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

Impact Of Choices When Creating Average Proximity Soundings As Applied To Lp/cl/hp Supercell Environments

The first half of this two-part study explores two ways of producing composite environmental soundings (feature averaging versus height averaging; FA vs. HA), why those composites differ from one another, how the compositing technique itself affects the resulting thermodynamic and wind parameters, and which technique results in preserving features. This exploration was applied to three groups of supercell proximity soundings: low-precipitation (LP), classic (CL), and high precipitation (HP) and the HA analysis from the Rasmusssen and Straka (RS98) paper are reanalyzed in both the FA and HA framework. The second half of this study investigates how well previously reported LP, CL, and HP supercell radar behavior (Beatty et al. 2009) is reproduced in an idealized three-dimensional cloud model using both the original and composite soundings. Reanalyzing the results from RS98 in both HA and FA frameworks, the LP group of soundings have a mean mixed-layer LCL (MLLCL) and mean MLLFC that are both significantly different (p \u3c 0.05) than those from the other sounding groups. Also, the HP group of soundings has a mean MLLFC that is significantly different (p \u3c 0.05) than the means from the other sounding groups. The HP sounding mean BL to 9 km shear and mean 4-10 km shear magnitude are significantly different (p \u3c 0.05; RS98 found p \u3c 0.02) and the mean HP sounding 9-10 km storm relative wind is significantly different (p \u3c 0.02) compared to the other sounding groups. Wind parameters and thermodynamic parameters computed from surface-based parcels for both the FA and HA composite sounding lay within one standard deviation of the distribution mean for each sounding group and mixed-layer parcel parameters lay farther from the distribution mean. The FA soundings parameters are not consistently closer to distribution means despite features such as the capping inversion and low-level moisture being preserved better within the FA sounding. Using relative humidity for the LP and CL FA and HA soundings (and vapor pressure for the HP soundings) produces the largest CAPE and least CIN, although averaging water vapor mixing ratio is arguably the most accurate and appropriate. From the dataset, 29 individual sounding cases were simulated--10 CL, 10 LP, and 9 HP supercells-- and only three storms in each class lasted at least 7200 seconds with an updraft helicity greater than 480 m2 s-2. Only two of these nine individual cases produced long lived supercells, one each from the LP and HP sounding classes, transitioned from a forward flank dominant to rear flank dominant maximum precipitation (following Beatty et al. 2009). The other seven cases maintained a forward flank dominant maximum precipitation. Compositing using only the three successful cases in each class only succeeded in producing long-lived supercells only for CL FA and HA composites and the HP HA composite. These cases produced forward flank dominant precipitation maximums, with no transition. Due to the lack of consistency in storm behavior within each class, it is concluded that cases should be simulated and studied individually, as compared to creating a composite sounding - particularly when studying environments with a very small sample size

A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning

Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. In order to fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plain language format that is targeted for the operational meteorological community. This is the second paper in a pair that aim to serve as a machine learning resource for meteorologists. While the first paper focused on traditional machine learning methods (e.g., random forest), here a broad spectrum of neural networks and deep learning methods are discussed. Specifically this paper covers perceptrons, artificial neural networks, convolutional neural networks and U-networks. Like the part 1 paper, this manuscript discusses the terms associated with neural networks and their training. Then the manuscript provides some intuition behind every method and concludes by showing each method used in a meteorological example of diagnosing thunderstorms from satellite images (e.g., lightning flashes). This paper is accompanied with an open-source code repository to allow readers to explore neural networks using either the dataset provided (which is used in the paper) or as a template for alternate datasets

Spectral analyses of the dual polarization Doppler weather radar data.

Echoes in clear air from biological scatterers mixed within the resolution volumes over a large region are presented. These echoes were observed with the polarimetric prototype of the forthcoming WSR-88D weather radar. The study case occurred in the evening of September 7, 2004, at the beginning of the bird migrating season. Novel polarimetric spectral analyses are used for distinguishing signatures of birds and insects in multimodal spectra. These biological scatterers were present at the same time in the radar resolution volumes over a large area. Spectral techniques for (1) data censoring, (2) wind retrieval and (3) estimation of intrinsic values/functions of polarimetric variables for different types of scatterers are presented. The technique for data censoring in the frequency domain allows detection of weak signals. Censoring is performed on the level of spectral densities, allowing exposure of contributions to the spectrum from multiple types of scatterers. The spectral techniques for wind retrieval allow simultaneous estimation of wind from the data that are severely contaminated by migrating birds, and assessment of bird migration parameters. The intrinsic polarimetric signatures associated with the variety of scatterers can be evaluated using presented methodology. Algorithms for echo classification can be built on these. The possibilities of spectral processing using parametric estimation techniques are explored for resolving contributions to the Doppler spectrum from the three types of scatterers: passive wind tracers, actively flying insects and birds. A combination of parametric and non-parametric polarimetric spectral analyses is used to estimate the small bias introduced to the wind velocity by actively flying insects

Prediction of convective morphology in near-cloud permitting WRF model simulations

Convective morphology was analyzed with 3-km WRF-ARW simulations for 37 events during the warm season from 2006 to 2010. Ten classifications were used to identify convective modes displayed in each event. An objective scoring method, based on normalized time and the type of mode exhibited, was developed to measure the accuracy of the modeled morphologies when compared to radar observations. Trends in the simulated evolution were discussed, as well as common discrepancies between the model and observed events. Environmental conditions before convective initiation were obtained from RUC analyses, and statistically significant associations between the parameters and the model accuracy scores were found. Finally, four cases highlighting the main morphological issues in the simulations were investigated further. Overall, the simulations entailed more cellular modes and fewer linear modes than the observed systems. Bow echoes and linear systems with trailing stratiform rain regions were especially difficult for the model to forecast. Of the 21 cases with an observed bow echo, only 8 featured a simulated bow echo. Twelve cases included a missed squall line with trailing stratiform rain. Cellular modes were simpler to forecast, as 75% of the forecast comparisons with an observed cellular mode also featured a modeled cellular mode. The simulations usually portrayed convective evolution more accurately when the initial synoptic environment included strong deep-layer shear and cool potential temperatures at the level of maximum theta-E. Major timing errors with convective initiation or dissipation in the simulations usually occurred when initial 0-6 km shear was very low, surface potential temperatures were cool, and when potential temperatures quickly warmed with height. The case studies showed that differences in wind shear and cold pool strength and development between the simulations and observations were responsible for many convective mode discrepancies. Strong boundary-normal shear, a warm cold pool, and very dry air within the rear inflow jet inhibited the formation of a trailing stratiform rain region in the simulation for the first case study. Weak deep-layer shear and the lack of a fully-developed cold pool in the model did not allow the simulated line to bow out in the second case study. Weaker forcing near the surface in the simulation compared to the observations allowed for clustered cells instead of a broken line in the third case study. Mid-layer shear not oriented along the boundary did not provide for proper cold pool merging for linear development in the WRF output for the fourth case study. Sensitivity tests were also conducted for microphysical schemes and initial and lateral boundary conditions for the first and second case studies. None of the microphysics schemes produced the observed convective mode, but GFS initial conditions in the first case study produced a trailing stratiform region

Quantitative precipitation estimates from dual-polarization weather radar in lazio region

Many phenomena (such as attenuation and range degradation) can influence the accuracy of rainfall radar estimates. They introduce errors that increase as the distance from the radar increases, thereby decreasing the reliability of radar estimates for applications that require quantitative precipitation estimation. The aim of the present work is to develop a range dependent error model called adjustment factor, that can be used as a range error pattern for allowing to correct the mean error which affects long-term quantitative precipitation estimates. A range dependent gauge adjustment technique was applied in combination with other processing of radar data in order to correct the range dependent error affecting radar measurements. Issues like beam blocking, path attenuation, vertical structure of precipitation related error, bright band, and incorrect Z-R relationship are implicitly treated with this type of method. In order to develop the adjustment factor, radar error was determined with respect to rain gauges measurements through a comparison between the two devices, based on the assumption that gauge rain was real. Therefore, the G/R ratio between the yearly rainfall amount measured in each rain gauge position during 2008 and the corresponding radar rainfall amount was calculated against the distance from radar. Trend of the G/R ratio shows two behaviors: a concave part due to the melting layer effect close to the radar location, and an almost linear increasing trend at greater distance. Then, a linear best fitting was used to find an adjustment factor, which estimates the radar error at a given range. The effectiveness of the methodology was verified by comparing pairs of rainfall time series that were observed simultaneously by collocated rain gauges and radar. Furthermore, the variability of the adjustment factor was investigated at the scale of event, both for convective and stratiform events. The main result is that there is not an univocal range error pattern, as it is also a function of the event characteristics. On the other hand, the adjustment factor tends to stabilize over long periods of observation as in the case of a whole year of measures

X-band synthetic aperture radar methods

Spaceborne Synthetic Aperture Radars (SARs), operating at L-band and above, offer microwave observations of the Earth at very high spatial resolution in almost all-weather conditions. Nevertheless, precipitating clouds can significantly affect the signal backscattered from the ground surface in both amplitude and phase, especially at X band and beyond. This evidence has been assessed by numerous recent efforts analyzing data collected by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions at X band. This sensitivity can be exploited to detect and quantify precipitations from SARs at the spatial resolution of a few hundred meters, a very appealing feature considering the current resolution of precipitation products from space. Forward models of SAR response in the presence of precipitation have been developed for analyzing SAR signature sensitivity and developing rainfall retrieval algorithms. Precipitation retrieval algorithms from SARs have also been proposed on a semi-empirical basis. This chapter will review experimental evidences, modelling approaches, retrieval methods and recent applications of X-band SAR data to rainfall estimation

Frequency requirements for active earth observation sensors

The foundation and rationale for the selection of microwave frequencies for active remote sensing usage and for subsequent use in determination of sharing criteria and allocation strategies for the WARC-79 are presented