The Experts below are selected from a list of 37077 Experts worldwide ranked by ideXlab platform
David A. Randall - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Generation of subgrid‐scale cloudy columns for large‐scale models
Quarterly Journal of the Royal Meteorological Society, 2004Co-Authors: Petri Räisänen, Howard W. Barker, Marat Khairoutdinov, David A. RandallAbstract:To use the Monte Carlo Independent Column Approximation method for computing domain-average radiative fluxes in large-scale atmospheric models (LSAMs), a method is needed for generating cloudy subcolumns within LSAM columns. Here, a Stochastic cloud generator is introduced to produce the subcolumns. The generator creates a cloud field on a column-by-column basis using information about layer cloud fraction, vertical overlap of cloud fraction and cloud condensate for adjacent layers, and density functions describing horizontal variations in cloud water content. The performance of the generator is assessed using a single day's worth of data from an LSAM simulation that employed a low-resolution two-dimensional cloud-resolving model (CRM) within each LSAM column (a total of ∼59 000 cloudy domains). Statistical characteristics of generated cloud fields are compared against original CRM data, and radiative-transfer biases associated with the generator are evaluated. When the generator is initialized to the greatest extent possible with information obtained from the CRM fields, overall biases are small. For example, global-mean total cloud fraction exhibits a bias of −0.004, as compared with −0.024 for maximum-random overlap (MRO) and 0.047 for random overlap. Biases in radiative fluxes and heating rates are in general ¼ to ½ those for MRO with horizontally homogeneous clouds. Copyright © 2004 Royal Meteorological Society
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Stochastic Generation of subgrid scale cloudy columns for large scale models
Quarterly Journal of the Royal Meteorological Society, 2004Co-Authors: Petri Räisänen, Howard W. Barker, Marat Khairoutdinov, David A. RandallAbstract:To use the Monte Carlo Independent Column Approximation method for computing domain-average radiative fluxes in large-scale atmospheric models (LSAMs), a method is needed for generating cloudy subcolumns within LSAM columns. Here, a Stochastic cloud generator is introduced to produce the subcolumns. The generator creates a cloud field on a column-by-column basis using information about layer cloud fraction, vertical overlap of cloud fraction and cloud condensate for adjacent layers, and density functions describing horizontal variations in cloud water content. The performance of the generator is assessed using a single day's worth of data from an LSAM simulation that employed a low-resolution two-dimensional cloud-resolving model (CRM) within each LSAM column (a total of ∼59 000 cloudy domains). Statistical characteristics of generated cloud fields are compared against original CRM data, and radiative-transfer biases associated with the generator are evaluated. When the generator is initialized to the greatest extent possible with information obtained from the CRM fields, overall biases are small. For example, global-mean total cloud fraction exhibits a bias of −0.004, as compared with −0.024 for maximum-random overlap (MRO) and 0.047 for random overlap. Biases in radiative fluxes and heating rates are in general ¼ to ½ those for MRO with horizontally homogeneous clouds. Copyright © 2004 Royal Meteorological Society
Petri Räisänen - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Generation of subgrid‐scale cloudy columns for large‐scale models
Quarterly Journal of the Royal Meteorological Society, 2004Co-Authors: Petri Räisänen, Howard W. Barker, Marat Khairoutdinov, David A. RandallAbstract:To use the Monte Carlo Independent Column Approximation method for computing domain-average radiative fluxes in large-scale atmospheric models (LSAMs), a method is needed for generating cloudy subcolumns within LSAM columns. Here, a Stochastic cloud generator is introduced to produce the subcolumns. The generator creates a cloud field on a column-by-column basis using information about layer cloud fraction, vertical overlap of cloud fraction and cloud condensate for adjacent layers, and density functions describing horizontal variations in cloud water content. The performance of the generator is assessed using a single day's worth of data from an LSAM simulation that employed a low-resolution two-dimensional cloud-resolving model (CRM) within each LSAM column (a total of ∼59 000 cloudy domains). Statistical characteristics of generated cloud fields are compared against original CRM data, and radiative-transfer biases associated with the generator are evaluated. When the generator is initialized to the greatest extent possible with information obtained from the CRM fields, overall biases are small. For example, global-mean total cloud fraction exhibits a bias of −0.004, as compared with −0.024 for maximum-random overlap (MRO) and 0.047 for random overlap. Biases in radiative fluxes and heating rates are in general ¼ to ½ those for MRO with horizontally homogeneous clouds. Copyright © 2004 Royal Meteorological Society
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Stochastic Generation of subgrid scale cloudy columns for large scale models
Quarterly Journal of the Royal Meteorological Society, 2004Co-Authors: Petri Räisänen, Howard W. Barker, Marat Khairoutdinov, David A. RandallAbstract:To use the Monte Carlo Independent Column Approximation method for computing domain-average radiative fluxes in large-scale atmospheric models (LSAMs), a method is needed for generating cloudy subcolumns within LSAM columns. Here, a Stochastic cloud generator is introduced to produce the subcolumns. The generator creates a cloud field on a column-by-column basis using information about layer cloud fraction, vertical overlap of cloud fraction and cloud condensate for adjacent layers, and density functions describing horizontal variations in cloud water content. The performance of the generator is assessed using a single day's worth of data from an LSAM simulation that employed a low-resolution two-dimensional cloud-resolving model (CRM) within each LSAM column (a total of ∼59 000 cloudy domains). Statistical characteristics of generated cloud fields are compared against original CRM data, and radiative-transfer biases associated with the generator are evaluated. When the generator is initialized to the greatest extent possible with information obtained from the CRM fields, overall biases are small. For example, global-mean total cloud fraction exhibits a bias of −0.004, as compared with −0.024 for maximum-random overlap (MRO) and 0.047 for random overlap. Biases in radiative fluxes and heating rates are in general ¼ to ½ those for MRO with horizontally homogeneous clouds. Copyright © 2004 Royal Meteorological Society
Ross Baldick - One of the best experts on this subject based on the ideXlab platform.
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Multi-year Stochastic Generation capacity expansion planning under environmental energy policy
Applied Energy, 2016Co-Authors: Heejung Park, Ross BaldickAbstract:Abstract We present a multi-year Stochastic Generation capacity expansion planning model to investigate changes in Generation building decisions and carbon dioxide (CO2) emissions under environmental energy policies, including carbon tax and a renewable portfolio standard (RPS). A multi-stage Stochastic mixed-integer program is formulated to solve the Generation expansion problem. The uncertain parameters of load and wind availability are modeled as random variables and their independent and identically distributed (i.i.d.) random samples are generated using the Gaussian copula method, which represents the correlation between random variables explicitly. A multi-stage scenario tree is formed with the generated random samples, and the scenario tree is reduced for improved computation performance. A rolling-horizon method is applied to obtain one Generation plan at each stage.
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Stochastic Generation Capacity Expansion Planning Reducing Greenhouse Gas Emissions
IEEE Transactions on Power Systems, 2015Co-Authors: Heejung Park, Ross BaldickAbstract:With increasing concerns about greenhouse gas emissions, a least-cost Generation capacity expansion model to control carbon dioxide (CO2) emissions is proposed in this paper. The mathematical model employs a decomposed two-stage Stochastic integer program. Realizations of uncertain load and wind are represented by independent and identically distributed (i.i.d.) random samples generated via the Gaussian copula method. Two policies that affect CO2 emissions directly and indirectly, carbon tax and renewable portfolio standard (RPS), are investigated to assess how much CO2 emissions are expected to be reduced through those policies.
Jinn-chuang Yang - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Generation of hourly rainstorm events
Stochastic Environmental Research and Risk Assessment, 2006Co-Authors: Yeou-koung Tung, Jinn-chuang YangAbstract:Occurrence of rainstorm events can be characterized by the number of events, storm duration, rainfall depth, inter-event time and temporal variation of rainfall within a rainstorm event. This paper presents a Monte-Carlo based Stochastic hourly rainfall Generation model considering correlated non-normal random rainstorm characteristics, as well as dependence of various rainstorm patterns on rainfall depth, duration, and season. The proposed model was verified by comparing the derived rainfall depth–duration–frequency relations from the simulated rainfall sequences with those from observed annual maximum rainfalls based on the hourly rainfall data at the Hong Kong Observatory over the period of 1884–1990. Through numerical experiments, the proposed model was found to be capable of capturing the essential statistical features of rainstorm characteristics and those of annual extreme rainstorm events according to the available data.
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Identification and Stochastic Generation of representative rainfall temporal patterns in Hong Kong territory
Stochastic Environmental Research and Risk Assessment, 2006Co-Authors: Jinn-chuang Yang, Yeou-koung TungAbstract:In hydrosystem engineering design and analysis, temporal pattern for rainfall events of interest is often required. In this paper, statistical cluster analysis of dimensionless rainfall pattern is applied to identify representative temporal rainfall patterns typically occurred in Hong Kong Territory. For purpose of selecting an appropriate rainfall pattern in engineering applications, factors affecting the occurrence of different rainfall patterns are examined by statistical contingency tables analysis through which the inter-dependence of the occurrence frequency of rainfall patterns with respect to geographical location, rainfall duration and depth, and seasonality is investigated. Furthermore, due to inherent variability of rainfall mass curves or hyetographs within each classified rainfall pattern, a practical procedure to probabilistically generate plausible rainfall patterns is described. The procedure preserves the inherent Stochastic features of random dimensionless rainfall hyetograph ordinates, which in general are correlated non-normal multivariate compositional variables.
Howard W. Barker - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Generation of subgrid‐scale cloudy columns for large‐scale models
Quarterly Journal of the Royal Meteorological Society, 2004Co-Authors: Petri Räisänen, Howard W. Barker, Marat Khairoutdinov, David A. RandallAbstract:To use the Monte Carlo Independent Column Approximation method for computing domain-average radiative fluxes in large-scale atmospheric models (LSAMs), a method is needed for generating cloudy subcolumns within LSAM columns. Here, a Stochastic cloud generator is introduced to produce the subcolumns. The generator creates a cloud field on a column-by-column basis using information about layer cloud fraction, vertical overlap of cloud fraction and cloud condensate for adjacent layers, and density functions describing horizontal variations in cloud water content. The performance of the generator is assessed using a single day's worth of data from an LSAM simulation that employed a low-resolution two-dimensional cloud-resolving model (CRM) within each LSAM column (a total of ∼59 000 cloudy domains). Statistical characteristics of generated cloud fields are compared against original CRM data, and radiative-transfer biases associated with the generator are evaluated. When the generator is initialized to the greatest extent possible with information obtained from the CRM fields, overall biases are small. For example, global-mean total cloud fraction exhibits a bias of −0.004, as compared with −0.024 for maximum-random overlap (MRO) and 0.047 for random overlap. Biases in radiative fluxes and heating rates are in general ¼ to ½ those for MRO with horizontally homogeneous clouds. Copyright © 2004 Royal Meteorological Society
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Stochastic Generation of subgrid scale cloudy columns for large scale models
Quarterly Journal of the Royal Meteorological Society, 2004Co-Authors: Petri Räisänen, Howard W. Barker, Marat Khairoutdinov, David A. RandallAbstract:To use the Monte Carlo Independent Column Approximation method for computing domain-average radiative fluxes in large-scale atmospheric models (LSAMs), a method is needed for generating cloudy subcolumns within LSAM columns. Here, a Stochastic cloud generator is introduced to produce the subcolumns. The generator creates a cloud field on a column-by-column basis using information about layer cloud fraction, vertical overlap of cloud fraction and cloud condensate for adjacent layers, and density functions describing horizontal variations in cloud water content. The performance of the generator is assessed using a single day's worth of data from an LSAM simulation that employed a low-resolution two-dimensional cloud-resolving model (CRM) within each LSAM column (a total of ∼59 000 cloudy domains). Statistical characteristics of generated cloud fields are compared against original CRM data, and radiative-transfer biases associated with the generator are evaluated. When the generator is initialized to the greatest extent possible with information obtained from the CRM fields, overall biases are small. For example, global-mean total cloud fraction exhibits a bias of −0.004, as compared with −0.024 for maximum-random overlap (MRO) and 0.047 for random overlap. Biases in radiative fluxes and heating rates are in general ¼ to ½ those for MRO with horizontally homogeneous clouds. Copyright © 2004 Royal Meteorological Society
