The FLASH project: using lightning data to better understand and predict flash floods

The FLASH project was implemented from 2006 to 2010 underthe EU FP6 framework. The project focused on using lightning observations to better understand and predict convective storms that result in flash floods. As part of the project 23 case studies of flash floods in the Mediterranean region were examined. For the analysis of these storms lightning data from the ZEUS network were used together with satellite derived rainfall estimates in orderto understand the storm development and electrification. In addition, these case studies were simulated using mesoscale meteorological models to better understand the meteorological and synoptic conditions leading up to these intense storms. As part of this project tools for short term predictions (nowcasts) of intenseconvection across the Mediterranean and Europe, and long term forecasts (a few days) of the likelihood of intense convection were developed. The project also focused on educationaloutreach through our website http://flashproject.orgsupplying real time lightning observations, real time experimental nowcasts, forecasts and educational materials. While flash floods and intense thunderstorms cannot be preventedas the climate changes, long-range regional lightning networks can supply valuable data, in realtime, for warningend-users and stakeholders of imminent intense rainfall and possible flash floods

Autocorrelation structure of convective rainfall in semiarid-arid climate derived from high-resolution X-Band radar estimates

Small scale rainfall variability is a key factor driving runoff response in fast responding systems, such as mountainous, urban and arid catchments. In this paper, the spatial-temporal autocorrelation structure of convective rainfall is derived with extremely high resolutions (60 m, 1 min) using estimates from an X-Band weather radar recently installed in a semiarid-arid area. The 2-dimensional spatial autocorrelation of convective rainfall fields and the temporal autocorrelation of point-wise and distributed rainfall fields are examined. The auto correlation structures are characterized by spatial anisotropy, correlation distances similar to 1.5-2.8 km and rarely exceeding 5 km, and time-correlation distances similar to 1.8-6.4 min and rarely exceeding 10 min. The observed spatial variability is expected to negatively affect estimates from rain gauges and microwave links rather than satellite and C-/S-Band radars; conversely, the temporal variability is expected to negatively affect remote sensing estimates rather than rain gauges. The presented results provide quantitative information for stochastic weather generators, cloud-resolving models, dryland hydrologic and agricultural models, and multi-sensor merging techniques

Use of radar QPE for the derivation of Intensity–Duration–Frequency curves in a range of climatic regimes

Intensity Duration Frequency (IDF) curves are widely used in flood risk management because they provide an easy link between the characteristics of a rainfall event and the probability of its occurrence. Weather radars provide distributed rainfall estimates with high spatial and temporal resolutions and overcome the scarce representativeness of point-based rainfall for regions characterized by large gradients in rainfall climatology. This work explores the use of radar quantitative precipitation estimation (QPE) for the identification of IDF curves over a region with steep climatic transitions (Israel) using a unique radar data record (23 yr) and combined physical and empirical adjustment of the radar data. IDF relationships were derived by fitting a generalized extreme value distribution to the annual maximum series for durations of 20 min, 1 h and 4 h. Arid, semi-arid and Mediterranean climates were explored using 14 study cases. IDF curves derived from the study rain gauges were compared to those derived from radar and from nearby rain gauges characterized by similar climatology, taking into account the uncertainty linked with the fitting technique. Radar annual maxima and IDF curves were generally overestimated but in 70% of the cases (60% for a 100 yr return period), they lay within the rain gauge IDF confidence intervals. Overestimation tended to increase with return period, and this effect was enhanced in arid climates. This was mainly associated with radar estimation uncertainty, even if other effects, such as rain gauge temporal resolution, cannot be neglected. Climatological classification remained meaningful for the analysis of rainfall extremes and radar was able to discern climatology from rainfall frequency analysis. (C) 2015 Elsevier B.V. All rights reserved

Data from: Projecting pest population dynamics under global warming: the combined effect of inter- and intra-annual variations

The typical short generation length of insects makes their population dynamics highly sensitive not only to mean annual temperatures but also to their intra-annual variations. To consider the combined effect of both thermal factors under global warming, we propose a modeling framework that links general circulation models (GCMs) with a stochastic weather generator and population dynamics models to predict species population responses to inter- and intra-annual temperature changes. This framework was utilized to explore future changes in populations of Bemisia tabaci, an invasive insect pest-species that affects multiple agricultural systems in the Mediterranean region. We considered three locations representing different pest status and climatic conditions: Montpellier (France), Seville (Spain), and Beit-Jamal (Israel). We produced ensembles of local daily temperature realizations representing current and future (mid-21st century) climatic conditions under two emission scenarios for the three locations. Our simulations predicted a significant increase in the average number of annual generations and in population size, and a significant lengthening of the growing season in all three locations. A negative effect was found only in Seville for the summer season, where future temperatures lead to a reduction in population size. High variability in population size was observed between years with similar annual mean temperatures, suggesting a strong effect of intra-annual temperature variation. Critical periods were from late spring to late summer in Montpellier and from late winter to early summer in Seville and Beit-Jamal. Although our analysis suggested that earlier seasonal activity does not necessarily lead to increased populations load unless an additional generation is produced, it is highly likely that the insect will become a significant pest of open-fields at Mediterranean latitudes above 40° during the next 50 years. Our simulations also implied that current predictions based on mean temperature anomalies are relatively conservative and it is better to apply stochastic tools to resolve complex responses to climate change while taking natural variability into account. In summary, we propose a modeling framework capable of determining distinct intra-annual temperature patterns leading to large or small population sizes, for pest risk assessment and management planning of both natural and agricultural ecosystems

Seville-BCC-CSM1.1-RCP4.5

Downscaled (bias-corrected) daily temperature data (150 simulations for 10 years) based on the BCC-CSM1.1 CMIP5 model. Seville station Spain. RCP4.5 climate scenario

Coastal and orographic effects on extreme precipitation revealed by weather radar observations

The yearly exceedance probability of extreme precipitation of multiple durations is crucial for infrastructure design, risk management, and policymaking. Local extremes emerge from the interaction of weather systems with local terrain features such as coastlines and orography; however, multi-duration extremes do not follow exactly the patterns of cumulative precipitation and are still not well understood. High-resolution information from weather radars could help us quantify their patterns better, but traditional extreme value analyses based on radar records were found to be too inaccurate for quantifying the extreme intensities required for impact studies. Here, we propose a novel methodology for extreme precipitation frequency analysis based on relatively short weather radar records, and we use it to investigate the coastal and orographic effects on extreme precipitation of durations between 10 min and 24 h. Combining 11 years of radar data with 10 min rain gauge data in the southeastern Mediterranean, we obtain estimates of the once in 100 years precipitation intensities with similar to 26 % standard error, which is lower than those obtained using traditional approaches on rain gauge data. We identify the following three distinct regimes which respond differently to coastal and orographic forcing: short durations (similar to 10 min), related to peak convective rain rates, hourly durations (similar to 1 h), related to the yield of individual convective cells, and long durations (similar to 6-24 h), related to the accumulation of multiple convective cells and to stratiform processes. At short and hourly durations, extreme return levels peak at the coastline, while at longer durations they peak corresponding to the orographic barriers. The distributions tail heaviness is rather uniform above the sea and rapidly changes in presence of orography, with opposing directions at short (decreasing tail heaviness, with a peak at hourly durations) and long (increasing) durations. These distinct effects suggest that short-scale hazards, such as urban pluvial floods, could be more of concern for the coastal regions, while longer-scale hazards, such as flash floods, could be more relevant in mountainous areas

Coastal and orographic effects on extreme precipitation revealed by weather radar observations

The yearly exceedance probability of extreme precipitation of multiple durations is crucial for infrastructure design, risk management, and policymaking. Local extremes emerge from the interaction of weather systems with local terrain features such as coastlines and orography; however, multi-duration extremes do not follow exactly the patterns of cumulative precipitation and are still not well understood. High-resolution information from weather radars could help us quantify their patterns better, but traditional extreme value analyses based on radar records were found to be too inaccurate for quantifying the extreme intensities required for impact studies. Here, we propose a novel methodology for extreme precipitation frequency analysis based on relatively short weather radar records, and we use it to investigate the coastal and orographic effects on extreme precipitation of durations between 10 min and 24 h. Combining 11 years of radar data with 10 min rain gauge data in the southeastern Mediterranean, we obtain estimates of the once in 100 years precipitation intensities with ∼26 % standard error, which is lower than those obtained using traditional approaches on rain gauge data. We identify the following three distinct regimes which respond differently to coastal and orographic forcing: short durations (∼10 min), related to peak convective rain rates, hourly durations (∼1 h), related to the yield of individual convective cells, and long durations (∼6–24 h), related to the accumulation of multiple convective cells and to stratiform processes. At short and hourly durations, extreme return levels peak at the coastline, while at longer durations they peak corresponding to the orographic barriers. The distributions tail heaviness is rather uniform above the sea and rapidly changes in presence of orography, with opposing directions at short (decreasing tail heaviness, with a peak at hourly durations) and long (increasing) durations. These distinct effects suggest that short-scale hazards, such as urban pluvial floods, could be more of concern for the coastal regions, while longer-scale hazards, such as flash floods, could be more relevant in mountainous areas.ISSN:1027-5606ISSN:1607-793

Montpellier-BCC-CSM1.1-Current

Downscaled (bias-corrected) daily temperature data (150 simulations for 10 years) based on the BCC-CSM1.1 CMIP5 model. Montpellier station France. Current climate scenario