scholarly journals Characteristics of Wintertime Daily Precipitation over the Australian Snowy Mountains

2017 ◽  
Vol 18 (10) ◽  
pp. 2849-2867 ◽  
Author(s):  
Fahimeh Sarmadi ◽  
Yi Huang ◽  
Steven T. Siems ◽  
Michael J. Manton
Keyword(s):  
Regression Model ◽  
Clustering Algorithm ◽  
Heavy Rain ◽  
Surface Precipitation ◽  
Low Level ◽  
Highly Nonlinear ◽  
Additional Input ◽  
Moderate Resolution ◽  
Model Output Statistics ◽  
The Relationship

Abstract The relationship between orographic precipitation, low-level thermodynamic stability, and the synoptic meteorology is explored for the Snowy Mountains of southeast Australia. A 21-yr dataset (May–October, 1995–2015) of upper-air soundings from an upwind site is used to define synoptic indicators and the low-level stability. A K-means clustering algorithm was employed to classify the daily meteorology into four synoptic classes. The initial classification, based only on six synoptic indicators, distinctly defines both the surface precipitation and the low-level stability by class. Consistent with theory, the wet classes are found to have weak low-level stability, and the dry classes have strong low-level stability. By including low-level stability as an additional input variable to the clustering method, statistically significant correlations were found between the precipitation and the low-level stability within each of the four classes. An examination of the joint PDF reveals a highly nonlinear relationship; heavy rain was associated with very weak low-level stability, and conversely, strong low-level stability was associated with very little precipitation. Building on these historical relationships, model output statistics (MOS) from a moderate resolution (12-km spatial resolution) operational forecast were used to develop stepwise regression models designed to improve the 24-h forecast of precipitation over the Snowy Mountains. A single regression model for all days was found to reduce the RMSE by 7% and the bias by 75%. A class-based regression model was found to reduce the overall RMSE by 30% and the bias by 85%.

scholarly journals Financial Literacy

2021 ◽  
Vol 9 (3) ◽  
pp. 47-62
Author(s):  
Ahmed Sameer El Khatib
Keyword(s):  
Regression Model ◽  
School Performance ◽  
Financial Literacy ◽  
Cognitive Process ◽  
Weak Dependence ◽  
Low Level ◽  
The Relationship

The present paper explores various approaches to the development of cognitive process dimensions in financial literacy. Applying the regression model, the study has examined the strength of the relationship between the actual financial competence of 413 Brazilian’s students aged 18-21, studying economic disciplines, and their school performance. The research has produced the following conclusions: (a) a weak dependence of actual skills on school performance; (b) inherently associated consequence of school performance is not corresponding to the actual financial skills; (c) a low level of financial competence in the dimensions - Remember, Understand and Apply.

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

Factor-Bounded Nonnegative Matrix Factorization

2021 ◽  
Vol 15 (6) ◽  
pp. 1-18
Author(s):  
Kai Liu ◽  
Xiangyu Li ◽  
Zhihui Zhu ◽  
Lodewijk Brand ◽  
Hua Wang
Keyword(s):  
Matrix Factorization ◽  
Clustering Algorithm ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Optimization Methods ◽  
Auxiliary Function ◽  
Image Clustering ◽  
Real World Datasets ◽  
The Relationship ◽  
Matrix Factors

Nonnegative Matrix Factorization (NMF) is broadly used to determine class membership in a variety of clustering applications. From movie recommendations and image clustering to visual feature extractions, NMF has applications to solve a large number of knowledge discovery and data mining problems. Traditional optimization methods, such as the Multiplicative Updating Algorithm (MUA), solves the NMF problem by utilizing an auxiliary function to ensure that the objective monotonically decreases. Although the objective in MUA converges, there exists no proof to show that the learned matrix factors converge as well. Without this rigorous analysis, the clustering performance and stability of the NMF algorithms cannot be guaranteed. To address this knowledge gap, in this article, we study the factor-bounded NMF problem and provide a solution algorithm with proven convergence by rigorous mathematical analysis, which ensures that both the objective and matrix factors converge. In addition, we show the relationship between MUA and our solution followed by an analysis of the convergence of MUA. Experiments on both toy data and real-world datasets validate the correctness of our proposed method and its utility as an effective clustering algorithm.

An Empirical Study of the Relationship between Seasonal Precipitation and Thermodynamic Environment in Puerto Rico

2019 ◽  
Vol 34 (2) ◽  
pp. 277-288 ◽  
Author(s):  
Paul W. Miller ◽  
Thomas L. Mote ◽  
Craig A. Ramseyer
Keyword(s):  
Puerto Rico ◽  
Annual Rainfall ◽  
Seasonal Precipitation ◽  
Surface Precipitation ◽  
Precipitation Forecasting ◽  
Caribbean Islands ◽  
K Index ◽  
Groundwater Reserves ◽  
The Tropics ◽  
The Relationship

Abstract With limited groundwater reserves and few reservoirs, Caribbean islands such as Puerto Rico are largely dependent on regular rainfall to meet societal and ecological water needs. Thus, the ability to anticipate seasonal rainfall shortages, such as the 2015 drought, is particularly important, yet few reliable tools exist for this purpose. Consequently, interpolated surface precipitation observations from the Daymet archive are summarized on daily, annual, and seasonal time scales and compared to the host thermodynamic environment as characterized by the Gálvez–Davison index (GDI), a convective potential parameter designed specifically for the tropics. Complementing the Daymet precipitation totals, ≥1.1 million WSR-88D volume scans between 2002 and 2016 were analyzed for echo tops ≥ 10 000 ft (~3 km) to establish a radar-inferred precipitation activity database for Puerto Rico. The 15-yr record reveals that the GDI outperforms several midlatitude-centric thermodynamic indices, explaining roughly 25% of daily 3-km echo top (ET) activity during each of Puerto Rico’s primary seasons. In contrast, neither mean-layer CAPE, the K index, nor total totals explain more than 11% during any season. When aggregated to the seasonal level, the GDI strongly relates to 3-km ET (R2 = 0.65) and Daymet precipitation totals (R2 = 0.82) during the early rainfall season (ERS; April–July), with correlations weaker outside of this period. The 4-month ERS explains 51% (41%) of the variability to Puerto Rico’s annual rainfall during exceptionally wet (dry) years. These findings are valuable for climate downscaling studies predicting Puerto Rico’s hydroclimate in future atmospheric states, and they could potentially be adapted for operational seasonal precipitation forecasting.

Imitating the Traditional Gods

Mnemosyne ◽  
2021 ◽  
pp. 1-22
Author(s):  
Vilius Bartninkas
Keyword(s):  
Role Models ◽  
Moral Virtues ◽  
The People ◽  
Additional Input ◽  
Virtuous Character ◽  
Ethical Role ◽  
Virtuous Life ◽  
The Relationship ◽  
Religious Aspect ◽  
The Ideal

Abstract This paper examines moral virtues and cult practice in Plato’s Laws. It explores the symposium and the chorus and their potential to provide a recognisable cultural setting, in which the Magnesian citizens can test their responses to pleasurable and painful experiences and thus train their moral virtues. The challenge to this reading is to explain what additional input to moral habituation is provided by the religious aspect of these institutions. This paper draws attention to the relationship between the people and the patron gods of the respective institutions. It argues that the cult practices are designed to reflect the virtuous character of the traditional gods, who serve as the ethical role models for the worshipers. In this way, the worship of the traditional gods not only facilitates moral progress by exemplifying the objective of virtuous life, but also gives an egalitarian version of the ideal of godlikeness to its citizens.

Cloud-Precipitation Hybrid Regimes and their Projection onto IMERG Precipitation Data

2021 ◽  
Author(s):  
Daeho Jin ◽  
Lazaros Oreopoulos ◽  
Dongmin Lee ◽  
Jackson Tan ◽  
Nayeong Cho
Keyword(s):  
Clustering Algorithm ◽  
Relative Weight ◽  
Heavy Precipitation ◽  
Optimal Number ◽  
Hybrid Regimes ◽  
Precipitation Regimes ◽  
Moderate Resolution ◽  
Application Potential ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Top Pressure

AbstractIn order to better understand cloud-precipitation relationships, we extend the concept of cloud regimes (CRs) developed from two-dimensional joint histograms of cloud optical thickness and cloud top pressure from the Moderate Resolution Imaging Spectroradiometer (MODIS), to include precipitation information. Taking advantage of the high-resolution Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation dataset, we derive cloud-precipitation “hybrid” regimes by implementing a k-means clustering algorithm with advanced initialization and objective measures to determine the optimal number of clusters. By expressing the variability of precipitation rates within 1-degree grid cells as histograms and varying the relative weight of cloud and precipitation information in the clustering algorithm, we obtain several editions of hybrid cloud-precipitation regimes (CPRs), and examine their characteristics.In the deep tropics, when precipitation is weighted weakly, the cloud part centroids of the hybrid regimes resemble their counterparts of cloud-only regimes, but combined clustering tightens the cloud-precipitation relationship by decreasing each regime’s precipitation variability. As precipitation weight progressively increases, the shape of the cloud part centroids becomes blunter, while the precipitation part sharpens. When cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct enough features in the precipitation parts of the centroids to allow us to project them onto the 30-min IMERG domain. Such a projection overcomes the temporal sparseness of MODIS cloud observations associated with substantial rainfall, suggesting great application potential for convection-focused studies where characterization of the diurnal cycle is essential.

The Heavy Rain Event of 29 October 2000 in Hana, Maui*

2005 ◽  
Vol 20 (4) ◽  
pp. 397-414 ◽  
Author(s):  
Ryan E. Lyman ◽  
Thomas A. Schroeder ◽  
Gary M. Barnes
Keyword(s):  
Heavy Rain ◽  
Rain Event ◽  
Low Level ◽  
Heavy Rain Event ◽  
Major Factors ◽  
Topographic Forcing

Abstract On 29 October 2000, the Hana region of Maui received 700 mm of rain in 7 h. Radar analyses revealed that the storm consisted of seven cells that were initiated along the southeast slope of Haleakala volcano. One of these cells survived for nearly 4 h and was responsible for 80% of the volumetric rainout from the storm. The interaction of low-level flow distorted by the island of Hawaii located farther east, the passage of a trough, and the topographic forcing caused by Haleakala volcano were major factors responsible for the evolution of the storm.

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