Relationship between storm structure and lightning activity in Colorado convection observed during STERO-A

November 1997.Includes bibliographical references.Concurrent measurements from the CSU-CHILL multiparameter Doppler radar, the ONERA VHF lightning interferometer, and the National Lightning Detection Network, obtained during Phase A of the Stratosphere-Troposphere Experiments: Radiation, Aerosols, Ozone (STERAO-A) field project, provided a unique data set with which to study the relationships between convective storm microphysics and associated lightning. Two events have been examined in detail: storms of 10 and 12 July 1996. Both storms underwent major organizational transitions during their lifetimes, identified by sharp changes in total lightning flash rates, dominant cloud-to-ground (CG) flash polarity, or dominant flash type (cloud-to-ground vs. intra-cloud). Both storms also featured relatively high intra-cloud (IC) flash rates. The 10 July 1996·storm evolved from a multicellular line to an intense unicellular storm. The unicellular stage was marked by a sharp peak in IC flash rate as identified by the interferometer. Cloud-to-ground flash rates were low throughout the storm’s lifetime. Small hail was produced during the entire observation period, suggesting storm updraft speeds were significant. The storm of 12 July evolved from an intense multicellular, hail­ producing storm to a weaker rainstorm. Before this transition, hail was being produced and the CG flash rates were low. After the transition, hail was no longer being produced and negative CG flash rates were significantly larger. Storm updraft speeds likely weakened during the transition. These observations are consistent with the elevated-dipole hypothesis to explain low CG production in convective storms, especially if the observed high IC flash rates mostly neutralized any charged core before it descended toward the ground. Alternatively, if significant charging does not occur during wet growth of hail and graupel, both these storms might have produced enough wet-growth ice to prevent the generation of a lower positive charge center that could act to stimulate CG production. However, the radar data, in particular the linear depolarization ratio (LOR) data, suggest that dry growth was more prevalent than wet growth.Sponsored by the National Science Foundation under grant ATM-9321361

Kinematic and microphysical evolution of the 29 June supercell observed during STEPS

Spring 2003.Also issued as Sarah A. Tessendorf's thesis (M.S.) -- Colorado State University, 2003.Includes bibliographical references.The focus of this thesis is to examine the kinematic and microphysical properties of a severe storm using polarimetric and Doppler radar data. The data were collected during the Severe Thunderstorm Electrification and Precipitation Study (STEPS) that took place between 17 May 2000 and 20 July 2000 in eastern Colorado and western Kansas. One goal of STEPS is to find a relationship between the microphysics and kinematics of severe storms on the High Plains and their unusual positive cloud-to­ ground lightning production. The severe storm observed on 29 June 2000 produced large hail, frequent positive cloud-to-ground lightning, an F1 tornado, and displayed characteristic storm splitting evolution during the sampling period. Unprecedented measurements from three Doppler radars were used to describe the kinematics and rnicrophysics of this storm. Radial components of the wind fields relative to the three Doppler radars were combined to produce the three-dimensional winds in the storm. Bulk precipitation types (e.g., rain, hail) were objectively determined using the multi­ parameter variables available on two of the radars. The Doppler-derived kinematic fields were compared with the microphysical classifications over a nearly three-hour period to examine trends during the lifecycle of the supercell. Results showed that the supercell intensified rapidly while storm splitting occurred. Prior to splitting, there was little cloud-to-ground lightning and little evidence of hail aloft. After storm splitting. hail volume and cloud-to-ground lightning activity quickly intensified. The updraft of this storm pulsated, with maximum speeds to nearly 50 m s·1. The peaks in hail production aloft, largely around -10° C, were well correlated with the updraft fluctuations as well as with peaks in the frequency of positive cloud-to­ ground lightning flashes. These results are consistent with experimental work that shows positive charging in ice-ice collisions around -10° C. The dynamics of the storm-splitting process, in terms of radar-derived updraft and vorticity fields, were shown to be consistent with current conceptual models. The results of this thesis advance our knowledge of supercell evolution and will be used to help determine the electrification mechanisms of severe storms that produce predominantly positive cloud-to-ground lightning.Sponsored by the National Science Foundation under grant ATM-9912051

Observations of winter storms with a video disdrometer and polarimetric radar

Spring 2007.Includes bibliographical references (pages 104-110).With efforts to upgrade the operational NEXRAD radars to include polarimetric capability underway, there are growing interests in developing radar-based algorithms for classifying hydrometeor types, quantifying winter precipitation, and improving the parameterization of winter precipitation in numerical forecast models. The capabilities of polarimetric radars, such as to better quantify warm season precipitation, have been demonstrated in various studies. However, these tasks are further complicated for winter precipitation by the need to know hydrometeor phase and bulk density of ice particles. In this study, data collected with a two-dimensional video disdrometer and S-band dual polarization radar during the Winter Icing and Storms Project 2004 (WISP04) storms are examined in support of ongoing research to develop radar-based algorithms for cold season precipitation. The capability to match radar-measured and disdrometer-based calculations of radar reflectivity factor and differential reflectivity is essential for retrieving hydrometeor characteristics with radar. During the WISP04, the disdrometer provided detailed information regarding hydrometeor size, number concentration, terminal velocity, and shape during the precipitation events. In this study, bulk ice particle density is estimated using an empirical relationship derived from disdrometer measurements of precipitation volume and rain gauge measurements of precipitation mass. Reflectivity and differential reflectivity, as measured by radar and computed from disdrometer observations are compared, and the combined dataset is used to examine storm microphysical properties. The measurements and computed values show good agreement and reveal that the radar detected subtle changes in the characteristics of winter precipitation. Additionally, sensitivity of the scattering computations to assumed ice particle characteristics is examined, and particle size distributions from radar measurements are retrieved for comparisons with the disdrometer observations

2-D spatial distribution of rainfall rate through combined use of radar reflectivity and rain gauge data

International audienceThis paper describes and comments the results obtained applying a data processing method to a joint set of radar and a rain gauge data for estimating the 2-D rainfall field at ground averaged over a given observation time T and over a radar coverage area that includes a rain gauge network. The estimate of the rainfall field is based on the processing of a data set composed by rain gauge and horizontal reflectivity radar data gathered during a rainfall phenomenon. The procedure has been tested on an experimental data set collected in Tuscany in 1999

A study of the structure of radar rainfall and its errors

Els objectius principals d’aquesta tesi són dos: d’una banda estudiar l’estructura de la variabilitat de la precipitació a diferents escales espacials i temporals, i de l’altra, estudiar l’estructura dels errors en les estimacions quantitatives de precipitació a través de radar. Pel que fa a l’estudi de l’estructura de la precipitació es proposa un marc de comparació per a mètodes de downscaling basat en valorar el grau amb què cada mètode és capaç de reproduir la variabilitat observada a les diferents escales de la pluja i la seva estructura multifractal. Finalment es proposa un mètode de downscaling tridimensional per a generar camps de precipitació d’alta resolució. Partint de dades mesurades amb radar, és capaç de reproduir la variabilitat a totes les escales de la pluja, i a la vegada, conservar l’estructura vertical de la precipitació observada pel radar. En aquesta tesi s’estudia també l’estructura dels errors associats a les mesures de radar, tant terrestre com embarcat en satèl·lit, que queden després de la cadena de correcció. Es realitza un estudi mitjançant simulació física de les observacions del radar, sobre un camp de precipitació d’alta resulució, per caracteritzar l’error relacionat amb la distància d’observació. També es caracteritza l’error total en les estimacions quantitatives de pluja dels radars terrestres mitjançant comparació contra un producte de referència basat en la combinació de radar i pluviòmetres. L’estructura de l’error trobada ha estat usada per generar un ensemble d’estimacions de pluja, que representa la incertesa en les estimacions, i pot ser emprat per aplicacions probabilístiques. Pel que fa a l’estudi de l’estructura de l’error associat a les estimacions de radar embarcat en satel·lit, s’han realitzat comparacions del radar embarcat en el satèl·lit TRMM contra equipament terrestre, per tal de caracteritzar, sota diverses condicions, les diferències en les mesures de precipitació.The principal objectives of this thesis are two: on one hand study the structure of the precipitation’s variability at different spatial and temporal scales, and on the other hand study the structure of the errors in the quantitative precipitation estimates by radar. In relation to the precipitation structure, a comparison framework for downscaling methods is proposed. Within this framework, the capability of each method reproducing the variability and multifractal behaviour observed in rainfall can be tested. A three-dimensional downscaling method to generate high-resolution precipitation fields from radar observations is proposed. The method is capable to reproduce the variability of rainfall at all scales and, at the same time, preserve the vertical structure of precipitation observed by the radar. In this thesis the structure of the errors that remain after the correction chain in radar measurements (both ground- and space-borne) is also studied. Simulation of the radar physical measurement process over high-resolution precipitation fields is performed to characterize the error related with range. The overall error in quantitative precipitation estimates by radar is characterized through comparison of radar estimates with a reference product based on a radar-raingauges merging. The error structure is used to generate a radar ensemble of precipitation estimates that represents the uncertainty in the measurements and can be used in probabilistic applications. Regarding the study of the errors associated to spaceborne radar measurements, comparisons of TRMM Precipitation Radar with ground equipment are performed to characterize the discrepancies between the precipitation estimates under different conditions

Machine learning-based fusion studies of rainfall estimation from spaceborne and ground-based radars

2019 Spring.Includes bibliographical references.Precipitation measurement by satellite radar plays a significant role in researching the water circle and forecasting extreme weather event. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has capability of providing a high-resolution vertical profile of precipitation over the tropics regions. Its successor, Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR), can provide detailed information on the microphysical properties of precipitation particles, quantify particle size distribution and quantitatively measure light rain and falling snow. This thesis presents a novel Machine Learning system for ground-based and space borne radar rainfall estimation. The system first trains ground radar data for rainfall estimation using rainfall measurements from gauges and subsequently uses the ground radar based rainfall estimates to train spaceborne radar data in order to get space based rainfall product. Therein, data alignment between spaceborne and ground radar is conducted using the methodology proposed by Bolen and Chandrasekar (2013), which can minimize the effects of potential geometric distortion of spaceborne radar observations. For demonstration purposes, rainfall measurements from three rain gauge networks near Melbourne, Florida, are used for training and validation purposes. These three gauge networks, which are located in Kennedy Space Center (KSC), South Florida Water Management District (SFL), and St. Johns Water Management District (STJ), include 33, 46, and 99 rain gauge stations, respectively. Collocated ground radar observations from the National Weather Service (NWS) Weather Surveillance Radar – 1988 Doppler (WSR-88D) in Melbourne (i.e., KMLB radar) are trained with the gauge measurements. The trained model is then used to derive KMLB radar based rainfall product, which is used to train both TRMM PR and GPM DPR data collected from coincident overpasses events. The machine learning based rainfall product is compared against the standard satellite products, which shows great potential of the machine learning concept in satellite radar rainfall estimation. Also, the local rain maps generated by machine learning system at KMLB area are demonstrate the application potential

Final report for the CSU-CHILL radar facility

Submitted to the National Science Foundation, Division of Atmospheric Sciences.6 May 1996.Cooperative agreement no. ATM-8919080

Quality Control and Calibration of the Dual-Polarization Radar at Kwajalein, RMI

Weather radars, recording information about precipitation around the globe, will soon be significantly upgraded. Most of today s weather radars transmit and receive microwave energy with horizontal orientation only, but upgraded systems have the capability to send and receive both horizontally and vertically oriented waves. These enhanced "dual-polarimetric" (DP) radars peer into precipitation and provide information on the size, shape, phase (liquid / frozen), and concentration of the falling particles (termed hydrometeors). This information is valuable for improved rain rate estimates, and for providing data on the release and absorption of heat in the atmosphere from condensation and evaporation (phase changes). The heating profiles in the atmosphere influence global circulation, and are a vital component in studies of Earth s changing climate. However, to provide the most accurate interpretation of radar data, the radar must be properly calibrated and data must be quality controlled (cleaned) to remove non-precipitation artifacts; both of which are challenging tasks for today s weather radar. The DP capability maximizes performance of these procedures using properties of the observed precipitation. In a notable paper published in 2005, scientists from the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) at the University of Oklahoma developed a method to calibrate radars using statistically averaged DP measurements within light rain. An additional publication by one of the same scientists at the National Severe Storms Laboratory (NSSL) in Norman, Oklahoma introduced several techniques to perform quality control of radar data using DP measurements. Following their lead, the Topical Rainfall Measuring Mission (TRMM) Satellite Validation Office at NASA s Goddard Space Flight Center has fine-tuned these methods for specific application to the weather radar at Kwajalein Island in the Republic of the Marshall Islands, approximately 2100 miles southwest of Hawaii and 1400 miles east of Guam in the tropical North Pacific Ocean. This tropical oceanic location is important because the majority of rain, and therefore the majority of atmospheric heating, occurs in the tropics where limited ground-based radar data are available