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Reese Mishler
,
Guifu Zhang
, and
Vivek N. Mahale

Abstract

Polarimetric variables such as differential phase ΦDP 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 ΦDP 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 squares fit (LSF) estimates. The results show the potential use of the differential backscattering phase signature in the detection of hail and tornado debris.

Free access
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 1-yr-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.

Significance Statement

We aim to showcase a method for improving the precision of hub-height estimation of wind resources offshore. This involves integrating wind observations obtained from near-surface satellites into the model simulations. To assess the accuracy of the simulations, we compare the simulated winds to data gathered from multiple offshore sources, including oil platforms, tall masts, and a wind lidar installed on a commercial ferry.

Free access
Qing Zheng
,
Wei Sun
,
Jian Li
,
Yong Feng
,
Zhiwei Heng
, and
Xingwen Jiang

Abstract

Water vapor transport is a crucial process in modeling and can contribute to errors in precipitation forecasts. To investigate the sensitivity of precipitation to the moisture advection scheme, this study introduced the two-step shape-preserving advection scheme (TSPAS), which has been proven to improve precipitation simulation over steep topography at lower resolutions, into the Southwest Center Weather Research and Forecast (WRF)-based Intelligent Numerical Grid Forecast System (SWC-WINGS) at a convection-permitting resolution. According to experiments conducted throughout the summer of 2021, the precipitation over the eastern slope of the Tibetan Plateau (TP) is highly sensitive to the moisture advection scheme. TSPAS successfully improved precipitation over the eastern slope of the TP, especially for torrential rainfall. The fractions skill score (FSS) is improved by 0.075 (27.78%) for daily precipitation with a threshold of 100 mm. Compared with the experiment with the original WRF advection scheme, the TSPAS reduced the overestimation of precipitation in the topographic region and excessive water vapor transport in a low-level atmosphere. To understand the precipitation improvement contributed by the advection scheme, additional experiments were conducted for a particular precipitation process from two approaches: switching advection schemes during the rainfall evolution and updating the variables related to moisture advection individually. Results demonstrate that the precipitation improvement is mainly contributed by the moisture advection scheme before the precipitation. Among the different variables, the combination of wind and water vapor was the most influential factor causing the precipitation improvement under the TSPAS.

Restricted access
Jacob T. Carlin
,
Elizabeth N. Smith
, and
Katherine Giannakopoulos

Abstract

Knowledge about the depth of the planetary boundary layer (PBL) is crucial for a variety of applications, but direct observations of PBL depth are spatiotemporally sparse. Recent studies have proposed using operational dual-polarization weather radars to observe the evolution of PBL depth by capitalizing on unique differential reflectivity (Z DR) signatures of Bragg scatter at the top of the PBL. While this approach appears promising and cost-effective, uncertainties remain about the representativeness of these estimates and how its efficacy may vary by geography and climatology. To address these outstanding uncertainties, this study compares collocated observations collected from two WSR-88D radars and two state-of-the-art mobile boundary layer profiling systems and evaluates the proposed methodology over the full diurnal cycle. Results indicate good overall correspondence between the profiling- and radar-based PBL depth estimates, with an abrupt divergence during the early evening transition and large discrepancies overnight. Relatively large root-mean-square-deviations (RMSDs) coupled with small biases match expectations when comparing spatially averaged data with point observations during PBL growth, which capture frequent fluctuations. A qualitative examination of the radar data reveals signatures of elevated residual layers, clouds, and ground clutter, all of which can obfuscate the desired surface-based PBL signal but which may have their own utility. The prominence of the Bragg scatter signal is found to be correlated with the observed moisture gradient at the top of the PBL, reflecting climatological variability that should be considered. These findings motivate further work to improve the automated detection of Bragg scatter layers from polarimetric radar data.

Significance Statement

Knowledge of the height of the planetary boundary layer matters for weather forecasting, air quality, and renewable energy production. Currently, boundary layer height measurements are taken at select locations twice a day. However, a method to use the existing national network of polarimetric weather radars for this purpose has been proposed. This work evaluates this method against specialized boundary layer measurements. The results show that the method is generally reliable during the daytime and could be used for a variety of applications including climatologies and model evaluation. There remain a number of situational caveats, including residual turbulence, clouds/precipitation, ground clutter, and certain meteorological environments, that may require modification of the approach and need to be considered in future work.

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

Abstract

Using passive microwave brightness temperatures Tb from the Global Precipitation Measurement (GPM) 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 collocated Tb and HID data from the GPM Validation Network are used for development and testing of the GMI-based HID retrieval, with 2015–20 used for training and 2021–22 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 Tb .

Open access
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 United States. 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 versus the eastern United States. 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-yr periods 1951–85 and 1986–2020, we show that since 1951, by critical measures (tornadogenesis events, tornado days, and tornado pathlength), tornado activity has shifted away from the Great Plains and toward the Midwest and Southeast United States. 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 United States is due to an increase in cold season tornadoes. Tornadoes in the western United States decreased 25% (from 8451 during 1951–85 to 6307 during 1986–2020), while tornadoes in the eastern United States. increased 12% (from 9469 during 1951–85 to 10 595 during 1986–2020). The cities with the largest increases and decreases in tornado activity since 1951 are determined.

Significance Statement

This paper quantifies in many ways (tornadoes, tornado days, and pathlength) the geographical shift in tornadoes from the central to the eastern United States and from the warm season to the cold season, since 1951. Where and when tornadoes most frequently occur is significant not only for the research and operational meteorology communities but also for public perception and risk awareness. Some research studies have shown that tornado casualties are more likely in the eastern United States and the cold season because of preconceived notions of a “tornado alley” in the Great Plains and a “tornado season” in the spring. Publication of the results of this research might help ameliorate this problem.

Restricted access
Mohammad Hadavi
and
Djordje Romanic

Abstract

Thunderstorms are recognized as one of the most disastrous weather threats in Canada because of their power to cause substantial damage to human-made structures and even result in fatalities. It is therefore essential for operational forecasting to diagnose thunderstorms that generate damaging downdrafts of negatively buoyant air, known as downbursts. This study develops several machine learning models to identify environments supportive of downbursts in Canada. The models are trained and evaluated using 38 convective parameters calculated based on ERA5 reanalysis vertical profiles prior to thunderstorms with (306 cases) and without (19 132 cases) downbursts across Canada. Various resampling techniques are implemented to adjust data imbalance. An increase in the performance of the random forest (RF) model is observed when the support vector machine synthetic minority oversampling technique is utilized. The RF model outperforms other tested models, as indicated by model performance metrics and calibration. Several model interpretability methods highlight that the RF model has learned physical trends and patterns from the input variables. Moreover, the thermodynamic parameters are deemed to have higher impacts on the model outcomes compared to parcel, kinematic, and composite variables. For example, a considerable rise in the downburst probability is detected with an increase in cold pool strength. This study serves as one of the earliest attempts toward the fledgling field of machine learning applications in weather forecasting systems in Canada. The findings suggest that the developed model has the potential to enhance the effectiveness of issuing severe thunderstorm warnings in Canada, although further assessment with operational meteorologists is needed to validate its practical application.

Significance Statement

Severe thunderstorms demand particular attention in forecasting as their outflow can pose a serious threat to both structures and human life. This study uses machine learning techniques to predict whether or not a thunderstorm generates a damaging outflow in Canada. Atmospheric conditions that could trigger a severe thunderstorm are identified and discussed. Results show that the models have the potential to assist forecasters in better analyzing and predicting thunderstorms that generate destructive winds. Consequently, taking advantage of promising machine learning tools can yield more reliable forecasts of damaging thunderstorms, thereby mitigating the economic and societal burdens of these storms on Canadian communities.

Restricted access
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 interior Alaska during nonprecipitating 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 mark 2, Karlsruhe edition (PPD-2K), instrument, which measures particles from 8 to 112 μm (sphere equivalent). Panoramic camera images were captured and saved every 10 min 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 and 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 power plant plume. 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 and 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 the particles were classified as liquid.

Significance Statement

Boundary layer ice particles are frequently present in the near-surface atmosphere when surface temperatures drop below −20°C. Substantial human impacts can occur due to visibility degradation and deposition of particles on surfaces. Understanding particle shape, size, and phase (liquid or solid) is important for understanding those impacts. This study presents the results of a 3-yr measurement campaign in Fairbanks, Alaska, in which we relate ice particle characteristics to lower atmospheric conditions. Results should improve weather forecasting and hazard prediction.

Open access
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 the Flexible Particle Weather Research and Forecasting (FLEXPART-WRF) Model, a Lagrangian particle dispersion model, with particle motion driven by output from a full-physics atmospheric model with a forest canopy submodel and 10-m horizontal grid spacing [Advanced Regional Prediction System (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 and (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 the 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.

Restricted access
Laurence Coursol
,
Sylvain Heilliette
, and
Pierre Gauthier

Abstract

With hyperspectral instruments measuring radiation emitted by Earth and its atmosphere in the thermal infrared range in multiple channels, several studies were made to select a subset of channels in order to reduce the number of channels to be used in a data assimilation system. An optimal selection of channels based on the information content depends on several factors related to observation and background error statistics and the assimilation system itself. An optimal channel selection for the Cross-track Infrared Sounder (CrIS) was obtained and then compared to selections made for different NWP systems. For instance, the channel selection of Carminati has 224 channels also present in our optimal selection, which includes 455 channels. However, in terms of analysis error variance, the difference between the two selections is small. Integrated over the whole profile, the relative difference is equal to 15.3% and 4.5% for temperature and humidity, respectively. Also, different observation error covariance matrices were considered to evaluate the impact of this matrix on channel selection. Even though the channels selected optimally were different in terms of which channels were selected for the various R matrices, the results in terms of analysis error are similar.

Significance Statement

Satellites measure radiation from Earth and its atmosphere in the thermal infrared. Those radiance data contain thousands of measurements, called channels, and thus, a selection needs to be done retaining most of the information content since the large number of individual pieces of information is not usable for numerical weather prediction systems. The goal of this paper is to find an optimal selection for the instrument CrIS and to compare this selection with selections made for different numerical weather prediction systems. It was found that even though the channels selected optimally were different in terms of which channels were selected compared to other selections, the results in terms of precision of the analysis are similar and the results in terms of analysis error are similar due to the nature of hyperspectral instruments, which have multiple Jacobians overlapping.

Open access