Mean Storm Research Articles

Quantifying and Visualizing Severe Thunderstorm Motion Uncertainty for Improved Decision Support

Abstract Forecasts and observations of thunderstorm motion are key components of severe thunderstorm communication and decision support for preparedness and protective behaviors, as all National Weather Service (NWS) Storm Prediction Center (SPC) convective watches and NWS warnings contain information about storm translational speed and direction of motion. The goal of this research was to quantify uncertainty in thunderstorm motion using the information contained in NWS watch and warning products. Analyzing severe thunderstorm and tornado watches and warnings from 2008 to 2020, we matched and compared forecasted storm motions given in NWS warnings with those given in SPC watches. Translational speed deviations between watches and warnings exhibited symmetry, with about one-third of warning speeds falling within ±2.5 m s−1 of forecasts. Directional deviations were also symmetrical, with one-third of warning directions within ±9° of forecasts. However, when stratified to account for translational speed, the comparisons revealed greater directional uncertainty for slower-moving storms. Temporal analysis revealed faster and rightward translations in warnings during the latter half of watch durations. Using the distributions of speed and directional deviations, we developed new visualizations to represent both mean storm motion vectors and associated uncertainty sectors, aiding the interpretation of motion uncertainties. Quantifiable storm motion uncertainty also can inform estimated ranges of storm arrival times, potentially enhancing NWS communication and decision support with key partners and a range of publics for safer outcomes during severe weather events. Significance Statement Severe thunderstorms are a frequent phenomenon in the United States, threatening lives and property. The goal of this research was to expand on deterministic forecasts of thunderstorm motion by quantifying uncertainty for translational speed and direction of movement. Using the results of the uncertainty analyses, we visualized a forecast motion vector together with a motion sector that showed how much variability in motion is likely based on past forecasts in NWS watch and warning products. The results demonstrated how the motion uncertainty sectors varied according to the forecasted speed, with greater directional uncertainty at slower speeds and reduced directional uncertainty at faster speeds. Findings from this study can improve forecast communication and decision support for stakeholders who use NWS convective watch and warning products.

Event-based rainfall analysis in Sinai, Egypt

ABSTRACT This study investigates event-based rainfall characteristics in Sinai (Egypt) using hourly precipitation data from the Global Satellite Mapping of Precipitation (GSMaP). A hierarchical cluster analysis of a 19-year dataset (2003–2021) identified five regions in Sinai. Distinct storms were identified using a minimum inter-event time of 5 h. The analysis of storm characteristics revealed that rainfall events in Sinai last from 1.7 to 3.6 h, with a mean storm volume of 6.4 mm. Rainfall intensity ranges from 1.7 to 4 mm/h, and the average dry period duration is 34 days. The northern region has the highest frequency of storms (25 events/year). The Weibull distribution was found to fit the best for all rainfall characteristics except for intensity, which was best represented by the generalized extreme value distribution. This study provides valuable insights about rainfall events in Sinai that can be applied to improve flood mitigation strategies and water resources management.

Nonstationary recharge responses to a drying climate in the Gnangara Groundwater System, Western Australia

The response of groundwater recharge to climate change needs to be understood to enable sustainable management of groundwater systems today and in the future, yet observations of recharge over long-enough time periods to reveal responses to climate trends are scarce. Here we present a meta-analysis of 60 years of recharge studies over the Gnangara Groundwater System of South-West Western Australia, covering a period of sustained drying consistent with climate change projections. The recharge process in the area is defined by a wet winter during which rain saturates a deep, highly permeable soil profile with very low water storage capacity. Measurements of recharge since the 1960s show near-linear reductions in potential recharge of 50%, in response to a 20% reduction in rainfall. For the best-represented land cover in the dataset (Banksia woodland), the reduction in potential recharge was closer to 70%. A simple analytical model suggests that reductions in the duration of winter, coupled with a decreased frequency of winter storms, were most responsible for these declines, and reveals the potential for nonlinear relationships between the recharge fraction (recharge/precipitation) and climatic variables such as mean storm frequency, mean storm depth, and the length of the winter wet season. Overall, results suggest that recharge declines in drying Mediterranean groundwater systems are likely to outstrip the declines in rainfall, and that leveraging existing observation networks worldwide to characterise recharge responses to changing climate is needed to overcome existing interpretation challenges created by inconsistent sites, methods and durations of recharge estimation.

Assessing RRFS versus HRRR in Predicting Widespread Convective Systems over the Eastern CONUS

Abstract This study provides a comparison of the operational HRRR version 4 and its eventual successor, the experimental Rapid Refresh Forecast System (RRFS) model (summer 2022 version), at predicting the evolution of convective storm characteristics during widespread convective events that occurred primarily over the eastern United States during summer 2022. In total 32 widespread convective events were selected using observations from the MRMS composite reflectivity, which includes an equal number of MCSs, quasi-linear convective systems (QLCSs), clusters, and cellular convection. Each storm system was assessed on four primary characteristics: total storm area, total storm count, storm area ratio (an indicator of mean storm size), and storm size distributions. It was found that the HRRR predictions of total storm area were comparable to MRMS, while the RRFS overpredicted total storm area by 40%–60% depending on forecast lead time. Both models tended to underpredict storm counts particularly during the storm initiation and growth period. This bias in storm counts originates early in the model runs (forecast hour 1) and propagates through the simulation in both models indicating that both miss storm initiation events and/or merge individual storm objects too quickly. Thus, both models end up with mean storm sizes that are much larger than observed (RRFS more so than HRRR). Additional analyses revealed that the storm area and individual storm biases were largest for the clusters and cellular convective modes. These results can serve as a benchmark for assessing future versions of RRFS and will aid model users in interpreting forecast guidance.

A Statistical Forecast Model for Extratropical Cyclones Including Intensity and Precipitation Type

Abstract Winter extratropical cyclones (ETCs) are dominant features of winter weather on the east coast of North America. These storms are characterized by high winds and heavy precipitation (rain, snow, and ice). ETCs are well predicted by numerical weather prediction models (NWPs) at short- to midrange forecast lead times, but prediction on seasonal time scales is lacking. We develop a set of multiple linear regression models, using stepwise regression and cross validation, to predict the number of storms expected to affect a specific location throughout the winter storm season. Each model in the set predicts a specific storm type (e.g., snow, rain, or bomb storms). This set of models is applied in a probabilistic forecast framework that uses the probability density function of the prediction in combination with climatological mean storm activity. The resulting forecast makes statements about the likelihood of below-average, average, or above-average activity for all storms and for each of the type-specific subsets of storms. Though this forecast framework could in theory be applied anywhere, we demonstrate its skill in forecasting the characteristics of the winter storm season experienced in Halifax, Nova Scotia, Canada. Significance Statement Winter storms are a disruptive but inevitable part of life on the eastern coast of North America all the way from the Carolinas to Labrador. Knowing each fall what to expect for the upcoming winter storm season is not only a matter of public interest, but also of great public safety and financial importance. Here we develop a model that uses the state of the atmosphere over the month of September to forecast the upcoming winter storm characteristics for a specified region of interest. Our model uses a multiple linear regression approach to make skilled forecasts including probability statements about the level and type of storm activity. Forecasts can be used to inform planning for the winter ahead.

Future changes in the extratropical storm tracks and cyclone intensity, wind speed, and structure

Abstract. Future changes in extratropical cyclones and the associated storm tracks are uncertain. Using the new CMIP6 models, we investigate changes to seasonal mean storm tracks and composite wind speeds at different levels of the troposphere for the winter and summer seasons in both the Northern Hemisphere (NH) and Southern Hemisphere (SH). Changes are assessed across four different climate scenarios. The seasonal mean storm tracks are predicted to shift polewards in the SH and also in the North Pacific, with an extension into Europe for the North Atlantic storm track. Overall, the number of cyclones will decrease by ∼5 % by the end of the 21st century, although the number of extreme cyclones will increase by 4 % in NH winter. Cyclone wind speeds are projected to strengthen throughout the troposphere in the winter seasons and also summer in the SH, with a weakening projected in NH summer, although there are minimal changes in the maximum wind speed in the lower troposphere. Changes in wind speeds are concentrated in the warm sector of cyclones, and the area of extreme winds may be up to 40 % larger by the end of the century. The largest changes are seen for the SSP5-85 scenario, although a large amount of change can be mitigated by restricting warming to that seen in the SSP1-26 and 2-45 scenarios. Extreme cyclones show larger increases in wind speed and peak vorticity than the average-strength cyclones, with the extreme cyclones showing a larger increase in wind speed in the warm sector.

Simulation of Vegetation Cover Based on the Theory of Ecohydrological Optimality in the Yongding River Watershed, China

During ecological restoration, it is necessary to comprehensively consider the state of vegetation in climate–soil–vegetation systems. The theory of ecohydrological optimality assumes that this state tends to reach long-term dynamic equilibrium between the available water supply of the system and the water demand of vegetation, which is driven by the maximization of productivity. This study aimed to understand the factors that affect the spatial distribution of vegetation and simulate the ideal vegetation coverage (M0) that a specific climate and soil can maintain under an equilibrium state. The ecohydrological optimality model was applied based on meteorological, soil, and vegetation data during the 2000–2018 growing seasons, and the sensitivity of the simulated results to input data under distinct vegetation and soil conditions was also considered in the Yongding River watershed, China. The results revealed that the average observed vegetation coverage (M) was affected by precipitation characteristic factors, followed by wind speed and relative humidity. The M, as a whole, exhibited horizontal zonal changes from a spatial perspective, with an average value of 0.502, whereas the average M0 was 0.475. The ecohydrological optimality theory ignores the drought resistance measures evolved by vegetation in high vegetation coverage areas and is applicable to simulate the long-term average vegetation coverage that minimizes water stress and maximizes productivity. The differences between M and M0 increased from the northwest to the southeast of this area, with a maximum value exceeding 0.3. Meteorological factors were the most sensitive factors of this model, and the M0 of the steppe was most sensitive to the stem fraction, mean storm depth, and air temperature. Whether soil factors are sensitive depends on soil texture. Overall, the study of the carrying capacity of vegetation in the natural environment contributes to providing new insights into vegetation restoration and the conservation of water resources.

Long-term ice-rich permafrost coast sensitivity to air temperatures and storm influence: lessons from Pullen Island, Northwest Territories, Canada

Response of erosive mechanisms to climate change is of mounting concern on Beaufort Sea coasts, which experience some of the highest erosion rates in the Arctic. Collapse of intact permafrost blocks and slumping within sprawling retrogressive thaw complexes are two predominant mechanisms that manifest as cliff retreat in this region. Using aerial imagery and ground survey data from Pullen Island, Northwest Territories., Canada, from 13 time points between 1947 and 2018, we observe increasing mean retreat rates from 0 ± 4.8 m a−1 in 1947 to 12 ± 0.3 m a−1 in 2018. Mean summer air temperature was positively correlated with cliff retreat over each time step via block failure (r2 = 0.08; p = 0.5) and slumping (r2 = 0.41; p = 0.05), as was mean storm duration with cliff retreat via block failure (r2 = 0.84; p = 0.0002) and slumping (r2 = 0.34; p = 0.08). These data indicate that air temperature has a greater impact in slump-dominated areas, whereas storm duration has greater control in areas of block failure. Increasingly, heterogeneous cliff retreat rates are likely resulting from different magnitudes of response to climate trends depending on mechanism, and on geomorphological variations that prescribe occurrences of retrogressive thaw slumps.

Trees and seasonal wind gust maxima in Ireland: acclimation and minimal factors of safety

ABSTRACT Daily maximum high gust wind speeds at Dublin, Ireland over a recent 30-year period were analysed. Maximal and mean storm gusts are compared to those occurring in the growing season period of xylogenesis in relation to thigmomorphogenetic acclimation of temperate trees to their wind environment and to the maintenance of notional minimal factors of biomechanical safety against major failure in storm winds. Maximal annual gust probabilities are estimated, and the correlation between the highest mean growing season gusts and extremum storm gusts is discussed. Successful acclimation to the mean highest 5% of growing season gusts is conjectured to confer a notional minimal factor of safety against major failure – in extremum winter storm gusts – of ~4. Tree risk assessment may be improved by shifting focus from “defects” to factors of inadaptation, which preclude or compromise a tree’s ability to maintain minimal factors of safety relative to its wind environment.

Equilibrium beach profile on sandy beach of the Mehdya coast of Morocco

The world’s coastlines are shaped by mean sea level, wave conditions and storm surge. Climate change driven variations in these environmental forcing’s will inevitably have a profound effect on the coastal zone. They will result in unprecedented coastal recession, threatening billions of dollars worth of coastal developments and infrastructure. Coastal erosion is observed in some locations along Atlantic alluvial plain (Kenitra coastal (Morocco)) and is an important factor to consider for the coastal zone management. Therefore, for coastal recession estimates are obtained via the simple, deterministic method (Bruun rule) especially, that has been widely used over the last 50 years. It is in widespread contemporary use at a global scale both as a management tool and as a scientific concept. We investigated the potential erosion at the site and the result was very important. The result shows a severe erosion of the 21st century.

Impacts of the North Atlantic Oscillation on winter precipitations and storm track variability in southeast Canada and the northeast United States

Abstract. The North Atlantic Oscillation (NAO) affects atmospheric variability from eastern North America to Europe. Although the link between the NAO and winter precipitations in eastern North America has been the focus of previous work, only few studies have considered extreme precipitation and hitherto provided clear physical explanations on these relationships. In this study we revisit and extend the analysis of the effect of the NAO on mean and heavy winter precipitations over a large domain covering southeast Canada and the northeastern United States. Furthermore, we use the recent ERA5 reanalysis dataset (1979–2018), which currently has the highest available horizontal resolution for a global reanalysis (0.25∘), to track extratropical cyclones to delve into the physical processes behind the relationship between NAO and precipitation, snowfall, snowfall-to-precipitation ratio (S∕P), and snow cover depth anomalies in the region. In particular, our results show that positive NAO phases are associated with less snowfall over a wide region covering Nova Scotia, New England and the Mid-Atlantic of the United States relative to negative NAO phases. Over the same area, the analysis of heavy snowfall revealed that there are up to twice as many heavy snowfall events during negative phases compared to positive phases. Therefore, a significant negative correlation is also seen between S∕P and the NAO over this region. This is due to a decrease (increase) in cyclogenesis of coastal storms near the United States east coast during positive (negative) NAO phases, as well as a northward (southward) displacement of the mean storm track over North America.

Modulation of South Asian Jet Wave Train on the Extreme Winter Precipitation over Southeast China: Comparison between 2015/16 and 2018/19

AbstractTwo extremely wet winters in 2015/16 and 2018/19 over Southeast China are compared in this study. South-to-north discrepancies appear in the spatial distribution of precipitation, with anomalous precipitation centered over the southeast coast in 2015/16 and the lower reaches of Yangtze River valley in 2018/19, respectively. Both instances of enhanced precipitation are ascribed mainly to warm and moist advection from the south, with transport in 2015/16 partly by a deepened India–Burma trough to the west, whereas with transport in 2018/19 mainly by a subtropical western North Pacific anticyclone (WNPAC). Both the India–Burma trough and WNPAC are maintained by the wave trains propagating along the South Asian jet, which are zonally offset by a quarter-wavelength. Further study of the wave train sources in 2015/16 and 2018/19 shows that they both tend to originate from extremely strong storm-track activity over the North Atlantic but have different displacement. The former is located more northeastward than the mean storm track and is modulated by a strong positive NAO, whereas the latter lies over the midlatitude central North Atlantic along with a circumglobal teleconnection. These differences further result in a quarter-wavelength offset in the Rossby wave source near the entrance of the South Asian jet by the convergence of upper-level divergent wind.

Real-time forecasting of pesticide concentrations in soil

Forecasting pesticide residues in soils in real time is essential for agronomic purposes, to manage phytotoxic effects, and in catchments to manage surface and ground water quality. This has not been possible in the past due to both modelling and measurement constraints. Here, the analytical transient probability distribution (pdf) of pesticide concentrations is derived. The pdf results from the random ways in which rain events occur after pesticide application. First-order degradation kinetics and linear equilibrium sorption are assumed. The analytical pdfs allow understanding of the relative contributions that climate (mean storm depth and mean rainfall event frequency) and chemical (sorption and degradation) properties have on the variability of soil concentrations into the future. We demonstrated the two uncertain reaction parameters can be constrained using Bayesian methods. An approach to a Bayesian informed forecast is then presented. With the use of new rapid tests capable of providing quantitative measurements of soil concentrations in the field, real-time forecasting of future pesticide concentrations now looks possible for the first time. Such an approach offers new means to manage crops, soils and water quality, and may be extended to other classes of pesticides for ecological risk assessment purposes.

PREDICTING DUNE EROSION WITH COMBINED PROCESS-BASED AND MULTIVARIATE STATISTICAL MODELS

Oceanographic variables such as mean sea level, tides, storm surges, and waves are drivers of erosion, and they act on different time scales ranging from hours (associated with weather) to seasonal and decadal variations and trends (associated with climate). Storm erosion of dunes, which often protect coastal communities and built infrastructure from flooding, poses a major risk. Dune erosion is also a challenge to analyze and predict, due to the complex processes involved and the computational costs that come with it. Here, we introduce a novel framework that combines advanced statistical techniques and a process-based numerical model to efficiently simulate storm-induced dune erosion.

Seasonal prediction skill and predictability of the Northern Hemisphere storm track variability in Project Minerva

The seasonal prediction skill and predictability of the Northern Hemisphere storm track anomalies in boreal winter (December–January–February, DJF) is examined using seasonal ensemble reforecasts for 1982–2009 from the ECMWF Integrated Forecast System at two different atmospheric resolutions in Project Minerva. It is found that the predictable signals of storm track variations are associated with the two leading EOF modes of ensemble-averaged DJF variances of the high-pass filtered daily meridional winds at 250-hPa level derived from each of the hindcast ensemble members. These two EOF modes are highly correlated both temporarily and spatially between two sets of reforecasts. The first mode (EOF1) mainly shows a latitudinal shift of the storm tracks over the central-eastern North Pacific and the North America continent. The second mode (EOF2) is primarily the pulsing signal exerting on the mean storm track background of the North Pacific. The model predictive skill is verified against observations. The first mode has higher prediction skills and larger skillful regions than the second one. In particular, the first predictable mode is generated by the ENSO-induced wave train, starting from tropical central Pacific and propagating to North America. The skillful region lies in the North Pacific to the west of California, corresponding to the southern lobe of EOF1. The second predictable mode is generated by the North Pacific Mode, which evokes a distinctive wave train, emanating from the tropical western Pacific and propagating northeastward. Its skillful region of the storm track prediction is confined to a small area of Canada western coastlines.

Potential Impacts of Sahara Dust Aerosol on Rainfall Vertical Structure Over the Atlantic Ocean as Identified From EOF Analysis

AbstractThe current study investigates the potential impacts of Sahara dust on rainfall vertical structure over the Atlantic Ocean by employing multisensor satellite observations. The variabilities in rainfall vertical structure are decomposed by empirical orthogonal function (EOF) analysis. For a given near surface rain rate, the mean storm height of stratiform rain under dust‐laden condition is significantly higher than that under the dust‐free condition. The stratiform rain rate at the layers well above the freezing level is substantially enhanced under dust‐laden condition. Those changes may result from dynamic and thermodynamic effect of Sahara Air Layer effect and/or the effect that dust aerosol acting as additional ice nuclei. For convective rain, there is no significant difference in the leading three normalized EOF modes between the dust‐laden and dust‐free sectors, implying that dust effect, if exists, is not strong enough to cause evident changes in convective rainfall profiles. For stratiform rain, the EOF1 shows no signals of dust effect either. However, significant difference is found in the EOF2 and EOF3 modes between dust‐laden and dust‐free conditions. The normalized EOF2 and EOF3 under dust‐laden condition both represent enhanced rain rate at the upper layer well above the freezing level, where heterogeneous ice nucleation prevails. The statistical study shows consistent results with the case study, except that the aerosol‐related changes in stratiform rainfall vertical structure is isolated in the EOF3 only. This study suggests that EOF analysis is a promising way for isolating the potential and relatively weak aerosol effects on precipitation from the strong dynamic effects.

Facies architecture of Miocene subaqueous clinothems of the New Jersey passive margin: Results from IODP-ICDP Expedition 313

The preserved, laterally correlative sedimentary record of continental erosion on passive margins has been used to reconstruct past sea level changes. However, the detailed nature of a basic clinothem progradational pattern observed on many of these margins is still poorly known. This paper describes the sedimentary facies and interprets the depositional environments and the architecture of the clinothems of the New Jersey shelf to depict the origin and controls of the distribution of the sediment on the margin. We analyze 612 cores totaling 1311 m in length collected at three sites 60 km offshore Atlantic City, New Jersey, USA during IODP-ICDP Expedition 313. The three sites sampled the Lower to Middle Miocene passive margin sediments of the New Jersey shelf clinothems. We also collected wireline logs at the three sites and tied the sedimentary architecture to the geometry observed on seismic profiles. The observed sediment distribution in the clinoform complex differs from current models based on seismic data, which predict a progressive increase in mud and decrease in sand contents in a seaward direction. In contrast, we observe that the clinoforms are largely composed of muds with sands and coarser material concentrated at the rollover, the bottomset and the toe of the slope. The shelf clinothem topsets are storm-influenced mud whereas the foreset slope is composed of a mud wedge largely dominated by low density turbidites and debrites. The architecture of the clinothem complex includes a composite stack of approximately 30 m thick clinothem units each composed of four systems tracts (TST, HST, FRWST, LST) building individual T-R sequences. The presence of mud-rich facies during highstands on the topset of the clinoform, 40 to 60 km offshore from the sand-prone shoreface deposit (observed in the New Jersey onshore delta plain) and the lack of subaerial erosion (and continental depositional environments) points to a depositional model involving a subaerial delta (onshore) feeding a distant subaqueous delta. During forced regressions, shelf edge deltas periodically overstep the stacks of flood-influenced, offshore-marine mud wedges of the New Jersey subaqueous delta bringing sand to the rollover building up the large-scale shelf-prism clinothems. The clinothem complex develops on a gently dipping platform with a ramp-like morphology (apparent dip of 0.75-0.5) below mean storm wave base, in 30-50 m of water depth, 40-60 km seaward of the coastal area. Its shape depends on the balance between accommodation and sedimentation rates. Subaqueous deltas show higher accumulation rates than their subaerial counterparts and prograde three times further and faster than their contemporaneous shoreline. The increase in the intensity of waves (height, recurrence intervals) favors the separation between subaqueous and subaerial deltas, and as a consequence, the formation of a flat topset geometry, the decrease in flood events, and fluvial discharge, the overall progressive decrease in sediment grain size (from sequence m5.45, approximately 17.8-17.7 Ma, onwards) as well as the increase in sedimentation rates on the foresets of the clinoforms. All of these are recognized as preliminary signals that might characterize the entry in the Neogene icehouse world.

Assessment of CLIGEN precipitation and storm pattern generation in China

The applicability of the uncalibrated CLIGEN (CLImate GENerator) model was assessed using long-term precipitation data at 1-min interval from 18 sites in eastern and central China. The model performance was evaluated in terms of daily precipitation depth, storm duration and peak intensities of 1-min (I1), 5-min (I5) and 30-min (I30) for all storms and four categories grouped by daily precipitation depth: light (<10 mm), moderate (10–25 mm), heavy (25–50 mm), and intense (≥50 mm) storms. Additionally, the applicability of CLIGEN in generating climate inputs for the Revised Universal Soil Loss Equation (RUSLE) in China and calculating the intensity-duration-frequency (IDF) values for a series of storm duration (5-min to 24-h) and return periods (2 to 100-years) were assessed. Results showed that CLIGEN was able to accurately reproduce the statistics and probability distributions of daily precipitation depth for all storms and the four categories. The mean storm duration was underestimated for 3 of the 4 storm categories but was overestimated for light storms. CLIGEN underestimated I1 and overestimated I30 in general, whereas no obvious bias was observed in I5 for these 18 sites. In addition, the relative error in the generated duration and peak intensity increased as the magnitude of precipitation increased from moderate to intense storms, which implies that the model does not perform as well for intense storms as other storms. Rainfall erosivity generated with CLIGEN outputs was systematically larger than, but well correlated with, the measured erosivity values (slope = 0.547, R2 = 0.96 for the R-factor; slope = 0.576, R2 = 0.81 for the 10-year storm erosivity). Extreme intensities for given duration and return periods were systematically over-predicted using the output from CLIGEN, and the bias between measured and CLIGEN generated intensity-duration-frequency (IDF) values varied with duration intervals. The average of the regression slopes for the six return periods (2-year to 100-year) between measured (X) and generated (Y) intensities (Y = b X) increased from 1.19 (5-min) to 1.43 (1-h), then decreased to 1.01 (12-h) and 0.96 (24-h). As a stochastic weather generator, CLIGEN is able to reproduce daily precipitation very well, but its capacity to simulate storm duration and the peak intensity for a given time interval needs to be improved, especially for heavy and intense storms, based on this study.

An Uncertainty Investigation of RCM Downscaling Ratios in Nonstationary Extreme Rainfall IDF Curves

Designed for rainstorms and flooding, hydrosystems are largely based on local rainfall Intensity–Duration–Frequency (IDF) curves which include nonstationary components accounting for climate variability. IDF curves are commonly calculated using downscaling outputs from General Circulation Models (GCMs) or Regional Circulation Models (RCMs). However, the downscaling procedures used in most studies are based on one specific time scale (e.g., 1 h) and generally ignore scale-driven uncertainty. This study analyzes the uncertainties in IDF curves stemming from RCM downscaling ratios for four representative weather stations in the United Kingdom. We constructed a series of IDF curves using distribution-based scaling bias-correction technology and a statistical downscaling method to explore the scale-driven uncertainty of IDF curves. The results revealed considerable scale-induced uncertainty of IDF curves for short durations and long return periods; however, there was no clear correlation with the mean storm intensity of the IDF curves of different RCM ensemble members for each duration and return period. The scale-driven uncertainty of IDF curves, which may be propagated or enhanced through hydrometeorological applications, is critical and cannot be ignored in the hydrosystem design process; therefore, a multi-scale method to derive IDF curves must be developed.

Thunderstorm hail and lightning detection parameters based on dual‐polarization Doppler weather radar data

ABSTRACTFour years (2011–2014) of the summer period (May–September) dual‐polarization Doppler C‐band weather radar data for Sürgavere, Estonia, were examined to determine the best indicator for cloud‐to‐ground lightning activity. Furthermore, the legacy radar‐derived hail indicator probability of hail and the storm maximum reflectivity were compared with the polarimetric hydrometeor classification algorithm (HCA) to establish a link between them and ensure data continuity. The study is based on convective storm cells identified from radar reflectivity data, using a 35 dBZ reflectivity threshold. Applying this threshold to the radar data resulted in 123 360 individual storm cells. It was found that 33.9% of the identified cells produced lightning and 25.9% hail. The number of individual storm cells on average is highest in the afternoon at 1600 local time and the mean storm cell area is largest in the evening at 2100 local time. Echo top 15 dBZ (ET15), ET20 and ET25 achieved the highest scores in terms of critical success index (0.39, 0.39 and 0.38 respectively), Heidke skill score (0.24, 0.26, 0.27) and equitable threat score (0.14, 0.15, 0.16). The graupel class of the dual‐polarization radar hydrometeor classification showed a similar performance to the 35 dBZ echo top. It was also shown that the reflectivity value just below 50 dBZ indicates the best skill for detection of hail, if HCA hail detection is used as the ground truth. The probability of hail value that shows the best agreement with the HCA hail class is 0.2.