Improving Short-Term, Near-Surface Temperature Forecasts by Integrating Weather Pattern Information into Model Output Statistics

Author:

Zech Matthias1ORCID,von Bremen Lueder1

Affiliation:

1. a German Aerospace Center, Institute of Networked Energy Systems, Stuttgart, Germany

Abstract

Abstract Dynamical numerical weather prediction has remarkably improved over the last decades. Yet postprocessing techniques are needed to calibrate forecasts which are based on statistical and machine learning techniques. With recent advances in the derivation of year-round, large-scale atmospheric circulations, or weather regimes, the question arises of whether this information can be valuable within forecast postprocessing methods. This paper investigates this by proposing a bias correction scheme to integrate the atmospheric circulation state derived from empirical orthogonal functions, referred to as weather patterns, for deterministic short-term, near-surface temperature forecasts based on least absolute shrinkage and selection operator (LASSO) regression. We propose a computational study which first evaluates different weather pattern definitions (spatial domain) to improve temperature forecasts in Europe. As a bias could be associated with the weather pattern at the model initialization time or at the realization time of the forecast, both variants are tested in this study. We show that forecasted weather patterns with the identical spatial domain as the forecast show best skill reaching mean-square-error skill improvements of up to 3% (day ahead) or 1% (week ahead). Only considering land surface improvements in Europe, improvements of 4%–6% for day-ahead forecasts and 1%–5% for week-ahead forecasts are observable. We believe that this study not only introduces a simple yet effective tool to reduce bias in temperature forecasts but also contributes to the active discussion of how valuable weather patterns are and how to use them within forecast calibration techniques.

Funder

Deutsche Bundesstiftung Umwelt

Publisher

American Meteorological Society

Reference55 articles.

1. Local temperature forecasts based on statistical post-processing of numerical weather prediction data;Alerskans, E.,2021

2. Regime-dependent statistical post-processing of ensemble forecasts;Allen, S.,2019

3. Recalibrating wind-speed forecasts using regime-dependent ensemble model output statistics;Allen, S.,2020

4. New approaches to postprocessing of multi-model ensemble forecasts;Barnes, C.,2019

5. The quiet revolution of numerical weather prediction;Bauer, P.,2015

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3