High-impact Weather Events Research Articles

FACETs: A Proposed Next-Generation Paradigm for High-Impact Weather Forecasting

AbstractRecommendations by the National Research Council (NRC), the National Institute of Standards and Technology (NIST), and Weather-Ready Nation workshop participants have encouraged the National Oceanic and Atmospheric Administration (NOAA) and the broader weather enterprise to explore and expand the use of probabilistic information to convey weather forecast uncertainty. Forecasting a Continuum of Environmental Threats (FACETs) is a concept being explored by NOAA to address those recommendations and also potentially shift the National Weather Service (NWS) from (primarily) teletype-era, deterministic watch–warning products to high-resolution, probabilistic hazard information (PHI) spanning periods from days (and longer) to within minutes of high-impact weather and water events. FACETs simultaneously i) considers a reinvention of the NWS hazard forecasting and communication paradigm so as to deliver multiscale, user-specific probabilistic guidance from numerical weather prediction ensembles and ii) provides a comprehensive framework to organize the physical, social, and behavioral sciences, the technology, and the practices needed to achieve that reinvention. The first applications of FACETs have focused on thunderstorm phenomena, but the FACETs concept is envisioned to extend to the attributes of any environmental hazards that can be described probabilistically (e.g., winter, tropical, and aviation weather). This paper introduces the FACETs vision, the motivation for its creation, the research and development under way to explore that vision, its relevance to operational forecasting and society, and possible strategies for implementation.

Value-added Impact of Geostationary Hyperspectral Infrared Sounders on Local Severe Storm Forecasts—via a Quick Regional OSSE

Accurate atmospheric temperature and moisture information with high temporal/spatial resolutions are two of the key parameters needed in regional numerical weather prediction (NWP) models to reliably predict high-impact weather events such as local severe storms (LSSs). High spectral resolution or hyperspectral infrared (HIR) sounders from geostationary orbit (GEO) provide an unprecedented source of near time-continuous, three-dimensional information on the dynamic and thermodynamic atmospheric fields—an important benefit for nowcasting and NWP-based forecasting. In order to demonstrate the value of GEO HIR sounder radiances on LSS forecasts, a quick regional OSSE (Observing System Simulation Experiment) framework has been developed, including high-resolution nature run generation, synthetic observation simulation and validation, and impact study on LSS forecasts. Results show that, on top of the existing LEO (low earth orbit) sounders, a GEO HIR sounder may provide value-added impact [a reduction of 3.56% in normalized root-mean-square difference (RMSD)] on LSS forecasts due to large spatial coverage and high temporal resolution, even though the data are assimilated every 6 h with a thinning of 60 km. Additionally, more frequent assimilations and smaller thinning distances allow more observations to be assimilated, and may further increase the positive impact from a GEO HIR sounder. On the other hand, with denser and more frequent observations assimilated, it becomes more difficult to handle the spatial error correlation in observations and gravity waves due to the limitations of current assimilation and forecast systems (such as a static background error covariance). The peak reduction of 4.6% in normalized RMSD is found when observations are assimilated every 3 h with a thinning distance of 30 km.

Impact of UAS Global Hawk Dropsonde Data on Tropical and Extratropical Cyclone Forecasts in 2016

AbstractA preliminary investigation into the impact of dropsonde observations from the Global Hawk (GH) on tropical and extratropical forecasts is performed using the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS). Experiments are performed during high-impact weather events that were sampled as part of the NOAA Unmanned Aerial Systems (UAS) Sensing Hazards with Operational Unmanned Technology (SHOUT) field campaigns in 2016: 1) three extratropical systems in February 2016 and 2) Hurricanes Matthew and Nicole in the western Atlantic. For these events, the benefits of GH observations under a satellite data gap scenario are also investigated. It is found that the assimilation of GH dropsondes reduces the track error for both Matthew and Nicole; the improvements are as high as 20% beyond 60 h. Additionally, the localized dropsondes reduce global forecast track error for four tropical cyclones by up to 9%. Results are mixed under a satellite gap scenario, where only Hurricane Matthew is improved from assimilated dropsondes. The improved storm track is attributed to a better representation of the steering flow and atmospheric midlevel pattern. For all cases, dropsondes reduce the root-mean-square error in temperature, relative humidity, wind, and sea level pressure by 3%–8% out to 96 h. Additional benefits from GH dropsondes are obtained for precipitation, with higher skill scores over the southeastern United States versus control forecasts of up to 8%, as well as for low-level parameters important for severe weather prediction. The findings from this study are preliminary and, therefore, more cases are needed for statistical significance.

Observation and Numerical Simulation of Cold Clouds and Snow Particles in the Yeongdong Region

The Yeongdong region of the Korean Peninsula is vulnerable to high-impact weather events, because of its complicated geographical characteristics and East Sea effect, such that heavy snowfall episodes have frequently occurred in winter. Snow crystal play an important role in cloud and precipitation physics because it is an essential element for improvement of numerical model, and remote sensing retrieval such as radar and satellite. In this study, the high-temporal resolution (3-hourly) dataset of radiosonde soundings and snow particle photographs during the intensive observation period for 2013–2016 has been used to understand characteristics of snow particles for the different meteorological conditions in the Yeongdong region. We also attempt to simulate two episodes that occurred on 23–24 February and 13–14 December 2016; one has a single-layered cloud and the other has two-layered cloud structure. This study demonstrates that the rimed particles in the first period tend to shift to aggregates of dendrites with the decrease of 850-hPa temperature. In general, the low-level clouds in the Yeongdong region are observed along with the distinctive wind shear and strong inversion in equivalent potential temperature around 2 ~ 3 km above the sea level. The simulation successfully represents the variations of the characteristics of snow particles as well as different cloud structure for both episodes. The observation and model simulations clearly suggest that snow particles primarily depend on the 850-hPa temperature.

On the low-frequency variability of wintertime Euro-Atlantic planetary wave-breaking

Planetary wave-breaking can lead to large-scale atmospheric circulation anomalies and favour high-impact weather occurrences. For example, the simultaneous occurrence of anti-cyclonic wave-breaking to the south of the North Atlantic jet and cyclonic wave-breaking to the north, here termed double wave-breaking, has been linked to heightened frequencies of explosive cyclones in the Atlantic basin and destructive windstorms over Western and Continental Europe. The present study analyses the long-term temporal variability of wintertime cyclonic and anti-cyclonic wave-breaking, and the resulting double wave-breaking, in the North Atlantic. We use reanalysis data, proxy reconstructions of the North Atlantic Oscillation (NAO) and a 1000-year coupled global climate model equilibrium simulation under constant pre-industrial forcing. The wave-breaking wavelet spectra highlight a significant ultra-centennial variability in double wave-breaking frequency, which is largely mirrored in the variability of the NAO. However, we note that the NAO wavelet spectra in the different datasets display significant discrepancies. The low-frequency wave-breaking variability is reflected in long-term anomalies of the large-scale atmospheric circulation in the Euro-Atlantic sector. The 100-year periods with the most and least double wave-breaking occurrences display significant and opposite anomalies in both upper and lower-level wind, as well as in the frequency of extreme temperature events and in the magnitude of wind destructiveness over Europe. The latter broadly resembles the wind destructiveness anomalies associated with individual double wave-breaking instances in reanalysis data. The existence of low-frequency variability in an atmospheric pattern related to high-impact weather events has important implications for the study and interpretation of climate change projections and of possible future NAO changes.

Improving Winter Storm Forecasts with Observing System Simulation Experiments (OSSEs). Part I: An Idealized Case Study of Three U.S. Storms

Abstract Severe weather events can have a significant impact on local communities because of the loss of life and property. Forecast busts associated with high-impact weather events have been attributed to initial condition errors over data-sparse regions, such as the Pacific Ocean. Numerous flight campaigns have found that targeted observations over these areas can improve forecasts. To better understand the impacts of measurement type and sampling domains on forecast performance, observing system simulation experiments are performed using the National Centers for Environmental Prediction Global Forecast System (GFS) with hybrid 3DEnVar data assimilation and the ECMWF T511 nature run. First, three types of simulated perfect dropsonde observations (temperature, specific humidity, and wind) are assimilated into the GFS over a large idealized sampling domain covering the Pacific Ocean. For the three winter storms studied, forecast error was found to be significantly reduced with all three types of measurements providing the most benefit (%–15% reduction in error). Instances when forecasts are not improved are investigated and concluded to be due to challenging meteorological structures, such as cutoff lows and interactions with atmospheric structures outside the sampling domain. Second, simulated dropsondes are assimilated over sensitive areas and flight tracks established using the ensemble transform sensitivity (ETS) technique. For all three winter storms, forecast error is reduced up to 5%, which is less than that found using an idealized domain. These results suggest that targeted observations over the Pacific Ocean may provide a small improvement to winter storm forecasts over the United States.

Downslope Windstorms of San Diego County. Part II: Physics Ensemble Analyses and Gust Forecasting

Abstract The “Santa Ana” winds of Southern California represent a high-impact weather event because their dry, fast winds can significantly elevate the wildfire threat. This high-resolution numerical study of six events of moderate or greater strength employs physics parameterization and stochastic perturbation ensembles to determine the optimal model configuration for predicting winds in San Diego County, with verification performed against observations from the San Diego Gas and Electric (SDG&E) mesonet. Results demonstrate model physics can have a material effect on the strength, location, and timing of the winds, with the land surface model playing an outsized role via its specification of surface roughness lengths. Even when bias in the network-averaged sustained wind forecasts is minimized, systematic biases remain in that many stations are consistently over- or underforecasted. The argument is made that this is an “unavoidable” error that represents localized anemometer exposure issues revealed through the station gust factor. A very simple gust parameterization is proposed for the mesonet based on the discovery that the network-averaged gust factor is independent of weather conditions and results in unbiased forecasts of gusts at individual stations and the mesonet as a whole. Combined with atmospheric humidity and fuel moisture information, gust forecasts can help in the assessment of wildfire risks.

Seeding Chaos: The Dire Consequences of Numerical Noise in NWP Perturbation Experiments

AbstractPerturbation experiments are a common technique used to study how differences between model simulations evolve within chaotic systems. Such perturbation experiments include modifications to initial conditions (including those involved with data assimilation), boundary conditions, and model parameterizations. We have discovered, however, that any difference between model simulations produces a rapid propagation of very small changes throughout all prognostic model variables at a rate many times the speed of sound. The rapid propagation seems to be due to the model’s higher-order spatial discretization schemes, allowing the communication of numerical error across many grid points with each time step. This phenomenon is found to be unavoidable within the Weather Research and Forecasting (WRF) Model even when using techniques such as digital filtering or numerical diffusion.These small differences quickly spread across the entire model domain. While these errors initially are on the order of a millionth of a degree with respect to temperature, for example, they can grow rapidly through nonlinear chaotic processes where moist processes are occurring. Subsequent evolution can produce within a day significant changes comparable in magnitude to high-impact weather events such as regions of heavy rainfall or the existence of rotating supercells. Most importantly, these unrealistic perturbations can contaminate experimental results, giving the false impression that realistic physical processes play a role. This study characterizes the propagation and growth of this type of noise through chaos, shows examples for various perturbation strategies, and discusses the important implications for past and future studies that are likely affected by this phenomenon.

Impact of SST on heavy rainfall events on eastern Adriatic during SOP1 of HyMeX

The season of late summer and autumn is favourable for intensive precipitation events (IPE) in the central Mediterranean. During that period the sea surface is warm and contributes to warming and moistening of the lowest portion of the atmosphere, particularly the planetary boundary layer (PBL). Adriatic sea is surrounded by mountains and the area often receives substantial amounts of precipitation in short time (24h). The IPEs are a consequence of convection triggered by topography acting on the southerly flow that has brought the unstable air to the coastline. Improvement in prediction of high impact weather events is one of the goals of The Hydrological cycle in the Mediterranean eXperiment (HyMeX). This study examines how precipitation patterns change in response to different SST forcing. We focus on the IPEs that occurred on the eastern Adriatic coast during the first HyMeX Special observing period (SOP1, 6 September to 5 November 2012). The operational forecast model ALADIN uses the same SST as the global meteorological model (ARPEGE from Meteo France), as well as the forecast lateral boundary conditions (LBCs). First we assess the SST used by the operational atmospheric model ALADIN and compare it to the in situ measurements, ROMS ocean model, OSTIA and MUR analyses. Results of this assessment show that SST in the eastern Adriatic was overestimated by up to 10K during HyMeX SOP1 period. Then we examine the sensitivity of 8km and 2km resolution forecasts of IPEs to the changes in the SST during whole SOP1 with special attention to the intensive precipitation event in Rijeka. Forecast runs in both resolutions are performed for the whole SOP1 using different SST fields prescribed at initial time and kept constant during the model forecast. Categorical verification of 24h accumulated precipitation did not show substantial improvement in verification scores when more realistic SST was used. Furthermore, the results show that the impact of introducing improved SST in the analysis on the precipitation forecast varies for different cases. There is generally a larger sensitivity to the SST in high resolution than in the lower one, although the forecast period of the latter is longer.

DRIHM(2US): An e-Science Environment for Hydrometeorological Research on High-Impact Weather Events

Abstract From 1970 to 2012, about 9,000 high-impact weather events were reported globally, causing the loss of 1.94 million lives and damage of $2.4 trillion (U.S. dollars). The scientific community is called to action to improve the predictive ability of such events and communicate forecasts and associated risks both to affected populations and to those making decisions. At the heart of this challenge lies the ability to have easy access to hydrometeorological data and models and to facilitate the necessary collaboration between meteorologists, hydrologists, and computer science experts to achieve accelerated scientific advances. Two European Union (EU)-funded projects, Distributed Research Infrastructure for Hydro-Meteorology (DRIHM) and DRIHM to United States of America (DRIHM2US), sought to help address this challenge by developing a prototype e-science environment providing advanced end-to-end services (models, datasets, and postprocessing tools), with the aim of paving the way to a step change in how scientists can approach studying these events, with a special focus on flood events in complex topographic areas. This paper describes the motivation and philosophy behind this prototype e-science environment together with certain key components, focusing on hydrometeorological aspects that are then illustrated through actionable research for a critical flash flood event that occurred in October 2014 in Liguria, Italy.

Sensitive indicators of Stipa bungeana response to precipitation under ambient and elevated CO2 concentration.

Precipitation is a primary environmental factor in the semiarid grasslands of northern China. With increased concentrations of atmospheric greenhouse gases, precipitation regimes will change, and high-impact weather events may be more common. Currently, many ecophysiological indicators are known to reflect drought conditions, but these indicators vary greatly among species, and few studies focus on the applicability of these drought indicators under high CO2 conditions. In this study, five precipitation levels (-30%, -15%, control, +15%, and +30%) were used to simulate the effects of precipitation change on 18 ecophysiological characteristics in Stipa bungeana, including leaf area, plant height, leaf nitrogen (N), and chlorophyll content, among others. Two levels of CO2 concentration (ambient, 390ppm; 550ppm) were used to simulate the effects of elevated CO2 on these drought indicators. Using gray relational analysis and phenotypic plasticity analysis, we found that total leaf area or leaf number (morphology), leaf water potential or leaf water content (physiology), and aboveground biomass better reflected the water status of S. bungeana under ambient and elevated CO2 than the 13 other analyzed variables. The sensitivity of drought indicators changed under the elevated CO2 condition. By quantifying the relationship between precipitation and the five most sensitive indicators, we found that the thresholds of precipitation decreased under elevated CO2 concentration. These results will be useful for objective monitoring and assessment of the occurrence and development of drought events in S. bungeana grasslands.

Cloud archiving and data mining of High-Resolution Rapid Refresh forecast model output

Weather-related research often requires synthesizing vast amounts of data that need archival solutions that are both economical and viable during and past the lifetime of the project. Public cloud computing services (e.g., from Amazon, Microsoft, or Google) or private clouds managed by research institutions are providing object data storage systems potentially appropriate for long-term archives of such large geophysical data sets. We illustrate the use of a private cloud object store developed by the Center for High Performance Computing (CHPC) at the University of Utah. Since early 2015, we have been archiving thousands of two-dimensional gridded fields (each one containing over 1.9 million values over the contiguous United States) from the High-Resolution Rapid Refresh (HRRR) data assimilation and forecast modeling system. The archive is being used for retrospective analyses of meteorological conditions during high-impact weather events, assessing the accuracy of the HRRR forecasts, and providing initial and boundary conditions for research simulations. The archive is accessible interactively and through automated download procedures for researchers at other institutions that can be tailored by the user to extract individual two-dimensional grids from within the highly compressed files. Characteristics of the CHPC object storage system are summarized relative to network file system storage or tape storage solutions. The CHPC storage system is proving to be a scalable, reliable, extensible, affordable, and usable archive solution for our research.

Understanding Institutional Deadlocks in Disaster Risk Reduction: The Financial and Legal Risk Root Causes in Genova, Italy

Many countries, regions and towns around the world experience lack of action or delayed actions in the implementation of disaster risk reduction measures. One of the common causal explanations is the insufficient funding available to reduce risk, especially at the local level. However, so far few studies have provided an in-depth analysis of the root causes of these deadlocks in disaster risk reduction. This paper analyses risk root causes in Genova, a town situated in North West Italy and exposed to high impact weather events. The town has experienced an increase in the intensity of flood events in the past 50 years, causing casualties, evacuations, displacements and severe damage. Decisions about flood risk mitigation progressively became a highly contested issue, especially because of the delays in reinforcing the banks of the Bisagno river and the city’s flood defences. The causes of these delays are not to be found in the lack of technical solutions, but rather in the socio-political and institutional dynamics. The Risk Root Cause Analysis (RRCA) framework provided the theoretical and methodological background to identify the key root causes of the risk reduction deadlock: corruption accusations and legal conflicts, unfair criteria for funding allocation at the regional level, lack of coordination among funding agencies, side effects of austerity measures, mismatches between the political and disaster risk cycles. The results show that the strengthening of a certain system — e.g., anti-corruption measures to avoid bribery in public spending — may cause the weakening of another one — e.g., delays in protection works execution — and generate increased risk exposure and vulnerability. Another similar example regards the implementation of national austerity policies, which caused human resources’ constraints at the local level and shifts of public funding from the disaster risk reduction to other sectors such as health. Anticipation of these and other unintended side effects discussed in the paper as well as a better understanding of the root causes of the deadlocks in risk reduction, can contribute to the improvement of disaster management strategies.

Impacts of Storm-Track Variations on Wintertime Extreme Weather Events over the Continental United States

Extratropical cyclones are responsible for many of the high-impact weather events over the United States, including extreme cold, extreme high wind, and extreme heavy precipitation. In this study, impacts from the variations of the cyclone (or storm-track) activity on these extreme events are examined through composites based on map-averaged cyclone activity. Increased cyclone activity enhances the frequency of extreme cold and high wind events over much of the United States, and impacts extreme precipitation around the Ohio River valley. These impacts are largely due to a changing of the tail of the distribution rather than a shifting of the mean. To systematically study these impacts, three singular value decomposition (SVD) analyses have been conducted, each one between the cyclone activity and one kind of extreme event frequency. All three SVD leading modes represent a pattern of overall increase or decrease of storm tracks over the United States. The average of the time series of these leading modes is highly correlated with the observed map-averaged storm track and strongly associated with the Pacific–North America (PNA) pattern and El Niño–Southern Oscillation (ENSO). However, composites based on either the PNA pattern or ENSO do not show as strong impacts as the map-averaged storm track. A second common SVD mode is found that correlates weakly with the North Pacific mode and is likely to be largely due to internal variability. Finally, the potential impacts of projected storm-track change on the frequency of extreme events are examined, indicating that the projected storm-track decrease over North America may give rise to some reduction in the frequency of extreme events.

Multiscale Upstream and In Situ Precursors to the Elevated Mixed Layer and High-Impact Weather over the Midwest United States

Abstract Two severe MCSs over the upper Midwest United States resulted in >100 mm of rain in a ~24-h period and >200 severe weather reports, respectively, during 30 June–2 July 2011. This period also featured 100 (104) daily maximum high (low) temperature records across the same region. These high-impact weather events occurred in the presence of an elevated mixed layer (EML) that influenced the development of the severe MCSs and the numerous record high temperatures. The antecedent large-scale flow evolution was influenced by early season Tropical Cyclone Meari over the western North Pacific. The recurvature and subsequent interaction of Meari with the extratropical large-scale flow occurred in conjunction with Rossby wave train amplification over the North Pacific and dispersion across North America during 22 June–2 July 2011. The Rossby wave train dispersion contributed to trough (ridge) development over western (central) North America and the development of an EML and the two MCSs over the upper Midwest United States. A composite analysis of 99 warm-season days with an EML at Minneapolis, Minnesota, suggests that Rossby wave train amplification and dispersion across the North Pacific may frequently occur in the 7 days leading up to EMLs across the upper Midwest. The composite analysis also demonstrates an increased frequency of severe weather and elevated temperatures relative to climatology on days with an EML. These results suggest that EMLs over the upper Midwest may often be preceded by Rossby wave train amplification over the North Pacific and be followed by a period of severe weather and elevated temperatures.

High-Definition Sounding System (HDSS) for Atmospheric Profiling

AbstractThe High-Definition Sounding System (HDSS) is an automated system deploying the expendable digital dropsonde (XDD) designed to measure wind and pressure–temperature–humidity (PTH) profiles, and skin sea surface temperature (SST) within and around tropical cyclones (TCs) and other high-impact weather events needing high sampling density. Three experiments were conducted to validate the XDD.On two successive days off the California coast, 10 XDDs and 14 Vaisala RD-94s were deployed from the navy’s Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft over offshore buoys. The Twin Otter made spiral descents from 4 km to 60 m at the same descent rate as the sondes. Differences between successive XDD and RD-94 profiles due to true meteorological variability were on the same order as the profile differences between the spirals, XDDs, and RD-94s. XDD SST measured via infrared microradiometer, referred to as infrared skin SST (SSTir), and surface wind measurements were within 0.5°C and 1.5 m s−1, respectively, of buoy and Twin Otter values.A NASA DC-8 flight launched six XDDs from 12 km between ex-TC Cosme and the Baja California coast. Repeatability was shown with good agreement between features in successive profiles. XDD SSTir measurements from 18° to 28°C and surface winds agreed well with drifting buoy- and satellite-derived estimates.Excellent agreement was found between PTH and wind profiles measured by XDDs deployed from a NASA WB-57 at 18-km altitude offshore from the Texas coast and NWS radiosonde profiles from Brownsville and Corpus Christi, Texas. Successful XDD profiles were obtained in the clear and within precipitation over an offshore squall line.

Reducing the prediction uncertainties of high-impact weather and climate events: An overview of studies at LASG

This paper summarizes recent progress at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences in studies on targeted observations, data assimilation, and ensemble prediction, which are three effective strategies to reduce the prediction uncertainties and improve the forecast skill of weather and climate events. Considering the limitations of traditional targeted observation approaches, LASG researchers have developed a conditional nonlinear optimal perturbation-based targeted observation strategy to optimize the design of the observing network. This strategy has been employed to identify sensitive areas for targeted observations of the El Nino–Southern Oscillation, Indian Ocean dipole, and tropical cyclones, and has been demonstrated to be effective in improving the forecast skill of these events. To assimilate the targeted observations into the initial state of a numerical model, a dimension-reducedprojection- based four-dimensional variational data assimilation (DRP-4DVar) approach has been proposed and is used operationally to supply accurate initial conditions in numerical forecasts. The performance of DRP-4DVar is good, and its computational cost is much lower than the standard 4DVar approach. Besides, ensemble prediction, which is a practical approach to generate probabilistic forecasts of the future state of a particular system, can be used to reduce the prediction uncertainties of single forecasts by taking the ensemble mean of forecast members. In this field, LASG researchers have proposed an ensemble forecast method that uses nonlinear local Lyapunov vectors (NLLVs) to yield ensemble initial perturbations. Its application in simple models has shown that NLLVs are more useful than bred vectors and singular vectors in improving the skill of the ensemble forecast. Therefore, NLLVs represent a candidate for possible development as an ensemble method in operational forecasts. Despite the considerable efforts made towards developing these methods to reduce prediction uncertainties, much challenging but highly important work remains in terms of improving the methods to further increase the skill in forecasting such weather and climate events.

Decadal predictability of regional-scale peak winds over Europe using the Earth System Model of the Max-Planck-Institute for Meteorology

The predictability of high impact weather events on multiple time scales is a crucial issue both in scientific and socio-economic terms. In this study, a statistical-dynamical downscaling (SDD) approach is applied to an ensemble of decadal hindcasts obtained with the Max-Planck-Institute Earth System Model (MPI-ESM) to estimate the decadal predictability of peak wind speeds (as a proxy for gusts) over Europe. Yearly initialized decadal ensemble simulations with ten members are investigated for the period 1979–2005. The SDD approach is trained with COSMO-CLM regional climate model simulations and ERA-Interim reanalysis data and applied to the MPI-ESM hindcasts. The simulations for the period 1990–1993, which was characterized by several windstorm clusters, are analyzed in detail. The anomalies of the 95 % peak wind quantile of the MPI-ESM hindcasts are in line with the positive anomalies in reanalysis data for this period. To evaluate both the skill of the decadal predictability system and the added value of the downscaling approach, quantile verification skill scores are calculated for both the MPI-ESM large-scale wind speeds and the SDD simulated regional peak winds. Skill scores are predominantly positive for the decadal predictability system, with the highest values for short lead times and for (peak) wind speeds equal or above the 75 % quantile. This provides evidence that the analyzed hindcasts and the downscaling technique are suitable for estimating wind and peak wind speeds over Central Europe on decadal time scales. The skill scores for SDD simulated peak winds are slightly lower than those for large-scale wind speeds. This behavior can be largely attributed to the fact that peak winds are a proxy for gusts, and thus have a higher variability than wind speeds. The introduced cost-efficient downscaling technique has the advantage of estimating not only wind speeds but also estimates peak winds (a proxy for gusts) and can be easily applied to large ensemble datasets like operational decadal prediction systems.

Overview of the first HyMeX special observation period over Croatia

Abstract. The HYdrological cycle in the Mediterranean EXperiment (HyMeX) is intended to improve the capabilities of predicting high-impact weather events. Within its framework, the aim of the first special observation period (SOP1), 5 September to 6 November 2012, was to study heavy precipitation events and flash floods. Here, we present high-impact weather events over Croatia that occurred during SOP1. Particular attention is given to eight intense observation periods (IOPs), during which high precipitation occurred over the eastern Adriatic and Dinaric Alps. During the entire SOP1, the operational model forecasts generally well represented medium intensity precipitation, but heavy precipitation was frequently underestimated by the ALADIN model at an 8 km grid spacing and was overestimated at a higher resolution (2 km grid spacing). During IOP2, intensive rainfall occurred over a wider area around the city of Rijeka in the northern Adriatic. The short-range maximum rainfall totals were the largest ever recorded at the Rijeka station since the beginning of measurements in 1958. The rainfall amounts measured in intervals of 20, 30 and 40 min were exceptional, with return periods that exceeded a thousand, a few hundred and one hundred years, respectively. The operational precipitation forecast using the ALADIN model at an 8 km grid spacing provided guidance regarding the event but underestimated the rainfall intensity. An evaluation of numerical sensitivity experiments suggested that the forecast was slightly enhanced by improving the initial conditions through variational data assimilation. The operational non-hydrostatic run at a 2 km grid spacing using a configuration with the ALARO physics package further improved the forecast. This article highlights the need for an intensive observation period in the future over the Adriatic region to validate the simulated mechanisms and improve numerical weather predictions via data assimilation and model improvements in descriptions of microphysics and air–sea interactions.

The meteorology of the exceptional winter of 2015/2016 across the UK and Ireland

The meteorological winter of 2015/2016 will be remembered as another exceptional winter across the UK and Ireland, with numerous climate records broken and high impact weather events causing considerable disruption from flooding and high winds. A succession of winter storms tracked across the region, bringing persistent and in places record-breaking rainfall, including the highest 24 and 48h rainfall accumulations on record, from storm Desmond on 4–6 December. Persistent rain, particularly through the first half of the winter, resulted in new records for both monthly and seasonal rainfall accumulations widely across Ireland, Scotland, Wales and northern England. Temperatures were also exceptionally high through much of December and in late January. In this paper we document the main meteorological and climate features that defined this exceptional winter season, and consider its wider historical context.