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Neil F. Laird
,
Caitlin C. Crossett
,
Catherine J. Britt
,
Nicholas D. Metz
,
Kelly Carmer
, and
Braedyn D. McBroom

Abstract

An investigation of lake effect (LE) and the associated synoptic environment is presented for days when all five lakes in the Great Lakes (GL) region had LE bands [five-lake days (5LDs)]. The study utilized an expanded database of observed LE clouds over the GL during 25 cold seasons (October–March) from 1997/98 to 2021/22. LE bands occurred on 2870 days (64% of all cold-season days). Nearly a third of all LE bands occurred during 5LDs, although 5LDs consisted of just 17.1% of LE days. A majority of 5LDs (56.5%) had lake-to-lake (L2L) bands, and these days comprised 43.5% of all L2L occurrences. 5LDs occurred with a mean of 26.1 (SD = 6.2) days per cold season until 2008/09 and then decreased to a mean of 13.8 (SD = 5.5) days during subsequent cold seasons. January and February had the largest number of consecutive LE days in the GL with a mean of 5.7 and 5.4 days, respectively. As the number of consecutive LE days increases, both the number of 5LDs and the occurrence of consecutive 5LD increase. This translates to an increased potential of heavy snowfall impacts in multiple, localized areas of the GL for extended time periods. The mean composite synoptic pattern of 5LDs exhibited characteristics consistent with lake-aggregate disturbances and showed similarity to synoptic patterns favorable for LE over one or two of the GL found by previous studies. The results demonstrate that several additional areas of the GL are often experiencing LE bands when a localized area has active LE bands occurring.

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Nicolas G. Alonso-De-Linaje
,
Andrea N. Hahmann
,
Ioanna Karagali
,
Krystallia Dimitriadou
, and
Merete Badger

Abstract

The paper aims to demonstrate how to enhance the accuracy of offshore wind resource estimation, specifically by incorporating near-surface satellite-derived wind observations into mesoscale models. We utilized the Weather Research and Forecasting (WRF) model and applied observational nudging by integrating ASCAT data over offshore areas to achieve this. We then evaluated the accuracy of the nudged WRF model simulations by comparing them with data from ocean oil platforms, tall masts, and a wind Lidar mounted on a commercial ferry crossing the southern Baltic Sea. Our findings indicate that including satellite-derived ASCAT wind speeds through nudging enhances the correlation and reduces the error of the mesoscale simulations across all validation platforms. Moreover, it consistently outperforms the control and previously published WRF-based wind atlases. Using satellite-derived winds directly in the model simulations also solves the issue of lifting near-surface winds to wind turbine heights, which has been challenging in estimating wind resources at such heights. The comparison of the one-year-long simulations with and without nudging reveals intriguing differences in the sign and magnitude between the Baltic and North Seas, which vary seasonally. The pattern highlights a distinct regional pattern attributed to regional dynamics, sea surface temperature, atmospheric stability, and the number of available ASCAT samples.

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Rory Laiho
,
Katja Friedrich
, and
Andrew C. Winters

Abstract

Situated in the Upper Midwest, Minnesota’s mid-continental location places it in a climate transition zone between eastern U.S. humid conditions and western semi-arid conditions as well as between warm, moist air from the Gulf of Mexico to the south and drier, polar air to the north. Potential adverse impacts on ecosystems due to changing climate and precipitation patterns, together with ongoing flash flooding risks, indicate that heavy rainfall occurrence and distribution are important considerations for Minnesota. This research used ERA5 reanalysis data with 0.25° grid spacing during May-September 1959-2021 to investigate the synoptic-scale drivers of Minnesota heavy rainfall. The study utilized a neural-network, self-organizing map (SOM) technique to identify sea-level pressure patterns and precipitation patterns associated with heavy rainfall and used composite analysis to explore the relationships between synoptic-scale conditions and environmental parameters during heavy rain hours. Six sea level pressure patterns were identified, three of which represented advancing surface cyclones and accounted for >70% of the heavy rain hours. The spatial distribution of heavy rainfall was represented by six precipitation patterns. The greatest frequency of heavy rain hours was associated with the northwest precipitation pattern, followed by the southwest and southeast patterns. Analysis of the frequency of pressure and heavy rain precipitation pattern pairs revealed that the top five most frequent pairs were associated with advancing surface cyclones and >26% of the total heavy rain hours. Composite analysis of environmental parameters showed favorable conditions related to moisture and lift were associated with heavy rainfall.

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Reese Mishler
,
Guifu Zhang
, and
Vivek N. Mahale

Abstract

Polarimetric variables such as differential phase (фDP) and its range derivative, specific differential phase (K DP), contain useful information for improving quantitative precipitation estimation (QPE) and microphysics retrieval. However, the usefulness of the current operationally utilized estimation method of K DP is limited by measurement error and artifacts resulting from the differential backscattering phase (δ). The contribution of δ can significantly influence the фDP measurements and therefore negatively affect the K DP estimates. Neglecting the presence of δ within non-Rayleigh scattering regimes has also led to the adoption of incorrect terminology regarding signatures seen within current operational K DP estimates implying associated regions of unrealistic liquid water content. A new processing method is proposed and developed to estimate both K DP and δ using classification and Linear Programming (LP) to reduce bias in K DP estimates caused by the δ component. It is shown that by applying the LP technique specifically to the rain regions of Rayleigh scattering along a radial profile, accurate estimates of differential propagation phase, specific differential phase and differential backscattering phase can be retrieved within regions of both Rayleigh and non-Rayleigh scattering. This new estimation method is applied to cases of reported hail and tornado debris and the LP results are compared to the operationally utilized least-square-fit (LSF) estimates. The results show the potential use of the differential backscattering phase signature in the detection of hail and tornado debris.

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Hiroyuki Kusaka
,
Yuma Imai
,
Hiroki Kobayashi
,
Quang-Van Doan
, and
Thanh Ngo-Duc

Abstract

North-central Vietnam often experiences high temperatures. Foehn winds originating from the Truong Son Mountains (also known as Laos winds) are believed to contribute to abnormally high temperatures; however, no quantitative research has focused on foehn warming in Vietnam. In this study, we conducted numerical simulations using the Weather Research and Forecasting (WRF) Model to investigate the contribution of foehn warming to abnormally high temperatures in north-central Vietnam in early June 2017. Generally, May–June is the monsoon period in Vietnam. Consequently, foehn warming during this season is thought to be mainly caused by latent heating and precipitation mechanism. However, the primary factor in the cases covered in this study was foehn warming with an isentropic drawdown mechanism. Diabatic heating with turbulent diffusion and sensible heat flux from mountain slopes also plays significant roles. The warming effect of the foehn winds on the temperatures during the events was approximately 2°–3°C. It was concluded that the high temperature events from 31 May to 5 June 2017 were caused by synoptic-scale warm advection and foehn warming. Sensitivity experiments were conducted on the WRF Model, utilizing three atmospheric boundary layer turbulence schemes (YSU, ACM2, and MYNN), consistently yielding results for simulated temperature and relative humidity. The wind speed bias for the MYNN scheme was found to be lower than that of the other schemes. However, this study did not delve into the underlying reasons for these differences. The optimal performance of each scheme remains an open question.

Significance Statement

It was hypothesized that north-central Vietnam often experiences high temperatures owing to foehn winds (also known as Laos winds) descending from the Truong Son Mountains. This study conducted numerical experiments to validate this hypothesis and investigate the associated mechanisms of foehn winds in this region. Surprisingly, despite the monsoon season, the isentropic drawdown mechanism without precipitation effects provides the primary explanation for the high temperatures caused by foehn winds in north-central Vietnam, and not the latent heating and precipitation mechanism with precipitation effects that researchers expected. The results of this study contribute to better prediction of high temperatures in this region and improve our understanding of foehn winds in tropical monsoon climate zones.

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Daniel J. Cecil
,
Michael B. Solomon
,
Retha Mecikalski
, and
Kenneth D. Leppert II

Abstract

Using passive microwave brightness temperatures (Tbs) from the Global Precipitation Measurement (GPM) mission Microwave Imager (GMI) and hydrometeor identification (HID) data from dual-polarization ground radars, empirical lookup tables are developed for a multifrequency estimation of the likelihood a precipitation column includes certain hydrometeor types, as a function of Tb . Eight years of co-located Tbs and HID data from the GPM Validation Network are used for development and testing of the GMI-based HID retrieval, with 2015-2020 used for training and 2021-2022 used for testing the GMI-based HID retrieval. The occurrence of profiles with hail and graupel are both slightly underpredicted by the lookup tables, but the percentage of profiles predicted is highly correlated with the percentage observed (0.98 correlation coefficient for hail, and 0.99 for graupel). By having snow appear before rain in the hierarchy, the sample size for rain, without ice aloft, is fairly small, and the percentage of rain profiles is less than snow for all Tbs.

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Carl G. Schmitt
,
Dragos Vas
,
Martin Schnaiter
,
Emma Järvinen
,
Lea Hartl
,
Telayna Wong
,
Victor Cassella
, and
Martin Stuefer

Abstract

A three-winter study has been conducted to better understand the relationship between atmospheric conditions and ice fog or diamond dust microphysics. Measurements were conducted east of downtown Fairbanks in the Interior Alaska during non-precipitating conditions. Atmospheric conditions were measured with several weather stations around the Fairbanks region and two Meteorological Temperature Profiler instruments (ATTEX MTP-5HE and MTP-5PE). Near surface ice particle microphysical observations were conducted with the Particle Phase Discriminator (PPD-2K) instrument which measures particles from 8 to 112 μm (sphere-equivalent). Panoramic camera images were captured and saved every ten minutes throughout the campaign for visual assessment of atmospheric conditions.

Machine Learning was used to classify both cloud particle microphysical characteristics from the PPD-2K data as well as to categorize boundary layer conditions using the panoramic camera images. For panoramic camera images, data were categorized as: cloudy, clear, fog, snowing, and a nearby powerplant plume was characterized. For the PPD-2K Machine Learning study the scattering pattern images were used to identify: rough surface, pristine, sublimating, and spherical particles. Three additional categories were used to identify indeterminant or saturated images. These categories as well as categories derived from weather station data (e.g., temperature ranges) are used to quantify ice microphysical properties under different conditions. For the complete microphysical dataset, pristine plates or columns accounted for 15.5%, 16.3% appeared to be sublimating particles and 43.4% were complex particles with either rough surfaces or multiple branches. Although the temperature was as warm as −20°C during measurements, only 1.3% of particles were classified as liquid.

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Michael T. Kiefer
,
Shiyuan Zhong
,
Joseph J. Charney
,
Xindi Bian
,
Warren E. Heilman
, and
Joseph Seitz

Abstract

Broadly speaking, prediction of the negative impacts of prescribed fire on air quality is limited by gaps in our understanding of the underlying fire, fuels, and atmospheric processes. These knowledge gaps hinder our ability to accurately predict smoke concentration distributions, leading to unintended smoke intrusions into nearby communities and subsequent threats to public health and safety. In this study, numerical simulations are performed using a Lagrangian particle dispersion model (FLEXPART-WRF), with particle motion driven by output from a full-physics atmospheric model with a forest canopy sub-model and 10-m horizontal grid spacing (ARPS-CANOPY), to address two research questions. First, what is the relationship between near-fire (within ~50–150 m of fire) smoke concentration distribution and (i) vertical canopy structure, (ii) fire heat source strength? Second, what roles do mean transport (i.e., transport by the mean wind) and turbulent diffusion play in shaping the near-fire smoke concentration distribution? To address these questions, simulations are run with 25 combinations of plant area density profile and fire sensible heat flux magnitude, and smoke is represented by particles with diameters ≤ 2.5 µm (PM2.5). Results show that near-fire PM2.5 concentration distribution is primarily controlled by vertical canopy structure, with fire heat source strength primarily controlling the PM2.5 concentration magnitude. Analysis of the underlying ARPS-CANOPY variables driving FLEXPART-WRF particle dispersion helps elucidate the roles of mean transport and turbulent diffusion. In total, the study findings suggest that the vertical distribution of canopy vegetation and fire heat source strength are important factors influencing PM2.5 dispersion and concentration distribution near low-intensity fires.

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Timothy A. Coleman
,
Richard L. Thompson
, and
Gregory S. Forbes

Abstract

Recent articles have shown that the long-portrayed “tornado alley” in the central plains is not an accurate portrayal of current tornado frequency over the United States. The greatest tornado threat now covers parts of the eastern U.S. This paper shows that there has been a true spatial shift in tornado frequency, dispelling any misconceptions caused by the better visibility of tornadoes in the Great Plains vs. the eastern U.S. Using F/EF1+ tornadoes (the dataset least affected by increasing awareness of tornado locations or by changing rating methods), a 1° × 1° grid, and data for the two 35-year periods 1951-1985 and 1986-2020, we show that since 1951, by critical measures (tornadogenesis events, tornado days, and tornado path length), tornado activity has shifted away from the Great Plains and toward the Midwestern and Southeast U.S.

In addition, tornadoes have trended away from the warm season, especially the Summer, and toward the cold season since 1951. Annual trends in tornadoes by season (Winter, Spring, Summer, and Autumn) confirm this. All of the increase in F/EF1+ tornadoes in the eastern U.S. is due to an increase in cold season tornadoes. Tornadoes in the western U.S. decreased 25% (from 8451 during 1951-1985 to 6307 during 1986-2020), while tornadoes in the eastern U.S. increased 12% (from 9469 during 1951-1985 to 10595 during 1986-2020). The cities with the largest increases and decreases in tornado activity since 1951 are determined.

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