Work

Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation

Year: 2018

Type: article

Source: Theoretical and Applied Climatology

DOI: 10.1007/s00704-018-2613-3

Authors: Thomas Vandal, Evan Kodra, Auroop R. Ganguly

Institution: Northeastern University

Language: English

Topic: Climate variability and models

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

Funders: National Science Foundation, Directorate for Computer and Information Science and Engineering, National Oceanic and Atmospheric Administration, Goddard Space Flight Center, Division of Computing and Communication Foundations, Division of Information and Intelligent Systems

Awards: G1610490375, BIGDATA: F: DKA: Collaborative Research: High-Dimensional Statistical Machine Learning for Spatio-Temporal Climate Data, G3482855789, G6984015672, CyberSEES: Type 2: SEA-MASCOT: Spatio-temporal Extremes and Associations : Marine Adaptation and Survivorship under Changes in extreme Ocean Temperatures, Collaborative Research: Understanding Climate Change: A Data Driven Approach, G8599669070

OpenAlex ID: W2889852608

FWCI: 6.331

Cites: 72

Cited by: 202

Related to: 10