Downscaling temperature and precipitation: a comparison of regression‐based methods and artificial neural networks

By J. Schoof, Sara C. Pryor. 2001

305 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/dd42f6617cae7d4ee89e21e1b4a840da4573e559

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models (2016, methodology): “For example, regression-based methods and artificial neural networks were used by Schoof and Pryor (2001), while first-order trivariate auto-regression that is conditional on precipitation occurrence as implemented in Weather GENerator (WGEN) by Richardson and Wright (1984) have also been applied…”
Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application (2013, methodology): “Generalized Linear Models (GLM) is also used, as it is a least computationally demanding technique and provides useful results (Weichert and Bürger 1998; Trigo and Palutikof 2001; Schoof and Pryor 2001).”
Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada (2012, background): “Both Schoof and Pryor (2001) and this study showed that ANN models yielded good performance for the daily air temperatures, whereas they showed worse performance than the MLR models for the daily precipitation amount.”
Deep learning in environmental remote sensing: Achievements and challenges (2020, background): “crucial roles in related environmental studies and management activities, including surface energy fluxes, climate change, agricultural productivity, urban heat island monitoring, and industrial studies (Schoof and Pryor, 2001; Zhan et al., 2013).”
Flood Prediction Using Machine Learning Models: Literature Review (2018, background): “include precipitation prediction [88,89] and peak-value prediction [90].”
Climatologies at high resolution for the earth’s land surface areas (2016, background): “ax being the maximum distance between the boundary layer height and all grid cell at the CHELSA resolution within an respective ERA grid cell, and f being a constant of 9000 m. with: 𝐵=𝐵𝐸𝑅�+�𝑙�𝑣𝐸𝑅�+� [9] B being the height of the monthly means of daily mean boundary layer from ERA-Interim, e”