Daily Precipitation over Southern Africa: A New Resource for Climate Studies

This paper describes a new high-resolution multiplatform multisensor satellite rainfall product for southern Africa covering the period 1993–2002. The microwave infrared rainfall algorithm (MIRA) employed to generate the rainfall estimates combines high spatial and temporal resolution Meteosat infrared data with infrequent Special Sensor Microwave Imager (SSM/I) overpasses. A transfer function relating Meteosat thermal infrared cloud brightness temperatures to SSM/I rainfall estimates is derived using collocated data from the two instruments and then applied to the full coverage of the Meteosat data. An extensive continental-scale validation against synoptic station data of both the daily MIRA precipitation product and a normalized geostationary IR-only Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI) demonstrates a consistent advantage using the former over the latter for rain delineation. Potential uses for the resulting high-resolution daily rainfall dataset are discussed

Shopping for Success: Numbers, Knowledge, Time & Energy

Working with Infrastructure Creation of Knowledge and Energy strategy Development (WICKED) is a UK-based research project seeking energy solutions for different retail market segments. Stakeholders include landlords, tenants, and owner-occupiers. Through cooperative research, WICKED investigates clusters of technical, legal, and organizational challenges faced by retail organizations, including those with smart meters and energy managers (the “data rich”) and those without (the “data poor”). This paper provides a snapshot of the existing energy data and analytics practices of six WICKED partners. Partners include four retail tenants (a multi- national, full-service department store; a home improvement chain; a café chain; and an electronics retailer) and two landlords/managing agents (a property owner of UK community shopping centers, and a managing agent for a budget shopping center). Using quantitative data from partners, it provides a glimpse of current energy analytics within organizations. Using interviews with staff, it provides new information on the organizational context of energy management according to a 4C’s “concern”, “capacity” and “conditions” within a “communities” framework. These cases show that the data rich and poor will need different energy management solutions to maximize their energy efficiency and behavioral opportunities. The data rich may hire third-party experts to turn numbers into knowledge, and then discover the need for further communications strategies to engage staff. The data poor, on the other hand, have fewer opportunities to engage staff with empirical evidence. Further investigation is needed into how organizational cultures frame employee duties, behaviors, and expectations, particularly with regard to data and analytics

Electrification of heating: the role of heat pumps

Electrification is seen as an important global contributor to mitigation of climate change, because low carbon electricity can, in theory, replace current fossil fuel use in buildings and surface transport. Heat pumps are the key technology for delivering electrification of heating. This paper focuses on the role of heat pumps in heating the residential sector, with both a global and UK perspective. The UK perspective is used to discuss detailed issues around scenarios, technologies, infrastructures, social factors, policies and barriers to adoption. The global focus necessarily excludes a lot of this detail, but shows how heat pump adoption would affect electricity demand, and particularly peak demand, at this much larger scale. A number of UK low carbon scenarios are summarised. Heat pumps are a key technology in many, expected to deliver up to 90% of heating energy. However, the future role of heat pumps has been reduced recently in some influential scenarios, due to greater recognition of the cost and complexity of this transition. Nevertheless, there is a huge gulf between annual UK sales of 21,000 heat pumps, and the Committee on Climate Change's revised projection of 4 million heat pump installations by 2030. UK analysis illustrates the challenges for heat pumps – economic, technical and social – at a variety of scales. A key issue at national and international scale is the effect of heat pumps on peak electricity demand in countries with cold winters. This is explored by developing a model of global heating energy use. This geographical model uses historical population-weighted temperature data, and assumptions about heating energy use and the efficiency of heat pumps to give peak instantaneous demand, calculated at three-hourly time steps. Results show that heating energy need is dominated globally by China, which is responsible for almost 40% the total. For the UK, a 100% use of heat pumps would increase national electricity demand by 25%, and peak electricity demand by 65%. The peak: mean electricity ratio would change from the current 1.58 to 4.1. Globally, 100% heat pump adoption would require 11% of current world electricity use and increase peak demand by 65%. This peak electricity capacity is unlikely to be delivered, given the huge costs entailed. The challenges of delivering electrification of heating, suggest policy makers should also encourage alternative options including deep efficiency retrofits, biomass, biogas and renewable district heating systems. There are also options for reducing the peak: mean ratio, including international interconnection, and using backup heating systems at times of extreme cold, which can be further investigated. The key research question emerging from this work is: does the peak issue set a limit on the percentage of residential heating demand that can be met by heat pumps, and if so, what is it

Power-use profile analysis of non-domestic consumers for electricity tariff switching

For both domestic and non-domestic consumers dynamic electricity tariffs have been proposed as a way to reduce their energy costs and to facilitate demand-side response. It is difficult for businesses which are tenants to adopt energy efficiency measures, thus tariff-switching is the easier option. Therefore, understanding the limits of the cost saving offered by tariff switching is an important step. This raises two questions: by how much could bills be reduced, and would all consumers benefit equally? Using a data set of half-hourly electricity readings from more than 7,500 British businesses, we performed an empirical analysis to discover which types of businesses might have lower or higher costs when changing between static and real-time tariffs. We identified differences in demand profiles that demonstrate that the decision whether to switch tariff-types is a subtle one which may have a significant cost impact. The data set was aggregated into five categories: Entertainment, Industry, Retail, Social, and Other. Our analytical methods can be used to distinguish the differences between typical electricity demand profiles for small to medium-sized businesses and sectors in different market options. Our analyses of switching to a real-time tariff suggests that most of those small to medium-sized businesses that would reduce their annual electricity bill, would gain by no more than ten percent. Most of these businesses would gain by less than five percent. This, we suggest, sets a realistic upper limit of the likely cash savings before energy efficiency or other measures must be taken to further reduce bills

Heat pumps and global residential heating

Electrification is seen as an important global contributor to mitigation of climate change, because low carbon electricity can, in theory, replace current fossil fuel use in buildings and surface transport. Heat pumps are the key technology for delivering electrification of heating. This paper investigates how heat pump adoption in the residential sector would affect total and peak electricity demand globally and for individual countries. It also analyses the role of improving efficiency in reducing heating energy demand. A model of global heating energy use has been developed. This geographical model uses historical population-weighted temperature data, and assumptions about heating energy use and the efficiency of heat pumps to give peak instantaneous demand, calculated at three-hourly time steps. Results show that heating energy need is dominated globally by China, which is responsible for almost 40% of the total. For the UK, 100% adoption of heat pumps would increase national electricity demand by 25%, and peak electricity demand by 65%. The peak: mean heating ratio is 4.1 and would change from the current total electricity peak:mean ratio from 1.58 to 2.11. Globally, 100% heat pump adoption would require 11% of current world electricity use and increase peak demand by 65%. This peak electricity capacity is unlikely to be delivered, given the huge costs entailed. Options for reducing the peak: mean ratio, including international interconnection, and using back-up heating systems at times of extreme cold, have been modelled. The model is then used to look at how results would change with a variety of climate change scenarios, and with energy service demand. In particular, scenarios with a much more insulated building stock are explored – highlighting the importance of efficiency in enabling a scenario with high heat pump uptake. Thus the modelling results are linked with real world concerns and policy options, to deliver a more sophisticated understanding of the challenges of mass heat pump adoption.</p

Extreme rainfall events over southern Africa: influence of Atlantic sea surface temperatures on rainfall variability

No description supplie

Shopping for Success: Numbers, Knowledge, Time and Energy

Working with Infrastructure Creation of Knowledge and Energy strategy Development (WICKED) is a UK-based research project seeking energy solutions for different retail market segments. Stakeholders include landlords, tenants, and owner-occupiers. Through cooperative research, WICKED investigates clusters of technical, legal, and organizational challenges faced by retail organizations, including those with smart meters and energy managers (the “data rich”) and those without (the “data poor”). This paper provides a snapshot of the existing energy data and analytics practices of six WICKED partners. Partners include four retail tenants (a multi- national, full-service department store; a home improvement chain; a café chain; and an electronics retailer) and two landlords/managing agents (a property owner of UK community shopping centers, and a managing agent for a budget shopping center). Using quantitative data from partners, it provides a glimpse of current energy analytics within organizations. Using interviews with staff, it provides new information on the organizational context of energy management according to a 4C’s “concern”, “capacity” and “conditions” within a “communities” framework. These cases show that the data rich and poor will need different energy management solutions to maximize their energy efficiency and behavioral opportunities. The data rich may hire third-party experts to turn numbers into knowledge, and then discover the need for further communications strategies to engage staff. The data poor, on the other hand, have fewer opportunities to engage staff with empirical evidence. Further investigation is needed into how organizational cultures frame employee duties, behaviors, and expectations, particularly with regard to data and analytics

Rainfall variability and extremes over southern Africa: assessment of a climate model to reproduce daily extremes

No description supplie

Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UKMeteorological Office Hadley Centre’s climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is underestimated (over-estimated) over wet (dry) regions of southern Africa

A new ice thickness and bedrock data set for the Greenland ice sheet.

1999 using a coherent ice penetrating radar system developed at the University of Kansas have been combined with data collected by the Technical University of Denmark in the 1970’s to produce a new ice thickness grid for Greenland. Cross-over analysis was used to assess the relative accuracy of the two data sets and they were weighted accordingly and interpolated onto a regular grid using an optimal interpolation procedure. A high resolution land-ice mask was used to help constrain the interpolation of the ice thickness near the ice sheet margins where, in the past, the relative errors have been largest. The ice thickness grid was combined with a new digital elevation model of the ice sheet and surrounding rock outcrops to produce a new bed elevation data set for the whole of Greenland. Relative measurements of the reflectivity of the bed/ice interface were extracted from the 1990’s data to show areas of potential basal melting, a pre-requisite for basal sliding