Search Results (27)

Prescribed fire is a critical land management practice in the Great Plains of North America, helping to maintain native rangelands and reduce wildfire risk. Barriers to prescribed fire practice remain due to concerns on potential fire escape and fire danger. A localized fire danger index can help address these concerns by providing clear, science-based guidance, encouraging safer and confident use of prescribed fire. Our goal is to support the development of a localized Grassland Fire Danger Index (GFDI) for prescribed fire management in the Great Plains. The specific objective of this study is to develop user-friendly sub-models for dead fuel moisture content (DFMC) and grass curing, which serve as components of the proposed GFDI. DFMC reflects short-term fuel moisture that affects ignition and fire spread, while grass curing represents seasonal drying that controls fuel availability. Both are critical for fire prediction and safe burns. Lower DFMC and higher grass curing levels are strongly associated with wildfire risks. Using Oklahoma Mesonet weather data, the DFMC sub-model improves the accuracy and sensitivity of existing models. The grass curing sub-model shows that 50% curing usually occurs around April 15–16, which matches the time for the most intensive prescribed fire activities in the region, indicating it as a safe and effective window for prescribed fire recognized by landowners. Our sub-models lay the foundation for development of GFDI in the region. Full article

Understanding the intricate dynamics of precipitation patterns is essential for effective water resource management and climate adaptation in Missouri. Existing analyses of Missouri’s climate variability lack the spatial granularity needed to capture nuanced variations across climate divisions. The Missouri historical agricultural weather database, an open-source tool that contains key weather measurements gathered at Mesonet stations across the state, is beginning to fill in the data sparsity gaps. The aim of this study is to identify core patterns associated with ENSO in the global wavelet output. Using a continuous wavelet transform analysis on data from 32 stations (2000–2024), we identified significant precipitation cycles. Where previous studies used just four Automated Surface Observing Systems (ASOSs) located at airports across Missouri to characterize climate variability, this study uses an additional 28 from the Missouri Mesonet. The use of a global wavelet power spectrum analysis reveals that precipitation patterns, with the exception of southeast Missouri, have a distinct annual cycle. Furthermore, separating the stations based on the significance of their ENSO (El Niño–Southern Oscillation) signal results in the identification of three precipitation zones: an annual, ENSO, and residual zone. This spatial data analysis reveals that the Missouri climate division boundaries broadly capture the three precipitation zones found in this study. Additionally, the results suggest a corridor in central Missouri where precipitation is particularly sensitive to an ENSO signal. These findings provide critical insights for improved water resource management and climate adaptation strategies. Full article

With more than half of the world already living in urban spaces—a number set to increase in the coming decades—the need is clear to understand urban microclimates and extremes. This study placed twenty MX2302a HOBOmobile weather microsensors placed in aerated housings across the ~4 km² of the campus of the University of South Alabama from September to November 2022 and recorded temperature, relative humidity, and dewpoint every minute during the study period. These sensors were placed in situ, which allowed for the diversity in land cover, canopy cover, and aspect—large microclimatic drivers—to be captured. Sensors were compared to a campus mesonet station, part of the South Alabama Mesonet, a member of the National Mesonet Program. During the study period, temperatures were found to vary as much as 13 °C at the same minute across campus, with substantial changes in humidities and dewpoints also found. For example, the campus mesonet may have read 32 °C, yet the sensors could read as low as 29 °C and as high as 42 °C at the same moment. This study shows that the world is far more complex than what is seen at the mesoscale under idealized conditions, and the implications for society are considered. Full article

Freezing rain and freezing drizzle can produce nearly undetectable hazards, with potentially catastrophic consequences for aircraft within low altitudes (e.g., the terminal area). However, the lack of direct observations of the low-altitude freezing precipitation environment creates a challenge for forecasters, flight crews, dispatchers, and air traffic controllers. This research demonstrates how unmanned aerial vehicles (UAVs) can be designed and instrumented to create unmanned aerial weather measurement systems (WxUAS) capable of characterizing the low-altitude freezing precipitation environment and providing insight into the mechanisms that govern it. In this article, we discuss the design considerations for WxUAS-based in situ sampling during active precipitation. We present results from controlled experiments at the Oklahoma Mesonet’s calibration laboratory as well as results from intercomparison studies with collocated well-established ground-based instruments in Oklahoma and Colorado. Additionally, we explore the insights provided by high-resolution thermodynamic and cloud droplet size distribution profiles and their potential contributions to a better understanding of the low-altitude freezing precipitation environment. Full article

A total solar eclipse provides an unparalleled opportunity to study the changes in the atmosphere’s planetary boundary layer (PBL) due to changes in radiative heating. Although previous eclipse studies have demonstrated that significant changes occur, few studies have explored the evolution of these changes. To better understand the changes in the lowest layers of the PBL during an eclipse, a multi-sensor sampling approach was taken. Radiosonde launches were used to explore the depth of the column, while Unmanned Aerial Vehicles (UAVs) were used to document with high-resolution the brief changes in the vertical structure of the PBL caused by the eclipse. These changes highlighted differences from previous studies that relied solely on radiosonde and/or mesonet data alone. Higher-resolution sampling of the lower PBL showed a delay in the local vertical mixing as well as changes in the PBL height from pre- to post-eclipse. Slow responses were noted at the top of the PBL while very rapid changes to the PBL profile were captured in the near-surface layer. These changes highlighted differences from previous studies that relied solely on radiosonde and/or mesonet data alone. A preliminary analysis of the collected data highlighted a slow response to the eclipse near the top of the planetary boundary layer (radiosonde data) with very rapid changes noted in the near surface layer (UAV data). Preliminary results show that PBL heights remained nearly constant until well after third contact when a 35 hPa lowering of the PBL heights was observed and were limited to the lowest 25 hPa. The UAV soundings demonstrated the development of a strong inversion where the air below 990 hPa rapidly cooled with a nearly 1 °C drop in temperature observed. These observed changes raise interesting questions about how the lower and upper parts of the planetary boundary layer interact. Full article

Deepfake technology uses artificial intelligence to create realistic but false audio, images, and videos. Deepfake technology poses a significant threat to the authenticity of visual content, particularly in live-stream scenarios where the immediacy of detection is crucial. Existing Deepfake detection approaches have limitations and challenges, prompting the need for more robust and accurate solutions. This research proposes an innovative approach: combining eye movement analysis with a hybrid deep learning model to address the need for real-time Deepfake detection. The proposed hybrid deep learning model integrates two deep neural network architectures, MesoNet4 and ResNet101, to leverage their respective architectures’ strengths for effective Deepfake classification. MesoNet4 is a lightweight CNN model designed explicitly to detect subtle manipulations in facial images. At the same time, ResNet101 handles complex visual data and robust feature extraction. Combining the localized feature learning of MesoNet4 with the deeper, more comprehensive feature representations of ResNet101, our robust hybrid model achieves enhanced performance in distinguishing between manipulated and authentic videos, which cannot be conducted with the naked eye or traditional methods. The model is evaluated on diverse datasets, including FaceForensics++, CelebV1, and CelebV2, demonstrating compelling accuracy results, with the hybrid model attaining an accuracy of 0.9873 on FaceForensics++, 0.9689 on CelebV1, and 0.9790 on CelebV2, showcasing its robustness and potential for real-world deployment in content integrity verification and video forensics applications. Full article

The total solar eclipse of 21 August 2017 was unique in that the path of totality swept across the high spatial and temporal resolution QuantumWeather^® mesonet and was very near the city of St. Louis Missouri. Thus, the meteorological response to the eclipse was complicated by the St. Louis urban heat island. Temperature changes of up 4 °C were observed across the network. Composite meteograms for rural, suburban, and urban stations displayed significant differences in the observed temperature and pressure response to the eclipse with a peak amplitude at the time of the eclipse. The differing response suggests that the urban heat island and changes in land surface characteristics alter the temperature and pressure response by the passage of the eclipse shadow. Oscillations in the composite meteograms appear to be the consequence of the passage of an outflow boundary across the network. As the outflow boundary moves north to south, the outflow boundary manifests its presence in the pressure field as a damped oscillation. Sounding data were collected along the center line of eclipse and along the southern edge of the eclipse before and during the eclipse. The soundings show that the eclipse altered the boundary layer height, the lowest layer of the atmosphere, in an unexpected way. Full article

This study develops a triple-collocation (TC) based, multi-source shallow-soil moisture product for Oklahoma. The method uses a least squared weights (LSW) optimization to find the set of parameters that result in the lowest root mean squared error (RMSE) with respect to the “unknown truth”. Soil moisture information from multiple sources and resolutions, including the Soil Moisture Active Passive SMAP L3_SM_P_E (9 km, daily), the physically-based, land surface model (LSM) estimates from NLDAS_NOAH0125_H (1/8°, hourly), and the Oklahoma Mesonet ground sensor network (9 km interpolated from point, 30 min) is merged into a 9 km spatial and daily temporal resolution product across the state of Oklahoma from April 2015 to July 2019. This multi-sensor surface soil moisture (MSSM) product is assessed in terms of a state-wide benchmark and previously tested, in situ-based soil moisture product and SMAP L4. Results show that: (1) independent source products have differential values according to the regional conditions they represent, including land cover type, soils, irrigation, or climate regime; (2) beyond serving as validation sets, in situ measurements are of significant value for improving the accuracy of multi-sensor soil moisture datasets through TC; and (3) state-wide RMSE values obtained with MSSM are similar to the typical measurement error found on in situ ground measurements which provides some degree of confidence on the new product. MSSM is an improvement over currently available products in Oklahoma due to its minimized uncertainty, easiness of production, and continuous temporal and geographic coverage. Nevertheless, to exploit its utility, further tests of this methodology are needed in different climates, land cover types, geographic regions, and for other independent products and spatiotemporal resolutions. Full article

Data-driven machine learning technology can learn and extract features, a factor which is well recognized to be powerful in the warning and prediction of severe weather. With the large-scale deployment of the radar wind profile (RWP) observational network in China, dynamical variables with higher temporal and spatial resolution in the vertical become strong supports for machine-learning-based severe convection prediction. Based on the RWP mesonet that has been deployed in Beijing, this study uses the measurements from four triangles composed of six RWP stations to determine the profiles of divergence, vorticity, and vertical velocity before rainfall onsets. These dynamic feature variables, combined with cloud properties from Himawari-8 and ERA-5 reanalysis, serve as key input parameters for two rainfall forecast models based on the random forest (RF) classification algorithm. One is for the rainfall/non-rainfall forecast and another for the rainfall grade forecast. The roles of dynamic features such as divergence, vorticity, and vertical velocity are examined from ERA-5 reanalysis data and RWP measurements. The contribution of each feature variable to the performance of the RF model in independent tests is also discussed here. The results show that the usage of RWP observational data as the RF model input tends to result in better performance in rainfall/non-rainfall forecast 30 min in advance of rainfall onset than using the ERA-5 data as inputs. For the rainfall grade forecast, the divergence and vorticity that were estimated from the RWP measurements at 800 hPa show importance in improving the model performance in heavy and moderate rain forecasts. This indicates that the atmospheric dynamic variable measurements from RWP have great potential to improve the prediction skill of convection with the aid of a machine learning model. Full article

Improvements in soil moisture observations and modeling play a vital role in drought, water resources, flooding, and landslide management and forecasting. However, the lack of multisensor products that integrate different spatial scales (i.e., from 1 m² to 10² km²) is a pressing need in the management and forecasting chain. Up to date, surface soil moisture estimates could be obtained through three primary approaches: (1) in situ measurements and their interpolations, (2) remote sensing observations, and (3) land surface model (LSM) outputs. Each source of soil moisture has its own spatiotemporal resolution, strengths, and weaknesses. Therefore, their correct interpretation and application require an in-depth understanding of their accuracy and appropriateness. In this study, we explore the utility of the triple collocation (TC) method for an independent assessment of three soil moisture products to characterize their uncertainty structures and make recommendations toward a potential product merge. The state of Oklahoma is an ideal domain to test the hypotheses of this work because of the presence of marked west-to-east gradients in climate, vegetation, and soils. The three target soil moisture products include (1) the remotely sensed microwave soil moisture active passive (SMAP) L3_SM_P_E (9 km, daily), (2) the physically based LSM estimates from NLDAS_NOAH0125_H (1/8°, hourly; Noah), and (3) the Oklahoma Mesonet ground sensor network (point, 30 min). The product assessment was conducted from April 2015 to July 2019. The results indicate that, in general, Mesonet and Noah are the most reliable products, although their performance varies geographically and by land cover type, reflecting the main spatiotemporal characteristics and scope of each product. Specifically, Mesonet provides the best estimates of volumetric soil moisture with a mean Pearson correlation coefficient of 0.805, followed by Noah with 0.747. However, Noah represents the true soil moisture variation better than the interpolated Mesonet product on the mesoscale, with an averaged RMSE of 0.026 m³⁄m³. Over different land cover types, Mesonet had the best performance in shrub/scrub, herbaceous, hay/pasture, and cultivated crops with an average correlation coefficient of 0.79, while Noah achieved the best performance in evergreen, mixed, and deciduous forests, with an average correlation coefficient of 0.74. The period-integrated TC intercomparison results over nine climate divisions indicated that Noah outperformed in the central, northeast, and east-central regions. TC provides not only a new perspective for comparatively assessing multisource soil moisture products but also a basis for objective data merging to capitalize on the strengths of multisensor, multiplatform soil moisture products. Full article

Evapotranspiration is an essential component of the hydrologic cycle, and its accurate quantification is crucial for managing crop water requirements and the operation of irrigation systems. Evapotranspiration data is key to hydrological and water management research investigations, including studying the impact of various climatic factors on crop water requirements. It has been estimated as the product of the reference crop evapotranspiration and crop coefficient. Daily reference crop evapotranspiration (ET_o) can be determined by several methods and equations. The Food and Agriculture Organization Penman-Monteith equation requires complete weather data, whereas empirical equations such as Hargreaves and Samani, Valiantzas, Priestley-Taylor, Makkink, and Stephens-Stewart require limited weather data. This work evaluated different empirical equations for West Texas using the standard FAO Penman-Monteith method and calibrated their parameters to improve ET_o estimation. Detailed meteorological data from West Texas Mesonet and high resolution (800 m) Parameter-elevation Regressions on Independent Slopes Model (PRISM) datasets from 2007 to 2016 were used. Daily ET_o calculated using the standard FAO Penman-Monteith equation was compared to ET_o estimated based on different empirical methods. The results show that all original empirical equations underestimated ET_o. Calibration improved the performance of tested equations; however, there seems to be underestimation of ET_o in the 8–16 mm range. Overall, the monthly Hargreaves and Samani equation with either original or calibrated values of its parameters outperformed all tested models. This equation seems to be a reasonable estimator, especially under limited weather data conditions. Full article

With the development of computer hardware and deep learning, face manipulation videos represented by Deepfake have been widely spread on social media. From the perspective of symmetry, many forensics methods have been raised, while most detection performance might drop under compression attacks. To solve this robustness issue, this paper proposes a Deepfake video detection method based on MesoNet with preprocessing module. First, the preprocessing module is established to preprocess the cropped face images, which increases the discrimination among multi-color channels. Next, the preprocessed images are fed into the classic MesoNet. The detection performance of proposed method is verified on two datasets; the AUC on FaceForensics++ can reach 0.974, and it can reach 0.943 on Celeb-DF which is better than the current methods. More importantly, even in the case of heavy compression, the detection rate can still be more than 88%. Full article

Measurements of atmospheric pressure by mesoscale transects of vehicle platforms such as the National Severe Storms Lab (NSSL) mobile mesonets have previously been collected in various targeted field campaigns. The challenges involved were specifically documented in the very different environments of tornadogenesis (Markowski et al., 2002) and orographic foehn winds (Raab and Mayr 2008). In recent years, the Jackson State University Mobile Meteorology Unit (MMU) has been developed with broad ranging applications in mind. Barometric pressure was originally expected only to be used for calculation of potential temperature over transects with significant elevation change. Previous studies have determined a dynamic change in measured pressure due to vehicle motion relative to the air that varies quadratically with speed, in agreement with theoretical expectations. This quadratic relationship is examined for the MMU under a variety of conditions. In order to consider least squares regression of this relationship, it was necessary to also have accurate speed and elevation data. Since even quite small elevation changes can produce measurable pressure changes, it was considered necessary to reduce pressures in each transect to the mean elevation using the methodology of Markowski et al. (2002). This required a combination of digital elevation model (DEM) and geographic positioning system (GPS) data to have sufficiently accurate elevations matched to the locations of the pressure measurements. Speed relative to ground from the GPS was used in place of actual air flow speed. Cases to be discussed include transects from approximately 20 to 200 km in length: approximately uniform conditions in flat terrain; crossing of orographic barriers; and cold fronts. Differences between pressure data collected with and without a pressure port are also considered. The impacts for determination of mesoscale pressure gradients, potential temperature, and other derived quantities will be evaluated. Full article

This study examines the role of information systems (IS) on environmental sustainability by gaining an understanding of how benefits may be realized from using IS in a green context (a particular IS, regional mesonet (RM) equipped with information- and communication-based technologies and a comprehensive information system) through the use of duel approaches: a survey (218 respondents) and a case study (six interviews of stakeholders of a RM). Our results provide evidence how IS use contributes to different goals at different levels of sustainability and advance knowledge of utilizing IS for providing actual as well as anticipated benefits to sustainability. In addition, our findings provide suggestions on how successful IS might be used to further induce actions and advance goals of environmental sustainability that can contribute to energy policy-making. Full article

Coastal ecosystems are vulnerable to climate change and have been identified as sources of uncertainty in the global carbon budget. Here we introduce a recently established mesonet of eddy covariance towers in South Carolina and describe the sensor arrays and data workflow used to produce three site-years of flux observations in coastal ecosystems. The tower sites represent tidal salt marsh (US-HB1), mature longleaf pine forest (US-HB2), and longleaf pine restoration (replanted clearcut; US-HB3). Coastal ecosystems remain less represented in climate studies despite their potential to sequester large amounts of carbon. Our goal in publishing this open access dataset is to contribute observations in understudied coastal ecosystems to facilitate synthesis and modeling analyses that advance carbon cycle science. Full article