scholarly journals Development of a probabilistic early health warning system based on meteorological parameters

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
A. K. Sahai ◽  
Raju Mandal ◽  
Susmitha Joseph ◽  
Shubhayu Saha ◽  
Pradip Awate ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Disease Incidence ◽  
Geographical Location ◽  
Warning System ◽  
Maximum Temperature ◽  
Probabilistic Forecasting ◽  
Health Warning ◽  
Probabilistic Forecasts ◽  
Early Health ◽  
Recognition Technique

Abstract Among the other diseases, malaria and diarrhoea have a large disease burden in India, especially among children. Changes in rainfall and temperature patterns likely play a major role in the increased incidence of these diseases across geographical locations. This study proposes a method for probabilistic forecasting of the disease incidences in extended range time scale (2–3 weeks in advance) over India based on an unsupervised pattern recognition technique that uses meteorological parameters as inputs and which can be applied to any geographical location over India. To verify the robustness of this newly developed early warning system, detailed analysis has been made in the incidence of malaria and diarrhoea over two districts of the State of Maharashtra. It is found that the increased probabilities of high (less) rainfall, high (low) minimum temperature and low (moderate) maximum temperature are more (less) conducive for both diseases over these locations, but have different thresholds. With the categorical probabilistic forecasts of disease incidences, this early health warning system is found to be a useful tool with reasonable skill to provide the climate-health outlook about possible disease incidence at least 2 weeks in advance for any location or grid over India.

scholarly journals Effect of meteorological parameters on Karnal bunt incidence in wheat under different agroclimatic zones of Punjab

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
SARABJOT KAUR SANDHU ◽  
ANURAG ATTRI ◽  
RITU BALA
Keyword(s):  
Relative Humidity ◽  
Negative Correlation ◽  
Significant Positive Correlation ◽  
Meteorological Parameters ◽  
Disease Incidence ◽  
Karnal Bunt ◽  
Maximum Temperature ◽  
Wheat Crop ◽  
Sunshine Hours ◽  
Rainy Days

To quantify the effect of meteorological parameters on incidence of Karnal bunt in wheat crop, an investigation was done using 9 to 12 season’s data of Bathinda and Ludhiana stations of Punjab. Maximum temperature during March in range of 25-31oC, minimum temperature of February (8.5-11.0oC), morning and evening relative humidity of March in range of 85-95 and 40-60 per cent respectively, rainfall more than 25 mm with sunshine hours 5.5-9.0 hrs/day during mid February to mid March favour Karnal bunt in wheat crop. Maximum temperature of March showed significant negative correlation with incidence of Karnal bunt whereas minimum temperature of February showed significant positive correlation with disease incidence at both locations. Morning and evening relative humidity showed significant positive correlation with disease incidence. Rain amount and rainy days during mid February to mid March significantly influenced disease incidence. Sunshine hours had negative correlation with disease incidence. Backward multiple linear regression (BMLR) analysis indicated maximum temperature, rainfall and sunshine hours play significant role in Karnal bunt incidence at Ludhiana. However, at Bathinda, maximum temperature, evening time relative humidity, rain amount and rainy days played significant role.

scholarly journals Machine learning approaches to identify thresholds in a heat‐health warning system context

2021 ◽  
Author(s):  
Pierre Masselot ◽  
Fateh Chebana ◽  
Céline Campagna ◽  
Éric Lavigne ◽  
Taha B.M.J. Ouarda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Warning System ◽  
Learning Approaches ◽  
Health Warning ◽  
System Context

scholarly journals Verification of Ensemble-Based Uncertainty Circles around Tropical Cyclone Track Forecasts

2011 ◽  
Vol 26 (5) ◽  
pp. 664-676 ◽  
Author(s):  
Thierry Dupont ◽  
Matthieu Plu ◽  
Philippe Caroff ◽  
Ghislain Faure
Keyword(s):  
Tropical Cyclone ◽  
Large Error ◽  
Ensemble Prediction ◽  
Probabilistic Forecasting ◽  
Cyclone Track ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Uncertainty Information

Abstract Several tropical cyclone forecasting centers issue uncertainty information with regard to their official track forecasts, generally using the climatological distribution of position error. However, such methods are not able to convey information that depends on the situation. The purpose of the present study is to assess the skill of the Ensemble Prediction System (EPS) from the European Centre for Medium-Range Weather Forecasts (ECMWF) at measuring the uncertainty of up to 3-day track forecasts issued by the Regional Specialized Meteorological Centre (RSMC) La Réunion in the southwestern Indian Ocean. The dispersion of cyclone positions in the EPS is extracted and translated at the RSMC forecast position. The verification relies on existing methods for probabilistic forecasts that are presently adapted to a cyclone-position metric. First, the probability distribution of forecast positions is compared to the climatological distribution using Brier scores. The probabilistic forecasts have better scores than the climatology, particularly after applying a simple calibration scheme. Second, uncertainty circles are built by fixing the probability at 75%. Their skill at detecting small and large error values is assessed. The circles have some skill for large errors up to the 3-day forecast (and maybe after); but the detection of small radii is skillful only up to 2-day forecasts. The applied methodology may be used to assess and to compare the skill of different probabilistic forecasting systems of cyclone position.

scholarly journals Evaluation of a Disease Warning System for Downy Mildew of Grapes

Plant Disease ◽  
2000 ◽  
Vol 84 (5) ◽  
pp. 549-554 ◽  
Author(s):  
L. V. Madden ◽  
M. A. Ellis ◽  
N. Lalancette ◽  
G. Hughes ◽  
L. L. Wilson
Keyword(s):  
Linear Model ◽  
Downy Mildew ◽  
Environmental Conditions ◽  
Generalized Linear Model ◽  
Disease Incidence ◽  
Warning System ◽  
Plasmopara Viticola ◽  
Effective Control ◽  
Subsequent Infection ◽  
Grape Downy Mildew

An electronic warning system for grape downy mildew— based on models for the infection of leaves of Vitis lambrusca, production of sporangia by Plasmopara viticola in lesions, and sporangial survival—was tested over 7 years in Ohio. Grapevines were sprayed with metalaxyl plus mancozeb (Ridomil MZ58) when the warning system indicated that environmental conditions were favorable for sporulation and subsequent infection. Over the 7 years, plots were sprayed from one to four times according to the warning system, and from four to 10 times according to the standard calendar-based schedule (depending on the date of the initiation of the experiment). The warning system resulted in yearly reductions of one to six sprays (with median of three sprays). Disease incidence (i.e., proportion of leaves with symptoms) in unsprayed plots at the end of the season ranged from 0 to 86%, with a median of 68%. Incidence generally was very similar for the warning-system and standard-schedule treatments (median of 7% of the leaves with symptoms), and both of these incidence values were significantly lower (P < 0.05) than that found for the unsprayed control, based on a generalized-linear-model analysis. Simplifications of the disease warning system, where sprays were applied based only on the infection or sporulation components of the system, were also effective in controlling the disease, although more fungicide applications sometimes were applied. Effective control of downy mildew, therefore, can be achieved with the use of the warning system with fewer sprays than a with a standard schedule.

scholarly journals Relationships between Meteorological Parameters and Particulate Matter in Mae Hong Son Province, Thailand

2018 ◽  
Vol 15 (12) ◽  
pp. 2801 ◽  
Author(s):  
Wissanupong Kliengchuay ◽  
Aronrag Cooper Meeyai ◽  
Suwalee Worakhunpiset ◽  
Kraichat Tantrakarnapa
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Meteorological Parameters ◽  
Warning System ◽  
Correlation Coefficients ◽  
Pm10 Concentration ◽  
P Value ◽  
Climate Data ◽  
High Concentration ◽  
Ambient Concentrations

Meteorological parameters play an important role in determining the prevalence of ambient particulate matter (PM) in the upper north of Thailand. Mae Hong Son is a province located in this region and which borders Myanmar. This study aimed to determine the relationships between meteorological parameters and ambient concentrations of particulate matter less than 10 µm in diameter (PM10) in Mae Hong Son. Parameters were measured at an air quality monitoring station, and consisted of PM10, carbon monoxide (CO), ozone (O3), and meteorological factors, including temperature, rainfall, pressure, wind speed, wind direction, and relative humidity (RH). Nine years (2009–2017) of pollution and climate data obtained from the Thai Pollution Control Department (PCD) were used for analysis. The results of this study indicate that PM10 is influenced by meteorological parameters; high concentration occurred during the dry season and northeastern monsoon seasons. Maximum concentrations were always observed in March. The PM10 concentrations were significantly related to CO and O3 concentrations and to RH, giving correlation coefficients of 0.73, 0.39, and −0.37, respectively (p-value < 0.001). Additionally, the hourly PM10 concentration fluctuated within each day. In general, it was found that the reporting of daily concentrations might be best suited to public announcements and presentations. Hourly concentrations are recommended for public declarations that might be useful for warning citizens and organizations about air pollution. Our findings could be used to improve the understanding of PM10 concentration patterns in Mae Hong Son and provide information to better air pollution measures and establish a warning system for the province.

scholarly journals ROUTINE, EXTREME AND ENGINEERING METEOROLOGY ANALYSIS FOR KARACHI COASTAL AREA

2020 ◽  
Vol 4 (1) ◽  
pp. 15-22
Author(s):  
Muhammad Taqui ◽  
Jabir Hussain Syed ◽  
Ghulam Hassan Askari
Keyword(s):  
Climate Change ◽  
Coastal Area ◽  
Meteorological Parameters ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Meteorological Station ◽  
Annual Precipitation ◽  
Maximum Temperature ◽  
Intergovernmental Panel ◽  
Interdisciplinary Field

Pakistan’s largest city, Karachi, which is industrial centre and economic hub needs focus in research and development of every field of Engineering, Science and Technology. Urbanization and industrialization is resulting bad weather conditions which prolongs until a climate change. Since, Meteorology serves as interdisciplinary field of study, an analytical study of real and region-specific meteorological data is conducted which focuses on routine, extreme and engineering meteorology of metropolitan city Karachi. Results of study endorse the meteorological parameters relationship and establish the variability of those parameters for Karachi Coastal Area. The rise of temperature, decreasing trend of atmospheric pressure, increment in precipitation and fall in relative humidity depict the effects of urbanization and industrialization. The recorded extreme maximum temperature of 45.50C (on June 11, 1988) and the extreme minimum temperature of 4.5 0C(on January 1, 2007) is observed at Karachi south meteorological station. The estimated temperature rise in 32 years is 0.9 0C, which is crossing the Intergovernmental Panel on Climate Change (IPCC) predicted/estimated limit of 2oC rise per century. The maximum annual precipitation of 487.0mm appearing in 1994 and the minimum annual precipitation of 2.5mm appearing in 1987 is observed at same station which is representative meteorological station for Karachi Coast. Further Engineering meteorological parameters for heating ventilation air condition (HVAC) system design for industrial purpose are deduced as supporting data for coastal area site study for industrial as well as any follow-up engineering work in the specified region.

scholarly journals Performances of Limited Area Models for the WORKLIMATE Heat–Health Warning System to Protect Worker’s Health and Productivity in Italy

2021 ◽  
Vol 18 (18) ◽  
pp. 9940
Author(s):  
Daniele Grifoni ◽  
Alessandro Messeri ◽  
Alfonso Crisci ◽  
Michela Bonafede ◽  
Francesco Pasi ◽  
...  
Keyword(s):  
Heat Stress ◽  
Warning System ◽  
Outdoor Environment ◽  
Limited Area ◽  
Warning Systems ◽  
Stress Indicators ◽  
Climate Conditions ◽  
Health Warning ◽  
Globe Temperature ◽  
Meteorological Models

Outdoor workers are particularly exposed to climate conditions, and in particular, the increase of environmental temperature directly affects their health and productivity. For these reasons, in recent years, heat-health warning systems have been developed for workers generally using heat stress indicators obtained by the combination of meteorological parameters to describe the thermal stress induced by the outdoor environment on the human body. There are several studies on the verification of the parameters predicted by meteorological models, but very few relating to the validation of heat stress indicators. This study aims to verify the performance of two limited area models, with different spatial resolution, potentially applicable in the occupational heat health warning system developed within the WORKLIMATE project for the Italian territory. A comparison between the Wet Bulb Globe Temperature predicted by the models and that obtained by data from 28 weather stations was carried out over about three summer seasons in different daily time slots, using the most common skill of performance. The two meteorological models were overall comparable for much of the Italian explored territory, while major limits have emerged in areas with complex topography. This study demonstrated the applicability of limited area models in occupational heat health warning systems.

scholarly journals Probabilistic forecasting of wind power production losses in cold climates: a case study

2018 ◽  
Vol 3 (2) ◽  
pp. 667-680 ◽  
Author(s):  
Jennie Molinder ◽  
Heiner Körnich ◽  
Esbjörn Olsson ◽  
Hans Bergström ◽  
Anna Sjöblom
Keyword(s):  
Wind Power ◽  
Energy Production ◽  
Spatial Representation ◽  
Forecast Skill ◽  
Cold Climates ◽  
Probabilistic Forecasting ◽  
Probabilistic Forecasts ◽  
Forecasting System ◽  
Production Losses

Abstract. The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next-day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a 2-week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climates can be valuable when planning next-day energy production, in the usage of de-icing systems and for site safety.

scholarly journals Probabilistic forecasting of wind power production losses in cold climates: A case study

2017 ◽  
Author(s):  
Jennie P. Söderman ◽  
Heiner Körnich ◽  
Esbjörn Olsson ◽  
Hans Bergström ◽  
Anna Sjöblom
Keyword(s):  
Wind Power ◽  
Energy Production ◽  
Cold Climate ◽  
Forecast Skill ◽  
Cold Climates ◽  
Probabilistic Forecasting ◽  
Probabilistic Forecasts ◽  
Forecasting System ◽  
Production Losses

Abstract. The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next- day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a two- week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climate can be valuable when planning next-day energy production, in the usage of de-icing systems, and for site safety.

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