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秘鲁逐日气温高分辨率格网数据集——新版 PISCOt v1.2 数据集。

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

机构信息

Servicio Nacional de Meteorología e Hidrología (SENAMHI), Lima, Perú.

Departamento de Física y Meteorología, Universidad Nacional Agraria La Molina (UNALM), Lima, Perú.

出版信息

Sci Data. 2023 Dec 1;10(1):847. doi: 10.1038/s41597-023-02777-w.

DOI:10.1038/s41597-023-02777-w
PMID:38040747
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10692097/
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.

摘要

网格化高分辨率气候数据集对于广泛的建模应用越来越重要。本文介绍了 PISCOt（v1.2），这是一个全新的秘鲁逐日气温高空间分辨率（0.01°）数据集（1981-2020 年）。数据集开发包括四个主要步骤：（i）质量控制；（ii）填补空缺；（iii）气象站的均一化，以及（iv）使用额外数据、修改后的计算顺序和增强的版本控制进行空间插值。与其他现有数据集（如 PISCOt v1.1、ERA5-Land、TerraClimate、CHIRTS）相比，这种改进的方法框架能够更准确地捕捉最大和最小气温的复杂空间变异性。PISCOt 在最大和最小气温方面的平均绝对误差分别为 1.4°C 和 1.2°C，表现良好。PISCOt v1.2 首次充分捕捉了高时空分辨率的复杂气候，因此为秘鲁地方-区域层面的众多应用提供了实质性的改进。考虑到秘鲁数据的稀缺性以及气候变化、水量平衡和生态系统评估研究中迫切需要基于模型的决策，这一点尤其有用。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5fa/10692097/eb6720acff33/41597_2023_2777_Fig8_HTML.jpg
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