Fredrick Semazzi - Academia.edu (original) (raw)

Papers by Fredrick Semazzi

Pressure dipoles in global climate data capture recurring and persistent, large-scale patterns of... more Pressure dipoles in global climate data capture recurring and persistent, large-scale patterns of pressure and circulation anomalies that span distant geographical areas (teleconnections). In this paper, we present a novel graph based approach called shared reciprocal nearest neighbors that considers only reciprocal positive and negative edges in the shared nearest neighbor graph to find dipoles in pressure data. To show the utility of finding dipoles using our approach, we show that the data driven dynamic climate indices generated from our algorithm always perform better than static indices formed from the fixed locations used by climate scientists in terms of capturing impact on land temperature and precipitation. Another salient point of this approach is that it can generate a single snapshot picture of all the dipole interconnections on the globe in a given dataset making it possible to differentiate between various climate model simulations via data driven dipole analysis. Given the importance of teleconnections in climate and the importance of model simulations in understanding the impact of climate change, this methodology has the potential to provide significant insights.

The connections among greenhouse-gas emissions scenarios, global warming, and frequencies of hurr... more The connections among greenhouse-gas emissions scenarios, global warming, and frequencies of hurricanes or tropical cyclones are among the least understood in climate science but among the most fiercely debated in the context of adaptation decisions or mitigation policies. Here we show that a knowledge discovery strategy, which leverages observations and climate model simulations, offers the promise of developing credible projections of tropical cyclones based on sea surface temperatures (SST) in a warming environment. While this study motivates the development of new methodologies in statistics and data mining, the ability to solve challenging climate science problems with innovative combinations of traditional and state-of-the-art methods is demonstrated. Here we develop new insights, albeit in a proof-of-concept sense, on the relationship between sea surface temperatures and hurricane frequencies, and generate the most likely projections with uncertainty bounds for storm counts in the 21st-century warming environment based in turn on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. Our preliminary insights point to the benefits that can be achieved for climate science and impacts analysis, as well as adaptation and mitigation policies, by a solution strategy that remains tailored to the climate domain and complements physics-based climate model simulations with a combination of existing and new computational and data science approaches.

Monthly Weather Review, Sep 1, 1994

Journal of the Atmospheric Sciences, Feb 1, 1985

A barotropic model over an equatorial beta-plane is used to investigate the response when a unifo... more A barotropic model over an equatorial beta-plane is used to investigate the response when a uniform zonal current crosses an isolated hypothetical mountain centered at the equator. The bounded derivative initialization method is applied to suppress gravity-inertia motions. A quasi-stationary trough (also reported in an earlier paper by Semazzi) is generated at the top of the orography. Its intensity is greater in the easterly case than in the westerly case of the initial zonal current. A diagnostic equation is derived to interpret this difference in the solution. The quasi-stationary orographic divergence field which is positive on the windward slope and negative on the leeward slope (for both easterly and westerly cases of the initial zonal current) is responsible for maintaining the trough. The difference in the intensity of the trough is due to the contribution of the u- effect, which changes sign with the direction of the initial zonal flow.

Proceedings of the First World Congress on World Congress of Nonlinear Analysts 92 Volume Iv, Dec 1, 1995

Advances in Meteorology, 2014

Previous water budget studies over Lake Victoria basin have shown that there is near balance betw... more Previous water budget studies over Lake Victoria basin have shown that there is near balance between rainfall and evaporation and that the variability of Lake Victoria levels is determined virtually entirely by changes in rainfall since evaporation is nearly constant. The variability of rainfall over East Africa is dominated by El Niño-Southern Oscillation (ENSO); however, the second and third most dominant rainfall climate modes also account for significant variability across the region. The relationship between ENSO and other significant modes of precipitation variability with Lake Victoria levels is nonlinear. This relationship should be studied to determine which modes need to be accurately modeled in order to accurately model Lake Victoria levels, which are important to the hydroelectric industry in East Africa. The objective of this analysis is to estimate the relative contributions of the dominant modes of annual precipitation variability to the modulation of Lake Victoria levels for the present day . The first mode of annual rainfall variability accounts for most of the variability in Lake Victoria levels, while the effects of the second and third modes are negligible even though these modes are also significant over the region.

Proceedings of the 2013 SIAM International Conference on Data Mining, 2013

ABSTRACT Real-world dynamic systems such as physical and atmosphere-ocean systems often exhibit a... more ABSTRACT Real-world dynamic systems such as physical and atmosphere-ocean systems often exhibit a hierarchical system-subsystem structure. However, the paradigm of making this hierarchical/modular structure and the rich properties they encode a “first-class citizen” of machine learning algorithms is largely absent from the literature. Furthermore, traditional data mining approaches focus on designing new classifiers or ensembles of classifiers, while there is a lack of study on detecting and correcting prediction errors of existing forecasting (or classification) algorithms. In this paper, we propose DETECTOR, a hierarchical method for detecting and correcting forecast errors by employing the whole-part relationships between the target system and non-target systems. Experimental results show that DETECTOR can successfully detect and correct forecasting errors made by state-of-art classifier ensemble techniques and traditional single classifier methods at an average rate of 22%, corresponding to a 11% average forecasting accuracy increase, in seasonal forecasting of hurricanes and landfalling hurricanes in North Atlantic and North African rainfall.

Proceedings of the 2012 SIAM International Conference on Data Mining, 2012

Statistical Analysis and Data Mining, 2013

ABSTRACT Pressure dipoles are important long distance climate phenomena (teleconnection) characte... more ABSTRACT Pressure dipoles are important long distance climate phenomena (teleconnection) characterized by pressure anomalies of the opposite polarity appearing at two different locations at the same time. Such dipoles have been proven important for understanding and explaining the variability in climate in many regions of the world, e.g. the El Niño Southern Oscillation (ENSO) climate phenomenon, which is described by opposite pressure anomalies between the west and east Pacific and is known to be responsible for precipitation and temperature anomalies worldwide. This paper presents a graph-based approach called shared reciprocal nearest neighbor approach that considers only reciprocal positive and negative edges in the shared nearest neighbor graph to find the dipoles. One crucial aspect of our approach to the analysis of such networks is a careful treatment of negative correlations, whose proper consideration is critical for finding the dipoles. Further, our work shows the importance of modeling the time-dependent patterns of the dipoles in a changing climate in order to better capture the impact of important climate phenomena on the globe. To show the utility of finding dipoles using our approach, we show that the data driven dynamic climate indices generated from our algorithm generally perform better than static indices formed from the fixed locations used by climate scientists in terms of capturing impact on global temperature and precipitation. Our approach can generate a single snapshot picture of all the dipole interconnections on the globe in a given dataset and thus makes it possible to study the changes in dipole interactions and movements. As teleconnections are crucial in the understanding of the global climate system, there is a pressing need to better understand the behavior and interactions of these atmospheric processes as well as to capture them precisely. Our systematic graph-based approach to find the teleconnections in climate data is an attempt in that direction. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 6: 158–179, 2013

2013 IEEE 13th International Conference on Data Mining, 2013

ABSTRACT The complex dynamic climate system often exhibits hierarchical modularity of its organiz... more ABSTRACT The complex dynamic climate system often exhibits hierarchical modularity of its organization and function. Scientists have spent decades trying to discover and understand the driving mechanisms behind western African Sahel summer rainfall variability, mostly via hypothesis-driven and/or first-principles based research. Their work has furthered theory regarding the connections between various climate patterns, but the key relationships are still not fully understood. We present Coupled Heterogeneous Association Rule Mining (CHARM), a computationally efficient methodology that mines higher-order relationships between these subsystems' anomalous temporal phases with respect to their effect on the system's response. We apply this to climate science data, aiming to infer putative pathways/cascades of modulating events and the modulating signs that collectively define the network of pathways for the rainfall anomaly in the Sahel. Experimental results are consistent with fundamental theories of phenomena in climate science, especially physical processes that best describe sub-regional climate.

2011 IEEE 11th International Conference on Data Mining Workshops, 2011

The application of complex networks to study complex phenomena, including the Internet, social ne... more The application of complex networks to study complex phenomena, including the Internet, social networks, food networks, and others, has seen a growing interest in recent years. In particular, the use of complex networks and network theory to analyze the behavior of the climate system is an emerging topic. This newfound interest is due to the difficulty of analyzing climate data-this analysis is notoriously difficult due to the strong spatio-temporal dependencies, multivariate nature, seasonal behavior, and nonlinear phenomena inherent in the climate system. Network-based approaches model the complex long-term dependencies of weather attributes (such as temperature or air pressure) between locations on the Earth as a network of relationships and analyze these networks to gather insights about the emergent behavior of the system as a whole.

Data Mining and Knowledge Discovery, 2012

The latent behavior of a physical system that can exhibit extreme events such as hurricanes or ra... more The latent behavior of a physical system that can exhibit extreme events such as hurricanes or rainfalls, is complex. Recently, a very promising means for studying complex systems has emerged through the concept of complex networks. Networks representing relationships between individual objects usually exhibit community dynamics. Conventional community detection methods mainly focus on either mining frequent subgraphs in a network or detecting stable communities in time-varying networks. In this paper, we formulate a novel problem-detection of predictive and phase-biased communities in contrasting groups of networks, and propose an efficient and effective machine learning solution for finding such anomalous communities. We build different groups of networks corresponding to different system's phases, such as higher or low hurricane activity, discover phase-related system components as seeds to help bound the search space of community generation in each network, and use the proposed contrast-based technique to identify the changing communities across different groups. The detected anomalous communities are hypothesized (1) to play an important role in defining the target system's state(s) and (2) to improve the predictive Responsible editor: Eamonn Keogh.

Geophys Res Lett, 1993

A nested high resolution atmospheric model is used to investigate the sensitivity of the Sahelian... more A nested high resolution atmospheric model is used to investigate the sensitivity of the Sahelian climate to large-scale sea-surface temperature (SST) anomalies. The nested system has realistic vegetation and detailed bottom orography. Two separate sets of northern hemispheric summer (June, July and August) numerical integrations are performed; one corresponding to the SST anomalies in 1950 when the Sahelian region was relatively much wetter than the long-term average conditions and a second integration based on 1984 SST anomalies when one of the driest rain seasons in the last few decades was experienced. Although the low resolution (R15 ≈ 4.5° by 7.5° latitude by longitude) stand-alone global climate model reasonably simulates the lower rainfall amounts in 1984 compared to 1950, the nested system yields more realistic regional climate because its forcing includes more detailed effects of topography, land-sea contrasts, and land surface processes. In particular, two distinct rainfall maxima primarily anchored to the regions of highest terrain are simulated by the model. One corresponding to the highlands in Cameroon over the Adamawa Plateau and a second maxima over Guinea and Sierra-Leone. Inspection of model circulation indicates that the weaker moist cross-equatorial monsoon flow in the 1984 is responsible for the lower amounts of the Sahelian rainfall compared to 1950. Our results are in agreement with several diagnostic and modeling studies performed in the recent years which show that deficient sub-Saharan rainy seasons tends to coincide with the southwesterly surface monsoon flow not extending as far north along the West African coast as in the wetter years (Lamb and Peppier, 1990, and others).

Understanding extreme events, such as hurricanes or forest fires, is of paramount importance beca... more Understanding extreme events, such as hurricanes or forest fires, is of paramount importance because of their adverse impacts on human beings. Such events often propagate in space and time. Predicting-even a few days in advance-what locations will get affected by the event tracks could benefit our society in many ways. Arguably, simulations from first principles, where underlying physics-based models are described by a system of equations, provide least reliable predictions for variables characterizing the dynamics of these extreme events. Data-driven model building has been recently emerging as a complementary approach that could learn the relationships between historically observed or simulated multiple, spatio-temporal ancillary variables and the dynamic behavior of extreme events of interest. While promising, the methodology for predictive learning from such complex data is still in its infancy. In this paper, we propose a dynamic networks-based methodology for in-advance prediction of the dynamic tracks of emerging extreme events. By associating a network model of the system with the known tracks, our method is capable of learning the recurrent network motifs that could be used as discriminatory signatures for the event's behavioral class. When applied to classifying the behavior of the hurricane tracks at their early formation stages in Western Africa region, our method is able to predict whether hurricane tracks will hit the land of the North Atlantic region at least 10-15 days lead lag time in advance with more than 90% accuracy using 10-fold cross-validation. To the best of our knowledge, no comparable methodology exists for solving this problem using data-driven models.

Monthly Weather Review, Aug 1, 2002

A computationally efficient mass-conservative transport scheme over the sphere is proposed and te... more A computationally efficient mass-conservative transport scheme over the sphere is proposed and tested. The scheme combines a conservative finite-volume method with an efficient semi-Lagrangian scheme based on the dimension splitting ''cascade'' method. In the regions near the poles where the conservative cascade procedure breaks down, a globally conservative, but locally approximate scheme is used. This procedure is currently restricted to polar meridional Courant numbers less than one. The resulting conservative cascade scheme is evaluated using a solid-body rotation test and deformational flow test, and found to be both accurate and efficient. Compared to the traditional semi-Lagrangian scheme employing a bicubic-Lagrange interpolator, the proposed scheme is considerably more accurate and almost twice as fast while conserving mass exactly.

Advances in Meteorology, 2014

By using a limited-area model (LAM) in combination with the scale-selective data assimilation (SS... more By using a limited-area model (LAM) in combination with the scale-selective data assimilation (SSDA) approach, wind energy resources in the contiguous United States (CONUS) were downscaled from IPCC CCSM3 global model projections for both current and future climate conditions. An assessment of climate change impacts on wind energy resources in the CONUS region was then conducted. Based on the downscaling results, when projecting into future climate under IPCC's A1B scenario, the average annual wind speed experiences an overall shift across the CONUS region. From the current climate to the 2040s, the average annual wind speed is expected to increase from 0.1 to 0.2 m s −1 over the Great Plains, Northern Great Lakes Region, and Southwestern United States located southwest of the Rocky Mountains. When projecting into the 2090s from current climate, there is an overall increase in the Great Plains Region and Southwestern United States located southwest of the Rockies with a mean wind speed increase between 0 and 0.1 m s −1 , while, the Northern Great Lakes Region experiences an even greater increase from current climate to 2090s than over the first few decades with an increase of mean wind speed from 0.1 to 0.4 m s −1 .

Proceedings of the First World Congress of Nonlinear Analysts, Tampa, Florida, August 19-26, 1992, 1996

A system of equations which describe the motion of a barotropic fluid in the presence of bottom t... more A system of equations which describe the motion of a barotropic fluid in the presence of bottom topography are presented. The mathematical expression for orography is developed and the bounded derivative initialization method is applied to suppress gravitational oscillations. A stationary orographic trough is simulated. The geopotential and zonal motion have maximum deviation from the mean state at the top of the mountain. Regarding meridional speed, outflow occurs on the windward slope and inflow on the leeward slope. Divergence of order (10(-6)s(-1) is found on the windward slope while convergence of the same order of magnitude resides on the leeward slope. This outcome may have interesting implications regarding real climatology occurring over the equatorial regions of continental land masses.

Pressure dipoles in global climate data capture recurring and persistent, large-scale patterns of... more Pressure dipoles in global climate data capture recurring and persistent, large-scale patterns of pressure and circulation anomalies that span distant geographical areas (teleconnections). In this paper, we present a novel graph based approach called shared reciprocal nearest neighbors that considers only reciprocal positive and negative edges in the shared nearest neighbor graph to find dipoles in pressure data. To show the utility of finding dipoles using our approach, we show that the data driven dynamic climate indices generated from our algorithm always perform better than static indices formed from the fixed locations used by climate scientists in terms of capturing impact on land temperature and precipitation. Another salient point of this approach is that it can generate a single snapshot picture of all the dipole interconnections on the globe in a given dataset making it possible to differentiate between various climate model simulations via data driven dipole analysis. Given the importance of teleconnections in climate and the importance of model simulations in understanding the impact of climate change, this methodology has the potential to provide significant insights.

The connections among greenhouse-gas emissions scenarios, global warming, and frequencies of hurr... more The connections among greenhouse-gas emissions scenarios, global warming, and frequencies of hurricanes or tropical cyclones are among the least understood in climate science but among the most fiercely debated in the context of adaptation decisions or mitigation policies. Here we show that a knowledge discovery strategy, which leverages observations and climate model simulations, offers the promise of developing credible projections of tropical cyclones based on sea surface temperatures (SST) in a warming environment. While this study motivates the development of new methodologies in statistics and data mining, the ability to solve challenging climate science problems with innovative combinations of traditional and state-of-the-art methods is demonstrated. Here we develop new insights, albeit in a proof-of-concept sense, on the relationship between sea surface temperatures and hurricane frequencies, and generate the most likely projections with uncertainty bounds for storm counts in the 21st-century warming environment based in turn on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. Our preliminary insights point to the benefits that can be achieved for climate science and impacts analysis, as well as adaptation and mitigation policies, by a solution strategy that remains tailored to the climate domain and complements physics-based climate model simulations with a combination of existing and new computational and data science approaches.

Monthly Weather Review, Sep 1, 1994

Journal of the Atmospheric Sciences, Feb 1, 1985

A barotropic model over an equatorial beta-plane is used to investigate the response when a unifo... more A barotropic model over an equatorial beta-plane is used to investigate the response when a uniform zonal current crosses an isolated hypothetical mountain centered at the equator. The bounded derivative initialization method is applied to suppress gravity-inertia motions. A quasi-stationary trough (also reported in an earlier paper by Semazzi) is generated at the top of the orography. Its intensity is greater in the easterly case than in the westerly case of the initial zonal current. A diagnostic equation is derived to interpret this difference in the solution. The quasi-stationary orographic divergence field which is positive on the windward slope and negative on the leeward slope (for both easterly and westerly cases of the initial zonal current) is responsible for maintaining the trough. The difference in the intensity of the trough is due to the contribution of the u- effect, which changes sign with the direction of the initial zonal flow.

Proceedings of the First World Congress on World Congress of Nonlinear Analysts 92 Volume Iv, Dec 1, 1995

Advances in Meteorology, 2014

Previous water budget studies over Lake Victoria basin have shown that there is near balance betw... more Previous water budget studies over Lake Victoria basin have shown that there is near balance between rainfall and evaporation and that the variability of Lake Victoria levels is determined virtually entirely by changes in rainfall since evaporation is nearly constant. The variability of rainfall over East Africa is dominated by El Niño-Southern Oscillation (ENSO); however, the second and third most dominant rainfall climate modes also account for significant variability across the region. The relationship between ENSO and other significant modes of precipitation variability with Lake Victoria levels is nonlinear. This relationship should be studied to determine which modes need to be accurately modeled in order to accurately model Lake Victoria levels, which are important to the hydroelectric industry in East Africa. The objective of this analysis is to estimate the relative contributions of the dominant modes of annual precipitation variability to the modulation of Lake Victoria levels for the present day . The first mode of annual rainfall variability accounts for most of the variability in Lake Victoria levels, while the effects of the second and third modes are negligible even though these modes are also significant over the region.

Proceedings of the 2013 SIAM International Conference on Data Mining, 2013

ABSTRACT Real-world dynamic systems such as physical and atmosphere-ocean systems often exhibit a... more ABSTRACT Real-world dynamic systems such as physical and atmosphere-ocean systems often exhibit a hierarchical system-subsystem structure. However, the paradigm of making this hierarchical/modular structure and the rich properties they encode a “first-class citizen” of machine learning algorithms is largely absent from the literature. Furthermore, traditional data mining approaches focus on designing new classifiers or ensembles of classifiers, while there is a lack of study on detecting and correcting prediction errors of existing forecasting (or classification) algorithms. In this paper, we propose DETECTOR, a hierarchical method for detecting and correcting forecast errors by employing the whole-part relationships between the target system and non-target systems. Experimental results show that DETECTOR can successfully detect and correct forecasting errors made by state-of-art classifier ensemble techniques and traditional single classifier methods at an average rate of 22%, corresponding to a 11% average forecasting accuracy increase, in seasonal forecasting of hurricanes and landfalling hurricanes in North Atlantic and North African rainfall.

Proceedings of the 2012 SIAM International Conference on Data Mining, 2012

Statistical Analysis and Data Mining, 2013

ABSTRACT Pressure dipoles are important long distance climate phenomena (teleconnection) characte... more ABSTRACT Pressure dipoles are important long distance climate phenomena (teleconnection) characterized by pressure anomalies of the opposite polarity appearing at two different locations at the same time. Such dipoles have been proven important for understanding and explaining the variability in climate in many regions of the world, e.g. the El Niño Southern Oscillation (ENSO) climate phenomenon, which is described by opposite pressure anomalies between the west and east Pacific and is known to be responsible for precipitation and temperature anomalies worldwide. This paper presents a graph-based approach called shared reciprocal nearest neighbor approach that considers only reciprocal positive and negative edges in the shared nearest neighbor graph to find the dipoles. One crucial aspect of our approach to the analysis of such networks is a careful treatment of negative correlations, whose proper consideration is critical for finding the dipoles. Further, our work shows the importance of modeling the time-dependent patterns of the dipoles in a changing climate in order to better capture the impact of important climate phenomena on the globe. To show the utility of finding dipoles using our approach, we show that the data driven dynamic climate indices generated from our algorithm generally perform better than static indices formed from the fixed locations used by climate scientists in terms of capturing impact on global temperature and precipitation. Our approach can generate a single snapshot picture of all the dipole interconnections on the globe in a given dataset and thus makes it possible to study the changes in dipole interactions and movements. As teleconnections are crucial in the understanding of the global climate system, there is a pressing need to better understand the behavior and interactions of these atmospheric processes as well as to capture them precisely. Our systematic graph-based approach to find the teleconnections in climate data is an attempt in that direction. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 6: 158–179, 2013

2013 IEEE 13th International Conference on Data Mining, 2013

ABSTRACT The complex dynamic climate system often exhibits hierarchical modularity of its organiz... more ABSTRACT The complex dynamic climate system often exhibits hierarchical modularity of its organization and function. Scientists have spent decades trying to discover and understand the driving mechanisms behind western African Sahel summer rainfall variability, mostly via hypothesis-driven and/or first-principles based research. Their work has furthered theory regarding the connections between various climate patterns, but the key relationships are still not fully understood. We present Coupled Heterogeneous Association Rule Mining (CHARM), a computationally efficient methodology that mines higher-order relationships between these subsystems' anomalous temporal phases with respect to their effect on the system's response. We apply this to climate science data, aiming to infer putative pathways/cascades of modulating events and the modulating signs that collectively define the network of pathways for the rainfall anomaly in the Sahel. Experimental results are consistent with fundamental theories of phenomena in climate science, especially physical processes that best describe sub-regional climate.

2011 IEEE 11th International Conference on Data Mining Workshops, 2011

The application of complex networks to study complex phenomena, including the Internet, social ne... more The application of complex networks to study complex phenomena, including the Internet, social networks, food networks, and others, has seen a growing interest in recent years. In particular, the use of complex networks and network theory to analyze the behavior of the climate system is an emerging topic. This newfound interest is due to the difficulty of analyzing climate data-this analysis is notoriously difficult due to the strong spatio-temporal dependencies, multivariate nature, seasonal behavior, and nonlinear phenomena inherent in the climate system. Network-based approaches model the complex long-term dependencies of weather attributes (such as temperature or air pressure) between locations on the Earth as a network of relationships and analyze these networks to gather insights about the emergent behavior of the system as a whole.

Data Mining and Knowledge Discovery, 2012

The latent behavior of a physical system that can exhibit extreme events such as hurricanes or ra... more The latent behavior of a physical system that can exhibit extreme events such as hurricanes or rainfalls, is complex. Recently, a very promising means for studying complex systems has emerged through the concept of complex networks. Networks representing relationships between individual objects usually exhibit community dynamics. Conventional community detection methods mainly focus on either mining frequent subgraphs in a network or detecting stable communities in time-varying networks. In this paper, we formulate a novel problem-detection of predictive and phase-biased communities in contrasting groups of networks, and propose an efficient and effective machine learning solution for finding such anomalous communities. We build different groups of networks corresponding to different system's phases, such as higher or low hurricane activity, discover phase-related system components as seeds to help bound the search space of community generation in each network, and use the proposed contrast-based technique to identify the changing communities across different groups. The detected anomalous communities are hypothesized (1) to play an important role in defining the target system's state(s) and (2) to improve the predictive Responsible editor: Eamonn Keogh.

Geophys Res Lett, 1993

A nested high resolution atmospheric model is used to investigate the sensitivity of the Sahelian... more A nested high resolution atmospheric model is used to investigate the sensitivity of the Sahelian climate to large-scale sea-surface temperature (SST) anomalies. The nested system has realistic vegetation and detailed bottom orography. Two separate sets of northern hemispheric summer (June, July and August) numerical integrations are performed; one corresponding to the SST anomalies in 1950 when the Sahelian region was relatively much wetter than the long-term average conditions and a second integration based on 1984 SST anomalies when one of the driest rain seasons in the last few decades was experienced. Although the low resolution (R15 ≈ 4.5° by 7.5° latitude by longitude) stand-alone global climate model reasonably simulates the lower rainfall amounts in 1984 compared to 1950, the nested system yields more realistic regional climate because its forcing includes more detailed effects of topography, land-sea contrasts, and land surface processes. In particular, two distinct rainfall maxima primarily anchored to the regions of highest terrain are simulated by the model. One corresponding to the highlands in Cameroon over the Adamawa Plateau and a second maxima over Guinea and Sierra-Leone. Inspection of model circulation indicates that the weaker moist cross-equatorial monsoon flow in the 1984 is responsible for the lower amounts of the Sahelian rainfall compared to 1950. Our results are in agreement with several diagnostic and modeling studies performed in the recent years which show that deficient sub-Saharan rainy seasons tends to coincide with the southwesterly surface monsoon flow not extending as far north along the West African coast as in the wetter years (Lamb and Peppier, 1990, and others).

Understanding extreme events, such as hurricanes or forest fires, is of paramount importance beca... more Understanding extreme events, such as hurricanes or forest fires, is of paramount importance because of their adverse impacts on human beings. Such events often propagate in space and time. Predicting-even a few days in advance-what locations will get affected by the event tracks could benefit our society in many ways. Arguably, simulations from first principles, where underlying physics-based models are described by a system of equations, provide least reliable predictions for variables characterizing the dynamics of these extreme events. Data-driven model building has been recently emerging as a complementary approach that could learn the relationships between historically observed or simulated multiple, spatio-temporal ancillary variables and the dynamic behavior of extreme events of interest. While promising, the methodology for predictive learning from such complex data is still in its infancy. In this paper, we propose a dynamic networks-based methodology for in-advance prediction of the dynamic tracks of emerging extreme events. By associating a network model of the system with the known tracks, our method is capable of learning the recurrent network motifs that could be used as discriminatory signatures for the event's behavioral class. When applied to classifying the behavior of the hurricane tracks at their early formation stages in Western Africa region, our method is able to predict whether hurricane tracks will hit the land of the North Atlantic region at least 10-15 days lead lag time in advance with more than 90% accuracy using 10-fold cross-validation. To the best of our knowledge, no comparable methodology exists for solving this problem using data-driven models.

Monthly Weather Review, Aug 1, 2002

A computationally efficient mass-conservative transport scheme over the sphere is proposed and te... more A computationally efficient mass-conservative transport scheme over the sphere is proposed and tested. The scheme combines a conservative finite-volume method with an efficient semi-Lagrangian scheme based on the dimension splitting ''cascade'' method. In the regions near the poles where the conservative cascade procedure breaks down, a globally conservative, but locally approximate scheme is used. This procedure is currently restricted to polar meridional Courant numbers less than one. The resulting conservative cascade scheme is evaluated using a solid-body rotation test and deformational flow test, and found to be both accurate and efficient. Compared to the traditional semi-Lagrangian scheme employing a bicubic-Lagrange interpolator, the proposed scheme is considerably more accurate and almost twice as fast while conserving mass exactly.

Advances in Meteorology, 2014

By using a limited-area model (LAM) in combination with the scale-selective data assimilation (SS... more By using a limited-area model (LAM) in combination with the scale-selective data assimilation (SSDA) approach, wind energy resources in the contiguous United States (CONUS) were downscaled from IPCC CCSM3 global model projections for both current and future climate conditions. An assessment of climate change impacts on wind energy resources in the CONUS region was then conducted. Based on the downscaling results, when projecting into future climate under IPCC's A1B scenario, the average annual wind speed experiences an overall shift across the CONUS region. From the current climate to the 2040s, the average annual wind speed is expected to increase from 0.1 to 0.2 m s −1 over the Great Plains, Northern Great Lakes Region, and Southwestern United States located southwest of the Rocky Mountains. When projecting into the 2090s from current climate, there is an overall increase in the Great Plains Region and Southwestern United States located southwest of the Rockies with a mean wind speed increase between 0 and 0.1 m s −1 , while, the Northern Great Lakes Region experiences an even greater increase from current climate to 2090s than over the first few decades with an increase of mean wind speed from 0.1 to 0.4 m s −1 .

Proceedings of the First World Congress of Nonlinear Analysts, Tampa, Florida, August 19-26, 1992, 1996

A system of equations which describe the motion of a barotropic fluid in the presence of bottom t... more A system of equations which describe the motion of a barotropic fluid in the presence of bottom topography are presented. The mathematical expression for orography is developed and the bounded derivative initialization method is applied to suppress gravitational oscillations. A stationary orographic trough is simulated. The geopotential and zonal motion have maximum deviation from the mean state at the top of the mountain. Regarding meridional speed, outflow occurs on the windward slope and inflow on the leeward slope. Divergence of order (10(-6)s(-1) is found on the windward slope while convergence of the same order of magnitude resides on the leeward slope. This outcome may have interesting implications regarding real climatology occurring over the equatorial regions of continental land masses.