Use Of Weather Forecasts Research Articles

The combination of climate information services in the decision-making process of farmers to reduce climate risks: Application of social cognition theory

One of the main instruments of risk management and climate risk reduction in the agricultural sector is the use of climate information services (CIS). Meteorological information can be useful for society by increasing the efficiency of resource consumption and helping to improve decision-making management. However, there is a gap between the production and implementation of weather forecasts, and that is the lack of practical use of weather forecasts in the agricultural decision-making process. In doing so, this research was conducted with the main objective of identifying components influencing the utilize of CIS by farmers in southwest Iran. The research utilized social cognitive theory (SCT) as the underlying framework, employing a survey method and employing Structural Equation Modeling (SEM) for analysis. The research encompassed all farmers residing in Dezful city within the Khuzestan province, located in the southwest of Iran. The results showed that the variables of SCT, namely outcome expectations, perception of others’ behavior, self-efficacy, socio-structural, and behavioral intentions are very powerful in identifying factors affecting farmers’ use of CIS and can account for 76.4% of the variance of farmers’ behavior. Therefore, these research results can provide essential help to authorities to mitigate the negative impacts of climate change on agriculture.

Classification of synoptic processes in the territory of Ukraine using a modified Jenkinson and Collison method

During the year, the territory of Ukraine is under the influence of various synoptic processes, which have a certain evolution in the surface conditions of the region and sometimes lead to the formation of unfavorable and dangerous weather conditions. The classification of synoptic processes makes it possible to create a set of daily synoptic situations taking into account the characteristics of a certain region, both for their use in weather forecasting and for studying the dynamics of regional synoptic processes under climate change. In this study, the modified version of Jenkinson and Collison method is used to classify synoptic processes on the territory of Ukraine, which is based on pressure data at the sea surface level and the geopotential heights at the level 500 hPa. The analysis of the obtained set of daily types of atmospheric circulation for the period 1948-2021 and the composite pressure fields built for each circulation type showed that all synoptic situations are clearly defined according to the classification and have seasonal features of frequency. During the year, advective processes of the western circulation group prevail, eastern advection is also often observed, and this type of circulation dominates in the spring months. The processes of the southern group are most relevant on the cold period of the year. Anticyclones occur more often over the region in winter and autumn, and cyclones are observed in summer and spring. The inclusion of 500 hPa level data in the classification makes it possible to take into account the three-dimensional structure of synoptic objects and distinguish such types of circulation as low cyclones and cold anticyclones, as well as troughs and cut-off lows at heights. It was found that low (thermal) cyclones and anticyclones make up a small proportionof synoptic situations, so the feasibility of their further inclusion in the typification for the territory of Ukraine is being discussed. The processes associated with the cut-off lows at the upper levels turned out to quite rare, but due to the possibility of the formation of dangerous weather conditions, this type of circulation is necessary in the classification of regional synoptic processes.

A stratospheric mission – design of a conceptual framework to bring weather balloons and STEM into the classroom

IntroductionIntegrated STEM education makes learning relevant and applicable, blending the mindset, skillset, and toolset necessary in developing a depth of understanding for science, technology, engineering and mathematics.MethodsBased on the theoretical framework of Kelley and Knowles the ‘Spaceship Earth’ project was designed to engage primary school children in 4th class (ages 9 and 10 years old) in real-world project-based experiments in the classroom that are used to develop collaborative problem-solving skills and a framework for asking and answering scientific questions. This project involved researchers from two Universities and the Irish National Meteorological Service working collaboratively on a high-altitude balloon mission.ResultsHigh-altitude balloons have an established track record of safe and effective use in weather forecasting, astronomy, and STEM outreach. During the project lifecycle the children devised experiments that they launched to the stratosphere using the high-altitude balloons. Once the experiments returned to Earth, the children engaged in analysis and discussion about their experiments that extended and deepened their learning.DiscussionThis celestial project framework represents a new dawn of innovation for STEM education and public engagement.

The Value-Add of Tailored Seasonal Forecast Information for Industry Decision Making

There is a growing need for more systematic, robust, and comprehensive information on the value-add of climate services from both the demand and supply sides. There is a shortage of published value-add assessments that focus on the decision-making context, involve participatory or co-evaluation approaches, avoid over-simplification, and address both the quantitative (e.g., economic) and qualitative (e.g., social) values of climate services. The 12 case studies that formed the basis of the European Union-funded SECLI-FIRM project were co-designed by industrial and research partners in order to address these gaps while focusing on the use of tailored sub-seasonal and seasonal forecasts in the energy and water industries. For eight of these case studies, it was possible to apply quantitative economic valuation methods: econometric modelling was used in five case studies while three case studies used a cost/loss (relative economic value) analysis and avoided costs. The case studies illustrated the challenges in attempting to produce quantitative estimates of the economic value-add of these forecasts. At the same time, many of them highlighted how practical value for users—transcending the actual economic value—can be enhanced; for example, through the provision of climate services as an extension to their current use of weather forecasts and with the visualisation tailored towards the user.

Quantifying the role of weather forecast error on the uncertainty of greenhouse energy prediction and power market trading

Currently the Dutch greenhouse horticultural sector has a high energy demand. The present use of weather forecasts can exacerbate this high energy consumption by contributing to suboptimal prediction and trading of the greenhouse's power demand. This study investigates the role of weather forecast errors on energy prediction power and trading uncertainty in greenhouse horticulture. This was done using an uncertainty analysis and computer model of a tomato producing Venlo style greenhouse in Bleiswijk, The Netherlands. This greenhouse model was used to predict the greenhouse's gas and electrical power demand. The study concluded that errors in the weather forecast of outdoor radiation, temperature and wind speed caused an overestimation of greenhouse energy demand. A sensitivity analysis showed that the radiation forecast error had the greatest impact on predicted greenhouse power demand errors with a mean relative error of 6.1%. Predicted gas demand errors were most dependent on the outside wind speed forecast mean relative error (18.0%) and temperature forecast error (17.2%). A power trading uncertainty analysis was done to investigate the impact of predicted energy demand errors on the cost of buying power on the Dutch imbalance and Amsterdam Power Exchange day-ahead market. This cost analysis found that the volume of initial power trading was greater than corrective trading. Additionally, the higher volatility in short term power prices resulted in higher corrective power costs per unit of power than if the power demand had been initially predicted with more accuracy.

A public decision support system for the assessment of plant disease infection risk shared by Italian regions.

Integrated pest management (IPM) practices proved to be efficient in reducing pesticide use and ensuring economic farming sustainability. Digital decision support systems (DSS) to support the adoption of IPM practices from plant protection services are required by European legislation. Available DSSs used by Italian plant protection services are heterogeneous with regards to disease forecasting models, datasets for their calibration, and level of integration in operational decision-making. This study presents the MISFITS-DSS, which has been jointly developed by a public research institution and nine regional plant protection services with the objective of harmonizing data collection and decision support for Italian farmers. Participatory approach allowed designing a predictive workflow relying on specific domain expertise, in order to explicitly match actual user needs. The DSS calibration entailed the risk of grapevine downy mildew infection (5-point scale from very low to very high), and phenological observations in 2012-2017 as reference data. Process-based models of primary and secondary infections have been implemented and tested via sensitivity analysis (Morris method) under contrasting weather conditions. Hindcast simulations of grapevine phenology, host susceptibility and disease pressure were post-processed by machine-learning classifiers to predict the reference infection risk. Results indicate that IPM principles are implemented by plant protection services since years. The accurate reproduction of grapevine phenology (RMSE=4-14 days), which drove the dynamic of host susceptibility, and the use of weather forecasts as model inputs contributed to reliably predict the reference infection risk (88% balanced accuracy). We did a pioneering effort to homogenize the methodology to deliver decision support to Italian farmers, by involving plant protection services in the DSS definition, to foster a further adoption of IPM practices.

Model inter-comparison for short-range forecasts over the southern African domain

Numerical weather prediction (NWP) models have been increasing in skill and their capability to simulate weather systems and provide valuable information at convective scales has improved in recent years. Much effort has been put into developing NWP models across the globe. Representation of physical processes is one of the critical issues in NWP, and it differs from one model to another. We investigated the performance of three regional NWP models used by the South African Weather Service over southern Africa, to identify the model that produces the best deterministic forecasts for the study domain. The three models – Unified Model (UM), Consortium for Small-scale Modelling (COSMO) and Weather Research and Forecasting (WRF) – were run at a horizontal grid spacing of about 4.4 km. Model forecasts for precipitation, 2-m temperature, and wind speed were verified against different observations. Snow was evaluated against reported snow records. Both the temporal and spatial verification of the model forecasts showed that the three models are comparable, with slight variations. Temperature and wind speed forecasts were similar for the three different models. Accumulated precipitation was mostly similar, except where WRF captured small rainfall amounts from a coastal low, while it over-estimated rainfall over the ocean. The UM showed a bubble-like shape towards the tropics, while COSMO cut-off part of the rainfall band that extended from the tropics to the sub-tropics. The COSMO and WRF models simulated a larger spatial coverage of precipitation than UM and snow-report records.Significance: Extreme weather events, such as tornadoes, floods, strong winds and heat waves, have significant impacts on society, the economy, infrastructure, agriculture and many other sectors. These impacts may be mitigated or even prevented through early warning systems which depend on the use of weather forecasts and information from NWP models. As South Africa depends on models from developed countries, these models may have shortcomings in capturing extreme weather events over the southern African region.

Perceptions of heat-health impacts and the effects of knowledge and preventive actions by outdoor workers in Hanoi, Vietnam

Extreme heat is an increasing climate threat, most pronounced in urban areas where poor populations are at particular risk. We analyzed heat impacts and vulnerabilities of 1027 outdoor workers who participated in a KAP survey in Hanoi, Vietnam in 2018, and the influence of their mitigation actions, their knowledge of heat-risks, and access to early warnings.We grouped respondents by their main income (vendors, builders, shippers, others, multiple jobs, and non-working) and analyzed their reported heat-health impacts, taking into consideration socioeconomics, knowledge of heat impacts and preventive measures, actions taken, access to air-conditioning, drinking amounts and use of weather forecasts. We applied linear and logistic regression analyses using R.Construction workers were younger and had less knowledge of heat-health impacts, but also reported fewer symptoms. Older females were more likely to report symptoms and visit a doctor. Access to air-conditioning in the bedroom depended on age and house ownership, but did not influence heat impacts as cooling was too expensive. Respondents who knew more heat exhaustion symptoms were more likely to report impacts (p < 0.01) or consult a doctor (p < 0.05). Similarly, those who checked weather updates were more likely to report heat impacts (p < 0.01) and experienced about 0.6 more symptoms (p < 0.01). Even though occupation type did not explain heat illness, builders knew considerably less (40%; p < 0.05) about heat than other groups but were twice as likely to consult a doctor than street vendors (p < 0.01). Knowledge of preventive actions and taking these actions both correlated positively with reporting of heat-health symptoms, while drinking water did not reduce these symptoms (p < 0.01). Child carers and homeowners experienced income losses in heatwaves (p < 0.01).The differences support directed actions, such as dissemination of educational materials and weather forecasts for construction workers. The Red Cross assisted all groups with cooling tents, provision of drinks and health advice.

WPŁYW POGODY NA ŻYCIE CODZIENNE CZŁOWIEKA

The study covers an analysis of the impact of meteorological factors on human well-being, as well as the importance of weather forecasts in people’s daily lives. Data obtained from an online questionnaire have been used to determine the characteristics of the influence of weather on human well-being and functioning. Women (65.5%), considerably more often than men (41%) maintain that they are or rather are susceptible to meteoropathy. What is more, this response prevailed in urban rather than suburban or rural areas. The research results also indicate that cyclonic weather (high cloudiness, rainfall, low atmospheric pressure) tends to intensify the body’s response to atmospheric stimuli. High productivity, for example at work, typically accompanies cloudless weather, with air temperatures ranging from 15°C to 24°C which 68.5% of the respondents consider the most optimal. As many as 83.7% of the respondents make use of weather forecast on a daily basis or almost every day, which shows the importance of verification thereof in the context of indirect threat to life, as well as possible unfavorable meteorotropic situations

Fishers’ Perceptions and Attitudes toward Weather and Climate Information Services for Climate Change Adaptation in Senegal

Climate variability has become a major issue for vital sectors in the context of climate change. In fisheries, in particular, the effects of climate change are reflected in the decline of fishing yield and loss of lives during extreme weather events in the sea. This study analyzed the perception of climate variability and change by fisher-folks, the attitude of fisher-folks toward the weather forecast and the adoption rate of the use of the weather forecast as well as the factors determining its use in Senegal. To this end, 576 fisher-folks belonging to 41 local fishing committees along the coastal areas were surveyed and focus group discussions were organized with key informants. The adoption rate was identified using the method of the average treatment effect (ATE) and the test of independency (chi-square) was used to analyze the perceptions of and beliefs on climate change. The results showed that 96% of fisher-folks perceive the change in the climate, though the effects are differently appreciated across the coastline. The most frequently observed effects are: coastal erosion, change in wind direction, increase in extreme swells and sea level rise. Nearly half of fisher-folks confirm that they noticed these changes over the past five years. In the Southern Coast in particular, 40% of fisher-folks stated that these changes happened 10 years ago. This statement is confirmed by the qualitative data. More than 90% of the respondents ascertain the weather forecast before going to fish, 63% regularly receive the weather forecast and 53% avoid going to sea during extreme events. In addition, the results showed that if the weather forecast was made accessible to the majority of fisher-folks, more than 83% would avoid going to sea during periods of extreme weather extreme events, thus reducing significantly the number of fatalities. The best way to protect the fisher-folks from the harmful effects of climate change is to ensure large-scale access to and use of accurate weather forecasts.

Fire Weather Index: the skill provided by the European Centre for Medium-Range Weather Forecasts ensemble prediction system

Abstract. In the framework of the EU Copernicus programme, the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the Joint Research Centre (JRC) is forecasting daily fire weather indices using its medium-range ensemble prediction system. The use of weather forecasts in place of local observations can extend early warnings by up to 1–2 weeks, allowing for greater proactive coordination of resource-sharing and mobilization within and across countries. Using 1 year of pre-operational service in 2017 and the Fire Weather Index (FWI), here we assess the capability of the system globally and analyse in detail three major events in Chile, Portugal and California. The analysis shows that the skill provided by the ensemble forecast system extends to more than 10 d when compared to the use of mean climate, making a case for extending the forecast range to the sub-seasonal to seasonal timescale. However, accurate FWI prediction does not translate into accuracy in the forecast of fire activity globally. Indeed, when all fires detected in 2017 are considered, including agricultural- and human-induced burning, high FWI values only occur in 50 % of the cases and are limited to the Boreal regions. Nevertheless for very large events which were driven by weather conditions, FWI forecasts provide advance warning that could be instrumental in setting up management and containment strategies.

Indigenous knowledge for seasonal weather and climate forecasting across East Africa

Climate information and agro-advisory services are crucial in helping smallholder farmers and pastoralists in East Africa manage climate-related risks and adapt to climate change. However, significant gaps exist in provision of climate information that effectively addresses the needs of farmers and pastoralists. Most farmers and pastoralists, therefore, rely on indigenous knowledge (IK), where local indicators and experiences are used to observe and forecast weather conditions. While IK-based forecasting is inbuilt and established in many communities in East Africa, coordinated research and systematic documentation of IK for weather forecasting, including accuracy and reliability of IK is largely lacking. This paper documents and synthesizes existing IK for weather forecasting in East Africa using case studies from Ethiopia, Tanzania, and Uganda. The results show that farmers and pastoralists use a combination of meteorological, biological, and astrological indicators to forecast local weather conditions. IK weather forecasting is, therefore, crucial in supporting efforts to improve access to climate information in East Africa, especially in resource-poor and vulnerable communities. The paper draws valuable lessons on how farmers and pastoralists in East Africa use IK weather forecasts for making crop and livestock production decisions and demonstrates that the trust and willingness to apply scientific forecasts by farmers and pastoralists is likely to increase when integrated with IK. Therefore, a systematic documentation of IK, and a framework for integrating IK and scientific weather forecasting from national meteorological agencies can improve accuracy, uptake, and use of weather forecasts.

Optimized scheduling for an air-conditioning system based on indoor thermal comfort using the multi-objective improved global particle swarm optimization

In energy management system (EMS), the scheduling of air-conditioning (AC) system has been shown to reduce considerable amount of its power consumption with relatively low implementation cost. However, most scheduling methods lack a systematic approach to ensuring optimal power consumption reduction and comfort experienced by occupants. The main contribution of this paper is a new optimized AC scheduling approach that focuses on indoor thermal comfort using a new multi-objective optimization algorithm, called the improved global particle swarm optimization (IGPSO), which able to find better optimal solutions faster than its original version, the global particle swarm optimization (GPSO) algorithm. IGPSO is used to model the building characteristics and to find optimum indoor temperature values for the room/building. The proposed technique is based on predicted mean vote (PMV) comfort index that is able to reduce AC power consumption while maintaining indoor comfort throughout its operation. The schedule is set in advance by making use of weather forecast and the estimation of building characteristic parameters. This technique can be implemented on existing buildings with existing HVAC systems with minimal modifications to the HVAC infrastructure. Experimental results show that the proposed method is able to provide good PMV while consuming less power compared to the commonly used extended pre-cooling technique.

Use of weather forecast for increasing the self-consumption rate of home solar systems: An Italian case study

With the aim of increasing the self-consumption rate of grid-connected Photovoltaic (PV) home systems, two main options can be implemented: the inclusion of an energy storage system, in particular a battery bank, and the adoption of a Demand Side Management (DSM) strategy. However, both the reshaping of the load consumption curve with the displacement of deferrable loads and the optimal management of the battery bank require estimation of the daily PV generation profile. The assessment of the on-site energy production can be carried out based on weather forecast data. However, the latter are characterized by uncertainty, which may affect the achievable self-consumption rate.This work investigates the influence of weather forecast errors on the performance of home PV systems equipped with a battery bank and characterized by a certain share of deferrable loads. Two different weather forecast services are considered, referring to the annual meteorological conditions occurring in Rome, and energy consumption data for 150 different households are analysed. The self-consumption rate is maximized by solving a suitable optimization problem, while different combinations of relative battery capacity, PV-to-load ratio and share of deferrable loads are considered. Two different approaches—deterministic and stochastic—are adopted and compared with an ideal approach where the PV generation profile is perfectly forecasted. The results show that the adoption of the deterministic approach leads to a reduction in the achievable self-consumption rate in the range of 0.5–4.5% compared to the ideal approach. The adoption of a stochastic approach further reduces the deviations from the ideal case, especially in the case of consumption profiles with a high share of deferrable loads. Finally, a preliminary economic analysis proves that the use of a battery bank is not yet a cost-effective solution and a price reduction of the current battery prices is therefore required.

Energy management in a domestic microgrid by means of model predictive controllers

The need to increase renewable energy sources deployment and to reduce consumption of fossil fuels has led to the diffusion of small-scale DG (Distributed Generation) systems, which may be effectively integrated in micro-grids. The role of control logic in defining microgrid performances and reliability is predominant and can be improved by using advanced control logics such as the ones based on MPC (Model Predictive Control).In a previous paper, a MPC logic, based on the use of weather forecasts to improve the performances, has been applied to the analysis of power management in a domestic micro-grid system composed by: PV (Photovoltaic panels), FC (Fuel Cells) and a battery pack; in that case, the system was not affected by real uncertainties. In this paper the same system has been considered for domestic microgrid applications. The system control logic has been implemented by assuming real weather forecast as input data. DMPC and SMPC (Deterministic and Stochastic Model Predictive Control) concepts have been applied to the system and results have been compared to both MPC and to a standard RBC (Rule Based Control) logic. The impact of forecast uncertainties has been evaluated showing the advantages of a stochastic approach. In that case, the SMPC showed encouraging performances compared to standard control logics, primarily in terms of primary energy savings and downsizing potential of the power-delivering sub-systems using programmable energy sources.

Winter weather decision-making: North Carolina school closures, 2010-2011

The use of weather forecasts for emergency management decision-making requires that those making decisions have access to information they can understand and can put into operation in a timely manner. This can create a gap between what emergency management decision-makers need and what is available. Using school decision-making under conditions of severe winter weather in North Carolina as a case study, this project traces decision-making processes and use of weather information to identify the nature of these gaps and to test prototypes that might fill those gaps. A mixed methods approach including focus groups, interviews and surveys provided an evaluation of the critical decisions and specific information needs of this group of decision-makers. All these methods collect information from a variety of sources, including on-line forecasts, conversations with other school districts and riding the roads to check conditions. The findings suggest that some of the needed forecast information is available, but is either misinterpreted or school officials were not aware of its existence. Location-specific and time-sensitive information is crucial. By delving into the decision-making processes, including needs and networks, and by developing prototypes to address the needs, the results provide a step in the direction of enhancing the infusion of weather and climate information into severe weather decision processes.

Forecasting Regional Photovoltaic Power Generation - A Comparison of Strategies to Obtain One-Day-Ahead Data

There are several ways to obtain forecasts of photovoltaic, PV, power depending on the required accuracy, forecast horizon, climate and other conditions. In this study, we evaluate 3 strategies to obtain one-day-ahead regional forecasts of PV power generation. The strategies were based on the use of weather forecast, past PV power generation and support vector regression. The strategies characterize different scenarios regarding what data are available to make the forecasts, and how these data are used. The first strategy, Strategy 1, is based on a scenario where local PV power generation data can be obtained. Strategy 2 is based on a scenario where regional PV power generation data are available, and all related weather forecast data are used. Strategy 3 is derived from the second one, but it used a principal component analysis to select only weather forecast data that is relevant to the forecasts. To evaluate the strategies data of 6 PV systems, 149 kWh of installed capacity, in different locations in Hokkaido, Japan, were used to make 1 year of forecasts. The annual results show a maximum variation of 9.3% of the forecast error among the strategies. Strategy 3 was the best, yielding a RMSE of 10.24 kWh, 2.7% lower than the one achieved with Strategy 1. On the other hand, Strategy 2 was the one with the worst annual performance with a RMSE of 11.16 kWh. Looking at the results in a monthly fashion the same trend occurs with Strategy 3 being the one with the lowest error in 9 of the 12 months analyzed. The results show that the use of principal component analysis can yield meaningful improvement of the regional forecast error. Moreover, the results give a preliminary assessment of the level of accuracy that can be achieved not only with this technique, but also with feasible strategies to forecast regional PV power.

Suomi NPP VIIRS prelaunch and on‐orbit geometric calibration and characterization

AbstractThe Visible Infrared Imaging Radiometer Suite (VIIRS) sensor was launched 28 October 2011 on the Suomi National Polar‐orbiting Partnership (SNPP) satellite. VIIRS has 22 spectral bands covering the spectrum between 0.412 µm and 12.01 µm, including 16 moderate resolution bands (M‐bands) with a nominal spatial resolution of 750 m at nadir, five imaging resolution bands (I‐bands) with a nominal spatial resolution of 375 m at nadir, and a day‐night band (DNB) with a near‐constant nominal 750 m spatial resolution throughout the scan. These bands are located in a visible and near‐infrared focal plane assembly (FPA), a shortwave and midwave infrared FPA, and a long‐wave infrared FPA. All bands, except the DNB, are coregistered for proper environmental data records retrievals. Observations from VIIRS instrument provide long‐term measurements of biogeophysical variables for climate research and polar satellite data stream for the operational community's use in weather forecasting and disaster relief and other applications. Well Earth‐located (geolocated) instrument data are important to retrieving accurate biogeophysical variables. This paper describes prelaunch pointing and alignment measurements, and the two sets of on‐orbit correction of geolocation errors, the first of which corrected error from 1300 m to within 75 m (20% I‐band pixel size) and the second of which fine‐tuned scan‐angle dependent errors, bringing VIIRS geolocation products to high maturity in one and a half years of the SNPP VIIRS on‐orbit operations. Prelaunch calibration and the on‐orbit characterization of sensor spatial impulse responses and band‐to‐band coregistration are also described.

Health forecasting and COPD

In cold weather Grandma used to say ‘‘wrap up warm.’’ But was she right? Exposure to extremes of both hot and cold weather is associated with excess mortality due to myocardial infarction, pneumonia and COPD. The hypothesis has therefore been formulated that if one can predict the advent of a cold snap, then COPD exacerbations and hospitalisation, and ultimately mortality, can be prevented. By sending high-risk patients a recorded phone message, they can be warned of the impending cold spell and be given advice about what they should do in order to promote self-management. The message sent is a reminder to have available a full course of medication, to contact their doctor or nurse if symptoms become worse, to make sure that their bedand living rooms are kept warm, and to keep active. The matter is not simple, however, and what little evidence there is seems to challenge this initially intuitive hypothesis. Interestingly, increased mortality associated with cold exposure is well described and may be related to physiological changes in the immune system, blood viscosity, blood pressure and the airway. Respiratory symptoms in the cold are also twice as common in smokers. However, to complicate matters, despite identical mean winter temperatures in London and Northern Italy, mortality from cardiorespiratoy disease is nearly three times higher in London. It is likely that factors other than a fall in temperature are interacting. Acclimatization to the cold, staying active, smoking, air quality, and rate of change in environmental or indoor temperature to name a few. Exacerbations of COPD are commonly caused by viruses, especially influenza. As such viruses are particularly common in winter months and spread by person-to-person contact, staying indoors might reduce exacerbations as a result of isolation alone. Vaccination against influenza is particularly effective in reducing admission to hospital as a consequence of COPD exacerbation. To confound matters further, will a patient heed the advice given? The treatment of COPD requires patients to take treatment on a daily basis and use an anticipatory care plan. The evidence suggests that, using didactic advice, about a half do not adhere to the treatment plan advised. The study described by Diar-Bakerly et al therefore is of interest. Many commissioners are implementing Health Forecasting for COPD, with little other than the premise ‘that it seems reasonable’. Now we are beginning to gather evidence concerning its use – and are coming up with what are perhaps surprising results. The paper describes the use of weather forecasts in anticipatory care for patients with mild and moderate COPD, and reports that it did not reduce admission rates to hospital, or reduce overall costs of healthcare. Similar results have been reported from Sheffield, where patients were found to like the service, but there was little evidence for any change in management. Where does this leave us? The hypothesis that exposure to cold is associated with an increase in mortality is sound, albeit complicated. Advice to patients about this seems reasonable; however, we need to consider how this should be done. Should it form part of a self-management or anticipatory care plan? If so, how should the plan be implemented and used? The evidence shows that simply giving advice does not change patient behaviour. We should use other techniques linked with the advice. If we wish to see, in response to a cold weather warning, a change in patient behaviour then we should engage patients in the decision making process. 8 This is clearly an important area and unfortunately we have little in the way of clear-cut evidence. The paucity of evidence means that we require more

The Use of Weather Forecasts to Characterise Near-Surface Optical Turbulence

The propagation of optical and electromagnetic waves is affected by small-scale atmospheric turbulence, quantified by the structure parameter of the refractive index. In the atmospheric surface layer, the mean structure parameter \({C_{n}^{2}}\) , as averaged over the large-scale turbulence, relates to meteorological forcings through well-documented relationships. Present-day numerical weather forecast models routinely produce these forcings at the global scale. This study introduces a method where the products of such a model are used to calculate the mean optical turbulence near the surface. The method is evaluated against scintillometry measurements over climatologically distinct sites in Western Europe. The diurnal cycle modulation, and regional and seasonal contrasts, are all reproduced by our predictions. Hence, the present method explains and predicts some essential aspects of the meteorological variability of \({C_{n}^{2}}\) near the surface. The noted discrepancies combine instrumental limitations, site peculiarities, differences related to distinct averaging procedures, and model errors, notably from weather forecasts. The minute-scale fluctuations of the measured scintillation rate are also analysed in the light of the forecast weather conditions. Fair-weather daytime periods consistently show a small short-term variability compared to the nighttime and perturbed weather periods. Thus, this short-term variability appears to have a predictable component.