ORCID Profile
0000-0001-7450-3477
Current Organisation
University of Reading
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Publisher: American Meteorological Society
Date: 13-09-2021
Abstract: The canonical view of the Maritime Continent (MC) diurnal cycle is deep convection occurring over land during the afternoon and evening, tending to propagate offshore overnight. However, there is considerable day-to-day variability in the convection, and the mechanism of the offshore propagation is not well understood. We test the hypothesis that large-scale drivers such as ENSO, the MJO and equatorial waves, through their modification of the local circulation, can modify the direction or strength of the propagation, or prevent the deep convection from triggering in the first place. Taking a local-to-large scale approach we use in situ observations, satellite data and reanalyses for five MC coastal regions, and show that the occurrence of the diurnal convection and its offshore propagation is closely tied to coastal wind regimes we define using the k -means cluster algorithm. Strong prevailing onshore winds are associated with a suppressed diurnal cycle of precipitation while prevailing offshore winds are associated with an active diurnal cycle, offshore propagation of convection and a greater risk of extreme rainfall. ENSO, the MJO, equatorial Rossby waves and westward mixed Rossby-gravity waves have varying levels of control over which coastal wind regime occurs, and therefore on precipitation, depending on the MC coastline in question. The large-scale drivers associated with dry and wet regimes are summarised for each location as a reference for forecasters.
Publisher: Wiley
Date: 07-2022
DOI: 10.1002/QJ.4338
Abstract: Equatorial waves (EWs) are synoptic‐ to planetary‐scale propagating disturbances at low latitudes with periods from a few days to several weeks. Here, this term includes Kelvin waves, equatorial Rossby waves, mixed Rossby–gravity waves, and inertio‐gravity waves, which are well described by linear wave theory, but it also other tropical disturbances such as easterly waves and the intraseasonal Madden–Julian Oscillation with more complex dynamics. EWs can couple with deep convection, leading to a substantial modulation of clouds and rainfall. EWs are amongst the dynamic features of the troposphere with the longest intrinsic predictability, and models are beginning to forecast them with an exploitable level of skill. Most of the methods developed to identify and objectively isolate EWs in observations and model fields rely on (or at least refer to) the adiabatic, frictionless linearized primitive equations on the sphere or the shallow‐water system on the equatorial ‐plane. Common ingredients to these methods are zonal wave‐number–frequency filtering (Fourier or wavelet) and/or projections onto predefined empirical or theoretical dynamical patterns. This paper gives an overview of six different methods to isolate EWs and their structures, discusses the underlying assumptions, evaluates the applicability to different problems, and provides a systematic comparison based on a case study (February 20–May 20, 2009) and a climatological analysis (2001–2018). In addition, the influence of different input fields (e.g., winds, geopotential, outgoing long‐wave radiation, rainfall) is investigated. Based on the results, we generally recommend employing a combination of wave‐number–frequency filtering and spatial‐projection methods (and of different input fields) to check for robustness of the identified signal. In cases of disagreement, one needs to carefully investigate which assumptions made for the in idual methods are most probably not fulfilled. This will help in choosing an approach optimally suited to a given problem at hand and avoid misinterpretation of the results.
Publisher: American Geophysical Union (AGU)
Date: 20-06-2022
DOI: 10.1029/2022JD036439
Abstract: Predicting the peak‐season (July–September) tropical cyclones (TCs) in Southeast Asia (SEA) several months ahead remains challenging, related to limited understanding and prediction of the dynamics affecting the variability of SEA TC activity. Here, we introduce a new statistical approach to sequentially identify mutually independent predictors for the occurrence frequency of peak‐season TCs in the South China Sea (SCS) and east of the Philippines (PHL). These predictors, which are identified from the preseason (April‐June) environmental fields, can capture the interannual variability of different clusters of peak‐season TCs, through a cross‐season effect on large‐scale environment that governs TC genesis and track. The physically oriented approach provides a skillful seasonal prediction in the 41‐year period (1979–2019), with r = 0.73 and 0.54 for SCS and PHL TC frequency, respectively. The lower performance for PHL TCs is likely related to the nonstationarity of the cross‐season TC‐environment relationship. We further develop the statistical approach to a hybrid method using the predictors derived from dynamical seasonal forecasts. The hybrid prediction shows a significant skill for both SCS and PHL TCs, for lead times up to four or 5 months ahead, related to the good performance of models for the sea surface temperatures and low‐level winds in the tropics. The statistical and hybrid predictions outperform the dynamical predictions, showing the potential for operational use.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Gui-Ying Yang.