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1. Decisive Atmospheric Circulation Indices for July–August Precipitation in North China Based on Tree Models.

Author

Tong, Xuan, Yan, Zhongwei, Xia, Jiangjiang, and Lou, Xiao

Subjects
ATMOSPHERIC circulation, CLIMATE change, NORTH Atlantic oscillation, ANTARCTIC oscillation, METEOROLOGICAL precipitation, TELECONNECTIONS (Climatology)
Abstract

Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

Published
2019
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2. ME-SERIES; HYDROLOGY; ALGORITHM; VARIABLES; RAINFALL; ISSUES; CHAOS

Author

Dikbaş, Fatih

Subjects
Precipitation (chemical), precipitation (climatology), Quantitative precipitation estimation, Turkey, 010504 meteorology & atmospheric sciences, Computer science, 0208 environmental biotechnology, Chaotic, Data driven modelling, Precipitation, 02 engineering and technology, computer.software_genre, 01 natural sciences, one-dimensional modeling, Statistics, Imputation (statistics), Linear regression, Safety, Risk, Reliability and Quality, General Environmental Science, Water Science and Technology, Multiple linear regression models, Frequency based imputation, precipitation assessment, climate change, Precipitation patterns, Expectation - maximizations, Data mining, Precipitation time series, Regression analysis, Environmental Engineering, Missing data, frequency analysis, Expectation–maximization algorithm, Maximum principle, Estimation of missing data, Environmental Chemistry, Time series, 0105 earth and related environmental sciences, Data-driven modelling, software, One-dimensional approach, modeling, 020801 environmental engineering, Frequency estimation, Data pre-processing, time series, Estimation, computer
Abstract

Changing climate and precipitation patterns make the estimation of precipitation, which exhibits two-dimensional and sometimes chaotic behavior, more challenging. In recent decades, numerous data-driven methods have been developed and applied to estimate precipitation; however, these methods suffer from the use of one-dimensional approaches, lack generality, require the use of neighboring stations and have low sensitivity. This paper aims to implement the first generally applicable, highly sensitive two-dimensional data-driven model of precipitation. This model, named frequency based imputation (FBI), relies on non-continuous monthly precipitation time series data. It requires no determination of input parameters and no data preprocessing, and it provides multiple estimations (from the most to the least probable) of each missing data unit utilizing the series itself. A total of 34,330 monthly total precipitation observations from 70 stations in 21 basins within Turkey were used to assess the success of the method by removing and estimating observation series in annual increments. Comparisons with the expectation maximization and multiple linear regression models illustrate that the FBI method is superior in its estimation of monthly precipitation. This paper also provides a link to the software code for the FBI method. © 2016, Springer-Verlag Berlin Heidelberg.

Published
2016

3. Hydrogeological Risk Management in Smart Cities: A New Approach to Rainfall Classification Based on LTE Cell Selection Parameters

Author

Roberta Avanzato and Francesco Beritelli

Subjects
Rainfall classification, General Computer Science, Computer science, 0211 other engineering and technologies, Climate change, 02 engineering and technology, computer.software_genre, radio signal quality, Smart city, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, Precipitation, Electrical and Electronic Engineering, Risk management, 021101 geological & geomatics engineering, Estimation, Hydrogeology, Artificial neural network, business.industry, General Engineering, 020206 networking & telecommunications, neural networks, 4G/LTE technologies, smart sensors, Pattern recognition (psychology), lcsh:Electrical engineering. Electronics. Nuclear engineering, Data mining, business, lcsh:TK1-9971, computer
Abstract

The sudden climate change, that has taken place in recent years, has generated calamitous phenomena linked to hydrogeological instability in many areas of the world. An accurate estimate of rainfall levels is fundamental in smart city application scenarios: it becomes essential to be able to warn of the imminent occurrence of a calamitous event and reduce the risk to human beings. Unfortunately, to date, traditional techniques for rainfall level estimation present numerous critical issues. This paper proposes a new approach to rainfall classification based on the LTE radio channel parameters adopted for the cell selection mechanism. In particular, this study highlights the correlation between the set of radio channel quality monitoring parameters and the relative rainfall intensity levels. Through a pattern recognition approach based on neural networks with Multi-Layer Perceptron (MLP), the proposed algorithm identifies five classes of rainfall levels with an average accuracy of 96 % and a F1 score of 93.6 %.

Published
2020