Dataset Information

Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques.

ABSTRACT: We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine-learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process.

SUBMITTER: Chiu JC

PROVIDER: S-EPMC7900997 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques.

Chiu J Christine JC Yang C Kevin CK van Leeuwen Peter Jan PJ Feingold Graham G Wood Robert R Blanchard Yann Y Mei Fan F Wang Jian J

Geophysical research letters 20210123 2

We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine-learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming exi ...[more]

PMID: 33678926

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Project description:BackgroundMachine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations.ObjectiveThis study aims to use classification techniques to identify the key patient predictors that are considered most important in the classification of poorer cognitive performance, which is an early risk factor for dementia.MethodsData were used from the Trinity-Ulster and Department of Agriculture study, which included detailed information on sociodemographic, clinical, biochemical, nutritional, and lifestyle factors in 5186 older adults recruited from the Republic of Ireland and Northern Ireland, a proportion of whom (987/5186, 19.03%) were followed up 5-7 years later for reassessment. Cognitive function at both time points was assessed using a battery of tests, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), with a score <70 classed as poorer cognitive performance. This study trained 3 classifiers-decision trees, Naïve Bayes, and random forests-to classify the RBANS score and to identify key health, nutritional, and environmental predictors of cognitive performance and cognitive decline over the follow-up period. It assessed their performance, taking note of the variables that were deemed important for the optimized classifiers for their computational diagnostics.ResultsIn the classification of a low RBANS score (<70), our models performed well (F1 score range 0.73-0.93), all highlighting the individual's score from the Timed Up and Go (TUG) test, the age at which the participant stopped education, and whether or not the participant's family reported memory concerns to be of key importance. The classification models performed well in classifying a greater rate of decline in the RBANS score (F1 score range 0.66-0.85), also indicating the TUG score to be of key importance, followed by blood indicators: plasma homocysteine, vitamin B6 biomarker (plasma pyridoxal-5-phosphate), and glycated hemoglobin.ConclusionsThe results suggest that it may be possible for a health care professional to make an initial evaluation, with a high level of confidence, of the potential for cognitive dysfunction using only a few short, noninvasive questions, thus providing a quick, efficient, and noninvasive way to help them decide whether or not a patient requires a full cognitive evaluation. This approach has the potential benefits of making time and cost savings for health service providers and avoiding stress created through unnecessary cognitive assessments in low-risk patients.

Dataset Information

Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques.

Publications

Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques.

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