Funded Research Grant Proposals - C.A. Doswell III


1. Principal Investigator (Co-PI - Daniel Weber)

Funding Agency: National Science Foundation Grant #ATM-0350539

Title: A study of moist deep convection: Generation of multiple updrafts in association with mesoscale forcing

Summary: The proposed work seeks to investigate the interaction of moist deep convection with mesoscale processes via new methods of initiating convection. The intent of this work is to investigate more realistic forcing from mesoscale processes on time scales that are much longer than the convective time scale with the goal of capturing the interaction between mesoscale forcing and convection. Specifically, the project team will investigate and observe the effects of several key environmental factors on the updraft and convective regeneration cycle of numerically simulated convection. The project team will apply, within a three-dimensional numerical cloud model, two initiating mechanisms, a constant near surface heat source and a momentum flux source within a non-dimensional parameter range study. These methods provide a continuous source of vertical motion that is consistent with environmental conditions commonly observed with organized convection. The results will be used to further classify and understand isolated multicellular thunderstorms.

Amount: $430,000 Running total: $430,000

Period: 07/15/04-07/14/07


2. Co-Principal Investigator (PI - Lance Leslie, Co-PI - Michael Richman)

Funding Agency: National Science Foundation Grant # ATM-050297

Title: Detecting synoptic-scale precursors of tornado outbreaks

Summary: Historically, synoptic-scale signals have played an elusive role in discriminating between tornado outbreak days and severe thunderstorm days without substantial tornadic activity.A question that needs to be answered is: "To what extent are tornado outbreaks attributable to processes on the synoptic-scale rather than on the mesoscale and smaller?" To explore this question, a series of numerical simulations are proposed that commence from smoothed, "synoptic-scale " initial conditions". These simulations will be run for lead-times of one to three days. The exclusion of mesoscale observational data is necessary to establish a baseline for determining the relationship between synoptic-scale signals and tornado outbreaks.

The MM5 and OU-HIRES numerical models both are extensively tested mesoscale numerical models. The models will be initialized using composite gridded fields from the NCEP/NCAR reanalysis data, which has a horizontal grid spacing of about 200 km. A family of such composites will be developed using Empirical Orthogonal Functions (EOF) that filter the data such that only the dominant synoptic-scale modes are retained. A range of meteorological covariates, including CAPE, low-level wind shear, storm-relative helicity, relative vorticity, and relative humidity, will be used as proxy variables for the occurrence of tornadoes. The covariates are necessary, as even the most sophisticated mesoscale models currently can predict supercell formation and motion but are incapable of explicitly predicting tornadoes, now and in the foreseeable future. Because we have no a priori hypotheses, we will investigate the spatial and temporal correlations between the simulated fields associated with tornado outbreak cases and cases involving primarily non-tornadic severe weather.

Statistical exploration of the outbreak and non-outbreak cases will enhance our physical understanding of the relationships between the synoptic environment and tornado outbreaks. A high statistical correlation between the outbreak and non-outbreak cases implies that even the most important tornado events, major tornado outbreaks, are controlled primarily at sub-synoptic scales. This finding has clear and profound implications for observing strategies, and for research programs: observation and prediction of major tornado outbreaks will depend almost exclusively on sub-synoptic-scale measurements. Alternatively, if we find low correlations, further study aimed at diagnosis of those processes that connect the synoptic scales to tornado outbreaks is likely to prove very fruitful.

Amount: $352,278 Running total: $782,278

Period: 01/16/05-01/15/08


3. Co-Principal Investigator (PI - Lance Leslie, Co-PI - Michael Richman)

Funding Agency: National Science Foundation Grant # ATM-080567

Title: Synoptic-Scale Influences on Outbreaks of Severe Convection

Summary: Recent studies have suggested that tornado outbreaks are linked to synoptic-scale processes, with lead times of at least 72 hours. However, many questions remain regarding the precise nature of these links. For example, the ability of mesoscale models to discriminate outbreak types using synoptic-scale input may be diminished for cases in the spring and fall. Furthermore, many severe weather outbreaks cannot be classified as primarily tornadic or primarily nontornadic. A mesoscale model's ability to distinguish these "nearly major" tornado outbreaks from tornadic and nontornadic outbreaks remains unknown. To explore these uncertainties, numerical simulations are proposed, using synoptic-scale initial conditions and test cases of the three outbreak types occurring in the spring and fall seasons.

Two mesoscale models, WRF and MM5, will be initialized using NCEP/NCAR reanalysis data, with a horizontal grid spacing of about 200 km. Simulations will be analyzed using meteorological covariates, including CAPE, wind shear, storm-relative helicity, relative vorticity, moisture parameters, and the lifting condensation level, as the explicit prediction of tornadoes, even with the most sophisticated mesoscale models, is not possible now or in the foreseeable future. A combination of subjective and objective techniques will be employed to analyze the predicted covariates. These techniques will determine the degree to which the outbreak types can be discriminated and what severe weather parameters can be exploited to distinguish the outbreaks. If mesoscale models are shown to be capable of distinguishing outbreak type using synoptic-scale input, implications for future research are profound, as further study aimed at the diagnosis of processes connecting the synoptic scales to tornado outbreaks is likely to prove fruitful.

Amount: $591,957 Running total: $1,374,235

Period: 01/01/09-12/31/11

4.  Co-Principal Investigator (PI - David Stensrud, Co-PI Michael Coniglio)

Funding Agency: National Science Foundation Grant # AGS-1230114

Title:  Collaborative Research:  Improved Understanding of Convective-Storm Predictability and Environmental Feedbacks from Observations during the Mesoscale Predicability Experiment (MPEX)

Summary:  The influence of organized regions of deep convection on its environment in both space and time has been recognized for many years. For example, organized deep convective regions are known to enhance upper-level jet streaks through modification of the direct mass circulation in jet entrance regions through diabatic heating. Individual thunderstorms modify the nearby surrounding mass and momentum fields within a few hours, likely assisting in storm maintenance and influencing storm severity. While past observational and modeling studies have documented these nearby and more distant feedback effects, this proposal represents the first attempt to conduct a careful comparison of model-simulated convective feedbacks with those diagnosed from dropsonde and Microwave Temperature Profiling (MTP) observations taken during the Mesoscale Predictability Experiment (MPEX). The improved capability of numerical weather prediction (NWP) models at convection-allowing grid spacing (1-4 km), and the availability of the NCAR GV airborne observing systems, argues strongly that it is time to understand how deep convection modifies the surrounding environment in much greater detail

A multi-institutional team with broad expertise has been assembled to pursue the fundamental scientific questions of convective storm-environmental feedbacks and predictability under MPEX Hypothesis 2. In particular, the team proposes to: 1) quantify the observed environmental modifications and upscale feedbacks from deep convection, and relate these back to the characteristics of the convection; 2) evaluate model simulations of upscale feedbacks from deep convection with MPEX observations; and 3) explore the predictability of convectively disturbed atmospheres. These objectives will be met using various diagnostic approaches applied to the dropsonde observations, including calculation of heat and moisture budgets; numerical model simulations with ensemble Kalman filter data assimilation at convection-allowing resolutions; and careful comparisons between MPEX observations and model simulations.

Amount:  $156,149  Running total: $1,530,384

Period: 10/01/12-09/30/15

5 [Pending].  Co-Principal Investigator (PI - Lance M. Leslie, Co-PI Michael Richman)

Funding Agency:  National Science Foundation Grant # AGS-__

Title:  Collaborative Research:  Physical Understanding of Synoptic-Scale Controls on Tornadic and Other Severe Weather Outbreaks

Summary:  Earlier studies by the PIs demonstrated that tornado outbreaks are linked to synoptic-scale processes, known as meteorological covariates (MC), with lead times of at least 72 hours. Moreover, the PIs have shown that mesoscale models can discriminate outbreak types, skill varies as a function of season, and severe weather outbreaks can be classified as tornadic or primarily nontornadic. The proposed research will expand our previous diagnostic work by (a) developing composites on a larger sample to reduce uncertainty in our composite MC patterns associated with severe weather outbreak occurrence and intensity, (b) expanding the case list of outbreaks and applying new techniques that optimize two or more variables simultaneously to improve discrimination of severe weather outbreaks, (c) application of a newly designed multiphysics mesoscale ensemble with perturbed initial conditions, to provide physical insight of the predictability of severe weather outbreaks and reveal systematic simulation biases that may affect implementation of discrimination techniques, (d) forming conditional and unconditional probabilities that severe weather occurs within some distance of a point, for a given MC magnitude, which will allow a greater understanding of the levels and sources of uncertainty associated with discrimination based on MC magnitudes, and (e) using diagnosed fields to determine how well the areal coverage of MCs matches the relative frequencies of severe weather outbreaks exceeding threshold severities to document differences from using model simulations of the MC areal coverages.

The magnitude and spatiotemporal extent of geostrophy will be determined in the composites using geostrophic analyses. We will apply PV surgery to our composites and predict severe weather outbreaks using WRF in ensemble mode. By exploiting this suite of analyses, we will increase our knowledge of processes that initiate major tornadic outbreaks by using analyses and model simulation fields. Minimizing the false alarm ratio (FAR) while maximizing the probability of detection (POD) for severe weather is critical. The skill gained by combining increased physical understanding and the optimization methods will advance the science of predicting major tornado outbreaks.