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The purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated.

The new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop varieties from 19,238 farms from 1996 to 2011. As well, corresponding weather data from 30 stations, including daily temperature and precipitation, are considered. Algorithms that combine screening regression, cross-validation and principal component analysis are evaluated for the purpose of achieving efficient dimension reduction and model selection.

The results show that the new yield forecasting model provides significant improvements over the classical regression model, both in terms of in-sample and out-of-sample forecasting abilities.

The empirical analysis is limited to data from the province of Manitoba, Canada, and other regions may show different results.

This research is useful from a risk management perspective for insurers and reinsurers, and the framework may also be used to develop improved weather risk management strategies to help manage adverse weather events.

This is the first paper to integrate a credibility estimator for crop yield forecasting, and develop a closed form reinsurance pricing formula.

The purpose of this paper is to compare the ability of popular temperature models, namely, the models given by Alaton et al., by Benth and Benth, by Campbell and Diebold and by Brody et al., to forecast the prices of heating/cooling degree days (HDD/CDD) futures for New York, Atlanta, and Chicago.

To verify the forecasting power of various temperature models, a statistical backtesting approach is utilised. The backtesting sample consists of the market data of daily settlement futures prices for New York, Atlanta, and Chicago. Settlement prices are separated into two groups, namely, “in‐period” and “out‐of‐period”.

The findings show that the models of Alaton et al. and Benth and Benth forecast the futures prices more accurately. The difference in the forecasting performance of models between “in‐period” and “out‐of‐period” valuation can be attributed to the meteorological temperature forecasts during the contract measurement periods.

In future studies, it may be useful to utilize the historical data for meteorological forecasts to assess the forecasting power of the new hybrid model considered.

Out‐of‐period backtesting helps reduce the effect of any meteorological forecast on the formation of futures prices. It is observed that the performance of models for out‐of‐period improves consistently. This indicates that the effects of available weather forecasts should be incorporated into the considered models.

To the best of the author's knowledge this is the first study to compare some of the popular temperature models in forecasting HDD/CDD futures. Furthermore, a new temperature modelling approach is proposed for incorporating available temperature forecasts into the considered dynamic models.

Climate change continues to pose a serious challenge to mankind. Given their socio-economic and vulnerable situations, resource-poor farmers will be hard hit and likely to be the most affected group in Africa – a continent that will bear the full brunt of inclement weather conditions. The purpose of this paper is to address the questions of how local farmers read and predict the weather, and how best they can collaborate with weather scientists in adapting to climate change and variability in the Okavango Delta of Botswana.

A multi-stage sampling procedure was employed in sampling a total of 592 households heads (both men and women) in eight rural communities in the Okavango Delta, Botswana.

Analysis indicates that about 80 per cent of the farmers had a good knowledge of weather forecasting. In a knowledge validation workshop organised and implemented in early August 2012, farmers and scientists identified a nine-point agenda and strategies for addressing the challenges posed by climate change to community well-being and agricultural production. Knowledge sharing, installation of community weather stations and local-level capacity building are amongst the strategies identified.

The research is only limited to the Okavango Delta, Botswana.

The paper emanates from original field research. The outcome of the paper provides pertinent information for policy formulation on how best to enhance small farmers’ adaptation to climate change.

Blown up theory is very important in modern forecasting science, and will result in revolution not only in forecasting theories but also in applied theories and applied methods. Moreover, the blown‐up theory will involve re‐thinking and re‐formulation of some concepts in traditional theories. This article is a record of dialogue between Professor OuYang and the author on some important issues. It is believed that this record will not only benefit us greatly, but also be inductive for young generations in developing their way of thinking and research directions.

After many years’ practice and experiments, it was found that quantitative analysis systems with unequal quantitative effect cannot be extended into that with equal quantitative effect. While it is related to such epistemological viewpoints as irregularity and continuity systems, an infrastructural form comparison has shown universally scientific and methodological characteristics. In combination with evolution of weather systems, our infrastructural analysis involves applications of super low temperatures, reversed information order, rolling currents infrastructure in reversal weather change and long‐term weather forecasting.

This research paper is informed by a study to assess performance of local knowledge drought forecasts (LKDFs) in the Mzingwane catchment which is located in the Limpopo River Basin in Zimbabwe. The purpose of this paper is to validate local traditional knowledge (LTK) indicators being applied in Mzingwane catchment and verify their accuracy and reliability in drought forecasting and early warning.

LTK forecast data for 2012/2013 season were collected through structured questionnaires administered to 40 selected household heads and focus group discussions. Observations and key informant interviews with chiefs and the elderly (>55 years) were also used to collect additional LTK forecast data. Meteorological data on seasonal rainfall were collected from the meteorological Services Department of Zimbabwe (MSD). Two sets of comparisons were conducted namely the hind-cast comparison where the LKDF system results were evaluated against what the season turned out to be and forecast comparison where local LKDF system results were compared with downscaled meteorological forecasts.

The results showed that the majority of the LTK indicators used were accurate in forecasting weather and drought conditions when compared to the observed data of what the season turned out to be. LTK forecasts were found to be more accurate than meteorological forecast at local scale. This study has shown that the reliability of LTKs is high as demonstrated by the fact that the predicted event occurs.

Further validation be carried out for a number of seasons, in order to standardise the LTK indicators per geographical area.

The research creates platform for adoption of LTKs into formal forecasting systems. The research is useful to both meteorological researchers and resource constrained communities in Mzingwane catchment.

The purpose of this paper is to introduce a holistic decision support system based on condition-based maintenance which utilizes meteorological forecasting information to support decision-making process in services of wind power enterprises.

A pilot conceptual system combining with meteorological information and operations management has been formulated in this study. The proposed system provides benchmarking to support decision making directly and indirectly basing on processing meteorological information and evaluating its impact on service operations. It collects meteorological data to predict failure probabilities in different areas which need corresponding maintenance service and schedule the optimal maintenance periods. In addition, it provides meteorological forecasting and decision support in case of extreme weather events (EWEs).

The conceptual study shows that there is a connection between the meteorological conditions and failures, and it is feasible to make service decisions based on the predictions of weather conditions and their impacts to failures.

The research presented at the present phase is not much beyond a conceptual framework. The actual implementation and all possible related practical issues will be dealt with in future research.

It helps decision makers to predict and identify possible categories of faults in wind turbine, make optimal service decisions to enhance the output performance of wind power generation, and take in advance emergency counteractions in case of EWEs.

It presents a novel concept and provides a roadmap to achieve optimal operations in wind park application through combining meteorological information system with service decision making.

The purpose of this paper is to propose a multi-variable analysis (MVA) model for predicting potential delays in the delivery of subsea inspection, maintenance and repair (IMR) services.

Based on data from 351 subsea IMR service jobs executed between 2006 and 2008, a MVA model is proposed for predicting the potential delays in the delivery of IMR services in different plausible scenarios.

A model for predicting the delays in IMR service delivery, based on four practical variables that are readily available during the planning phase, was developed and tested. The factors contributing to delays in petroleum subsea IMR services based on importance are: water depth, weather, job complexity, job uncertainty as well as job complexity mix.

The MVA model is developed based on analyzing subsea IMR service jobs performed in the petroleum industry from 2006-2008. The model can be used in the planning stage to predict potential delays in service delivery based on practical variables available.

The research proposes a MVA model for predicting delays in service delivery. The model is useful for predicting potential delays in service delivery and for improving the plan based on model analysis results.

This paper contains an application of the study on shallow water currents. With the employment of the tool of the first and the second lines of defense of the blown‐up graphs, and based on the test forecasting done in 1994 using the software developed for the blown‐up graphs, we will analyze the characteristics of the blown‐ups of 1994 torrential rains in Southern China, and pose a new theory on the formation of the rains.