ALBERT

Description: We describe the salient features of a field study whose goals are to quantify the vertical distribution of plant-emitted hydrocarbons and their contribution to aerosol and cloud condensation nuclei production above a central Amazonian rain forest. Using observing systems deployed on a 50-m meteorological tower, complemented with tethered balloon deployments, the vertical distribution of hydrocarbons and aerosols was determined under different boundary layer thermodynamic states. The rain forest emits sufficient reactive hydrocarbons, such as isoprene and monoterpenes, to provide precursors of secondary organic aerosols and cloud condensation nuclei. Mesoscale convective systems transport ozone from the middle troposphere, enriching the atmospheric boundary layer as well as the forest canopy and surface layer. Through multiple chemical transformations, the ozone-enriched atmospheric surface layer can oxidize rain forest–emitted hydrocarbons. One conclusion derived from the field studies is that the rain forest produces the necessary chemical species and in sufficient amounts to undergo oxidation and generate aerosols that subsequently activate into cloud condensation nuclei.

Description: 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.

Description: Tornadic and nontornadic outbreaks occur within the United States and elsewhere around the world each year with devastating effect. However, few studies have considered the physical differences between these two outbreak types. To address this issue, synoptic-scale pattern composites of tornadic and nontornadic outbreaks are formulated over North America using a rotated principal component analysis (RPCA). A cluster analysis of the RPC loadings group similar outbreak events, and the resulting map types represent an idealized composite of the constituent cases in each cluster. These composites are used to initialize a Weather Research and Forecasting Model (WRF) simulation of each hypothetical composite outbreak type in an effort to determine the WRF’s capability to distinguish the outbreak type each composite represents. Synoptic-scale pattern analyses of the composites reveal strikingly different characteristics within each outbreak type, particularly in the wind fields. The tornado outbreak composites reveal a strong low- and midlevel cyclone over the eastern Rockies, which is likely responsible for the observed surface low pressure system in the plains. Composite soundings from the hypothetical outbreak centroids reveal significantly greater bulk shear and storm-relative environmental helicity values in the tornado outbreak environment, whereas instability fields are similar between the two outbreak types. The WRF simulations of the map types confirm results observed in the composite soundings.

Description: The areal extent of severe weather parameters favorable for significant severe weather is evaluated as a means of identifying major severe weather outbreaks. The first areal coverage method uses kernel density estimation (KDE) to identify severe weather outbreak locations. A selected severe weather parameter value is computed at each grid point within the region identified by KDE. The average, median, or sum value is used to diagnose the event’s severity. The second areal coverage method finds the largest contiguous region where a severe weather parameter exceeds a specified threshold that intersects the KDE region. The severe weather parameter values at grid points within the parameter exceedance region are computed, with the average, median, or sum value used to diagnose the event’s severity. A total of 4057 severe weather outbreaks from 1979 to 2008 are analyzed. An event is considered a major outbreak if it exceeds a selected ranking index score (developed in previous work), and is a minor event otherwise. The areal coverage method is also compared to Storm Prediction Center (SPC) day-1 convective outlooks from 2003 to 2008. Comparisons of the SPC forecasts and areal coverage diagnoses indicate the areal coverage methods have similar skill to SPC convective outlooks in discriminating major and minor severe weather outbreaks. Despite a seemingly large sample size, the rare-events nature of the dataset leads to sample size sensitivities. Nevertheless, the findings of this study suggest that areal coverage should be tested in a forecasting environment as a means of providing guidance in future outbreak scenarios.

Description: Downslope windstorms are of major concern to those living in and around Boulder, Colorado, often striking with little warning, occasionally bringing clear-air wind gusts of 35–50 m s−1 or higher, and producing widespread damage. Historically, numerical models used for forecasting these events had lower than desired accuracy. This observation provides the motivation to study the potential for improving windstorm forecasting through the use of linear and nonlinear statistical modeling techniques with a perfect prog approach. A 10-yr mountain-windstorm dataset and a set of 18 predictors are used to train and test the models. For the linear model, a stepwise regression is applied. It is difficult to determine which predictor is the most important, although significance testing suggests that 700-hPa flow is selected often. The nonlinear techniques employed, feedforward neural networks (NN) and support vector regression (SVR), do not filter out predictors as the former uses a hidden layer to account for the nonlinearities in the data, whereas the latter fits a kernel function to the data to optimize prediction. The models are evaluated using root-mean-square error (RMSE) and median residuals. The SVR model has the lowest forecast errors, consistently, and is not prone to creating outlier forecasts. Stepwise linear regression (LR) yielded results that were accurate to within an RMSE of 8 m s−1; whereas an NN had errors of 7–9 m s−1 and SVR had errors of 4–6 m s−1. For SVR, 85% of the forecasts predicted maximum wind gusts with an RMSE of less than 6 m s−1 and all forecasts predicted wind gusts with an RMSE of below 12 m s−1. The LR method performed slightly better in most evaluations than NNs; however, SVR was the optimal technique.

Description: A large portion of the lower Mississippi River alluvial valley (LMRAV) relies on irrigation from the regional alluvial aquifer for crop sustainability, which is expensive both in terms of water resources and farmer expenditures because of the large volume of water necessary to maintain crop production. As a result, knowledge of the seasonal frequency and distribution of precipitation over the LMRAV is critical for water resources management, the development of irrigation strategies, and economic planning. This project addresses the need for a detailed assessment of regional precipitation patterns through the use of rotated principal component analysis (RPCA) of high-resolution gridded radar-derived rainfall data, which provides quantification of the spatial and temporal characteristics of rainfall over the LMRAV from 1996 to 2011. Results of the project show that precipitation depths over the LMRAV are generally lower and more variable than adjacent eastern areas throughout the year, although there is substantial variability between seasons. This pattern seems to be influenced more by variations during the cool season (January–March), which has a higher overall precipitation depth and lower spatial variability than the warm season (July–September). Results further indicate that warm season rainfall is generally lower and less predictable over the LMRAV as compared to the cool season, which may be detrimental to regional water resources since irrigation planning and permitting is heavily based on seasonal rainfall predictions.

Description: The fundamental purpose of this research is to highlight the spatial seasonality of tornado risk. This requires the use of objective methods to determine the appropriate spatial extent of the bandwidth used to calculate tornado density values (i.e., smoothing the raw tornado data). With the understanding that a smoothing radius depends partially upon the period of study, the next step is to identify objectively ideal periods of tornado analysis. To avoid decisions about spatial or temporal boundaries, this project makes use of storm speed and tornado pathlength data, along with statistical cluster analysis, to establish tornado seasons that display significantly different temporal and spatial patterns. This method yields four seasons with unique characteristics of storm speed and tornado pathlength. The results show that the ideal bandwidth depends partially upon the temporal analysis period and the lengths of the tornadoes studied. Hence, there is not a “one size fits all,” but the bandwidth can be quantitatively chosen for a given dataset. Results from this research, based upon tornado data for 1950–2011, yield ideal bandwidths ranging from 55 to 180 km. The ideal smoothing radii are then applied via a kernel density analysis of each new tornado season.

Description: Uncertainty exists concerning the links between synoptic-scale processes and tornado outbreaks. With continuously improving computer technology, a large number of high-resolution model simulations can be conducted to study these outbreaks to the storm scale, to determine the degree to which synoptic-scale processes appear to influence the occurrence of tornado outbreaks, and to determine how far in advance these processes are important. To this end, 50 tornado outbreak simulations are compared with 50 primarily nontornadic outbreak simulations initialized with synoptic-scale input using the Weather Research and Forecasting (WRF) mesoscale model to determine if the model is able to distinguish the outbreak type 1, 2, and 3 days in advance of the event. The model simulations cannot resolve tornadoes explicitly; thus, the use of meteorological covariates (in the form of numerous severe-weather parameters) is necessary to determine whether or not the model is predicting a tornado outbreak. Results indicate that, using the covariates, the WRF model can discriminate outbreak type consistently at least up to 3 days in advance. The severe-weather parameters that are most helpful in discriminating between outbreak types include low-level and deep-layer shear variables and the lifting condensation level. An analysis of the spatial structures and temporal evolution, as well as the magnitudes, of the severe-weather parameters is critical to diagnose the outbreak type correctly. Thermodynamic instability parameters are not helpful in distinguishing the outbreak type, primarily because of a strong seasonal dependence and convective modification in the simulations.