A Quantitative Analysis of the Enhanced-V Feature in Relation to Severe Weather

2007 ◽  
Vol 22 (4) ◽  
pp. 853-872 ◽  
Author(s):  
Jason C. Brunner ◽  
Steven A. Ackerman ◽  
A. Scott Bachmeier ◽  
Robert M. Rabin
Keyword(s):  
Satellite Imagery ◽  
Detection Algorithm ◽  
Low Earth Orbit ◽  
Severe Weather ◽  
Probability Of Detection ◽  
Quantitative Parameters ◽  
Critical Success ◽  
Temporal Sampling ◽  
Upper Level ◽  
The Impact

Abstract Early enhanced-V studies used 8-km ground-sampled distance and 30-min temporal-sampling Geostationary Operational Environmental Satellite (GOES) infrared (IR) imagery. In contrast, the ground-sampled distance of current satellite imagery is 1 km for low earth orbit (LEO) satellite IR imagery. This improved spatial resolution is used to detect and investigate quantitative parameters of the enhanced-V feature. One of the goals of this study is to use the 1-km-resolution LEO data to help understand the impact of higher-resolution GOES data (GOES-R) when it becomes available. A second goal is to use the LEO data available now to provide better severe storm information than GOES when it is available. This study investigates the enhanced-V feature observed with 1-km-resolution satellite imagery as an aid for severe weather warning forecasters by comparing with McCann’s enhanced-V study. Therefore, verification statistics such as the probability of detection, false alarm ratio, and critical success index were calculated. Additionally, the importance of upper-level winds to severe weather occurrence will be compared with that of the quantitative parameters of the enhanced-V feature. The main goal is to provide a basis for the development of an automated detection algorithm for enhanced-V features from the results in this study. Another goal is to examine daytime versus nighttime satellite overpass distributions with the enhanced-V feature.

scholarly journals Numerical simulations of June 7, 2020 convective precipitation over Slovakia using deterministic, probabilistic, and convection-permitting approaches

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 571-607
Author(s):  
André Simon ◽  
Martin Belluš ◽  
Katarína Čatlošová ◽  
Mária Derková ◽  
Martin Dian ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Weather Prediction ◽  
Severe Weather ◽  
Precipitation Forecast ◽  
Convective Precipitation ◽  
Small Scale ◽  
Limited Area ◽  
Systematic Effect ◽  
The Impact

The paper presented is dedicated to the evaluation of the influence of various improvements to the numerical weather prediction (NWP) systems exploited at the Slovak Hydrometeorological Institute (SHMÚ). The impact was illustrated in a case study with multicell thunderstorms and the results were confronted with the reference analyses from the INCA nowcasting system, regional radar reflectivity data, and METEOSAT satellite imagery. The convective cells evolution was diagnosed in non-hydrostatic dynamics experiments to study weak mesoscale vortices and updrafts. The growth of simulated clouds and evolution of the temperature at their top were compared with the brightness temperature analyzed from satellite imagery. The results obtained indicated the potential for modeling and diagnostics of small-scale structures within the convective cloudiness, which could be related to severe weather. Furthermore, the non-hydrostatic dynamics experiments related to the stability and performance improvement of the time scheme led to the formulation of a new approach to linear operator definition for semi-implicit scheme (in text referred as NHHY). We demonstrate that the execution efficiency has improved by more than 20%. The exploitation of several high resolution measurement types in data assimilation contributed to more precise position of predicted patterns and precipitation representation in the case study. The non-hydrostatic dynamics provided more detailed structures. On the other hand, the potential of a single deterministic forecast of prefrontal heavy precipitation was not as high as provided by the ensemble system. The prediction of a regional ensemble system A-LAEF (ALARO Limited Area Ensemble Forecast) enhanced the localization of precipitation patterns. Though, this was rather due to the simulation of uncertainty in the initial conditions and also because of the stochastic perturbation of physics tendencies. The various physical parameterization setups of A-LAEF members did not exhibit a systematic effect on precipitation forecast in the evaluated case. Moreover, the ensemble system allowed an estimation of uncertainty in a rapidly developing severe weather case, which was high even at very short range.

scholarly journals The Advantages of a Mixed-Band Radar Network for Severe Weather Operations: A Case Study of 13 May 2009

2014 ◽  
Vol 29 (1) ◽  
pp. 78-98 ◽  
Author(s):  
Vivek N. Mahale ◽  
Jerald A. Brotzge ◽  
Howard B. Bluestein
Keyword(s):  
Situational Awareness ◽  
Severe Weather ◽  
Combined System ◽  
Low Level ◽  
Dynamic Events ◽  
Temporal Sampling ◽  
Radar Network ◽  
Upper Level ◽  
Using Data

Abstract Adding a mix of X- or C-band radars to the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network could address several limitations of the network, including improvements to spatial gaps in low-level coverage and temporal sampling of volume scans. These limitations can result in missing critical information in highly dynamic events, such as tornadoes and severe straight-line wind episodes. To evaluate the potential value of a mixed-band radar network for severe weather operations, a case study is examined using data from X- and S-band radars. On 13 May 2009, a thunderstorm complex associated with a cold front moved southward into southwest Oklahoma. A tornado rapidly developed from an embedded supercell within the complex. The life cycle of the tornado and subsequent wind event was sampled by the experimental Collaborative Adaptive Sensing of the Atmosphere (CASA) radar testbed of four X-band radars as well as two operational WSR-88Ds. In this study, the advantages of a mixed-band radar network are demonstrated through a chronological analysis of the event. The two radar networks provided enhanced overall situational awareness. Data from the WSR-88Ds provided 1) clear-air sensitivity, 2) a broad overview of the storm complex, 3) a large maximum unambiguous range, and 4) upper-level scans up to 19.5°. Data from the CASA radars provided 1) high-temporal, 1-min updates; 2) overlapping coverage for dual-Doppler analysis; and 3) dense low-level coverage. The combined system allowed for detailed, dual- and single-Doppler observations of a wind surge, a mesocyclone contraction, and a downburst.

scholarly journals A Preliminary Statistical Study of Correlations between Inflow Feeder Clouds, Supercell or Multicell Thunderstorms, and Severe Weather

2009 ◽  
Vol 24 (4) ◽  
pp. 921-934 ◽  
Author(s):  
Rebecca J. Mazur ◽  
John F. Weaver ◽  
Thomas H. Vonder Haar
Keyword(s):  
Satellite Imagery ◽  
Statistical Study ◽  
Detection Algorithm ◽  
Severe Weather ◽  
Potential Utility ◽  
Geostationary Operational Environmental Satellite ◽  
The Relationship ◽  
Visible Imagery ◽  
Cloud Analysis

Abstract This study examines the relationship between severe weather and organized lines of cumulus towers, called feeder clouds, which form in the inflow region of supercell and multicell thunderstorms. Using Geostationary Operational Environmental Satellite (GOES) imagery, correlations between the occurrence of feeder clouds and severe weather reports are explored. Output from the Weather Surveillance Radar-1988 Doppler (WSR-88D) mesocyclone detection algorithm (MDA) is also assessed for a subset of the satellite case days. Statistics from the satellite and radar datasets are assembled to estimate not only the effectiveness of feeder cloud signatures as sole predictors of severe weather, but also the potential utility of combining feeder cloud analysis with the radar’s MDA output. Results from this study suggest that the formation of feeder clouds as seen in visible satellite imagery is often followed by the occurrence of severe weather in a storm. The study finds that feeder cloud signatures by themselves have low skill in predicting severe weather. However, if feeder clouds are observed in a storm, there is a 77% chance that severe weather will occur within 30 min of the observation. For the cases considered, the MDA turns out to be the more effective predictor of severe weather. However, results show that combined predictions (feeder clouds plus mesocyclones) outperform both feeder cloud signatures and the MDA as separate predictors by ∼10%–20%. Thus, the presence of feeder clouds as observed in visible imagery is a useful adjunct to the MDA in diagnosing a storm’s potential for producing severe weather.

scholarly journals Potential Vorticity Diagnosis of the Severe Convective Regime. Part II: The Impact of Idealized PV Anomalies

2008 ◽  
Vol 136 (5) ◽  
pp. 1582-1592 ◽  
Author(s):  
John W. Nielsen-Gammon ◽  
David A. Gold
Keyword(s):  
Potential Vorticity ◽  
Severe Weather ◽  
Jet Stream ◽  
Balance Equations ◽  
Upper Level ◽  
Height Fields ◽  
Upper Level Jet ◽  
The Impact ◽  
Analysis Errors ◽  
Pv Gradient

Abstract Idealized numerical experiments are conducted to understand the effect of upper-tropospheric potential vorticity (PV) anomalies on an environment conducive to severe weather. Anomalies are specified as a single isolated vortex, a string of vortices analogous to a negatively tilted trough, and a pair of string vortices analogous to a position error in a negatively tilted trough. The anomalies are placed adjacent to the tropopause along a strong upper-level jet at a time just prior to a major tornado outbreak and inverted using the nonlinear balance equations. In addition to the expected destabilization beneath and adjacent to a cyclonic PV anomaly, the spatial pattern of the inverted balanced streamfunction and height fields is distorted by the presence of the horizontal PV gradient along the upper-tropospheric jet stream. Streamfunction anomalies are elongated in the cross-jet direction, while height and temperature anomalies are elongated in the along-jet direction. The amplitude of the inverted fields, as well as the changes in CAPE associated with the inverted temperature perturbations, are linearly proportional to the amplitudes of the PV anomalies themselves, and the responses to complex PV perturbation structures are approximated by the sum of the responses to individual simple PV anomalies. This is true for the range of PV amplitudes tested, which was designed to mimic typical 6-h forecast or analysis errors and produced changes in CAPE beneath the trough of well over 100 J kg−1. Impacts on inverted fields are largest when the PV anomaly is on the anticyclonic shear side of the jet, where background PV is small, compared with the cyclonic shear side of the jet, where background PV is large.

scholarly journals Polarimetric Radar Characteristics of Melting Hail. Part III: Validation of the Algorithm for Hail Size Discrimination

2016 ◽  
Vol 55 (4) ◽  
pp. 829-848 ◽  
Author(s):  
Kiel L. Ortega ◽  
John M. Krause ◽  
Alexander V. Ryzhkov
Keyword(s):  
Radar Data ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Polarimetric Radar ◽  
Size Discrimination ◽  
Critical Success ◽  
Single Polarization ◽  
Differential Reflectivity

AbstractThis study is the third part of a paper series investigating the polarimetric radar properties of melting hail and application of those properties for operational polarimetric hail detection and determination of its size. The results of theoretical simulations in Part I were used to develop a hail size discrimination algorithm (HSDA) described in Part II. The HSDA uses radar reflectivity Z, differential reflectivity ZDR, and cross-correlation coefficient ρhv along with melting-level height within a fuzzy-logic scheme to distinguish among three hail size classes: small hail (with diameter D < 2.5 cm), large hail (2.5 < D < 5.0 cm), and giant hail (D > 5.0 cm). The HSDA validation is performed using radar data collected by numerous WSR-88D sites and more than 3000 surface hail reports obtained from the Severe Hazards Analysis and Verification Experiment (SHAVE). The original HSDA version was modified in the process of validation, and the modified algorithm demonstrates probability of detection of 0.594, false-alarm ratio of 0.136, and resulting critical success index (CSI) equal to 0.543. The HSDA outperformed the current operational single-polarization hail detection algorithm, which only provides a single hail size estimate per storm and is characterized by CSI equal to 0.324. It is shown that HSDA is particularly sensitive to the quality of ZDR measurements, which might be affected by possible radar miscalibration and anomalously high differential attenuation.

The Impact of Ice Phase Cloud Parameterizations on Tropical Cyclone Prediction

2014 ◽  
Vol 142 (2) ◽  
pp. 606-625 ◽  
Author(s):  
Yi Jin ◽  
Shouping Wang ◽  
Jason Nachamkin ◽  
James D. Doyle ◽  
Gregory Thompson ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Satellite Imagery ◽  
Hybrid Approach ◽  
Radiative Heating ◽  
Basin Scale ◽  
Single Moment ◽  
Upper Level ◽  
Cloud Ice ◽  
The Impact ◽  
Ice Phase

Abstract The impact of ice phase cloud microphysical processes on prediction of tropical cyclone environment is examined for two microphysical parameterizations using the Coupled Ocean–Atmosphere Mesoscale Prediction System–Tropical Cyclone (COAMPS-TC) model. An older version of microphysical parameterization is a relatively typical single-moment scheme with five hydrometeor species: cloud water and ice, rain, snow, and graupel. An alternative newer method uses a hybrid approach of double moment in cloud ice and rain and single moment in the other three species. Basin-scale synoptic flow simulations point to important differences between these two schemes. The upper-level cloud ice concentrations produced by the older scheme are up to two orders of magnitude greater than the newer scheme, primarily due to differing assumptions concerning the ice nucleation parameterization. Significant (1°–2°C) warm biases near the 300-hPa level in the control experiments are not present using the newer scheme. The warm bias in the control simulations is associated with the longwave radiative heating near the base of the cloud ice layer. The two schemes produced different track and intensity forecasts for 15 Atlantic storms. Rightward cross-track bias and positive intensity bias in the control forecasts are significantly reduced using the newer scheme. Synthetic satellite imagery of Hurricane Igor (2010) shows more realistic brightness temperatures from the simulations using the newer scheme, in which the inner core structure is clearly discernible. Applying the synthetic satellite imagery in both quantitative and qualitative analyses helped to pinpoint the issue of excessive upper-level cloud ice in the older scheme.

scholarly journals Combined Dust Detection Algorithm for Asian Dust Events Over East Asia Using GK2A/AMI: a Case Study in October 2019

2021 ◽  
Author(s):  
Jae-Cheol Jang ◽  
Soobong Lee ◽  
Eun-Ha Sohn ◽  
Yoo-Jeong Noh ◽  
Steven D. Miller
Keyword(s):  
Detection Algorithm ◽  
Low Earth Orbit ◽  
Probability Of Detection ◽  
Dust Storms ◽  
Detection Methods ◽  
False Alarms ◽  
Monitoring Methods ◽  
Dust Detection ◽  
Ground Based Lidar ◽  
Combined Algorithm

AbstractA combined algorithm comprising multiple dust detection methods was developed using infrared (IR) channels onboard the GEOstationary Korea Multi-Purpose SATellite 2A equipped with the Advanced Meteorological Imager (GK2A/AMI). Six cloud tests using brightness temperature difference (BTD) were utilized to reduce errors caused by clouds. For detecting dust storms, three standard BTD tests (i.e., $${BT}_{12.3}-{BT}_{10.5}$$, $${BT}_{8.7}-{BT}_{10.5}$$, and $${BT}_{11.2}-{BT}_{10.5}$$) were combined with the polarized optical depth index (PODI). The combined algorithm normalizes the indices for cloud and dust detection, and adopts weighted combinations of dust tests depending on the observation time (day/night) and surface type (land/sea). The dust detection results were produced as quantitative confidence factors and displayed as false color imagery, applying a dynamic enhancement background reduction algorithm (DEBRA). The combined dust detection algorithm was qualitatively assessed by comparing it with dust RGB imageries and ground-based lidar data. The combined algorithm especially improved the discontinuity in weak dust advection to the sea and considerably reduced false alarms as compared to previous dust monitoring methods. For quantitative validation, we used aerosol optical thickness (AOT) and fine mode fraction (FMF) derived from low Earth orbit (LEO) satellites in daytime. For both severe and weakened dust cases, the probability of detection (POD) ranged from 0.667 to 0.850 and it indicated that the combined algorithm detects more potential dust pixels than other satellites. In particular, the combined algorithm was advantageous in detecting weak dust storms passing over the warm and humid Yellow Sea with low dust height and small AOT.

Preliminary Study of the Impact of Critical Success Factors on Project Success in Pakistan

2014 ◽  
Vol 1 (4) ◽  
pp. 9-13 ◽  
Author(s):  
Aqeel Ahmed ◽  
Muhammad Sehail Younis
Keyword(s):  
Critical Success Factors ◽  
Success Factors ◽  
Project Success ◽  
Workforce Planning ◽  
Survey Instrument ◽  
Past Performance ◽  
Cronbach Alpha ◽  
Critical Success ◽  
Preliminary Study ◽  
The Impact

This preliminary study attempts to link among the critical success factors on overall project success in public sector organizations in Pakistan.  In this study it’s reflected that major critical success factors (soundness of Business & workforce, planning & control, quality performance and past performance) can enhance the success of the project in Pakistan.  The purpose of this preliminary study was to verify the reliability of the survey instrument which has been used in European countries. It was found that the planning & control was the highest Cronbach Alpha value, while the ranged for each constructs in the present study from 0.68 to 0.88.  Therefore, based on the Cronbach alpha value score, the proposed survey instrument has fulfilled the basic requirement of a valid instrument.

scholarly journals Evaluating Satellite Imagery‐Based Land Use Data for Describing Forestland Development in Western Washington

2009 ◽  
Vol 24 (4) ◽  
pp. 214-222 ◽  
Author(s):  
Jeffrey D. Kline ◽  
Alissa Moses ◽  
David Azuma ◽  
Andrew Gray
Keyword(s):  
Land Use ◽  
Satellite Imagery ◽  
Econometric Model ◽  
Landsat Imagery ◽  
Forest Inventory And Analysis ◽  
The Us ◽  
Western Washington ◽  
Us Census ◽  
The Impact ◽  
Density Development

Abstract Forestry professionals are concerned about how forestlands are affected by residential and other development. To address those concerns, researchers must find appropriate data with which to describe and evaluate rates and patterns of forestland development and the impact of development on the management of remaining forestlands. We examine land use data gathered from Landsat imagery for western Washington and evaluate its usefulness for characterizing low-density development of forestland. We evaluate the accuracy of the satellite imagery‐based land use classifications by comparing them with other data from US Forest Service's Forest Inventory and Analysis inventories and the US census. We then use the data to estimate an econometric model describing development as a function of socioeconomic and topographic factors and project future rates of development and forestland loss to 2020. We conclude by discussing how best to meet the land use data needs of researchers, forestry policymakers, and managers.

scholarly journals Rainfall Threshold for Flash Flood Warning Based on Model Output of Soil Moisture: Case Study Wernersbach, Germany

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1061
Author(s):  
Thanh Thi Luong ◽  
Judith Pöschmann ◽  
Rico Kronenberg ◽  
Christian Bernhofer
Keyword(s):  
Soil Moisture ◽  
Flash Flood ◽  
Rainfall Threshold ◽  
Probability Of Detection ◽  
Water Levels ◽  
Critical Success ◽  
Flood Warning ◽  
Critical Discharge ◽  
Critical Success Index

Convective rainfall can cause dangerous flash floods within less than six hours. Thus, simple approaches are required for issuing quick warnings. The flash flood guidance (FFG) approach pre-calculates rainfall levels (thresholds) potentially causing critical water levels for a specific catchment. Afterwards, only rainfall and soil moisture information are required to issue warnings. This study applied the principle of FFG to the Wernersbach Catchment (Germany) with excellent data coverage using the BROOK90 water budget model. The rainfall thresholds were determined for durations of 1 to 24 h, by running BROOK90 in “inverse” mode, identifying rainfall values for each duration that led to exceedance of critical discharge (fixed value). After calibrating the model based on its runoff, we ran it in hourly mode with four precipitation types and various levels of initial soil moisture for the period 1996–2010. The rainfall threshold curves showed a very high probability of detection (POD) of 91% for the 40 extracted flash flood events in the study period, however, the false alarm rate (FAR) of 56% and the critical success index (CSI) of 42% should be improved in further studies. The proposed adjusted FFG approach has the potential to provide reliable support in flash flood forecasting.

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