Cloud banding and winds in intense European cyclones: results from the DIAMET project

The DIAMET (DIAbatic influences on Mesoscale structures in ExTratropical storms) project aims to improve forecasts of high-impact weather in extratropical cyclones through field measurements, high-resolution numerical modeling, and improved design of ensemble forecasting and data assimilation systems. This article introduces DIAMET and presents some of the first results. Four field campaigns were conducted by the project, one of which, in late 2011, coincided with an exceptionally stormy period marked by an unusually strong, zonal North Atlantic jet stream and a succession of severe windstorms in northwest Europe. As a result, December 2011 had the highest monthly North Atlantic Oscillation index (2.52) of any December in the last 60 years. Detailed observations of several of these storms were gathered using the UK’s BAe146 research aircraft and extensive ground-based measurements. As an example of the results obtained during the campaign, observations are presented of cyclone Friedhelm on 8 December 2011, when surface winds with gusts exceeding 30 m s-1 crossed central Scotland, leading to widespread disruption to transportation and electricity supply. Friedhelm deepened 44 hPa in 24 hours and developed a pronounced bent-back front wrapping around the storm center. The strongest winds at 850 hPa and the surface occurred in the southern quadrant of the storm, and detailed measurements showed these to be most intense in clear air between bands of showers. High-resolution ensemble forecasts from the Met Office showed similar features, with the strongest winds aligned in linear swaths between the bands, suggesting that there is potential for improved skill in forecasts of damaging winds.

Verification of European subseasonal wind speed forecasts

Analysis of the forecasts and hindcasts from the ECMWF 32-day forecast model reveals that there is statistically significant skill in predicting weekly mean wind speeds over areas of Europe at lead times of at least 14–20 days. Previous research on wind speed predictability has focused on the short- to medium-range time scales, typically finding that forecasts lose all skill by the later part of the medium-range forecast. To the authors’ knowledge, this research is the first to look beyond the medium-range time scale by taking weekly mean wind speeds, instead of averages at hourly or daily resolution, for the ECMWF monthly forecasting system. It is shown that the operational forecasts have high levels of correlation (~0.6) between the forecasts and observations over the winters of 2008–12 for some areas of Europe. Hindcasts covering 20 winters show a more modest level of correlation but are still skillful. Additional analysis examines the probabilistic skill for the United Kingdom with the application of wind power forecasting in mind. It is also shown that there is forecast “value” for end users (operating in a simple cost/loss ratio decision-making framework). End users that are sensitive to winter wind speed variability over the United Kingdom, Germany, and some other areas of Europe should therefore consider forecasts beyond the medium-range time scale as it is clear there is useful information contained within the forecast.

Review: the predictability of the extra-tropical stratosphere on monthly timescales and its impact on the skill of tropospheric forecasts

Extreme variability of the winter- and spring-time stratospheric polar vortex has been shown to affect extratropical tropospheric weather. Therefore, reducing stratospheric forecast error may be one way to improve the skill of tropospheric weather forecasts. In this review, the basis for this idea is examined. A range of studies of different stratospheric extreme vortex events shows that they can be skilfully forecasted beyond five days and into the sub-seasonal range (0-30 days) in some cases. Separate studies show that typical errors in forecasting a stratospheric extreme vortex event can alter tropospheric forecasts skill by 5-7% in the extratropics on sub-seasonal timescales. Thus understanding what limits stratospheric predictability is of significant interest to operational forecasting centres. Both limitations in forecasting tropospheric planetary waves and stratospheric model biases have been shown to be important in this context.

Are professional macroeconomic forecasters able to do better than forecasting trends?

This paper investigates whether survey forecasters are able to make more accurate forecasts than simply supposing that the future values of the variable will move monotonically to the long-run expectation. We consider the forecasts individually, and the consensus forecasts. Consensus survey forecasts are able to do so to varying degrees depending on the variable, but this ability is largely limited to forecasts of the current quarter.

Robust approaches to forecasting

We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium-correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, impulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods are likely to perform well. The robust methods are applied to forecasting US GDP using autoregressive models, and also to autoregressive models with factors extracted from a large dataset of macroeconomic variables. We consider forecasting performance over the Great Recession, and over an earlier more quiescent period.

Forecasting with Bayesian multivariate vintage-based VARs

We consider the forecasting of macroeconomic variables that are subject to revisions, using Bayesian vintage-based vector autoregressions. The prior incorporates the belief that, after the first few data releases, subsequent ones are likely to consist of revisions that are largely unpredictable. The Bayesian approach allows the joint modelling of the data revisions of more than one variable, while keeping the concomitant increase in parameter estimation uncertainty manageable. Our model provides markedly more accurate forecasts of post-revision values of inflation than do other models in the literature.

Influence of short-term solar disturbances on the fair weather conduction current

The fair weather atmospheric electrical current (Jz) couples the ionosphere to the lower atmosphere and thus provides a route by which changes in solar activity can modify processes in the lower troposphere. This paper examines the temporal variations and spectral characteristics of continuous measurements of Jz conducted at the Wise Observatory in Mitzpe-Ramon, Israel (30°35′ N, 34°45′ E), during two large CMEs, and during periods of increased solar wind density. Evidence is presented for the effects of geomagnetic storms and sub-storms on low latitude Jz during two coronal mass ejections (CMEs), on 24–25th October 2011 and 7–8th March 2012, when the variability in Jz increased by an order of magnitude compared to normal fair weather conditions. The dynamic spectrum of the increased Jz fluctuations exhibit peaks in the Pc5 frequency range. Similar low frequency characteristics occur during periods of enhanced solar wind proton density. During the October 2011 event, the periods of increased fluctuations in Jz lasted for 7 h and coincided with fluctuations of the inter-planetary magnetic field (IMF) detected by the ACE satellite. We suggest downward mapping of ionospheric electric fields as a possible mechanism for the increased fluctuations.

Calibration of structure in a distributed forecasting model for a semiarid flash flood: dynamic surface storage and channel roughness

Flash floods pose a significant danger for life and property. Unfortunately, in arid and semiarid environment the runoff generation shows a complex non-linear behavior with a strong spatial and temporal non-uniformity. As a result, the predictions made by physically-based simulations in semiarid areas are subject to great uncertainty, and a failure in the predictive behavior of existing models is common. Thus better descriptions of physical processes at the watershed scale need to be incorporated into the hydrological model structures. For example, terrain relief has been systematically considered static in flood modelling at the watershed scale. Here, we show that the integrated effect of small distributed relief variations originated through concurrent hydrological processes within a storm event was significant on the watershed scale hydrograph. We model these observations by introducing dynamic formulations of two relief-related parameters at diverse scales: maximum depression storage, and roughness coefficient in channels. In the final (a posteriori) model structure these parameters are allowed to be both time-constant or time-varying. The case under study is a convective storm in a semiarid Mediterranean watershed with ephemeral channels and high agricultural pressures (the Rambla del Albujón watershed; 556 km 2 ), which showed a complex multi-peak response. First, to obtain quasi-sensible simulations in the (a priori) model with time-constant relief-related parameters, a spatially distributed parameterization was strictly required. Second, a generalized likelihood uncertainty estimation (GLUE) inference applied to the improved model structure, and conditioned to observed nested hydrographs, showed that accounting for dynamic relief-related parameters led to improved simulations. The discussion is finally broadened by considering the use of the calibrated model both to analyze the sensitivity of the watershed to storm motion and to attempt the flood forecasting of a stratiform event with highly different behavior.

Global forecasting of thermal health hazards: the skill of probabilistic predictions of the Universal Thermal Climate Index (UTCI)

Although over a hundred thermal indices can be used for assessing thermal health hazards, many ignore the human heat budget, physiology and clothing. The Universal Thermal Climate Index (UTCI) addresses these shortcomings by using an advanced thermo-physiological model. This paper assesses the potential of using the UTCI for forecasting thermal health hazards. Traditionally, such hazard forecasting has had two further limitations: it has been narrowly focused on a particular region or nation and has relied on the use of single ‘deterministic’ forecasts. Here, the UTCI is computed on a global scale,which is essential for international health-hazard warnings and disaster preparedness, and it is provided as a probabilistic forecast. It is shown that probabilistic UTCI forecasts are superior in skill to deterministic forecasts and that despite global variations, the UTCI forecast is skilful for lead times up to 10 days. The paper also demonstrates the utility of probabilistic UTCI forecasts on the example of the 2010 heat wave in Russia.

Evaluation of boundary-layer type in a weather forecast model utilising long-term Doppler lidar observations

Many studies evaluating model boundary-layer schemes focus either on near-surface parameters or on short-term observational campaigns. This reflects the observational datasets that are widely available for use in model evaluation. In this paper we show how surface and long-term Doppler lidar observations, combined in a way to match model representation of the boundary layer as closely as possible, can be used to evaluate the skill of boundary-layer forecasts. We use a 2-year observational dataset from a rural site in the UK to evaluate a climatology of boundary layer type forecast by the UK Met Office Unified Model. In addition, we demonstrate the use of a binary skill score (Symmetric Extremal Dependence Index) to investigate the dependence of forecast skill on season, horizontal resolution and forecast leadtime. A clear diurnal and seasonal cycle can be seen in the climatology of both the model and observations, with the main discrepancies being the model overpredicting cumulus capped and decoupled stratocumulus capped boundary-layers and underpredicting well mixed boundary-layers. Using the SEDI skill score the model is most skillful at predicting the surface stability. The skill of the model in predicting cumulus capped and stratocumulus capped stable boundary layer forecasts is low but greater than a 24 hr persistence forecast. In contrast, the prediction of decoupled boundary-layers and boundary-layers with multiple cloud layers is lower than persistence. This process based evaluation approach has the potential to be applied to other boundary-layer parameterisation schemes with similar decision structures.

An integrated study of urban microclimates in Chongqing, China: historical weather data, transverse measurement and numerical simulation

Chongqing is the largest central-government-controlled municipality in China, which is now under going a rapid urbanization. The question remains open: What are the consequences of such rapid urbanization in Chongqing in terms of urban microclimates? An integrated study comprising three different research approaches is adopted in the present paper. By analyzing the observed annual climate data, an average rising trend of 0.10◦C/decade was found for the annual mean temperature from 1951 to 2010 in Chongqing,indicating a higher degree of urban warming in Chongqing. In addition, two complementary types of field measurements were conducted: fixed weather stations and mobile transverse measurement. Numerical simulations using a house-developed program are able to predict the urban air temperature in Chongqing.The urban heat island intensity in Chongqing is stronger in summer compared to autumn and winter.The maximum urban heat island intensity occurs at around midnight, and can be as high as 2.5◦C. In the day time, an urban cool island exists. Local greenery has a great impact on the local thermal environment.Urban green spaces can reduce urban air temperature and therefore mitigate the urban heat island. The cooling effect of an urban river is limited in Chongqing, as both sides of the river are the most developed areas, but the relative humidity is much higher near the river compared with the places far from it.

The Solar Stormwatch CME catalogue: results from the first space weather citizen science project

Solar Stormwatch was the first space weather citizen science project, the aim of which was to identify and track coronal mass ejections (CMEs) observed by the Heliospheric Imagers aboard the STEREO satellites. The project has now been running for approximately 4 years, with input from >16000 citizen scientists, resulting in a dataset of >38000 time-elongation profiles of CME trajectories, observed over 18 pre-selected position angles. We present our method for reducing this data set into aCME catalogue. The resulting catalogue consists of 144 CMEs over the period January-2007 to February-2010, of which 110 were observed by STEREO-A and 77 were observed by STEREO-B. For each CME, the time-elongation profiles generated by the citizen scientists are averaged into a consensus profile along each position angle that the event was tracked. We consider this catalogue to be unique, being at present the only citizen science generated CME catalogue, tracking CMEs over an elongation range of 4 degrees out to a maximum of approximately 70 degrees. Using single spacecraft fitting techniques, we estimate the speed, direction, solar source region and latitudinal width of each CME. This shows that, at present, the Solar Stormwatch catalogue (which covers only solar minimum years) contains almost exclusively slow CMEs, with a mean speed of approximately 350 kms−1. The full catalogue is available for public access at www.met.reading.ac.uk/spate/stormwatch. This includes, for each event, the unprocessed time-elongation profiles generated by Solar Stormwatch, the consensus time-elongation profiles and a set of summary plots, as well as the estimated CME properties.

Structure of the planetary boundary layer over southeast England: modeling and measurements

The Weather Research and Forecasting model was applied to analyze variations in the planetary boundary layer (PBL) structure over Southeast England including central and suburban London. The parameterizations and predictive skills of two nonlocal mixing PBL schemes, YSU and ACM2, and two local mixing PBL schemes, MYJ and MYNN2, were evaluated over a variety of stability conditions, with model predictions at a 3 km grid spacing. The PBL height predictions, which are critical for scaling turbulence and diffusion in meteorological and air quality models, show significant intra-scheme variance (> 20%), and the reasons are presented. ACM2 diagnoses the PBL height thermodynamically using the bulk Richardson number method, which leads to a good agreement with the lidar data for both unstable and stable conditions. The modeled vertical profiles in the PBL, such as wind speed, turbulent kinetic energy (TKE), and heat flux, exhibit large spreads across the PBL schemes. The TKE predicted by MYJ were found to be too small and show much less diurnal variation as compared with observations over London. MYNN2 produces better TKE predictions at low levels than MYJ, but its turbulent length scale increases with height in the upper part of the strongly convective PBL, where it should decrease. The local PBL schemes considerably underestimate the entrainment heat fluxes for convective cases. The nonlocal PBL schemes exhibit stronger mixing in the mean wind fields under convective conditions than the local PBL schemes and agree better with large-eddy simulation (LES) studies.

Impacts of increasing the aerosol complexity in the Met Office global numerical weather prediction model.

The inclusion of the direct and indirect radiative effects of aerosols in high-resolution global numerical weather prediction (NWP) models is being increasingly recognised as important for the improved accuracy of short-range weather forecasts. In this study the impacts of increasing the aerosol complexity in the global NWP configuration of the Met Office Unified Model (MetUM) are investigated. A hierarchy of aerosol representations are evaluated including three-dimensional monthly mean speciated aerosol climatologies, fully prognostic aerosols modelled using the CLASSIC aerosol scheme and finally, initialised aerosols using assimilated aerosol fields from the GEMS project. The prognostic aerosol schemes are better able to predict the temporal and spatial variation of atmospheric aerosol optical depth, which is particularly important in cases of large sporadic aerosol events such as large dust storms or forest fires. Including the direct effect of aerosols improves model biases in outgoing long-wave radiation over West Africa due to a better representation of dust. However, uncertainties in dust optical properties propagate to its direct effect and the subsequent model response. Inclusion of the indirect aerosol effects improves surface radiation biases at the North Slope of Alaska ARM site due to lower cloud amounts in high-latitude clean-air regions. This leads to improved temperature and height forecasts in this region. Impacts on the global mean model precipitation and large-scale circulation fields were found to be generally small in the short-range forecasts. However, the indirect aerosol effect leads to a strengthening of the low-level monsoon flow over the Arabian Sea and Bay of Bengal and an increase in precipitation over Southeast Asia. Regional impacts on the African Easterly Jet (AEJ) are also presented with the large dust loading in the aerosol climatology enhancing of the heat low over West Africa and weakening the AEJ. This study highlights the importance of including a more realistic treatment of aerosol–cloud interactions in global NWP models and the potential for improved global environmental prediction systems through the incorporation of more complex aerosol schemes.

Attribution of extreme weather events in Africa: A preliminary exploration of the science and policy implications

At the most recent session of the Conference of the Parties (COP19) in Warsaw (November 2013) the Warsaw international mechanism for loss and damage associated with climate change impacts was established under the United Nations Framework Convention on Climate Change (UNFCCC). The mechanism aims at promoting the implementation of approaches to address loss and damage associated with the adverse effects of climate change. Specifically, it aims to enhance understanding of risk management approaches to address loss and damage. Understanding risks associated with impacts due to highly predictable (slow onset) events like sea-level rise is relatively straightforward whereas assessing the effects of climate change on extreme weather events and their impacts is much more difficult. However, extreme weather events are a significant cause of loss of life and livelihoods, particularly in vulnerable countries and communities in Africa. The emerging science of probabilistic event attribution is relevant as it provides scientific evidence on the contribution of anthropogenic climate change to changes in risk of extreme events. It thus provides the opportunity to explore scientifically-backed assessments of the human influence on such events. However, different ways of framing attribution questions can lead to very different assessments of change in risk. Here we explain the methods of, and implications of different approaches to attributing extreme weather events with a focus on Africa. Crucially, it demonstrates that defining the most appropriate attribution question to ask is not a science decision but needs to be made in dialogue with those stakeholders who will use the answers.