Lloyd Treinish - Academia.edu (original) (raw)

Papers by Lloyd Treinish

Atmospheric Research, 2022

Environmental Research Letters

Urban environments and heat waves interact synergistically and aggravate the thermal 11 environme... more Urban environments and heat waves interact synergistically and aggravate the thermal 11 environment through the urban heat island effect. Of concern is the potential for a 12 projected warmer future climate to further compound heat waves in urban environments. 13 The present study investigates the interaction of a 2006 heat wave in North America with 14 two urban environments (Phoenix and New York City) in current climate and future 15 climate simulations. The future climate conditions were generated using the pseudo 16 global warming methodology. Multiple high-resolution (3 km) simulations were 17 performed using the Weather Research and Forecasting (WRF) model coupled with the 18 single layer urban canopy model to improve representation of urban processes and we 19 explore how irrigated green roofs and cool roofs can mitigate heat wave amplification by 20 urban heat islands. To quantify heat wave intensity, an analytical model is applied to the 21 WRF model output that considers the urban surface heat and water vapor exchanges with 22 the atmosphere. A future, warmer climate is found to amplify the urban heat island 23 intensity during heat waves in both Phoenix (21%) and New York City (48%), but the 24 amplification is of great uncertainty as its magnitude is comparable to the temporal 25 variability of temperatures. The increase in urban heat index can be almost completely 26 offset by adopting irrigated green roofs in urban areas, and partially offset by adopting 27 cool roofs.

Our ongoing work focuses on systems for and applications of operational mesoscale numerical weath... more Our ongoing work focuses on systems for and applications of operational mesoscale numerical weather prediction. In particular, our goal is to provide weather forecasts at a level of precision and fast enough to address specific weather-sensitive operations. Hence, we are addressing problems of high-performance computing , visualization, and automation while designing, evaluating and optimizing an integrated system that includes receiving and processing data, modelling, and post-processing analysis and dissemination (Treinish and Praino, 2004). In addition to considering both business and meteorological value of such mesoscale models in a number of application areas, we are also addressing whether a practical and usable system can be implemented at reasonable cost? To begin to answer these questions, a prototype system, dubbed "Deep Thunder", has been implemented for several metropolitan areas (initially, New York City, followed more recently by Chicago, Kansas City and Bal...

In many industries weather conditions are a critical factor in planning business operations and m... more In many industries weather conditions are a critical factor in planning business operations and making effective decisions. Typically, what optimization that is applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or weather forecasts of limited precision. Alternatively, numerical weather models operating at higher resolution in space and time with more detailed physics exist for short-term forecasting (i.e., a few days at the mesoscale) that offer greater precision and accuracy for a more limited region. Although such a model has occasionally been adapted for the specific three-dimensional geographic area and time-scale relevant to the aforementioned decision making (e.g., Carpenter and Bassett, 2001; Snook, 2001), usually it is not.

Visualization is critical to the effective analysis, dissemination and assessment of data generat... more Visualization is critical to the effective analysis, dissemination and assessment of data generated by numerical weather prediction. In that regard, consider two aspects of our previous work. First is the need to develop appropriate mapping of user goals to the design of pictorial content by considering both the underlying data characteristics and the perception of the visualization (Treinish, 2001). The second is the adaptation of these ideas from workstation or PC/game-class threedimensional graphics systems with sufficient bandwidth for timely access to the model data to remote access via the world-wide-web (Treinish, 2002b). In this situation, the limitation in bandwidth is the primary bottleneck since desktop systems can support interactive visualization of typical model data.

Weather-sensitive business operations are primarily reactive to short-term (3 to 36 hours), local... more Weather-sensitive business operations are primarily reactive to short-term (3 to 36 hours), local conditions (city, county, state) due to unavailability of appropriate predicted data at this temporal and spatial scale. This situation is commonplace in a number of applications including, but not limited to transportation, agriculture, energy, insurance, entertainment, construction, communications and emergency planning. Typically, what optimization that is applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or the results of synoptic-scale weather models. Alternatively, mesoscale (cloud-scale) numerical weather models operating at higher resolution in space and time with more detailed physics has shown "promise" for many years as a potential enabler of pro-active decision making for both economic and societal value. They may offer greater precision and accuracy within a limited geographic region for problems...

A number of operations in the aviation industry, particularly in the terminal area, are weather-s... more A number of operations in the aviation industry, particularly in the terminal area, are weather-sensitive to local conditions in the short-term (3 to 18 hours). Often, they are reactive due to unavailability of appropriate predicted data at the required temporal and spatial scale. Hence, whatever planning that may be applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or the results of synoptic-to meso-beta-scale weather models. Since this time range is beyond what is feasible with modern now-casting techniques, near-real-time assessment of observations of current weather conditions may have the appropriate geographic locality, by its very nature is only directly suitable for reactive response.

For many applications, expected local weather conditions during the next day or two are critical ... more For many applications, expected local weather conditions during the next day or two are critical factors in planning operations and making effective decisions. Typically, what optimization that is applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or the results of synoptic-scale weather models. Alternatively, mesos-cale numerical weather models operating at higher resolution in space and time with more detailed physics may offer greater precision and accuracy within a limited geographic region for problems with short-term weather sensitivity (e.g., Mass et al, 2002; Gall and Shapiro, 2000). Such forecasts can be used for competitive advantage or to improve operational efficiency and safety. To evaluate this hypothesis, a prototype system, dubbed "Deep Thunder", has been implemented for the New York City area.

We appreciate the many people who contributed to the editorial process, including the many member... more We appreciate the many people who contributed to the editorial process, including the many members of the program committee; all of the reviewers; and, most importantly, the authors. We thank the reviewers for providing detailed comments and corrections, and for meeting the strict deadlines required by the review and publication processes. The reviewers were:

<p&amp... more <p>The Building Effect Parameterization + Building Energy Model (BEP+BEM) with a detailed urban parameterization coupled with the Weather Research and Forecasting (WRF) model is used to simulate the summertime local circulation in the Houston, Texas metropolitan area. Six numerical model simulations at 3km horizontal resolutions (within the nested parent domain of 9km) are performed using land use data representative of 2010, and 2100.They include:</p> <p>(a) Control Simulation (with 2010 land use with current and future climate)</p> <p>(b) same as (a) but with less aggressive urban expansion</p> <p>(c) same as (a) but with more aggressive urban expansion</p> <p>For future climate simulation, CCSM4 data (RCP8.5 scenario) were used to generate the climate perturbation, which was then applied to the current forcing data (NCEP final analyses) used for the numerical model simulations. Validation is based on comparison between model simulations and observations and it shows reasonably good model performance. Numerical simulations show an important interaction between the sea breeze and the urban heat island (UHI) circulation. The UHI forms a strong convergence zone in the center of the city and accelerates the sea-breeze front toward it. This phenomenon raises several questions.  (1) With urban expansion, how is the sea breeze penetration modified?  What is its impact on energy consumption in the city during the summer season, (2) After the dissipation of the UHI, how does the penetration of sea breeze change?  (3) How is the speed of the sea breeze modified with climate change and/or urban expansion? We will discuss our approach and present our results that help answer these questions.</p>

IBM Journal of Research and Development

Efficient, resilient, and safe operation of many cities is dependent on the local weather conditi... more Efficient, resilient, and safe operation of many cities is dependent on the local weather conditions at the scale of their critical infrastructure (electric, communications and water utilities, transportation, etc.) This includes both routine and severe weather events such as tropical storms, tornadoes, snowstorms, damaging winds and hail. For example, with precipitation events, local topography and weather influence water runoff and infiltration, which directly affect flooding as well as drinking water quality and availability. The impact of such events creates issues of public safety for both citizens and first responders. Therefore, the availability of highly localized weather model predictions focused on municipal public safety and operations of infrastructure has the potential to mitigate the impact of severe weather. This is especially true if the lead time for the availability of such predictions enables proactive allocation and deployment of resources (people and equipment) to minimize time for restoration of damage from severe events. Typically, information at such a scale is simply not available. Hence, what optimization that is applied to these processes to enable proactive efforts utilizes either historical weather data as a predictor of trends or the results of continental-or regionalscale weather models. Neither source of information is appropriately matched to the temporal or spatial scale of many such operations. While near-real-time assessment of observations of current weather conditions may have the appropriate geographic locality, by its very nature it is only directly suitable for reactive response. The initial step to address this gap is the application of state-of-the-art physical weather models at the spatial scale of the city's infrastructure, calibrated to avoid this mismatch in predictability. The results of such a model are then coupled to a data-driven stochastic model to represent the actionable prediction of weather (business) impacts. In some cases, an intermediate physical model may be required to translate predicted weather into the phenomena that leads to such impacts. We have applied these ideas to several cities with a diversity of impacts and weather concerns. This coupled model methodology has enabled operational prediction of storm impacts on local infrastructure, as well as measurement of the model error associated with such forecasts. We have defined a flexible approach for such one-way coupling that includes an abstraction of the weather forecasting component. We will present the implementation of these urban weather impact predictions and the ongoing challenges they represent. We will then discuss how we can extend this concept to a climate scale in order to evaluate the potential localized impacts of a warming planet and the effectiveness of strategies being used to mitigate such impacts.

Atmospheric Science Letters

Ieee Computer Society, 1993

As part of the Great Rivers Project, IBM is collaborating with The Nature Conservancy and the Cen... more As part of the Great Rivers Project, IBM is collaborating with The Nature Conservancy and the Center for Sustainability and the Global Environment (SAGE) at the University of Wisconsin, Madison to build a Modeling Framework and Decision Support System (DSS) designed to help policy makers and a variety of stakeholders (farmers, fish & wildlife managers, hydropower operators, et al.) to assess, come to consensus, and act on land use decisions representing effective compromises between human use and ecosystem preservation/restoration. Initially focused on Brazil's Paraguay-Parana, China's Yangtze, and the Mississippi Basin in the US, the DSS integrates data and models from a wide variety of environmental sectors, including water balance, water quality, carbon balance, crop production, hydropower, and biodiversity. In this presentation we focus on the modeling framework aspect of this project. In our approach to these and other environmental modeling projects, we see a flexible, extensible modeling framework infrastructure for defining and running multi-step analytic simulations as critical. In this framework, we divide monolithic models into atomic components with clearly defined semantics encoded via rich metadata representation. Once models and their semantics and composition rules have been registered with the system by their authors or other experts, non-expert users may construct simulations as workflows of these atomic model components. A model composition engine enforces rules/constraints for composing model components into simulations, to avoid the creation of Frankenmodels, models that execute but produce scientifically invalid results. A common software environment and common representations of data and models are required, as well as an adapter strategy for code written in e.g., Fortran or python, that still enables efficient simulation runs, including parallelization. Since each new simulation, as a new composition of model components, requires calibration of parameters (fudge factors) to produce scientifically valid results, we are also developing an autocalibration engine. Finally, visualization is a key element of this modeling framework strategy, both to convey complex scientific data effectively, and also to enable non-expert users to make full use of the relevant features of the framework. We are developing a visualization environment with a strong data model, to enable visualizations, model results, and data all to be handled similarly.

Atmospheric Research, 2022

Environmental Research Letters

Urban environments and heat waves interact synergistically and aggravate the thermal 11 environme... more Urban environments and heat waves interact synergistically and aggravate the thermal 11 environment through the urban heat island effect. Of concern is the potential for a 12 projected warmer future climate to further compound heat waves in urban environments. 13 The present study investigates the interaction of a 2006 heat wave in North America with 14 two urban environments (Phoenix and New York City) in current climate and future 15 climate simulations. The future climate conditions were generated using the pseudo 16 global warming methodology. Multiple high-resolution (3 km) simulations were 17 performed using the Weather Research and Forecasting (WRF) model coupled with the 18 single layer urban canopy model to improve representation of urban processes and we 19 explore how irrigated green roofs and cool roofs can mitigate heat wave amplification by 20 urban heat islands. To quantify heat wave intensity, an analytical model is applied to the 21 WRF model output that considers the urban surface heat and water vapor exchanges with 22 the atmosphere. A future, warmer climate is found to amplify the urban heat island 23 intensity during heat waves in both Phoenix (21%) and New York City (48%), but the 24 amplification is of great uncertainty as its magnitude is comparable to the temporal 25 variability of temperatures. The increase in urban heat index can be almost completely 26 offset by adopting irrigated green roofs in urban areas, and partially offset by adopting 27 cool roofs.

Our ongoing work focuses on systems for and applications of operational mesoscale numerical weath... more Our ongoing work focuses on systems for and applications of operational mesoscale numerical weather prediction. In particular, our goal is to provide weather forecasts at a level of precision and fast enough to address specific weather-sensitive operations. Hence, we are addressing problems of high-performance computing , visualization, and automation while designing, evaluating and optimizing an integrated system that includes receiving and processing data, modelling, and post-processing analysis and dissemination (Treinish and Praino, 2004). In addition to considering both business and meteorological value of such mesoscale models in a number of application areas, we are also addressing whether a practical and usable system can be implemented at reasonable cost? To begin to answer these questions, a prototype system, dubbed "Deep Thunder", has been implemented for several metropolitan areas (initially, New York City, followed more recently by Chicago, Kansas City and Bal...

In many industries weather conditions are a critical factor in planning business operations and m... more In many industries weather conditions are a critical factor in planning business operations and making effective decisions. Typically, what optimization that is applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or weather forecasts of limited precision. Alternatively, numerical weather models operating at higher resolution in space and time with more detailed physics exist for short-term forecasting (i.e., a few days at the mesoscale) that offer greater precision and accuracy for a more limited region. Although such a model has occasionally been adapted for the specific three-dimensional geographic area and time-scale relevant to the aforementioned decision making (e.g., Carpenter and Bassett, 2001; Snook, 2001), usually it is not.

Visualization is critical to the effective analysis, dissemination and assessment of data generat... more Visualization is critical to the effective analysis, dissemination and assessment of data generated by numerical weather prediction. In that regard, consider two aspects of our previous work. First is the need to develop appropriate mapping of user goals to the design of pictorial content by considering both the underlying data characteristics and the perception of the visualization (Treinish, 2001). The second is the adaptation of these ideas from workstation or PC/game-class threedimensional graphics systems with sufficient bandwidth for timely access to the model data to remote access via the world-wide-web (Treinish, 2002b). In this situation, the limitation in bandwidth is the primary bottleneck since desktop systems can support interactive visualization of typical model data.

Weather-sensitive business operations are primarily reactive to short-term (3 to 36 hours), local... more Weather-sensitive business operations are primarily reactive to short-term (3 to 36 hours), local conditions (city, county, state) due to unavailability of appropriate predicted data at this temporal and spatial scale. This situation is commonplace in a number of applications including, but not limited to transportation, agriculture, energy, insurance, entertainment, construction, communications and emergency planning. Typically, what optimization that is applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or the results of synoptic-scale weather models. Alternatively, mesoscale (cloud-scale) numerical weather models operating at higher resolution in space and time with more detailed physics has shown "promise" for many years as a potential enabler of pro-active decision making for both economic and societal value. They may offer greater precision and accuracy within a limited geographic region for problems...

A number of operations in the aviation industry, particularly in the terminal area, are weather-s... more A number of operations in the aviation industry, particularly in the terminal area, are weather-sensitive to local conditions in the short-term (3 to 18 hours). Often, they are reactive due to unavailability of appropriate predicted data at the required temporal and spatial scale. Hence, whatever planning that may be applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or the results of synoptic-to meso-beta-scale weather models. Since this time range is beyond what is feasible with modern now-casting techniques, near-real-time assessment of observations of current weather conditions may have the appropriate geographic locality, by its very nature is only directly suitable for reactive response.

For many applications, expected local weather conditions during the next day or two are critical ... more For many applications, expected local weather conditions during the next day or two are critical factors in planning operations and making effective decisions. Typically, what optimization that is applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or the results of synoptic-scale weather models. Alternatively, mesos-cale numerical weather models operating at higher resolution in space and time with more detailed physics may offer greater precision and accuracy within a limited geographic region for problems with short-term weather sensitivity (e.g., Mass et al, 2002; Gall and Shapiro, 2000). Such forecasts can be used for competitive advantage or to improve operational efficiency and safety. To evaluate this hypothesis, a prototype system, dubbed "Deep Thunder", has been implemented for the New York City area.

We appreciate the many people who contributed to the editorial process, including the many member... more We appreciate the many people who contributed to the editorial process, including the many members of the program committee; all of the reviewers; and, most importantly, the authors. We thank the reviewers for providing detailed comments and corrections, and for meeting the strict deadlines required by the review and publication processes. The reviewers were:

<p&amp... more <p>The Building Effect Parameterization + Building Energy Model (BEP+BEM) with a detailed urban parameterization coupled with the Weather Research and Forecasting (WRF) model is used to simulate the summertime local circulation in the Houston, Texas metropolitan area. Six numerical model simulations at 3km horizontal resolutions (within the nested parent domain of 9km) are performed using land use data representative of 2010, and 2100.They include:</p> <p>(a) Control Simulation (with 2010 land use with current and future climate)</p> <p>(b) same as (a) but with less aggressive urban expansion</p> <p>(c) same as (a) but with more aggressive urban expansion</p> <p>For future climate simulation, CCSM4 data (RCP8.5 scenario) were used to generate the climate perturbation, which was then applied to the current forcing data (NCEP final analyses) used for the numerical model simulations. Validation is based on comparison between model simulations and observations and it shows reasonably good model performance. Numerical simulations show an important interaction between the sea breeze and the urban heat island (UHI) circulation. The UHI forms a strong convergence zone in the center of the city and accelerates the sea-breeze front toward it. This phenomenon raises several questions.  (1) With urban expansion, how is the sea breeze penetration modified?  What is its impact on energy consumption in the city during the summer season, (2) After the dissipation of the UHI, how does the penetration of sea breeze change?  (3) How is the speed of the sea breeze modified with climate change and/or urban expansion? We will discuss our approach and present our results that help answer these questions.</p>

IBM Journal of Research and Development

Efficient, resilient, and safe operation of many cities is dependent on the local weather conditi... more Efficient, resilient, and safe operation of many cities is dependent on the local weather conditions at the scale of their critical infrastructure (electric, communications and water utilities, transportation, etc.) This includes both routine and severe weather events such as tropical storms, tornadoes, snowstorms, damaging winds and hail. For example, with precipitation events, local topography and weather influence water runoff and infiltration, which directly affect flooding as well as drinking water quality and availability. The impact of such events creates issues of public safety for both citizens and first responders. Therefore, the availability of highly localized weather model predictions focused on municipal public safety and operations of infrastructure has the potential to mitigate the impact of severe weather. This is especially true if the lead time for the availability of such predictions enables proactive allocation and deployment of resources (people and equipment) to minimize time for restoration of damage from severe events. Typically, information at such a scale is simply not available. Hence, what optimization that is applied to these processes to enable proactive efforts utilizes either historical weather data as a predictor of trends or the results of continental-or regionalscale weather models. Neither source of information is appropriately matched to the temporal or spatial scale of many such operations. While near-real-time assessment of observations of current weather conditions may have the appropriate geographic locality, by its very nature it is only directly suitable for reactive response. The initial step to address this gap is the application of state-of-the-art physical weather models at the spatial scale of the city's infrastructure, calibrated to avoid this mismatch in predictability. The results of such a model are then coupled to a data-driven stochastic model to represent the actionable prediction of weather (business) impacts. In some cases, an intermediate physical model may be required to translate predicted weather into the phenomena that leads to such impacts. We have applied these ideas to several cities with a diversity of impacts and weather concerns. This coupled model methodology has enabled operational prediction of storm impacts on local infrastructure, as well as measurement of the model error associated with such forecasts. We have defined a flexible approach for such one-way coupling that includes an abstraction of the weather forecasting component. We will present the implementation of these urban weather impact predictions and the ongoing challenges they represent. We will then discuss how we can extend this concept to a climate scale in order to evaluate the potential localized impacts of a warming planet and the effectiveness of strategies being used to mitigate such impacts.

Atmospheric Science Letters

Ieee Computer Society, 1993

As part of the Great Rivers Project, IBM is collaborating with The Nature Conservancy and the Cen... more As part of the Great Rivers Project, IBM is collaborating with The Nature Conservancy and the Center for Sustainability and the Global Environment (SAGE) at the University of Wisconsin, Madison to build a Modeling Framework and Decision Support System (DSS) designed to help policy makers and a variety of stakeholders (farmers, fish & wildlife managers, hydropower operators, et al.) to assess, come to consensus, and act on land use decisions representing effective compromises between human use and ecosystem preservation/restoration. Initially focused on Brazil's Paraguay-Parana, China's Yangtze, and the Mississippi Basin in the US, the DSS integrates data and models from a wide variety of environmental sectors, including water balance, water quality, carbon balance, crop production, hydropower, and biodiversity. In this presentation we focus on the modeling framework aspect of this project. In our approach to these and other environmental modeling projects, we see a flexible, extensible modeling framework infrastructure for defining and running multi-step analytic simulations as critical. In this framework, we divide monolithic models into atomic components with clearly defined semantics encoded via rich metadata representation. Once models and their semantics and composition rules have been registered with the system by their authors or other experts, non-expert users may construct simulations as workflows of these atomic model components. A model composition engine enforces rules/constraints for composing model components into simulations, to avoid the creation of Frankenmodels, models that execute but produce scientifically invalid results. A common software environment and common representations of data and models are required, as well as an adapter strategy for code written in e.g., Fortran or python, that still enables efficient simulation runs, including parallelization. Since each new simulation, as a new composition of model components, requires calibration of parameters (fudge factors) to produce scientifically valid results, we are also developing an autocalibration engine. Finally, visualization is a key element of this modeling framework strategy, both to convey complex scientific data effectively, and also to enable non-expert users to make full use of the relevant features of the framework. We are developing a visualization environment with a strong data model, to enable visualizations, model results, and data all to be handled similarly.