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At least 235 records · Page 13
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NASA NTRS2012

Validation of Satellite-Based Objective Overshooting Cloud-Top Detection Methods Using CloudSat Cloud Profiling Radar Observations

Two satellite infrared-based overshooting convective cloud-top (OT) detection methods have recently been described in the literature: 1) the 11-mm infrared window channel texture (IRW texture) method, which uses IRW channel brightness temperature (BT) spatial gradients and thresholds, and 2) the water vapor minus IRW BT difference (WV-IRW BTD). While both methods show good performance in published case study examples, it is important to quantitatively validate these methods relative to overshooting top events across the globe. Unfortunately, no overshooting top database currently exists that could be used in such study. This study examines National Aeronautics and Space Administration CloudSat Cloud Profiling Radar data to develop an OT detection validation database that is used to evaluate the IRW-texture and WV-IRW BTD OT detection methods. CloudSat data were manually examined over a 1.5-yr period to identify cases in which the cloud top penetrates above the tropopause height defined by a numerical weather prediction model and the surrounding cirrus anvil cloud top, producing 111 confirmed overshooting top events. When applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-based Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager proxy data, the IRW-texture (WV-IRW BTD) method offered a 76% (96%) probability of OT detection (POD) and 16% (81%) false-alarm ratio. Case study examples show that WV-IRW BTD.0 K identifies much of the deep convective cloud top, while the IRW-texture method focuses only on regions with a spatial scale near that of commonly observed OTs. The POD decreases by 20% when IRW-texture is applied to current geostationary imager data, highlighting the importance of imager spatial resolution for observing and detecting OT regions.

Bedka, Kristopher M.↗
NASA NTRS2013

Modulation of Terrestrial Convection by Tropospheric Humidity, and Implications for Other Planets

For decades, deep cumulus convection was viewed as consisting partly of undilute plumes that do not interact with their surrounding environment in order to explain their observed tendency to reach or penetrate the tropical tropopause. This behavior was built into all cumulus parameterizations used in terrestrial global climate and numerical weather prediction models, and it still persists in some models today. In the past decade, though, some embarrassing failures of global models have come to light, notably their tendency to rain over land near noon rather than in late afternoon or evening as observed, and the absence in the models of the Madden-Julian Oscillation (MJO), the major source of intraseasonal (30-90 day) precipitation variability in the Indian Ocean, West Pacific, and surrounding continental regions. In the past decade it has become clear that an important missing component of parameterizations is strong turbulent entrainment of drier environmental air into cumulus updrafts, which reduces the buoyancy of the updrafts and thus limits their vertical development. Tropospheric humidity thus serves as a throttle on convective penetration to high altitudes and delays the convective response to large-scale destabilizing influences in the environment.

tropical regions↗
NASA NTRS2014

Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Department

Flooding and drought are two key forecasting challenges for the Kenya Meteorological Department (KMD). Atmospheric processes leading to excessive precipitation and/or prolonged drought can be quite sensitive to the state of the land surface, which interacts with the boundary layer of the atmosphere providing a source of heat and moisture. The development and evolution of precipitation systems are affected by heat and moisture fluxes from the land surface within weakly-sheared environments, such as in the tropics and sub-tropics. These heat and moisture fluxes during the day can be strongly influenced by land cover, vegetation, and soil moisture content. Therefore, it is important to represent the land surface state as accurately as possible in numerical weather prediction models. Enhanced regional modeling capabilities have the potential to improve forecast guidance in support of daily operations and high-end events over east Africa. KMD currently runs a configuration of the Weather Research and Forecasting (WRF) model in real time to support its daily forecasting operations, invoking the Nonhydrostatic Mesoscale Model (NMM) dynamical core. They make use of the National Oceanic and Atmospheric Administration / National Weather Service Science and Training Resource Center's Environmental Modeling System (EMS) to manage and produce the WRF-NMM model runs on a 7-km regional grid over eastern Africa. Two organizations at the National Aeronautics and Space Administration Marshall Space Flight Center in Huntsville, AL, SERVIR and the Short-term Prediction Research and Transition (SPoRT) Center, have established a working partnership with KMD for enhancing its regional modeling capabilities. To accomplish this goal, SPoRT and SERVIR will provide experimental land surface initialization datasets and model verification capabilities to KMD. To produce a land-surface initialization more consistent with the resolution of the KMD-WRF runs, the NASA Land Information System (LIS) will be run at a comparable resolution to provide real-time, daily soil initialization data in place of interpolated Global Forecast System soil moisture and temperature data. Additionally, real-time green vegetation fraction data from the Visible Infrared Imaging Radiometer Suite will be incorporated into the KMD-WRF runs, once it becomes publicly available from the National Environmental Satellite Data and Information Service. Finally, model verification capabilities will be transitioned to KMD using the Model Evaluation Tools (MET) package, in order to quantify possible improvements in simulated temperature, moisture and precipitation resulting from the experimental land surface initialization. The transition of these MET tools will enable KMD to monitor model forecast accuracy in near real time. This presentation will highlight preliminary verification results of WRF runs over east Africa using the LIS land surface initialization.

Case. Jonathan↗
NASA NTRS2014

The GOES-R Geostationary Lightning Mapper (GLM) and the Global Observing System for Total Lightning

for the existing GOES system currently operating over the Western Hemisphere. New and improved instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved temporal, spatial, and spectral resolution for the next generation Advanced Baseline Imager (ABI). The GLM will map total lightning continuously day and night with near-uniform spatial resolution of 8 km with a product latency of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency among a number of potential applications. The GLM will help address the National Weather Service requirement for total lightning observations globally to support warning decision-making and forecast services. Science and application development along with pre-operational product demonstrations and evaluations at NWS national centers, forecast offices, and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and check-out of GOES-R in 2016. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings.

Goodman, Steven J.↗
NASA NTRS2014

Configuration and Evaluation of a Dual-Doppler 3-D Wind Field System

Current LSP, GSDO, and SLS space vehicle operations are halted when wind speeds from specific directions exceed defined thresholds and when lightning is a threat. Strong winds and lightning are difficult parameters for the 45th Weather Squadron (45 WS) to forecast, yet are important in the protection of customer vehicle operations and the personnel that conduct them. A display of the low-level horizontal wind field to reveal areas of high winds or convergence would be a valuable tool for forecasters in assessing the timing of high winds, or convection initiation and subsequent lightning occurrence. This is especially important for areas where no weather observation platforms exist. Developing a dual-Doppler radar capability would provide such a display to assist forecasters in predicting high winds and convection initiation. The wind fields can also be used to initialize a local mesoscale numerical weather prediction model to help improve the model forecast winds, convection initiation, and other phenomena. The 45 WS and NWS MLB tasked the Applied Meteorology Unit (AMU) to develop a dual- Doppler wind field display using data from the 45th Space Wing radar, known as the Weather Surveillance Radar (WSR), NWS MLB Weather Surveillance Radar 1988 Doppler (KMLB), and the Orlando International Airport Terminal Doppler Weather Radar (KMCO). They also stipulated that the software used should be freely available. The AMU evaluated two software packages and, with concurrence from NWS MLB and the 45 WS, chose the Warning Decision Support System-Integrated Information (WDSS-II). The AMU collected data from two significant weather cases: a tornadic event on 14 April 2013 and a severe wind and hail event on 12 February 2014. For the 14 April case, the data were from WSR and KMLB. For the 12 February case, the data were from KMCO and KMLB. The AMU installed WDSS-II on a Linux PC, then processed and quality controlled the radar data for display and analysis using WDSS-II tools. Because of issues with de-aliasing the WSR velocity field, the AMU did not use data from this radar in this study and only analyzed the 12 February case. Merging the data to create the dual-Doppler analysis involved several steps. The AMU used instructions from the WDSS-II website and discussion forum to determine the correct tools to use for the analysis, and was successful in creating a merged reflectivity field, which was critical to the success of creating a merged velocity field. However, the AMU was unable to create a merged velocity field. The AMU researched the WDSS-II forum for discussions on similar issues, asked questions on the forum, and tested different options and values in the merger tool with no success. Developing a dual-Doppler wind field was the main goal of this task, but that was not accomplished. It could be an issue of not using the correct options or the correct value for the options used, or there could be issues with the radar data. There is a follow-on AMU task to install the operational version of WDSS-II in the NWS MLB office. This will provide more opportunities to try different options and input values in order to create a merged wind field from KMCO and KMLB.

Doppler radar↗
NASA NTRS2014

Near-Real Time Satellite-Retrieved Cloud and Surface Properties for Weather and Aviation Safety Applications

Cloud properties determined from satellite imager radiances provide a valuable source of information for nowcasting and weather forecasting. In recent years, it has been shown that assimilation of cloud top temperature, optical depth, and total water path can increase the accuracies of weather analyses and forecasts. Aircraft icing conditions can be accurately diagnosed in near-­‐real time (NRT) retrievals of cloud effective particle size, phase, and water path, providing valuable data for pilots. NRT retrievals of surface skin temperature can also be assimilated in numerical weather prediction models to provide more accurate representations of solar heating and longwave cooling at the surface, where convective initiation. These and other applications are being exploited more frequently as the value of NRT cloud data become recognized. At NASA Langley, cloud properties and surface skin temperature are being retrieved in near-­‐real time globally from both geostationary (GEO) and low-­‐earth orbiting (LEO) satellite imagers for weather model assimilation and nowcasting for hazards such as aircraft icing. Cloud data from GEO satellites over North America are disseminated through NCEP, while those data and global LEO and GEO retrievals are disseminated from a Langley website. This paper presents an overview of the various available datasets, provides examples of their application, and discusses the use of the various datasets downstream. Future challenges and areas of improvement are also presented.

Minnis, Patrick↗
NASA NTRS2015

Simultaneous Radar and Satellite Data Storm-Scale Assimilation Using an Ensemble Kalman Filter Approach for 24 May 2011

Assimilating high-resolution radar reflectivity and radial velocity into convection-permitting numerical weather prediction models has proven to be an important tool for improving forecast skill of convection. The use of satellite data for the application is much less well understood, only recently receiving significant attention. Since both radar and satellite data provide independent information, combing these two sources of data in a robust manner potentially represents the future of high-resolution data assimilation. This research combines Geostationary Operational Environmental Satellite 13 (GOES-13) cloud water path (CWP) retrievals with Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity to examine the impacts of assimilating each for a severe weather event occurring in Oklahoma on 24 May 2011. Data are assimilated into a 3-km model using an ensemble adjustment Kalman filter approach with 36 members over a 2-h assimilation window between 1800 and 2000 UTC. Forecasts are then generated for 90 min at 5-min intervals starting at 1930 and 2000 UTC. Results show that both satellite and radar data are able to initiate convection, but that assimilating both spins up a storm much faster. Assimilating CWP also performs well at suppressing spurious precipitation and cloud cover in the model as well as capturing the anvil characteristics of developed storms. Radar data are most effective at resolving the 3D characteristics of the core convection. Assimilating both satellite and radar data generally resulted in the best model analysis and most skillful forecast for this event.

Jones, Thomas A.↗
NASA NTRS2017

Global Clear-Sky Surface Skin Temperature from Multiple Satellites Using a Single-Channel Algorithm with Angular Anisotropy Corrections

Surface skin temperature (T(sub s)) is an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared (TIR) method to retrieve T(sub s) over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of T(sub s) over the diurnal cycle in non-polar regions, while polar T(sub s) retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR), can complement the GEO measurements. The combined global coverage of remotely sensed T(sub s), along with accompanying cloud and surface radiation parameters, produced in near-realtime and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-realtime hourly T(sub s) observations can be assimilated in high-temporal-resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived T(sub s) data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing and illumination angles. Therefore, T(sub s) validation with established references is essential, as is proper evaluation of T(sub s) sensitivity to atmospheric correction source. This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based T(sub s) product that is derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirically adjusted theoretical model of satellite land surface temperature (LST) angular anisotropy is tested to improve satellite LST retrievals. Application of the anisotropic correction yields reduced mean bias and improved precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program ground station measurements. It also significantly reduces inter-satellite differences between LSTs retrieved simultaneously from two different imagers. The implementation of these universal corrections into the SatCORPS product can yield significant improvement in near-global-scale, near-realtime, satellite-based LST measurements. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

Scarino, Benjamin R.↗
NASA NTRS2017

GC13I-0857: Designing a Frost Forecasting Service for Small Scale Tea Farmers in East Africa

Kenya is the third largest tea exporter in the world, producing 10% of the world's black tea. Sixty percent of this production occurs largely by small scale tea holders, with an average farm size of 1.04 acres, and an annual net income of $1,075. According to a recent evaluation, a typical frost event in the tea growing region causes about $200 dollars in losses which can be catastrophic for a small holder farm. A 72-hour frost forecast would provide these small-scale tea farmers with enough notice to reduce losses by approximately 80 USD annually. With this knowledge, SERVIR, a joint NASA-USAID initiative that brings Earth observations for improved decision making in developing countries, sought to design a frost monitoring and forecasting service that would provide farmers with enough lead time to react to and protect against a forecasted frost occurrence on their farm. SERVIR Eastern and Southern Africa, through its implementing partner, the Regional Centre for Mapping of Resources for Development (RCMRD), designed a service that included multiple stakeholder engagement events whereby stakeholders from the tea industry value chain were invited to share their experiences so that the exact needs and flow of information could be identified. This unique event allowed enabled the design of a service that fit the specifications of the stakeholders. The monitoring service component uses the MODIS Land Surface Temperature product to identify frost occurrences in near-real time. The prediction component, currently under testing, uses the 2-m air temperature, relative humidity, and 10-m wind speed from a series of high-resolution Weather Research and Forecasting (WRF) numerical weather prediction model runs over eastern Kenya as inputs into a frost prediction algorithm. Accuracy and sensitivity of the algorithm is being assessed with observations collected from the farmers using a smart phone app developed specifically to report frost occurrences, and from data shared through our partner network developed at the stakeholder engagement meeting. This presentation will illustrate the efficacy of our frost forecasting algorithm, and a way forward for incorporating these forecasts in a meaningful way to the key decision makers - the small-scale farmers of East Africa.

frost↗
NASA NTRS2018

Determination of Best Low-Frequency Microwave Antenna Approach for Future High Resolution Measurements from Space

Microwave remote sensing measurements at L-band (~1.2-1.6 GHz) of geophysical parameters such as soil moisture will need to be at higher spatial resolution than current systems (SMOS/ SMAP/ Aquarius) in order to meet the requirements of land surface, ocean, and numerical weather prediction models in the near future, which will operate at ~9-15 km global grids and 1-3 km regional grids in the next few years. In order to make progress toward these needed spatial resolutions, advancements in technology are necessary which would lead to improved effective (i.e. equivalent) antenna size. An architecture trade study was conducted to quantitatively define the value and limits of different microwave technology paths, and to select the most appropriate path to achieve the high spatial resolution required by science in the future without sacrificing performance, accuracy, and global coverage.

soil moisture↗
NASA NTRS2019

An Integrated Approach to Weather Radar Calibration and Monitoring Using Ground Clutter and Satellite Comparisons

The stability and accuracy of weather radar reflectivity calibration are imperative for quantitative applications, such as rainfall estimation, severe weather monitoring and nowcasting, and assimilation in numerical weather prediction models. Various radar calibration and monitoring techniques have been developed, but only recently have integrated approaches been proposed, that is, using different calibration techniques in combination. In this paper the following three techniques are used: 1) ground clutter monitoring, 2) comparisons with spaceborne radars, and 3) the self-consistency of polarimetric variables. These techniques are applied to a C-band polarimetric radar (CPOL) located in the Australian tropics since 1998. The ground clutter monitoring technique is applied to each radar volumetric scan and provides a means to reliably detect changes in calibration, relative to a baseline. It is remarkably stable to within a standard deviation of 0.1 dB (decibels). To obtain an absolute calibration value, CPOL observations are compared to spaceborne radars on board TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Measurement) using a volume-matching technique. Using an iterative procedure and stable calibration periods identified by the ground echoes technique, we improve the accuracy of this technique to about 1 dB. Finally, we review the self-consistency technique and constrain its assumptions using results from the hybrid TRMM-GPM and ground echo technique. Small changes in the self-consistency parameterization can lead to 5 dB of variation in the reflectivity calibration. We find that the drop-shape model of Brandes et al. with a standard deviation of the canting angle of 12 degrees best matches our dataset.

Louf, Valentin↗
NASA NTRSDate not supplied

TPSAS-NF1676L-31539-DND

TROPICS (Time-Resolved Observations of Precipitation Structure and Storm Intensity with a Constellation of Smallsats) Science Objectives - Relate precipitation structure evolution, including diurnal cycle, to the evolution of the upper-level warm core and associated intensity changes - Relate the occurrence of intense precipitation cores (convective bursts) to storm intensity evolution - Relate retrieved environmental moisture measurements to coincident measures of storm structure (including size) and intensity - Assimilate microwave observations in mesoscale and global numerical weather prediction models to assess impacts on storm track and intensity

William Blackwell↗
NASA NTRS2022

Novel Parameterization of Ice Cloud Effective Diameter from Collocated CALIOP-IIR and CloudSat Retrievals

Satellite-based measurements of global ice cloud microphysical properties are sampled to develop a novel set of physical parameterizations, relating to cloud layer temperature and effective diameter D(e), that can be implemented for two separate applications: in numerical weather prediction models and lidar-based cloud radiative forcing studies. Ice cloud optical properties (i.e., spectral scattering and absorption) are estimated based on the effective size and habit mixture of the cloud particles. Historically, the ice cloud D(e) has been parameterized from aircraft in situ measurements. However, aircraft-based parameterizations are opportunistic in that they only represent specific types of clouds (e.g., convective anvil, tropopause-topped cirrus) in the regions in which they were sampled and, in some cases, are limited in fully resolving the entire vertical cloud layer. Breaking away from the aircraft-based parameterization paradigm, this study is the first of its kind to attempt a parameterization of D(e) as a function of temperature, ice water content (IWC), and lidar-derived extinction from satellite-based global oceanic measurements of ice clouds. Data from both active and passive remote sensing sensors from two of NASA’s A-Train satellites, CloudSat and CALIPSO, are collected to guide development of globally robust parameterizations of all ice cloud types and one exclusively for cirrus clouds.

Ice Cloud↗
NASA NTRSDate not supplied

NASA SPoRT Land Information System Products for Soil Moisture Analysis

The NASA Short-term Prediction Research and Transition (SPoRT) Program has been producing a near real-time instance of NASA’s Land Information System (hereafter known as SPoRT-LIS) since 2010, which contains output of soil moisture and temperature at layered depths. The unique configuration of the SPoRT-LIS enables decision-making on operational timescales since it assimilates near real-time observations such as Green Vegetation Fraction from the Visible Infrared Imaging Radiometer Suite and Quantitative Precipitation Estimates from the Multi-Radar Multi-Sensor System. The SPoRT-LIS was developed initially to provide land surface initialization variables for local numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model. The earliest documented uses of the SPoRT-LIS as a tool for local drought analysis came from the National Weather Service Office in Huntsville, AL in 2011, in which data were used to provide recommendations for drought category changes to the U.S. Drought Monitor. Through engagement with SPoRT collaborative partners, use of the SPoRT-LIS has gradually expanded in recent years as a component of drought analysis and feedback to the U.S. Drought Monitor. This effort was aided by feedback from end users who expressed needs for specific soil moisture variables, which led to increased applicability for analysis by others in the drought community. This presentation will provide information on the SPoRT-LIS for drought analysis and the collaborative process that has led to changes in product output to meet user needs, with focus in the Southern Appalachian region. However, applications for hydrology and fire weather will also be discussed.

Land surface modeling↗
NASA NTRSDate not supplied

Simulating Microwave and Radar Signals in Severe Weather Conditions

The development of radiative transfer simulators for radar and microwave signals, spanning a range of frequencies from 10 to 800 GHz, is paramount for enhancing weather forecasting accuracy, particularly for severe weather events. These tools facilitate the assimilation of microwave and radar observations into numerical weather prediction models, thereby improving accuracy of weather forecasts. Additionally, they directly simulate radar signals crucial for autonomous vehicle operation, with frequencies commonly used in radar systems such as 24, 74, 77, and 79 GHz. However, these frequencies are susceptible to weather phenomena like severe rain and snow, which can significantly impact vehicle safety and performance. These simulation tools, including radiative transfer simulators, serve a dual purpose. Firstly, they enhance forecasts for severe weather events, contributing to autonomous vehicle safety by providing early warnings and risk assessments. Secondly, they enable the direct simulation of radar signals in autonomous vehicle driving systems, allowing researchers and engineers to evaluate radar system performance under various weather conditions. In addition to the radar signal simulator, this abstract discusses the incorporation of advanced scattering properties developed using the discrete dipole approximation (DDA). The DDA technique enhances scattering calculations for frozen hydrometeors at microwave frequencies, thereby improving the accuracy of radar signal simulations and enabling more accurate assessments of radar system performance in adverse weather conditions. In summary, this abstract explores the development and utilization of comprehensive simulation tools, emphasizing their significance in simulating microwave and radar signals and improving weather forecasts. Special attention is given to the simulation of radar signals at critical frequencies for autonomous vehicle sensing and navigation, addressing challenges posed by severe weather phenomena and their effects on signal propagation and detection

Isaac Moradi↗
NASA NTRS2024

Explicitly Resolving Lightning and Electrification Processes from the 10-12 April 2019 Thundersnow Outbreak

The 10-12 April 2019 thundersnow (i.e., lightning within snowfall) outbreak was examined via ground- and space-based lightning observations and was simulated using a numerical weather prediction model with an explicit electrification parameterization. When compared to observations, the simulation propagated the synoptic snowband two to six hours faster while also exaggerating the 3-D reflectivity structure. Throughout the event, the simulation produced 1,733 thundersnow flashes which was less than what was observed by ground- and space-based lightning sensors. In general, simulated thundersnow flashes were spatially offset from the largest reflectivities within the synoptic snowband and tended to occur within elevated convection that traversed isentropically along the top of mid-level frontogenesis. These simulated thundersnow flashes were associated with a tripole charge structure with ice/snow hydrometeors contributing most to the main negative charge region. Both simulated and observed thundersnow flashes initiated in conditionally unstable environments. Lastly, a conceptual model was developed to explain the spatial separation between the largest reflectivities in the snowband and the occurrence of thundersnow. It is hypothesized that the spatial offset of thundersnow initiation from the reflectivity cores within the synoptic snowband arose from a thermal circulation – induced by mid-level frontogenesis – that advects positively charged ice/snow hydrometeors towards the surface and creates a nearly homogeneous vertical charge structure.

lightning↗