Mesonet Stations Research Articles

Optimizing Numerical Weather Prediction Utility of the Maryland Mesonet with Observing System Simulation Experiments

Abstract The Maryland Mesonet Project will construct a network of 75 surface observing stations with aims that include mitigating the statewide impact of severe convective storms and improving analyses of record. The spatial configuration of mesonet stations is expected to affect the utility newly provided observations will have via data assimilation, making it desirable to study the effects of mesonet configuration. Furthermore, the impact associated with any observing system configuration is constrained by errors inherent to the prediction systems used to generate forecasts, which may change with future advances in data assimilation methodology, physical parameterization schemes, and resource availability. To address such possibilities, we perform sets of observing system simulation experiments using a high-resolution regional modeling system to assess the expected impact of four candidate mesonet configurations. Experiments cover seven 18-hour case-study events featuring moist convective regimes associated with severe weather over the state of Maryland and are performed using two versions of our experimental modeling system: a ’standard-uncertainty’ configuration tuned to be representative of existing convective-allowing prediction systems, and a ’constrained-uncertainty’ configuration with reduced boundary condition and model error that reflects a possible trajectory for future prediction systems. We find that the assimilation of mesonet data produces definitive improvements to analysis fields below 1000 m that are mediated by modeling system uncertainty. Conversely, mesonet impact on forecast verification is inconclusive and strongly variable across verification metrics. The impact of mesonet configuration appears limited by a saturation effect that caps local analysis improvements past a minimal density of observing stations.

NYSolarCast: A solar power forecasting system for New York State

Solar energy generation capacity will need to be greatly increased to meet aggressive clean energy targets by New York State (NYS), which are 70% renewable energy (RE) generation by 2030, and 100% by 2040. Because solar energy is a variable, weather-driven resource, accurate forecasts of solar energy generation both in nowcast (intra-day) and day-ahead time horizons are necessary for electric utilities and independent system operators, as they maintain grid stability and maximize RE use.Meeting this need for NYS, a gridded, open-source solar power forecasting system called NYSolarCast was developed (https://github.com/ncar/NYSolarCast_delivery). NYSolarCast makes 15-min resolution predictions of global horizontal irradiance (GHI) on a 3-km grid covering all of NYS, which are then used to estimate solar power generation both for select utility-scale PV plants (15-min resolution) and for zone-aggregated distributed PV (1-h resolution). The statewide GHI forecasts are made by applying the StatCast statistical forecasting model, which is trained on over two years of GHI observations from pyranometers at all 126 NYS Mesonet stations and gridded numerical weather prediction forecasts from both the Weather Research and Forecasting model tuned for solar applications (WRF-Solar®) and NOAA’s operational High-Resolution Rapid Refresh (HRRR) model. Forecasts are made at each NYS Mesonet site and then blended outward into the rest of the grid. This paper gives an overview of NYSolarCast performance for intra-day (0–6-h) GHI and power forecasts during a one-year period.

Impact of real-time weather conditions on crash injury severity in Kentucky using the correlated random parameters logit model with heterogeneity in means

The present study investigated the impact of real-time weather (air temperature, relative humidity, precipitation, wind speed, and solar radiation) on crash injury severity. Recent crash data (January 2016 to April 2021) on Interstate-75 in the state of Kentucky were merged with real-time weather information (retrieved from Kentucky Mesonet stations) at the 1-hour level. The severity index “SI” (i.e., the ratio of percent severe crashes to percent exposure of a specific weather state during the crash period) was introduced to evaluate the impact of different real-time weather states on fatal and severe injury crashes. Furthermore, the standard mixed logit (MXL), correlated mixed logit (CMXL), and correlated mixed logit with heterogeneity in means (CMXLHM) models were fitted and compared to identify the risk factors contributing to crash injury severity while accounting for unobserved heterogeneity. The results showed that the CMXLHM model was statistically superior to the CMXL and MXL models based on various goodness-of-fit measures (e.g., Akaike information criterion “AIC” and McFadden pseudo R-squared). Results from the SI analysis and CMXLHM model showed that real-time weather-related factors (e.g., air temperature ≥ 70 0F and relative humidity ≥ 90 %) were significantly associated with higher severe injury likelihood. Further, driving under the influence (DUI), young drivers, and vehicle travel speed were associated with greater injury severities. On the other hand, presence of horizontal curve, passenger cars, and hourly traffic volume were associated with lower injury severity likelihood. The study outcomes can help in incident management by suggesting specific real-time weather-related states to feed to dynamic message signs (DMS) to enhance travelers’ safety along the interstates.

PSIII-13 Effects of Weather, Body Weight, and Feed Intake on Total and Liquid Water Intake in Beef Bulls

Abstract Research conducted in the 1950s to determine water requirements used cattle that vary greatly from the animals being raised today. Changes in lean composition and growth rate in young cattle and milk production and mature size in cows have likely altered water requirements. Furthermore, most of the research has been conducted on animals being fed for slaughter, rather than bulls or reproductive females. The objective of this study was to analyze the effect of weather variables, dry matter intake (DMI), and body weight (BW) on total daily water intake (TDWI) in growing beef bulls. Angus (n = 15) and Simmental x Angus bulls (n = 29) with an average initial BW of 362.5 ± 27.0 kg were housed in an open front, monoslope building at the SDSU Cow-Calf Education and Research Facility (CCERF) from November 9, 2021, to March 1, 2022. Bulls were provided with ad libitum access to feed (1.15 Mcal NEg/kg dry matter, 14% crude protein, 52.2% dry matter) and water using the Insentec Roughage Intake Control system (Hokofarm, Marknesse, Netherlands). Feed and water disappearance were assumed to be caused by intake. Water intake was measured in kg; however, the data were converted to L based on a correction factor determined by weighing a known amount of water. Body weights were taken every 28 d to determine ADG and predict daily BW during the duration of the study. Daily weather variables were collected from the South Dakota Mesonet station located 3.86 km from the CCERF. Weather variables analyzed included: average windchill (°C), solar radiation (W/m2), and wind speed (km/h); and minimum, maximum, and average relative humidity (%) and average temperature (°C). R-studio (R-4.1.2.pkg) was used to develop a linear mixed effects model with random intercept and slope used to account for the within subject correlations. Total daily water intake increased with increasing DMI (1.921 L TDWI per kg DMI; P < 0.0001), BW (0.025 L TDWI per kg BW; P = 0.0055), and maximum daily temperature (0.171 L TDWI per 1°C; P = 0.0023). For every 1% increase in the minimum relative humidity, TDWI tended to increase by 0.041 L (P = 0.0616). Warmer wind chill also tended to increase TDWI (0.463 L per 1°C; P = 0.0707). The effects of DMI, BW, maximum daily temperature, minimum relative humidity, and wind chill were predictive of TDWI in growing beef bulls fed during the winter months in South Dakota. This model will be useful for predicting TDWI in growing beef bulls that are raised in areas that experience similar winter weather conditions.

Tracking the Effect of Human Activity on MeSO-net Noise Using Seismic Data Traffic—Did Seismic Noise in Tokyo Truly Decrease during the COVID-19 State of Emergency?

Abstract Human activities cause seismic noise over 1 Hz (cultural noise), and the recent articles have reported that the curtailing of socioeconomic activities during the novel coronavirus (COVID-19) pandemic in 2020 appeared to reduce high-frequency seismic noise amplitudes in cities. The Tokyo metropolitan area in Japan, where seismic stations are densely distributed and various anthropogenic activities have been closely monitored, is an ideal study area to investigate the effect of human activity on high-frequency seismic noise during the pandemic. We demonstrated that the magnitude of seismic data traffic (SDT), indexed by the packet size of continuous seismic data in WIN32 format, is a good indicator for monitoring time-dependent changes in high-frequency noise levels. The SDT of 169 Metropolitan Seismic Observation network (MeSO-net) stations—a continuous accelerometer network that is mostly located at schools in the Tokyo metropolitan area—decreased by approximately 1%–3% from March to June 2020, when a state of emergency in Japan was first declared, compared with that in the previous year. We revealed that the SDT decrease was prominent only at stations near school buildings, and the SDT trend was uncorrelated with the temporal changes in the population and vehicular traffic volume near the seismic stations. We also found strong correlations between the SDT reduction and school size (classified by the number of students enrolled), implying that the noise decrease at the MeSO-net stations during the pandemic was strongly influenced by school-based activities. Thus, the noise reduction observed at MeSO-net stations during the COVID-19 pandemic in 2020 did not provide strong evidence of quieting in the Tokyo metropolitan area.

Influence of synoptic weather conditions on atmometers on the Delmarva Peninsula, USA

Reference evapotranspiration data from atmometers at three locations on the Delmarva Peninsula (USA) were compared to Penman-Monteith reference evapotranspiration (ETo) data across two growing seasons. Atmometer reference evapotranspiration (ETa) was found to underestimate ETo by 22.8% in 2016 and 30.4% in 2017. Stepwise linear regression was used to examine the relationship between both ET datasets and local meteorological conditions measured by Delaware Environmental Observing System (DEOS) mesonet stations that were co-located with the atmometers. Variability of ETa and ETo are well explained (R2 equal to 0.890 and 0.956, respectively) by a combination of meteorological variables, though the R2 in 2017 (R2 = 0.754) was notably lower than in 2016 (R2 = 0.890). The ET datasets were further examined by partitioning the data into days with similar synoptic conditions using a temporal synoptic index (TSI). Using the TSI results, three dominant synoptic categories were defined during the study period: High Pressure (HP), Southwest Flow (SW), and Cold Fronts (CF). Overall, the similarity between ETa and ETo was greatest on HP days, followed by CF and SW days. This relationship was primarily driven by wind speed, which had the greatest influence on ETa-ETo differences under all synoptic weather patterns. The 2016 growing season consisted of more days with synoptic conditions that are associated with smaller ETa -ETo differences than the 2017 growing season. Thus, changes in synoptic category frequency impact the nature of the ETa-ETo relationship from season to season. This study improves upon previous atmometer comparison studies by associating atmometer correction factors with synoptic weather patterns and descriptions in order to improve the utility of atmometers and remove the need for expensive meteorological equipment to correct atmometer data.

An Evaluation of Strong-Motion Parameters at the S-net Ocean-Bottom Seismograph Sites Near the Kanto Basin for Earthquake Early Warning

We analyzed strong-motion records at the ground and borehole in and around the Kanto Basin and the seafloor in the Japan Trench area from three nearby offshore earthquakes of similar magnitudes (Mw 5.8–5.9). The seafloor strong-motion records were obtained from S-net, which was established to enhance tsunami and earthquake early warnings after the 2011 great Tohoku-oki earthquake disaster. The borehole records were obtained from MeSO-net, a dense network of seismometers installed at a depth of 20 m in the Tokyo metropolitan area. The ground records were obtained from the K-NET and KiK-net networks, established after the 1995 great Hanshin-Awaji earthquake disaster. The MeSO-net and S-net stations record the shakings continuously, while the K-NET and KiK-net records are based on triggering thresholds. It is crucial to evaluate the properties of strong motions recorded by the S-net for earthquake early warning (EEW). This paper compared the peak ground accelerations (PGAs) and peak ground velocities (PGVs) between the S-net and K-NET/KiK-net stations. Because the MeSO-net records were from the borehole, we compared the PGAs and significant durations of the low-frequency motions (0.1–0.5 Hz) between the S-net and MeSO-net stations from identical record lengths. We found that the horizontal PGAs and PGVs at the S-net sites were similar to or larger than the K-NET/KiK-net sites for the S wave. In contrast, the vertical PGAs and PGVs at the S-net sites were similar to or smaller than those at the K-NET/KiK-net sites for the S wave. Particularly, the PGAs and PGVs for the P-wave parts on the vertical-component records of S-net were, on average, much smaller than those of K-NET/KiK-net records. The difference was more evident in the PGAs. The average ratios of S-wave horizontal to vertical PGAs were about 2.5 and 5 for the land and S-net sites, respectively. The low-frequency PGAs at the S-net sites were similar to or larger than those of the MeSO-net borehole records. The significant durations between the two-networks low-frequency records were generally comparable. Quantification of the results from a larger dataset may contribute to ground-motion prediction for EEW and the design of the offshore facilities.

MesoSoil v2.0: An updated soil physical property database for the Oklahoma Mesonet

AbstractSoil moisture data from the Oklahoma Mesonet have been used in numerous fields within the Earth sciences, including agriculture, hydrology, and meteorology. Soil matric potentials measured by heat dissipation sensors at Oklahoma Mesonet stations have been converted to soil volumetric water content estimates using soil water retention curve parameters estimated by the Rosetta pedotransfer function. Recently, an improved version of Rosetta, Rosetta3, was released. Informed by this new pedotransfer function and soil sampling at additional locations, an improved version of the Oklahoma Mesonet soil physical property database, MesoSoil v2.0, has been created. This article presents this new soil database, compares soil water retention parameters estimated using Rosetta3 with those derived from the original Rosetta model, and describes the effects of changes in those parameters on estimated volumetric water content. Using the Rosetta3 model led to changes in the estimated water retention parameters, most notably decreases in the parameter α, which is related to the inverse of the air‐entry potential. These changes resulted in volumetric water content estimates that differed from those based on Rosetta1, with mean absolute differences averaging 0.02 cm3 cm–3 across all site‐years and the greatest differences occurring during wet periods. The mean volumetric water content estimated using the Rosetta3 parameters across more than 100 sites was not significantly different from that determined by soil sampling. The updated database is publicly available and may be found here: http://soilphysics.okstate.edu/data.

Developing and Validating Heat Exposure Products Using the U.S. Climate Reference Network

AbstractExtreme heat is one of the most pressing climate risks in the United States and is exacerbated by a warming climate and aging population. Much work in heat health has focused only on temperature-based metrics, which do not fully measure the physiological impact of heat stress on the human body. The U.S. Climate Reference Network (USCRN) consists of 139 sites across the United States and includes meteorological parameters that fully encompass human tolerance to heat, including relative humidity, wind, and solar radiation. Hourly and 5-min observations from USCRN are used to develop heat exposure products, including heat index (HI), apparent temperature (AT), and wet-bulb globe temperature (WBGT). Validation of this product is conducted with nearby airport and mesonet stations, with reanalysis data used to fill in data gaps. Using these derived heat products, two separate analyses are conducted. The first is based on standardized anomalies, which place current heat state in the context of a long-term climate record. In the second study, heat events are classified by time spent at various levels of severity of conditions. There is no consensus as to what defines a heat event, so a comparison of absolute thresholds (i.e., ≥30.0°, 35.0°, and 40.0°C) and relative thresholds (≥90th, 95th, and 98th percentile) will be examined. The efficacy of the product set will be studied using an extreme heat case study in the southeastern United States. While no heat exposure metric is deemed superior, each has their own advantages and caveats, especially in the context of public communication.

Grain sorghum production: Influence of planting date, hybrid selection, and insecticide application

AbstractGrain sorghum [Sorghum bicolor (L.) Moench] has been a valuable crop for the southern Great Plains due to its ability to overcome the high temperatures and sporadic rain events common in the region. Optimizing production practices has always been critical for grain sorghum to overcome these environmental challenges. In response to sugarcane aphids (SCA), a detrimental pest in grain sorghum production, common agronomic management practices rapidly shifted with no regard to how such changes would affect overall production. This research is aimed to evaluate the effects of the hasty management changes on grain sorghum plant stands and yields in Oklahoma. Trials were established in 2016 and 2017 at the Northcentral Research Station in Lahoma, OK, and the Efaw Research Station near Stillwater, OK. Four planting dates, two hybrid types, and insecticide applications were evaluated on their impact on early‐season stands and crop yield. Findings showed that planting date had the most consistent impact on sorghum yields, across hybrid types and insecticide applications. Planting in April resulted in higher yields in 2016. However, results from 2017 showed that cooler soil temperatures, as measured by Mesonet stations at each location, can still limit yield when grain sorghum is planted too early. Early‐season plant stands followed the same trend as crop yields, with the highest early‐season stands occurring in the same period as highest yields. Yields and stands consistently decreased with later planting. It was also during these later planting dates that hybrid selection and insecticide applications were found to have a significant impact on yield.

Two-step seismic noise reduction caused by COVID-19 induced reduction in social activity in metropolitan Tokyo, Japan

The COVID-19 pandemic that started at the end of 2019 forced populations around the world to reduce social and economic activities; it is believed that this can prevent the spread of the disease. In this paper, we report an analysis of the seismic noise during such an induced social activity reduction in the Tokyo metropolitan area, Japan. Using seismic data obtained from 18 stations in the Metropolitan Seismic Observation Network (MeSO-net), a two-step seismic noise reduction was observed during the timeline of COVID-19 in Tokyo. The first noise reduction occurred at the beginning of March 2020 in the frequency band of 20–40 Hz. This corresponded with the request by the Prime Minister of Japan for a nationwide shutdown of schools. Although social activity was not reduced significantly at this juncture, local reduction of seismic wave excitation in the high-frequency band, 20–40 Hz, was recorded at some MeSO-net stations located in school properties. The second reduction of seismic noise occurred at the end of March to the beginning of April 2020 in a wider frequency band including lower frequency bands of 1–20 Hz. This timing corresponds to when the Governors of the Tokyo metropolitan area requested citizens to stay home and when the state of emergency was declared for the Tokyo metropolitan area by the government, respectively. Since then, the estimated population at train stations abruptly dropped, which suggests that social activity was severely reduced. Such large-scale changes in social activity affect the seismic noise level in low-frequency bands. The seismic noise level started to increase from the middle of May correlating with increase in population at the train stations. This suggests that social activity restarted even before the state of emergency was lifted at the end of May. The two-step seismic noise reduction observed in this study has not been reported in other cities around the world. Unexpected reduction of social activity due to COVID-19 provided a rare opportunity to investigate the characteristics of seismic noise caused by human activities.

More science with less: evaluation of a 3D-printed weather station

Abstract. A weather station built using 3D-printed parts and low-cost sensors, based on plans and guidance provided by the University Corporation for Atmospheric Research 3D-Printed Automatic Weather Station Initiative, was deployed alongside an Oklahoma Mesonet station to compare its performance against standard commercial sensors and determine the longevity and durability of the system. Temperature, relative humidity, atmospheric pressure, wind speed and direction, solar radiation, and precipitation measurements were collected over an 8-month field deployment in Norman, Oklahoma. Measurements were comparable to the commercial sensors except for wind direction, which proved to be problematic. Longevity and durability of the system varied, as some sensors and 3D-printed components failed during the deployment. Overall, results show that these low-cost sensors are comparable to the more expensive commercial counterparts and could serve as viable alternatives for researchers and educators with limited resources for short-term deployments. Long-term deployments are feasible with proper maintenance and regular replacement of sensors and 3D-printed components.

Spatially Explicit Model for Statistical Downscaling of Satellite Passive Microwave Soil Moisture

We introduce a spatially explicit statistical downscaling (SESD) method that fuses multiscale geospatial data with the soil moisture (SM) product from NASA’s SM Active and Passive (SMAP) satellite. The multiscale data included the 9-km resolution SMAP SM image, 1-km resolution normalized difference vegetation index (NDVI), 1-km digital elevation model (DEM), 1-km resolution MODIS land surface temperature (LST), 500-m resolution gross primary productivity (GPP), 30-m resolution topographical water index (TWI), and West Texas Mesonet (WTM) station data. We used the random forest (RF) machine learning method to make a downscaled SM prediction at the 1-km resolution. Then, a regression kriging was applied to model the unpredicted variability at local scales to produce downscaled SM using the WTM station data. Due to the low number of ground truth samples, the validation was based on Monte -Carlo cross validation (CV) to calculate the unbiased root-mean-square deviation (ubRMSD), root-mean-square deviation (RMSD), and bias of the test set randomly separated from the training set from the WTM station data. Model validation showed that the downscaled SM data at the 1-km resolution can significantly improve the accuracy of the SM product as well as enhancing its spatial resolution. This article has its novelty in using the spatially explicit model to reconcile the scale difference from satellite data and ground observations.

Deterministic chaotic dynamics in soil moisture across Nebraska

Soil moisture systems generally exhibit complex spatiotemporal patterns due to their nonlinear interactions with surrounding environments. In this paper, the possible existence of chaotic behaviors in soil moisture time series was investigated using the chaos theory, specifically based on the techniques of phase space reconstruction, correlation dimension (CD), largest Lyapunov exponent (LLE), and local approximation prediction (LAP) methods. Daily soil moisture data for six years along with different influencing factors were obtained for 35 Nebraska Mesonet (NM) stations with varying hydroclimatic conditions and soil properties. The results of CDs and LLEs revealed the presence of chaos in soil moisture systems, implying that soil moisture dynamics was controlled by deterministic systems with a limited number of governing variables. The results obtained by the LAP method offered further evidence on the chaotic behavior of soil moisture systems. Specifically, Median of CD values of 4.51, 4.06, 3.23, and 1.40 were obtained for soil moisture time series at the depths of 10, 25, 50, and 100 cm, respectively, indicating that the complexity of soil moisture systems decreased with increasing depth. Moreover, the correlations of CDs with different influencing factors showed that the dominant controls on soil moisture dynamics switched at different depths. At shallower depths (e.g., 10 and 25 cm), CDs were more correlated with meteorological forcings, while stronger correlations emerged between CDs and soil texture in deeper soil layers (e.g., 50 and 100 cm). As the first attempt to analyze the complexity of soil moisture systems from the perspective of the chaos theory, the results of this study offer additional avenues for understanding chaotic behaviors of soil moisture dynamics.

368 Effects of average temperature, wind speed, and solar radiation on daily water intake of beef bulls in winter and early spring in Eastern South Dakota

Abstract Few studies have estimated effects of weather on water intake in beef cattle, and most of these studies estimated effects during summer months. Our objective was to estimate effects of temperature, wind speed, and sunlight on daily water intake (DWI) of beef bulls in winter and early spring. Angus and SimAngus® bulls (n = 36; average starting BW = 369.5 ± 81.8 kg) were sampled from the South Dakota State University Cow-Calf Education and Research Facility (CCERF, Brookings, SD) from December 22, 2017 through April 5, 2018 (104 d total). Bulls were fed a diet (51.97% DM, 14% CP, and 1.08 Mcal NEg/kg DM) of high-moisture corn, corn silage, alfalfa hay, and supplement pellet. Individual feed and water intake were measured with an Insentec RIC system (Hokofarm, Marknesse, The Netherlands). Weather data, including average temperature, wind speed, and solar radiation were obtained from a Mesonet station located 3.9 km from the CCERF. Body weights were collected before feeding at four time points: December 13-14 (averaged), January 23, February 20, and April 2. Average daily gain was calculated from BW and used to predict BW on each day of the trial. Daily water and feed intakes were summed across all bulls each day; total DWI for all 36 bulls was the dependent variable. Average DWI per bull was 17.8 ± 7.05 kg/d and average total DWI per bull (including feed) was 28.9 ± 10.1 kg/d. Effects of average temperature, wind speed, and solar radiation were estimated with a linear model including a covariate of ADFI as percent predicted BW. Increased average temperature (P < 0.001) and solar radiation (P = 0.032) were associated with increased DWI, while increased average wind speed was associated with decreased DWI (P = 0.009). Temperature, wind speed, and amount of sunlight were associated with water intake of beef bulls fed from December until early April in Eastern South Dakota.

A Modified Framework for Quantifying Land–Atmosphere Covariability during Hydrometeorological and Soil Wetness Extremes in Oklahoma

AbstractGlobal “hot spots” for land–atmosphere coupling have been identified through various modeling studies—both local and global in scope. One hot spot that is common to many of these analyses is the U.S. southern Great Plains (SGP). In this study, we perform a mesoscale analysis, enabled by the Oklahoma Mesonet, that bridges the spatial and temporal gaps between preceding local and global analyses of coupling. We focus primarily on east–west variations in seasonal coupling in the context of interannual variability over the period spanning 2000–15. Using North American Regional Reanalysis (NARR)-derived standardized anomalies of convective triggering potential (CTP) and the low-level humidity index (HI), we investigate changes in the covariance of soil moisture and the atmospheric low-level thermodynamic profile during seasonal hydrometeorological extremes. Daily CTP and HI z scores, dependent upon climatology at individual NARR grid points, were computed and compared to in situ soil moisture observations at the nearest mesonet station to provide nearly collocated annual composites over dry and wet soils. Extreme dry and wet year CTP and HI z-score distributions are shown to deviate significantly from climatology and therefore may constitute atmospheric precursors to extreme events. The most extreme rainfall years differ from climatology but also from one another, indicating variability in the strength of land–atmosphere coupling during these years. Overall, the covariance between soil moisture and CTP/HI is much greater during drought years, and coupling appears more consistent. For example, propagation of drought during 2011 occurred under antecedent CTP and HI conditions that were identified by this study as being conducive to positive dry feedbacks demonstrating potential utility of this framework in forecasting regional drought propagation.

Inference, Simulation and Application of a Latent Trawl Model for Extreme Values

We extend the study of a parametric latent model for extreme values from Noven et al. (2018) which captures serial dependence in the exceedances above a threshold using so-called trawl processes (Barndorff-Nielsen (2011)) - a family of stationary and infinitely divisible random processes. In this regard, this article comprises a new approximation of the autocorrelation function at small lags. Applying this result, we unveil a unprecedented way to estimate key trawl parameters along with their convergence in probability to the true value under reasonable technical assumptions. We also investigate an identifiability issue from both theoretical arguments and numerical examples with a focus on a simulation study. This leads to apply this model on solar energy intake data (ARNE Mesonet station, Oklahoma, USA) with negative shape parameter which corroborates the flexibility and goodness-of-fit originally tested in Noven et al. (2018).

Investigation of Liquid Cloud Microphysical Properties of Deep Convective Systems: 2. Parameterization of Raindrop Size Distribution and its Application for Convective Rain Estimation

AbstractThe liquid cloud microphysical properties for stratiform rain (SR) have been investigated in Part 1 of this series. Since convective rain (CR) has characteristics in raindrop size distribution (DSD) and precipitation that are distinct from SR, we investigate the CR properties in this study by using 20 hr of CR samples collected by 17 Automatic Parsivel Units disdrometers during the Midlatitude Continental Convective Clouds Experiment over the Atmospheric Radiation Measurement Southern Great Plains site. A full spectrum of DSD is constructed based on a total of 23 size channels (0.321 to 9.785 mm), and both Gamma and Exponential fitted functions are applied to extract the DSD shape parameters. Compared to SR properties, CR has distinct features including broader size range and narrower exponential slope parameter (λE). These results indicate that the assumption of constant exponential intercept parameter (N0E) is inappropriate for CR rain rate estimates. Additionally, the subsetting scheme of the CR DSD spectra also has a strong impact on the Gamma/Exponential functions. Therefore, a new CR DSD parameterization scheme is developed by choosing the appropriate spectra subset with the constraint of rain rate and using the constant λE instead of constant N0E. With the input of the radar reflectivity at the lowest observed height, the newly calculated CR rain rates match well with the collocated surface rain gauge measurements (127 Mesonet stations and 17 Automatic Parsivel Units), while the rain rates calculated using traditional Z‐R relationship are 3–4 times larger, indicating that constant λE is a better assumption for CR DSD.

Observing the May 2015 Record Rainfall at Norman, Oklahoma, Using Various Methods

Abstract The May 2015 record rainfall that occurred across Oklahoma was the result of a large number of high-intensity rain events. A unique set of observations from gauges in the Oklahoma Mesonet, the NWS Cooperative Observer (COOP) network, the Community Collaborative Rain, Hail and Snow (CoCoRaHS) network, an experimental pit gauge system, and NWS radar was available that covered an area in and around Norman, Oklahoma. This paper documents the performance of the various gauges throughout the course of the month. Key findings are 1) observations from all methods significantly exceeded the 200-yr return interval; 2) a weighing-bucket gauge at ground level recorded amounts up to 4.5% higher than a similarly located ground-level tipping-bucket gauge and up to 8.2% higher than a nearby aboveground tipping-bucket gauge; 3) a manual COOP gauge recorded nearly identical (within 1.2%) observations as compared to an automated tipping-bucket gauge at a collocated Mesonet station; and 4) observations from 26 CoCoRaHS gauges yielded an average rainfall within 1% of the aerially averaged radar rainfall derived from the Multisensor Precipitation Estimator.

Characterization of the 14 June 2011 Norman, Oklahoma, Downburst through Dual-Polarization Radar Observations and Hydrometeor Classification

AbstractOn 14 June 2011, thunderstorms developed along a cold front in central Oklahoma in a thermodynamic environment that was conducive for downbursts. One of the thunderstorms produced a wet downburst in Norman, Oklahoma, that resulted in surface winds in excess of 35 m s−1 (>80 mi h−1) and hailstones in excess of 4 cm in diameter. Unique 1-min observations of the downburst were recorded by an Oklahoma Mesonet station. These observations indicated a 6.6-hPa pressure rise that was coincident with a rain rate of 213 mm h−1 at the center of the downburst. In this event, both the research KOUN (Norman) and operational KTLX (Oklahoma City, Oklahoma) Weather Surveillance Radar-1988 Doppler (WSR-88D) instruments were scanning this downburst and its parent storm at close range (<30 km). KOUN provided polarimetric radar data (PRD) while both radars provided limited dual-Doppler coverage. The evolution of the downburst is analyzed mostly through the use of reconstructed range–height indicators of the PRD. A hydrometeor classification algorithm (HCA) is applied to the PRD to gain further understanding of the microphysical evolution of the downburst. Through the analyses, it is seen that graupel aloft made a transition to a nearly all rain and hail mixture above the 0°C level. This large area of mixed rain and hail eventually descended to the ground, causing the downburst. In this study, the HCA analyses are utilized to develop a conceptual model that characterizes the hydrometeor evolution of the parent downburst storm.