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[1] Observational analyses and mesoscale modeling studies, using the Weather Research and Forecasting (WRF) model, are used to dissect the mechanisms associated with record lightning, rainfall, and flooding over the Baltimore metropolitan region on 7 July 2004. Storm evolution on 7 July 2004 exhibited characteristic features of warm season thunderstorms producing flash flooding over the Baltimore-Washington DC metropolitan region. The storm system was initiated along the Blue Ridge mountains, with model simulations suggesting that convergence-induced spin-up of a meso-low was responsible for initial thunderstorm development. Observations and model analyses show that thermal effects associated with Chesapeake Bay had a pronounced impact on storm evolution and rainfall distribution. Analyses of radar reflectivity and lightning observations suggest that the urban environment played a significant role in storm evolution and heavy rainfall distribution. Model analyses show that urban canopy effects from both the Baltimore and Washington DC urban regions play an important role in determining the storm environment associated with heavy rainfall. Urban Heat Island effects did not play a significant role in the storm evolution. Observations of aerosols and drop-size distributions from a vertically pointing LIDAR and a disdrometer and model analyses suggest that the aerosols may have played an important role in stimulating efficient precipitation mechanisms and extreme rainfall rates for the 7 July 2004 storm.

The climatology of thunderstorms and flash floods in the Baltimore, Maryland, metropolitan region is examined through analyses of cloud-to-ground (CG) lightning observations from the National Lightning Detection Network (NLDN) and discharge observations from 11 U.S. Geological Survey (USGS) stream gauging stations. A point process framework is used for analyses of CG lightning strikes and the occurrences of flash floods. Analyses of lightning strikes as a space-time point process focus on the mean intensity function, from which the seasonal, diurnal, and spatial variation in mean lightning frequency are examined. Important elements of the spatial variation of mean lightning frequency are 1) initiation of thunderstorms along the Blue Ridge, 2) large variability of lightning frequency around the urban cores of Baltimore and Washington D.C., and 3) decreased lightning frequency over the Chesapeake Bay and Atlantic Ocean. Lightning frequency has a sharp seasonal maximum around mid-July, and the diurnal cycle of lightning frequency peaks between 2100 and 2200 UTC with a frequency that is more than an order of magnitude larger than the minimum frequency at 1200 UTC. The seasonal and diurnal variation of flash flood occurrence in urban streams of Baltimore mimics the seasonal and diurnal variation of lightning. The peak of the diurnal frequency of flash floods in Moores Run, a 9.1-km 2 urban watershed in Baltimore City, occurs at 2200 UTC. Analyses of the lightning and flood peak data also show a close link between the occurrence of major thunderstorms systems and flash flooding on a regional scale.

ABSTRACT:Analyses of extreme flooding in Austria is performed using daily discharge time series from 27 stations over the period . The main research questions revolve around: (1) temporal non-stationarities in the flood record, (2) upper tail and scaling properties of the flood peak records, and (3) relation between magnitude and frequency of flooding and the North Atlantic Oscillation (NAO). Two datasets are derived from the daily discharge time series: annual maximum daily discharge and peaks-over-threshold (POT) data. The validity of the stationarity assumption in the annual maximum discharge record is assessed by investigating the presence of abrupt and slowly varying changes using nonparametric tests. The time series are tested for abrupt changes both in the mean and variance of the flood peak distributions by means of the Pettitt test. The presence of monotonic trends is investigated by means of the Mann-Kendall and Spearman tests. Violations of the stationarity assumption are associated with abrupt rather than gradual changes. These step changes generally involve river regulation through construction of dams or other major engineering works. It is not possible to make conclusive statements about the presence of an anthropogenic climate change signal in the flood peak record. Similar conclusions are obtained when focussing on the frequency of POT floods. The Generalised Extreme Value distribution is used to study the upper tail and scaling properties of annual maximum daily discharge records. The location and scale parameters exhibit power-law behaviour as a function of drainage area. The shape parameters indicate that the flood peak distributions for Austria have a heavy tail. Non-stationary modelling of the annual maximum daily discharge and POT time series is used to explore the relation between flood magnitude and frequency and NAO. The results indicate that NAO is a significant covariate in explaining the magnitude and frequency of occurrence of flooding over a large part of Austria.

[1] A stochastic model of rainfall rate is used to examine the temporal variability of rainfall during heavy convective rain periods. The model represents the microstructure of rainfall rate at time scales that are important for land surface processes associated with infiltration and runoff production. The representation of rainfall rate is based on a marked point process model of raindrop size distributions, which yields a gamma raindrop spectrum with parameters that are time-varying stochastic processes. Raindrop size distribution observations from a Joss-Waldvogel disdrometer in Princeton, New Jersey, during the period May-October 2006 are used along with the stochastic model to examine rainfall rate variability. Analyses focus on a sample of 60-min time periods in which heavy convective rainfall occurred. Central elements of the analyses entail examination of the relationships between rainfall rate and the time-varying model parameters that characterize the raindrop size distribution. We also examine the dependence structure among these processes. ''Scaling law'' formulations of raindrop size distributions are used to examine variability of raindrop size distributions. Analyses of the Princeton heavy rainfall periods also point to seasonal and diurnal heterogeneities as important elements of the distribution of extreme rainfall rates. Convective intensity, as reflected in cloud-to-ground lightning observations, plays an important role in the distribution of extreme rainfall rates and the evolution of raindrop size distributions associated with heavy rainfall.