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Christopher J. Schultz
,
Phillip M. Bitzer
,
Michael Antia
,
Jonathan L. Case
, and
Christopher R. Hain

Abstract

Twenty-six years of lightning data were paired with over 68 000 lightning-initiated wildfire (LIW) reports to understand lightning flash characteristics responsible for ignition in between 1995 and 2020. Results indicate that 92% of LIW were started by negative cloud-to-ground (CG) lightning flashes and 57% were single stroke flashes. Moreover, 62% of LIW reports did not have a positive CG within 10 km of the start location, contrary to the science literature’s suggestion that positive CG flashes are a dominant fire-starting mechanism. Nearly ⅓ of wildfire events were holdovers, meaning 1 or more days elapsed between lightning occurrence and fire report. However, fires that were reported less than a day after lightning occurrence statistically burned more acreage. Peak current was not found to be a statistically significant delineator between fire starters and non–fire starters for negative CGs but was for positive CGs. Results highlighted the need for reassessing the role of positive CG lightning and subsequently long-continuing current in wildfire ignition started by lightning. One potential outcome of this study’s results is the development of real-time tools to identify ignition potential during lightning events to aid in fire mitigation efforts.

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Pieter B. Smit
,
Galen Egan
, and
Isabel A Houghton

Abstract

Peak periods estimated from finite resolution frequency spectra are necessarily discrete. For wind generated surface gravity waves, conflicting considerations of robust (quasi)-stationary statistics, and high spectral resolution, combined with the inverse relation between frequency and period, this typically implies that swell periods (above 10 s) are resolved at best at 𝒪(1) s intervals. Here we consider a method to improve peak period estimates for finite resolution spectra. Specifically, we propose to define the peak period based on continuous spectra derived from a spline-based interpolation of the discretely sampled monotone cumulative distribution function. The method may directly be applied to existing discrete spectra—the original time-domain data (which may not be available) are not required. We compare reconstructed spectra and derived peak periods to parametric shapes and field data. Peak estimates are markedly improved, allowing for better tracking of e.g., swells. The proposed method also marginally improves spectral levels and shape for a given discretely sampled estimate.

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Eun-Pa Lim
,
Harry H. Hendon
,
Amy H. Butler
,
David W. J. Thompson
,
Zachary D. Lawrence
,
Adam A. Scaife
,
Theodore G. Shepherd
,
Inna Polichtchouk
,
Hisashi Nakamura
,
Chiaki Kobayashi
,
Ruth Comer
,
Lawrence Coy
,
Andrew Dowdy
,
Rene D. Garreaud
,
Paul A. Newman
, and
Guomin Wang
Open access
Matthew Patterson
,
Christopher O’Reilly
,
Jon Robson
, and
Tim Woollings

Abstract

The coupled nature of the ocean-atmosphere system frequently makes understanding the direction of causality difficult in ocean-atmosphere interactions. This study presents a method to decompose turbulent surface heat fluxes into a component which is directly forced by atmospheric circulation, and a residual which is assumed to be primarily ‘ocean-forced’. This method is applied to the North Atlantic in a 500-year pre-industrial control run using the Met Office’s HadGEM3-GC3.1-MM model. The method shows that atmospheric circulation dominates interannual to decadal heat flux variability in the Labrador Sea, in contrast to the Gulf Stream where the Ocean primarily drives the variability. An empirical orthogonal function analysis identifies several residual heat flux modes associated with variations in ocean circulation. The first of these modes is characterised by the ocean warming the atmosphere along the Gulf Stream and North Atlantic Current and the second by a dipole of cooling in the western subtropical North Atlantic and warming in the sub-polar North Atlantic. Lead-lag regression analysis suggests that atmospheric circulation anomalies in prior years partly drive the ocean heat flux modes, however there is no significant atmospheric circulation response in years following the peaks of the modes. Overall, the heat flux dynamical decomposition method provides a useful way to separate the effects of the ocean and atmosphere on heat flux and could be applied to other ocean basins and to either models or reanalysis datasets.

Open access
Ning Yang
,
Debin Su
,
Luyao Sun
, and
Tao Wang

Abstract

Atmospheric ducting is a highly refractive propagation condition that frequently occurs at sea and significantly impacts radar and communication equipment. This paper analyzes the spatiotemporal distribution of lower atmospheric ducts (LAD) in the South China Sea (SCS) and the variation of their occurrence rate with the monsoon by using reanalysis data from the ECMWF from 1980 to 2022. Additionally, the study discusses the relationship between ducting occurrences and atmospheric and oceanic conditions. The results indicate that wind dynamics in the SCS significantly impact ducting incidents. During the high-incidence period of LAD, humidity-gradient-constructed ducts are the primary mechanism. Before the onset of the monsoon, the mountains in the western part of Luzon Island obstruct the easterly wind, resulting in high temperatures and strong evaporation along the western coast of the mountains. Meanwhile, low temperatures and humidity prevail in the eastern part of the mountains, and they lead to a stratified atmosphere characterized by dry and cold upper layers and warm and humid lower layers in the western part of Luzon Island, which causes a distinct decrease in humidity with height. After the onset of the monsoon, the air from the Indochina Peninsula to the ocean is dry and cold, but the high-altitude area blocks it. This weakens the horizontal mobility of the low-level humid atmosphere over the sea, resulting in atmospheric stratification in the eastern coastal area of the Indochina Peninsula. This stratification leads to dry and cold upper layers and warm and humid lower layers.

Significance Statement

Atmospheric ducting is a superrefractive propagation condition that frequently occurs at sea and has a significant impact on radar and communication equipment and is related to large-scale or medium- and small-scale atmospheric stratification. The distribution of land and sea around the South China Sea (SCS) and the monsoon are important factors affecting the existence of atmospheric ducts in this region. Many scholars have studied the mechanism of atmospheric ducts in local areas based on observation data (or reanalysis data). The literature on the atmospheric ducts in the SCS mainly focuses on the spatial and temporal statistical distribution of seasons, months, and days, and gives the spatial and temporal distribution characteristics of the region within the statistical time, emphasizing the important influence of the monsoon on the duct, but there is no relevant research on the reasons for the existence of the specific relationship and its temporal and spatial distribution characteristics. The manuscript analyzes the temporal and spatial distribution of lower atmospheric ducts in the SCS and the variation of their occurrence rate with the monsoon, quantifies the contributions of temperature, humidity, and air pressure to the ducting occurrence, meanwhile discussing the ducting occurrence relation with atmospheric and oceanic conditions. In the end, we demonstrate that the development of high-incidence areas for SCS ducts prior to and following the onset of monsoon season is connected to factors such as wind patterns, seawater evaporation, and topography. Furthermore, unstable vertical transport of water vapor in both the atmosphere and oceanic conditions plays a crucial role in facilitating the creation of humidity-type ducts.

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Free access
Free access
Susan L. Belak
,
Robin L. Tanamachi
,
Matthew L. Asel
,
Grant Dennany
,
Abhiram Gnanasambandam
,
Stephen J. Frasier
, and
Francesc Rocadenbosch

Abstract

This study describes a novel combination of methods to remove spurious spectral peaks, or “spurs,” from Doppler spectra produced by a vertically pointing, S-band radar. The University of Massachusetts S-band frequency-modulated, continuous-wave radar (UMass FMCW) was deployed to monitor the growth of the CBL over northern Alabama during the VORTEX–Southeast field campaign in 2016. The Doppler spectra contained spurs caused by high-voltage switching power supplies in the traveling wave tube amplifier. In the original data-processing scheme for this radar, a median filtering method was used to eliminate most of the spurs, but the largest ones persisted, which significantly degraded the quality of derived radar moments (e.g., reflectivity, Doppler velocity, and spectrum width) and hindered further analysis of these data (e.g., hydrometeor classification and boundary layer height tracking). Our technique for removing the spurs consists of three steps: (i) a Laplacian filter identifies and masks peaks in the spectra that are characteristic of the spurs in shape and amplitude, (ii) an in-painting method then fills in the masked area based on surrounding data, and (iii) the moments data (e.g., reflectivity, Doppler velocity, and spectrum width) are then recomputed using a coherent power technique. This combination of techniques was more effective than the median filter at removing the largest spurs from the Doppler spectra and preserved more of the underlying Doppler spectral structure of the scatterers. Performance of both the median-filter and the in-painting methods is assessed through statistical analysis of the spectral power differences. Downstream products, such as boundary layer height detection, are more easily derived from the recomputed moments.

Significance Statement

This manuscript describes a novel combination of image and signal processing techniques used to recover meteorological observations from corrupted Doppler radar spectra. This successful recovery of meteorologically significant information illustrates the importance of retaining Doppler spectra when practical. In seeking solutions to data quality issues, the atmospheric science community should remain cognizant of promising techniques offered by other disciplines. We present this data rescue study as an example to the meteorological community.

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John M. Lewis
and
S. Lakshmivarahan

Abstract

A single-day meeting between two theoretical meteorologists took place in 1961 at the Travelers Research Center (TRC) in Hartford, Connecticut. The two scientists were Barry Saltzman and Edward Lorenz, former proteges of V. P. Starr at MIT. Several years before this meeting, Lorenz discovered the following profound result: extended-range weather forecasting was not feasible in the presence of slight errors in initial conditions. The model used was the geostrophic form of a two-level baroclinic model with twelve spectral variables. These results were presented a year earlier at the First Symposium on Numerical Weather Prediction (NWP) in Tokyo, Japan, and met with some skepticism from the NWP elite, dynamical meteorologists, and pioneers in operational NWP. Lorenz held faint hope that Saltzman’s recently developed model of Rayleigh- Bénard convection would produce the profound result found earlier. One of the numerical experiments executed that eventful day with Saltzman’s 7-mode truncated spectral model produced an unexpected result: inability of the model’s 7 variables to settle down and approach a steady state. This occurred when the key parameter, the Rayleigh number, assumed an especially large value, one associated with turbulent convection. And further experimentation with the case delivered the sought-after result that Lorenz had found earlier, and now convincingly found with a simpler model. It built the bridge to chaos theory. The pathway to this exceptional result is explored by revisiting Saltzman’s and Lorenz’s mentorship under V. P. Starr, the authors’ interview with Lorenz in 2002 that complements information in Lorenz’s scientific autobiography, and the authors’ published perspective on Salzman’s 7-mode model.

Open access
Yuanyuan Zhou
and
Liang Gao

Abstract

The spatiotemporal variations of annual tropical-cyclone-induced rainfall (TCR) and non-tropical-cyclone-induced rainfall (NTCR) during 1960–2017 in Southeast China are investigated in this study. The teleconnections to sea surface temperature, the Arctic Oscillation, the Southern Oscillation, and the Indian Ocean dipole are examined. A significant decrease in annual TCR in the Pearl River basin was detected, while an increase in annual TCR in rainstorms was observed in the northeast of the Pearl River basin and south of the Yangtze River basin. A northward migration of a TCR belt was identified, which was also indicated by the pronounced anomalies of annual TCR. There was in general an increasing trend of non-tropical-cyclone-induced moderate rain, heavy rain, and rainstorms in Southeast China. Compared with the non-tropical-cyclone-induced heavy rain, the abnormal non-tropical-cyclone-induced rainstorms are more northerly. Both monthly TCR and NTCR were remarkably affected by the Arctic Oscillation, Southern Oscillation, and Indian Ocean dipole. TCR was more easily affected by the Arctic Oscillation compared to NTCR.

Significance Statement

Tropical-cyclone- and non-tropical-cyclone-induced rainfall (TCR and NTCR) prevails in Southeast China, and their characteristics of spatiotemporal variability are of significance in predicting rainfall over the study area. Therefore, this study aims to detect the degree to which rainfall varies in time and space, respectively, using the Mann–Kendall test and the empirical orthogonal function method. Moreover, to explore which climatic factor contributes the most to the spatiotemporal variability of TCR and NTCR, the teleconnections to the large-scale climatic indices including sea surface temperature, the Arctic Oscillation, the Southern Oscillation, and the Indian Ocean dipole are studied. The spatiotemporal variations of TCR and NTCR were affected by the sea surface temperature and the other three large-scale climatic indices. The findings in this study are expected to deepen the understanding of spatiotemporal variations of TCR and NTCR over Southeast China and the teleconnections to climatic indices.

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