Meteorological measurement and simulation datasets to understand the complex circulation over a tropical inter-Andean valleyMendeley Data

Atmospheric circulation over tropical mountainous regions is intrinsically complex, and frequently difficult to reproduce in simulation models, due to the convolution of synoptic, mesoscale, local convective, and topographically induced effects. Moreover, ground-level measurements are frequently sca...

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Bibliographic Details
Main Authors: Andres V. Ardila, Carlos M. González, Felipe Cifuentes, German Rueda-Saa, Rodrigo Jimenez
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Data in Brief
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Online Access:http://www.sciencedirect.com/science/article/pii/S2352340925003464
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Summary:Atmospheric circulation over tropical mountainous regions is intrinsically complex, and frequently difficult to reproduce in simulation models, due to the convolution of synoptic, mesoscale, local convective, and topographically induced effects. Moreover, ground-level measurements are frequently scant and sparse. We present a 10-year meteorological dataset for the Cauca River valley (CRV) located in southwest Colombia and simulation results for various parameterization schemes. Meteorological data from 25 sites for the 2010-2020 period were quality-controlled and compiled. Hourly-resolved daily profiles for barometric pressure, temperature, relative humidity, wind speed, and direction are presented, along with monthly profiles for precipitation. Following a rigorous validation process, 83% of the 4.7 million original data points were retained, with solar radiation exhibiting the highest rate of invalidity (37.6%). Furthermore, twelve WRF-CHEM simulations were performed to assess the performance of different schemes suitable for the CRV conditions. The simulated periods in 2018 covered a dry to rainy season period (February-April), and a predominantly dry period (July-September). The analysis of observations and validated simulation results allowed us to identify atmospheric circulation patterns, and to improve our understanding of their impact on the regional air quality.
ISSN:2352-3409