics processes, radiative transfer in a cloudy atmosphere, data assimilation, and weather modification.

EarthCube - Earth System Bridge: Spanning Scientific Communities with Interoperable Modeling Frameworks

NASA Astrophysics Data System (ADS)

Peckham, S. D.; DeLuca, C.; Gochis, D. J.; Arrigo, J.; Kelbert, A.; Choi, E.; Dunlap, R.

2014-12-01

In order to better understand and predict environmental hazards of weather/climate, ecology and deep earth processes, geoscientists develop and use physics-based computational models. These models are used widely both in academic and federal communities. Because of the large effort required to develop and test models, there is widespread interest in component-based modeling, which promotes model reuse and simplified coupling to tackle problems that often cross discipline boundaries. In component-based modeling, the goal is to make relatively small changes to models that make it easy to reuse them as "plug-and-play" components. Sophisticated modeling frameworks exist to rapidly couple these components to create new composite models. They allow component models to exchange variables while accommodating different programming languages, computational grids, time-stepping schemes, variable names and units. Modeling frameworks have arisen in many modeling communities. CSDMS (Community Surface Dynamics Modeling System) serves the academic earth surface process dynamics community, while ESMF (Earth System Modeling Framework) serves many federal Earth system modeling projects. Others exist in both the academic and federal domains and each satisfies design criteria that are determined by the community they serve. While they may use different interface standards or semantic mediation strategies, they share fundamental similarities. The purpose of the Earth System Bridge project is to develop mechanisms for interoperability between modeling frameworks, such as the ability to share a model or service component. This project has three main goals: (1) Develop a Framework Description Language (ES-FDL) that allows modeling frameworks to be described in a standard way so that their differences and similarities can be assessed. (2) Demonstrate that if a model is augmented with a framework-agnostic Basic Model Interface (BMI), then simple, universal adapters can go from BMI to a modeling framework's native component interface. (3) Create semantic mappings between modeling frameworks that support semantic mediation. This third goal involves creating a crosswalk between the CF Standard Names and the CSDMS Standard Names (a set of naming conventions). This talk will summarize progress towards these goals.

A combination of HARMONIE short time direct normal irradiance forecasts and machine learning: The #hashtdim procedure

NASA Astrophysics Data System (ADS)

Gastón, Martín; Fernández-Peruchena, Carlos; Körnich, Heiner; Landelius, Tomas

2017-06-01

The present work describes the first approach of a new procedure to forecast Direct Normal Irradiance (DNI): the #hashtdim that treats to combine ground information and Numerical Weather Predictions. The system is centered in generate predictions for the very short time. It combines the outputs from the Numerical Weather Prediction Model HARMONIE with an adaptive methodology based on Machine Learning. The DNI predictions are generated with 15-minute and hourly temporal resolutions and presents 3-hourly updates. Each update offers forecasts to the next 12 hours, the first nine hours are generated with 15-minute temporal resolution meanwhile the last three hours present hourly temporal resolution. The system is proved over a Spanish emplacement with BSRN operative station in south of Spain (PSA station). The #hashtdim has been implemented in the framework of the Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies (DNICast) project, under the European Union's Seventh Programme for research, technological development and demonstration framework.

Transforming the sensing and numerical prediction of high-impact local weather through dynamic adaptation.

PubMed

Droegemeier, Kelvin K

2009-03-13

Mesoscale weather, such as convective systems, intense local rainfall resulting in flash floods and lake effect snows, frequently is characterized by unpredictable rapid onset and evolution, heterogeneity and spatial and temporal intermittency. Ironically, most of the technologies used to observe the atmosphere, predict its evolution and compute, transmit or store information about it, operate in a static pre-scheduled framework that is fundamentally inconsistent with, and does not accommodate, the dynamic behaviour of mesoscale weather. As a result, today's weather technology is highly constrained and far from optimal when applied to any particular situation. This paper describes a new cyberinfrastructure framework, in which remote and in situ atmospheric sensors, data acquisition and storage systems, assimilation and prediction codes, data mining and visualization engines, and the information technology frameworks within which they operate, can change configuration automatically, in response to evolving weather. Such dynamic adaptation is designed to allow system components to achieve greater overall effectiveness, relative to their static counterparts, for any given situation. The associated service-oriented architecture, known as Linked Environments for Atmospheric Discovery (LEAD), makes advanced meteorological and cyber tools as easy to use as ordering a book on the web. LEAD has been applied in a variety of settings, including experimental forecasting by the US National Weather Service, and allows users to focus much more attention on the problem at hand and less on the nuances of data formats, communication protocols and job execution environments.

Resilient Grid Operational Strategies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pasqualini, Donatella

Extreme weather-related disturbances, such as hurricanes, are a leading cause of grid outages historically. Although physical asset hardening is perhaps the most common way to mitigate the impacts of severe weather, operational strategies may be deployed to limit the extent of societal and economic losses associated with weather-related physical damage.1 The purpose of this study is to examine bulk power-system operational strategies that can be deployed to mitigate the impact of severe weather disruptions caused by hurricanes, thereby increasing grid resilience to maintain continuity of critical infrastructure during extreme weather. To estimate the impacts of resilient grid operational strategies, Losmore » Alamos National Laboratory (LANL) developed a framework for hurricane probabilistic risk analysis (PRA). The probabilistic nature of this framework allows us to estimate the probability distribution of likely impacts, as opposed to the worst-case impacts. The project scope does not include strategies that are not operations related, such as transmission system hardening (e.g., undergrounding, transmission tower reinforcement and substation flood protection) and solutions in the distribution network.« less

Modeling Hydrologic Transport through the Critical Zone: Lessons from Catchment-Scale and Lysimeter Studies

NASA Astrophysics Data System (ADS)

Benettin, P.; Queloz, P.; Bailey, S. W.; McGuire, K. J.; Rinaldo, A.; Botter, G.

2015-12-01

Water age distributions can be used to address a number of environmental challenges, such as modeling the dynamics of river water quality, quantifying the interactions between shallow and deep flow systems and understanding nutrient loading persistence. Moreover, as the travel time of a water particle is the time available for biogeochemical reactions, it can be explicitly used to predict the concentration of non-conservative solutes, as e.g. those derived by mineral weathering. In recent years, many studies acknowledged the dynamic nature of streamflow age and linked it to observed variations in stream water quality. In this new framework, water stored within a catchment can be seen as a pool that is selectively "sampled" by streams and vegetation, determining the chemical composition of discharge and evapotranspiration. We present results from a controlled lysimeter experiment and real-world catchments, where the theoretical framework has been used to reproduce water quality datasets including conservative tracers (e.g. chloride and water stable isotopes) and weathering-derived solutes (like silicon and sodium). The approach proves useful to estimate the catchment water storage involved in solute mixing and sheds light on how solutes and water of different ages are selectively removed by vegetation and soil drainage.

Interoperability challenges in river discharge modelling: A cross domain application scenario

NASA Astrophysics Data System (ADS)

Santoro, Mattia; Andres, Volker; Jirka, Simon; Koike, Toshio; Looser, Ulrich; Nativi, Stefano; Pappenberger, Florian; Schlummer, Manuela; Strauch, Adrian; Utech, Michael; Zsoter, Ervin

2018-06-01

River discharge is a critical water cycle variable, as it integrates all the processes (e.g. runoff and evapotranspiration) occurring within a river basin and provides a hydrological output variable that can be readily measured. Its prediction is of invaluable help for many water-related tasks including water resources assessment and management, flood protection, and disaster mitigation. Observations of river discharge are important to calibrate and validate hydrological or coupled land, atmosphere and ocean models. This requires using datasets from different scientific domains (Water, Weather, etc.). Typically, such datasets are provided using different technological solutions. This complicates the integration of new hydrological data sources into application systems. Therefore, a considerable effort is often spent on data access issues instead of the actual scientific question. This paper describes the work performed to address multidisciplinary interoperability challenges related to river discharge modeling and validation. This includes definition and standardization of domain specific interoperability standards for hydrological data sharing and their support in global frameworks such as the Global Earth Observation System of Systems (GEOSS). The research was developed in the context of the EU FP7-funded project GEOWOW (GEOSS Interoperability for Weather, Ocean and Water), which implemented a "River Discharge" application scenario. This scenario demonstrates the combination of river discharge observations data from the Global Runoff Data Centre (GRDC) database and model outputs produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) predicting river discharge based on weather forecast information in the context of the GEOSS.

Probabilistic forecasting of extreme weather events based on extreme value theory

NASA Astrophysics Data System (ADS)

Van De Vyver, Hans; Van Schaeybroeck, Bert

2016-04-01

Extreme events in weather and climate such as high wind gusts, heavy precipitation or extreme temperatures are commonly associated with high impacts on both environment and society. Forecasting extreme weather events is difficult, and very high-resolution models are needed to describe explicitly extreme weather phenomena. A prediction system for such events should therefore preferably be probabilistic in nature. Probabilistic forecasts and state estimations are nowadays common in the numerical weather prediction community. In this work, we develop a new probabilistic framework based on extreme value theory that aims to provide early warnings up to several days in advance. We consider the combined events when an observation variable Y (for instance wind speed) exceeds a high threshold y and its corresponding deterministic forecasts X also exceeds a high forecast threshold y. More specifically two problems are addressed:} We consider pairs (X,Y) of extreme events where X represents a deterministic forecast, and Y the observation variable (for instance wind speed). More specifically two problems are addressed: Given a high forecast X=x_0, what is the probability that Y>y? In other words: provide inference on the conditional probability: [ Pr{Y>y|X=x_0}. ] Given a probabilistic model for Problem 1, what is the impact on the verification analysis of extreme events. These problems can be solved with bivariate extremes (Coles, 2001), and the verification analysis in (Ferro, 2007). We apply the Ramos and Ledford (2009) parametric model for bivariate tail estimation of the pair (X,Y). The model accommodates different types of extremal dependence and asymmetry within a parsimonious representation. Results are presented using the ensemble reforecast system of the European Centre of Weather Forecasts (Hagedorn, 2008). Coles, S. (2001) An Introduction to Statistical modelling of Extreme Values. Springer-Verlag.Ferro, C.A.T. (2007) A probability model for verifying deterministic forecasts of extreme events. Wea. Forecasting {22}, 1089-1100.Hagedorn, R. (2008) Using the ECMWF reforecast dataset to calibrate EPS forecasts. ECMWF Newsletter, {117}, 8-13.Ramos, A., Ledford, A. (2009) A new class of models for bivariate joint tails. J.R. Statist. Soc. B {71}, 219-241.

Prediction Activities at NASA's Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2010-01-01

The Global Modeling and Assimilation Office (GMAO) is a core NASA resource for the development and use of satellite observations through the integrating tools of models and assimilation systems. Global ocean, atmosphere and land surface models are developed as components of assimilation and forecast systems that are used for addressing the weather and climate research questions identified in NASA's science mission. In fact, the GMAO is actively engaged in addressing one of NASA's science mission s key questions concerning how well transient climate variations can be understood and predicted. At weather time scales the GMAO is developing ultra-high resolution global climate models capable of resolving high impact weather systems such as hurricanes. The ability to resolve the detailed characteristics of weather systems within a global framework greatly facilitates addressing fundamental questions concerning the link between weather and climate variability. At sub-seasonal time scales, the GMAO is engaged in research and development to improve the use of land information (especially soil moisture), and in the improved representation and initialization of various sub-seasonal atmospheric variability (such as the MJO) that evolves on time scales longer than weather and involves exchanges with both the land and ocean The GMAO has a long history of development for advancing the seasonal-to-interannual (S-I) prediction problem using an older version of the coupled atmosphere-ocean general circulation model (AOGCM). This includes the development of an Ensemble Kalman Filter (EnKF) to facilitate the multivariate assimilation of ocean surface altimetry, and an EnKF developed for the highly inhomogeneous nature of the errors in land surface models, as well as the multivariate assimilation needed to take advantage of surface soil moisture and snow observations. The importance of decadal variability, especially that associated with long-term droughts is well recognized by the climate community. An improved understanding of the nature of decadal variability and its predictability has important implications for efforts to assess the impacts of global change in the coming decades. In fact, the GMAO has taken on the challenge of carrying out experimental decadal predictions in support of the IPCC AR5 effort.

Mechanical weathering and rock erosion by climate-dependent subcritical cracking

NASA Astrophysics Data System (ADS)

Eppes, Martha-Cary; Keanini, Russell

2017-06-01

This work constructs a fracture mechanics framework for conceptualizing mechanical rock breakdown and consequent regolith production and erosion on the surface of Earth and other terrestrial bodies. Here our analysis of fracture mechanics literature explicitly establishes for the first time that all mechanical weathering in most rock types likely progresses by climate-dependent subcritical cracking under virtually all Earth surface and near-surface environmental conditions. We substantiate and quantify this finding through development of physically based subcritical cracking and rock erosion models founded in well-vetted fracture mechanics and mechanical weathering, theory, and observation. The models show that subcritical cracking can culminate in significant rock fracture and erosion under commonly experienced environmental stress magnitudes that are significantly lower than rock critical strength. Our calculations also indicate that climate strongly influences subcritical cracking—and thus rock weathering rates—irrespective of the source of the stress (e.g., freezing, thermal cycling, and unloading). The climate dependence of subcritical cracking rates is due to the chemophysical processes acting to break bonds at crack tips experiencing these low stresses. We find that for any stress or combination of stresses lower than a rock's critical strength, linear increases in humidity lead to exponential acceleration of subcritical cracking and associated rock erosion. Our modeling also shows that these rates are sensitive to numerous other environment, rock, and mineral properties that are currently not well characterized. We propose that confining pressure from overlying soil or rock may serve to suppress subcritical cracking in near-surface environments. These results are applicable to all weathering processes.

The Separate Physics and Dynamics Experiment (SPADE) framework for determining resolution awareness: A case study of microphysics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gustafson, William I.; Ma, Po-Lun; Xiao, Heng

2013-08-29

The ability to use multi-resolution dynamical cores for weather and climate modeling is pushing the atmospheric community towards developing scale aware or, more specifically, resolution aware parameterizations that will function properly across a range of grid spacings. Determining the resolution dependence of specific model parameterizations is difficult due to strong resolution dependencies in many pieces of the model. This study presents the Separate Physics and Dynamics Experiment (SPADE) framework that can be used to isolate the resolution dependent behavior of specific parameterizations without conflating resolution dependencies from other portions of the model. To demonstrate the SPADE framework, the resolution dependencemore » of the Morrison microphysics from the Weather Research and Forecasting model and the Morrison-Gettelman microphysics from the Community Atmosphere Model are compared for grid spacings spanning the cloud modeling gray zone. It is shown that the Morrison scheme has stronger resolution dependence than Morrison-Gettelman, and that the ability of Morrison-Gettelman to use partial cloud fractions is not the primary reason for this difference. This study also discusses how to frame the issue of resolution dependence, the meaning of which has often been assumed, but not clearly expressed in the atmospheric modeling community. It is proposed that parameterization resolution dependence can be expressed in terms of "resolution dependence of the first type," RA1, which implies that the parameterization behavior converges towards observations with increasing resolution, or as "resolution dependence of the second type," RA2, which requires that the parameterization reproduces the same behavior across a range of grid spacings when compared at a given coarser resolution. RA2 behavior is considered the ideal, but brings with it serious implications due to limitations of parameterizations to accurately estimate reality with coarse grid spacing. The type of resolution awareness developers should target in their development depends upon the particular modeler’s application.« less

Economic Impact of Fire Weather Forecasts

Treesearch

Don Gunasekera; Graham Mills; Mark Williams

2006-01-01

Southeastern Australia, where the State of Victoria is located is regarded as one of the most fire prone areas in the world. The Australian Bureau of Meteorology provides fire weather services in Victoria as part of a national framework for the provision of such services. These services range from fire weather warnings to special forecasts for hazard reduction burns....

Weather Effects on Crop Diseases in Eastern Germany

NASA Astrophysics Data System (ADS)

Conradt, Tobias

2017-04-01

Since the 1970s there are several long-term monitoring programmes for plant diseases and pests in Germany. Within the framework of a national research project, some otherwise confidential databases comprising 77 111 samples from numerous sites accross Eastern Germany could be accessed and analysed. The pest data covered leaf rust (Puccinia triticina) and powdery mildew (Blumeria graminis) in winter wheat, aphids (Aphididae, four genera) on wheat and other cereal crops, late blight (Phytophthora infestans) in potatoes, and pollen beetles (Brassicogethes aeneus) on rape. These data were complemented by daily weather observations from the German Weather Service (DWD). In a first step, Pearson correlations between weather variables and pest frequencies were calculated for seasonal time periods of different start months and durations and ordered into so-called correlograms. This revealed principal weather effects on disease spread - e. g. that wind is favourable for mildew throughout the year or that rape pollen beetles like it warm, but not during wintertime. Secondly, the pest frequency samples were found to resemble gamma distributions, and a generalised linear model was fitted to describe their parameter shift depending on end-of-winter temperatures for aphids on cereals. The method clearly shows potential for systematic pest risk assessments regarding climate change.

The acid and alkalinity budgets of weathering in the Andes-Amazon system: Insights into the erosional control of global biogeochemical cycles

NASA Astrophysics Data System (ADS)

Torres, Mark A.; West, A. Joshua; Clark, Kathryn E.; Paris, Guillaume; Bouchez, Julien; Ponton, Camilo; Feakins, Sarah J.; Galy, Valier; Adkins, Jess F.

2016-09-01

The correlation between chemical weathering fluxes and denudation rates suggests that tectonic activity can force variations in atmospheric pCO2 by modulating weathering fluxes. However, the effect of weathering on pCO2 is not solely determined by the total mass flux. Instead, the effect of weathering on pCO2 also depends upon the balance between 1) alkalinity generation by carbonate and silicate mineral dissolution and 2) sulfuric acid generation by the oxidation of sulfide minerals. In this study, we explore how the balance between acid and alkalinity generation varies with tectonic uplift to better understand the links between tectonics and the long-term carbon cycle. To trace weathering reactions across the transition from the Peruvian Andes to the Amazonian foreland basin, we measured a suite of elemental concentrations (Na, K, Ca, Mg, Sr, Si, Li, SO4, and Cl) and isotopic ratios (87Sr/86Sr and δ34S) on both dissolved and solid phase samples. Using an inverse model, we quantitatively link systematic changes in solute geochemistry with elevation to downstream declines in sulfuric acid weathering as well as the proportion of cations sourced from silicates. With a new carbonate-system framework, we show that weathering in the Andes Mountains is a CO2 source whereas foreland weathering is a CO2 sink. These results are consistent with the theoretical expectation that the ratio of sulfide oxidation to silicate weathering increases with increasing erosion. Altogether, our results suggest that the effect of tectonically-enhanced weathering on atmospheric pCO2 is strongly modulated by sulfide mineral oxidation.

Weather forecast performances for complex orographic areas: Impact of different grid resolutions and of geographic data on heavy rainfall event simulations in Sicily

NASA Astrophysics Data System (ADS)

Caccamo, M. T.; Castorina, G.; Colombo, F.; Insinga, V.; Maiorana, E.; Magazù, S.

2017-12-01

Over the past decades, Sicily has undergone an increasing sequence of extreme weather events that have produced, besides huge damages to both environment and territory, the death of hundreds of people together with the evacuation of thousands of residents, which have permanently lost their properties. In this framework, with this paper we have investigated the impact of different grid spacing and geographic data on the performance of forecasts over complex orographic areas. In order to test the validity of this approach we have analyzed and discussed, as case study, the heavy rainfall occurred in Sicily during the night of October 10, 2015. In just 9 h, a Mediterranean depression, centered on the Tunisian coastline, produced a violent mesoscale storm localized on the Peloritani Mountains with a maximum rain accumulation of about 200 mm. The results of these simulations were obtained using the Weather Research and Forecasting (WRF-ARW) Model, version 3.7.1, at different grid spacing values and the Two Way Nesting procedure with a sub-domain centered on the area of interest. The results highlighted that providing correct and timely forecasts of extreme weather events is a challenge that could have been efficiently and effectively countered using proper employment of high spatial resolution models.

Fixed points, stable manifolds, weather regimes, and their predictability

DOE PAGES

Deremble, Bruno; D'Andrea, Fabio; Ghil, Michael

2009-10-27

In a simple, one-layer atmospheric model, we study the links between low-frequency variability and the model’s fixed points in phase space. The model dynamics is characterized by the coexistence of multiple ''weather regimes.'' To investigate the transitions from one regime to another, we focus on the identification of stable manifolds associated with fixed points. We show that these manifolds act as separatrices between regimes. We track each manifold by making use of two local predictability measures arising from the meteorological applications of nonlinear dynamics, namely, ''bred vectors'' and singular vectors. These results are then verified in the framework of ensemblemore » forecasts issued from clouds (ensembles) of initial states. The divergence of the trajectories allows us to establish the connections between zones of low predictability, the geometry of the stable manifolds, and transitions between regimes.« less

Expansion of the Real-Time SPoRT-Land Information System for NOAA/National Weather Service Situational Awareness and Local Modeling Applications

NASA Technical Reports Server (NTRS)

Case, Jonathan L; White, Kristopher D.

2014-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center in Huntsville, AL is running a real-time configuration of the Noah land surface model (LSM) within the NASA Land Information System (LIS) framework (hereafter referred to as the "SPoRT-LIS"). Output from the real-time SPoRT-LIS is used for (1) initializing land surface variables for local modeling applications, and (2) displaying in decision support systems for situational awareness and drought monitoring at select NOAA/National Weather Service (NWS) partner offices. The experimental CONUS run incorporates hourly quantitative precipitation estimation (QPE) from the National Severe Storms Laboratory Multi- Radar Multi-Sensor (MRMS) which will be transitioned into operations at the National Centers for Environmental Prediction (NCEP) in Fall 2014.This paper describes the current and experimental SPoRT-LIS configurations, and documents some of the limitations still remaining through the advent of MRMS precipitation analyses in the SPoRT-LIS land surface model (LSM) simulations.

Using Space Weather for Enhanced, Extreme Terrestrial Weather Predictions.

NASA Astrophysics Data System (ADS)

McKenna, M. H.; Lee, T. A., III

2017-12-01

Considering the complexities of the Sun-Earth system, the impacts of space weather to weather here on Earth are not fully understood. This study attempts to analyze this interrelationship by providing a theoretical framework for studying the varied modalities of solar inclination and explores the extent to which they contribute, both in formation and intensity, to extreme terrestrial weather. Using basic topologic and ontology engineering concepts (TOEC), the transdisciplinary syntaxes of space physics, geophysics, and meteorology are analyzed as a seamless interrelated system. This paper reports this investigation's initial findings and examines the validity of the question "Does space weather contribute to extreme weather on Earth, and if so, to what degree?"

Implementation and Evaluation of Weather Responsive Traffic Estimation and Prediction System

DOT National Transportation Integrated Search

2012-06-01

The objective of the project is to develop a framework and procedures for implementing and evaluating weather-responsive traffic management (WRTM) strategies using Traffic Estimation and Prediction System (TrEPS) methodologies. In a previous FHWA-fun...

Development of extended WRF variational data assimilation system (WRFDA) for WRF non-hydrostatic mesoscale model

NASA Astrophysics Data System (ADS)

Pattanayak, Sujata; Mohanty, U. C.

2018-06-01

The paper intends to present the development of the extended weather research forecasting data assimilation (WRFDA) system in the framework of the non-hydrostatic mesoscale model core of weather research forecasting system (WRF-NMM), as an imperative aspect of numerical modeling studies. Though originally the WRFDA provides improved initial conditions for advanced research WRF, we have successfully developed a unified WRFDA utility that can be used by the WRF-NMM core, as well. After critical evaluation, it has been strategized to develop a code to merge WRFDA framework and WRF-NMM output. In this paper, we have provided a few selected implementations and initial results through single observation test, and background error statistics like eigenvalues, eigenvector and length scale among others, which showcase the successful development of extended WRFDA code for WRF-NMM model. Furthermore, the extended WRFDA system is applied for the forecast of three severe cyclonic storms: Nargis (27 April-3 May 2008), Aila (23-26 May 2009) and Jal (4-8 November 2010) formed over the Bay of Bengal. Model results are compared and contrasted within the analysis fields and later on with high-resolution model forecasts. The mean initial position error is reduced by 33% with WRFDA as compared to GFS analysis. The vector displacement errors in track forecast are reduced by 33, 31, 30 and 20% to 24, 48, 72 and 96 hr forecasts respectively, in data assimilation experiments as compared to control run. The model diagnostics indicates successful implementation of WRFDA within the WRF-NMM system.

AWIPS II+: An Open-Source SOA Solution Enabling Environmental Remote Sensing Integration, Analysis, and Decision Support

NASA Astrophysics Data System (ADS)

Ardanuy, P. E.; Hood, C. A.; Moran, S. G.; Ritchie, A. A.; Tarro, A. M.; Nappi, A. J.

2008-12-01

Our shared future demands a renewed focus on sound environment stewardship-on the GEOSS socioeconomic imperatives, as well as the interdisciplinary relationships interconnecting our environment, climate, ecosystems, energy, carbon, water-and national security. Data volumes are now measured in the many petabytes. An increasingly urgent and accelerated tempo of changing requirements and responsive solutions demands data exploitation, and transparent, seamless, effortless, bidirectional, and interdisciplinary interoperability across models and observations. There is today a robust working paradigm established with the Advanced Weather Interactive Processing System (AWIPS)-NOAA/NWS's information integration and fusion capability. This process model extends vertically, and seamlessly, from environmental sensing through the direct delivery of societal benefit. NWS, via AWIPS, is the primary source of weather forecast and warning information in the nation. AWIPS is the tested and proven "the nerve center of operations" at all 122 NWS Weather Forecast Offices and 13 River Forecast Centers. Raytheon, in partnership with NOAA, has now evolved AWIPS into an open-source 2nd generation capability to satisfy climate, ecosystems, weather, and water mission goals. Just as AWIPS II supports NOAA decision- making, it is at the same time a platform funded by Raytheon IRAD and Government investment that can be cost-effectively leveraged across all of the GEOSS and IEOS societal benefit areas. The core principles in the AWIPS II evolution to a service-oriented architecture (SOA) were to minimize coupling, increase cohesion, minimize size of code base, maximize simplicity, and incorporate a pull-style data flow. We focused on "ilities" to drive the new AWIPS architecture-our shared architecture framework vision included six elements: - Create a new, low-cost framework for hosting a full range of environmental services, including thick-client visualization via virtual Earth's and GIS - Scale down framework to a small laptop and through workstations to clusters of enterprise servers without software change - "Plug-n-play"- plug-ins can be hot deployable, or system cycled to pick up new plug-ins - Base the framework on highly reusable design patterns that maximize reuse and have datatype independence and fast adaptability - Open Source leveraged to maximize reuse - "Gaming-style" interaction with the data This talk addresses the challenges that we meet to realize benefits in applications that couple environmental data from many disparate remote sensing and ancillary sources and disciplines. By leveraging the existing AWIPS II weather, water, ecosystems, and climate functionality and these six elements, along with well- thought-out displays with the end user's specific needs in mind, we demonstrate an easily adapted, extremely powerful, open-source remote sensing software tool that will help non-geospatial-experts make better use of these remote sensing resources to enhance environmental mapping and analysis and help guide environmental decision making at the national, regional, local and citizen levels.

Aggregation of Environmental Model Data for Decision Support

NASA Astrophysics Data System (ADS)

Alpert, J. C.

2013-12-01

Weather forecasts and warnings must be prepared and then delivered so as to reach their intended audience in good time to enable effective decision-making. An effort to mitigate these difficulties was studied at a Workshop, 'Sustaining National Meteorological Services - Strengthening WMO Regional and Global Centers' convened, June , 2013, by the World Bank, WMO and the US National Weather Service (NWS). The skill and accuracy of atmospheric forecasts from deterministic models have increased and there are now ensembles of such models that improve decisions to protect life, property and commerce. The NWS production of numerical weather prediction products result in model output from global and high resolution regional ensemble forecasts. Ensembles are constructed by changing the initial conditions to make a 'cloud' of forecasts that attempt to span the space of possible atmospheric realizations which can quantify not only the most likely forecast, but also the uncertainty. This has led to an unprecedented increase in data production and information content from higher resolution, multi-model output and secondary calculations. One difficulty is to obtain the needed subset of data required to estimate the probability of events, and report the information. The calibration required to reliably estimate the probability of events, and honing of threshold adjustments to reduce false alarms for decision makers is also needed. To meet the future needs of the ever-broadening user community and address these issues on a national and international basis, the weather service implemented the NOAA Operational Model Archive and Distribution System (NOMADS). NOMADS provides real-time and retrospective format independent access to climate, ocean and weather model data and delivers high availability content services as part of NOAA's official real time data dissemination at its new NCWCP web operations center. An important aspect of the server's abilities is to aggregate the matrix of model output offering access to probability and calibrating information for real time decision making. The aggregation content server reports over ensemble component and forecast time in addition to the other data dimensions of vertical layer and position for each variable. The unpacking, organization and reading of many binary packed files is accomplished most efficiently on the server while weather element event probability calculations, the thresholds for more accurate decision support, or display remain for the client. Our goal is to reduce uncertainty for variables of interest, e.g, agricultural importance. The weather service operational GFS model ensemble and short range ensemble forecasts can make skillful probability forecasts to alert users if and when their selected weather events will occur. A description of how this framework operates and how it can be implemented using existing NOMADS content services and applications is described.

Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data in mountainous terrain

NASA Astrophysics Data System (ADS)

Havens, S.; Marks, D. G.; Kormos, P.; Hedrick, A. R.; Johnson, M.; Robertson, M.; Sandusky, M.

2017-12-01

In the Western US, operational water supply managers rely on statistical techniques to forecast the volume of water left to enter the reservoirs. As the climate changes and the demand increases for stored water utilized for irrigation, flood control, power generation, and ecosystem services, water managers have begun to move from statistical techniques towards using physically based models. To assist with the transition, a new open source framework was developed, the Spatial Modeling for Resources Framework (SMRF), to automate and simplify the most common forcing data distribution methods. SMRF is computationally efficient and can be implemented for both research and operational applications. Currently, SMRF is able to generate all of the forcing data required to run physically based snow or hydrologic models at 50-100 m resolution over regions of 500-10,000 km2, and has been successfully applied in real time and historical applications for the Boise River Basin in Idaho, USA, the Tuolumne River Basin and San Joaquin in California, USA, and Reynolds Creek Experimental Watershed in Idaho, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input data. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of physics-based snow and hydrologic models possible.

A framework to simulate small shallow inland water bodies in semi-arid regions

NASA Astrophysics Data System (ADS)

Abbasi, Ali; Ohene Annor, Frank; van de Giesen, Nick

2017-12-01

In this study, a framework for simulating the flow field and heat transfer processes in small shallow inland water bodies has been developed. As the dynamics and thermal structure of these water bodies are crucial in studying the quality of stored water , and in assessing the heat fluxes from their surfaces as well, the heat transfer and temperature simulations were modeled. The proposed model is able to simulate the full 3-D water flow and heat transfer in the water body by applying complex and time varying boundary conditions. In this model, the continuity, momentum and temperature equations together with the turbulence equations, which comprise the buoyancy effect, have been solved. This model is built on the Reynolds Averaged Navier Stokes (RANS) equations with the widely used Boussinesq approach to solve the turbulence issues of the flow field. Micrometeorological data were obtained from an Automatic Weather Station (AWS) installed on the site and combined with field bathymetric measurements for the model. In the framework developed, a simple, applicable and generalizable approach is proposed for preparing the geometry of small shallow water bodies using coarsely measured bathymetry. All parts of the framework are based on open-source tools, which is essential for developing countries.

How Reliable Is the Prediction of Solar Wind Background?

NASA Astrophysics Data System (ADS)

Jian, Lan K.; MacNeice, Peter; Taktakishvili, Aleksandre; Odstrcil, Dusan; Jackson, Bernard; Yu, Hsiu-Shan; Riley, Pete; Sokolov, Igor

2015-04-01

The prediction of solar wind background is a necessary part of space weather forecasting. Multiple coronal and heliospheric models have been installed at the Community Coordinated Modeling Center (CCMC) to produce the solar wind, including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and heliospheric tomography using interplanetary scintillation (IPS) data. By comparing the modeling results with the OMNI data over 7 Carrington rotations in 2007, we have conducted a third-party validation of these models for the near-Earth solar wind. This work will help the models get ready for the transition from research to operation. Besides visual comparison, we have quantitatively assessed the models’ capabilities in reproducing the time series and statistics of solar wind parameters. Using improved algorithms, we have identified magnetic field sector boundaries (SBs) and slow-to-fast stream interaction regions (SIRs) as focused structures. The success rate of capturing them and the time offset vary largely with models. For this period, the 2014 version of MAS-Enlil model works best for SBs, and the heliospheric tomography works best for SIRs. General strengths and weaknesses for each model are identified to provide an unbiased reference to model developers and users.

Approach to Integrate Global-Sun Models of Magnetic Flux Emergence and Transport for Space Weather Studies

NASA Technical Reports Server (NTRS)

Mansour, Nagi N.; Wray, Alan A.; Mehrotra, Piyush; Henney, Carl; Arge, Nick; Godinez, H.; Manchester, Ward; Koller, J.; Kosovichev, A.; Scherrer, P.;

Peak Stress Testing Protocol Framework

EPA Science Inventory

Treatment of peak flows during wet weather is a common challenge across the country for municipal wastewater utilities with separate and/or combined sewer systems. Increases in wastewater flow resulting from infiltration and inflow (I/I) during wet weather events can result in op...

A framework to monitor activities of satellite data processing in real-time

NASA Astrophysics Data System (ADS)

Nguyen, M. D.; Kryukov, A. P.

2018-01-01

Space Monitoring Data Center (SMDC) of SINP MSU is one of the several centers in the world that collects data on the radiational conditions in near-Earth orbit from various Russian (Lomonosov, Electro-L1, Electro-L2, Meteor-M1, Meteor-M2, etc.) and foreign (GOES 13, GOES 15, ACE, SDO, etc.) satellites. The primary purposes of SMDC are: aggregating heterogeneous data from different sources; providing a unified interface for data retrieval, visualization, analysis, as well as development and testing new space weather models; and controlling the correctness and completeness of data. Space weather models rely on data provided by SMDC to produce forecasts. Therefore, monitoring the whole data processing cycle is crucial for further success in the modeling of physical processes in near-Earth orbit based on the collected data. To solve the problem described above, we have developed a framework called Live Monitor at SMDC. Live Monitor allows watching all stages and program components involved in each data processing cycle. All activities of each stage are logged by Live Monitor and shown in real-time on a web interface. When an error occurs, a notification message will be sent to satellite operators via email and the Telegram messenger service so that they could take measures in time. The Live Monitor’s API can be used to create a customized monitoring service with minimum coding.

Comprehensive Assessment of Models and Events based on Library tools (CAMEL)

NASA Astrophysics Data System (ADS)

Rastaetter, L.; Boblitt, J. M.; DeZeeuw, D.; Mays, M. L.; Kuznetsova, M. M.; Wiegand, C.

2017-12-01

At the Community Coordinated Modeling Center (CCMC), the assessment of modeling skill using a library of model-data comparison metrics is taken to the next level by fully integrating the ability to request a series of runs with the same model parameters for a list of events. The CAMEL framework initiates and runs a series of selected, pre-defined simulation settings for participating models (e.g., WSA-ENLIL, SWMF-SC+IH for the heliosphere, SWMF-GM, OpenGGCM, LFM, GUMICS for the magnetosphere) and performs post-processing using existing tools for a host of different output parameters. The framework compares the resulting time series data with respective observational data and computes a suite of metrics such as Prediction Efficiency, Root Mean Square Error, Probability of Detection, Probability of False Detection, Heidke Skill Score for each model-data pair. The system then plots scores by event and aggregated over all events for all participating models and run settings. We are building on past experiences with model-data comparisons of magnetosphere and ionosphere model outputs in GEM2008, GEM-CEDAR CETI2010 and Operational Space Weather Model challenges (2010-2013). We can apply the framework also to solar-heliosphere as well as radiation belt models. The CAMEL framework takes advantage of model simulations described with Space Physics Archive Search and Extract (SPASE) metadata and a database backend design developed for a next-generation Run-on-Request system at the CCMC.

Performance of a coupled lagged ensemble weather and river runoff prediction model system for the Alpine Ammer River catchment

NASA Astrophysics Data System (ADS)

Smiatek, G.; Kunstmann, H.; Werhahn, J.

2012-04-01

The Ammer River catchment located in the Bavarian Ammergau Alps and alpine forelands, Germany, represents with elevations reaching 2185 m and annual mean precipitation between1100 and 2000 mm a very demanding test ground for a river runoff prediction system. Large flooding events in 1999 and 2005 motivated the development of a physically based prediction tool in this area. Such a tool is the coupled high resolution numerical weather and river runoff forecasting system AM-POE that is being studied in several configurations in various experiments starting from the year 2005. Corner stones of the coupled system are the hydrological water balance model WaSiM-ETH run at 100 m grid resolution, the numerical weather prediction model (NWP) MM5 driven at 3.5 km grid cell resolution and the Perl Object Environment (POE) framework. POE implements the input data download from various sources, the input data provision via SOAP based WEB services as well as the runs of the hydrology model both with observed and with NWP predicted meteorology input. The one way coupled system utilizes a lagged ensemble prediction system (EPS) taking into account combination of recent and previous NWP forecasts. Results obtained in the years 2005-2011 reveal that river runoff simulations depict high correlation with observed runoff when driven with monitored observations in hindcast experiments. The ability to runoff forecasts is depending on lead times in the lagged ensemble prediction and shows still limitations resulting from errors in timing and total amount of the predicted precipitation in the complex mountainous area. The presentation describes the system implementation, and demonstrates the application of the POE framework in networking, distributed computing and in the setup of various experiments as well as long term results of the system application in the years 2005 - 2011.

Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach

PubMed Central

Kneifel, Joshua; Webb, David

2016-01-01

Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid climate zone, and compares these estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the experimental data collected from the NZERTF. PMID:27956756

Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach.

PubMed

Kneifel, Joshua; Webb, David

2016-09-01

Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid climate zone, and compares these estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the experimental data collected from the NZERTF.

New efficient optimizing techniques for Kalman filters and numerical weather prediction models

NASA Astrophysics Data System (ADS)

Famelis, Ioannis; Galanis, George; Liakatas, Aristotelis

2016-06-01

The need for accurate local environmental predictions and simulations beyond the classical meteorological forecasts are increasing the last years due to the great number of applications that are directly or not affected: renewable energy resource assessment, natural hazards early warning systems, global warming and questions on the climate change can be listed among them. Within this framework the utilization of numerical weather and wave prediction systems in conjunction with advanced statistical techniques that support the elimination of the model bias and the reduction of the error variability may successfully address the above issues. In the present work, new optimization methods are studied and tested in selected areas of Greece where the use of renewable energy sources is of critical. The added value of the proposed work is due to the solid mathematical background adopted making use of Information Geometry and Statistical techniques, new versions of Kalman filters and state of the art numerical analysis tools.

Real-time SWMF-Geospace at CCMC: assessing the quality of output from continuous operational simulations

NASA Astrophysics Data System (ADS)

Liemohn, M. W.; Welling, D. T.; De Zeeuw, D.; Kuznetsova, M. M.; Rastaetter, L.; Ganushkina, N. Y.; Ilie, R.; Toth, G.; Gombosi, T. I.; van der Holst, B.

2016-12-01

The ground-based magnetometer index Dst is a decent measure of the near-Earth current systems, in particular those in the storm-time inner magnetosphere. The ability of a large-scale, physics-based model to reproduce, or even predict, this index is therefore a tangible measure of the overall validity of the code for space weather research and space weather operational usage. Experimental real-time simulations of the Space Weather Modeling Framework (SWMF) are conducted at the Community Coordinated Modeling Center (CCMC), with results available there (http://ccmc.gsfc.nasa.gov/realtime.php), through the CCMC Integrated Space Weather Analysis (iSWA) site (http://iswa.ccmc.gsfc.nasa.gov/IswaSystemWebApp/), and the Michigan SWMF site (http://csem.engin.umich.edu/realtime). Presently, two configurations of the SWMF are running in real time at CCMC, both focusing on the geospace modules, using the BATS-R-US magnetohydrodynamic model, the Ridley Ionosphere Model, and with and without the Rice Convection Model for inner magnetospheric drift physics. While both have been running for several years, nearly continuous results are available since July 2015. Dst from the model output is compared against the Kyoto real-time Dst. Various quantitative measures are presented to assess the goodness of fit between the models and observations. In particular, correlation coefficients, RMSE and prediction efficiency are calculated and discussed. In addition, contingency tables are presented, demonstrating the ability of the model to predict "disturbed times" as defined by Dst values below some critical threshold. It is shown that the SWMF run with the inner magnetosphere model is significantly better at reproducing storm-time values, with prediction efficiencies above 0.25 and Heidke skill scores above 0.5. This work was funded by NASA and NSF grants, and the European Union's Horizon 2020 research and innovation programme under grant agreement 637302 PROGRESS.

Validation of BlueSky Smoke Prediction System using surface and satellite observations during major wildland fire events in Northern California

Treesearch

Lesley Fusina; Sharon Zhong; Julide Koracin; Tim Brown; Annie Esperanza; Leland Tarney; Haiganoush Preisler

2007-01-01

The BlueSky Smoke Prediction System developed by the U.S. Department of Agriculture, Forest Service, AirFire Team under the National Fire Plan is a modeling framework that integrates tools, knowledge of fuels, moisture, combustion, emissions, plume dynamics, and weather to produce real-time predictions of the cumulative impacts of smoke from wildfires, prescribed fires...

Scaling an urban emergency evacuation framework : challenges and practices.

DOT National Transportation Integrated Search

2014-01-01

Critical infrastructure disruption, caused by severe weather events, natural disasters, terrorist : attacks, etc., has significant impacts on urban transportation systems. We built a computational : framework to simulate urban transportation systems ...

Changing risks of resonance in extreme weather events for higher atmospheric greenhouse gas concentrations

NASA Astrophysics Data System (ADS)

Huntingford, Chris; Mitchell, Dann; Osprey, Scott

2015-04-01

A recent paper by Petoukhov et al (2013) demonstrates that many of the recent major extreme events in the NH may have been caused by resonant conditions driving very high meridional winds around slowly moving centres-of-action. Besides high amplitudes of planetary wave numbers 6,7 and 8, additional features are identified through 4 further conditions that trigger system resonance. These make the potential for high amplitude waves more likely as well as the possibility of more persistent events. A concern is that human-induced climate change could create conditions more conducive to tropospheric Rossby wave resonance, thereby forcing any periods of extreme weather to become more commonplace and longer lasting. Whilst the CMIP5 ensemble provides much information on expected changes, to fully address changing probabilities of extreme event occurrence - which by definition are relatively rare - is, though, best approached through a massive ensemble modeling framework. The climateprediction-dot-net citizen-science massive ensemble GCM modeling framework provides order 104 simulations for sea-surface temperature, sea-ice extent and atmospheric gas composition representative of both pre-industrial and contemporary conditions. Here we present what these families of simulations imply in terms of the changing likelihood of conditions for mid-latitude resonance, and implications for amplitudes of Rossby waves

Investigating atmospheric transport processes of trace gases with ICON-ART on different scales

NASA Astrophysics Data System (ADS)

Schröter, Jennifer; Ruhnke, Roland; Rieger, Daniel; Vogel, Heike; Vogel, Bernhard

2016-04-01

We have extended the global ICON [1] (ICOsahedral Nonhydrostatic) modelling framework by introducing ICON-ART [2]. ICON is jointly developed by the German Weather Service (DWD) and Max-Planck-Institute for Meteorology (MPI-M), and is used for numerical weather prediction as well as for future climate predictions. ICON-ART is developed at the KIT with the goal to simulate interactions between trace substances and the state of the atmosphere. For the dynamics (transport and diffusion) of gaseous tracers, the original ICON tracer framework is used. A process splitting approach separates the physical processes. In this study, we present results of the ICON-ART extension, including the full gas-phase chemistry module. This module uses the kpp formalism [3] to generate chemistry modules and the photolysis module is based on Cloud-J7.3 [4]. Photolysis rates are calculated online based on the meteorological state of the atmosphere, as well as on the actual ozone profile and cloud optical parameters. Two simulations are performed with ICON-ART. The first one with physics parameterisations for the numerical weather prediction (NWP) and the second one with that for climate simulation in order to investigate the dynamical influence on the distribution of long-lived as well as of short-lived species by comparing both simulations. The results are evaluated with other model results and with observation. In addition to that, we use aircraft campaign data to validate the results on the regional scale for short term simulations by using the NWP physics. [1] Zängl, G., Reinert, D., Ripodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamicalcore, Q. J. Roy. Meteor. Soc,141, 563-579, doi:10.1002/qj.2378, 2015 [2] Rieger, D., Bangert, M., Bischoff-Gauss, I., Förstner, J., Lundgren, K., Reinert, D., Schröter, J., Vogel, H., Zängl, G., Ruhnke, R., and Vogel, B.: ICON-ART 1.0 - a new online-coupled model system from the global to regional scale, Geosci. Model Dev., 8, 1659-1676, doi:10.5194/gmd-8-1659-2015, 2015 [3] Sandu, A. and Sander, R.: Technical note: Simulating chemical systems in Fortran90 and Matlab with the Kinetic PreProcessor KPP-2.1, Atmos. Chem. Phys., 6, 187-195, doi:10.5194/acp-6-187-2006, 2006 [4] Prather, M. J.: Photolysis rates in correlated overlapping cloud fields: Cloud-J 7.3c, Geosci. Model Dev., 8, 2587-2595, doi:10.5194/gmd-8-2587-2015, 2015

The Impact of Climate Projection Method on the Analysis of Climate Change in Semi-arid Basins

NASA Astrophysics Data System (ADS)

Halper, E.; Shamir, E.

2016-12-01

In small basins with arid climates, rainfall characteristics are highly variable and stream flow is tightly coupled with the nuances of rainfall events (e.g. hourly precipitation patterns Climate change assessments in these basins typically employ CMIP5 projections downscaled with Bias Corrected Statistical Downscaling and Bias Correction/Constructed Analogs (BCSD-BCCA) methods, but these products have drawbacks. Specifically, BCSD-BCCA these projections do not explicitly account for localized physical precipitation mechanisms (e.g. monsoon and snowfall) that are essential to many hydrological systems in the U. S. Southwest. An investigation of the impact of different types of precipitation projections for two kinds of hydrologic studies is being conducted under the U.S. Bureau of Reclamation's Science and Technology Grant Program. An innovative modeling framework consisting of a weather generator of likely hourly precipitation scenarios, coupled with rainfall-runoff, river routing and groundwater models, has been developed in the Nogales, Arizona area. This framework can simulate the impact of future climate on municipal water operations. This framework allows the rigorous comparison of the BCSD-BCCA methods with alternative approaches including rainfall output from dynamical downscaled Regional Climate Models (RCM), a stochastic rainfall generator forced by either Global Climate Models (GCM) or RCM, and projections using historical records conditioned on either GCM or RCM. The results will provide guide for the use of climate change projections into hydrologic studies of semi-arid areas. The project extends this comparison to analyses of flood control. Large flows on the Bill Williams River are a concern for the operation of dams along the Lower Colorado River. After adapting the weather generator for this region, we will evaluate the model performance for rainfall and stream flow, with emphasis on statistical features important to the specific needs of flood management. The end product of the research is to develop a test to guide selection of a precipitation projection method (including downscaling procedure) for a given region and objective.

Modeling and Observational Framework for Diagnosing Local Land-Atmosphere Coupling on Diurnal Time Scales

NASA Technical Reports Server (NTRS)

Santanello, Joseph A., Jr.; Peters-Lidard, Christa D.; Kumar, Sujay V.; Alonge, Charles; Tao, Wei-Kuo

2009-01-01

Land-atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed due to the complex interactions and feedbacks present across a range of scales. Further, uncoupled systems or experiments (e.g., the Project for Intercomparison of Land Parameterization Schemes, PILPS) may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land-atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U. S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The co-evolution of these variables and the convective PBL is sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land-atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Large CO 2 effluxes at night and during synoptic weather events significantly contribute to CO 2 emissions from a reservoir

DOE PAGES

Liu, Heping; Zhang, Qianyu; Katul, Gabriel G.; ...

2016-05-24

CO 2 emissions from inland waters are commonly determined by indirect methods that are based on the product of a gas transfer coefficient and the concentration gradient at the air water interface (e.g., wind-based gas transfer models). The measurements of concentration gradient are typically collected during the day in fair weather throughout the course of a year. Direct measurements of eddy covariance CO 2 fluxes from a large inland water body (Ross Barnett reservoir, Mississippi, USA) show that CO 2 effluxes at night are approximately 70% greater than those during the day. At longer time scales, frequent synoptic weather eventsmore » associated with extratropical cyclones induce CO 2 flux pulses, resulting in further increase in annual CO 2 effluxes by 16%. Therefore, CO 2 emission rates from this reservoir, if these diel and synoptic processes are under-sampled, are likely to be underestimated by approximately 40%. Our results also indicate that the CO 2 emission rates from global inland waters reported in the literature, when based on indirect methods, are likely underestimated. Field samplings and indirect modeling frameworks that estimate CO 2 emissions should account for both daytime-nighttime efflux difference and enhanced emissions during synoptic weather events. Furthermore, the analysis here can guide carbon emission sampling to improve regional carbon estimates.« less

Large CO 2 effluxes at night and during synoptic weather events significantly contribute to CO 2 emissions from a reservoir

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Heping; Zhang, Qianyu; Katul, Gabriel G.

CO 2 emissions from inland waters are commonly determined by indirect methods that are based on the product of a gas transfer coefficient and the concentration gradient at the air water interface (e.g., wind-based gas transfer models). The measurements of concentration gradient are typically collected during the day in fair weather throughout the course of a year. Direct measurements of eddy covariance CO 2 fluxes from a large inland water body (Ross Barnett reservoir, Mississippi, USA) show that CO 2 effluxes at night are approximately 70% greater than those during the day. At longer time scales, frequent synoptic weather eventsmore » associated with extratropical cyclones induce CO 2 flux pulses, resulting in further increase in annual CO 2 effluxes by 16%. Therefore, CO 2 emission rates from this reservoir, if these diel and synoptic processes are under-sampled, are likely to be underestimated by approximately 40%. Our results also indicate that the CO 2 emission rates from global inland waters reported in the literature, when based on indirect methods, are likely underestimated. Field samplings and indirect modeling frameworks that estimate CO 2 emissions should account for both daytime-nighttime efflux difference and enhanced emissions during synoptic weather events. Furthermore, the analysis here can guide carbon emission sampling to improve regional carbon estimates.« less

Online coupled regional meteorology-chemistry models in Europe: current status and prospects

NASA Astrophysics Data System (ADS)

Baklanov, A.; Schluenzen, K. H.; Suppan, P.; Baldasano, J.; Brunner, D.; Aksoyoglu, S.; Carmichael, G.; Douros, J.; Flemming, J.; Forkel, R.; Galmarini, S.; Gauss, M.; Grell, G.; Hirtl, M.; Joffre, S.; Jorba, O.; Kaas, E.; Kaasik, M.; Kallos, G.; Kong, X.; Korsholm, U.; Kurganskiy, A.; Kushta, J.; Lohmann, U.; Mahura, A.; Manders-Groot, A.; Maurizi, A.; Moussiopoulos, N.; Rao, S. T.; Savage, N.; Seigneur, C.; Sokhi, R.; Solazzo, E.; Solomos, S.; Sørensen, B.; Tsegas, G.; Vignati, E.; Vogel, B.; Zhang, Y.

2013-05-01

The simulation of the coupled evolution of atmospheric dynamics, pollutant transport, chemical reactions and atmospheric composition is one of the most challenging tasks in environmental modelling, climate change studies, and weather forecasting for the next decades as they all involve strongly integrated processes. Weather strongly influences air quality (AQ) and atmospheric transport of hazardous materials, while atmospheric composition can influence both weather and climate by directly modifying the atmospheric radiation budget or indirectly affecting cloud formation. Until recently, however, due to the scientific complexities and lack of computational power, atmospheric chemistry and weather forecasting have developed as separate disciplines, leading to the development of separate modelling systems that are only loosely coupled. The continuous increase in computer power has now reached a stage that enables us to perform online coupling of regional meteorological models with atmospheric chemical transport models. The focus on integrated systems is timely, since recent research has shown that meteorology and chemistry feedbacks are important in the context of many research areas and applications, including numerical weather prediction (NWP), AQ forecasting as well as climate and Earth system modelling. However, the relative importance of online integration and its priorities, requirements and levels of detail necessary for representing different processes and feedbacks can greatly vary for these related communities: (i) NWP, (ii) AQ forecasting and assessments, (iii) climate and earth system modelling. Additional applications are likely to benefit from online modelling, e.g.: simulation of volcanic ash or forest fire plumes, pollen warnings, dust storms, oil/gas fires, geo-engineering tests involving changes in the radiation balance. The COST Action ES1004 - European framework for online integrated air quality and meteorology modelling (EuMetChem) - aims at paving the way towards a new generation of online integrated atmospheric chemical transport and meteorology modelling with two-way interactions between different atmospheric processes including dynamics, chemistry, clouds, radiation, boundary layer and emissions. As its first task, we summarise the current status of European modelling practices and experience with online coupled modelling of meteorology with atmospheric chemistry including feedback mechanisms and attempt reviewing the various issues connected to the different modules of such online coupled models but also providing recommendations for coping with them for the benefit of the modelling community at large.

POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Auld, Joshua; Hope, Michael; Ley, Hubert

This paper discusses the development of an agent-based modelling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database IO library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typicallymore » done with separate stand-alone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a long standing issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Initial tests with case studies involving traffic management center impacts on various network events such as accidents, congestion and weather events, show the potential of the system.« less

Probabilistic flood inundation prediction within a coupled hydrodynamic, distributed hydrologic modeling framework

NASA Astrophysics Data System (ADS)

Adams, T. E.

2016-12-01

Accurate and timely predictions of the lateral exent of floodwaters and water level depth in floodplain areas are critical globally. This paper demonstrates the coupling of hydrologic ensembles, derived from the use of numerical weather prediction (NWP) model forcings as input to a fully distributed hydrologic model. Resulting ensemble output from the distributed hydrologic model are used as upstream flow boundaries and lateral inflows to a 1-D hydrodynamic model. An example is presented for the Potomac River in the vicinity of Washington, DC (USA). The approach taken falls within the broader goals of the Hydrologic Ensemble Prediction EXperiment (HEPEX).

Using Virtualization to Integrate Weather, Climate, and Coastal Science Education

NASA Astrophysics Data System (ADS)

Davis, J. R.; Paramygin, V. A.; Figueiredo, R.; Sheng, Y.

2012-12-01

To better understand and communicate the important roles of weather and climate on the coastal environment, a unique publically available tool is being developed to support research, education, and outreach activities. This tool uses virtualization technologies to facilitate an interactive, hands-on environment in which students, researchers, and general public can perform their own numerical modeling experiments. While prior efforts have focused solely on the study of the coastal and estuary environments, this effort incorporates the community supported weather and climate model (WRF-ARW) into the Coastal Science Educational Virtual Appliance (CSEVA), an education tool used to assist in the learning of coastal transport processes; storm surge and inundation; and evacuation modeling. The Weather Research and Forecasting (WRF) Model is a next-generation, community developed and supported, mesoscale numerical weather prediction system designed to be used internationally for research, operations, and teaching. It includes two dynamical solvers (ARW - Advanced Research WRF and NMM - Nonhydrostatic Mesoscale Model) as well as a data assimilation system. WRF-ARW is the ARW dynamics solver combined with other components of the WRF system which was developed primarily at NCAR, community support provided by the Mesoscale and Microscale Meteorology (MMM) division of National Center for Atmospheric Research (NCAR). Included with WRF is the WRF Pre-processing System (WPS) which is a set of programs to prepare input for real-data simulations. The CSEVA is based on the Grid Appliance (GA) framework and is built using virtual machine (VM) and virtual networking technologies. Virtualization supports integration of an operating system, libraries (e.g. Fortran, C, Perl, NetCDF, etc. necessary to build WRF), web server, numerical models/grids/inputs, pre-/post-processing tools (e.g. WPS / RIP4 or UPS), graphical user interfaces, "Cloud"-computing infrastructure and other tools into a single ready-to-use package. Thus, the previous ornery task of setting up and compiling these tools becomes obsolete and the research, educator or student can focus on using the tools to study the interactions between weather, climate and the coastal environment. The incorporation of WRF into the CSEVA has been designed to be synergistic with the extensive online tutorials and biannual tutorials hosted by NCAR. Included are working examples of the idealized test simulations provided with WRF (2D sea breeze and squalls, a large eddy simulation, a Held and Suarez simulation, etc.) To demonstrate the integration of weather, coastal and coastal science education, example applications are being developed to demonstrate how the system can be used to couple a coastal and estuarine circulation, transport and storm surge model with downscale reanalysis weather and future climate predictions. Documentation, tutorials and the enhanced CSEVA itself will be found on the web at: http://cseva.coastal.ufl.edu.

Aurorasaurus Database of Real-Time, Soft-Sensor Sourced Aurora Data for Space Weather Research

NASA Astrophysics Data System (ADS)

Kosar, B.; MacDonald, E.; Heavner, M.

2017-12-01

Aurorasaurus is an innovative citizen science project focused on two fundamental objectives i.e., collecting real-time, ground-based signals of auroral visibility from citizen scientists (soft-sensors) and incorporating this new type of data into scientific investigations pertaining to aurora. The project has been live since the Fall of 2014, and as of Summer 2017, the database compiled approximately 12,000 observations (5295 direct reports and 6413 verified tweets). In this presentation, we will focus on demonstrating the utility of this robust science quality data for space weather research needs. These data scale with the size of the event and are well-suited to capture the largest, rarest events. Emerging state-of-the-art computational methods based on statistical inference such as machine learning frameworks and data-model integration methods can offer new insights that could potentially lead to better real-time assessment and space weather prediction when citizen science data are combined with traditional sources.

GEOS Atmospheric Model: Challenges at Exascale

NASA Technical Reports Server (NTRS)

Putman, William M.; Suarez, Max J.

2017-01-01

The Goddard Earth Observing System (GEOS) model at NASA's Global Modeling and Assimilation Office (GMAO) is used to simulate the multi-scale variability of the Earth's weather and climate, and is used primarily to assimilate conventional and satellite-based observations for weather forecasting and reanalysis. In addition, assimilations coupled to an ocean model are used for longer-term forecasting (e.g., El Nino) on seasonal to interannual times-scales. The GMAO's research activities, including system development, focus on numerous time and space scales, as detailed on the GMAO website, where they are tabbed under five major themes: Weather Analysis and Prediction; Seasonal-Decadal Analysis and Prediction; Reanalysis; Global Mesoscale Modeling, and Observing System Science. A brief description of the GEOS systems can also be found at the GMAO website. GEOS executes as a collection of earth system components connected through the Earth System Modeling Framework (ESMF). The ESMF layer is supplemented with the MAPL (Modeling, Analysis, and Prediction Layer) software toolkit developed at the GMAO, which facilitates the organization of the computational components into a hierarchical architecture. GEOS systems run in parallel using a horizontal decomposition of the Earth's sphere into processing elements (PEs). Communication between PEs is primarily through a message passing framework, using the message passing interface (MPI), and through explicit use of node-level shared memory access via the SHMEM (Symmetric Hierarchical Memory access) protocol. Production GEOS weather prediction systems currently run at 12.5-kilometer horizontal resolution with 72 vertical levels decomposed into PEs associated with 5,400 MPI processes. Research GEOS systems run at resolutions as fine as 1.5 kilometers globally using as many as 30,000 MPI processes. Looking forward, these systems can be expected to see a 2 times increase in horizontal resolution every two to three years, as well as less frequent increases in vertical resolution. Coupling these resolution changes with increases in complexity, the computational demands on the GEOS production and research systems should easily increase 100-fold over the next five years. Currently, our 12.5 kilometer weather prediction system narrowly meets the time-to-solution demands of a near-real-time production system. Work is now in progress to take advantage of a hybrid MPI-OpenMP parallelism strategy, in an attempt to achieve a modest two-fold speed-up to accommodate an immediate demand due to increased scientific complexity and an increase in vertical resolution. Pursuing demands that require a 10- to 100-fold increases or more, however, would require a detailed exploration of the computational profile of GEOS, as well as targeted solutions using more advanced high-performance computing technologies. Increased computing demands of 100-fold will be required within five years based on anticipated changes in the GEOS production systems, increases of 1000-fold can be anticipated over the next ten years.

A sensor simulation framework for the testing and evaluation of external hazard monitors and integrated alerting and notification functions

NASA Astrophysics Data System (ADS)

Uijt de Haag, Maarten; Venable, Kyle; Bezawada, Rajesh; Adami, Tony; Vadlamani, Ananth K.

2009-05-01

This paper discusses a sensor simulator/synthesizer framework that can be used to test and evaluate various sensor integration strategies for the implementation of an External Hazard Monitor (EHM) and Integrated Alerting and Notification (IAN) function as part of NASA's Integrated Intelligent Flight Deck (IIFD) project. The IIFD project under the NASA's Aviation Safety program "pursues technologies related to the flight deck that ensure crew workload and situational awareness are both safely optimized and adapted to the future operational environment as envisioned by NextGen." Within the simulation framework, various inputs to the IIFD and its subsystems, the EHM and IAN, are simulated, synthesized from actual collected data, or played back from actual flight test sensor data. Sensors and avionics included in this framework are TCAS, ADS-B, Forward-Looking Infrared, Vision cameras, GPS, Inertial navigators, EGPWS, Laser Detection and Ranging sensors, altimeters, communication links with ATC, and weather radar. The framework is implemented in Simulink, a modeling language developed by The Mathworks. This modeling language allows for test and evaluation of various sensor and communication link configurations as well as the inclusion of feedback from the pilot on the performance of the aircraft. Specifically, this paper addresses the architecture of the simulator, the sensor model interfaces, the timing and database (environment) aspects of the sensor models, the user interface of the modeling environment, and the various avionics implementations.

Modeling Urban Scenarios & Experiments: Fort Indiantown Gap Data Collections Summary and Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Archer, Daniel E.; Bandstra, Mark S.; Davidson, Gregory G.

This report summarizes experimental radiation detector, contextual sensor, weather, and global positioning system (GPS) data collected to inform and validate a comprehensive, operational radiation transport modeling framework to evaluate radiation detector system and algorithm performance. This framework will be used to study the influence of systematic effects (such as geometry, background activity, background variability, environmental shielding, etc.) on detector responses and algorithm performance using synthetic time series data. This work consists of performing data collection campaigns at a canonical, controlled environment for complete radiological characterization to help construct and benchmark a high-fidelity model with quantified system geometries, detector response functions,more » and source terms for background and threat objects. This data also provides an archival, benchmark dataset that can be used by the radiation detection community. The data reported here spans four data collection campaigns conducted between May 2015 and September 2016.« less

A stochastic ensemble-based model to predict crop water requirements from numerical weather forecasts and VIS-NIR high resolution satellite images in Southern Italy

NASA Astrophysics Data System (ADS)

Pelosi, Anna; Falanga Bolognesi, Salvatore; De Michele, Carlo; Medina Gonzalez, Hanoi; Villani, Paolo; D'Urso, Guido; Battista Chirico, Giovanni

2015-04-01

Irrigation agriculture is one the biggest consumer of water in Europe, especially in southern regions, where it accounts for up to 70% of the total water consumption. The EU Common Agricultural Policy, combined with the Water Framework Directive, imposes to farmers and irrigation managers a substantial increase of the efficiency in the use of water in agriculture for the next decade. Ensemble numerical weather predictions can be valuable data for developing operational advisory irrigation services. We propose a stochastic ensemble-based model providing spatial and temporal estimates of crop water requirements, implemented within an advisory service offering detailed maps of irrigation water requirements and crop water consumption estimates, to be used by water irrigation managers and farmers. The stochastic model combines estimates of crop potential evapotranspiration retrieved from ensemble numerical weather forecasts (COSMO-LEPS, 16 members, 7 km resolution) and canopy parameters (LAI, albedo, fractional vegetation cover) derived from high resolution satellite images in the visible and near infrared wavelengths. The service provides users with daily estimates of crop water requirements for lead times up to five days. The temporal evolution of the crop potential evapotranspiration is simulated with autoregressive models. An ensemble Kalman filter is employed for updating model states by assimilating both ground based meteorological variables (where available) and numerical weather forecasts. The model has been applied in Campania region (Southern Italy), where a satellite assisted irrigation advisory service has been operating since 2006. This work presents the results of the system performance for one year of experimental service. The results suggest that the proposed model can be an effective support for a sustainable use and management of irrigation water, under conditions of water scarcity and drought. Since the evapotranspiration term represents a staple component in the water balance of a catchment, as outstanding future development, the model could also offer an advanced support for water resources management decisions at catchment scale.

Mineral stimulation of subsurface microorganisms: release of limiting nutrients from silicates

USGS Publications Warehouse

Roger, Jennifer Roberts; Bennett, Philip C.

2004-01-01

Microorganisms play an important role in the weathering of silicate minerals in many subsurface environments, but an unanswered question is whether the mineral plays an important role in the microbial ecology. Silicate minerals often contain nutrients necessary for microbial growth, but whether the microbial community benefits from their release during weathering is unclear. In this study, we used field and laboratory approaches to investigate microbial interactions with minerals and glasses containing beneficial nutrients and metals. Field experiments from a petroleum-contaminated aquifer, where silicate weathering is substantially accelerated in the contaminated zone, revealed that phosphorus (P) and iron (Fe)-bearing silicate glasses were preferentially colonized and weathered, while glasses without these elements were typically barren of colonizing microorganisms, corroborating previous studies using feldspars. In laboratory studies, we investigated microbial weathering of silicates and the release of nutrients using a model ligand-promoted pathway. A metal-chelating organic ligand 3,4 dihydroxybenzoic acid (3,4 DHBA) was used as a source of chelated ferric iron, and a carbon source, to investigate mineral weathering rate and microbial metabolism.In the investigated aquifer, we hypothesize that microbes produce organic ligands to chelate metals, particularly Fe, for metabolic processes and also form stable complexes with Al and occasionally with Si. Further, the concentration of these ligands is apparently sufficient near an attached microorganism to destroy the silicate framework while releasing the nutrient of interest. In microcosms containing silicates and glasses with trace phosphate mineral inclusions, microbial biomass increased, indicating that the microbial community can use silicate-bound phosphate inclusions. The addition of a native microbial consortium to microcosms containing silicates or glasses with iron oxide inclusions correlated to accelerated weathering and release of Si into solution as well as the accelerated degradation of the model substrate 3,4 DHBA. We propose that silicate-bound P and Fe inclusions are bioavailable, and microorganisms may use organic ligands to dissolve the silicate matrix and access these otherwise limiting nutrients.

HESFIRE: a global fire model to explore the role of anthropogenic and weather drivers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Le Page, Yannick LB; Morton, Douglas; Bond-Lamberty, Benjamin

Vegetation fires are a major driver of ecosystem dynamics and greenhouse gas emissions. Anticipating potential changes in fire activity and their impacts relies first on a realistic model of fire activity (e.g., fire incidence and interannual variability) and second on a model accounting for fire impacts (e.g., mortality and emissions). In this paper, we focus on our understanding of fire activity and describe a new fire model, HESFIRE (Human–Earth System FIRE), which integrates the influence of weather, vegetation characteristics, and human activities on fires in a stand-alone framework. It was developed with a particular emphasis on allowing fires to spreadmore » over consecutive days given their major contribution to burned areas in many ecosystems. A subset of the model parameters was calibrated through an optimization procedure using observation data to enhance our knowledge of regional drivers of fire activity and improve the performance of the model on a global scale. Modeled fire activity showed reasonable agreement with observations of burned area, fire seasonality, and interannual variability in many regions, including for spatial and temporal domains not included in the optimization procedure. Significant discrepancies are investigated, most notably regarding fires in boreal regions and in xeric ecosystems and also fire size distribution. The sensitivity of fire activity to model parameters is analyzed to explore the dominance of specific drivers across regions and ecosystems. The characteristics of HESFIRE and the outcome of its evaluation provide insights into the influence of anthropogenic activities and weather, and their interactions, on fire activity.« less

HESFIRE: a global fire model to explore the role of anthropogenic and weather drivers

DOE PAGES

Le Page, Yannick LB; Morton, Douglas; Bond-Lamberty, Benjamin; ...

2015-02-13

Vegetation fires are a major driver of ecosystem dynamics and greenhouse gas emissions. Anticipating potential changes in fire activity and their impacts relies first on a realistic model of fire activity (e.g., fire incidence and interannual variability) and second on a model accounting for fire impacts (e.g., mortality and emissions). In this paper, we focus on our understanding of fire activity and describe a new fire model, HESFIRE (Human–Earth System FIRE), which integrates the influence of weather, vegetation characteristics, and human activities on fires in a stand-alone framework. It was developed with a particular emphasis on allowing fires to spreadmore » over consecutive days given their major contribution to burned areas in many ecosystems. A subset of the model parameters was calibrated through an optimization procedure using observation data to enhance our knowledge of regional drivers of fire activity and improve the performance of the model on a global scale. Modeled fire activity showed reasonable agreement with observations of burned area, fire seasonality, and interannual variability in many regions, including for spatial and temporal domains not included in the optimization procedure. Significant discrepancies are investigated, most notably regarding fires in boreal regions and in xeric ecosystems and also fire size distribution. The sensitivity of fire activity to model parameters is analyzed to explore the dominance of specific drivers across regions and ecosystems. The characteristics of HESFIRE and the outcome of its evaluation provide insights into the influence of anthropogenic activities and weather, and their interactions, on fire activity.« less

A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes

NASA Astrophysics Data System (ADS)

Krishnamurti, T. N.; Kumar, V.; Simon, A.; Bhardwaj, A.; Ghosh, T.; Ross, R.

2016-06-01

This review provides a summary of work in the area of ensemble forecasts for weather, climate, oceans, and hurricanes. This includes a combination of multiple forecast model results that does not dwell on the ensemble mean but uses a unique collective bias reduction procedure. A theoretical framework for this procedure is provided, utilizing a suite of models that is constructed from the well-known Lorenz low-order nonlinear system. A tutorial that includes a walk-through table and illustrates the inner workings of the multimodel superensemble's principle is provided. Systematic errors in a single deterministic model arise from a host of features that range from the model's initial state (data assimilation), resolution, representation of physics, dynamics, and ocean processes, local aspects of orography, water bodies, and details of the land surface. Models, in their diversity of representation of such features, end up leaving unique signatures of systematic errors. The multimodel superensemble utilizes as many as 10 million weights to take into account the bias errors arising from these diverse features of multimodels. The design of a single deterministic forecast models that utilizes multiple features from the use of the large volume of weights is provided here. This has led to a better understanding of the error growths and the collective bias reductions for several of the physical parameterizations within diverse models, such as cumulus convection, planetary boundary layer physics, and radiative transfer. A number of examples for weather, seasonal climate, hurricanes and sub surface oceanic forecast skills of member models, the ensemble mean, and the superensemble are provided.

Magnetic field extrapolation with MHD relaxation using AWSoM

NASA Astrophysics Data System (ADS)

Shi, T.; Manchester, W.; Landi, E.

2017-12-01

Coronal mass ejections are known to be the major source of disturbances in the solar wind capable of affecting geomagnetic environments. In order for accurate predictions of such space weather events, a data-driven simulation is needed. The first step towards such a simulation is to extrapolate the magnetic field from the observed field that is only at the solar surface. Here we present results of a new code of magnetic field extrapolation with direct magnetohydrodynamics (MHD) relaxation using the Alfvén Wave Solar Model (AWSoM) in the Space Weather Modeling Framework. The obtained field is self-consistent with our model and can be used later in time-dependent simulations without modifications of the equations. We use the Low and Lou analytical solution to test our results and they reach a good agreement. We also extrapolate the magnetic field from the observed data. We then specify the active region corona field with this extrapolation result in the AWSoM model and self-consistently calculate the temperature of the active region loops with Alfvén wave dissipation. Multi-wavelength images are also synthesized.

Lessons learned from recent geomagnetic disturbance model validation activities

NASA Astrophysics Data System (ADS)

Pulkkinen, A. A.; Welling, D. T.

2017-12-01

Due to concerns pertaining to geomagnetically induced current impact on ground-based infrastructure, there has been significantly elevated interest in applying models for local geomagnetic disturbance or "delta-B" predictions. Correspondingly there has been elevated need for testing the quality of the delta-B predictions generated by the modern empirical and physics-based models. To address this need, community-wide activities were launched under the GEM Challenge framework and one culmination of the activities was the validation and selection of models that were transitioned into operations at NOAA SWPC. The community-wide delta-B action is continued under the CCMC-facilitated International Forum for Space Weather Capabilities Assessment and its "Ground Magnetic Perturbations: dBdt, delta-B, GICs, FACs" working group. The new delta-B working group builds on the past experiences and expands the collaborations to cover the entire international space weather community. In this paper, we discuss the key lessons learned from the past delta-B validation exercises and lay out the path forward for building on those experience under the new delta-B working group.

The representation of low-level clouds during the West African monsoon in weather and climate models

NASA Astrophysics Data System (ADS)

Kniffka, Anke; Hannak, Lisa; Knippertz, Peter; Fink, Andreas

2016-04-01

The West African monsoon is one of the most important large-scale circulation features in the tropics and the associated seasonal rainfalls are crucial to rain-fed agriculture and water resources for hundreds of millions of people. However, numerical weather and climate models still struggle to realistically represent salient features of the monsoon across a wide range of scales. Recently it has been shown that substantial errors in radiation and clouds exist in the southern parts of West Africa (8°W-8°E, 5-10°N) during summer. This area is characterised by strong low-level jets associated with the formation of extensive ultra-low stratus clouds. Often persisting long after sunrise, these clouds have a substantial impact on the radiation budget at the surface and thus the diurnal evolution of the planetary boundary layer (PBL). Here we present some first results from a detailed analysis of the representation of these clouds and the associated PBL features across a range of weather and climate models. Recent climate model simulations for the period 1991-2010 run in the framework of the Year of Tropical Convection (YOTC) offer a great opportunity for this analysis. The models are those used for the latest Assessment Report of the Intergovernmental Panel on Climate Change, but for YOTC the model output has a much better temporal resolution, allowing to resolve the diurnal cycle, and includes diabatic terms, allowing to much better assess physical reasons for errors in low-level temperature, moisture and thus cloudiness. These more statistical climate model analyses are complemented by experiments using ICON (Icosahedral non-hydrostatic general circulation model), the new numerical weather prediction model of the German Weather Service and the Max Planck Institute for Meteorology. ICON allows testing sensitivities to model resolution and numerical schemes. These model simulations are validated against (re-)analysis data, satellite observations (e.g. CM SAF cloud and radiation data) and ground-based eye observations of clouds and radiation measurements from weather stations. Our results show that many of the climate models have great difficulties representing the diurnal cycle of winds and clouds, leading to associated errors in radiation. Typical errors include a substantial underestimation of the lowest clouds accompanied by an overestimation of clouds at the top of the monsoon layer, indicating systematic problems in vertical exchange processes, which are also reflected in large errors in jet speed. Consequently, many models show a too flat diurnal cycle in cloudiness. This contribution is part of the EU-funded DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) project that aims to investigate the impact of the drastic increase in anthropogenic emissions in West Africa on the local weather and climate, for example through cloud-aerosol interactions. The analysis of the capability of state-of-the-art numerical models to represent low-level cloudiness presented here is an important requisite for the planned assessments of the influence of anthropogenic aerosol.

Dynamic Weather Routes Architecture Overview

NASA Technical Reports Server (NTRS)

Eslami, Hassan; Eshow, Michelle

2014-01-01

Dynamic Weather Routes Architecture Overview, presents the high level software architecture of DWR, based on the CTAS software framework and the Direct-To automation tool. The document also covers external and internal data flows, required dataset, changes to the Direct-To software for DWR, collection of software statistics, and the code structure.

Steeply dipping heaving bedrock, Colorado: Part 1 - Heave features and physical geological framework

USGS Publications Warehouse

Noe, D.C.; Higgins, J.D.; Olsen, H.W.

2007-01-01

Differentially heaving bedrock has caused severe damage near the Denver metropolitan area. This paper describes heave-feature morphologies, the underlying bedrock framework, and their inter-relationship. The heave features are linear to curvilinear and may attain heights of 0.7 m (2.4 ft), widths of 58 m (190 ft), and lengths of 1,067 m (3,500 ft). They are nearly symmetrical to highly asymmetrical in cross section, with width-to-height ratios of 45:1 to 400:1, and most are oriented parallel with the mountain front. The bedrock consists of Mesozoic sedimentary formations having dip angles of 30 degrees to vertical to overturned. Mixed claystone-siltstone bedding sequences up to 36-m (118-ft) thick are common in the heave-prone areas, and interbeds of bentonite, limestone, or sandstone may be present. Highly fractured zones of weathered to variably weathered claystone extend to depths of 19.5 to 22.3 m (64 to 73 ft). Fracture spacings are 0.1 to 0.2 m (0.3 to 0.7 ft) in the weathered and variably weathered bedrock and up to 0.75 m (2.5 ft) in the underlying, unweathered bedrock. Curvilinear shear planes in the weathered claystone show thrust or reverse offsets up to 1.2 m (3.9 ft). Three associations between heave-feature morphologies and the geological framework are recognized: (1) Linear, symmetrical to asymmetrical heaves are associated with primary bedding composition changes. (2) Linear, highly asymmetrical heaves are associated with shear planes along bedding. (3) Curvi-linear, highly asymmetrical heaves are associated with bedding-oblique shear planes.

The link between eddy-driven jet variability and weather regimes in the North Atlantic-European sector

NASA Astrophysics Data System (ADS)

Madonna, E.; Li, C.; Grams, C. M.; Woollings, T.

2017-12-01

Understanding the variability of the North Atlantic eddy-driven jet is key to unravelling the dynamics, predictability and climate change response of extratropical weather in the region. This study aims to 1) reconcile two perspectives on wintertime variability in the North Atlantic-European sector and 2) clarify their link to atmospheric blocking. Two common views of wintertime variability in the North Atlantic are the zonal-mean framework comprising three preferred locations of the eddy-driven jet (southern, central, northern), and the weather regime framework comprising four classical North Atlantic-European regimes (Atlantic ridge AR, zonal ZO, European/Scandinavian blocking BL, Greenland anticyclone GA). We use a k-means clustering algorithm to characterize the two-dimensional variability of the eddy-driven jet stream, defined by the lower tropospheric zonal wind in the ERA-Interim reanalysis. The first three clusters capture the central jet and northern jet, along with a new mixed jet configuration; a fourth cluster is needed to recover the southern jet. The mixed cluster represents a split or strongly tilted jet, neither of which is well described in the zonal-mean framework, and has a persistence of about one week, similar to the other clusters. Connections between the preferred jet locations and weather regimes are corroborated - southern to GA, central to ZO, and northern to AR. In addition, the new mixed cluster is found to be linked to European/Scandinavian blocking, whose relation to the eddy-driven jet was previously unclear. The results highlight the necessity of bridging from weather to climate scales for a deeper understanding of atmospheric circulation variability.

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

Critical Zone Architecture and the Last Glacial Legacy in Unglaciated North America

NASA Astrophysics Data System (ADS)

Marshall, J. A.; Roering, J. J.; Rempel, A. W.; Bartlein, P. J.; Merritts, D. J.; Walter, R. C.

2015-12-01

As fresh bedrock is exhumed into the Critical Zone and intersects with water and life, rock attributes controlling geochemical reactions, hydrologic routing, accommodation space for roots, surface area, and the mobile fraction of regolith are set not just by present-day processes, but are predicated on the 'ghosts' of past processes embedded in the subsurface architecture. Easily observable modern ecosystem processes such as tree throw can erase the past and bias our interpretation of landscape evolution. Abundant paleoenvironmental records demonstrate that unglaciated regions experienced profound climate changes through the late Pleistocene-Holocene transition, but studies quantifying how environmental variables affect erosion and weathering rates in these settings often marginalize or even forego consideration of the role of past climate regimes. Here we combine seven downscaled Last Glacial Maximum (LGM) paleoclimate reconstructions with a state of the art frost cracking model to explore frost weathering potential across the North American continent 21 ka. We analyze existing evidence of LGM periglacial processes and features to better constrain frost weathering model predictions. All seven models predict frost cracking across a large swath to the west of the Continental Divide, with the southernmost extent at ~ latitude 35° N, and increasing latitude towards the buffering influence of the Pacific Ocean. All models predict significant frost cracking in the unglaciated Rocky Mountains. To the east of the Continental Divide, models results diverge more, but all predict regions with LGM temperatures too cold for significant frost cracking (mean annual temperatures < 15 °C), corroborated by observations of permafrost relics such as ice wedges in some areas. Our results provide a framework for coupling paleoclimate reconstructions with a predictive frost weathering model, and importantly, suggest that modeling modern Critical Zone process evolution may require a consideration of vastly different processes when rock was first exhumed into the Critical Zone reactor.

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

NASA Astrophysics Data System (ADS)

Rueda, Ana; Hegermiller, Christie A.; Antolinez, Jose A. A.; Camus, Paula; Vitousek, Sean; Ruggiero, Peter; Barnard, Patrick L.; Erikson, Li H.; Tomás, Antonio; Mendez, Fernando J.

2017-02-01

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this complex problem tractable using computationally expensive numerical models. So far, the skill of statistical-downscaling model-based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

Multiscale Climate Emulator of Multimodal Wave Spectra: MUSCLE-spectra

NASA Astrophysics Data System (ADS)

Rueda, A.; Hegermiller, C.; Alvarez Antolinez, J. A.; Camus, P.; Vitousek, S.; Ruggiero, P.; Barnard, P.; Erikson, L. H.; Tomas, A.; Mendez, F. J.

2016-12-01

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this problem complex yet tractable using computationally-expensive numerical models. So far, the skill of statistical-downscaling models based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical-downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the Southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

Data-Model and Inter-Model Comparisons of the GEM Outflow Events Using the Space Weather Modeling Framework

NASA Astrophysics Data System (ADS)

Welling, D. T.; Eccles, J. V.; Barakat, A. R.; Kistler, L. M.; Haaland, S.; Schunk, R. W.; Chappell, C. R.

2015-12-01

Two storm periods were selected by the Geospace Environment Modeling Ionospheric Outflow focus group for community collaborative study because of its high magnetospheric activity and extensive data coverage: the September 27 - October 4, 2002 corotating interaction region event and the October 22 - 29 coronal mass ejection event. During both events, the FAST, Polar, Cluster, and other missions made key observations, creating prime periods for data-model comparison. The GEM community has come together to simulate this period using many different methods in order to evaluate models, compare results, and expand our knowledge of ionospheric outflow and its effects on global dynamics. This paper presents Space Weather Modeling Framework (SWMF) simulations of these important periods compared against observations from the Polar TIDE, Cluster CODIF and EFW instruments. Emphasis will be given to the second event. Density and velocity of oxygen and hydrogen throughout the lobes, plasma sheet, and inner magnetosphere will be the focus of these comparisons. For these simulations, the SWMF couples the multifluid version of BATS-R-US MHD to a variety of ionospheric outflow models of varying complexity. The simplest is outflow arising from constant MHD inner boundary conditions. Two first-principles-based models are also leveraged: the Polar Wind Outflow Model (PWOM), a fluid treatment of outflow dynamics, and the Generalized Polar Wind (GPW) model, which combines fluid and particle-in-cell approaches. Each model is capable of capturing a different set of energization mechanisms, yielding different outflow results. The data-model comparisons will illustrate how well each approach captures reality and which energization mechanisms are most important. Inter-model comparisons will illustrate how the different outflow specifications affect the magnetosphere. Specifically, it is found that the GPW provides increased heavy ion outflow over a broader spatial range than the alternative models, improving comparisons in some regions but degrading the agreement in others. This work will also assess our current capability to reproduce ionosphere-magnetosphere mass coupling.

Year of Tropical Convection (YOTC): Status and Research Agenda

NASA Astrophysics Data System (ADS)

Moncrieff, M. W.; Waliser, D. E.

2009-12-01

The realistic representation of tropical convection in global models is a long-standing challenge for numerical weather prediction and an emerging grand challenge for climate prediction in respect to its physical basis. Insufficient knowledge and practical capabilities in this area disadvantage the modeling and prediction of prominent multi-scale phenomena such as the ITCZ, ENSO, monsoons and their active/break periods, the MJO, subtropical stratus decks, near-surface ocean properties, and tropical cyclones. Science elements include the diurnal cycle of precipitation, multi-scale convective organization, the global energy and water cycle, and interaction between the tropics and extra-tropics which interact strongly on timescales of weeks-to-months: the intersection of weather and climate. To address such challenges, the WCRP and WWRP/THORPEX are conducting a joint international research project, the Year of Tropical Convection (YOTC) which is a coordinated observing, modeling and forecasting project. The focus-year and integrated framework is intended to exploit the vast observational datasets, the modern high-resolution modeling frameworks, and theoretical insights. The over-arching objective is to advance the characterization, diagnosis, modeling, parameterization and prediction of multi-scale organized tropical phenomena and their interaction with the global circulation. The “Year” (May 2008 - April 2010) is intended to leverage recent major investments in Earth Science infrastructure and overlapping observational activities, e.g., Asian Monsoon Years (AMY) and the THORPEX Pacific Asian Regional Campaign (T-PARC). The research agenda involves phenomena and scale-interactions that are problematic for prediction models and have important socio-economic implications: MJO and convectively coupled equatorial waves; easterly waves and tropical cyclones; the monsoons including their intraseasonal variability; the diurnal cycle of precipitation; and two-way tropical-extratropical interaction. This presentation will summarize the status of the above.

Remote sensing of rainfall for flash flood prediction in the United States

NASA Astrophysics Data System (ADS)

Gourley, J. J.; Flamig, Z.; Vergara, H. J.; Clark, R. A.; Kirstetter, P.; Terti, G.; Hong, Y.; Howard, K.

2015-12-01

This presentation will briefly describe the Multi-Radar Multi-Sensor (MRMS) system that ingests all NEXRAD and Canadian weather radar data and produces accurate rainfall estimates at 1-km resolution every 2 min. This real-time system, which was recently transitioned for operational use in the National Weather Service, provides forcing to a suite of flash flood prediction tools. The Flooded Locations and Simulated Hydrographs (FLASH) project provides 6-hr forecasts of impending flash flooding across the US at the same 1-km grid cell resolution as the MRMS rainfall forcing. This presentation will describe the ensemble hydrologic modeling framework, provide an evaluation at gauged basins over a 10-year period, and show the FLASH tools' performance during the record-setting floods in Oklahoma and Texas in May and June 2015.

Simulations of Tornadoes, Tropical Cyclones, MJOs, and QBOs, using GFDL's multi-scale global climate modeling system

NASA Astrophysics Data System (ADS)

Lin, Shian-Jiann; Harris, Lucas; Chen, Jan-Huey; Zhao, Ming

2014-05-01

A multi-scale High-Resolution Atmosphere Model (HiRAM) is being developed at NOAA/Geophysical Fluid Dynamics Laboratory. The model's dynamical framework is the non-hydrostatic extension of the vertically Lagrangian finite-volume dynamical core (Lin 2004, Monthly Wea. Rev.) constructed on a stretchable (via Schmidt transformation) cubed-sphere grid. Physical parametrizations originally designed for IPCC-type climate predictions are in the process of being modified and made more "scale-aware", in an effort to make the model suitable for multi-scale weather-climate applications, with horizontal resolution ranging from 1 km (near the target high-resolution region) to as low as 400 km (near the antipodal point). One of the main goals of this development is to enable simulation of high impact weather phenomena (such as tornadoes, thunderstorms, category-5 hurricanes) within an IPCC-class climate modeling system previously thought impossible. We will present preliminary results, covering a very wide spectrum of temporal-spatial scales, ranging from simulation of tornado genesis (hours), Madden-Julian Oscillations (intra-seasonal), topical cyclones (seasonal), to Quasi Biennial Oscillations (intra-decadal), using the same global multi-scale modeling system.

Interactions between Cool Roofs and Urban Irrigation: Do Cooling Strategies Reduce Water Consumption in the San Francisco Bay Area?

NASA Astrophysics Data System (ADS)

Vahmani, P.; Jones, A. D.

2016-12-01

California has experienced progressive drought since 2012, with 2012-2014 constituting a nearly 10,000-year drought event, resulting in a suite of policies with the goal of reducing water consumption. At the same time, climate warming effects of accelerated urbanization along with projected global climate change raise an urgent need for sustainable mitigation and adaptation strategies to cool urban climates. In this study, for the first time, we assess the overarching benefits of cooling strategies on urban water consumption. We employ a satellite-supported regional climate-modeling framework over the San Francisco Bay Area to assess the effects of cool roofs on urban irrigation, a topic of increasing importance as it accounts for a significant fraction of urban water use particularly in arid and semi-arid regions. We use a suit of climatological simulations at high (1.5 km) spatial resolution, based on a Weather Research and Forecasting (WRF)-Urban Canopy Model (UCM) modeling framework, reinforced with remotely sensed observations of Green Vegetation Fraction (GVF), leaf area index (LAI), and albedo. Our analysis shows that widespread incorporation of cool roofs would result in a mean daytime cooling of about 0.7° C, which in turn results in roughly 4% reduction in irrigation water, largely due to decreases in surface evapotranspiration rates. We further investigate the critical interactions between cool roofs, wind, and sea-breeze patterns as well as fog formation, a dominant weather pattern in San Francisco Bay area.

Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions.

PubMed

Heslot, Nicolas; Akdemir, Deniz; Sorrells, Mark E; Jannink, Jean-Luc

2014-02-01

Development of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection. Application to a large winter wheat dataset. Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. The method is tested using a large winter wheat dataset, representative of the type of data available in a large-scale commercial breeding program. Accuracy in predicting genotype performance in unobserved environments for which weather data were available increased by 11.1% on average and the variability in prediction accuracy decreased by 10.8%. By leveraging agronomic knowledge and the large historical datasets generated by breeding programs, this new model provides insight into the genetic architecture of genotype by environment interactions and could predict genotype performance based on past and future weather scenarios.

Flash floods in small Alpine catchments in a changing climate

NASA Astrophysics Data System (ADS)

Breinl, Korbinian; Di Baldassarre, Giuliano

2017-04-01

Climate change is expected to increase the frequency and intensity of hazardous meteorological and hydrological events in numerous mountainous areas. The mountain environment is becoming more and more important for urbanization and the tourism-based economy. Here we show new and innovative methodologies for assessing intensity and frequency of flash floods in small Alpine catchments, in South Tyrol (Italy), under climate change. This research is done within the STEEP STREAMS project, whereby we work closely with decision makers in Italian authorities, and the final goal is to provide them with clear guidelines on how to adapt current structural solutions for mitigating hazardous events under future climate conditions. To this end, we develop a coupled framework of weather generation (i.e. extrapolation of observations and trained with climate projections), time series disaggregation and hydrological modelling using the conceptual HBV model. One of the key challenges is the transfer of comparatively coarse RCM projections to small catchments, whose sizes range from only about 10km2 to 100km2. We examine different strategies to downscale the RCM data from e.g. the EURO-CORDEX dataset using our weather generator. The selected projections represent combinations of warmer, milder, drier and wetter conditions. In general, our main focus is to develop an improved understanding of the impact of the multiple sources of uncertainty in this modelling framework, and make these uncertainties tangible. The output of this study (i.e. discharge with a return period and associated uncertainty) will allow hydraulic and sediment transport modelling of flash floods and debris flows.

Comparison of Selected Weather Translation Products

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak

2017-01-01

Weather is a primary contributor to the air traffic delays within the National Airspace System (NAS). At present, it is the individual decision makers who use weather information and assess its operational impact in creating effective air traffic management solutions. As a result, the estimation of the impact of forecast weather and the quality of ATM response relies on the skill and experience level of the decision maker. FAA Weather-ATM working groups have developed a Weather-ATM integration framework that consists of weather collection, weather translation, ATM impact conversion and ATM decision support. Some weather translation measures have been developed for hypothetical operations such as decentralized free flight, whereas others are meant to be relevant in current operations. This paper does comparative study of two different weather translation products relevant in current operations and finds that these products have strong correlation with each other. Given inaccuracies in prediction of weather, these differences would not be expected to be of significance in statistical study of a large number of decisions made with a look-ahead time of two hours or more.

Hurdles to Overcome to Model Carrington Class Events

NASA Astrophysics Data System (ADS)

Engel, M.; Henderson, M. G.; Jordanova, V. K.; Morley, S.

2017-12-01

Large geomagnetic storms pose a threat to both space and ground based infrastructure. In order to help mitigate that threat a better understanding of the specifics of these storms is required. Various computer models are being used around the world to analyze the magnetospheric environment, however they are largely inadequate for analyzing the large and extreme storm time environments. Here we report on the first steps towards expanding and robustifying the RAM-SCB inner magnetospheric model, used in conjunction with BATS-R-US and the Space Weather Modeling Framework, in order to simulate storms with Dst > -400. These results will then be used to help expand our modelling capabilities towards including Carrington-class events.

Application of global weather and climate model output to the design and operation of wind-energy systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curry, Judith

This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatorymore » environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.« less

Image-based optimization of coronal magnetic field models for improved space weather forecasting

NASA Astrophysics Data System (ADS)

Uritsky, V. M.; Davila, J. M.; Jones, S. I.; MacNeice, P. J.

2017-12-01

The existing space weather forecasting frameworks show a significant dependence on the accuracy of the photospheric magnetograms and the extrapolation models used to reconstruct the magnetic filed in the solar corona. Minor uncertainties in the magnetic field magnitude and direction near the Sun, when propagated through the heliosphere, can lead to unacceptible prediction errors at 1 AU. We argue that ground based and satellite coronagraph images can provide valid geometric constraints that could be used for improving coronal magnetic field extrapolation results, enabling more reliable forecasts of extreme space weather events such as major CMEs. In contrast to the previously developed loop segmentation codes designed for detecting compact closed-field structures above solar active regions, we focus on the large-scale geometry of the open-field coronal regions up to 1-2 solar radii above the photosphere. By applying the developed image processing techniques to high-resolution Mauna Loa Solar Observatory images, we perform an optimized 3D B-line tracing for a full Carrington rotation using the magnetic field extrapolation code developed S. Jones at al. (ApJ 2016, 2017). Our tracing results are shown to be in a good qualitative agreement with the large-scale configuration of the optical corona, and lead to a more consistent reconstruction of the large-scale coronal magnetic field geometry, and potentially more accurate global heliospheric simulation results. Several upcoming data products for the space weather forecasting community will be also discussed.

Relationships between CO 2, thermodynamic limits on silicate weathering, and the strength of the silicate weathering feedback

DOE PAGES

Winnick, Matthew J.; Maher, Kate

2018-01-27

Recent studies have suggested that thermodynamic limitations on chemical weathering rates exert a first-order control on riverine solute fluxes and by extension, global chemical weathering rates. As such, these limitations may play a prominent role in the regulation of carbon dioxide levels (pCO 2) over geologic timescales by constraining the maximum global weathering flux. In this study, we develop a theoretical scaling relationship between equilibrium solute concentrations and pCO 2 based on equilibrium constants and reaction stoichiometry relating primary mineral dissolution and secondary mineral precipitation. Here, we test this theoretical scaling relationship against reactive transport simulations of chemical weathering profilesmore » under open-and closed-system conditions, representing partially and fully water-saturated regolith, respectively. Under open-system conditions, equilibrium bicarbonate concentrations vary as a power-law function of pCO 2(y =kx n)where nis dependent on reaction stoichiometry and kis dependent on both reaction stoichiometry and the equilibrium constant. Under closed-system conditions, bicarbonate concentrations vary linearly with pCO 2 at low values and approach open-system scaling at high pCO 2. To describe the potential role of thermodynamic limitations in the global silicate weathering feedback, we develop a new mathematical framework to assess weathering feedback strength in terms of both (1) steady-state atmospheric pCO 2 concentrations, and (2) susceptibility to secular changes in degassing rates and transient carbon cycle perturbations, which we term 1st and 2nd order feedback strength, respectively. Finally, we discuss the implications of these results for the effects of vascular land plant evolution on feedback strength, the potential role of vegetation in controlling modern solute fluxes, and the application of these frameworks to a more complete functional description of the silicate weathering feedback. Most notably, the dependence of equilibrium solute concentrations on pCO 2 may represent a direct weathering feedback largely independent of climate and modulated by belowground organic carbon respiration.« less

Relationships between CO 2, thermodynamic limits on silicate weathering, and the strength of the silicate weathering feedback

DOE Office of Scientific and Technical Information (OSTI.GOV)

Winnick, Matthew J.; Maher, Kate

Recent studies have suggested that thermodynamic limitations on chemical weathering rates exert a first-order control on riverine solute fluxes and by extension, global chemical weathering rates. As such, these limitations may play a prominent role in the regulation of carbon dioxide levels (pCO 2) over geologic timescales by constraining the maximum global weathering flux. In this study, we develop a theoretical scaling relationship between equilibrium solute concentrations and pCO 2 based on equilibrium constants and reaction stoichiometry relating primary mineral dissolution and secondary mineral precipitation. Here, we test this theoretical scaling relationship against reactive transport simulations of chemical weathering profilesmore » under open-and closed-system conditions, representing partially and fully water-saturated regolith, respectively. Under open-system conditions, equilibrium bicarbonate concentrations vary as a power-law function of pCO 2(y =kx n)where nis dependent on reaction stoichiometry and kis dependent on both reaction stoichiometry and the equilibrium constant. Under closed-system conditions, bicarbonate concentrations vary linearly with pCO 2 at low values and approach open-system scaling at high pCO 2. To describe the potential role of thermodynamic limitations in the global silicate weathering feedback, we develop a new mathematical framework to assess weathering feedback strength in terms of both (1) steady-state atmospheric pCO 2 concentrations, and (2) susceptibility to secular changes in degassing rates and transient carbon cycle perturbations, which we term 1st and 2nd order feedback strength, respectively. Finally, we discuss the implications of these results for the effects of vascular land plant evolution on feedback strength, the potential role of vegetation in controlling modern solute fluxes, and the application of these frameworks to a more complete functional description of the silicate weathering feedback. Most notably, the dependence of equilibrium solute concentrations on pCO 2 may represent a direct weathering feedback largely independent of climate and modulated by belowground organic carbon respiration.« less

Relationships between CO2, thermodynamic limits on silicate weathering, and the strength of the silicate weathering feedback

NASA Astrophysics Data System (ADS)

Winnick, Matthew J.; Maher, Kate

2018-03-01

Recent studies have suggested that thermodynamic limitations on chemical weathering rates exert a first-order control on riverine solute fluxes and by extension, global chemical weathering rates. As such, these limitations may play a prominent role in the regulation of carbon dioxide levels (pCO2) over geologic timescales by constraining the maximum global weathering flux. In this study, we develop a theoretical scaling relationship between equilibrium solute concentrations and pCO2 based on equilibrium constants and reaction stoichiometry relating primary mineral dissolution and secondary mineral precipitation. We test this theoretical scaling relationship against reactive transport simulations of chemical weathering profiles under open- and closed-system conditions, representing partially and fully water-saturated regolith, respectively. Under open-system conditions, equilibrium bicarbonate concentrations vary as a power-law function of pCO2 (y = kxn) where n is dependent on reaction stoichiometry and k is dependent on both reaction stoichiometry and the equilibrium constant. Under closed-system conditions, bicarbonate concentrations vary linearly with pCO2 at low values and approach open-system scaling at high pCO2. To describe the potential role of thermodynamic limitations in the global silicate weathering feedback, we develop a new mathematical framework to assess weathering feedback strength in terms of both (1) steady-state atmospheric pCO2 concentrations, and (2) susceptibility to secular changes in degassing rates and transient carbon cycle perturbations, which we term 1st and 2nd order feedback strength, respectively. Finally, we discuss the implications of these results for the effects of vascular land plant evolution on feedback strength, the potential role of vegetation in controlling modern solute fluxes, and the application of these frameworks to a more complete functional description of the silicate weathering feedback. Most notably, the dependence of equilibrium solute concentrations on pCO2 may represent a direct weathering feedback largely independent of climate and modulated by belowground organic carbon respiration.

A new open-source Python-based Space Weather data access, visualization, and analysis toolkit

NASA Astrophysics Data System (ADS)

de Larquier, S.; Ribeiro, A.; Frissell, N. A.; Spaleta, J.; Kunduri, B.; Thomas, E. G.; Ruohoniemi, J.; Baker, J. B.

2013-12-01

Space weather research relies heavily on combining and comparing data from multiple observational platforms. Current frameworks exist to aggregate some of the data sources, most based on file downloads via web or ftp interfaces. Empirical models are mostly fortran based and lack interfaces with more useful scripting languages. In an effort to improve data and model access, the SuperDARN community has been developing a Python-based Space Science Data Visualization Toolkit (DaViTpy). At the center of this development was a redesign of how our data (from 30 years of SuperDARN radars) was made available. Several access solutions are now wrapped into one convenient Python interface which probes local directories, a new remote NoSQL database, and an FTP server to retrieve the requested data based on availability. Motivated by the efficiency of this interface and the inherent need for data from multiple instruments, we implemented similar modules for other space science datasets (POES, OMNI, Kp, AE...), and also included fundamental empirical models with Python interfaces to enhance data analysis (IRI, HWM, MSIS...). All these modules and more are gathered in a single convenient toolkit, which is collaboratively developed and distributed using Github and continues to grow. While still in its early stages, we expect this toolkit will facilitate multi-instrument space weather research and improve scientific productivity.

Evaluation of numerical weather predictions performed in the context of the project DAPHNE

NASA Astrophysics Data System (ADS)

Tegoulias, Ioannis; Pytharoulis, Ioannis; Bampzelis, Dimitris; Karacostas, Theodore

2014-05-01

The region of Thessaly in central Greece is one of the main areas of agricultural production in Greece. Severe weather phenomena affect the agricultural production in this region with adverse effects for farmers and the national economy. For this reason the project DAPHNE aims at tackling the problem of drought by means of weather modification through the development of the necessary tools to support the application of a rainfall enhancement program. In the present study the numerical weather prediction system WRF-ARW is used, in order to assess its ability to represent extreme weather phenomena in the region of Thessaly. WRF is integrated in three domains covering Europe, Eastern Mediterranean and Central-Northern Greece (Thessaly and a large part of Macedonia) using telescoping nesting with grid spacing of 15km, 5km and 1.667km, respectively. The cases examined span throughout the transitional and warm period (April to September) of the years 2008 to 2013, including days with thunderstorm activity. Model results are evaluated against all available surface observations and radar products, taking into account the spatial characteristics and intensity of the storms. Preliminary results indicate a good level of agreement between the simulated and observed fields as far as the standard parameters (such as temperature, humidity and precipitation) are concerned. Moreover, the model generally exhibits a potential to represent the occurrence of the convective activity, but not its exact spatiotemporal characteristics. Acknowledgements This research work has been co-financed by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" (contract number 11SYN_8_1088 - DAPHNE) in the framework of the operational programme "Competitiveness and Entrepreneurship" and Regions in Transition (OPC II, NSRF 2007-2013)

3D Exploration of Meteorological Data: Facing the challenges of operational forecasters

NASA Astrophysics Data System (ADS)

Koutek, Michal; Debie, Frans; van der Neut, Ian

2016-04-01

In the past years the Royal Netherlands Meteorological Institute (KNMI) has been working on innovation in the field of meteorological data visualization. We are dealing with Numerical Weather Prediction (NWP) model data and observational data, i.e. satellite images, precipitation radar, ground and air-borne measurements. These multidimensional multivariate data are geo-referenced and can be combined in 3D space to provide more intuitive views on the atmospheric phenomena. We developed the Weather3DeXplorer (W3DX), a visualization framework for processing and interactive exploration and visualization using Virtual Reality (VR) technology. We managed to have great successes with research studies on extreme weather situations. In this paper we will elaborate what we have learned from application of interactive 3D visualization in the operational weather room. We will explain how important it is to control the degrees-of-freedom during interaction that are given to the users: forecasters/scientists; (3D camera and 3D slicing-plane navigation appear to be rather difficult for the users, when not implemented properly). We will present a novel approach of operational 3D visualization user interfaces (UI) that for a great deal eliminates the obstacle and the time it usually takes to set up the visualization parameters and an appropriate camera view on a certain atmospheric phenomenon. We have found our inspiration in the way our operational forecasters work in the weather room. We decided to form a bridge between 2D visualization images and interactive 3D exploration. Our method combines WEB-based 2D UI's, pre-rendered 3D visualization catalog for the latest NWP model runs, with immediate entry into interactive 3D session for selected visualization setting. Finally, we would like to present the first user experiences with this approach.

Quantitative methods for stochastic high frequency spatio-temporal and non-linear analysis: Assessing health effects of exposure to extreme ambient temperature

NASA Astrophysics Data System (ADS)

Liss, Alexander

Extreme weather events, such as heat waves and cold spells, cause substantial excess mortality and morbidity in the vulnerable elderly population, and cost billions of dollars. The accurate and reliable assessment of adverse effects of extreme weather events on human health is crucial for environmental scientists, economists, and public health officials to ensure proper protection of vulnerable populations and efficient allocation of scarce resources. However, the methodology for the analysis of large national databases is yet to be developed. The overarching objective of this dissertation is to examine the effect of extreme weather on the elderly population of the Conterminous US (ConUS) with respect to seasonality in temperature in different climatic regions by utilizing heterogeneous high frequency and spatio-temporal resolution data. To achieve these goals the author: 1) incorporated dissimilar stochastic high frequency big data streams and distinct data types into the integrated data base for use in analytical and decision support frameworks; 2) created an automated climate regionalization system based on remote sensing and machine learning to define climate regions for the Conterminous US; 3) systematically surveyed the current state of the art and identified existing gaps in the scientific knowledge; 4) assessed the dose-response relationship of exposure to temperature extremes on human health in relatively homogeneous climate regions using different statistical models, such as parametric and non-parametric, contemporaneous and asynchronous, applied to the same data; 5) assessed seasonal peak timing and synchronization delay of the exposure and the disease within the framework of contemporaneous high frequency harmonic time series analysis and modification of the effect by the regional climate; 6) modeled using hyperbolic functional form non-linear properties of the effect of exposure to extreme temperature on human health. The proposed climate regionalization method algorithmically forms eight climatically homogeneous regions for Conterminous US from satellite Remote Sensing inputs. The relative risk of hospitalizations due to extreme ambient temperature varied across climatic regions. Difference in regional hospitalization rates suggests presence of an adaptation effect to a prevailing climate. In various climatic regions the hospitalizations peaked earlier than the peak of exposure. This suggests disproportionally high impact of extreme weather events, such as cold spells or heat waves when they occur early in the season. These findings provide an insight into the use of high frequency disjoint data sets for the assessment of the magnitude, timing, synchronization and non-linear properties of adverse health consequences due to exposure to extreme weather events to the elderly in defined climatic regions. These findings assist in the creation of decision support frameworks targeting preventions and adaptation strategies such as improving infrastructure, providing energy assistance, education and early warning notifications for the vulnerable population. This dissertation offers a number of methodological innovations for the assessment of the high frequency spatio-temporal and non-linear impacts of extreme weather events on human health. These innovations help to ensure an improved protection of the elderly population, aid policy makers in the development of efficient disaster prevention strategies, and facilitate more efficient allocation of scarce resources.

Assessment of Predictive Capabilities of L1 Orbiters using Realtime Solar Wind Data

NASA Astrophysics Data System (ADS)

Holmes, J.; Kasper, J. C.; Welling, D. T.

2017-12-01

Realtime measurements of solar wind conditions at L1 point allow us to predict geomagnetic activity at Earth up to an hour in advance. These predictions are quantified in the form of geomagnetic indices such as Kp and Ap, allowing for a concise, standardized prediction and measurement system. For years, the Space Weather Prediction Center used ACE realtime solar wind data to develop its one and four-hour Kp forecasts, but has in the past year switched to using DSCOVR data as its source. In this study, the performance of both orbiters in predicting Kp over the course of one month was assessed in an attempt to determine whether or not switching to DSCOVR data has resulted in improved forecasts. The period of study was chosen to encompass a time when the satellites were close to each other, and when moderate to high activity was observed. Kp predictions were made using the Geospace Model, part of the Space Weather Modeling Framework, to simulate conditions based on observed solar wind parameters. The performance of each satellite was assessed by comparing the model output to observed data.

Toward GEOS-6, A Global Cloud System Resolving Atmospheric Model

NASA Technical Reports Server (NTRS)

Putman, William M.

2010-01-01

NASA is committed to observing and understanding the weather and climate of our home planet through the use of multi-scale modeling systems and space-based observations. Global climate models have evolved to take advantage of the influx of multi- and many-core computing technologies and the availability of large clusters of multi-core microprocessors. GEOS-6 is a next-generation cloud system resolving atmospheric model that will place NASA at the forefront of scientific exploration of our atmosphere and climate. Model simulations with GEOS-6 will produce a realistic representation of our atmosphere on the scale of typical satellite observations, bringing a visual comprehension of model results to a new level among the climate enthusiasts. In preparation for GEOS-6, the agency's flagship Earth System Modeling Framework [JDl] has been enhanced to support cutting-edge high-resolution global climate and weather simulations. Improvements include a cubed-sphere grid that exposes parallelism; a non-hydrostatic finite volume dynamical core, and algorithm designed for co-processor technologies, among others. GEOS-6 represents a fundamental advancement in the capability of global Earth system models. The ability to directly compare global simulations at the resolution of spaceborne satellite images will lead to algorithm improvements and better utilization of space-based observations within the GOES data assimilation system

Predicting the Magnetic Properties of ICMEs: A Pragmatic View

NASA Astrophysics Data System (ADS)

Riley, P.; Linker, J.; Ben-Nun, M.; Torok, T.; Ulrich, R. K.; Russell, C. T.; Lai, H.; de Koning, C. A.; Pizzo, V. J.; Liu, Y.; Hoeksema, J. T.

2017-12-01

The southward component of the interplanetary magnetic field plays a crucial role in being able to successfully predict space weather phenomena. Yet, thus far, it has proven extremely difficult to forecast with any degree of accuracy. In this presentation, we describe an empirically-based modeling framework for estimating Bz values during the passage of interplanetary coronal mass ejections (ICMEs). The model includes: (1) an empirically-based estimate of the magnetic properties of the flux rope in the low corona (including helicity and field strength); (2) an empirically-based estimate of the dynamic properties of the flux rope in the high corona (including direction, speed, and mass); and (3) a physics-based estimate of the evolution of the flux rope during its passage to 1 AU driven by the output from (1) and (2). We compare model output with observations for a selection of events to estimate the accuracy of this approach. Importantly, we pay specific attention to the uncertainties introduced by the components within the framework, separating intrinsic limitations from those that can be improved upon, either by better observations or more sophisticated modeling. Our analysis suggests that current observations/modeling are insufficient for this empirically-based framework to provide reliable and actionable prediction of the magnetic properties of ICMEs. We suggest several paths that may lead to better forecasts.

A framework for WRF to WRF-IBM grid nesting to enable multiscale simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wiersema, David John; Lundquist, Katherine A.; Chow, Fotini Katapodes

With advances in computational power, mesoscale models, such as the Weather Research and Forecasting (WRF) model, are often pushed to higher resolutions. As the model’s horizontal resolution is refined, the maximum resolved terrain slope will increase. Because WRF uses a terrain-following coordinate, this increase in resolved terrain slopes introduces additional grid skewness. At high resolutions and over complex terrain, this grid skewness can introduce large numerical errors that require methods, such as the immersed boundary method, to keep the model accurate and stable. Our implementation of the immersed boundary method in the WRF model, WRF-IBM, has proven effective at microscalemore » simulations over complex terrain. WRF-IBM uses a non-conforming grid that extends beneath the model’s terrain. Boundary conditions at the immersed boundary, the terrain, are enforced by introducing a body force term to the governing equations at points directly beneath the immersed boundary. Nesting between a WRF parent grid and a WRF-IBM child grid requires a new framework for initialization and forcing of the child WRF-IBM grid. This framework will enable concurrent multi-scale simulations within the WRF model, improving the accuracy of high-resolution simulations and enabling simulations across a wide range of scales.« less

The Effect of Subsurface Parameterizations on Modeled Flows in the Catchment Land Surface Model, Fortuna 2.5

NASA Astrophysics Data System (ADS)

Roningen, J. M.; Eylander, J. B.

2014-12-01

Groundwater use and management is subject to economic, legal, technical, and informational constraints and incentives at a variety of spatial and temporal scales. Planned and de facto management practices influenced by tax structures, legal frameworks, and agricultural and trade policies that vary at the country scale may have medium- and long-term effects on the ability of a region to support current and projected agricultural and industrial development. USACE is working to explore and develop global-scale, physically-based frameworks to serve as a baseline for hydrologic policy comparisons and consequence assessment, and such frameworks must include a reasonable representation of groundwater systems. To this end, we demonstrate the effects of different subsurface parameterizations, scaling, and meteorological forcings on surface and subsurface components of the Catchment Land Surface Model Fortuna v2.5 (Koster et al. 2000). We use the Land Information System 7 (Kumar et al. 2006) to process model runs using meteorological components of the Air Force Weather Agency's AGRMET forcing data from 2006 through 2011. Seasonal patterns and trends are examined in areas of the Upper Nile basin, northern China, and the Mississippi Valley. We also discuss the relevance of the model's representation of the catchment deficit with respect to local hydrogeologic structures.

Simulation of the 23 July 2012 Extreme Space Weather Event: What if This Extremely Rare CME Was Earth Directed?

NASA Technical Reports Server (NTRS)

Ngwira, Chigomezyo M.; Pulkkinen, Antti; Mays, M. Leila; Kuznetsova, Maria M.; Galvin, A. B.; Simunac, Kristin; Baker, Daniel N.; Li, Xinlin; Zheng, Yihua; Glocer, Alex

2013-01-01

Extreme space weather events are known to cause adverse impacts on critical modern day technological infrastructure such as high-voltage electric power transmission grids. On 23 July 2012, NASA's Solar Terrestrial Relations Observatory-Ahead (STEREO-A) spacecraft observed in situ an extremely fast coronal mass ejection (CME) that traveled 0.96 astronomical units (approx. 1 AU) in about 19 h. Here we use the SpaceWeather Modeling Framework (SWMF) to perform a simulation of this rare CME.We consider STEREO-A in situ observations to represent the upstream L1 solar wind boundary conditions. The goal of this study is to examine what would have happened if this Rare-type CME was Earth-bound. Global SWMF-generated ground geomagnetic field perturbations are used to compute the simulated induced geoelectric field at specific ground-based active INTERMAGNET magnetometer sites. Simulation results show that while modeled global SYM-H index, a high-resolution equivalent of the Dst index, was comparable to previously observed severe geomagnetic storms such as the Halloween 2003 storm, the 23 July CME would have produced some of the largest geomagnetically induced electric fields, making it very geoeffective. These results have important practical applications for risk management of electrical power grids.

A Goddard Multi-Scale Modeling System with Unified Physics

NASA Technical Reports Server (NTRS)

Tao, W.K.; Anderson, D.; Atlas, R.; Chern, J.; Houser, P.; Hou, A.; Lang, S.; Lau, W.; Peters-Lidard, C.; Kakar, R.;

A Portable Regional Weather and Climate Downscaling System Using GEOS-5, LIS-6, WRF, and the NASA Workflow Tool

NASA Astrophysics Data System (ADS)

Kemp, E. M.; Putman, W. M.; Gurganus, J.; Burns, R. W.; Damon, M. R.; McConaughy, G. R.; Seablom, M. S.; Wojcik, G. S.

2009-12-01

We present a regional downscaling system (RDS) suitable for high-resolution weather and climate simulations in multiple supercomputing environments. The RDS is built on the NASA Workflow Tool, a software framework for configuring, running, and managing computer models on multiple platforms with a graphical user interface. The Workflow Tool is used to run the NASA Goddard Earth Observing System Model Version 5 (GEOS-5), a global atmospheric-ocean model for weather and climate simulations down to 1/4 degree resolution; the NASA Land Information System Version 6 (LIS-6), a land surface modeling system that can simulate soil temperature and moisture profiles; and the Weather Research and Forecasting (WRF) community model, a limited-area atmospheric model for weather and climate simulations down to 1-km resolution. The Workflow Tool allows users to customize model settings to user needs; saves and organizes simulation experiments; distributes model runs across different computer clusters (e.g., the DISCOVER cluster at Goddard Space Flight Center, the Cray CX-1 Desktop Supercomputer, etc.); and handles all file transfers and network communications (e.g., scp connections). Together, the RDS is intended to aid researchers by making simulations as easy as possible to generate on the computer resources available. Initial conditions for LIS-6 and GEOS-5 are provided by Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis data stored on DISCOVER. The LIS-6 is first run for 2-4 years forced by MERRA atmospheric analyses, generating initial conditions for the WRF soil physics. GEOS-5 is then initialized from MERRA data and run for the period of interest. Large-scale atmospheric data, sea-surface temperatures, and sea ice coverage from GEOS-5 are used as boundary conditions for WRF, which is run for the same period of interest. Multiply nested grids are used for both LIS-6 and WRF, with the innermost grid run at a resolution sufficient for typical local weather features (terrain, convection, etc.) All model runs, restarts, and file transfers are coordinated by the Workflow Tool. Two use cases are being pursued. First, the RDS generates regional climate simulations down to 4-km for the Chesapeake Bay region, with WRF output provided as input to more specialized models (e.g., ocean/lake, hydrological, marine biology, and air pollution). This will allow assessment of climate impact on local interests (e.g., changes in Bay water levels and temperatures, innundation, fish kills, etc.) Second, the RDS generates high-resolution hurricane simulations in the tropical North Atlantic. This use case will support Observing System Simulation Experiments (OSSEs) of dynamically-targeted lidar observations as part of the NASA Sensor Web Simulator project. Sample results will be presented at the AGU Fall Meeting.

Towards energy-efficient nonoscillatory forward-in-time integrations on lat-lon grids

NASA Astrophysics Data System (ADS)

Polkowski, Marcin; Piotrowski, Zbigniew; Ryczkowski, Adam

2017-04-01

The design of the next-generation weather prediction models calls for new algorithmic approaches allowing for robust integrations of atmospheric flow over complex orography at sub-km resolutions. These need to be accompanied by efficient implementations exposing multi-level parallelism, capable to run on modern supercomputing architectures. Here we present the recent advances in the energy-efficient implementation of the consistent soundproof/implicit compressible EULAG dynamical core of the COSMO weather prediction framework. Based on the experiences of the atmospheric dwarfs developed within H2020 ESCAPE project, we develop efficient, architecture agnostic implementations of fully three-dimensional MPDATA advection schemes and generalized diffusion operator in curvilinear coordinates and spherical geometry. We compare optimized Fortran implementation with preliminary C++ implementation employing the Gridtools library, allowing for integrations on CPU and GPU while maintaining single source code.

Identifying crash-prone traffic conditions under different weather on freeways.

PubMed

Xu, Chengcheng; Wang, Wei; Liu, Pan

2013-09-01

Understanding the relationships between traffic flow characteristics and crash risk under adverse weather conditions will help highway agencies develop proactive safety management strategies to improve traffic safety in adverse weather conditions. The primary objective is to develop separate crash risk prediction models for different weather conditions. The crash data, weather data, and traffic data used in this study were collected on the I-880N freeway in California in 2008 and 2010. This study considered three different weather conditions: clear weather, rainy weather, and reduced visibility weather. The preliminary analysis showed that there was some heterogeneity in the risk estimates for traffic flow characteristics by weather conditions, and that the crash risk prediction model for all weather conditions cannot capture the impacts of the traffic flow variables on crash risk under adverse weather conditions. The Bayesian random intercept logistic regression models were applied to link the likelihood of crash occurrence with various traffic flow characteristics under different weather conditions. The crash risk prediction models were compared to their corresponding logistic regression model. It was found that the random intercept model improved the goodness-of-fit of the crash risk prediction models. The model estimation results showed that the traffic flow characteristics contributing to crash risk were different across different weather conditions. The speed difference between upstream and downstream stations was found to be significant in each crash risk prediction model. Speed difference between upstream and downstream stations had the largest impact on crash risk in reduced visibility weather, followed by that in rainy weather. The ROC curves were further developed to evaluate the predictive performance of the crash risk prediction models under different weather conditions. The predictive performance of the crash risk model for clear weather was better than those of the crash risk models for adverse weather conditions. The research results could promote a better understanding of the impacts of traffic flow characteristics on crash risk under adverse weather conditions, which will help transportation professionals to develop better crash prevention strategies in adverse weather. Copyright © 2013 National Safety Council and Elsevier Ltd. All rights reserved.

Mesoscale weather and climate modeling with the global non-hydrostatic Goddard Earth Observing System Model (GEOS-5) at cloud-permitting resolutions

NASA Astrophysics Data System (ADS)

Putman, W. M.; Suarez, M.

2009-12-01

The Goddard Earth Observing System Model (GEOS-5), an earth system model developed in the NASA Global Modeling and Assimilation Office (GMAO), has integrated the non-hydrostatic finite-volume dynamical core on the cubed-sphere grid. The extension to a non-hydrostatic dynamical framework and the quasi-uniform cubed-sphere geometry permits the efficient exploration of global weather and climate modeling at cloud permitting resolutions of 10- to 4-km on today's high performance computing platforms. We have explored a series of incremental increases in global resolution with GEOS-5 from it's standard 72-level 27-km resolution (~5.5 million cells covering the globe from the surface to 0.1 hPa) down to 3.5-km (~3.6 billion cells). We will present results from a series of forecast experiments exploring the impact of the non-hydrostatic dynamics at transition resolutions of 14- to 7-km, and the influence of increased horizontal/vertical resolution on convection and physical parameterizations within GEOS-5. Regional and mesoscale features of 5- to 10-day weather forecasts will be presented and compared with satellite observations. Our results will highlight the impact of resolution on the structure of cloud features including tropical convection and tropical cyclone predicability, cloud streets, von Karman vortices, and the marine stratocumulus cloud layer. We will also present experiment design and early results from climate impact experiments for global non-hydrostatic models using GEOS-5. Our climate experiments will focus on support for the Year of Tropical Convection (YOTC). We will also discuss a seasonal climate time-slice experiment design for downscaling coarse resolution century scale climate simulations to global non-hydrostatic resolutions of 14- to 7-km with GEOS-5.

Global perspectives on oxidative weathering of organic carbon in sedimentary rocks

NASA Astrophysics Data System (ADS)

Dellinger, M.; Hilton, R. G.; West, A. J.; Horan, K.; Gaillardet, J.

2016-12-01

Over geological timescales, the oxidation of organic carbon in sedimentary rocks is major source of carbon dioxide (CO2) to the atmosphere. The global magnitude of this flux remains poorly constrained, but it is likely to be between 40-100 x 1012 g C yr-1, similar to the CO2 emissions from volcanism. The rates of CO2 emission ultimately set the rate of silicate weathering by carbonic acid and new organic carbon burial, which act together to stabilise the climate system. To constrain how the geological carbon cycle operates and modifies Earth's climate over millions of years, we must better understand the controls on the oxidation of sedimentary rock-derived organic carbon (`petrogenic' OC, OCpetro). Here we examine new and published constraints on OCpetro oxidation flux, which come from indirect measurements (e.g. trace element proxies such as rhenium) and direct measurements (e.g. CO2 trapping and 14C). Existing datasets track the gaseous and dissolved products of weathering as well as the solid residues over a range of spatial scales, from soil profiles to large river catchments. Although the datasets are still sparse, they indicate that physical denudation plays a major role in setting OCpetro oxidation flux. These measurements are interrogated in the framework of a catchment-scale numerical model of OCpetro oxidation. By harnessing approaches developed to examine and quantify acid-hydrolysis reactions (i.e. silicate mineral weathering by carbonic acid) the model considers realistic geochemical processes and the links between erosion and weathering. Key parameters emerge, such as the `weathering thickness' which describes a depth to which oxidative waters penetrate. The reaction kinetics of OCpetro remain poorly constrained, but nevertheless, the model predicts that the kinetic limitation of OCpetro oxidation is not reached until physical erosion rates exceed 2 mm yr-1, which is much higher than for CO2 consumption by silicate weathering. These findings mirror data on sulphide oxidation, demonstrating that physical erosion can drive an important CO2 release to the atmosphere both from inorganic and organic reduced phases in sedimentary rocks. The degree to which this release is compensated by CO2 drawdown associated with the erosion and transfer of biospheric OC will also be considered.

An Introduction to Observing System Simulation Experiments

NASA Technical Reports Server (NTRS)

Prive, Nikki C.

2017-01-01

Observing System Simulation Experiments (OSSEs) are used to estimate the potential impact of proposed new instruments and data on numerical weather prediction. OSSEs can also be used to help design new observing platforms and to investigate the behavior of data assimilation systems. A basic overview of how to design and perform an OSSE will be given, as well as best practices and pitfalls. Some examples using the OSSE framework developed at the NASA Global Modeling and Assimilation Office will be shown.

Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data for snow modeling in mountain basins

NASA Astrophysics Data System (ADS)

Havens, Scott; Marks, Danny; Kormos, Patrick; Hedrick, Andrew

2017-12-01

In the Western US and many mountainous regions of the world, critical water resources and climate conditions are difficult to monitor because the observation network is generally very sparse. The critical resource from the mountain snowpack is water flowing into streams and reservoirs that will provide for irrigation, flood control, power generation, and ecosystem services. Water supply forecasting in a rapidly changing climate has become increasingly difficult because of non-stationary conditions. In response, operational water supply managers have begun to move from statistical techniques towards the use of physically based models. As we begin to transition physically based models from research to operational use, we must address the most difficult and time-consuming aspect of model initiation: the need for robust methods to develop and distribute the input forcing data. In this paper, we present a new open source framework, the Spatial Modeling for Resources Framework (SMRF), which automates and simplifies the common forcing data distribution methods. It is computationally efficient and can be implemented for both research and operational applications. We present an example of how SMRF is able to generate all of the forcing data required to a run physically based snow model at 50-100 m resolution over regions of 1000-7000 km2. The approach has been successfully applied in real time and historical applications for both the Boise River Basin in Idaho, USA and the Tuolumne River Basin in California, USA. These applications use meteorological station measurements and numerical weather prediction model outputs as input. SMRF has significantly streamlined the modeling workflow, decreased model set up time from weeks to days, and made near real-time application of a physically based snow model possible.

Verification of Advances in a Coupled Snow-runoff Modeling Framework for Operational Streamflow Forecasts

NASA Astrophysics Data System (ADS)

Barik, M. G.; Hogue, T. S.; Franz, K. J.; He, M.

2011-12-01

The National Oceanic and Atmospheric Administration's (NOAA's) River Forecast Centers (RFCs) issue hydrologic forecasts related to flood events, reservoir operations for water supply, streamflow regulation, and recreation on the nation's streams and rivers. The RFCs use the National Weather Service River Forecast System (NWSRFS) for streamflow forecasting which relies on a coupled snow model (i.e. SNOW17) and rainfall-runoff model (i.e. SAC-SMA) in snow-dominated regions of the US. Errors arise in various steps of the forecasting system from input data, model structure, model parameters, and initial states. The goal of the current study is to undertake verification of potential improvements in the SNOW17-SAC-SMA modeling framework developed for operational streamflow forecasts. We undertake verification for a range of parameters sets (i.e. RFC, DREAM (Differential Evolution Adaptive Metropolis)) as well as a data assimilation (DA) framework developed for the coupled models. Verification is also undertaken for various initial conditions to observe the influence of variability in initial conditions on the forecast. The study basin is the North Fork America River Basin (NFARB) located on the western side of the Sierra Nevada Mountains in northern California. Hindcasts are verified using both deterministic (i.e. Nash Sutcliffe efficiency, root mean square error, and joint distribution) and probabilistic (i.e. reliability diagram, discrimination diagram, containing ratio, and Quantile plots) statistics. Our presentation includes comparison of the performance of different optimized parameters and the DA framework as well as assessment of the impact associated with the initial conditions used for streamflow forecasts for the NFARB.

The CAMI Project - Weather and Climate Services for Caribbean Food Security

NASA Astrophysics Data System (ADS)

Trotman, Adrian; Van Meerbeeck, Cedric

2013-04-01

Food security is major focus of Caribbean governments, with production being of particular concern. For the past three decades, Caribbean agriculture has been declining in relative importance, both in terms of its contribution to GDP and its share of the labour force. One of the problems Caribbean agriculture faces is the destructive impacts from weather and climate extremes. These include flood, drought, extreme temperatures, and strong winds from tropical cyclones. Other potential disasters, such as from pests and diseases attacks, are also weather and climate driven. These make weather and climate information critically important to decision-making in agriculture in the Caribbean region. In an effort to help reduce weather and climate related risks to the food security sector, The Caribbean Institute for Meteorology and Hydrology, along with its partners the Caribbean Agricultural Research and Development Institute, the World Meteorological Organization (WMO) and ten National Meteorological Services from within the Caribbean Community launched and implemented the Caribbean Agrometeorological Initiative (CAMI). From 2010 to 2013, CAMI set out to provide relevant information to farmers, and the industry in general, for decision and policy making. The project is funded by the European Union through the Science and Technology Programme of the African, Caribbean and Pacific Group of Countries' (ACP). The overarching objective of CAMI was to increase and sustain agricultural productivity at the farm level in the Caribbean region through improved applications of weather and climate information, using an integrated and coordinated approach. Currently, this is done through (i) provision of relevant climate information appropriately disseminated, (ii) predictions on seasonal rainfall and temperature, (iii) support for improved irrigation management, (iv) the development of strategically selected weather-driven pest and disease models, (v) use of crop simulation models, (vi) training of staff of National Meteorological Services (NMS) and two relevant regional research institutions (vi) and the staging of forums for farmers and Agriculture Extension officers. With its innovative actions and generated products, the thrusts of CAMI link well to the components of the WMO's Global Framework for Climate Services.

Statistical wave climate projections for coastal impact assessments

NASA Astrophysics Data System (ADS)

Camus, P.; Losada, I. J.; Izaguirre, C.; Espejo, A.; Menéndez, M.; Pérez, J.

2017-09-01

Global multimodel wave climate projections are obtained at 1.0° × 1.0° scale from 30 Coupled Model Intercomparison Project Phase 5 (CMIP5) global circulation model (GCM) realizations. A semi-supervised weather-typing approach based on a characterization of the ocean wave generation areas and the historical wave information from the recent GOW2 database are used to train the statistical model. This framework is also applied to obtain high resolution projections of coastal wave climate and coastal impacts as port operability and coastal flooding. Regional projections are estimated using the collection of weather types at spacing of 1.0°. This assumption is feasible because the predictor is defined based on the wave generation area and the classification is guided by the local wave climate. The assessment of future changes in coastal impacts is based on direct downscaling of indicators defined by empirical formulations (total water level for coastal flooding and number of hours per year with overtopping for port operability). Global multimodel projections of the significant wave height and peak period are consistent with changes obtained in previous studies. Statistical confidence of expected changes is obtained due to the large number of GCMs to construct the ensemble. The proposed methodology is proved to be flexible to project wave climate at different spatial scales. Regional changes of additional variables as wave direction or other statistics can be estimated from the future empirical distribution with extreme values restricted to high percentiles (i.e., 95th, 99th percentiles). The statistical framework can also be applied to evaluate regional coastal impacts integrating changes in storminess and sea level rise.

Comparison of predictive estimates of high-latitude electrodynamics with observations of global-scale Birkeland currents

NASA Astrophysics Data System (ADS)

Anderson, Brian J.; Korth, Haje; Welling, Daniel T.; Merkin, Viacheslav G.; Wiltberger, Michael J.; Raeder, Joachim; Barnes, Robin J.; Waters, Colin L.; Pulkkinen, Antti A.; Rastaetter, Lutz

2017-02-01

Two of the geomagnetic storms for the Space Weather Prediction Center Geospace Environment Modeling challenge occurred after data were first acquired by the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). We compare Birkeland currents from AMPERE with predictions from four models for the 4-5 April 2010 and 5-6 August 2011 storms. The four models are the Weimer (2005b) field-aligned current statistical model, the Lyon-Fedder-Mobarry magnetohydrodynamic (MHD) simulation, the Open Global Geospace Circulation Model MHD simulation, and the Space Weather Modeling Framework MHD simulation. The MHD simulations were run as described in Pulkkinen et al. (2013) and the results obtained from the Community Coordinated Modeling Center. The total radial Birkeland current, ITotal, and the distribution of radial current density, Jr, for all models are compared with AMPERE results. While the total currents are well correlated, the quantitative agreement varies considerably. The Jr distributions reveal discrepancies between the models and observations related to the latitude distribution, morphologies, and lack of nightside current systems in the models. The results motivate enhancing the simulations first by increasing the simulation resolution and then by examining the relative merits of implementing more sophisticated ionospheric conductance models, including ionospheric outflows or other omitted physical processes. Some aspects of the system, including substorm timing and location, may remain challenging to simulate, implying a continuing need for real-time specification.

DEVELOPMENT OF A DECISION SUPPORT FRAMEWORK FOR PLACEMENT OF BMPS IN URBAN-WATERSHEDS

EPA Science Inventory

This paper will present an on-going development of an integrated decision support framework (IDSF) for cost-effective placement of best management practices (BMPs) for managing wet weather flows (WWF) in urban watersheds. This decision tool will facilitate the selection and plac...

AN INTEGRATED DECISION SUPPORT FRAMEWORK FOR PLACEMENT OF BMPS IN URBAN-WATERSHEDS

EPA Science Inventory

This paper will present an on-going development of an integrated decision support framework (IDSF) for cost-effective placement of best management practices (BMPs) for managing wet weather flows (WWF) in urban watersheds. This decision tool will facilitate the selection and plac...

A dynamic water-quality modeling framework for the Neuse River estuary, North Carolina

USGS Publications Warehouse

Bales, Jerad D.; Robbins, Jeanne C.

1999-01-01

As a result of fish kills in the Neuse River estuary in 1995, nutrient reduction strategies were developed for point and nonpoint sources in the basin. However, because of the interannual variability in the natural system and the resulting complex hydrologic-nutrient inter- actions, it is difficult to detect through a short-term observational program the effects of management activities on Neuse River estuary water quality and aquatic health. A properly constructed water-quality model can be used to evaluate some of the potential effects of manage- ment actions on estuarine water quality. Such a model can be used to predict estuarine response to present and proposed nutrient strategies under the same set of meteorological and hydrologic conditions, thus removing the vagaries of weather and streamflow from the analysis. A two-dimensional, laterally averaged hydrodynamic and water-quality modeling framework was developed for the Neuse River estuary by using previously collected data. Development of the modeling framework consisted of (1) computational grid development, (2) assembly of data for model boundary conditions and model testing, (3) selection of initial values of model parameters, and (4) limited model testing. The model domain extends from Streets Ferry to Oriental, N.C., includes seven lateral embayments that have continual exchange with the main- stem of the estuary, three point-source discharges, and three tributary streams. Thirty-five computational segments represent the mainstem of the estuary, and the entire framework contains a total of 60 computa- tional segments. Each computational cell is 0.5 meter thick; segment lengths range from 500 meters to 7,125 meters. Data that were used to develop the modeling framework were collected during March through October 1991 and represent the most comprehensive data set available prior to 1997. Most of the data were collected by the North Carolina Division of Water Quality, the University of North Carolina Institute of Marine Sciences, and the U.S. Geological Survey. Limitations in the modeling framework were clearly identified. These limitations formed the basis for a set of suggestions to refine the Neuse River estuary water-quality model.

Modelling Precipitation and Temperature Extremes: The Importance of Horizontal Resolution

NASA Astrophysics Data System (ADS)

Shields, C. A.; Kiehl, J. T.; Meehl, G. A.

2013-12-01

Understanding Earth's water cycle on a warming planet is of critical importance in society's ability to adapt to climate change. Extreme weather events, such as floods, heat waves, and drought will likely change with the water cycle as greenhouse gases continue to rise. Location, duration, and intensity of extreme events can be studied using complex earth system models. Here, we employ the fully coupled Community Earth System Model (CESM1.0) to evaluate extreme event impacts for different possible future forcing scenarios. Simulations applying the Representative Concentration Pathway (RCP) scenarios 2.6 and 8.5 were chosen to bracket the range of model responses. Because extreme weather events happen on a regional scale, there is a tendency to favor using higher resolution models, i.e. models that can represent regional features with greater accuracy. Within the CESM1.0 framework, we evaluate both the standard 1 degree resolution (1 degree atmosphere/land coupled to 1 degree ocean/sea ice), and the higher 0.5 degree resolution version (0.5 degree atmosphere/land coupled to 1 degree ocean/sea ice), focusing on extreme precipitation events, heat waves, and droughts. We analyze a variety of geographical regions, but generally find that benefits from increased horizontal resolution are most significant on the regional scale.

Building resilience to weather-related hazards through better preparedness

NASA Astrophysics Data System (ADS)

Keller, Julia; Golding, Brian; Johnston, David; Ruti, Paolo

2017-04-01

Recent developments in weather forecasting have transformed our ability to predict weather-related hazards, while mobile communication is radically changing the way that people receive information. At the same time, vulnerability to weather-related hazards is growing through urban expansion, population growth and climate change. This talk will address issues facing the science community in responding to the Sendai Framework objective to "substantially increase the availability of and access to multi-hazard early warning systems" in the context of weather-related hazards. It will also provide an overview of activities and approaches developed in the World Meteorological Organisation's High Impact Weather (HIWeather) project. HIWeather has identified and is promoting research in key multi-disciplinary gaps in our knowledge, including in basic meteorology, risk prediction, communication and decision making, that affect our ability to provide effective warnings. The results will be pulled together in demonstration projects that will both showcase leading edge capability and build developing country capacity.

An Implementing Strategy for Improving Wildland Fire Environmental Literacy

NASA Astrophysics Data System (ADS)

McCalla, M. R.; Andrus, D.; Barnett, K.

2007-12-01

Wildland fire is any planned or unplanned fire which occurs in wildland ecosystems. Wildland fires affect millions of acres annually in the U.S. An average of 5.4 million acres a year were burned in the U.S. between 1995 and 2004, approximately 142 percent of the average burned area between 1984 and 1994. In 2005 alone, Federal agencies spent nearly $1 billion on fire suppression and state and local agencies contributed millions more. Many Americans prefer to live and vacation in relatively remote surroundings, (i.e., woods and rangelands). These choices offer many benefits, but they also present significant risks. Most of North America is fire-prone and every day developed areas and home sites are extending further into natural wildlands, which increases the chances of catastrophic fire. In addition, an abundance of accumulated biomass in forests and rangelands and persistent drought conditions are contributing to larger, costlier wildland fires. To effectively prevent, manage, suppress, respond to, and recover from wildland fires, fire managers, and other communities which are impacted by wildland fires (e.g., the business community; healthcare providers; federal, state, and local policymakers; the media; the public, etc.) need timely, accurate, and detailed wildland fire weather and climate information to support their decision-making activities. But what are the wildland fire weather and climate data, products, and information, as well as information dissemination technologies, needed to reach out and promote wildland fire environmental literacy in these communities? The Office of the Federal Coordinator for Meteorological Services and Supporting Research (OFCM) conducted a comprehensive review and assessment of weather and climate needs of providers and users in their wildland fire and fuels management activities. The assessment has nine focus areas, one of which is environmental literacy (e.g., education, training, outreach, partnering, and collaboration). The OFCM model for promoting wildland fire environmental literacy, the model's component parts, as well as an implementing strategy to execute the model will be presented. That is, the presentation will lay out the framework and methodology which the OFCM used to systematically define the wildland fire weather and climate education and outreach needs through interdepartmental collaboration within the OFCM coordinating infrastructure. A key element of the methodology is to improve the overall understanding and use of wildland fire forecast and warning climate and weather products and to exploit current and emerging technologies to improve the dissemination of customer-tailored forecast and warning information and products to stakeholders and users. Thus, the framework and methodology define the method used to determine the target public, private, and academic sector audiences. The methodology also identifies the means for determining the optimal channels, formats, and content for informing end users in time for effective action to be taken.

SMOS Soil Moisture Data Assimilation in the NASA Land Information System: Impact on LSM Initialization and NWP Forecasts

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Case, Jonathan L.; Zavodsky, Bradley

2015-01-01

Land surface models are important components of numerical weather prediction (NWP) models, partitioning incoming energy into latent and sensitive heat fluxes that affect boundary layer growth and destabilization. During warm-season months, diurnal heating and convective initiation depend strongly on evapotranspiration and available boundary layer moisture, which are substantially affected by soil moisture content. Therefore, to properly simulate warm-season processes in NWP models, an accurate initialization of the land surface state is important for accurately depicting the exchange of heat and moisture between the surface and boundary layer. In this study, soil moisture retrievals from the Soil Moisture and Ocean Salinity (SMOS) satellite radiometer are assimilated into the Noah Land Surface Model via an Ensemble Kalman Filter embedded within the NASA Land Information System (LIS) software framework. The output from LIS-Noah is subsequently used to initialize runs of the Weather Research and Forecasting (WRF) NWP model. The impact of assimilating SMOS retrievals is assessed by initializing the WRF model with LIS-Noah output obtained with and without SMOS data assimilation. The southeastern United States is used as the domain for a preliminary case study. During the summer months, there is extensive irrigation in the lower Mississippi Valley for rice and other crops. The irrigation is not represented in the meteorological forcing used to drive the LIS-Noah integration, but the irrigated areas show up clearly in the SMOS soil moisture retrievals, resulting in a case with a large difference in initial soil moisture conditions. The impact of SMOS data assimilation on both Noah soil moisture fields and on short-term (0-48 hour) WRF weather forecasts will be presented.

A New Framework for Cumulus Parametrization - A CPT in action

NASA Astrophysics Data System (ADS)

Jakob, C.; Peters, K.; Protat, A.; Kumar, V.

2016-12-01

The representation of convection in climate model remains a major Achilles Heel in our pursuit of better predictions of global and regional climate. The basic principle underpinning the parametrisation of tropical convection in global weather and climate models is that there exist discernible interactions between the resolved model scale and the parametrised cumulus scale. Furthermore, there must be at least some predictive power in the larger scales for the statistical behaviour on small scales for us to be able to formally close the parametrised equations. The presentation will discuss a new framework for cumulus parametrisation based on the idea of separating the prediction of cloud area from that of velocity. This idea is put into practice by combining an existing multi-scale stochastic cloud model with observations to arrive at the prediction of the area fraction for deep precipitating convection. Using mid-tropospheric humidity and vertical motion as predictors, the model is shown to reproduce the observed behaviour of both mean and variability of deep convective area fraction well. The framework allows for the inclusion of convective organisation and can - in principle - be made resolution-aware or resolution-independent. When combined with simple assumptions about cloud-base vertical motion the model can be used as a closure assumption in any existing cumulus parametrisation. Results of applying this idea in the the ECHAM model indicate significant improvements in the simulation of tropical variability, including but not limited to the MJO. This presentation will highlight how the close collaboration of the observational, theoretical and model development community in the spirit of the climate process teams can lead to significant progress in long-standing issues in climate modelling while preserving the freedom of individual groups in pursuing their specific implementation of an agreed framework.

An Integrated Uncertainty Analysis and Ensemble-based Data Assimilation Framework for Operational Snow Predictions

NASA Astrophysics Data System (ADS)

He, M.; Hogue, T. S.; Franz, K.; Margulis, S. A.; Vrugt, J. A.

2009-12-01

The National Weather Service (NWS), the agency responsible for short- and long-term streamflow predictions across the nation, primarily applies the SNOW17 model for operational forecasting of snow accumulation and melt. The SNOW17-forecasted snowmelt serves as an input to a rainfall-runoff model for streamflow forecasts in snow-dominated areas. The accuracy of streamflow predictions in these areas largely relies on the accuracy of snowmelt. However, no direct snowmelt measurements are available to validate the SNOW17 predictions. Instead, indirect measurements such as snow water equivalent (SWE) measurements or discharge are typically used to calibrate SNOW17 parameters. In addition, the forecast practice is inherently deterministic, lacking tools to systematically address forecasting uncertainties (e.g., uncertainties in parameters, forcing, SWE and discharge observations, etc.). The current research presents an Integrated Uncertainty analysis and Ensemble-based data Assimilation (IUEA) framework to improve predictions of snowmelt and discharge while simultaneously providing meaningful estimates of the associated uncertainty. The IUEA approach uses the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) to simultaneously estimate uncertainties in model parameters, forcing, and observations. The robustness and usefulness of the IUEA-SNOW17 framework is evaluated for snow-dominated watersheds in the northern Sierra Mountains, using the coupled IUEA-SNOW17 and an operational soil moisture accounting model (SAC-SMA). Preliminary results are promising and indicate successful performance of the coupled IUEA-SNOW17 framework. Implementation of the SNOW17 with the IUEA is straightforward and requires no major modification to the SNOW17 model structure. The IUEA-SNOW17 framework is intended to be modular and transferable and should assist the NWS in advancing the current forecasting system and reinforcing current operational forecasting skill.

Why Is Rainfall Error Analysis Requisite for Data Assimilation and Climate Modeling?

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.; Zhang, Sara Q.

2004-01-01

Given the large temporal and spatial variability of precipitation processes, errors in rainfall observations are difficult to quantify yet crucial to making effective use of rainfall data for improving atmospheric analysis, weather forecasting, and climate modeling. We highlight the need for developing a quantitative understanding of systematic and random errors in precipitation observations by examining explicit examples of how each type of errors can affect forecasts and analyses in global data assimilation. We characterize the error information needed from the precipitation measurement community and how it may be used to improve data usage within the general framework of analysis techniques, as well as accuracy requirements from the perspective of climate modeling and global data assimilation.

Simulation of all-scale atmospheric dynamics on unstructured meshes

NASA Astrophysics Data System (ADS)

Smolarkiewicz, Piotr K.; Szmelter, Joanna; Xiao, Feng

2016-10-01

The advance of massively parallel computing in the nineteen nineties and beyond encouraged finer grid intervals in numerical weather-prediction models. This has improved resolution of weather systems and enhanced the accuracy of forecasts, while setting the trend for development of unified all-scale atmospheric models. This paper first outlines the historical background to a wide range of numerical methods advanced in the process. Next, the trend is illustrated with a technical review of a versatile nonoscillatory forward-in-time finite-volume (NFTFV) approach, proven effective in simulations of atmospheric flows from small-scale dynamics to global circulations and climate. The outlined approach exploits the synergy of two specific ingredients: the MPDATA methods for the simulation of fluid flows based on the sign-preserving properties of upstream differencing; and the flexible finite-volume median-dual unstructured-mesh discretisation of the spatial differential operators comprising PDEs of atmospheric dynamics. The paper consolidates the concepts leading to a family of generalised nonhydrostatic NFTFV flow solvers that include soundproof PDEs of incompressible Boussinesq, anelastic and pseudo-incompressible systems, common in large-eddy simulation of small- and meso-scale dynamics, as well as all-scale compressible Euler equations. Such a framework naturally extends predictive skills of large-eddy simulation to the global atmosphere, providing a bottom-up alternative to the reverse approach pursued in the weather-prediction models. Theoretical considerations are substantiated by calculations attesting to the versatility and efficacy of the NFTFV approach. Some prospective developments are also discussed.

Developing of operational hydro-meteorological simulating and displaying system

NASA Astrophysics Data System (ADS)

Wang, Y.; Shih, D.; Chen, C.

2010-12-01

Hydrological hazards, which often occur in conjunction with extreme precipitation events, are the most frequent type of natural disaster in Taiwan. Hence, the researchers at the Taiwan Typhoon and Flood Research Institute (TTFRI) are devoted to analyzing and gaining a better understanding of the causes and effects of natural disasters, and in particular, typhoons and floods. The long-term goal of the TTFRI is to develop a unified weather-hydrological-oceanic model suitable for simulations with local parameterizations in Taiwan. The development of a fully coupled weather-hydrology interaction model is not yet completed but some operational hydro-meteorological simulations are presented as a step in the direction of completing a full model. The predicted rainfall data from Weather Research Forecasting (WRF) are used as our meteorological forcing on watershed modeling. The hydrology and hydraulic modeling are conducted by WASH123D numerical model. And the WRF/WASH123D coupled system is applied to simulate floods during the typhoon landfall periods. The daily operational runs start at 04UTC, 10UTC, 16UTC and 22UTC, about 4 hours after data downloaded from NCEP GFS. This system will execute 72-hr weather forecasts. The simulation of WASH123D will sequentially trigger after receiving WRF rainfall data. This study presents the preliminary framework of establishing this system, and our goal is to build this earlier warning system to alert the public form dangerous. The simulation results are further display by a 3D GIS web service system. This system is established following the Open Geospatial Consortium (OGC) standardization process for GIS web service, such as Web Map Service (WMS) and Web Feature Service (WFS). The traditional 2D GIS data, such as high resolution aerial photomaps and satellite images are integrated into 3D landscape model. The simulated flooding and inundation area can be dynamically mapped on Wed 3D world. The final goal of this system is to real-time forecast flood and the results can be visually displayed on the virtual catchment. The policymaker can easily and real-time gain visual information for decision making at any site through internet.

Quantitative Assessment of the CCMC's Experimental Real-time SWMF-Geospace Results

NASA Astrophysics Data System (ADS)

Liemohn, Michael; Ganushkina, Natalia; De Zeeuw, Darren; Welling, Daniel; Toth, Gabor; Ilie, Raluca; Gombosi, Tamas; van der Holst, Bart; Kuznetsova, Maria; Maddox, Marlo; Rastaetter, Lutz

2016-04-01

Experimental real-time simulations of the Space Weather Modeling Framework (SWMF) are conducted at the Community Coordinated Modeling Center (CCMC), with results available there (http://ccmc.gsfc.nasa.gov/realtime.php), through the CCMC Integrated Space Weather Analysis (iSWA) site (http://iswa.ccmc.gsfc.nasa.gov/IswaSystemWebApp/), and the Michigan SWMF site (http://csem.engin.umich.edu/realtime). Presently, two configurations of the SWMF are running in real time at CCMC, both focusing on the geospace modules, using the BATS-R-US magnetohydrodynamic model, the Ridley Ionosphere Model, and with and without the Rice Convection Model for inner magnetospheric drift physics. While both have been running for several years, nearly continuous results are available since July 2015. Dst from the model output is compared against the Kyoto real-time Dst, in particular the daily minimum value of Dst to quantify the ability of the model to capture storms. Contingency tables are presented, showing that the run with the inner magnetosphere model is much better at reproducing storm-time values. For disturbances with a minimum Dst lower than -50 nT, this version yields a probability of event detection of 0.86 and a Heidke Skill Score of 0.60. In the other version of the SWMF, without the inner magnetospheric module included, the modeled Dst never dropped below -50 nT during the examined epoch.

Integrated Decision Tools for Sustainable Watershed/Ground Water and Crop Health using Predictive Weather, Remote Sensing, and Irrigation Decision Tools

NASA Astrophysics Data System (ADS)

Jones, A. S.; Andales, A.; McGovern, C.; Smith, G. E. B.; David, O.; Fletcher, S. J.

2017-12-01

US agricultural and Govt. lands have a unique co-dependent relationship, particularly in the Western US. More than 30% of all irrigated US agricultural output comes from lands sustained by the Ogallala Aquifer in the western Great Plains. Six US Forest Service National Grasslands reside within the aquifer region, consisting of over 375,000 ha (3,759 km2) of USFS managed lands. Likewise, National Forest lands are the headwaters to many intensive agricultural regions. Our Ogallala Aquifer team is enhancing crop irrigation decision tools with predictive weather and remote sensing data to better manage water for irrigated crops within these regions. An integrated multi-model software framework is used to link irrigation decision tools, resulting in positive management benefits on natural water resources. Teams and teams-of-teams can build upon these multi-disciplinary multi-faceted modeling capabilities. For example, the CSU Catalyst for Innovative Partnerships program has formed a new multidisciplinary team that will address "Rural Wealth Creation" focusing on the many integrated links between economic, agricultural production and management, natural resource availabilities, and key social aspects of govt. policy recommendations. By enhancing tools like these with predictive weather and other related data (like in situ measurements, hydrologic models, remotely sensed data sets, and (in the near future) linking to agro-economic and life cycle assessment models) this work demonstrates an integrated data-driven future vision of inter-meshed dynamic systems that can address challenging multi-system problems. We will present the present state of the work and opportunities for future involvement.

AN overview of the FLYSAFE datalink solution for the exchange of weather information: supporting aircrew decision making processes.

NASA Astrophysics Data System (ADS)

Mirza, A.; Drouin, A.

2009-09-01

FLYSAFE is an Integrated Project of the 6th framework of the European Commission with the aim to improve flight safety through the development of an avionics solution the Next Generation Integrated Surveillance System (NGISS), which is supported by a ground based network of Weather Information Management Systems (WIMS) and access points in the form of the Ground Weather Processor (GWP). The NGISS provides information to the flight crew on the three major external hazards for aviation: weather, air traffic and terrain. The NGISS has the capability of displaying data about all three hazards on a single display screen, facilitating rapid appreciation of the situation by the flight crew. Weather Information Management Systems (WIMS) were developed to provide the NGISS and the flight crew with weather related information on in-flight icing, thunderstorms and clear-air turbulence. These products are generated on the ground from observations and model forecasts. WIMS will supply relevant information on three different scales: global, regional and local (over airport Terminal Manoeuvring Area). The Ground Weather Processor is a client-server architecture that utilises open source components, which include a geospatial database and web feature services. The GWP stores Weather Objects generated by the WIMS. An aviation user can retrieve on-demand all Weather Objects that intersect the volume of space that is of interest to them. The Weather Objects are fused with in-situ observation data and can be used by the flight management system to propose a route to avoid the hazard. In addition they can be used to display the current hazardous weather to the Flight Crew thereby raising their awareness. Within the FLYSAFE program, around 120 hours of flight trials were performed during February 2008 and August 2008. Two aircraft were involved each with separate objectives: - to assess FLYSAFE's innovative solutions for the data-link, on-board data-fusion and data-display and data-updates during flight; - to evaluate the new weather information management systems (in-flight icing and thunderstorms) using in-situ measurements recorded on-board the test aircraft. In this presentation we will focus on the data link solution to uplink the Weather Objects to the NGISS. As part of the solution, a brief description is given on how grid data created by the WIMS are transformed to Weather Objects; which describe the weather hazard and are formatted using the Geospatial Mark-up Language.

Multifluid MHD Simulations of the Plasma Environment of Comet Churyumov-Gerasimenko at Different Heliocentric Distances

NASA Astrophysics Data System (ADS)

Huang, Z.; Jia, X.; Rubin, M.; Fougere, N.; Gombosi, T. I.; Tenishev, V.; Combi, M. R.; Bieler, A. M.; Toth, G.; Hansen, K. C.; Shou, Y.

2014-12-01

We study the plasma environment of the comet Churyumov-Gerasimenko, which is the target of the Rosetta mission, by performing large scale numerical simulations. Our model is based on BATS-R-US within the Space Weather Modeling Framework that solves the governing multifluid MHD equations, which describe the behavior of the cometary heavy ions, the solar wind protons, and electrons. The model includes various mass loading processes, including ionization, charge exchange, dissociative ion-electron recombination, as well as collisional interactions between different fluids. The neutral background used in our MHD simulations is provided by a kinetic Direct Simulation Monte Carlo (DSMC) model. We will simulate how the cometary plasma environment changes at different heliocentric distances.

Numerical Weather Prediction Models on Linux Boxes as tools in meteorological education in Hungary

NASA Astrophysics Data System (ADS)

Gyongyosi, A. Z.; Andre, K.; Salavec, P.; Horanyi, A.; Szepszo, G.; Mille, M.; Tasnadi, P.; Weidiger, T.

2012-04-01

Education of Meteorologist in Hungary - according to the Bologna Process - has three stages: BSc, MSc and PhD, and students graduating at each stage get the respective degree (BSc, MSc and PhD). The three year long base BSc course in Meteorology can be chosen by undergraduate students in the fields of Geosciences, Environmental Sciences and Physics. BasicsFundamentals in Mathematics (Calculus), Physics (General and Theoretical) Physics and Informatics are emphasized during their elementary education. The two year long MSc course - in which about 15 to 25 students are admitted each year - can be studied only at our the Eötvös Loránd uUniversity in the our country. Our aim is to give a basic education in all fields of Meteorology. Main topics are: Climatology, Atmospheric Physics, Atmospheric Chemistry, Dynamic and Synoptic Meteorology, Numerical Weather Prediction, modeling Modeling of surfaceSurface-atmosphere Iinteractions and Cclimate change. Education is performed in two branches: Climate Researcher and Forecaster. Education of Meteorologist in Hungary - according to the Bologna Process - has three stages: BSc, MSc and PhD, and students graduating at each stage get the respective degree. The three year long BSc course in Meteorology can be chosen by undergraduate students in the fields of Geosciences, Environmental Sciences and Physics. Fundamentals in Mathematics (Calculus), (General and Theoretical) Physics and Informatics are emphasized during their elementary education. The two year long MSc course - in which about 15 to 25 students are admitted each year - can be studied only at the Eötvös Loránd University in our country. Our aim is to give a basic education in all fields of Meteorology: Climatology, Atmospheric Physics, Atmospheric Chemistry, Dynamic and Synoptic Meteorology, Numerical Weather Prediction, Modeling of Surface-atmosphere Interactions and Climate change. Education is performed in two branches: Climate Researcher and Forecaster. Numerical modeling became a common tool in the daily practice of weather experts forecasters due to the i) increasing user demands for weather data by the costumers, ii) the growth in computer resources, iii) numerical weather prediction systems available for integration on affordable, off the shelf computers and iv) available input data (from ECMWF or NCEP) for model integrations. Beside learning the theoretical basis, since the last year. Students in their MSc or BSc Thesis Research or in Student's Research ProjectsStudent's Research Projects h have the opportunity to run numerical models and to analyze the outputs for different purposes including wind energy estimation, simulation of the dynamics of a polar low, and subtropical cyclones, analysis of the isentropic potential vorticity field, examination of coupled atmospheric dispersion models, etc. A special course in the application of numerical modeling has been held (is being announced for the upcoming semester) (is being announced for the upcoming semester) for our students in order to improve their skills on this field. Several numerical model (NRIPR ETA and WRF) systems have been adapted in the University and integrated WRF have been tested and used for the geographical region of the Carpathian Basin (NRIPR, ETA and WRF). Recently ALADIN/CHAPEAU the academic version of the ARPEGE ALADIN cy33t1 meso-scale numerical weather prediction model system (which is the operational forecasting tool of our National Weather Service) has been installed at our Institute. ALADIN is the operational forecasting model of the Hungarian Meteorological Service and developed in the framework of the international ALADIN co-operation. Our main objectives are i) the analysis of different typical weather situations, ii) fine tuning of parameterization schemes and the iii) comparison of the ALADIN/CHAPEAU and WRF model outputs based on case studies. The necessary hardware and software innovations has have been done. In the presentation the computer resources needed for the integration of both WRF and ALADIN/CHAPEAU models will be briefly described. The software developments performed for the evaluation and comparison of the different modeling systems will be demonstrated. The main objectives of the education program on the practical numerical weather modeling will be introduced, as well as its detailed thematics and the structure of the labs.

WRF model performance under flash-flood associated rainfall

NASA Astrophysics Data System (ADS)

Mejia-Estrada, Iskra; Bates, Paul; Ángel Rico-Ramírez, Miguel

2017-04-01

Understanding the natural processes that precede the occurrence of flash floods is crucial to improve the future flood projections in a changing climate. Using numerical weather prediction tools allows to determine one of the triggering conditions for these particularly dangerous events, difficult to forecast due to their short lead-time. However, simulating the spatial and temporal evolution of the rainfall that leads to a rapid rise in river levels requires determining the best model configuration without compromising the computational efficiency. The current research involves the results of the first part of a cascade modeling approach, where the Weather Research and Forecasting (WRF) model is used to simulate the heavy rainfall in the east of the UK in June 2012 when stationary thunderstorms caused 2-hour accumulated values to match those expected in the whole month of June over the city of Newcastle. The optimum model set-up was obtained after extensive testing regarding physics parameterizations, spin-up times, datasets used as initial conditions and model resolution and nesting, hence determining its sensitivity to reproduce localised events of short duration. The outputs were qualitatively and quantitatively assessed using information from the national weather radar network as well as interpolated rainfall values from gauges, respectively. Statistical and skill score values show that the model is able to produce reliable accumulated precipitation values while explicitly solving the atmospheric equations in high resolution domains as long as several hydrometeors are considered with a spin-up time that allows the model to assimilate the initial conditions without going too far back in time from the event of interest. The results from the WRF model will serve as input to run a semi-distributed hydrological model to determine the rainfall-runoff relationship within an uncertainty assessment framework that will allow evaluating the implications of assumptions at the top of the modeling process in the final outputs of the cascade.

NOAA's weather forecasts go hyper-local with next-generation weather

Science.gov Websites

model NOAA HOME WEATHER OCEANS FISHERIES CHARTING SATELLITES CLIMATE RESEARCH COASTS CAREERS with next-generation weather model New model will help forecasters predict a storm's path, timing and intensity better than ever September 30, 2014 This is a comparison of two weather forecast models looking

Bayesian hierarchical modelling of North Atlantic windiness

NASA Astrophysics Data System (ADS)

Vanem, E.; Breivik, O. N.

2013-03-01

Extreme weather conditions represent serious natural hazards to ship operations and may be the direct cause or contributing factor to maritime accidents. Such severe environmental conditions can be taken into account in ship design and operational windows can be defined that limits hazardous operations to less extreme conditions. Nevertheless, possible changes in the statistics of extreme weather conditions, possibly due to anthropogenic climate change, represent an additional hazard to ship operations that is less straightforward to account for in a consistent way. Obviously, there are large uncertainties as to how future climate change will affect the extreme weather conditions at sea and there is a need for stochastic models that can describe the variability in both space and time at various scales of the environmental conditions. Previously, Bayesian hierarchical space-time models have been developed to describe the variability and complex dependence structures of significant wave height in space and time. These models were found to perform reasonably well and provided some interesting results, in particular, pertaining to long-term trends in the wave climate. In this paper, a similar framework is applied to oceanic windiness and the spatial and temporal variability of the 10-m wind speed over an area in the North Atlantic ocean is investigated. When the results from the model for North Atlantic windiness is compared to the results for significant wave height over the same area, it is interesting to observe that whereas an increasing trend in significant wave height was identified, no statistically significant long-term trend was estimated in windiness. This may indicate that the increase in significant wave height is not due to an increase in locally generated wind waves, but rather to increased swell. This observation is also consistent with studies that have suggested a poleward shift of the main storm tracks.

Mitigating wildland fire hazard using complex network centrality measures

NASA Astrophysics Data System (ADS)

Russo, Lucia; Russo, Paola; Siettos, Constantinos I.

2016-12-01

We show how to distribute firebreaks in heterogeneous forest landscapes in the presence of strong wind using complex network centrality measures. The proposed framework is essentially a two-tire one: at the inner part a state-of- the-art Cellular Automata model is used to compute the weights of the underlying lattice network while at the outer part the allocation of the fire breaks is scheduled in terms of a hierarchy of centralities which influence the most the spread of fire. For illustration purposes we applied the proposed framework to a real-case wildfire that broke up in Spetses Island, Greece in 1990. We evaluate the scheme against the benchmark of random allocation of firebreaks under the weather conditions of the real incident i.e. in the presence of relatively strong winds.

Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions

Treesearch

Joseph J. Charney; Lesley A. Fusina

2006-01-01

This paper presents an assessment of fire weather and fire behavior predictions produced by a numerical weather prediction model similar to those used by operational weather forecasters when preparing their forecasts. The PSU/NCAR MM5 model is used to simulate the weather conditions associated with three fire episodes in June 2005. Extreme fire behavior was reported...

Space Weather Products at the Community Coordinated Modeling Center

NASA Technical Reports Server (NTRS)

Hesse, Michael; Kuznetsova, M.; Pulkkinen, A.; Maddox, M.; Rastaetter, L.; Berrios, D.; MacNeice, P.

2010-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involves model evaluations, model transitions to operations, and the development of space weather forecasting tools. Owing to the pace of development in the science community, new model capabilities emerge frequently. Consequently, space weather products and tools involve not only increased validity, but often entirely new capabilities. This presentation will review the present state of space weather tools as well as point out emerging future capabilities.

Space Weather Models at the CCMC And Their Capabilities

NASA Technical Reports Server (NTRS)

Hesse, Michael; Rastatter, Lutz; MacNeice, Peter; Kuznetsova, Masha

2007-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second focus of CCMC activities is on validation and verification of space weather models, and on the transition of appropriate models to space weather forecast centers. As part of the latter activity, the CCMC develops real-time simulation systems that stress models through routine execution. A by-product of these real-time calculations is the ability to derive model products, which may be useful for space weather operators. In this presentation, we will provide an overview of the community-provided, space weather-relevant, model suite, which resides at CCMC. We will discuss current capabilities, and analyze expected future developments of space weather related modeling.

Improved Modeling of Land-Atmosphere Interactions using a Coupled Version of WRF with the Land Information System

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaCasse, Katherine M.; Santanello, Joseph A., Jr.; Lapenta, William M.; Petars-Lidard, Christa D.

2007-01-01

The exchange of energy and moisture between the Earth's surface and the atmospheric boundary layer plays a critical role in many hydrometeorological processes. Accurate and high-resolution representations of surface properties such as sea-surface temperature (SST), vegetation, soil temperature and moisture content, and ground fluxes are necessary to better understand the Earth-atmosphere interactions and improve numerical predictions of weather and climate phenomena. The NASA/NWS Short-term Prediction Research and Transition (SPORT) Center is currently investigating the potential benefits of assimilating high-resolution datasets derived from the NASA moderate resolution imaging spectroradiometer (MODIS) instruments using the Weather Research and Forecasting (WRF) model and the Goddard Space Flight Center Land Information System (LIS). The LIS is a software framework that integrates satellite and ground-based observational and modeled data along with multiple land surface models (LSMs) and advanced computing tools to accurately characterize land surface states and fluxes. The LIS can be run uncoupled to provide a high-resolution land surface initial condition, and can also be run in a coupled mode with WRF to integrate surface and soil quantities using any of the LSMs available in LIS. The LIS also includes the ability to optimize the initialization of surface and soil variables by tuning the spin-up time period and atmospheric forcing parameters, which cannot be done in the standard WRF. Among the datasets available from MODIS, a leaf-area index field and composite SST analysis are used to improve the lower boundary and initial conditions to the LIS/WRF coupled model over both land and water. Experiments will be conducted to measure the potential benefits from using the coupled LIS/WRF model over the Florida peninsula during May 2004. This month experienced relatively benign weather conditions, which will allow the experiments to focus on the local and mesoscale impacts of the high-resolution MODIS datasets and optimized soil and surface initial conditions. Follow-on experiments will examine the utility of such an optimized WRF configuration for more complex weather scenarios such as convective initiation. This paper will provide an overview of the experiment design and present preliminary results from selected cases in May 2004.

Modeling and simulation of offshore wind farm O&M processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joschko, Philip, E-mail: joschko@informatik.uni-hamburg.de; Widok, Andi H., E-mail: a.widok@htw-berlin.de; Appel, Susanne, E-mail: susanne.appel@hs-bremen.de

2015-04-15

This paper describes a holistic approach to operation and maintenance (O&M) processes in the domain of offshore wind farm power generation. The acquisition and process visualization is followed by a risk analysis of all relevant processes. Hereafter, a tool was designed, which is able to model the defined processes in a BPMN 2.0 notation, as well as connect and simulate them. Furthermore, the notation was enriched with new elements, representing other relevant factors that were, to date, only displayable with much higher effort. In that regard a variety of more complex situations were integrated, such as for example new processmore » interactions depending on different weather influences, in which case a stochastic weather generator was combined with the business simulation or other wind farm aspects important to the smooth running of the offshore wind farms. In addition, the choices for different methodologies, such as the simulation framework or the business process notation will be presented and elaborated depending on the impact they had on the development of the approach and the software solution. - Highlights: • Analysis of operation and maintenance processes of offshore wind farms • Process modeling with BPMN 2.0 • Domain-specific simulation tool.« less

Smsynth: AN Imagery Synthesis System for Soil Moisture Retrieval

NASA Astrophysics Data System (ADS)

Cao, Y.; Xu, L.; Peng, J.

2018-04-01

Soil moisture (SM) is a important variable in various research areas, such as weather and climate forecasting, agriculture, drought and flood monitoring and prediction, and human health. An ongoing challenge in estimating SM via synthetic aperture radar (SAR) is the development of the retrieval SM methods, especially the empirical models needs as training samples a lot of measurements of SM and soil roughness parameters which are very difficult to acquire. As such, it is difficult to develop empirical models using realistic SAR imagery and it is necessary to develop methods to synthesis SAR imagery. To tackle this issue, a SAR imagery synthesis system based on the SM named SMSynth is presented, which can simulate radar signals that are realistic as far as possible to the real SAR imagery. In SMSynth, SAR backscatter coefficients for each soil type are simulated via the Oh model under the Bayesian framework, where the spatial correlation is modeled by the Markov random field (MRF) model. The backscattering coefficients simulated based on the designed soil parameters and sensor parameters are added into the Bayesian framework through the data likelihood where the soil parameters and sensor parameters are set as realistic as possible to the circumstances on the ground and in the validity range of the Oh model. In this way, a complete and coherent Bayesian probabilistic framework is established. Experimental results show that SMSynth is capable of generating realistic SAR images that suit the needs of a large amount of training samples of empirical models.

Advancing the Explicit Representation of Lake Processes in WRF-Hydro

NASA Astrophysics Data System (ADS)

Yates, D. N.; Read, L.; Barlage, M. J.; Gochis, D.

2017-12-01

Realistic simulation of physical processes in lakes is essential for closing the water and energy budgets in a coupled land-surface and hydrologic model, such as the Weather Research and Forecasting (WRF) model's WRF-Hydro framework. A current version of WRF-Hydro, the National Water Model (NWM), includes 1,506 waterbodies derived from the National Hydrography Database, each of which is modeled using a level-pool routing scheme. This presentation discusses the integration of WRF's one-dimensional lake model into WRF-Hydro, which is used to estimate waterbody fluxes and thus explicitly represent latent and sensible heat and the mass balance occurring over the lakes. Results of these developments are presented through a case study from Lake Winnebago, Wisconsin. Scalability and computational benchmarks to expand to the continental-scale NWM are discussed.

An Independent Assessment of Anthropogenic Attribution Statements for Recent Extreme Temperature and Rainfall Events

DOE Office of Scientific and Technical Information (OSTI.GOV)

Angélil, Oliver; Stone, Dáithí; Wehner, Michael

The annual "State of the Climate" report, published in the Bulletin of the American Meteorological Society (BAMS), has included a supplement since 2011 composed of brief analyses of the human influence on recent major extreme weather events. There are now several dozen extreme weather events examined in these supplements, but these studies have all differed in their data sources as well as their approaches to defining the events, analyzing the events, and the consideration of the role of anthropogenic emissions. This study reexamines most of these events using a single analytical approach and a single set of climate model andmore » observational data sources. In response to recent studies recommending the importance of using multiple methods for extreme weather event attribution, results are compared from these analyses to those reported in the BAMS supplements collectively, with the aim of characterizing the degree to which the lack of a common methodological framework may or may not influence overall conclusions. Results are broadly similar to those reported earlier for extreme temperature events but disagree for a number of extreme precipitation events. Based on this, it is advised that the lack of comprehensive uncertainty analysis in recent extreme weather attribution studies is important and should be considered when interpreting results, but as yet it has not introduced a systematic bias across these studies.« less

An Independent Assessment of Anthropogenic Attribution Statements for Recent Extreme Temperature and Rainfall Events

DOE PAGES

Angélil, Oliver; Stone, Dáithí; Wehner, Michael; ...

2016-12-16

The annual "State of the Climate" report, published in the Bulletin of the American Meteorological Society (BAMS), has included a supplement since 2011 composed of brief analyses of the human influence on recent major extreme weather events. There are now several dozen extreme weather events examined in these supplements, but these studies have all differed in their data sources as well as their approaches to defining the events, analyzing the events, and the consideration of the role of anthropogenic emissions. This study reexamines most of these events using a single analytical approach and a single set of climate model andmore » observational data sources. In response to recent studies recommending the importance of using multiple methods for extreme weather event attribution, results are compared from these analyses to those reported in the BAMS supplements collectively, with the aim of characterizing the degree to which the lack of a common methodological framework may or may not influence overall conclusions. Results are broadly similar to those reported earlier for extreme temperature events but disagree for a number of extreme precipitation events. Based on this, it is advised that the lack of comprehensive uncertainty analysis in recent extreme weather attribution studies is important and should be considered when interpreting results, but as yet it has not introduced a systematic bias across these studies.« less

Impact of Representing Model Error in a Hybrid Ensemble-Variational Data Assimilation System for Track Forecast of Tropical Cyclones over the Bay of Bengal

NASA Astrophysics Data System (ADS)

Kutty, Govindan; Muraleedharan, Rohit; Kesarkar, Amit P.

2018-03-01

Uncertainties in the numerical weather prediction models are generally not well-represented in ensemble-based data assimilation (DA) systems. The performance of an ensemble-based DA system becomes suboptimal, if the sources of error are undersampled in the forecast system. The present study examines the effect of accounting for model error treatments in the hybrid ensemble transform Kalman filter—three-dimensional variational (3DVAR) DA system (hybrid) in the track forecast of two tropical cyclones viz. Hudhud and Thane, formed over the Bay of Bengal, using Advanced Research Weather Research and Forecasting (ARW-WRF) model. We investigated the effect of two types of model error treatment schemes and their combination on the hybrid DA system; (i) multiphysics approach, which uses different combination of cumulus, microphysics and planetary boundary layer schemes, (ii) stochastic kinetic energy backscatter (SKEB) scheme, which perturbs the horizontal wind and potential temperature tendencies, (iii) a combination of both multiphysics and SKEB scheme. Substantial improvements are noticed in the track positions of both the cyclones, when flow-dependent ensemble covariance is used in 3DVAR framework. Explicit model error representation is found to be beneficial in treating the underdispersive ensembles. Among the model error schemes used in this study, a combination of multiphysics and SKEB schemes has outperformed the other two schemes with improved track forecast for both the tropical cyclones.

A new statistical model to find bedrock, a prequel to geochemical mass balance

NASA Astrophysics Data System (ADS)

Fisher, B.; Rendahl, A. K.; Aufdenkampe, A. K.; Yoo, K.

2016-12-01

We present a new statistical model to assess weathering trends in deep weathering profiles. The Weathering Trends (WT) model is presented as an extension of the geochemical mass balance model (Brimhall & Dietrich, 1987), and is available as an open-source R library on GitHub (https://github.com/AaronRendahl/WeatheringTrends). WT uses element concentration data to determine the depth to fresh bedrock by assessing the maximum extent of weathering for all elements and the model applies confidence intervals on the depth to bedrock. WT models near-surface features and the shape of the weathering profile using a log transformation of data to capture the magnitude of changes that are relevant to geochemical kinetics and thermodynamics. The WT model offers a new, enhanced opportunity to characterize and understand biogeochemical weathering in heterogeneous rock types. We apply the model to two 21-meter drill cores in the Laurels Schist bedrock in the Christina River Basin Critical Zone Observatory in the Pennsylvania Piedmont. The Laurels Schist had inconclusive weathering indicators prior to development and application of WT model. The model differentiated between rock variability and weathering to delineate the maximum extent of weathering at 12.3 (CI 95% [9.2, 21.3]) meters in Ridge Well 1 and 7.2 (CI 95% [4.3, 13.0]) meters in Interfluve Well 2. The modeled extent to weathering is decoupled from the water table at the ridge, but coincides with the water table at the interfluve. These depths were applied as the parent material for the geochemical mass balance for the Laurels Schist. We test statistical approaches to assess the variability and correlation of immobile elements to facilitate the selection of the best immobile element for use in both models. We apply the model to other published data where the geochemical mass balance was applied, to demonstrate how the WT model provides additional information about weathering depth and weathering trends.

The problem of predicting the size distribution of sediment supplied by hillslopes to rivers

NASA Astrophysics Data System (ADS)

Sklar, Leonard S.; Riebe, Clifford S.; Marshall, Jill A.; Genetti, Jennifer; Leclere, Shirin; Lukens, Claire L.; Merces, Viviane

2017-01-01

Sediments link hillslopes to river channels. The size of sediments entering channels is a key control on river morphodynamics across a range of scales, from channel response to human land use to landscape response to changes in tectonic and climatic forcing. However, very little is known about what controls the size distribution of particles eroded from bedrock on hillslopes, and how particle sizes evolve before sediments are delivered to channels. Here we take the first steps toward building a geomorphic transport law to predict the size distribution of particles produced on hillslopes and supplied to channels. We begin by identifying independent variables that can be used to quantify the influence of five key boundary conditions: lithology, climate, life, erosion rate, and topography, which together determine the suite of geomorphic processes that produce and transport sediments on hillslopes. We then consider the physical and chemical mechanisms that determine the initial size distribution of rock fragments supplied to the hillslope weathering system, and the duration and intensity of weathering experienced by particles on their journey from bedrock to the channel. We propose a simple modeling framework with two components. First, the initial rock fragment sizes are set by the distribution of spacing between fractures in unweathered rock, which is influenced by stresses encountered by rock during exhumation and by rock resistance to fracture propagation. That initial size distribution is then transformed by a weathering function that captures the influence of climate and mineralogy on chemical weathering potential, and the influence of erosion rate and soil depth on residence time and the extent of particle size reduction. Model applications illustrate how spatial variation in weathering regime can lead to bimodal size distributions and downstream fining of channel sediment by down-valley fining of hillslope sediment supply, two examples of hillslope control on river sediment size. Overall, this work highlights the rich opportunities for future research into the controls on the size of sediments produced on hillslopes and delivered to channels.

Argonne simulation framework for intelligent transportation systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ewing, T.; Doss, E.; Hanebutte, U.

1996-04-01

A simulation framework has been developed which defines a high-level architecture for a large-scale, comprehensive, scalable simulation of an Intelligent Transportation System (ITS). The simulator is designed to run on parallel computers and distributed (networked) computer systems; however, a version for a stand alone workstation is also available. The ITS simulator includes an Expert Driver Model (EDM) of instrumented ``smart`` vehicles with in-vehicle navigation units. The EDM is capable of performing optimal route planning and communicating with Traffic Management Centers (TMC). A dynamic road map data base is sued for optimum route planning, where the data is updated periodically tomore » reflect any changes in road or weather conditions. The TMC has probe vehicle tracking capabilities (display position and attributes of instrumented vehicles), and can provide 2-way interaction with traffic to provide advisories and link times. Both the in-vehicle navigation module and the TMC feature detailed graphical user interfaces that includes human-factors studies to support safety and operational research. Realistic modeling of variations of the posted driving speed are based on human factor studies that take into consideration weather, road conditions, driver`s personality and behavior and vehicle type. The simulator has been developed on a distributed system of networked UNIX computers, but is designed to run on ANL`s IBM SP-X parallel computer system for large scale problems. A novel feature of the developed simulator is that vehicles will be represented by autonomous computer processes, each with a behavior model which performs independent route selection and reacts to external traffic events much like real vehicles. Vehicle processes interact with each other and with ITS components by exchanging messages. With this approach, one will be able to take advantage of emerging massively parallel processor (MPP) systems.« less

Inverse Geochemical Reaction Path Modelling and the Impact of Climate Change on Hydrologic Structure in Snowmelt-Dominated Catchments in the Southwestern USA

NASA Astrophysics Data System (ADS)

Driscoll, J. M.; Meixner, T.; Molotch, N. P.; Sickman, J. O.; Williams, M. W.; McIntosh, J. C.; Brooks, P. D.

2011-12-01

Snowmelt from alpine catchments provides 70-80% of the American Southwest's water resources. Climate change threatens to alter the timing and duration of snowmelt in high elevation catchments, which may also impact the quantity and the quality of these water resources. Modelling of these systems provides a robust theoretical framework to process the information extracted from the sparse physical measurement available in these sites due to their remote locations. Mass-balance inverse geochemical models (via PHREEQC, developed by the USGS) were applied to two snowmelt-dominated catchments; Green Lake 4 (GL4) in the Rockies and Emerald Lake (EMD) in the Sierra Nevada. Both catchments primarily consist of granite and granodiorite with a similar bulk geochemistry. The inputs for the models were the initial (snowpack) and final (catchment output) hydrochemistry and a catchment-specific suite of mineral weathering reactions. Models were run for wet and dry snow years, for early and late time periods (defined hydrologically as 1/2 of the total volume for the year). Multiple model solutions were reduced to a representative suite of reactions by choosing the model solution with the fewest phases and least overall phase change. The dominant weathering reactions (those which contributed the most solutes) were plagioclase for GL4 and albite for EMD. Results for GL4 show overall more plagioclase weathering during the dry year (214.2g) than wet year (89.9g). Both wet and dry years show more weathering in the early time periods (63% and 56%, respectively). These results show that the snowpack and outlet are chemically more similar during wet years than dry years. A possible hypothesis to explain this difference is a change in contribution from subsurface storage; during the wet year the saturated catchment reduces contact with surface materials that would result in mineral weathering reactions by some combination of reduced infiltration and decreased subsurface transit time. By contrast, during the dry year infiltration and subsequent displacement of stored water that has had longer contact time with minerals and therefore has become more geochemically evolved to produce a greater difference between snowmelt and catchment outlet hydrochemistry. The results for EMD show little distinction between albite weathering for wet and dry years (55.9g and 66.0g, relatively). A hypothesis for this lack of difference in mineral phase changes may be due to less subsurface storage capacity in EMD relative to GL4. The spatial distribution of snowmelt has also been shown to influence the integrated watershed response, and future work includes using the Alpine Hydrochemical Model (AHM) to further investigate catchment response to these spatial data. The AHM will also provide further insight of surface-groundwater interactions through a more integrated model which includes hydrochemical, biological and physical processes to elucidate catchment response to changes in snowmelt dynamics.

Implementation of weather stations at Ghanaian high schools

NASA Astrophysics Data System (ADS)

Pieron, M.

2012-04-01

The Trans-African Hydro-Meteorological Observatory (www.tahmo.org) is an initiative that aims to develop a dense weather observation network in Sub-Sahara Africa. The ambition is to have 20.000 low-cost innovative weather stations in place in 2015. An increased amount of weather data is locally required to provide stakeholders that are dependent on the weather, such as farmers and fishermen, with accurate forecasts. As a first proof of concept, showing that sensors can be built at costs lower than commercially available, a disdrometer was developed. In parallel with the design of the measurement instruments, a high school curriculum is developed that covers environmental sciences. In order to find out which requirements the TAHMO weather station and accompanying educational materials should meet for optimal use at Junior High Schools research was done at Ghanaian schools. Useful insights regarding the future African context of the weather station and requirements for an implementation strategy were obtained during workshops with teachers and students, visits to WMO observatories and case studies regarding use of educational materials. The poster presents the conclusions of this research, which is part of the bigger TAHMO framework.

Weather Forecaster Understanding of Climate Models

NASA Astrophysics Data System (ADS)

Bol, A.; Kiehl, J. T.; Abshire, W. E.

2013-12-01

Weather forecasters, particularly those in broadcasting, are the primary conduit to the public for information on climate and climate change. However, many weather forecasters remain skeptical of model-based climate projections. To address this issue, The COMET Program developed an hour-long online lesson of how climate models work, targeting an audience of weather forecasters. The module draws on forecasters' pre-existing knowledge of weather, climate, and numerical weather prediction (NWP) models. In order to measure learning outcomes, quizzes were given before and after the lesson. Preliminary results show large learning gains. For all people that took both pre and post-tests (n=238), scores improved from 48% to 80%. Similar pre/post improvement occurred for National Weather Service employees (51% to 87%, n=22 ) and college faculty (50% to 90%, n=7). We believe these results indicate a fundamental misunderstanding among many weather forecasters of (1) the difference between weather and climate models, (2) how researchers use climate models, and (3) how they interpret model results. The quiz results indicate that efforts to educate the public about climate change need to include weather forecasters, a vital link between the research community and the general public.

SCALING AN URBAN EMERGENCY EVACUATION FRAMEWORK: CHALLENGES AND PRACTICES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karthik, Rajasekar; Lu, Wei

2014-01-01

Critical infrastructure disruption, caused by severe weather events, natural disasters, terrorist attacks, etc., has significant impacts on urban transportation systems. We built a computational framework to simulate urban transportation systems under critical infrastructure disruption in order to aid real-time emergency evacuation. This framework will use large scale datasets to provide a scalable tool for emergency planning and management. Our framework, World-Wide Emergency Evacuation (WWEE), integrates population distribution and urban infrastructure networks to model travel demand in emergency situations at global level. Also, a computational model of agent-based traffic simulation is used to provide an optimal evacuation plan for traffic operationmore » purpose [1]. In addition, our framework provides a web-based high resolution visualization tool for emergency evacuation modelers and practitioners. We have successfully tested our framework with scenarios in both United States (Alexandria, VA) and Europe (Berlin, Germany) [2]. However, there are still some major drawbacks for scaling this framework to handle big data workloads in real time. On our back-end, lack of proper infrastructure limits us in ability to process large amounts of data, run the simulation efficiently and quickly, and provide fast retrieval and serving of data. On the front-end, the visualization performance of microscopic evacuation results is still not efficient enough due to high volume data communication between server and client. We are addressing these drawbacks by using cloud computing and next-generation web technologies, namely Node.js, NoSQL, WebGL, Open Layers 3 and HTML5 technologies. We will describe briefly about each one and how we are using and leveraging these technologies to provide an efficient tool for emergency management organizations. Our early experimentation demonstrates that using above technologies is a promising approach to build a scalable and high performance urban emergency evacuation framework that can improve traffic mobility and safety under critical infrastructure disruption in today s socially connected world.« less

Useful and Usable Climate Science: Frameworks for Bridging the Social and Physical domains.

NASA Astrophysics Data System (ADS)

Buja, L.

2016-12-01

Society is transforming the Earth's system in unprecedented ways, often with significant variations across space and time. In turn, the impacts of climate change on the human system vary dramatically due to differences in cultural, socioeconomic, institutional, and physical processes at the local level. The Climate Science and Applications Program (CSAP) at the National Center for Atmospheric Research in Boulder Colorado addresses societal vulnerability, impacts and adaptation to climate change through the development of frameworks and methods for analyzing current and future vulnerability, and integrated analyses of climate impacts and adaptation at local, regional and global scales. CSAP relies heavily on GIS-based scientific data and knowledge systems to bridge social and physical science approaches in its five focus areas: Governance of inter-linked natural and managed resource systems. The role of urban areas in driving emissions of climate change Weather, climate and global human health, GIS-based science data & knowledge systems. Regional Climate Science and Services for Adaptation Advanced methodologies and frameworks for assessing current and future risks to environmental hazards through the integration of physical and social science models, research results, and remote sensing data are presented in the context of recent national and international projects on climate change and food/water security, urban carbon emissions, metropolitan extreme heat and global health. In addition, innovative CSAP international capacity building programs teaching interdisciplinary approaches for using geospatial technologies to integrate multi-scale spatial information of weather, climate change into important sectors such as disaster reduction, agriculture, tourism and society for decision-making are discussed.

Probabilistic Space Weather Forecasting: a Bayesian Perspective

NASA Astrophysics Data System (ADS)

Camporeale, E.; Chandorkar, M.; Borovsky, J.; Care', A.

2017-12-01

Most of the Space Weather forecasts, both at operational and research level, are not probabilistic in nature. Unfortunately, a prediction that does not provide a confidence level is not very useful in a decision-making scenario. Nowadays, forecast models range from purely data-driven, machine learning algorithms, to physics-based approximation of first-principle equations (and everything that sits in between). Uncertainties pervade all such models, at every level: from the raw data to finite-precision implementation of numerical methods. The most rigorous way of quantifying the propagation of uncertainties is by embracing a Bayesian probabilistic approach. One of the simplest and most robust machine learning technique in the Bayesian framework is Gaussian Process regression and classification. Here, we present the application of Gaussian Processes to the problems of the DST geomagnetic index forecast, the solar wind type classification, and the estimation of diffusion parameters in radiation belt modeling. In each of these very diverse problems, the GP approach rigorously provide forecasts in the form of predictive distributions. In turn, these distributions can be used as input for ensemble simulations in order to quantify the amplification of uncertainties. We show that we have achieved excellent results in all of the standard metrics to evaluate our models, with very modest computational cost.

Review of Tropical-Extratropical Teleconnections on Intraseasonal Time Scales

NASA Astrophysics Data System (ADS)

Stan, Cristiana; Straus, David M.; Frederiksen, Jorgen S.; Lin, Hai; Maloney, Eric D.; Schumacher, Courtney

2017-12-01

The interactions and teleconnections between the tropical and midlatitude regions on intraseasonal time scales are an important modulator of tropical and extratropical circulation anomalies and their associated weather patterns. These interactions arise due to the impact of the tropics on the extratropics, the impact of the midlatitudes on the tropics, and two-way interactions between the regions. Observational evidence, as well as theoretical studies with models of complexity ranging from the linear barotropic framework to intricate Earth system models, suggest the involvement of a myriad of processes and mechanisms in generating and maintaining these interconnections. At this stage, our understanding of these teleconnections is primarily a collection of concepts; a comprehensive theoretical framework has yet to be established. These intraseasonal teleconnections are increasingly recognized as an untapped source of potential subseasonal predictability. However, the complexity and diversity of mechanisms associated with these teleconnections, along with the lack of a conceptual framework to relate them, prevent this potential predictability from being translated into realized forecast skill. This review synthesizes our progress in understanding the observed characteristics of intraseasonal tropical-extratropical interactions and their associated mechanisms, identifies the significant gaps in this understanding, and recommends new research endeavors to address the remaining challenges.

Data Integration Plans for the NOAA National Climate Model Portal (NCMP) (Invited)

NASA Astrophysics Data System (ADS)

Rutledge, G. K.; Williams, D. N.; Deluca, C.; Hankin, S. C.; Compo, G. P.

2010-12-01

NOAA’s National Climatic Data Center (NCDC) and its collaborators have initiated a five-year development and implementation of an operational access capability for the next generation weather and climate model datasets. The NOAA National Climate Model Portal (NCMP) is being designed using format neutral open web based standards and tools where users at all levels of expertise can gain access and understanding to many of NOAA’s climate and weather model products. NCMP will closely coordinate with and reside under the emerging NOAA Climate Services Portal (NCSP). To carry out its mission, NOAA must be able to successfully integrate model output and other data and information from all of its discipline specific areas to understand and address the complexity of many environmental problems. The NCMP will be an initial access point for the emerging NOAA Climate Services Portal (NCSP), which is the basis for unified access to NOAA climate products and services. NCMP is currently collaborating with the emerging Environmental Projection Center (EPC) expected to be developed at the Earth System Research Laboratory in Boulder CO. Specifically, NCMP is being designed to: - Enable policy makers and resource managers to make informed national and global policy decisions using integrated climate and weather model outputs, observations, information, products, and other services for the scientist and the non-scientist; - Identify model to observational interoperability requirements for climate and weather system analysis and diagnostics; - Promote the coordination of an international reanalysis observational clearinghouse (i.e.., Reanalysis.org) spanning the worlds numerical processing Center’s for an “Ongoing Analysis of the Climate System”. NCMP will initially provide access capabilities to 3 of NOAA’s high volume Reanalysis data sets of the weather and climate systems: 1) NCEP’s Climate Forecast System Reanalysis (CFS-R); 2) NOAA’s Climate Diagnostics Center/ Earth System Research Laboratory (ESRL) Twentieth Century Reanalysis Project data set (20CR, G. Compo, et al.), a historical reanalysis that will provide climate information dating back to 1850 to the present; and 3) the CPC’s Upper Air Reanlaysis. NCMP will advance the highly successful NOAA National Operational Model Archive and Distribution System (NOMADS, Rutledge, BAMS 2006), and standards already in use including Unidata’s THREDDS (TDS), PMEL’s Live Access Server (LAS) and the GrADS Data Server (GDS) from COLA; the Department of Energy (DOE) Earth System Grid (ESG) and the associated IPCC Climate model archive located at the Program for Climate Model Diagnostics and Inter-comparison (PCMDI) through the ESG; and NOAA’s Unified Access Framework (UAF) effort; and core standards developed by Open Geospatial Consortium (OGC). The format neutral OPeNDAP protocol as used in the NOMADS system will also be a key aspect of the design of NCMP.

Statistical characterization of spatial patterns of rainfall cells in extratropical cyclones

NASA Astrophysics Data System (ADS)

Bacchi, Baldassare; Ranzi, Roberto; Borga, Marco

1996-11-01

The assumption of a particular type of distribution of rainfall cells in space is needed for the formulation of several space-time rainfall models. In this study, weather radar-derived rain rate maps are employed to evaluate different types of spatial organization of rainfall cells in storms through the use of distance functions and second-moment measures. In particular the spatial point patterns of the local maxima of rainfall intensity are compared to a completely spatially random (CSR) point process by applying an objective distance measure. For all the analyzed radar maps the CSR assumption is rejected, indicating that at the resolution of the observation considered, rainfall cells are clustered. Therefore a theoretical framework for evaluating and fitting alternative models to the CSR is needed. This paper shows how the "reduced second-moment measure" of the point pattern can be employed to estimate the parameters of a Neyman-Scott model and to evaluate the degree of adequacy to the experimental data. Some limitations of this theoretical framework, and also its effectiveness, in comparison to the use of scaling functions, are discussed.

A top-down approach to projecting market impacts of climate change

NASA Astrophysics Data System (ADS)

Lemoine, Derek; Kapnick, Sarah

2016-01-01

To evaluate policies to reduce greenhouse-gas emissions, economic models require estimates of how future climate change will affect well-being. So far, nearly all estimates of the economic impacts of future warming have been developed by combining estimates of impacts in individual sectors of the economy. Recent work has used variation in warming over time and space to produce top-down estimates of how past climate and weather shocks have affected economic output. Here we propose a statistical framework for converting these top-down estimates of past economic costs of regional warming into projections of the economic cost of future global warming. Combining the latest physical climate models, socioeconomic projections, and economic estimates of past impacts, we find that future warming could raise the expected rate of economic growth in richer countries, reduce the expected rate of economic growth in poorer countries, and increase the variability of growth by increasing the climate's variability. This study suggests we should rethink the focus on global impacts and the use of deterministic frameworks for modelling impacts and policy.

Reproducibility of Carbon and Water Cycle by an Ecosystem Process Based Model Using a Weather Generator and Effect of Temporal Concentration of Precipitation on Model Outputs

NASA Astrophysics Data System (ADS)

Miyauchi, T.; Machimura, T.

2014-12-01

GCM is generally used to produce input weather data for the simulation of carbon and water cycle by ecosystem process based models under climate change however its temporal resolution is sometimes incompatible to requirement. A weather generator (WG) is used for temporal downscaling of input weather data for models, where the effect of WG algorithms on reproducibility of ecosystem model outputs must be assessed. In this study simulated carbon and water cycle by Biome-BGC model using weather data measured and generated by CLIMGEN weather generator were compared. The measured weather data (daily precipitation, maximum, minimum air temperature) at a few sites for 30 years was collected from NNDC Online weather data. The generated weather data was produced by CLIMGEN parameterized using the measured weather data. NPP, heterotrophic respiration (HR), NEE and water outflow were simulated by Biome-BGC using measured and generated weather data. In the case of deciduous broad leaf forest in Lushi, Henan Province, China, 30 years average monthly NPP by WG was 10% larger than that by measured weather in the growing season. HR by WG was larger than that by measured weather in all months by 15% in average. NEE by WG was more negative in winter and was close to that by measured weather in summer. These differences in carbon cycle were because the soil water content by WG was larger than that by measured weather. The difference between monthly water outflow by WG and by measured weather was large and variable, and annual outflow by WG was 50% of that by measured weather. The inconsistency in carbon and water cycle by WG and measured weather was suggested be affected by the difference in temporal concentration of precipitation, which was assessed.

WRF-Fire: coupled weather-wildland fire modeling with the weather research and forecasting model

Treesearch

Janice L. Coen; Marques Cameron; John Michalakes; Edward G. Patton; Philip J. Riggan; Kara M. Yedinak

2012-01-01

A wildland fire behavior module (WRF-Fire) was integrated into the Weather Research and Forecasting (WRF) public domain numerical weather prediction model. The fire module is a surface fire behavior model that is two-way coupled with the atmospheric model. Near-surface winds from the atmospheric model are interpolated to a finer fire grid and used, with fuel properties...

Effects of Weather on Tourism and its Moderation

NASA Astrophysics Data System (ADS)

Park, J. H.; Kim, S.; Lee, D. K.

2016-12-01

Tourism is weather sensitive industry (Gómez Martín, 2005). As climate change has been intensifying, the concerns about negative effects of weather on tourism also have been increasing. This study attempted to find ways that mitigate the negative effects from weather on tourism, by analyzing a path of the effects of weather on intention to revisit and its moderation. The data of the study were collected by a self-recording online questionnaire survey of South Korean domestic tourists during August 2015, and 2,412 samples were gathered. A path model of effects of weather on intention to revisit that including moderating effects from physical attraction satisfaction and service satisfaction was ran. Season was controlled in the path model. The model fit was adequate (CMIN/DF=2.372(p=.000), CFI=.974, RMSEA=.024, SRMR=0.040), and the Model Comparison, which assumes that the base model to be correct with season constrained model, showed that there was a seasonal differences in the model ( DF=24, CMIN=32.430, P=.117). By the analysis, it was figured out that weather and weather expectation affected weather satisfaction, and the weather satisfaction affected intention to revisit (spring/fall: .167**, summer: .104**, and winter: .114**). Meanwhile physical attraction satisfaction (.200**), and service satisfaction (.210**) of tourism positively moderated weather satisfaction in summer, and weather satisfaction positively moderated physical attraction (.238**) satisfaction and service satisfaction (.339**). In other words, in summer, dissatisfaction from hot weather was moderated by satisfaction from physical attractions and services, and in spring/fall, comfort weather conditions promoted tourists to accept tourism experience and be satisfied from attractions and services positively. Based on the result, it was expected that if industries focus on offering the good attractions and services based on weather conditions, there would be positive effects to alleviate tourists' discomfort from weather in climate change.

Toward improving hurricane forecasts using the JPL Tropical Cyclone Information System (TCIS): A framework to address the issues of Big Data

NASA Astrophysics Data System (ADS)

Hristova-Veleva, S. M.; Boothe, M.; Gopalakrishnan, S.; Haddad, Z. S.; Knosp, B.; Lambrigtsen, B.; Li, P.; montgomery, M. T.; Niamsuwan, N.; Tallapragada, V. S.; Tanelli, S.; Turk, J.; Vukicevic, T.

2013-12-01

Accurate forecasting of extreme weather requires the use of both regional models as well as global General Circulation Models (GCMs). The regional models have higher resolution and more accurate physics - two critical components needed for properly representing the key convective processes. GCMs, on the other hand, have better depiction of the large-scale environment and, thus, are necessary for properly capturing the important scale interactions. But how to evaluate the models, understand their shortcomings and improve them? Satellite observations can provide invaluable information. And this is where the issues of Big Data come: satellite observations are very complex and have large variety while model forecast are very voluminous. We are developing a system - TCIS - that addresses the issues of model evaluation and process understanding with the goal of improving the accuracy of hurricane forecasts. This NASA/ESTO/AIST-funded project aims at bringing satellite/airborne observations and model forecasts into a common system and developing on-line tools for joint analysis. To properly evaluate the models we go beyond the comparison of the geophysical fields. We input the model fields into instrument simulators (NEOS3, CRTM, etc.) and compute synthetic observations for a more direct comparison to the observed parameters. In this presentation we will start by describing the scientific questions. We will then outline our current framework to provide fusion of models and observations. Next, we will illustrate how the system can be used to evaluate several models (HWRF, GFS, ECMWF) by applying a couple of our analysis tools to several hurricanes observed during the 2013 season. Finally, we will outline our future plans. Our goal is to go beyond the image comparison and point-by-point statistics, by focusing instead on understanding multi-parameter correlations and providing robust statistics. By developing on-line analysis tools, our framework will allow for consistent model evaluation, providing results that are much more robust than those produced by case studies - the current paradigm imposed by the Big Data issues (voluminous data and incompatible analysis tools). We believe that this collaborative approach, with contributions of models, observations and analysis approaches used by the research and operational communities, will help untangle the complex interactions that lead to hurricane genesis and rapid intensity changes - two processes that still pose many unanswered questions. The developed framework for evaluation of the global models will also have implications for the improvement of the climate models, which output only a limited amount of information making it difficult to evaluate them. Our TCIS will help by investigating the GCMs under current weather scenarios and with much more detailed model output, making it possible to compare the models to multiple observed parameters to help narrow down the uncertainty in their performance. This knowledge could then be transferred to the climate models to lower the uncertainty in their predictions. The work described here was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

High-severity fire: evaluating its key drivers and mapping its probability across western US forests

NASA Astrophysics Data System (ADS)

Parks, Sean A.; Holsinger, Lisa M.; Panunto, Matthew H.; Jolly, W. Matt; Dobrowski, Solomon Z.; Dillon, Gregory K.

2018-04-01

Wildland fire is a critical process in forests of the western United States (US). Variation in fire behavior, which is heavily influenced by fuel loading, terrain, weather, and vegetation type, leads to heterogeneity in fire severity across landscapes. The relative influence of these factors in driving fire severity, however, is poorly understood. Here, we explore the drivers of high-severity fire for forested ecoregions in the western US over the period 2002–2015. Fire severity was quantified using a satellite-inferred index of severity, the relativized burn ratio. For each ecoregion, we used boosted regression trees to model high-severity fire as a function of live fuel, topography, climate, and fire weather. We found that live fuel, on average, was the most important factor driving high-severity fire among ecoregions (average relative influence = 53.1%) and was the most important factor in 14 of 19 ecoregions. Fire weather was the second most important factor among ecoregions (average relative influence = 22.9%) and was the most important factor in five ecoregions. Climate (13.7%) and topography (10.3%) were less influential. We also predicted the probability of high-severity fire, were a fire to occur, using recent (2016) satellite imagery to characterize live fuel for a subset of ecoregions in which the model skill was deemed acceptable (n = 13). These ‘wall-to-wall’ gridded ecoregional maps provide relevant and up-to-date information for scientists and managers who are tasked with managing fuel and wildland fire. Lastly, we provide an example of the predicted likelihood of high-severity fire under moderate and extreme fire weather before and after fuel reduction treatments, thereby demonstrating how our framework and model predictions can potentially serve as a performance metric for land management agencies tasked with reducing hazardous fuel across large landscapes.

Optimal Control of Hybrid Systems in Air Traffic Applications

NASA Astrophysics Data System (ADS)

Kamgarpour, Maryam

Growing concerns over the scalability of air traffic operations, air transportation fuel emissions and prices, as well as the advent of communication and sensing technologies motivate improvements to the air traffic management system. To address such improvements, in this thesis a hybrid dynamical model as an abstraction of the air traffic system is considered. Wind and hazardous weather impacts are included using a stochastic model. This thesis focuses on the design of algorithms for verification and control of hybrid and stochastic dynamical systems and the application of these algorithms to air traffic management problems. In the deterministic setting, a numerically efficient algorithm for optimal control of hybrid systems is proposed based on extensions of classical optimal control techniques. This algorithm is applied to optimize the trajectory of an Airbus 320 aircraft in the presence of wind and storms. In the stochastic setting, the verification problem of reaching a target set while avoiding obstacles (reach-avoid) is formulated as a two-player game to account for external agents' influence on system dynamics. The solution approach is applied to air traffic conflict prediction in the presence of stochastic wind. Due to the uncertainty in forecasts of the hazardous weather, and hence the unsafe regions of airspace for aircraft flight, the reach-avoid framework is extended to account for stochastic target and safe sets. This methodology is used to maximize the probability of the safety of aircraft paths through hazardous weather. Finally, the problem of modeling and optimization of arrival air traffic and runway configuration in dense airspace subject to stochastic weather data is addressed. This problem is formulated as a hybrid optimal control problem and is solved with a hierarchical approach that decouples safety and performance. As illustrated with this problem, the large scale of air traffic operations motivates future work on the efficient implementation of the proposed algorithms.

Basement Fracturing and Weathering On- and Offshore Norway - Genesis, Age, and Landscape Development

NASA Astrophysics Data System (ADS)

Knies, J.; van der Lelij, R.; Faust, J.; Scheiber, T.; Broenner, M.; Fredin, O.; Mueller, A.; Viola, G.

2014-12-01

Saprolite remnants onshore Scandinavia have been investigated only sporadically. The nature and age of the deeply weathered material thus remains only loosely constrained. The type and degree of weathering of in situ weathered soils are indicative of the environmental conditions during their formation. When external forcing changes, properties related to previous weathering conditions are usually preserved, for example in clay mineral assemblages. By constraining the age and rate of weathering onshore and by isotopically dating selected faults determined to be intimately linked to weathered basement blocks, the influence of climate development, brittle deformation and landscape processes on weathering can be quantified. The "BASE" project aims to establish a temporal and conceptual framework for brittle tectonics, weathering patterns and landscape evolution affecting the basement onshore and offshore Norway. We will study the formation of saprolite in pre-Quaternary times, the influence of deep weathering on landscape development and establish a conceptual structural template of the evolution of the brittle deformational features that are exposed on onshore (weathered) basement blocks. Moreover, saprolitic material may have been eroded and preserved along the Norwegian continental margin during Cenozoic times. By studying both the onshore remnants and offshore erosional products deposited during periods of extreme changes of climate and tectonic boundary conditions (e..g Miocene-Pliocene), new inferences on the timing and controlling mechanisms of denudation, and on the relevance of deep weathering on Late Cenozoic global cooling can be drawn.

Increasing risk over time of weather-related hazards to the European population: a data-driven prognostic study.

PubMed

Forzieri, Giovanni; Cescatti, Alessandro; E Silva, Filipe Batista; Feyen, Luc

2017-08-01

The observed increase in the effects on human beings of weather-related disasters has been largely attributed to the rise in population exposed, with a possible influence of global warming. Yet, future risks of weather-related hazards on human lives in view of climate and demographic changes have not been comprehensively investigated. We assessed the risk of weather-related hazards to the European population in terms of annual numbers of deaths in 30 year intervals relative to the reference period (1981-2010) up to the year 2100 (2011-40, 2041-70, and 2071-100) by combining disaster records with high-resolution hazard and demographic projections in a prognostic modelling framework. We focused on the hazards with the greatest impacts-heatwaves and cold waves, wildfires, droughts, river and coastal floods, and windstorms-and evaluated their spatial and temporal variations in intensity and frequency under a business-as-usual scenario of greenhouse gas emissions. We modelled long-term demographic dynamics through a territorial modelling platform to represent the evolution of human exposure under a corresponding middle-of-the-road socioeconomic scenario. We appraised human vulnerability to weather extremes on the basis of more than 2300 records collected from disaster databases during the reference period and assumed it to be static under a scenario of no adaptation. We found that weather-related disasters could affect about two-thirds of the European population annually by the year 2100 (351 million people exposed per year [uncertainty range 126 million to 523 million] during the period 2071-100) compared with 5% during the reference period (1981-2010; 25 million people exposed per year). About 50 times the number of fatalities occurring annually during the reference period (3000 deaths) could occur by the year 2100 (152 000 deaths [80 500-239 800]). Future effects show a prominent latitudinal gradient, increasing towards southern Europe, where the premature mortality rate due to weather extremes (about 700 annual fatalities per million inhabitants [482-957] during the period 2071-100 vs 11 during the reference period) could become the greatest environmental risk factor. The projected changes are dominated by global warming (accounting for more than 90% of the rise in risk to human beings), mainly through a rise in the frequency of heatwaves (about 2700 heat-related fatalities per year during the reference period vs 151 500 [80 100-239 000] during the period 2071-100). Global warming could result in rapidly rising costs of weather-related hazards to human beings in Europe unless adequate adaptation measures are taken. Our results could aid in prioritisation of regional investments to address the unequal burden of effects on human beings of weather-related hazards and differences in adaptation capacities. European Commission. Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

The U.S. National Tsunami Hazard Mitigation Program: Successes in Tsunami Preparedness

NASA Astrophysics Data System (ADS)

Whitmore, P.; Wilson, R. I.

2012-12-01

Formed in 1995 by Congressional Action, the National Tsunami Hazards Mitigation Program (NTHMP) provides the framework for tsunami preparedness activities in the United States. The Program consists of the 28 U.S. coastal states, territories, and commonwealths (STCs), as well as three Federal agencies: the National Oceanic and Atmospheric Administration (NOAA), the Federal Emergency Management Agency (FEMA), and the United States Geological Survey (USGS). Since its inception, the NTHMP has advanced tsunami preparedness in the United States through accomplishments in many areas of tsunami preparedness: - Coordination and funding of tsunami hazard analysis and preparedness activities in STCs; - Development and execution of a coordinated plan to address education and outreach activities (materials, signage, and guides) within its membership; - Lead the effort to assist communities in meeting National Weather Service (NWS) TsunamiReady guidelines through development of evacuation maps and other planning activities; - Determination of tsunami hazard zones in most highly threatened coastal communities throughout the country by detailed tsunami inundation studies; - Development of a benchmarking procedure for numerical tsunami models to ensure models used in the inundation studies meet consistent, NOAA standards; - Creation of a national tsunami exercise framework to test tsunami warning system response; - Funding community tsunami warning dissemination and reception systems such as sirens and NOAA Weather Radios; and, - Providing guidance to NOAA's Tsunami Warning Centers regarding warning dissemination and content. NTHMP activities have advanced the state of preparedness of United States coastal communities, and have helped save lives and property during recent tsunamis. Program successes as well as future plans, including maritime preparedness, are discussed.

Notes on a Vision for the Global Space Weather Enterprise

NASA Astrophysics Data System (ADS)

Head, James N.

2015-07-01

Space weather phenomena impacts human civilization on a global scale and hence calls for a global approach to research, monitoring, and operational forecasting. The Global Space Weather Enterprise (GSWE) could be arranged along lines well established in existing international frameworks related to space exploration or to the use of space to benefit humanity. The Enterprise need not establish a new organization, but could evolve from existing international organizations. A GSWE employing open architectural concepts could be arranged to promote participation by all interested States regardless of current differences in science and technical capacity. Such an Enterprise would engender capacity building and burden sharing opportunities.

Using a High-Resolution Ensemble Modeling Method to Inform Risk-Based Decision-Making at Taylor Park Dam, Colorado

NASA Astrophysics Data System (ADS)

Mueller, M.; Mahoney, K. M.; Holman, K. D.

2015-12-01

The Bureau of Reclamation (Reclamation) is responsible for the safety of Taylor Park Dam, located in central Colorado at an elevation of 9300 feet. A key aspect of dam safety is anticipating extreme precipitation, runoff and the associated inflow of water to the reservoir within a probabilistic framework for risk analyses. The Cooperative Institute for Research in Environmental Sciences (CIRES) has partnered with Reclamation to improve understanding and estimation of precipitation in the western United States, including the Taylor Park watershed. A significant challenge is that Taylor Park Dam is located in a relatively data-sparse region, surrounded by mountains exceeding 12,000 feet. To better estimate heavy precipitation events in this basin, a high-resolution modeling approach is used. The Weather Research and Forecasting (WRF) model is employed to simulate events that have produced observed peaks in streamflow at the location of interest. Importantly, an ensemble of model simulations are run on each event so that uncertainty bounds (i.e., forecast error) may be provided such that the model outputs may be more effectively used in Reclamation's risk assessment framework. Model estimates of precipitation (and the uncertainty thereof) are then used in rainfall runoff models to determine the probability of inflows to the reservoir for use in Reclamation's dam safety risk analyses.

An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting.

PubMed

Hippert, Henrique S; Taylor, James W

2010-04-01

Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for automatic neural network modelling within a Bayesian framework, as applied to six samples containing daily load and weather data for four different countries. We analyse input selection as carried out by the Bayesian 'automatic relevance determination', and the usefulness of the Bayesian 'evidence' for the selection of the best structure (in terms of number of neurones), as compared to methods based on cross-validation. Copyright 2009 Elsevier Ltd. All rights reserved.

The Representation of Tropical Cyclones Within the Global William Putman Non-Hydrostatic Goddard Earth Observing System Model (GEOS-5) at Cloud-Permitting Resolutions

NASA Technical Reports Server (NTRS)

Putman, William M.

2010-01-01

The Goddard Earth Observing System Model (GEOS-S), an earth system model developed in the NASA Global Modeling and Assimilation Office (GMAO), has integrated the non-hydrostatic finite-volume dynamical core on the cubed-sphere grid. The extension to a non-hydrostatic dynamical framework and the quasi-uniform cubed-sphere geometry permits the efficient exploration of global weather and climate modeling at cloud permitting resolutions of 10- to 4-km on today's high performance computing platforms. We have explored a series of incremental increases in global resolution with GEOS-S from irs standard 72-level 27-km resolution (approx.5.5 million cells covering the globe from the surface to 0.1 hPa) down to 3.5-km (approx. 3.6 billion cells).

Numerical Hindcast Experiments for Study Tropical Convections and MJO Events during Year of Tropical Convection

NASA Astrophysics Data System (ADS)

Chern, J.; Tao, W.; Shen, B.

2011-12-01

The Madden-Julian oscillation (MJO) is the dominant component of intraseasonal variability in the tropic. It interacts and influences a wide range of weather and climate phenomena across different temporal and spatial scales. Despite the important role the MJO plays in the weather and climate system, past multi-model MJO intercomparison studies have shown that current global general circulation models (GCMs) still have considerable shortcomings in representing and forecasting this phenomenon. To improve representation of MJO and tropical convective cloud systems in global model, an Multiscale Modeling Framework (MMF) in which a cloud-resolving model takes the place of the sing-column cumulus parameterization used in convectional GCMs has been successfully developed at NAAS Goddard (Tao et al. 2009). To evaluate and improve the ability of this modeling system in representation and prediction of the MJO, several numerical hindcast experiments of a few selected MJO events during YOTC have been carried out. The ability of the model to simulate the MJO events is examined using diagnostic and skill metrics developed by the CLIVAR MJO Working Group Project as well as comparisons with a high-resolution global mesoscale model simulations, satellite observations, and analysis dataset. Several key variables associated with the MJO are investigated, including precipitation, outgoing longwave radiation, large-scale circulation, surface latent heat flux, low-level moisture convergence, vertical structure of moisture and hydrometers, and vertical diabatic heating profiles to gain insight of cloud processes associated with the MJO events.

Effects of atmospheric composition on apparent activation energy of silicate weathering: I. Model formulation

NASA Astrophysics Data System (ADS)

Kanzaki, Yoshiki; Murakami, Takashi

2018-07-01

We have developed a weathering model to comprehensively understand the determining factors of the apparent activation energy of silicate weathering in order to better estimate the silicate-weathering flux in the Precambrian. The model formulates the reaction rate of a mineral as a basis, then the elemental loss by summing the reaction rates of whole minerals, and finally the weathering flux from a given weathering profile by integrating the elemental losses along the depth of the profile. The rate expressions are formulated with physicochemical parameters relevant to weathering, including solution and atmospheric compositions. The apparent activation energies of silicate weathering are then represented by the temperature dependences of the physicochemical parameters based on the rate expressions. It was found that the interactions between individual mineral-reactions and the compositions of solution and atmosphere are necessarily accompanied by those of temperature-dependence counterparts. Indeed, the model calculates the apparent activation energy of silicate weathering as a function of the temperature dependence of atmospheric CO2 (Δ HCO2‧) . The dependence of the apparent activation energy of silicate weathering on Δ HCO2‧ may explain the empirical dependence of silicate weathering on the atmospheric composition. We further introduce a compensation law between the apparent activation energy and the pre-exponential factor to obtain the relationship between the silicate-weathering flux (FCO2), temperature and the apparent activation energy. The model calculation and the compensation law enable us to predict FCO2 as a function of temperature, once Δ HCO2‧ is given. The validity of the model is supported by agreements between the model prediction and observations of the apparent activation energy and FCO2 in the modern weathering systems. The present weathering model will be useful for the estimation of FCO2 in the Precambrian, for which Δ HCO2‧ can be deduced from the greenhouse effect of atmospheric CO2.

The Impact of Microphysics on Intensity and Structure of Hurricanes

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Shi, Jainn; Lang, Steve; Peters-Lidard, Christa

2006-01-01

During the past decade, both research and operational numerical weather prediction models, e.g. Weather Research and Forecast (WRF) model, have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with a 1-2 km or less horizontal resolutions. WFW is a next-generation mesoscale forecast model and assimilation system that has incorporated modern software framework, advanced dynamics, numeric and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WFW model can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options such as Lin et al. (1983), WSM 6-class and Thompson microphysics schemes. We have recently implemented three sophisticated cloud microphysics schemes into WRF. The cloud microphysics schemes have been extensively tested and applied for different mesoscale systems in different geographical locations. The performances of these schemes have been compared to those from other WRF microphysics options. We are performing sensitivity tests in using WW to examine the impact of six different cloud microphysical schemes on hurricane track, intensity and rainfall forecast. We are also performing the inline tracer calculation to comprehend the physical processes @e., boundary layer and each quadrant in the boundary layer) related to the development and structure of hurricanes.

Process-based modeling of silicate mineral weathering responses to increasing atmospheric CO2 and climate change

NASA Astrophysics Data System (ADS)

Banwart, Steven A.; Berg, Astrid; Beerling, David J.

2009-12-01

A mathematical model describes silicate mineral weathering processes in modern soils located in the boreal coniferous region of northern Europe. The process model results demonstrate a stabilizing biological feedback mechanism between atmospheric CO2 levels and silicate weathering rates as is generally postulated for atmospheric evolution. The process model feedback response agrees within a factor of 2 of that calculated by a weathering feedback function of the type generally employed in global geochemical carbon cycle models of the Earth's Phanerozoic CO2 history. Sensitivity analysis of parameter values in the process model provides insight into the key mechanisms that influence the strength of the biological feedback to weathering. First, the process model accounts for the alkalinity released by weathering, whereby its acceleration stabilizes pH at values that are higher than expected. Although the process model yields faster weathering with increasing temperature, because of activation energy effects on mineral dissolution kinetics at warmer temperature, the mineral dissolution rate laws utilized in the process model also result in lower dissolution rates at higher pH values. Hence, as dissolution rates increase under warmer conditions, more alkalinity is released by the weathering reaction, helping maintain higher pH values thus stabilizing the weathering rate. Second, the process model yields a relatively low sensitivity of soil pH to increasing plant productivity. This is due to more rapid decomposition of dissolved organic carbon (DOC) under warmer conditions. Because DOC fluxes strongly influence the soil water proton balance and pH, this increased decomposition rate dampens the feedback between productivity and weathering. The process model is most sensitive to parameters reflecting soil structure; depth, porosity, and water content. This suggests that the role of biota to influence these characteristics of the weathering profile is as important, if not more important, than the role of biota to influence mineral dissolution rates through changes in soil water chemistry. This process-modeling approach to quantify the biological weathering feedback to atmospheric CO2 demonstrates the potential for a far more mechanistic description of weathering feedback in simulations of the global geochemical carbon cycle.

North American Observing Systems: An Interagency Group Runs Tests at the NCCS

NASA Technical Reports Server (NTRS)

2002-01-01

Some 250,000 weather reports are collected by the National Weather Service (NWS) every day. Important measurements are taken by satellites, weather balloons, ground weather stations, airplanes, oceangoing ships, and tethered ocean buoys. Local or global weather models rely on these reports to provide the raw data used as initial conditions for the models to produce a weather prediction.

Assessing the Value of Post-processed State-of-the-art Long-term Weather Forecast Ensembles within An Integrated Agronomic Modelling Framework

NASA Astrophysics Data System (ADS)

LI, Y.; Castelletti, A.; Giuliani, M.

2014-12-01

Over recent years, long-term climate forecast from global circulation models (GCMs) has been demonstrated to show increasing skills over the climatology, thanks to the advances in the modelling of coupled ocean-atmosphere dynamics. Improved information from long-term forecast is supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping time) and for more effectively coping with the adverse impacts of climate variability. Yet, evaluating how valuable this information can be is not straightforward and farmers' response must be taken into consideration. Indeed, while long-range forecast are traditionally evaluated in terms of accuracy by comparison of hindcast and observed values, in the context of agricultural systems, potentially useful forecast information should alter the stakeholders' expectation, modify their decisions and ultimately have an impact on their annual benefit. Therefore, it is more desirable to assess the value of those long-term forecasts via decision-making models so as to extract direct indication of probable decision outcomes from farmers, i.e. from an end-to-end perspective. In this work, we evaluate the operational value of thirteen state-of-the-art long-range forecast ensembles against climatology forecast and subjective prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of farmers' behavior. Collected ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in order to address the mismatch of the spatio-temporal scale between forecast data from GCMs and distributed crop simulation model. The agronomic model is first simulated using the forecast information (ex-ante), followed by a second run with actual climate (ex-post). Multi-year simulations are performed to account for climate variability and the value of the different climate forecast is evaluated against the perfect foresight scenario based on the expected crop productivity as well as the land-use decisions. Our results show that not all the products generate beneficial effects to farmers and that the forecast errors might be amplified by the farmers decisions.

WRF4SG: A Scientific Gateway for climate experiment workflows

NASA Astrophysics Data System (ADS)

Blanco, Carlos; Cofino, Antonio S.; Fernandez-Quiruelas, Valvanuz

2013-04-01

The Weather Research and Forecasting model (WRF) is a community-driven and public domain model widely used by the weather and climate communities. As opposite to other application-oriented models, WRF provides a flexible and computationally-efficient framework which allows solving a variety of problems for different time-scales, from weather forecast to climate change projection. Furthermore, WRF is also widely used as a research tool in modeling physics, dynamics, and data assimilation by the research community. Climate experiment workflows based on Weather Research and Forecasting (WRF) are nowadays among the one of the most cutting-edge applications. These workflows are complex due to both large storage and the huge number of simulations executed. In order to manage that, we have developed a scientific gateway (SG) called WRF for Scientific Gateway (WRF4SG) based on WS-PGRADE/gUSE and WRF4G frameworks to ease achieve WRF users needs (see [1] and [2]). WRF4SG provides services for different use cases that describe the different interactions between WRF users and the WRF4SG interface in order to show how to run a climate experiment. As WS-PGRADE/gUSE uses portlets (see [1]) to interact with users, its portlets will support these use cases. A typical experiment to be carried on by a WRF user will consist on a high-resolution regional re-forecast. These re-forecasts are common experiments used as input data form wind power energy and natural hazards (wind and precipitation fields). In the cases below, the user is able to access to different resources such as Grid due to the fact that WRF needs a huge amount of computing resources in order to generate useful simulations: * Resource configuration and user authentication: The first step is to authenticate on users' Grid resources by virtual organizations. After login, the user is able to select which virtual organization is going to be used by the experiment. * Data assimilation: In order to assimilate the data sources, the user has to select them browsing through LFC Portlet. * Design Experiment workflow: In order to configure the experiment, the user will define the type of experiment (i.e. re-forecast), and its attributes to simulate. In this case the main attributes are: the field of interest (wind, precipitation, ...), the start and end date simulation and the requirements of the experiment. * Monitor workflow: In order to monitor the experiment the user will receive notification messages based on events and also the gateway will display the progress of the experiment. * Data storage: Like Data assimilation case, the user is able to browse and view the output data simulations using LFC Portlet. The objectives of WRF4SG can be described by considering two goals. The first goal is to show how WRF4SG facilitates to execute, monitor and manage climate workflows based on the WRF4G framework. And the second goal of WRF4SG is to help WRF users to execute their experiment workflows concurrently using heterogeneous computing resources such as HPC and Grid. [1] Kacsuk, P.: P-GRADE portal family for grid infrastructures. Concurrency and Computation: Practice and Experience. 23, 235-245 (2011). [2] http://www.meteo.unican.es/software/wrf4g

Predicting Power Outages Using Multi-Model Ensemble Forecasts

NASA Astrophysics Data System (ADS)

Cerrai, D.; Anagnostou, E. N.; Yang, J.; Astitha, M.

2017-12-01

Power outages affect every year millions of people in the United States, affecting the economy and conditioning the everyday life. An Outage Prediction Model (OPM) has been developed at the University of Connecticut for helping utilities to quickly restore outages and to limit their adverse consequences on the population. The OPM, operational since 2015, combines several non-parametric machine learning (ML) models that use historical weather storm simulations and high-resolution weather forecasts, satellite remote sensing data, and infrastructure and land cover data to predict the number and spatial distribution of power outages. A new methodology, developed for improving the outage model performances by combining weather- and soil-related variables using three different weather models (WRF 3.7, WRF 3.8 and RAMS/ICLAMS), will be presented in this study. First, we will present a performance evaluation of each model variable, by comparing historical weather analyses with station data or reanalysis over the entire storm data set. Hence, each variable of the new outage model version is extracted from the best performing weather model for that variable, and sensitivity tests are performed for investigating the most efficient variable combination for outage prediction purposes. Despite that the final variables combination is extracted from different weather models, this ensemble based on multi-weather forcing and multi-statistical model power outage prediction outperforms the currently operational OPM version that is based on a single weather forcing variable (WRF 3.7), because each model component is the closest to the actual atmospheric state.

Exploring the correlation between annual precipitation and potential evaporation

NASA Astrophysics Data System (ADS)

Chen, X.; Buchberger, S. G.

2017-12-01

The interdependence between precipitation and potential evaporation is closely related to the classic Budyko framework. In this study, a systematic investigation of the correlation between precipitation and potential evaporation at the annual time step is conducted at both point scale and watershed scale. The point scale precipitation and potential evaporation data over the period of 1984-2015 are collected from 259 weather stations across the United States. The watershed scale precipitation data of 203 watersheds across the United States are obtained from the Model Parameter Estimation Experiment (MOPEX) dataset from 1983 to 2002; and potential evaporation data of these 203 watersheds in the same period are obtained from a remote-sensing algorithm. The results show that majority of the weather stations (77%) and watersheds (79%) exhibit a statistically significant negative correlation between annual precipitation and annual potential evaporation. The aggregated data cloud of precipitation versus potential evaporation follows a curve based on the combination of the Budyko-type equation and Bouchet's complementary relationship. Our result suggests that annual precipitation and potential evaporation are not independent when both Budyko's hypothesis and Bouchet's hypothesis are valid. Furthermore, we find that the wet surface evaporation, which is controlled primarily by short wave radiation as defined in Bouchet's hypothesis, exhibits less dependence on precipitation than the potential evaporation. As a result, we suggest that wet surface evaporation is a better representation of energy supply than potential evaporation in the Budyko framework.

Concept of Operations for the NASA Weather Accident Prevention (WxAP) Project. Version 2.0

NASA Technical Reports Server (NTRS)

Green, Walter S.; Tsoucalas, George; Tanger, Thomas

2003-01-01

The Weather Accident Prevention Concept of Operations (CONOPS) serves as a decision-making framework for research and technology development planning. It is intended for use by the WxAP members and other related programs in NASA and the FAA that support aircraft accident reduction initiatives. The concept outlines the project overview for program level 3 elements-such as AWIN, WINCOMM, and TPAWS (Turbulence)-that develop the technologies and operating capabilities to form the building blocks for WxAP. Those building blocks include both retrofit of equipment and systems and development of new aircraft, training technologies, and operating infrastructure systems and capabilities. This Concept of operations document provides the basis for the WxAP project to develop requirements based on the operational needs ofthe system users. It provides the scenarios that the flight crews, airline operations centers (AOCs), air traffic control (ATC), and flight service stations (FSS) utilize to reduce weather related accidents. The provision to the flight crew of timely weather information provides awareness of weather situations that allows replanning to avoid weather hazards. The ability of the flight crew to locate and avoid weather hazards, such as turbulence and hail, contributes to safer flight practices.

Assessing Fire Weather Index using statistical downscaling and spatial interpolation techniques in Greece

NASA Astrophysics Data System (ADS)

Karali, Anna; Giannakopoulos, Christos; Frias, Maria Dolores; Hatzaki, Maria; Roussos, Anargyros; Casanueva, Ana

2013-04-01

Forest fires have always been present in the Mediterranean ecosystems, thus they constitute a major ecological and socio-economic issue. The last few decades though, the number of forest fires has significantly increased, as well as their severity and impact on the environment. Local fire danger projections are often required when dealing with wild fire research. In the present study the application of statistical downscaling and spatial interpolation methods was performed to the Canadian Fire Weather Index (FWI), in order to assess forest fire risk in Greece. The FWI is used worldwide (including the Mediterranean basin) to estimate the fire danger in a generalized fuel type, based solely on weather observations. The meteorological inputs to the FWI System are noon values of dry-bulb temperature, air relative humidity, 10m wind speed and precipitation during the previous 24 hours. The statistical downscaling methods are based on a statistical model that takes into account empirical relationships between large scale variables (used as predictors) and local scale variables. In the framework of the current study the statistical downscaling portal developed by the Santander Meteorology Group (https://www.meteo.unican.es/downscaling) in the framework of the EU project CLIMRUN (www.climrun.eu) was used to downscale non standard parameters related to forest fire risk. In this study, two different approaches were adopted. Firstly, the analogue downscaling technique was directly performed to the FWI index values and secondly the same downscaling technique was performed indirectly through the meteorological inputs of the index. In both cases, the statistical downscaling portal was used considering the ERA-Interim reanalysis as predictands due to the lack of observations at noon. Additionally, a three-dimensional (3D) interpolation method of position and elevation, based on Thin Plate Splines (TPS) was used, to interpolate the ERA-Interim data used to calculate the index. Results from this method were compared with the statistical downscaling results obtained from the portal. Finally, FWI was computed using weather observations obtained from the Hellenic National Meteorological Service, mainly in the south continental part of Greece and a comparison with the previous results was performed.

UA-ICON - A non-hydrostatic global model for studying gravity waves from the troposphere to the thermosphere

NASA Astrophysics Data System (ADS)

Borchert, Sebastian; Zängl, Günther; Baldauf, Michael; Zhou, Guidi; Schmidt, Hauke; Manzini, Elisa

2017-04-01

In numerical weather prediction as well as climate simulations, there are ongoing efforts to raise the upper model lid, acknowledging the possible influence of middle and upper atmosphere dynamics on tropospheric weather and climate. As the momentum deposition of gravity waves (GWs) is responsible for key features of the large scale flow in the middle and upper atmosphere, the upward model extension has put GWs in the focus of atmospheric research needs. The Max Planck Institute for Meteorology (MPI-M) and the German Weather Service (DWD) have been developing jointly the non-hydrostatic global model ICON (Zängl et al, 2015) which features a new dynamical core based on an icosahedral grid. The extension of ICON beyond the mesosphere, where most GWs deposit their momentum, requires, e.g., relaxing the shallow-atmosphere and other traditional approximations as well as implementing additional physical processes that are important to the upper atmosphere. We would like to present aspects of the model development and its evaluation, and first results from a simulation of a period of the DEEPWAVE campaign in New Zealand in 2014 (Fritts et al, 2016) using grid nesting up to a horizontal mesh size of about 1.25 km. This work is part of the research unit: Multi-Scale Dynamics of Gravity Waves (MS-GWaves: sub-project GWING, https://ms-gwaves.iau.uni-frankfurt.de/index.php), funded by the German Research Foundation. Fritts, D.C. and Coauthors, 2016: "The Deep Propagating Gravity Wave Experiment (DEEPWAVE): An airborne and ground-based exploration of gravity wave propagation and effects from their sources throughout the lower and middle atmosphere". Bull. Amer. Meteor. Soc., 97, 425 - 453, doi:10.1175/BAMS-D-14-00269.1 Zängl, G., Reinert, D., Ripodas, P., Baldauf, M., 2015: "The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core". Quart. J. Roy. Met. Soc., 141, 563 - 579, doi:10.1002/qj.2378

Multi-year Estimates of Methane Fluxes in Alaska from an Atmospheric Inverse Model

NASA Astrophysics Data System (ADS)

Miller, S. M.; Commane, R.; Chang, R. Y. W.; Miller, C. E.; Michalak, A. M.; Dinardo, S. J.; Dlugokencky, E. J.; Hartery, S.; Karion, A.; Lindaas, J.; Sweeney, C.; Wofsy, S. C.

2015-12-01

We estimate methane fluxes across Alaska over a multi-year period using observations from a three-year aircraft campaign, the Carbon Arctic Reservoirs Vulnerability Experiment (CARVE). Existing estimates of methane from Alaska and other Arctic regions disagree in both magnitude and distribution, and before the CARVE campaign, atmospheric observations in the region were sparse. We combine these observations with an atmospheric particle trajectory model and a geostatistical inversion to estimate surface fluxes at the model grid scale. We first use this framework to estimate the spatial distribution of methane fluxes across the state. We find the largest fluxes in the south-east and North Slope regions of Alaska. This distribution is consistent with several estimates of wetland extent but contrasts with the distribution in most existing flux models. These flux models concentrate methane in warmer or more southerly regions of Alaska compared to the estimate presented here. This result suggests a discrepancy in how existing bottom-up models translate wetland area into methane fluxes across the state. We next use the inversion framework to explore inter-annual variability in regional-scale methane fluxes for 2012-2014. We examine the extent to which this variability correlates with weather or other environmental conditions. These results indicate the possible sensitivity of wetland fluxes to near-term variability in climate.

The potential predictability of fire danger provided by ECMWF forecast

NASA Astrophysics Data System (ADS)

Di Giuseppe, Francesca

2017-04-01

The European Forest Fire Information System (EFFIS), is currently being developed in the framework of the Copernicus Emergency Management Services to monitor and forecast fire danger in Europe. The system provides timely information to civil protection authorities in 38 nations across Europe and mostly concentrates on flagging regions which might be at high danger of spontaneous ignition due to persistent drought. The daily predictions of fire danger conditions are based on the US Forest Service National Fire Danger Rating System (NFDRS), the Canadian forest service Fire Weather Index Rating System (FWI) and the Australian McArthur (MARK-5) rating systems. Weather forcings are provided in real time by the European Centre for Medium range Weather Forecasts (ECMWF) forecasting system. The global system's potential predictability is assessed using re-analysis fields as weather forcings. The Global Fire Emissions Database (GFED4) provides 11 years of observed burned areas from satellite measurements and is used as a validation dataset. The fire indices implemented are good predictors to highlight dangerous conditions. High values are correlated with observed fire and low values correspond to non observed events. A more quantitative skill evaluation was performed using the Extremal Dependency Index which is a skill score specifically designed for rare events. It revealed that the three indices were more skilful on a global scale than the random forecast to detect large fires. The performance peaks in the boreal forests, in the Mediterranean, the Amazon rain-forests and southeast Asia. The skill-scores were then aggregated at country level to reveal which nations could potentiallty benefit from the system information in aid of decision making and fire control support. Overall we found that fire danger modelling based on weather forecasts, can provide reasonable predictability over large parts of the global landmass.

East African weathering dynamics controlled by vegetation-climate feedbacks

USGS Publications Warehouse

Ivory, Sarah J.; McGlue, Michael M.; Ellis, Geoffrey S.; Boehlke, Adam; Lézine, Anne-Marie; Vincens, Annie; Cohen, Andrew S.

2017-01-01

Tropical weathering has important linkages to global biogeochemistry and landscape evolution in the East African rift. We disentangle the influences of climate and terrestrial vegetation on chemical weathering intensity and erosion at Lake Malawi using a long sediment record. Fossil pollen, microcharcoal, particle size, and mineralogy data affirm that the detrital clays accumulating in deep water within the lake are controlled by feedbacks between climate and hinterland forest composition. Particle-size patterns are also best explained by vegetation, through feedbacks with lake levels, wildfires, and erosion. We develop a new source-to-sink framework that links lacustrine sedimentation to hinterland vegetation in tropical rifts. Our analysis suggests that climate-vegetation interactions and their coupling to weathering/erosion could threaten future food security and has implications for accurately predicting petroleum play elements in continental rift basins.

Improving High-resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with Upgraded Kain-Fritsch Cumulus Scheme

EPA Science Inventory

High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of inco...

Application of optimal data assimilation techniques in oceanography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, R.N.

Application of optimal data assimilation methods in oceanography is, if anything, more important than it is in numerical weather prediction, due to the sparsity of data. Here, a general framework is presented and practical examples taken from the author`s work are described, with the purpose of conveying to the reader some idea of the state of the art of data assimilation in oceanography. While no attempt is made to be exhaustive, references to other lines of research are included. Major challenges to the community include design of statistical error models and handling of strong nonlinearity.

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models.

PubMed

Lovejoy, S; de Lima, M I P

2015-07-01

Over the range of time scales from about 10 days to 30-100 years, in addition to the familiar weather and climate regimes, there is an intermediate "macroweather" regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spite of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be "homogenized" by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time.

Evaluation of operational numerical weather predictions in relation to the prevailing synoptic conditions

NASA Astrophysics Data System (ADS)

Pytharoulis, Ioannis; Tegoulias, Ioannis; Karacostas, Theodore; Kotsopoulos, Stylianos; Kartsios, Stergios; Bampzelis, Dimitrios

2015-04-01

The Thessaly plain, which is located in central Greece, has a vital role in the financial life of the country, because of its significant agricultural production. The aim of DAPHNE project (http://www.daphne-meteo.gr) is to tackle the problem of drought in this area by means of Weather Modification in convective clouds. This problem is reinforced by the increase of population and the water demand for irrigation, especially during the warm period of the year. The nonhydrostatic Weather Research and Forecasting model (WRF), is utilized for research and operational purposes of DAPHNE project. The WRF output fields are employed by the partners in order to provide high-resolution meteorological guidance and plan the project's operations. The model domains cover: i) Europe, the Mediterranean sea and northern Africa, ii) Greece and iii) the wider region of Thessaly (at selected periods), at horizontal grid-spacings of 15km, 5km and 1km, respectively, using 2-way telescoping nesting. The aim of this research work is to investigate the model performance in relation to the prevailing upper-air synoptic circulation. The statistical evaluation of the high-resolution operational forecasts of near-surface and upper air fields is performed at a selected period of the operational phase of the project using surface observations, gridded fields and weather radar data. The verification is based on gridded, point and object oriented techniques. The 10 upper-air circulation types, which describe the prevailing conditions over Greece, are employed in the synoptic classification. This methodology allows the identification of model errors that occur and/or are maximized at specific synoptic conditions and may otherwise be obscured in aggregate statistics. Preliminary analysis indicates that the largest errors are associated with cyclonic conditions. Acknowledgments This research work of Daphne project (11SYN_8_1088) is co-funded by the European Union (European Regional Development Fund) and Greek national funds, through the action "COOPERATION 2011: Partnerships of Production and Research Institutions in Focused Research and Technology Sectors" in the framework of the Operational Programme "Competitiveness and Entrepreneurship" and Regions in Transition (OPC II, NSRF 2007-2013).

Robust permanence for ecological equations with internal and external feedbacks.

PubMed

Patel, Swati; Schreiber, Sebastian J

2018-07-01

Species experience both internal feedbacks with endogenous factors such as trait evolution and external feedbacks with exogenous factors such as weather. These feedbacks can play an important role in determining whether populations persist or communities of species coexist. To provide a general mathematical framework for studying these effects, we develop a theorem for coexistence for ecological models accounting for internal and external feedbacks. Specifically, we use average Lyapunov functions and Morse decompositions to develop sufficient and necessary conditions for robust permanence, a form of coexistence robust to large perturbations of the population densities and small structural perturbations of the models. We illustrate how our results can be applied to verify permanence in non-autonomous models, structured population models, including those with frequency-dependent feedbacks, and models of eco-evolutionary dynamics. In these applications, we discuss how our results relate to previous results for models with particular types of feedbacks.

TECA: Petascale pattern recognition for climate science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prabhat, .; Byna, Surendra; Vishwanath, Venkatram

Climate Change is one of the most pressing challenges facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to examine effects of anthropogenic emissions. Highresolution climate simulations produce “Big Data”: contemporary climate archives are ≈ 5PB in size and we expect future archives to measure on the order of Exa-Bytes. In this work, we present the successful application of TECA (Toolkit for Extreme Climate Analysis) framework, for extracting extreme weather patterns such as Tropical Cyclones, Atmospheric Rivers and Extra-Tropical Cyclones from TB-sized simulation datasets. TECA has been run at full-scale on Cray XE6 and IBMmore » BG/Q systems, and has reduced the runtime for pattern detection tasks from years to hours. TECA has been utilized to evaluate the performance of various computational models in reproducing the statistics of extreme weather events, and for characterizing the change in frequency of storm systems in the future.« less

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

DOE PAGES

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

2018-03-01

We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space-time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, themore » samples are shown to produce realistic wind scenarios based on sample spectra and space-time correlation structure.« less

Microphysics in the Multi-Scale Modeling Systems with Unified Physics

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Chern, J.; Lamg, S.; Matsui, T.; Shen, B.; Zeng, X.; Shi, R.

2011-01-01

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (l) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, the microphysics developments of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the heavy precipitation processes will be presented.

Progress in the Development of a Global Quasi-3-D Multiscale Modeling Framework

NASA Astrophysics Data System (ADS)

Jung, J.; Konor, C. S.; Randall, D. A.

2017-12-01

The Quasi-3-D Multiscale Modeling Framework (Q3D MMF) is a second-generation MMF, which has following advances over the first-generation MMF: 1) The cloud-resolving models (CRMs) that replace conventional parameterizations are not confined to the large-scale dynamical-core grid cells, and are seamlessly connected to each other, 2) The CRMs sense the three-dimensional large- and cloud-scale environment, 3) Two perpendicular sets of CRM channels are used, and 4) The CRMs can resolve the steep surface topography along the channel direction. The basic design of the Q3D MMF has been developed and successfully tested in a limited-area modeling framework. Currently, global versions of the Q3D MMF are being developed for both weather and climate applications. The dynamical cores governing the large-scale circulation in the global Q3D MMF are selected from two cube-based global atmospheric models. The CRM used in the model is the 3-D nonhydrostatic anelastic Vector-Vorticity Model (VVM), which has been tested with the limited-area version for its suitability for this framework. As a first step of the development, the VVM has been reconstructed on the cubed-sphere grid so that it can be applied to global channel domains and also easily fitted to the large-scale dynamical cores. We have successfully tested the new VVM by advecting a bell-shaped passive tracer and simulating the evolutions of waves resulted from idealized barotropic and baroclinic instabilities. For improvement of the model, we also modified the tracer advection scheme to yield positive-definite results and plan to implement a new physics package that includes a double-moment microphysics and an aerosol physics. The interface for coupling the large-scale dynamical core and the VVM is under development. In this presentation, we shall describe the recent progress in the development and show some test results.

Studies on Factors affecting the Evolution of Agroecosystems in the Dakotas

NASA Astrophysics Data System (ADS)

Arora, Gaurav

This dissertation combines remote sensing and applied economics tools to study land use conversions in North Dakota and South Dakota that are tied to this region's overall socio-economic welfare. Specifically, the region's corn and soybeans cultivation expanded significantly over the past decade replacing the region's grasslands and grain crops. In paper I, we estimate the localized impacts of the advent of corn-based ethanol plants on the Dakotas' corn acreage. We implement a Difference-in-Difference framework through more flexible assumptions as the Parallel Paths assumption of the standard model fails to hold. We find strong trends in the Dakotas' corn acreage over the past decade, but surprisingly some ethanol plants were found to have a negative impact on local corn acreage. In paper II, we evaluate crop competitiveness due to heterogeneous weather impacts on crop yields, and then test whether annual weather fluctuations explain land allocations among the Dakotas' major land uses. Our integrated framework suggests that annual weather variability is an important determinant of regional land use decisions. Under the A1B emissions scenario of climate change, we find that the yields of all of the Dakotas' major crops will decline by 2031-2060 relative to 1981-2010, leading to lower (higher) spring wheat (alfalfa) acres in Eastern (Western) Dakotas. In paper III, we develop and implement a satellite image-processing algorithm to estimate historical land use acres using raw Landsat sensor data, thereby extending the existing Cropland Data Layers back to 1984 in eastern Dakotas. We demonstrate that the availability of a longer time-series is useful as the rate of land use change may differ among different time-spans. In paper IV, we evaluate the cost-effectiveness of grassland conservation easements when spatial spillovers are present among private landowners. We first develop a conceptual model to incorporate social spillovers in evaluating the role of easements in inhibiting grassland conversions. We empirically test whether social spillovers are present by estimating hazard rates of conversion as a function of neighborhood density of grasslands and easements. Our findings suggest that easements are strategic complements to existing grasslands in preventing grassland conversions in the Dakotas.

It's All About the Data: Workflow Systems and Weather

NASA Astrophysics Data System (ADS)

Plale, B.

2009-05-01

Digital data is fueling new advances in the computational sciences, particularly geospatial research as environmental sensing grows more practical through reduced technology costs, broader network coverage, and better instruments. e-Science research (i.e., cyberinfrastructure research) has responded to data intensive computing with tools, systems, and frameworks that support computationally oriented activities such as modeling, analysis, and data mining. Workflow systems support execution of sequences of tasks on behalf of a scientist. These systems, such as Taverna, Apache ODE, and Kepler, when built as part of a larger cyberinfrastructure framework, give the scientist tools to construct task graphs of execution sequences, often through a visual interface for connecting task boxes together with arcs representing control flow or data flow. Unlike business processing workflows, scientific workflows expose a high degree of detail and control during configuration and execution. Data-driven science imposes unique needs on workflow frameworks. Our research is focused on two issues. The first is the support for workflow-driven analysis over all kinds of data sets, including real time streaming data and locally owned and hosted data. The second is the essential role metadata/provenance collection plays in data driven science, for discovery, determining quality, for science reproducibility, and for long-term preservation. The research has been conducted over the last 6 years in the context of cyberinfrastructure for mesoscale weather research carried out as part of the Linked Environments for Atmospheric Discovery (LEAD) project. LEAD has pioneered new approaches for integrating complex weather data, assimilation, modeling, mining, and cyberinfrastructure systems. Workflow systems have the potential to generate huge volumes of data. Without some form of automated metadata capture, either metadata description becomes largely a manual task that is difficult if not impossible under high-volume conditions, or the searchability and manageability of the resulting data products is disappointingly low. The provenance of a data product is a record of its lineage, or trace of the execution history that resulted in the product. The provenance of a forecast model result, e.g., captures information about the executable version of the model, configuration parameters, input data products, execution environment, and owner. Provenance enables data to be properly attributed and captures critical parameters about the model run so the quality of the result can be ascertained. Proper provenance is essential to providing reproducible scientific computing results. Workflow languages used in science discovery are complete programming languages, and in theory can support any logic expressible by a programming language. The execution environments supporting the workflow engines, on the other hand, are subject to constraints on physical resources, and hence in practice the workflow task graphs used in science utilize relatively few of the cataloged workflow patterns. It is important to note that these workflows are executed on demand, and are executed once. Into this context is introduced the need for science discovery that is responsive to real time information. If we can use simple programming models and abstractions to make scientific discovery involving real-time data accessible to specialists who share and utilize data across scientific domains, we bring science one step closer to solving the largest of human problems.

A Vision for Incorporating Environmental Effects into Nitrogen Management Decision Support Tools for U.S. Maize Production

PubMed Central

Banger, Kamaljit; Yuan, Mingwei; Wang, Junming; Nafziger, Emerson D.; Pittelkow, Cameron M.

2017-01-01

Meeting crop nitrogen (N) demand while minimizing N losses to the environment has proven difficult despite significant field research and modeling efforts. To improve N management, several real-time N management tools have been developed with a primary focus on enhancing crop production. However, no coordinated effort exists to simultaneously address sustainability concerns related to N losses at field- and regional-scales. In this perspective, we highlight the opportunity for incorporating environmental effects into N management decision support tools for United States maize production systems by integrating publicly available crop models with grower-entered management information and gridded soil and climate data in a geospatial framework specifically designed to quantify environmental and crop production tradeoffs. To facilitate advances in this area, we assess the capability of existing crop models to provide in-season N recommendations while estimating N leaching and nitrous oxide emissions, discuss several considerations for initial framework development, and highlight important challenges related to improving the accuracy of crop model predictions. Such a framework would benefit the development of regional sustainable intensification strategies by enabling the identification of N loss hotspots which could be used to implement spatially explicit mitigation efforts in relation to current environmental quality goals and real-time weather conditions. Nevertheless, we argue that this long-term vision can only be realized by leveraging a variety of existing research efforts to overcome challenges related to improving model structure, accessing field data to enhance model performance, and addressing the numerous social difficulties in delivery and adoption of such tool by stakeholders. PMID:28804490

A Vision for Incorporating Environmental Effects into Nitrogen Management Decision Support Tools for U.S. Maize Production.

PubMed

Banger, Kamaljit; Yuan, Mingwei; Wang, Junming; Nafziger, Emerson D; Pittelkow, Cameron M

2017-01-01

Meeting crop nitrogen (N) demand while minimizing N losses to the environment has proven difficult despite significant field research and modeling efforts. To improve N management, several real-time N management tools have been developed with a primary focus on enhancing crop production. However, no coordinated effort exists to simultaneously address sustainability concerns related to N losses at field- and regional-scales. In this perspective, we highlight the opportunity for incorporating environmental effects into N management decision support tools for United States maize production systems by integrating publicly available crop models with grower-entered management information and gridded soil and climate data in a geospatial framework specifically designed to quantify environmental and crop production tradeoffs. To facilitate advances in this area, we assess the capability of existing crop models to provide in-season N recommendations while estimating N leaching and nitrous oxide emissions, discuss several considerations for initial framework development, and highlight important challenges related to improving the accuracy of crop model predictions. Such a framework would benefit the development of regional sustainable intensification strategies by enabling the identification of N loss hotspots which could be used to implement spatially explicit mitigation efforts in relation to current environmental quality goals and real-time weather conditions. Nevertheless, we argue that this long-term vision can only be realized by leveraging a variety of existing research efforts to overcome challenges related to improving model structure, accessing field data to enhance model performance, and addressing the numerous social difficulties in delivery and adoption of such tool by stakeholders.

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

PubMed Central

Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel; Meze-Hausken, Elisabeth

2013-01-01

Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models. PMID:23396890

Integration of the Total Lightning Jump Algorithm into Current Operational Warning Environment Conceptual Models

NASA Technical Reports Server (NTRS)

Schultz, C. J.; Carey, L. D.; Schultz, E. V.; Stano, G. T.; Blakeslee, R.; Goodman, S. J.

2014-01-01

The purpose of the total lightning jump algorithm (LJA) is to provide forecasters with an additional tool to identify potentially hazardous thunderstorms, yielding increased confidence in decisions within the operational warning environment. The LJA was first developed to objectively indentify rapid increases in total lightning (also termed "lightning jumps") that occur prior to the observance of severe and hazardous weather (Williams et al. 1999, Schultz et al. 2009, Gatlin and Goodman 2010, Schultz et al. 2011). However, a physical and framework leading up to and through the time of a lightning jump is still lacking within the literature. Many studies infer that there is a large increase in the updraft prior to or during the jump, but are not specific on what properties of the updraft are indeed increasing (e.g., maximum updraft speed vs volume or both) likely because these properties were not specifically observed. Therefore, the purpose of this work is to physically associate lightning jump occurrence to polarimetric and multi-Doppler radar measured thunderstorm intensity metrics and severe weather occurrence, thus providing a conceptual model that can be used to adapt the LJA to current operations.

Frost for the trees: Did climate increase erosion in unglaciated landscapes during the late Pleistocene?

PubMed

Marshall, Jill A; Roering, Joshua J; Bartlein, Patrick J; Gavin, Daniel G; Granger, Darryl E; Rempel, Alan W; Praskievicz, Sarah J; Hales, Tristram C

2015-11-01

Understanding climatic influences on the rates and mechanisms of landscape erosion is an unresolved problem in Earth science that is important for quantifying soil formation rates, sediment and solute fluxes to oceans, and atmospheric CO2 regulation by silicate weathering. Glaciated landscapes record the erosional legacy of glacial intervals through moraine deposits and U-shaped valleys, whereas more widespread unglaciated hillslopes and rivers lack obvious climate signatures, hampering mechanistic theory for how climate sets fluxes and form. Today, periglacial processes in high-elevation settings promote vigorous bedrock-to-regolith conversion and regolith transport, but the extent to which frost processes shaped vast swaths of low- to moderate-elevation terrain during past climate regimes is not well established. By combining a mechanistic frost weathering model with a regional Last Glacial Maximum (LGM) climate reconstruction derived from a paleo-Earth System Model, paleovegetation data, and a paleoerosion archive, we propose that frost-driven sediment production was pervasive during the LGM in our unglaciated Pacific Northwest study site, coincident with a 2.5 times increase in erosion relative to modern rates. Our findings provide a novel framework to quantify how climate modulates sediment production over glacial-interglacial cycles in mid-latitude unglaciated terrain.

Integration of Weather Avoidance and Traffic Separation

NASA Technical Reports Server (NTRS)

Consiglio, Maria C.; Chamberlain, James P.; Wilson, Sara R.

2011-01-01

This paper describes a dynamic convective weather avoidance concept that compensates for weather motion uncertainties; the integration of this weather avoidance concept into a prototype 4-D trajectory-based Airborne Separation Assurance System (ASAS) application; and test results from a batch (non-piloted) simulation of the integrated application with high traffic densities and a dynamic convective weather model. The weather model can simulate a number of pseudo-random hazardous weather patterns, such as slow- or fast-moving cells and opening or closing weather gaps, and also allows for modeling of onboard weather radar limitations in range and azimuth. The weather avoidance concept employs nested "core" and "avoid" polygons around convective weather cells, and the simulations assess the effectiveness of various avoid polygon sizes in the presence of different weather patterns, using traffic scenarios representing approximately two times the current traffic density in en-route airspace. Results from the simulation experiment show that the weather avoidance concept is effective over a wide range of weather patterns and cell speeds. Avoid polygons that are only 2-3 miles larger than their core polygons are sufficient to account for weather uncertainties in almost all cases, and traffic separation performance does not appear to degrade with the addition of weather polygon avoidance. Additional "lessons learned" from the batch simulation study are discussed in the paper, along with insights for improving the weather avoidance concept. Introduction

Colluvial deposits as a possible weathering reservoir in uplifting mountains

NASA Astrophysics Data System (ADS)

Carretier, Sébastien; Goddéris, Yves; Martinez, Javier; Reich, Martin; Martinod, Pierre

2018-03-01

The role of mountain uplift in the evolution of the global climate over geological times is controversial. At the heart of this debate is the capacity of rapid denudation to drive silicate weathering, which consumes CO2. Here we present the results of a 3-D model that couples erosion and weathering during mountain uplift, in which, for the first time, the weathered material is traced during its stochastic transport from the hillslopes to the mountain outlet. To explore the response of weathering fluxes to progressively cooler and drier climatic conditions, we run model simulations accounting for a decrease in temperature with or without modifications in the rainfall pattern based on a simple orographic model. At this stage, the model does not simulate the deep water circulation, the precipitation of secondary minerals, variations in the pH, below-ground pCO2, and the chemical affinity of the water in contact with minerals. Consequently, the predicted silicate weathering fluxes probably represent a maximum, although the predicted silicate weathering rates are within the range of silicate and total weathering rates estimated from field data. In all cases, the erosion rate increases during mountain uplift, which thins the regolith and produces a hump in the weathering rate evolution. This model thus predicts that the weathering outflux reaches a peak and then falls, consistent with predictions of previous 1-D models. By tracking the pathways of particles, the model can also consider how lateral river erosion drives mass wasting and the temporary storage of colluvial deposits on the valley sides. This reservoir is comprised of fresh material that has a residence time ranging from several years up to several thousand years. During this period, the weathering of colluvium appears to sustain the mountain weathering flux. The relative weathering contribution of colluvium depends on the area covered by regolith on the hillslopes. For mountains sparsely covered by regolith during cold periods, colluvium produces most of the simulated weathering flux for a large range of erosion parameters and precipitation rate patterns. In addition to other reservoirs such as deep fractured bedrock, colluvial deposits may help to maintain a substantial and constant weathering flux in rapidly uplifting mountains during cooling periods.

Total probabilities of ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2017-04-01

Ensemble forecasting has a long history from meteorological modelling, as an indication of the uncertainty of the forecasts. However, it is necessary to calibrate and post-process the ensembles as the they often exhibit both bias and dispersion errors. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters varying in space and time, while giving a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, which makes it unsuitable for our purpose. Our post-processing method of the ensembles is developed in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu), where we are making forecasts for whole Europe, and based on observations from around 700 catchments. As the target is flood forecasting, we are also more interested in improving the forecast skill for high-flows rather than in a good prediction of the entire flow regime. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different meteorological forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to estimate the total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but we are adding a spatial penalty in the calibration process to force a spatial correlation of the parameters. The penalty takes distance, stream-connectivity and size of the catchment areas into account. This can in some cases have a slight negative impact on the calibration error, but avoids large differences between parameters of nearby locations, whether stream connected or not. The spatial calibration also makes it easier to interpolate the post-processing parameters to uncalibrated locations. We also look into different methods for handling the non-normal distributions of runoff data and the effect of different data transformations on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Engeland, K. and Steinsland, I.: Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times, Water Resour. Res., 50(1), 182-197, doi:10.1002/2012WR012757, 2014. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133(5), 1155-1174, doi:10.1175/MWR2906.1, 2005.

Total probabilities of ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2016-04-01

Ensemble forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the ensembles often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative impact on the calibration error, but makes it easier to interpolate the post-processing parameters to uncalibrated locations. We also look into different methods for handling the non-normal distributions of runoff data and the effect of different data transformations on forecasts skills in general and for floods in particular. Berrocal, V. J., Raftery, A. E. and Gneiting, T.: Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Mon. Weather Rev., 135(4), 1386-1402, doi:10.1175/MWR3341.1, 2007. Engeland, K. and Steinsland, I.: Probabilistic postprocessing models for flow forecasts for a system of catchments and several lead times, Water Resour. Res., 50(1), 182-197, doi:10.1002/2012WR012757, 2014. Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098-1118, doi:10.1175/MWR2904.1, 2005. Hemri, S., Fundel, F. and Zappa, M.: Simultaneous calibration of ensemble river flow predictions over an entire range of lead times, Water Resour. Res., 49(10), 6744-6755, doi:10.1002/wrcr.20542, 2013. Raftery, A. E., Gneiting, T., Balabdaoui, F. and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133(5), 1155-1174, doi:10.1175/MWR2906.1, 2005.

Microphysics in Multi-scale Modeling System with Unified Physics

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo

2012-01-01

Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (1) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, a review of developments and applications of the multi-scale modeling system will be presented. In particular, the microphysics development and its performance for the multi-scale modeling system will be presented.

Predictability of the atmospheric conditions leading to extreme weather events in the Western Mediterranean Region in comparison with the seasonal mean conditions

NASA Astrophysics Data System (ADS)

Khodayar, Samiro; Kalthoff, Norbert

2013-04-01

Among all severe convective weather situations, fall season heavy rainfall represents the most threatening phenomenon in the western Mediterranean region. Devastating flash floods occur every year somewhere in eastern Spain, southern France, Italy, or North Africa, being responsible for a great proportion of the fatalities, property losses, and destruction of infrastructure caused by natural hazards. Investigations in the area have shown that most of the heavy rainfall events in this region can be attributed to mesoscale convective systems. The main goal of this investigation is to understand and identify the atmospheric conditions that favor the initiation and development of such systems. Insight of the involved processes and conditions will improve their predictability and help preventing some of the fatal consequences related with the occurrence of these weather phenomena. The HyMeX (Hydrological cycle in the Mediterranean eXperiment) provides a unique framework to investigate this issue. Making use of high-resolution seasonal simulations with the COSMO-CLM model the mean atmospheric conditions of the fall season, September, October and November, are investigated in different western Mediterranean regions such as eastern Spain, Southern France, northern Africa and Italy. The precipitation distribution, its daily cycle, and probability distribution function are evaluated to ascertain the similarities and differences between the regions of interest, as well as the spatial distribution of extreme events. Additionally, the regional differences of the boundary layer and mid-tropospheric conditions, atmospheric stability and inhibition, and low-level triggering are presented. Selected high impact weather HyMeX episodes' are analyzed with special focus on the atmospheric pre-conditions leading to the extreme weather situations. These pre-conditions are then compared to the mean seasonal conditions to identify and point out possible anomalies in the atmospheric conditions which could favor the initiation and intensification of extreme precipitation weather events.

Using Mesoscale Weather Model Output as Boundary Conditions for Atmospheric Large-Eddy Simulations and Wind-Plant Aerodynamic Simulations (Presentation)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Churchfield, M. J.; Michalakes, J.; Vanderwende, B.

Wind plant aerodynamics are directly affected by the microscale weather, which is directly influenced by the mesoscale weather. Microscale weather refers to processes that occur within the atmospheric boundary layer with the largest scales being a few hundred meters to a few kilometers depending on the atmospheric stability of the boundary layer. Mesoscale weather refers to large weather patterns, such as weather fronts, with the largest scales being hundreds of kilometers wide. Sometimes microscale simulations that capture mesoscale-driven variations (changes in wind speed and direction over time or across the spatial extent of a wind plant) are important in windmore » plant analysis. In this paper, we present our preliminary work in coupling a mesoscale weather model with a microscale atmospheric large-eddy simulation model. The coupling is one-way beginning with the weather model and ending with a computational fluid dynamics solver using the weather model in coarse large-eddy simulation mode as an intermediary. We simulate one hour of daytime moderately convective microscale development driven by the mesoscale data, which are applied as initial and boundary conditions to the microscale domain, at a site in Iowa. We analyze the time and distance necessary for the smallest resolvable microscales to develop.« less

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations

USGS Publications Warehouse

Gerber, Brian D.; Kendall, William L.; Hooten, Mevin B.; Dubovsky, James A.; Drewien, Roderick C.

2015-01-01

Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment.Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression.Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect.Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond.Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

Landslide risk mitigation by means of early warning systems

NASA Astrophysics Data System (ADS)

Calvello, Michele

2017-04-01

Among the many options available to mitigate landslide risk, early warning systems may be used where, in specific circumstances, the risk to life increases above tolerable levels. A coherent framework to classify and analyse landslide early warning systems (LEWS) is herein presented. Once the objectives of an early warning strategy are defined depending on the scale of analysis and the type of landslides to address, the process of designing and managing a LEWS should synergically employ technical and social skills. A classification scheme for the main components of LEWSs is proposed for weather-induced landslides. The scheme is based on a clear distinction among: i) the landslide model, i.e. a functional relationship between weather characteristics and landslide events considering the geotechnical, geomorphological and hydro-geological characterization of the area as well as an adequate monitoring strategy; ii) the warning model, i.e. the landslide model plus procedures to define the warning events and to issue the warnings; iii) the warning system, i.e. the warning model plus warning dissemination procedures, communication and education tools, strategies for community involvement and emergency plans. Each component of a LEWS is related to a number of actors involved with their deployment, operational activities and management. For instance, communication and education, community involvement and emergency plans are all significantly influenced by people's risk perception and by operational aspects system managers need to address in cooperation with scientists.

Weather Safety - NOAA's National Weather Service

Science.gov Websites

Statistical Models... MOS Prod GFS-LAMP Prod Climate Past Weather Predictions Weather Safety Weather Radio National Weather Service on FaceBook NWS on Facebook NWS Director Home > Safety Weather Safety This page weather safety. StormReady NOAA Weather Radio Emergency Managers Information Network U.S. Hazard Assmt

Water age and stream solute dynamics at the Hubbard Brook Experimental Forest (US)

NASA Astrophysics Data System (ADS)

Botter, Gianluca; Benettin, Paolo; McGuire, Kevin; Rinaldo, Andrea

2016-04-01

The contribution discusses experimental and modeling results from a headwater catchment at the Hubbard Brook Experimental Forest (New Hampshire, USA) to explore the link between stream solute dynamics and water age. A theoretical framework based on water age dynamics, which represents a general basis for characterizing solute transport at the catchment scale, is used to model both conservative and weathering-derived solutes. Based on the available information about the hydrology of the site, an integrated transport model was developed and used to estimate the relevant hydrochemical fluxes. The model was designed to reproduce the deuterium content of streamflow and allowed for the estimate of catchment water storage and dynamic travel time distributions (TTDs). Within this framework, dissolved silicon and sodium concentration in streamflow were simulated by implementing first-order chemical kinetics based explicitly on dynamic TTD, thus upscaling local geochemical processes to catchment scale. Our results highlight the key role of water stored within the subsoil glacial material in both the short-term and long-term solute circulation at Hubbard Brook. The analysis of the results provided by the calibrated model allowed a robust estimate of the emerging concentration-discharge relationship, streamflow age distributions (including the fraction of event water) and storage size, and their evolution in time due to hydrologic variability.

The NASA-Langley Wake Vortex Modelling Effort in Support of an Operational Aircraft Spacing System

NASA Technical Reports Server (NTRS)

Proctor, Fred H.

1998-01-01

Two numerical modelling efforts, one using a large eddy simulation model and the other a numerical weather prediction model, are underway in support of NASA's Terminal Area Productivity program. The large-eddy simulation model (LES) has a meteorological framework and permits the interaction of wake vortices with environments characterized by crosswind shear, stratification, humidity, and atmospheric turbulence. Results from the numerical simulations are being used to assist in the development of algorithms for an operational wake-vortex aircraft spacing system. A mesoscale weather forecast model is being adapted for providing operational forecast of winds, temperature, and turbulence parameters to be used in the terminal area. This paper describes the goals and modelling approach, as well as achievements obtained to date. Simulation results will be presented from the LES model for both two and three dimensions. The 2-D model is found to be generally valid for studying wake vortex transport, while the 3-D approach is necessary for realistic treatment of decay via interaction of wake vortices and atmospheric boundary layer turbulence. Meteorology is shown to have an important affect on vortex transport and decay. Presented are results showing that wake vortex transport is unaffected by uniform fog or rain, but wake vortex transport can be strongly affected by nonlinear vertical change in the ambient crosswind. Both simulation and observations show that atmospheric vortices decay from the outside with minimal expansion of the core. Vortex decay and the onset three-dimensional instabilities are found to be enhanced by the presence of ambient turbulence.

Southern Alaska Coastal Relief Model

NASA Astrophysics Data System (ADS)

Lim, E.; Eakins, B.; Wigley, R.

2009-12-01

The National Geophysical Data Center (NGDC), an office of the National Oceanic and Atmospheric Administration (NOAA), in conjunction with the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado at Boulder, has developed a 24 arc-second integrated bathymetric-topographic digital elevation model of Southern Alaska. This Coastal Relief Model (CRM) was generated from diverse digital datasets that were obtained from NGDC, the United States Geological Survey, and other U.S. and international agencies. The CRM spans 170° to 230° E and 48.5° to 66.5° N, including the Gulf of Alaska, Bering Sea, Aleutian Islands, and Alaska’s largest communities: Anchorage, Fairbanks, and Juneau. The CRM provides a framework for enabling scientists to refine tsunami propagation and ocean circulation modeling through increased resolution of geomorphologic features. It may also be useful for benthic habitat research, weather forecasting, and environmental stewardship. Shaded-relief image of the Southern Alaska Coastal Relief Model.

Four-fluid MHD Simulations of the Plasma and Neutral Gas Environment of Comet Churyumov-Gerasimenko Near Perihelion

NASA Astrophysics Data System (ADS)

Huang, Z.; Toth, G.; Gombosi, T.; Jia, X.; Rubin, M.; Fougere, N.; Tenishev, V.; Combi, M.; Bieler, A.; Hansen, K.; Shou, Y.; Altwegg, K.

2015-10-01

We develop a 3-D four fluid model to study the plasma environment of comet Churyumov- Gerasimenko (CG), which is the target of the Rosetta mission. Our model is based on BATS-R-US within the SWMF (Space Weather Modeling Framework) that solves the governing multifluid MHD equations and and the Euler equations for the neutral gas fluid. These equations describe the behavior and interactions of the cometary heavy ions, the solar wind protons, the electrons, and the neutrals. This model incorporates mass loading processes, including photo and electron impact ionization, furthermore taken into account are charge exchange, dissociative ion-electron recombination, as well as collisional interactions between different fluids. We simulate the near nucleus plasma and neutral gas environment with a realistic shape model of CG near perihelion and compare our simulation results with Rosetta observations.

Extending the Community Multiscale Air Quality (CMAQ) Modeling System to Hemispheric Scales: Overview of Process Considerations and Initial Applications

PubMed Central

Mathur, Rohit; Xing, Jia; Gilliam, Robert; Sarwar, Golam; Hogrefe, Christian; Pleim, Jonathan; Pouliot, George; Roselle, Shawn; Spero, Tanya L.; Wong, David C.; Young, Jeffrey

2018-01-01

The Community Multiscale Air Quality (CMAQ) modeling system is extended to simulate ozone, particulate matter, and related precursor distributions throughout the Northern Hemisphere. Modelled processes were examined and enhanced to suitably represent the extended space and time scales for such applications. Hemispheric scale simulations with CMAQ and the Weather Research and Forecasting (WRF) model are performed for multiple years. Model capabilities for a range of applications including episodic long-range pollutant transport, long-term trends in air pollution across the Northern Hemisphere, and air pollution-climate interactions are evaluated through detailed comparison with available surface, aloft, and remotely sensed observations. The expansion of CMAQ to simulate the hemispheric scales provides a framework to examine interactions between atmospheric processes occurring at various spatial and temporal scales with physical, chemical, and dynamical consistency. PMID:29681922

Evaluating impacts of climate change on future water scarcity in an intensively managed semi-arid region using a coupled model of biophysical processes and water rights

NASA Astrophysics Data System (ADS)

Han, B.; Flores, A. N.; Benner, S. G.

2017-12-01

In semiarid and arid regions where water supply is intensively managed, future water scarcity is a product of complex interactions between climate change and human activities. Evaluating future water scarcity under alternative scenarios of climate change, therefore, necessitates modeling approaches that explicitly represent the coupled biophysical and social processes responsible for the redistribution of water in these regions. At regional scales a particular challenge lies in adequately capturing not only the central tendencies of change in projections of climate change, but also the associated plausible range of variability in those projections. This study develops a framework that combines a stochastic weather generator, historical climate observations, and statistically downscaled General Circulation Model (GCM) projections. The method generates a large ensemble of daily climate realizations, avoiding deficiencies of using a few or mean values of individual GCM realizations. Three climate change scenario groups reflecting the historical, RCP4.5, and RCP8.5 future projections are developed. Importantly, the model explicitly captures the spatiotemporally varying irrigation activities as constrained by local water rights in a rapidly growing, semi-arid human-environment system in southwest Idaho. We use this modeling framework to project water use and scarcity patterns under the three future climate change scenarios. The model is built using the Envision alternative futures modeling framework. Climate projections for the region show future increases in both precipitation and temperature, especially under the RCP8.5 scenario. The increase of temperature has a direct influence on the increase of the irrigation water use and water scarcity, while the influence of increased precipitation on water use is less clear. The predicted changes are potentially useful in identifying areas in the watershed particularly sensitive to water scarcity, the relative importance of changes in precipitation versus temperature as a driver of scarcity, and potential shortcomings of the current water management framework in the region.

Meteorological risks are drivers of environmental innovation in agro-ecosystem management

NASA Astrophysics Data System (ADS)

Gobin, Anne; Van de Vijver, Hans; Vanwindekens, Frédéric; de Frutos Cachorro, Julia; Verspecht, Ann; Planchon, Viviane; Buyse, Jeroen

2017-04-01

Agricultural crop production is to a great extent determined by weather conditions. The research hypothesis is that meteorological risks act as drivers of environmental innovation in agro-ecosystem management. The methodology comprised five major parts: the hazard, its impact on different agro-ecosystems, vulnerability, risk management and risk communication. Generalized Extreme Value (GEV) theory was used to model annual maxima of meteorological variables based on a location-, scale- and shape-parameter that determine the center of the distribution, the deviation of the location-parameter and the upper tail decay, respectively. Spatial interpolation of GEV-derived return levels resulted in spatial temperature extremes, precipitation deficits and wet periods. The temporal overlap between extreme weather conditions and sensitive periods in the agro-ecosystem was realised using a bio-physically based modelling framework that couples phenology, a soil water balance and crop growth. 20-year return values for drought and waterlogging during different crop stages were related to arable yields. The method helped quantify agricultural production risks and rate both weather and crop-based agricultural insurance. The spatial extent of vulnerability is developed on different layers of geo-information to include meteorology, soil-landscapes, crop cover and management. Vulnerability of agroecosystems was mapped based on rules set by experts' knowledge and implemented by Fuzzy Inference System modelling and Geographical Information System tools. The approach was applied for cropland vulnerability to heavy rain and grassland vulnerability to drought. The level of vulnerability and resilience of an agro-ecosystem was also determined by risk management which differed across sectors and farm types. A calibrated agro-economic model demonstrated a marked influence of climate adapted land allocation and crop management on individual utility. The "chain of risk" approach allowed for investigating the hypothesis that meteorological risks act as drivers for agricultural innovation. Risk types were quantified in terms of probability and distribution, and further distinguished according to production type. Examples of strategies and options were provided at field, farm and policy level using different modelling methods.

Directable weathering of concave rock using curvature estimation.

PubMed

Jones, Michael D; Farley, McKay; Butler, Joseph; Beardall, Matthew

2010-01-01

We address the problem of directable weathering of exposed concave rock for use in computer-generated animation or games. Previous weathering models that admit concave surfaces are computationally inefficient and difficult to control. In nature, the spheroidal and cavernous weathering rates depend on the surface curvature. Spheroidal weathering is fastest in areas with large positive mean curvature and cavernous weathering is fastest in areas with large negative mean curvature. We simulate both processes using an approximation of mean curvature on a voxel grid. Both weathering rates are also influenced by rock durability. The user controls rock durability by editing a durability graph before and during weathering simulation. Simulations of rockfall and colluvium deposition further improve realism. The profile of the final weathered rock matches the shape of the durability graph up to the effects of weathering and colluvium deposition. We demonstrate the top-down directability and visual plausibility of the resulting model through a series of screenshots and rendered images. The results include the weathering of a cube into a sphere and of a sheltered inside corner into a cavern as predicted by the underlying geomorphological models.

Operational Space Weather Activities in the US

NASA Astrophysics Data System (ADS)

Berger, Thomas; Singer, Howard; Onsager, Terrance; Viereck, Rodney; Murtagh, William; Rutledge, Robert

2016-07-01

We review the current activities in the civil operational space weather forecasting enterprise of the United States. The NOAA/Space Weather Prediction Center is the nation's official source of space weather watches, warnings, and alerts, working with partners in the Air Force as well as international operational forecast services to provide predictions, data, and products on a large variety of space weather phenomena and impacts. In October 2015, the White House Office of Science and Technology Policy released the National Space Weather Strategy (NSWS) and associated Space Weather Action Plan (SWAP) that define how the nation will better forecast, mitigate, and respond to an extreme space weather event. The SWAP defines actions involving multiple federal agencies and mandates coordination and collaboration with academia, the private sector, and international bodies to, among other things, develop and sustain an operational space weather observing system; develop and deploy new models of space weather impacts to critical infrastructure systems; define new mechanisms for the transition of research models to operations and to ensure that the research community is supported for, and has access to, operational model upgrade paths; and to enhance fundamental understanding of space weather through support of research models and observations. The SWAP will guide significant aspects of space weather operational and research activities for the next decade, with opportunities to revisit the strategy in the coming years through the auspices of the National Science and Technology Council.

Validation for Global Solar Wind Prediction Using Ulysses Comparison: Multiple Coronal and Heliospheric Models Installed at the Community Coordinated Modeling Center

NASA Technical Reports Server (NTRS)

Jian, L. K.; MacNeice, P. J.; Mays, M. L.; Taktakishvili, A.; Odstrcil, D.; Jackson, B.; Yu, H.-S.; Riley, P.; Sokolov, I. V.

2016-01-01

The prediction of the background global solar wind is a necessary part of space weather forecasting. Several coronal and heliospheric models have been installed and/or recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and Heliospheric tomography using interplanetary scintillation data. Ulysses recorded the last fast latitudinal scan from southern to northern poles in 2007. By comparing the modeling results with Ulysses observations over seven Carrington rotations, we have extended our third-party validation from the previous near-Earth solar wind to middle to high latitudes, in the same late declining phase of solar cycle 23. Besides visual comparison, wehave quantitatively assessed the models capabilities in reproducing the time series, statistics, and latitudinal variations of solar wind parameters for a specific range of model parameter settings, inputs, and grid configurations available at CCMC. The WSA-Enlil model results vary with three different magnetogram inputs.The MAS-Enlil model captures the solar wind parameters well, despite its underestimation of the speed at middle to high latitudes. The new version of SWMF misses many solar wind variations probably because it uses lower grid resolution than other models. The interplanetary scintillation-tomography cannot capture the latitudinal variations of solar wind well yet. Because the model performance varies with parameter settings which are optimized for different epochs or flow states, the performance metric study provided here can serve as a template that researchers can use to validate the models for the time periods and conditions of interest to them.

Validation for global solar wind prediction using Ulysses comparison: Multiple coronal and heliospheric models installed at the Community Coordinated Modeling Center

NASA Astrophysics Data System (ADS)

Jian, L. K.; MacNeice, P. J.; Mays, M. L.; Taktakishvili, A.; Odstrcil, D.; Jackson, B.; Yu, H.-S.; Riley, P.; Sokolov, I. V.

2016-08-01

The prediction of the background global solar wind is a necessary part of space weather forecasting. Several coronal and heliospheric models have been installed and/or recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and heliospheric tomography using interplanetary scintillation data. Ulysses recorded the last fast latitudinal scan from southern to northern poles in 2007. By comparing the modeling results with Ulysses observations over seven Carrington rotations, we have extended our third-party validation from the previous near-Earth solar wind to middle to high latitudes, in the same late declining phase of solar cycle 23. Besides visual comparison, we have quantitatively assessed the models' capabilities in reproducing the time series, statistics, and latitudinal variations of solar wind parameters for a specific range of model parameter settings, inputs, and grid configurations available at CCMC. The WSA-Enlil model results vary with three different magnetogram inputs. The MAS-Enlil model captures the solar wind parameters well, despite its underestimation of the speed at middle to high latitudes. The new version of SWMF misses many solar wind variations probably because it uses lower grid resolution than other models. The interplanetary scintillation-tomography cannot capture the latitudinal variations of solar wind well yet. Because the model performance varies with parameter settings which are optimized for different epochs or flow states, the performance metric study provided here can serve as a template that researchers can use to validate the models for the time periods and conditions of interest to them.

Two Way Coupling RAM-SCB to the Space Weather Modeling Framework

NASA Astrophysics Data System (ADS)

Welling, D. T.; Jordanova, V. K.; Zaharia, S. G.; Toth, G.

2010-12-01

The Ring current Atmosphere interaction Model with Self-Consistently calculated 3D Magnetic field (RAM-SCB) has been used to successfully study inner magnetosphere dynamics during different solar wind and magnetosphere conditions. Recently, one way coupling of RAM-SCB with the Space Weather Modeling Framework (SWMF) has been achieved to replace all data or empirical inputs with those obtained through first-principles-based codes: magnetic field and plasma flux outer boundary conditions are provided by the Block Adaptive Tree Solar wind Roe-type Upwind Scheme (BATS-R-US) MHD code, convection electric field is provided by the Ridley Ionosphere Model (RIM), and ion composition is provided by the Polar Wind Outflow Model (PWOM) combined with a multi-species MHD approach. These advances, though creating a powerful inner magnetosphere virtual laboratory, neglect the important mechanisms through which the ring current feeds back into the whole system, primarily the stretching of the magnetic field lines and shielding of the convection electric field through strong region two Field Aligned Currents (FACs). In turn, changing the magnetosphere in this way changes the evolution of the ring current. To address this shortcoming, the coupling has been expanded to include feedback from RAM-SCB to the other coupled codes: region two FACs are returned to the RIM while total plasma pressure is used to nudge the MHD solution towards the RAM-SCB values. The impacts of the two way coupling are evaluated on three levels: the global magnetospheric level, focusing on the impact on the ionosphere and the shape of the magnetosphere, the regional level, examining the impact on the development of the ring current in terms of energy density, anisotropy, and plasma distribution, and the local level to compare the new results to in-situ measurements of magnetic and electric field and plasma. The results will also be compared to past simulations using the one way coupling and no coupling whatsoever. This work is the first to fully couple an anisotropic kinetic ring current code with a self-consistently calculated magnetic field to a set of global models.

Two way coupling RAM-SCB to the space weather modeling framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Welling, Daniel T; Jordanova, Vania K; Zaharia, Sorin G

The Ring current Atmosphere interaction Model with Self-Consistently calculated 3D Magnetic field (RAM-SCB) has been used to successfully study inner magnetosphere dynamics during different solar wind and magnetosphere conditions. Recently, one way coupling of RAM-SCB with the Space Weather Modeling Framework (SWMF) has been achieved to replace all data or empirical inputs with those obtained through first-principles-based codes: magnetic field and plasma flux outer boundary conditions are provided by the Block Adaptive Tree Solar wind Roe-type Upwind Scheme (BATS-R-US) MHO code, convection electric field is provided by the Ridley Ionosphere Model (RIM), and ion composition is provided by the Polarmore » Wind Outflow Model (PWOM) combined with a multi-species MHO approach. These advances, though creating a powerful inner magnetosphere virtual laboratory, neglect the important mechanisms through which the ring current feeds back into the whole system, primarily the stretching of the magnetic field lines and shielding of the convection electric field through strong region two Field Aligned Currents (FACs). In turn, changing the magnetosphere in this way changes the evolution of the ring current. To address this shortcoming, the coupling has been expanded to include feedback from RAM-SCB to the other coupled codes: region two FACs are returned to the RIM while total plasma pressure is used to nudge the MHO solution towards the RAMSCB values. The impacts of the two way coupling are evaluated on three levels: the global magnetospheric level, focusing on the impact on the ionosphere and the shape of the magnetosphere, the regional level, examining the impact on the development of the ring current in terms of energy density, anisotropy, and plasma distribution, and the local level to compare the new results to in-situ measurements of magnetic and electric field and plasma. The results will also be compared to past simulations using the one way coupling and no coupling whatsoever. This work is the first to fully couple an anisotropic kinetic ring current code with a selfconsistently calculated magnetic field to a set of global models.« less

ED(MF)n: Humidity-Convection Feedbacks in a Mass Flux Scheme Based on Resolved Size Densities

NASA Astrophysics Data System (ADS)

Neggers, R.

2014-12-01

Cumulus cloud populations remain at least partially unresolved in present-day numerical simulations of global weather and climate, and accordingly their impact on the larger-scale flow has to be represented through parameterization. Various methods have been developed over the years, ranging in complexity from the early bulk models relying on a single plume to more recent approaches that attempt to reconstruct the underlying probability density functions, such as statistical schemes and multiple plume approaches. Most of these "classic" methods capture key aspects of cumulus cloud populations, and have been successfully implemented in operational weather and climate models. However, the ever finer discretizations of operational circulation models, driven by advances in the computational efficiency of supercomputers, is creating new problems for existing sub-grid schemes. Ideally, a sub-grid scheme should automatically adapt its impact on the resolved scales to the dimension of the grid-box within which it is supposed to act. It can be argued that this is only possible when i) the scheme is aware of the range of scales of the processes it represents, and ii) it can distinguish between contributions as a function of size. How to conceptually represent this knowledge of scale in existing parameterization schemes remains an open question that is actively researched. This study considers a relatively new class of models for sub-grid transport in which ideas from the field of population dynamics are merged with the concept of multi plume modelling. More precisely, a multiple mass flux framework for moist convective transport is formulated in which the ensemble of plumes is created in "size-space". It is argued that thus resolving the underlying size-densities creates opportunities for introducing scale-awareness and scale-adaptivity in the scheme. The behavior of an implementation of this framework in the Eddy Diffusivity Mass Flux (EDMF) model, named ED(MF)n, is examined for a standard case of subtropical marine shallow cumulus. We ask if a system of multiple independently resolved plumes is able to automatically create the vertical profile of bulk (mass) flux at which the sub-grid scale transport balances the imposed larger-scale forcings in the cloud layer.

A Physically Based Coupled Chemical and Physical Weathering Model for Simulating Soilscape Evolution

NASA Astrophysics Data System (ADS)

Willgoose, G. R.; Welivitiya, D.; Hancock, G. R.

2015-12-01

A critical missing link in existing landscape evolution models is a dynamic soil evolution models where soils co-evolve with the landform. Work by the authors over the last decade has demonstrated a computationally manageable model for soil profile evolution (soilscape evolution) based on physical weathering. For chemical weathering it is clear that full geochemistry models such as CrunchFlow and PHREEQC are too computationally intensive to be couplable to existing soilscape and landscape evolution models. This paper presents a simplification of CrunchFlow chemistry and physics that makes the task feasible, and generalises it for hillslope geomorphology applications. Results from this simplified model will be compared with field data for soil pedogenesis. Other researchers have previously proposed a number of very simple weathering functions (e.g. exponential, humped, reverse exponential) as conceptual models of the in-profile weathering process. The paper will show that all of these functions are possible for specific combinations of in-soil environmental, geochemical and geologic conditions, and the presentation will outline the key variables controlling which of these conceptual models can be realistic models of in-profile processes and under what conditions. The presentation will finish by discussing the coupling of this model with a physical weathering model, and will show sample results from our SSSPAM soilscape evolution model to illustrate the implications of including chemical weathering in the soilscape evolution model.

Dimensions and dynamics of citizen observatories: The case of online amateur weather networks

NASA Astrophysics Data System (ADS)

Gharesifard, Mohammad; Wehn, Uta; van der Zaag, Pieter

2016-04-01

Crowd-sourced environmental observations are being increasingly considered as having the potential to enhance the spatial and temporal resolution of current data streams from terrestrial and areal sensors. The rapid diffusion of ICTs during the past decades has facilitated the process of data collection and sharing by the general public (so-called citizen science) and has resulted in the formation of various online environmental citizen observatory networks. Online amateur weather networks are a particular example of such ICT-mediated citizen observatories as one of the oldest and most widely practiced citizen science activities. The objective of this paper is to introduce a conceptual framework that enables a systematic review of different dimensions of these mushrooming/expanding networks. These dimensions include the geographic scope and types of network participants; the network's establishment mechanism, revenue stream(s) and existing communication paradigm; efforts required by citizens and support offered by platform providers; and issues such as data accessibility, availability and quality. An in-depth understanding of these dimensions helps to analyze various dynamics such as interactions between different stakeholders, motivations to run these networks, sustainability of the platforms, data ownership and level of transparency of each network. This framework is then utilized to perform a critical and normative review of six existing online amateur weather networks based on publicly available data. The main findings of this analysis suggest that: (1) There are several key stakeholders such as emergency services and local authorities that are not (yet) engaged in these networks. (2) The revenue stream(s) of online amateur weather networks is one of the least discussed but most important dimensions that is crucial for the sustainability of these networks. (3) Although all of the networks included in this study have one or more explicit pattern of two-way communications, there is no sign (yet) of interactive information exchange among the triangle of weather observers, data aggregators and policy makers. KEYWORDS Citizen Science, Citizen Observatories, ICT-enabled citizen participation, online amateur weather networks

Status report on the geology of the Oak Ridge Reservation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hatcher, R.D. Jr.; Lemiszki, P.J.; Foreman, J.L.

1992-10-01

This report provides an introduction to the present state of knowledge of the geology of the Oak Ridge Reservation (ORR) and a cursory introduction to the hydrogeology. An important element of this work is the construction of a modern detailed geologic map of the ORR (Plate 1), which remains in progress. An understanding of the geologic framework of the ORR is essential to many current and proposed activities related to land-use planning, waste management, environmental restoration, and waste remediation. Therefore, this report is also intended to convey the present state of knowledge of the geologic and geohydrologic framework of themore » ORR and vicinity and to present some of the available data that provide the basic framework for additional geologic mapping, subsurface geologic, and geohydrologic studies. In addition, some recently completed, detailed work on soils and other surficial materials is included because of the close relationships to bedrock geology and the need to recognize the weathered products of bedrock units. Weathering processes also have some influence on hydrologic systems and processes at depth.« less

Between the Rock and a Hard Place: The CCMC as a Transit Station Between Modelers and Forecasters

NASA Technical Reports Server (NTRS)

Hesse, Michael

2009-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second CCMC activity is to support Space Weather forecasting at national Space Weather Forecasting Centers. This second activity involved model evaluations, model transitions to operations, and the development of draft Space Weather forecasting tools. This presentation will focus on the latter element. Specifically, we will discuss the process of transition research models, or information generated by research models, to Space Weather Forecasting organizations. We will analyze successes as well as obstacles to further progress, and we will suggest avenues for increased transitioning success.

Integrated Wind Power Planning Tool

NASA Astrophysics Data System (ADS)

Rosgaard, Martin; Giebel, Gregor; Skov Nielsen, Torben; Hahmann, Andrea; Sørensen, Poul; Madsen, Henrik

2013-04-01

This poster presents the current state of the public service obligation (PSO) funded project PSO 10464, with the title "Integrated Wind Power Planning Tool". The goal is to integrate a mesoscale numerical weather prediction (NWP) model with purely statistical tools in order to assess wind power fluctuations, with focus on long term power system planning for future wind farms as well as short term forecasting for existing wind farms. Currently, wind power fluctuation models are either purely statistical or integrated with NWP models of limited resolution. Using the state-of-the-art mesoscale NWP model Weather Research & Forecasting model (WRF) the forecast error is sought quantified in dependence of the time scale involved. This task constitutes a preparative study for later implementation of features accounting for NWP forecast errors in the DTU Wind Energy maintained Corwind code - a long term wind power planning tool. Within the framework of PSO 10464 research related to operational short term wind power prediction will be carried out, including a comparison of forecast quality at different mesoscale NWP model resolutions and development of a statistical wind power prediction tool taking input from WRF. The short term prediction part of the project is carried out in collaboration with ENFOR A/S; a Danish company that specialises in forecasting and optimisation for the energy sector. The integrated prediction model will allow for the description of the expected variability in wind power production in the coming hours to days, accounting for its spatio-temporal dependencies, and depending on the prevailing weather conditions defined by the WRF output. The output from the integrated short term prediction tool constitutes scenario forecasts for the coming period, which can then be fed into any type of system model or decision making problem to be solved. The high resolution of the WRF results loaded into the integrated prediction model will ensure a high accuracy data basis is available for use in the decision making process of the Danish transmission system operator. The need for high accuracy predictions will only increase over the next decade as Denmark approaches the goal of 50% wind power based electricity in 2025 from the current 20%.

Real-Time Kennedy Space Center and Cape Canaveral Air Force Station High-Resolution Model Implementation and Verification

NASA Technical Reports Server (NTRS)

Shafer, Jaclyn; Watson, Leela R.

2015-01-01

NASA's Launch Services Program, Ground Systems Development and Operations, Space Launch System and other programs at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) use the daily and weekly weather forecasts issued by the 45th Weather Squadron (45 WS) as decision tools for their day-to-day and launch operations on the Eastern Range (ER). Examples include determining if they need to limit activities such as vehicle transport to the launch pad, protect people, structures or exposed launch vehicles given a threat of severe weather, or reschedule other critical operations. The 45 WS uses numerical weather prediction models as a guide for these weather forecasts, particularly the Air Force Weather Agency (AFWA) 1.67 km Weather Research and Forecasting (WRF) model. Considering the 45 WS forecasters' and Launch Weather Officers' (LWO) extensive use of the AFWA model, the 45 WS proposed a task at the September 2013 Applied Meteorology Unit (AMU) Tasking Meeting requesting the AMU verify this model. Due to the lack of archived model data available from AFWA, verification is not yet possible. Instead, the AMU proposed to implement and verify the performance of an ER version of the high-resolution WRF Environmental Modeling System (EMS) model configured by the AMU (Watson 2013) in real time. Implementing a real-time version of the ER WRF-EMS would generate a larger database of model output than in the previous AMU task for determining model performance, and allows the AMU more control over and access to the model output archive. The tasking group agreed to this proposal; therefore the AMU implemented the WRF-EMS model on the second of two NASA AMU modeling clusters. The AMU also calculated verification statistics to determine model performance compared to observational data. Finally, the AMU made the model output available on the AMU Advanced Weather Interactive Processing System II (AWIPS II) servers, which allows the 45 WS and AMU staff to customize the model output display on the AMU and Range Weather Operations (RWO) AWIPS II client computers and conduct real-time subjective analyses.

Browsing Space Weather Data and Models with the Integrated Space Weather Analysis (iSWA) System

NASA Technical Reports Server (NTRS)

Maddox, Marlo M.; Mullinix, Richard E.; Berrios, David H.; Hesse, Michael; Rastaetter, Lutz; Pulkkinen, Antti; Hourcle, Joseph A.; Thompson, Barbara J.

2011-01-01

The Integrated Space Weather Analysis (iSWA) System is a comprehensive web-based platform for space weather information that combines data from solar, heliospheric and geospace observatories with forecasts based on the most advanced space weather models. The iSWA system collects, generates, and presents a wide array of space weather resources in an intuitive, user-configurable, and adaptable format - thus enabling users to respond to current and future space weather impacts as well as enabling post-impact analysis. iSWA currently provides over 200 data and modeling products, and features a variety of tools that allow the user to browse, combine, and examine data and models from various sources. This presentation will consist of a summary of the iSWA products and an overview of the customizable user interfaces, and will feature several tutorial demonstrations highlighting the interactive tools and advanced capabilities.

Prediction of Weather Impacted Airport Capacity using Ensemble Learning

NASA Technical Reports Server (NTRS)

Wang, Yao Xun

2011-01-01

Ensemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.

Application of an integrated Weather Research and Forecasting (WRF)/CALPUFF modeling tool for source apportionment of atmospheric pollutants for air quality management: A case study in the urban area of Benxi, China.

PubMed

Wu, Hao; Zhang, Yan; Yu, Qi; Ma, Weichun

2018-04-01

In this study, the authors endeavored to develop an effective framework for improving local urban air quality on meso-micro scales in cities in China that are experiencing rapid urbanization. Within this framework, the integrated Weather Research and Forecasting (WRF)/CALPUFF modeling system was applied to simulate the concentration distributions of typical pollutants (particulate matter with an aerodynamic diameter <10 μm [PM 10 ], sulfur dioxide [SO 2 ], and nitrogen oxides [NO x ]) in the urban area of Benxi. Statistical analyses were performed to verify the credibility of this simulation, including the meteorological fields and concentration fields. The sources were then categorized using two different classification methods (the district-based and type-based methods), and the contributions to the pollutant concentrations from each source category were computed to provide a basis for appropriate control measures. The statistical indexes showed that CALMET had sufficient ability to predict the meteorological conditions, such as the wind fields and temperatures, which provided meteorological data for the subsequent CALPUFF run. The simulated concentrations from CALPUFF showed considerable agreement with the observed values but were generally underestimated. The spatial-temporal concentration pattern revealed that the maximum concentrations tended to appear in the urban centers and during the winter. In terms of their contributions to pollutant concentrations, the districts of Xihu, Pingshan, and Mingshan all affected the urban air quality to different degrees. According to the type-based classification, which categorized the pollution sources as belonging to the Bengang Group, large point sources, small point sources, and area sources, the source apportionment showed that the Bengang Group, the large point sources, and the area sources had considerable impacts on urban air quality. Finally, combined with the industrial characteristics, detailed control measures were proposed with which local policy makers could improve the urban air quality in Benxi. In summary, the results of this study showed that this framework has credibility for effectively improving urban air quality, based on the source apportionment of atmospheric pollutants. The authors endeavored to build up an effective framework based on the integrated WRF/CALPUFF to improve the air quality in many cities on meso-micro scales in China. Via this framework, the integrated modeling tool is accurately used to study the characteristics of meteorological fields, concentration fields, and source apportionments of pollutants in target area. The impacts of classified sources on air quality together with the industrial characteristics can provide more effective control measures for improving air quality. Through the case study, the technical framework developed in this study, particularly the source apportionment, could provide important data and technical support for policy makers to assess air pollution on the scale of a city in China or even the world.

Model Analyses and Guidance

Science.gov Websites

Fire Weather Sun/Moon Long Range Forecasts Climate Prediction Past Weather Past Weather Heating/Cooling Space Weather Sun (Ultraviolet Radiation) Safety Campaigns Wind Drought Winter Weather Information

Modeling extreme (Carrington-type) space weather events using three-dimensional MHD code simulations

NASA Astrophysics Data System (ADS)

Ngwira, C. M.; Pulkkinen, A. A.; Kuznetsova, M. M.; Glocer, A.

2013-12-01

There is growing concern over possible severe societal consequences related to adverse space weather impacts on man-made technological infrastructure and systems. In the last two decades, significant progress has been made towards the modeling of space weather events. Three-dimensional (3-D) global magnetohydrodynamics (MHD) models have been at the forefront of this transition, and have played a critical role in advancing our understanding of space weather. However, the modeling of extreme space weather events is still a major challenge even for existing global MHD models. In this study, we introduce a specially adapted University of Michigan 3-D global MHD model for simulating extreme space weather events that have a ground footprint comparable (or larger) to the Carrington superstorm. Results are presented for an initial simulation run with ``very extreme'' constructed/idealized solar wind boundary conditions driving the magnetosphere. In particular, we describe the reaction of the magnetosphere-ionosphere system and the associated ground induced geoelectric field to such extreme driving conditions. We also discuss the results and what they might mean for the accuracy of the simulations. The model is further tested using input data for an observed space weather event to verify the MHD model consistence and to draw guidance for future work. This extreme space weather MHD model is designed specifically for practical application to the modeling of extreme geomagnetically induced electric fields, which can drive large currents in earth conductors such as power transmission grids.

Integrating Environmental and Mosquito Data to Model Disease: Evaluating Alternative Modeling Approaches for Forecasting West Nile Virus in South Dakota, USA

NASA Astrophysics Data System (ADS)

Davis, J. K.; Vincent, G. P.; Hildreth, M.; Kightlinger, L.; Carlson, C.; Wimberly, M. C.

2017-12-01

South Dakota has the highest annual incidence of human cases of West Nile virus (WNV) in all US states, and human cases can vary wildly among years; predicting WNV risk in advance is a necessary exercise if public health officials are to respond efficiently and effectively to risk. Case counts are associated with environmental factors that affect mosquitoes, avian hosts, and the virus itself. They are also correlated with entomological risk indices obtained by trapping and testing mosquitoes. However, neither weather nor insect data alone provide a sufficient basis to make timely and accurate predictions, and combining them into models of human disease is not necessarily straightforward. Here we present lessons learned in three years of making real-time forecasts of this threat to public health. Various methods of integrating data from NASA's North American Land Data Assimilation System (NLDAS) with mosquito surveillance data were explored in a model comparison framework. We found that a model of human disease summarizing weather data (by polynomial distributed lags with seasonally-varying coefficients) and mosquito data (by a mixed-effects model that smooths out these sparse and highly-variable data) made accurate predictions of risk, and was generalizable enough to be recommended in similar applications. A model based on lagged effects of temperature and humidity provided the most accurate predictions. We also found that model accuracy was improved by allowing coefficients to vary smoothly throughout the season, giving different weights to different predictor variables during different parts of the season.

21st Century Changes in Precipitation Extremes Over the United States: Can Climate Analogues Help or Hinder?

NASA Astrophysics Data System (ADS)

Gao, X.; Schlosser, C. A.

2013-12-01

Global warming is expected to alter the frequency and/or magnitude of extreme precipitation events. Such changes could have substantial ecological, economic, and sociological consequences. However, climate models in general do not correctly reproduce the frequency and intensity distribution of precipitation, especially at the regional scale. In this study, gridded data from a dense network of surface precipitation gauges and a global atmospheric analysis at a coarser scale are combined to develop a diagnostic framework for the large-scale meteorological conditions (i.e. flow features, moisture supply) that dominate during extreme precipitation. Such diagnostic framework is first evaluated with the late 20th century simulations from an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and is found to produce more consistent (and less uncertain) total and interannaul number of extreme days with the observations than the model-based precipitation over the south-central United States and the Western United States examined in this study. The framework is then applied to the CMIP5 multi-model projections for two radiative forcing scenarios (Representative Concentration Pathways 4.5 and 8.5) to assess the potential future changes in the probability of precipitation extremes over the same study regions. We further analyze the accompanying circulation features and their changes that may be responsible for shifts in extreme precipitation in response to changed climate. The results from this study may guide hazardous weather watches and help society develop adaptive strategies for preventing catastrophic losses.

Enhancing the hydrogeological landscape (HGL) characterisation of the Greater Launceston area (GLA) through better understanding of dolerite weathering, stream water properties and a revised landscape evolution model

NASA Astrophysics Data System (ADS)

Moore, Leah; Nicholson, Allan; Cook, Wayne; Sweeney, Margaret

2014-05-01

In the Greater Launceston Area (GLA) in northern Tasmania, Australia, there is a widespread urban salinity problem with severe impacts on urban/peri-urban infrastructure in localised areas. Salinity patterns in the landscape (elevated flux to waterways; salt efflorescence at the land surface) could be related to: the underlying rock type, the thickness of regolith materials and hence the volume of the salt store, the landforms present and the amount of water passing over and through the landscape. In northern Tasmania secondary mineralogy on dolerite typically includes formation of Fe/Ca smectite phases (e.g. nontronite, saponite) and Fe-Ti oxides/sesquioxides (e.g. hematite, goethite) with some primary phases (e.g. Ca-plagioclase feldspar, augite) weathering through to a suite dominated by kaolinite clay and Fe-Ti oxides/sesquioxides. Deeply weathered profiles in the GLA have weathered to the kaolintite-clay dominant mineralogy and in places there are gibbsite/beidellite/hematite/goethite bauxites developed. Most existing salinity mapping emphasises salt manifestation over paleo-estuarine sediments of the Paleogene Tamar-Esk River system, so incorporation of deeply weathered Jurassic dolerite materials into the salt budget considerably augments the estimated potential hazard. Rapid stream surveys provide a snapshot of stream electrical conductivity (EC) over the study area at regular intervals allowing a broad evaluation of salt flux patterns in surfaces waters. Higher EC readings were obtained from selected streams draining: deeply weathered dolerite profiles (0.37 1.86 dS/m) and deeply weathered Paleogene paleo-estuarine sediments (0.49 to 1.16 dS/m). Lower values were measured on up-faulted dolerite blocks (<0.10 dS/m); moderately weathered, high relief dolerite (<0.03 dS/m), and in incised streams flowing over a rocky dolerite substrate (<0.03 dS/m). The patterns of stream EC reflect the nature of the regolith materials the streams drain, and match mapped patterns for distribution of deeply weathered Jurassic dolerite and moderately to deeply weathered bedded paleo-estuarine sediments of the Paleogene Tamar-Esk river system, some Quaternary terrace deposits along the Tamar and Esk Rivers; and some Holocene estuarine sediments. Recent geomorphic mapping has enabled development of a more comprehensive and consistent landscape evolution model that builds on existing knowledge. This model describes the influence of a progressively incising Tamar-Esk river system in response to episodic lowering of the local base level, with multiple episodes of valley widening as the river system stabilised after incision. Successive lowering events dissected earlier landforms, but locally remnant surfaces are preserved that represent former fluvial plain and terrace features. These processes were partially controlled by the structural configuration and contrasting resistance of the underlying lithologies, influencing the planform geometries of the rivers, and consequently the potential to preserve paleo-fluvial features. Because the Tamar River is an estuarine system, some of the lowermost preserved surfaces are likely to reflect marine processes (e.g. 5-7m; 10-12m ASL). The geomorphic mapping was conducted independently of the hydrogeological landscape (HGL) characterisation in the GLA, but there is strong correlation between the areas identified as having elevated salinity hazard (HGL) and newly mapped remnant surfaces in this landscape. This work complements HGL research and supports development of an increasingly rigorous evidence-based framework for GLA salinity hazard management.

Space weather in the EU's FP7 Space Theme. Preface to the special issue on "EU-FP7 funded space weather projects"

NASA Astrophysics Data System (ADS)

Chiarini, Paola

2013-11-01

Technological infrastructures in space and on ground provide services on which modern society and economies rely. Space weather related research is funded under the 7th Framework Programme for Research and Innovation (FP7) of the European Union in response to the need of protecting such critical infrastructures from the damage which could be caused by extreme space weather events. The calls for proposals published under the topic "Security of space assets from space weather events" of the FP7 Space Theme aimed to improve forecasts and predictions of disruptive space weather events as well as identify best practices to limit the impacts on space- and ground-based infrastructures and their data provision. Space weather related work was also funded under the topic "Exploitation of space science and exploration data", which aims to add value to space missions and Earth-based observations by contributing to the effective scientific exploitation of collected data. Since 2007 a total of 20 collaborative projects have been funded, covering a variety of physical phenomena associated with space weather, from ionospheric disturbances and scintillation, to geomagnetically induced currents at Earth's surface, to coronal mass ejections and solar energetic particles. This article provides an overview of the funded projects, touching upon some results and referring to specific websites for a more exhaustive description of the projects' outcomes.

A framework for predicting global silicate weathering and CO2 drawdown rates over geologic time-scales.

PubMed

Hilley, George E; Porder, Stephen

2008-11-04

Global silicate weathering drives long-time-scale fluctuations in atmospheric CO(2). While tectonics, climate, and rock-type influence silicate weathering, it is unclear how these factors combine to drive global rates. Here, we explore whether local erosion rates, GCM-derived dust fluxes, temperature, and water balance can capture global variation in silicate weathering. Our spatially explicit approach predicts 1.9-4.6 x 10(13) mols of Si weathered globally per year, within a factor of 4-10 of estimates of global silicate fluxes derived from riverine measurements. Similarly, our watershed-based estimates are within a factor of 4-18 (mean of 5.3) of the silica fluxes measured in the world's ten largest rivers. Eighty percent of total global silicate weathering product traveling as dissolved load occurs within a narrow range (0.01-0.5 mm/year) of erosion rates. Assuming each mol of Mg or Ca reacts with 1 mol of CO(2), 1.5-3.3 x 10(8) tons/year of CO(2) is consumed by silicate weathering, consistent with previously published estimates. Approximately 50% of this drawdown occurs in the world's active mountain belts, emphasizing the importance of tectonic regulation of global climate over geologic timescales.

A tri-modular model for the computation of the meteorological and oceanographic fields in the Adriatic Sea

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lionello, P.; Pernigotti, D.; Zampato, L.

1994-12-31

The purpose of this research program is the construction of the modelling framework to describe and predict the development of the sea and of the atmosphere in the Adriatic region. There are two time scales that are considered: the medium range time scale of the weather-surge-oceanwave forecast and the interseasonal time scale of the thermohaline circulation in the Adriatic Sea. The phenomenology associated with the medium range is represented by the intense storms that take place in the Adriatic Sea, in spite of its relatively small extension, when the presence of a pressure minimum over Italy generates an intense Sciroccomore » wind which, channeled by the mountain ridges surrounding the basin, blows along its whole length. Because of the long fetch, approximately 1,000 Km., this situation produces high ocean waves and the storm surge that is associated with the flooding of Venice. The interseasonal phenomenology is represented by the formation of dense water in the Northern part of the basin during winter. This is presumably caused by Bora, a strong South-Westerly wind, cold and dry, which produces cooling and evaporation in the shallow water coastal region of the Northern Adriatic. The complex orography surrounding the Adriatic and the short duration of this phenomena require a model framework capable of high space and time resolution on a limited area. This is the motivation for addressing these issues in a coupled model framework consisting of a limited area atmospheric circulation model, an ocean circulation model, and a ocean wave model with high resolution both in space and time.« less

Key processes for the eastward propagation of the Madden-Julian Oscillation based on multimodel simulations

NASA Astrophysics Data System (ADS)

Jiang, Xianan

2017-01-01

As a prominent climate variability mode with widespread influences on global weather extremes, the Madden-Julian Oscillation (MJO) remains poorly represented in the latest generation of general circulation models (GCMs), with a particular challenge in simulating its eastward propagating convective signals. In this study, by analyzing multimodel simulations from a recent global MJO model evaluation project, an effort is made to identify key processes for the eastward propagation of the MJO through analyses of moisture entropy (ME) processes under a "moisture mode" framework for the MJO. The column-integrated horizontal ME advection is found to play a critical role for the eastward propagation of the MJO in both observations and good MJO models, with a primary contribution through advection of the lower tropospheric seasonal mean ME by the MJO anomalous circulations. By contrast, the horizontal ME advection effect for the eastward propagation is greatly underestimated in poor MJO GCMs, due to model deficiencies in simulating both the seasonal mean ME pattern and MJO circulations, leading to a largely stationary MJO mode in these GCMs. These results thus pinpoint an important guidance toward improved representation of the MJO in climate and weather forecast models. While this study mainly focuses on fundamental physics for the MJO propagation over the Indian Ocean, complex influences by the Maritime Continent on the MJO and also ME processes associated with the MJO over the western Pacific warrant further investigations.

Stochastic Hourly Weather Generator HOWGH: Validation and its Use in Pest Modelling under Present and Future Climates

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Hirschi, M.; Spirig, C.

2014-12-01

To quantify impact of the climate change on a specific pest (or any weather-dependent process) in a specific site, we may use a site-calibrated pest (or other) model and compare its outputs obtained with site-specific weather data representing present vs. perturbed climates. The input weather data may be produced by the stochastic weather generator. Apart from the quality of the pest model, the reliability of the results obtained in such experiment depend on an ability of the generator to represent the statistical structure of the real world weather series, and on the sensitivity of the pest model to possible imperfections of the generator. This contribution deals with the multivariate HOWGH weather generator, which is based on a combination of parametric and non-parametric statistical methods. Here, HOWGH is used to generate synthetic hourly series of three weather variables (solar radiation, temperature and precipitation) required by a dynamic pest model SOPRA to simulate the development of codling moth. The contribution presents results of the direct and indirect validation of HOWGH. In the direct validation, the synthetic series generated by HOWGH (various settings of its underlying model are assumed) are validated in terms of multiple climatic characteristics, focusing on the subdaily wet/dry and hot/cold spells. In the indirect validation, we assess the generator in terms of characteristics derived from the outputs of SOPRA model fed by the observed vs. synthetic series. The weather generator may be used to produce weather series representing present and future climates. In the latter case, the parameters of the generator may be modified by the climate change scenarios based on Global or Regional Climate Models. To demonstrate this feature, the results of codling moth simulations for future climate will be shown. Acknowledgements: The weather generator is developed and validated within the frame of projects WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports of CR), and VALUE (COST ES 1102 action).

The Evolution of Land Plants and the Silicate Weathering Feedback

NASA Astrophysics Data System (ADS)

Ibarra, D. E.; Caves Rugenstein, J. K.; Bachan, A.; Baresch, A.; Lau, K. V.; Thomas, D.; Lee, J. E.; Boyce, C. K.; Chamberlain, C. P.

2017-12-01

It has long been recognized that the advent of vascular plants in the Paleozoic must have changed silicate weathering and fundamentally altered the long-term carbon cycle. Efforts to quantify these effects have been formulated in carbon cycle models that are, in part, calibrated by weathering studies of modern plant communities. In models of the long-term carbon cycle, plants play a key role in controlling atmospheric CO2, particularly in the late Paleozoic. We test the impact of some established and recent theories regarding plant-enhanced weathering by coupling a one-dimensional vapor transport model to a reactive transport model of silicate weathering. In this coupled model, we evaluate consequences of plant evolutionary innovation that have not been mechanistically incorporated into most existing models: 1) the role of evolutionary shifts in plant transpiration in enhancing silicate weathering by increasing downwind transport and recycling of water vapor to continental interiors; 2) the importance of deeply-rooted plants and their associated microbial communities in increasing soil CO2 and weathering zone length scales; and, 3) the cumulative effect of these processes. Our modeling approach is framed by energy/supply constraints calibrated for minimally vegetated-, vascular plant forested-, and angiosperm-worlds. We find that the emergence of widespread transpiration and associated inland vapor recycling approximately doubles weathering solute concentrations when deep-rooted vascular plants (Devonian-Carboniferous) fully replace a minimally vegetated (pre-Devonian) world. The later evolution of angiosperms (Cretaceous and Cenozoic) and subsequent increase in transpiration fluxes increase weathering solute concentrations by approximately an additional 20%. Our estimates of the changes in weatherability caused by land plant evolution are of a similar magnitude, but explained with new process-based mechanisms, than those used in existing carbon cycle models. We suggest a feedback where the increase in solute concentrations is compensated by a decrease in runoff and temperature, permitting lower steady-state atmospheric pCO2. Consequently, plants have increased the strength of the climatic feedback on silicate weathering since the late Paleozoic.

NOAA SWPC / NASA CCMC Space Weather Modeling Assessment Project: Toward the Validation of Advancements in Heliospheric Space Weather Prediction Within WSA-Enlil

NASA Astrophysics Data System (ADS)

Adamson, E. T.; Pizzo, V. J.; Biesecker, D. A.; Mays, M. L.; MacNeice, P. J.; Taktakishvili, A.; Viereck, R. A.

2017-12-01

In 2011, NOAA's Space Weather Prediction Center (SWPC) transitioned the world's first operational space weather model into use at the National Weather Service's Weather and Climate Operational Supercomputing System (WCOSS). This operational forecasting tool is comprised of the Wang-Sheeley-Arge (WSA) solar wind model coupled with the Enlil heliospheric MHD model. Relying on daily-updated photospheric magnetograms produced by the National Solar Observatory's Global Oscillation Network Group (GONG), this tool provides critical predictive knowledge of heliospheric dynamics such as high speed streams and coronal mass ejections. With the goal of advancing this predictive model and quantifying progress, SWPC and NASA's Community Coordinated Modeling Center (CCMC) have initiated a collaborative effort to assess improvements in space weather forecasts at Earth by moving from a single daily-updated magnetogram to a sequence of time-dependent magnetograms to drive the ambient inputs for the WSA-Enlil model as well as incorporating the newly developed Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model. We will provide a detailed overview of the scope of this effort and discuss preliminary results from the first phase focusing on the impact of time-dependent magnetogram inputs to the WSA-Enlil model.

Community Coordinated Modeling Center: Paving the Way for Progress in Space Science Research to Operational Space Weather Forecasting

NASA Astrophysics Data System (ADS)

Kuznetsova, M. M.; Maddox, M. M.; Mays, M. L.; Mullinix, R.; MacNeice, P. J.; Pulkkinen, A. A.; Rastaetter, L.; Shim, J.; Taktakishvili, A.; Zheng, Y.; Wiegand, C.

2013-12-01

Community Coordinated Modeling Center (CCMC) was established at the dawn of the millennium as an essential element on the National Space Weather Program. One of the CCMC goals was to pave the way for progress in space science research to operational space weather forecasting. Over the years the CCMC acquired the unique experience in preparing complex models and model chains for operational environment, in developing and maintaining powerful web-based tools and systems ready to be used by space weather service providers and decision makers as well as in space weather prediction capabilities assessments. The presentation will showcase latest innovative solutions for space weather research, analysis, forecasting and validation and review on-going community-wide initiatives enabled by CCMC applications.

Fuzzy time-series based on Fibonacci sequence for stock price forecasting

NASA Astrophysics Data System (ADS)

Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

2007-07-01

Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

SIMULATION-BASED WEATHER NORMALIZATION APPROACH TO STUDY THE IMPACT OF WEATHER ON ENERGY USE OF BUILDINGS IN THE U.S.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Makhmalbaf, Atefe; Srivastava, Viraj; Wang, Na

Weather normalization is a crucial task in several applications related to building energy conservation such as retrofit measurements and energy rating. This paper documents preliminary results found from an effort to determine a set of weather adjustment coefficients that can be used to smooth out impacts of weather on energy use of buildings in 1020 weather location sites available in the U.S. The U.S. Department of Energy (DOE) commercial reference building models are adopted as hypothetical models with standard operations to deliver consistency in modeling. The correlation between building envelop design, HVAC system design and properties for different building typesmore » and the change in heating and cooling energy consumption caused by variations in weather is examined.« less

A coupled human-natural system to assess the operational value of weather and climate services for agriculture

NASA Astrophysics Data System (ADS)

Li, Yu; Giuliani, Matteo; Castelletti, Andrea

2017-09-01

Recent advances in weather and climate (W&C) services are showing increasing forecast skills over seasonal and longer timescales, potentially providing valuable support in informing decisions in a variety of economic sectors. Quantifying this value, however, might not be straightforward as better forecast quality does not necessarily imply better decisions by the end users, especially when forecasts do not reach their final users, when providers are not trusted, or when forecasts are not appropriately understood. In this study, we contribute an assessment framework to evaluate the operational value of W&C services for informing agricultural practices by complementing traditional forecast quality assessments with a coupled human-natural system behavioural model which reproduces farmers' decisions. This allows a more critical assessment of the forecast value mediated by the end users' perspective, including farmers' risk attitudes and behavioural factors. The application to an agricultural area in northern Italy shows that the quality of state-of-the-art W&C services is still limited in predicting the weather and the crop yield of the incoming agricultural season, with ECMWF annual products simulated by the IFS/HOPE model resulting in the most skillful product in the study area. However, we also show that the accuracy of estimating crop yield and the probability of making optimal decisions are not necessarily linearly correlated, with the overall assessment procedure being strongly impacted by the behavioural attitudes of farmers, which can produce rank reversals in the quantification of the W&C services operational value depending on the different perceptions of risk and uncertainty.

Toward an Improved Representation of Middle Atmospheric Dynamics Thanks to the ARISE Project

NASA Astrophysics Data System (ADS)

Blanc, E.; Ceranna, L.; Hauchecorne, A.; Charlton-Perez, A.; Marchetti, E.; Evers, L. G.; Kvaerna, T.; Lastovicka, J.; Eliasson, L.; Crosby, N. B.; Blanc-Benon, P.; Le Pichon, A.; Brachet, N.; Pilger, C.; Keckhut, P.; Assink, J. D.; Smets, P. S. M.; Lee, C. F.; Kero, J.; Sindelarova, T.; Kämpfer, N.; Rüfenacht, R.; Farges, T.; Millet, C.; Näsholm, S. P.; Gibbons, S. J.; Espy, P. J.; Hibbins, R. E.; Heinrich, P.; Ripepe, M.; Khaykin, S.; Mze, N.; Chum, J.

2018-03-01

This paper reviews recent progress toward understanding the dynamics of the middle atmosphere in the framework of the Atmospheric Dynamics Research InfraStructure in Europe (ARISE) initiative. The middle atmosphere, integrating the stratosphere and mesosphere, is a crucial region which influences tropospheric weather and climate. Enhancing the understanding of middle atmosphere dynamics requires improved measurement of the propagation and breaking of planetary and gravity waves originating in the lowest levels of the atmosphere. Inter-comparison studies have shown large discrepancies between observations and models, especially during unresolved disturbances such as sudden stratospheric warmings for which model accuracy is poorer due to a lack of observational constraints. Correctly predicting the variability of the middle atmosphere can lead to improvements in tropospheric weather forecasts on timescales of weeks to season. The ARISE project integrates different station networks providing observations from ground to the lower thermosphere, including the infrasound system developed for the Comprehensive Nuclear-Test-Ban Treaty verification, the Lidar Network for the Detection of Atmospheric Composition Change, complementary meteor radars, wind radiometers, ionospheric sounders and satellites. This paper presents several examples which show how multi-instrument observations can provide a better description of the vertical dynamics structure of the middle atmosphere, especially during large disturbances such as gravity waves activity and stratospheric warming events. The paper then demonstrates the interest of ARISE data in data assimilation for weather forecasting and re-analyzes the determination of dynamics evolution with climate change and the monitoring of atmospheric extreme events which have an atmospheric signature, such as thunderstorms or volcanic eruptions.

Quality assurance of weather data for agricultural system model input

USDA-ARS?s Scientific Manuscript database

It is well known that crop production and hydrologic variation on watersheds is weather related. Rarely, however, is meteorological data quality checks reported for agricultural systems model research. We present quality assurance procedures for agricultural system model weather data input. Problems...

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

2012-01-01

Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

Constraining climate sensitivity and continental versus seafloor weathering using an inverse geological carbon cycle model.

PubMed

Krissansen-Totton, Joshua; Catling, David C

2017-05-22

The relative influences of tectonics, continental weathering and seafloor weathering in controlling the geological carbon cycle are unknown. Here we develop a new carbon cycle model that explicitly captures the kinetics of seafloor weathering to investigate carbon fluxes and the evolution of atmospheric CO 2 and ocean pH since 100 Myr ago. We compare model outputs to proxy data, and rigorously constrain model parameters using Bayesian inverse methods. Assuming our forward model is an accurate representation of the carbon cycle, to fit proxies the temperature dependence of continental weathering must be weaker than commonly assumed. We find that 15-31 °C (1σ) surface warming is required to double the continental weathering flux, versus 3-10 °C in previous work. In addition, continental weatherability has increased 1.7-3.3 times since 100 Myr ago, demanding explanation by uplift and sea-level changes. The average Earth system climate sensitivity is  K (1σ) per CO 2 doubling, which is notably higher than fast-feedback estimates. These conclusions are robust to assumptions about outgassing, modern fluxes and seafloor weathering kinetics.

Constraining climate sensitivity and continental versus seafloor weathering using an inverse geological carbon cycle model

PubMed Central

Krissansen-Totton, Joshua; Catling, David C.

2017-01-01

The relative influences of tectonics, continental weathering and seafloor weathering in controlling the geological carbon cycle are unknown. Here we develop a new carbon cycle model that explicitly captures the kinetics of seafloor weathering to investigate carbon fluxes and the evolution of atmospheric CO2 and ocean pH since 100 Myr ago. We compare model outputs to proxy data, and rigorously constrain model parameters using Bayesian inverse methods. Assuming our forward model is an accurate representation of the carbon cycle, to fit proxies the temperature dependence of continental weathering must be weaker than commonly assumed. We find that 15–31 °C (1σ) surface warming is required to double the continental weathering flux, versus 3–10 °C in previous work. In addition, continental weatherability has increased 1.7–3.3 times since 100 Myr ago, demanding explanation by uplift and sea-level changes. The average Earth system climate sensitivity is  K (1σ) per CO2 doubling, which is notably higher than fast-feedback estimates. These conclusions are robust to assumptions about outgassing, modern fluxes and seafloor weathering kinetics. PMID:28530231

Models Required to Mitigate Impacts of Space Weather on Space Systems

NASA Technical Reports Server (NTRS)

Barth, Janet L.

2003-01-01

This viewgraph presentation attempts to develop a model of factors which need to be considered in the design and construction of spacecraft to lessen the effects of space weather on these vehicles. Topics considered include: space environments and effects, radiation environments and effects, space weather drivers, space weather models, climate models, solar proton activity and mission design for the GOES mission. The authors conclude that space environment models need to address issues from mission planning through operations and a program to develop and validate authoritative space environment models for application to spacecraft design does not exist at this time.

Beyond climate envelopes: effects of weather on regional population trends in butterflies.

PubMed

WallisDeVries, Michiel F; Baxter, Wendy; Van Vliet, Arnold J H

2011-10-01

Although the effects of climate change on biodiversity are increasingly evident by the shifts in species ranges across taxonomical groups, the underlying mechanisms affecting individual species are still poorly understood. The power of climate envelopes to predict future ranges has been seriously questioned in recent studies. Amongst others, an improved understanding of the effects of current weather on population trends is required. We analysed the relation between butterfly abundance and the weather experienced during the life cycle for successive years using data collected within the framework of the Dutch Butterfly Monitoring Scheme for 40 species over a 15-year period and corresponding climate data. Both average and extreme temperature and precipitation events were identified, and multiple regression was applied to explain annual changes in population indices. Significant weather effects were obtained for 39 species, with the most frequent effects associated with temperature. However, positive density-dependence suggested climatic independent trends in at least 12 species. Validation of the short-term predictions revealed a good potential for climate-based predictions of population trends in 20 species. Nevertheless, data from the warm and dry year of 2003 indicate that negative effects of climatic extremes are generally underestimated for habitat specialists in drought-susceptible habitats, whereas generalists remain unaffected. Further climatic warming is expected to influence the trends of 13 species, leading to an improvement for nine species, but a continued decline in the majority of species. Expectations from climate envelope models overestimate the positive effects of climate change in northwestern Europe. Our results underline the challenge to include population trends in predicting range shifts in response to climate change.

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa D.

2011-01-01

The Land Information System (LIS; http://lis.gsfc.nasa.gov) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite-and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected as a co-winner of NASA?s 2005 Software of the Year award.LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has e volved from two earlier efforts -- North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations.In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins". LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling be enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation, who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs.LIS has also recently been demonstrated for multi-model data assimilation using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature.Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation.Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeorological modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems

Joint Meteorological Statistics of Observing Sites for the Event Horizon Telescope

NASA Astrophysics Data System (ADS)

Lope Córdova Rosado, Rodrigo Eduardo; Doeleman, Sheperd; Paine, Scott; Johnson, Michael; Event Horizon Telescope (EHT)

2018-01-01

The Event Horizon Telescope (EHT) aims to resolve the general relativistic shadow of Sgr A*, the supermassive black hole at the center of our galaxy, via Very Long Baseline Interferometry (VLBI) measurements with a multinational array of radio observatories. In order to optimize the scheduling of future observations, we have developed tools to model the atmospheric opacity at each EHT site using the past 10 years of Global Forecast System (GFS) data describing the atmospheric state. These tools allow us to determine the ideal observing windows for EHT observations and to assess the suitability and impact of new EHT sites. We describe our modeling framework, compare our models to in-situ measurements at EHT sites, and discuss the implications of weather limitations for planned extensions of the EHT to higher frequencies, as well as additional sites and observation windows.

Weather models as virtual sensors to data-driven rainfall predictions in urban watersheds

NASA Astrophysics Data System (ADS)

Cozzi, Lorenzo; Galelli, Stefano; Pascal, Samuel Jolivet De Marc; Castelletti, Andrea

2013-04-01

Weather and climate predictions are a key element of urban hydrology where they are used to inform water management and assist in flood warning delivering. Indeed, the modelling of the very fast dynamics of urbanized catchments can be substantially improved by the use of weather/rainfall predictions. For example, in Singapore Marina Reservoir catchment runoff processes have a very short time of concentration (roughly one hour) and observational data are thus nearly useless for runoff predictions and weather prediction are required. Unfortunately, radar nowcasting methods do not allow to carrying out long - term weather predictions, whereas numerical models are limited by their coarse spatial scale. Moreover, numerical models are usually poorly reliable because of the fast motion and limited spatial extension of rainfall events. In this study we investigate the combined use of data-driven modelling techniques and weather variables observed/simulated with a numerical model as a way to improve rainfall prediction accuracy and lead time in the Singapore metropolitan area. To explore the feasibility of the approach, we use a Weather Research and Forecast (WRF) model as a virtual sensor network for the input variables (the states of the WRF model) to a machine learning rainfall prediction model. More precisely, we combine an input variable selection method and a non-parametric tree-based model to characterize the empirical relation between the rainfall measured at the catchment level and all possible weather input variables provided by WRF model. We explore different lead time to evaluate the model reliability for different long - term predictions, as well as different time lags to see how past information could improve results. Results show that the proposed approach allow a significant improvement of the prediction accuracy of the WRF model on the Singapore urban area.

Big Data Analytics for Modelling and Forecasting of Geomagnetic Field Indices

NASA Astrophysics Data System (ADS)

Wei, H. L.

2016-12-01

A massive amount of data are produced and stored in research areas of space weather and space climate. However, the value of a vast majority of the data acquired every day may not be effectively or efficiently exploited in our daily practice when we try to forecast solar wind parameters and geomagnetic field indices using these recorded measurements or digital signals, probably due to the challenges stemming from the dealing with big data which are characterized by the 4V futures: volume (a massively large amount of data), variety (a great number of different types of data), velocity (a requirement of quick processing of the data), and veracity (the trustworthiness and usability of the data). In order to obtain more reliable and accurate predictive models for geomagnetic field indices, it requires that models should be developed from the big data analytics perspective (or it at least benefits from such a perspective). This study proposes a few data-based modelling frameworks which aim to produce more efficient predictive models for space weather parameters forecasting by means of system identification and big data analytics. More specifically, it aims to build more reliable mathematical models that characterise the relationship between solar wind parameters and geomagnetic filed indices, for example the dependent relationship of Dst and Kp indices on a few solar wind parameters and magnetic field indices, namely, solar wind velocity (V), southward interplanetary magnetic field (Bs), solar wind rectified electric field (VBs), and dynamic flow pressure (P). Examples are provided to illustrate how the proposed modelling approaches are applied to Dst and Kp index prediction.

The NASA-Goddard Multi-Scale Modeling Framework - Land Information System: Global Land/atmosphere Interaction with Resolved Convection

NASA Technical Reports Server (NTRS)

Mohr, Karen Irene; Tao, Wei-Kuo; Chern, Jiun-Dar; Kumar, Sujay V.; Peters-Lidard, Christa D.

2013-01-01

The present generation of general circulation models (GCM) use parameterized cumulus schemes and run at hydrostatic grid resolutions. To improve the representation of cloud-scale moist processes and landeatmosphere interactions, a global, Multi-scale Modeling Framework (MMF) coupled to the Land Information System (LIS) has been developed at NASA-Goddard Space Flight Center. The MMFeLIS has three components, a finite-volume (fv) GCM (Goddard Earth Observing System Ver. 4, GEOS-4), a 2D cloud-resolving model (Goddard Cumulus Ensemble, GCE), and the LIS, representing the large-scale atmospheric circulation, cloud processes, and land surface processes, respectively. The non-hydrostatic GCE model replaces the single-column cumulus parameterization of fvGCM. The model grid is composed of an array of fvGCM gridcells each with a series of embedded GCE models. A horizontal coupling strategy, GCE4fvGCM4Coupler4LIS, offered significant computational efficiency, with the scalability and I/O capabilities of LIS permitting landeatmosphere interactions at cloud-scale. Global simulations of 2007e2008 and comparisons to observations and reanalysis products were conducted. Using two different versions of the same land surface model but the same initial conditions, divergence in regional, synoptic-scale surface pressure patterns emerged within two weeks. The sensitivity of largescale circulations to land surface model physics revealed significant functional value to using a scalable, multi-model land surface modeling system in global weather and climate prediction.

New York State energy-analytic information system: first-stage implementation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allentuck, J.; Carroll, O.; Fiore, L.

1979-09-01

So that energy policy by state government may be formulated within the constraints imposed by policy determined at the national level - yet reflect the diverse interests of its citizens - large quantities of data and sophisticated analytic capabilities are required. This report presents the design of an energy-information/analytic system for New York State, the data for a base year, 1976, and projections of these data. At the county level, 1976 energy-supply demand data and electric generating plant data are provided as well. Data-base management is based on System 2000. Three computerized models provide the system's basic analytic capacity. Themore » Brookhaven Energy System Network Simulator provides an integrating framework while a price-response model and a weather sensitive energy demand model furnished a short-term energy response estimation capability. The operation of these computerized models is described. 62 references, 25 figures, 39 tables.« less

Four-fluid MHD Simulations of the Plasma and Neutral Gas Environment of Comet Churyumov-Gerasimenko Near Perihelio

NASA Astrophysics Data System (ADS)

Huang, Z.; Toth, G.; Gombosi, T. I.; Jia, X.; Rubin, M.; Hansen, K. C.; Fougere, N.; Bieler, A. M.; Shou, Y.; Altwegg, K.; Combi, M. R.; Tenishev, V.

2015-12-01

The neutral and plasma environment is critical in understanding the interaction of comet Churyumov-Gerasimenko (CG), the target of the Rosetta mission, and the solar wind. To serve this need and support the Rosetta mission, we develop a 3-D four fluid model, which is based on BATS-R-US within the SWMF (Space Weather Modeling Framework) that solves the governing multi-fluid MHD equations and the Euler equations for the neutral gas fluid. These equations describe the behavior and interactions of the cometary heavy ions, the solar wind protons, the electrons, and the neutrals. This model incorporates different mass loading processes, including photo and electron impact ionization, charge exchange, dissociative ion-electron recombination, and collisional interactions between different fluids. We simulate the near nucleus plasma and neutral gas environment near perihelion with a realistic shape model of CG and compare our simulation results with Rosetta observations.

Benchmark Campaign of the COST Action GNSS4SWEC: Main Goals and Achievements

NASA Astrophysics Data System (ADS)

Dick, G.; Dousa, J.; Kacmarik, M.; Pottiaux, E.; Zus, F.; Brenot, H. H.; Moeller, G.; Kaplon, J.; Morel, L.; Hordyniec, P.

2016-12-01

This talk will give an overview of achievements of the Benchmark campaign, one of the central activities in the framework of the COST Action ES 1206 GNSS4SWEC. The main goal of the campaign is supporting the development and validation of advanced Global Navigation Satellite System (GNSS) tropospheric products, in particular high-resolution and ultra-fast/real-time zenith total delays (ZTD) and asymmetry products in terms of tropospheric horizontal gradients and slant delays.For the Benchmark campaign a complex data set of GNSS observations and various meteorological data were collected for a two-month period in 2013 (May-June) which included severe weather events in central Europe. An initial processing of data sets from GNSS and numerical weather models (NWM) provided independently estimated tropospheric reference products - ZTDs, tropospheric horizontal gradients and others. The comparison of horizontal tropospheric gradients from GNSS and NWM data demonstrated a very good agreement among independent solutions with negligible biases and an accuracy of about 0.5 mm. Visual comparisons of maps of zenith wet delays and tropospheric horizontal gradients showed very promising results for future exploitations of advanced GNSS tropospheric products in meteorological applications such as severe weather event monitoring and weather nowcasting.The benchmark data set is also used for an extensive validation of line-of-sight tropospheric Slant Total Delays (STD) from GNSS, NWM-raytracing and Water Vapour Radiometer (WVR) solutions. Six institutions delivered their STDs based on GNSS observations processed using different software and strategies. STDs from NWM ray-tracing came from three institutions using three different NWM models. Results show generally a very good mutual agreement among all solutions from all techniques. Among all an influence of adding not cleaned as well as cleaned GNSS post-fit residuals, i.e. residuals with eliminated and not eliminated non-tropospheric systematic effects such as multipath, to estimated STDs will be presented.

Analog ensemble and Bayesian regression techniques to improve the wind speed prediction during extreme storms in the NE U.S.

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Delle Monache, L.; Alessandrini, S.

2016-12-01

Accuracy of weather forecasts in Northeast U.S. has become very important in recent years, given the serious and devastating effects of extreme weather events. Despite the use of evolved forecasting tools and techniques strengthened by increased super-computing resources, the weather forecasting systems still have their limitations in predicting extreme events. In this study, we examine the combination of analog ensemble and Bayesian regression techniques to improve the prediction of storms that have impacted NE U.S., mostly defined by the occurrence of high wind speeds (i.e. blizzards, winter storms, hurricanes and thunderstorms). The predicted wind speed, wind direction and temperature by two state-of-the-science atmospheric models (WRF and RAMS/ICLAMS) are combined using the mentioned techniques, exploring various ways that those variables influence the minimization of the prediction error (systematic and random). This study is focused on retrospective simulations of 146 storms that affected the NE U.S. in the period 2005-2016. In order to evaluate the techniques, leave-one-out cross validation procedure was implemented regarding 145 storms as the training dataset. The analog ensemble method selects a set of past observations that corresponded to the best analogs of the numerical weather prediction and provides a set of ensemble members of the selected observation dataset. The set of ensemble members can then be used in a deterministic or probabilistic way. In the Bayesian regression framework, optimal variances are estimated for the training partition by minimizing the root mean square error and are applied to the out-of-sample storm. The preliminary results indicate a significant improvement in the statistical metrics of 10-m wind speed for 146 storms using both techniques (20-30% bias and error reduction in all observation-model pairs). In this presentation, we discuss the various combinations of atmospheric predictors and techniques and illustrate how the long record of predicted storms is valuable in the improvement of wind speed prediction.

Initial Demonstration of the Real-Time Safety Monitoring Framework for the National Airspace System Using Flight Data

NASA Technical Reports Server (NTRS)

Roychoudhury, Indranil; Daigle, Matthew; Goebel, Kai; Spirkovska, Lilly; Sankararaman, Shankar; Ossenfort, John; Kulkarni, Chetan; McDermott, William; Poll, Scott

2016-01-01

As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have an automated framework to provide an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecast, predicted health of assets in the airspace, and so on. Towards this end, as part of our earlier work, we formulated the Real-Time Safety Monitoring (RTSM) framework for monitoring and predicting the state of safety and to predict unsafe events. In our previous work, the RTSM framework was demonstrated in simulation on three different constructed scenarios. In this paper, we further develop the framework and demonstrate it on real flight data from multiple data sources. Specifically, the flight data is obtained through the Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART-NAS) Testbed that serves as a central point of collection, integration, and access of information from these different data sources. By testing and evaluating using real-world scenarios, we may accelerate the acceptance of the RTSM framework towards deployment. In this paper we demonstrate the framework's capability to not only estimate the state of safety in the NAS, but predict the time and location of unsafe events such as a loss of separation between two aircraft, or an aircraft encountering convective weather. The experimental results highlight the capability of the approach, and the kind of information that can be provided to operators to improve their situational awareness in the context of safety.

Pesticide risk assessment in free-ranging bees is weather and landscape dependent.

PubMed

Henry, Mickaël; Bertrand, Colette; Le Féon, Violette; Requier, Fabrice; Odoux, Jean-François; Aupinel, Pierrick; Bretagnolle, Vincent; Decourtye, Axel

2014-07-10

The risk assessment of plant protection products on pollinators is currently based on the evaluation of lethal doses through repeatable lethal toxicity laboratory trials. Recent advances in honeybee toxicology have, however, raised interest on assessing sublethal effects in free-ranging individuals. Here, we show that the sublethal effects of a neonicotinoid pesticide are modified in magnitude by environmental interactions specific to the landscape and time of exposure events. Field sublethal assessment is therefore context dependent and should be addressed in a temporally and spatially explicit way, especially regarding weather and landscape physiognomy. We further develop an analytical Effective Dose (ED) framework to help disentangle context-induced from treatment-induced effects and thus to alleviate uncertainty in field studies. Although the ED framework involves trials at concentrations above the expected field exposure levels, it allows to explicitly delineating the climatic and landscape contexts that should be targeted for in-depth higher tier risk assessment.

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lovejoy, S., E-mail: lovejoy@physics.mcgill.ca; Lima, M. I. P. de; Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra

2015-07-15

Over the range of time scales from about 10 days to 30–100 years, in addition to the familiar weather and climate regimes, there is an intermediate “macroweather” regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spitemore » of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be “homogenized” by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time.« less

Climate, weather, space weather: model development in an operational context

NASA Astrophysics Data System (ADS)

Folini, Doris

2018-05-01

Aspects of operational modeling for climate, weather, and space weather forecasts are contrasted, with a particular focus on the somewhat conflicting demands of "operational stability" versus "dynamic development" of the involved models. Some common key elements are identified, indicating potential for fruitful exchange across communities. Operational model development is compelling, driven by factors that broadly fall into four categories: model skill, basic physics, advances in computer architecture, and new aspects to be covered, from costumer needs over physics to observational data. Evaluation of model skill as part of the operational chain goes beyond an automated skill score. Permanent interaction between "pure research" and "operational forecast" people is beneficial to both sides. This includes joint model development projects, although ultimate responsibility for the operational code remains with the forecast provider. The pace of model development reflects operational lead times. The points are illustrated with selected examples, many of which reflect the author's background and personal contacts, notably with the Swiss Weather Service and the Max Planck Institute for Meteorology, Hamburg, Germany. In view of current and future challenges, large collaborations covering a range of expertise are a must - within and across climate, weather, and space weather. To profit from and cope with the rapid progress of computer architectures, supercompute centers must form part of the team.

A new climate modeling framework for convection-resolving simulation at continental scale

NASA Astrophysics Data System (ADS)

Charpilloz, Christophe; di Girolamo, Salvatore; Arteaga, Andrea; Fuhrer, Oliver; Hoefler, Torsten; Schulthess, Thomas; Schär, Christoph

2017-04-01

Major uncertainties remain in our understanding of the processes that govern the water cycle in a changing climate and their representation in weather and climate models. Of particular concern are heavy precipitation events of convective origin (thunderstorms and rain showers). The aim of the crCLIM project [1] is to propose a new climate modeling framework that alleviates the I/O-bottleneck in large-scale, convection-resolving climate simulations and thus to enable new analysis techniques for climate scientists. Due to the large computational costs, convection-resolving simulations are currently restricted to small computational domains or very short time scales, unless the largest available supercomputers system such as hybrid CPU-GPU architectures are used [3]. Hence, the COSMO model has been adapted to run on these architectures for research and production purposes [2]. However, the amount of generated data also increases and storing this data becomes infeasible making the analysis of simulations results impractical. To circumvent this problem and enable high-resolution models in climate we propose a data-virtualization layer (DVL) that re-runs simulations on demand and transparently manages the data for the analysis, that means we trade off computational effort (time) for storage (space). This approach also requires a bit-reproducible version of the COSMO model that produces identical results on different architectures (CPUs and GPUs) [4] that will be coupled with a performance model in order enable optimal re-runs depending on requirements of the re-run and available resources. In this contribution, we discuss the strategy to develop the DVL, a first performance model, the challenge of bit-reproducibility and the first results of the crCLIM project. [1] http://www.c2sm.ethz.ch/research/crCLIM.html [2] O. Fuhrer, C. Osuna, X. Lapillonne, T. Gysi, M. Bianco, and T. Schulthess. "Towards gpu-accelerated operational weather forecasting." In The GPU Technology Conference, GTC. 2013. [3] D. Leutwyler, O. Fuhrer, X. Lapillonne, D. Lüthi, and C. Schär. "Towards European-scale convection-resolving climate simulations with GPUs: a study with COSMO 4.19." Geoscientific Model Development 9, no. 9 (2016): 3393. [4] A. Arteaga, O. Fuhrer, and T. Hoefler. "Designing bit-reproducible portable high-performance applications." In Parallel and Distributed Processing Symposium, 2014 IEEE 28th International, pp. 1235-1244. IEEE, 2014.

Towards an Archaeology of Early Islamic Ports on the Western Red Sea Coast

NASA Astrophysics Data System (ADS)

Breen, Colin

2013-12-01

Against a background of developing research on Red Sea ports, a hypothetical model of the morphology of port towns during the early Islamic period is presented here. These places went through constant cycles of change as economic and political frameworks fluctuated. While their physical shape and form was strongly influenced by architectural features of the Islamic world their functionality was more aligned to commercial interaction. These were dynamic spaces where the daily life of their inhabitants was guided by trade, religion, weather and politics. The ports were intrinsically tied to the trade networks that connected Africa with Arabia and the broader Indian Ocean world.

Observations and simulations of the interactions between clouds, radiation, and precipitation

NASA Astrophysics Data System (ADS)

Naegele, Alexandra Claire

Increasing precipitation and warming temperatures associated with climate change have been documented across the globe, including in the Northeast US. These climate changes threaten human health in many ways. Research is necessary to understand and explain the relationship between climate change and human health. Extreme weather events such as extreme temperatures, convective storms, floods, lightning events, wintry precipitation, and low visibility, are frequently associated with adverse effects on human health. While more media attention is typically given to events that cause the most structural or economic damage (e.g., tornadoes, hurricanes, earthquakes, etc.), extreme temperatures ultimately account for the greatest loss of life in the US. Extreme weather events can be unpredictable; however, improved knowledge and technology allow meteorologists to accurately forecast many of these events, specifically extreme temperature and precipitation events. Advancing our knowledge of climate variability and trends in extreme weather can inform: public education programs to alert the community of the dangers of extreme heat or cold, emergency response plans to hazardous weather conditions, and current thresholds for emergency alerts. This study evaluates trends in extreme weather events across New Hampshire and links these extreme events to adverse health outcomes. Using data from NCEI Global Historical Climatological Network (GHCN) - Daily dataset (1981 - 2015), five daily xiii Extreme Weather Metrics (EWMs) were defined: Daily Maximum Temperature ≤32°F, Daily Maximum Temperature ≥90°F, Daily Maximum Temperature ≥95°F, Daily Precipitation ≥1", and Daily Precipitation ≥2". Relevant human health outcomes were extracted from the New Hampshire Hospital Discharge Dataset for the years 2001-2009. Health cases were defined based on the International Classification of Disease 9th Revision (ICD-9). Outcomes in this analysis include: All-Cause Injury, Vehicle Accidents, Accidental Falls, Accidents Due to Natural and Environmental (including excessive heat, excessive cold, exposure due to weather conditions, lightning, and storms and floods), Accidental Drowning, and Carbon Monoxide Poisoning. Temporal and spatial trends were assessed, and the associations between all health outcomes and EWMs, daily maximum temperature, and daily precipitation were evaluated via Spearman correlations. Once the four strongest correlations were determined, a quasi-Poisson regression model was used to evaluate the relationship between each exposureoutcome pair. These pairs were modeled to show the relation between maximum temperature and all-cause hospital visits, hospital visits related to vehicle accidents, hospital visits related to accidental falls, and hospital visits related to heat. Future work will incorporate these findings into public health planning and programming. This project is a collaboration with New Hampshire Department of Health and Human Services (NH DHHS) who have a shared interest in understanding the impact of extreme weather events on the citizens of New Hampshire. Furthermore, this work supports an ongoing effort to implement the Centers for Disease Control (CDC) Building Resilience Against Climate Effects (BRACE) Framework, which focuses on identifying climate and weather-related hazards and estimating the associated disease burden.

Integrated impact assessment of climate change, land use, and adaptation policies on water quality in Austria

NASA Astrophysics Data System (ADS)

Trautvetter, Helen; Schoenhart, Martin; Parajaka, Juraj; Schmid, Erwin; Zessner, Matthias

2017-04-01

Climate change is one of the major challenges of our time and adds considerable stress to the human society and environment. A change in climate will not only shift general weather patterns, but might also increase the recurrence of extreme weather events such as drought and heavy rainfall. These changes in climatic conditions will affect the quality and quantity of water resources both directly as well as indirectly through autonomous adaptation by farmers (e.g. cultivar choices, fertilization intensity or soil management). This will influence the compliance with the good ecological and chemical status according to the EU Water Framework Directive. We present results from an integrated impact modelling framework (IIMF) to tackle those direct and indirect impacts and analyze policy options for planned adaptation in agricultural land use and sustainable management of land and water resources until 2040. The IIMF is the result of an interdisciplinary collaboration among economists, agronomists, and hydrologists. It consists of the bio-physical process model EPIC, the regional land use optimization model PASMA[grid], the quantitative precipitation/runoff TUWmodel and the surface water emission model MONERIS. Scenarios have been developed and parameterized in collaboration with stakeholders in order to facilitate multi-actor knowledge transfer. The set of climate change scenarios until 2040 includes three scenarios with equal temperature changes but varying precipitation patterns. They are combined with potential socio-economic and policy development. The latter include water protection measures on fertilization management, soil management, or crop rotation choices. We will presented the development of interfaces among the research, the definition of scenarios and major scenario results for Austria. We will focus on nutrient emissions to surface waters, which are the major link between the different models. The results, available at watershed level indicate the significant impact on future precipitation development on the risk of not achieving nutrient criteria of the good ecological water quality status of surface waters. Policy measures show relatively low impacts for nitrogen, while they may highly affect the phosphorus emissions and hence the compliance with environmental quality standards for phosphate phosphorus.

Parametric vs. non-parametric daily weather generator: validation and comparison

NASA Astrophysics Data System (ADS)

Dubrovsky, Martin

2016-04-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30 years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series from several European stations available from the ECA&D database.

Inter-comparison of isotropic and anisotropic sea ice rheology in a fully coupled model

NASA Astrophysics Data System (ADS)

Roberts, A.; Cassano, J. J.; Maslowski, W.; Osinski, R.; Seefeldt, M. W.; Hughes, M.; Duvivier, A.; Nijssen, B.; Hamman, J.; Hutchings, J. K.; Hunke, E. C.

2015-12-01

We present the sea ice climate of the Regional Arctic System Model (RASM), using a suite of new physics available in the Los Alamos Sea Ice Model (CICE5). RASM is a high-resolution fully coupled pan-Arctic model that also includes the Parallel Ocean Program (POP), the Weather Research and Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) land model. The model domain extends from ~45˚N to the North Pole and is configured to run at ~9km resolution for the ice and ocean components, coupled to 50km resolution atmosphere and land models. The baseline sea ice model configuration includes mushy-layer sea ice thermodynamics and level-ice melt ponds. Using this configuration, we compare the use of isotropic and anisotropic sea ice mechanics, and evaluate model performance using these two variants against observations including Arctic buoy drift and deformation, satellite-derived drift and deformation, and sea ice volume estimates from ICESat. We find that the isotropic rheology better approximates spatial patterns of thickness observed across the Arctic, but that both rheologies closely approximate scaling laws observed in the pack using buoys and RGPS data. A fundamental component of both ice mechanics variants, the so called Elastic-Viscous-Plastic (EVP) and Anisotropic-Elastic-Plastic (EAP), is that they are highly sensitive to the timestep used for elastic sub-cycling in an inertial-resolving coupled framework, and this has a significant affect on surface fluxes in the fully coupled framework.

Intercomparison of Streamflow Simulations between WRF-Hydro and Hydrology Laboratory-Research Distributed Hydrologic Model Frameworks

NASA Astrophysics Data System (ADS)

KIM, J.; Smith, M. B.; Koren, V.; Salas, F.; Cui, Z.; Johnson, D.

2017-12-01

The National Oceanic and Atmospheric Administration (NOAA)-National Weather Service (NWS) developed the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) framework as an initial step towards spatially distributed modeling at River Forecast Centers (RFCs). Recently, the NOAA/NWS worked with the National Center for Atmospheric Research (NCAR) to implement the National Water Model (NWM) for nationally-consistent water resources prediction. The NWM is based on the WRF-Hydro framework and is run at a 1km spatial resolution and 1-hour time step over the contiguous United States (CONUS) and contributing areas in Canada and Mexico. In this study, we compare streamflow simulations from HL-RDHM and WRF-Hydro to observations from 279 USGS stations. For streamflow simulations, HL-RDHM is run on 4km grids with the temporal resolution of 1 hour for a 5-year period (Water Years 2008-2012), using a priori parameters provided by NOAA-NWS. The WRF-Hydro streamflow simulations for the same time period are extracted from NCAR's 23 retrospective run of the NWM (version 1.0) over CONUS based on 1km grids. We choose 279 USGS stations which are relatively less affected by dams or reservoirs, in the domains of six different RFCs. We use the daily average values of simulations and observations for the convenience of comparison. The main purpose of this research is to evaluate how HL-RDHM and WRF-Hydro perform at USGS gauge stations. We compare daily time-series of observations and both simulations, and calculate the error values using a variety of error functions. Using these plots and error values, we evaluate the performances of HL-RDHM and WRF-Hydro models. Our results show a mix of model performance across geographic regions.

Investigation and Modeling of Cranberry Weather Stress.

NASA Astrophysics Data System (ADS)

Croft, Paul Joseph

Cranberry bog weather conditions and weather-related stress were investigated for development of crop yield prediction models and models to predict daily weather conditions in the bog. Field investigations and data gathering were completed at the Rutgers University Blueberry/Cranberry Research Center experimental bogs in Chatsworth, New Jersey. Study indicated that although cranberries generally exhibit little or no stomatal response to changing atmospheric conditions, the evaluation of weather-related stress could be accomplished via use of micrometeorological data. Definition of weather -related stress was made by establishing critical thresholds of the frequencies of occurrence, and magnitudes of, temperature and precipitation in the bog based on values determined by a review of the literature and a grower questionnaire. Stress frequencies were correlated with cranberry yield to develop predictive models based on the previous season's yield, prior season data, prior and current season data, current season data; and prior and current season data through July 31 of the current season. The predictive ability of the prior season models was best and could be used in crop planning and production. Further examination of bog micrometeorological data permitted the isolation of those weather conditions conducive to cranberry scald and allowed for the institution of a pilot scald advisory program during the 1991 season. The micrometeorological data from the bog was also used to develop models to predict daily canopy temperature and precipitation, based on upper air data, for grower use. Models were developed for each month for maximum and minimum temperatures and for precipitation and generally performed well. The modeling of bog weather conditions is an important first step toward daily prediction of cranberry weather-related stress.

Bridging the Transition from Mesoscale to Microscale Turbulence in Numerical Weather Prediction Models

NASA Astrophysics Data System (ADS)

Muñoz-Esparza, Domingo; Kosović, Branko; Mirocha, Jeff; van Beeck, Jeroen

2014-12-01

With a focus towards developing multiscale capabilities in numerical weather prediction models, the specific problem of the transition from the mesoscale to the microscale is investigated. For that purpose, idealized one-way nested mesoscale to large-eddy simulation (LES) experiments were carried out using the Weather Research and Forecasting model framework. It is demonstrated that switching from one-dimensional turbulent diffusion in the mesoscale model to three-dimensional LES mixing does not necessarily result in an instantaneous development of turbulence in the LES domain. On the contrary, very large fetches are needed for the natural transition to turbulence to occur. The computational burden imposed by these long fetches necessitates the development of methods to accelerate the generation of turbulence on a nested LES domain forced by a smooth mesoscale inflow. To that end, four new methods based upon finite amplitude perturbations of the potential temperature field along the LES inflow boundaries are developed, and investigated under convective conditions. Each method accelerated the development of turbulence within the LES domain, with two of the methods resulting in a rapid generation of production and inertial range energy content associated to microscales that is consistent with non-nested simulations using periodic boundary conditions. The cell perturbation approach, the simplest and most efficient of the best performing methods, was investigated further under neutral and stable conditions. Successful results were obtained in all the regimes, where satisfactory agreement of mean velocity, variances and turbulent fluxes, as well as velocity and temperature spectra, was achieved with reference non-nested simulations. In contrast, the non-perturbed LES solution exhibited important energy deficits associated to a delayed establishment of fully-developed turbulence. The cell perturbation method has negligible computational cost, significantly accelerates the generation of realistic turbulence, and requires minimal parameter tuning, with the necessary information relatable to mean inflow conditions provided by the mesoscale solution.

Enhancing the Awareness of the Interaction of the Space Weather and Public: Some Case Studies in Turkey

NASA Astrophysics Data System (ADS)

Tulunay, Y.; Tulunay, E.; Kocabas, Z.; Altuntas, E.; Yapici, T.; Senalp, E. T.; Hippler, R.

2009-04-01

Space Weather has important effects on many systems and peripherals that human interacts with. However, most of the people are not aware of those interactions. During the FP6 SWEETS, COST 724 and the ‘I love my Sun' activities it was aimed to create basis to bring together academicians from universities, experts from industry, scientific institutes, and the public, especially the school children of age 7-11, in order to enhance the awareness of space weather effects and to discuss appropriate countermeasures by different education and promotion methods including non-technical ones. This work mentions the activities performed in Turkey within the framework. Since 1990, a small group at METU has been developing data driven models in order to forecast some critical system parameters related with the near-Earth space processes. With the background on the subject the group feels responsible to organise activities in Turkey to inform public on enhancing the awareness of space weather effects. In order to inform and educate public on their interaction with the Space Weather, distinct social activities which take quick and strong attention were organised. Those include art shows and workshops, quizes, movies and entertainments, special programs for school children of age 7-11 under the ‘I love my Sun' activities, press releases, audio-visual media including webpages [Tulunay, 2007]. The impact of the activities can be evaluated considering the before and after activity record materials of the participants. For instance, under the ‘I love my Sun' activities, the school children drew pictures related with Sun before and after the informative programs. The performance of reaching the school children on the subject is very promising. Sub-activities conducted under the action are: 1. Space Weather Dance Show "Sonnensturm" 2. Web Quiz all over Europe: In Türkiye 3. Space Weather / Sun / Heliospheric Public Science Festivals in 27 Countries: In Türkiye 4. Space Weather on Tour-Mobile Bus 5. Rocket / balloon launch participation for European web quiz winner and journalists 6. Space Weather / Solar / Aurora / Rocket / Balloon movie production for TV 7. Space Weather / Sun /Heliospheric public science festival & public fair in Schwerin castle (main SWEETS festival during ESW 2007) 8. Space Weather telescope video link with Australian (Antarctic Mawson station) and Japanese locations for Schwerin castle festival (no. 7 deliverable) 9. Space Weather planetarium show in Poland, Finland, France and Portugal (4 new languages) 10. Updated Space Weather / Solar CD-Rom / DVD in 7 new languages, poster / flyer 11. Cosmic ray spark chambers 12. Space Weather storm forecast map 13. Mirror system for solar movie 14. FP6 SWEETS / IHY / COST 724 Case Sub-project: "I LOVE MY SUN" (An outreach Activity in Turkey: The Space Weather and the Sun as conceived by the School Children of age 7-11) 15. Press Releases 16. FP6 SWEETS Related Art 17. Turkish Translations in IHY and COST webpages 18. Impact of the SWEETS References Tulunay Y. (2007), FP6 SWEETS (SSA) Activity Report of the Participant No. 16: the METU in Ankara, Türkiye, 31 December 2007, www.ae.metu.edu.tr/~cost.

Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction

NASA Astrophysics Data System (ADS)

Subramanian, Aneesh C.; Palmer, Tim N.

2017-06-01

Stochastic schemes to represent model uncertainty in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system has helped improve its probabilistic forecast skill over the past decade by both improving its reliability and reducing the ensemble mean error. The largest uncertainties in the model arise from the model physics parameterizations. In the tropics, the parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate. Superparameterization is a promising alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud-resolving model (CRM) embedded within a global climate model (GCM). In this paper, we compare the impact of initial random perturbations in embedded CRMs, within the ECMWF ensemble prediction system, with stochastically perturbed physical tendency (SPPT) scheme as a way to represent model uncertainty in medium-range tropical weather forecasts. We especially focus on forecasts of tropical convection and dynamics during MJO events in October-November 2011. These are well-studied events for MJO dynamics as they were also heavily observed during the DYNAMO field campaign. We show that a multiscale ensemble modeling approach helps improve forecasts of certain aspects of tropical convection during the MJO events, while it also tends to deteriorate certain large-scale dynamic fields with respect to stochastically perturbed physical tendencies approach that is used operationally at ECMWF.