Subseasonal Climate Prediction in the Western US using Bayesian Spatial Models

Vishwak Srinivasan, Justin Khim, Arindam Banerjee, Pradeep Ravikumar

Research output: Contribution to conferencePaperpeer-review

Abstract

Subseasonal climate forecasting is the task of predicting climate variables, such as temperature and precipitation, in a two-week to two-month time horizon. The primary predictors for such prediction problem are spatio-temporal satellite and ground measurements of a variety of climate variables in the atmosphere, ocean, and land, which however have rather limited predictive signal at the subseasonal time horizon. We propose a carefully constructed spatial hierarchical Bayesian regression model that makes use of the inherent spatial structure of the subseasonal climate prediction task. We use our Bayesian model to then derive decision-theoretically optimal point estimates with respect to various performance measures of interest to climate science. As we show, our approach handily improves on various off-the-shelf ML baselines. Since our method is based on a Bayesian framework, we are also able to quantify the uncertainty in our predictions, which is particularly crucial for difficult tasks such as the subseasonal prediction, where we expect any model to have considerable uncertainty at different test locations under different scenarios.

Original languageEnglish (US)
Pages961-970
Number of pages10
StatePublished - 2021
Externally publishedYes
Event37th Conference on Uncertainty in Artificial Intelligence, UAI 2021 - Virtual, Online
Duration: Jul 27 2021Jul 30 2021

Conference

Conference37th Conference on Uncertainty in Artificial Intelligence, UAI 2021
CityVirtual, Online
Period7/27/217/30/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Applied Mathematics

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