Toward Data-driven, Semi-automatic Inference of Phenomenological Physical Models: Application to Eastern Sahel Rainfall

Pendse, Saurabh V.; Tetteh, Isaac Kow; Semazzi, Fredrick H. M.; Kumar, Vipin; Samatova, Nagiza F.

doi:10.1137/1.9781611972825.4

Cited by 5 publications

(17 citation statements)

References 23 publications

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“…CHARM first prepares the input data through a synergy of preprocessing methods, and then employ Lasso multivariate regression as described in [2] to infer the causal relationships among the variables of interest and identify the key players in the network of modulatory pathways. We then calculate asymmetric ratios using the identified key players to create data couplings, and employ percentile thresholds to detect anomalies with respect to the desired response (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…The model explored by [2] effectively identifies the intrinsic causal relationships among the local and global climate variables and the Sahel rainfall using Lasso multivariate regression. It also proposes methods for pruning the search space, significance estimation and impact analysis that provide quantifiable metrics, and is validated by its consistency with many well-known causal climate relationships, and its results complement existing physical models and help climate scientists derive a better physical rationale for Sahel rainfall variability.…”

Section: A Identification Of Key Players In the Climate Causal Networkmentioning

confidence: 99%

“…However, it places emphasis on final outcomes, ignoring the vital signals embedded in intermediary pathways associated with the dynamic predictor phases vis-à-vis the static phases of the rainfall response. The prominent phases of the climate drivers and/or dynamic features are of more relevance to our work, which could suggest clues for the development of a framework for exploring the intermediary mechanisms, so we employ [2]'s method to identify the top ρ = 3 prominent seasons in the data, highlighted in Table I.…”

Section: A Identification Of Key Players In the Climate Causal Networkmentioning

confidence: 99%

“…Pendse et al [2] presents a promising approach toward a data-driven, semi-automatic inference of phenomenological physical models based on Lasso multivariate regression, and quantified the influence of key "players" on the Sahel rainfall anomaly through use of the Expected Causality Impact (ECI) score. This enabled the formulation of the North Atlantic Oscillation (NAO)-driven hypothesis, among others: NAO modulates the climate drivers of West African climate-the Atlantic Dipole & EATL-via the low-level westerly (LLW) jet.…”

Section: Introductionmentioning

confidence: 99%

“…To gain further knowledge over the results in [2], we must elucidate the putative modulatory pathways that define key mechanisms controlling a system's anomalous response (i.e. Sahelian droughts).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Coupled Heterogeneous Association Rule Mining (CHARM): Application Toward Inference of Modulatory Climate Relationships

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et al. 2013

2013 IEEE 13th International Conference on Data Mining

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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 firstprinciples 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.

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Section: Methodsmentioning

confidence: 99%

Section: A Identification Of Key Players In the Climate Causal Networkmentioning

confidence: 99%

Section: A Identification Of Key Players In the Climate Causal Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Coupled Heterogeneous Association Rule Mining (CHARM): Application Toward Inference of Modulatory Climate Relationships

Gonzalez

Pendse

Padmanabhan

et al. 2013

2013 IEEE 13th International Conference on Data Mining

Self Cite

View full text Add to dashboard Cite

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Detecting Opinions in a Temporally Evolving Conversation on Twitter

Bhattacharjee

Petzold

2015

Lecture Notes in Computer Science

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Understanding Dominant Factors for Precipitation over the Great Lakes Region

Chatterjee

Ließ

Banerjee

et al. 2016

AAAI

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Statistical modeling of local precipitation involves understanding local, regional and global factors informative of precipitation variability in a region. Modern machine learning methods for feature selection can potentially be explored for identifying statistically significant features from pool of potential predictors of precipitation. In this work, we consider sparse regression, which simultaneously performs feature selection and regression, followed by random permutation tests for selecting dominant factors. We consider average winter precipitation over Great Lakes Region in order to identify its dominant influencing factors.Experiments show that global climate indices, computed at different temporal lags, offer predictive information for winter precipitation. Further, among the dominant factors identified using randomized permutation tests, multiple climate indices indicate the influence of geopotential height patterns on winter precipitation.Using composite analysis, we illustrate that certain patterns are indeed typical in high and low precipitation years, and offer plausible scientific reasons for variations in precipitation.Thus, feature selection methods can be useful in identifying influential climate processes and variables, and thereby provide useful hypotheses over physical mechanisms affecting local precipitation.

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Toward Data-driven, Semi-automatic Inference of Phenomenological Physical Models: Application to Eastern Sahel Rainfall

Cited by 5 publications

References 23 publications

Coupled Heterogeneous Association Rule Mining (CHARM): Application Toward Inference of Modulatory Climate Relationships

Coupled Heterogeneous Association Rule Mining (CHARM): Application Toward Inference of Modulatory Climate Relationships

Detecting Opinions in a Temporally Evolving Conversation on Twitter

Understanding Dominant Factors for Precipitation over the Great Lakes Region

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