High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1038/s41597-023-02777-w. This is version 4 of this Preprint.

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Authors

Adrian Huerta , Cesar Luis Aybar Camacho, Noemi Imfeld, Kris Correa, Oscar Felipe-Obando, Pedro Rau, Fabian Drenkhan, Waldo Lavado-Casimiro

Abstract

Gridded high-resolution climate datasets are increasingly important for a wide range of modelling applications. Here we present PISCOt (v1.2), a novel high spatial resolution (0.01°) dataset of daily air temperature for entire Peru (1981-2020). The dataset development involves four main steps: i) quality control; ii) gap-filling; iii) homogenisation of weather stations, and iv) spatial interpolation using additional data, a revised calculation sequence and an enhanced version control. This improved methodological framework enables capturing complex spatial variability of maximum and minimum air temperature at a more accurate scale compared to other existing datasets (e.g. PISCOt v1.1, ERA5-Land, TerraClimate, CHIRTS). PISCOt performs well with mean absolute errors of 1.4 °C and 1.2 °C for maximum and minimum air temperature, respectively. For the first time, PISCOt v1.2 adequately captures complex climatology at high spatiotemporal resolution and therefore provides a substantial improvement for numerous applications at local-regional level. This is particularly useful in view of data scarcity and urgently needed model-based decision making for climate change, water balance and ecosystem assessment studies in Peru.

DOI

https://doi.org/10.31223/X5P93V

Subjects

Physical Sciences and Mathematics

Keywords

Dates

Published: 2022-12-30 18:35

Last Updated: 2023-12-01 23:02

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
https://doi.org/10.6084/m9.figshare.c.5959863