Assessing Variations in the Predictive Skill of Ensemble Snowband Forecasts with Object-Oriented Verification and Self-Organizing Maps

IF 3 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Forecasting Pub Date : 2023-07-12 DOI:10.1175/waf-d-23-0004.1

Jacob T. Radford, G. Lackmann

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引用次数: 0

Abstract

We used object-oriented verification and self-organizing maps (SOMs) to identify patterns in environmental parameters correlating with mesoscale snowband predictive skill by the High-Resolution Ensemble Forecast (HREF) system between 2017 and 2022. First, HREF snowband forecasts for 305 banding events were verified based on similarities between forecast and observed feature properties. HREF members performed comparably, demonstrating large positional errors, but the High-Resolution Rapid Refresh member demonstrated the greatest overall skill. Observed banding events were clustered by 500-hPa geopotential height anomalies, mean sea level pressure, vertical velocity, frontogenesis, and saturation equivalent potential vorticity from the European Center for Medium-Range Weather Forecasts Reanalysis version 5 using SOMs. Clusters reaffirmed the presence of mid-level frontogenesis, ascent, and reduced stability in most banding cases, and the predominant synoptic environments conducive to band development. Clusters were compared to determine whether patterns in the variables were correlated with predictive skill. Strength of upward motion was correlated with skill, with the strongest upward motion cases verifying 10% better than the weakest upward motion cases due to smaller positional error. Additionally, events with a single region of strong upward motion verified better than events with disorganized, but comparably intense, upward motion. Magnitude of frontogenesis was uncorrelated with skill, but events with more upright frontogenesis collocated with the band centroid were better predicted than events with shallower slopes and low-level frontogenesis displaced towards warmer air. The skill variance associated with different vertical motion magnitudes could assist forecasters in modulating forecast confidence, while the most common types of errors may be beneficial to model developers in refining HREF member snowfall forecasts.

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基于面向对象验证和自组织地图的集合雪带预报预测技能变化评估

我们使用面向对象的验证和自组织映射（SOM）来识别2017年至2022年间高分辨率集合预报（HREF）系统中与中尺度雪带预测技能相关的环境参数模式。首先，基于预测和观测到的特征属性之间的相似性，验证了305个带状事件的HREF雪带预测。HREF成员的表现相当，显示出较大的位置错误，但高分辨率快速刷新成员显示出了最高的整体技能。观测到的带状事件由500 hPa位势高度异常、平均海平面压力、垂直速度、锋生和欧洲中期天气预报中心使用SOMs重新分析第5版的饱和等效位涡度进行聚类。集群重申了在大多数带状情况下存在中层锋生、上升和稳定性降低，以及有利于带状发展的主要天气环境。对聚类进行比较，以确定变量中的模式是否与预测技能相关。向上运动的强度与技能相关，由于位置误差较小，最强的向上运动情况比最弱的向上移动情况好10%。此外，具有单一强烈向上运动区域的事件比具有无组织但相对强烈的向上运动的事件得到了更好的验证。锋生的强度与技巧无关，但与较浅斜坡和低水平锋生向温暖空气移动的事件相比，锋生与带质心并置的事件更能预测。与不同垂直运动幅度相关的技能差异可以帮助预报员调整预报信心，而最常见的错误类型可能有利于模型开发人员完善HREF成员降雪预报。

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来源期刊

Weather and Forecasting 地学-气象与大气科学

CiteScore

5.20

自引率

17.20%

发文量

131

审稿时长

6-12 weeks

期刊介绍： Weather and Forecasting (WAF) (ISSN: 0882-8156; eISSN: 1520-0434) publishes research that is relevant to operational forecasting. This includes papers on significant weather events, forecasting techniques, forecast verification, model parameterizations, data assimilation, model ensembles, statistical postprocessing techniques, the transfer of research results to the forecasting community, and the societal use and value of forecasts. The scope of WAF includes research relevant to forecast lead times ranging from short-term “nowcasts” through seasonal time scales out to approximately two years.