276 research outputs found

    APPLICATION OF NEURAL NETWORKS TO EMULATION OF RADIATION PARAMETERIZATIONS IN GENERAL CIRCULATION MODELS

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    A novel approach based on using neural network (NN) techniques for approximation of physical components of complex environmental systems has been applied and further developed in this dissertation. A new type of a numerical model, a complex hybrid environmental model, based on a combination of deterministic and statistical learning model components, has been explored. Conceptual and practical aspects of developing hybrid models have been formalized as a methodology for applications to climate modeling and numerical weather prediction. The approach uses NN as a machine or statistical learning technique to develop highly accurate and fast emulations for model physics components/parameterizations. The NN emulations of the most time consuming model physics components, short and long wave radiation (LWR and SWR) parameterizations have been combined with the remaining deterministic components of a general circulation model (GCM) to constitute a hybrid GCM (HGCM). The parallel GCM and HGCM simulations produce very similar results but HGCM is significantly faster. The high accuracy, which is of a paramount importance for the approach, and a speed-up of model calculations when using NN emulations, open the opportunity for model improvement. It includes using extended NN ensembles and/or more frequent calculations of full model radiation resulting in an improvement of radiation-cloud interaction, a better consistency with model dynamics and other model physics components. First, the approach was successfully applied to a moderate resolution (T42L26) uncoupled NCAR Community Atmospheric Model driven by climatological SST for a decadal climate simulation mode. Then it has been further developed and subsequently implemented into a coupled GCM, the NCEP Climate Forecast System with significantly higher resolution (T126L64) and time dependent CO2 and tested for decadal climate simulations, seasonal prediction, and short- to medium term forecasts. The developed highly accurate NN emulations of radiation parameterizations are on average one to two orders of magnitude faster than the original radiation parameterizations. The NN approach was extended by introduction of NN ensembles and a compound parameterization with quality control of larger errors. Applicability of other statistical learning techniques, such as approximate nearest neighbor approximation and random trees, to emulation of model physics has also been explore

    Using Machine Learning for Model Physics: an Overview

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    In the overview, a generic mathematical object (mapping) is introduced, and its relation to model physics parameterization is explained. Machine learning (ML) tools that can be used to emulate and/or approximate mappings are introduced. Applications of ML to emulate existing parameterizations, to develop new parameterizations, to ensure physical constraints, and control the accuracy of developed applications are described. Some ML approaches that allow developers to go beyond the standard parameterization paradigm are discussed.Comment: 50 pages, 3 figures, 1 tabl

    100 Years of Earth System Model Development

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    This is the final version. Available from American Meteorological Society via the DOI in this recordToday’s global Earth System Models began as simple regional models of tropospheric weather systems. Over the past century, the physical realism of the models has steadily increased, while the scope of the models has broadened to include the global troposphere and stratosphere, the ocean, the vegetated land surface, and terrestrial ice sheets. This chapter gives an approximately chronological account of the many and profound conceptual and technological advances that made today’s models possible. For brevity, we omit any discussion of the roles of chemistry and biogeochemistry, and terrestrial ice sheets

    Evaluation and Improvements of the Offline CLM4 Using ARM Data

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    Hourly ground observations for year 2004 from the Atmospheric Radiation Measurement (ARM) program of the Department of Energy were used to examine the surface and subsurface energy simulations of the Community Land Model version 4 (CLM4). The 2 m air temperature, wind speed, solar radiation, downward longwave radiation, and precipitation observed by the ARM project were used to force the offline CLM4, and the ARM land surface and soil observations including skin temperature (Tskin), soil temperature and moisture, and sensible, latent, and ground heat fluxes were used to evaluate the model outputs. The default and ARM-forced CLM4 runs for 2004 were compared to assess the improvements to the model for hourly, daily, and seasonal timescales. The root mean square error and the Pearson correlation coefficient show that the ARM-forced offline CLM4 leads to improved accuracy in surface and soil energy fluxes in comparison with the default offline CLM4. Nevertheless, a warm bias of 2°C to 3°C was assessed on Tskin in summer due to warm maximum temperatures and in winter due to warm minimum temperatures. To improve CLM4 Tskin simulations, a proposed vegetation emissivity parameterization was evaluated locally and globally using both ARM and Moderate Resolution Imaging Spectroradiometer remote-sensing observations. This new algorithm results in cooling and an improvement of 0.17 K for the ARM site. Global evaluation revealed improvement in areas of intermediate canopy density

    An Overview of the Atmospheric Component of the Energy Exascale Earth System Model

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    The Energy Exascale Earth System Model Atmosphere Model version 1, the atmospheric component of the Department of Energy’s Energy Exascale Earth System Model is described. The model began as a fork of the wellâ known Community Atmosphere Model, but it has evolved in new ways, and coding, performance, resolution, physical processes (primarily cloud and aerosols formulations), testing and development procedures now differ significantly. Vertical resolution was increased (from 30 to 72 layers), and the model top extended to 60 km (~0.1 hPa). A simple ozone photochemistry predicts stratospheric ozone, and the model now supports increased and more realistic variability in the upper troposphere and stratosphere. An optional improved treatment of lightâ absorbing particle deposition to snowpack and ice is available, and stronger connections with Earth system biogeochemistry can be used for some science problems. Satellite and groundâ based cloud and aerosol simulators were implemented to facilitate evaluation of clouds, aerosols, and aerosolâ cloud interactions. Higher horizontal and vertical resolution, increased complexity, and more predicted and transported variables have increased the model computational cost and changed the simulations considerably. These changes required development of alternate strategies for tuning and evaluation as it was not feasible to â brute forceâ tune the highâ resolution configurations, so shortâ term hindcasts, perturbed parameter ensemble simulations, and regionally refined simulations provided guidance on tuning and parameterization sensitivity to higher resolution. A brief overview of the model and model climate is provided. Model fidelity has generally improved compared to its predecessors and the CMIP5 generation of climate models.Plain Language SummaryThis study provides an overview of a new computer model of the Earth’s atmosphere that is used as one component of the Department of Energy’s latest Earth system model. The model can be used to help understand past, present, and future changes in Earth’s behavior as the system responds to changes in atmospheric composition (like pollution and greenhouse gases), land, and water use and to explore how the atmosphere interacts with other components of the Earth system (ocean, land, biology, etc.). Physical, chemical, and biogeochemical processes treated within the atmospheric model are described, and pointers to previous and recent work are listed to provide additional information. The model is compared to presentâ day observations and evaluated for some important tests that provide information about what could happen to clouds and the environment as changes occur. Strengths and weaknesses of the model are listed, as well as opportunities for future work.Key PointsA brief description and evaluation is provided for the atmospheric component of the Department of Energy’s Energy Exascale Earth System ModelModel fidelity has generally improved compared to predecessors and models participating in past international model evaluationsStrengths and weaknesses of the model, as well as opportunities for future work, are describedPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151811/1/jame20932_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151811/2/jame20932.pd

    A mechanistic ecohydrological model to investigate complex interactions in cold and warm water‐controlled environments: 1. Theoretical framework and plot‐scale analysis

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95321/1/jame60.pd

    Electromagnetic Radiation

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    The application of electromagnetic radiation in modern life is one of the most developing technologies. In this timely book, the authors comprehensively treat two integrated aspects of electromagnetic radiation, theory and application. It covers a wide scope of practical topics, including medical treatment, telecommunication systems, and radiation effects. The book sections have clear presentation, some state of the art examples, which makes this book an indispensable reference book for electromagnetic radiation applications

    Oak forest carbon and water simulations:Model intercomparisons and evaluations against independent data

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    Models represent our primary method for integration of small-scale, process-level phenomena into a comprehensive description of forest-stand or ecosystem function. They also represent a key method for testing hypotheses about the response of forest ecosystems to multiple changing environmental conditions. This paper describes the evaluation of 13 stand-level models varying in their spatial, mechanistic, and temporal complexity for their ability to capture intra- and interannual components of the water and carbon cycle for an upland, oak-dominated forest of eastern Tennessee. Comparisons between model simulations and observations were conducted for hourly, daily, and annual time steps. Data for the comparisons were obtained from a wide range of methods including: eddy covariance, sapflow, chamber-based soil respiration, biometric estimates of stand-level net primary production and growth, and soil water content by time or frequency domain reflectometry. Response surfaces of carbon and water flux as a function of environmental drivers, and a variety of goodness-of-fit statistics (bias, absolute bias, and model efficiency) were used to judge model performance. A single model did not consistently perform the best at all time steps or for all variables considered. Intermodel comparisons showed good agreement for water cycle fluxes, but considerable disagreement among models for predicted carbon fluxes. The mean of all model outputs, however, was nearly always the best fit to the observations. Not surprisingly, models missing key forest components or processes, such as roots or modeled soil water content, were unable to provide accurate predictions of ecosystem responses to short-term drought phenomenon. Nevertheless, an inability to correctly capture short-term physiological processes under drought was not necessarily an indicator of poor annual water and carbon budget simulations. This is possible because droughts in the subject ecosystem were of short duration and therefore had a small cumulative impact. Models using hourly time steps and detailed mechanistic processes, and having a realistic spatial representation of the forest ecosystem provided the best predictions of observed data. Predictive ability of all models deteriorated under drought conditions, suggesting that further work is needed to evaluate and improve ecosystem model performance under unusual conditions, such as drought, that are a common focus of environmental change discussions
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