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

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.

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Main Authors: Huerta, Adrian, Aybar, Cesar, Correa, Kris, Noemi, Imfeld, Felipe-Obando, Oscar, Rau, Pedro, Drenkhan, Fabian
Format: info:eu-repo/semantics/article biblioteca
Language:spa
Published: Nature
Subjects:Climate Change, Ecosystem,
Online Access:https://hdl.handle.net/20.500.12542/3050
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spelling dig-senamhi-pe-20.500.12542-30502023-12-29T15:01:44Z High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset Huerta, Adrian Aybar, Cesar Correa, Kris Noemi, Imfeld Correa, Kris Felipe-Obando, Oscar Rau, Pedro Drenkhan, Fabian Climate Change Ecosystem 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. 2023 info:eu-repo/semantics/article https://hdl.handle.net/20.500.12542/3050 spa info:eu-repo/semantics/openAccess application/pdf application/pdf Nature Repositorio Institucional - SENAMHI Servicio Nacional de Meteorología e Hidrología del Perú
institution SENAMHI PE
collection DSpace
country Perú
countrycode PE
component Bibliográfico
access En linea
databasecode dig-senamhi-pe
tag biblioteca
region America del Sur
libraryname Biblioteca del SENAMHI Perú
language spa
topic Climate Change
Ecosystem
Climate Change
Ecosystem
spellingShingle Climate Change
Ecosystem
Climate Change
Ecosystem
Huerta, Adrian
Aybar, Cesar
Correa, Kris
Noemi, Imfeld
Correa, Kris
Felipe-Obando, Oscar
Rau, Pedro
Drenkhan, Fabian
High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset
description 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.
format info:eu-repo/semantics/article
topic_facet Climate Change
Ecosystem
author Huerta, Adrian
Aybar, Cesar
Correa, Kris
Noemi, Imfeld
Correa, Kris
Felipe-Obando, Oscar
Rau, Pedro
Drenkhan, Fabian
author_facet Huerta, Adrian
Aybar, Cesar
Correa, Kris
Noemi, Imfeld
Correa, Kris
Felipe-Obando, Oscar
Rau, Pedro
Drenkhan, Fabian
author_sort Huerta, Adrian
title High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset
title_short High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset
title_full High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset
title_fullStr High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset
title_full_unstemmed High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset
title_sort high-resolution grids of daily air temperature for peru - the new piscot v1.2 dataset
publisher Nature
url https://hdl.handle.net/20.500.12542/3050
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