An objective separation of rainfall classes in the high tropical Andes by using a clustering analysis.

2021 ◽  
Author(s):  
Gabriela Urgilés ◽  
Rolando Célleri ◽  
Katja Trachte ◽  
Jörg Bendix ◽  
Johanna Orellana-Alvear
Keyword(s):  
Clustering Analysis ◽  
Rainfall Variability ◽  
Tropical Andes ◽  
Mountain Area ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Melting Layer ◽  
Tropical Mountain ◽  
Layer Characteristic ◽  
Using Data

<p>Information about the temporal rainfall variability at high-resolution is scarce, especially in regions with complex topography as the Tropical Andes, and this hinders the study rainfall dynamics. The identification of rainfall types is usually determined using thresholds of some rainfall characteristics as rain rate and velocity. Nevertheless, these thresholds are identified for a specific study area and thus they cannot be extrapolated to other places to identify rainfall classes. Thus, the aim of this study is to investigate rainfall-event classes based on a clustering approach by using the k-means algorithm. The clustering analysis is used to group objects (i.e., rainfall-events) based on its characteristics (e.g., duration, intensity, drop size distribution, melting layer identification). This study was carried out using data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The instruments were located in the tropical Andes, at 2600 m a.s.l., in the city of Cuenca, Ecuador.  Three years of data were available for the study. Firstly, the rainfall events were selected by using the criteria: minimum inter-event, minimum total accumulation and minimum duration. Then, by using the k-means algorithm, two principal rainfall classes were identified in the study area. These rainfall classes (i.e., convective, stratiform) showed marked differences in their rainfall characteristics. Besides, a third rainfall class (mixed class) was identified as a subclass of the stratiform class. The stratiform class was more common during the year in the study area. Also, short duration rainfall events (less than 70 min) were dominant. Furthermore, the melting layer characteristic – that is used to determine rainfall classes – did not influence the rainfall class identification using the clustering analysis, especially in two classes; thus, its prior study is not necessary, and this makes the clustering analysis highly beneficial. Finally, this clustering analysis ensured an objective separation of rainfall classes in the tropical high Andes. This rainfall classification provided new insights about the rainfall dynamics in this tropical mountain area.</p>

scholarly journals Assessing spatio-temporal rainfall variability in a tropical mountain area (Ethiopia) using NOAA’s rainfall estimates

2013 ◽  
Vol 34 (23) ◽  
pp. 8319-8335 ◽  
Author(s):  
Miro Jacob ◽  
Amaury Frankl ◽  
Mitiku Haile ◽  
Ann Zwertvaegher ◽  
Jan Nyssen
Keyword(s):  
Rainfall Variability ◽  
Mountain Area ◽  
Tropical Mountain ◽  
Spatio Temporal ◽  
Rainfall Estimates

scholarly journals Evaluation and Application of Multi-Source Satellite Rainfall Product CHIRPS to Assess Spatio-Temporal Rainfall Variability on Data-Sparse Western Margins of Ethiopian Highlands

2019 ◽  
Vol 11 (22) ◽  
pp. 2688 ◽  
Author(s):  
Ashebir Sewale Belay ◽  
Ayele Almaw Fenta ◽  
Alemu Yenehun ◽  
Fenta Nigate ◽  
Seifu A. Tilahun ◽  
...  
Keyword(s):  
Rainfall Variability ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Integration Time ◽  
Spatial And Temporal Variability ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Mean Annual Rainfall ◽  
Time Space ◽  
Spatio Temporal

The spatio-temporal characteristic of rainfall in the Beles Basin of Ethiopia is poorly understood, mainly due to lack of data. With recent advances in remote sensing, satellite derived rainfall products have become alternative sources of rainfall data for such poorly gauged areas. The objectives of this study were: (i) to evaluate a multi-source rainfall product (Climate Hazards Group Infrared Precipitation with Stations: CHIRPS) for the Beles Basin using gauge measurements and (ii) to assess the spatial and temporal variability of rainfall across the basin using validated CHIRPS data for the period 1981–2017. Categorical and continuous validation statistics were used to evaluate the performance, and time-space variability of rainfall was analyzed using GIS operations and statistical methods. Results showed a slight overestimation of rainfall occurrence by CHIRPS for the lowland region and underestimation for the highland region. CHIRPS underestimated the proportion of light daily rainfall events and overestimated the proportion of high intensity daily rainfall events. CHIRPS rainfall amount estimates were better in highland regions than in lowland regions, and became more accurate as the duration of the integration time increases from days to months. The annual spatio-temporal analysis result using CHIRPS revealed: a mean annual rainfall of the basin is 1490 mm (1050–2090 mm), a 50 mm increase of mean annual rainfall per 100 m elevation rise, periodical and persistent drought occurrence every 8 to 10 years, a significant increasing trend of rainfall (~5 mm year−1), high rainfall variability observed at the lowland and drier parts of the basin and high coefficient of variation of monthly rainfall in March and April (revealing occurrence of bimodal rainfall characteristics). This study shows that the performance of CHIRPS product can vary spatially within a small basin level, and CHIRPS can help for better decision making in poorly gauged areas by giving an option to understand the space-time variability of rainfall characteristics.

A novel classification rainfall type using a clustering approach in the tropical Andes.

2020 ◽  
Author(s):  
Gabriela Urgiles ◽  
Johanna Orellana-Alvear ◽  
Katja Trachte ◽  
Jörg Bendix ◽  
Rolando Célleri
Keyword(s):  
Size Distribution ◽  
Vertical Profile ◽  
Drop Size ◽  
Drop Size Distribution ◽  
Tropical Andes ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Rainfall Type ◽  
Clustering Approach ◽  
Microphysical Processes

<p>Information on the vertical profile of rainfall is important to improve our knowledge about microphysical processes that govern the formation of the hydrometeors. In addition, the vertical profile helps improving the quantitative precipitation estimation from scanning weather radars and may be useful to improve the parameterization of cloud microphysical processes in numerical models. Usually, rainfall types (e.g, stratiform and convective) are defined by using some rainfall characteristics of its vertical profile such as intensity and velocity. Furthermore, certain thresholds for these variables need to be defined to separate the rainfall classes. However, studies about the vertical profile of rainfall showed that the vertical variability of rainfall highly depends on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Besides, the identification of thresholds can become too subjective and, thus, influence the identification of rainfall types. In regions of complex topography such as the Tropical Andes, rainfall vertical profile studies are very scarce and they show that rainfall classification has similar drawbacks such as the identification of thresholds. Thus, this study aims to develop a new methodology for rainfall events classification by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics (e.g., duration, intensity, drop size distribution) of each rainfall type. The study was carried out using data retrieved from a K-band Doppler Micro Rain Radar (MRR) that records rainfall characteristics such as rainfall intensity, drop velocity, reflectivity profile, drop size distribution (DSD), and liquid water content (LWC). The MRR was located in the tropical Andes, at 2600 m a.s.l., in the city of Cuenca, Ecuador.  Three years of data were available for the study with a temporal resolution of 1 minute.  First, the rainfall events were identified by using three criteria: minimum inter-event, minimum total accumulation, and minimum duration. Then, by using the k-means approach, several iterations with different number of clusters each were evaluated and consequently, three representative rainfall classes were found. These classes showed certain transitions (e.g., for rainfall intensity, velocity and drop size distribution) that separated the rainfall classes. The distributions of these rainfall event characteristics were compared with those found in the literature. This novel classification provided new insights about the variability of the rainfall in this tropical mountain setting and how its characteristics revealed distinctive patterns of the rainfall processes. Finally, since the rain types were identified by a data-driven method, it ensured an objective separation of the rainfall events. Thus, the application of this method in other sites will allow contrasting previous findings regarding the suitability of the tailor-used thresholds for rainfall classification.</p>

scholarly journals Clustering of Rainfall Types Using Micro Rain Radar and Laser Disdrometer Observations in the Tropical Andes

2021 ◽  
Vol 13 (5) ◽  
pp. 991
Author(s):  
Gabriela Urgilés ◽  
Rolando Célleri ◽  
Katja Trachte ◽  
Jörg Bendix ◽  
Johanna Orellana-Alvear
Keyword(s):  
Data Driven ◽  
High Temporal Resolution ◽  
Formation Processes ◽  
Tropical Andes ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Local Climate ◽  
Rainfall Type ◽  
Clustering Approach ◽  
Rain Radar

Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some rainfall characteristics such as intensity and velocity. However, these thresholds highly depend on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Thus, this study aims to analyze rainfall-event types by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics of each rainfall type. It was carried out using three years of data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The results show two main rainfall types (convective and stratiform) in the area which highly differ in their rainfall features. In addition, a mixed type was found as a subgroup of the stratiform type. The stratiform type was found more frequently throughout the year. Furthermore, rainfall events of short duration (less than 70 min) were prevalent in the study area. This study will contribute to analyze the rainfall formation processes and the vertical profile.

scholarly journals Soil Moisture Dynamics in Response to Precipitation and Thinning in a Semi-Dry Forest in Northern Mexico

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 105
Author(s):  
Argelia E. Rascón-Ramos ◽  
Martín Martínez-Salvador ◽  
Gabriel Sosa-Pérez ◽  
Federico Villarreal-Guerrero ◽  
Alfredo Pinedo-Alvarez ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Forest Management ◽  
Water Availability ◽  
Dry Forest ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Northern Mexico ◽  
The Difference ◽  
The Impact ◽  
Moisture Dynamics

Understanding soil moisture behavior in semi-dry forests is essential for evaluating the impact of forest management on water availability. The objective of the study was to analyze soil moisture based in storm observations in three micro-catchments (0.19, 0.20, and 0.27 ha) with similar tree densities, and subject to different thinning intensities in a semi-dry forest in Chihuahua, Mexico. Vegetation, soil characteristics, precipitation, and volumetric water content were measured before thinning (2018), and after 0%, 40%, and 80% thinning for each micro-catchment (2019). Soil moisture was low and relatively similar among the three micro-catchments in 2018 (mean = 8.5%), and only large rainfall events (>30 mm) increased soil moisture significantly (29–52%). After thinning, soil moisture was higher and significantly different among the micro-catchments only during small rainfall events (<10 mm), while a difference was not noted during large events. The difference before–after during small rainfall events was not significant for the control (0% thinning); whereas 40% and 80% thinning increased soil moisture significantly by 40% and 53%, respectively. Knowledge of the response of soil moisture as a result of thinning and rainfall characteristics has important implications, especially for evaluating the impact of forest management on water availability.

scholarly journals Mechanisms Associated with Large Daily Rainfall Events in Northeast Brazil

2011 ◽  
Vol 24 (2) ◽  
pp. 376-396 ◽  
Author(s):  
Brant Liebmann ◽  
George N. Kiladis ◽  
Dave Allured ◽  
Carolina S. Vera ◽  
Charles Jones ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Annual Rainfall ◽  
Northeast Brazil ◽  
Kelvin Wave ◽  
Rainfall Events ◽  
Independent Events ◽  
Precipitation Anomalies ◽  
Wave Trains ◽  
Using Data ◽  
Forcing Mechanisms

Abstract The mechanisms resulting in large daily rainfall events in Northeast Brazil are analyzed using data filtering to exclude periods longer than 30 days. Composites of circulation fields that include all independent events do not reveal any obvious forcing mechanisms as multiple patterns contribute to Northeast Brazil precipitation variability. To isolate coherent patterns, subsets of events are selected based on anomalies that precede the Northeast Brazil precipitation events at different locations. The results indicate that at 10°S, 40°W, the area of lowest annual rainfall in Brazil, precipitation occurs mainly in association with trailing midlatitude synoptic wave trains originating in either hemisphere. Closer to the equator at 5°S, 37.5°W, an additional convection precursor is found to the west, with a spatial structure consistent with that of a Kelvin wave. Although these two sites are located within only several hundred kilometers of each other and the midlatitude patterns that induce precipitation appear to be quite similar, the dates on which large precipitation anomalies occur at each location are almost entirely independent, pointing to separate forcing mechanisms.

Assessing the Job Satisfaction of Registered Nurses Using Sentiment Analysis and Clustering Analysis

2021 ◽  
pp. 107755872110352
Author(s):  
Matthew Jura ◽  
Joanne Spetz ◽  
Der-Ming Liou
Keyword(s):  
Job Satisfaction ◽  
Registered Nurses ◽  
Sentiment Analysis ◽  
Support Services ◽  
Clustering Analysis ◽  
Multivariate Analyses ◽  
Free Text ◽  
Registered Nursing ◽  
High Satisfaction ◽  
Using Data

Job satisfaction is a critical component of the professional work environment and is often ascertained through surveys that include structured or open-ended questions. Using data from 24,543 respondents to California Board of Registered Nursing biennial surveys, this study examines the job satisfaction of registered nurses (RNs) by applying clustering analysis to structured job satisfaction items and sentiment analysis to free-text comments. The clustering analysis identified three job satisfaction groups (low, medium, and high satisfaction). Sentiment analysis scores were significantly associated with the job satisfaction groups in both bivariate and multivariate analyses. Differences between the job satisfaction clusters were mostly driven by satisfaction with workload, adequacy of the clerical support services, adequacy of the number of RN staff, and skills of RN colleagues. In addition, there was dispersion in satisfaction related to involvement in management and policy decisions, recognition for a job well done, and opportunities for professional development.

scholarly journals Variabilidade Espacial da Erosividade das Chuvas para o Estado da Paraíba (Variability of Spatial Erosivity of Rainfall in the Paraíba State)

2014 ◽  
Vol 7 (4) ◽  
pp. 691
Author(s):  
Bernardo Starling Dorta do Amaral ◽  
João Filadelfo de Carvalho Neto ◽  
Richarde Marques da Silva ◽  
José Carlos Dantas
Keyword(s):  
Water Resources ◽  
Rainfall Variability ◽  
Climatic Conditions ◽  
Climate Data ◽  
Rainfall Characteristics ◽  
R Factor ◽  
Erosivity Index ◽  
The Mean ◽  
Weather Stations ◽  
Soil Loss Equation

As características específicas das chuvas variam entre regiões, e o conhecimento da sua potencialidade erosiva é necessário para o planejamento dos recursos hídricos. Este estudo determinou a erosividade, analisou a variabilidade espacial da precipitação e o coeficiente de chuva para o Estado da Paraíba mediante técnicas de Sistemas de Informação Geográfica. Para a realização deste estudo foram utilizados dados climatológicos de 98 estações climatológicas da Embrapa, com séries de 1911 a 1990. Em seguida as informações sobre a erosividade foram processadas cartograficamente. O valor médio anual da erosividade das chuvas com base no índice EI30 para o Estado da Paraíba foi de 5.032,03 MJ.mm/ha/h, valor que representa o Fator “R” da Equação Universal de Perdas de Solo (USLE). As equações de regressão entre erosividade e precipitação e coeficiente de chuva não foram significativas. As principais conclusões são que: (a) os índices de erosividade encontrados são maiores na zona litorânea do que nas demais porções do Estado, e (b) as erosividades encontradas variaram de acordo com os valores da precipitação.   A B S T R A C T Specific rainfall characteristics vary among regions and their erosion potential must be known for the planning of water resources. This study analyzed the erosivity and rainfall variability and precipitation coefficient for Paraíba State based on Geographic Information Systems techniques. In order In this paper 98 climatological stations of Embrapa were used, with rainfall data of 1911 to 1990. For this study we use d climate data from 98 weather stations of Embrapa, with series from 1911 to 1990. Additionally we processed the information of the erosivity index cartographically by year and microregions. The mean annual value of erosivity was 5,032.03 MJ.mm/ha/h, which is to be used as “R” Factor in the Universal Soil Loss Equation (USLE) for Paraíba State and surrounding regions with similar climatic conditions. The main conclusions are that: (a) erosivity indexes are higher in coastal areas than in inland areas, and (b) the erosivity range according to the precipitation.   Keywords: erosivity, rainfall, water resources   

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