A Review: Anomaly-Based versus Full-Field-Based Weather Analysis and Forecasting

Abstract

Comparisons between anomaly and full-field methods have been carried out in weather analysis and forecasting over the last decade. Evidence from these studies has demonstrated the superiority of anomaly to full-field in the following four aspects: depiction of weather systems, anomaly forecasts, diagnostic parameters and model prediction. To promote the use and further discussion of the anomaly approach, this article summarizes those findings.After examining many types of weather events, anomaly weather maps show at least five advantages in weather system depiction: (1) less vagueness in visually connecting the location of an event with its associated meteorological conditions; (2) clearer and more complete depictions of vertical structures of a disturbance; (3) easier observation of time and spatial evolution of an event and its interaction or connection with other weather systems; (4) simplification of conceptual models by unifying different weather systems into one pattern; and (5) extension of model forecast length due to earlier detection of predictors. Anomaly verification is also mentioned. The anomaly forecast is useful for raising one’s awareness of potential societal impact. Combining the anomaly forecast with an ensemble is emphasized, where a societal impact index is discussed. For diagnostic parameters, two examples are given: an anomalous convective instability index for convection, and seven vorticity and divergence related parameters for heavy rain. Both showed positive contributions from the anomalous fields. For model prediction, the anomaly version of the beta-advection model consistently outperformed its full-field version in predicting typhoon tracks with clearer physical explanation. Application of anomaly global models to seasonal forecasts is also reviewed.

Anomaly Based Synoptic Analysis and Model Prediction of Six Dust Storms Moving From Mongolia to Northern China in Spring 2021

“…The hourly climatology contains the diurnal (24 hr a day) and annual (365 days a year) cycles. Once the climatology is known, the anomaly can be extracted from a total variable by subtracting its climatology (Qian, Du, & Ai, 2021). Although such derived anomaly includes multi‐scale disturbances such as synoptic, weekly, biweekly, low‐frequency, and even long‐term trend deviations related to climate change, previous studies show that synoptic‐scale component of anomaly is the largest and other components are small, especially when an extreme weather occurs (Qian, 2017; Qian, Ai, et al., 2021).…”

“…Dust storm is one of the weather extremes similar to haze episodes and fog events (Qian & Huang, 2019; Qian, Leung, Chen, et al., 2019). These weather extremes can be visually more striking in spatial structure if they are described by an approach of anomaly based weather analysis (Qian, Du, et al., 2021). Besides the better revealing of spatial structure, there are other advantages for the anomaly based approach including easier observation of a weather system's interaction or connection with other weather systems near or far; simplification of conceptual models by unifying different weather systems into one pattern; and extension of model forecast length due to earlier detection of predictors.…”

“…For a detailed comparison between anomaly and full‐field‐based methods, readers are referred to Qian, Du, et al. (2021). The anomaly method will also be described briefly in Section 2.2 below.…”

“…All these advantages are due to the fact that many important detailed features of weather disturbances are embedded in a large-value background (climate state) in a full field but revealed and enhanced in an anomaly field. For a detailed comparison between anomaly and full-field-based methods, readers are referred to Qian, Du, et al (2021). The anomaly method will also be described briefly in Section 2.2 below.…”

Opposite anomalous synoptic patterns for potential California large wildfire spread and extinguishing in 2018 cases

“…1) separates climatic variable ṽd (λ, φ, p, t) and anomalous component v ′ (d,y) (λ, φ, p, t) from a total field v (d,y) (λ, φ, p, t). This approach of decomposition has been proven to be useful in weather extremes (Qian et al, 2021b), mostly for tropical cyclones (Qian et al, 2014) and heavy rainfall (Qian et al, 2021a).…”