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Huerta A, Aybar C, Imfeld N, Correa K, Felipe-Obando O, Rau P, Drenkhan F, Lavado-Casimiro W. High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset. Sci Data 2023; 10:847. [PMID: 38040747 PMCID: PMC10692097 DOI: 10.1038/s41597-023-02777-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 11/23/2023] [Indexed: 12/03/2023] Open
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.
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Affiliation(s)
- Adrian Huerta
- Servicio Nacional de Meteorología e Hidrología (SENAMHI), Lima, Perú.
- Departamento de Física y Meteorología, Universidad Nacional Agraria La Molina (UNALM), Lima, Perú.
- Institute of Geography and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland.
| | - Cesar Aybar
- Image Processing Laboratory, University of Valencia, 46980, Valencia, Spain
- High Mountain Ecosystem Research Group, National University of San Marcos, 15081, Lima, Peru
| | - Noemi Imfeld
- Institute of Geography, University of Bern, Bern, Switzerland
- Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
| | - Kris Correa
- Servicio Nacional de Meteorología e Hidrología (SENAMHI), Lima, Perú
| | | | - Pedro Rau
- Centro de Investigación y Tecnología del Agua (CITA), Departamento de Ingeniería Ambiental, Universidad de Ingeniería y Tecnología (UTEC), Lima, Perú
| | - Fabian Drenkhan
- Geography and the Environment, Department of Humanities, Pontificia Universidad Católica del Perú, Lima, Peru
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Singh M, Acharya N, Jamshidi S, Jiao J, Yang ZL, Coudert M, Baumer Z, Niyogi D. DownScaleBench for developing and applying a deep learning based urban climate downscaling- first results for high-resolution urban precipitation climatology over Austin, Texas. COMPUTATIONAL URBAN SCIENCE 2023; 3:22. [PMID: 37274379 PMCID: PMC10232592 DOI: 10.1007/s43762-023-00096-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 04/05/2023] [Accepted: 04/17/2023] [Indexed: 06/06/2023]
Abstract
Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 - 10 km) and neighborhood (order of 0.1 - 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This 'DownScaleBench' tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.
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Affiliation(s)
- Manmeet Singh
- Jackson School of Geosciences, The University of Texas at Austin, Austin, 78712 TX USA
- Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, 411008 MH India
- IDP in Climate Studies, Indian Institute of Technology Bombay, Mumbai, 400076 MH India
| | - Nachiketa Acharya
- CIRES, University of Colorado Boulder, NOAA/Physical Sciences Laboratory, Boulder, 80309 CO USA
| | - Sajad Jamshidi
- Department of Agronomy, Purdue University, West Lafayette, 47906 IN USA
| | - Junfeng Jiao
- Department of Civil, Architectural, and Environmental Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, 78712 TX USA
| | - Zong-Liang Yang
- Jackson School of Geosciences, The University of Texas at Austin, Austin, 78712 TX USA
| | - Marc Coudert
- Office of Sustainability, City of Austin, Austin, 78712 TX USA
| | - Zach Baumer
- Office of Sustainability, City of Austin, Austin, 78712 TX USA
| | - Dev Niyogi
- Jackson School of Geosciences, The University of Texas at Austin, Austin, 78712 TX USA
- Department of Civil, Architectural, and Environmental Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, 78712 TX USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, 78712 TX USA
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