scholarly journals Evaluation of a Technique for Radar Identification of Large Hail across the Upper Midwest and Central Plains of the United States

2007 ◽  
Vol 22 (2) ◽  
pp. 244-254 ◽  
Author(s):  
Rodney A. Donavon ◽  
Karl A. Jungbluth
Keyword(s):  
United States ◽  
Linear Regression ◽  
Linear Relationship ◽  
Radar Data ◽  
The United States ◽  
Probability Of Detection ◽  
Coefficient Of Determination ◽  
Event Time ◽  
Severe Thunderstorms ◽  
Melting Level

Abstract Radar data were analyzed for severe thunderstorms that produced severe hail (>19 mm diameter) across the central and northern plains of the United States during the 2001–04 convective seasons. Results showed a strongly linear relationship between the 50-dBZ echo height and the height of the melting level—so strong that a severe hail warning methodology was successfully deployed at the National Weather Service Warning and Forecast Offices in North Dakota and Iowa. Specifically, for each of 183 severe hailstorms, the 50-dBZ echo height near the hail event time was plotted against the depth of the environmental melting level. Linear regression revealed a coefficient of determination of 0.86, which suggested a strong linear relationship between the 50-dBZ echo height and the melting-level depth for the severe hail producing storms. As the height of the melting level increased, the expected 50-dBZ echo height increased. A severe warning criterion for large hail was based on the 10th percentile from the linear regression, producing a probability of detection of 90% and a false alarm rate of 22%. Additional analysis found that the 50-dBZ echo-height technique performs very well for weakly to moderately sheared thunderstorm environments. However, for strongly sheared, supercell-type environments, signatures such as weak-echo regions and three-body scatter spikes led to more rapid severe thunderstorm detection in many cases.

scholarly journals Development of a semi-parametric PAR (Photosynthetically Active Radiation) partitioning model for the United States, version 1.0

2014 ◽  
Vol 7 (5) ◽  
pp. 2477-2484 ◽  
Author(s):  
J. C. Kathilankal ◽  
T. L. O'Halloran ◽  
A. Schmidt ◽  
C. V. Hanson ◽  
B. E. Law
Keyword(s):  
United States ◽  
Elevation Angle ◽  
Model Performance ◽  
Diffuse Radiation ◽  
The United States ◽  
Polynomial Model ◽  
Coefficient Of Determination ◽  
Data Set ◽  
Cubic Polynomial ◽  
Solar Elevation Angle

Abstract. A semi-parametric PAR diffuse radiation model was developed using commonly measured climatic variables from 108 site-years of data from 17 AmeriFlux sites. The model has a logistic form and improves upon previous efforts using a larger data set and physically viable climate variables as predictors, including relative humidity, clearness index, surface albedo and solar elevation angle. Model performance was evaluated by comparison with a simple cubic polynomial model developed for the PAR spectral range. The logistic model outperformed the polynomial model with an improved coefficient of determination and slope relative to measured data (logistic: R2 = 0.76; slope = 0.76; cubic: R2 = 0.73; slope = 0.72), making this the most robust PAR-partitioning model for the United States currently available.

An Analysis of 2016-2018 Tornadoes and National Weather Service Tornado Warnings across the contiguous United States

2021 ◽  
Author(s):  
Evan S. Bentley ◽  
Richard L. Thompson ◽  
Barry R. Bowers ◽  
Justin G. Gibbs ◽  
Steven E. Nelson
Keyword(s):  
United States ◽  
Rotational Velocity ◽  
Radar Data ◽  
Probability Of Detection ◽  
False Alarms ◽  
National Weather Service ◽  
Low Level ◽  
Tornado Warnings ◽  
Tornado Warning ◽  
Weather Service

AbstractPrevious work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–2018) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance.Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1nm) circulations in a poor (STP=0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.

scholarly journals A 5-yr 40-km-Resolution Global Climatology of Superrefraction for Ground-Based Weather Radars

2009 ◽  
Vol 48 (1) ◽  
pp. 89-110 ◽  
Author(s):  
Philippe Lopez
Keyword(s):  
United States ◽  
Electromagnetic Waves ◽  
Vertical Gradient ◽  
Radar Data ◽  
The United States ◽  
Horizontal Resolution ◽  
Trade Wind ◽  
Late Afternoon ◽  
The Balkans ◽  
Radar Images

Abstract The propagation of electromagnetic waves emitted from ground-based meteorological radars is determined by the stratification of the atmosphere. In extreme superrefractive situations characterized by strong temperature inversions or strong vertical gradients of moisture, the radar beam can be deflected toward the ground (ducting or trapping). This phenomenon often results in spurious returned echoes and misinterpretation of radar images such as erroneous precipitation detection. In this work, a 5-yr global climatology of the frequency of superrefractive and ducting conditions and of trapping-layer base height has been produced using refractivity computations from ECMWF temperature, moisture, and pressure analyses at a 40-km horizontal resolution. The aim of this climatology is to better document how frequent such events are, which is a prerequisite for fully benefiting from radar data information for the multiple purposes of model validation, precipitation analysis, and data assimilation. First, the main climatological features are summarized for the whole globe: high- and midlatitude oceans seldom experience superrefraction or ducting whereas tropical oceans are strongly affected, especially in regions where the trade wind inversion is intense and lying near the surface. Over land, seasonal averages of superrefraction (ducting) frequencies reach 80% (40%) over tropical moist areas year-round but remain below 40% (15%) in most other regions. A particular focus is then laid on Europe and the United States, where extensive precipitation radar networks already exist. Seasonal statistics exhibit a pronounced diurnal cycle of ducting occurrences, with averaged frequencies peaking at 60% in summer late afternoon over the eastern half of the United States, the Balkans, and the Po Valley but no ducts by midday. Similarly high ducting frequencies are found over the southwestern coast of the United States at night. A potentially strong reduction of ducting occurrences with increased radar height (especially in midlatitude summer late afternoon) is evidenced by initiating refractivity vertical gradient computations from either the lowest or the second lowest model level. However, installing radar on tall towers also brings other problems, such as a possible amplification of sidelobe clutter echoes.

A study about the linearity of trends between extreme and mean temperatures in Argentina

2021 ◽  
Author(s):  
Solange Suli ◽  
Matilde Rusticucci ◽  
Soledad Collazo
Keyword(s):  
Linear Regression ◽  
Linear Relationship ◽  
Statistical Significance ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Mean Temperature ◽  
Change In The Mean ◽  
The Mean ◽  
Student’S T ◽  
Cold Extremes

&lt;p&gt;Small variations in the mean state of the atmosphere can cause large changes in the frequency of extreme events. In order to deepen and extend previous results in time, in this work we analyzed the linear relationship between extreme and mean temperature (&amp;#932;) on a climate change scale in Argentina. Two monthly extreme indices, cold nights (TN10) and warm days (TX90), were calculated based on the quality-controlled daily minimum and maximum temperature data provided by the Argentine National Meteorological Service from 58 conventional weather stations located over Argentina in the 1977&amp;#8211;2017 period. Subsequently, we evaluated the relationship between the linear trends of extremes and mean temperature on a seasonal basis (JFM, AMJ, JAS, and OND). Student's T-test was performed to analyze their statistical significance at 5%. Firstly, positive (negative) and significant linear regressions were found between TX90 (TN10) trends and mean temperature trends for the four studied seasons. Therefore, an increase in the &amp;#932;-trend maintains a linear relationship with significant increase (decrease) of warm days (cold nights). Moreover, we found that JFM was the one with the highest coefficient of determination (0.602 for hot extremes and 0.511 for cold extremes), implying that 60.2% (51.1%) of the TX90 (TN10) trend could be explained as a function of the &amp;#932;-trend by a linear regression. In addition, in the JFM (OND) quarter, the TX90 index increased by 7.02 (6.02) % of days each with a 1 &amp;#186;C increase in the mean temperature. Likewise, the TN10 index decreased by 4.94 (and 4.99) % of days from a 1&amp;#186;C increase in the mean temperature for the JFM (AMJ) quarter. Finally, it is worthwhile to highlight the uneven behavior between hot and cold extremes and the mean temperature. Specifically, it was observed that the slopes of the linear regression calculated for the TX90 index and &amp;#932;&amp;#160;presented a higher absolute value than those registered for the TN10 index and &amp;#932;. Therefore, a change in the mean temperature affects hot extremes to a greater extent than cold ones in Argentina.&lt;/p&gt;

Statistical Assessment of Operating Experiences of Power Reactors in France, Japan, and the United States

2004 ◽  
Author(s):  
Hitoshi Sakamoto
Keyword(s):  
United States ◽  
Linear Regression ◽  
Nuclear Power ◽  
The United States ◽  
Major Type ◽  
Regression Analyses ◽  
Statistical Assessment ◽  
Statistical Correlations ◽  
The U.S

Statistical correlations for unavailability of power reactors across countries are sought. France, Japan and the United States, are selected because of their different political climates surrounding nuclear power through the 1990s. Outage data reveal that the dominating type of outage is different in each of the countries in spite of the similar plant types. In France, unplanned, externally caused, partial outages overwhelm other types of outages in number. In Japan, planned outages dominate in terms of number and duration. Unplanned outages are the major type in the U.S. These differences are not only due to technical differences but also to differences in economic and regulatory environments. Results of linear regression analyses suggest that unavailability factors are so random that country of operation, age of reactor and type of reactor cannot predict them well. This finding seems contrasting to an earlier study in the literature.

scholarly journals Upright Convection in Extratropical Cyclones: A Survey Using Ground‐Based Radar Data Over the United States

2020 ◽  
Vol 47 (4) ◽  
Author(s):  
Jeyavinoth Jeyaratnam ◽  
James F. Booth ◽  
Catherine M. Naud ◽  
Z. Johnny Luo ◽  
Cameron R. Homeyer
Keyword(s):  
United States ◽  
Radar Data ◽  
The United States ◽  
Extratropical Cyclones

scholarly journals Precipitation and Fatal Motor Vehicle Crashes: Continental Analysis with High-Resolution Radar Data

2019 ◽  
Vol 100 (8) ◽  
pp. 1453-1461 ◽  
Author(s):  
Scott E. Stevens ◽  
Carl J. Schreck ◽  
Shubhayu Saha ◽  
Jesse E. Bell ◽  
Kenneth E. Kunkel
Keyword(s):  
United States ◽  
High Resolution ◽  
Motor Vehicle ◽  
Radar Data ◽  
The United States ◽  
Motor Vehicle Crashes ◽  
Vehicle Crashes ◽  
Fatal Crash ◽  
Fatal Crashes ◽  
High Resolution Radar

AbstractMotor vehicle crashes remain a leading cause of accidental death in the United States, and weather is frequently cited as a contributing factor in fatal crashes. Previous studies have investigated the link between these crashes and precipitation typically using station-based observations that, while providing a good estimate of the prevailing conditions on a given day or hour, often fail to capture the conditions present at the actual time and location of a crash. Using a multiyear, high-resolution radar reanalysis and information on 125,012 fatal crashes spanning the entire continental United States over a 6-yr period, we find that the overall risk of a fatal crash increases by approximately 34% during active precipitation. The risk is significant in all regions of the continental United States, and it is highest during the morning rush hour and during the winter months.

scholarly journals Evaluation of the Solus One Salmonella Assay in Select Foods: Collaborative Study: First Action 2020.03

2020 ◽  
Vol 103 (6) ◽  
pp. 1568-1581
Author(s):  
Benjamin Bastin ◽  
M Joseph Benzinger ◽  
Erin S Crowley ◽  
James Agin ◽  
Raymond Wakefield
Keyword(s):  
United States ◽  
Statistical Analysis ◽  
Collaborative Study ◽  
Control Level ◽  
Reference Method ◽  
The United States ◽  
Probability Of Detection ◽  
Inoculum Level ◽  
Test Portion ◽  
The Matrix

Abstract Background The Solus One Salmonella immunoassay utilizes Salmonella specific selective media and automated liquid handling, for the rapid and specific detection of Salmonella species in select food types. Objective The candidate method was evaluated using 375 g test portions in an unpaired study design for a single matrix, instant non-fat dry milk (NFDM) powder. Method The matrix was compared to the United States Food and Drug Administration/Bacteriological Analytical Manual (FDA/BAM) Chapter 5 Salmonella reference method. Eleven participants from 10 laboratories within academia and industry, located within the United States, Mexico, South Africa, Germany, and the United Kingdom, contributed data for the collaborative study. Three levels of contamination were evaluated for each matrix: an uninoculated control level [0 colony forming units (CFU)/test portion], a low inoculum level (0.2–2 CFU/test portion) and a high inoculum level (2–5 CFU/test portion). Statistical analysis was conducted according to the Probability of Detection (POD) statistical model. Results Results obtained for the low inoculum level test portions produced a dLPOD value with a 95% confidence interval between the candidate method confirmed (both alternative and conventional confirmation procedures) and the reference method of 0.07 (−0.02, 0.15). Conclusions The dLPOD results indicate equivalence between the candidate method and the reference method for the matrix evaluated and the method demonstrated acceptable inter-laboratory reproducibility as determined in the collaborative evaluation. False positive and false negative rates were determined for the matrix and produce values of &lt;2%. Highlights Based on the data generated, the method demonstrated acceptable inter-laboratory reproducibility data and statistical analysis.

scholarly journals Tornado Damage Rating Probabilities Derived from WSR-88D Data

2017 ◽  
Vol 32 (4) ◽  
pp. 1509-1528 ◽  
Author(s):  
Richard L. Thompson ◽  
Bryan T. Smith ◽  
Jeremy S. Grams ◽  
Andrew R. Dean ◽  
Joseph C. Picca ◽  
...  
Keyword(s):  
United States ◽  
Great Plains ◽  
Probability Distributions ◽  
Rotational Velocity ◽  
The United States ◽  
Low Level ◽  
Convective Systems ◽  
Severe Thunderstorms ◽  
Damage Indicators ◽  
Tornado Damage

Abstract Previous work with observations from the NEXRAD (WSR-88D) network in the United States has shown that the probability of damage from a tornado, as represented by EF-scale ratings, increases as low-level rotational velocity increases. This work expands on previous studies by including reported tornadoes from 2014 to 2015, as well as a robust sample of nontornadic severe thunderstorms [≥1-in.- (2.54 cm) diameter hail, thunderstorm wind gusts ≥ 50 kt (25 m s−1), or reported wind damage] with low-level cyclonic rotation. The addition of the nontornadic sample allows the computation of tornado damage rating probabilities across a spectrum of organized severe thunderstorms represented by right-moving supercells and quasi-linear convective systems. Dual-polarization variables are used to ensure proper use of velocity data in the identification of tornadic and nontornadic cases. Tornado damage rating probabilities increase as low-level rotational velocity Vrot increases and circulation diameter decreases. The influence of height above radar level (or range from radar) is less obvious, with a muted tendency for tornado damage rating probabilities to increase as rotation (of the same Vrot magnitude) is observed closer to the ground. Consistent with previous work on gate-to-gate shear signatures such as the tornadic vortex signature, easily identifiable rotation poses a greater tornado risk compared to more nebulous areas of cyclonic azimuthal shear. Additionally, tornado probability distributions vary substantially (for similar sample sizes) when comparing the southeast United States, which has a high density of damage indicators, to the Great Plains, where damage indicators are more sparse.

Comparison of Ground-Based Radar and Geosynchronous Satellite Climatologies of Warm-Season Precipitation over the United States

2008 ◽  
Vol 47 (12) ◽  
pp. 3264-3270 ◽  
Author(s):  
John D. Tuttle ◽  
Richard E. Carbone ◽  
Phillip A. Arkin
Keyword(s):  
United States ◽  
Brightness Temperature ◽  
Satellite Data ◽  
Warm Season ◽  
Propagation Speed ◽  
Radar Data ◽  
The United States ◽  
Temperature Threshold ◽  
Brightness Temperatures ◽  
The Difference

Abstract Studies in the past several years have documented the climatology of warm-season precipitation-episode statistics (propagation speed, span, and duration) over the United States using a national composited radar dataset. These climatological studies have recently been extended to other continents, including Asia, Africa, and Australia. However, continental regions outside the United States have insufficient radar coverage, and the newer studies have had to rely on geostationary satellite data at infrared (IR) frequencies as a proxy for rainfall. It is well known that the use of IR brightness temperatures to infer rainfall is subject to large errors. In this study, the statistics of warm-season precipitation episodes derived from radar and satellite IR measurements over the United States are compared and biases introduced by the satellite data are evaluated. It is found that the satellite span and duration statistics are highly dependent upon the brightness temperature threshold used but with the appropriate choices of thresholds can be brought into good agreement with those based upon radar data. The propagation-speed statistics of satellite events are on average ∼4 m s−1 faster than radar events and are relatively insensitive to the brightness temperature threshold. A simple correction procedure based upon the difference between the steering winds for the precipitation core and the winds at the level of maximum anvil outflow is developed.

Sign in / Sign up

Export Citation Format

Share Document