scholarly journals An Automated Velocity Dealiasing Scheme for Radar Data Observed from Typhoons and Hurricanes

2019 ◽  
Vol 36 (1) ◽  
pp. 139-149 ◽  
Author(s):  
Guangxin He ◽  
Juanzhen Sun ◽  
Zhuming Ying
Keyword(s):  
Weather Prediction ◽  
Weather Conditions ◽  
Data Interpretation ◽  
Radar Data ◽  
The United States ◽  
Test Cases ◽  
Radar Network ◽  
Error Check ◽  
New Generation ◽  
Further Development

AbstractAccurate and automated dealiasing of radar data is important for data interpretation and downstream applications such as numerical weather prediction (NWP) models. In this paper an improved radial velocity dealiasing scheme is presented and evaluated using observations from several S-band radars under the severe weather conditions of typhoons and hurricanes. This scheme, named Automated Dealiasing for Typhoon and Hurricane (ADTH), is a further development of the China New Generation Doppler Weather Radar (CINRAD) improved dealiasing algorithm (CIDA). The upgraded algorithm ADTH includes three modules designed to select the first radial from which the dealiasing process starts, to conduct a two-way multipass dealiasing, and to perform an error check for a final local dealiasing. The dealiasing algorithm is applied to two typhoon hurricane cases and four typhoon cases observed with radars from CINRAD, NEXRAD of the United States, and the Taiwan radar network for a continuous period of 12 h for each of the selected cases. The results show that ADTH outperforms CIDA for all of the test cases.

scholarly journals Usefulness of the United States National Weather Service Radar Display as Rated by Website Users

2018 ◽  
Vol 10 (4) ◽  
pp. 673-691 ◽  
Author(s):  
Michelle E. Saunders ◽  
Kevin D. Ash ◽  
Jennifer M. Collins
Keyword(s):  
United States ◽  
Spatial Clustering ◽  
Weather Radar ◽  
Weather Conditions ◽  
Radar Data ◽  
The United States ◽  
Perceived Usefulness ◽  
National Weather Service ◽  
Weather Information ◽  
Weather Service

Abstract Weather radar is now widely viewed by the general public in the United States via television, computers/tablets, and smartphones. Anyone can consult near-real-time maps and animations of weather radar data when weather conditions are a factor. However, the usefulness of weather radar data for each user depends on a complex interaction of factors. There have been few studies providing conceptual arguments and empirical data to better understand what the most important factors are and to comprehend patterns of public weather radar use across the United States. The first part of this research provides a basic conceptual framework for research investigating the usefulness of weather radar displays as a source of weather information and as a decision aid. The second part aims to uncover several factors that influence the perceived usefulness rating of the National Weather Service (NWS) website’s weather radar display at both national and regional levels using variables gathered from the 2014 NWS customer satisfaction survey alongside relevant geographic and climatological variables. Data analyses include spatial clustering and ordinal regression utilized within a generalized linear model methodology. Overall, respondents who are more familiar with the NWS and their products, as well as those who indicate they are more likely to take action based on information provided by the NWS, are more likely to find the NWS radar display useful. Geographically, the NWS radar display is most useful to persons residing in the southern United States. Lightning is the most important hazard associated with higher radar usefulness ratings.

scholarly journals A Radar Radial Velocity Dealiasing Algorithm for Radar Data Assimilation and its Evaluation with Observations from Multiple Radar Networks

2019 ◽  
Vol 11 (20) ◽  
pp. 2457
Author(s):  
Guangxin He ◽  
Juanzhen Sun ◽  
Zhuming Ying ◽  
Lejian Zhang
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Weather Forecasting ◽  
Weather Radar ◽  
Weather Conditions ◽  
Radar Data ◽  
Next Generation ◽  
Radar Network ◽  
Numerical Weather Forecasting ◽  
Radar Data Assimilation

Automated and accurate radar dealiasing algorithms are very important for their assimilation into operational numerical weather forecasting models. A radar radial velocity dealiasing algorithm aimed at radar data assimilation is introduced and assessed using from several S-band and C-band radar observations under the severe weather conditions of hurricanes, typhoons, and deep continental convection in this paper. This dealiasing algorithm, named automated dealiasing for data assimilation (ADDA), is a further development of the dealiasing algorithm named the China radar network (CINRAD) improved dealiasing algorithm (CIDA), originally developed for China’s CINRAD (China Next Generation Weather Radar) radar network. The improved scheme contains five modules employed to remove noisy data, select the suitable first radial, preserve the convective regions, execute multipass dealiasing in both azimuthal and radial directions and conduct the final local dealiasing with an error check. This new dealiasing algorithm was applied to two hurricane cases, two typhoon cases, and three intense-convection cases that were observed from the CINRAD of China, Taiwan‘s radar network, and NEXRAD (Next Generation Weather Radar) of the U.S. with a continuous period of more than 12 h for each case. The dealiasing results demonstrated that ADDA performed better than CIDA for all selected cases. This algorithm not only produced a high success rate for the S-band radar, but also a reasonable performance for the C-band radar.

Industry Payments to Plastic Surgeons: What Has Changed Over the Last Six-Years Following Implementation of the Physician Payment Sunshine Act?

2021 ◽  
Author(s):  
Rowland W Pettit ◽  
Jordan Kaplan ◽  
Matthew M Delancy ◽  
Edward Reece ◽  
Sebastian Winocour ◽  
...  
Keyword(s):  
United States ◽  
User Interface ◽  
Stock Options ◽  
Data Interpretation ◽  
The United States ◽  
Inclusion Criteria ◽  
Physician Payment ◽  
The Past ◽  
To Receive ◽  
Open Payments

Abstract Background The Open Payments Program, as designated by the Physician Payments Sunshine Act is the single largest repository of industry payments made to licensed physicians within the United States. Though sizeable in its dataset, the database and user interface are limited in their ability to permit expansive data interpretation and summarization. Objectives We sought to comprehensively compare industry payments made to plastic surgeons with payments made to all surgeons and all physicians to elucidate industry relationships since implementation. Methods The Open Payments Database was queried between 2014 and 2019, and inclusion criteria were applied. These data were evaluated in aggregate and for yearly totals, payment type, and geographic distribution. Results 61,000,728 unique payments totaling $11,815,248,549 were identified over the six-year study period. 9,089 plastic surgeons, 121,151 surgeons, and 796,260 total physicians received these payments. Plastic surgeons annually received significantly less payment than all surgeons (p=0.0005). However, plastic surgeons did not receive significantly more payment than all physicians (p = 0.0840). Cash and cash equivalents proved to be the most common form of payment; Stock and stock options were least commonly transferred. Plastic surgeons in Tennessee received the most in payments between 2014-2019 (mean $ 76,420.75). California had the greatest number of plastic surgeons to receive payments (1,452 surgeons). Conclusions Plastic surgeons received more in industry payments than the average of all physicians but received less than all surgeons. The most common payment was cash transactions. Over the past six years, geographic trends in industry payments have remained stable.

scholarly journals The MJO in a Coarse-Resolution GCM with a Stochastic Multicloud Parameterization

2015 ◽  
Vol 72 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Qiang Deng ◽  
Boualem Khouider ◽  
Andrew J. Majda
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Deterministic Model ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Radar Data ◽  
Equation Model ◽  
Death Process ◽  
Relevant Parameter ◽  
Convective Systems

Abstract The representation of the Madden–Julian oscillation (MJO) is still a challenge for numerical weather prediction and general circulation models (GCMs) because of the inadequate treatment of convection and the associated interactions across scales by the underlying cumulus parameterizations. One new promising direction is the use of the stochastic multicloud model (SMCM) that has been designed specifically to capture the missing variability due to unresolved processes of convection and their impact on the large-scale flow. The SMCM specifically models the area fractions of the three cloud types (congestus, deep, and stratiform) that characterize organized convective systems on all scales. The SMCM captures the stochastic behavior of these three cloud types via a judiciously constructed Markov birth–death process using a particle interacting lattice model. The SMCM has been successfully applied for convectively coupled waves in a simplified primitive equation model and validated against radar data of tropical precipitation. In this work, the authors use for the first time the SMCM in a GCM. The authors build on previous work of coupling the High-Order Methods Modeling Environment (HOMME) NCAR GCM to a simple multicloud model. The authors tested the new SMCM-HOMME model in the parameter regime considered previously and found that the stochastic model drastically improves the results of the deterministic model. Clear MJO-like structures with many realistic features from nature are reproduced by SMCM-HOMME in the physically relevant parameter regime including wave trains of MJOs that organize intermittently in time. Also one of the caveats of the deterministic simulation of requiring a doubling of the moisture background is not required anymore.

scholarly journals The National Earth System Prediction Capability: Coordinating the Giant

2017 ◽  
Vol 98 (2) ◽  
pp. 239-252 ◽  
Author(s):  
Jessie C. Carman ◽  
Daniel P. Eleuterio ◽  
Timothy C. Gallaudet ◽  
Gerald L. Geernaert ◽  
Patrick A. Harr ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The United States ◽  
Department Of Energy ◽  
Earth System ◽  
Numerical Weather ◽  
Interagency Coordination ◽  
Research Technology ◽  
Prediction Capability ◽  
The U.S

Abstract The United States has had three operational numerical weather prediction centers since the Joint Numerical Weather Prediction Unit was closed in 1958. This led to separate paths for U.S. numerical weather prediction, research, technology, and operations, resulting in multiple community calls for better coordination. Since 2006, the three operational organizations—the U.S. Air Force, the U.S. Navy, and the National Weather Service—and, more recently, the Department of Energy, the National Aeronautics and Space Administration, the National Science Foundation, and the National Oceanic and Atmospheric Administration/Office of Oceanic and Atmospheric Research, have been working to increase coordination. This increasingly successful effort has resulted in the establishment of a National Earth System Prediction Capability (National ESPC) office with responsibility to further interagency coordination and collaboration. It has also resulted in sharing of data through an operational global ensemble, common software standards, and model components among the agencies. This article discusses the drivers, the progress, and the future of interagency collaboration.

Weatherwise: Evaluation of a Cue-Based Training Approach for the Recognition of Deteriorating Weather Conditions during Flight

2003 ◽  
Vol 45 (2) ◽  
pp. 337-345 ◽  
Author(s):  
Mark Wiggins ◽  
David O'Hare
Keyword(s):  
Decision Making ◽  
Significant Proportion ◽  
Evaluation Process ◽  
Weather Conditions ◽  
The United States ◽  
Training System ◽  
General Aviation ◽  
Self Report ◽  
Computer Based Training ◽  
Computer Based

Inappropriate and ineffective weather-related decision making continues to account for a significant proportion of general aviation fatalities in the United States and elsewhere. This study details the evaluation of a computer-based training system that was developed to provide visual pilots with the skills necessary to recognize and respond to the cues associated with deteriorating weather conditions during flight. A total of 66 pilots were assigned to one of two groups, and the evaluation process was undertaken at both a self-report and performance level. At the self-report level, the results suggested that pilots were more likely to use the cues following exposure to the training program. From a performance perspective, there is evidence to suggest that cue-based training can improve the timeliness of weather-related decision making during visual flight rules flight. Actual or potential applications of this research include the development of computer-based training systems for fault diagnosis in complex industrial environments.

Urban Finescale Forecasting Reveals Weather Conditions with Unprecedented Detail

2017 ◽  
Vol 98 (12) ◽  
pp. 2675-2688 ◽  
Author(s):  
R. J. Ronda ◽  
G. J. Steeneveld ◽  
B. G. Heusinkveld ◽  
J. J. Attema ◽  
A. A. M. Holtslag
Keyword(s):  
Local Governments ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Morphological Characteristics ◽  
Weather Conditions ◽  
Human Thermal Comfort ◽  
Forecasting System ◽  
The Impact ◽  
Urban Weather ◽  
Neighborhood Level

Abstract Urban landscapes impact the lives of urban dwellers by influencing local weather conditions. However, weather forecasting down to the street and neighborhood scale has been beyond the capabilities of numerical weather prediction (NWP) despite the fact that observational systems are now able to monitor urban climate at these scales. In this study, weather forecasts at intra-urban scales were achieved by exploiting recent advances in topographic element mapping and aerial photography as well as looking at detailed mappings of soil characteristics and urban morphological properties, which were subsequently incorporated into a specifically adapted Weather Research and Forecasting (WRF) Model. The urban weather forecasting system (UFS) was applied to the Amsterdam, Netherlands, metropolitan area during the summer of 2015, where it produced forecasts for the city down to the neighborhood level (a few hundred meters). Comparing these forecasts to the dense network of urban weather station observations within the Amsterdam metropolitan region showed that the forecasting system successfully determined the impact of urban morphological characteristics and urban spatial structure on local temperatures, including the cooling effect of large water bodies on local urban temperatures. The forecasting system has important practical applications for end users such as public health agencies, local governments, and energy companies. It appears that the forecasting system enables forecasts of events on a neighborhood level where human thermal comfort indices exceeded risk thresholds during warm weather episodes. These results prove that worldwide urban weather forecasting is within reach of NWP, provided that appropriate data and computing resources become available to ensure timely and efficient forecasts.

50th Anniversary of Operational Numerical Weather Prediction

2007 ◽  
Vol 88 (5) ◽  
pp. 639-650 ◽  
Author(s):  
Kristine Harper ◽  
Louis W. Uccellini ◽  
Eugenia Kalnay ◽  
Kenneth Carey ◽  
Lauren Morone
Keyword(s):  
Air Force ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
50Th Anniversary ◽  
Well Being ◽  
The United States ◽  
Numerical Weather ◽  
American Meteorological Society ◽  
College Park ◽  
The U.S

The National Centers for Environmental Prediction (NCEP), Air Force Weather Agency (AFWA), Fleet Numerical Meteorology and Oceanography Center (FNMOC), National Weather Association, and American Meteorological Society (AMS) cosponsored a “Symposium on the 50th Anniversary of Operational Numerical Weather Prediction,” on 14–17 June 2004 at the University of Maryland, College Park in College Park, Maryland. Operational numerical weather prediction (NWP) in the United States started with the Joint Numerical Weather Prediction Unit (JNWPU) on 1 July 1954, staffed by members of the U.S. Weather Bureau, the U.S. Air Force and the U.S. Navy. The origins of NCEP, AFWA, and FNMOC can all be traced to the JNWPU. The symposium celebrated the pioneering developments in NWP and the remarkable improvements in forecast skill and support of the nation's economy, well being, and national defense achieved over the last 50 years. This essay was inspired by the presentations from that symposium.

scholarly journals The Effect of Upstream Convection on Downstream Precipitation

2007 ◽  
Vol 22 (2) ◽  
pp. 255-277 ◽  
Author(s):  
Kelly M. Mahoney ◽  
Gary M. Lackmann
Keyword(s):  
Moisture Transport ◽  
Weather Prediction ◽  
The United States ◽  
Cold Season ◽  
Precipitation Forecast ◽  
Study Region ◽  
Low Level ◽  
Convective Systems ◽  
Eta Model ◽  
Nwp Model

Abstract Operational forecasters in the southeast and mid-Atlantic regions of the United States have noted a positive quantitative precipitation forecast (QPF) bias in numerical weather prediction (NWP) model forecasts downstream of some organized, cold-season convective systems. Examination of cold-season cases in which model QPF guidance exhibited large errors allowed identification of two representative cases for detailed analysis. The goals of the case study analyses are to (i) identify physical mechanisms through which the upstream convection (UC) alters downstream precipitation amounts, (ii) determine why operational models are challenged to provide accurate guidance in these situations, and (iii) suggest future research directions that would improve model forecasts in these situations and allow forecasters to better anticipate such events. Two primary scenarios are identified during which downstream precipitation is altered in the presence of UC for the study region: (i) a fast-moving convective (FC) scenario in which organized convective systems oriented parallel to the lower-tropospheric flow are progressive relative to the parent synoptic system, and appear to disrupt poleward moisture transport, and (ii) a situation characterized by slower-moving convection (SC) relative to the parent system. Analysis of a representative FC case indicated that moisture consumption, stabilization via convective overturning, and modification of the low-level flow to a more westerly direction in the postconvective environment all appear to contribute to the reduction of downstream precipitation. In the FC case, operational Eta Model forecasts moved the organized UC too slowly, resulting in an overestimate of downstream moisture transport. A 4-km explicit convection model forecast from the Weather Research and Forecasting model produced a faster-moving upstream convective system and improved downstream QPF. In contrast to the FC event, latent heat release in the primary rainband is shown to enhance the low-level jet ahead of the convection in the SC case, thereby increasing moisture transport into the downstream region. A negative model QPF bias was observed in Eta Model forecasts for the SC event. Suggestions are made for precipitation forecasting in UC situations, and implications for NWP model configuration are discussed.

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