High-Temporal-Resolution In Situ Tropical Meteorological Dataset from Santo Domingo, Dominican Republic for Energy and Environmental Applications

High-temporal-resolution meteorological data collected under tropical climatic conditions are scarce yet highly valuable, because they facilitate a more precise characterisation of atmospheric variability. This paper presents a high-temporal-resolution meteorological database collected in Santo Domingo, Dominican Republic, using a Davis Vantage Pro2 Plus weather station. The database encompasses 170,861 observations covering 35 meteorological parameters, all of which were acquired at a temporal resolution of one minute. The database was prepared through a rigorous process of data organisation and cleaning. In the initial stage, NaN values representing missing data were identified and removed. In the second stage, based on the temporal and statistical characteristics of the database, missing values were reconstructed using an imputation technique. This approach ensures data quality and consistency, which are essential for the reliable utilisation of the database in scientific, energy-related, and engineering applications. The database is a valuable resource for the development of predictive tools based on machine learning and deep learning techniques to forecast solar radiation and wind speed. Additionally, it can serve as a useful input for the modelling and simulation of renewable energy systems.

Authors

Institutions

Publication Details

Journal
Data
Published
2026-05-21
DOI
https://doi.org/10.3390/data11050124
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

High-Temporal-Resolution In Situ Tropical Meteorological Dataset from Santo Domingo, Dominican Republic for Energy and Environmental Applications

Francisco A. Ramírez‐Rivera, Néstor F. Guerrero-Rodríguez
Data
Meteorological Phenomena and Simulations
article

High-Temporal-Resolution In Situ Tropical Meteorological Dataset from Santo Domingo, Dominican Republic for Energy and Environmental Applications

Francisco A. Ramírez‐Rivera, Néstor F. Guerrero-Rodríguez
article en

Abstract

High-temporal-resolution meteorological data collected under tropical climatic conditions are scarce yet highly valuable, because they facilitate a more precise characterisation of atmospheric variability. This paper presents a high-temporal-resolution meteorological database collected in Santo Domingo, Dominican Republic, using a Davis Vantage Pro2 Plus weather station. The database encompasses 170,861 observations covering 35 meteorological parameters, all of which were acquired at a temporal resolution of one minute. The database was prepared through a rigorous process of data organisation and cleaning. In the initial stage, NaN values representing missing data were identified and removed. In the second stage, based on the temporal and statistical characteristics of the database, missing values were reconstructed using an imputation technique. This approach ensures data quality and consistency, which are essential for the reliable utilisation of the database in scientific, energy-related, and engineering applications. The database is a valuable resource for the development of predictive tools based on machine learning and deep learning techniques to forecast solar radiation and wind speed. Additionally, it can serve as a useful input for the modelling and simulation of renewable energy systems.

DataVol. 11(5)
Pontificia Universidad Católica Madre y Maestra (DO)
Ministerio de Educación Superior, Ciencia y Tecnología, República Dominicana
Openalex Percentile: Top 6%
Meteorological Phenomena and Simulations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.