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Sample records for africa conditional forecasts

  1. Aggregate electricity demand in South Africa: Conditional forecasts to 2030

    International Nuclear Information System (INIS)

    Inglesi, Roula

    2010-01-01

    In 2008, South Africa experienced a severe electricity crisis. Domestic and industrial electricity users had to suffer from black outs all over the country. It is argued that partially the reason was the lack of research on energy, locally. However, Eskom argues that the lack of capacity can only be solved by building new power plants. The objective of this study is to specify the variables that explain the electricity demand in South Africa and to forecast electricity demand by creating a model using the Engle-Granger methodology for co-integration and Error Correction models. By producing reliable results, this study will make a significant contribution that will improve the status quo of energy research in South Africa. The findings indicate that there is a long run relationship between electricity consumption and price as well as economic growth/income. The last few years in South Africa, price elasticity was rarely taken into account because of the low and decreasing prices in the past. The short-run dynamics of the system are affected by population growth, too After the energy crisis, Eskom, the national electricity supplier, is in search for substantial funding in order to build new power plants that will help with the envisaged lack of capacity that the company experienced. By using two scenarios for the future of growth, this study shows that the electricity demand will drop substantially due to the price policies agreed - until now - by Eskom and the National Energy Regulator South Africa (NERSA) that will affect the demand for some years. (author)

  2. Aggregate electricity demand in South Africa: Conditional forecasts to 2030

    Energy Technology Data Exchange (ETDEWEB)

    Inglesi, Roula [Department of Economics, Faculty of Economic and Management Sciences, University of Pretoria, Main Campus, Pretoria 0002 (South Africa)

    2010-01-15

    In 2008, South Africa experienced a severe electricity crisis. Domestic and industrial electricity users had to suffer from black outs all over the country. It is argued that partially the reason was the lack of research on energy, locally. However, Eskom argues that the lack of capacity can only be solved by building new power plants. The objective of this study is to specify the variables that explain the electricity demand in South Africa and to forecast electricity demand by creating a model using the Engle-Granger methodology for co-integration and Error Correction models. By producing reliable results, this study will make a significant contribution that will improve the status quo of energy research in South Africa. The findings indicate that there is a long run relationship between electricity consumption and price as well as economic growth/income. The last few years in South Africa, price elasticity was rarely taken into account because of the low and decreasing prices in the past. The short-run dynamics of the system are affected by population growth, too After the energy crisis, Eskom, the national electricity supplier, is in search for substantial funding in order to build new power plants that will help with the envisaged lack of capacity that the company experienced. By using two scenarios for the future of growth, this study shows that the electricity demand will drop substantially due to the price policies agreed - until now - by Eskom and the National Energy Regulator South Africa (NERSA) that will affect the demand for some years. (author)

  3. Using Satellite Data and Land Surface Models to Monitor and Forecast Drought Conditions in Africa and Middle East

    Science.gov (United States)

    Arsenault, K. R.; Shukla, S.; Getirana, A.; Peters-Lidard, C. D.; Kumar, S.; McNally, A.; Zaitchik, B. F.; Badr, H. S.; Funk, C. C.; Koster, R. D.; Narapusetty, B.; Jung, H. C.; Roningen, J. M.

    2017-12-01

    Drought and water scarcity are among the important issues facing several regions within Africa and the Middle East. In addition, these regions typically have sparse ground-based data networks, where sometimes remotely sensed observations may be the only data available. Long-term satellite records can help with determining historic and current drought conditions. In recent years, several new satellites have come on-line that monitor different hydrological variables, including soil moisture and terrestrial water storage. Though these recent data records may be considered too short for the use in identifying major droughts, they do provide additional information that can better characterize where water deficits may occur. We utilize recent satellite data records of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and the European Space Agency's Advanced Scatterometer (ASCAT) soil moisture retrievals. Combining these records with land surface models (LSMs), NASA's Catchment and the Noah Multi-Physics (MP), is aimed at improving the land model states and initialization for seasonal drought forecasts. The LSMs' total runoff is routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics, which can provide an additional means of validation against in situ streamflow data. The NASA Land Information System (LIS) software framework drives the LSMs and HyMAP and also supports the capability to assimilate these satellite retrievals, such as soil moisture and TWS. The LSMs are driven for 30+ years with NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS/UCSB Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall dataset. The seasonal water deficit forecasts are generated using downscaled and bias-corrected versions of NASA's Goddard Earth Observing System Model (GEOS-5), and NOAA's Climate Forecast System (CFSv2) forecasts

  4. Regional Model Nesting Within GFS Daily Forecasts Over West Africa

    Science.gov (United States)

    Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

    2010-01-01

    The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger

  5. Forecasting tourist arrivals in South Africa

    Directory of Open Access Journals (Sweden)

    Andrea Saayman

    2010-12-01

    Full Text Available Purpose: The aim of this paper is to model and forecast tourism to South Africa from the country's main intercontinental tourism markets. These include Great Britain, Germany, the Netherlands, the United States of America and France. Problem investigated: Tourism to South Africa has grown substantially since the first democratic elections in 1994. It is currently the third largest industry in the country and a vital source of foreign exchange earnings. Tourist arrivals continue to grow annually, and have shown some resilience to a number of emerging market crises, including the terrorist attacks in the USA. Business success, marketing decisions, government's investment policy as well as macroeconomic policy are influenced by the accuracy of tourism forecasts, since the tourism product comprises a number of services that cannot be accumulated. Accurate forecasts of tourism demand are paramount to ensure the availability of such services when demanded. In addition, the seasonal nature of tourism leads to a pattern of excess capacity followed by shortage in capacity. Method: Since univariate time series modelling has proved to be a very successful method for forecasting tourist arrivals, it is also the method employed in this paper. The naïve model is tested against a standard ARIMA model, as well as the Holt-Winters exponential smoothing and seasonal-non-seasonal ARIMA models. Forecasting accuracy is assessed using the mean absolute percentage error, root mean square error and Theill's U of the various models. Monthly tourist arrivals from 1994 to 2006 are used in the analysis, and arrivals are forecasted for 2007. Findings: The results show that seasonal ARIMA models deliver the most accurate predictions of arrivals over three time horizons, namely three months, six months and 12 months. Value: This paper is the first tourist arrivals forecast using South African data for the country as a whole, and therefore it forms an interesting case study

  6. Forecasting rodent outbreaks in Africa

    DEFF Research Database (Denmark)

    Leirs, Herwig; Verhagen, Ron; Verheyen, Walter

    1996-01-01

    1. Rainfall data were collated for years preceding historical outbreaks of Mastomys rats in East Africa in order to test the hypothesis that such outbreaks occur after long dry periods. 2. Rodent outbreaks were generally not preceded by long dry periods. 3. Population dynamics of Mastomys...

  7. Seasonal forecasts: communicating current climate variability in southern Africa

    CSIR Research Space (South Africa)

    Landman, WA

    2011-11-01

    Full Text Available In southern Africa, seasonal rainfall and temperature forecasts have been made for almost two decades already and these forecasts have been developed to improve the ability of users to cope with fluctuations in rainfall and temperatures on a...

  8. Drought Monitoring and Forecasting: Experiences from the US and Africa

    Science.gov (United States)

    Sheffield, Justin; Chaney, Nate; Yuan, Xing; Wood, Eric

    2013-04-01

    Drought has important but very different consequences regionally due to differences in vulnerability. These differences derive from variations in exposure related to climate variability and change, sensitivity of local populations, and coping capacity at all levels. Managing the risk of drought impacts relies on a variety of measures to reduce vulnerability that includes forewarning of drought development through early-warning systems. Existing systems rely on a variety of observing systems from satellites to local observers, modeling tools, and data dissemination methods. They range from sophisticated state-of-the-art systems to simple ground reports. In some regions, systems are virtually non-existent due to limited national capacity. This talk describes our experiences in developing and implementing drought monitoring and seasonal forecast systems in the US and sub-Saharan Africa as contrasting examples of the scientific challenges and user needs in developing early warning systems. In particular, early warning can help improve livelihoods based on subsistence farming in sub-Saharan Africa; whist reduction of economic impacts is generally foremost in the US. For the US, our national drought monitoring and seasonal forecast system has been operational for over 8 years and provides near real-time updates on hydrological states at ~12km resolution and hydrological forecasts out to 9 months. Output from the system contributes to national assessments such as from the NOAA Climate Prediction Center (CPC) and the US National Drought Monitor (USDM). For sub-Saharan Africa, our experimental drought monitoring system was developed as a translation of the US system but presents generally greater challenges due to, for example, lack of ground data and unique user needs. The system provides near real-time updates based on hydrological modeling and satellite based precipitation estimates, and has recently been augmented by a seasonal forecast component. We discuss the

  9. Operational forecasting of human-biometeorological conditions

    Science.gov (United States)

    Giannaros, T. M.; Lagouvardos, K.; Kotroni, V.; Matzarakis, A.

    2018-03-01

    This paper presents the development of an operational forecasting service focusing on human-biometeorological conditions. The service is based on the coupling of numerical weather prediction models with an advanced human-biometeorological model. Human thermal perception and stress forecasts are issued on a daily basis for Greece, in both point and gridded format. A user-friendly presentation approach is adopted for communicating the forecasts to the public via the worldwide web. The development of the presented service highlights the feasibility of replacing standard meteorological parameters and/or indices used in operational weather forecasting activities for assessing the thermal environment. This is of particular significance for providing effective, human-biometeorology-oriented, warnings for both heat waves and cold outbreaks.

  10. Daily peak electricity load forecasting in South Africa using a ...

    African Journals Online (AJOL)

    A multivariate adaptive regression splines (MARS) modelling approach towards daily peak electricity load forecasting in South Africa is presented in this paper for the period 2000 to 2009. MARS is a non-parametric multivariate regression method which is used in high-dimensional problems with complex model structures, ...

  11. Current Status on Flood Forecasting and Early Warning in Africa

    NARCIS (Netherlands)

    Thiemig, V.; Roo, A.P.J. de

    2011-01-01

    An overview of the current state of flood forecasting and early warning in Africa is provided in order to identify future user needs and research. Information was collected by reviewing previously published research in the scientific literature and from institutional websites. This information was

  12. Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

    Science.gov (United States)

    Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris

    2013-01-01

    The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period

  13. Forecasting droughts in West Africa: Operational practice and refined seasonal precipitation forecasts

    Science.gov (United States)

    Bliefernicht, Jan; Siegmund, Jonatan; Seidel, Jochen; Arnold, Hanna; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald

    2016-04-01

    Precipitation forecasts for the upcoming rainy seasons are one of the most important sources of information for an early warning of droughts and water scarcity in West Africa. The meteorological services in West Africa perform seasonal precipitation forecasts within the framework of PRESAO (the West African climate outlook forum) since the end of the 1990s. Various sources of information and statistical techniques are used by the individual services to provide a harmonized seasonal precipitation forecasts for decision makers in West Africa. In this study, we present a detailed overview of the operational practice in West Africa including a first statistical assessment of the performance of the precipitation forecasts for drought situations for the past 18 years (1998 to 2015). In addition, a long-term hindcasts (1982 to 2009) and a semi-operational experiment for the rainy season 2013 using statistical and/or dynamical downscaling are performed to refine the precipitation forecasts from the Climate Forecast System Version 2 (CFSv2), a global ensemble prediction system. This information is post-processed to provide user-oriented precipitation indices such as the onset of the rainy season for supporting water and land use management for rain-fed agriculture. The evaluation of the individual techniques is performed focusing on water-scarce regions of the Volta basin in Burkina Faso and Ghana. The forecasts of the individual techniques are compared to state-of-the-art global observed precipitation products and a novel precipitation database based on long-term daily rain-gage measurements provided by the national meteorological services. The statistical assessment of the PRESAO forecasts indicates skillful seasonal precipitation forecasts for many locations in the Volta basin, particularly for years with water deficits. The operational experiment for the rainy season 2013 illustrates the high potential of a physically-based downscaling for this region but still shows

  14. A seasonal agricultural drought forecast system for food-insecure regions of East Africa

    Science.gov (United States)

    Shukla, S.; McNally, A.; Husak, G.; Funk, C.

    2014-10-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agropastoral management decisions, support optimal allocation of the region's water resources, and mitigate socioeconomic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's (FEWS NET) science team. We evaluate this forecast system for a region of equatorial EA (2° S-8° N, 36-46° E) for the March-April-May (MAM) growing season. This domain encompasses one of the most food-insecure, climatically variable, and socioeconomically vulnerable regions in EA, and potentially the world; this region has experienced famine as recently as 2011. To produce an "agricultural outlook", our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios describing the upcoming season. First, we forced the VIC model with high-quality atmospheric observations to produce baseline soil moisture (SM) estimates (here after referred as SM a posteriori estimates). These compared favorably (correlation = 0.75) with the water requirement satisfaction index (WRSI), an index that the FEWS NET uses to estimate crop yields. Next, we evaluated the SM forecasts generated by this system on 5 March and 5 April of each year between 1993 and 2012 by comparing them with the corresponding SM a posteriori estimates. We found that initializing SM forecasts with start-of-season (SOS) (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month and, in some cases, 3-month lead times. Similarly, when the forecast was initialized with midseason (i.e., 5

  15. Seasonal Forecast Skill And Teleconnections Over East Africa

    Science.gov (United States)

    MacLeod, D.; Palmer, T.

    2017-12-01

    Many people living in East Africa are significantly exposed to risks arising from climate variability. The region experiences two rainy seasons and poor performance of either or both of these (such as seen recently in 2016/17) reduces agricultural productivity and threatens food security. In combination with other factors this can lead to famine. By utilizing seasonal climate forecasts, preparatory actions can be taken in order to mitigate the risks arising from such climate variability. As part of the project ForPAc: "Towards forecast-based preparedness action", we are working with humanitarian agencies in Kenya to build such early warning systems on subseasonal-to-seasonal timescales. Here, the seasonal predictability and forecast skill of the two East African rainy seasons will be presented. Results from the new ECMWF operational forecasting system SEAS5 will be shown and compared to the previous System 4. Analysis of a new 110 year long atmosphere-only simulation will also be discussed, demonstrating impacts of atmosphere-ocean coupling as well as putting operational forecast skill in a long-term context. Particular focus will be given to the model representation of teleconnections of seasonal climate with global sea surface temperatures; highlighting sources of forecast error and informing future model development.

  16. Forecasting Investment Risks in Conditions of Uncertainty

    Directory of Open Access Journals (Sweden)

    Andrenko Elena A.

    2017-04-01

    Full Text Available The article is aimed at studying the topical problem of evaluation and forecasting risks of investment activity of enterprises in conditions of uncertainty. Generalizing the researches on qualitative and quantitative methods for evaluating investment risks has helped to reveal certain shortcomings of the proposed approaches, to note in most of the publications there are no results as to any practical application, and to allocate promising directions. On the basis of the theory of fuzzy sets, a model of forecasting the expected risk has been proposed, making use of the Gauss membership function, which has certain advantages over the multi-angular membership functions. Dependences of investment risk from the parameters characterizing the investment project have been obtained. Using the formulas obtained, the total risk of investing in innovation project depending on the boundary conditions has been defined. As the researched target, index of profitability has been selected. The model provides the potential investors and developers with forecasting possible scenarios of investment process to make informed managerial decisions about the appropriateness of introduction and implementation of a project.

  17. Multivariate forecasting of total water storage anomalies over West Africa from multi-satellite data

    Science.gov (United States)

    Kusche, Jürgen; Forootan, Ehsan; Krasbutter, Ina; Schuh, Wolf-Dieter; Eicker, Annette; Diekkrüger, Bernd; Schmidt, Michael; Shum, Ck

    2013-04-01

    For West Africa, large-scale weather-related extreme hydrological conditions such as droughts or floods may persist over several months and usually have devastating environmental, social and economic impacts. Assessing and forecasting these conditions is therefore an important activity, in which data from the Gravity Recovery and Climate Experiment (GRACE) mission has been shown to be very useful. In this study, we describe a new statistical, data-driven approach to predict total water storage anomalies over West Africa from gravity data obtained from of GRACE, rainfall data from the Tropical Rainfall Measuring Mission (TRMM), and sea surface temperature data products over the Atlantic, Pacific and Indian oceans. Major teleconnections within these data sets were identified by independent component analysis, and linked via low-degree autoregressive models to build a predictive framework for forecasting total water storage, a quantity which is hard to observe in the field but important for agricultural and water resource management. After a learning phase of 80 months, our approach predicts water storage from rainfall and sea surface temperature data alone that fits to observed GRACE data at 79% after one year and 62% after two years. This means, our approach should be able to bridge the present GRACE data gaps of one month about each 162 days as well as a - hopefully - limited gap between GRACE and the GRACE-FO mission for West Africa. Keywords: Forecasting GRACE-TWS, West-Africa, ICA; AR model

  18. Skill of ECMWF system-4 ensemble seasonal climate forecasts for East Africa

    NARCIS (Netherlands)

    Ogutu, Geoffrey E.O.; Franssen, Wietse H.P.; Supit, Iwan; Omondi, P.; Hutjes, Ronald W.A.

    2017-01-01

    This study evaluates the potential use of the ECMWF System-4 seasonal forecasts (S4) for impact analysis over East Africa. For use, these forecasts should have skill and small biases. We used the 15-member ensemble of 7-month forecasts initiated every month, and tested forecast skill of

  19. Seasonal rainfall prediction skill over South Africa: one- versus two-tiered forecasting systems

    CSIR Research Space (South Africa)

    Landman, WA

    2012-04-01

    Full Text Available Forecast performance by coupled ocean–atmosphere or one-tiered models predicting seasonal rainfall totals over South Africa is compared with forecasts produced by computationally less demanding two-tiered systems where prescribed sea surface...

  20. Application of seasonal rainfall forecasts and satellite rainfall observations to crop yield forecasting for Africa

    Science.gov (United States)

    Greatrex, H. L.; Grimes, D. I. F.; Wheeler, T. R.

    2009-04-01

    Rain-fed agriculture is of utmost importance in sub-Saharan Africa; the FAO estimates that over 90% of food consumed in the region is grown in rain-fed farming systems. As the climate in sub-Saharan Africa has a high interannual variability, this dependence on rainfall can leave communities extremely vulnerable to food shortages, especially when coupled with a lack of crop management options. The ability to make a regional forecast of crop yield on a timescale of months would be of enormous benefit; it would enable both governmental and non-governmental organisations to be alerted in advance to crop failure and could facilitate national and regional economic planning. Such a system would also enable individual communities to make more informed crop management decisions, increasing their resilience to climate variability and change. It should be noted that the majority of crops in the region are rainfall limited, therefore the ability to create a seasonal crop forecast depends on the ability to forecast rainfall at a monthly or seasonal timescale and to temporally downscale this to a daily time-series of rainfall. The aim of this project is to develop a regional-scale seasonal forecast for sub-Saharan crops, utilising the General Large Area Model for annual crops (GLAM). GLAM would initially be driven using both dynamical and statistical seasonal rainfall forecasts to provide an initial estimate of crop yield. The system would then be continuously updated throughout the season by replacing the seasonal rainfall forecast with daily weather observations. TAMSAT satellite rainfall estimates are used rather than rain-gauge data due to the scarcity of ground based observations. An important feature of the system is the use of the geo-statistical method of sequential simulation to create an ensemble of daily weather inputs from both the statistical seasonal rainfall forecasts and the satellite rainfall estimates. This allows a range of possible yield outputs to be

  1. A seasonal agricultural drought forecast system for food-insecure regions of East Africa

    Science.gov (United States)

    Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.

    2014-01-01

     The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agricultural management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this forecast system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing season. This domain encompasses one of the most food insecure, climatically variable and socio-economically vulnerable regions in EA, and potentially the world: this region has experienced famine as recently as 2011. To assess the agricultural outlook for the upcoming season our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming season. First, to show that the VIC model is appropriate for this application we forced the model with high quality atmospheric observations and found that the resulting SM values were consistent with the Food and Agriculture Organization's (FAO's) Water Requirement Satisfaction Index (WRSI), an index used by FEWS NET to estimate crop yields. Next we tested our forecasting system with hindcast runs (1993–2012). We found that initializing SM forecasts with start-of-season (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the forecast was initialized with mid-season (i.e. 5 April) SM conditions the skill until the end-of-season improved. This shows that early-season rainfall

  2. Seasonal maximum temperature prediction skill over Southern Africa: 1- vs 2-tiered forecasting systems

    CSIR Research Space (South Africa)

    Lazenby, MJ

    2011-09-01

    Full Text Available TEMPERATURE PREDICTION SKILL OVER SOUTHERN AFRICA: 1- VS. 2-TIERED FORECASTING SYSTEMS Melissa J. Lazenby University of Pretoria, Private Bag X20, Pretoria, 0028, South Africa Willem A. Landman Council for Scientific and Industrial...) (Graham et al., 2000; Goddard & Mason, 2002). Using such a two-tiered modelling system to forecast the seasonal outcome of an area has been employed in South Africa for several years already (e.g., Landman et al., 2001). Coupled ocean...

  3. A forecast of energy requirements in South Africa

    International Nuclear Information System (INIS)

    Kotze, D.J.

    1975-01-01

    The aim of this paper is to evaluate the adequacy of South Africa's energy resources relative to projected demands. The forecasting procedure embraces the construction of suitable energy balances and the development of econometric demand models. An energy balance is employed which integrates supply and demand data on all forms of energy for a particular year. The demand side of the balance is divided into both final demand and demand by the conversion sector. Useful energy consumption in each sector is estimated by applying utilisation efficiency co-efficients to the physics energy content of each energy form. Total final demand is determined by developing sub-models for each sector of final demand including households, industry, mining and transport. In these sub-models, economic series representing the type of activity in the particular sub-sector, are used as explanatory variables. Further relationships, quantifying the contributions of each form of energy to the sectorial totals, are constructed. Having established the future value of final useful energy demand, total future production and final consumption is obtained. The forecast of primary energy requirements is therefore made via a reversed calculation from the final energy demand through all conversion processes to the primary energy stage. Once the future distribution of energy by source, form and end use sector is known it is possible to plan the optimum allocation of energy resources in the country. It is also possible to evaluate the life of indigenous energy resources, their adequacy, and import requirements

  4. Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa

    Science.gov (United States)

    Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh

    2014-01-01

    Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

  5. Multi-model forecast skill for mid-summer rainfall over southern Africa

    CSIR Research Space (South Africa)

    Landman, WA

    2012-02-01

    Full Text Available Pa forecasts, and then downscaling them using CCA. Downscaling is performed onto the 0.5° × 0.5° resolution of the CRU rainfall data set south of 10° south over Africa. Forecast verification is performed using the relative operating characteristic (ROC...

  6. Forecasting Future Sea Ice Conditions: A Lagrangian Approach

    Science.gov (United States)

    2015-09-30

    Journal of Climate, in revision). The decadal forecasting of the minimum sea ice extent based on the output of 30 ensemble members of the Community...1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Forecasting Future Sea Ice Conditions: A Lagrangian...1- Show from observations whether the dynamics of the multi -year pack ice has a influence on the location of the following summer MIZ. 2

  7. Developing a Climate Service: Using Hydroclimate Monitoring and Forecasting to Aid Decision Making in Africa and Latin America

    Science.gov (United States)

    Wood, E. F.; Sheffield, J.; Fisher, C. K.; Chaney, N.; Wanders, N.

    2015-12-01

    Hydrological and water scarcity predictions have the potential to provide vital information for a variety of needs including water resources management, agricultural and urban water supply, and flood mitigation. In particular, seasonal forecasts of drought risk can enable farmers to make adaptive choices on crop varieties, labor usage, and technology investments. Forecast skill is generally derived from teleconnections with ocean variability specifically sea surface temperature (SST) anomalies and, equally important persistence in the state of the land in terms of soil moisture, snowpack, or streamflow conditions. Short term precipitation forecasts are critical in flood prediction by extending flood prediction lead times beyond the basin travel time, and thus allows for extended warnings. The Global Framework for Climate Services (GFCS) is a UN-wide initiative in which WMO Members and inter- and non- governmental, regional, national and local stakeholders work in partnership to develop targeted climate services. Thus, GFCS offers the potential for hydroclimatologists to develop products (hydroclimatic forecasts) and information services (i.e. product dissemination) to users with the expectation that GFCS will increase the resilience of the society to weather and climate events and to reduce operational costs for economic sectors and regions dependent on water. This presentation will discuss the development of a nascent climate service system focused on hydroclimatic monitoring and forecasting, and initially developed by the authors for Africa and Latin America. Central to this system is the use of satellite remote sensing and hydroclimate forecasts (from days to seasons) in the development of weather and climate information useful for water management in sectors such as flood protection (precipitation and streamflow forecasting) and agriculture (drought and crop forecasting). The elements of this system will be discussed, including the challenges of monitoring and

  8. Forecasting South Africa's performance at the 2010 Commonwealth ...

    African Journals Online (AJOL)

    Objectives. This paper predicts South Africa's performance at the Delhi 2010 Commonwealth Games. Methods. Potential scenarios are developed based on South Africa's previous performances. Results. South Africa will win up to 15 gold medals and 43 medals in total. Conclusions. After Delhi 2010, the actual results ...

  9. Local Air Quality Conditions and Forecasts

    Science.gov (United States)

    ... 500) Health warnings of emergency conditions. The entire population is more likely to be affected. Action Day Maps by Monitor Location Archived Maps by Region Canada Air Quality Air Quality on Google Earth Links A-Z About AirNow AirNow International Air ...

  10. Seasonal prediction for Southern Africa: Maximising the skill from forecast systems

    CSIR Research Space (South Africa)

    Landman, WA

    2012-06-01

    Full Text Available recalibrated and combined at the same time 2. Each model recalibrated, then averaged ToR 1: To facilitate cooperation between the centres within southern Africa that run an operational global scale long-range forecasting (LRF - from 30 days up to 2 years...) system ToR 2: To produce global forecasts from dynamical forecasting systems ToR 3: To establish a web based environment for non commercial product dissemination ToR 4: The consortium will be managed by a committee ToR 5: To compile archived hindcasts...

  11. Forecasting conditional climate-change using a hybrid approach

    Science.gov (United States)

    Esfahani, Akbar Akbari; Friedel, Michael J.

    2014-01-01

    A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

  12. A Machine LearningFramework to Forecast Wave Conditions

    Science.gov (United States)

    Zhang, Y.; James, S. C.; O'Donncha, F.

    2017-12-01

    Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in

  13. Operational seasonal forecast system development in South Africa

    CSIR Research Space (South Africa)

    Landman, WA

    2011-09-01

    Full Text Available -1 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ????? ???????????????? ???????? ?????????? ? ? SON ROC analysis 0 0.5 1 Reg1 Reg2 Reg3 Reg 4 Reg5 Reg6 Reg7 Reg8 Regions RO C ar ea s Below-Normal Near-Normal Above-Normal DJF ROC analysis 0 0.5 1 Reg1 Reg2 Reg3 Reg 4 Reg5 Reg6 Reg7 Reg8 Regions RO C ar ea s Below-Normal Near...-Normal Above-Normal New objective multi-model forecast Old subjective consensus forecast MOS post-processing and forecast combination Multi-model ensemble of N1+N2+N3+N4 +N5 +N6 +N7 +N8 +N9 members Ensemble 1 CCAM at CSIR NRE N1 members Ensemble 2...

  14. Statistical evaluation of CFS seasonal precipitation forecasts for large-scale droughts in Africa and India

    Science.gov (United States)

    Siegmund, Jonatan; Bliefernicht, Jan; Laux, Patrick; Kunstmann, Harald

    2013-04-01

    Monthly and seasonal meteorological forecasts are routinely produced by several international weather services using global coupled ocean-atmosphere general circulation models. This kind of information can be used as source of information in operational hydrological monitoring and forecasting systems to improve early drought warnings. In March 2011, a new version of the global coupled model of the National Centre for Environmental Prediction, the Climate Forecast System (CFS) Version 2, became operational providing real-time ensemble forecasts up to nine months. However, a comprehensive analysis of the CFS forecast for the prediction of droughts in water stress regions has not yet been performed. In this study we evaluate the CFS precipitation forecasts for large-scale droughts that occurred during the rainy season in West Africa, East Africa and India. The target areas are large-scale river-basins like Volta (West Africa), Ganges (India) and the administrative area of Kenya. The forecasts are compared to monthly precipitation observations provided on a regular grid by the Global Precipitation Climatology Centre. In addition, the CFS performance is evaluated using areal monthly precipitation amount of the river basin of interest as an indicator for dry months. The verification is done for the period 1982-2009 using all ensemble members of the retrospective CFS archive. The outcomes of this study illustrate, that the CFS in some cases can simulate general features of the monthly precipitation regime for the respective river basins. However, an evaluation using the entire retrospective CFS forecasts demonstrates a low accuracy. Furthermore, the seasonal forecasts of monthly precipitation are characterized by a large over- and underestimation during the rainy season depending on the target region. In this presentation, the following issues are highlighted: (i) The performance of the CFS precipitation forecast for individual events such as the severe India drought in

  15. Forecasting Instability Indicators in the Horn of Africa

    Science.gov (United States)

    2008-03-01

    Westview Press, Boulder CO, 1996. 6 7 Wiener , Norbert . Extrapolation, Interpolation, and Smoothing of Stationary Time Series. New York: John Wiley...an algorithm ( Wiener , 1949: 9). Extrapolation and forecasting methods generate values for a set more subject to uncertainty than imputation. The

  16. Identifying needs for streamflow forecasting in the Incomati basin, Southern Africa

    Science.gov (United States)

    Sunday, Robert; Werner, Micha; Masih, Ilyas; van der Zaag, Pieter

    2013-04-01

    Despite being widely recognised as an efficient tool in the operational management of water resources, rainfall and streamflow forecasts are currently not utilised in water management practice in the Incomati Basin in Southern Africa. Although, there have been initiatives for forecasting streamflow in the Sabie and Crocodile sub-basins, the outputs of these have found little use because of scepticism on the accuracy and reliability of the information, or the relevance of the information provided to the needs of the water managers. The process of improving these forecasts is underway, but as yet the actual needs of the forecasts are unclear and scope of the ongoing initiatives remains very limited. In this study questionnaires and focused group interviews were used to establish the need, potential use, benefit and required accuracy of rainfall and streamflow forecasts in the Incomati Basin. Thirty five interviews were conducted with professionals engaged in water sector and detailed discussions were held with water institutions, including the Inkomati Catchment Management Agency (ICMA), Komati Basin Water Authority (KOBWA), South African Weather Service (SAWS), water managers, dam operators, water experts, farmers and other water users in the Basin. Survey results show that about 97% of the respondents receive weather forecasts. In contrast to expectations, only 5% have access to the streamflow forecast. In the weather forecast, the most important variables were considered to be rainfall and temperature at daily and weekly time scales. Moreover, forecasts of global climatic indices such as El Niño or La Niña were neither received nor demanded. There was limited demand and/or awareness of flood and drought forecasts including the information on their linkages with global climatic indices. While the majority of respondents indicate the need and indeed use the weather forecast, the provision, communication and interpretation were in general found to be with too

  17. Integrating observation and statistical forecasts over sub-Saharan Africa to support Famine Early Warning

    Science.gov (United States)

    Funk, Chris; Verdin, James P.; Husak, Gregory

    2007-01-01

    Famine early warning in Africa presents unique challenges and rewards. Hydrologic extremes must be tracked and anticipated over complex and changing climate regimes. The successful anticipation and interpretation of hydrologic shocks can initiate effective government response, saving lives and softening the impacts of droughts and floods. While both monitoring and forecast technologies continue to advance, discontinuities between monitoring and forecast systems inhibit effective decision making. Monitoring systems typically rely on high resolution satellite remote-sensed normalized difference vegetation index (NDVI) and rainfall imagery. Forecast systems provide information on a variety of scales and formats. Non-meteorologists are often unable or unwilling to connect the dots between these disparate sources of information. To mitigate these problem researchers at UCSB's Climate Hazard Group, NASA GIMMS and USGS/EROS are implementing a NASA-funded integrated decision support system that combines the monitoring of precipitation and NDVI with statistical one-to-three month forecasts. We present the monitoring/forecast system, assess its accuracy, and demonstrate its application in food insecure sub-Saharan Africa.

  18. A statistical scheme to forecast the daily lightning threat over southern Africa using the Unified Model

    Science.gov (United States)

    Gijben, Morné; Dyson, Liesl L.; Loots, Mattheus T.

    2017-09-01

    Cloud-to-ground lightning data from the Southern Africa Lightning Detection Network and numerical weather prediction model parameters from the Unified Model are used to develop a lightning threat index (LTI) for South Africa. The aim is to predict lightning for austral summer days (September to February) by means of a statistical approach. The austral summer months are divided into spring and summer seasons and analysed separately. Stepwise logistic regression techniques are used to select the most appropriate model parameters to predict lightning. These parameters are then utilized in a rare-event logistic regression analysis to produce equations for the LTI that predicts the probability of the occurrence of lightning. Results show that LTI forecasts have a high sensitivity and specificity for spring and summer. The LTI is less reliable during spring, since it over-forecasts the occurrence of lightning. However, during summer, the LTI forecast is reliable, only slightly over-forecasting lightning activity. The LTI produces sharp forecasts during spring and summer. These results show that the LTI will be useful early in the morning in areas where lightning can be expected during the day.

  19. Perspectives on model forecasts of the 2014-2015 Ebola epidemic in West Africa

    DEFF Research Database (Denmark)

    Chowell, Gerardo; Viboud, Cécile; Simonsen, Lone

    2017-01-01

    The unprecedented impact and modeling efforts associated with the 2014–2015 Ebola epidemic in West Africa provides a unique opportunity to document the performances and caveats of forecasting approaches used in near-real time for generating evidence and to guide policy. A number of international...... changes and case clustering; (3) challenges in forecasting the long-term epidemic impact very early in the outbreak; and (4) ways to move forward. We conclude that rapid availability of aggregated population-level data and detailed information on a subset of transmission chains is crucial to characterize...

  20. Assessing existing drought monitoring and forecasting capacities, mitigation and adaptation practices in Africa

    Science.gov (United States)

    Nyabeze, W. R.; Dlamini, L.; Lahlou, O.; Imani, Y.; Alaoui, S. B.; Vermooten, J. S. A.

    2012-04-01

    Drought is one of the major natural hazards in many parts of the world, including Africa and some regions in Europe. Drought events have resulted in extensive damages to livelihoods, environment and economy. In 2011, a consortium consisting of 19 organisations from both Africa and Europe started a project (DEWFORA) aimed at developing a framework for the provision of early warning and response through drought impact mitigation for Africa. This framework covers the whole chain from monitoring and vulnerability assessment to forecasting, warning, response and knowledge dissemination. This paper presents the first results of the capacity assessment of drought monitoring and forecasting systems in Africa, the existing institutional frameworks and drought mitigation and adaptation practices. Its focus is particularly on the historical drought mitigation and adaptation actions identified in the North Africa - Maghreb Region (Morocco, Algeria and Tunisia) and in the Southern Africa - Limpopo Basin. This is based on an extensive review of historical drought experiences. From the 1920's to 2009, the study identified 37 drought seasons in the North African - Maghreb Region and 33 drought seasons in the Southern Africa - Limpopo Basin. Existing literature tends to capture the spatial extent of drought at national and administrative scale in great detail. This is driven by the need to map drought impacts (food shortage, communities affected) in order to inform drought relief efforts (short-term drought mitigation measures). However, the mapping of drought at catchment scale (hydrological unit), required for longer-term measures, is not well documented. At regional level, both in North Africa and Southern Africa, two organisations are involved in drought monitoring and forecasting, while at national level 22 organisations are involved in North Africa and 37 in Southern Africa. Regarding drought related mitigation actions, the inventory shows that the most common actions

  1. The use of ambient humidity conditions to improve influenza forecast.

    Directory of Open Access Journals (Sweden)

    Jeffrey Shaman

    2017-11-01

    Full Text Available Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1-4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively. These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast.

  2. The use of ambient humidity conditions to improve influenza forecast.

    Science.gov (United States)

    Shaman, Jeffrey; Kandula, Sasikiran; Yang, Wan; Karspeck, Alicia

    2017-11-01

    Laboratory and epidemiological evidence indicate that ambient humidity modulates the survival and transmission of influenza. Here we explore whether the inclusion of humidity forcing in mathematical models describing influenza transmission improves the accuracy of forecasts generated with those models. We generate retrospective forecasts for 95 cities over 10 seasons in the United States and assess both forecast accuracy and error. Overall, we find that humidity forcing improves forecast performance (at 1-4 lead weeks, 3.8% more peak week and 4.4% more peak intensity forecasts are accurate than with no forcing) and that forecasts generated using daily climatological humidity forcing generally outperform forecasts that utilize daily observed humidity forcing (4.4% and 2.6% respectively). These findings hold for predictions of outbreak peak intensity, peak timing, and incidence over 2- and 4-week horizons. The results indicate that use of climatological humidity forcing is warranted for current operational influenza forecast.

  3. European models reliability over West Africa: from seasonal forecasting to climate scenarios

    Science.gov (United States)

    Caminade, C.; Morse, A.; Jones, A.

    2009-04-01

    The severe drought that stroke the Sahel during the 1970's and the 1980's had dramatic consequences regarding to impacts in terms of food security and health. Improving the prediction of the West African Monsoon (WAM) system and its impacts on health, water resources and food security became a priority at all time scales, namely from seasonal forecasting to longer climate change perspectives. However, the actual state of the art General Circulation Model (GCM) mainly fail in reproducing key features of the WAM when a full ocean-atmosphere coupled approach is considered. This leads to strong uncertainties in simulated future rainfall changes over Africa at the end of the 21st century. This work proposes to highlight the differences and similarities of the GCM biases in both forecasting (seasonal to decadal) and climatic approaches. This is carried out using seasonal and decadal forecasting outputs from the ENSEMBLES project and climate historical runs from the CMIP3 dataset, used within the IPCC fourth report assessment. Preliminary results highlight consistent warm biases over the Gulf of Guinea, weak predictability of rainfall over the Sahel and problems in reproducing precipitation / orography feedbacks. The major biases highlighted in forecasting mode are generally similar to the ones depicted in climate simulations. This leads to the intermediate conclusion that the GCM biases are mainly related to their intrinsic parameterization whatever the approach considered.

  4. Designing a Frost Forecasting Service for Small Scale Tea Farmers in East Africa

    Science.gov (United States)

    Adams, E. C.; Nyaga, J. W.; Ellenburg, W. L.; Limaye, A. S.; Mugo, R. M.; Flores Cordova, A. I.; Irwin, D.; Case, J.; Malaso, S.; Sedah, A.

    2017-12-01

    Kenya is the third largest tea exporter in the world, producing 10% of the world's black tea. Sixty percent of this production occurs largely by small scale tea holders, with an average farm size of 1.04 acres, and an annual net income of 1,075. According to a recent evaluation, a typical frost event in the tea growing region causes about 200 dollars in losses which can be catastrophic for a small holder farm. A 72-hour frost forecast would provide these small-scale tea farmers with enough notice to reduce losses by approximately $80 annually. With this knowledge, SERVIR, a joint NASA-USAID initiative that brings Earth observations for improved decision making in developing countries, sought to design a frost monitoring and forecasting service that would provide farmers with enough lead time to react to and protect against a forecasted frost occurrence on their farm. SERVIR Eastern and Southern Africa, through its implementing partner, the Regional Centre for Mapping of Resources for Development (RCMRD), designed a service that included multiple stakeholder engagement events whereby stakeholders from the tea industry value chain were invited to share their experiences so that the exact needs and flow of information could be identified. This unique event allowed enabled the design of a service that fit the specifications of the stakeholders. The monitoring service component uses the MODIS Land Surface Temperature product to identify frost occurrences in near-real time. The prediction component, currently under testing, uses the 2-m air temperature, relative humidity, and 10-m wind speed from a series of high-resolution Weather Research and Forecasting (WRF) numerical weather prediction model runs over eastern Kenya as inputs into a frost prediction algorithm. Accuracy and sensitivity of the algorithm is being assessed with observations collected from the farmers using a smart phone app developed specifically to report frost occurrences, and from data shared through

  5. Is Information Enough? User Responses to Seasonal Climate Forecasts in Southern Africa. Report to the World Bank, AFTE1-ENVGC. Adaptation to Climate Change and Variability in Sub{sub S}aharan Africa, Phase II

    Energy Technology Data Exchange (ETDEWEB)

    O' Brien, Karen; Sygna, Linda; Naess, Lars Otto; Kingamkono, Robert; Hochobeb, Ben

    2000-05-01

    Since the mid-1980s, long-lead climate forecasts have been developed and used to predict the onset of El Nino events and their impact on climate variability. This report discusses user responses to seasonal climate forecasts in southern Africa, with an emphasis on small-scale farmers in Namibia and Tanzania. The study examines how farmers received and used the forecasts in the agricultural season of 1997/1998. It also summarises a workshop on user responses to seasonal forecasts in southern Africa. Comparison of case studies across south Africa revealed differences in forecast dissemination strategies and in the capacity to respond to extreme events. However, improving these strategies and the capacity to respond to the forecasts would yield net profit to agriculture in southern Africa. In anticipation of potential changes in the frequency or magnitude of extreme events associated with global climate change, there clearly is a need for improved seasonal forecasts and improved information dissemination.

  6. Incorporating Medium-Range Weather Forecasts in Seasonal Crop Scenarios over the Greater Horn of Africa to Support National/Regional/Local Decision Makers

    Science.gov (United States)

    Shukla, S.; Husak, G. J.; Funk, C. C.; Verdin, J. P.

    2015-12-01

    The USAID's Famine Early Warning Systems Network (FEWS NET) provides seasonal assessments of crop conditions over the Greater Horn of Africa (GHA) and other food insecure regions. These assessments and current livelihood, nutrition, market conditions and conflicts are used to generate food security scenarios that help national, regional and local decision makers target their resources and mitigate socio-economic losses. Among the various tools that FEWS NET uses is the FAO's Water Requirement Satisfaction Index (WRSI). The WRSI is a simple yet powerful crop assessment model that incorporates current moisture conditions (at the time of the issuance of forecast), precipitation scenarios, potential evapotranspiration and crop parameters to categorize crop conditions into different classes ranging from "failure" to "very good". The WRSI tool has been shown to have a good agreement with local crop yields in the GHA region. At present, the precipitation scenarios used to drive the WRSI are based on either a climatological forecast (that assigns equal chances of occurrence to all possible scenarios and has no skill over the forecast period) or a sea-surface temperature anomaly based scenario (which at best have skill at the seasonal scale). In both cases, the scenarios fail to capture the skill that can be attained by initial atmospheric conditions (i.e., medium-range weather forecasts). During the middle of a cropping season, when a week or two of poor rains can have a devastating effect, two weeks worth of skillful precipitation forecasts could improve the skill of the crop scenarios. With this working hypothesis, we examine the value of incorporating medium-range weather forecasts in improving the skill of crop scenarios in the GHA region. We use the NCEP's Global Ensemble Forecast system (GEFS) weather forecasts and examine the skill of crop scenarios generated using the GEFS weather forecasts with respect to the scenarios based solely on the climatological forecast

  7. Statistical forecasting for precipitation over West Africa based on spatio-temporal precipitation properties and tropical wave activity

    Science.gov (United States)

    Vogel, Peter; Klar, Manuel; Schlüter, Andreas; Knippertz, Peter; Fink, Andreas H.; Gneiting, Tilmann

    2017-04-01

    Precipitation forecasts for one up to several days are of high socioeconomic importance for agriculturally dominated societies in West Africa, regarding both the occurrence as well as the amount of precipitation. However, disappointingly forecasts based on numerical weather prediction models and even statistically postprocessed forecasts still do not outperform simple reference forecasts such as climatology or persistence. More elaborate statistical forecasts can hopefully lead to an improvement in the quality of precipitation forecasts above climatological or persistent ones. In this contribution, we concentrate on the potential of statistical forecasts to predict the occurrence of precipitation, while the prediction of the amount will be addressed in the future. Using increasingly sophisticated statistical models, we start with forecasts solely relying on the spatio-temporal information contained in precipitation observations. With the necessity of a full spatial coverage of precipitation observations in order to understand its spatio-temporal properties, we rely on Tropical Rainfall Measuring Mission (TRMM) observations and use accumulation periods of 1 to 5 days for the monsoon seasons from May to mid-October of the years 2007 to 2014. Especially for the full monsoon from the end of June to the end of September, the precipitation fields exhibit clear spatio-temporal information that is meteorologically interpretable and statistically meaningful. Using Markov models, we do in fact find an increased forecast quality for this period. While such forecasts already outperform persistent and climatological forecasts for the full monsoon, the forecast quality increases further and also covers the whole monsoon period from May to mid-October, when we add additional predictors. We find the activity of tropical waves such as Kelvin or African Easterly waves or the Madden-Julian Oscillation to be informative predictors and test for additional predictors closely linked to

  8. Moral Education and the Condition of Africa

    African Journals Online (AJOL)

    Reginald M. J. Oduor

    There are many problems in Africa today which could be addressed, partially or wholly, through moral ..... fall away in most serious discussion of moral issues, such as slavery and abortion, not because religion is not important ... Democracy as the government by the people and for the people has become an honorific term ...

  9. Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing

    NARCIS (Netherlands)

    Yossef, N.C.; Winsemius, H.; Weerts, A.H.; Beek, van R.; Bierkens, M.F.P.

    2013-01-01

    We investigate the relative contributions of initial conditions (ICs) and meteorological forcing (MF) to the skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCRaster Global Water Balance. Potential improvement in forecasting skill through

  10. Conditional time series forecasting with convolutional neural networks

    NARCIS (Netherlands)

    A. Borovykh (Anastasia); S.M. Bohte (Sander); C.W. Oosterlee (Cornelis)

    2017-01-01

    textabstractForecasting financial time series using past observations has been a significant topic of interest. While temporal relationships in the data exist, they are difficult to analyze and predict accurately due to the non-linear trends and noise present in the series. We propose to learn these

  11. Assessing Seasonal Climate Forecasts over Africa to Support Decision-Making : Bridging Science and Policy Implication for Managing Climate Extremes

    NARCIS (Netherlands)

    Wanders, Niko|info:eu-repo/dai/nl/364253940; Wood, Eric F.

    2018-01-01

    Recent drought events like in the 2011 Horn of Africa and the ongoing drought in California have an enormous impact on nature and society. Reliable seasonal weather outlooks are critical for drought management and other applications like, crop modelling, flood forecasting and planning of reservoir

  12. Common paediatric renal conditions: Few children in South Africa ...

    African Journals Online (AJOL)

    Common paediatric renal conditions: Few children in South Africa have access to dialysis or renal transplantation, so it is important to recognise kidney disease early enough to prevent progression to end-stage disease.

  13. Two-Step Forecast of Geomagnetic Storm Using Coronal Mass Ejection and Solar Wind Condition

    Science.gov (United States)

    Kim, R.-S.; Moon, Y.-J.; Gopalswamy, N.; Park, Y.-D.; Kim, Y.-H.

    2014-01-01

    To forecast geomagnetic storms, we had examined initially observed parameters of coronal mass ejections (CMEs) and introduced an empirical storm forecast model in a previous study. Now we suggest a two-step forecast considering not only CME parameters observed in the solar vicinity but also solar wind conditions near Earth to improve the forecast capability. We consider the empirical solar wind criteria derived in this study (Bz = -5 nT or Ey = 3 mV/m for t = 2 h for moderate storms with minimum Dst less than -50 nT) (i.e. Magnetic Field Magnitude, B (sub z) less than or equal to -5 nanoTeslas or duskward Electrical Field, E (sub y) greater than or equal to 3 millivolts per meter for time greater than or equal to 2 hours for moderate storms with Minimum Disturbance Storm Time, Dst less than -50 nanoTeslas) and a Dst model developed by Temerin and Li (2002, 2006) (TL [i.e. Temerin Li] model). Using 55 CME-Dst pairs during 1997 to 2003, our solar wind criteria produce slightly better forecasts for 31 storm events (90 percent) than the forecasts based on the TL model (87 percent). However, the latter produces better forecasts for 24 nonstorm events (88 percent), while the former correctly forecasts only 71 percent of them. We then performed the two-step forecast. The results are as follows: (i) for 15 events that are incorrectly forecasted using CME parameters, 12 cases (80 percent) can be properly predicted based on solar wind conditions; (ii) if we forecast a storm when both CME and solar wind conditions are satisfied (n, i.e. cap operator - the intersection set that is comprised of all the elements that are common to both), the critical success index becomes higher than that from the forecast using CME parameters alone, however, only 25 storm events (81 percent) are correctly forecasted; and (iii) if we forecast a storm when either set of these conditions is satisfied (?, i.e. cup operator - the union set that is comprised of all the elements of either or both

  14. The influence of antecedent conditions on flood risk in sub-Saharan Africa

    OpenAIRE

    Bischiniotis, Konstantinos; Hurk, Bart; Jongman, Brenden; Coughlan de Perez, Erin; Veldkamp, Ted; Aerts, Jeroen

    2017-01-01

    Most flood early warning systems have predominantly focused on forecasting floods with lead times of hours or days. However, physical processes during longer – seasonal – time scales can also contribute to flood generation. In this study, the hydro-meteorological pre-conditions of 501 historical damaging flood events over the period 1980 to 2010 in sub-Saharan Africa are analyzed. These are separated into a) a short-term weather scale period (0–7 days) and b) a long-term sea...

  15. Forecasting electricity demand in South Africa: A critique of Eskom’s projections

    Directory of Open Access Journals (Sweden)

    Anastassios Pouris

    2010-03-01

    Full Text Available Within a short period, Eskom has applied to the National Energy Regulator of South Africa (NERSA for the third time since the 2008 electricity crisis, proposing a multiyear price determination for the periods 2010−2011 and 2012−2013. The new application, submitted at the end of September 2009, motivated for the debate of strategies with which the consequences of the proposed price hikes could be predicted, measured and controlled. In his presentation to Parliament in February 2009, Eskom’s then CEO, Mr Jacob Maroga presented the current energy situation in the country, the reasons for the crisis in 2007−2008, as well as the challenges of the future. The purpose of this paper is to contribute some new ideas and perspectives to Eskom’s existing arguments regarding the demand for electricity. The most important issue is the fact that Eskom does not sufficiently take into account the impact of the electricity prices in their electricity demand forecast. This study proposed that prices have a high impact on the demand for electricity (price elasticity of -0.5. Employing similar assumptions for the country’s economic growth as Eskom, the results of the forecasting exercise indicated a substantial decrease in demand (scenario 1: -31% in 2025 and scenario 2:-18% in 2025. This study’s findings contrasted significantly with Eskom’s projection, which has extensive implications as far as policy is concerned.

  16. Conditions for successful land reform in Africa

    Directory of Open Access Journals (Sweden)

    JA Groenewald

    2004-11-01

    Full Text Available Land reform has traditionally had two objectives: equity and productivity. Food insecurity and the need for agriculture to contribute to development emphasise the need to maintain and improve productivity while improving equitability. Land must foster production and agriculture must attract good human material. The following areas need to be considered in policy formulation and delivery: an effective institutional framework involving all the relevant public and private bodies; efficient fiscal planning is essential; potentially successful farmers must be selected and given special support, including extension and adult education; complementary services and infrastructure are needed; prioritisation of functions and land tenure reform is often necessary. In addition, international agricultural markets are very important for Africa.  Wealthy nations should cease trade-distorting protection of their own farmers.

  17. Evaluation of the ECMWF Sub-seasonal to Seasonal Precipitation Forecasts during the Peak of West Africa Monsoon in Nigeria

    Directory of Open Access Journals (Sweden)

    Eniola Olaniyan

    2018-02-01

    Full Text Available Motivated by the increasing needs for reliable seasonal climate forecasts for enhanced living and protection of property, this study evaluates the predictive skill of the European Center for Medium-range Weather Forecast's Sub-seasonal to Seasonal (ECMWF-S2S precipitation forecasts during the peak of West Africa Monsoon in Nigeria. It investigates the ability of the ECMWF-S2S model to reproduce the atmospheric dynamics that influence the monsoon variability in West-Africa. Rain gauge values of 46 meteorological stations and 10-member ensemble of ECMWF-S2S forecasts from the Ensemble Prediction System (EPS version of the ECMWF were subjected to quantitative statistical analyses. Results show that the model has weak capability in predicting wind strength at 700 mb level to depict the African Easterly Jet (AEJ. However, irrespective of the ENSO phases, ECMWF-S2S model is capable of adequately and reliably predicting the latitudinal positions of the Inter-Tropical Discontinuity (ITD, mean sea level pressure component of the thermal lows and sea surface temperature (SST anomalies over the Pacific and Atlantic Oceans. On inter-annual time-scales, results also show that ECMWF-S2S model performs best over the Savannah in forecasting of rainfall anomalies (synchronization = 75% and over the Sahel in the prediction of rainfall accumulation. The model may however not be able to forecast extreme precipitation reliably because the disagreement between the model's ensemble members increases as higher rainfall accumulation values are attained. The implication here is that the reproducibility of the atmospheric dynamic by the model is a better measure of rainfall prediction than the actual quantitative rainfall forecasts especially in areas south of latitude 10°N. The study therefore suggests considering some climate driving mechanisms as predictability sources for the ECMWF-S2S model to enable the atmospheric dynamics to be better represented in the model.

  18. Contingent valuation study of the benefits of seasonal climate forecasts for maize farmers in the Republic of Benin, West Africa

    Directory of Open Access Journals (Sweden)

    Cocou Jaurès Amegnaglo

    2017-04-01

    Full Text Available This study aims to assess the economic benefits of seasonal climate forecasts in West Africa based on a random survey of 354 maize farmers and to use the contingent valuation method. Results indicate that farmers need accurate seasonal climate forecasts between 1 and 2 months before the onset of rains. The most desirable dissemination channels are radio, local elders, local farmer meetings and extension agents. The most likely used farming strategies are change of: planting date, crop acreage, crop variety, and production intensification. The vast majority of farmers are willing to pay for seasonal climate forecasts, and the average annual economic value of seasonal climate forecasts are about USD 5492 for the 354 sampled farmers and USD 66.5 million dollar at the national level. Furthermore, benefits of seasonal climate forecasts are likely to increase with better access to farmer based organisation, to extension services, to financial services, to modern communication tools, intensity of use of fertilizer and with larger farm sizes. Seasonal climate forecasts are a source of improvement of farmers’ performance and the service should be integrated in extension programmes and in national agricultural development agenda.

  19. Influence of Met-Ocean Condition Forecasting Uncertainties on Weather Window Predictions for Offshore Operations

    DEFF Research Database (Denmark)

    Gintautas, Tomas; Sørensen, John Dalsgaard

    2017-01-01

    The article briefly presents a novel methodology of weather window estimation for offshore operations and mainly focuses on effects of met-ocean condition forecasting uncertainties on weather window predictions when using the proposed methodology. It is demonstrated that the proposed methodology...... to include stochastic variables, representing met-ocean forecasting uncertainties and the results of such modification are given in terms of predicted weather windows for a selected test case....

  20. How the International Research Institute for Climate and Society has contributed towards seasonal climate forecast modelling and operations in South Africa

    CSIR Research Space (South Africa)

    Landman, WA

    2014-06-01

    Full Text Available The production of seasonal forecasts on a routine basis in South Africa started in the early 1990s. Most of the modelling then was based on linear statistical approaches. The subsequent evolution of the seasonal forecasting enterprise in South...

  1. Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

    Science.gov (United States)

    Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

    2013-12-01

    To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the statistical properties of the ensemble. The effectiveness of the method was demonstrated in a real-time operational hydrologic seasonal forecasts system for the Columbia River basin operated by the Bonneville Power Administration. The forecast skill of the k-nn resampler was tested against the original ESP for three basins at the long-range seasonal time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive forecast skill scores were found for the resampler method conditioned on different indices for the prediction of spring peak flows in the Dworshak and Hungry Horse basin. For the Libby Dam basin however, no improvement of skill was found. The proposed resampling method is a promising practical approach that can add skill to ESP forecasts at the seasonal time scale. Further improvement is possible by fine tuning the method and selecting the most

  2. Seasonal streamflow forecasting: experiences with precipitation bias correction and SPI conditioning to improve performance for hydrological events

    Science.gov (United States)

    Ramos, M. H.; Crochemore, L.; Pappenberger, F.; Perrin, C.

    2016-12-01

    Many fields such as drought risk assessment or reservoir management can benefit from seasonal streamflow forecasts. This study presents the results of two analyses aiming to: 1) assess the skill of seasonal precipitation forecasts in France and provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times, and 2) evaluate how the conditioning of historical data based on the Standardized Precipitation Index (SPI) from bias-corrected GCM precipitation forecasts can be useful to select traces within the historical data and further improve the forecast of droughts. We evaluated several bias correction approaches and conditioned precipitation scenarios in sixteen catchments in France, with the help of ECMWF System 4 seasonal precipitation forecasts and the GR6J hydrological model. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often sharper than the conventional ESP method. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. The simple linear scaling of monthly values contributed mainly to increase forecast sharpness and accuracy, while the empirical distribution mapping of daily values was successful in improving forecast reliability. Our results also show that conditioning past observations based on the three-month Standardized Precipitation Index (SPI3) can improve the sharpness of ensemble forecasts based on historical data, while maintaining good reliability. An evaluation of forecast ensembles for low-flow forecasting showed that the SPI3-conditioned ensembles provided reliable forecasts of low-flow duration and deficit volume based on the 80th exceedance percentile. Drought risk forecasting is illustrated for the 2003 drought event.

  3. Recent advances in operational seasonal forecasting in South Africa: Models, infrastructure and networks

    CSIR Research Space (South Africa)

    Landman, WA

    2011-11-01

    Full Text Available The various institutions involved with seasonal forecast development and production are discussed. New modelling approaches and the establishment of infrastructures to improve forecast dissemination are discussed....

  4. Evaluation of short-term weather forecasts in South Africa | Banitz ...

    African Journals Online (AJOL)

    In this paper a brief overview will be given for the reasons for doing evaluations of short-term weather forecasts as well as the methodology thereof. Short-term weather forecasts are defined as a forecast valid for the current day as well as the next day. In other words up to 48 h ahead. Results are given for South African ...

  5. Spatial Bayesian methods of forecasting house prices in six metropolitan areas of South Africa

    CSIR Research Space (South Africa)

    Gupta, R

    2008-06-01

    Full Text Available :07 to 2005:06. The authors then forecast one- to six-months-ahead house prices over the forecast horizon of 2005:07 to 2007:06. They then compare forecasts generated from the SBVAR's with those from an unrestricted Vector Autoregressive (VAR) and the Bayesian...

  6. Toward Improved Reliability of Seasonal Hydrologic Forecast: Accounting for Initial Condition and State-Parameter Uncertainties

    Science.gov (United States)

    DeChant, C. M.; Moradkhani, H.

    2012-12-01

    Providing reliable estimates of seasonal water supply is a primary goal in operational hydro-meteorological prediction. In order to achieve this goal, it is accepted that hydrologists must accurately estimate forecast initial conditions (land surface states prior to forecast) and the future climate conditions, and quantify the uncertainty in these two forecast stages to provide a full estimation of the uncertainty in a given forecast. Recent work has highlighted the benefits of such a framework through advancing both land surface state estimation techniques and future climate estimation/modeling, within the operational Ensemble Streamflow Prediction (ESP) methodology. Often overlooked in this framework, the uncertainty in land surface state estimates play a key role in providing reliable seasonal forecasts. In order to quantify and reduce this uncertainty, land surface state-parameter estimation, through ensemble data assimilation, is performed with observations of snow and streamflow in a mountainous basin. Through incorporation of both snow and streamflow data for estimation of land surface states and parameters, the quantity of water stored at the land surface can be estimated, and parameter uncertainty can be estimated for seasonal simulations. With the inclusion of parameter uncertainty in the hydrologic forecasting framework, more robust quantification of hydrologic uncertainty is possible, leading to more useful forecasts for end users. This study seeks to examine the role of combined state-parameter estimation for characterization of initial conditions with the potential to be formally adopted in operational ESP framework, and validates results with probabilistic verification of both ESP and ESP with state-parameter estimation.

  7. Using Seasonal Climate Forecasts to Guide Disaster Management: The Red Cross Experience during the 2008 West Africa Floods

    Directory of Open Access Journals (Sweden)

    Arame Tall

    2012-01-01

    Full Text Available In 2008, the seasonal forecast issued at the Seasonal Climate Outlook Forum for West Africa (PRESAO announced a high risk of above-normal rainfall for the July–September rainy season. With probabilities for above-normal rainfall of 0.45, this forecast indicated noteworthy increases in the risk of heavy rainfall. When this information reached the International Federation of Red Cross and Red Crescent Societies (IFRC West and Central Africa Office, it led to significant changes in the organization’s flood response operations. The IFRC regional office requested funds in advance of anticipated floods, prepositioned disaster relief items in strategic locations across West Africa to benefit up to 9,500 families, updated its flood contingency plans, and alerted vulnerable communities and decision-makers across the region. This forecast-based preparedness resulted in a decrease in the number of lives, property, and livelihoods lost to floods, compared to just one year prior in 2007 when similar floods claimed above 300 lives in the region. This article demonstrates how a science-based early warning informed decisions and saved lives by triggering action in anticipation of forecast events. It analyses what it took to move decision-makers to action, based on seasonal climate information, and to overcome traditional barriers to the uptake of seasonal climate information in the region, providing evidence that these barriers can be overcome. While some institutional, communication and technical barriers were addressed in 2008, many challenges remain. Scientists and humanitarians need to build more common ground.

  8. Seasonal scale water deficit forecasting in Africa and the Middle East using NASA's Land Information System (LIS)

    Science.gov (United States)

    Peters-Lidard, C. D.; Arsenault, K. R.; Shukla, S.; Getirana, A.; McNally, A.; Koster, R. D.; Zaitchik, B. F.; Badr, H. S.; Roningen, J. M.; Kumar, S.; Funk, C. C.

    2017-12-01

    A seamless and effective water deficit monitoring and early warning system is critical for assessing food security in Africa and the Middle East. In this presentation, we report on the ongoing development and validation of a seasonal scale water deficit forecasting system based on NASA's Land Information System (LIS) and seasonal climate forecasts. First, our presentation will focus on the implementation and validation of drought and water availability monitoring products in the region. Next, it will focus on evaluating drought and water availability forecasts. Finally, details will be provided of our ongoing collaboration with end-user partners in the region (e.g., USAID's Famine Early Warning Systems Network, FEWS NET), on formulating meaningful early warning indicators, effective communication and seamless dissemination of the products through NASA's web-services. The water deficit forecasting system thus far incorporates NASA GMAO's Catchment and the Noah Multi-Physics (MP) LSMs. In addition, the LSMs' surface and subsurface runoff are routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics. To establish a climatology from 1981-2015, the two LSMs are driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS and UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall dataset. Comparison of the models' energy and hydrological budgets with independent observations suggests that major droughts are well-reflected in the climatology. The system uses seasonal climate forecasts from NASA's GEOS-5 (the Goddard Earth Observing System Model-5) and NCEP's Climate Forecast System-2, and it produces forecasts of soil moisture, ET and streamflow out to 6 months in the future. Forecasts of those variables are formulated in terms of indicators to provide forecasts of drought and water availability in the region. Current work suggests

  9. Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

    Science.gov (United States)

    Bernier, Natacha B.; Bélair, Stéphane; Bilodeau, Bernard; Tong, Linying

    2014-01-01

    A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today's optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf's initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.

  10. Influence of Met-Ocean Condition Forecasting Uncertainties on Weather Window Predictions for Offshore Operations

    DEFF Research Database (Denmark)

    Gintautas, Tomas; Sørensen, John Dalsgaard

    2017-01-01

    The article briefly presents a novel methodology of weather window estimation for offshore operations and mainly focuses on effects of met-ocean condition forecasting uncertainties on weather window predictions when using the proposed methodology. It is demonstrated that the proposed methodology...... to include stochastic variables, representing met-ocean forecasting uncertainties and the results of such modification are given in terms of predicted weather windows for a selected test case....... has the capacity to retain the uncertainties of met-ocean condition forecasting and transfer them into uncertainties of probability of operation failure. In addition to that, improvements to the failure function, used to define operation failure are presented. The failure function is modified...

  11. Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing

    Science.gov (United States)

    Yossef, Naze Candogan; Winsemius, Hessel; Weerts, Albrecht; van Beek, Rens; Bierkens, Marc F. P.

    2013-08-01

    We investigate the relative contributions of initial conditions (ICs) and meteorological forcing (MF) to the skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCRaster Global Water Balance. Potential improvement in forecasting skill through better climate prediction or by better estimation of ICs through data assimilation depends on the relative importance of these sources of uncertainty. We use the Ensemble Streamflow Prediction (ESP) and reverse ESP (revESP) procedure to explore the impact of both sources of uncertainty at 78 stations on large global basins for lead times upto 6 months. We compare the ESP and revESP forecast ensembles with retrospective model simulations driven by meteorological observations. For each location, we determine the critical lead time after which the importance of ICs is surpassed by that of MF. We analyze these results in the context of prevailing hydroclimatic conditions for larger basins. This analysis suggests that in some basins forecast skill may be improved by better estimation of initial hydrologic states through data assimilation; whereas in others skill improvement depends on better climate prediction. For arctic and snowfed rivers, forecasts of high flows may benefit from assimilation of snow and ice data. In some snowfed basins where the onset of melting is highly sensitive to temperature changes, forecast skill depends on better climate prediction. In monsoonal basins, the variability of the monsoon dominates forecasting skill, except for those where snow and ice contribute to streamflow. In large basins, initial surface water and groundwater states are important sources of skill.

  12. Forecasting the condition of petroleum impregnated load bearing ...

    African Journals Online (AJOL)

    Petroleum products (PP) used in industrial processes systematically fall on the load-bearing CRC structures and gradually impregnate therein. Currently, available guidelines for the assessment of technical condition and reliability of load-bearing CRC structures do not fully take into account the effect of viscosity of PP that ...

  13. Methods of forecasting crack growth rate under creep conditions

    International Nuclear Information System (INIS)

    Ol'kin, S.I.

    1979-01-01

    Using construction aluminium alloy application possibility of linear mechanics of the destruction for quantitative description of crack development process under creepage conditions is investigated. It is shown, that the grade dependence between the stress intensity coefficient and the crack growth rate takes place only at certain combination of the sample geometry and creepage parameters, and consequently, its applicability in every given case must necessarily be tested experimentally

  14. How can we best use climate information and hydrologic initial conditions to improve seasonal streamflow forecasts?

    Science.gov (United States)

    Mendoza, P. A.; Wood, A. W.; Rothwell, E.; Clark, M. P.; Brekke, L. D.; Arnold, J. R.

    2015-12-01

    Over the last decades, a number of forecasting centers around the world have offered seasonal streamflow predictions, using methodologies that span a wide range of data requirements and complexity. In the western United States, two primary approaches have been adopted for operational purposes: (i) development of regression equations between future streamflow and in situ observations (e.g. rainfall, snow water equivalent), and (ii) ensemble hydrologic model simulations that combine initial watershed moisture states with historically observed weather sequences for the forecast period (e.g., Ensemble Streamflow Prediction, ESP). Nevertheless, none of these methodologies makes use of analyzed or forecast climate information, which might increase the skill of seasonal predictions. Further, there is a need to better understand the marginal benefits of using more complex methods (from statistical to dynamical) and different types of information. In this work, we provide a systematic intercomparison of various seasonal streamflow forecasting techniques, including: (1) a dynamical approach based on conceptual hydrologic modeling and ESP, (2) statistical regression using climate information and/or initial hydrologic conditions, (3) an ESP trace weighting scheme based on analog climatic conditions, and (4) combination of dynamical and statistical forecasts (i.e. hybrid approach). Climate information is taken from the NCEP CFSv2 reanalysis and reforecast datasets. These methods are tested for predicting spring (e.g., May-September) runoff volumes at case study basins located in the US Pacific Northwest, and results obtained for several initialization times are evaluated in terms of accuracy, probabilistic skill and statistical consistency. Preliminary results show that for earlier initialization times (October 1 to December 1), statistical and hybrid techniques that make use of climate information outperform ESP in terms of correlation and probabilistic skill. Although ESP at

  15. Development of a Decision Support System for Monitoring, Reporting, Forecasting Ecological Conditions of the Appalachian Trail

    Science.gov (United States)

    Y. Wang; R. Nemani; F. Dieffenbach; K. Stolte; G. Holcomb

    2010-01-01

    This paper introduces a collaborative multi-agency effort to develop an Appalachian Trail (A.T.) MEGA-Transect Decision Support System (DSS) for monitoring, reporting and forecasting ecological conditions of the A.T. and the surrounding lands. The project is to improve decision-making on management of the A.T. by providing a coherent framework for data integration,...

  16. Forecasting changes of arid geosystems under ecological destabilizing conditions in the Aral Sea region

    Directory of Open Access Journals (Sweden)

    V.A. Rifikov

    2014-05-01

    Full Text Available We discuss the main natural and anthropogenic factors of forecasting and establish the basic tendencies to change natural complexes. We conclude that the Aral Sea and the Aral Sea region are genetically uniform and paragenetically dynamical macro geosystems. By considering properties and features of structural and dynamic conditions of superaqual, subequal, and eluvial geosystems of the Aral Sea region and the Aral Sea, a forecast of its transformation by 2020 year is developed. We develop a practical plan of action for cardinal improvement of the environment in the Amu Darya Delta and the dried bottom of the Aral Sea.

  17. Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa

    Directory of Open Access Journals (Sweden)

    Tadesse Kassahun Birhanu

    2017-12-01

    Full Text Available Knowledge of future river flow information is fundamental for development and management of a river system. In this study, Waterval River flow was forecasted by SARIMA model using GRETL statistical software. Mean monthly flows from 1960 to 2016 were used for modelling and forecasting. Different unit root and Mann–Kendall trend analysis proved the stationarity of the observed flow time series. Based on seasonally differenced correlogram characteristics, different SARIMA models were evaluated; their parameters were optimized, and diagnostic check up of forecasts was made using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AI and Hannan–Quinn (HQ criteria, SARIMA (3, 0, 2 x (3, 1, 312 model was selected for Waterval River flow forecasting. Comparison of forecast performance of SARIMA models with that of computational intelligent forecasting techniques was recommended for future study.

  18. Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of hydrometeorological extremes over East Africa

    Science.gov (United States)

    Shukla, Shraddhanand; Roberts, Jason B.; Hoell. Andrew,; Funk, Chris; Robertson, Franklin R.; Kirtmann, Benjamin

    2016-01-01

    The skill of North American multimodel ensemble (NMME) seasonal forecasts in East Africa (EA), which encompasses one of the most food and water insecure areas of the world, is evaluated using deterministic, categorical, and probabilistic evaluation methods. The skill is estimated for all three primary growing seasons: March–May (MAM), July–September (JAS), and October–December (OND). It is found that the precipitation forecast skill in this region is generally limited and statistically significant over only a small part of the domain. In the case of MAM (JAS) [OND] season it exceeds the skill of climatological forecasts in parts of equatorial EA (Northern Ethiopia) [equatorial EA] for up to 2 (5) [5] months lead. Temperature forecast skill is generally much higher than precipitation forecast skill (in terms of deterministic and probabilistic skill scores) and statistically significant over a majority of the region. Over the region as a whole, temperature forecasts also exhibit greater reliability than the precipitation forecasts. The NMME ensemble forecasts are found to be more skillful and reliable than the forecast from any individual model. The results also demonstrate that for some seasons (e.g. JAS), the predictability of precipitation signals varies and is higher during certain climate events (e.g. ENSO). Finally, potential room for improvement in forecast skill is identified in some models by comparing homogeneous predictability in individual NMME models with their respective forecast skill.

  19. Access to Risk Mitigating Weather Forecasts and Changes in Farming Operations in East and West Africa: Evidence from a Baseline Survey

    Directory of Open Access Journals (Sweden)

    Abayomi Samuel Oyekale

    2015-10-01

    Full Text Available Unfavorable weather currently ranks among the major challenges facing agricultural development in many African countries. Impact mitigation through access to reliable and timely weather forecasts and other adaptive mechanisms are foremost in Africa’s policy dialogues and socio-economic development agendas. This paper analyzed the factors influencing access to forecasts on incidence of pests/diseases (PD and start of rainfall (SR. The data were collected by Climate Change Agriculture and Food Security (CCAFS and analyzed with Probit regression separately for East Africa, West Africa and the combined dataset. The results show that 62.7% and 56.4% of the farmers from East and West Africa had access to forecasts on start of rainfall, respectively. In addition, 39.3% and 49.4% of the farmers from East Africa indicated that forecasts on outbreak of pests/diseases and start of rainfall were respectively accompanied with advice as against 18.2% and 41.9% for West Africa. Having received forecasts on start of rainfall, 24.0% and 17.6% of the farmers from East and West Africa made decisions on timing of farming activities respectively. Probabilities of having access to forecasts on PD significantly increased with access to formal education, farm income and previous exposure to climatic shocks. Furthermore, probabilities of having access to forecasts on SR significantly increased (p < 0.05 with access to business income, radio and perception of more erratic rainfall, among others. It was recommended that promotion of informal education among illiterate farmers would enhance their climatic resilience, among others.

  20. Shared investment projects and forecasting errors: setting framework conditions for coordination and sequencing data quality activities.

    Science.gov (United States)

    Leitner, Stephan; Brauneis, Alexander; Rausch, Alexandra

    2015-01-01

    In this paper, we investigate the impact of inaccurate forecasting on the coordination of distributed investment decisions. In particular, by setting up a computational multi-agent model of a stylized firm, we investigate the case of investment opportunities that are mutually carried out by organizational departments. The forecasts of concern pertain to the initial amount of money necessary to launch and operate an investment opportunity, to the expected intertemporal distribution of cash flows, and the departments' efficiency in operating the investment opportunity at hand. We propose a budget allocation mechanism for coordinating such distributed decisions The paper provides guidance on how to set framework conditions, in terms of the number of investment opportunities considered in one round of funding and the number of departments operating one investment opportunity, so that the coordination mechanism is highly robust to forecasting errors. Furthermore, we show that-in some setups-a certain extent of misforecasting is desirable from the firm's point of view as it supports the achievement of the corporate objective of value maximization. We then address the question of how to improve forecasting quality in the best possible way, and provide policy advice on how to sequence activities for improving forecasting quality so that the robustness of the coordination mechanism to errors increases in the best possible way. At the same time, we show that wrong decisions regarding the sequencing can lead to a decrease in robustness. Finally, we conduct a comprehensive sensitivity analysis and prove that-in particular for relatively good forecasters-most of our results are robust to changes in setting the parameters of our multi-agent simulation model.

  1. Evaluating the performance of real-time streamflow forecasting using multi-satellite precipitation products in the Upper Zambezi, Africa

    Science.gov (United States)

    Demaria, E. M.; Valdes, J. B.; Wi, S.; Serrat-Capdevila, A.; Valdés-Pineda, R.; Durcik, M.

    2016-12-01

    In under-instrumented basins around the world, accurate and timely forecasts of river streamflows have the potential of assisting water and natural resource managers in their management decisions. The Upper Zambezi river basin is the largest basin in southern Africa and its water resources are critical to sustainable economic growth and poverty reduction in eight riparian countries. We present a real-time streamflow forecast for the basin using a multi-model-multi-satellite approach that allows accounting for model and input uncertainties. Three distributed hydrologic models with different levels of complexity: VIC, HYMOD_DS, and HBV_DS are setup at a daily time step and a 0.25 degree spatial resolution for the basin. The hydrologic models are calibrated against daily observed streamflows at the Katima-Mulilo station using a Genetic Algorithm. Three real-time satellite products: Climate Prediction Center's morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM-3B42RT) are bias-corrected with daily CHIRPS estimates. Uncertainty bounds for predicted flows are estimated with the Inverse Variance Weighting method. Because concentration times in the basin range from a few days to more than a week, we include the use of precipitation forecasts from the Global Forecasting System (GFS) to predict daily streamflows in the basin with a 10-days lead time. The skill of GFS-predicted streamflows is evaluated and the usefulness of the forecasts for short term water allocations is presented.

  2. Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions

    Directory of Open Access Journals (Sweden)

    X. Tang

    2011-12-01

    Full Text Available In order to improve the surface ozone forecast over Beijing and surrounding regions, data assimilation method integrated into a high-resolution regional air quality model and a regional air quality monitoring network are employed. Several advanced data assimilation strategies based on ensemble Kalman filter are designed to adjust O3 initial conditions, NOx initial conditions and emissions, VOCs initial conditions and emissions separately or jointly through assimilating ozone observations. As a result, adjusting precursor initial conditions demonstrates potential improvement of the 1-h ozone forecast almost as great as shown by adjusting precursor emissions. Nevertheless, either adjusting precursor initial conditions or emissions show deficiency in improving the short-term ozone forecast at suburban areas. Adjusting ozone initial values brings significant improvement to the 1-h ozone forecast, and its limitations lie in the difficulty in improving the 1-h forecast at some urban site. A simultaneous adjustment of the above five variables is found to be able to reduce these limitations and display an overall better performance in improving both the 1-h and 24-h ozone forecast over these areas. The root mean square errors of 1-h ozone forecast at urban sites and suburban sites decrease by 51% and 58% respectively compared with those in free run. Through these experiments, we found that assimilating local ozone observations is determinant for ozone forecast over the observational area, while assimilating remote ozone observations could reduce the uncertainty in regional transport ozone.

  3. Future sea ice conditions and weather forecasts in the Arctic: Implications for Arctic shipping.

    Science.gov (United States)

    Gascard, Jean-Claude; Riemann-Campe, Kathrin; Gerdes, Rüdiger; Schyberg, Harald; Randriamampianina, Roger; Karcher, Michael; Zhang, Jinlun; Rafizadeh, Mehrad

    2017-12-01

    The ability to forecast sea ice (both extent and thickness) and weather conditions are the major factors when it comes to safe marine transportation in the Arctic Ocean. This paper presents findings focusing on sea ice and weather prediction in the Arctic Ocean for navigation purposes, in particular along the Northeast Passage. Based on comparison with the observed sea ice concentrations for validation, the best performing Earth system models from the Intergovernmental Panel on Climate Change (IPCC) program (CMIP5-Coupled Model Intercomparison Project phase 5) were selected to provide ranges of potential future sea ice conditions. Our results showed that, despite a general tendency toward less sea ice cover in summer, internal variability will still be large and shipping along the Northeast Passage might still be hampered by sea ice blocking narrow passages. This will make sea ice forecasts on shorter time and space scales and Arctic weather prediction even more important.

  4. Shared Investment Projects and Forecasting Errors: Setting Framework Conditions for Coordination and Sequencing Data Quality Activities

    Science.gov (United States)

    Leitner, Stephan; Brauneis, Alexander; Rausch, Alexandra

    2015-01-01

    In this paper, we investigate the impact of inaccurate forecasting on the coordination of distributed investment decisions. In particular, by setting up a computational multi-agent model of a stylized firm, we investigate the case of investment opportunities that are mutually carried out by organizational departments. The forecasts of concern pertain to the initial amount of money necessary to launch and operate an investment opportunity, to the expected intertemporal distribution of cash flows, and the departments’ efficiency in operating the investment opportunity at hand. We propose a budget allocation mechanism for coordinating such distributed decisions The paper provides guidance on how to set framework conditions, in terms of the number of investment opportunities considered in one round of funding and the number of departments operating one investment opportunity, so that the coordination mechanism is highly robust to forecasting errors. Furthermore, we show that—in some setups—a certain extent of misforecasting is desirable from the firm’s point of view as it supports the achievement of the corporate objective of value maximization. We then address the question of how to improve forecasting quality in the best possible way, and provide policy advice on how to sequence activities for improving forecasting quality so that the robustness of the coordination mechanism to errors increases in the best possible way. At the same time, we show that wrong decisions regarding the sequencing can lead to a decrease in robustness. Finally, we conduct a comprehensive sensitivity analysis and prove that—in particular for relatively good forecasters—most of our results are robust to changes in setting the parameters of our multi-agent simulation model. PMID:25803736

  5. Seasonal scale water deficit forecasting in Africa and the Middle East using NASA's Land Information System (LIS)

    Science.gov (United States)

    Shukla, Shraddhanand; Arsenault, Kristi R.; Getirana, Augusto; Kumar, Sujay V.; Roningen, Jeanne; Zaitchik, Ben; McNally, Amy; Koster, Randal D.; Peters-Lidard, Christa

    2017-04-01

    Drought and water scarcity are among the important issues facing several regions within Africa and the Middle East. A seamless and effective monitoring and early warning system is needed by regional/national stakeholders. Such system should support a proactive drought management approach and mitigate the socio-economic losses up to the extent possible. In this presentation, we report on the ongoing development and validation of a seasonal scale water deficit forecasting system based on NASA's Land Information System (LIS) and seasonal climate forecasts. First, our presentation will focus on the implementation and validation of the LIS models used for drought and water availability monitoring in the region. The second part will focus on evaluating drought and water availability forecasts. Finally, details will be provided of our ongoing collaboration with end-user partners in the region (e.g., USAID's Famine Early Warning Systems Network, FEWS NET), on formulating meaningful early warning indicators, effective communication and seamless dissemination of the monitoring and forecasting products through NASA's web-services. The water deficit forecasting system thus far incorporates NOAA's Noah land surface model (LSM), version 3.3, the Variable Infiltration Capacity (VIC) model, version 4.12, NASA GMAO's Catchment LSM, and the Noah Multi-Physics (MP) LSM (the latter two incorporate prognostic water table schemes). In addition, the LSMs' surface and subsurface runoff are routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics. The LSMs are driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS and UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall dataset. The LIS software framework integrates these forcing datasets and drives the four LSMs and HyMAP. The Land Verification Toolkit (LVT) is used for the evaluation of the

  6. The quality and value of seasonal precipitation forecasts for an early warning of large-scale droughts and floods in West Africa

    Science.gov (United States)

    Bliefernicht, Jan; Seidel, Jochen; Salack, Seyni; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald

    2017-04-01

    Seasonal precipitation forecasts are a crucial source of information for an early warning of hydro-meteorological extremes in West Africa. However, the current seasonal forecasting system used by the West African weather services in the framework of the West African Climate Outlook forum (PRESAO) is limited to probabilistic precipitation forecasts of 1-month lead time. To improve this provision, we use an ensemble-based quantile-quantile transformation for bias correction of precipitation forecasts provided by a global seasonal ensemble prediction system, the Climate Forecast System Version 2 (CFS2). The statistical technique eliminates systematic differences between global forecasts and observations with the potential to preserve the signal from the model. The technique has also the advantage that it can be easily implemented at national weather services with low capacities. The statistical technique is used to generate probabilistic forecasts of monthly and seasonal precipitation amount and other precipitation indices useful for an early warning of large-scale drought and floods in West Africa. The evaluation of the statistical technique is done using CFS hindcasts (1982 to 2009) in a cross-validation mode to determine the performance of the precipitation forecasts for several lead times focusing on drought and flood events depicted over the Volta and Niger basins. In addition, operational forecasts provided by PRESAO are analyzed from 1998 to 2015. The precipitation forecasts are compared to low-skill reference forecasts generated from gridded observations (i.e. GPCC, CHIRPS) and a novel in-situ gauge database from national observation networks (see Poster EGU2017-10271). The forecasts are evaluated using state-of-the-art verification techniques to determine specific quality attributes of probabilistic forecasts such as reliability, accuracy and skill. In addition, cost-loss approaches are used to determine the value of probabilistic forecasts for multiple users

  7. Regional air-quality forecasting for the Pacific Northwest using MOPITT/TERRA assimilated carbon monoxide MOZART-4 forecasts as a near real-time boundary condition

    Directory of Open Access Journals (Sweden)

    F. L. Herron-Thorpe

    2012-06-01

    Full Text Available Results from a regional air quality forecast model, AIRPACT-3, were compared to AIRS carbon monoxide column densities for the spring of 2010 over the Pacific Northwest. AIRPACT-3 column densities showed high correlation (R > 0.9 but were significantly biased (~25% with consistent under-predictions for spring months when there is significant transport from Asia. The AIRPACT-3 CO bias relative to AIRS was eliminated by incorporating dynamic boundary conditions derived from NCAR's MOZART forecasts with assimilated MOPITT carbon monoxide. Changes in ozone-related boundary conditions derived from MOZART forecasts are also discussed and found to affect background levels by ± 10 ppb but not found to significantly affect peak ozone surface concentrations.

  8. Forecasting the atmospheric composition of southern West Africa with COSMO-ART during the DACCIWA measurement campaign

    Science.gov (United States)

    Deetz, Konrad; Vogel, Bernhard

    2017-04-01

    The Dynamics-aerosol-chemistry-cloud interactions in West Africa (DACCIWA) project (Knippertz et al., 2015) investigates the influence of anthropogenic and natural emissions on the atmospheric composition over Southern West Africa (SWA). Between 1 June and 31 July 2016 the DACCIWA measurement campaign took place in SWA, including ground based and airborne observations. By using the regional scale comprehensive model system COSMO-ART (Vogel et al., 2009), operational numerical forecasts of the atmospheric composition including aerosols and gas phase compounds were conducted between 8 May and 31 July 2016. The forecasts cover the domain 25°W to 35°E and 20°S to 30°N with a grid mesh size of 28km and a lead time of 57h. The primary assignment of the forecasts was to support the DACCIWA aircraft campaign (27 June to 17 July 2016) in terms of the decision making of the flight routes of the research aircrafts. Visualizations of the forecast results were daily uploaded to the public available server dacciwa.sedoo.fr. Apart from the support of the DACCIWA measurement campaign, the COSMO-ART model dataset is highly valuable for identifying time periods feasible for post-campaign case study simulations, the extensive validation of COSMO-ART with observational data and the derivation of model climatologies to raise knowledge in meteorological and the atmospheric composition characteristics of SWA. The presentation will show validation results of the COSMO-ART forecasts with ground based and airborne measurements from the DACCIWA campaign as well as remote sensing observations. COSMO-ART well reproduces the diurnal cycle of the observed ozone concentration at Savé site and shows very good agreement of mineral dust AOD compared to CAMS model results whereas the anthropogenic aerosol seems to be overestimated by COSMO-ART compared to MODIS AOD and AERONET observations. We will present model climatologies of the NLLS characteristics and the spatial structure of the pollution

  9. The influence of antecedent conditions on flood risk in sub-Saharan Africa

    Directory of Open Access Journals (Sweden)

    K. Bischiniotis

    2018-01-01

    Full Text Available Most flood early warning systems have predominantly focused on forecasting floods with lead times of hours or days. However, physical processes during longer timescales can also contribute to flood generation. In this study, we follow a pragmatic approach to analyse the hydro-meteorological pre-conditions of 501 historical damaging floods from 1980 to 2010 in sub-Saharan Africa. These are separated into (a weather timescale (0–6 days and (b seasonal timescale conditions (up to 6 months before the event. The 7-day precipitation preceding a flood event (PRE7 and the standardized precipitation evapotranspiration index (SPEI are analysed for the two timescale domains, respectively. Results indicate that high PRE7 does not always generate floods by itself. Seasonal SPEIs, which are not directly correlated with PRE7, exhibit positive (wet values prior to most flood events across different averaging times, indicating a relationship with flooding. This paper provides evidence that bringing together weather and seasonal conditions can lead to improved flood risk preparedness.

  10. The influence of antecedent conditions on flood risk in sub-Saharan Africa

    Science.gov (United States)

    Bischiniotis, Konstantinos; van den Hurk, Bart; Jongman, Brenden; Coughlan de Perez, Erin; Veldkamp, Ted; de Moel, Hans; Aerts, Jeroen

    2018-01-01

    Most flood early warning systems have predominantly focused on forecasting floods with lead times of hours or days. However, physical processes during longer timescales can also contribute to flood generation. In this study, we follow a pragmatic approach to analyse the hydro-meteorological pre-conditions of 501 historical damaging floods from 1980 to 2010 in sub-Saharan Africa. These are separated into (a) weather timescale (0-6 days) and (b) seasonal timescale conditions (up to 6 months) before the event. The 7-day precipitation preceding a flood event (PRE7) and the standardized precipitation evapotranspiration index (SPEI) are analysed for the two timescale domains, respectively. Results indicate that high PRE7 does not always generate floods by itself. Seasonal SPEIs, which are not directly correlated with PRE7, exhibit positive (wet) values prior to most flood events across different averaging times, indicating a relationship with flooding. This paper provides evidence that bringing together weather and seasonal conditions can lead to improved flood risk preparedness.

  11. Seasonal forecasts of the SINTEX-F coupled model applied to maize yield and streamflow estimates over north-eastern South Africa

    CSIR Research Space (South Africa)

    Malherbe, J

    2014-07-01

    Full Text Available Forecasts of a Global Coupled Model for austral summer with a 1 month lead are downscaled to end-of-season maize yields and accumulated streamflow over the Limpopo Province and adjacent districts in northeastern South Africa through application...

  12. SST prediction methodologies and verification considerations for dynamical mid-summer rainfall forecasts for South Africa

    CSIR Research Space (South Africa)

    Landman, WA

    2014-10-01

    Full Text Available Seasonal-to-interannual hindcasts (re-forecasts) for December-January-February (DJF) produced at a 1-month lead-time by the ECHAM4.5 atmospheric general circulation model (AGCM) are verified after calibrating model output to DJF rainfall at 94...

  13. Solar radiation forecasting in the short- and medium-term under all sky conditions

    International Nuclear Information System (INIS)

    Alonso-Montesinos, J.; Batlles, F.J.

    2015-01-01

    Meteorological conditions are decisive in solar plant management and electricity generation. Any increases or decreases in solar radiation mean a plant has to adapt its operation method to the climatological phenomena. An unexpected atmospheric change can provoke a range of problems related to various solar plant components affecting the electricity generation system and, in consequence, causing alterations in the electricity grid. Therefore, predicting atmospheric features is key to managing solar plants and is therefore necessary for correct electrical grid management. Accordingly, a solar radiation forecast model is presented, where the three solar components (beam, diffuse and global) are predicted over the short- and medium-term (up to three hours) for all sky conditions, demonstrating its potential as a useful application in decision-making processes at solar power plants. - Highlights: • A solar radiation forecasting has been proposed over the short- and medium-term. • The three radiation components have been predicted under all sky conditions. • Cloud motion and the Heliosat-2 model are combined for predicting solar radiation. • Results have been presented for cloudless, partially-cloudy and overcast conditions. • For beam and global radiation, the nRMSE value is lower than 10% under clear skies

  14. L band microwave remote sensing and land data assimilation improve the representation of prestorm soil moisture conditions for hydrologic forecasting

    Science.gov (United States)

    Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

    2017-06-01

    Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total streamflow divided by total rainfall accumulation in depth units) and prestorm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting streamflow response to future rainfall events.type="synopsis">type="main">Plain Language SummaryForecasting streamflow conditions is important for minimizing loss of life and property during flooding and adequately planning for low streamflow conditions accompanying drought. One way to improve these forecasts is measuring the amount of water in the soil—since soil moisture conditions determine what fraction of rainfall will run off horizontally into stream channels (versus vertically infiltrate into the soil column). Within the past 5 years, there have been important advances in our ability to monitor soil moisture over large scales using both satellite-based sensors and the application of new land data assimilation techniques. This paper illustrates that these advances have significantly improved our capacity to forecast how much streamflow will be generated by future precipitation events. These results may eventually be used by operational forecasters to improve flash flood forecasting and agricultural water use management.

  15. GARCH based artificial neural networks in forecasting conditional variance of stock returns

    Directory of Open Access Journals (Sweden)

    Josip Arnerić

    2014-12-01

    Full Text Available Portfolio managers, option traders and market makers are all interested in volatility forecasting in order to get higher profits or less risky positions. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most popular models in modelling volatility are GARCH type models because they can account excess kurtosis and asymmetric effects of financial time series. A standard GARCH(1,1 model usually indicates high persistence in the conditional variance, which may originate from structural changes. The first objective of this paper is to develop a parsimonious neural networks (NN model, which can capture the nonlinear relationship between past return innovations and conditional variance. Therefore, the goal is to develop a neural network with an appropriate recurrent connection in the context of nonlinear ARMA models, i.e., the Jordan neural network (JNN. The second objective of this paper is to determine if JNN outperforms the standard GARCH model. Out-of-sample forecasts of the JNN and the GARCH model will be compared to determine their predictive accuracy. The data set consists of returns of the CROBEX index daily closing prices obtained from the Zagreb Stock Exchange. The results indicate that the selected JNN(1,1,1 model has superior performances compared to the standard GARCH(1,1 model. The contribution of this paper can be seen in determining the appropriate NN that is comparable to the standard GARCH(1,1 model and its application in forecasting conditional variance of stock returns. Moreover, from the econometric perspective, NN models are used as a semi-parametric method that combines flexibility of nonparametric methods and the interpretability of parameters of parametric methods.

  16. Demand Forecasting at Low Aggregation Levels using Factored Conditional Restricted Boltzmann Machine

    DEFF Research Database (Denmark)

    Mocanu, Elena; Nguyen, Phuong H.; Gibescu, Madeleine

    2016-01-01

    approaches have been proposed in the literature. As an evolution of neural network-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by allowing stochastic formulations and bi-directional connections between neurons. In this paper, we investigate a newly...... developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for electrical demand forecasting. The assessment is made on the EcoGrid dataset, originating from the Bornholm island experiment in Denmark, consisting of aggregated...

  17. THE ANALYSIS OF THE COMMODITY PRICE FORECASTING SUCCESS CONSIDERING DIFFERENT LENGTHS OF THE INITIAL CONDITION DRIFT

    Directory of Open Access Journals (Sweden)

    Marcela Lascsáková

    2015-09-01

    Full Text Available In the paper the numerical model based on the exponential approximation of commodity stock exchanges was derived. The price prognoses of aluminium on the London Metal Exchange were determined as numerical solution of the Cauchy initial problem for the 1st order ordinary differential equation. To make the numerical model more accurate the idea of the modification of the initial condition value by the stock exchange was realized. By having analyzed the forecasting success of the chosen initial condition drift types, the initial condition drift providing the most accurate prognoses for the commodity price movements was determined. The suggested modification of the original model made the commodity price prognoses more accurate.

  18. Evaluation of effect of initial condition on seasonal interannual streamflow forecasting system in Rangitata and Waitaiki river basins (New Zealand)

    Science.gov (United States)

    Zammit, C.; Singh, S.; Hreinsson, E.; Woods, R. A.; Clark, M. P.; Hamlet, A. F.

    2012-12-01

    New Zealand currently does not have a centralized, comprehensive, and state-of-the-art system in place for providing operational seasonal to interannual streamflow forecasts to guide water resources management decisions. As a pilot effort, we implement and evaluate an experimental ensemble streamflow forecasting system for the Waitaki and Rangitata River basins on New Zealand's South Island using a hydrologic simulation model (TopNet) and the familiar ensemble streamflow prediction (ESP) paradigm for estimating forecast uncertainty. To provide a comprehensive database for evaluation of the forecasting system, first a set of retrospective model states simulated by the hydrologic model on the first day of each month were archived from 1972-2009. Then, using the hydrologic simulation model, each of these historical model states was paired with the retrospective temperature and precipitation time series from each historical water year to create a database of retrospective hindcasts. Using the resulting database, the relative importance of initial state variables (such as soil moisture and snowpack) as fundamental drivers of uncertainties in forecasts were evaluated for different seasons and lead times. The analysis indicates that the sensitivity of seasonal flow forecast to intial condition uncertainty is limited. As a result seasonal hydrological forecast based on ESP technique may be plausible in South island New Zealand catchment

  19. Quantifying uncertainty in Gulf of Mexico forecasts stemming from uncertain initial conditions

    KAUST Repository

    Iskandarani, Mohamed

    2016-06-09

    Polynomial Chaos (PC) methods are used to quantify the impacts of initial conditions uncertainties on oceanic forecasts of the Gulf of Mexico circulation. Empirical Orthogonal Functions are used as initial conditions perturbations with their modal amplitudes considered as uniformly distributed uncertain random variables. These perturbations impact primarily the Loop Current system and several frontal eddies located in its vicinity. A small ensemble is used to sample the space of the modal amplitudes and to construct a surrogate for the evolution of the model predictions via a nonintrusive Galerkin projection. The analysis of the surrogate yields verification measures for the surrogate\\'s reliability and statistical information for the model output. A variance analysis indicates that the sea surface height predictability in the vicinity of the Loop Current is limited to about 20 days. © 2016. American Geophysical Union. All Rights Reserved.

  20. Using constructed analogs to improve the skill of National Multi-Model Ensemble March–April–May precipitation forecasts in equatorial East Africa

    International Nuclear Information System (INIS)

    Shukla, Shraddhanand; Funk, Christopher; Hoell, Andrew

    2014-01-01

    In this study we implement and evaluate a simple ‘hybrid’ forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble’s (NMME) March–April–May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2°S to 8°N and 36°E to 46°E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The ‘hybrid approach’ described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30°S to 30°N and 30°E to 270°E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45. (letter)

  1. Some Drivers of Change in Forest Conditions in Africa | Kowero ...

    African Journals Online (AJOL)

    Forests in Africa are important to livelihoods of rural communities, as habitats of wildlife, sources of genetic resources and for mitigation to climate change among many other uses. Africa's area under forests is, however, declining at rates faster than those in other continents despite efforts to improve forest management by, ...

  2. Generating Southern Africa Precipitation Forecast Using the FEWS Engine, a New Application for the Google Earth Engine

    Science.gov (United States)

    Landsfeld, M. F.; Hegewisch, K.; Daudert, B.; Morton, C.; Husak, G. J.; Friedrichs, M.; Funk, C. C.; Huntington, J. L.; Abatzoglou, J. T.; Verdin, J. P.

    2016-12-01

    The Famine Early Warning Systems Network (FEWS NET) focuses on food insecurity in developing nations and provides objective, evidence-based analysis to help government decision-makers and relief agencies plan for and respond to humanitarian emergencies. The network of FEWS NET analysts and scientists require flexible, interactive tools to aid in their monitoring and research efforts. Because they often work in bandwidth-limited regions, lightweight Internet tools and services that bypass the need for downloading massive datasets are preferred for their work. To support food security analysis FEWS NET developed a custom interface for the Google Earth Engine (GEE). GEE is a platform developed by Google to support scientific analysis of environmental data in their cloud computing environment. This platform allows scientists and independent researchers to mine massive collections of environmental data, leveraging Google's vast computational resources for purposes of detecting changes and monitoring the Earth's surface and climate. GEE hosts an enormous amount of satellite imagery and climate archives, one of which is the Climate Hazards Group Infrared Precipitation with Stations dataset (CHIRPS). CHIRPS precipitation dataset is a key input for FEWS NET monitoring and forecasting efforts. In this talk we introduce the FEWS Engine interface. We present an application that highlights the utility of FEWS Engine for forecasting the upcoming seasonal precipitation of southern Africa. Specifically, the current state of ENSO is assessed and used to identify similar historical seasons. The FEWS Engine compositing tool is used to examine rainfall and other environmental data for these analog seasons. The application illustrates the unique benefits of using FEWS Engine for on-the-fly food security scenario development.

  3. Profiling of Saharan dust from the Caribbean to western Africa – Part 2: Shipborne lidar measurements versus forecasts

    Directory of Open Access Journals (Sweden)

    A. Ansmann

    2017-12-01

    Full Text Available A unique 4-week ship cruise from Guadeloupe to Cabo Verde in April–May 2013 see part 1, Rittmeister et al. (2017 is used for an in-depth comparison of dust profiles observed with a polarization/Raman lidar aboard the German research vessel Meteor over the remote tropical Atlantic and respective dust forecasts of a regional (SKIRON and two global atmospheric (dust transport models (NMMB/BSC-Dust, MACC/CAMS. New options of model–observation comparisons are presented. We analyze how well the modeled fine dust (submicrometer particles and coarse dust contributions to light extinction and mass concentration match respective lidar observations, and to what extent models, adjusted to aerosol optical thickness observations, are able to reproduce the observed layering and mixing of dust and non-dust (mostly marine aerosol components over the remote tropical Atlantic. Based on the coherent set of dust profiles at well-defined distances from Africa (without any disturbance by anthropogenic aerosol sources over the ocean, we investigate how accurately the models handle dust removal at distances of 1500 km to more than 5000 km west of the Saharan dust source regions. It was found that (a dust predictions are of acceptable quality for the first several days after dust emission up to 2000 km west of the African continent, (b the removal of dust from the atmosphere is too strong for large transport paths in the global models, and (c the simulated fine-to-coarse dust ratio (in terms of mass concentration and light extinction is too high in the models compared to the observations. This deviation occurs initially close to the dust sources and then increases with distance from Africa and thus points to an overestimation of fine dust emission in the models.

  4. Profiling of Saharan dust from the Caribbean to western Africa - Part 2: Shipborne lidar measurements versus forecasts

    Science.gov (United States)

    Ansmann, Albert; Rittmeister, Franziska; Engelmann, Ronny; Basart, Sara; Jorba, Oriol; Spyrou, Christos; Remy, Samuel; Skupin, Annett; Baars, Holger; Seifert, Patric; Senf, Fabian; Kanitz, Thomas

    2017-12-01

    A unique 4-week ship cruise from Guadeloupe to Cabo Verde in April-May 2013 see part 1, Rittmeister et al. (2017) is used for an in-depth comparison of dust profiles observed with a polarization/Raman lidar aboard the German research vessel Meteor over the remote tropical Atlantic and respective dust forecasts of a regional (SKIRON) and two global atmospheric (dust) transport models (NMMB/BSC-Dust, MACC/CAMS). New options of model-observation comparisons are presented. We analyze how well the modeled fine dust (submicrometer particles) and coarse dust contributions to light extinction and mass concentration match respective lidar observations, and to what extent models, adjusted to aerosol optical thickness observations, are able to reproduce the observed layering and mixing of dust and non-dust (mostly marine) aerosol components over the remote tropical Atlantic. Based on the coherent set of dust profiles at well-defined distances from Africa (without any disturbance by anthropogenic aerosol sources over the ocean), we investigate how accurately the models handle dust removal at distances of 1500 km to more than 5000 km west of the Saharan dust source regions. It was found that (a) dust predictions are of acceptable quality for the first several days after dust emission up to 2000 km west of the African continent, (b) the removal of dust from the atmosphere is too strong for large transport paths in the global models, and (c) the simulated fine-to-coarse dust ratio (in terms of mass concentration and light extinction) is too high in the models compared to the observations. This deviation occurs initially close to the dust sources and then increases with distance from Africa and thus points to an overestimation of fine dust emission in the models.

  5. Pavement condition assessment to forecast maintenance program on JKR state roads in Petaling district

    Science.gov (United States)

    Hamsan, R.; Hafiz, H.; Azlan, A.; Keprawi, M. F.; Malik, A. K. A.; Adamuddin, A.; Abdullah, A. H.; Shafie, A. M.

    2018-02-01

    This research allows local authorities to project road maintenance in term of activities and financial expenditure through pavement condition assessment and then Highway Development and Management (HDM-4) analysis. Current form of road maintenance carried out by local authority is on reactive manner where corrective actions were taken based on reports recorded. Some went unrecorded hence causing prolonged damages. This causes the local authority unable to project the required cost to maintain the roads. This affects the socio-economy of the surrounding routes. Hence, it is seen, as preventive maintenance of the roads will provide more feasible option in term of work force and finance to the local authority. To overcome this issue, a preventive model was introduced. This was done through pavement condition assessment (PCA) where analysis was done through HDM-4. Nondestructive test and destructive test were conducted in order to provide an indicator to the road's health. This were then analyzed in HDM-4 where the result was benchmarked with maintenance standard. The scope of this research is set to PCA where DT and NDT were performed on the routes of Petaling and the output is analyzed in HDM-4. The result of this research provides a 10 years forecast maintenance budget in maintaining the roads in Petaling. This allows the local authority to perform good practice in term of maintaining the roads while at the same time helps them in forecasting their budget for the upcoming years. This research will have a strong impact on the local socio-economy as well as local road user confidence towards the authority over good practices. This research can be further expanded to other type of roads as well as highway bridges.

  6. Volatility forecasting and value-at-risk estimation in emerging markets: the case of the stock market index portfolio in South Africa

    Directory of Open Access Journals (Sweden)

    Lumengo Bonga-Bonga

    2011-04-01

    Full Text Available Accurate modelling of volatility is important as it relates to the forecasting of Value-at-Risk (VaR. The RiskMetrics model to forecast volatility is the benchmark in the financial sector. In an important regulatory innovation, the Basel Committee has proposed the use of an internal method for modelling VaR instead of the strict use of the benchmark model. The aim of this paper is to evaluate the performance of RiskMetrics in comparison to other models of volatility forecasting, such as some family classes of the Generalised Auto Regressive Conditional Heteroscedasticity models, in forecasting the VaR in emerging markets. This paper makes use of the stock market index portfolio, the All-Share Index, as a case study to evaluate the market risk in emerging markets. The paper underlines the importance of asymmetric behaviour for VaR forecasting in emerging markets’ economies.

  7. West-WRF Sensitivity to Sea Surface Temperature Boundary Condition in California Precipitation Forecasts of AR Related Events

    Science.gov (United States)

    Zhang, X.; Cornuelle, B. D.; Martin, A.; Weihs, R. R.; Ralph, M.

    2017-12-01

    We evaluated the merit in coastal precipitation forecasts by inclusion of high resolution sea surface temperature (SST) from blended satellite and in situ observations as a boundary condition (BC) to the Weather Research and Forecast (WRF) mesoscale model through simple perturbation tests. Our sensitivity analyses shows that the limited improvement of watershed scale precipitation forecast is credible. When only SST BC is changed, there is an uncertainty introduced because of artificial model state equilibrium and the nonlinear nature of the WRF model system. With the change of SST on the order of a fraction of a degree centigrade, we found that the part of random perturbation forecast response is saturated after 48 hours when it reaches to the order magnitude of the linear response. It is important to update the SST at a shorter time period, so that the independent excited nonlinear modes can cancel each other. The uncertainty in our SST configuration is quantitatively equivalent to adding to a spatially uncorrelated Guasian noise of zero mean and 0.05 degree of standard deviation to the SST. At this random noise perturbation magnitude, the ensemble average behaves well within a convergent range. It is also found that the sensitivity of forecast changes in response to SST changes. This is measured by the ratio of the spatial variability of mean of the ensemble perturbations over the spatial variability of the corresponding forecast. The ratio is about 10% for surface latent heat flux, 5 % for IWV, and less than 1% for surface pressure.

  8. A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator

    Directory of Open Access Journals (Sweden)

    D. Mellor

    2000-01-01

    Full Text Available The need for the development of a method for generating an ensemble of rainfall scenarios, which are conditioned on the observed rainfall, and its place in the HYREX programme is discussed. A review of stochastic models for rainfall, and rainfall forecasting techniques, is followed by a justification for the choice of the Modified Turning Bands (MTB model in this context. This is a stochastic model of rainfall which is continuous over space and time, and which reproduces features of real rainfall fields at four distinct scales: raincells, cluster potential regions, rainbands and the overall outline of a storm at the synoptic scale. The model can be used to produce synthetic data sets, in the same format as data from a radar. An inversion procedure for inferring a construction of the MTB model which generates a given sequence of radar images is described. This procedure is used to generate an ensemble of future rainfall scenarios which are consistent with a currently observed storm. The combination of deterministic modelling at the large scales and stochastic modelling at smaller scales, within the MTB model, makes the system particularly suitable for short-term forecasts. As the lead time increases, so too does the variability across the set of generated scenarios. Keywords: MTB model, space-time rainfall field model, rainfall radar, HYREX, real-time flow forecasting

  9. Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional "Upwind" Scheme.

    Science.gov (United States)

    Owens, Mathew J; Riley, Pete

    2017-11-01

    Long lead-time space-weather forecasting requires accurate prediction of the near-Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near-Sun solar wind and magnetic field conditions provide the inner boundary condition to three-dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics-based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near-Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near-Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near-Sun solar wind speed at a range of latitudes about the sub-Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun-Earth line. Propagating these conditions to Earth by a three-dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one-dimensional "upwind" scheme is used. The variance in the resulting near-Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996-2016, the upwind ensemble is found to provide a more "actionable" forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

  10. Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional "Upwind" Scheme

    Science.gov (United States)

    Owens, Mathew J.; Riley, Pete

    2017-11-01

    Long lead-time space-weather forecasting requires accurate prediction of the near-Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near-Sun solar wind and magnetic field conditions provide the inner boundary condition to three-dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics-based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near-Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near-Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near-Sun solar wind speed at a range of latitudes about the sub-Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun-Earth line. Propagating these conditions to Earth by a three-dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one-dimensional "upwind" scheme is used. The variance in the resulting near-Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996-2016, the upwind ensemble is found to provide a more "actionable" forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

  11. Moral Education and the Condition of Africa | Nyabul | Thought and ...

    African Journals Online (AJOL)

    This paper explores the relationships among moral education on the one hand, and culture, politics, poverty and religion in Africa on the other. It sets out by examining the theory and practice of moral education, before reflecting on moral education and virtue ethics. Thereafter, the paper examines moral education in African ...

  12. Impact of chemical lateral boundary conditions in a regional air quality forecast model on surface ozone predictions during stratospheric intrusions

    Science.gov (United States)

    Pendlebury, Diane; Gravel, Sylvie; Moran, Michael D.; Lupu, Alexandru

    2018-02-01

    A regional air quality forecast model, GEM-MACH, is used to examine the conditions under which a limited-area air quality model can accurately forecast near-surface ozone concentrations during stratospheric intrusions. Periods in 2010 and 2014 with known stratospheric intrusions over North America were modelled using four different ozone lateral boundary conditions obtained from a seasonal climatology, a dynamically-interpolated monthly climatology, global air quality forecasts, and global air quality reanalyses. It is shown that the mean bias and correlation in surface ozone over the course of a season can be improved by using time-varying ozone lateral boundary conditions, particularly through the correct assignment of stratospheric vs. tropospheric ozone along the western lateral boundary (for North America). Part of the improvement in surface ozone forecasts results from improvements in the characterization of near-surface ozone along the lateral boundaries that then directly impact surface locations near the boundaries. However, there is an additional benefit from the correct characterization of the location of the tropopause along the western lateral boundary such that the model can correctly simulate stratospheric intrusions and their associated exchange of ozone from stratosphere to troposphere. Over a three-month period in spring 2010, the mean bias was seen to improve by as much as 5 ppbv and the correlation by 0.1 depending on location, and on the form of the chemical lateral boundary condition.

  13. Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model

    Directory of Open Access Journals (Sweden)

    Adeboye Azeez

    2016-07-01

    Full Text Available Background: Tuberculosis (TB is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA and neural network auto-regression (SARIMA-NNAR models of TB incidence and analyse its seasonality in South Africa. Methods: TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net. A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE, root mean square error (RMSE, mean absolute error (MAE, mean percent error (MPE, mean absolute scaled error (MASE and mean absolute percentage error (MAPE were applied to assess the better performance of prediction between the models. Results: Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC, second-order AIC (AICc and Bayesian information criterion (BIC are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. Conclusions: The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute

  14. Groundwater ecohydrology: GIScience tools to forecast change and sustainability of global ecosystems, studies in Africa, Europe and North America

    Science.gov (United States)

    Steward, D. R.; de Lange, W. J.; Yang, X.; Vasak, S. L.; Olsthoorn, T. N.

    2009-03-01

    This study examines the interface between groundwater hydrology and ecology, and addresses a scientific grand challenge to develop a comprehensive, systematic understanding of continental water dynamics by linking the hydrosphere and biosphere. There exists a current lack of data interoperability between groundwater modeling tools due to differences in numerical techniques - Analytic Element Method (AEM), Finite Difference Method (FDM), and Finite Element Method (FEM) - which lend themselves well to either vector or raster data, and legacy input/output file formats that are not well suited across models. Nonetheless, investigative computational tools are all founded in the same conceptualization of hydrologic properties associated with mass, flux, pathways and residence time. A consistent framework is developed using modern Geographic Information Science (GIScience) methods to organize and archive important information from international datasets and previous groundwater ecohydrology studies organized around aquifer and water point, line, polygon and raster features. Case studies illustrate the efficacy of this platform to address existing data interoperability issues for representative groundwater ecohydrology problems of global significance including the impact of human-induced forcings, change in species, and forcings by natural processes on groundwater ecohydrology. In North America, we study the relationships between groundwater pumping in the Ogallala Aquifer and changes in riparian habitat and phreatophyte species composition. In Europe, we study the impacts of changes in forest species composition on groundwater recharge and baseflow to biologically diverse fens and wetlands in the Veluwe sand hill region of The Netherlands. In Africa, we study the wetlands of the Okavango Delta in Botswana that forms an oasis in the midst of the Kalahari Desert and the role of groundwater in flushing salts from this freshwater ecosystem. In each study, we document the

  15. Evaluating Weather Research and Forecasting Model Sensitivity to Land and Soil Conditions Representative of Karst Landscapes

    Science.gov (United States)

    Johnson, Christopher M.; Fan, Xingang; Mahmood, Rezaul; Groves, Chris; Polk, Jason S.; Yan, Jun

    2017-10-01

    Due to their particular physiographic, geomorphic, soil cover, and complex surface-subsurface hydrologic conditions, karst regions produce distinct land-atmosphere interactions. It has been found that floods and droughts over karst regions can be more pronounced than those in non-karst regions following a given rainfall event. Five convective weather events are simulated using the Weather Research and Forecasting model to explore the potential impacts of land-surface conditions on weather simulations over karst regions. Since no existing weather or climate model has the ability to represent karst landscapes, simulation experiments in this exploratory study consist of a control (default land-cover/soil types) and three land-surface conditions, including barren ground, forest, and sandy soils over the karst areas, which mimic certain karst characteristics. Results from sensitivity experiments are compared with the control simulation, as well as with the National Centers for Environmental Prediction multi-sensor precipitation analysis Stage-IV data, and near-surface atmospheric observations. Mesoscale features of surface energy partition, surface water and energy exchange, the resulting surface-air temperature and humidity, and low-level instability and convective energy are analyzed to investigate the potential land-surface impact on weather over karst regions. We conclude that: (1) barren ground used over karst regions has a pronounced effect on the overall simulation of precipitation. Barren ground provides the overall lowest root-mean-square errors and bias scores in precipitation over the peak-rain periods. Contingency table-based equitable threat and frequency bias scores suggest that the barren and forest experiments are more successful in simulating light to moderate rainfall. Variables dependent on local surface conditions show stronger contrasts between karst and non-karst regions than variables dominated by large-scale synoptic systems; (2) significant

  16. Evaluating Weather Research and Forecasting Model Sensitivity to Land and Soil Conditions Representative of Karst Landscapes

    Science.gov (United States)

    Johnson, Christopher M.; Fan, Xingang; Mahmood, Rezaul; Groves, Chris; Polk, Jason S.; Yan, Jun

    2018-03-01

    Due to their particular physiographic, geomorphic, soil cover, and complex surface-subsurface hydrologic conditions, karst regions produce distinct land-atmosphere interactions. It has been found that floods and droughts over karst regions can be more pronounced than those in non-karst regions following a given rainfall event. Five convective weather events are simulated using the Weather Research and Forecasting model to explore the potential impacts of land-surface conditions on weather simulations over karst regions. Since no existing weather or climate model has the ability to represent karst landscapes, simulation experiments in this exploratory study consist of a control (default land-cover/soil types) and three land-surface conditions, including barren ground, forest, and sandy soils over the karst areas, which mimic certain karst characteristics. Results from sensitivity experiments are compared with the control simulation, as well as with the National Centers for Environmental Prediction multi-sensor precipitation analysis Stage-IV data, and near-surface atmospheric observations. Mesoscale features of surface energy partition, surface water and energy exchange, the resulting surface-air temperature and humidity, and low-level instability and convective energy are analyzed to investigate the potential land-surface impact on weather over karst regions. We conclude that: (1) barren ground used over karst regions has a pronounced effect on the overall simulation of precipitation. Barren ground provides the overall lowest root-mean-square errors and bias scores in precipitation over the peak-rain periods. Contingency table-based equitable threat and frequency bias scores suggest that the barren and forest experiments are more successful in simulating light to moderate rainfall. Variables dependent on local surface conditions show stronger contrasts between karst and non-karst regions than variables dominated by large-scale synoptic systems; (2) significant

  17. Satellite-based hybrid drought monitoring tool for prediction of vegetation condition in Eastern Africa: A case study for Ethiopia

    Science.gov (United States)

    Tadesse, Tsegaye; Demisse, Getachew Berhan; Zaitchik, Ben; Dinku, Tufa

    2014-03-01

    An experimental drought monitoring tool has been developed that predicts the vegetation condition (Vegetation Outlook) using a regression-tree technique at a monthly time step during the growing season in Eastern Africa. This prediction tool (VegOut-Ethiopia) is demonstrated for Ethiopia as a case study. VegOut-Ethiopia predicts the standardized values of the Normalized Difference Vegetation Index (NDVI) at multiple time steps (weeks to months into the future) based on analysis of "historical patterns" of satellite, climate, and oceanic data over historical records. The model underlying VegOut-Ethiopia capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Niño and the Southern Oscillation (ENSO)) expressed over the 24 year data record and also considers several environmental characteristics (e.g., land cover and elevation) that influence vegetation's response to weather conditions to produce 8 km maps that depict future general vegetation conditions. VegOut-Ethiopia could provide vegetation monitoring capabilities at local, national, and regional levels that can complement more traditional remote sensing-based approaches that monitor "current" vegetation conditions. The preliminary results of this case study showed that the models were able to predict the vegetation stress (both spatial extent and severity) in drought years 1-3 months ahead during the growing season in Ethiopia. The correlation coefficients between the predicted and satellite-observed vegetation condition range from 0.50 to 0.90. Based on the lessons learned from past research activities and emerging experimental forecast models, future studies are recommended that could help Eastern Africa in advancing knowledge of climate, remote sensing, hydrology, and water resources.

  18. Comparative study of holt-winters triples exponential smoothing and seasonal Arima: Forecasting short term seasonal car sales in South Africa

    Directory of Open Access Journals (Sweden)

    Katleho Daniel Makatjane

    2016-02-01

    Full Text Available In this paper, both Seasonal ARIMA and Holt-Winters models are developed to predict the monthly car sales in South Africa using data for the period of January 1994 to December 2013. The purpose of this study is to choose an optimal model suited for the sector. The three error metrics; mean absolute error, mean absolute percentage error and root mean square error were used in making such a choice. Upon realizing that the three forecast errors could not provide concrete basis to make conclusion, the power test was calculated for each model proving Holt-Winters to having about 0.3% more predictive power. Empirical results also indicate that Holt-Winters model produced more precise short-term seasonal forecasts. The findings also revealed a structural break in April 2009, implying that the car industry was significantly affected by the 2008 and 2009 US financial crisis

  19. Technology data characterizing space conditioning in commercial buildings: Application to end-use forecasting with COMMEND 4.0

    Energy Technology Data Exchange (ETDEWEB)

    Sezgen, O.; Franconi, E.M.; Koomey, J.G.; Greenberg, S.E.; Afzal, A.; Shown, L.

    1995-12-01

    In the US, energy consumption is increasing most rapidly in the commercial sector. Consequently, the commercial sector is becoming an increasingly important target for state and federal energy policies and also for utility-sponsored demand side management (DSM) programs. The rapid growth in commercial-sector energy consumption also makes it important for analysts working on energy policy and DSM issues to have access to energy end-use forecasting models that include more detailed representations of energy-using technologies in the commercial sector. These new forecasting models disaggregate energy consumption not only by fuel type, end use, and building type, but also by specific technology. The disaggregation of space conditioning end uses in terms of specific technologies is complicated by several factors. First, the number of configurations of heating, ventilating, and air conditioning (HVAC) systems and heating and cooling plants is very large. Second, the properties of the building envelope are an integral part of a building`s HVAC energy consumption characteristics. Third, the characteristics of commercial buildings vary greatly by building type. The Electric Power Research Institute`s (EPRI`s) Commercial End-Use Planning System (COMMEND 4.0) and the associated data development presented in this report attempt to address the above complications and create a consistent forecasting framework. This report describes the process by which the authors collected space-conditioning technology data and then mapped it into the COMMEND 4.0 input format. The data are also generally applicable to other end-use forecasting frameworks for the commercial sector.

  20. Forecasting of the industrial power consumption in the conditions of volatility price signals

    Directory of Open Access Journals (Sweden)

    Igor Aleksandrovich Baev

    2012-12-01

    Full Text Available Article is devoted to problems of purchase of the electric power in the wholesale market for the industry of Russia. Authors considered the mechanism of pricing and various combinations between the prices of the market for days forward and the prices of the balancing market. Favorable and adverseratios between the prices of the balancing market and submitted plans for power consumption are revealed. The urgency of forecasting of the industrial power consumption, allowing providing a sustainable development not only power supply systems and the power companies, but also region economy as a whole is proved. Recommendations about improvement of forecasting of the power consumption, based on the account not only the factors defining requirement for the electric power, but also factors considering tendencies of the balancing market are offered. As methods of forecasting sharing of methods of the regression analysis and method of expert evaluations is offered. Results of research will allow to increase accuracy of forecasting and to reduce financial losses not only at level of the concrete enterprises, but also at region level as a whole.

  1. Long Range River Discharge Forecasting Using the Gravity Recovery and Climate Experiment (GRACE) Satellite to Predict Conditions for Endemic Cholera

    Science.gov (United States)

    Jutla, A.; Akanda, A. S.; Colwell, R. R.

    2014-12-01

    Prediction of conditions of an impending disease outbreak remains a challenge but is achievable if the associated and appropriate large scale hydroclimatic process can be estimated in advance. Outbreaks of diarrheal diseases such as cholera, are related to episodic seasonal variability in river discharge in the regions where water and sanitation infrastructure are inadequate and insufficient. However, forecasting river discharge, few months in advance, remains elusive where cholera outbreaks are frequent, probably due to non-availability of geophysical data as well as transboundary water stresses. Here, we show that satellite derived water storage from Gravity Recovery and Climate Experiment Forecasting (GRACE) sensors can provide reliable estimates on river discharge atleast two months in advance over regional scales. Bayesian regression models predicted flooding and drought conditions, a prerequisite for cholera outbreaks, in Bengal Delta with an overall accuracy of 70% for upto 60 days in advance without using any other ancillary ground based data. Forecasting of river discharge will have significant impacts on planning and designing intervention strategies for potential cholera outbreaks in the coastal regions where the disease remain endemic and often fatal.

  2. Forecasting of flowrate under rolling motion flow instability condition based on on-line sequential extreme learning machine

    International Nuclear Information System (INIS)

    Chen Hanying; Gao Puzhen; Tan Sichao; Tang Jiguo; Hou Xiaofan; Xu Huiqiang; Wu Xiangcheng

    2015-01-01

    The coupling of multiple thermal-hydraulic parameters can result in complex flow instability in natural circulation system under rolling motion. A real-time thermal-hydraulic condition prediction is helpful to the operation of systems in such condition. A single hidden layer feedforward neural networks algorithm named extreme learning machine (ELM) is considered as suitable method for this application because of its extremely fast training time, good accuracy and simplicity. However, traditional ELM assumes that all the training data are ready before the training process, while the training data is received sequentially in practical forecasting of flowrate. Therefore, this paper proposes a forecasting method for flowrate under rolling motion based on on-line sequential ELM (OS-ELM), which can learn the data one by one or chunk-by-chunk. The experiment results show that the OS-ELM method can achieve a better forecasting performance than basic ELM method and still keep the advantage of fast training and simplicity. (author)

  3. Humans as Sensors: Assessing the Information Value of Qualitative Farmer's Crop Condition Surveys for Crop Yield Monitoring and Forecasting

    Science.gov (United States)

    Beguería, S.

    2017-12-01

    While large efforts are devoted to developing crop status monitoring and yield forecasting systems trough the use of Earth observation data (mostly remotely sensed satellite imagery) and observational and modeled weather data, here we focus on the information value of qualitative data on crop status from direct observations made by humans. This kind of data has a high value as it reflects the expert opinion of individuals directly involved in the development of the crop. However, they have issues that prevent their direct use in crop monitoring and yield forecasting systems, such as their non-spatially explicit nature, or most importantly their qualitative nature. Indeed, while the human brain is good at categorizing the status of physical systems in terms of qualitative scales (`very good', `good', `fair', etcetera), it has difficulties in quantifying it in physical units. This has prevented the incorporation of this kind of data into systems that make extensive use of numerical information. Here we show an example of using qualitative crop condition data to estimate yields of the most important crops in the US early in the season. We use USDA weekly crop condition reports, which are based on a sample of thousands of reporters including mostly farmers and people in direct contact with them. These reporters provide subjective evaluations of crop conditions, in a scale including five levels ranging from `very poor' to `excellent'. The USDA report indicates, for each state, the proportion of reporters fort each condition level. We show how is it possible to model the underlying non-observed quantitative variable that reflects the crop status on each state, and how this model is consistent across states and years. Furthermore, we show how this information can be used to monitor the status of the crops and to produce yield forecasts early in the season. Finally, we discuss approaches for blending this information source with other, more classical earth data sources

  4. An experimental seasonal hydrological forecasting system over the Yellow River basin - Part 1: Understanding the role of initial hydrological conditions

    Science.gov (United States)

    Yuan, Xing; Ma, Feng; Wang, Linying; Zheng, Ziyan; Ma, Zhuguo; Ye, Aizhong; Peng, Shaoming

    2016-06-01

    The hydrological cycle over the Yellow River has been altered by the climate change and human interventions greatly during past decades, with a decadal drying trend mixed with a large variation of seasonal hydrological extremes. To provide support for the adaptation to a changing environment, an experimental seasonal hydrological forecasting system is established over the Yellow River basin. The system draws from a legacy of a global hydrological forecasting system that is able to make use of real-time seasonal climate predictions from North American Multimodel Ensemble (NMME) climate models through a statistical downscaling approach but with a higher resolution and a spatially disaggregated calibration procedure that is based on a newly compiled hydrological observation dataset with 5 decades of naturalized streamflow at 12 mainstream gauges and a newly released meteorological observation dataset including 324 meteorological stations over the Yellow River basin. While the evaluation of the NMME-based seasonal hydrological forecasting will be presented in a companion paper to explore the added values from climate forecast models, this paper investigates the role of initial hydrological conditions (ICs) by carrying out 6-month Ensemble Streamflow Prediction (ESP) and reverse ESP-type simulations for each calendar month during 1982-2010 with the hydrological models in the forecasting system, i.e., a large-scale land surface hydrological model and a global routing model that is regionalized over the Yellow River. In terms of streamflow predictability, the ICs outweigh the meteorological forcings up to 2-5 months during the cold and dry seasons, but the latter prevails over the former in the predictability after the first month during the warm and wet seasons. For the streamflow forecasts initialized at the end of the rainy season, the influence of ICs for lower reaches of the Yellow River can be 5 months longer than that for the upper reaches, while such a difference

  5. Seasonal forecasting of synoptic type variability: potential intraseasonal predictability relevant to the Cape south coast of South Africa

    CSIR Research Space (South Africa)

    Engelbrecht, CJ

    2015-09-01

    Full Text Available systems. Weather and Forecasting. 27: 489-501. DOI: 10.1175/WAF-D-11-00078.1 MacLachlan, C., Arribas, A., Peterson, K.A., Maidens, A., Fereday, D., Scaife, A.A., Gordan, M., Vellinga, M., Williams, A., Comer, R.E., Camp, J., Xavier, P. and Madec, G...

  6. The Advantages of Hybrid 4DEnVar in the Context of the Forecast Sensitivity to Initial Conditions

    Science.gov (United States)

    Song, Hyo-Jong; Shin, Seoleun; Ha, Ji-Hyun; Lim, Sujeong

    2017-11-01

    Hybrid four-dimensional ensemble variational data assimilation (hybrid 4DEnVar) is a prospective successor to three-dimensional variational data assimilation (3DVar) in operational weather prediction centers currently developing a new weather prediction model and those that do not operate adjoint models. In experiments using real observations, hybrid 4DEnVar improved Northern Hemisphere (NH; 20°N-90°N) 500 hPa geopotential height forecasts up to 5 days in a NH summer month compared to 3DVar, with statistical significance. This result is verified against ERA-Interim through a Monte Carlo test. By a regression analysis, the sensitivity of 5 day forecast is associated with the quality of the initial condition. The increased analysis skill for midtropospheric midlatitude temperature and subtropical moisture has the most apparent effect on forecast skill in the NH including a typhoon prediction case. Through attributing the analysis improvements by hybrid 4DEnVar separately to the ensemble background error covariance (BEC), its four-dimensional (4-D) extension, and climatological BEC, it is revealed that the ensemble BEC contributes to the subtropical moisture analysis, whereas the 4-D extension does to the midtropospheric midlatitude temperature. This result implies that hourly wind-mass correlation in 6 h analysis window is required to extract the potential of hybrid 4DEnVar for the midlatitude temperature analysis to the maximum. However, the temporal ensemble correlation, in hourly time scale, between moisture and another variable is invalid so that it could not work for improving the hybrid 4DEnVar analysis.

  7. The Use of Rainfall Forecasts as a Decision Guide for Small-Scale Farming in Limpopo Province, South Africa

    Science.gov (United States)

    Moeletsi, M. E.; Mellaart, E. A. R.; Mpandeli, N. S.; Hamandawana, H.

    2013-01-01

    Purpose: New innovative ways of communicating agrometeorological information are needed to help farmers, especially subsistence/small-scale farmers, to cope with the high climate variability experienced in most parts of southern Africa. Design/methodology/approach: The article introduces an early warning system for farmers. It utilizes short…

  8. Mechanistic Drifting Forecast Model for A Small Semi-Submersible Drifter Under Tide-Wind-Wave Conditions

    Science.gov (United States)

    Zhang, Wei-Na; Huang, Hui-ming; Wang, Yi-gang; Chen, Da-ke; Zhang, lin

    2018-03-01

    Understanding the drifting motion of a small semi-submersible drifter is of vital importance regarding monitoring surface currents and the floating pollutants in coastal regions. This work addresses this issue by establishing a mechanistic drifting forecast model based on kinetic analysis. Taking tide-wind-wave into consideration, the forecast model is validated against in situ drifting experiment in the Radial Sand Ridges. Model results show good performance with respect to the measured drifting features, characterized by migrating back and forth twice a day with daily downwind displacements. Trajectory models are used to evaluate the influence of the individual hydrodynamic forcing. The tidal current is the fundamental dynamic condition in the Radial Sand Ridges and has the greatest impact on the drifting distance. However, it loses its leading position in the field of the daily displacement of the used drifter. The simulations reveal that different hydrodynamic forces dominate the daily displacement of the used drifter at different wind scales. The wave-induced mass transport has the greatest influence on the daily displacement at Beaufort wind scale 5-6; while wind drag contributes mostly at wind scale 2-4.

  9. Africa

    DEFF Research Database (Denmark)

    Mol, Michael J.; Stadler, Christian; Ariño, Africa

    2017-01-01

    Context matters in the global strategy literature. We discuss how Africa, as a setting that received limited attention in the past, offers opportunity to challenge existing theory and develop new insights. The overall goal is to ask: What will the field of global strategic management look like on...... we have engaged with Africa in a similar manner as we have done with other emerging economies? We also introduce the papers published in this special issue and highlight directions for future research.......Context matters in the global strategy literature. We discuss how Africa, as a setting that received limited attention in the past, offers opportunity to challenge existing theory and develop new insights. The overall goal is to ask: What will the field of global strategic management look like once...

  10. Africa

    OpenAIRE

    Vidal, Dominique

    2017-01-01

    Laurent Fourchard & Aurelia Segatti, eds, Africa, 2015, 85 (1) : The Politics of Exclusion and Inclusion in Africa, Cambridge, Cambridge University Press, 2015, 151 p. La vie en commun dans les villes africaines a été étudiée aussi bien par le prisme des conflits opposant des groupes définis par une appartenance revendiquée ou attribuée, que par celui des multiples formes d’échange et de coopération qui, à l’inverse, voient des individus d’origines diverses dépasser les assignations identitai...

  11. Development of a decision support system for monitoring, reporting and forecasting ecological conditions of the Appalachian Trail

    Science.gov (United States)

    Wang, Yeqiao; Nemani, Ramakrishna; Dieffenbach, Fred; Stolte, Kenneth; Holcomb, Glenn B.; Robinson, Matt; Reese, Casey C.; McNiff, Marcia; Duhaime, Roland; Tierney, Geri; Mitchell, Brian; August, Peter; Paton, Peter; LaBash, Charles

    2010-01-01

    This paper introduces a collaborative multi-agency effort to develop an Appalachian Trail (A.T.) MEGA-Transect Decision Support System (DSS) for monitoring, reporting and forecasting ecological conditions of the A.T. and the surrounding lands. The project is to improve decisionmaking on management of the A.T. by providing a coherent framework for data integration, status reporting and trend analysis. The A.T. MEGA-Transect DSS is to integrate NASA multi-platform sensor data and modeling through the Terrestrial Observation and Prediction System (TOPS) and in situ measurements from A.T. MEGA-Transect partners to address identified natural resource priorities and improve resource management decisions.

  12. The Condition of Young Children in Sub-Saharan Africa: The Convergence of Health, Nutrition, and Early Education. World Bank Technical Paper No. 326, Africa Technical Department Series.

    Science.gov (United States)

    Colletta, Nat J.; And Others

    In Sub-Saharan Africa, severe adverse conditions have placed children at high risk: persistent and worsening poverty, rapid economic change and population growth, increasing urbanization, a changing family structure, growing numbers of orphaned refugees, and displaced women and children from internal civil strife. These conditions make a viable…

  13. In Brief: Forecasting meningitis threats

    Science.gov (United States)

    Showstack, Randy

    2008-12-01

    The University Corporation for Atmospheric Research (UCAR), in conjunction with a team of health and weather organizations, has launched a project to provide weather forecasts to medical officials in Africa to help reduce outbreaks of meningitis. The forecasts will enable local health care providers to target vaccination programs more effectively. In 2009, meteorologists with the National Center for Atmospheric Research, which is managed by UCAR, will begin issuing 14-day forecasts of atmospheric conditions in Ghana. Later, UCAR plans to work closely with health experts from several African countries to design and test a decision support system to provide health officials with useful meteorological information. ``By targeting forecasts in regions where meningitis is a threat, we may be able to help vulnerable populations. Ultimately, we hope to build on this project and provide information to public health programs battling weather-related diseases in other parts of the world,'' said Rajul Pandya, director of UCAR's Community Building Program. Funding for the project comes from a $900,000 grant from Google.org, the philanthropic arm of the Internet search company.

  14. Probabilistic Solar Wind Forecasting Using Large Ensembles of Near‐Sun Conditions With a Simple One‐Dimensional “Upwind” Scheme

    Science.gov (United States)

    Riley, Pete

    2017-01-01

    Abstract Long lead‐time space‐weather forecasting requires accurate prediction of the near‐Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near‐Sun solar wind and magnetic field conditions provide the inner boundary condition to three‐dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics‐based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near‐Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near‐Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near‐Sun solar wind speed at a range of latitudes about the sub‐Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun‐Earth line. Propagating these conditions to Earth by a three‐dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one‐dimensional “upwind” scheme is used. The variance in the resulting near‐Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996–2016, the upwind ensemble is found to provide a more “actionable” forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large). PMID:29398982

  15. Urban fine-scale forecasting reveals weather conditions with unprecedented detail

    NARCIS (Netherlands)

    Ronda, R.J.; Steeneveld, G.J.; Heusinkveld, B.G.; Attema, Jisk; Holtslag, A.A.M.

    2017-01-01

    Feasibility of Numerical Weather Prediction at urban neighborhood and street scales demonstrated for summer conditions in the Amsterdam metropolitan region (Netherlands). As the number of urban dwellers increases from an estimated 4 billion in 2014 to an expected 6.5 billion by 2050 (UN 2014),

  16. Driving range estimation for electric vehicles based on driving condition identification and forecast

    Directory of Open Access Journals (Sweden)

    Chaofeng Pan

    2017-10-01

    Full Text Available With the impact of serious environmental pollution in our cities combined with the ongoing depletion of oil resources, electric vehicles are becoming highly favored as means of transport. Not only for the advantage of low noise, but for their high energy efficiency and zero pollution. The Power battery is used as the energy source of electric vehicles. However, it does currently still have a few shortcomings, noticeably the low energy density, with high costs and short cycle life results in limited mileage compared with conventional passenger vehicles. There is great difference in vehicle energy consumption rate under different environment and driving conditions. Estimation error of current driving range is relatively large due to without considering the effects of environmental temperature and driving conditions. The development of a driving range estimation method will have a great impact on the electric vehicles. A new driving range estimation model based on the combination of driving cycle identification and prediction is proposed and investigated. This model can effectively eliminate mileage errors and has good convergence with added robustness. Initially the identification of the driving cycle is based on Kernel Principal Component feature parameters and fuzzy C referring to clustering algorithm. Secondly, a fuzzy rule between the characteristic parameters and energy consumption is established under MATLAB/Simulink environment. Furthermore the Markov algorithm and BP(Back Propagation neural network method is utilized to predict the future driving conditions to improve the accuracy of the remaining range estimation. Finally, driving range estimation method is carried out under the ECE 15 condition by using the rotary drum test bench, and the experimental results are compared with the estimation results. Results now show that the proposed driving range estimation method can not only estimate the remaining mileage, but also eliminate the

  17. Driving range estimation for electric vehicles based on driving condition identification and forecast

    Science.gov (United States)

    Pan, Chaofeng; Dai, Wei; Chen, Liao; Chen, Long; Wang, Limei

    2017-10-01

    With the impact of serious environmental pollution in our cities combined with the ongoing depletion of oil resources, electric vehicles are becoming highly favored as means of transport. Not only for the advantage of low noise, but for their high energy efficiency and zero pollution. The Power battery is used as the energy source of electric vehicles. However, it does currently still have a few shortcomings, noticeably the low energy density, with high costs and short cycle life results in limited mileage compared with conventional passenger vehicles. There is great difference in vehicle energy consumption rate under different environment and driving conditions. Estimation error of current driving range is relatively large due to without considering the effects of environmental temperature and driving conditions. The development of a driving range estimation method will have a great impact on the electric vehicles. A new driving range estimation model based on the combination of driving cycle identification and prediction is proposed and investigated. This model can effectively eliminate mileage errors and has good convergence with added robustness. Initially the identification of the driving cycle is based on Kernel Principal Component feature parameters and fuzzy C referring to clustering algorithm. Secondly, a fuzzy rule between the characteristic parameters and energy consumption is established under MATLAB/Simulink environment. Furthermore the Markov algorithm and BP(Back Propagation) neural network method is utilized to predict the future driving conditions to improve the accuracy of the remaining range estimation. Finally, driving range estimation method is carried out under the ECE 15 condition by using the rotary drum test bench, and the experimental results are compared with the estimation results. Results now show that the proposed driving range estimation method can not only estimate the remaining mileage, but also eliminate the fluctuation of the

  18. Southern Hemisphere anticyclonic circulation drives oceanic and climatic conditions in late Holocene southernmost Africa

    Science.gov (United States)

    Hahn, Annette; Schefuß, Enno; Andò, Sergio; Cawthra, Hayley C.; Frenzel, Peter; Kugel, Martin; Meschner, Stephanie; Mollenhauer, Gesine; Zabel, Matthias

    2017-06-01

    Due to the high sensitivity of southern Africa to climate change, a reliable understanding of its hydrological system is crucial. Recent studies of the regional climatic system have revealed a highly complex interplay of forcing factors on precipitation regimes. This includes the influence of the tropical easterlies, the strength of the southern hemispheric westerlies as well as sea surface temperatures along the coast of the subcontinent. However, very few marine records have been available in order to study the coupling of marine and atmospheric circulation systems. Here we present results from a marine sediment core, recovered in shallow waters off the Gouritz River mouth on the south coast of South Africa. Core GeoB18308-1 allows a closer view of the last ˜ 4 kyr. Climate sensitive organic proxies, like the distribution and isotopic composition of plant-wax lipids as well as indicators for sea surface temperatures and soil input, give information on oceanographic and hydrologic changes during the recorded time period. Moreover, the micropaleontology, mineralogical and elemental composition of the sediments reflect the variability of the terrigenous input to the core site. The combination of down-core sediment signatures and a catchment-wide provenance study indicate that the Little Ice Age ( ˜ 300-650 cal yr BP) was characterized by climatic conditions favorable to torrential flood events. The Medieval Climate Anomaly ( ˜ 950-650 cal yr BP) is expressed by lower sea surface temperatures in the Mossel Bay area and humid conditions in the Gouritz River catchment. These new results suggest that the coincidence of humid conditions and cooler sea surface temperatures along the south coast of South Africa resulted from a strengthened and more southerly anticyclonic circulation. Most probably, the transport of moisture from the Indian Ocean by strong subtropical easterlies was coupled with Agulhas Bank upwelling pulses, which were initiated by an increase in

  19. Relation of weather forecasts to the prediction of dangerous forest fire conditions

    Science.gov (United States)

    R. H. Weidman

    1923-01-01

    The purpose of predicting dangerous forest-fire conditions, of course, is to reduce the great cost and damage caused by forest fires. In the region of Montana and northern Idaho alone the average cost to the United States Forest Service of fire protection and suppression is over $1,000,000 a year. Although the causes of forest fires will gradually be reduced by...

  20. INFORMATION-ANALYTICAL SYSTEM OF FORECAST VEGETATION FIRES IN NATURAL CONDITIONS

    Directory of Open Access Journals (Sweden)

    R. M. Kogan

    2015-01-01

    Full Text Available A system for spatial prediction for fire danger as function of weather and pyrological vegetation characteristics was constructed. The method of calculating the time conducted vegetable combustible materials in fire condition of each month of the season was suggested. Calculate the probability of fires and danger periods of plant formations in a monsoon climate. The geographic information system was developed, it was tested in the Middle Amur region in the Russian Far East.

  1. Forecasting changes of hydrological and hydrochemical conditions in the Aral Sea

    Directory of Open Access Journals (Sweden)

    Vakhob Rafikov

    2014-08-01

    Full Text Available The increase of irretrievable river water withdrawals and regulation of river flow has a negative effect on the natural regime of the Aral Sea. The Amu Darya River and the Syr Darya River Basins are the largest irrigated fanning areas. Their favorable soil and climatic conditions ensure guaranteed yields of various crops on irrigated lands. Since 1961, for the drastic increase of irretrievable river water withdrawal, mainly for irrigation, the inflow of river water into the Aral Sea has started to decrease significantly, accordingly the sea's hydrological and hydrochemical regimes disrupted dramatically. The sea level has continued to drop as evaporation exceeds inflow. This negatively transforms the natural environment and worsens socio-economic conditions in Priaralie as a whole, especially in the lower reaches of Amu Darya and Syr Darya, where natural conditions are largely determined by the sea's impact. At present, this causes desertification of the nonirrigated zone in the deltas, spreading to new areas as the Aral Sea dries out.

  2. The paediatric surgeon and his working conditions in Francophone sub-Saharan Africa

    Directory of Open Access Journals (Sweden)

    K Gnassingbé

    2011-01-01

    Full Text Available Background: This study described the current conditions of work of paediatric surgeons in Francophone sub-Saharan Africa (FSSA and set the debate at the level of the humanist thinking in medicine. Patients and Methods: This was a multicentre study from 1 st May to 30 th October 2008. The African Society of paediatric surgeons′ directory was used to identify paediatric surgeons in the Francophone′s countries in Sub Saharan Africa. The parameters studied were number of surgeons per country, means of training, working conditions, remunerations, needs for continuous training and the research. Results: A total of 41 paediatric surgeons (68.33% responded. The average number of paediatric surgeons per country was 5. The means of training included government scholarships among 7 paediatric surgeons (17.07%, scholarship from a non-governmental organisations in 14 (34.15% and self-sponsorships in 20 (48.78%. The average salary was 450 Euros (€ (range: 120-1 400 Euros. Most of the paediatric surgeons (68.29% had internet services for continuous update courses and research. Thirty six paediatric surgeons (87.80% had no subscription to specialised scientific journals. Conclusion: The paediatric surgeon in FSSA faces many problems related to his working and living conditions that may have a negative impact on their competences.

  3. Forecasts of forest conditions in regions of the United States under future scenarios: a technical document supporting the Forest Service 2012 RPA Assessment

    Science.gov (United States)

    David N. Wear; Robert Huggett; Ruhong Li; Benjamin Perryman; Shan Liu

    2013-01-01

    The 626 million acres of forests in the conterminous United States represent significant reserves of biodiversity and terrestrial carbon and provide substantial flows of highly valued ecosystem services, including timber products, watershed protection benefits, and recreation. This report describes forecasts of forest conditions for the conterminous United States in...

  4. Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model.

    Science.gov (United States)

    Olatinwo, Rabiu O; Prabha, Thara V; Paz, Joel O; Hoogenboom, Gerrit

    2012-03-01

    Early leaf spot of peanut (Arachis hypogaea L.), a disease caused by Cercospora arachidicola S. Hori, is responsible for an annual crop loss of several million dollars in the southeastern United States alone. The development of early leaf spot on peanut and subsequent spread of the spores of C. arachidicola relies on favorable weather conditions. Accurate spatio-temporal weather information is crucial for monitoring the progression of favorable conditions and determining the potential threat of the disease. Therefore, the development of a prediction model for mitigating the risk of early leaf spot in peanut production is important. The specific objective of this study was to demonstrate the application of the high-resolution Weather Research and Forecasting (WRF) model for management of early leaf spot in peanut. We coupled high-resolution weather output of the WRF, i.e. relative humidity and temperature, with the Oklahoma peanut leaf spot advisory model in predicting favorable conditions for early leaf spot infection over Georgia in 2007. Results showed a more favorable infection condition in the southeastern coastline of Georgia where the infection threshold were met sooner compared to the southwestern and central part of Georgia where the disease risk was lower. A newly introduced infection threat index indicates that the leaf spot threat threshold was met sooner at Alma, GA, compared to Tifton and Cordele, GA. The short-term prediction of weather parameters and their use in the management of peanut diseases is a viable and promising technique, which could help growers make accurate management decisions, and lower disease impact through optimum timing of fungicide applications.

  5. Estimating conditional probability of volcanic flows for forecasting event distribution and making evacuation decisions

    Science.gov (United States)

    Stefanescu, E. R.; Patra, A.; Sheridan, M. F.; Cordoba, G.

    2012-04-01

    In this study we propose a conditional probability framework for Galeras volcano, which is one of the most active volcanoes on the world. Nearly 400,000 people currently live near the volcano; 10,000 of them reside within the zone of high volcanic hazard. Pyroclastic flows pose a major hazard for this population. Some of the questions we try to answer when studying conditional probabilities for volcanic hazards are: "Should a village be evacuated and villagers moved to a different location?", "Should we construct a road along this valley or along a different one?", "Should this university be evacuated?" Here, we try to identify critical regions such as villages, infrastructures, cities, university to determine their relative probability of inundation in case of an volcanic eruption. In this study, a set of numerical simulation were performed using a computational tool TITAN2D which simulates granular flow over digital representation of the natural terrain. The particular choice from among the methods described below can be based on the amount of information necessary in the evacuation decision and on the complexity of the analysis required in taking such decision. A set of 4200 TITAN2D runs were performed for several different location so that the area of all probably vents is covered. The output of the geophysical model provides a flow map which contains the maximum flow depth over time. Frequency approach - In estimating the conditional probability of volcanic flows we define two discrete random variables (r.v.) A and B, where P(A =1) and P(B=1) represents the probability of having a flow at location A, and B, respectively. For this analysis we choose two critical locations identified by their UTM coordinates. The flow map is then used in identifying at the pixel level, flow or non-flow at the two locations. By counting the number of times there is flow or non-flow, we are able to find the marginal probabilities along with the joint probability associated with an

  6. An ensemble prediction approach to weekly Dengue cases forecasting based on climatic and terrain conditions

    Directory of Open Access Journals (Sweden)

    Sougata Deb

    2017-11-01

    Full Text Available Introduction: Dengue fever has been one of the most concerning endemic diseases of recent times. Every year, 50-100 million people get infected by the dengue virus across the world. Historically, it has been most prevalent in Southeast Asia and the Pacific Islands. In recent years, frequent dengue epidemics have started occurring in Latin America as well. This study focused on assessing the impact of different short and long-term lagged climatic predictors on dengue cases. Additionally, it assessed the impact of building an ensemble model using multiple time series and regression models, in improving prediction accuracy. Materials and Methods: Experimental data were based on two Latin American cities, viz. San Juan (Puerto Rico and Iquitos (Peru. Due to weather and geographic differences, San Juan recorded higher dengue incidences than Iquitos. Using lagged cross-correlations, this study confirmed the impact of temperature and vegetation on the number of dengue cases for both cities, though in varied degrees and time lags. An ensemble of multiple predictive models using an elaborate set of derived predictors was built and validated. Results: The proposed ensemble prediction achieved a mean absolute error of 21.55, 4.26 points lower than the 25.81 obtained by a standard negative binomial model. Changes in climatic conditions and urbanization were found to be strong predictors as established empirically in other researches. Some of the predictors were new and informative, which have not been explored in any other relevant studies yet. Discussion and Conclusions: Two original contributions were made in this research. Firstly, a focused and extensive feature engineering aligned with the mosquito lifecycle. Secondly, a novel covariate pattern-matching based prediction approach using past time series trend of the predictor variables. Increased accuracy of the proposed model over the benchmark model proved the appropriateness of the analytical approach

  7. Application of probabilistic precipitation forecasts from a ...

    African Journals Online (AJOL)

    Application of probabilistic precipitation forecasts from a deterministic model towards increasing the lead-time of flash flood forecasts in South Africa. ... The procedure is applied to a real flash flood event and the ensemble-based rainfall forecasts are verified against rainfall estimated by the SAFFG system. The approach ...

  8. Toward Improved Land Surface Initialization in Support of Regional WRF Forecasts at the Kenya Meteorological Service (KMS)

    Science.gov (United States)

    Case, Jonathan L.; Mungai, John; Sakwa, Vincent; Kabuchanga, Eric; Zavodsky, Bradley T.; Limaye, Ashutosh S.

    2014-01-01

    SPoRT/SERVIR/RCMRD/KMS Collaboration: Builds off strengths of each organization. SPoRT: Transition of satellite, modeling and verification capabilities; SERVIR-Africa/RCMRD: International capacity-building expertise; KMS: Operational organization with regional weather forecasting expertise in East Africa. Hypothesis: Improved land-surface initialization over Eastern Africa can lead to better temperature, moisture, and ultimately precipitation forecasts in NWP models. KMS currently initializes Weather Research and Forecasting (WRF) model with NCEP/Global Forecast System (GFS) model 0.5-deg initial / boundary condition data. LIS will provide much higher-resolution land-surface data at a scale more representative to regional WRF configuration. Future implementation of real-time NESDIS/VIIRS vegetation fraction to further improve land surface representativeness.

  9. Pre-conception counselling for key cardiovascular conditions in Africa: optimising pregnancy outcomes.

    Science.gov (United States)

    Zühlke, Liesl; Acquah, Letitia

    2016-01-01

    The World Health Organisation (WHO) supports pre-conception care (PCC) towards improving health and pregnancy outcomes. PPC entails a continuum of promotive, preventative and curative health and social interventions. PPC identifies current and potential medical problems of women of childbearing age towards strategising optimal pregnancy outcomes, whereas antenatal care constitutes the care provided during pregnancy. Optimised PPC and antenatal care would improve civil society and maternal, child and public health. Multiple factors bar most African women from receiving antenatal care. Additionally, PPC is rarely available as a standard of care in many African settings, despite the high maternal mortality rate throughout Africa. African women and healthcare facilitators must cooperate to strategise cost-effective and cost-efficient PPC. This should streamline their limited resources within their socio-cultural preferences, towards short- and long-term improvement of pregnancy outcomes. This review discusses the relevance of and need for PPC in resource-challenged African settings, and emphasises preventative and curative health interventions for congenital and acquired heart disease. We also consider two additional conditions, HIV/AIDS and hypertension, as these are two of the most important co-morbidities encountered in Africa, with significant burden of disease. Finally we advocate strongly for PPC to be considered as a key intervention for reducing maternal mortality rates on the African continent.

  10. Policy and regulatory framework conditions for small hydro power in Sub-Saharan Africa

    Energy Technology Data Exchange (ETDEWEB)

    Koelling, Fritz [Sustainable Energy and Environment, Karlsruhe (Germany); Gaul, Mirco; Schroeder, Miriam [SiNERGi Consultancy for Renewable Energies, Berlin (Germany)

    2011-07-01

    The vast potential of mini and micro hydro power (MHP) in Sub-Saharan African countries is one promising option to cover increasing energy demand and to enable electricity access for remote rural communities. Based on the analysis of 6 African countries (Ethiopia, Kenya, Mozambique, Nigeria, Rwanda, South Africa), this study sheds light on some of the main barriers on the level of political and regulatory framework conditions which include gap between the national-level policies and regulations and local MHP project implementation, lack of financing and limited capacities for project planning, building and operation. The paper also identifies some promising practices employed in several SSA countries of how to overcome these barriers and concludes with recommendations of how to create positive feed-backs between ambitious policies and regulations and MHP financing and capacity development needs in order to scale up MHP deployment and MHP sector development. (orig.)

  11. Attributing Climate Conditions for Stable Malaria Transmission to Human Activity in sub-Saharan Africa

    Science.gov (United States)

    Sheldrake, L.; Mitchell, D.; Allen, M. R.

    2015-12-01

    Temperature and precipitation limit areas of stable malaria transmission, but the effects of climate change on the disease remain controversial. Previously, studies have not separated the influence of anthropogenic climate change and natural variability, despite being an essential step in the attribution of climate change impacts. Ensembles of 2900 simulations of regional climate in sub-Saharan Africa for the year 2013, one representing realistic conditions and the other how climate might have been in the absence of human influence, were used to force a P.falciparium climate suitability model developed by the Mapping Malaria Risk in Africa project. Strongest signals were detected in areas of unstable transmission, indicating their heightened sensitivity to climatic factors. Evidently, impacts of human-induced climate change were unevenly distributed: the probability of conditions being suitable for stable malaria transmission were substantially reduced (increased) in the Sahel (Greater Horn of Africa (GHOA), particularly in the Ethiopian and Kenyan highlands). The length of the transmission season was correspondingly shortened in the Sahel and extended in the GHOA, by 1 to 2 months, including in Kericho (Kenya), where the role of climate change in driving recent malaria occurrence is hotly contested. Human-induced warming was primarily responsible for positive anomalies in the GHOA, while reduced rainfall caused negative anomalies in the Sahel. The latter was associated with anthropogenic impacts on the West African Monsoon, but uncertainty in the RCM's ability to reproduce precipitation trends in the region weakens confidence in the result. That said, outputs correspond well with broad-scale changes in observed endemicity, implying a potentially important contribution of anthropogenic climate change to the malaria burden during the past century. Results support the health-framing of climate risk and help indicate hotspots of climate vulnerability, providing

  12. The high-resolution global SST forecast set of the CSIR

    CSIR Research Space (South Africa)

    Landman, WA

    2011-09-01

    Full Text Available -RESOLUTION GLOBAL SST FORECAST SET OF THE CSIR Willem A. Landman Council for Scientific and Industrial Research, P. O. Box 395, Pretoria, 0001, South Africa David G. DeWitt and Dong-Eun Lee International Research Institute for Climate and Society, Lamont... seasonal forecast production (e.g. DeWitt, 2005). Furthermore, coupled models can predict both the evolution of SSTs and atmospheric conditions at elevated levels of skill. However, when skilful SST forecasts are used AGCMs may perform equally well...

  13. Spatiotemporal characteristics of severe dry and wet conditions in the Free State Province, South Africa

    Science.gov (United States)

    Mbiriri, M.; Mukwada, G.; Manatsa, D.

    2018-02-01

    This paper assesses the spatiotemporal characteristics of agricultural droughts and wet conditions in the Free State Province of South Africa for the period between 1960 and 2013. Since agriculturally, the Free State Province is considered the bread basket of the country, understanding the variability of drought and wet conditions becomes necessary. The Standardised Precipitation Index (SPI) computed from gridded monthly precipitation data was used to assess the rainfall extreme conditions. Hot spot analysis was used to divide the province into five homogenous clusters where the spatiotemporal characteristics for each cluster were analysed. The results show a west to east increase in seasonal average total precipitation. However, the eastern part of the province demonstrates higher occurrences of droughts, with SPI ≤ - 1.282. This is despite the observation that the region shows a recent increase in droughts unlike the western region. It is also noted that significant differences in drought/wet intensities between clusters are more pronounced during the early compared to the late summer period.

  14. The use of prescribed drugs for common chronic conditions in South Africa in 1998.

    Science.gov (United States)

    Steyn, Krisela; Bradshaw, Debbie; Norman, Rosana; Bradley, Hazel; Laubscher, Ria

    2005-02-01

    To determine the prescribed drug-utilisation pattern for six common chronic conditions in adult South Africans in a cross-sectional survey. 13,826 randomly selected participants, 15 years and older, were surveyed by trained fieldworkers at their homes in 1998. Questionnaires included socio-demographic, chronic-disease and drug-use data. The prescribed drugs were recorded from participants' medication containers. The Anatomical Therapeutic Classification (ATC) code of the drugs for tuberculosis (TB), diabetes, hypertension, hyperlipidaemia, other atherosclerosis-related conditions, such as heart conditions or cerebrovascular accidents (CVA), and asthma or chronic obstructive pulmonary disease (COPD), was recorded. The use of logistic regression analyses identified the determinants of those patients who used prescription medication for these six conditions. 18.4% of the women and 12.5% of the men used drugs for the six chronic conditions. Men used drugs most frequently for hypertension (50.9%) and asthma or chronic bronchitis (24.3%), while in women it was for hypertension (59.9%) and diabetes (17.5%). The logistic regression analyses showed that women, wealthier and older people, and those with medical insurance used these chronic-disease drugs more frequently compared to men, younger or poor people, or those without medical insurance. The African population group used these drugs less frequently than any other ethnic group. The inappropriate use of methyldopa was found for 14.8% of all antihypertensive drugs, while very few people used aspirin. The methodology of this study provides a means of ascertaining the chronic-disease drug-utilisation pattern in national health surveys. The pattern described, suggests an inequitable use of chronic-disease drugs and inadequate use of some effective drugs to control the burden of chronic diseases in South Africa.

  15. The influence of antecedent conditions on flood risk in sub-Saharan Africa

    Science.gov (United States)

    Bischiniotis, Konstantinos; van den Hurk, Bart; Coughlan de Perez, Erin; Jongman, Brenden; Veldkamp, Ted; Aerts, Jeroen

    2017-04-01

    Traditionally, flood risk management has focused on long-term flood protection measures. However, many countries are often not able to afford hard infrastructure that provides sufficient safety levels due to the high investment costs. As a consequence, they rely more on post disaster response and timely warning systems. Most early warning systems have predominantly focused on precipitation as the main predictive factor, having usually lead times of hours or days. However, other variables could also play a role. For instance, anomalous positive water storage, soil saturation and evapotranspiration are physical factors that may influence the length of the flood build-up period. This period can vary from some days to several months before the event and it is particularly important in flood risk management since longer flood warning lead times during this period could result in better flood preparation actions. This study addresses how the antecedent conditions of historical reported flood events over the period 1980 to 2010 in sub-Saharan Africa relate to flood generation. The seasonal-scale conditions are reflected in the Standardized Precipitation Evapotranspiration Index (SPEI), which is calculated using monthly precipitation and temperature data and accounts for the wetness/dryness of an area. Antecedent conditions are separated into a) a short term 'weather-scale' period (0-7 days) and b) a 'seasonal-scale' period (up to 6 months) before the flood event in such a way that they do not overlap. Total 7-day precipitation, which is based on daily meteorological data, was used to evaluate the short-term weather-scale conditions. Using a pair of coordinates, derived from the NatCatSERVICE database on global flood losses, each flood event is positioned on a 0.5°x 0.5° grid cell. The antecedent SPEI conditions of the two periods and their joint influence in flood generation are compared to the same period conditions of the other years of the dataset. First results

  16. Weather forecast

    CERN Document Server

    Courtier, P

    1994-02-07

    Weather prediction is performed using the numerical model of the atmosphere evolution.The evolution equations are derived from the Navier Stokes equation for the adiabatic part but the are very much complicated by the change of phase of water, the radiation porocess and the boundary layer.The technique used operationally is described. Weather prediction is an initial value problem and accurate initial conditions need to be specified. Due to the small number of observations available (105 ) as compared to the dimension of the model state variable (107),the problem is largely underdetermined. Techniques of optimal control and inverse problems are used and have been adapted to the large dimension of our problem. our problem.The at mosphere is a chaotic system; the implication for weather prediction is discussed. Ensemble prediction is used operationally and the technique for generating initial conditions which lead to a numerical divergence of the subsequent forecasts is described.

  17. Forecasting Skill

    Science.gov (United States)

    1981-01-01

    indicated that forecasting experience has little relationship to forecasting performance. In the latter three studies, neophyte forecasters became... Europe . Within a few months after a new commander was assigned, this unit’s performance rose to first place in the theater and remained there

  18. NYHOPS Forecast Model Results

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — 3D Marine Nowcast/Forecast System for the New York Bight NYHOPS subdomain. Currents, waves, surface meteorology, and water conditions.

  19. ENSO forecasts in South Africa

    CSIR Research Space (South Africa)

    Landman, WA

    2015-11-01

    Full Text Available The SST prediction systems currently being used at the Council for Scientific and Industrial Research (CSIR) and the South African Weather Service (SAWS) are presented. In particular, the skill of these systems to predict Niño3.4 SST and how...

  20. Analyzing sanitation characteristics in the urban slums of East Africa

    NARCIS (Netherlands)

    Szanto, G.L.; Letema, S.C.; Tukahirwa, J.; Mgana, S.; Oosterveer, P.J.M.; Buuren, van J.C.L.

    2012-01-01

    Urban slums in East Africa exhibit deplorable sanitary conditions. Despite (inter)national efforts, slum sanitation provision remains inadequate and the projected population growth forecasts a worsening of this crisis. The core of the problem is that available knowledge about the local feasibility

  1. Forecasting olive crop yields based on long-term aerobiological data series and bioclimatic conditions for the southern Iberian Peninsula

    Directory of Open Access Journals (Sweden)

    Fátima Aguilera

    2014-02-01

    Full Text Available In the present study, bio-meteorological models for predicting olive-crop production in the southern Iberian Peninsula were developed. These covered a 16-year period: 1994-2009. The forecasting models were constructed using the partial least-squares regression method, taking the annual olive yield as the dependent variable, and both aerobiological and meteorological parameters as the independent variables. Two regression models were built for the prediction of crop production prior to the final harvest at two different times of the year: July and November. The percentage variance explained by the models was between 83% and 93%. Through these forecasting models, the main factors that influence olive-crop yield were identified. Pollen index and accumulated precipitation, especially as rain recorded during the pre-flowering months, were the most important parameters for providing an explanation of fluctuations in fruit production. The temperature recorded during the two months preceding budburst was another important variable, which showed positive effects on the final yield. The July model that provides accurate predictions of fruit production eight months prior to the final harvest is proposed as an optimal model to forecast fruit produced by olive trees in western Mediterranean areas.

  2. Musculoskeletal health conditions among older populations in urban slums in sub-Saharan Africa.

    Science.gov (United States)

    Aboderin, Isabella; Nanyonjo, Agnes

    2017-04-01

    Debate on the burden of musculoskeletal (MSK) conditions in lower and middle income countries is intensifying; yet, little knowledge so far exists on patterns and impacts of such conditions among general or older adult populations in sub-Saharan Africa (SSA). The objectives of this study are to examine the prevalence, potential predictors, and sequelae of MSK among older adults residing in two low resource informal urban settlements or "slums" in Nairobi Kenya. Data on older adults aged 60 years and over from two unrelated cross-sectional surveys on the older slum populations are used: a 2006/7 survey on the social, health, and overall well-being of older people (sample N = 831), and a 2016 survey on realities and impacts of long-term care and social protection for older adults (sample n = 1026). Uni and multivariate regressions on the 2006/7 data are employed to examine relationships of back pain and symptoms of arthritis with sex, age, wealth, unemployment, diagnoses of hypertension, and diabetes; and with indicators of subjective well-being and functional ability. Descriptive frequencies and chi-squared tests of association are used on 2016 data to identify the overall prevalence and locations of activity limiting MSK pain, and sex differences in these. Prevalence of past month back pain and past 2 week symptoms of arthritis was 44% and 42.6%, respectively. Respective prevalence of past month activity limiting back pain and joint pain was 13.9% and 22.7%. A total of 42.6% of slum residents with a current health problem report MSK as the most severe problem. In multivariate regressions, female sex, unemployment, and diagnosis of hypertension are predictive of back pain and symptoms of arthritis. Both conditions are associated with raised odds of having lower quality of life, poorer life satisfaction, and depressive symptoms, and with mobility impairments and self-care difficulties. MSK conditions are salient, and a likely key cause of impaired subjective well

  3. How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments

    Directory of Open Access Journals (Sweden)

    L. Berthet

    2009-06-01

    Full Text Available This paper compares event-based and continuous hydrological modelling approaches for real-time forecasting of river flows. Both approaches are compared using a lumped hydrologic model (whose structure includes a soil moisture accounting (SMA store and a routing store on a data set of 178 French catchments. The main focus of this study was to investigate the actual impact of soil moisture initial conditions on the performance of flood forecasting models and the possible compensations with updating techniques. The rainfall-runoff model assimilation technique we used does not impact the SMA component of the model but only its routing part. Tests were made by running the SMA store continuously or on event basis, everything else being equal. The results show that the continuous approach remains the reference to ensure good forecasting performances. We show, however, that the possibility to assimilate the last observed flow considerably reduces the differences in performance. Last, we present a robust alternative to initialize the SMA store where continuous approaches are impossible because of data availability problems.

  4. Investigating the environmental costs of deteriorating road conditions in South Africa

    CSIR Research Space (South Africa)

    Mashoko, L

    2014-07-01

    Full Text Available Africa.” The 5th Annual State of Logistics Survey for South Africa: Value and cost drivers from a macro and micro-economic perspective 2008, King, D. (ed), CSIR. 2. Steyn, W.J.v.d.M., and Bean, W.L. (2010) “Cost of bad roads to the economy....” The 6th Annual State of Logistics Survey for South Africa: Logistics value and cost driving macro and micro-economic change towards global competitiveness and sustainability 2009, King, D. (ed), CSIR. 3. Bean, W. L., and Steyn, W.J.v.d.M., 2013...

  5. Seasonal Streamflow Forecasts for African Basins

    Science.gov (United States)

    Serrat-Capdevila, A.; Valdes, J. B.; Wi, S.; Roy, T.; Roberts, J. B.; Robertson, F. R.; Demaria, E. M.

    2015-12-01

    Using high resolution downscaled seasonal meteorological forecasts we present the development and evaluation of seasonal hydrologic forecasts with Stakeholder Agencies for selected African basins. The meteorological forecasts are produced using the Bias Correction and Spatial Disaggregation (BCSD) methodology applied to NMME hindcasts (North American Multi-Model Ensemble prediction system) to generate a bootstrap resampling of plausible weather forecasts from historical observational data. This set of downscaled forecasts is then used to drive hydrologic models to produce a range of forecasts with uncertainty estimates suitable for water resources planning in African pilot basins (i.e. Upper Zambezi, Mara Basin). In an effort to characterize the utility of these forecasts, we will present an evaluation of these forecast ensembles over the pilot basins, and discuss insights as to their operational applicability by regional actors. Further, these forecasts will be contrasted with those from a standard Ensemble Streamflow Prediction (ESP) approach to seasonal forecasting. The case studies presented here have been developed in the setting of the NASA SERVIR Applied Sciences Team and within the broader context of operational seasonal forecasting in Africa. These efforts are part of a dialogue with relevant planning and management agencies and institutions in Africa, which are in turn exploring how to best use uncertain forecasts for decision making.

  6. GEOS-5 seasonal forecast system

    Science.gov (United States)

    Borovikov, Anna; Cullather, Richard; Kovach, Robin; Marshak, Jelena; Vernieres, Guillaume; Vikhliaev, Yury; Zhao, Bin; Li, Zhao

    2017-09-01

    Ensembles of numerical forecasts based on perturbed initial conditions have long been used to improve estimates of both weather and climate forecasts. The Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model, Version 5 (GEOS-5 AOGCM) Seasonal-to-Interannual Forecast System has been used routinely by the GMAO since 2008, the current version since 2012. A coupled reanalysis starting in 1980 provides the initial conditions for the 9-month experimental forecasts. Once a month, sea surface temperature from a suite of 11 ensemble forecasts is contributed to the North American Multi-Model Ensemble (NMME) consensus project, which compares and distributes seasonal forecasts of ENSO events. Since June 2013, GEOS-5 forecasts of the Arctic sea-ice distribution were provided to the Sea-Ice Outlook project. The seasonal forecast output data includes surface fields, atmospheric and ocean fields, as well as sea ice thickness and area, and soil moisture variables. The current paper aims to document the characteristics of the GEOS-5 seasonal forecast system and to highlight forecast biases and skills of selected variables (sea surface temperature, air temperature at 2 m, precipitation and sea ice extent) to be used as a benchmark for the future GMAO seasonal forecast systems and to facilitate comparison with other global seasonal forecast systems.

  7. Impacts of boundary condition changes on regional climate projections over West Africa

    Science.gov (United States)

    Kim, Jee Hee; Kim, Yeonjoo; Wang, Guiling

    2017-06-01

    Future projections using regional climate models (RCMs) are driven with boundary conditions (BCs) typically derived from global climate models. Understanding the impact of the various BCs on regional climate projections is critical for characterizing their robustness and uncertainties. In this study, the International Center for Theoretical Physics Regional Climate Model Version 4 (RegCM4) is used to investigate the impact of different aspects of boundary conditions, including lateral BCs and sea surface temperature (SST), on projected future changes of regional climate in West Africa, and BCs from the coupled European Community-Hamburg Atmospheric Model 5/Max Planck Institute Ocean Model are used as an example. Historical, future, and several sensitivity experiments are conducted with various combinations of BCs and CO2 concentration, and differences among the experiments are compared to identify the most important drivers for RCMs. When driven by changes in all factors, the RegCM4-produced future climate changes include significantly drier conditions in Sahel and wetter conditions along the Guinean coast. Changes in CO2 concentration within the RCM domain alone or changes in wind vectors at the domain boundaries alone have minor impact on projected future climate changes. Changes in the atmospheric humidity alone at the domain boundaries lead to a wetter Sahel due to the northward migration of rain belts during summer. This impact, although significant, is offset and dominated by changes of other BC factors (primarily temperature) that cause a drying signal. Future changes of atmospheric temperature at the domain boundaries combined with SST changes over oceans are sufficient to cause a future climate that closely resembles the projection that accounts for all factors combined. Therefore, climate variability and changes simulated by RCMs depend primarily on the variability and change of temperature aspects of the RCM BCs. Moreover, it is found that the response

  8. Some implications of time series analysis for describing climatologic conditions and for forecasting. An illustrative case: Veracruz, Mexico

    Energy Technology Data Exchange (ETDEWEB)

    Gay, C.; Estrada, F.; Conde, C. [Centro de Ciencias de la Atmosfera, Universidad Nacional Autonoma de Mexico, Mexico, D.F. (Mexico)]. E:mail: feporrua@atmosfera.unam.mx

    2007-04-15

    The common practice of using 30-year sub-samples of climatological data for describing past, present and future conditions has been widely applied, in many cases without considering the properties of the time series analyzed. This paper shows that this practice can lead to an inefficient use of the information contained in the data and to an inaccurate characterization of present, and especially future, climatological conditions because parameters are time and sub-sample size dependent. Furthermore, this approach can lead to the detection of spurious changes in distribution parameters. The time series analysis of observed monthly temperature in Veracruz, Mexico, is used to illustrate the fact that these techniques permit to make a better description of the mean and variability of the series, which in turn allows (depending on the class of process) to restrain uncertainty of forecasts, and therefore provides a better estimation of present and future risk of observing values outside a given coping range. Results presented in this paper show that, although a significant trend is found in the temperatures, giving possible evidence of observed climate change in the region, there is no evidence to support changes in the variability of the series and therefore there is neither observed evidence to support that monthly temperature variability will increase (or decrease) in the future. That is, if climate change is already occurring, it has manifested itself as a change-in-the-mean of these processes and has not affected other moments of their distributions (homogeneous non-stationary processes). The Magicc-Scengen, a software useful for constructing climate change scenarios, uses 20-year sub-samples to estimate future climate variability. For comparison purposes, possible future probability density functions are constructed following two different approaches: one, using solely the Magicc-Scengen output, and another one using a combination of this information and the time

  9. Air Pollution Forecasts: An Overview

    Directory of Open Access Journals (Sweden)

    Lu Bai

    2018-04-01

    Full Text Available Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

  10. Air Pollution Forecasts: An Overview.

    Science.gov (United States)

    Bai, Lu; Wang, Jianzhou; Ma, Xuejiao; Lu, Haiyan

    2018-04-17

    Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

  11. Load forecasting

    International Nuclear Information System (INIS)

    Mak, H.

    1995-01-01

    Slides used in a presentation at The Power of Change Conference in Vancouver, BC in April 1995 about the changing needs for load forecasting were presented. Technological innovations and population increase were said to be the prime driving forces behind the changing needs in load forecasting. Structural changes, market place changes, electricity supply planning changes, and changes in planning objectives were other factors discussed. It was concluded that load forecasting was a form of information gathering, that provided important market intelligence

  12. Reconstruction of late Quaternary marine and terrestrial environmental conditions of Northwest Africa and Southeast Australia : a multiple organic proxy study using marine sediments

    NARCIS (Netherlands)

    Alfama Lopes dos Santos, R.

    2012-01-01

    NW Africa and SE Australia are regions which are particularly vulnerable to climate change. In this thesis, organic proxies are used from marine sediment cores to reconstruct past environmental conditions from these areas. In sediments from NW Africa, the UK'37 showed an efficient proxy for sea

  13. Climate Change Adaptation in Cities: the conditions for success. Feedback from Sub-Saharan Africa, South Africa, and Colombia

    International Nuclear Information System (INIS)

    Paugam, Anne; Henry, Alain

    2014-11-01

    Until recently, the actions to promote climate change adaptation have mainly taken the form of occasional projects for reducing vulnerabilities (infrastructures for rain drainage, early-warning systems, etc.). But for greater effective action, it is better to both develop real public policies dedicated to this theme and to incorporate this concern into the other sectoral policies and in national strategies. To this end, AFD launched three research projects to grasp a better understanding of the conditions needed for effective adaptation. The three studies look into the institutional, political, and social factors that make for success or failure in adaptation programs on a city scale. The cities studied were selected because they have initiated adaptation procedures that enable feedback not only on how adaptation has been taken into account within local priorities, but also on the implementation of strategies, which represents a relatively new research subject. The study Institutional Pathways for Local Climate Adaptation was produced by South African academics from the University of Cape Town and University of KwaZulu- Natal in 2012-2013. It identifies the political, institutional and social dimensions of effective adaptation at the municipal level, in three South African cities (Durban, Cape Town, Theewaterskloof). The 2014 study Understanding the Assessment and Reduction of Vulnerability to Climate Change in African Cities by the British research institute International Institute for Environment and Development (IIED) is more sociological and concerns social vulnerability to climate change in African cities, especially in poor neighborhoods (case studies in Kampala, Accra, and Dakar). Finally, in 2013 the Colombian research institute Fedesarrollo and the Institut de recherche et debat sur la gouvernance (IRG) produced the set of documents Ciudades y cambio climatico en Colombia, which contains an institutional analysis of climate change management in 11

  14. Evaluation of Probabilistic Disease Forecasts.

    Science.gov (United States)

    Hughes, Gareth; Burnett, Fiona J

    2017-10-01

    The statistical evaluation of probabilistic disease forecasts often involves calculation of metrics defined conditionally on disease status, such as sensitivity and specificity. However, for the purpose of disease management decision making, metrics defined conditionally on the result of the forecast-predictive values-are also important, although less frequently reported. In this context, the application of scoring rules in the evaluation of probabilistic disease forecasts is discussed. An index of separation with application in the evaluation of probabilistic disease forecasts, described in the clinical literature, is also considered and its relation to scoring rules illustrated. Scoring rules provide a principled basis for the evaluation of probabilistic forecasts used in plant disease management. In particular, the decomposition of scoring rules into interpretable components is an advantageous feature of their application in the evaluation of disease forecasts.

  15. Yields and quality of Phaseolus bean cultivars under farmers’ conditions in eastern and southern Africa

    DEFF Research Database (Denmark)

    Jensen, Henning Høgh; Kamalongo, Donwell; Ngwira, Amos

    2014-01-01

    Common bean (Phaseolus vulgaris L.) is a dominant grain legume in eastern and southern Africa, where it constitutes a major source of protein and microminerals in peoples’ diet. The current studies aimed at determining how initially promising genotypes of bean responded in terms of yield and grain...... may best be secured by selecting for high-yielding cultivars as the amounts of phosphorus (P), Fe and Zn in the grains correlated strongly (r2 > 0.93) to the dry matter grain yield....

  16. Examining the value of global seasonal reference evapotranspiration forecasts tosupport FEWS NET's food insecurity outlooks

    Science.gov (United States)

    Shukla, S.; McEvoy, D.; Hobbins, M.; Husak, G. J.; Huntington, J. L.; Funk, C.; Verdin, J.; Macharia, D.

    2017-12-01

    The Famine Early Warning Systems Network (FEWS NET) team provides food insecurity outlooks for several developing countries in Africa, Central Asia, and Central America. Thus far in terms of agroclimatic conditions that influence food insecurity, FEWS NET's primary focus has been on the seasonal precipitation forecasts while not adequately accounting for the atmospheric evaporative demand, which is also directly related to agricultural production and hence food insecurity, and is most often estimated by reference evapotranspiration (ETo). This presentation reports on the development of a new global ETo seasonal reforecast and skill evaluation with a particular emphasis on the potential use of this dataset by the FEWS NET to support food insecurity early warning. The ETo reforecasts span the 1982-2009 period and are calculated following ASCE's formulation of Penman-Monteith method driven by seasonal climate forecasts of monthly mean temperature, humidity, wind speed, and solar radiation from NCEP's CFSv2 and NASA's GEOS-5 models. The skill evaluation using deterministic and probabilistic scores focuses on the December-February (DJF), March-May (MAM), June-August (JJA) and September-November (SON) seasons. The results indicate that ETo forecasts are a promising tool for early warning of drought and food insecurity. The FEWS NET regions with promising level of skill (correlation >0.35 at lead times of 3 months) include Northern Sub-Saharan Africa (DJF, dry season), Central America (DJF, dry season), parts of East Africa (JJA, wet Season), Southern Africa (JJA, dry season), and Central Asia (MAM, wet season). A case study over parts of East Africa for the JJA season shows that, in combination with the precipitation forecasts, ETo forecasts could have provided early warning of recent severe drought events (e.g., 2002, 2004, 2009) that contributed to substantial food insecurity in the region.

  17. Strategic Forecasting

    DEFF Research Database (Denmark)

    Duus, Henrik Johannsen

    2016-01-01

    as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority. Research limitations/implications: The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts......Purpose: The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions. Design/methodology/approach: This article is conceptual but also informed by the author’s long contact...... and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management. Findings: Strategic forecasting is seen...

  18. Exposure Forecaster

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Exposure Forecaster Database (ExpoCastDB) is EPA's database for aggregating chemical exposure information and can be used to help with chemical exposure...

  19. Evolving forecasting classifications and applications in health forecasting

    Directory of Open Access Journals (Sweden)

    Soyiri IN

    2012-05-01

    Full Text Available Ireneous N Soyiri1,2, Daniel D Reidpath11Global Public Health, JCSMHS, MONASH University, Selangor, Malaysia; 2School of Public Health, University of Ghana, Legon, Accra, GhanaAbstract: Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation.Keywords: health forecast, health data, electronic health records, accuracy, cross validation, method, strengths and limitations

  20. Early forecasting of crop condition using an integrative remote sensing method for corn and soybeans in Iowa and Illinois, USA

    Science.gov (United States)

    Seo, Bumsuk; Lee, Jihye; Kang, Sinkyu

    2017-04-01

    The weather-related risks in crop production is not only crucial for farmers but also for market participants and policy makers since securing food supply is an important issue for society. While crop growth condition and phenology are essential information about such risks, the extensive observations on those are often non-existent in many parts of the world. In this study, we have developed a novel integrative approach to remotely sense crop growth condition and phenology at a large scale. For corn and soybeans in Iowa and Illinois of USA (2003-2014), we assessed crop growth condition and crop phenology by EO data and validated it against the United States Department of Agriculture (USDA) National Agriculture Statistics System (NASS) crop statistics. For growth condition, we used two distinguished approaches to acquire crop condition indicators: a process-based crop growth modelling and a satellite NDVI based method. Based on their pixel-wise historic distributions, we determined relative growth conditions and scaled-down to the state-level. For crop phenology, we calculated three crop phenology metrics [i.e., start of season (SOS), end of season (EOS), and peak of season (POS)] at the pixel level from MODIS 8-day Normalized Difference Vegetation Index (NDVI). The estimates were compared with the Crop Progress and Condition (CPC) data of NASS. For the condition, the state-level 10-day estimates showed a moderate agreement (RMSE 70%). Notably, the condition estimates corresponded to the severe soybeans disease in 2003 and the drought in 2012 for both crops. For the phenology, the average RMSE of the estimates was 8.6 day for the all three metrics. The average |ME| was smaller than 1.0 day after bias correction. The proposed method enables us to evaluate crop growth at any given period and place. Global climate changes are increasing the risk in agricultural production such as long-term drought. We hope that the presented remote sensing method for crop condition

  1. A conditional extreme value theory approach in value-at-risk forecasting: Evidence from Southeastern Europe and USA market

    Directory of Open Access Journals (Sweden)

    Totić Selena

    2015-01-01

    Full Text Available As a consequence of the recent financial crisis, the adequacy of different Value-at-Risk (VaR methodologies was heavily questioned. Current practice in VaR assessment relies on modeling the whole distribution of returns. As an alternative, in this paper we model tail behavior of returns, and thus VaR, using conditional Extreme Value Theory (EVT, which combines EVT and GARCH methodology. Moreover, we examine the performance of conditional EVT with the daily returns of seven stock market indices, of which six are from Southeastern Europe (BelexLine, BET, BUX, CROBEX, SBITOP, SOFIX from the period of September 2004 - April 2013, and one from USA market (Standard&Poors 500 Index from the period January 1998 - April 2013. Backtesting of historical daily returns proves that conditional EVT model gives good predictions for all indices and for all confidence levels.

  2. Satellite assessment of early-season forecasts for vegetation conditions of grazing allotments in Nevada, United States

    Science.gov (United States)

    Fifteen years of enhanced vegetation index data from the MODIS sensor are examined in conjunction with precipitation and the Palmer drought severity index to assess how well growing season conditions for vegetation within grazing allotments of Nevada can be predicted at different times of the year. ...

  3. Forecasting metal prices: Do forecasters herd?

    DEFF Research Database (Denmark)

    Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

    2013-01-01

    We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

  4. Paleoarchean sulfur cycle and biogeochemical surface conditions on the early Earth, Barberton, South Africa

    Science.gov (United States)

    Grosch, Eugene G.; McLoughlin, Nicola

    2013-09-01

    This study presents the first multiple sulfur isotope dataset on sulfides from the ca. 3.5-3.2 Ga Onverwacht Group in the Paleoarchean Barberton Greenstone Belt (BGB) of South Africa. In situ δ34SCDT and Δ33S values of pyrite (n=568) are reported from a wide range of hydrothermal, volcanic and sedimentary environments and are used to explore Mid-Archean biogeochemical sulfur cycling. Samples are from fresh drill core collected by the Barberton Scientific Drilling Project that intercepted cherts, metabasalts and sheared ultramafics of the ˜3.3-3.35 Ga Kromberg Formation; the sedimentary units of the ˜3.432 Ga Noisy formation; and the unconformably underlying metabasaltic pillow lavas of the ˜3.472 Ga Hooggenoeg Formation.

  5. Method of forecasting power distribution

    International Nuclear Information System (INIS)

    Kaneto, Kunikazu.

    1981-01-01

    Purpose: To obtain forecasting results at high accuracy by reflecting the signals from neutron detectors disposed in the reactor core on the forecasting results. Method: An on-line computer transfers, to a simulator, those process data such as temperature and flow rate for coolants in each of the sections and various measuring signals such as control rod positions from the nuclear reactor. The simulator calculates the present power distribution before the control operation. The signals from the neutron detectors at each of the positions in the reactor core are estimated from the power distribution and errors are determined based on the estimated values and the measured values to determine the smooth error distribution in the axial direction. Then, input conditions at the time to be forecast are set by a data setter. The simulator calculates the forecast power distribution after the control operation based on the set conditions. The forecast power distribution is corrected using the error distribution. (Yoshino, Y.)

  6. Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island

    Directory of Open Access Journals (Sweden)

    Julien Morel

    2014-07-01

    Full Text Available Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1 an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2 the Kumar-Monteith efficiency model, and (3 a forced-coupling method with a sugarcane crop model (MOSICAS and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.4 t∙ha−1. Because this method is also the simplest to set up and requires less input data, it appears that it is the most suitable for performing operational estimations and forecasts of sugarcane yield at the field scale. The main limitation is the acquisition of a minimum of five satellite images. The upcoming open-access Sentinel-2 Earth observation system should overcome this limitation because it will provide 10-m resolution satellite images with a 5-day frequency.

  7. Recurrent networks for wave forecasting

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    The tremendous increase in offshore operational activities demands improved wave forecasting techniques. With the knowledge of accurate wave conditions, it is possible to carry out the marine activities such as offshore drilling, naval operations...

  8. The daily hour forecasting of the electrical energy production from renewable energy sources – a required condition for the operation of the new energy market model

    International Nuclear Information System (INIS)

    Kalpachka, Gergana; Kalpachki, Georgi

    2011-01-01

    The report presented the new energy market model in Bulgaria and the main attention is directed to a daily hour forecasting of the electrical energy production from renewable energy sources. The need of development of a methodology and the development of the most precise methods for predicting is reviewed and some of the used methods at the moment are presented. An analysis of the problems related to the daily hour forecasting is done using data from the producers of electrical energy from renewable energy sources in the territory of western Bulgaria. Keywords: Renewable energy sources, daily hour forecasting, electrical energy

  9. Job Burnout, Work Engagement and Self-reported Treatment for Health Conditions in South Africa.

    Science.gov (United States)

    de Beer, Leon T; Pienaar, Jaco; Rothmann, Sebastiaan

    2016-02-01

    The purpose of the study being reported here was to investigate the relationship of job burnout and work engagement with self-reported received treatment for health conditions (cardiovascular condition, high cholesterol, depression, diabetes, hypertension and irritable bowel syndrome), while controlling for age, gender, smoking and alcohol use. The sample comprised 7895 employees from a broad range of economic sectors in the South African working population. A cross-sectional survey design was used for the study. Structural equation modelling methods were implemented with a weighted least squares approach. The results showed that job burnout had a positive relationship with self-reported received treatment for depression, diabetes, hypertension and irritable bowel syndrome. Work engagement did not have any significant negative or positive relationships with the treatment for these health conditions. The results of this study make stakeholders aware of the relationship between job burnout, work engagement and self-reported treatment for health conditions. Evidence for increased reporting of treatment for ill-health conditions due to burnout was found. Therefore, attempts should be made to manage job burnout to prevent ill-health outcomes. Copyright © 2014 John Wiley & Sons, Ltd.

  10. Experts' adjustment to model-based forecasts: Does the forecast horizon matter?

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Legerstee (Rianne)

    2007-01-01

    textabstractExperts may have domain-specific knowledge that is not included in a statistical model and that can improve forecasts. While one-step-ahead forecasts address the conditional mean of the variable, model-based forecasts for longer horizons have a tendency to convert to the unconditional

  11. Heat tolerance in Field Grown Tomatoes (Lycopersicon esculentum Mill.) under Semi Arid Conditions of West Africa

    DEFF Research Database (Denmark)

    Kugblenu, Y O; Oppong Danso, E; Ofori, K

    2013-01-01

    to October, which has a mean temperature of 23°C. Heat tolerant tomato cultivars were grown from April to July with a mean temperature of 25°C to evaluate their performance under these conditions and to assess the effect of shading on the production of one of the genotypes. Fruiting percentage...... was significantly lower in exotic hybrids compared to a local variety. Different genotypes showed no differences in the production of viable pollen. Shading decreased final shoot and root biomass by 67 and 47%, respectively, whiles fruit yield was not affected. Also among cultivars yields were similar....

  12. The cold climate geomorphology of the Eastern Cape Drakensberg: A reevaluation of past climatic conditions during the last glacial cycle in Southern Africa

    OpenAIRE

    Mills, SC; Barrows, TT; Telfer, MW; Fifield, LK

    2017-01-01

    publisher: Elsevier articletitle: The cold climate geomorphology of the Eastern Cape Drakensberg: A reevaluation of past climatic conditions during the last glacial cycle in Southern Africa journaltitle: Geomorphology articlelink: http://dx.doi.org/10.1016/j.geomorph.2016.11.011 content_type: article copyright: Crown Copyright © 2016 Published by Elsevier B.V. All rights reserved.

  13. Management through decentralisation and local economic development: A condition for sustainable urbanisation in Africa

    Directory of Open Access Journals (Sweden)

    Emmanuel Innocents Edoun

    2017-02-01

    Full Text Available This paper is based on the premise that, urbanisation could be effective only if decentralisation policy is at the centre of development initiatives. In this way the paper argues, local authorities could utilize local resources to ignite local economic development (LED through for instance trade activities and investments.LED initiatives aim at empowering local stakeholders to utilise business enterprises, labour, capital and other local resources effectively to maximise local benefits in order to contribute to poverty reduction and the uplifting of citizens life conditions. The paper is divided into four major parts. The first part gives a background of the notion of decentralisation, urbanisation and local economic development. The second part provides an overview of the review of the related literature while the third part gives an account on how the above are inter-related. The fourth part provides the challenges faced by urbanisation in achieving local economic development and part five is presented as conclusion and recommendations.

  14. Seasonal prevalence, body condition score and risk factors of bovine fasciolosis in South Africa

    Directory of Open Access Journals (Sweden)

    Ishmael Festus Jaja

    2017-12-01

    Full Text Available Fasciolosis is an important zoonotic disease that is responsible for a significant loss in food resource and animal productivity. The objectives of this study were to determine the seasonal prevalence and risk factors associated with Fasciola infection in cattle. The results were obtained by coprology, antemortem and post-mortem survey of three abattoirs (HTPA1, n = 500, HTPA2, n = 400, and LTPA, n = 220. The seasonal prevalence of Fasciola infection was 10.4%, 12.8% and 10.9%, during summer, 11.2%, 10.8% and 8.6%, during autumn, 9.8%, 6.5% and 5.9% during winter and 8.2%, 7.8% and 5.9%, during spring in the three abattoirs HTPA1, HTPA and LTPA respectively. There was a significant association (p < 0.05 between the intensity of infection and body condition score (BCS of cattle at each abattoir. Factors such as age [HTPA1 (OR = 3.6, CI = 1.2, 10.2, and LTPA (OR = 3.8, CI= 2.4, 6.1], sex [LTPA (OR = 4.2, CI= 2.5, 7.0], breed [HTPA2 (OR = 2.3, CI = 1.3, 4.1 and LTPA (OR = 2.5, CI= 1.3, 5.0] and BCS had significant (p < 0.01–0.001 influence on the prevalence of fasciolosis. In conclusion, the infection with Fasciola spp was higher in the summer than in the winter; a positive association was established between the prevalence of fasciolosis and poor body condition in study animals. This study, therefore, suggests that fasciolosis could be causing substantial production losses, mainly due to cattle weight loss and liver condemnation.

  15. Hydroclimate Forecasts in Ethiopia: Benefits, Impediments, and Ways Forward

    Science.gov (United States)

    Block, P. J.

    2014-12-01

    Numerous hydroclimate forecast models, tools, and guidance exist for application across Ethiopia and East Africa in the agricultural, water, energy, disasters, and economic sectors. This has resulted from concerted local and international interdisciplinary efforts, yet little evidence exists of rapid forecast uptake and use. We will review projected benefits and gains of seasonal forecast application, impediments, and options for the way forward. Specific case studies regarding floods, agricultural-economic links, and hydropower will be reviewed.

  16. Uses and applications of climate forecasts for power utilities

    Energy Technology Data Exchange (ETDEWEB)

    Changnon, S.A.; Changnon, J.M.; Changnon, D. [Changnon Climatologist, Mahomet, IL (United States)

    1995-05-01

    The uses and potential applications of climate forecasts for electric and gas utilities were assessed: (1) to discern needs for improving climate forecasts and guiding future research, and (2) to assist utilities in making wise use of forecasts. In-depth structured interviews were conducted with 56 decision makers in six utilities to assess existing and potential uses of climate forecasts. Only 3 of the 56 use forecasts. Eighty percent of those sampled envisioned applications of climate forecasts, given certain changes and additional information. Primary applications exist in power trading, load forecasting, fuel acquisition, and systems planning, with slight differences in interests between utilities. Utility staff understand probability-based forecasts but desire climatological information related to forecasted outcomes, including analogs similar to the forecasts, and explanations of the forecasts. Desired lead times vary from a week to three months, along with forecasts of up to four seasons ahead. The new NOAA forecasts initiated in 1995 provide the lead times and longer-term forecasts desired. Major hindrances to use of forecasts are hard-to-understand formats, lack of corporate acceptance, and lack of access to expertise. Recent changes in government regulations altered the utility industry, leading to a more competitive world wherein information about future weather conditions assumes much more value. Outreach efforts by government forecast agencies appear valuable to help achieve the appropriate and enhanced use of climate forecasts by the utility industry. An opportunity for service exists also for the private weather sector. 17 refs., 1 fig., 9 tabs.

  17. Pilot pathfinder survey of oral hygiene and periodontal conditions in the rural population of The Gambia (West Africa).

    Science.gov (United States)

    Jordan, R A; Lucaciu, A; Fotouhi, K; Markovic, L; Gaengler, P; Zimmer, S

    2011-02-01

    To document oral hygiene and periodontal conditions in the rural population of The Gambia. Cross-sectional study according to the recommendations of the WHO for oral health surveys. Examination by two calibrated investigators in the health centres of rural communities after a public radio call. Patients were randomly allocated to the investigators.   162 patients (20-54 years old; 52.5% female, 47.5% male). Patients were interviewed for personal information and examined in a full-mouth recording. Oral Hygiene Index (OHI), Gingival Index (GI), Community Periodontal Index (CPI), and the Gingivitis-Periodontitis-Missing/Teeth Index (GPM/T). Statistical analysis was performed using the Wilcoxon-rank-sum test and Kruskal-Wallis test with statistical significance at P Periodontal Index codes increased by age (P periodontitis in males (P periodontitis and missing teeth (P periodontitis than women. No statistical associations were found between ethnic groups or for different oral hygiene methods concerning CPI or GPM/T. Prevalence of predominantly mild to moderate periodontal disease indicates treatment needs that should be considered when developing a national oral health care plan in The Gambia (West Africa). © 2009 John Wiley & Sons A/S.

  18. Assessment of future agricultural conditions in southwestern Africa using fuzzy logic and high-resolution climate model scenarios

    Directory of Open Access Journals (Sweden)

    Weinzierl, Thomas

    2015-12-01

    Full Text Available Climate change is expected to have a major impact on the arid savanna regions of southwestern Africa, such as the Okavango Basin. Precipitation is a major constraint for agriculture in countries like Namibia and Botswana and assessments of future crop growth conditions are in high demand. This GIS-based approach uses reanalysis data and climate model output for two scenarios and compares them to the precipitation requirements of the five most important crops grown in the region: maize, pearl millet, sorghum, cassava and cow pea. It also takes into account the dominant soil types, as plant growth is also limited by nutrient-poor soils with unfavorable physical and chemical properties. The two factors are then combined using a fuzzy logic algorithm. The assessment visualizes the expected shifts in suitable zones and identifies areas where farming without irrigation may experience a decline in yields or may even no longer be possible at the end of the 21st century. The results show that pearl millet is the most suitable crop in all scenarios while especially the cultivation of maize, sorghum and cow pea may be affected by a possible reduction of precipitation under the high-emission scenario.

  19. A Wind Forecasting System for Energy Application

    Science.gov (United States)

    Courtney, Jennifer; Lynch, Peter; Sweeney, Conor

    2010-05-01

    Accurate forecasting of available energy is crucial for the efficient management and use of wind power in the national power grid. With energy output critically dependent upon wind strength there is a need to reduce the errors associated wind forecasting. The objective of this research is to get the best possible wind forecasts for the wind energy industry. To achieve this goal, three methods are being applied. First, a mesoscale numerical weather prediction (NWP) model called WRF (Weather Research and Forecasting) is being used to predict wind values over Ireland. Currently, a gird resolution of 10km is used and higher model resolutions are being evaluated to establish whether they are economically viable given the forecast skill improvement they produce. Second, the WRF model is being used in conjunction with ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble forecasts to produce a probabilistic weather forecasting product. Due to the chaotic nature of the atmosphere, a single, deterministic weather forecast can only have limited skill. The ECMWF ensemble methods produce an ensemble of 51 global forecasts, twice a day, by perturbing initial conditions of a 'control' forecast which is the best estimate of the initial state of the atmosphere. This method provides an indication of the reliability of the forecast and a quantitative basis for probabilistic forecasting. The limitation of ensemble forecasting lies in the fact that the perturbed model runs behave differently under different weather patterns and each model run is equally likely to be closest to the observed weather situation. Models have biases, and involve assumptions about physical processes and forcing factors such as underlying topography. Third, Bayesian Model Averaging (BMA) is being applied to the output from the ensemble forecasts in order to statistically post-process the results and achieve a better wind forecasting system. BMA is a promising technique that will offer calibrated

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  12. ktcs Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. kdnl Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. kmgw Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  15. kryy Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. kgtf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. kjax Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. ktvf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. kfat Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. kink Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  1. kshv Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  2. pajn Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  3. kpna Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  4. ktph Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  5. ksux Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  6. kcon Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  7. kpnc Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  8. kgsp Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  9. kgpt Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  10. kgcn Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  11. kart Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  12. pagk Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  13. korf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  14. kpsm Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  15. kcre Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  16. krsw Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  17. papg Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  18. kblf Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  19. krdu Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  20. kluk Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  1. keed Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  2. kiwd Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  3. kttn Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  4. kagc Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  5. kbmi Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  6. kapn Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  7. kgon Terminal Aerodrome Forecast

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — TAF (terminal aerodrome forecast or terminal area forecast) is a format for reporting weather forecast information, particularly as it relates to aviation. TAFs are...

  8. Evaluation of the Weather Research and Forecasting mesoscale model for GABLS3: Impact of boundary-layer schemes, boundary conditions and spin-up

    NARCIS (Netherlands)

    Kleczek, M.A.; Steeneveld, G.J.; Holtslag, A.A.M.

    2014-01-01

    We evaluated the performance of the three-dimensional Weather Research and Forecasting (WRF) mesoscale model, specifically the performance of the planetary boundary-layer (PBL) parametrizations. For this purpose, Cabauw tower observations were used, with the study extending beyond the third GEWEX

  9. L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

    Science.gov (United States)

    Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i...

  10. On the Economic Evaluation of Volatility Forecasts

    DEFF Research Database (Denmark)

    Voev, Valeri

    We analyze the applicability of economic criteria for volatility forecast evaluation based on unconditional measures of portfolio performance. The main theoretical finding is that such unconditional measures generally fail to rank conditional forecasts correctly due to the presence of a bias term...... driven by the variability of the conditional mean and portfolio weights. Simulations and a small empirical study suggest that the bias can be empirically substantial and lead to distortions in forecast evaluation. An important implication is that forecasting superiority of models using high frequency...

  11. Probabilistic Solar Forecasting Using Quantile Regression Models

    Directory of Open Access Journals (Sweden)

    Philippe Lauret

    2017-10-01

    Full Text Available In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h–6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of solar irradiance as endogenous inputs and day-ahead forecasts as exogenous inputs. Day-ahead irradiance forecasts are obtained from the Integrated Forecast System (IFS, a Numerical Weather Prediction (NWP model maintained by the European Center for Medium-Range Weather Forecast (ECMWF. Several metrics, mainly originated from the weather forecasting community, are used to evaluate the performance of the probabilistic forecasts. The results demonstrated that the NWP exogenous inputs improve the quality of the intra-day probabilistic forecasts. The analysis considered two locations with very dissimilar solar variability. Comparison between the two locations highlighted that the statistical performance of the probabilistic models depends on the local sky conditions.

  12. Ensemble Forecasts with Useful Skill-Spread Relationships for African meningitis and Asia Streamflow Forecasting

    Science.gov (United States)

    Hopson, T. M.

    2014-12-01

    One potential benefit of an ensemble prediction system (EPS) is its capacity to forecast its own forecast error through the ensemble spread-error relationship. In practice, an EPS is often quite limited in its ability to represent the variable expectation of forecast error through the variable dispersion of the ensemble, and perhaps more fundamentally, in its ability to provide enough variability in the ensembles dispersion to make the skill-spread relationship even potentially useful (irrespective of whether the EPS is well-calibrated or not). In this paper we examine the ensemble skill-spread relationship of an ensemble constructed from the TIGGE (THORPEX Interactive Grand Global Ensemble) dataset of global forecasts and a combination of multi-model and post-processing approaches. Both of the multi-model and post-processing techniques are based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. The methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. A context for these concepts is provided by assessing the constructed ensemble in forecasting district-level humidity impacting the incidence of meningitis in the meningitis belt of Africa, and in forecasting flooding events in the Brahmaputra and Ganges basins of South Asia.

  13. SHORT-TERM FORECASTING OF MORTGAGE LENDING

    Directory of Open Access Journals (Sweden)

    Irina V. Orlova

    2013-01-01

    Full Text Available The article considers the methodological and algorithmic problems arising in modeling and forecasting of time series of mortgage loans. Focuses on the processes of formation of the levels of time series of mortgage loans and the problem of choice and identification of models in the conditions of small samples. For forecasting options are selected and implemented a model of autoregressive and moving average, which allowed to obtain reliable forecasts.

  14. Forecasting olive crop yields based on long-term aero biological data series and bio climatic conditions for the southern Iberian Peninsula

    Energy Technology Data Exchange (ETDEWEB)

    Aguilera, F.; Ruiz-Valenzuela, L.

    2014-06-01

    In the present study, bio-meteorological models for predicting olive-crop production in the southern Iberian Peninsula were developed. These covered a 16-year period: 1994-2009. The forecasting models were constructed using the partial least-squares regression method, taking the annual olive yield as the dependent variable, and both aero biological and meteorological parameters as the independent variables. Two regression models were built for the prediction of crop production prior to the final harvest at two different times of the year: July and November. The percentage variance explained by the models was between 83% and 93%. Through these forecasting models, the main factors that influence olive-crop yield were identified. Pollen index and accumulated precipitation, especially as rain recorded during the pre-flowering months, were the most important parameters for providing an explanation of fluctuations in fruit production. The temperature recorded during the two months preceding budburst was another important variable, which showed positive effects on the final yield. The July model that provides accurate predictions of fruit production eight months prior to the final harvest is proposed as an optimal model to forecast fruit produced by olive trees in western Mediterranean areas. (Author)

  15. Municipal water consumption forecast accuracy

    Science.gov (United States)

    Fullerton, Thomas M.; Molina, Angel L.

    2010-06-01

    Municipal water consumption planning is an active area of research because of infrastructure construction and maintenance costs, supply constraints, and water quality assurance. In spite of that, relatively few water forecast accuracy assessments have been completed to date, although some internal documentation may exist as part of the proprietary "grey literature." This study utilizes a data set of previously published municipal consumption forecasts to partially fill that gap in the empirical water economics literature. Previously published municipal water econometric forecasts for three public utilities are examined for predictive accuracy against two random walk benchmarks commonly used in regional analyses. Descriptive metrics used to quantify forecast accuracy include root-mean-square error and Theil inequality statistics. Formal statistical assessments are completed using four-pronged error differential regression F tests. Similar to studies for other metropolitan econometric forecasts in areas with similar demographic and labor market characteristics, model predictive performances for the municipal water aggregates in this effort are mixed for each of the municipalities included in the sample. Given the competitiveness of the benchmarks, analysts should employ care when utilizing econometric forecasts of municipal water consumption for planning purposes, comparing them to recent historical observations and trends to insure reliability. Comparative results using data from other markets, including regions facing differing labor and demographic conditions, would also be helpful.

  16. The role of climate forecasts in smallholder agriculture: Lessons from participatory research in two communities in Senegal

    Directory of Open Access Journals (Sweden)

    P. Roudier

    2014-01-01

    Full Text Available Climate forecasts have shown potential for improving resilience of African agriculture to climate shocks, but uncertainty remains about how farmers would use such information in crop management decisions and whether doing so would benefit them. This article presents results from participatory research with farmers from two agro-ecological zones of Senegal, West Africa. Based on simulation exercises, the introduction of seasonal and dekadal forecasts induced changes in farmers’ practices in almost 75% of the cases. Responses were categorized as either implying pure intensification of cropping systems (21% of cases, non-intensified strategies (31% or a mix of both (24%. Among non-intensified strategies, the most common forecast uses are changes in sowing date and crop variety with the latter being more prevalent where a wider repertoire of varieties existed. Mixed strategies generally used more inputs like manure or chemical fertilizers coupled with another strategy such as changing sowing date. Yield estimates suggest that forecast use led to yield gains in about one-third of the cases, with relatively few losses. Impacts varied according to the nature of the actual rainy season, forecasts accuracy and the type of response, positive ones being higher in wetter years, with intensified strategies and with accurate predictions. These results validate prior evidence that climate forecasts may be able to help Senegalese farmers adapt to climate variability, especially helping them capitalize on anticipated favorable conditions. Realization of potential advantages appears associated with a context where there is greater varietal choice and options for intensification.

  17. Precipitation Ensembles from Single-Value Forecasts for Hydrological Ensemble Forecasting

    Science.gov (United States)

    Demargne, J.; Schaake, J.; Wu, L.; Welles, E.; Herr, H.; Seo, D.

    2005-05-01

    An ensemble pre-processor was developed to produce short-term precipitation ensembles using operational single-value forecasts. The methodology attempts to quantify the uncertainty in the single-value forecast and to capture the skill therein. These precipitation ensemble forecasts could be then ingested in the NOAA/National Weather Service (NWS) Ensemble Streamflow Prediction (ESP) system to produce probabilistic hydrological forecasts that reflect the uncertainty in forecast precipitation. The procedure constructs the joint distribution of forecast and observed precipitation from historical pairs of forecast and observed values. The probability distribution function of the future events that may occur given a particular single-value forecast is then the conditional distribution of observed precipitation given the forecast. To generate individual ensemble members for each lead time and each location, the historical observed values are replaced with values sampled from the conditional distribution given the single-value forecast. The replacement procedure matches the ranks of historical and rescaled values to preserve the space-time properties of observed precipitation in the ensemble traces. Currently, the ensemble pre-processor is being tested and evaluated at four NOAA/NWS River Forecast Centers (RFCs) in the U.S.A. In this contribution, we present the results thus far from the field and retrospective evaluations, and key science issues that must be addressed toward national operational implementation.

  18. Evidence for environmental conditions during the last 20 000 years in Southern Africa from C-13 in fossil hyrax dung

    CSIR Research Space (South Africa)

    Scott, L

    2000-11-01

    Full Text Available of fossil isotope values, more modern controls, and palynological research are needed to make an accurate reconstruction of past vegetation changes possible. To be effective controls from the modern environment should take into account site peculiarities... to the rock hyrax problem in South West Africa. Madoqua 13, 177?196. Scott, L., 1989. Late Quaternary vegetation history and climatic change in the eastern O.F.S. S. Afr. J. Bot. 55, 107?116. . . middens in paleoenvironmental studies in Africa. In: Betan...

  19. Modeled Forecasts of Dengue Fever in San Juan, Puerto Rico Using NASA Satellite Enhanced Weather Forecasts

    Science.gov (United States)

    Morin, C.; Quattrochi, D. A.; Zavodsky, B.; Case, J.

    2015-12-01

    Dengue fever (DF) is an important mosquito transmitted disease that is strongly influenced by meteorological and environmental conditions. Recent research has focused on forecasting DF case numbers based on meteorological data. However, these forecasting tools have generally relied on empirical models that require long DF time series to train. Additionally, their accuracy has been tested retrospectively, using past meteorological data. Consequently, the operational utility of the forecasts are still in question because the error associated with weather and climate forecasts are not reflected in the results. Using up-to-date weekly dengue case numbers for model parameterization and weather forecast data as meteorological input, we produced weekly forecasts of DF cases in San Juan, Puerto Rico. Each week, the past weeks' case counts were used to re-parameterize a process-based DF model driven with updated weather forecast data to generate forecasts of DF case numbers. Real-time weather forecast data was produced using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) system enhanced using additional high-resolution NASA satellite data. This methodology was conducted in a weekly iterative process with each DF forecast being evaluated using county-level DF cases reported by the Puerto Rico Department of Health. The one week DF forecasts were accurate especially considering the two sources of model error. First, weather forecasts were sometimes inaccurate and generally produced lower than observed temperatures. Second, the DF model was often overly influenced by the previous weeks DF case numbers, though this phenomenon could be lessened by increasing the number of simulations included in the forecast. Although these results are promising, we would like to develop a methodology to produce longer range forecasts so that public health workers can better prepare for dengue epidemics.

  20. Performance of four European hemp cultivars cultivated under different agronomic experimental conditions in the Eastern Cape Province, South Africa

    CSIR Research Space (South Africa)

    Blouw, LS

    2005-10-01

    Full Text Available The purpose of this work was to obtain information on the performance of four European hemp cultivars piloted at five different sites in the Eastern Cape (South Africa), by assessing the fibre content of each cultivar grown under different agronomic...

  1. Combining forecast weights: Why and how?

    Science.gov (United States)

    Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim

    2012-09-01

    This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.

  2. Fuel cycle forecasting - there are forecasts and there are forecasts

    International Nuclear Information System (INIS)

    Puechl, K.H.

    1975-01-01

    The FORECAST-NUCLEAR computer program described recognizes that forecasts are made to answer a variety of questions and, therefore, that no single forecast is universally appropriate. Also, it recognizes that no two individuals will completely agree as to the input data that are appropriate for obtaining an answer to even a single simple question. Accordingly, the program was written from a utilitarian standpoint: it allows working with multiple projections; data inputting is simple to allow game-playing; computation time is short to minimize the cost of 'what if' assessements; and detail is internally carried to allow meaningful analysis. (author)

  3. Climate and Health Vulnerability to Vector-Borne Diseases: Increasing Resilience under Climate Change Conditions in Africa

    Science.gov (United States)

    Ceccato, P.

    2015-12-01

    The International Research Institute for Climate and Society (IRI), the City University of New York (CUNY) and NASA Jet Propulsion Laboratory (JPL) in collaboration with NASA SERVIR are developing tools to monitor climate variables (precipitation, temperature, vegetation, water bodies, inundation) that help projects in Africa to increase resilience to climate change for vector-borne diseases ( malaria, trypanosomiasis, leishmaniasis, and schistosomiasis). Through the development of new products to monitor precipitation, water bodies and inundation, IRI, CUNY and JPL provide tools and capacity building to research communities; ministries of health; the WMO Global Framework for Climate and Services; and World Health Organization in Africa to: 1) Develop research teams' ability to appropriately use climate data as part of their research 2) Enable research teams and ministries to integrate climate information into social and economic drivers of vulnerability and opportunities for adaptation to climate change 3) Inform better policies and programs for climate change adaptation. This oral presentation will demonstrate how IRI, CUNY, and JPL developed new products, tools and capacity building to achieve the three objectives mentioned above with examples in South Africa, Zimbabwe, Tanzania and Malawi.

  4. Very short range forecasts of visibility and ceiling

    Science.gov (United States)

    Hilsenrod, A.

    1980-01-01

    The development of methods for the short range forecasting of visibility and ceiling conditions is discussed. Short range forecasts of one hour or less (5 or 30 minutes), immediately after a series of local observations can be expected to be more accurate and reliable than any forecast of more than one hour. These forecasts can be accomplished by the operational implementation of fully automated aviation observation systems and the utilization of statistical techniques such as the Generalized Equivalent Markov model.

  5. Improving Software Reliability Forecasting

    NARCIS (Netherlands)

    Burtsy, Bernard; Albeanu, Grigore; Boros, Dragos N.; Popentiu, Florin; Nicola, V.F.

    1996-01-01

    This work investigates some methods for software reliability forecasting. A supermodel is presented as a suited tool for prediction of reliability in software project development. Also, times series forecasting for cumulative interfailure time is proposed and illustrated.

  6. Global Energy Forecasting Competition 2012

    DEFF Research Database (Denmark)

    Hong, Tao; Pinson, Pierre; Fan, Shu

    2014-01-01

    The Global Energy Forecasting Competition (GEFCom2012) attracted hundreds of participants worldwide, who contributed many novel ideas to the energy forecasting field. This paper introduces both tracks of GEFCom2012, hierarchical load forecasting and wind power forecasting, with details...

  7. Ensemble forecasts of road surface temperatures

    Science.gov (United States)

    Sokol, Zbyněk; Bližňák, Vojtěch; Sedlák, Pavel; Zacharov, Petr; Pešice, Petr; Škuthan, Miroslav

    2017-05-01

    This paper describes a new ensemble technique for road surface temperature (RST) forecasting using an energy balance and heat conduction model. Compared to currently used deterministic forecasts, the proposed technique allows the estimation of forecast uncertainty and probabilistic forecasts. The ensemble technique is applied to the METRo-CZ model and stems from error covariance analyses of the forecasted air temperature and humidity 2 m above the ground, wind speed at 10 m and total cloud cover N in octas by the numerical weather prediction (NWP) model. N is used to estimate the shortwave and longwave radiation fluxes. These variables are used to calculate the boundary conditions in the METRo-CZ model. We found that the variable N is crucial for generating the ensembles. Nevertheless, the ensemble spread is too small and underestimates the uncertainty in the RST forecast. One of the reasons is not considering errors in the rain and snow forecast by the NWP model when generating ensembles. Technical issues, such as incorrect sky view factors and the current state of road surface conditions also contribute to errors. Although the ensemble technique underestimates the uncertainty in the RST forecasts, it provides additional information to road authorities who provide winter road maintenance.

  8. Inaccuracy in traffic forecasts

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent; Holm, Mette K. Skamris; Buhl, Søren Ladegaard

    2006-01-01

    , the difference between actual and forecasted traffic is more than +-20%; for 25% of road projects, the difference is larger than +-40%. Forecasts for roads are more accurate and more balanced than for rail, with no significant difference between the frequency of inflated versus deflated forecasts. But for both...

  9. Managing Sales Forecasters

    NARCIS (Netherlands)

    L.P. de Bruijn (Bert); Ph.H.B.F. Franses (Philip Hans)

    2012-01-01

    textabstractA Forecast Support System (FSS), which generates sales forecasts, is a sophisticated business analytical tool that can help to improve targeted business decisions. Many companies use such a tool, although at the same time they may allow managers to quote their own forecasts. These sales

  10. Forecasting The Onset Of The East African Rains

    Science.gov (United States)

    MacLeod, D.; Palmer, T.

    2017-12-01

    The timing of the rainy seasons is critical for East Africa, where many livelihoods depend on rain-fed agriculture. The exact onset date of the rains varies from year to year and a delayed start has significant implications for food security. Early warning of anomalous onset can help mitigate risks by informing farmer decisions on crop choice and timing of planting. Onset forecasts may also pre-warn governments and NGOs of upcoming need for financial support and humanitarian intervention. Here we assess the potential to forecast the onset of both the short and long rains over East Africa at subseasonal to seasonal timescales. Based on operational reforecasts from ECMWF, we will demonstrate skilful prediction of onset anomalies. An investigation to determine potential sources of this forecast skill will also be presented. This work has been carried out as part of the project ForPAc: "Towards forecast-based preparedness action".

  11. Scenarios for energy forecasting: papers of the symposium

    International Nuclear Information System (INIS)

    1987-01-01

    Energy planning is important for every developed country and therefore also for South Africa. However, during 1984 it was felt by interested parties that the work in this field should be coordinated through mutual discussion. With this in mind a 'Task Team for Energy Forecasting' was formed with the task to generate acceptable forecasts of the energy set-up in South Africa. Knowledge of the relationship between energy and variables such as the economy and the population is necessary to the Task Team. However, the Task Team also needs some insight into the future paths of such variables if it has to generate energy forecasts. It is the purpose of this symposium to improve this insight through having experts in all relevant fields to set out and develop their possible future scenarios independently of energy forecasting

  12. Estimation of predictive hydrological uncertainty using quantile regression : Examples from the National Flood Forecasting System (England and Wales)

    NARCIS (Netherlands)

    Weerts, A.H.; Winsemius, H.C.; Verkade, J.S.

    2011-01-01

    In this paper, a technique is presented for assessing the predictive uncertainty of rainfall-runoff and hydraulic forecasts. The technique conditions forecast uncertainty on the forecasted value itself, based on retrospective Quantile Regression of hindcasted water level forecasts and forecast

  13. Morphological changes in the spiracles of Anopheles gambiae s.l (Diptera) as a response to the dry season conditions in Burkina Faso (West Africa).

    Science.gov (United States)

    Mamai, Wadaka; Mouline, Karine; Parvy, Jean-Philippe; Le Lannic, Jo; Dabiré, Kounbobr Roch; Ouédraogo, Georges Anicet; Renault, David; Simard, Frederic

    2016-01-07

    Survival to dry season conditions of sub-Saharan savannahs is a major challenge for insects inhabiting such environments, especially regarding the desiccation threat they are exposed to. While extensive literature about insect seasonality has revealed morphologic, metabolic and physiological changes in many species, only a few studies have explored the responses following exposure to the stressful dry season conditions in major malaria vectors. Here, we explored morphological changes triggered by exposure to dry season conditions in An. gambiae s.l. mosquitoes by comparing females reared in climatic chambers reflecting environmental conditions found in mosquito habitats during the rainy and dry seasons in a savannah area of Burkina Faso (West Africa). Using scanning electron microscopy (SEM) and confocal imaging, we revealed significant changes in morphological features of the spiracles in females An. gambiae s.l. exposed to contrasted environmental conditions. Hence, the hairs surrounding the spiracles were thicker in the three species when raised under dry season environmental conditions. The thicker hairs were in some cases totally obstructing spiracular openings. Specific staining provided evidence against contamination by external microorganisms such as bacteria and fungi. However, only further analysis would unequivocally rule out the hypothesis of experimental artifact. Morphological changes in spiracular features probably help to limit body water loss during desiccating conditions, therefore contributing to insect survival. Differences between species within the An. gambiae complex might therefore reflect different survival strategies used by these species to overcome the detrimental dry season conditions in the wild.

  14. Gold sales forecasting: The Box-Jenkins methodology

    OpenAIRE

    Johannes Tshepiso Tsoku; Nonofo Phukuntsi; Daniel Metsileng

    2017-01-01

    The study employs the Box-Jenkins Methodology to forecast South African gold sales. For a resource economy like South Africa where metals and minerals account for a high proportion of GDP and export earnings, the decline in gold sales is very disturbing. Box-Jenkins time series technique was used to perform time series analysis of monthly gold sales for the period January 2000 to June 2013 with the following steps: model identification, model estimation, diagnostic checking and forecasting. F...

  15. Inaccuracy in traffic forecasts

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent; Holm, Mette K. Skamris; Buhl, Søren Ladegaard

    2006-01-01

    that forecasters generally do a poor job of estimating the demand for transportation infrastructure projects. The result is substantial downside financial and economic risk. Forecasts have not become more accurate over the 30-year period studied. If techniques and skills for arriving at accurate demand forecasts...... forecasting. Highly inaccurate traffic forecasts combined with large standard deviations translate into large financial and economic risks. But such risks are typically ignored or downplayed by planners and decision-makers, to the detriment of social and economic welfare. The paper presents the data...

  16. Electricity demand forecasting techniques

    International Nuclear Information System (INIS)

    Gnanalingam, K.

    1994-01-01

    Electricity demand forecasting plays an important role in power generation. The two areas of data that have to be forecasted in a power system are peak demand which determines the capacity (MW) of the plant required and annual energy demand (GWH). Methods used in electricity demand forecasting include time trend analysis and econometric methods. In forecasting, identification of manpower demand, identification of key planning factors, decision on planning horizon, differentiation between prediction and projection (i.e. development of different scenarios) and choosing from different forecasting techniques are important

  17. Operational forecast products and applications based on WRF/Chem

    Science.gov (United States)

    Hirtl, Marcus; Flandorfer, Claudia; Langer, Matthias; Mantovani, Simone; Olefs, Marc; Schellander-Gorgas, Theresa

    2015-04-01

    The responsibilities of the national weather service of Austria (ZAMG) include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. The ZAMG conducts daily Air-Quality forecasts using the on-line coupled model WRF/Chem. The mother domain expands over Europe, North Africa and parts of Russia. The nested domain includes the alpine region and has a horizontal resolution of 4 km. Local emissions (Austria) are used in combination with European inventories (TNO and EMEP) for the simulations. The modeling system is presented and the results from the evaluation of the assimilation of pollutants using the 3D-VAR software GSI is shown. Currently observational data (PM10 and O3) from the Austrian Air-Quality network and from European stations (EEA) are assimilated into the model on an operational basis. In addition PM maps are produced using Aerosol Optical Thickness (AOT) observations from MODIS in combination with model data using machine learning techniques. The modeling system is operationally evaluated with different data sets. The emphasis of the application is on the forecast of pollutants which are compared to the hourly values (PM10, O3 and NO2) of the Austrian Air-Quality network. As the meteorological conditions are important for transport and chemical processes, some parameters like wind and precipitation are automatically evaluated (SAL diagrams, maps, …) with other models (e.g. ECMWF, AROME, …) and ground stations via web interface. The prediction of the AOT is also important for operators of solar power plants. In the past Numerical Weather Prediction (NWP) models were used to predict the AOT based on cloud forecasts at the ZAMG. These models do not consider the spatial and temporal variation of the aerosol distribution in the atmosphere with a consequent impact on the accuracy of forecasts especially during clear-sky days

  18. Climate and Population Health Vulnerabilities to Vector-Borne Diseases: Increasing Resilience Under Climate Change Conditions in Africa

    Science.gov (United States)

    Ceccato, P.; McDonald, K. C.; Podest, E.; De La Torre Juarez, M.; Kruczkiewicz, A.; Lessel, J.; Jensen, K.; Thomson, M. C.

    2014-12-01

    The International Research Institute for Climate and Society (IRI), the City University of New York (CUNY) and NASA Jet Propulsion Laboratory (JPL) in collaboration with NASA SERVIR are developing tools to monitor climate variables (precipitation, temperature, vegetation, water bodies, inundation) that help projects in Africa to increase resilience to climate change for vector-borne diseases (i.e. malaria, trypanosomiasis, leishmaniasis, and schistosomiasis). Through the development of new products to monitor precipitation, water bodies and inundation, IRI, CUNY and JPL provide tools and capacity building to research communities, ministries of health and World Health Organization in Africa to: 1) Develop research teams' ability to appropriately use climate data as part of their research 2) Enable research teams and ministries to integrate climate information into social and economic drivers of vulnerability and opportunities for adaptation to climate change 3) Inform better policies and programs for climate change adaptation. This oral presentation will demonstrate how IRI, CUNY, and JPL developed new products, tools and capacity building to achieve the three objectives mentioned above.

  19. Improving the Performance of Water Demand Forecasting Models by Using Weather Input

    NARCIS (Netherlands)

    Bakker, M.; Van Duist, H.; Van Schagen, K.; Vreeburg, J.; Rietveld, L.

    2014-01-01

    Literature shows that water demand forecasting models which use water demand as single input, are capable of generating a fairly accurate forecast. However, at changing weather conditions the forecasting errors are quite large. In this paper three different forecasting models are studied: an

  20. Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment

    Science.gov (United States)

    Molthan, Andrew; Case, Jonathan; Venner, Jason; Moreno-Madrinan, Max J.; Delgado, Francisco

    2012-01-01

    Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.

  1. Associations of demographics, living conditions, work and lifestyle, with levels of satisfaction of nursing personnel in Grahamstown, South Africa

    CSIR Research Space (South Africa)

    Hodgskiss, J

    2015-10-01

    Full Text Available Diverse demographics, living conditions, working conditions and lifestyles in the South African workforce are likely to affect levels of satisfaction and quality of life. Stressors facing nursing personnel include high mental and physical demands...

  2. House Price Forecasts, Forecaster Herding, and the Recent Crisis

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Pierdzioch; Ruelke

    2013-01-01

    We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-)herding of forecasters. Forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from...

  3. Towards a medium-range coastal station fog forecasting system

    CSIR Research Space (South Africa)

    Landman, S

    2013-09-01

    Full Text Available -1 29th Annual conference of South African Society for Atmospheric Sciences (SASAS) 2013 http://sasas.ukzn.ac.za/homepage.aspx Towards a Medium-Range Coastal Station Fog Forecasting System Stephanie Landman*1, Estelle Marx1, Willem A. Landman2..., Simon J. Mason3 1 South African Weather Service, Pretoria, South Africa 2 Natural Resources and the Environment, Council for Scientific and Industrial Research, Pretoria, South Africa 3 International Research Institute for Climate and Society...

  4. Risky Business: Development, Communication and Use of Hydroclimatic Forecasts

    Science.gov (United States)

    Lall, U.

    2012-12-01

    Inter-seasonal and longer hydroclimatic forecasts have been made increasingly in the last two decades following the increase in ENSO activity since the early 1980s and the success in seasonal ENSO forecasting. Yet, the number of examples of systematic use of these forecasts and their incorporation into water systems operation continue to be few. This may be due in part to the limited skill in such forecasts over much of the world, but is also likely due to the limited evolution of methods and opportunities to "safely" use uncertain forecasts. There has been a trend to rely more on "physically based" rather than "physically informed" empirical forecasts, and this may in part explain the limited success in developing usable products in more locations. Given the limited skill, forecasters have tended to "dumb" down their forecasts - either formally or subjectively shrinking the forecasts towards climatology, or reducing them to tercile forecasts that serve to obscure the potential information in the forecast. Consequently, the potential utility of such forecasts for decision making is compromised. Water system operating rules are often designed to be robust in the face of historical climate variability, and consequently are adapted to the potential conditions that a forecast seeks to inform. In such situations, there is understandable reluctance by managers to use the forecasts as presented, except in special cases where an alternate course of action is pragmatically appealing in any case. In this talk, I review opportunities to present targeted forecasts for use with decision systems that directly address climate risk and the risk induced by unbiased yet uncertain forecasts, focusing especially on extreme events and water allocation in a competitive environment. Examples from Brazil and India covering surface and ground water conjunctive use strategies that could potentially be insured and lead to improvements over the traditional system operation and resource

  5. Forecasting in Planning

    OpenAIRE

    Ike, P.; Voogd, Henk; Voogd, Henk; Linden, Gerard

    2004-01-01

    This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively. Quantitative forecasting is illustrated with a brief overview of time series methods. Both qualitative and quantitative methods are illustrated by an example. The role and relative importance of forec...

  6. Commuter Airline Forecasts,

    Science.gov (United States)

    1981-05-01

    conterminous United States (48 contiguous states and the District of Columbia), for the State of Hawaii, and for the U.S. Carribean areas, Puerto Rico and U.S...FAA 15. Supplementary Notes I Abstract This publication presents forecasts of cammuter air carrier activity and describes the models designed for...forecasting Contenninous United States, Puerto Rico and the Virgin Islands, Hawaii, and individual airport activity. These forecasts take into account the

  7. Changes in hydro-meteorological conditions over tropical West Africa (1980-2015) and links to global climate

    Science.gov (United States)

    Ndehedehe, Christopher E.; Awange, Joseph L.; Agutu, Nathan O.; Okwuashi, Onuwa

    2018-03-01

    The role of global sea surface temperature (SST) anomalies in modulating rainfall in the African region has been widely studied and is now less debated. However, their impacts and links to terrestrial water storage (TWS) in general, have not been studied. This study presents the pioneer results of canonical correlation analysis (CCA) of TWS derived from both global reanalysis data (1980-2015) and GRACE (Gravity Recovery and Climate Experiment) (2002-2014) with SST fields. The main issues discussed include, (i) oceanic hot spots that impact on TWS over tropical West Africa (TWA) based on CCA, (ii) long term changes in model and global reanalysis data (soil moisture, TWS, and groundwater) and the influence of climate variability on these hydrological indicators, and (iii) the hydrological characteristics of the Equatorial region of Africa (i.e., the Congo basin) based on GRACE-derived TWS, river discharge, and precipitation. Results of the CCA diagnostics show that El-Niño Southern Oscillation related equatorial Pacific SST fluctuations is a major index of climate variability identified in the main portion of the CCA procedure that indicates a significant association with long term TWS reanalysis data over TWA (r = 0.50, ρ < 0.05). Based on Mann-Kendall's statistics, the study found fairly large long term declines (ρ < 0.05) in TWS and soil moisture (1982 - 2015), mostly over the Congo basin, which coincided with warming of the land surface and the surrounding oceans. Meanwhile, some parts of the Sahel show significant wetting (rainfall, soil moisture, groundwater, and TWS) trends during the same period (1982-2015) and aligns with the ongoing narratives of rainfall recovery in the region. Results of singular spectral analysis and regression confirm that multi-annual changes in the Congo River discharge explained a considerable proportion of variability in GRACE-hydrological signal over the Congo basin (r = 0.86 and R2 = 0.70, ρ < 0.05). Finally, leading

  8. Muslim-State Relations in East Africa Under Conditions of Military and Civilian or One-party Dictatorships

    Directory of Open Access Journals (Sweden)

    Abdin Chande

    2009-12-01

    Full Text Available Este trabajo examina cómo los musulmanes de África Oriental han sido alternativamente marginados, cooptados e incluso en una ocasión favorecidos en la política de dictaduras militares o de un único partido. En respuesta a las políticas basadas en el regionalismo o en la etnia del periodo poscolonial, los grupos musulmanes han desarrollado estrategias para promover las metas o intereses de su comunidad dentro o fuera de los círculos oficiales reconocidos. Estas estrategias han oscilado entre el buscar la acomodación con los partidos gobernantes, posicionándose estos mismo como grupos de presión articulando sus intereses y objetivos a, en raras ocasiones, organizar la oposición armada contra el gobierno.____________________ABSTRACT:This paper examines how Muslims in East Africa have been alternately peripheralized, coopted and even on one occasion favored in the politics of military or one-party dictatorships. In response to the regional or ethnically-based politics of the post-colonial period, Muslim groups have devised strategies to promote their communities’ goals or interests either within or outside officially sanctioned organizational circles. These strategies have ranged from seeking accomodation with governing parties, positioning themselves as pressure groups articulating the interests and grievances of their religious constituencies to, on rare occasions, resorting to outright armed opposition against the government.

  9. CORRECTION OF FORECASTS OF INTERRELATED CURRENCY PAIRS IN TERMS OF SYSTEMS OF BALANCE RATIOS

    OpenAIRE

    Gertsekovich D. A.

    2015-01-01

    In this paper the problem of exchange rates forecast is logically considered a) traditionally as a task of forecast on the base of «stand-alone» equations of autoregression for each currency pair and b) as a result of forecast correction of autoregression equations system on the base of boundary conditions of balance ratios systems. As a criterion for quality of forecast constructed with empirical models we take the sum of deficiency quadrates of forecasts estimated for deductive currency pai...

  10. Uncertainty Analysis of Multi-Model Flood Forecasts

    Directory of Open Access Journals (Sweden)

    Erich J. Plate

    2015-12-01

    Full Text Available This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf, calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables.

  11. Household dynamics and socioeconomic conditions in the context of incident adolescent orphaning in KwaZulu-Natal, South Africa

    Science.gov (United States)

    DeSilva, Mary Bachman; Skalicky, Anne; Beard, Jennifer; Cakwe, Mandisa; Zhuwau, Tom; Quinlan, Tim; Simon, Jonathon L.

    2012-01-01

    We compared demographics, socioeconomic status, and food insecurity between households with and without recent orphans in a region of high HIV/AIDS mortality in South Africa. We recruited a cohort of 197 recent orphans and 528 non-orphans ages 9–15 and their households using stratified cluster sampling. Households were classified into three groups: orphan-only (N=50); non-orphan-only (N=377); and mixed (N=210). Between September 2004 and May 2007, households were interviewed three times regarding demographics, income and assets, and food insecurity. Baseline bivariate associations were assessed using chi-square- and t-tests. Longitudinal bivariate associations and multivariate models were tested using generalized estimating equations. At baseline, mixed households generally exhibited greater characteristics of vulnerability than orphan and non-orphan households. They were larger, had older, less educated household heads, and reported a much smaller annual per capita income. Orphan households were more likely to report a death in the previous year, and less likely to have an adult employed. These differences persisted over the study. Even non-orphan households exhibited characteristics of vulnerability, with 14% reporting a death one year before baseline, 45% of whom were prime-age adults. At baseline, a much smaller proportion of orphan households reported receiving the child support grant than the other household types, but notably, there were no differences among households in receipt of the grant by Round 3. Household food insecurity was highly prevalent: more than one in five orphan-only and mixed households reported being food insecure in the previous month. These findings suggest that the effects of HIV/AIDS only exacerbate existing high levels of poverty in the district, as virtually all households are vulnerable regardless of orphan status. Community-level programs must help families address a spectrum of needs, including food security, caregiving, and

  12. House Price Forecasts, Forecaster Herding, and the Recent Crisis

    Directory of Open Access Journals (Sweden)

    Christian Pierdzioch

    2012-11-01

    Full Text Available We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-herding of forecasters. Forecasts are consistent with herding (anti-herding of forecasters if forecasts are biased towards (away from the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time.

  13. World Area Forecast System (WAFS)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The World Area Forecast System (WAFS) is a worldwide system by which world area forecast centers provide aeronautical meteorological en-route forecasts in uniform...

  14. How to compare what seems incomparable in seasonal hydrological forecasting?

    Science.gov (United States)

    Crochemore, Louise; Pechlivanidis, Ilias; Ramos, Maria-Helena

    2017-04-01

    A number of climate forecasting systems have been developed at the global scale allowing the production of seasonal hydrological services at the regional, national and continental scales. With these services becoming increasingly available to dissemination institutes and occasionally directly to the public, forecasters have been requesting information on the reliability of the forecasts, particularly when they need to select one system for a specific application. The quality of the forecasts depends on the hydrological model used and, consequently, on its setup, structure, objective, and performance. These characteristics can be very different among models, adding a degree of complexity to any model output inter-comparison analysis. Here, we propose a framework to compare outputs from different modelling systems. We compare the seasonal streamflow forecasts produced by a continentally-calibrated complex model (E-HYPE) and a regionally-calibrated parsimonious model (GR6J) to forecast streamflow in a set of French catchments. Streamflow forecasts are obtained by using bias adjusted ECMWF System 4 seasonal precipitation and temperature forecasts as input to the E-HYPE and GR6J hydrological models. We first identify within the forecasting chain the origin of the differences between the two hydrological systems. We use the evaluation of forecast skill to highlight and isolate the differences in meteorological forcing, initial hydrological conditions and historical model performance, respectively. Forecast skill is thus evaluated by considering different benchmarks based on: i) historical observed streamflow, ii) historical simulated streamflow, and iii) the Extended Streamflow Prediction (ESP) system which uses meteorological climatology as input to the hydrological models. We also present the dependence of forecast quality (i.e., sharpness and reliability) on the hydrological models used. Lastly, we assess the impact of the two different model structures on forecast

  15. Forecasting in Planning

    NARCIS (Netherlands)

    Ike, P.; Voogd, Henk; Voogd, Henk; Linden, Gerard

    2004-01-01

    This chapter begins with a discussion of qualitative forecasting by describing a number of methods that depend on judgements made by stakeholders, experts or other interested parties to arrive at forecasts. Two qualitative approaches are illuminated, the Delphi and scenario methods respectively.

  16. A review of effective flood forecasting, warning and response system ...

    African Journals Online (AJOL)

    Last mentioned are the reason for absence in South Africa of a formal flood forecast, warning and response system (FFWRS). In most cases where a flood warning system exists, there is evidence that it is insufficient, mainly because of a lack of knowledge and understanding of a well-functioning, appropriate FFWRS.

  17. A hindcast archive to assess forecast uncertainty of seasonal forecasts for the Columbia River Basin

    Science.gov (United States)

    Nijssen, B.

    2006-12-01

    More than half of the electricity in the northwestern United States is generated by hydropower facilities in the Columbia River Basin. Consequently, seasonal hydrologic forecasts of naturalized streamflow are of interest to system operators, energy traders and financial institutions. Much of the seasonal streamflow predictability is derived from the importance of snow melt in the Columbia River Basin. Further predictability is derived from the ENSO-state (El Niño Southern Oscillation), which affects precipitation patterns in the basin. Typically, the Pacific Northwest experiences a greater likelihood of reduced precipitation during El Niño episodes, and a greater likelihood of increased precipitation during La Niña episodes. For the 2006 water year, we created long-range operational hydrologic forecasts for selected locations in the basin using a macroscale hydrologic model and an ensemble streamflow prediction (ESP) methodology. Although our ESP approach provided a measure of the range of expected streamflow conditions, it did not account for the uncertainty in forecast initial conditions, parameter uncertainty or model uncertainty. To assess the total uncertainty associated with our hydrologic forecasts, we have created a hindcast database for the period 1950-2005, which includes 12-month forecasts made on the start of each month during the period November May. This hindcast archive enables us to assess the total uncertainty associated with our seasonal forecasts. We will present forecast verification results for selected locations in the Columbia River Basin as a function of lead time and ENSO condition.

  18. Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting

    Directory of Open Access Journals (Sweden)

    Jui-Yi Ho

    2015-04-01

    Full Text Available The dynamic relationship between watershed characteristics and rainfall-runoff has been widely studied in recent decades. Since watershed rainfall-runoff is a non-stationary process, most deterministic flood forecasting approaches are ineffective without the assistance of adaptive algorithms. The purpose of this paper is to propose an effective flow forecasting system that integrates a rainfall forecasting model, watershed runoff model, and real-time updating algorithm. This study adopted a grey rainfall forecasting technique, based on existing hourly rainfall data. A geomorphology-based runoff model can be used for simulating impacts of the changing geo-climatic conditions on the hydrologic response of unsteady and non-linear watershed system, and flow updating algorithm were combined to estimate watershed runoff according to measured flow data. The proposed flood forecasting system was applied to three watersheds; one in the United States and two in Northern Taiwan. Four sets of rainfall-runoff simulations were performed to test the accuracy of the proposed flow forecasting technique. The results indicated that the forecast and observed hydrographs are in good agreement for all three watersheds. The proposed flow forecasting system could assist authorities in minimizing loss of life and property during flood events.

  19. Status of mineral resources evaluation and forecast

    International Nuclear Information System (INIS)

    Ma Hanfeng; Li Ziying; Luo Yi; Li Shengxiang; Sun Wenpeng

    2007-01-01

    The work of resources evaluation and forecast is a focus to the governments of every country in the world, it is related to the establishment of strategic policy on the national mineral resources. In order to quantitatively evaluate the general potential of uranium resources in China and better forecast uranium deposits, this paper briefly introduces the method of evaluating total amount of mineral resources, especially 6 usual prospective methods which are recommended in international geology comparison programs, as well as principle of usual mineral resources quantitative prediction and its steps. The work history of mineral resources evaluation and forecast is reviewed concisely. Advantages and disadvantages of each method, their application field and condition are also explained briefly. At last, the history of uranium resources evaluation and forecast in China and its status are concisely outlined. (authors)

  20. Seasonal UK hydrological forecasts using rainfall forecasts - what level of skill?

    Science.gov (United States)

    Bell, Victoria; Davies, Helen; Kay, Alison; Scaife, Adam

    2017-04-01

    Skilful winter seasonal predictions for the North Atlantic circulation and Northern Europe, including the UK have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. The Hydrological Outlook UK (HOUK: www.hydoutuk.net) is the first operational hydrological forecast system for the UK that delivers monthly outlooks of the water situation for both river flow and groundwater levels. The output from the HOUK are publicly available and used each month by government agencies, practitioners and academics alongside other sources of information such as flood warnings and meteorological forecasts. The HOUK brings together information on current and forecast weather conditions, and river flows, and uses several modelling approaches to explore possible future hydrological conditions. One of the techniques combines ensembles of monthly-resolution seasonal rainfall forecasts provided by the Met Office GloSea5 forecast system with hydrological modelling tools to provide estimates of river flows up to a few months ahead. The approach combines a high resolution, spatially distributed hydrological initial condition (HIC) provided by a hydrological model (Grid-to-Grid) driven by weather observations up to the forecast time origin. Considerable efforts have been made to accommodate the temporal and spatial resolution of the GloSea5 rainfall forecasts (monthly time-step and national-scale) in a spatially distributed forecasting system, leading to the development of a monthly resolution water balance model (WBM) to forecast regional mean river flows for the next 1 and 3 months ahead. The work presented here provides the first assessment of the skill in the HOUK national-scale flow forecasts using an ensemble of rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009). The skill in the combined modelling system has been assessed for different seasons and regions of Britain, and compared to what might be achieved using

  1. Non-relational conditions necessary for mentoring of black small business owner–managers in South Africa

    Directory of Open Access Journals (Sweden)

    Chantal Rootman

    2017-10-01

    Contribution and value-add: Knowledge of non-relational conditions required for effective mentoring could result in successful skills development of owner–managers. Ultimately, the decision-making of owner–managers could be improved, and the success and longevity of their businesses could be enhanced.

  2. conditions

    Directory of Open Access Journals (Sweden)

    M. Venkatesulu

    1996-01-01

    Full Text Available Solutions of initial value problems associated with a pair of ordinary differential systems (L1,L2 defined on two adjacent intervals I1 and I2 and satisfying certain interface-spatial conditions at the common end (interface point are studied.

  3. The strategy of professional forecasting

    DEFF Research Database (Denmark)

    Ottaviani, Marco; Sørensen, Peter Norman

    2006-01-01

    We develop and compare two theories of professional forecasters’ strategic behavior. The first theory, reputational cheap talk, posits that forecasters endeavor to convince the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts and the ......We develop and compare two theories of professional forecasters’ strategic behavior. The first theory, reputational cheap talk, posits that forecasters endeavor to convince the market that they are well informed. The market evaluates their forecasting talent on the basis of the forecasts...... and the realized state. If the market expects forecasters to report their posterior expectations honestly, then forecasts are shaded toward the prior mean. With correct market expectations, equilibrium forecasts are imprecise but not shaded. The second theory posits that forecasters compete in a forecasting...

  4. Monitoring and seasonal forecasting of meteorological droughts

    Science.gov (United States)

    Dutra, Emanuel; Pozzi, Will; Wetterhall, Fredrik; Di Giuseppe, Francesca; Magnusson, Linus; Naumann, Gustavo; Barbosa, Paulo; Vogt, Jurgen; Pappenberger, Florian

    2015-04-01

    Near-real time drought monitoring can provide decision makers valuable information for use in several areas, such as water resources management, or international aid. Unfortunately, a major constraint in current drought outlooks is the lack of reliable monitoring capability for observed precipitation globally in near-real time. Furthermore, drought monitoring systems requires a long record of past observations to provide mean climatological conditions. We address these constraints by developing a novel drought monitoring approach in which monthly mean precipitation is derived from short-range using ECMWF probabilistic forecasts and then merged with the long term precipitation climatology of the Global Precipitation Climatology Centre (GPCC) dataset. Merging the two makes available a real-time global precipitation product out of which the Standardized Precipitation Index (SPI) can be estimated and used for global or regional drought monitoring work. This approach provides stability in that by-passes problems of latency (lags) in having local rain-gauge measurements available in real time or lags in satellite precipitation products. Seasonal drought forecasts can also be prepared using the common methodology and based upon two data sources used to provide initial conditions (GPCC and the ECMWF ERA-Interim reanalysis (ERAI) combined with either the current ECMWF seasonal forecast or a climatology based upon ensemble forecasts. Verification of the forecasts as a function of lead time revealed a reduced impact on skill for: (i) long lead times using different initial conditions, and (ii) short lead times using different precipitation forecasts. The memory effect of initial conditions was found to be 1 month lead time for the SPI-3, 3 to 4 months for the SPI-6 and 5 months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value, a skill similar to or better than climatological forecasts. In some cases, particularly for long SPI time

  5. Impact of AIRS Thermodynamic Profile on Regional Weather Forecast

    Science.gov (United States)

    Chou, Shih-Hung; Zavodsky, Brad; Jedlovee, Gary

    2010-01-01

    Prudent assimilation of AIRS thermodynamic profiles and quality indicators can improve initial conditions for regional weather models. AIRS-enhanced analysis has warmer and moister PBL. Forecasts with AIRS profiles are generally closer to NAM analyses than CNTL. Assimilation of AIRS leads to an overall QPF improvement in 6-h accumulated precipitation forecasts. Including AIRS profiles in assimilation process enhances the moist instability and produces stronger updrafts and a better precipitation forecast than the CNTL run.

  6. Spatial electric load forecasting

    CERN Document Server

    Willis, H Lee

    2002-01-01

    Spatial Electric Load Forecasting Consumer Demand for Power and ReliabilityCoincidence and Load BehaviorLoad Curve and End-Use ModelingWeather and Electric LoadWeather Design Criteria and Forecast NormalizationSpatial Load Growth BehaviorSpatial Forecast Accuracy and Error MeasuresTrending MethodsSimulation Method: Basic ConceptsA Detailed Look at the Simulation MethodBasics of Computerized SimulationAnalytical Building Blocks for Spatial SimulationAdvanced Elements of Computerized SimulationHybrid Trending-Simulation MethodsAdvanced

  7. Predicting the Vegetation Condition of the 2015/16 Drought across the Great Horn of Africa (GHA): Model Evaluation and Preliminary Result

    Science.gov (United States)

    Tadesse, T.; Bayissa, Y. A.; Demisse, G. B.; Wardlow, B.

    2017-12-01

    The National Drought Mitigation Center (NDMC) funded by NASA has developed a new tool for predicting the general vegetation condition called: the "Vegetation outlook for the Greater Africa (VegOut-GHA)." In this study, the 2015/16 drought across the GHA that has been considered one of the worst in decades across the region was assessed and evaluated using the VegOut-GHA models and products. The VegOut-GHA maps (hindsight prediction maps) for the growing season (June - September) were generated to predict a standardized seasonal greenness (SSG) that is based on seasonally integrated normalized difference vegetation index (a measure that represents a general indicator of relative vegetation health within a growing season). The vegetation condition outlooks were made for 10-day, 1-month, 2-month, and 3-month in hindsight and compared to the observed values of the SSG. The VegOut-GHA model was evaluated and compared to crop yield and other satellite-derived data (e.g., standardized seasonal precipitation based on "Enhancing National Climate Services (ENACTS)" datasets for GHA). Thus, the VegOut-GHA model and its evaluation results will be discussed based on the 2015/2016 drought season in the region. This preliminary results suggest an opportunity to improve management of drought risk in agriculture and food security.

  8. Solar UV forecasts: a randomized trial assessing their impact on adults' sun-protection behavior.

    Science.gov (United States)

    Dixon, Helen G; Hill, David J; Karoly, David J; Jolley, Damien J; Aden, Said M

    2007-06-01

    This study examined the effectiveness of solar UV forecasts and supporting communications in assisting adults to protect themselves from excessive weekend sun exposure. The study was conducted in Australia, where 557 adult participants with workplace e-mail and Internet access were randomly allocated to one of three weather forecast conditions: standard forecast (no UV), standard forecast + UV, standard forecast + UV + sun-protection messages. From late spring through summer and early autumn, they were e-mailed weekend weather forecasts late in the working week. Each Monday they were e-mailed a prompt to complete a Web-based questionnaire to report sun-related behavior and any sunburn experienced during the previous weekend. There were no significant differences between weather forecast conditions in reported hat use, sunscreen use, sun avoidance, or sunburn. Results indicate that provision of solar-UV forecasts in weather forecasts did not promote markedly enhanced personal sun-protection practices among the adults surveyed.

  9. Wind conditions and geography shape the first outbound migration of juvenile honey buzzards and their distribution across sub-Saharan Africa.

    Science.gov (United States)

    Vansteelant, W M G; Kekkonen, J; Byholm, P

    2017-05-31

    Contemporary tracking studies reveal that low migratory connectivity between breeding and non-breeding ranges is common in migrant landbirds. It is unclear, however, how internal factors and early-life experiences of individual migrants shape the development of their migration routes and concomitant population-level non-breeding distributions. Stochastic wind conditions and geography may determine whether and where migrants end up by the end of their journey. We tested this hypothesis by satellite-tagging 31 fledgling honey buzzards Pernis apivorus from southern Finland and used a global atmospheric reanalysis model to estimate the wind conditions they encountered on their first outbound migration. Migration routes diverged rapidly upon departure and the birds eventually spread out across 3340 km of longitude. Using linear regression models, we show that the birds' longitudinal speeds were strongly affected by zonal wind speed, and negatively affected by latitudinal wind, with significant but minor differences between individuals. Eventually, 49% of variability in the birds' total longitudinal displacements was accounted for by wind conditions on migration. Some birds circumvented the Baltic Sea via Scandinavia or engaged in unusual downwind movements over the Mediterranean, which also affected the longitude at which these individuals arrived in sub-Saharan Africa. To understand why adult migrants use the migration routes and non-breeding sites they use, we must take into account the way in which wind conditions moulded their very first journeys. Our results present some of the first evidence into the mechanisms through which low migratory connectivity emerges. © 2017 The Authors.

  10. Improving seasonal forecast through the state of large-scale climate signals

    Science.gov (United States)

    Samale, Chiara; Zimmerman, Brian; Giuliani, Matteo; Castelletti, Andrea; Block, Paul

    2017-04-01

    Increasingly uncertain hydrologic regimes are challenging water systems management worldwide, emphasizing the need of accurate medium- to long-term predictions to timely prompt anticipatory operations. In fact, forecasts are usually skillful over short lead time (from hours to days), but predictability tends to decrease on longer lead times. The forecast lead time might be extended by using climate teleconnection, such as El Nino Southern Oscillation (ENSO). Despite the ENSO teleconnection is well defined in some locations such as Western USA and Australia, there is no consensus on how it can be detected and used in other river basins, particularly in Europe, Africa, and Asia. In this work, we propose the use of the Nino Index Phase Analysis for capturing the state of multiple large-scale climate signals (i.e., ENSO, North Atlantic Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, Dipole Mode Index). This climate state information is used for distinguishing the different phases of the climate signals and for identifying relevant teleconnections between the observations of Sea Surface Temperature (SST) that mostly influence the local hydrologic conditions. The framework is applied to the Lake Como system, a regulated lake in northern Italy which is mainly operated for flood control and irrigation supply. Preliminary results show high correlations between SST and three to six months ahead precipitation in the Lake Como basin. This forecast represents a valuable information to partially anticipate the summer water availability, ultimately supporting the improvement of the Lake Como operations.

  11. Operational Streamflow Forecasts Development Using GCM Predicted Precipitation Fields

    Science.gov (United States)

    Arumugam, S.; Lall, U.

    2004-12-01

    Monthly updates of streamflow forecasts are required for deriving reservoir operation strategies as well as for quantifying surplus and shortfall for the allocated water contracts. In this study, an operational streamflow forecasts are developed using Atmospheric General Circulation Models (AGCM) predicted precipitation for managing the Angat Reservoir System, Philippines. The methodology employs principal components regression (PCR) for downscaling the AGCM predicted precipitation fields to monthly streamflow forecasts. The performance of this downscaling approach is analyzed with AGCM being forced using the observed sea surface temperature (SST) conditions as well under persisted SST conditions. The ability of downscaled streamflow forecasts in explaining the intraseasonal variability is also explored. Conditional distribution of streamflows obtained from the PCR downscaling approach is also compared with a simple, semi-parametric resampling algorithm that obtains ensembles of streamflow forecasts by identifying similar conditions in that season's climatic predictors state space.

  12. The Stevens Integrated Maritime Surveillance Forecast System: Expansion and Enhancement

    National Research Council Canada - National Science Library

    Bruno, Michael S; Blumberg, Alan F

    2006-01-01

    ... for the real-time assessment of ocean, weather, environmental, and vessel traffic conditions throughout the New York Harbor region, and the forecast of conditions in the near and long-term and under specific threat scenarios...

  13. Study of atmospheric condition during the heavy rain event in Bojonegoro using weather research and forecasting (WRF) model: case study 9 February 2017

    Science.gov (United States)

    Saragih, I. J. A.; Meygatama, A. G.; Sugihartati, F. M.; Sidauruk, M.; Mulsandi, A.

    2018-03-01

    During 2016, there are frequent heavy rains in the Bojonegoro region, one of which is rain on 9 February 2016. The occurrence of heavy rainfall can cause the floods that inundate the settlements, rice fields, roads, and public facilities. This makes it important to analyze the atmospheric conditions during the heavy rainfall events in Bojonegoro. One of the analytical methods that can be used is using WRF-Advanced Research WRF (WRF-ARW) model. This study was conducted by comparing the rain analysis from WRF-ARW model with the Himawari-8 satellite imagery. The data used are Final Analysis (FNL) data for the WRF-ARW model and infrared (IR) channel for Himawari-8 satellite imagery. The data are processed into the time-series images and then analyzed descriptively. The meteorological parameters selected to be analyzed are relative humidity, vortices, divergences, air stability index, and precipitation. These parameters are expected to indicate the existence of a convective activity in Bojonegoro during the heavy rainfall event. The Himawari-8 satellite imagery shows that there is a cluster of convective clouds in Bojonegoro during the heavy rainfall event. The lowest value of the cloud top temperature indicates that the cluster of convective clouds is a cluster of Cumulonimbus cloud (CB).

  14. Use of Operational Climate Forecasts in Reservoir Management and Operation

    Science.gov (United States)

    Arumugam, S.; Lall, U.

    2005-12-01

    Seasonal streamflow forecasts contingent on climate information are essential for short-term planning and for setting up contingency measures during extreme years. Similarly, monthly updates of streamflow forecasts are useful in quantifying surplus and shortfall in addressing the change in streamflow potential during the season. In this study, an operational streamflow forecasts for managing the Angat Reservoir System, Philippines, is developed using the precipitation forecasts from Atmospheric General Circulation Models (AGCM) that are forced by persisted Sea Surface Temperature (SST) conditions. The methodology employs principal components regression (PCR) to downscale the AGCM predicted precipitation fields to monthly streamflow forecasts. By performing retrospective analyses that combines streamflow forecasts with a dynamic water allocation model, we show that use of updated climate forecasts in reservoir operation results in increased reservoir system yields in comparison to using the seasonal streamflow forecasts alone. Revising the reservoir operation strategy based on updated streamflow forecasts is particularly critical in hydropower systems, since the increased yields from reduced spillage could be effectively utilized for power generation during above-normal inflow years. Further, analyzing the system performance under different scenarios of storage and demand, we show that the utility of climate information based reservoir inflow forecasts is more pronounced for systems with low storage to demand ratio.

  15. Recent changes in continentality and aridity conditions over the Middle East and North Africa region, and their association with circulation patterns

    KAUST Repository

    El Kenawy, Ahmed M.

    2016-05-30

    A long-term (1960-2013) assessment of the variability of continentality and aridity conditions over the Middle East and North Africa (MENA) region was undertaken. Monthly gridded temperature and precipitation data from the Climate Research Unit (CRU) (TS3.22 version) were used to compute the Johansson Continentality Index (JCI) and the Marsz Oceanity Index (MOI). In addition, the De Martonne index and the Pinna index were employed to assess recent changes in aridity conditions. All indices revealed a statistically significant increase in continental influences over the region, particularly in the Nile Basin and the Fertile Crescent. For aridity, the results suggested a generally statistically insignificant increase, with the most rapid changes occurring over the most humid regions (i.e. the Ethiopian Highlands and the Fertile Crescent). In order to explain the observed changes in the continentality and aridity conditions, we assessed the relationship between aridity and continentality indices and a wide range of large-scale circulation patterns. Results indicate that the spatial variability of continentality (as well as aridity) was closely coupled with the Atlantic modes of variability, e.g. the Eastern Atlantic pattern and the Atlantic Meridional Mode, compared to those of the Mediterranean Sea and the Indian Ocean. The results of this work highlight change processes in 2 important climate features in one of the hottest regions on Earth. Improving our understanding of the spatio-temporal characteristics of climate continentality and aridity has implications for a diversity of socio-political, economic, hydrological, and ecological activities in the MENA region.

  16. Integration of Behind-the-Meter PV Fleet Forecasts into Utility Grid System Operations

    Energy Technology Data Exchange (ETDEWEB)

    Hoff, Thomas Hoff [Clean Power Research, L.L.C., Napa, CA (United States); Kankiewicz, Adam [Clean Power Research, L.L.C., Napa, CA (United States)

    2016-02-26

    Four major research objectives were completed over the course of this study. Three of the objectives were to evaluate three, new, state-of-the-art solar irradiance forecasting models. The fourth objective was to improve the California Independent System Operator’s (ISO) load forecasts by integrating behind-the-meter (BTM) PV forecasts. The three, new, state-of-the-art solar irradiance forecasting models included: the infrared (IR) satellite-based cloud motion vector (CMV) model; the WRF-SolarCA model and variants; and the Optimized Deep Machine Learning (ODML)-training model. The first two forecasting models targeted known weaknesses in current operational solar forecasts. They were benchmarked against existing operational numerical weather prediction (NWP) forecasts, visible satellite CMV forecasts, and measured PV plant power production. IR CMV, WRF-SolarCA, and ODML-training forecasting models all improved the forecast to a significant degree. Improvements varied depending on time of day, cloudiness index, and geographic location. The fourth objective was to demonstrate that the California ISO’s load forecasts could be improved by integrating BTM PV forecasts. This objective represented the project’s most exciting and applicable gains. Operational BTM forecasts consisting of 200,000+ individual rooftop PV forecasts were delivered into the California ISO’s real-time automated load forecasting (ALFS) environment. They were then evaluated side-by-side with operational load forecasts with no BTM-treatment. Overall, ALFS-BTM day-ahead (DA) forecasts performed better than baseline ALFS forecasts when compared to actual load data. Specifically, ALFS-BTM DA forecasts were observed to have the largest reduction of error during the afternoon on cloudy days. Shorter term 30 minute-ahead ALFS-BTM forecasts were shown to have less error under all sky conditions, especially during the morning time periods when traditional load forecasts often experience their largest

  17. Gold sales forecasting: The Box-Jenkins methodology

    Directory of Open Access Journals (Sweden)

    Johannes Tshepiso Tsoku

    2017-02-01

    Full Text Available The study employs the Box-Jenkins Methodology to forecast South African gold sales. For a resource economy like South Africa where metals and minerals account for a high proportion of GDP and export earnings, the decline in gold sales is very disturbing. Box-Jenkins time series technique was used to perform time series analysis of monthly gold sales for the period January 2000 to June 2013 with the following steps: model identification, model estimation, diagnostic checking and forecasting. Furthermore, the prediction accuracy is tested using mean absolute percentage error (MAPE. From the analysis, a seasonal ARIMA(4,1,4×(0,1,112 was found to be the “best fit model” with an MAPE value of 11% indicating that the model is fit to be used to predict or forecast future gold sales for South Africa. In addition, the forecast values show that there will be a decrease in the overall gold sales for the first six months of 2014. It is hoped that the study will help the public and private sectors to understand the gold sales or output scenario and later plan the gold mining activities in South Africa. Furthermore, it is hoped that this research paper has demonstrated the significance of Box-Jenkins technique for this area of research and that they will be applied in the future.

  18. Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models

    DEFF Research Database (Denmark)

    Johansen, Simon Vestergaard; Bendtsen, Jan Dimon; Riisgaard-Jensen, Martin

    2017-01-01

    In this article, the dynamic influence of environmental broiler house conditions and broiler growth is investigated. Dynamic neural network forecasting models have been trained on farm-scale broiler batch production data from 12 batches from the same house. The model forecasts future broiler weight...... and uses environmental conditions such as heating, ventilation, and temperature along with broiler behavior such as feed and water consumption. Training data and forecasting data is analyzed to explain when the model might fail at generalizing. We present ensemble broiler weight forecasts to day 7, 14, 21......, 28 and 34 from all preceding days and provide our interpretation of the results. Results indicate that the dynamic interconnection between environmental conditions and broiler growth can be captured by the model. Furthermore, we found that a comparable forecast can be obtained by using input data...

  19. Statistical ensemble postprocessing for precipitation forecasting during the West African Monsoon

    Science.gov (United States)

    Vogel, Peter; Gneiting, Tilmann; Knippertz, Peter; Fink, Andreas H.; Schlüter, Andreas

    2017-04-01

    Precipitation forecasts for one up to several days are of high socioeconomic importance for agriculturally dominated societies in West Africa. In this contribution, we evaluate the performance of operational European Centre for Medium-Range Weather Forecasts (ECWMF) raw ensemble and statistically postprocessed against climatological precipitation forecasts for accumulation periods of 1 to 5 days for the monsoon periods (May to mid-October) from 2007 to 2014. We use Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS) as state-of-the-art postprocessing methods and verify against station and gridded Tropical Rainfall Measuring Mission (TRMM) observations. Based on a subset of past forecast—observation-pairs, statistical postprocessing corrects ensemble forecasts for biases and dispersion errors. For the midlatitudes, statistical postprocessing has demonstrated its added value for a wide range of meteorological quantities and this contribution is the first to apply it to precipitation forecasts over West Africa, where the high degree of convective organization at the mesoscale makes precipitation forecasts particularly challenging. The raw ECMWF ensemble predictions of accumulated precipitation are poor compared to climatological forecasts and exhibit strong dispersion errors and biases. For the Guinea Coast, we find a substantial wet bias of the ECMWF ensemble and more than every second ensemble forecast fails to capture the verifying observation within its forecast range. Postprocessed forecasts clearly outperform ECMWF raw ensemble forecasts by correcting for biases and dispersion errors, but disappointingly reveal only slight, if any, improvements compared to climatological forecasts. These results hold across verification regions and years, for 1 to 5-day accumulated precipitation forecasts, and for station and gridded observations. We investigate different spatial accumulation sizes from 0.25 x 0.25° to 5 x 2° longitude

  20. FORECASTING OF PERFORMANCE EVALUATION OF NEW VEHICLES

    Directory of Open Access Journals (Sweden)

    O. S. Krasheninin

    2016-12-01

    Full Text Available Purpose. The research work focuses on forecasting of performance evaluation of the tractive and non-tractive vehicles that will satisfy and meet the needs and requirements of the railway industry, which is constantly evolving. Methodology. Analysis of the technical condition of the existing fleet of rolling stock (tractive and non-tractive of Ukrainian Railways shows a substantial reduction that occurs in connection with its moral and physical wear and tear, as well as insufficient and limited purchase of new units of the tractive and non-tractive rolling stock in the desired quantity. In this situation there is a necessity of search of the methods for determination of rolling stock technical characteristics. One of such urgent and effective measures is to conduct forecasting of the defining characteristics of the vehicles based on the processes of their reproduction in conditions of limited resources using a continuous exponential function. The function of the growth rate of the projected figure degree for the vehicle determines the logistic characteristic that with unlimited resources has the form of an exponent, and with low ones – that of a line. Findings. The data obtained according to the proposed method allowed determining the expected (future value, that is the ratio of load to volume of the body for non-tractive rolling stock (gondola cars and weight-to-power for tractive rolling stock, the degree of forecast reliability and the standard forecast error, which show high prediction accuracy for the completed procedure. As a result, this will allow estimating the required characteristics of vehicles in the forecast year with high accuracy. Originality. The concept of forecasting the characteristics of the vehicles for decision-making on the evaluation of their prospects was proposed. Practical value. The forecasting methodology will reliably determine the technical parameters of tractive and non-tractive rolling stock, which will meet

  1. The Potential Role of Neglected and Underutilised Crop Species as Future Crops under Water Scarce Conditions in Sub-Saharan Africa

    Directory of Open Access Journals (Sweden)

    Pauline Chivenge

    2015-05-01

    Full Text Available Modern agricultural systems that promote cultivation of a very limited number of crop species have relegated indigenous crops to the status of neglected and underutilised crop species (NUCS. The complex interactions of water scarcity associated with climate change and variability in sub-Saharan Africa (SSA, and population pressure require innovative strategies to address food insecurity and undernourishment. Current research efforts have identified NUCS as having potential to reduce food and nutrition insecurity, particularly for resource poor households in SSA. This is because of their adaptability to low input agricultural systems and nutritional composition. However, what is required to promote NUCS is scientific research including agronomy, breeding, post-harvest handling and value addition, and linking farmers to markets. Among the essential knowledge base is reliable information about water utilisation by NUCS with potential for commercialisation. This commentary identifies and characterises NUCS with agronomic potential in SSA, especially in the semi-arid areas taking into consideration inter alia: (i what can grow under water-scarce conditions, (ii water requirements, and (iii water productivity. Several representative leafy vegetables, tuber crops, cereal crops and grain legumes were identified as fitting the NUCS category. Agro-biodiversity remains essential for sustainable agriculture.

  2. The Potential Role of Neglected and Underutilised Crop Species as Future Crops under Water Scarce Conditions in Sub-Saharan Africa

    Science.gov (United States)

    Chivenge, Pauline; Mabhaudhi, Tafadzwanashe; Modi, Albert T.; Mafongoya, Paramu

    2015-01-01

    Modern agricultural systems that promote cultivation of a very limited number of crop species have relegated indigenous crops to the status of neglected and underutilised crop species (NUCS). The complex interactions of water scarcity associated with climate change and variability in sub-Saharan Africa (SSA), and population pressure require innovative strategies to address food insecurity and undernourishment. Current research efforts have identified NUCS as having potential to reduce food and nutrition insecurity, particularly for resource poor households in SSA. This is because of their adaptability to low input agricultural systems and nutritional composition. However, what is required to promote NUCS is scientific research including agronomy, breeding, post-harvest handling and value addition, and linking farmers to markets. Among the essential knowledge base is reliable information about water utilisation by NUCS with potential for commercialisation. This commentary identifies and characterises NUCS with agronomic potential in SSA, especially in the semi-arid areas taking into consideration inter alia: (i) what can grow under water-scarce conditions, (ii) water requirements, and (iii) water productivity. Several representative leafy vegetables, tuber crops, cereal crops and grain legumes were identified as fitting the NUCS category. Agro-biodiversity remains essential for sustainable agriculture. PMID:26016431

  3. Effect of Planting Density and Harvest Interval on the Leaf Yield and Quality of Moringa (Moringa oleifera under Diverse Agroecological Conditions of Northern South Africa

    Directory of Open Access Journals (Sweden)

    M. P. Mabapa

    2017-01-01

    Full Text Available Smallholder livestock farmers who depend on natural communal grazing lands are particularly vulnerable to climate change as well as to food insecurity and should be encouraged to grow drought-tolerant fodder crops. Moringa oleifera is a highly valued plant, due to its exceptionally high nutritional content. This study was conducted at two experimental sites in the Limpopo province of northern South Africa to evaluate for the first time the effect of plant density and cutting interval on biomass production and chemical composition of moringa grown under two diverse climatic conditions. Four different planting densities (435,000, 300,000, 200,000, and 100,000 plants/ha were arranged in a randomized complete block design and experimental samples were replicated four times. Data for biomass and gravimetric soil moisture content were collected each time the plants reached a height of 50 cm. Harvested leaves were analysed for chemical composition. An increase in the plant density led to elevated biomass production at both study locations, ranging between 527 and 2867 kg/ha. Moringa is capable of meeting all nutrient requirements of livestock depending on harvest time and location.

  4. The oceanic forecasting system near the Shimokita Peninsula, Japan

    International Nuclear Information System (INIS)

    In, Teiji; Nakayama, Tomoharu; Matsuura, Yasutaka; Shima, Shigeki; Ishikawa, Yoichi; Awaji, Toshiyuki; Kobayashi, Takuya; Kawamura, Hideyuki; Togawa, Orihiko; Toyoda, Takahiro

    2007-01-01

    The oceanic forecasting system off the Shimokita Peninsula was constructed. To evaluate the performance of this system, we carried out the hindcast experiment for the oceanic conditions in 2003. The results showed the system had good reproducibility. Especially, it was able to reproduce the feature of seasonal variation of the Tsugaru Warm Water (TWW). We expect it has enough performance in actual forecasting. (author)

  5. Verification of Ensemble Water Supply Forecasts for Sierra Nevada Watersheds

    Directory of Open Access Journals (Sweden)

    Minxue He

    2016-11-01

    Full Text Available This study verifies the skill and reliability of ensemble water supply forecasts issued by an innovative operational Hydrologic Ensemble Forecast Service (HEFS of the U.S. National Weather Service (NWS at eight Sierra Nevada watersheds in the State of California. The factors potentially influencing the forecast skill and reliability are also explored. Retrospective ensemble forecasts of April–July runoff with 60 traces for these watersheds from 1985 to 2010 are generated with the HEFS driven by raw precipitation and temperature reforecasts from operational Global Ensemble Forecast System (GEFS for the first 15 days and climatology from day 16 up to day 365. Results indicate that the forecast skill is limited when the lead time is long (over three months or before January but increases through the forecast period. There is generally a negative bias in the most probable forecast (median forecast for most study watersheds. When the mean forecast is investigated instead, the bias becomes mostly positive and generally smaller in magnitude. The forecasts, particularly the wet forecasts (with less than 10% exceedance probability are reliable on the average. The low April–July flows (with higher than 90% exceedance probability are forecast more frequently than their actual occurrence frequency, while the medium April–July flows (90% to 10% exceedance are forecast to occur less frequently. The forecast skill and reliability tend to be sensitive to extreme conditions. Particularly, the wet extremes show more significant impact than the dry extremes. Using different forcing data, including pure climatology and Climate Forecast System version 2 (CFSv2 shows no consistent improvement in the forecast skill and reliability, neither does using a longer (than the study period 1985–2010 period of record. Overall, this study is meaningful in the context of (1 establishing a benchmark for future enhancements (i.e., newer version of HEFS, GEFS and CFSv2 to

  6. Towards reliable seasonal ensemble streamflow forecasts for ephemeral rivers

    Science.gov (United States)

    Bennett, James; Wang, Qj; Li, Ming; Robertson, David

    2016-04-01

    inference. Third, FoGSS applies a linear bias-correction to transformed data, which is able to handle strongly non-linear and conditional biases that are sometimes present in forecasts of ephemeral rivers. In this presentation we focus on recent developments of FoGGS that have been necessary to ensure that forecasts for ephemeral rivers are reliable. We show that if more than half of observations are zero, it is necessary to treat both observations and simulations as censored data. This requires a more complex procedure to infer parameters, but is able to produce reliable forecasts in even the driest catchments. However, the new inference procedure did not resolve all instances of negative forecast skill. We discuss prospects for eliminating negative forecast skill in ephemeral rivers.

  7. Drought Monitoring and Forecasting Using the Princeton/U Washington National Hydrologic Forecasting System

    Science.gov (United States)

    Wood, E. F.; Yuan, X.; Roundy, J. K.; Lettenmaier, D. P.; Mo, K. C.; Xia, Y.; Ek, M. B.

    2011-12-01

    observed atmospheric forcing. The forecast skills from the dynamical seasonal models (CFSv1, CFSv2, EUROSIP) and CPC are also compared with forecasts based on the Ensemble Streamflow Prediction (ESP) method, which uses initial conditions and historical forcings to generate seasonal forecasts. The skill of the system to predict drought, drought recovery and related hydrological conditions such as low-flows is assessed, along with quantified uncertainty.

  8. Using Seasonal Forecasting Data for Vessel Routing

    Science.gov (United States)

    Bell, Ray; Kirtman, Ben

    2017-04-01

    We present an assessment of seasonal forecasting of surface wind speed, significant wave height and ocean surface current speed in the North Pacific for potential use of vessel routing from Singapore to San Diego. WaveWatchIII is forced with surface winds and ocean surface currents from the Community Climate System Model 4 (CCSM4) retrospective forecasts for the period of 1982-2015. Several lead time forecasts are used from zero months to six months resulting in 2,720 model years, ensuring the findings from this study are robust. July surface wind speed and significant wave height can be skillfully forecast with a one month lead time, with the western North Pacific being the most predictable region. Beyond May initial conditions (lead time of two months) the El Niño Southern Oscillation (ENSO) Spring predictability barrier limits skill of significant wave height but there is skill for surface wind speed with January initial conditions (lead time of six months). In a separate study of vessel routing between Norfolk, Virginia and Gibraltar we demonstrate the benefit of a multimodel approach using the North American Multimodel Ensemble (NMME). In collaboration with Charles River Analytics an all-encompassing forecast is presented by using machine learning on the various ensembles which can be using used for industry applications.

  9. MACROECONOMIC FORECASTING USING BAYESIAN VECTOR AUTOREGRESSIVE APPROACH

    Directory of Open Access Journals (Sweden)

    D. Tutberidze

    2017-04-01

    Full Text Available There are many arguments that can be advanced to support the forecasting activities of business entities. The underlying argument in favor of forecasting is that managerial decisions are significantly dependent on proper evaluation of future trends as market conditions are constantly changing and require a detailed analysis of future dynamics. The article discusses the importance of using reasonable macro-econometric tool by suggesting the idea of conditional forecasting through a Vector Autoregressive (VAR modeling framework. Under this framework, a macroeconomic model for Georgian economy is constructed with the few variables believed to be shaping business environment. Based on the model, forecasts of macroeconomic variables are produced, and three types of scenarios are analyzed - a baseline and two alternative ones. The results of the study provide confirmatory evidence that suggested methodology is adequately addressing the research phenomenon and can be used widely by business entities in responding their strategic and operational planning challenges. Given this set-up, it is shown empirically that Bayesian Vector Autoregressive approach provides reasonable forecasts for the variables of interest.

  10. Should seasonal rainfall forecasts be used for flood preparedness?

    Directory of Open Access Journals (Sweden)

    E. Coughlan de Perez

    2017-09-01

    Full Text Available In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.

  11. Africa Insight

    African Journals Online (AJOL)

    Africa Insight is a quarterly, peer-reviewed journal of the Africa Institute of South Africa. It is accredited by the South African National Department of Higher Education and Training (DHET) and is indexed in the International Bibliography of Social Science (IBSS). It is a multi-disciplinary journal primarily focusing on African ...

  12. Integrating indigenous knowledge with conventional science: Enhancing localised climate and weather forecasts in Nessa, Mulanje, Malawi

    Science.gov (United States)

    Kalanda-Joshua, Miriam; Ngongondo, Cosmo; Chipeta, Lucy; Mpembeka, F.

    Subsistence rain fed agriculture underpins rural livelihoods in the Sub Saharan Africa. The overdependence on rainfall suggests the need for more reliable climate and weather forecasts to guide farm level decision making. Traditionally, African farmers have used indigenous knowledge (IK) to understand weather and climate patterns and make decisions about crops and farming practices. However, increased rainfall variability in recent years associated with climate change has reduced their confidence in indigenous knowledge, hence reducing their adaptive capacity and increasing their vulnerability to climate change. To address this problem, researchers are advocating the integration of indigenous knowledge into scientific climate forecasts at the local level, where it can be used to enhance the resilience of communities vulnerable to climate change. A study was therefore conducted to establish commonly used IK indicators in weather and climate forecasting and people’s perceptions of climate change and variability in Nessa Village, Southern Malawi. We further compared the people’s perceptions on climate change and variability with empirical evidence from a nearby weather station during 1971-2003 and the major constraints that the people face to fully utilise conventional weather and climate forecasts. Our results show various forms of traditional indicators that have been used to predict weather and climate for generations. These include certain patterns and behaviour of flora and fauna as well as environmental conditions. We further established that the peoples documentation of major climatic events over the years in the area agreed with the empirical evidence from the temperature and rainfall data. Overall, rainfall in the area has reduced since 1971 with increasing temperatures. The people were however of the view that current scientific weather and climate predictions in Malawi were not that useful at village level because they do not incorporate IK.

  13. Improving seasonal forecasts of hydroclimatic variables through the state of multiple large-scale climate signals

    Science.gov (United States)

    Castelletti, A.; Giuliani, M.; Block, P. J.

    2017-12-01

    Increasingly uncertain hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide, emphasizing the need of accurate medium- to long-term predictions to timely prompt anticipatory operations. Despite modern forecasts are skillful over short lead time (from hours to days), predictability generally tends to decrease on longer lead times. Global climate teleconnection, such as El Niño Southern Oscillation (ENSO), may contribute in extending forecast lead times. However, ENSO teleconnection is well defined in some locations, such as Western USA and Australia, while there is no consensus on how it can be detected and used in other regions, particularly in Europe, Africa, and Asia. In this work, we generalize the Niño Index Phase Analysis (NIPA) framework by contributing the Multi Variate Niño Index Phase Analysis (MV-NIPA), which allows capturing the state of multiple large-scale climate signals (i.e. ENSO, North Atlantic Oscillation, Pacific Decadal Oscillation, Atlantic Multi-decadal Oscillation, Indian Ocean Dipole) to forecast hydroclimatic variables on a seasonal time scale. Specifically, our approach distinguishes the different phases of the considered climate signals and, for each phase, identifies relevant anomalies in Sea Surface Temperature (SST) that influence the local hydrologic conditions. The potential of the MV-NIPA framework is demonstrated through an application to the Lake Como system, a regulated lake in northern Italy which is mainly operated for flood control and irrigation supply. Numerical results show high correlations between seasonal SST values and one season-ahead precipitation in the Lake Como basin. The skill of the resulting MV-NIPA forecast outperforms the one of ECMWF products. This information represents a valuable contribution to partially anticipate the summer water availability, especially during drought events, ultimately supporting the improvement of the Lake Como

  14. A framework for improving a seasonal hydrological forecasting system using sensitivity analysis

    Science.gov (United States)

    Arnal, Louise; Pappenberger, Florian; Smith, Paul; Cloke, Hannah

    2017-04-01

    Seasonal streamflow forecasts are of great value for the socio-economic sector, for applications such as navigation, flood and drought mitigation and reservoir management for hydropower generation and water allocation to agriculture and drinking water. However, as we speak, the performance of dynamical seasonal hydrological forecasting systems (systems based on running seasonal meteorological forecasts through a hydrological model to produce seasonal hydrological forecasts) is still limited in space and time. In this context, the ESP (Ensemble Streamflow Prediction) remains an attractive forecasting method for seasonal streamflow forecasting as it relies on forcing a hydrological model (starting from the latest observed or simulated initial hydrological conditions) with historical meteorological observations. This makes it cheaper to run than a standard dynamical seasonal hydrological forecasting system, for which the seasonal meteorological forecasts will first have to be produced, while still producing skilful forecasts. There is thus the need to focus resources and time towards improvements in dynamical seasonal hydrological forecasting systems which will eventually lead to significant improvements in the skill of the streamflow forecasts generated. Sensitivity analyses are a powerful tool that can be used to disentangle the relative contributions of the two main sources of errors in seasonal streamflow forecasts, namely the initial hydrological conditions (IHC; e.g., soil moisture, snow cover, initial streamflow, among others) and the meteorological forcing (MF; i.e., seasonal meteorological forecasts of precipitation and temperature, input to the hydrological model). Sensitivity analyses are however most useful if they inform and change current operational practices. To this end, we propose a method to improve the design of a seasonal hydrological forecasting system. This method is based on sensitivity analyses, informing the forecasters as to which element of

  15. Space Weather Forecasting at IZMIRAN

    Science.gov (United States)

    Gaidash, S. P.; Belov, A. V.; Abunina, M. A.; Abunin, A. A.

    2017-12-01

    Since 1998, the Institute of Terrestrial Magnetism, Ionosphere, and Radio Wave Propagation (IZMIRAN) has had an operating heliogeophysical service—the Center for Space Weather Forecasts. This center transfers the results of basic research in solar-terrestrial physics into daily forecasting of various space weather parameters for various lead times. The forecasts are promptly available to interested consumers. This article describes the center and the main types of forecasts it provides: solar and geomagnetic activity, magnetospheric electron fluxes, and probabilities of proton increases. The challenges associated with the forecasting of effects of coronal mass ejections and coronal holes are discussed. Verification data are provided for the center's forecasts.

  16. Financial Analysts’ Forecasts

    DEFF Research Database (Denmark)

    Stæhr, Simone

    in the decision making and the magnitude of these constraints does sometimes vary with personal traits. Therefore, to the extent that financial analysts are subjects to behavioral biases their outputs to the investors are likely to be biased by their interpretation of information. Because investors need accuracy....... The primary focus is on financial analysts in the task of conducting earnings forecasts while a secondary focus is on investors’ abilities to interpret and make use of these forecasts. Simply put, financial analysts can be seen as information intermediators receiving inputs to their analyses from firm...... management and providing outputs to the investors. Amongst various outputs from the analysts are forecasts of earnings. According to decision theories mostly from the literature in psychology all humans are affected by cognitive constraints to some degree. These constraints may lead to unintentional biases...

  17. A fuzzy inference model for short-term load forecasting

    International Nuclear Information System (INIS)

    Mamlook, Rustum; Badran, Omar; Abdulhadi, Emad

    2009-01-01

    This paper is concerned with the short-term load forecasting (STLF) in power system operations. It provides load prediction for generation scheduling and unit commitment decisions, and therefore precise load forecasting plays an important role in reducing the generation cost and the spinning reserve capacity. Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand)) is one of the most important tools by which an electric utility/company plans, dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. In the present study, a proposed methodology has been introduced to decrease the forecasted error and the processing time by using fuzzy logic controller on an hourly base. Therefore, it predicts the effect of different conditional parameters (i.e., weather, time, historical data, and random disturbances) on load forecasting in terms of fuzzy sets during the generation process. These parameters are chosen with respect to their priority and importance. The forecasted values obtained by fuzzy method were compared with the conventionally forecasted ones. The results showed that the STLF of the fuzzy implementation have more accuracy and better outcomes

  18. Spatial load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Willis, H.L.; Engel, M.V.; Buri, M.J.

    1995-04-01

    The reliability, efficiency, and economy of a power delivery system depend mainly on how well its substations, transmission lines, and distribution feeders are located within the utility service area, and how well their capacities match power needs in their respective localities. Often, utility planners are forced to commit to sites, rights of way, and equipment capacities year in advance. A necessary element of effective expansion planning is a forecast of where and how much demand must be served by the future T and D system. This article reports that a three-stage method forecasts with accuracy and detail, allowing meaningful determination of sties and sizes for future substation, transmission, and distribution facilities.

  19. Forecast of auroral activity

    International Nuclear Information System (INIS)

    Lui, A.T.Y.

    2004-01-01

    A new technique is developed to predict auroral activity based on a sample of over 9000 auroral sites identified in global auroral images obtained by an ultraviolet imager on the NASA Polar satellite during a 6-month period. Four attributes of auroral activity sites are utilized in forecasting, namely, the area, the power, and the rates of change in area and power. This new technique is quite accurate, as indicated by the high true skill scores for forecasting three different levels of auroral dissipation during the activity lifetime. The corresponding advanced warning time ranges from 22 to 79 min from low to high dissipation levels

  20. South Africa's Unintended Experiment in School Choice: How the National Education Policy Act, the South Africa Schools Act and the Employment of Educators Act Create the Enabling Conditions for Quasi-Markets in Schools

    Science.gov (United States)

    Woolman, Stuart; Fleisch, Brahm

    2006-01-01

    School choice is often identified with right-leaning, voucher-happy, market-oriented public school systems like those found in the United States. Thus, the proposition that a social democratic state such as South Africa will offer many primary and secondary school learners far greater choice strikes many as counter-intuitive and implausible. The…

  1. Assimilation scheme of the Mediterranean Forecasting System: operational implementation

    Directory of Open Access Journals (Sweden)

    E. Demirov

    Full Text Available This paper describes the operational implementation of the data assimilation scheme for the Mediterranean Forecasting System Pilot Project (MFSPP. The assimilation scheme, System for Ocean Forecast and Analysis (SOFA, is a reduced order Optimal Interpolation (OI scheme. The order reduction is achieved by projection of the state vector into vertical Empirical Orthogonal Functions (EOF. The data assimilated are Sea Level Anomaly (SLA and temperature profiles from Expandable Bathy Termographs (XBT. The data collection, quality control, assimilation and forecast procedures are all done in Near Real Time (NRT. The OI is used intermittently with an assimilation cycle of one week so that an analysis is produced once a week. The forecast is then done for ten days following the analysis day. The root mean square (RMS between the model forecast and the analysis (the forecast RMS is below 0.7°C in the surface layers and below 0.2°C in the layers deeper than 200 m for all the ten forecast days. The RMS between forecast and initial condition (persistence RMS is higher than forecast RMS after the first day. This means that the model improves forecast with respect to persistence. The calculation of the misfit between the forecast and the satellite data suggests that the model solution represents well the main space and time variability of the SLA except for a relatively short period of three – four weeks during the summer when the data show a fast transition between the cyclonic winter and anti-cyclonic summer regimes. This occurs in the surface layers that are not corrected by our assimilation scheme hypothesis. On the basis of the forecast skill scores analysis, conclusions are drawn about future improvements.

    Key words. Oceanography; general (marginal and semi-enclosed seas; numerical modeling; ocean prediction

  2. Assimilation scheme of the Mediterranean Forecasting System: operational implementation

    Directory of Open Access Journals (Sweden)

    E. Demirov

    2003-01-01

    Full Text Available This paper describes the operational implementation of the data assimilation scheme for the Mediterranean Forecasting System Pilot Project (MFSPP. The assimilation scheme, System for Ocean Forecast and Analysis (SOFA, is a reduced order Optimal Interpolation (OI scheme. The order reduction is achieved by projection of the state vector into vertical Empirical Orthogonal Functions (EOF. The data assimilated are Sea Level Anomaly (SLA and temperature profiles from Expandable Bathy Termographs (XBT. The data collection, quality control, assimilation and forecast procedures are all done in Near Real Time (NRT. The OI is used intermittently with an assimilation cycle of one week so that an analysis is produced once a week. The forecast is then done for ten days following the analysis day. The root mean square (RMS between the model forecast and the analysis (the forecast RMS is below 0.7°C in the surface layers and below 0.2°C in the layers deeper than 200 m for all the ten forecast days. The RMS between forecast and initial condition (persistence RMS is higher than forecast RMS after the first day. This means that the model improves forecast with respect to persistence. The calculation of the misfit between the forecast and the satellite data suggests that the model solution represents well the main space and time variability of the SLA except for a relatively short period of three – four weeks during the summer when the data show a fast transition between the cyclonic winter and anti-cyclonic summer regimes. This occurs in the surface layers that are not corrected by our assimilation scheme hypothesis. On the basis of the forecast skill scores analysis, conclusions are drawn about future improvements. Key words. Oceanography; general (marginal and semi-enclosed seas; numerical modeling; ocean prediction

  3. Combining forecasts in short term load forecasting: Empirical ...

    Indian Academy of Sciences (India)

    We present an empirical analysis to show that combination of short term load forecasts leads to better accuracy. We also discuss other aspects of combination, i.e.,distribution of weights, effect of variation in the historical window and distribution of forecast errors. The distribution of forecast errors is analyzed in order to get a ...

  4. Forecasting Housing Approvals in Australia: Do Forecasters Herd?

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Pierdzioch; Rülke

    2012-01-01

    Price trends in housing markets may reflect herding of market participants. A natural question is whether such herding, to the extent that it occurred, reflects herding in forecasts of professional forecasters. Using more than 6,000 forecasts of housing approvals for Australia, we did not find...

  5. Forecasting volatility of crude oil markets

    International Nuclear Information System (INIS)

    Kang, Sang Hoon; Kang, Sang-Mok; Yoon, Seong-Min

    2009-01-01

    This article investigates the efficacy of a volatility model for three crude oil markets - Brent, Dubai, and West Texas Intermediate (WTI) - with regard to its ability to forecast and identify volatility stylized facts, in particular volatility persistence or long memory. In this context, we assess persistence in the volatility of the three crude oil prices using conditional volatility models. The CGARCH and FIGARCH models are better equipped to capture persistence than are the GARCH and IGARCH models. The CGARCH and FIGARCH models also provide superior performance in out-of-sample volatility forecasts. We conclude that the CGARCH and FIGARCH models are useful for modeling and forecasting persistence in the volatility of crude oil prices. (author)

  6. Carbon monoxide concentration forecasting in Santiago, Chile.

    Science.gov (United States)

    Perez, Patricio; Palacios, Rodrigo; Castillo, Alejandro

    2004-08-01

    In the city of Santiago, Chile, air quality is defined in terms of particulate matter with an aerodynamic diameter < or = 10 microm (PM10) concentrations. An air quality forecasting model based on past concentrations of PM10 and meteorological conditions currently is used by the metropolitan agency for the environment, which allows restrictions to emissions to be imposed in advance. This model, however, fails to forecast between 40 and 50% of the days considered to be harmful for the inhabitants every year. Given that a high correlation between particulate matter and carbon monoxide (CO) concentrations is observed at monitoring stations in the city, a model for CO concentration forecasting would be a useful tool to complement information about expected air quality in the city. Here, the results of a neural network-based model aimed to forecast maximum values of the 8-hr moving average of CO concentrations for the next day are presented. Forecasts from the neural network model are compared with those produced with linear regressions. The neural network model seems to leave more room to adjust free parameters with 1-yr data to predict the following year's values. We have worked with 3 yr of data measured at the monitoring station located in the zone with the worst air quality in the city of Santiago, Chile.

  7. Industrial Production Forecasting with Kalman Filtering

    Science.gov (United States)

    Teodoro, M. Filomena

    2009-09-01

    Under certains conditions commonly used forecast methods reveal themselves inappropriate for the desired result. Thus it is therefore required to resort to other methods deemed more adequate to the desired results. The main purpose of this work is to investigate the suitability of the Kalman filtering as short term forecast method. The work data set was made of qualitative surveys of conjunture and the index of industrial production. Along the work it is studied the application of this method to a set of qualitative and quantitative information. The ultimate objective is the attainment of short term forecast models for the industrial production index of the transforming industry. After the previous treatment of the data, a number of models are estimated and validated. At the end of this process two models are selected: the two better adjusted in accordance with criteria previously established. The next step in process, following the choice of the models, is the repetition of the sellected models estimation using all sample, after which the actual forecast is done. The results obtained with the described methods have shown sufficiently coincident with the estimates supplied by Portuguese National Institute of Statistics for the same period. The application of Kalman filtering to a set of qualitative and quantitative information in the attainment of models of short term forecasts appears quit promising thus.

  8. CDM Convective Forecast Planning guidance

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The CDM Convective Forecast Planning (CCFP) guidance product provides a foreast of en-route aviation convective hazards. The forecasts are updated every 2 hours and...

  9. Battlescale Forecast Model Sensitivity Study

    National Research Council Canada - National Science Library

    Sauter, Barbara

    2003-01-01

    .... Changes to the surface observations used in the Battlescale Forecast Model initialization led to no significant changes in the resulting forecast values of temperature, relative humidity, wind speed, or wind direction...

  10. Challenges of operational river forecasting

    NARCIS (Netherlands)

    Pagano, T.C.; Wood, A.W.; Ramos, M.H.; Cloke, H.L.; Pappenbreger, F.; Verkade, J.S.

    2014-01-01

    Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models.

  11. Are demand forecasting techniques applicable to libraries?

    OpenAIRE

    Sridhar, M. S.

    1984-01-01

    Examines the nature and limitations of demand forecasting, discuses plausible methods of forecasting demand for information, suggests some useful hints for demand forecasting and concludes by emphasizing unified approach to demand forecasting.

  12. Evaluating the skill of seasonal weather forecasts in predicting aflatoxin contamination of groundnut in Senegal

    Science.gov (United States)

    Brak, B.; Challinor, A.

    2011-12-01

    Aflatoxins, a group of toxic secondary metabolites produced by some strains of a number of species within Aspergillus section Flavi, contaminate a range of crops grown at latitudes between 40N° and 40S° of the equator. Digestion of food products derived from aflatoxin-contaminated crops may result in acute and chronic health problems in human beings. Countries in sub-Saharan Africa in particular have seen large percentages of the human population exposed to aflatoxin. A recent study showed that over 98% of subjects in West Africa tested positive for aflatoxin biomarkers. According to other research, every year 250,000 people die from hepato-cellular carcinoma related causes due to aflatoxin ingestion in parts of West Africa. Strict aflatoxin levels set by importing countries in accordance with the WTO Agreement on the Application of Sanitary and Phytosanitary Measures (SPS Agreement) also impair the value of agricultural trade. Over the last thirty years this has led to a reduction of African exports of groundnut by 19% despite the consumption of groundnut derived food products going up by 209%. The occurrence of aflatoxin on crops is strongly influenced by weather. Empirical studies in the US have shown that pre-harvest, aflatoxin contamination of groundnuts is induced by conditions of drought stress in combination with soil temperatures between 25°C and 31°C. Post-harvest, aflatoxin production of stored, Aspergillus-contaminated groundnuts is exacerbated in conditions where relative humidity is above 83%. The GLAM crop model was extended to include a soil temperature subroutine and subroutines containing pre- and post-harvest aflatoxin algorithms. The algorithms used to estimate aflatoxin contamination indices are based on findings from multiple empirical studies and the pre-harvest aflatoxin model has been validated for Australian conditions. Hence, there was sufficient scope to use GLAM with these algorithms to answer the foremost research question: Is the

  13. Forecasting inbound tourists in Cambodia

    OpenAIRE

    Tanaka, Kiyoyasu

    2016-01-01

    Forecasting tourism demand is crucial for management decisions in the tourism sector. Estimating a vector autoregressive (VAR) model for monthly visitor arrivals disaggregated by three entry points in Cambodia for the years 2006–2015, I forecast the number of arrivals for years 2016 and 2017. The results show that the VAR model fits well with the data on visitor arrivals for each entry point. Ex post forecasting shows that the forecasts closely match the observed data for visitor arrivals, th...

  14. Forecasters' Objectives and Strategies

    DEFF Research Database (Denmark)

    Marinovic, Iván; Ottaviani, Marco; Sørensen, Peter Norman

    2013-01-01

    This chapter develops a unified modeling framework for analyzing the strategic behavior of forecasters. The theoretical model encompasses reputational objectives, competition for the best accuracy, and bias. Also drawing from the extensive lit- erature on analysts, we review the empirical evidenc...

  15. Housing Price Forecastability

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    2016-01-01

    We examine U.S. housing price forecastability using principal component analysis (PCA), partial least squares (PLS), and sparse PLS (SPLS). We incorporate information from a large panel of 128 economic time series and show that macroeconomic fundamentals have strong predictive power for future mo...

  16. Hydrology and flow forecasting

    NARCIS (Netherlands)

    Vrijling, J.K.; Kwadijk, J.; Van Duivendijk, J.; Van Gelder, P.; Pang, H.; Rao, S.Q.; Wang, G.Q.; Huang, X.Q.

    2002-01-01

    We have studied and applied the statistic model (i.e. MMC) and hydrological models to Upper Yellow River. This report introduces the results and some conclusions from the model. The three models, MMC, MWBM and NAM, have be applied in the research area. The forecasted discharge by the three models

  17. Reference class forecasting

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent

    Underbudgettering og budgetoverskridelser forekommer i et flertal af større bygge- og anlægsprojekter. Problemet skyldes optimisme og/eller strategisk misinformation i budgetteringsprocessen. Reference class forecasting (RCF) er en prognosemetode, som er udviklet for at reducere eller eliminere...

  18. Iron and sulfur isotope constraints on redox conditions associated with the 3.2 Ga barite deposits of the Mapepe Formation (Barberton Greenstone Belt, South Africa)

    Science.gov (United States)

    Busigny, Vincent; Marin-Carbonne, Johanna; Muller, Elodie; Cartigny, Pierre; Rollion-Bard, Claire; Assayag, Nelly; Philippot, Pascal

    2017-08-01

    The occurrence of Early Archean barite deposits is intriguing since this type of sediment requires high availability of dissolved sulfate (SO42-), the oxidized form of sulfur, although most authors argued that the Archean eon was dominated by reducing conditions, with low oceanic sulfate concentration (state of the paleo-atmosphere and -oceans, we examined Fe and S isotope compositions in a sedimentary sequence from the 3.2 Ga-old Mendon and Mapepe formations (Kaapvaal craton, South Africa), recovered from the drill-core BBDP2 of the Barberton Barite Drilling Project. Major elements were also analyzed to constrain the respective imprints of detrital vs metasomatic processes, in particular using Al, Ti and K interrelations. Bulk rock Fe isotope compositions are linked to mineralogy, with δ56Fe values varying between -2.04‰ in Fe sulfide-dominated barite beds, to +2.14‰ in Fe oxide-bearing cherts. δ34S values of sulfides vary between -10.84 and +3.56‰, with Δ33S in a range comprised between -0.35 and +2.55‰, thus supporting an O2-depleted atmosphere (<10-5 PAL). Iron isotope variations together with major element correlations show that, although the sediments experienced a pervasive stage of hydrothermal alteration, the rocks preserved a primary/authigenic signature predating subsequent hydrothermal stage. Highly positive δ56Fe values recorded in primary Fe-oxides from ferruginous cherts support partial Fe oxidation in a reducing oceanic environment (O2 < 10-4 μM), but are incompatible with a model of complete oxidation at the redox boundary of a stratified water column. Iron oxide precipitation under low O2 levels was likely mediated by anoxygenic photosynthesis, and/or abiotic photo-oxidation processes. Our results are consistent with global anoxic conditions in the 3.2 Ga-old sediments, implying that the barite deposits were most likely sourced by atmospheric photolysis of S gases produced by large subaerial volcanic events, and possibly SO42

  19. Is It Going to Rain Today? Understanding the Weather Forecast.

    Science.gov (United States)

    Allsopp, Jim; And Others

    1996-01-01

    Presents a resource for science teachers to develop a better understanding of weather forecasts, including outlooks, watches, warnings, advisories, severe local storms, winter storms, floods, hurricanes, nonprecipitation hazards, precipitation probabilities, sky condition, and UV index. (MKR)

  20. Evaluation and Application of the Weather Research and Forecast Model

    National Research Council Canada - National Science Library

    Passner, Jeffrey E

    2007-01-01

    ... by the U.S. Army Research Laboratory (ARL) to determine how accurate and robust the model is under a variety of meteorological conditions, with an emphasis on fine resolution, short-range forecasts in complex terrain...

  1. The potential value of seasonal forecasts in a changing climate

    CSIR Research Space (South Africa)

    Winsemius, HC

    2013-12-01

    Full Text Available and mortality. Climate change may affect the frequency and severity of extreme weather conditions, impacting on the success of subsistence farming. A potentially interesting adaptation 10 measure comprises the timely forecasting and warning of such extreme...

  2. The Wind Forecast Improvement Project (WFIP). A Public-Private Partnership Addressing Wind Energy Forecast Needs

    Energy Technology Data Exchange (ETDEWEB)

    Wilczak, James M. [NOAA, Boulder, CO (United States); Finley, Cathy [WindLogics, Inc., St. Paul, MN (United States); Freedman, Jeff [AWS Truepower, Albany, NY (United States); Cline, Joel [USDOE Office of Energy Efficiency and Renewable Energy, Washington, DC (United States); Bianco, L. [Univ. of Colorado, Boulder, CO (United States); Olson, J. [Univ. of Colorado, Boulder, CO (United States); Djalaova, I. [Univ. of Colorado, Boulder, CO (United States); Sheridan, L. [WindLogics, Inc., St. Paul, MN (United States); Ahlstrom, M. [WindLogics, Inc., St. Paul, MN (United States); Manobianco, J. [Meso, Inc., Troy, NY (United States); Zack, J. [Meso, Inc., Troy, NY (United States); Carley, J. [National Oceanic and Atmospheric Administration (NOAA), College Park, MD (United States); Benjamin, S. [NOAA, Boulder, CO (United States); Coulter, R. L. [Argonne National Lab. (ANL), Lemont, IL (United States); Berg, Larry K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Mirocha, Jeff D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Clawson, K. [National Oceanic and Atmospheric Administration (NOAA), Idaho Falls, ID (United States); Natenberg, E. [Meso, Inc., Troy, NY (United States); Marquis, M. [NOAA, Boulder, CO (United States)

    2015-10-30

    The Wind Forecast Improvement Project (WFIP) is a public-private research program, the goals of which are to improve the accuracy of short-term (0-6 hr) wind power forecasts for the wind energy industry and then to quantify the economic savings that accrue from more efficient integration of wind energy into the electrical grid. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that include the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models to improve model initial conditions; and second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the U.S. (the upper Great Plains, and Texas), and included 12 wind profiling radars, 12 sodars, 184 instrumented tall towers and over 400 nacelle anemometers (provided by private industry), lidar, and several surface flux stations. Results demonstrate that a substantial improvement of up to 14% relative reduction in power root mean square error (RMSE) was achieved from the combination of improved NOAA numerical weather prediction (NWP) models and assimilation of the new observations. Data denial experiments run over select periods of time demonstrate that up to a 6% relative improvement came from the new observations. The use of ensemble forecasts produced even larger forecast improvements. Based on the success of WFIP, DOE is planning follow-on field programs.

  3. Timetable of an operational flood forecasting system

    Science.gov (United States)

    Liechti, Katharina; Jaun, Simon; Zappa, Massimiliano

    2010-05-01

    At present a new underground part of Zurich main station is under construction. For this purpose the runoff capacity of river Sihl, which is passing beneath the main station, is reduced by 40%. If a flood is to occur the construction site is evacuated and gates can be opened for full runoff capacity to prevent bigger damages. However, flooding the construction site, even if it is controlled, is coupled with costs and retardation. The evacuation of the construction site at Zurich main station takes about 2 to 4 hours and opening the gates takes another 1 to 2 hours each. In the upper part of the 336 km2 Sihl catchment the Sihl lake, a reservoir lake, is situated. It belongs and is used by the Swiss Railway Company for hydropower production. This lake can act as a retention basin for about 46% of the Sihl catchment. Lowering the lake level to gain retention capacity, and therewith safety, is coupled with direct loss for the Railway Company. To calculate the needed retention volume and the water to be released facing unfavourable weather conditions, forecasts with a minimum lead time of 2 to 3 days are needed. Since the catchment is rather small, this can only be realised by the use of meteorological forecast data. Thus the management of the construction site depends on accurate forecasts to base their decisions on. Therefore an operational hydrological ensemble prediction system (HEPS) was introduced in September 2008 by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). It delivers daily discharge forecasts with a time horizon of 5 days. The meteorological forecasts are provided by MeteoSwiss and stem from the operational limited-area COSMO-LEPS which downscales the ECMWF ensemble prediction system to a spatial resolution of 7 km. Additional meteorological data for model calibration and initialisation (air temperature, precipitation, water vapour pressure, global radiation, wind speed and sunshine duration) and radar data are also provided by

  4. Verification of operational weather forecasts from the POSEIDON system across the Eastern Mediterranean

    OpenAIRE

    A. Papadopoulos; P. Katsafados

    2009-01-01

    The POSEIDON weather forecasting system became operational at the Hellenic Centre for Marine Research (HCMR) in October 1999. The system with its nesting capability provided 72-h forecasts in two different model domains, i.e. 25- and 10-km grid spacing. The lower-resolution domain covered an extended area that included most of Europe, Mediterranean Sea and N. Africa, while the higher resolution domain focused on the Eastern Mediterranean. A major upgrade of the system was recently implemented...

  5. Group on Earth Observations (GEO) Global Drought Monitor Portal: Adding Capabilities for Forecasting Hydrological Extremes and Early Warning Networking

    Science.gov (United States)

    Pozzi, W.; de Roo, A.; Vogt, J.; Lawford, R. G.; Pappenberger, F.; Heim, R. R.; Stefanski, R.

    2011-12-01

    The Intergovernmental Panel on Climate Change (IPCC 2007) has suggested the hydrometeorological extremes of both drought and flooding may increase under climate change. Drought zones can grow over large tracts of continental area and are a global-scale phenomenon (Sheffield and Wood 2011). The Group on Earth Observations Global Drought Monitor Portal (GDMP) was established as a demonstration for the 5th Earth Observation Ministerial Summit in Beijing in 2010. The European Drought Observatory, the North American Drought Monitor, the Princeton University experimental African Drought Monitor, and the University College London experimental global drought monitor were made "interoperable" through installation of Open Geospatial Consortium (OGC) Web Mapping Services (WMS) on their respective servers, allowing maps of current drought conditions to be exchanged and assembled into maps of global drought coverage on the NIDIS portal. Partners from the Republic of Argentina, the Commonwealth of Australia, China, Jordan, Brazil, and Uruguay have also joined. The GEO Global Drought Monitoring, Forecasting, and Early Warning effort involves multiple parties and institutions, including the World Meteorological Organization, the World Climate Research Program Drought Interest Group, NASA, and others. The GEO Secretariat held a launch workshop in Geneva on 4-6 May 2010 to initiate drafting the final GEO Work Plan, and, during this meeting, additional capabilities were added to the existing GDMP: 1) drought forecasting was added to drought "current conditions" monitoring, in a partnership with Joint Research Centre (and other partners) aiming at a combined platform for Hydrological Extremes (drought and flooding); 2) extending drought forecasts from the medium-range 15-day window to a 30-day window; this will be tested through pilot projects over Europe and Africa, as part of the Global Water Scarcity Information Service (GLOWASIS)and the Improved Drought Early Warning Forecasting

  6. Load forecasting for supermarket refrigeration

    DEFF Research Database (Denmark)

    Bacher, Peder; Madsen, Henrik; Aalborg Nielsen, Henrik

    This report presents a study of models for forecasting the load for supermarket refrigeration. The data used for building the forecasting models consists of load measurements, local climate measurements and weather forecasts. The load measurements are from a supermarket located in a village...... in Denmark. The load for refrigeration is the sum of all cabinets in the supermarket, both low and medium temperature cabinets, and spans a period of one year. As input to the forecasting models the ambient temperature observed near the supermarket together with weather forecasts are used. Every hour...... the hourly load for refrigeration for the following 42 hours is forecasted. The forecast models are adaptive linear time-series models which are fitted with a computationally efficient recursive least squares scheme. The dynamic relations between the inputs and the load is modeled by simple transfer...

  7. Biofuels in West Africa: prospective analysis of substitution for petroleum products

    International Nuclear Information System (INIS)

    Tonato, A.

    1993-01-01

    Is it viable and realistic to believe that biofuels can relieve the energy bill for certain countries in West Africa, restimulate whole areas often cut off from the development process, and diversify the outlets of the agricultural sector, hard struck as it is by the price drop in raw materials. The answer here is drawn from a micro-economic analysis of Benin, Burkina Faso, the Ivory Coast, and Niger, concerning the competitiveness of the three main products considered: - ethanol, produced from cane sugar; - methanol, obtained from eucalyptus by cryogenic oxygen gasification process; methyl ester, from palm oil. The result of this study is a forecast, for the year 2010, of conditions under which methyl alcohols and ester might be substituted for refined gasoline and fuel oil, and a hierarchical classification of the various biofuels in West Africa. (author). 15 refs., 8 tabs

  8. Methodical bases of geodemographic forecasting

    Directory of Open Access Journals (Sweden)

    Катерина Сегіда

    2016-10-01

    Full Text Available The article deals with methodological features of the forecast of population size and composition. The essence and features of probabilistic demographic forecasting, methods, a component and dynamic ranks are considered; requirements to initial indicators for each type of the forecast are provided. It is noted that geo-demographic forecast is an important component of regional geo-demographic characteristic. Features of the demographic forecast development by component method (recursors of age are given, basic formulae of calculation, including the equation of demographic balance, a formula recursors taking into account gender and age indicators, survival coefficient are presented. The basic methodical principles of the demographic forecast are given by an extrapolation method (dynamic ranks, calculation features by means of the generalized indicators, such as extrapolation on the basis of indicators of an average pure gain, average growth rate and average rate of a gain are presented. To develop population forecast, the method of retrospective extrapolation (for the short-term forecast and a component method (for the mid-term forecast are mostly used. The example of such development by component method for gender and age structure of the population of Kharkiv region with step-by-step explanation of calculation is provided. The example of Kharkiv region’s population forecast development is provided by the method of dynamic ranks. Having carried out calculations of the main forecast indicators by administrative units, it is possible to determine features of further regional demographic development, to reveal internal territorial distinctions in demographic development. Application of separate forecasting methods allows to develop the forecast for certain indicators, however essential a variety, nonlinearity and not stationarity of the processes constituting demographic development forces to look +for new approaches and

  9. Why building capacity is a necessary but insufficient condition for improved waste management in South Africa: The knowledge–behaviour relationship

    CSIR Research Space (South Africa)

    Godfrey, Linda K

    2010-10-01

    Full Text Available as a way of addressing these challenges. This paper explores whether building capacity in the field of waste management in South Africa is sufficient to improve the way that waste is currently managed in the country. The Theory of Planned Behaviour...

  10. Forecasting Turbine Icing Events

    DEFF Research Database (Denmark)

    Davis, Neil; Hahmann, Andrea N.; Clausen, Niels-Erik

    2012-01-01

    In this study, we present a method for forecasting icing events. The method is validated at two European wind farms in with known icing events. The icing model used was developed using current ice accretion methods, and newly developed ablation algorithms. The model is driven by inputs from the WRF...... mesoscale model, allowing for both climatological estimates of icing and short term icing forecasts. The current model was able to detect periods of icing reasonably well at the warmer site. However at the cold climate site, the model was not able to remove ice quickly enough leading to large ice...... accumulations, which have not been seen in observations. In addition to the model evaluation we were able to investigate the potential occurrence of ice induced power loss at two wind parks in Europe using observed data. We found that the potential loss during an icing event is large even when the turbine...

  11. Forecasting Infectious Disease Outbreaks

    Science.gov (United States)

    Shaman, J. L.

    2015-12-01

    Dynamic models of infectious disease systems abound and are used to study the epidemiological characteristics of disease outbreaks, the ecological mechanisms affecting transmission, and the suitability of various control and intervention strategies. The dynamics of disease transmission are non-linear and consequently difficult to forecast. Here, we describe combined model-inference frameworks developed for the prediction of infectious diseases. We show that accurate and reliable predictions of seasonal influenza outbreaks can be made using a mathematical model representing population-level influenza transmission dynamics that has been recursively optimized using ensemble data assimilation techniques and real-time estimates of influenza incidence. Operational real-time forecasts of influenza and other infectious diseases have been and are currently being generated.

  12. Forecasting carbon dioxide emissions.

    Science.gov (United States)

    Zhao, Xiaobing; Du, Ding

    2015-09-01

    This study extends the literature on forecasting carbon dioxide (CO2) emissions by applying the reduced-form econometrics approach of Schmalensee et al. (1998) to a more recent sample period, the post-1997 period. Using the post-1997 period is motivated by the observation that the strengthening pace of global climate policy may have been accelerated since 1997. Based on our parameter estimates, we project 25% reduction in CO2 emissions by 2050 according to an economic and population growth scenario that is more consistent with recent global trends. Our forecasts are conservative due to that we do not have sufficient data to fully take into account recent developments in the global economy. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Uranium price forecasting methods

    International Nuclear Information System (INIS)

    Fuller, D.M.

    1994-01-01

    This article reviews a number of forecasting methods that have been applied to uranium prices and compares their relative strengths and weaknesses. The methods reviewed are: (1) judgemental methods, (2) technical analysis, (3) time-series methods, (4) fundamental analysis, and (5) econometric methods. Historically, none of these methods has performed very well, but a well-thought-out model is still useful as a basis from which to adjust to new circumstances and try again

  14. Statistical methods for forecasting

    CERN Document Server

    Abraham, Bovas

    2009-01-01

    The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists."This book, it must be said, lives up to the words on its advertising cover: ''Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.'' It does just that!"-Journal of the Royal Statistical Society"A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series ...

  15. PyForecastTools

    Energy Technology Data Exchange (ETDEWEB)

    2017-09-22

    The PyForecastTools package provides Python routines for calculating metrics for model validation, forecast verification and model comparison. For continuous predictands the package provides functions for calculating bias (mean error, mean percentage error, median log accuracy, symmetric signed bias), and for calculating accuracy (mean squared error, mean absolute error, mean absolute scaled error, normalized RMSE, median symmetric accuracy). Convenience routines to calculate the component parts (e.g. forecast error, scaled error) of each metric are also provided. To compare models the package provides: generic skill score; percent better. Robust measures of scale including median absolute deviation, robust standard deviation, robust coefficient of variation and the Sn estimator are all provided by the package. Finally, the package implements Python classes for NxN contingency tables. In the case of a multi-class prediction, accuracy and skill metrics such as proportion correct and the Heidke and Peirce skill scores are provided as object methods. The special case of a 2x2 contingency table inherits from the NxN class and provides many additional metrics for binary classification: probability of detection, probability of false detection, false alarm ration, threat score, equitable threat score, bias. Confidence intervals for many of these quantities can be calculated using either the Wald method or Agresti-Coull intervals.

  16. Forecasting for dynamic line rating

    DEFF Research Database (Denmark)

    Michiorri, Andrea; Nguyen, Huu-Minh; Alessandrini, Stefano

    2015-01-01

    This paper presents an overview of the state of the art on the research on Dynamic Line Rating forecasting. It is directed at researchers and decision-makers in the renewable energy and smart grids domain, and in particular at members of both the power system and meteorological community. Its aim...... on environmental conditions such as the value of ambient temperature, solar radiation, and wind speed and direction. Currently, conservative static seasonal estimations of meteorological values are used to determine ampacity. In a DLR framework, the ampacity is estimated in real time or quasi-real time using...... sensors on the line that measure conductor temperature, tension, sag or environmental parameters such as wind speed and air temperature. Because of the conservative assumptions used to calculate static seasonal ampacity limits and the variability of weather parameters, DLRs are considerably higher than...

  17. Forecasting Container Throughput at the Doraleh Port in Djibouti through Time Series Analysis

    Science.gov (United States)

    Mohamed Ismael, Hawa; Vandyck, George Kobina

    The Doraleh Container Terminal (DCT) located in Djibouti has been noted as the most technologically advanced container terminal on the African continent. DCT's strategic location at the crossroads of the main shipping lanes connecting Asia, Africa and Europe put it in a unique position to provide important shipping services to vessels plying that route. This paper aims to forecast container throughput through the Doraleh Container Port in Djibouti by Time Series Analysis. A selection of univariate forecasting models has been used, namely Triple Exponential Smoothing Model, Grey Model and Linear Regression Model. By utilizing the above three models and their combination, the forecast of container throughput through the Doraleh port was realized. A comparison of the different forecasting results of the three models, in addition to the combination forecast is then undertaken, based on commonly used evaluation criteria Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The study found that the Linear Regression forecasting Model was the best prediction method for forecasting the container throughput, since its forecast error was the least. Based on the regression model, a ten (10) year forecast for container throughput at DCT has been made.

  18. Operational river discharge forecasting in poorly gauged basins: the Kavango River basin case study

    DEFF Research Database (Denmark)

    Bauer-Gottwein, Peter; Jensen, Iris Hedegaard; Guzinski, R.

    2015-01-01

    assimilation techniques. However, few studies have attempted to develop operational probabilistic forecasting approaches for large and poorly gauged river basins. The objective of this study is to develop open-source software tools to support hydrologic forecasting and integrated water resources management......Operational probabilistic forecasts of river discharge are essential for effective water resources management. Many studies have addressed this topic using different approaches ranging from purely statistical black-box approaches to physically based and distributed modeling schemes employing data...... in Africa. We present an operational probabilistic forecasting approach which uses public-domain climate forcing data and a hydrologic-hydrodynamic model which is entirely based on open-source software. Data assimilation techniques are used to inform the forecasts with the latest available observations...

  19. Indo-Pacific sea surface temperature influences on failed consecutive rainy seasons over eastern Africa

    Science.gov (United States)

    Hoell, Andrew; Funk, Christopher C.

    2014-01-01

    Rainfall over eastern Africa (10°S–10°N; 35°E–50°E) is bimodal, with seasonal maxima during the "long rains" of March–April–May (MAM) and the "short rains" of October–November–December (OND). Below average precipitation during consecutive long and short rains seasons over eastern Africa can have devastating long-term impacts on water availability and agriculture. Here, we examine the forcing of drought during consecutive long and short rains seasons over eastern Africa by Indo-Pacific sea surface temperatures (SSTs). The forcing of eastern Africa precipitation and circulation by SSTs is tested using ten ensemble simulations of a global weather forecast model forced by 1950–2010 observed global SSTs. Since the 1980s, Indo-Pacific SSTs have forced more frequent droughts spanning consecutive long and short rains seasons over eastern Africa. The increased frequency of dry conditions is linked to warming SSTs over the Indo-west Pacific and to a lesser degree to Pacific Decadal Variability. During MAM, long-term warming of tropical west Pacific SSTs from 1950–2010 has forced statistically significant precipitation reductions over eastern Africa. The warming west Pacific SSTs have forced changes in the regional lower tropospheric circulation by weakening the Somali Jet, which has reduced moisture and rainfall over the Horn of Africa. During OND, reductions in precipitation over recent decades are oftentimes overshadowed by strong year-to-year precipitation variability forced by the Indian Ocean Dipole and the El Niño–Southern Oscillation.

  20. Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

    Science.gov (United States)

    Roberts, J. Brent; Roberts, Franklin R.

    2013-01-01

    The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

  1. Analysing UK real estate market forecast disagreement

    OpenAIRE

    McAllister, Patrick; Newell, G.; Matysiak, George

    2005-01-01

    Given the significance of forecasting in real estate investment decisions, this paper investigates forecast uncertainty and disagreement in real estate market forecasts. Using the Investment Property Forum (IPF) quarterly survey amongst UK independent real estate forecasters, these real estate forecasts are compared with actual real estate performance to assess a number of real estate forecasting issues in the UK over 1999-2004, including real estate forecast error, bias and consensus. The re...

  2. Development of an Experimental African Drought Monitoring and Seasonal Forecasting System: A First Step towards a Global Drought Information System

    Science.gov (United States)

    Wood, E. F.; Chaney, N.; Sheffield, J.; Yuan, X.

    2012-12-01

    Extreme hydrologic events in the form of droughts are a significant source of social and economic damage. Internationally, organizations such as UNESCO, the Group on Earth Observations (GEO), and the World Climate Research Programme (WCRP) have recognized the need for drought monitoring, especially for the developing world where drought has had devastating impacts on local populations through food insecurity and famine. Having the capacity to monitor droughts in real-time, and to provide drought forecasts with sufficient warning will help developing countries and international programs move from the management of drought crises to the management of drought risk. While observation-based assessments, such as those produced by the US Drought Monitor, are effective for monitoring in countries with extensive observation networks (of precipitation in particular), their utility is lessened in areas (e.g., Africa) where observing networks are sparse. For countries with sparse networks and weak reporting systems, remote sensing observations can provide the real-time data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for the construction of a climatology against which current conditions can be compared. In this presentation we discuss the development of our multi-lingual experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML). At the request of UNESCO, the ADM system has been installed at AGRHYMET, a regional climate and agricultural center in Niamey, Niger and at the ICPAC climate center in Nairobi, Kenya. The ADM system leverages off our U.S. drought monitoring and forecasting system (http://hydrology.princeton.edu/forecasting) that uses the NLDAS data to force the VIC land surface model (LSM) at 1/8th degree spatial resolution for the estimation of our soil moisture drought index (Sheffield et al., 2004). For the seasonal forecast of drought, CFSv2 climate

  3. An Approach for Long-lead Probabilistic Forecast of Droughts

    Science.gov (United States)

    Madadgar, S.; Moradkhani, H.

    2013-12-01

    Spatio-temporal analysis of historical droughts across the Gunnison river Basin in CO, USA is studied and the probability distribution of future droughts is obtained. The Standardized Runoff Index (SRI) is employed to analyze the drought status across the spatial extent of the basin. To apply SRI in drought forecasting, the Precipitation Runoff Modeling System (PRMS) is used to estimate the runoff generated in the spatial units of the basin. A recently developed multivariate forecast technique is then used to model the joint behavior between the correlated variables of accumulated runoff over the forecast and predicting periods. The probability of future droughts in the forecast season given the observed drought in the last season is evaluated by the conditional probabilities derived from the forecast model. Using the conditional probabilities of future droughts, the runoff variation over the basin with the particular chance of occurrence is obtained as well. The forecast model also provides the uncertainty bound of future runoff produced at each spatial unit across the basin. Our results indicate that the statistical method developed in this study is a useful procedure in presenting the probabilistic forecasting of droughts given the spatio-temporal characteristics of droughts in the past.

  4. Improving wave forecasting by integrating ensemble modelling and machine learning

    Science.gov (United States)

    O'Donncha, F.; Zhang, Y.; James, S. C.

    2017-12-01

    Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

  5. An economic framework for forecasting land-use and ecosystem change

    International Nuclear Information System (INIS)

    Lewis, David J.

    2010-01-01

    This paper develops a joint econometric-simulation framework to forecast detailed empirical distributions of the spatial pattern of land-use and ecosystem change. In-sample and out-of-sample forecasting tests are used to examine the performance of the parcel-scale econometric and simulation models, and the importance of multiple forecasting challenges is assessed. The econometric-simulation method is integrated with an ecological model to generate forecasts of the probability of localized extinctions of an amphibian species. The paper demonstrates the potential of integrating economic and ecological models to generate ecological forecasts in the presence of alternative market conditions and land-use policy constraints. (author)

  6. NMME Monthly / Seasonal Forecasts for NASA SERVIR Applications Science

    Science.gov (United States)

    Robertson, F. R.; Roberts, J. B.

    2014-12-01

    This work details use of the North American Multi-Model Ensemble (NMME) experimental forecasts as drivers for Decision Support Systems (DSSs) in the NASA / USAID initiative, SERVIR (a Spanish acronym meaning "to serve"). SERVIR integrates satellite observations, ground-based data and forecast models to monitor and forecast environmental changes and to improve response to natural disasters. Through the use of DSSs whose "front ends" are physically based models, the SERVIR activity provides a natural testbed to determine the extent to which NMME monthly to seasonal projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of seasonal hydrologic anomalies in assessing agricultural / food security impacts, water availability, and risk to societal infrastructure. The multi-model NMME framework provides a "best practices" approach to probabilistic forecasting. The NMME forecasts are generated at resolution more coarse than that required to support DSS models; downscaling in both space and time is necessary. The methodology adopted here applied model output statistics where we use NMME ensemble monthly projections of sea-surface temperature (SST) and precipitation from 30 years of hindcasts with observations of precipitation and temperature for target regions. Since raw model forecasts are well-known to have structural biases, a cross-validated multivariate regression methodology (CCA) is used to link the model projected states as predictors to the predictands of the target region. The target regions include a number of basins in East and South Africa as well as the Ganges / Baramaputra / Meghna basin complex. The MOS approach used address spatial downscaling. Temporal disaggregation of monthly seasonal forecasts is achieved through use of a tercile bootstrapping approach. We interpret the results of these studies, the levels of skill by several metrics, and key uncertainties.

  7. NMME Monthly / Seasonal Forecasts for NASA SERVIR Applications Science

    Science.gov (United States)

    Robertson, Franklin R.; Roberts, Jason B.

    2014-01-01

    This work details use of the North American Multi-Model Ensemble (NMME) experimental forecasts as drivers for Decision Support Systems (DSSs) in the NASA / USAID initiative, SERVIR (a Spanish acronym meaning "to serve"). SERVIR integrates satellite observations, ground-based data and forecast models to monitor and forecast environmental changes and to improve response to natural disasters. Through the use of DSSs whose "front ends" are physically based models, the SERVIR activity provides a natural testbed to determine the extent to which NMME monthly to seasonal projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of seasonal hydrologic anomalies in assessing agricultural / food security impacts, water availability, and risk to societal infrastructure. The multi-model NMME framework provides a "best practices" approach to probabilistic forecasting. The NMME forecasts are generated at resolution more coarse than that required to support DSS models; downscaling in both space and time is necessary. The methodology adopted here applied model output statistics where we use NMME ensemble monthly projections of sea-surface temperature (SST) and precipitation from 30 years of hindcasts with observations of precipitation and temperature for target regions. Since raw model forecasts are well-known to have structural biases, a cross-validated multivariate regression methodology (CCA) is used to link the model projected states as predictors to the predictands of the target region. The target regions include a number of basins in East and South Africa as well as the Ganges / Baramaputra / Meghna basin complex. The MOS approach used address spatial downscaling. Temporal disaggregation of monthly seasonal forecasts is achieved through use of a tercile bootstrapping approach. We interpret the results of these studies, the levels of skill by several metrics, and key uncertainties.

  8. Forecast of water demand in Beijing in 2030

    Science.gov (United States)

    Liu, Zongxian; Xue, Linli

    2017-08-01

    Beijing has a large population and insufficient resources, in order to optimize water resources allocation in the future, using the method of water quota, multiple linear regression method and the grey model prediction method of three kinds of methods about water demand forecast, it is concluded that in 2030 the Beijing water demand of about 4.211-4.405 billion m3. Comparison on three kinds of forecasting method, found that the water quota method and grey prediction model method higher prediction accuracy than the multiple linear regression method, and analyzes three kinds of prediction method suitable conditions, so as to provide reference for the future water demand forecasting.

  9. Linear Algorithms for Radioelectric Spectrum Forecast

    Directory of Open Access Journals (Sweden)

    Luis F. Pedraza

    2016-12-01

    Full Text Available This paper presents the development and evaluation of two linear algorithms for forecasting reception power for different channels at an assigned spectrum band of global systems for mobile communications (GSM, in order to analyze the spatial opportunity for reuse of frequencies by secondary users (SUs in a cognitive radio (CR network. The algorithms employed correspond to seasonal autoregressive integrated moving average (SARIMA and generalized autoregressive conditional heteroskedasticity (GARCH, which allow for a forecast of channel occupancy status. Results are evaluated using the following criteria: availability and occupancy time for channels, different types of mean absolute error, and observation time. The contributions of this work include a more integral forecast as the algorithm not only forecasts reception power but also the occupancy and availability time of a channel to determine its precision percentage during the use by primary users (PUs and SUs within a CR system. Algorithm analyses demonstrate a better performance for SARIMA over GARCH algorithm in most of the evaluated variables.

  10. Weighting of NMME temperature and precipitation forecasts across Europe

    Science.gov (United States)

    Slater, Louise J.; Villarini, Gabriele; Bradley, A. Allen

    2017-09-01

    Multi-model ensemble forecasts are obtained by weighting multiple General Circulation Model (GCM) outputs to heighten forecast skill and reduce uncertainties. The North American Multi-Model Ensemble (NMME) project facilitates the development of such multi-model forecasting schemes by providing publicly-available hindcasts and forecasts online. Here, temperature and precipitation forecasts are enhanced by leveraging the strengths of eight NMME GCMs (CCSM3, CCSM4, CanCM3, CanCM4, CFSv2, GEOS5, GFDL2.1, and FLORb01) across all forecast months and lead times, for four broad climatic European regions: Temperate, Mediterranean, Humid-Continental and Subarctic-Polar. We compare five different approaches to multi-model weighting based on the equally weighted eight single-model ensembles (EW-8), Bayesian updating (BU) of the eight single-model ensembles (BU-8), BU of the 94 model members (BU-94), BU of the principal components of the eight single-model ensembles (BU-PCA-8) and BU of the principal components of the 94 model members (BU-PCA-94). We assess the forecasting skill of these five multi-models and evaluate their ability to predict some of the costliest historical droughts and floods in recent decades. Results indicate that the simplest approach based on EW-8 preserves model skill, but has considerable biases. The BU and BU-PCA approaches reduce the unconditional biases and negative skill in the forecasts considerably, but they can also sometimes diminish the positive skill in the original forecasts. The BU-PCA models tend to produce lower conditional biases than the BU models and have more homogeneous skill than the other multi-models, but with some loss of skill. The use of 94 NMME model members does not present significant benefits over the use of the 8 single model ensembles. These findings may provide valuable insights for the development of skillful, operational multi-model forecasting systems.

  11. Ensemble hydromoeteorological forecasting in Denmark

    DEFF Research Database (Denmark)

    Lucatero Villasenor, Diana

    of the main sources of uncertainty in hydrological forecasts. This is the reason why substantiated efforts to include information from Numerical Weather Predictors (NWP) or General Circulation Models (GCM) have been made over the last couple of decades. The present thesis expects to advance the field...... forecasts only about 15% and ET0 being the lowest at 15% for some months. The lowest skill of ET0 can be attributable to the combination of both T and incoming shortwave radiation (ISWR) bias from the GCM in addition to the added uncertainty for the model of the ET0 chosen (Makkink formula). Attempts...... steps. First, GCM-based streamflow forecasts exhibit biases that increase with lead time and, although these forecasts are sharper than the ESP forecasts, these biases lead to lower accuracy relative to ESP forecasts, especially at lead times larger than two months. Corrected GCM-based streamflow...

  12. Cash flow forecast for South African firms

    Directory of Open Access Journals (Sweden)

    Yun Li

    2015-06-01

    Full Text Available This paper applies models in the extant literature that have been used to forecast operating cash flows to predict the cash flows of South African firms listed on the Johannesburg Stock Exchange. Out-of-sample performance is examined for each model and compared between them. The reported results show that some accrual terms, i.e. depreciation and changes in inventory do not enhance cash flow prediction for the average South African firm in contrast to the reported results of studies in USA and Australia. Inclusion of more explanatory variables does not necessarily improve the models, according to the out-of-sample results. The paper proposes the application of moving average model in panel data, and vector regressive model for multi-period-ahead prediction of cash flows for South Africa firms.

  13. A national-scale seasonal hydrological forecast system: development and evaluation over Britain

    Directory of Open Access Journals (Sweden)

    V. A. Bell

    2017-09-01

    Full Text Available Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts from the GloSea5 model (1996 to 2009 are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region. Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 % in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows, whereas for the 3-month ahead lead time, GloSea5 forecasts account for  ∼ 70

  14. A national-scale seasonal hydrological forecast system: development and evaluation over Britain

    Science.gov (United States)

    Bell, Victoria A.; Davies, Helen N.; Kay, Alison L.; Brookshaw, Anca; Scaife, Adam A.

    2017-09-01

    Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ˜ 70 % of the forecast

  15. Forecasting volatility of wind power production

    OpenAIRE

    Zhiwei Shen; Matthias Ritter

    2015-01-01

    Abstract: The increasing share of wind energy in the portfolio of energy sources highlights its uncertainties due to changing weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility forecasting abilities of different GARCH-type models for wind power production. Moreover, due to characteristic features of the wind power process, such as heteroscedasticity and nonlinearity, we also investigate the use of a Markov regime-switch...

  16. IEA Wind Task 36 Forecasting

    Science.gov (United States)

    Giebel, Gregor; Cline, Joel; Frank, Helmut; Shaw, Will; Pinson, Pierre; Hodge, Bri-Mathias; Kariniotakis, Georges; Sempreviva, Anna Maria; Draxl, Caroline

    2017-04-01

    Wind power forecasts have been used operatively for over 20 years. Despite this fact, there are still several possibilities to improve the forecasts, both from the weather prediction side and from the usage of the forecasts. The new International Energy Agency (IEA) Task on Wind Power Forecasting tries to organise international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, UK MetOffice, …) and operational forecaster and forecast users. The Task is divided in three work packages: Firstly, a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction model physics, but also widely distributed information on accessible datasets for verification. Secondly, we will be aiming at an international pre-standard (an IEA Recommended Practice) on benchmarking and comparing wind power forecasts, including probabilistic forecasts aiming at industry and forecasters alike. This WP will also organise benchmarks, in cooperation with the IEA Task WakeBench. Thirdly, we will be engaging end users aiming at dissemination of the best practice in the usage of wind power predictions, especially probabilistic ones. The Operating Agent is Gregor Giebel of DTU, Co-Operating Agent is Joel Cline of the US Department of Energy. Collaboration in the task is solicited from everyone interested in the forecasting business. We will collaborate with IEA Task 31 Wakebench, which developed the Windbench benchmarking platform, which this task will use for forecasting benchmarks. The task runs for three years, 2016-2018. Main deliverables are an up-to-date list of current projects and main project results, including datasets which can be used by researchers around the world to improve their own models, an IEA Recommended Practice on performance evaluation of probabilistic forecasts, a position paper regarding the use of probabilistic forecasts

  17. Earthquake forecasting and its verification

    Directory of Open Access Journals (Sweden)

    J. R. Holliday

    2005-01-01

    Full Text Available No proven method is currently available for the reliable short time prediction of earthquakes (minutes to months. However, it is possible to make probabilistic hazard assessments for earthquake risk. In this paper we discuss a new approach to earthquake forecasting based on a pattern informatics (PI method which quantifies temporal variations in seismicity. The output, which is based on an association of small earthquakes with future large earthquakes, is a map of areas in a seismogenic region ('hotspots'' where earthquakes are forecast to occur in a future 10-year time span. This approach has been successfully applied to California, to Japan, and on a worldwide basis. Because a sharp decision threshold is used, these forecasts are binary--an earthquake is forecast either to occur or to not occur. The standard approach to the evaluation of a binary forecast is the use of the relative (or receiver operating characteristic (ROC diagram, which is a more restrictive test and less subject to bias than maximum likelihood tests. To test our PI method, we made two types of retrospective forecasts for California. The first is the PI method and the second is a relative intensity (RI forecast based on the hypothesis that future large earthquakes will occur where most smaller earthquakes have occurred in the recent past. While both retrospective forecasts are for the ten year period 1 January 2000 to 31 December 2009, we performed an interim analysis 5 years into the forecast. The PI method out performs the RI method under most circumstances.

  18. Medium-range fire weather forecasts

    Science.gov (United States)

    J.O. Roads; K. Ueyoshi; S.C. Chen; J. Alpert; F. Fujioka

    1991-01-01

    The forecast skill of theNational Meteorological Center's medium range forecast (MRF) numerical forecasts of fire weather variables is assessed for the period June 1,1988 to May 31,1990. Near-surface virtual temperature, relative humidity, wind speed and a derived fire weather index (FWI) are forecast well by the MRF model. However, forecast relative humidity has...

  19. Seasonal forecasting of discharge for the Raccoon River, Iowa

    Science.gov (United States)

    Slater, Louise; Villarini, Gabriele; Bradley, Allen; Vecchi, Gabriel

    2016-04-01

    The state of Iowa (central United States) is regularly afflicted by severe natural hazards such as the 2008/2013 floods and the 2012 drought. To improve preparedness for these catastrophic events and allow Iowans to make more informed decisions about the most suitable water management strategies, we have developed a framework for medium to long range probabilistic seasonal streamflow forecasting for the Raccoon River at Van Meter, a 8900-km2 catchment located in central-western Iowa. Our flow forecasts use statistical models to predict seasonal discharge for low to high flows, with lead forecasting times ranging from one to ten months. Historical measurements of daily discharge are obtained from the U.S. Geological Survey (USGS) at the Van Meter stream gage, and used to compute quantile time series from minimum to maximum seasonal flow. The model is forced with basin-averaged total seasonal precipitation records from the PRISM Climate Group and annual row crop production acreage from the U.S. Department of Agriculture's National Agricultural Statistics Services database. For the forecasts, we use corn and soybean production from the previous year (persistence forecast) as a proxy for the impacts of agricultural practices on streamflow. The monthly precipitation forecasts are provided by eight Global Climate Models (GCMs) from the North American Multi-Model Ensemble (NMME), with lead times ranging from 0.5 to 11.5 months, and a resolution of 1 decimal degree. Additionally, precipitation from the month preceding each season is used to characterize antecedent soil moisture conditions. The accuracy of our modelled (1927-2015) and forecasted (2001-2015) discharge values is assessed by comparison with the observed USGS data. We explore the sensitivity of forecast skill over the full range of lead times, flow quantiles, forecast seasons, and with each GCM. Forecast skill is also examined using different formulations of the statistical models, as well as NMME forecast

  20. Evaluating dynamic covariance matrix forecasting and portfolio optimization

    OpenAIRE

    Sendstad, Lars Hegnes; Holten, Dag Martin

    2012-01-01

    In this thesis we have evaluated the covariance forecasting ability of the simple moving average, the exponential moving average and the dynamic conditional correlation models. Overall we found that a dynamic portfolio can gain significant improvements by implementing a multivariate GARCH forecast. We further divided the global investment universe into sectors and regions in order to investigate the relative portfolio performance of several asset allocation strategies with both variance and c...

  1. Forecasting freight flows

    DEFF Research Database (Denmark)

    Lyk-Jensen, Stéphanie

    2011-01-01

    model. The analysis uses a structural relationship to explain the structure of the exchange of the goods—a relationship that can be used in the year of forecast. This article also provides a new methodology for converting monetary aggregates into quantity aggregates. The resulting commodity growth rates....... This article models long-term dynamic physical trade flows and estimates a dynamic panel data model for foreign trade for the EU15 and two countries from the EFTA (European Free Trade Association) 1967–2002. The analysis suggests that a dynamic three-way-effects gravity equation is the best-fitted econometric...

  2. Utility usage forecasting

    Science.gov (United States)

    Hosking, Jonathan R. M.; Natarajan, Ramesh

    2017-08-22

    The computer creates a utility demand forecast model for weather parameters by receiving a plurality of utility parameter values, wherein each received utility parameter value corresponds to a weather parameter value. Determining that a range of weather parameter values lacks a sufficient amount of corresponding received utility parameter values. Determining one or more utility parameter values that corresponds to the range of weather parameter values. Creating a model which correlates the received and the determined utility parameter values with the corresponding weather parameters values.

  3. Ecological Forecasting Project Management with Examples

    Science.gov (United States)

    Skiles, J. W.; Schmidt, Cindy; Estes, Maury; Turner, Woody

    2017-01-01

    Once scientists publish results of their projects and studies, all too often they end up on the shelf and are not otherwise used. The NASA Earth Science Division established its Applied Sciences Program (ASP) to apply research findings to help solve and manage real-world problems and needs. ASP-funded projects generally produce decision support systems for operational applications which are expected to last beyond the end of the NASA funding. Because of NASAs unique perspective of looking down on the Earth from space, ASP studies involve the use of remotely sensed information consisting of satellite data and imagery as well as information from sub-orbital platforms. ASP regularly solicits Earth science proposals that address one or more focus areas; disasters mitigation, ecological forecasting, health and air quality, and water resources. Reporting requirements for ASP-funded projects are different from those typical for research grants from NASA and other granting agencies, requiring management approaches different from other programs. This presentation will address the foregoing in some detail and give examples of three ASP-funded ecological forecasting projects that include: 1) the detection and survey of chimpanzee habitat in Africa from space, 2) harmful algal blooms (HABs) in the California Current System affecting aquaculture facilities and marine mammal populations, and 3) a call for the public to identify North America wildlife in Wisconsin using trail camera photos. Contact information to propose to ASP solicitations for those PIs interested is also provided.

  4. Ensemble Streamflow Forecast Improvements in NYC's Operations Support Tool

    Science.gov (United States)

    Wang, L.; Weiss, W. J.; Porter, J.; Schaake, J. C.; Day, G. N.; Sheer, D. P.

    2013-12-01

    Like most other water supply utilities, New York City's Department of Environmental Protection (DEP) has operational challenges associated with drought and wet weather events. During drought conditions, DEP must maintain water supply reliability to 9 million customers as well as meet environmental release requirements downstream of its reservoirs. During and after wet weather events, DEP must maintain turbidity compliance in its unfiltered Catskill and Delaware reservoir systems and minimize spills to mitigate downstream flooding. Proactive reservoir management - such as release restrictions to prepare for a drought or preventative drawdown in advance of a large storm - can alleviate negative impacts associated with extreme events. It is important for water managers to understand the risks associated with proactive operations so unintended consequences such as endangering water supply reliability with excessive drawdown prior to a storm event are minimized. Probabilistic hydrologic forecasts are a critical tool in quantifying these risks and allow water managers to make more informed operational decisions. DEP has recently completed development of an Operations Support Tool (OST) that integrates ensemble streamflow forecasts, real-time observations, and a reservoir system operations model into a user-friendly graphical interface that allows its water managers to take robust and defensible proactive measures in the face of challenging system conditions. Since initial development of OST was first presented at the 2011 AGU Fall Meeting, significant improvements have been made to the forecast system. First, the monthly AR1 forecasts ('Hirsch method') were upgraded with a generalized linear model (GLM) utilizing historical daily correlations ('Extended Hirsch method' or 'eHirsch'). The development of eHirsch forecasts improved predictive skill over the Hirsch method in the first week to a month from the forecast date and produced more realistic hydrographs on the tail

  5. On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models

    OpenAIRE

    Fay, D.; Ringwood, John; Condon, M.

    2004-01-01

    Weather information is an important factor in load forecasting models. This weather information usually takes the form of actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. A technique is proposed to model weather forecast errors to reflect current accuracy. A load forecasting model is then proposed which combines the forecasts of several load forecasting models. This approach allows the...

  6. Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

    Directory of Open Access Journals (Sweden)

    Jinyin Ye

    2016-01-01

    Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.

  7. Interactive Forecasting with the National Weather Service River Forecast System

    Science.gov (United States)

    Smith, George F.; Page, Donna

    1993-01-01

    The National Weather Service River Forecast System (NWSRFS) consists of several major hydrometeorologic subcomponents to model the physics of the flow of water through the hydrologic cycle. The entire NWSRFS currently runs in both mainframe and minicomputer environments, using command oriented text input to control the system computations. As computationally powerful and graphically sophisticated scientific workstations became available, the National Weather Service (NWS) recognized that a graphically based, interactive environment would enhance the accuracy and timeliness of NWS river and flood forecasts. Consequently, the operational forecasting portion of the NWSRFS has been ported to run under a UNIX operating system, with X windows as the display environment on a system of networked scientific workstations. In addition, the NWSRFS Interactive Forecast Program was developed to provide a graphical user interface to allow the forecaster to control NWSRFS program flow and to make adjustments to forecasts as necessary. The potential market for water resources forecasting is immense and largely untapped. Any private company able to market the river forecasting technologies currently developed by the NWS Office of Hydrology could provide benefits to many information users and profit from providing these services.

  8. Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond

    DEFF Research Database (Denmark)

    Hong, Tao; Pinson, Pierre; Fan, Shu

    2016-01-01

    The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged fundamenta...... competition with four tracks on load, price, wind and solar forecasting, which attracted 581 participants from 61 countries. We conclude the paper with 12 predictions for the next decade of energy forecasting.......The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged...... fundamentally. In this competitive and dynamic environment, many decision-making processes rely on probabilistic forecasts to quantify the uncertain future. Although most of the papers in the energy forecasting literature focus on point or singlevalued forecasts, the research interest in probabilistic energy...

  9. THE STUDY OF THE FORECASTING PROCESS INFRASTRUCTURAL SUPPORT BUSINESS

    Directory of Open Access Journals (Sweden)

    E. V. Sibirskaia

    2014-01-01

    Full Text Available Summary. When forecasting the necessary infrastructural support entrepreneurship predict rational distribution of the potential and expected results based on capacity development component of infrastructural maintenance, efficient use of resources, expertise and development of regional economies, the rationalization of administrative decisions, etc. According to the authors, the process of predicting business infrastructure software includes the following steps: analysis of the existing infrastructure support business to the top of the forecast period, the structure of resources, identifying disparities, their causes, identifying positive trends in the analysis and the results of research; research component of infrastructural support entrepreneurship, assesses complex system of social relations, institutions, structures and objects made findings and conclusions of the study; identification of areas of strategic change and the possibility of eliminating weaknesses and imbalances, identifying prospects for the development of entrepreneurship; identifying a set of factors and conditions affecting each component of infrastructure software, calculated the degree of influence of each of them and the total effect of all factors; adjustment indicators infrastructure forecasts. Research of views of category says a method of strategic planning and forecasting that methods of strategic planning are considered separately from forecasting methods. In a combination methods of strategic planning and forecasting, in relation to infrastructure ensuring business activity aren't given in literature. Nevertheless, authors consider that this category should be defined for the characteristic of the intrinsic and substantial nature of strategic planning and forecasting of infrastructure ensuring business activity.processing.

  10. Precipitation forecasts and their uncertainty as input into hydrological models

    Directory of Open Access Journals (Sweden)

    M. Kobold

    2005-01-01

    Full Text Available Torrential streams and fast runoff are characteristic of most Slovenian rivers and extensive damage is caused almost every year by rainstorms affecting different regions of Slovenia. Rainfall-runoff models which are tools for runoff calculation can be used for flood forecasting. In Slovenia, the lag time between rainfall and runoff is only a few hours and on-line data are used only for now-casting. Predicted precipitation is necessary in flood forecasting some days ahead. The ECMWF (European Centre for Medium-Range Weather Forecasts model gives general forecasts several days ahead while more detailed precipitation data with the ALADIN/SI model are available for two days ahead. Combining the weather forecasts with the information on catchment conditions and a hydrological forecasting model can give advance warning of potential flooding notwithstanding a certain degree of uncertainty in using precipitation forecasts based on meteorological models. Analysis of the sensitivity of the hydrological model to the rainfall error has shown that the deviation in runoff is much larger than the rainfall deviation. Therefore, verification of predicted precipitation for large precipitation events was performed with the ECMWF model. Measured precipitation data were interpolated on a regular grid and compared with the results from the ECMWF model. The deviation in predicted precipitation from interpolated measurements is shown with the model bias resulting from the inability of the model to predict the precipitation correctly and a bias for horizontal resolution of the model and natural variability of precipitation.

  11. Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

    Directory of Open Access Journals (Sweden)

    Nicolas Vigaud

    2017-10-01

    Full Text Available Subseasonal forecast skill over the broadly defined North American (NAM, West African (WAM and Asian (AM summer monsoon regions is investigated using three Ensemble Prediction Systems (EPS at sub-monthly lead times. Extended Logistic Regression (ELR is used to produce probabilistic forecasts of weekly and week 3–4 averages of precipitation with starts in May–Aug, over the 1999–2010 period. The ELR tercile category probabilities for each model gridpoint are then averaged together with equal weight. The resulting Multi-Model Ensemble (MME forecasts exhibit good reliability, but have generally low sharpness for forecasts beyond 1 week; Multi-model ensembling largely removes negative values of the Ranked Probability Skill Score (RPSS seen in individual forecasts, and broadly improves the skill obtained in any of the three individual models except for the AM. The MME week 3–4 forecasts have generally higher RPSS and comparable reliability over all monsoon regions, compared to week 3 or week 4 forecast separately. Skill is higher during La Niña compared to El Niño and ENSO-neutral conditions over the 1999–2010 period, especially for the NAM. Regionally averaged RPSS is significantly correlated with the Maden-Julian Oscillation (MJO for the AM and WAM. Our results indicate potential for skillful predictions at subseasonal time-scales over the three summer monsoon regions of the Northern Hemisphere.

  12. Issues in Forecasting CMEs

    Science.gov (United States)

    Pizzo, V. J.

    2017-12-01

    I will present my view of the current status of space weather forecasting abilities related to CMEs. This talk will address the large-scale aspects, but specifically not energetic particle phenomena. A key point is that all models, whether sophisticated numerical contraptions or quasi-empirical ones, are only as good as the data you feed them. Hence the emphasis will be on observations and analysis methods. First I will review where we stand with regard to the near-Sun quantitative data needed to drive any model, no matter how complex or simple-minded, and I will discuss technological roadblocks that suggest it may be some time before we see any meaningful improvements beyond what we have today. Then I cover issues related to characterizing CME propagation out through the corona and into interplanetary space, as well as to observational limitations in the vicinity of 1 AU. Since none of these observational constraints are likely to be resolved anytime soon, the real challenge is to make more informed use of what is available. Thus, this talk will focus on how we may identify and pursue the most profitable approaches, for both forecast and research applications. The discussion will highlight a number of promising leads, including those related to inclusion of solar backside information, joint magnetograph observations from L5 and Earth, how to use (not just run) ensembles, more rational use of HI observations, and suggestions for using cube-sats for deep space observations of CMEs and MCs.

  13. Global warming forecasts unreliability

    International Nuclear Information System (INIS)

    Baker, A.B.

    1993-01-01

    This paper reports the opinions of a series of experts who have recently commented on the reliability of predictions of global warning in relation to observed and forecasted increases in carbon dioxide emissions. One of the more difficult to explain observations, evidenced through the analysis of past meteorological data, was the rapid increase in global temperature that took place during the period preceding 1940 and which was followed by a gradual decrease, during a thirty year period of heightened industrialization and consumption of fossil fuels, up to 1970 when global temperatures began again to rise rapidly. Variations in solar activity was suggested to explain this apparently anomalous trend in global temperatures. This question as to the existence of a strict correlation between global warming and rises in carbon dioxide emissions, as well as, forecasted increases in concentrations of atmospheric carbon dioxide due to the expected population growth in China are putting a strain on attempts by OECD (Organization for Economic Co-operation and Development) environmental policy makers to gain support for energy tax proposals

  14. Human-model hybrid Korean air quality forecasting system.

    Science.gov (United States)

    Chang, Lim-Seok; Cho, Ara; Park, Hyunju; Nam, Kipyo; Kim, Deokrae; Hong, Ji-Hyoung; Song, Chang-Keun

    2016-09-01

    The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters' capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters' error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m(3) and the R(2) of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the

  15. Household conditions, eczema symptoms and rhinitis symptoms: relationship with wheeze and severe wheeze in children living in the Polokwane area, South Africa

    DEFF Research Database (Denmark)

    Wichmann, Janine; Wolvaardt, Jacqueline E; Maritz, Chantelle

    2009-01-01

    BACKGROUND: This is the fifth study that applied the International Study of Asthma and Allergies in Childhood (ISAAC) methodology in the Southern African Development Community (SADC region). However, it is the first ISAAC study that focused on 6- to 7-year-old children living in South Africa...... and the presence of eczema symptoms and rhinoconjunctivitis symptoms increased the likelihood of wheeze by 77%, 104% and 226%, respectively. Only the presence of rhinoconjunctivitis symptoms increased the likelihood of severe wheeze by 107%. CONCLUSION: Wheeze appears to be an emerging public health problem...

  16. Location specific forecasting of maximum and minimum ...

    Indian Academy of Sciences (India)

    . The global NWP models, though able to provide reasonably good short- to medium-range weather forecasts, have comparatively less skill in forecasting surface parameters. It is well known that NWP model forecasts contain systematic biases ...

  17. Global Ensemble Forecast System (GEFS) [1 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  18. Improving Local Weather Forecasts for Agricultural Applications

    NARCIS (Netherlands)

    Doeswijk, T.G.; Keesman, K.J.

    2005-01-01

    For controlling agricultural systems, weather forecasts can be of substantial importance. Studies have shown that forecast errors can be reduced in terms of bias and standard deviation using forecasts and meteorological measurements from one specific meteorological station. For agricultural systems

  19. Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

    Science.gov (United States)

    Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan

    2016-04-01

    Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more

  20. Ecological niche and potential distribution of Anopheles arabiensis in Africa in 2050.

    Science.gov (United States)

    Drake, John M; Beier, John C

    2014-06-03

    The future distribution of malaria in Africa is likely to be much more dependent on environmental conditions than the current distribution due to the effectiveness of indoor and therapeutic anti-malarial interventions, such as insecticide-treated nets (ITNs), indoor residual spraying for mosquitoes (IRS), artemisinin-combination therapy (ACT), and intermittent presumptive treatment (IPT). Future malaria epidemiology is therefore expected to be increasingly dominated by Anopheles arabiensis, which is the most abundant exophagic mosquito competent to transmit Plasmodium falciparum and exhibits a wide geographic range. To map the potential distribution of An. arabiensis in Africa, ecological niche models were fit to 20th century collection records. Many common species distribution modelling techniques aim to discriminate species habitat from the background distribution of environments. Since these methods arguably result in unnecessarily large Type I and Type II errors, LOBAG-OC was used to identify the niche boundary using only data on An. arabiensis occurrences. The future distribution of An. arabiensis in Africa was forecasted by projecting the fit model onto maps of simulated climate change following three climate change scenarios. Ecological niche modelling revealed An. arabiensis to be a climate generalist in the sense that it can occur in most of Africa's contemporary environmental range. Under three climate change scenarios, the future distribution of An. arabiensis is expected to be reduced by 48%-61%. Map differences between baseline and projected climate suggest that habitat reductions will be especially extensive in Western and Central Africa; portions of Botswana, Namibia, and Angola in Southern Africa; and portions of Sudan, South Sudan, Somalia, and Kenya in East Africa. The East African Rift Valley and Eastern Coast of Africa are expected to remain habitable. Some modest gains in habitat are predicted at the margins of the current range in South Sudan