scholarly journals The NSSL Hydrometeor Classification Algorithm in Winter Surface Precipitation: Evaluation and Future Development

2011 ◽  
Vol 26 (5) ◽  
pp. 756-765 ◽  
Author(s):  
Kimberly L. Elmore
Keyword(s):  
Warm Season ◽  
Radar Data ◽  
Skill Score ◽  
Detection Algorithm ◽  
Classification Algorithm ◽  
Environmental Data ◽  
Surface Precipitation ◽  
Freezing Level ◽  
American Meteorological Society ◽  
Precipitation Type

Abstract The National Severe Storms Laboratory (NSSL) has developed a hydrometeor classification algorithm (HCA) for use with the polarimetric upgrade of the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network. The algorithm was developed specifically for warm-season convection, but it will run regardless of season, and so its performance on surface precipitation type during winter events is examined here. The HCA output is compared with collocated (in time and space) observations of precipitation type provided by the public. The Peirce skill score (PSS) shows that the NSSL HCA applied to winter surface precipitation displays little skill, with a PSS of only 0.115. Further analysis indicates that HCA failures are strongly linked to the inability of HCA to accommodate refreezing below the first freezing level and to errors in the melting-level detection algorithm. Entrants in the 2009 American Meteorological Society second annual artificial intelligence competition developed classification methods that yield a PSS of 0.35 using a subset of available radar data merged with limited environmental data. Thus, when polarimetric radar data and environmental data are appropriately combined, more information about winter surface precipitation type is available than from either data source alone.

scholarly journals A Dual-Polarization Radar Hydrometeor Classification Algorithm for Winter Precipitation

2014 ◽  
Vol 31 (7) ◽  
pp. 1457-1481 ◽  
Author(s):  
Elizabeth J. Thompson ◽  
Steven A. Rutledge ◽  
Brenda Dolan ◽  
V. Chandrasekar ◽  
Boon Leng Cheong
Keyword(s):  
Electromagnetic Scattering ◽  
Radar Data ◽  
Detection Algorithm ◽  
Cold Season ◽  
Classification Algorithm ◽  
Data Quality Control ◽  
Freezing Rain ◽  
External Temperature ◽  
Snow Crystals ◽  
Melting Level

Abstract The purpose of this study is to demonstrate the use of polarimetric observations in a radar-based winter hydrometeor classification algorithm. This is accomplished by deriving bulk electromagnetic scattering properties of stratiform, cold-season rain, freezing rain, sleet, dry aggregated snowflakes, dendritic snow crystals, and platelike snow crystals at X-, C-, and S-band wavelengths based on microphysical theory and previous observational studies. These results are then used to define the expected value ranges, or membership beta functions, of a simple fuzzy-logic hydrometeor classification algorithm. To test the algorithm’s validity and robustness, polarimetric radar data and algorithm output from four unique winter storms are investigated alongside surface observations and thermodynamic soundings. This analysis supports that the algorithm is able to realistically discern regions dominated by wet snow, aggregates, plates, dendrites, and other small ice crystals based solely on polarimetric data, with guidance from a melting-level detection algorithm but without external temperature information. Temperature is still used to distinguish rain from freezing rain or sleet below the radar-detected melting level. After appropriate data quality control, little modification of the algorithm was required to produce physically reasonable results on four different radar platforms at X, C, and S bands. However, classification seemed more robust at shorter wavelengths because the specific differential phase is heavily weighted in ice crystal classification decisions. It is suggested that parts, or all, of this algorithm could be applicable in both operational and research settings.

scholarly journals A Study of hailstorm of 19th April 2010 over Delhi using Doppler Weather Radar observations

MAUSAM ◽  
2021 ◽  
Vol 62 (3) ◽  
pp. 433-440
Author(s):  
HARI SINGH ◽  
R. K. DATTA ◽  
SURESH CHAND ◽  
D.P. MISHRA ◽  
B.A.M. KANNAN
Keyword(s):  
India Meteorological Department ◽  
Weather Radar ◽  
Threshold Value ◽  
Radar Data ◽  
Detection Algorithm ◽  
Doppler Weather Radar ◽  
Radar Observations ◽  
Freezing Level ◽  
Function Diagram ◽  
Very High

Hailstorm of 19th April 2010 over Delhi has been studied using observations from Doppler Weather Radar (DWR) installed at Palam. The data was analysed at Central Server located at India Meteorological Department HQ using IRIS software (of M/s SIGMET-VAISALA, Finland) installed in the server. Reflectivity of 45 dBZ level was found to be 6.3 km above freezing level at the time of hailstorm which corresponds to 100% (obtained from probability function diagram of Witt et al. (1998)) probability of hail. Reflectivity was more than 55 dBZ upto 10 km and 7 km at 1110 UTC and 1120 UTC respectively which exceeds the hail threshold limit adopted in NEXRAD (USA). Maximum of 62 dBZ was observed at about 3 km at 1110 UTC and 64 dBZ at 3.5 km at 1120 UTC in Radar Data. Very high values of Vertical Integrated Liquid (VIL) ranging from 58.7 kg/m2 to 64.1 kg/m2 were observed between 1040 UTC and 1120 UTC which is higher than 43 kg/m2, the threshold value for occurrence of hail. Severe Hail Index (SHI), Probability of Severe Hail (POSH) and Maximum Expected Hail Size (MEHS) were computed to verify the applicability of enhancedHail Detection Algorithm (HDA) outlined by Witt et al. (1998) to Indian conditions. The Maximum Expected Hail Size (MEHS) computed using Doppler Weather Radar observations were 2.5 cm, 2.6 cm and 2.0 cm respectively at 1050 UTC, 1100 UTC and 1110 UTC which are in close agreement with the reported hail size. The study confirms that HDA and other thresholds of reflectivity and VIL used for hail detection and warnings in NEXRAD (USA) can be used in Indian conditions also.

Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

2014 ◽  
Vol 53 (8) ◽  
pp. 2017-2033 ◽  
Author(s):  
Vivek N. Mahale ◽  
Guifu Zhang ◽  
Ming Xue
Keyword(s):  
Fuzzy Logic ◽  
Standard Deviation ◽  
Radar Data ◽  
Classification Algorithm ◽  
Radar Reflectivity ◽  
Membership Functions ◽  
Polarimetric Radar ◽  
Radar Echo ◽  
Three Body ◽  
Reflectivity Factor

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

Large-Sample Application of Radar Reflectivity Object-Based Verification to Evaluate HRRR Warm-Season Forecasts

2021 ◽  
Author(s):  
Jeffrey D. Duda ◽  
David D. Turner
Keyword(s):  
Warm Season ◽  
Skill Score ◽  
Central Plains ◽  
Attribute Weights ◽  
Object Based ◽  
Convective Scale ◽  
Heidke Skill Score ◽  
Sample Application ◽  
Storm Cells ◽  
The Impact

AbstractThe Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification of approximately 1400 forecasts of composite reflectivity from the operational HRRR from April – September 2019. In this study, MODE is configured to prioritize deep, moist convective storm cells typical of those that produce severe weather across the central and eastern US during the warm season. In particular, attributes related to distance and size are given the greatest attribute weights for computing interest in MODE.HRRR tends to over-forecast all objects, but substantially over-forecasts both small objects at low reflectivity thresholds and large objects at high reflectivity thresholds. HRRR tends to either under-forecast objects in the southern and central Plains or has a correct frequency bias there, whereas it over-forecasts objects across the southern and eastern US. Attribute comparisons reveal the inability of the HRRR to fully resolve convective scale features and the impact of data assimilation and loss of skill during the initial hours of the forecasts.Scalar metrics are defined and computed based on MODE output, chiefly relying on the interest value. The object-based threat score (OTS), in particular, reveals similar performance of HRRR forecasts as does the Heidke Skill Score, but with differing magnitudes, suggesting value in adopting an object-based approach to forecast verification. The typical distance between centroids of objects is also analyzed and shows gradual degradation with increasing forecast length.

An Objective Method of Forecasting Maximum Temperatures at Salt Lake City, Utah

1958 ◽  
Vol 39 (4) ◽  
pp. 202-204
Author(s):  
Philip Williams
Keyword(s):  
Surface Temperature ◽  
Salt Lake ◽  
Salt Lake City ◽  
Warm Season ◽  
Maximum Temperature ◽  
Objective Method ◽  
Surface Temperature Change ◽  
Freezing Level ◽  
Lake City ◽  
Maximum Temperatures

An objective method is developed for forecasting the current day's maximum temperature at Salt Lake City during the warm season, May–October. Good results are obtained by using either the height of the freezing level or the 700-mb temperature at 0800 MST at Salt Lake City combined with the 0830 MST surface temperature and the 0530–0830 MST surface temperature change. Results are compared with subjective forecasts.

scholarly journals Using Radar-Derived Parameters to Develop Probabilistic Guidance for Lightning Cessation within Isolated Convection near Cape Canaveral, Florida

2019 ◽  
Vol 34 (3) ◽  
pp. 559-575 ◽  
Author(s):  
Joseph R. Patton ◽  
Henry E. Fuelberg
Keyword(s):  
Generalized Linear Model ◽  
Probabilistic Method ◽  
Warm Season ◽  
Radar Data ◽  
Probabilistic Prediction ◽  
Central Florida ◽  
Maximum Reflectivity ◽  
Statistical Verification ◽  
Random Samples ◽  
High Skill

Abstract Thunderstorms in central Florida frequently halt outdoor activities, requiring that one wait some prescribed time after an assumed last flash before safely resuming activities. The goal of this research is to develop a high-skill probabilistic method that can be used in high pressure real-world operations to terminate lightning warnings more quickly while maintaining safety. Probabilistic guidance tools are created for isolated warm season storms in central Florida using dual-polarized radar data at 1-min intervals. The parameters examined are maximum reflectivity and graupel presence at the 0°, −5°, −10°, −15°, and −20°C levels as well as composite reflectivity. Random samples of the radar data are used to train a generalized linear model (GLM) to make a probabilistic prediction whether a given flash is the storm’s last flash. The most statistically significant predictors for lightning cessation are found to be the storm’s maximum reflectivity in the composite and the 0°C levels, along with graupel presence or absence at the −5°, −10°, −15°, and −20°C levels. Statistical verification is used to analyze the performance of the two GLMs at different probability thresholds (95.0%, 97.5%, and 99.0%). When applying the cessation guidance as though storms are occurring in real time, results showed ~99% of the storms produced no additional lightning after the GLM suggested cessation had already occurred. Although these results are encouraging, the procedure must be tested on much larger datasets having different convective modes and different areal coverages to prove its value compared to operational forecasters.

scholarly journals On the Introduction of Canny Operator in an Advanced Imaging Algorithm for Real-Time Detection of Hyperbolas in Ground-Penetrating Radar Data

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 541 ◽  
Author(s):  
Željko Bugarinović ◽  
Lara Pajewski ◽  
Aleksandar Ristić ◽  
Milan Vrtunski ◽  
Miro Govedarica ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Penetrating Radar ◽  
Urban Areas ◽  
Radar Data ◽  
Detection Algorithm ◽  
Advanced Imaging ◽  
Canny Operator ◽  
Imaging Algorithm ◽  
Canny Edge ◽  
Ground Penetrating

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB.

scholarly journals Microphysical Properties and Radar Polarimetric Features within a Warm Front

2018 ◽  
Vol 146 (7) ◽  
pp. 2003-2022 ◽  
Author(s):  
S. Ch. Keppas ◽  
J. Crosier ◽  
T. W. Choularton ◽  
K. N. Bower
Keyword(s):  
Radar Data ◽  
Conveyor Belt ◽  
Pressure System ◽  
Surface Precipitation ◽  
Wind Speeds ◽  
Microphysical Properties ◽  
Warm Front ◽  
Level 3 ◽  
Ice Multiplication ◽  
Earth’S Climate

Abstract On 21 January 2009, the warm front of an extensive low pressure system affected U.K. weather. In this work, macroscopic and microphysical characteristics of this warm front are investigated using in situ (optical array probes, temperatures sensors, and radiosondes) and S-band polarimetric radar data from the Aerosol Properties, Processes and Influences on the Earth’s Climate–Clouds project. The warm front was associated with a warm conveyor belt, a zone of wind speeds of up to 26 m s−1, which played a key role in the formation of extensive mixed-phase cloud mass by ascending significant liquid water (LWC; ~0.22 g m−3) at a level ~3 km and creating an ideal environment at temperatures ~ −5°C for ice multiplication. Then, “generating cells,” which formed in the unstable and sheared layer above the warm conveyor belt, influenced the structure of the stratiform cloud layer, dividing it into two types of elongated and slanted ice fall streaks: one depicted by large ZDR values and the other by large ZH values. The different polarimetric characteristics of these ice fall streaks reveal their different microphysical properties, such as the ice habit, concentration, and size. We investigate their evolution, which was affected by the warm conveyor belt, and their impact on the surface precipitation.

scholarly journals The Ice Water Paths of Small and Large Ice Species in Hurricanes Arthur (2014) and Irene (2011)

2017 ◽  
Vol 56 (5) ◽  
pp. 1383-1404 ◽  
Author(s):  
Evan A. Kalina ◽  
Sergey Y. Matrosov ◽  
Joseph J. Cione ◽  
Frank D. Marks ◽  
Jothiram Vivekanandan ◽  
...  
Keyword(s):  
Small Particle ◽  
Large Particle ◽  
Radar Data ◽  
Particle Identification ◽  
Convective Precipitation ◽  
Ratio Distribution ◽  
Identification Scheme ◽  
Ice Particles ◽  
Precipitation Type ◽  
Ice Water

AbstractDual-polarization scanning radar measurements, air temperature soundings, and a polarimetric radar-based particle identification scheme are used to generate maps and probability density functions (PDFs) of the ice water path (IWP) in Hurricanes Arthur (2014) and Irene (2011) at landfall. The IWP is separated into the contribution from small ice (i.e., ice crystals), termed small-particle IWP, and large ice (i.e., graupel and snow), termed large-particle IWP. Vertically profiling radar data from Hurricane Arthur suggest that the small ice particles detected by the scanning radar have fall velocities mostly greater than 0.25 m s−1 and that the particle identification scheme is capable of distinguishing between small and large ice particles in a mean sense. The IWP maps and PDFs reveal that the total and large-particle IWPs range up to 10 kg m−2, with the largest values confined to intense convective precipitation within the rainbands and eyewall. Small-particle IWP remains mostly <4 kg m−2, with the largest small-particle IWP values collocated with maxima in the total IWP. PDFs of the small-to-total IWP ratio have shapes that depend on the precipitation type (i.e., intense convective, stratiform, or weak-echo precipitation). The IWP ratio distribution is narrowest (broadest) in intense convective (weak echo) precipitation and peaks at a ratio of about 0.1 (0.3).

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