Journal article
On the use of convolutional Gaussian processes to improve the seasonal forecasting of precipitation and temperature
Journal of hydrology (Amsterdam), Vol.593, p.125862
02/2021
DOI: 10.1016/j.jhydrol.2020.125862
Abstract
•The convolutional Gaussian process (CGP) is applied to climatic seasonal predictions.•Analyses focus on Iowa, 2 target months and lead times from 1 month to 1 year.•CGP leads to an improved prediction of precipitation and temperature.•The skill improves for all lead times and target months.
This study examines the potential improvement in seasonal predictability of monthly precipitation and temperature using a novel machine learning approach, the convolutional Gaussian process (CGP). This approach allows us to take into account multiple quantities and their interdependencies simultaneously. We use one global climate model (FLORb01) part of the North American Multi-Model Ensemble (NMME) project and quantify its skill in reproducing precipitation and temperature in March and July across Iowa (central United States) for lead times from one month to one year. As a first step we train the CGP over the 1985–2005 period, and then apply it out of sample from 2006 to 2019. Over the validation period, our results indicate that the CGP is able to increase the skill (i.e., increased correlation coefficient and reduced root mean squared error) in predicting precipitation and temperature compared to both the raw outputs and after standard bias correction. These statements are consistent across different lead times and target month (i.e., March or July). These encouraging findings provide a new potential path towards improved predictability of the regional climate at the seasonal scale.
Details
- Title: Subtitle
- On the use of convolutional Gaussian processes to improve the seasonal forecasting of precipitation and temperature
- Creators
- Chao Wang - University of IowaWei Zhang - University of IowaGabriele Villarini - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of hydrology (Amsterdam), Vol.593, p.125862
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.jhydrol.2020.125862
- ISSN
- 0022-1694
- eISSN
- 1879-2707
- Grant note
- DOI: 10.13039/100006752, name: U.S. Army Corps of Engineers
- Language
- English
- Date published
- 02/2021
- Academic Unit
- Civil and Environmental Engineering; Industrial and Systems Engineering; IIHR--Hydroscience and Engineering
- Record Identifier
- 9984186971302771
Metrics
5 Record Views