Predictive Capacity of Meteorological Data - Will it rain tomorrow

With the availability of high precision digital sensors and cheap storage medium, it is not uncommon to find large amounts of data collected on almost all measurable attributes, both in nature and man-made habitats. Weather in particular has been an area of keen interest for researchers to develop more accurate and reliable prediction models. This paper presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict the day of the week given the weather data for that particular day i.e. temperature, wind, rain etc., and test their reliability across four cities in Australia {Brisbane, Adelaide, Perth, Hobart}. The results provide a comparison of accuracy of these machine learning techniques and their reliability to predict the day of the week by analysing the weather data. We then apply the models to predict weather conditions based on the available data.Comment: 7 pages, 2 Result Set

Integrated model for the hydro-mechanical effects of vegetation against shallow landslides

Shallow landslides are instability events that lead to dramatic soil mass wasting in sloping areas and are commonly triggered by intense rainfall episodes. Vegetation may reduce the likelihood of slope failure through different hydro-mechanical mechanisms that take place at the soil-plant-atmosphere interface. However, while vegetation’s mechanical contribution has been widely recognized, its hydrological effects have been poorly quantified. In addition, most of the existing models lack a holistic approach, require difficult to measure parameters or are commercially based, making them hardly transferable to land planners and other researchers.In this paper an integrated, robust and reproducible model framework is proposed and evaluated with the aim of assessing the hydro-mechanical effects of different vegetation types on slope stability using easily measureable and quantifiable input parameters. The output shows that the model framework is able to simulate the hydro-mechanical effects of vegetation in a realistic manner and that it can be readily applied to any vegetation, soil and climate types. It also demonstrates that vegetation has positive hydro-mechanical effects against shallow landslides, where plant biomass and evapotranspiration play an important role

Weather forecasts: up to one week in advance

In this chapter, we examine how weather forecasts are made and how they may be used by the health sector. We explore why, where and when we might obtain good forecasts and why sometimes the forecasts go wrong. We consider the theoretical and practical constraints on weather forecasts and why their accuracy declines rapidly after only a few days. Knowing the limitations of weather forecasts helps us to learn how to make best use of such information

A study to define meteorological uses and performance requirements for the Synchronous Earth Observatory Satellite

The potential meteorological uses of the Synchronous Earth Observatory Satellite (SEOS) were studied for detecting and predicting hazards to life, property, or the quality of the environment. Mesoscale meteorological phenonmena, and the observations requirements for SEOS are discussed along with the sensor parameters

A Gap Analysis for the Implementation of the Global Climate Observing System Programme in Africa

This report is the main output from a Gap Analysis, financed by the United Kingdom's Department for International Development (DfID) as a major contribution to a Global Climate Observing System (GCOS) workshop to address climate and development issues in sub-Saharan Africa. The requirement is to assess key gaps in the use of climate information from the perspective of the decision-making community at household, community, district, national and regional levels, for a range of sectors, with particular emphasis on health, agriculture and water. The report was prepared by a group of people with broad, deep and diverse experience with climate sensitive development problems, and with climate and development communities in Africa. It was strengthened by review by a larger community of development experts

Connecting climate information with health outcomes

In this chapter we consider a range of factors that need to be taken into account when seeking to use climate information to improve health decision-making. Identifying causal mechanisms that link climate drivers with specific health issues is an important starting point for policy-makers. Matching decision time-horizons to climate information in a way that takes account of scale issues, uncertainties in the underlying data and modelling approaches as well as institutional barriers to knowledge and data sharing is also critical. And of course, all of this is dependent on a solid understanding of the climate information (including its limitations) that is available to health decision-makers. A researcher may be satisfied with a simple times-series of climate data from an authoritative source; a decision-maker needs to know that the climate information is robust, available for routine use and scalable (i.e., can be used over the entire region of interest)

Epidemiological versus meteorological forecasts: Best practice for linking models to policymaking

Weather forecasts, climate change projections, and epidemiological predictions all represent domains that are using forecast data to take early action for risk management. However, the methods and applications of the modeling efforts in each of these three fields have been developed and applied with little cross-fertilization. This perspective identifies best practices in each domain that can be adopted by the others, which can be used to inform each field separately as well as to facilitate a more effective combined use for the management of compound and evolving risks. In light of increased attention to predictive modeling during the COVID-19 pandemic, we identify three major areas that all three of these modeling fields should prioritize for future investment and improvement: (1) decision support, (2) conveying uncertainty, and (3) capturing vulnerability

Wind power forecasting and integration to power grids

This is a summary of the presentation in the special session: "Digital Signal Processing for Green Power Systems and Delivery". In recent years, wind power penetration level in power systems has increased significantly. Grid integration has become one of the major issues for wind power growth due to the intermittent characteristics of wind power. The uncertainty of power generation from wind farms may result in power system stability and security problems. Accurate wind power forecasting could reduce the uncertainty to generation scheduling to certain extent, hence increase the wind power penetration level in the system. © 2010 IEEE.published_or_final_versionThe 1st International Conference on Green Circuits and Systems (ICGCS 2010), Shanghai, China, 21-23 June 2010. In Proceedings of ICGCS, 2010, p. 555-56