Station Data Research Articles

A computer vision–aided methodology for bridge flexibility identification from ambient vibrations

AbstractThis paper presents the implementation of a novel monitoring system in which video images and conventional sensor network data are simultaneously analyzed to identify the structural flexibility from the ambient vibrations. The magnitude ratio between the flexibility estimated from known/unknown input force are theoretically derived and decomposed into two parts: and . The first scale factor related to basic modal parameters can be acquired using the general modal identification methods. Aiming to tackle the difficulty in identifying the second scale factor related to the force intensity, a video stream of traffic is processed to detect and classify vehicles to determine the vehicle's location while displacement measurements are simultaneously collected. By integrating the toll station data, the vehicle loads are assigned to the vehicle on the bridge deck through the uniqueness of the license plate number. Thus, a structural input–output relationship is established to solve the second scale factor . Finally, the flexibility estimated from the ambient vibration are scaled by and , respectively to obtain the exact flexibility , which are same as the analytical ones . Both numerical example and a laboratory test are performed to demonstrate the accuracy of the proposed methodology. The algorithms, approaches, and results given in the paper demonstrate its effectiveness and shows great potential for its application on a real‐life bridge's condition assessment.

Estimation of design precipitation using weather radar in Germany: A comparison of statistical methods

Study region:Germany. Study focus:Estimation of precipitation return levels on various temporal scales and with high spatial resolution is crucial for risk management and hydrological applications. Weather radars may provide this information, but design precipitation estimates from these instruments suffer from estimation biases and from the limited length of the available records. Here, the performance of two statistical methods for deriving design precipitation from 20 years of radar data for Germany is investigated: (a) a method based on peaks-over-threshold and an exponential distribution, operationally used for decades to compute design precipitation in Germany (DWA); and (b) a non-asymptotic approach that was recently shown to reduce estimation uncertainties related to short records (SMEV). The most recent official design precipitation for Germany derived from station data (KOSTRA-DWD-2020) are used as a benchmark. New Hydrological Insights for the RegionDesign precipitation from radar data tends to be lower than those derived from stations, due to the scale mismatch (point scale versus ∼1km2 of radar) and to biases in radar estimates of extremes. SMEV tends to underestimate more than DWA, especially for short durations. Larger uncertainties are reported for the DWA method, while SMEV estimates tend to be more stable and less influenced by statistical outliers. Application of the KOSTRA-DWD-2020 method on radar data leads to results closer to SMEV.

Automated PIM Detection in Wireless Networks via Smart Data-Driven Techniques

As the number of base stations keeps increasing due to ever-growing wireless services and pervasive connectivity, the operational costs for network service providers rises proportionally. In addition, with the evolution of wireless technologies and the development of next generation networks such as 5G and 6G, network operators are inevitably expected to encounter more complex challenges. One such network issue is the Passive Intermodulation (PIM) problem that is observed in both 4G and 5G networks. It degrades the user experience and radio resource efficiency while leading to an operational overhead for detecting and mitigating it on the operator side. Although there is a significant body of work regarding PIM detection and cancellation methods, the majority of such studies depends on hardware solutions and manual investigation by network engineers, which is costly in terms of time and labor. In this paper, we propose two methods using unsupervised and semi-supervised Machine Learning (ML) approaches, namely a time-series-based anomaly detection technique and an autoencoder-based one, for identifying PIM problems in network sites. The proposed solutions utilize a set of Key Performance Indicator (KPI) data of base stations obtained from network management systems for a significantly long time interval and detect possible PIM problems without the need for a human in the loop. We measure and analyze the performance of our solutions, with the guidance of experienced network engineers, on our collected dataset.

Predicting agricultural and meteorological droughts using Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM).

Drought is an extended shortage of rainfall resulting in water scarcity and affecting a region's social and economic conditions through environmental deterioration. Its adverse environmental effects can be minimised by timely prediction. Drought detection uses only ground observation stations, but satellite-based supervision scans huge land mass stretches and offers highly effective monitoring. This paper puts forward a novel drought monitoring system using satellite imagery by considering the effects of droughts that devastated agriculture in Thanjavur district, Tamil Nadu, between 2000 and 2022. The proposed method uses Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM) to forecast meteorological and agricultural droughts. It employs Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data precipitation index datasets, MODIS 11A1 temperature index, and MODIS 13Q1 vegetation index. It extracts the time series data from satellite images using trend and seasonal patterns and smoothens them using Holt Winter alpha, beta, and gamma parameters. Finally, an effective drought prediction procedure is developed using Conv2D-LSTM to calculate the spatiotemporal correlation amongst drought indices. The HW-Conv2DLSTM offers a better R2 value of 0.97. It holds promise as an effective computer-assisted strategy to predict droughts and maintain agricultural productivity, which is vital to feed the ever-increasing human population.

Estimation of Urban Greenhouse Gas Fluxes from Mole Fraction Measurements Using Monin–Obukhov Similarity Theory

Abstract The purpose of this study is to determine whether urban greenhouse gas (GHG) fluxes can be quantified from tower-based mole fraction measurements using Monin–Obukhov similarity theory (MOST). Tower-based GHG mole fraction networks are used in many cities to quantify whole-city GHG emissions. Local-scale, micrometeorological flux estimates would complement whole-city estimates from atmospheric inversions. CO2 mole fraction and eddy-covariance flux data at an urban site in Indianapolis, Indiana, from October 2020 through January 2022 are analyzed. Using MOST flux–variance and flux–gradient relationships, CO2 fluxes were calculated using these mole fraction data and compared to the eddy-covariance fluxes. MOST-based fluxes were calculated using varying measurement heights and methods of estimating stability. The MOST flux–variance relationship method showed good temporal correlation with eddy-covariance fluxes at this site but overestimated flux magnitudes. Fluxes calculated using flux–gradient relationships showed lower temporal correlation with eddy-covariance fluxes but closer magnitudes to eddy-covariance fluxes. Measurement heights closer to ground level produce more precise flux estimates for both MOST-based methods. For flux–gradient methods, flux estimates are more accurate and precise when low-altitude measurements are combined with a large vertical separation between measurement heights. When stability estimates based on eddy-covariance flux measurements are replaced with stability estimates based on the weather station or net radiation data, the MOST-based fluxes still capture the temporal patterns measured via eddy covariance. Based on these results, MOST can be used to estimate the temporal patterns in local GHG fluxes at mole fraction tower sites, complementing the small number of eddy-covariance flux measurements available in urban settings.

Research on the refinement of atmospheric weighted average temperature model in Xi’an based on machine learning

One of the most important parameters for calculating the atmospheric precipitable water vapor (PWV) is the atmospheric weighted mean temperature (Tm). Meteorological parameters that are measured typically constrain the accuracy and regional applicability of commonly used empirical models. When precipitable water is inverted using the Global Navigation Satellite System (GNSS), it results in erroneous Tm being obtained. Thus, in this paper, a small-scale Tm model for Xi’an is proposed by least squares method based on data provided by the European Centre for Mesoscale Weather Forecasts (ECMWF) from 2019 to 2022, while improving the functional model’s capacity to fit nonlinear residuals through the Random Forest method and enabling it to predict peaks more accurately. It is based on a random forest to confirm the weighted average temperature model’s correctness (RF Tm). When comparing the RF Tm model’s projected Tm accuracy to the GPT3, UNB3, and regional Bevis models, using the 2023 ERA5 data as the experimental data, the improvements are 23.6 %, 18.6 %, and 11.4 %, respectively. With the sounding station data as the reference value, the BIAS of RF Tm is only 1.51 K. In determining the atmospheric weighted average temperatures, the two models’ accuracy was comparable to that of the Long Short-Term Memory neural network model (LSTM). However, the LSTM model requires short-term forward data for practical usage, the RF Tm model compensated for the shortfall. The RF Tm model is used to invert the atmospheric precipitable water vapor at the appropriate monitoring stations using data from GNSS monitoring stations in Xi’an. It was demonstrated that the RF Tm model’s PWV inversion accuracy outperformed the PWV inverted by the GPT3 model by at least 30 %. Moreover, Xi’an rainfall data integration shows that the PWV inverted by the RF Tm model reacts to brief rainfall episodes efficiently. To sum up, this study’s RF Tm model provides a dependable and effective approach to meteorological observation and climate research.

Mapping seamless monthly XCO2 in East Asia: Utilizing OCO-2 data and machine learning

High spatial resolution XCO2 data is key to investigating the mechanisms of carbon sources and sinks. However, current carbon satellites have a narrow swath and uneven observation points, making it difficult to obtain seamless and full-coverage data. We propose a novel method combining extreme gradient boosting (XGBoost) with particle swarm optimization (PSO) to construct the relationship between OCO-2 XCO2 data and auxiliary data (i.e., vegetation, meteorological, anthropogenic emissions, and LST data), and to map the seamless monthly XCO2 concentration in East Asia from 2015 to 2020. Validation results based on TCCON ground station data demonstrate the high accuracy of the model with an average R2 of 0.93, Root Mean Square Error (RMSE) of 1.33 and Mean Absolute Percentage Error (MAPE) of 0.24 % in five sites. The results show that the average atmospheric XCO2 concentration in East Asia shows a continuous increasing trend from 2015 to 2020, with an average annual growth rate of 2.21 ppm/yr. This trend is accompanied by clear seasonal variations, with the highest XCO2 concentration in winter and the lowest in summer. Additionally, anthropogenic activities contributed significantly to XCO2 concentrations, which were higher in urban areas. These findings highlight the dynamics of regional XCO2 concentrations over time and their association with human activities. This study provides a detailed examination of XCO2 distribution and trends in East Asia, enhancing our comprehension of atmospheric CO2 dynamics.

Geodetic Matched Filter Slow Slip Event Detection Along the Northern Japan Subduction Zones

AbstractWe apply a template matching method on GNSS data for stations located in Honshu, Japan, to detect slow slip events associated with the subducting Philippine Sea and Pacific plates during the period from 1997 to 2020. A measure of the minimum detectable moment magnitude is proposed, from which we infer that the method could potentially detect SSEs as small as Mw 5.2 on the westernmost part of the Philippine Sea plate and Mw 6 on the Pacific plate below Honshu eastern coastline. We find 12 slow slip events on the Philippine Sea plate, among which eight are located on the known Boso slow slip event asperity and the four others are located offshore north‐east relative to the Boso SSEs, at the transition with the Pacific plate. We find 9 SSEs on the Pacific plate, mainly on the northern section, offshore Iwate prefecture. A clear gap with no SSEs coincides with the main asperity that broke during the 2011 Tohoku earthquake. Most event locations correlate with low locking areas. We do not find any clear temporal pattern apart from the regular occurrence of the largest Boso SSEs.

ASSESSING SATELLITE RAINFALL ACCURACY IN DENSE TROPICAL SABAH EAST COAST FOREST, MALAYSIA: A CROSS-VALIDATION OF DOWNSCALING TECHNIQUE

Rainfall is one of pivotal elements in the hydrological cycle that essential for ensuring the water balance in present and in the future. Traditional in-situ observations have been the conventional method for obtaining rainfall data, but their limitations arise from discrete point measurements that fail to represent the entire area. To overcome these limitations, this study utilizes satellite based TRMM data products for rainfall estimation. The research aims to cross-validate the TRMM 3B43-v7 product against corresponding in-situ measured rainfall, focusing on error localization in Sabah's east coast. The derivation of the rainfall rate from TRMM data is adequate with additional data from three manual gauges within the plots. The correlation between TRMM and ground was good (R2 = 0.78, RMSE = 65.65 mm). The Nash and Sutcliffe Error results closed to value 1 indicate that the accuracy of the TRMM data compared to the rain-gauge data in Danum has a good agreement. This is due to the IDW downscaling method for satellite rainfall data using additional data from three manual-gauges within the plots to increase the accuracy of the TRMM data. In summary, the downscaling method proves capable of providing fine spatial resolution and increasing the number of pixels in the study area. Future research endeavours may benefit from incorporating more station data to further improve the interpolation of satellite data and derive more precise results.

Long-Term Wind and Air Temperature Patterns in the Southeastern Region of Iran through Model Simulation and Ground Observations

Dust storms are one of the important natural hazards that affect the lives of inhabitants all around the world, especially in North Africa and the Middle East. In this study, wind speed, wind direction, and air temperature patterns are investigated in one of the dustiest cities in Sistan Basin, Zahedan City, located in southeast Iran, over a 17-year period (2004–2020) using a WRF model and ground observation data. The city is located near a dust source and is mostly affected by local dust storms. The World Meteorology Organization (WMO) dust-related codes show that the city was affected by local dust, with 52 percent of the total dust events occurring during the period (2004–2021). The city’s weather station reported that 17.5% and 43% were the minimum and maximum dusty days, respectively, during 2004–2021. The summer and July were considered the dustiest season and month in the city. Since air temperature, wind speed, and wind direction are important factors in dust rising and propagation, these meteorological factors were simulated using the Weather Research and Forecasting (WRF) model for the Zahedan weather station. The WRF model’s output was found to be highly correlated with the station data; however, the WRF simulation mostly overestimated when compared with station data during the study period (2004–2020). The model had a reasonable performance in wind class frequency distribution at the station, demonstrating that 42.6% of the wind was between 0.5 and 2, which is in good agreement with the station data (42% in the range of 0.5–2). So, the WRF model effectively simulated the wind class frequency distribution and the wind direction at Zahedan station, despite overestimating the wind speed as well as minimum, maximum, and average air temperatures during the 17-year period.

Identification of the Runoff Evolutions and Driving Forces during the Dry Season in the Xijiang River Basin

During the dry season, river flow gradually diminishes, and surface water flow dries up. Therefore, the investigation of runoff during the dry season is of great practical significance for rational water allocation and water resource management. Based on hydrological station data from the Xijiang River Basin (XRB) from 1961 to 2020, this study examines the trend and periodic characteristics of dry-season runoff, identifies fluctuation and variability in dry-season runoff, and investigates the main circulation factor combinations influencing dynamic changes in dry-season runoff. The results indicate the following: (1) the characteristics of dry-season runoff variations are basically consistent across sub-basins in the XRB during the study period, with the minimum (21.96 × 108 m3) and maximum (54.67 × 108 m3) average monthly runoff occurring in February and October, respectively; (2) interannual-scale dry-season runoff exhibits periodicity of 3.53 years and 7.5 years; (3) using the Bayesian estimator of abrupt seasonal and trend change algorithm (BEAST), a seasonal abrupt point with a probability of 20.5% occurs in 1983, and the confidence interval for this abrupt point is from 1980 to 1986; (4) based on the cross wavelet approach, solar sunspots are identified as the primary circulation factor contributing to dry-season runoff in the XRB, exhibiting a significant 8–14 years resonance cycle of negative correlation with runoff during the high-energy phase from 1972 to 2006. These findings offer a new perspective on understanding the evolution of dry-season runoff and circulation factor variations, which are crucial for accurate prediction, early warning, and rational allocation of water resources during the dry season.

Power Forecasting for Photovoltaic Microgrid Based on MultiScale CNN-LSTM Network Models

Photovoltaic (PV) microgrids comprise a multitude of small PV power stations distributed across a specific geographical area in a decentralized manner. Computational services for forecasting the output power of power stations are crucial for optimizing resource deployment. This paper proposes a deep-learning-based architecture for short-term prediction of PV power. Firstly, in order to make full use of the spatial information between different power stations, a spatio–temporal feature fusion method is proposed. This method is capable of exploiting both the power information of neighboring power stations with strong correlations and meteorological information with the PV feature data of the target power station. By using a multiscale convolutional neural network–long short-term memory (CNN-LSTM) network model, it is capable of generating a PV feature dataset containing spatio–temporal attributes that expand the data source and enhance the feature constraints. It is capable of predicting the output power sequences of power stations in PV microgrids with high model generalization and responsiveness. To validate the effectiveness of the proposed framework, an extensive numerical analysis is also conducted based on a real-world PV dataset.

Investigation of Temperature Multifractrality According to Zugspitze Weather Station Data

The main multifractal properties of time series of mean, maximum and minimum daily temperatures are analyzed using the method of multifractal fluctuation analysis. As initial data, we used the results of instrumental temperature observations made at the Zugspitze meteorological station in the period from August 1, 1900 to January 31, 2023. In general, variations in the mean, maximum and minimum daily temperatures demonstrate multifractal behavior, especially for small time scales, up to about 90 days An analysis of the generalized Hurst exponent found that the considered time series have a long-term positive correlation and that the multifractality is weaker with large fluctuations. The singularity spectrum for all time series is truncated to the left, which means that the time series have a multifractal structure that is insensitive to local fluctuations of large values.

Seasonal and vertical tidal variability in the Southeastern Mediterranean Sea

Currents and pressure records from the DeepLev mooring station and drifter data in the eastern Levantine Basin were analyzed to identify the dominant tidal constituents and their seasonal and depth variability. Harmonic and spectral analysis of seasonal segments of currents and pressure reveal key attributes of the tidal regime: (1) dominant semidiurnal sea level variability; (2) seasonal variation of semidiurnal and diurnal tides in both currents and pressure datasets; and (3) significant diurnal currents with weak semidiurnal currents across all seasons. The most dominant tidal constituent from the pressure dataset is the M2 (12.4 h). Results from pressure datasets align with previous models and observations of semidiurnal tides. In contrast, the diurnal tides are larger than previously reported by 8—9 cm in the winter and 1—2 cm in the summer. The surface current tidal regime differs from prior reports in the eastern Levantine Basin, with M2 magnitudes weaker by 1 cm s-1, while the diurnal tides (K1, O1) are 1—2 cm s-1 larger. Seasonal segments showed seasonal differences in the local tidal regime’s amplitudes. The most pronounced seasonal differences were with the K1 and S2 tides, with differences between winter and fall of 7 cm for the K1 and 4 cm between summer and fall for the S2. We utilized the DeepLev datasets to compare a moored device with surface drifters near DeepLev by analyzing the M2 and S2 tides. Additionally, we examined data at different dataset lengths, considering the time constraints needed to resolve the tides adequately. Longer datasets improved the resolution of the tidal analysis and reduced amplitude leakages from nearby frequencies, resulting in a more realistic and accurate analysis of the tidal currents. Conversely, longer datasets resulted in fewer drifters remaining our study region for the allotted dataset length. From 32 available segments in 15 day dataset to 7 with a 30 day dataset, reducing the effectiveness of drifters as a local tidal research tool.

The dominant warming season shifted from winter to spring in the arid region of Northwest China

The arid region of Northwest China (ARNC) has experienced a significantly higher warming rate than the global average and exhibits pronounced seasonal asymmetry, which has important implications for the region’s water-dependent systems. To understand the spatiotemporal patterns and driving mechanisms of seasonal asymmetric warming in the ARNC, we investigated seasonal changes in temperature rise and their underlying causes based on station and reanalysis data. We found that the dominant season of temperature increase shifted from winter to spring. The contribution of spring warming to the total temperature increase rose from −5%–7% to 58%–59%, while the contribution of winter warming decreased from 60%–75% to −4%–9%. However, the mechanisms underlying spring warming and winter cooling differ. An increase in solar radiation caused by a decrease in cloud cover (R = −0.64) was the main reason for spring warming, while a strengthening Siberian High primarily drove winter cooling.

Daily sunshine grids for Austria since 1961 – combining station and satellite observations for a multi-decadal climate-monitoring dataset

Grid datasets of sunshine duration at high spatial resolution and extending over many decades are required for quantitative applications in regional climatology and environmental change (e.g., modelling of droughts and snow/ice covers, evaluation of clouds in numerical models, mapping of solar energy potentials). We present a new gridded dataset of relative (and derived absolute) sunshine duration for Austria at a grid spacing of 1 km, extending back until 1961 at daily time resolution. Challenges in the dataset construction were consistency issues in the available station data, the scarcity of long time series, and the high variation of cloudiness in the study region. The challenges were addressed by special efforts to correct evident breaks in the station series and by adopting an analysis method, which combines station data with satellite data. The methodology merges the data sources non-contemporaneously, using statistical patterns distilled over a short period, which allowed involving satellite data even for the early part of the study period. The resulting fields contain plausible mesoscale structures, which could not be resolved by the station network alone. On average, the analyses explain 47% of the spatial variance in daily sunshine duration at the stations. Evaluation revealed a slight systematic underestimation (− 1.5%) and a mean absolute error of 9.2%. The average error is larger during winter, at high altitudes, and around the 1990s. The dataset exhibits a conditional bias, which can lead to considerable systematic errors (up to 15%) when calculating sunshine-related climate indices.

Using the Data of Meteorological and Satellite Station in Determining the Intensity of Rainfall in the Kan Basin

Using the Data of Meteorological and Satellite Station in Determining the Intensity of Rainfall in the Kan Basin

Evolution characteristics and mechanism of meso-γ-scale vortex in an atypical bow echo in the North China Plain

On June 25, 2020, an atypical bow echo (ATBE) accompanied by a meso-γ-scale vortex (MV) incurred the strongest gust (41.4 m s−1) recorded since 1957 in the North China Plain. In this article, we investigate the evolution characteristics of MV and its mechanism for the extreme wind by using the observation data of Doppler weather radar and automatic weather stations as well as the four-dimensional Variational Doppler Radar Analysis System (VDRAS). The results show that the MV in this process was initially born at a height of 0.9–2.4 km, and the contracting and stretching vertical vortex rapidly descended to surface from the 2.0 km height with the rotation speed increasing rapidly. Six minutes later, the extreme wind occurred. The generation mechanism of the MV is that, under the strong vertical wind shear and downdraft in the lower troposphere, the vertical vortex tube of MV was transformed from the horizontal vortex tube generated in the zone of perturbation temperature gradient. The influence mechanism of MV on the extreme wind is that, as the MV continued to intensify and extend downward, a negative perturbation low-pressure center formed in the lower troposphere near the MV. Meanwhile, the descending rear-inflow jet (RIJ) was blocked by the prefrontal upward motion and a positive perturbation high-pressure center formed at around 1 km height. The violent positive-negative couplets of perturbation pressure gradient produced a downward nonhydrostatic perturbation pressure gradient force (NPPGF) that further intensified the vertical downward velocity. In addition to the strong downdraft velocity, the Coriolis force also enhanced the formation of the extreme wind through the action of ageostrophic winds. Besides, this paper also compares the mechanism of this process with that of a typical bow echo MV and the extreme wind it affected.

Drought risk in Moldova under global warming and possible crop adaptation strategies.

This study analyzes the relationship between drought processes and crop yields in Moldova, together with the effects of possible future climate change on crops. The severity of drought is analyzed over time in Moldova using the Standard Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and their relationship with crop yields. In addition, rainfall variability and its relationship with crop yields are examined using spectral analysis and squared wavelet coherence. Observed station data (1950-2020 and 1850-2020), ERA5 reanalysis data (1950-2020), and climate model simulations (period 1970-2100) are used. Crop yield data (maize, sunflower, grape), data from experimental plots (wheat), and the Enhanced Vegetation Index from Moderate Resolution Imaging Spectroradiometer satellites were also used. Results show that although the severity of meteorological droughts has decreased in the last 170 years, the impact of precipitation deficits on different crop yields has increased, concurrent with a sharp increase in temperature, which negatively affected crop yields. Annual crops are now more vulnerable to natural rainfall variability and, in years characterized by rainfall deficits, the possibility of reductions in crop yield increases due to sharp increases in temperature. Projections reveal a pessimistic outlook in the absence of adaptation, highlighting the urgency of developing new agricultural management strategies.

On the Role of the Meridional Jet and Horizontal Potential Vorticity Dipole in the Iowa Derecho of 10 August 2020

Abstract On 10 August 2020, a derecho caused widespread damage across Iowa and Illinois. Des Moines station data show that the arrival of the gust front was characterized by an abrupt shift to northerly flow, exceeding 22 m s−1 for ∼20 min. To test the hypothesis that this northerly jet is associated with a horizontal potential vorticity (PV) dipole in the lower troposphere, we investigated the structure of PV in the University of Wisconsin Nonhydrostatic Modeling System (UWNMS) and of absolute vorticity in High-Resolution Rapid Refresh (HRRR) forecast analyses. This structure is described here for the first time. The negative PV member coincides with the downdraft, while the positive PV member coincides with the updraft, with a northerly jet between. The westerly inflow jet descends anticyclonically in the downdraft, joining with northerly flow from the surface anticyclone. The resulting northerly outflow jet creates the trailing comma-shaped radar echo. The speed of propagation of the derecho is similar to the westerly wind maximum in the 3–5-km layer associated with the approaching synoptic cyclone, which acts as a steering level for resonant amplification. Idealized diagrams and 3D isosurfaces illustrate the commonality of the PV dipole/northerly jet structure. Differences in this structure among the three model states are related to low-level wind shear theory. The PV dipole coincides with the pattern of diabatic stretching tendency, which shifts westward and downward relative to the updraft/downdraft with increasing tilt. The PV dipole can contribute toward dynamical stability in a derecho. Significance Statement The purpose of this work is to investigate the structure of potential vorticity (PV) in the lower troposphere in a derecho. It is found that a northerly outflow jet occurs between an east–west-oriented horizontal PV dipole, which is described here for the first time. The negative PV member coincides with the downdraft and is inertially unstable, while the positive PV member coincides with the updraft. This work contributes toward the theory of resonant structures and longevity. The 3–5-km westerly inflow layer constitutes a steering level, which controls propagation speed despite differences in structure. The degree of westward tilt with height is related to the pattern of forcing by diabatic stretching in producing the PV dipole.