scholarly journals The NOAA/CIMSS ProbSevere Model: Incorporation of Total Lightning and Validation

2018 ◽  
Vol 33 (1) ◽  
pp. 331-345 ◽  
Author(s):  
John L. Cintineo ◽  
Michael J. Pavolonis ◽  
Justin M. Sieglaff ◽  
Daniel T. Lindsey ◽  
Lee Cronce ◽  
...  
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Severe Weather ◽  
Short Term ◽  
Lightning Detection ◽  
Empirical Probability ◽  
Storm Cells ◽  
Total Lightning ◽  
Meteorological Satellite

Abstract The empirical Probability of Severe (ProbSevere) model, developed by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS), automatically extracts information related to thunderstorm development from several data sources to produce timely, short-term, statistical forecasts of thunderstorm intensity. More specifically, ProbSevere utilizes short-term numerical weather prediction guidance (NWP), geostationary satellite, ground-based radar, and ground-based lightning data to determine the probability that convective storm cells will produce severe weather up to 90 min in the future. ProbSevere guidance, which updates approximately every 2 min, is available to National Weather Service (NWS) Weather Forecast Offices with very short latency. This paper focuses on the integration of ground-based lightning detection data into ProbSevere. In addition, a thorough validation analysis is presented. The validation analysis demonstrates that ProbSevere has slightly less skill compared to NWS severe weather warnings, but can offer greater lead time to initial hazards. Feedback from NWS users has been highly favorable, with most forecasters responding that ProbSevere increases confidence and lead time in numerous warning situations.

Introducing Lightning Threat Messaging Using the GOES-16 Day Cloud Phase Distinction RGB Composite

2019 ◽  
Vol 34 (5) ◽  
pp. 1587-1600
Author(s):  
Cynthia B. Elsenheimer ◽  
Chad M. Gravelle
Keyword(s):  
Lead Time ◽  
Weather Forecast ◽  
The United States ◽  
Short Term ◽  
Awareness Campaign ◽  
Large Numbers ◽  
Safety Awareness ◽  
Cloud To Ground Lightning ◽  
Total Lightning ◽  
Cg Lightning

Abstract In 2001, the National Weather Service (NWS) began a Lightning Safety Awareness Campaign to reduce lightning-related fatalities in the United States. Although fatalities have decreased 41% since the campaign began, lightning still poses a significant threat to public safety as the majority of victims have little or no warning of cloud-to-ground lightning. This suggests it would be valuable to message the threat of lightning before it occurs, especially to NWS core partners that have the responsibility to protect large numbers of people. During the summer of 2018, a subset of forecasters from the Jacksonville, Florida, NWS Weather Forecast Office investigated if messaging the threat of cloud-to-ground (CG) lightning in developing convection was possible. Based on previous CG lightning forecasting research, forecasters incorporated new high-resolution Geostationary Operational Environmental Satellite (GOES)-16 Day Cloud Phase Distinction red–green–blue (RGB) composite imagery with Multi-Radar Multi-Sensor isothermal reflectivity and total lightning data to determine if there was enough confidence to message the threat of CG lightning before it occurred. This paper will introduce the Day Cloud Phase Distinction RGB composite, show how it can add value for short-term lightning forecasting, and provide an operational example illustrating how fusing these datasets together may be able to provide confidence and extend the lead time when messaging the threat of cloud-to-ground lightning before it occurs.

scholarly journals The Above-Anvil Cirrus Plume: An Important Severe Weather Indicator in Visible and Infrared Satellite Imagery

2018 ◽  
Vol 33 (5) ◽  
pp. 1159-1181 ◽  
Author(s):  
Kristopher Bedka ◽  
Elisa M. Murillo ◽  
Cameron R. Homeyer ◽  
Benjamin Scarino ◽  
Haiden Mersiovsky
Keyword(s):  
Satellite Imagery ◽  
Wave Breaking ◽  
Lead Time ◽  
Doppler Radar ◽  
Severe Weather ◽  
Rapid Scan ◽  
The World ◽  
Total Lightning ◽  
Anvil Cirrus ◽  
Rapid Scanning

Abstract Intense tropopause-penetrating updrafts and gravity wave breaking generate cirrus plumes that reside above the primary anvil. These “above anvil cirrus plumes” (AACPs) exhibit unique temperature and reflectance patterns in satellite imagery, best recognized within 1-min “super rapid scan” observations. AACPs are often evident during severe weather outbreaks and, due to their importance, have been studied for 35+ years. Despite this research, there is uncertainty regarding why some storms produce AACPs but other nearby storms do not, exactly how severe are storms with AACPs, and how AACP identification can assist with severe weather warning. These uncertainties are addressed through analysis of severe weather reports, NOAA/National Weather Service (NWS) severe weather warnings, metrics of updraft cloud height, intensity, and rotation derived from Doppler radars, as well as ground-based total lightning observations for 4583 storms observed by GOES super rapid scanning, 405 of which produced an AACP. Datasets are accumulated throughout storm lifetimes through radar object tracking. It is found that 1) AACP storms generated 14 times the number of reports per storm compared to non-AACP storms; 2) AACPs appeared, on average, 31 min in advance of severe weather; 3) 73% of significant severe weather reports were produced by AACP storms; 4) AACP recognition can provide comparable warning lead time to that provided by a forecaster; and 5) the presence of an AACP can increase forecaster confidence that large hail will occur. Given that AACPs occur throughout the world, and most of the world is not observed by Doppler radar, AACP-based severe storm identification and warning would be extremely helpful for protecting lives and property.

The Relationship between Total Cloud Lightning Behavior and Radar-Derived Thunderstorm Structure

2013 ◽  
Vol 28 (1) ◽  
pp. 237-253 ◽  
Author(s):  
Eric Metzger ◽  
Wendell A. Nuss
Keyword(s):  
Weather Type ◽  
Severe Weather ◽  
Characteristic Change ◽  
Lightning Flash ◽  
Fort Worth ◽  
Detection Systems ◽  
Lightning Detection ◽  
Rapid Changes ◽  
Total Lightning ◽  
The Relationship

Abstract Total lightning detection systems have been in development since the mid-1980s and deployed in several areas around the world. Previous studies on total lightning found intra- and intercloud lightning (IC) activity tends to fluctuate significantly during the lifetime of thunderstorms and have indicated that lightning jumps or rapid changes in lightning flash rates are closely linked to changes in the vertical integrated liquid (VIL) reading on the National Weather Service’s Weather Surveillance Radar-1988 Doppler (WSR-88D) systems. This study examines the total lightning and its relationship to WSR-88D signatures used operationally to determine thunderstorm severity to highlight the potential benefit of a combined forecast approach. Lightning and thunderstorm data from the Dallas–Fort Worth, Texas, and Tucson, Arizona, areas from 2006 to 2009, were used to relate total lightning behavior and radar interrogation techniques. The results indicate that lightning jumps can be classified into severe wind, hail, or mixed-type jumps based on the behavior of various radar-based parameters. In 25 of 34 hail-type jumps and in 18 of 20 wind-type jumps, a characteristic change in cloud-to-ground (CG) versus IC lightning flash rates occurred prior to the report of severe weather. For hail-type jumps, IC flash rates increased, while CG flash rates were steady or decreased. For wind-type jumps, CG flash rates increased, while IC flash rates either increased (12 of 18) or were steady or decreased (6 of 18). Although not every lightning jump resulted in a severe weather report, the characteristic behavior in flash rates adds information to radar-based approaches for nowcasting the severe weather type.

scholarly journals Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks

2009 ◽  
Vol 137 (12) ◽  
pp. 4355-4368 ◽  
Author(s):  
Andrew E. Mercer ◽  
Chad M. Shafer ◽  
Charles A. Doswell ◽  
Lance M. Leslie ◽  
Michael B. Richman
Keyword(s):  
Statistical Methods ◽  
Lead Time ◽  
Weather Prediction ◽  
The United States ◽  
Severe Weather ◽  
Probability Of Detection ◽  
Support Vector ◽  
Synoptic Scale ◽  
Skill Scores ◽  
Objective Classification

Abstract Tornadoes often strike as isolated events, but many occur as part of a major outbreak of tornadoes. Nontornadic outbreaks of severe convective storms are more common across the United States but pose different threats than do those associated with a tornado outbreak. The main goal of this work is to distinguish between significant instances of these outbreak types objectively by using statistical modeling techniques on numerical weather prediction output initialized with synoptic-scale data. The synoptic-scale structure contains information that can be utilized to discriminate between the two types of severe weather outbreaks through statistical methods. The Weather Research and Forecast model (WRF) is initialized with synoptic-scale input data (the NCEP–NCAR reanalysis dataset) on a set of 50 significant tornado outbreaks and 50 nontornadic severe weather outbreaks. Output from the WRF at 18-km grid spacing is used in the objective classification. Individual severe weather parameters forecast by the model near the time of the outbreak are analyzed from simulations initialized at 24, 48, and 72 h prior to the outbreak. An initial candidate set of 15 variables expected to be related to severe storms is reduced to a set of 6 or 7, depending on lead time, that possess the greatest classification capability through permutation testing. These variables serve as inputs into two statistical methods, support vector machines and logistic regression, to classify outbreak type. Each technique is assessed based on bootstrap confidence limits of contingency statistics. An additional backward selection of the reduced variable set is conducted to determine which variable combination provides the optimal contingency statistics. Results for the contingency statistics regarding the verification of discrimination capability are best at 24 h; at 48 h, modest degradation is present. By 72 h, the contingency statistics decline by up to 15%. Overall, results are encouraging, with probability of detection values often exceeding 0.8 and Heidke skill scores in excess of 0.7 at 24-h lead time.

scholarly journals Improving QPF by blending techniques at the Meteorological Service of Catalonia

2010 ◽  
Vol 10 (7) ◽  
pp. 1443-1455 ◽  
Author(s):  
A. Atencia ◽  
T. Rigo ◽  
A. Sairouni ◽  
J. Moré ◽  
J. Bech ◽  
...  
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Short Term ◽  
Convective Structures ◽  
Prediction Horizon ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Nwp Model

Abstract. The current operational very short-term and short-term quantitative precipitation forecast (QPF) at the Meteorological Service of Catalonia (SMC) is made by three different methodologies: Advection of the radar reflectivity field (ADV), Identification, tracking and forecasting of convective structures (CST) and numerical weather prediction (NWP) models using observational data assimilation (radar, satellite, etc.). These precipitation forecasts have different characteristics, lead time and spatial resolutions. The objective of this study is to combine these methods in order to obtain a single and optimized QPF at each lead time. This combination (blending) of the radar forecast (ADV and CST) and precipitation forecast from NWP model is carried out by means of different methodologies according to the prediction horizon. Firstly, in order to take advantage of the rainfall location and intensity from radar observations, a phase correction technique is applied to the NWP output to derive an additional corrected forecast (MCO). To select the best precipitation estimation in the first and second hour (t+1 h and t+2 h), the information from radar advection (ADV) and the corrected outputs from the model (MCO) are mixed by using different weights, which vary dynamically, according to indexes that quantify the quality of these predictions. This procedure has the ability to integrate the skill of rainfall location and patterns that are given by the advection of radar reflectivity field with the capacity of generating new precipitation areas from the NWP models. From the third hour (t+3 h), as radar-based forecasting has generally low skills, only the quantitative precipitation forecast from model is used. This blending of different sources of prediction is verified for different types of episodes (convective, moderately convective and stratiform) to obtain a robust methodology for implementing it in an operational and dynamic way.

scholarly journals Initial results of the project SINOPTICA (Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM) 

2021 ◽  
Author(s):  
Laura Esbri ◽  
Maria Carmen Llasat ◽  
Tomeu Rigo ◽  
Massimo Milelli ◽  
Vincenzo Mazzarella ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Wrf Model ◽  
Remote Sensing Data ◽  
Weather Forecast ◽  
Relevant Information ◽  
Air Traffic ◽  
Severe Weather ◽  
Radar Images ◽  
Weather Events ◽  
Adverse Weather

<p>In the framework of the SINOPTICA project (EU H2020 SESAR, 2020 – 2022), different meteorological forecasting techniques are being tested to better nowcast severe weather events affecting Air Traffic Management (ATM) operations. Short-range severe weather forecasts with very high spatial resolution will be obtained starting from radar images, through an application of nowcasting techniques combined with Numerical Weather Prediction (NWP) model with data assimilation. The final goal is to integrate compact nowcast information into an Arrival Manager to support Air Traffic Controllers (ATCO) when sequencing and guiding approaching aircraft even in adverse weather situations. The guidance-support system will enable the visualization of dynamic weather information on the radar display of the controller, and the 4D-trajectory calculation for diversion coordination around severe weather areas. This meteorological information must be compact and concise to not interfere with other relevant information on the radar display of the controller.</p><p>Three severe weather events impacting different Italian airports have been selected for a preliminary radar analysis. Some products are considered for obtaining the best radar approach to characterize the severity of the events for ATM interests. Combining the Vertical Integrated Liquid and the Echo Top Maximum products, hazard thresholds are defined for different domains around the airports. The Weather Research and Forecasting (WRF) model has been used to simulate the formation and development of the aforementioned convective events. In order to produce a more accurate very short-term weather forecast (nowcasting), remote sensing data (e.g. radar, GNSS) and conventional observations are assimilated by using a cycling three-dimensional variational technique. This contribution presents some preliminary results on the progress of the project.</p>

scholarly journals Cell-tracking with lightning data from LINET

2008 ◽  
Vol 17 ◽  
pp. 55-61 ◽  
Author(s):  
H. D. Betz ◽  
K. Schmidt ◽  
W. P. Oettinger ◽  
B. Montag
Keyword(s):  
Cell Tracking ◽  
Planning Stage ◽  
Lightning Detection ◽  
University Of Munich ◽  
A Cell ◽  
Storm Cells ◽  
Total Lightning ◽  
Convective Cells ◽  
The University ◽  
Tracking Module

Abstract. A new lightning detection network (LINET) has been developed at the University of Munich, which locates and reports both cloud discharges and cloud-to-ground strokes with high accuracy. The network started operation in May 2006; since then lightning data for Europe are being delivered to many scientific groups, and to the German Weather Service (DWD) on an operational basis (powered by nowcast GmbH, Germany). Using about 90 lightning sensors in 17 countries, an area from longitude 10° W–25° E to latitude 35° N–65° N is covered. Further expansion is in the planning stage with the aim to attain higher efficiency for Mediterranean storms. The total lightning capability, not readily available otherwise in large areas, is particularly helpful because it can provide useful information about the development of severe weather and strong storm cells. A cell-tracking module has been developed that allows the investigation of lightning parameters for specific convective cells. Present efforts are devoted to the question for what kind of storms and to what extent lightning-based cell tracking allows improved nowcasting. Numerous case studies have been carried out and typical examples will be presented.

scholarly journals Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 789
Author(s):  
Elena Collino ◽  
Dario Ronzio
Keyword(s):  
Prediction Models ◽  
Moving Average ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Small Scale ◽  
Multiple Time ◽  
Short Term ◽  
Production Forecasting ◽  
Moving Average Model ◽  
Power Forecasting

The relentless spread of photovoltaic production drives searches of smart approaches to mitigate unbalances in power demand and supply, instability on the grid and ensuring stable revenues to the producer. Because of the development of energy markets with multiple time sessions, there is a growing need of power forecasting for multiple time steps, from fifteen minutes up to days ahead. To address this issue, in this study both a short-term-horizon of three days and a very-short-term-horizon of three hours photovoltaic production forecasting methods are presented. The short-term is based on a multimodel approach and referred to several configurations of the Analog Ensemble method, using the weather forecast of four numerical weather prediction models. The very-short-term consists of an Auto-Regressive Integrated Moving Average Model with eXogenous input (ARIMAX) that uses the short-term power forecast and the irradiance from satellite elaborations as exogenous variables. The methods, applied for one year to four small-scale grid-connected plants in Italy, have obtained promising improvements with respect to refence methods. The time horizon after which the short-term was able to outperform the very-short-term has also been analyzed. The study also revealed the usefulness of satellite data on cloudiness to properly interpret the results of the performance analysis.

scholarly journals Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose

2013 ◽  
Vol 17 (5) ◽  
pp. 1913-1931 ◽  
Author(s):  
D. L. Shrestha ◽  
D. E. Robertson ◽  
Q. J. Wang ◽  
T. C. Pagano ◽  
H. A. P. Hapuarachchi
Keyword(s):  
Numerical Weather Prediction ◽  
Diurnal Cycle ◽  
Lead Time ◽  
Weather Prediction ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Sampling Uncertainty ◽  
Numerical Weather ◽  
Skill Scores ◽  
Forecast Lead Time

Abstract. The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT) and regional (ACCESS-R) NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G) consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly), the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

scholarly journals The Usefulness and Economic Value of Total Lightning Forecasts Made with a Dynamic Lightning Scheme Coupled with Lightning Data Assimilation

2017 ◽  
Vol 32 (2) ◽  
pp. 645-663 ◽  
Author(s):  
Barry H. Lynn
Keyword(s):  
Initial Conditions ◽  
Economic Value ◽  
Early Summer ◽  
Severe Weather ◽  
Lightning Flash ◽  
Spatial Smoothing ◽  
Short Term ◽  
Convective Initiation ◽  
Skill Scores ◽  
Total Lightning

Abstract Total lightning probability forecasts for 26 mostly springtime days and 27 summertime days were analyzed for their usefulness and economic value. The mostly springtime forecast days had a relatively high number of severe weather reports compared with the summertime forecast days. The lightning forecasts were made with a dynamic lightning forecast scheme (DLS), and each forecast dataset used lightning assimilation to hasten convective initiation and, in most cases, to improve short-term forecasts. A spatial smoothing parameter σ of 48 km yielded more skillful, reliable, and economically valuable hourly forecasts than other values of σ. Mostly springtime forecasts were more skillful and had more hours of useful skill than summertime forecasts, but the latter still demonstrated useful skill during the first two forecast hours. The DLS forecasts were compared to those obtained with the “McCaul” diagnostic scheme, which diagnoses lightning flash data. The DLS had significantly higher fractions skill scores than the McCaul scheme for or at least one event/flash (10 min)−1. Bias values of the forecast lightning fields with both schemes were overall small. Yet, DLS forecasts started in the early summer evening with RAP data did have positive bias, which was attributed to initial conditions within the RAP. Correlating fractions skill scores for lightning and precipitation indicated that more accurate forecasts of lightning were associated with more accurate precipitation forecasts for convection with a high, but not lower, number of severe weather reports.

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