Errors of Opportunity: Using Neural Networks to Predict Errors in the Global Ensemble Forecast System (GEFS) on S2S Timescales

人工神经网络 计算机科学 集合预报 气象学 环境科学 气候学 机器学习 地理 地质学
作者
J. S. Cahill,Elizabeth A. Barnes,Eric D. Maloney,Stephan R. Sain,Patrick A. Harr,Luke Madaus
出处
期刊:Weather and Forecasting [American Meteorological Society]
标识
DOI:10.1175/waf-d-23-0125.1
摘要

Abstract Making predictions of impactful weather on timescales of weeks to months (subseasonal to seasonal; S2S) in advance is incredibly challenging. Dynamical models often struggle to simulate tropical systems that evolve over multiple weeks such as the Madden Julian Oscillation (MJO) and the Boreal Summer Intraseasonal Oscillation (BSISO), and these errors can impact geopotential heights, precipitation, and other variables in the continental United States through teleconnections. While many data-driven S2S studies attempt to predict future midlatitude variables using current conditions, here we instead focus on post-processing of the National Oceanic and Atmospheric Association’s (NOAA) Global Ensemble Forecast System (GEFS) to predict GEFS errors. Specifically, by looking at when/where there are errors in the GEFS, neural networks can be used to understand what atmospheric conditions helped produce these errors via explainability methods. Our ‘Errors of Opportunity’ approach identifies phase 4 of the MJO and phases 1 and 2 of the BSISO as significant factors in aiding GEFS error prediction across different regions and seasons. Specifically, we see high accuracy for overestimates of 500 hPa geopotential height (h500) anomalies in the Pacific Northwest during Spring and as well as high accuracy for underestimates of geopotential heights in Northwest Mexico during Summer. Furthermore, we demonstrate enhanced error prediction skill for overestimates of Summer precipitation in the Midwest following BSISO phases 1 and 2. Most notably, our findings highlight that the identified errors stem from the GEFS’s failure to accurately forecast teleconnection patterns.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Elena完成签到,获得积分10
刚刚
林少玮发布了新的文献求助10
刚刚
自觉若剑发布了新的文献求助30
1秒前
1秒前
2秒前
zyt应助潇洒南烟采纳,获得10
2秒前
朴素小鸟胃完成签到,获得积分10
2秒前
3秒前
3秒前
3秒前
小古完成签到,获得积分10
3秒前
研友_浑不斜完成签到,获得积分10
5秒前
5秒前
5秒前
健壮问兰发布了新的文献求助10
5秒前
6秒前
wguanmc完成签到,获得积分10
6秒前
闪闪的咖啡完成签到,获得积分10
7秒前
7秒前
7秒前
拼搏飞柏发布了新的文献求助10
8秒前
天涯发布了新的文献求助10
8秒前
王茶茶发布了新的文献求助10
8秒前
米虫完成签到,获得积分10
9秒前
SciGPT应助张小度ever采纳,获得10
11秒前
你好发布了新的文献求助10
12秒前
12秒前
1h1m完成签到,获得积分10
13秒前
13秒前
乐观尔容发布了新的文献求助10
14秒前
15秒前
哈尼发布了新的文献求助20
15秒前
矮小的笑槐完成签到,获得积分10
15秒前
纯真的莫茗完成签到 ,获得积分10
16秒前
桐桐应助yuan采纳,获得10
16秒前
17秒前
17秒前
17秒前
是的给是的的求助进行了留言
18秒前
18秒前
高分求助中
The ACS Guide to Scholarly Communication 2500
Sustainability in Tides Chemistry 2000
Pharmacogenomics: Applications to Patient Care, Third Edition 1000
Studien zur Ideengeschichte der Gesetzgebung 1000
TM 5-855-1(Fundamentals of protective design for conventional weapons) 1000
Threaded Harmony: A Sustainable Approach to Fashion 810
《粉体与多孔固体材料的吸附原理、方法及应用》(需要中文翻译版,化学工业出版社,陈建,周力,王奋英等译) 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3083403
求助须知:如何正确求助?哪些是违规求助? 2736768
关于积分的说明 7542379
捐赠科研通 2386033
什么是DOI,文献DOI怎么找? 1265316
科研通“疑难数据库(出版商)”最低求助积分说明 613035
版权声明 597816