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Sample records for long-lead seasonal forecast

  1. Long-lead probabilistic forecasting of streamflow using ocean-atmospheric and hydrological predictors

    NASA Astrophysics Data System (ADS)

    Araghinejad, Shahab; Burn, Donald H.; Karamouz, Mohammad

    2006-03-01

    A geostatistically based approach with a local regression method is used to predict the magnitude of seasonal streamflow using ocean-atmospheric signals and the hydrological condition of a basin as predictors. The model characterizes the stochastic behavior of a forecast variable by generating a conditional distribution of the predicted value for different hydroclimatic conditions. The correlation structure between dependent and independent variables is represented by the variography of the predicted values in which the distance variable in the variogram is determined by measuring the distance between the predictors. This variogram in a virtual field constructed from the predictors makes it possible to predict variables as unmeasured points while considering historic information as measurement points of the field. Different types of kriging, as well as a generalized linear model regression, are used to predict data in interpolation and extrapolation modes. The forecast skill is evaluated using a linear error in probability space score for different combinations of predictors and different kriging methods. The method is applied to a case study of the Zayandeh-rud River in Isfahan, Iran. The utility of the method is demonstrated for forecasting autumn-winter and spring streamflow using the Southern Oscillation Index, the North Atlantic Oscillation, serial correlation between seasonal streamflow series, and the snow budget. The study analyzes the application of the proposed method in comparison with a K-nearest neighbor regression method. The results of this study show that the proposed method can significantly improve the long-lead probabilistic forecast skill for a nonlinear relationship between hydroclimatic predictors and streamflow in a region.

  2. Real time tests for long lead-time forecasting of the magnetic field vectors within CMEs

    NASA Astrophysics Data System (ADS)

    Savani, Neel; Vourlidas, Angelos; Pulkkinen, Antti; Wold, Alexandra M.

    2016-07-01

    The direction of magnetic vectors within coronal mass ejections, CMEs, has significant importance for forecasting terrestrial behavior. We have developed a technique to estimate the time-varying magnetic field at Earth for periods within CMEs (Savani et al 2015, 2016). This technique reduces the complex dynamics in order to create a reliable prediction methodology to operate everyday under robust conditions. In this presentation, we focus on the results and skill scores of the forecasting technique calculated from 40 historical CME events from the pre-STEREO mission. Since these results provided substantial improvements in the long lead-time Kp index forecasts, we have now begun testing under real-time conditions. We will also show the preliminary results of our methodology under these real-time conditions within the CCMC hosted at NASA Goddard Space Flight Center.

  3. Using oceanic-atmospheric oscillations for long lead time streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Kalra, Ajay; Ahmad, Sajjad

    2009-03-01

    We present a data-driven model, Support Vector Machine (SVM), for long lead time streamflow forecasting using oceanic-atmospheric oscillations. The SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach and has been used to predict a quantity forward in time on the basis of training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. The SVM model is applied to three gages, i.e., Cisco, Green River, and Lees Ferry in the Upper Colorado River Basin in the western United States. Annual oceanic-atmospheric indices, comprising Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Nino-Southern Oscillations (ENSO) for a period of 1906-2001 are used to generate annual streamflow volumes with 3 years lead time. The SVM model is trained with 86 years of data (1906-1991) and tested with 10 years of data (1992-2001). On the basis of correlation coefficient, root means square error, and Nash Sutcliffe Efficiency Coefficient the model shows satisfactory results, and the predictions are in good agreement with measured streamflow volumes. Sensitivity analysis, performed to evaluate the effect of individual and coupled oscillations, reveals a strong signal for ENSO and NAO indices as compared to PDO and AMO indices for the long lead time streamflow forecast. Streamflow predictions from the SVM model are found to be better when compared with the predictions obtained from feedforward back propagation artificial neural network model and linear regression.

  4. Regional-seasonal weather forecasting

    SciTech Connect

    Abarbanel, H.; Foley, H.; MacDonald, G.; Rothaus, O.; Rudermann, M.; Vesecky, J.

    1980-08-01

    In the interest of allocating heating fuels optimally, the state-of-the-art for seasonal weather forecasting is reviewed. A model using an enormous data base of past weather data is contemplated to improve seasonal forecasts, but present skills do not make that practicable. 90 references. (PSB)

  5. Long-Lead Quantitative Flood Forecasts in Ungauged Basins Using Bayesian Neural Networks

    NASA Astrophysics Data System (ADS)

    Barros, A. P.; Yoo, J.

    2004-05-01

    Previously, Kim and Barros (2001) demonstrated the use of a hierarchy of neural network models to forecast flood peaks in four small and medium size ungauged basins (750 to about 9,000 km-sq) in the Northern Appalachian Mountains in Pennsylvania. Using regional rainfall, radiosonde and mesoscale infrared (IR) satellite imagery, their approach consisted of identifying the presence and type of convective activity from the IR imagery, information which was subsequently used to characterize the dominant synoptic scale weather patters and predict storm path and evolution using rainfall and radiosonde data far away from the forecast location. In this regard, the organizational skeleton of the inputs is built to mimic our understanding of physical processes associated with rainstorms. The approach was very successful with skill scores on the order of 80-90 per cent for 18-hour lead-time forecasts of winter and spring floods in response to heavy rainfall (i.e. not associated with snowmelt alone). One weakness of this work was however the lack of a measure of forecast uncertainty, or alternatively a measure of forecast reliability that could be used in hydrometeorological operations. To address this question, we have modified and adapted the existing neural network models according to the principles of Bayesian statistics. In this context, forecasts are issued along with an error bar and are associated with a known probability distribution. One additional advantage of this methodology is that it provides an objective basis for selecting the best model during learning based on the posterior distribution of the parameters. In this context, forecasts are issued along with an error bar and are associated with a known probability distribution. An intercomparison study against Kim and Barros (2001) shows that the 18- and 24-hour lead time BNN forecasts are statistically more robust than those generated by the standard backward-learning NNs. We submit that given the consistently

  6. Forecasting seasonal outbreaks of influenza

    PubMed Central

    Shaman, Jeffrey; Karspeck, Alicia

    2012-01-01

    Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003–2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza. PMID:23184969

  7. Forecasting seasonal outbreaks of influenza.

    PubMed

    Shaman, Jeffrey; Karspeck, Alicia

    2012-12-11

    Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003-2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.

  8. Monitoring and seasonal forecasting of meteorological droughts

    NASA Astrophysics Data System (ADS)

    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

  9. Improving the Long-Lead Predictability of El Niño Using a Novel Forecasting Scheme Based on a Dynamic Components Model

    NASA Astrophysics Data System (ADS)

    Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Garcia, Markel; Rodo, Xavier

    2016-04-01

    El Niño Southern Oscillation (ENSO) is a dominant feature of climate variability on inter-annual time scales and predictions for it are issued on a regular basis by a wide array of prediction schemes and climate centres around the world. We have explored a novel method for ENSO forecasting. In the state-of-the-art the advantageous statistical technique of Structural (Unobserved Components) Time Series has not been applied. Therefore, we have developed such a model with regression parameters obtained by a State Space approach. Its distinguishing feature is that observations consist of several unobserved components - trend, seasonality, cycles, disturbance, and explanatory regression covariates. These components are modeled separately and ultimately combined in a single forecasting scheme. We introduce a new domain of predictor regression variables accounting for the state of the subsurface ocean temperature in the western and central equatorial Pacific as it has been shown by previous studies that subsurface processes and heat accumulation there are fundamental for the genesis of El Niño. An important feature of the scheme is that different regression predictors are used at different lead months, thus capturing the dynamical evolution of the system and rendering more efficient forecasts. The new model has been tested with the prediction of all warm events that occurred in the period 1980-2015. Retrospective forecasts of these events were successfully made for long lead times of at least two years. Hence, we demonstrate that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted "Spring Barrier". Our statistical approach is found to exhibit similar skill to the best dynamical forecasting models for ENSO. Thus, the novel way in which the proposed modeling scheme has been structured could also be used for improving other statistical and dynamical prediction systems.

  10. Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model

    NASA Astrophysics Data System (ADS)

    Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodó, Xavier

    2016-05-01

    events were made for long lead times of at least two and a half years. Hence, the present study demonstrates that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted "Spring Barrier". The high correspondence between the forecasts and observations indicates that the proposed model outperforms all current operational statistical models, and behaves comparably to the best dynamical models used for EN prediction. Thus, the novel way in which the modeling scheme has been structured could also be used for improving other statistical and dynamical modeling systems.

  11. Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model

    NASA Astrophysics Data System (ADS)

    Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodó, Xavier

    2017-02-01

    events were made for long lead times of at least two and a half years. Hence, the present study demonstrates that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted "Spring Barrier". The high correspondence between the forecasts and observations indicates that the proposed model outperforms all current operational statistical models, and behaves comparably to the best dynamical models used for EN prediction. Thus, the novel way in which the modeling scheme has been structured could also be used for improving other statistical and dynamical modeling systems.

  12. Seasonal Streamflow Forecasts for African Basins

    NASA Astrophysics Data System (ADS)

    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.

  13. Long Lead-Time Forecasting of Snowpack and Precipitation in the Upper Snake River Basin using Pacific Oceanic-Atmospheric Variability

    NASA Astrophysics Data System (ADS)

    Anderson, S.; Tootle, G.; Parkinson, S.; Holbrook, P.; Blestrud, D.

    2012-12-01

    Water managers and planners in the western United States are challenged with managing resources for various uses, including hydropower. Hydropower is especially important throughout the Upper Snake River Basin, where a series of hydropower projects provide a low cost renewable energy source to the region. These hydropower projects include several dams that are managed by Idaho Power Company (IPC). Planners and managers rely heavily on forecasts of snowpack and precipitation to plan for hydropower availability and the need for other generation sources. There is a pressing need for improved snowpack and precipitation forecast models in the Upper Snake River Basin. This research investigates the ability of Pacific oceanic-atmospheric data and climatic variables to provide skillful long lead-time (three to nine months) forecasts of snowpack and precipitation, and examines the benefits of segregating the warm and cold phases of the Pacific Decadal Oscillation (PDO) to reduce the temperature variability within the target dataset. Singular value decomposition (SVD) was used to identify regions of Pacific Ocean sea surface temperatures (SST) and 500mbar geopotential heights (Z500) for various lead times (three, six, and nine months) that were teleconnected with snowpack and precipitation stations in Upper Snake River Basin headwaters. The identified Pacific Ocean SST and Z500 regions were used to create indices that became predictors in a non-parametric forecasting model. The majority of forecasts resulted in positive statistical skill, which indicated an improvement of the forecast over the climatology forecast (no-skill forecast). The results from the forecasts models indicated that derived indices from the SVD analysis resulted in improved forecast skill when compared to forecasts using established climate indices. Segregation of the cold phase PDO years resulted in the identification of different regions in the Pacific Ocean and vastly improved skill for the nine month

  14. Seasonal streamflow forecasting with the global hydrological forecasting system FEWS-World

    NASA Astrophysics Data System (ADS)

    Candogan Yossef, N.; Van Beek, L. P.; Winsemius, H.; Bierkens, M. F.

    2011-12-01

    The year-to-year variability of river discharge brings about risks and opportunities in water resources management. Reliable hydrological forecasts and effective communication allow several sectors to make more informed management decisions. In many developing regions of the world, there are no efficient hydrological forecasting systems. For these regions, a global forecasting system which indicates increased probabilities of streamflow excesses or shortages over long lead-times can be of great value. FEWS-World is developed for this purpose. The system incorporates the global hydrological model PCR-GLOBWB and delivers streamflow forecasts on a global scale. This study investigates the skill and value of FEWS-World. Skill is defined as the ability of the system to forecast discharge extremes; and value is its usefulness for possible users and ultimately for affected populations. Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from the European Center for Medium-Range Weather Forecasts (ECMWF). The results will be disseminated on the internet to provide valuable information for users in data and model-poor regions of the world. The preliminary skill assessment of PCR-GLOBWB in reproducing flow extremes is carried out for a selection of 20 large rivers of the world. The model is run for a historical period, with a meteorological forcing data set based on observations from the Climate Research Unit of the University of East Anglia, and the ERA-40 reanalysis of ECMWF. Model skill in reproducing monthly anomalies as well as floods and droughts is assessed by applying verification measures developed for deterministic meteorological forecasts. The results of this preliminary analysis shows that even where the simulated hydrographs are biased, higher skills can be attained in reproducing monthly

  15. Seasonal hydrological ensemble forecasts over Europe

    NASA Astrophysics Data System (ADS)

    Arnal, Louise; Wetterhall, Fredrik; Stephens, Elisabeth; Cloke, Hannah; Pappenberger, Florian

    2016-04-01

    This study investigates the limits of predictability in dynamical seasonal discharge forecasting, in both space and time, over Europe. Seasonal forecasts have an important socioeconomic value. Applications are numerous and cover hydropower management, spring flood prediction, low flow prediction for navigation and agricultural water demands. Additionally, the constant increase in NWP skill for longer lead times and the predicted increase in the intensity and frequency of hydro-meteorological extremes, have amplified the incentive to promote and further improve hydrological forecasts on sub-seasonal to seasonal timescales. In this study, seasonal hydrological forecasts (SEA), driven by the ECMWF's System 4 in hindcast mode, were analysed against an Ensemble Streamflow Prediction (ESP) benchmark. The ESP was forced with an ensemble of resampled historical meteorological observations and started with perfect initial conditions. Both forecasts were produced by the LISFLOOD model, run on the pan-European scale with a spatial resolution of 5 by 5 km. The forecasts were issued monthly on a daily time step, from 1990 until the current time, up to a lead time of 7 months. The seasonal discharge forecasts were analysed against the ESP on a catchment scale in terms of their accuracy, skill and sharpness, using a diverse set of verification metrics (e.g. KGE, CRPSS and ROC). Additionally, a reverse-ESP was constructed by forcing the LISFLOOD model with a single perfect meteorological set of observations and initiated from an ensemble of resampled historical initial conditions. The comparison of the ESP with the reverse-ESP approach enabled the identification of the respective contribution of meteorological forcings and hydrologic initial conditions errors to seasonal discharge forecasting uncertainties in Europe. These results could help pinpoint target elements of the forecasting chain which, after being improved, could lead to substantial increase in discharge predictability

  16. Analogue Downscaling of Seasonal Rainfall Forecasts

    NASA Astrophysics Data System (ADS)

    Charles, A. N.; Timbal, B.; Hendon, H.

    2010-12-01

    We have taken an existing statistical downscaling model (SDM), based on meteorological analogues that was developed for downscaling climate change projections (Timbal et al 2009), and applied it in the seasonal forecasting context to produce downscaled rainfall hindcasts from a coupled model seasonal forecast system (POAMA). Downscaling of POAMA forecasts is required to provide seasonal climate information at local scales of interest. Analogue downscaling is a simple technique to generate rainfall forecasts appropriate to the local scale by conditioning on the large scale predicted GCM circulation and the local topography and climate. Analogue methods are flexible and have been shown to produce good results when downscaling 20th century South Eastern Australian rainfall output from climate models. A set of re-forecasts for three month rainfall at 170 observing stations in the South Murray Darling region of Australia were generated using predictors from the POAMA re-forecasts as input for the analogue SDM. The predictors were optimised over a number of different GCMS in previous climate change downscaling studies. Downscaling with the analogue SDM results in predicted rainfall with realistic variance while maintaining the modest predictive skill of the dynamical model. Evaluation of the consistency between the large scale mean of downscaled and direct GCM output precipitation is encouraging.

  17. Operational seasonal forecasting of crop performance

    PubMed Central

    Stone, Roger C; Meinke, Holger

    2005-01-01

    Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production. PMID:16433097

  18. Operational seasonal forecasting of crop performance.

    PubMed

    Stone, Roger C; Meinke, Holger

    2005-11-29

    Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.

  19. Seasonal Water Balance Forecasts for Drought Early Warning in Ethiopia

    NASA Astrophysics Data System (ADS)

    Spirig, Christoph; Bhend, Jonas; Liniger, Mark

    2016-04-01

    Droughts severely impact Ethiopian agricultural production. Successful early warning for drought conditions in the upcoming harvest season therefore contributes to better managing food shortages arising from adverse climatic conditions. So far, however, meteorological seasonal forecasts have not been used in Ethiopia's national food security early warning system (i.e. the LEAP platform). Here we analyse the forecast quality of seasonal forecasts of total rainfall and of the meteorological water balance as a proxy for plant available water. We analyse forecast skill of June to September rainfall and water balance from dynamical seasonal forecast systems, the ECMWF System4 and EC-EARTH global forecasting systems. Rainfall forecasts outperform forecasts assuming a stationary climate mainly in north-eastern Ethiopia - an area that is particularly vulnerable to droughts. Forecasts of the water balance index seem to be even more skilful and thus more useful than pure rainfall forecasts. The results vary though for different lead times and skill measures employed. We further explore the potential added value of dynamically downscaling the forecasts through several dynamical regional climate models made available through the EU FP7 project EUPORIAS. Preliminary results suggest that dynamically downscaled seasonal forecasts are not significantly better compared with seasonal forecasts from the global models. We conclude that seasonal forecasts of a simple climate index such as the water balance have the potential to benefit drought early warning in Ethiopia, both due to its positive predictive skill and higher usefulness than seasonal mean quantities.

  20. Increased Accuracy in Statistical Seasonal Hurricane Forecasting

    NASA Astrophysics Data System (ADS)

    Nateghi, R.; Quiring, S. M.; Guikema, S. D.

    2012-12-01

    Hurricanes are among the costliest and most destructive natural hazards in the U.S. Accurate hurricane forecasts are crucial to optimal preparedness and mitigation decisions in the U.S. where 50 percent of the population lives within 50 miles of the coast. We developed a flexible statistical approach to forecast annual number of hurricanes in the Atlantic region during the hurricane season. Our model is based on the method of Random Forest and captures the complex relationship between hurricane activity and climatic conditions through careful variable selection, model testing and validation. We used the National Hurricane Center's Best Track hurricane data from 1949-2011 and sixty-one candidate climate descriptors to develop our model. The model includes information prior to the hurricane season, i.e., from the last three months of the previous year (Oct. through Dec.) and the first five months of the current year (January through May). Our forecast errors are substantially lower than other leading forecasts such as that of the National Oceanic and Atmospheric Administration (NOAA).

  1. Seasonal forecasting of fire over Kalimantan, Indonesia

    NASA Astrophysics Data System (ADS)

    Spessa, A. C.; Field, R. D.; Pappenberger, F.; Langner, A.; Englhart, S.; Weber, U.; Stockdale, T.; Siegert, F.; Kaiser, J. W.; Moore, J.

    2015-03-01

    Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, biodiversity, health, and economic activities. In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean-atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire). The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70% of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire management policy.

  2. Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe

    NASA Astrophysics Data System (ADS)

    Orth, René; Seneviratne, Sonia I.

    2014-12-01

    Soil moisture exhibits outstanding memory characteristics and plays a key role within the climate system. Especially through its impacts on the evapotranspiration of soils and plants, it may influence the land energy balance and therefore surface temperature. These attributes make soil moisture an important variable in the context of weather and climate forecasting. In this study we investigate the value of (initial) soil moisture information for sub-seasonal temperature forecasts. For this purpose we employ a simple water balance model to infer soil moisture from streamflow observations in 400 catchments across Europe. Running this model with forecasted atmospheric forcing, we derive soil moisture forecasts, which we then translate into temperature forecasts using simple linear relationships. The resulting temperature forecasts show skill beyond climatology up to 2 weeks in most of the considered catchments. Even if forecasting skills are rather small at longer lead times with significant skill only in some catchments at lead times of 3 and 4 weeks, this soil moisture-based approach shows local improvements compared to the monthly European Centre for Medium Range Weather Forecasting (ECMWF) temperature forecasts at these lead times. For both products (soil moisture-only forecast and ECMWF forecast), we find comparable or better forecast performance in the case of extreme events, especially at long lead times. Even though a product based on soil moisture information alone is not of practical relevance, our results indicate that soil moisture (memory) is a potentially valuable contributor to temperature forecast skill. Investigating the underlying soil moisture of the ECMWF forecasts we find good agreement with the simple model forecasts, especially at longer lead times. Analyzing the drivers of the temperature forecast skills we find that they are mainly controlled by the strengths of (1) the soil moisture-temperature coupling and (2) the soil moisture memory. We

  3. Improving Groundwater Predictions using Seasonal Precipitation Forecasts

    NASA Astrophysics Data System (ADS)

    Almanaseer, N.; Arumugam, S.; Bales, J. D.

    2011-12-01

    This research aims to evaluate the utility of precipitation forecasts in improving groundwater and streamflow predictions at seasonal and monthly time scales using statistical modeling techniques. For this purpose, we select ten groundwater wells from the Groundwater Climate Response Network (GCRN) and nine streamgauges from the Hydro-Climatic Data Network (HCDN) to represent groundwater and surface water variability with minimal anthropogenic influences over Flint River Basin (FRB) in Georgia, U.S. Preliminary analysis shows significant correlation between precipitation forecasts over FRB with observed precipitation (P), streamflow discharges (Q) and depth to groundwater (G). Three statistical models are developed using principle component regression (PCR) and canonical correlation analysis (CCA) with leave-5-out cross-validation to predict winter (JFM) and spring (AMJ) as well as monthly (Jan through Jun) groundwater and streamflow for the selected sites. The three models starts at the end of Dec and uses Oct, Nov and Dec (OND) observed records to predict 2-seasons and 6-months ahead. Model-1 is the "null model" that does not include precipitation forecasts as predictors. It is developed using PCR to predict seasonal and monthly Q and G independently based on previous (Oct. Nov. and Dec; OND) observations of Q or G at a given site without using climate information. Model predictands are JFM, AMJ for seasonal and Jan. through Jun for monthly. Model-2 is also developed using PCR, but it uses the issued at January precipitation forecasts from nine ECHAM 4.5 grid points as additional predictors. Model-3 is developed using CCA and it aims to integrate additional information on the predictands (i.e., groundwater) from adjacent basins to improve the prediction. Model-3 is designed to evaluate the role of climate versus the role groundwater and surface water flows in the selected basins. Finally, comparisons between the three models for each site and across the sites

  4. Consensus Seasonal Flood Forecasts and Warning Response System (FFWRS): an alternate for nonstructural flood management in Bangladesh.

    PubMed

    Chowdhury, Rashed

    2005-06-01

    Despite advances in short-range flood forecasting and information dissemination systems in Bangladesh, the present system is less than satisfactory. This is because of short lead-time products, outdated dissemination networks, and lack of direct feedback from the end-user. One viable solution is to produce long-lead seasonal forecasts--the demand for which is significantly increasing in Bangladesh--and disseminate these products through the appropriate channels. As observed in other regions, the success of seasonal forecasts, in contrast to short-term forecast, depends on consensus among the participating institutions. The Flood Forecasting and Warning Response System (henceforth, FFWRS) has been found to be an important component in a comprehensive and participatory approach to seasonal flood management. A general consensus in producing seasonal forecasts can thus be achieved by enhancing the existing FFWRS. Therefore, the primary objective of this paper is to revisit and modify the framework of an ideal warning response system for issuance of consensus seasonal flood forecasts in Bangladesh. The five-stage FFWRS-i) Flood forecasting, ii) Forecast interpretation and message formulation, iii) Warning preparation and dissemination, iv) Responses, and v) Review and analysis-has been modified. To apply the concept of consensus forecast, a framework similar to that of the Southern African Regional Climate Outlook Forum (SARCOF) has been discussed. Finally, the need for a climate Outlook Fora has been emphasized for a comprehensive and participatory approach to seasonal flood hazard management in Bangladesh.

  5. Potential for long-lead prediction of the western North Pacific monsoon circulation beyond seasonal time scales

    NASA Astrophysics Data System (ADS)

    Choi, Jung; Son, Seok-Woo; Seo, Kyong-Hwan; Lee, June-Yi; Kang, Hyun-Suk

    2016-02-01

    Although the western North Pacific (WNP) monsoon circulation significantly impacts the socioeconomic communities around Asia, its prediction is only limited to a few months. By examining the Coupled Model Intercomparison Project phase 5 decadal hindcast experiments, we explore a possibility of the extended prediction skill for the WNP monsoon circulation beyond seasonal time scales. It is found that the multimodel ensemble (MME) predictions, initialized in January, successfully predict the WNP circulation in spring and early summer. Somewhat surprisingly, a reliable prediction of the WNP circulation appears even in the following spring with a maximum lead time of 14 months. This unexpected prediction skill is likely caused by the improved El Niño-Southern Oscillation (ENSO) prediction and the exaggerated dynamical link between the ENSO and premonsoon circulation in the MME prediction. Although further studies are needed, this result may open up new opportunities for the multiseasonal prediction of the WNP monsoon circulation.

  6. An experimental seasonal hydrological forecasting system over the Yellow River basin - Part 2: The added value from climate forecast models

    NASA Astrophysics Data System (ADS)

    Yuan, Xing

    2016-06-01

    This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982-2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08-0.2. To compare with the observed

  7. Seasonal UK Drought Forecasting using Statistical Methods

    NASA Astrophysics Data System (ADS)

    Richardson, Doug; Fowler, Hayley; Kilsby, Chris; Serinaldi, Francesco

    2016-04-01

    In the UK drought is a recurrent feature of climate with potentially large impacts on public water supply. Water companies' ability to mitigate the impacts of drought by managing diminishing availability depends on forward planning and it would be extremely valuable to improve forecasts of drought on monthly to seasonal time scales. By focusing on statistical forecasting methods, this research aims to provide techniques that are simpler, faster and computationally cheaper than physically based models. In general, statistical forecasting is done by relating the variable of interest (some hydro-meteorological variable such as rainfall or streamflow, or a drought index) to one or more predictors via some formal dependence. These predictors are generally antecedent values of the response variable or external factors such as teleconnections. A candidate model is Generalised Additive Models for Location, Scale and Shape parameters (GAMLSS). GAMLSS is a very flexible class allowing for more general distribution functions (e.g. highly skewed and/or kurtotic distributions) and the modelling of not just the location parameter but also the scale and shape parameters. Additionally GAMLSS permits the forecasting of an entire distribution, allowing the output to be assessed in probabilistic terms rather than simply the mean and confidence intervals. Exploratory analysis of the relationship between long-memory processes (e.g. large-scale atmospheric circulation patterns, sea surface temperatures and soil moisture content) and drought should result in the identification of suitable predictors to be included in the forecasting model, and further our understanding of the drivers of UK drought.

  8. Unorganized machines for seasonal streamflow series forecasting.

    PubMed

    Siqueira, Hugo; Boccato, Levy; Attux, Romis; Lyra, Christiano

    2014-05-01

    Modern unorganized machines--extreme learning machines and echo state networks--provide an elegant balance between processing capability and mathematical simplicity, circumventing the difficulties associated with the conventional training approaches of feedforward/recurrent neural networks (FNNs/RNNs). This work performs a detailed investigation of the applicability of unorganized architectures to the problem of seasonal streamflow series forecasting, considering scenarios associated with four Brazilian hydroelectric plants and four distinct prediction horizons. Experimental results indicate the pertinence of these models to the focused task.

  9. Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach

    NASA Astrophysics Data System (ADS)

    Zhao, Tongtiegang; Schepen, Andrew; Wang, Q. J.

    2016-10-01

    The Bayesian joint probability (BJP) modelling approach is used operationally to produce seasonal (three-month-total) ensemble streamflow forecasts in Australia. However, water resource managers are calling for more informative sub-seasonal forecasts. Taking advantage of BJP's capability of handling multiple predictands, ensemble forecasting of sub-seasonal to seasonal streamflows is investigated for 23 catchments around Australia. Using antecedent streamflow and climate indices as predictors, monthly forecasts are developed for the three-month period ahead. Forecast reliability and skill are evaluated for the period 1982-2011 using a rigorous leave-five-years-out cross validation strategy. BJP ensemble forecasts of monthly streamflow volumes are generally reliable in ensemble spread. Forecast skill, relative to climatology, is positive in 74% of cases in the first month, decreasing to 57% and 46% respectively for streamflow forecasts for the final two months of the season. As forecast skill diminishes with increasing lead time, the monthly forecasts approach climatology. Seasonal forecasts accumulated from monthly forecasts are found to be similarly skilful to forecasts from BJP models based on seasonal totals directly. The BJP modelling approach is demonstrated to be a viable option for producing ensemble time-series sub-seasonal to seasonal streamflow forecasts.

  10. Seasonal forecast of St. Louis encephalitis virus transmission, Florida.

    PubMed

    Shaman, Jeffrey; Day, Jonathan F; Stieglitz, Marc; Zebiak, Stephen; Cane, Mark

    2004-05-01

    Disease transmission forecasts can help minimize human and domestic animal health risks by indicating where disease control and prevention efforts should be focused. For disease systems in which weather-related variables affect pathogen proliferation, dispersal, or transmission, the potential for disease forecasting exists. We present a seasonal forecast of St. Louis encephalitis virus transmission in Indian River County, Florida. We derive an empiric relationship between modeled land surface wetness and levels of SLEV transmission in humans. We then use these data to forecast SLEV transmission with a seasonal lead. Forecast skill is demonstrated, and a real-time seasonal forecast of epidemic SLEV transmission is presented. This study demonstrates how weather and climate forecast skill-verification analyses may be applied to test the predictability of an empiric disease forecast model.

  11. Sources of seasonal water-supply forecast skill in the western US

    USGS Publications Warehouse

    Dettinger, Michael

    2007-01-01

    Many water supplies in the western US depend on water that is stored in snowpacks and reservoirs during the cool, wet seasons for release and use in the following warm seasons. Managers of these water supplies must decide each winter how much water will be available in subsequent seasons so that they can proactively capture and store water and can make reliable commitments for later deliveries. Long-lead water-supply forecasts are thus important components of water managers' decisionmaking. Present-day operational water-supply forecasts draw skill from observations of the amount of water in upland snowpacks, along with estimates of the amount of water otherwise available (often via surrogates for antecedent precipitation, soil moisture or baseflows). Occasionally, the historical hydroclimatic influences of various global climate conditions may be factored in to forecasts. The relative contributions of (potential) forecast skill for January-March and April-July seasonal water- supply availability from these sources are mapped across the western US as lag correlations among elements of the inputs and outputs from a physically based, regional land-surface hydrology model of the western US from 1950-1999. Information about snow-water contents is the most valuable predictor for forecasts made through much of the cool-season but, before the snows begin to fall, indices of El Nino-Southern Oscillation are the primary source of whatever meager skill is available. The contributions to forecast skill made available by knowledge of antecedent flows (a traditional predictor) and soil moisture at the time the long-lead forecast is issued are compared, to gain insights into the potential usefulness of new soil-moisture monitoring options in the region. When similar computations are applied to simulated flows under historical conditions, but with a uniform +2°C warming imposed, the widespread diminution of snowpacks reduces forecast skills, although skill contributed by measures

  12. Skillful seasonal forecasts of Arctic sea ice retreat and advance dates in a dynamical forecast system

    NASA Astrophysics Data System (ADS)

    Sigmond, M.; Reader, M. C.; Flato, G. M.; Merryfield, W. J.; Tivy, A.

    2016-12-01

    The need for skillful seasonal forecasts of Arctic sea ice is rapidly increasing. Technology to perform such forecasts with coupled atmosphere-ocean-sea ice systems has only recently become available, with previous skill evaluations mainly limited to area-integrated quantities. Here we show, based on a large set of retrospective ensemble model forecasts, that a dynamical forecast system produces skillful seasonal forecasts of local sea ice retreat and advance dates - variables that are of great interest to a wide range of end users. Advance dates can generally be skillfully predicted at longer lead times ( 5 months on average) than retreat dates ( 3 months). The skill of retreat date forecasts mainly stems from persistence of initial sea ice anomalies, whereas advance date forecasts benefit from longer time scale and more predictable variability in ocean temperatures. These results suggest that further investments in the development of dynamical seasonal forecast systems may result in significant socioeconomic benefits.

  13. Forecasting fluctuating outbreaks in seasonally driven epidemics

    NASA Astrophysics Data System (ADS)

    Stone, Lewi

    2009-03-01

    Seasonality is a driving force that has major impact on the spatio-temporal dynamics of natural systems and their populations. This is especially true for the transmission of common infectious diseases such as influenza, measles, chickenpox, and pertussis. Here we gain new insights into the nonlinear dynamics of recurrent diseases through the analysis of the classical seasonally forced SIR epidemic model. Despite many efforts over the last decades, it has been difficult to gain general analytical insights because of the complex synchronization effects that can evolve between the external forcing and the model's natural oscillations. The analysis advanced here attempts to make progress in this direction by focusing on the dynamics of ``skips'' where we identify and predict years in which the epidemic is absent rather than outbreak years. Skipping events are intrinsic to the forced SIR model when parameterised in the chaotic regime. In fact, it is difficult if not impossible to locate realistic chaotic parameter regimes in which outbreaks occur regularly each year. This contrasts with the well known Rossler oscillator whose outbreaks recur regularly but whose amplitude vary chaotically in time (Uniform Phase Chaotic Amplitude oscillations). The goal of the present study is to develop a ``language of skips'' that makes it possible to predict under what conditions the next outbreak is likely to occur, and how many ``skips'' might be expected after any given outbreak. We identify a new threshold effect and give clear analytical conditions that allow accurate predictions. Moreover, the time of occurrence (i.e., phase) of an outbreak proves to be a useful new parameter that carries important epidemiological information. In forced systems, seasonal changes can prevent late-initiating outbreaks (i.e., having high phase) from running to completion. These principles yield forecasting tools that should have relevance for the study of newly emerging and reemerging diseases.

  14. Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian

    2016-09-01

    Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping

  15. Seasonal streamflow forecasting by conditioning climatology with precipitation indices

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian; Perrin, Charles

    2017-03-01

    Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

  16. Validation of Seasonal Forecast of Indian Summer Monsoon Rainfall

    NASA Astrophysics Data System (ADS)

    Das, Sukanta Kumar; Deb, Sanjib Kumar; Kishtawal, C. M.; Pal, Pradip Kumar

    2015-06-01

    The experimental seasonal forecast of Indian summer monsoon (ISM) rainfall during June through September using Community Atmosphere Model (CAM) version 3 has been carried out at the Space Applications Centre Ahmedabad since 2009. The forecasts, based on a number of ensemble members (ten minimum) of CAM, are generated in several phases and updated on regular basis. On completion of 5 years of experimental seasonal forecasts in operational mode, it is required that the overall validation or correctness of the forecast system is quantified and that the scope is assessed for further improvements of the forecast over time, if any. The ensemble model climatology generated by a set of 20 identical CAM simulations is considered as the model control simulation. The performance of the forecast has been evaluated by assuming the control simulation as the model reference. The forecast improvement factor shows positive improvements, with higher values for the recent forecasted years as compared to the control experiment over the Indian landmass. The Taylor diagram representation of the Pearson correlation coefficient (PCC), standard deviation and centered root mean square difference has been used to demonstrate the best PCC, in the order of 0.74-0.79, recorded for the seasonal forecast made during 2013. Further, the bias score of different phases of experiment revealed the fact that the ISM rainfall forecast is affected by overestimation in predicting the low rain-rate (less than 7 mm/day), but by underestimation in the medium and high rain-rate (higher than 11 mm/day). Overall, the analysis shows significant improvement of the ISM forecast over the last 5 years, viz. 2009-2013, due to several important modifications that have been implemented in the forecast system. The validation exercise has also pointed out a number of shortcomings in the forecast system; these will be addressed in the upcoming years of experiments to improve the quality of the ISM prediction.

  17. Seasonal Drought Predictability and Forecast Skill over China

    NASA Astrophysics Data System (ADS)

    Ma, Feng; Yuan, Xing

    2015-04-01

    With global warming and land use changes, seasonal drought events occur more intensely and frequently. However, the understanding of drought mechanism and predictability is limited. Based on the hindcasts from multiple ocean-atmosphere coupled general circulations models (CGCMs) participating in the North American Multi-Model Ensemble (NMME) project, this paper investigates the potential predictability and forecast skill of seasonal droughts over China. A 3-month standardized precipitation index (SPI3) is used as the drought index, and the anomaly correlation (AC) of the ensemble mean of an individual model against the observed SPI3 values are used as measures of forecast skill. Predictability is calculated by verifying a single member against its ensemble mean, i.e., how well does the model predict itself. Preliminary results indicate that drought predictability is higher than forecast skill, suggesting there is room for improving the drought forecast. There are positive correlations between potential predictability and actual forecast skill, i.e., the models with higher potential predictability also have higher forecast skill. Such relationship holds for different seasons too. For the ENSO-affected river basins in China, both the drought predictability and forecast skill are higher in ENSO years than neutral years, which is consistent with the predictability and forecast skill of ENSO SST.

  18. Toward Seasonal Forecasting of Global Droughts: Evaluation over USA and Africa

    NASA Astrophysics Data System (ADS)

    Wood, Eric; Yuan, Xing; Roundy, Joshua; Sheffield, Justin; Pan, Ming

    2013-04-01

    in different seasons for different basins. The R2 of drought severity accumulated over USA is higher during winter, and climate models present added value especially at long leads. For countries with sparse networks and weak reporting systems, remote sensing observations can provide the realtime data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for estimating a climatology against which current conditions can be compared. Based on our established experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML), we use the downscaled CFSv2 climate forcings to drive the re-calibrated VIC model and produce 6-month, 20-member ensemble hydrologic forecasts over Africa starting on the 1st of each calendar month during 1982-2007. Our CHM-based seasonal hydrologic forecasts are now being analyzed for its skill in predicting short-term soil moisture droughts over Africa. Besides relying on a single seasonal climate model or a single drought index, preliminary forecast results will be presented using multiple seasonal climate models based on the NOAA-supported National Multi-Model Ensemble (NMME) project, and with multiple drought indices. Results will be presented for the USA NIDIS test beds such as Southeast US and Colorado NIDIS (National Integrated Drought Information System) test beds, and potentially for other regions of the globe.

  19. Seasonal forecasts of drought indices in African basins

    NASA Astrophysics Data System (ADS)

    Dutra, E.; Di Giuseppe, F.; Wetterhall, F.; Pappenberger, F.

    2012-09-01

    Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas which often have a very low resilience and limited capabilities to mitigate their effects. This paper tries to assess the predictive capabilities of an integrated drought monitoring and forecasting system based on the Standard precipitation index (SPI). The system is firstly constructed by temporally extending near real-time precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System-Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with forecasted fields as provided by the ECMWF seasonal forecasting system and then is evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. All the datasets show similar patterns in the South and North West Africa, while there is a low correlation in the tropical region which makes it difficult to define ground truth and choose an adequate product for monitoring. The Seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depends strongly on the SPI time-scale, and more skill is observed at larger time-scales. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in two to four months lead time.

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

    NASA Astrophysics Data System (ADS)

    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

  1. Application and verification of ECMWF seasonal forecast for wind energy

    NASA Astrophysics Data System (ADS)

    Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line

    2015-04-01

    A good understanding of long-term annual energy production (AEP) is crucial when assessing the business case of investing in green energy like wind power. The art of wind-resource assessment has emerged into a scientific discipline on its own, which has advanced at high pace over the last decade. This has resulted in continuous improvement of the AEP accuracy and, therefore, increase in business case certainty. Harvesting the full potential output of a wind farm or a portfolio of wind farms depends heavily on optimizing operation and management strategy. The necessary information for short-term planning (up to 14 days) is provided by standard weather and power forecasting services, and the long-term plans are based on climatology. However, the wind-power industry is lacking quality information on intermediate scales of the expected variability in seasonal and intra-annual variations and their geographical distribution. The seasonal power forecast presented here is designed to bridge this gap. The seasonal power production forecast is based on the ECMWF seasonal weather forecast and the Vestas' high-resolution, mesoscale weather library. The seasonal weather forecast is enriched through a layer of statistical post-processing added to relate large-scale wind speed anomalies to mesoscale climatology. The resulting predicted energy production anomalies, thus, include mesoscale effects not captured by the global forecasting systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power forecast. In theory, the wind power is proportional to the cube of wind speed. However, due to the nature of turbine design, this exponent is close to 3 only at low wind speeds, becomes smaller as the wind speed increases, and above 11-13 m/s the power output remains constant, called the rated power. The non-linear relationship between wind speed and the power output generally increases sensitivity of the forecasted power

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

    NASA Technical Reports Server (NTRS)

    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

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

    NASA Technical Reports Server (NTRS)

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

    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.

  4. Application of fuzzy logic to forecast seasonal runoff

    NASA Astrophysics Data System (ADS)

    Mahabir, C.; Hicks, F. E.; Robinson Fayek, A.

    2003-12-01

    Each spring in Alberta, Canada, the potential snowmelt runoff is forecast for several basins to assess the water supply situation. Water managers need this forecast to plan water allocations for the following summer season. The Lodge Creek and Middle Creek basins, located in southeastern Alberta, are two basins that require this type of late winter forecast of potential spring runoff. Historically, the forecast has been based upon a combination of regression equations. These results are then interpreted by a forecaster and are modified based on the forecaster's heuristic knowledge of the basin. Unfortunately, this approach has had limited success in the past, in terms of the accuracy of these forecasts, and consequently an alternative methodology is needed.In this study, the applicability of fuzzy logic modelling techniques for forecasting water supply was investigated. Fuzzy logic has been applied successfully in several fields where the relationship between cause and effect (variable and results) are vague. Fuzzy variables were used to organize knowledge that is expressed linguistically into a formal analysis. For example, high snowpack, average snowpack and low snowpack became variables. By applying fuzzy logic, a water supply forecast was created that classified potential runoff into three forecast zones: low, average and high. Spring runoff forecasts from the fuzzy expert systems were found to be considerably more reliable than the regression models in forecasting the appropriate runoff zone, especially in terms of identifying low or average runoff years. Based on the modelling results in these two basins, it is concluded that fuzzy logic has a promising potential for providing reliable water supply forecasts. Copyright

  5. Seasonal forecasts of drought indices in African basins

    NASA Astrophysics Data System (ADS)

    Dutra, Emanuel; Di Giuseppe, Francesca; Wetterhall, Fredrik; Pappenberge, Florian

    2013-04-01

    Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas which often have a very low resilience and limited capabilities to mitigate drought impacts. In this work we assess the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near real-time monthly precipitation fields (ERA-Interim reanalysis and the Climate Anomaly Monitoring System-Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system. This new seamless approach to merge monitoring and forecasting fields of precipitation to generate SPI at different time-scales is evaluated within the framework of the EU project DEWFORA. In particular, the evaluation was preformed over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. All the datasets show similar spatial and temporal patterns in the South and North West Africa, while there is a low correlation in the equatorial area which makes it difficult to define ground truth and choose an adequate product for monitoring. The Seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depends strongly on the SPI time-scale, and more skill is observed at longer time-scales. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near real-time data is available, the seasonal forecast can be used for monitoring (first

  6. Linking seasonal climate forecasts with crop models in Iberian Peninsula

    NASA Astrophysics Data System (ADS)

    Capa, Mirian; Ines, Amor; Baethgen, Walter; Rodriguez-Fonseca, Belen; Han, Eunjin; Ruiz-Ramos, Margarita

    2015-04-01

    Translating seasonal climate forecasts into agricultural production forecasts could help to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. In this study, we use seasonal rainfall forecasts and crop models to improve predictability of wheat yield in the Iberian Peninsula (IP). Additionally, we estimate economic margins and production risks associated with extreme scenarios of seasonal rainfall forecast. This study evaluates two methods for disaggregating seasonal climate forecasts into daily weather data: 1) a stochastic weather generator (CondWG), and 2) a forecast tercile resampler (FResampler). Both methods were used to generate 100 (with FResampler) and 110 (with CondWG) weather series/sequences for three scenarios of seasonal rainfall forecasts. Simulated wheat yield is computed with the crop model CERES-wheat (Ritchie and Otter, 1985), which is included in Decision Support System for Agrotechnology Transfer (DSSAT v.4.5, Hoogenboom et al., 2010). Simulations were run at two locations in northeastern Spain where the crop model was calibrated and validated with independent field data. Once simulated yields were obtained, an assessment of farmer's gross margin for different seasonal climate forecasts was accomplished to estimate production risks under different climate scenarios. This methodology allows farmers to assess the benefits and risks of a seasonal weather forecast in IP prior to the crop growing season. The results of this study may have important implications on both, public (agricultural planning) and private (decision support to farmers, insurance companies) sectors. Acknowledgements Research by M. Capa-Morocho has been partly supported by a PICATA predoctoral fellowship of the Moncloa Campus of International Excellence (UCM-UPM) and MULCLIVAR project (CGL2012-38923-C02-02) References Hoogenboom, G. et al., 2010. The Decision

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

    NASA Astrophysics Data System (ADS)

    Winsemius, H. C.; Dutra, E.; Engelbrecht, F. A.; Archer Van Garderen, E.; Wetterhall, F.; Pappenberger, F.; Werner, M. G. F.

    2013-12-01

    Subsistence farming in Southern Africa is vulnerable to extreme weather conditions. The yield of rain-fed agriculture depends largely on rainfall-related factors such as total seasonal rainfall, anomalous onsets and lengths of the rainy season and the frequency of occurrence of dry spells. Livestock, in turn, may be seriously impacted by climatic stress with, for example, exceptionally hot days, affecting condition, reproduction, vulnerability to pests and pathogens and, ultimately, morbidity 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 measure comprises the timely forecasting and warning of such extreme events, combined with mitigation measures that allow farmers to prepare for the event occurring. This paper investigates how the frequency of extreme events may change in the future due to climate change over southern Africa and, in more detail, the Limpopo basin using a set of climate change projections from several regional climate model downscalings. Furthermore the paper assesses the predictability of these indicators by seasonal meteorological forecasts of the European Centre for Medium-range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the Temperature Heat Index. In areas where their frequency of occurrence increases in the future and predictability is found, seasonal forecasts will gain importance in the future as they can more often lead to informed decision making to implement mitigation measures. The multi-model climate projections suggest that the frequency of dry spells is not likely to increase substantially, whereas there is a clear and coherent signal among the models, of an increase in the frequency of heat stress conditions by the end of the century. The skill analysis of the seasonal forecast

  8. Towards reliable seasonal ensemble streamflow forecasts for ephemeral rivers

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

    Despite their inherently variable nature, ephemeral rivers are an important water resource in many dry regions. Water managers are likely benefit considerably from even mildly skilful ensemble forecasts of streamflow in ephemeral rivers. As with any ensemble forecast, forecast uncertainty - i.e., the spread of the ensemble - must be reliably quantified to allow users of the forecasts to make well-founded decisions. Correctly quantifying uncertainty in ephemeral rivers is particularly challenging because of the high incidence of zero flows, which are difficult to handle with conventional statistical techniques. Here we apply a seasonal streamflow forecasting system, the model for generating Forecast Guided Stochastic Scenarios (FoGSS), to 26 Australian ephemeral rivers. FoGSS uses post-processed ensemble rainfall forecasts from a coupled ocean-atmosphere prediction system to force an initialised monthly rainfall runoff model, and then applies a staged hydrological error model to describe and propagate hydrological uncertainty in the forecast. FoGSS produces 12-month streamflow forecasts; as forecast skill declines with lead time, the forecasts are designed to transit seamlessly to stochastic scenarios. The ensemble rainfall forecasts used in FoGSS are known to be unbiased and reliable, and we concentrate here on the hydrological error model. The FoGSS error model has several features that make it well suited to forecasting ephemeral rivers. First, FoGSS models the error after data is transformed with a log-sinh transformation. The log-sinh transformation is able to normalise even highly skewed data and homogenise its variance, allowing us to assume that errors are Gaussian. Second, FoGSS handles zero values using data censoring. Data censoring allows streamflow in ephemeral rivers to be treated as a continuous variable, rather than having to model the occurrence of non-zero values and the distribution of non-zero values separately. This greatly simplifies parameter

  9. Using NMME in Region-Specific Operational Seasonal Climate Forecasts

    NASA Astrophysics Data System (ADS)

    Gronewold, A.; Bolinger, R. A.; Fry, L. M.; Kompoltowicz, K.

    2015-12-01

    The National Oceanic and Atmospheric Administration's Climate Prediction Center (NOAA/CPC) provides access to a suite of real-time monthly climate forecasts that comprise the North American Multi-Model Ensemble (NMME) in an attempt to meet increasing demands for monthly to seasonal climate prediction. While the graphical map forecasts of the NMME are informative, there is a need to provide decision-makers with probabilistic forecasts specific to their region of interest. Here, we demonstrate the potential application of the NMME to address regional climate projection needs by developing new forecasts of temperature and precipitation for the North American Great Lakes, the largest system of lakes on Earth. Regional opertional water budget forecasts rely on these outlooks to initiate monthly forecasts not only of the water budget, but of monthly lake water levels as well. More specifically, we present an alternative for improving existing operational protocols that currently involve a relatively time-consuming and subjective procedure based on interpreting the maps of the NMME. In addition, all forecasts are currently presented in the NMME in a probabilistic format, with equal weighting given to each member of the ensemble. In our new evolution of this product, we provide historical context for the forecasts by superimposing them (in an on-line graphical user interface) with the historical range of observations. Implementation of this new tool has already led to noticeable advantages in regional water budget forecasting, and has the potential to be transferred to other regional decision-making authorities as well.

  10. Sources of Errors in Developing Monthly to Seasonal Nutrient Forecasts

    NASA Astrophysics Data System (ADS)

    Libera, D.; Arumugam, S.

    2014-12-01

    Excess nitrogen in a river system can cause an overabundance of aquatic plant growth that can cause negative effects on larger water bodies downstream. This can result in eutrophication resulting in large algae blooms that hurt local recreation and fish populations. Recent studies have focused on developing seasonal nutrient forecasts that can be used to control nonpoint reduction strategies. Given that the seasonal nutrients are developed using large-scale climate forecasts, it needs to be pre-processed for ingesting into a water quality model. By considering the LOADEST model, a USGS constituent load estimator, and the Soil &Water Assessment Tool (SWAT) this study quantifies the sources of errors in developing monthly to seasonal nutrient forecasts using climate information. For this purpose, we consider the observed streamflow and nutrient loadings at the Tar River at Tarboro, NC station for developing and testing the water quality models. This streamgage was chosen since it is part of the Hydro-Climatic Data Network (HCDN) which naturally considers basins that are relatively undeveloped with limited storage and pumping. The study also proposes two bias-correction procedures, a bivariate copula-based model and a canonical correlation model, for preserving the cross-correlation structure between the observed nutrients and streamflows. Climate forecasts from the ECHAM4.5 model and NOAA NCEP Climate Forecast System (CFS) will be downscaled and disaggregated for developing nutrient forecasts from the SWAT model and the LOADEST model. Using both the canonical correlation model and the bi-variate copula based bias-correction procedures, the forecasted streamflow and TN loadings will be bias-corrected to preserve the correlation structure. The study will also quantify and compare different sources of errors that propagate in developing monthly to seasonal nutrient forecasts using climate information.

  11. Forecasting the 2013–2014 influenza season using Wikipedia

    DOE PAGES

    Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; ...

    2015-05-14

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are appliedmore » to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.« less

  12. Forecasting the 2013–2014 Influenza Season Using Wikipedia

    PubMed Central

    Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.

    2015-01-01

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed. PMID:25974758

  13. Forecasting the 2013–2014 influenza season using Wikipedia

    SciTech Connect

    Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.; Salathé, Marcel

    2015-05-14

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.

  14. Forecasting the 2013-2014 influenza season using Wikipedia.

    PubMed

    Hickmann, Kyle S; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M; Deshpande, Alina; Del Valle, Sara Y

    2015-05-01

    Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.

  15. The Impact of Soil Moisture Initialization On Seasonal Precipitation Forecasts

    NASA Technical Reports Server (NTRS)

    Koster, R. D.; Suarez, M. J.; Tyahla, L.; Houser, Paul (Technical Monitor)

    2002-01-01

    Some studies suggest that the proper initialization of soil moisture in a forecasting model may contribute significantly to the accurate prediction of seasonal precipitation, especially over mid-latitude continents. In order for the initialization to have any impact at all, however, two conditions must be satisfied: (1) the initial soil moisture anomaly must be "remembered" into the forecasted season, and (2) the atmosphere must respond in a predictable way to the soil moisture anomaly. In our previous studies, we identified the key land surface and atmospheric properties needed to satisfy each condition. Here, we tie these studies together with an analysis of an ensemble of seasonal forecasts. Initial soil moisture conditions for the forecasts are established by forcing the land surface model with realistic precipitation prior to the start of the forecast period. As expected, the impacts on forecasted precipitation (relative to an ensemble of runs that do not utilize soil moisture information) tend to be localized over the small fraction of the earth with all of the required land and atmosphere properties.

  16. Extra-tropical Cyclones and Windstorms in Seasonal Forecasts

    NASA Astrophysics Data System (ADS)

    Leckebusch, Gregor C.; Befort, Daniel J.; Weisheimer, Antje; Knight, Jeff; Thornton, Hazel; Roberts, Julia; Hermanson, Leon

    2015-04-01

    Severe damages and large insured losses over Europe related to natural phenomena are mostly caused by extra-tropical cyclones and their related windstorm fields. Thus, an adequate representation of these events in seasonal prediction systems and reliable forecasts up to a season in advance would be of high value for society and economy. In this study, state-of-the-art seasonal forecast prediction systems are analysed (ECMWF, UK Met Office) regarding the general climatological representation and the seasonal prediction of extra-tropical cyclones and windstorms during the core winter season (DJF) with a lead time of up to four months. Two different algorithms are used to identify cyclones and windstorm events in these datasets. Firstly, we apply a cyclone identification and tracking algorithm based on the Laplacian of MSLP and secondly, we use an objective wind field tracking algorithm to identify and track continuous areas of extreme high wind speeds (cf. Leckebusch et al., 2008), which can be related to extra-tropical winter cyclones. Thus, for the first time, we can analyse the forecast of severe wind events near to the surface caused by extra-tropical cyclones. First results suggest a successful validation of the spatial climatological distributions of wind storm and cyclone occurrence in the seasonal forecast systems in comparison with reanalysis data (ECMWF-ERA40 & ERAInterim) in general. However, large biases are found for some areas. The skill of the seasonal forecast systems in simulating the year-to-year variability of the frequency of severe windstorm events and cyclones is investigated using the ranked probability skill score. Positive skill is found over large parts of the Northern Hemisphere as well as for the most intense extra-tropical cyclones and its related wind fields.

  17. Forecasting Seasonal Water Needs Under Current and Future Climate

    NASA Astrophysics Data System (ADS)

    Spisni, A.; Pratizzoli, W.; Tomei, F.; Mariani, M. C.; Villani, G.; Pavan, V.; Tomozeiu, R.; Marletto, V.

    2010-12-01

    This work outlines the complex strategy being developed at ARPA-SIMC for the integrated exploitation of remote sensing, soil water modelling, seasonal forecasting and climate projections, in view of better monitoring and management of water in agriculture at the scale of the Emilia-Romagna region, northern Italy. Remote sensing and field surveys are being used to map crops early in the season, a geographical soil water model uses the crop map together with a soil map and weather data to simulate soil water status up to the beginning of the irrigation season. Downscaled seasonal forecasts are then used to assess the summer irrigation needs. This operational framework is also used to evaluate the impacts of climate change for years 2021-2050 relative to current climate conditions. First tests on kiwifruit in the Romagna subregion show a modest increase in irrigation water demand.

  18. Communicating uncertainty in seasonal and interannual climate forecasts in Europe.

    PubMed

    Taylor, Andrea L; Dessai, Suraje; de Bruin, Wändi Bruine

    2015-11-28

    Across Europe, organizations in different sectors are sensitive to climate variability and change, at a range of temporal scales from the seasonal to the interannual to the multi-decadal. Climate forecast providers face the challenge of communicating the uncertainty inherent in these forecasts to these decision-makers in a way that is transparent, understandable and does not lead to a false sense of certainty. This article reports the findings of a user-needs survey, conducted with 50 representatives of organizations in Europe from a variety of sectors (e.g. water management, forestry, energy, tourism, health) interested in seasonal and interannual climate forecasts. We find that while many participating organizations perform their own 'in house' risk analysis most require some form of processing and interpretation by forecast providers. However, we also find that while users tend to perceive seasonal and interannual forecasts to be useful, they often find them difficult to understand, highlighting the need for communication formats suitable for both expert and non-expert users. In addition, our results show that people tend to prefer familiar formats for receiving information about uncertainty. The implications of these findings for both the providers and users of climate information are discussed.

  19. Optimization of Evaporative Demand Models for Seasonal Drought Forecasting

    NASA Astrophysics Data System (ADS)

    McEvoy, D.; Huntington, J. L.; Hobbins, M.

    2015-12-01

    Providing reliable seasonal drought forecasts continues to pose a major challenge for scientists, end-users, and the water resources and agricultural communities. Precipitation (Prcp) forecasts beyond weather time scales are largely unreliable, so exploring new avenues to improve seasonal drought prediction is necessary to move towards applications and decision-making based on seasonal forecasts. A recent study has shown that evaporative demand (E0) anomaly forecasts from the Climate Forecast System Version 2 (CFSv2) are consistently more skillful than Prcp anomaly forecasts during drought events over CONUS, and E0 drought forecasts may be particularly useful during the growing season in the farming belts of the central and Midwestern CONUS. For this recent study, we used CFSv2 reforecasts to assess the skill of E0 and of its individual drivers (temperature, humidity, wind speed, and solar radiation), using the American Society for Civil Engineers Standardized Reference Evapotranspiration (ET0) Equation. Moderate skill was found in ET0, temperature, and humidity, with lesser skill in solar radiation, and no skill in wind. Therefore, forecasts of E0 based on models with no wind or solar radiation inputs may prove to be more skillful than the ASCE ET0. For this presentation we evaluate CFSv2 E0 reforecasts (1982-2009) from three different E0 models: (1) ASCE ET0; (2) Hargreaves and Samani (ET-HS), which is estimated from maximum and minimum temperature alone; and (3) Valiantzas (ET-V), which is a modified version of the Penman method for use when wind speed data are not available (or of poor quality) and is driven only by temperature, humidity, and solar radiation. The University of Idaho's gridded meteorological data (METDATA) were used as observations to evaluate CFSv2 and also to determine if ET0, ET-HS, and ET-V identify similar historical drought periods. We focus specifically on CFSv2 lead times of one, two, and three months, and season one forecasts; which are

  20. An Assessment of the Skill of GEOS-5 Seasonal Forecasts

    NASA Technical Reports Server (NTRS)

    Ham, Yoo-Geun; Schubert, Siegfried D.; Rienecker, Michele M.

    2013-01-01

    The seasonal forecast skill of the NASA Global Modeling and Assimilation Office coupled global climate model (CGCM) is evaluated based on an ensemble of 9-month lead forecasts for the period 1993 to 2010. The results from the current version (V2) of the CGCM consisting of the GEOS-5 AGM coupled to the MOM4 ocean model are compared with those from an earlier version (V1) in which the AGCM (the NSIPP model) was coupled to the Poseidon Ocean Model. It was found that the correlation skill of the Sea Surface Temperature (SST) forecasts is generally better in V2, especially over the sub-tropical and tropical central and eastern Pacific, Atlantic, and Indian Ocean. Furthermore, the improvement in skill in V2 mainly comes from better forecasts of the developing phase of ENSO from boreal spring to summer. The skill of ENSO forecasts initiated during the boreal winter season, however, shows no improvement in terms of correlation skill, and is in fact slightly worse in terms of root mean square error (RMSE). The degradation of skill is found to be due to an excessive ENSO amplitude. For V1, the ENSO amplitude is too strong in forecasts starting in boreal spring and summer, which causes large RMSE in the forecast. For V2, the ENSO amplitude is slightly stronger than that in observations and V1 for forecasts starting in boreal winter season. An analysis of the terms in the SST tendency equation, shows that this is mainly due to an excessive zonal advective feedback. In addition, V2 forecasts that are initiated during boreal winter season, exhibit a slower phase transition of El Nino, which is consistent with larger amplitude of ENSO after the ENSO peak season. It is found that this is due to weak discharge of equatorial Warm Water Volume (WWV). In both observations and V1, the discharge of equatorial WWV leads the equatorial geostrophic easterly current so as to damp the El Nino starting in January. This process is delayed by about 2 months in V2 due to the slower phase

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

    NASA Technical Reports Server (NTRS)

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  3. The value of the North American Multi Model Ensemble phase 2 for sub-seasonal hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Wanders, Niko; Wood, Eric

    2016-04-01

    from 30 days to 12 days (2.5 month lead) and an increased predictability of the high flow season from 45 days to 20 days (3-4 months lead). Additionally, we show that snow accumulation and melt onset in the Northern hemisphere can be forecasted with an uncertainty of 10 days (2.5 months lead). Both the overall skill, and the skill found in these last two examples, indicates that the new NMME-2 forecast dataset is valuable for sub-seasonal forecast applications. The high temporal resolution (daily), long leads (one year leads) and large hindcast archive enable new sub-seasonal forecasting applications to be explored. We show that the NMME-2 has a large potential for sub-seasonal hydrological forecasting and other potential hydrological applications (e.g. reservoir management), which could benefit from these new forecasts.

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

    NASA Astrophysics Data System (ADS)

    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

  5. Skill improvement of dynamical seasonal Arctic sea ice forecasts

    NASA Astrophysics Data System (ADS)

    Krikken, Folmer; Schmeits, Maurice; Vlot, Willem; Guemas, Virginie; Hazeleger, Wilco

    2016-05-01

    We explore the error and improve the skill of the outcome from dynamical seasonal Arctic sea ice reforecasts using different bias correction and ensemble calibration methods. These reforecasts consist of a five-member ensemble from 1979 to 2012 using the general circulation model EC-Earth. The raw model reforecasts show large biases in Arctic sea ice area, mainly due to a differently simulated seasonal cycle and long term trend compared to observations. This translates very quickly (1-3 months) into large biases. We find that (heteroscedastic) extended logistic regressions are viable ensemble calibration methods, as the forecast skill is improved compared to standard bias correction methods. Analysis of regional skill of Arctic sea ice shows that the Northeast Passage and the Kara and Barents Sea are most predictable. These results show the importance of reducing model error and the potential for ensemble calibration in improving skill of seasonal forecasts of Arctic sea ice.

  6. Multimodel Ensembling in Seasonal Climate Forecasting at IRI.

    NASA Astrophysics Data System (ADS)

    Barnston, Anthony G.; Mason, Simon J.; Goddard, Lisa; Dewitt, David G.; Zebiak, Stephen E.

    2003-12-01

    The International Research Institute (IRI) for Climate Prediction seasonal forecast system is based largely on the predictions of ensembles of several atmospheric general circulation models (AGCMs) forced by two versions of an SST prediction—one consisting of persisted SST anomalies from the current observations and one of evolving SST anomalies as predicted by a set of dynamical and statistical SST prediction models. Recently, an objective multimodel ensembling procedure has replaced a more laborious and subjective weighting of the predictions of the several AGCMs. Here the skills of the multimodel predictions produced retrospectively over the first 4 years of IRI forecasts are examined and compared with the skills of the more subjectively derived forecasts actually issued. The multimodel ensemble predictions are generally found to be an acceptable replacement, although the precipitation forecasts do benefit from inclusion of empirical forecast tools. Planned pattern-level model output statistics (MOS) corrections for systematic biases in the AGCM forecasts may render them more sufficient in their own right.

  7. Benchmark analysis of forecasted seasonal temperature over different climatic areas

    NASA Astrophysics Data System (ADS)

    Giunta, G.; Salerno, R.; Ceppi, A.; Ercolani, G.; Mancini, M.

    2015-12-01

    From a long-term perspective, an improvement of seasonal forecasting, which is often exclusively based on climatology, could provide a new capability for the management of energy resources in a time scale of just a few months. This paper regards a benchmark analysis in relation to long-term temperature forecasts over Italy in the year 2010, comparing the eni-kassandra meteo forecast (e-kmf®) model, the Climate Forecast System-National Centers for Environmental Prediction (CFS-NCEP) model, and the climatological reference (based on 25-year data) with observations. Statistical indexes are used to understand the reliability of the prediction of 2-m monthly air temperatures with a perspective of 12 weeks ahead. The results show how the best performance is achieved by the e-kmf® system which improves the reliability for long-term forecasts compared to climatology and the CFS-NCEP model. By using the reliable high-performance forecast system, it is possible to optimize the natural gas portfolio and management operations, thereby obtaining a competitive advantage in the European energy market.

  8. Seasonal forecast quality of the West African monsoon rainfall regimes by multiple forecast systems

    NASA Astrophysics Data System (ADS)

    Rodrigues, Luis Ricardo Lage; García-Serrano, Javier; Doblas-Reyes, Francisco

    2014-07-01

    A targeted methodology to study the West African monsoon (WAM) rainfall variability is considered where monthly rainfall is averaged over 10°W-10°E to take into account the latitudinal migration and temporal distribution of the WAM summer rainfall. Two observational rainfall data sets and a large number of quasi-operational forecast systems, among them two systems from the European Seasonal to Interannual Prediction initiative and six systems from the North American Multi-model Ensemble project, are used in this research. The two leading modes of the WAM rainfall variability, namely, the Guinean and Sahelian regimes, are estimated by applying principal component analysis (PCA) on the longitudinally averaged precipitation. The PCA is performed upon the observations and each forecast system and lead time separately. A statistical model based on simple linear regression using sea surface temperature indices as predictors is considered both as a benchmark and an additional forecast system. The combination of the dynamical forecast systems and the statistical model is performed using different methods of combination. It is shown that most forecast systems capture the main features associated with the Guinean regime, that is, rainfall located mainly south of 10°N and the northward migration of rainfall over the season. On the other hand, only a fraction of the forecast systems capture the characteristics of the rainfall signal north of 10°N associated with the Sahelian regime. A simple statistical model proves to be of great value and outperforms most state-of-the-art dynamical forecast systems when predicting the principal components associated with the Guinean and Sahelian regimes. Combining all forecast systems do not lead to improved forecasts when compared to the best single forecast system, the European Centre for Medium-Range Weather Forecasts System 4 (S4). In fact, S4 is far better than any forecast system when predicting the variability of the WAM rainfall

  9. The Canadian seasonal forecast and the APCC exchange.

    NASA Astrophysics Data System (ADS)

    Archambault, B.; Fontecilla, J.; Kharin, V.; Bourgouin, P.; Ashok, K.; Lee, D.

    2009-05-01

    In this talk, we will first describe the Canadian seasonal forecast system. This system uses a 4 model ensemble approach with each of these models generating a 10 members ensemble. Multi-model issues related to this system will be describes. Secondly, we will describe an international multi-system initiative. The Asia-Pacific Economic Cooperation (APEC) is a forum for 21 Pacific Rim countries or regions including Canada. The APEC Climate Center (APCC) provides seasonal forecasts to their regional climate centers with a Multi Model Ensemble (MME) approach. The APCC MME is based on 13 ensemble prediction systems from different institutions including MSC(Canada), NCEP(USA), COLA(USA), KMA(Korea), JMA(Japan), BOM(Australia) and others. In this presentation, we will describe the basics of this international cooperation.

  10. Use of Seasonal Forecasts for Dams Management in Spain

    NASA Astrophysics Data System (ADS)

    Voces, Jose; Sanchez, Eroteida; Navascues, Beatriz; Rodriguez-Camino, Ernesto

    2016-04-01

    This presentation describes the potential use of seasonal climate predictions for water management in Spain. Given the low skill provided by seasonal forecasting systems based on the current operational models, and after analyzing the response to the climate drivers at different time scales for selected river basins in Spain, a two steps empirical forecasting technique has been developed and tested as a pilot study in the framework of the Euporias project. In the first step the winter North Atlantic Oscillation index is forecasted from the observed Eurasian Snow Advance Index in October taking advantage of the high correlation between them. Then, a synthetic probability distribution function is generated to sample the NAO index in order to assess the forecasting uncertainty. In the second step, a KNN algorithm is used to retrieve a set of reservoir inflows associated with each of the NAO index samples. The final result is an ensemble of possible scenarios (reservoir inflows) which are used as input for the water allocation decision support models.

  11. Dynamically downscaled multi-model ensemble seasonal forecasts over Ethiopia

    NASA Astrophysics Data System (ADS)

    Asharaf, Shakeel; Fröhlich, Kristina; Fernandez, Jesus; Cardoso, Rita; Nikulin, Grigory; Früh, Barbara

    2016-04-01

    Truthful and reliable seasonal rainfall predictions have an important social and economic value for the east African countries as their economy is highly dependent on rain-fed agriculture and pastoral systems. Only June to September (JJAS) seasonal rainfall accounts to more than 80% crop production in Ethiopia. Hence, seasonal foresting is a crucial concern for the region. The European Provision of Regional Impact Assessment on a seasonal to decadal timescale (EUPORIAS) project offers a common framework to understand hindcast uncertainties through the use of multi-model and multi-member simulations over east Africa. Under this program, the participating regional climate models (RCMs) were driven by the atmospheric-only version of the ECEARTH global climate model, which provides hindcasts of a five-months period (May to September) from 1991-2012. In this study the RCMs downscaled rainfall is evaluated with respect to the observed JJAS rainfall over Ethiopia. Both deterministic and probabilistic based forecast skills are assessed. Our preliminary results show the potential usefulness of multi-model ensemble simulations in forecasting the seasonal rainfall over the region.

  12. FORWINE - Statistical Downscaling of Seasonal forecasts for wine

    NASA Astrophysics Data System (ADS)

    Cardoso, Rita M.; Soares, Pedro M. M.; Miranda, Pedro M. A.

    2016-04-01

    The most renowned viticulture regions in the Iberian Peninsula have a long standing tradition in winemaking and are considered world-class grapevine (Vitis Vinifera L.) producing regions. Portugal is the 11th wine producer in the world, with internationally acclaimed wines, such as Port wine, and vineyards across the whole territory. Climate is widely acknowledged of one of the most important factors for grapevine development and growth (Fraga et al. 2014a and b; Jackson et al. 1993; Keller 2010). During the growing season (April-October in the Northern Hemisphere) of this perennial and deciduous crop, the climatic conditions are responsible for numerous morphologically and physiological changes. Anomalously low February-March mean temperature, anomalously high May mean temperature and anomalously high March precipitation tend to be favourable to wine production in the Douro Valley. Seasonal forecast of precipitation and temperature tailored to fit critical thresholds, for crucial seasons, can be used to inform management practices (viz. phytosanitary measures, land operations, marketing campaigns) and develop a wine production forecast. Statistical downscaling of precipitation, maximum, minimum temperatures is used to model wine production following Santos et al. (2013) and to calculate bioclimatic indices. The skill of the ensemble forecast is evaluated through anomaly correlation, ROC area, spread-error ratio and CRPS

  13. Seasonal forecasting of tropical cyclogenesis over the North Indian Ocean

    NASA Astrophysics Data System (ADS)

    Pattanaik, D. R.; Mohapatra, M.

    2016-03-01

    Over the North Indian Ocean (NIO) and particularly over the Bay of Bengal (BoB), the post-monsoon season from October to December (OND) are known to produce tropical cyclones, which cause damage to life and property over India and many neighbouring countries. The variability of frequency of cyclonic disturbances (CDs) during OND season is found to be associated with variability of previous large-scale features during monsoon season from June to September, which is used to develop seasonal forecast model of CDs frequency over the BoB and NIO based on principal component regression (PCR). Six dynamical/thermodynamical parameters during previous June-August, viz., (i) sea surface temperature (SST) over the equatorial central Pacific, (ii) sea level pressure (SLP) over the southeastern equatorial Indian Ocean, (iii) meridional wind over the eastern equatorial Indian Ocean at 850 hPa, (iv) strength of upper level easterly, (v) strength of monsoon westerly over North Indian Ocean at 850 hPa, and (vi) SST over the northwest Pacific having significant and stable relationship with CDs over BoB in subsequent OND season are used in PCR model for a training period of 40 years (1971-2010) and the latest four years (2011-2014) are used for validation. The PCR model indicates highly significant correlation coefficient of 0.77 (0.76) between forecast and observed frequency of CD over the BoB (NIO) for the whole period of 44 years and is associated with the root mean square error and mean absolute error ≤ 1 CD. With respect to the category forecast of CD frequency over BoB and NIO, the Hit score is found to be about 63% and the Relative Operating Curves (ROC) for above and below normal forecast is found to be having much better forecast skill than the climatology. The PCR model performs very well, particularly for the above and below normal CD year over the BoB and the NIO, during the test period from 2011 to 2014.

  14. Statistical calibration and bridging of ECMWF System4 outputs for forecasting seasonal precipitation over China

    NASA Astrophysics Data System (ADS)

    Peng, Zhaoliang; Wang, Q. J.; Bennett, James C.; Schepen, Andrew; Pappenberger, Florian; Pokhrel, Prafulla; Wang, Ziru

    2014-06-01

    This study evaluates seasonal precipitation forecasts over China produced by statistically postprocessing multiple-output fields from the European Centre for Medium-Range Weather Forecasts' System4 (SYS4) coupled ocean-atmosphere general circulation model (CGCM). To ameliorate systematic deficiencies in the SYS4 precipitation forecasts, we apply a Bayesian joint probability (BJP) modeling approach to calibrate the raw forecasts. To improve the skill of the calibration forecasts, we use six large-scale climate indices, calculated from SYS4 sea surface temperature forecasts, to establish a set of BJP statistical bridging models to forecast precipitation. The calibration forecasts and bridging forecasts are merged through Bayesian model averaging to combine strengths of the different models. The BJP calibration effectively removes bias and improves statistical reliability of the raw forecasts. The calibration forecasts are skillful at a 0 month lead in most seasons, but skill decreases sharply at a 1 month lead. The skill of the bridging forecasts is more stable at different lead times. Consequently, the merged calibration and bridging forecasts at a 1 month lead are clearly more skillful than the calibration forecasts, and the skill is maintained out to a 4 month lead. The forecast framework used in this study can help to better realize the potential of CGCM ensemble forecasts. The increased reliability as well as improved skill of seasonal precipitation forecasts suggests that the system proposed here could be a useful operational forecasting tool.

  15. Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales

    NASA Astrophysics Data System (ADS)

    Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.

    2016-10-01

    Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

  16. Seasonal Water Resources Management and Probabilistic Operations Forecast in the San Juan Basin

    NASA Astrophysics Data System (ADS)

    Daugherty, L.; Zagona, E. A.; Rajagopalan, B.; Grantz, K.; Miller, W. P.; Werner, K.

    2013-12-01

    within the NWS Community Hydrologic Prediction System (CHPS) to produce an ensemble streamflow forecast. The ensemble traces are used to drive the MTOM with the initial conditions of the water resources system and the operating rules, to provide ensembles of water resources management and operation metrics. We applied this integrated approach to forecasting in the San Juan River Basin (SJRB) using a portion of the Colorado River MTOM. The management objectives in the basin include water supply for irrigation, tribal water rights, environmental flows, and flood control. The spring streamflow ensembles were issued at four different lead times on the first of each month from January - April, and are incorporated into the MTOM for the period 2002-2010. Ensembles of operational performance metrics for the SJRB such as Navajo Reservoir releases, end of water year storage, environmental flows and water supply for irrigation were computed and their skills evaluated against variables obtained in a baseline simulation using historical streamflow. Preliminary results indicate that thus obtained probabilistic forecasts may produce increased skill especially at long lead time (e.g., on Jan and Feb 1st). The probabilistic information for water management variables provide risks of system vulnerabilities and thus enables risk-based efficient planning and operations.

  17. Optimising seasonal streamflow forecast lead time for operational decision making in Australia

    NASA Astrophysics Data System (ADS)

    Schepen, Andrew; Zhao, Tongtiegang; Wang, Q. J.; Zhou, Senlin; Feikema, Paul

    2016-10-01

    Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0-14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within +5 to -15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to

  18. Application of seasonal forecasting for the drought forecasting in Catalonia (Spain)

    NASA Astrophysics Data System (ADS)

    Llasat, Maria-Carmen; Zaragoza, Albert; Aznar, Blanca; Cabot, Jordi

    2010-05-01

    Low flows and droughts are a hydro-climatic feature in Spain (Alvarez et al, 2008). The construction of dams as water reservoirs has been a usual tool to manage the water resources for agriculture and livestock, industries and human needs (MIMAM, 2000, 2007). The last drought that has affected Spain has last four years in Catalonia, from 2004 to the spring of 2008, and it has been particularly hard as a consequence of the precipitation deficit in the upper part of the rivers that nourish the main dams. This problem increases when the water scarcity affects very populated areas, like big cities. The Barcelona city, with more than 3.000.000 people concentrated in the downtown and surrounding areas is a clear example. One of the objectives of the SOSTAQUA project is to improve the water resources management in real time, in order to improve the water supply in the cities in the framework of sustainable development. The work presented here deals with the application of seasonal forecasting to improve the water management in Catalonia, particularly in drought conditions. A seasonal prediction index has been created as a linear combination of climatic data and the ECM4 prediction that has been validated too. This information has implemented into a hydrological model and it has been applied to the last drought considering the real water demands of population, as well as to the water storage evolution in the last months. It has been found a considerable advance in the forecasting of water volume into reservoirs. The advantage of this methodology is that it only requires seasonal forecasting free through internet. Due to the fact that the principal rivers that supply water to Barcelona, birth on the Pyrenees and Pre-Pyrenees region, the analysis and precipitation forecasting is focused on this region (Zaragoza, 2008).

  19. CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

    NASA Astrophysics Data System (ADS)

    Tian, Di; Wood, Eric F.; Yuan, Xing

    2017-03-01

    This paper explored the potential of a global climate model for sub-seasonal forecasting of precipitation and 2 m air temperature. The categorical forecast skill of 10 precipitation and temperature indices was investigated using the 28-year sub-seasonal hindcasts from the Climate Forecast System version 2 (CFSv2) over the contiguous United States (CONUS). The forecast skill for mean precipitation and temperature as well as for the frequency and duration of extremes was highly dependent on the forecasting indices, regions, seasons, and leads. Forecasts for 7- and 14-day temperature indices showed skill even at weeks 3 and 4, and generally were more skillful than precipitation indices. Overall, temperature indices showed higher skill than precipitation indices over the entire CONUS region at sub-seasonal scale. While the forecast skill related to mean precipitations was low in summer over the CONUS, the number of rainy days, number of consecutive rainy days, and number of consecutive dry days showed considerably high skill for the western coastal region. The presence of active Madden-Julian Oscillation (MJO) events improved CFSv2 weekly mean precipitation forecast skill over most parts of the CONUS, but it did not necessarily improve the weekly mean temperature forecasts. The 30-day forecasts of precipitation and temperature indices calculated from the downscaled monthly CFSv2 forecasts were less skillful than those calculated directly from CFSv2 daily forecasts, suggesting the usefulness of CFSv2 for sub-seasonal hydrological forecasting.

  20. Bias correcting precipitation forecasts for extended-range skilful seasonal streamflow predictions

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian

    2016-04-01

    Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to also benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France in order to provide insights into the way bias correcting seasonal precipitation forecasts can contribute to maintain skill of seasonal flow predictions at extended lead times. First, we evaluate the skill of raw (i.e., without bias correction) seasonal precipitation ensemble forecasts for streamflow forecasting in sixteen French catchments. A lumped daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. A reference prediction system based on historic observed precipitation and watershed initial conditions at the time of forecast (i.e., ESP method) is used as benchmark. In a second step, we apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the flow forecasts. The approaches were based on the linear scaling and the distribution mapping methods. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy, and overall performance. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, reliability is an attribute that is not significantly improved. Forecast skill is generally improved when applying bias correction. Two bias correction methods showed the best performance for the studied catchments, with, however, each method being more successful in improving specific attributes of forecast quality: the simple linear scaling of monthly values contributed mainly to increase forecast

  1. Assessing the skill of seasonal precipitation and streamflow forecasts in sixteen French catchments

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian

    2015-04-01

    Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful. Streamflow forecasting is one of the many applications than can benefit from these efforts. Seasonal flow forecasts generated using seasonal ensemble precipitation forecasts as input to a hydrological model can help to take anticipatory measures for water supply reservoir operation or drought risk management. The objective of the study is to assess the skill of seasonal precipitation and streamflow forecasts in France. First, we evaluated the skill of ECMWF SYS4 seasonal precipitation forecasts for streamflow forecasting in sixteen French catchments. Daily flow forecasts were produced using raw seasonal precipitation forecasts as input to the GR6J hydrological model. Ensemble forecasts are issued every month with 15 or 51 members according to the month of the year and evaluated for up to 90 days ahead. In a second step, we applied eight variants of bias correction approaches to the precipitation forecasts prior to generating the flow forecasts. The approaches were based on the linear scaling and the distribution mapping methods. The skill of the ensemble forecasts was assessed in accuracy (MAE), reliability (PIT Diagram) and overall performance (CRPS). The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are more skilful in terms of accuracy and overall performance than a reference prediction based on historic observed precipitation and watershed initial conditions at the time of forecast. Reliability is the only attribute that is not significantly improved. The skill of the forecasts is, in general, improved when applying bias correction. Two bias correction methods showed the best performance for the studied catchments: the simple linear scaling of monthly values and the empirical distribution mapping of daily values. L. Crochemore is funded by the Interreg IVB DROP Project (Benefit of governance in DROught adaPtation).

  2. Snow: A New Model Diagnostic and Seasonal Forecast Influences

    NASA Astrophysics Data System (ADS)

    Slater, A. G.; Lawrence, D. M.; Koven, C.

    2015-12-01

    Snow is the most variable of terrestrial surface condition on the planet with the seasonal extent of snow cover varying by about 48% of land area in the Northern Hemisphere. Physical properties of snow such as high albedo, high insulation along with its ability to store moisture make it an integral component of mid- and high-latitude climates and it is therefore important that models capture these properties and associated processes. In this work we explore two items associated with snow and their role in the climate system. Firstly, a diagnostic measure of snow insulation that is rooted in the physics of heat transfer is introduced. Insulation of the ground during cold Arctic winters heavily influences the rate and depth of ground freezing (or thawing), which can then influence hydrologic and biogeochemical fluxes. The ability of models to simulate snow insulation varies widely. Secondly, the role of snow upon seasonal forecasts is demonstrated within a currently operational modeling system. Due to model system biases, mass and longevity of snow can vary with forecasts. In turn, a longer lasting and greater moisture store can have impacts upon the surface temperature. These impacts can linger for over two months after all snow has melted. The cause of the biases is identified and a solution posed.

  3. Seasonal forecasts for regional onset of the West African monsoon

    NASA Astrophysics Data System (ADS)

    Vellinga, Michael; Arribas, Alberto; Graham, Richard

    2013-06-01

    The West African monsoon has over the years proven difficult to represent in global coupled models. The current operational seasonal forecasting system of the UK Met Office (GloSea4) has a good representation of monsoon rainfall over West Africa. It reproduces the various stages of the monsoon: a coastal phase in May and June, followed by onset of the Sahelian phase in July when rainfall maxima shift northward of 10N until September; and a secondary coastal rainfall maximum in October. We explore the dynamics of monsoon onset in GloSea4 and compare it to reanalyses. An important difference is the change in the Saharan heat low around the time of Sahelian onset. In Glosea4 the deepening heat low introduces moisture convergence across an east-west Sahelian band, whereas in the reanalyses such an east-west organisation of moisture does not occur and moisture is transported northwards to the Sahara. Lack of observations in the southern Sahara makes it difficult to verify this process in GloSea4 and also suggests that reanalyses may not be strongly constrained by station observations in an area key to Sahelian onset. Timing of monsoon onset has socio-economic importance for many countries in West Africa and we explore onset predictability in GloSea4. We use tercile categories to calculate probabilities for onset occurring before, near and after average in four different onset indicators. Glosea4 has modest skill at 2-3 months' lead time, with ROC scores of 0.6-0.8. Similar skill is seen in hindcasts with models from the ENSEMBLES project, even in models with large rainfall biases over the Sahel. Forecast skill derives from tropical SST in June and many models capture at least the influence of the tropical Atlantic. This suggests that long-range skill for onset could be present in other seasonal forecasting systems in spite of mean rainfall biases.

  4. Real-Time Influenza Forecasts during the 2012–2013 Season

    PubMed Central

    Shaman, Jeffrey; Karspeck, Alicia; Yang, Wan; Tamerius, James; Lipsitch, Marc

    2013-01-01

    Recently, we developed a seasonal influenza prediction system that uses an advanced data assimilation technique and real-time estimates of influenza incidence to optimize and initialize a population-based mathematical model of influenza transmission dynamics. This system was used to generate and evaluate retrospective forecasts of influenza peak timing in New York City. Here we present weekly forecasts of seasonal influenza developed and run in real time for 108 cites in the United States during the recent 2012–2013 season. Reliable ensemble forecasts of influenza outbreak peak timing with leads of up to 9 weeks were produced. Forecast accuracy increased as the season progressed, and the forecasts significantly outperformed alternate, analog prediction methods. By Week 52, prior to peak for the majority of cities, 63% of all ensemble forecasts were accurate. To our knowledge, this is the first time predictions of seasonal influenza have been made in real time and with demonstrated accuracy. PMID:24302074

  5. Downscaling and extrapolating dynamic seasonal marine forecasts for coastal ocean users

    NASA Astrophysics Data System (ADS)

    Vanhatalo, Jarno; Hobday, Alistair J.; Little, L. Richard; Spillman, Claire M.

    2016-04-01

    Marine weather and climate forecasts are essential in planning strategies and activities on a range of temporal and spatial scales. However, seasonal dynamical forecast models, that provide forecasts in monthly scale, often have low offshore resolution and limited information for inshore coastal areas. Hence, there is increasing demand for methods capable of fine scale seasonal forecasts covering coastal waters. Here, we have developed a method to combine observational data with dynamical forecasts from POAMA (Predictive Ocean Atmosphere Model for Australia; Australian Bureau of Meteorology) in order to produce seasonal downscaled, corrected forecasts, extrapolated to include inshore regions that POAMA does not cover. We demonstrate the method in forecasting the monthly sea surface temperature anomalies in the Great Australian Bight (GAB) region. The resolution of POAMA in the GAB is approximately 2° × 1° (lon. × lat.) and the resolution of our downscaled forecast is approximately 1° × 0.25°. We use data and model hindcasts for the period 1994-2010 for forecast validation. The predictive performance of our statistical downscaling model improves on the original POAMA forecast. Additionally, this statistical downscaling model extrapolates forecasts to coastal regions not covered by POAMA and its forecasts are probabilistic which allows straightforward assessment of uncertainty in downscaling and prediction. A range of marine users will benefit from access to downscaled and nearshore forecasts at seasonal timescales.

  6. Usefulness of ECMWF system-4 ensemble seasonal climate forecasts for East Africa

    NASA Astrophysics Data System (ADS)

    Ogutu, Geoffrey; Franssen, Wietse; Supit, Iwan; Omondi, Philip; Hutjes, Ronald

    2016-04-01

    This study evaluates whether European Centre for Medium-Range Weather Forecast (ECMWF) system-4 seasonal forecasts can potentially be used as input for impact analysis over East Africa. To be of any use, these forecasts should have skill. We used the 15-member ensemble runs and tested potential forecast skill of precipitation (tp), near surface air temperature (tas) and surface downwelling shortwave radiation (rsds) for future use in impact models. Probabilistic measures verified the ECMWF ensemble forecasts against the WATCH Forcing Data methodology applied to ERA-Interim data (WFDEI) climatology for the period 1981-2010. The Ranked Probability Skill Score (RPSS) assesses the overall forecast skill, whereas the Relative Operating Curve Skill Score (ROCSS) analyses skill of the forecasted tercile at both grid cell and over sub-regions with homogeneous rainfall characteristics. The results show that predictability of the three variables varies with season, location and forecast month (lead-time) before start of the seasons. Quantile-quantile bias correction clears biases in the raw forecasts but does not improve probabilistic skills. The October-December (OND) tp forecasts show skill over a larger area up to lead-time of three months compared to the March-May (MAM) and June-August (JJA) seasons. Temperature forecasts are skillful up to a minimum three months lead-time in all seasons, while the rsds is less skillful. ROCSS analyses indicate high skill in simulation of upper- and lower-tercile forecasts compared to simulation of the middle-terciles. Upper- and lower-tercile precipitation forecasts are 20-80% better than climatology over a larger area at 0-3 month lead-time; tas forecasts are 40-100% better at shorter lead-times while rsds forecasts are less skillful in all seasons. The forecast system captures manifestations of strong El Niño and La Niña years in terms of precipitation but the skill scores are region dependent.

  7. Seasonal Scale Water Deficit Forecasting in East Africa and the Middle East Region Using the NMME Models Forecasts

    NASA Astrophysics Data System (ADS)

    Shukla, S.; Funk, C. C.; Zaitchik, B. F.; Narapusetty, B.; Arsenault, K. R.; Peters-Lidard, C. D.

    2015-12-01

    In this presentation we report on our ongoing efforts to provide seasonal scale water deficit forecasts in East Africa and the Middle East regions. First, we report on the skill of the seasonal climate forecasts from the North American Multimodel Ensemble (NMME) models over this region. We evaluated deterministic (anomaly correlation), categorical (the equitable threat score) and probabilistic (the ranked probabilistic skill score) skill of the NMME models forecasts over the hindcast period of 1982-2010, focusing on the primary rainy seasons of March-May (MAM), July-September (JAS) and October-December (OND). We also examined the potential predictability of the NMME models using the anomaly correlation between the ensemble mean forecasts from a given model against a single ensemble member of the same model (homogenous predictability) and rest of the models (heterogeneous predictability), and observations (forecast skill). Overall, we found precipitation forecast skill in this region to be sparse and limited (up to three month of lead) to some locations and seasons, and temperature forecast skill to be much more skillful than the precipitation forecast skill. Highest level of skill exists over equatorial East Africa (OND season) and over parts of northern Ethiopia and southern Sudan (JAS season). Categorical and probabilistic forecast skills are also higher in those regions. We found the homogeneous predictability to be greater than the forecast skill indicating potential for forecast skill improvement. In the rest of the presentation we describe implementation and evaluation of a hybrid approach (that combines statistical and dynamical approaches) of downscaling climate forecasts to improve the precipitation forecast skill in this region. For this part of the analysis we mainly focus on two of the NMME models (NASA's GMAO and NCEP's CFSv2). Past research on a hybrid approach focusing only over equatorial East Africa has shown promising results. We found that MAM

  8. Seasonal Forecasting of Fires across Southern Borneo, 1997-2010

    NASA Astrophysics Data System (ADS)

    Spessa, Allan; Field, Robert; Kaiser, Johannes; Langner, Andreas; Moore, Jonathan; Pappenberger, Florian; Siegert, Florian; Weber, Ulrich

    2014-05-01

    several studies using historical data have established negative relationships between fires and antecedent rainfall, and/or positive relationships between fires and deforestation in regions affected by El Nino, comparatively little work has attempted to predict fires and emissions in such regions. Ensemble seasonal climate forecasts issued with several months lead-time have been applied to support risk assessment systems in many fields, notably agricultural production and natural disaster management of flooding, heat waves, drought and fire. The USA, for example, has a long-standing seasonal fire danger prediction system. Fire danger monitoring systems have been operating in Indonesia for over a decade, but, as of yet, no fire danger prediction systems exist. Given the effort required to mobilise suppression and prevention measures in Indonesia, one could argue that high fire danger periods must be anticipated months in advance for mitigation and response measures to be effective. To address this need, the goal of our work was to examine the utility of seasonal rainfall forecasts in predicting severe fires in Indonesia more than one month in advance, using southern Borneo (comprising the bulk of Kalimantan) as a case study. Here we present the results of comparing seasonal forecasts of monthly rainfall from ECMWF's System 4 against i) observed rainfall (GPCP), and ii) burnt area and deforestation (MODIS, AVHRR and Landsat) across southern Borneo for the period 1997-2010. Our results demonstrate the utility of using ECMWF's seasonal climate forecasts for predicting fire activity in the region. Potential applications include improved fire mitigation and responsiveness, and improved risk assessments of biodiversity and carbon losses through fire. These are important considerations for forest protection programmes (e.g. REDD+), forest carbon markets and forest (re)insurance enterprises.

  9. Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

    NASA Technical Reports Server (NTRS)

    Mazrooei, Amirhossein; Sinah, Tusshar; Sankarasubramanian, A.; Kumar, Sujay V.; Peters-Lidard, Christa D.

    2015-01-01

    Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

  10. Decomposition of sources of errors in seasonal streamflow forecasting over the U.S. Sunbelt

    NASA Astrophysics Data System (ADS)

    Mazrooei, Amirhossein; Sinha, Tushar; Sankarasubramanian, A.; Kumar, Sujay; Peters-Lidard, Christa D.

    2015-12-01

    Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8° to 1/8° spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

  11. The Influence of Seasonal Forecast Accuracy on Farmer Behavior: An Agent-Based Modeling Approach

    NASA Astrophysics Data System (ADS)

    Jacobi, J. H.; Nay, J.; Gilligan, J. M.

    2013-12-01

    Seasonal climates dictate the livelihoods of farmers in developing countries. While farmers in developed countries often have seasonal forecasts on which to base their cropping decisions, developing world farmers usually make plans for the season without such information. Climate change increases the seasonal uncertainty, making things more difficult for farmers. Providing seasonal forecasts to these farmers is seen as a way to help buffer these typically marginal groups from the effects of climate change, though how to do so and the efficacy of such an effort is still uncertain. In Sri Lanka, an effort is underway to provide such forecasts to farmers. The accuracy of these forecasts is likely to have large impacts on how farmers accept and respond to the information they receive. We present an agent-based model to explore how the accuracy of seasonal rainfall forecasts affects the growing decisions and behavior of farmers in Sri Lanka. Using a decision function based on prospect theory, this model simulates farmers' behavior in the face of a wet, dry, or normal forecast. Farmers can either choose to grow paddy rice or plant a cash crop. Prospect theory is used to evaluate outcomes of the growing season; the farmer's memory of the level of success under a certain set of conditions affects next season's decision. Results from this study have implications for policy makers and seasonal forecasters.

  12. Streamflow forecasts on seasonal and interannual time scales for reservoir management

    NASA Astrophysics Data System (ADS)

    Robertson, A. W.; Lu, M.; Lall, U.

    2014-12-01

    Seasonal climate forecasts are beginning to be complemented by improved forecasting capabilities at both sub-seasonal and interannual annual timescales, with the future prospect of seamless climate forecasts for water system operations. While seasonal predictability is often very limited by physical and modeling constraints, harnessing additional predictable components of the climate system may in some cases substantially increase their usable information content, and provide more flexible forecasts in terms of the kinds of management decisions that can be informed. Here we present an example of combining season and year-ahead streamflow forecasts as input to a multi-use reservoir optimization model, applied to the Bhakra Dam in NW India. Bi-timescale forecasts are made with a seasonal periodic autoregressive (PAR) model with exogenous climate-forecast inputs, together with an annual PAR model fit to observed flows used as a baseline for year-ahead forecasts. Annual net revenue from irrigation and hydropower supplies are calculated with contracts optimized using the reservoir optimization model. With Bhakra Dam inflows deriving from both winter storms/snow melt and the summer monsoon, it is found that net annual revenue is maximized when new contracts are initiated in March and June. We explore various choices of PARX model seasonal predictors based on climate model output and data and show that, with the choice of a good start date, even forecasts with relatively low skill can have value.

  13. Skill improvement of seasonal Arctic sea ice forecasts using bias-correction and ensemble calibration

    NASA Astrophysics Data System (ADS)

    Krikken, Folmer; Hazeleger, Wilco; Vlot, Willem; Schmeits, Maurice; Guemas, Virginie

    2016-04-01

    We explore the standard error and skill of dynamical seasonal sea ice forecasts of the Arctic using different bias-correction and ensemble calibration methods. The latter is often used in weather forecasting, but so far has not been applied to Arctic sea ice forecasts. We use seasonal predictions of Arctic sea ice of a 5-member ensemble forecast using the fully coupled GCM EC-Earth, with model initial states obtained by nudging towards ORAS4 and ERA-Interim. The raw model forecasts contain large biases in total sea ice area, especially during the summer months. This is mainly caused by a difference in average seasonal cycle between EC-Earth and observations, which translates directly into the forecasts yielding large biases. Further errors are introduced by the differences in long term trend between the observed sea ice, and the uninitialised EC-earth simulation. We find that extended logistic regression (ELR) and heteroscedastic extended logistic regression (HELR) both prove viable ensemble calibration methods, and improve the forecasts substantially compared to standard bias correction techniques. No clear distinction between ELR and HELR is found. Forecasts starting in May have higher skill (CRPSS > 0 up to 5 months lead time) than forecasts starting in August (2-3 months) and November (2-3 months), with trend-corrected climatology as reference. Analysis of regional skill in the Arctic shows distinct differences, where mainly the Arctic ocean and the Kara and Barents sea prove to be one of the more predictable regions with skilful forecasts starting in May up to 5-6 months lead time. Again, forecasts starting in August and November show much lower regional skill. Overall, it is still difficult to beat relative simple statistical forecasts, but by using ELR and HELR we are getting reasonably close to skilful seasonal forecasts up to 12 months lead time. These results show there is large potential, and need, for using ensemble calibration in seasonal forecasts of

  14. The verification of seasonal precipitation forecasts for early warning in Zambia and Malawi

    NASA Astrophysics Data System (ADS)

    Hyvärinen, O.; Mtilatila, L.; Pilli-Sihvola, K.; Venäläinen, A.; Gregow, H.

    2015-04-01

    We assess the probabilistic seasonal precipitation forecasts issued by Regional Climate Outlook Forum (RCOF) for the area of two southern African countries, Malawi and Zambia from 2002 to 2013. The forecasts, issued in August, are of rainy season rainfall accumulations in three categories (above normal, normal, and below normal), for early season (October-December) and late season (January-March). As observations we used in-situ observations and interpolated precipitation products from Global Precipitation Climatology Project (GPCP), Global Precipitation Climatology Centre (GPCC), and Climate Prediction Centre (CPC) Merged Analysis of Precipitation (CMAP). Differences between results from different data products are smaller than confidence intervals calculated by bootstrap. We focus on below normal forecasts as they were deemed to be the most important for society. The well-known decomposition of Brier score into three terms (Reliability, Resolution, and Uncertainty) shows that the forecasts are rather reliable or well-calibrated, but have a very low resolution; that is, they are not able to discriminate different events. The forecasts also lack sharpness as forecasts for one category are rarely higher than 40 % or less than 25 %. However, these results might be unnecessarily pessimistic, because seasonal forecasts have gone through much development during the period when the forecasts verified in this paper were issued, and forecasts using current methodology might have performed better.

  15. Seasonal Forecasts of Climate Indices: Impact of Definition and Spatial Aggregation on Predictive Skill

    NASA Astrophysics Data System (ADS)

    Bhend, Jonas; Mahlstein, Irina; Liniger, Mark

    2016-04-01

    Seasonal forecasting models are increasingly being used to forecast application-relevant aspects. A simple way to make such user-oriented predictions are application-specific climate indices. Little is known, however, on how the predictive skill of forecasts of such climate indices relates to the predictive skill in forecasting seasonal mean conditions. Here we analyse forecasts of two types of indices derived from daily precipitation and temperature: counts of events such as the number of dry days and accumulated threshold exceedances such as degree days. We find that the predictive skill of forecasts of heating and cooling degree days and of consecutive dry days is generally lower than the skill of seasonal mean temperature and rainfall forecasts respectively. By use of a toy model we demonstrate that this reduction in skill is more pronounced for skilful forecasts and climate indices with a threshold in the tail of the statistical distribution. We further analyse the impact of spatial aggregation and find that aggregation generally improves the predictive skill. Using appropriate covariates for weighting - for example population density to derive a proxy for the national energy demand for heating - the usefulness of forecasts of climate indices can be further enhanced while retaining predictive skill. We conclude that processing of direct model output to derive climate indices in combination with spatial aggregation can be used to render still skilful and even more useful seasonal forecasts of user-relevant quantities.

  16. Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

    NASA Technical Reports Server (NTRS)

    Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

    2015-01-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

  17. Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

    NASA Technical Reports Server (NTRS)

    Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

    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. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

  18. Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

    NASA Astrophysics Data System (ADS)

    Tian, Di

    Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in

  19. Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe

    NASA Astrophysics Data System (ADS)

    Prodhomme, Chloé; Doblas-Reyes, Francisco; Bellprat, Omar; Dutra, Emanuel

    2016-08-01

    Land surfaces and soil conditions are key sources of climate predictability at the seasonal time scale. In order to estimate how the initialization of the land surface affects the predictability at seasonal time scale, we run two sets of seasonal hindcasts with the general circulation model EC-Earth2.3. The initialization of those hindcasts is done either with climatological or realistic land initialization in May using the ERA-Land re-analysis. Results show significant improvements in the initialized run occurring up to the last forecast month. The prediction of near-surface summer temperatures and precipitation at the global scale and over Europe are improved, as well as the warm extremes prediction. As an illustration, we show that the 2010 Russian heat wave is only predicted when soil moisture is initialized. No significant improvement is found for the retrospective prediction of the 2003 European heat wave, suggesting this event to be mainly large-scale driven. Thus, we confirm that late-spring soil moisture conditions can be decisive in triggering high-impact events in the following summer in Europe. Accordingly, accurate land-surface initial conditions are essential for seasonal predictions.

  20. Value of seasonal flow forecast to reservoir operation for water supply in snow-dominated catchments

    NASA Astrophysics Data System (ADS)

    Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea; Pianosi, Francesca; Nijssen, Bart; Lettenmaier, Dennis

    2014-05-01

    The recursive application of forecasting and optimization can make management strategies more flexible and efficient by improving the potential for anticipating, and thus adapting, to adverse events. In the field of reservoir operation, this means enriching the information base on which release decisions are made. At a minimum, this includes the available reservoir storage, but reservoir management can greatly benefit from consideration of other pieces of information as, for instance, weather and flow forecasts. However, the utility or value of inflow forecasts is directly related to forecast quality. In this work, we focus on snow-dominated water resource systems, where the prediction of the volume and timing of snowmelt can greatly enhance the operational performance. We use the Oroville-Thermalito reservoir complex in the Feather River Basin, California, as a case study to explore the effect of forecast quality on optimal release strategies. We use Deterministic Dynamic Programming to optimize medium-range and seasonal reservoir operation based on different forecasts of reservoir inflows. We determine maximum reservoir operation performance by forcing the optimization with observed inflows, which is equivalent to a perfect forecast. The forecast quality is then progressively degraded to relate forecast skill to changes in release decisions and to determine the minimum forecast skill that is required to affect decision-making. We generate forecasted inflow sequences using the Variable Infiltration Capacity (VIC) hydrology model. Forecast initial conditions are created using observed meteorology, while inflow forecasts are based on seasonal climate forecasts. Although the forecast skill level is specific to the Feather River basin, the methodology should be transferable to other systems with strong seasonal runoff regimes. We assess the transferability of the case study results to other systems using alternative reservoir characteristics of the Oroville

  1. Skill Assessment of National Multi-Model Ensemble Forecasts for Seasonal Drought Prediction in East Africa

    NASA Astrophysics Data System (ADS)

    Shukla, S.; Hoell, A.; Roberts, J. B.; Funk, C. C.; Robertson, F. R.

    2014-12-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as 2011, part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at a seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However, seasonal drought prediction in this region faces several challenges including lack of skillful seasonal rainfall forecasts. The National Multi-model Ensemble (NMME); a state-of-the-art dynamical climate forecast system is potentially a promising tool for drought prediction in this region. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ forecasts ensemble members. Recent studies have indicated that in general NMME offers improvement over forecasts from any of the individual model. However, thus far the skill of NMME for forecasting rainfall in a vulnerable region like East Africa has largely been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons of the region. (i.e. March-April-May, July-August-September, and October-November-December). Additionally we describe a hybrid approach that combines statistical method with NMME forecasts to improve rainfall forecast skill in the region when raw NMME forecasts skill is lacking. This approach uses constructed analog method to improve NMME's March-April-May rainfall forecast skill in East Africa.

  2. Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the US Sunbelt

    NASA Astrophysics Data System (ADS)

    Mazrooei, A.; Sinha, T.; Kumar, S.; Peters-Lidard, C. D.; Arumugam, S.

    2014-12-01

    In order to better manage water demands from multiple uses (e.g., municipal water demands, hydroelectric power generation, and agricultural operations), water resources managers and operators are interested to know potential changes in seasonal streamflow based on climate forecasts. However, various sources of uncertainty in forecasting streamflow pose significant challenges to utilize streamflow forecasts in real time operations. In this study we systematically decompose various sources of errors in developing seasonal streamflow forecasts from multiple Land Surface Models (LSMs) forced with downscaled and disaggregated climate forecasts. The objectives of this study are: 1) Quantifying various sources of errors arising from each LSM, climate forecasts, and downscaling/disaggregation techniques employed in developing streamflow forecasts, and 2) Comparing the performance and the skill of different LSMs in streamflow forecasting over selected target basins in the study area. First, three-month ahead precipitation forecasts from ECHAM4.5 GCM for each season were statistically downscaled from 2.8° to 1/8° scale using Principal Component Regression (PCR) and then temporally disaggregated from monthly to daily time step using K-Nearest-Neighbor (K-NN) approach. For other climatic forcings excluding precipitation we obtained hourly climatology over almost 30 years (from 1979 to 2010) from NLDAS-2 dataset. Then LSMs such as NOAH3.2 and CLM2 were forced with precipitation forecasts and climatological forcings to develop retrospective seasonal streamflow forecast over the period of 20 years (1991-2010). Finally, the performance of different LSMs in forecasting streamflow under different schemes were analyzed to quantify sources of errors and to validate forecasted streamflow.

  3. Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

    PubMed Central

    Johansson, Michael A.; Reich, Nicholas G.; Hota, Aditi; Brownstein, John S.; Santillana, Mauricio

    2016-01-01

    Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model. PMID:27665707

  4. Using seasonal forecasts in a drought forecasting system for water management: case-study of the Arzal dam in Brittany

    NASA Astrophysics Data System (ADS)

    Crochemore, Louise; Ramos, Maria-Helena; Perrin, Charles; Penasso, Aldo

    2014-05-01

    The Arzal dam is located at the outlet of the Vilaine River basin (10,000 km2) in Brittany, France. It controls a reservoir (50 hm3) managed for multiple water uses: drinking water, flood control, irrigation, sailing and fish by-passing. Its location in the estuary creates a physical divide between upstream freshwater and downstream saline water. The reservoir thus plays an essential role in the regional water management system. Its operational management during the summer season poses several challenges, mainly related to the quantification of future water inflows and the risks of having restricted water availability for its different uses. Indeed, the occurrence of severe drought periods between May and October may increase the risk of salt intrusion and drinking water contamination due to lock operations. Therefore it is important to provide decision-makers with reliable low-flow forecasts and risk-based visualization tools, which will support their choice of the best strategy for allocation of water among different users and stakeholders. This study focuses on an integrated hydro-meteorological forecasting system developed to forecast low flows upstream the Arzal dam and based on a lumped hydrological model. Medium-range meteorological forecasts from the ECMWF ensemble prediction system (51 scenarios up to 9 days ahead) are combined with seasonal meteorological forecasts also from ECMWF to provide extended streamflow forecasts for the summer period. The performance of the forecasts obtained by this method is compared with the performance of two benchmarks: (i) flow forecasts obtained using an ensemble of past observed precipitation series as precipitation scenarios, i.e. without any use of forecasts from meteorological models and (ii) flow forecasts obtained using the seasonal forecasts only, i.e. without medium-term information. First, the performance of ensemble forecasts is evaluated and compared by means of probabilistic scores. Then, a risk

  5. Modelling dinoflagellates as an approach to the seasonal forecasting of bioluminescence in the North Atlantic

    NASA Astrophysics Data System (ADS)

    Marcinko, Charlotte L. J.; Martin, Adrian P.; Allen, John T.

    2014-11-01

    Bioluminescence within ocean surface waters is of significant interest because it can enhance the study of subsurface movement and organisms. Little is known about how bioluminescence potential (BPOT) varies spatially and temporally in the open ocean. However, light emitted from dinoflagellates often dominates the stimulated bioluminescence field. As a first step towards forecasting surface ocean bioluminescence in the open ocean, a simple ecological model is developed which simulates seasonal changes in dinoflagellate abundance. How forecasting seasonal changes in BPOT may be achieved through combining such a model with relationships derived from observations is discussed and an example is given. The study illustrates a potential new approach to forecasting BPOT through explicitly modelling the population dynamics of a prolific bioluminescent phylum. The model developed here offers a promising platform for the future operational forecasting of the broad temporal changes in bioluminescence within the North Atlantic. Such forecasting of seasonal patterns could provide valuable information for the targeting of scientific field campaigns.

  6. Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

    NASA Astrophysics Data System (ADS)

    Dutra, E.; Di Giuseppe, F.; Wetterhall, F.; Pappenberger, F.

    2013-06-01

    Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System-Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore

  7. Constraints and Suggestions in Adopting Seasonal Climate Forecasts by Farmers in South India

    ERIC Educational Resources Information Center

    Shankar, K. Ravi; Nagasree, K.; Venkateswarlu, B.; Maraty, Pochaiah

    2011-01-01

    The main objective of this study was to determine constraints and suggestions of farmers towards adopting seasonal climate forecasts. It addresses the question: Which forms of providing forecasts will be helpful to farmers in agricultural decision making? For the study, farmers were selected from Andhra Pradesh state of South India. One hundred…

  8. Reductions in seasonal climate forecast dependability as a result of downscaling

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This research determines whether NOAA/CPC seasonal climate forecasts are skillful enough to retain utility after they have been downscaled for use in crop models. Utility is assessed using net dependability, the product of the large-scale 3-month forecast dependability and a factor accounting for l...

  9. Modelled seasonal forecasts of snow water equivalent and runoff in alpine catchments

    NASA Astrophysics Data System (ADS)

    Förster, Kristian; Hanzer, Florian; Schöber, Johannes; Huttenlau, Matthias; Achleitner, Stefan; Strasser, Ulrich

    2016-04-01

    Seasonal forecasts of water balance components are becoming increasingly important for hydrological applications. These forecasts are typically derived from coupled atmosphere-ocean climate models, which enable physically based seasonal forecasts. In mountainous regions, however, topography is complex whilst typical spatial resolutions of the climate models are still comparably coarse, i.e in the data, ridges and valleys are not represented with sufficient accuracy. Therefore, seasonal predictions of atmospheric variables require consideration of representative gradients. We present first results of seasonal forecasts and re-forecasts processed by the NCEP (National Centers for Environmental Prediction) Climate Forecast System version 2 (CFSv2). These are prepared for monthly time steps in order to be used for ensemble runs of water balance simulation using the Alpine Water balance And Runoff Estimation model (AWARE). This model has been designed for monthly seasonal predictions in ice- and snowmelt dominated catchments. The study area is the Inn catchment in Tyrol/Austria, including its headwaters in Switzerland. Results are evaluated for both anomalies of meteorological input data (temperature and precipitation), as well as balance components including snow water equivalent and runoff, both simulated with AWARE. Based on model skill evaluations derived from forecasts and observations, the model chain CFSv2 - AWARE proves helpful to analyse possible future hydrological system states of mountainous catchments with emphasis on spatio-temporal snow cover evolution.

  10. Forecasting of Amount of Rainfall in Rainy Season by using Information Obtained from El Nino Data

    NASA Astrophysics Data System (ADS)

    Yamada, Fujihiro; Yamamoto, Nobuyuki; Sugimoto, Shigeyuki; Ichiyanagi, Katsuhiro; Hibino, Yasuyuki; Nakano, Hiroyuki; Mizuno, Katsunori; Yukita, Kazuto; Goto, Yasuyuki

    In electric power system operation and dam management, it is important that we forecast the rainfall depth in the rainy season. This paper studies the technique using neural network in order to forecast the amount of rainfall in rainy season on upper district of dam for hydro power plant. A case study was carried out on Central Japan. We were able to confirm the effectiveness of the information obtained from El Nino and La Nina data.

  11. Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model

    NASA Astrophysics Data System (ADS)

    MacLeod, Dave A.; Jones, Anne; Di Giuseppe, Francesca; Caminade, Cyril; Morse, Andrew P.

    2015-04-01

    The severity and timing of seasonal malaria epidemics is strongly linked with temperature and rainfall. Advance warning of meteorological conditions from seasonal climate models can therefore potentially anticipate unusually strong epidemic events, building resilience and adapting to possible changes in the frequency of such events. Here we present validation of a process-based, dynamic malaria model driven by hindcasts from a state-of-the-art seasonal climate model from the European Centre for Medium-Range Weather Forecasts. We validate the climate and malaria models against observed meteorological and incidence data for Botswana over the period 1982-2006 the longest record of observed incidence data which has been used to validate a modeling system of this kind. We consider the impact of climate model biases, the relationship between climate and epidemiological predictability and the potential for skillful malaria forecasts. Forecast skill is demonstrated for upper tercile malaria incidence for the Botswana malaria season (January-May), using forecasts issued at the start of November; the forecast system anticipates six out of the seven upper tercile malaria seasons in the observational period. The length of the validation time series gives confidence in the conclusion that it is possible to make reliable forecasts of seasonal malaria risk, forming a key part of a health early warning system for Botswana and contributing to efforts to adapt to climate change.

  12. Improved Water and Energy Management Utilizing Seasonal to Interannual Hydroclimatic Forecasts

    NASA Astrophysics Data System (ADS)

    Arumugam, S.; Lall, U.

    2014-12-01

    Seasonal to interannual climate forecasts provide valuable information for improving water and energy management. Given that the climatic attributes over these time periods are typically expressed as probabilistic information, we propose an adaptive water and energy management framework that uses probabilistic inflow forecasts to allocate water for uses with pre-specified reliabilities. To ensure that the system needs are not compromised due to forecast uncertainty, we propose uncertainty reduction using model combination and based on a probabilistic constraint in meeting the target storage. The talk will present findings from recent studies from various basins that include (a) role of multimodel combination in reducing the uncertainty in allocation (b) relevant system characteristics that improve the utility of forecasts, (c) significance of streamflow forecasts in promoting interbasin transfers and (d) scope for developing power demand forecasts utilizing temperature forecasts. Potential for developing seasonal nutrient forecasts using climate forecasts for supporting water quality trading will also be presented. Findings and synthesis from the panel discussion from the recently concluded AGU chapman conference on "Seaonal to Interannual Hydroclimatic Forecasts and Water Management" will also be summarized.

  13. Recalibration of CFS seasonal precipitation forecasts using statistical techniques for bias correction

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

    The development and application of statistical techniques with a special focus on a recalibration of meteorological or hydrological forecasts to eliminate the bias between forecasts and observations has received a great deal of attention in recent years. One reason is that retrospective forecasts are nowadays available which allows for a proper training and validation of this kind of techniques. The objective of this presentation is to propose several statistical techniques with different degree of complexity and to evaluate and compare their performance for a recalibration of seasonal ensemble forecasts of monthly precipitation. The techniques selected in this study range from straightforward normal score and quantile-quantile transformation, local scaling, to more sophisticated and novel statistical techniques such as Copula-based methodology recently proposed by Laux et al. (2011). The seasonal forecasts are derived from the Climate Forecast System Version 2. This version is the current coupled ocean-atmosphere general circulation model of the U.S. National Centers for Environmental Prediction used to provide forecasts up to nine months. The CFS precipitation forecasts are compared to monthly precipitation observations from the Global Precipitation Climatology Centre. The statistical techniques are tested for semi-arid regions in West Africa and the Indian subcontinent focusing on large-scale river basins such as the Ganges and the Volta basin. In both regions seasonal precipitation forecasts are a crucial source of information for the prediction of hydro-meteorological extremes, in particular for droughts. The evaluation is done using retrospective CFS ensemble forecast from 1982 to 2009. The training of the statistical techniques is done in a cross-validation mode. The outcome of this investigation illustrates large systematic differences between forecasts and observations, in particular for the Volta basin in West Africa. The selection of straightforward

  14. Performance of univariate forecasting on seasonal diseases: the case of tuberculosis.

    PubMed

    Permanasari, Adhistya Erna; Rambli, Dayang Rohaya Awang; Dominic, P Dhanapal Durai

    2011-01-01

    The annual disease incident worldwide is desirable to be predicted for taking appropriate policy to prevent disease outbreak. This chapter considers the performance of different forecasting method to predict the future number of disease incidence, especially for seasonal disease. Six forecasting methods, namely linear regression, moving average, decomposition, Holt-Winter's, ARIMA, and artificial neural network (ANN), were used for disease forecasting on tuberculosis monthly data. The model derived met the requirement of time series with seasonality pattern and downward trend. The forecasting performance was compared using similar error measure in the base of the last 5 years forecast result. The findings indicate that ARIMA model was the most appropriate model since it obtained the less relatively error than the other model.

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

    USGS Publications Warehouse

    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

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

    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 is

  17. Incorporating Seasonal Forecast of Inflow into Existing Water Resource Management at Ubolratana Dam, Thailand

    NASA Astrophysics Data System (ADS)

    Chatikavanij, V.; Block, P. J.; Lall, U.

    2011-12-01

    Seasonal forecast of streamflow, coupled with dynamic reservoir operation policy, has been shown to improve water allocation and flood control. By considering current and expected future reservoir level, water managers can make more adaptive and informed judgments about reservoir operation and allocation policy. While seasonal forecast application has shown substantial positive results, its adoption is still lagging due to the difficulties in integrating forecast into the current reservoir management system. This project offers a simple framework for incorporating seasonal streamflow forecasts into existing operation to optimize reservoir management decisions at the Ubolratana Dam in the Northeastern region of Thailand. The framework allows for the retention of existing reservoir policy while altering the application of the upper rule curve based on forecast information. Our objective is to maximize water release for hydropower generation, minimize spill and shortages while meeting demands for irrigation and water quality control during the peak summer monsoon season. Climate variables, including sea-surface temperature and sea-level pressure in March-May, are used to develop streamflow forecast ensembles for September-November. We use a dynamically linked system of forecast and reservoir management models to specify operating rules for end of the month storage levels at the Ubolratana Reservoir. Benefits and reliability based on the forecast ensembles are compared with historical operations and a climatological-based approach. Forecast ensembles are also divided into terciles to evaluate performance during dry and wet years, of particular interest to water managers. Results demonstrate that the dynamic operations contingent on forecasts may increase water releases for hydropower, decrease spill and deficit, and improve reliability compared with the status quo.

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

    NASA Astrophysics Data System (ADS)

    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

  19. Statistical guidance on seasonal forecast of Korean dust days over South Korea in the springtime

    NASA Astrophysics Data System (ADS)

    Sohn, Keon Tae

    2013-09-01

    This study aimed to develop the seasonal forecast models of Korean dust days over South Korea in the springtime. Forecast mode was a ternary forecast (below normal, normal, above normal) which was classified based on the mean and the standard deviation of Korean dust days for a period of 30 years (1981-2010). In this study, we used three kinds of monthly data: the Korean dust days observed in South Korea, the National Center for Environmental Prediction in National Center for Atmospheric Research (NCEP/NCAR) reanalysis data for meteorological factors over source regions of Asian dust, and the large-scale climate indices offered from the Climate Diagnostic Center and Climate Prediction Center in NOAA. Forecast guidance consisted of two components; ordinal logistic regression model to generate trinomial distributions, and conversion algorithm to generate ternary forecast by two thresholds. Forecast guidance was proposed for each month separately and its predictability was evaluated based on skill scores.

  20. Evaluating National Weather Service Seasonal Forecast Products in Reservoir Operation Case Studies

    NASA Astrophysics Data System (ADS)

    Nielson, A.; Guihan, R.; Polebistki, A.; Palmer, R. N.; Werner, K.; Wood, A. W.

    2014-12-01

    Forecasts of future weather and streamflow can provide valuable information for reservoir operations and water management. A challenge confronting reservoir operators today is how to incorporate both climate and streamflow products into their operations and which of these forecast products are most informative and useful for optimized water management. This study incorporates several reforecast products provided by the Colorado Basin River Forecast Center (CBRFC) which allows a complete retrospective analysis of climate forecasts, resulting in an evaluation of each product's skill in the context of water resources management. The accuracy and value of forecasts generated from the Climate Forecast System version 2 (CFSv2) are compared to the accuracy and value of using an Ensemble Streamflow Predictions (ESP) approach. Using the CFSv2 may offer more insight when responding to climate driven extremes than the ESP approach because the CFSv2 incorporates a fully coupled climate model into its forecasts rather than using all of the historic climate record as being equally probable. The role of forecast updating frequency will also be explored. Decision support systems (DSS) for both Salt Lake City Parley's System and the Snohomish County Public Utility Department's (SnoPUD) Jackson project will be used to illustrate the utility of forecasts. Both DSS include a coupled simulation and optimization model that will incorporate system constraints, operating policies, and environmental flow requirements. To determine the value of the reforecast products, performance metrics meaningful to the managers of each system are to be identified and quantified. Without such metrics and awareness of seasonal operational nuances, it is difficult to identify forecast improvements in meaningful ways. These metrics of system performance are compared using the different forecast products to evaluate the potential benefits of using CFSv2 seasonal forecasts in systems decision making.

  1. Seasonal drought forecast system for food-insecure regions of East Africa

    NASA Astrophysics Data System (ADS)

    Shukla, Shraddhanand; McNally, Amy; Husak, Greg; Funk, Chris

    2014-05-01

    In East Africa, agriculture is mostly rainfed and hence sensitive to interannual rainfall variability, and the increasing food and water demands of a growing population place further stresses on the water resources of this region. Skillful seasonal agricultural drought forecasts for this region can inform timely water and agricultural management decisions, support the proper allocation of the region's water resources, and help mitigate socio-economic losses. Here we describe the development and implementation of a seasonal drought forecast system that is being used for providing seasonal outlooks of agricultural drought in East Africa. We present a test case of the evaluation and applicability of this system for March-April-May growing season over equatorial East Africa (latitude 20 south to 80 North and 360 E to 460E) that encompasses one of the most food insecure and climatically and socio-economically vulnerable regions in East Africa. This region experienced famine as recently as in 2011. The system described here combines advanced satellite and re-analysis as well as station-based long term and real-time observations (e.g. NASA's TRMM, Infra-red remote sensing, Climate Forecast System Reanalysis), state-of-the-art dynamical climate forecast system (NCEP's Climate Forecast System Verison-2) and large scale land surface models (e.g. Variable Infiltration Capacity, NASA's Land Information System) to provide forecasts of seasonal rainfall, soil moisture and Water Requirement Satisfaction Index (WRSI) throughout the season - with an emphasis on times when water is the most critical: start of season/planting and the mid-season/crop reproductive phase. Based on the hindcast assessment of this system, we demonstrate the value of this approach to the US Agency for International Development (USAID)'s efforts to mitigate future losses of lives and economic losses by allowing a proactive approach of drought management that includes early warning and timely action.

  2. Short to sub-seasonal hydrologic forecast to manage water and agricultural resources in India

    NASA Astrophysics Data System (ADS)

    Shah, Reepal; Sahai, Atul Kumar; Mishra, Vimal

    2017-02-01

    Water resources and agriculture are often affected by the weather anomalies in India resulting in disproportionate damage. While short to sub-seasonal prediction systems and forecast products are available, a skilful hydrologic forecast of runoff and root-zone soil moisture that can provide timely information has been lacking in India. Using precipitation and air temperature forecasts from the Climate Forecast System v2 (CFSv2), the Global Ensemble Forecast System (GEFSv2) and four products from the Indian Institute of Tropical Meteorology (IITM), here we show that the IITM ensemble mean (mean of all four products from the IITM) can be used operationally to provide a hydrologic forecast in India at a 7-45-day accumulation period. The IITM ensemble mean forecast was further improved using bias correction for precipitation and air temperature. Bias corrected precipitation forecast showed an improvement of 2.1 mm (on the all-India median mean absolute error - MAE), while all-India median bias corrected temperature forecast was improved by 2.1 °C for a 45-day accumulation period. Moreover, the Variable Infiltration Capacity (VIC) model simulated forecast of runoff and soil moisture successfully captured the observed anomalies during the severe drought years. The findings reported herein have strong implications for providing timely information that can help farmers and water managers in decision making in India.

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

    NASA Technical Reports Server (NTRS)

    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.

  4. Application of Medium and Seasonal Flood Forecasts for Agriculture Damage Assessment

    NASA Astrophysics Data System (ADS)

    Fakhruddin, Shamsul; Ballio, Francesco; Menoni, Scira

    2015-04-01

    Early warning is a key element for disaster risk reduction. In recent decades, major advancements have been made in medium range and seasonal flood forecasting. This progress provides a great opportunity to reduce agriculture damage and improve advisories for early action and planning for flood hazards. This approach can facilitate proactive rather than reactive management of the adverse consequences of floods. In the agricultural sector, for instance, farmers can take a diversity of options such as changing cropping patterns, applying fertilizer, irrigating and changing planting timing. An experimental medium range (1-10 day) and seasonal (20-25 days) flood forecasting model has been developed for Thailand and Bangladesh. It provides 51 sets of discharge ensemble forecasts of 1-10 days with significant persistence and high certainty and qualitative outlooks for 20-25 days. This type of forecast could assist farmers and other stakeholders for differential preparedness activities. These ensembles probabilistic flood forecasts have been customized based on user-needs for community-level application focused on agriculture system. The vulnerabilities of agriculture system were calculated based on exposure, sensitivity and adaptive capacity. Indicators for risk and vulnerability assessment were conducted through community consultations. The forecast lead time requirement, user-needs, impacts and management options for crops were identified through focus group discussions, informal interviews and community surveys. This paper illustrates potential applications of such ensembles for probabilistic medium range and seasonal flood forecasts in a way that is not commonly practiced globally today.

  5. Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation Downscaling

    NASA Technical Reports Server (NTRS)

    Koster, Randal; Walker, Greg; Mahanama, Sarith; Reichle, Rolf

    2012-01-01

    Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which the downscaling of seasonal precipitation forecasts, if it could be done accurately, would improve streamflow forecasts. The reduction in streamflow forecast skill (with forecasted streamflow measured against observations) associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself. This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized ("perfect model") analysis, precipitation downscaling is found to have an impact on large-scale streamflow forecasts only if two conditions are met: (i) evaporation variance is significant relative to the precipitation variance, and (ii) the subgrid spatial variance of precipitation is adequately large. In the large-scale continental region studied (the conterminous United States), these two conditions are met in only a somewhat limited area.

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

    NASA Astrophysics Data System (ADS)

    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

  7. Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

    SciTech Connect

    Shukla, Shraddhanand; Voisin, Nathalie; Lettenmaier, D. P.

    2012-08-15

    We investigated the contribution of medium range weather forecasts with lead times up to 14 days to seasonal hydrologic prediction skill over the Conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP)-based experiments were performed for the period 1980-2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980-2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts, for runoff (SM) forecasts generally varies from 0 to 0.8 (0 to 0.5) as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

  8. The potential value of seasonal forecasts in a changing climate in southern Africa

    NASA Astrophysics Data System (ADS)

    Winsemius, H. C.; Dutra, E.; Engelbrecht, F. A.; Archer Van Garderen, E.; Wetterhall, F.; Pappenberger, F.; Werner, M. G. F.

    2014-04-01

    Subsistence farming in southern Africa is vulnerable to extreme weather conditions. The yield of rain-fed agriculture depends largely on rainfall-related factors such as total seasonal rainfall, anomalous onsets and lengths of the rainy season and the frequency of occurrence of dry spells. Livestock, in turn, may be seriously impacted by climatic stress with, for example, exceptionally hot days, affecting condition, reproduction, vulnerability to pests and pathogens and, ultimately, morbidity 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 measure comprises the timely forecasting and warning of such extreme events, combined with mitigation measures that allow farmers to prepare for the event occurring. This paper investigates how the frequency of extreme events may change in the future due to climate change over southern Africa and, in more detail, the Limpopo Basin using a set of climate change projections from several regional climate model downscalings based on an extreme climate scenario. Furthermore, the paper assesses the predictability of these indicators by seasonal meteorological forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system. The focus is on the frequency of dry spells as well as the frequency of heat stress conditions expressed in the temperature heat index. In areas where their frequency of occurrence increases in the future and predictability is found, seasonal forecasts will gain importance in the future, as they can more often lead to informed decision-making to implement mitigation measures. The multi-model climate projections suggest that the frequency of dry spells is not likely to increase substantially, whereas there is a clear and coherent signal among the models of an increase in the frequency of heat stress conditions by the end of the century. The

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

    NASA Astrophysics Data System (ADS)

    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

  10. Seasonal forecasting of global hydrologic extremes using the North American Multi-model Ensemble system

    NASA Astrophysics Data System (ADS)

    Wood, Eric F.; Yuan, Xing; Roundy, Joshua K.; Sheffield, Justin

    2015-04-01

    Seasonal hydrologic extremes in the form of droughts and wet spells have devastating impacts on human and natural systems. Improving our understanding and predictive capability of hydrologic extremes, and facilitating adaptations through establishing climate service systems at regional to global scales, are among the grand challenges proposed by the World Climate Research Programme (WCRP), and are the core themes of the Regional Hydroclimate Projects (RHP) under the Global Energy and Water Exchanges Project (GEWEX). An experimental global seasonal hydrologic forecasting system has been developed, which is based on coupled climate forecast models participating in the North American Multi-Model Ensemble (NMME) project and an advanced land surface hydrologic model. The system is evaluated over major GEWEX/RHP river basins by comparing with Ensemble Streamflow Prediction (ESP). The multi-model seasonal forecast system provides higher detectability for soil moisture droughts, more reliable low and high flow ensemble forecasts, and better "real-time" prediction for the 2012 North American extreme drought. The association of the onset of extreme hydrologic events with oceanic and land precursors is also investigated based on the joint distribution of forecasts and observations. Climate models have a higher probability of missing the onset of hydrologic extremes when there is no oceanic precursor. But oceanic precursor alone is insufficient to guarantee a correct forecast, a land precursor is also critical in avoiding a false alarm for forecasting extremes. This study is targeted at providing the scientific underpinning for the predictability of hydrologic extremes over GEWEX/RHP basins, and serves as a prototype for seasonal hydrologic forecasts within the Global Framework for Climate Services (GFCS).

  11. Application of seasonal climate forecasts in agricultural crop monitoring in Brazil

    NASA Astrophysics Data System (ADS)

    de Avila, A. M. H.; Pereira, V. R.; Lopes, F. A.

    2014-12-01

    This work is investigating the contribution of seasonal climate forecasts of Eta regional climate model to support crops in Brazil. The weather conditions are directed related with the crop yield, being a basic parameter for its forecast. The southern region has a subtropical climate and is the major national producer of rice and wheat and also is the second one for soybean, bean and corn. The Eta seasonal forecast model data for southern Brazil was evaluated from 2001 to 2010. Observed data from National and state meteorological agencies were used to evaluate the monthly model performance. The model performance was evaluated by calculating two parameters. The Root Mean Square Error (RMSE) was used to evaluate the monthly forecast averages and the observed precipitation standard deviation. The Skill Score Climatology (SSC) was used to compare the accuracy between the forecast and the climatology. The RMSE showed that in some locations the predicted values by the model were closer to the observed. The SSC showed a systematic error for the predicted values by the Eta seasonal model. This behavior indicates that the climatological analysis is more accurate to predict the monthly climate than the ETA model forecast. Also the consecutive negative bias was observed in some locations that can be corrected removing the systematic error.

  12. Probabilistic maize yield simulation over East Africa using ensemble seasonal climate forecasts

    NASA Astrophysics Data System (ADS)

    Ogutu, Geoffrey; Supit, Iwan; Hutjes, Ronald

    2016-04-01

    Seasonal climate variability influences crop yields, especially in areas where rain fed agriculture is widely practiced such as in the East African region. Assuming that seasonal climate prediction skill would translate to similarly skillful prediction of impacts, an ensemble seasonal climate hindcast (ECMWF system4 EPS) for the period 1981 to 2010 at different initialization dates (lead months) before sowing is used to drive a crop simulation model: the World Food Studies (WOFOST) model, implemented for a single season Maize crop. The water-limited yield predictions were assessed against reference yields produced by the same crop model forced by the WATCH Forcing Data ERA-Interim (WFDEI) at grid point level. We focus on the main sowing dates of June/July (Northern region), March (Equatorial East Africa) and November (Southern region). Deviation of yields from the mean over the study period is used to indicate regions in which probabilistic yield forecasts would be useful while the Relative Operating Curve Skill Score (ROCSS) indicates prediction skill of above normal, near normal and below normal yield prediction. Equatorial regions of East Africa and coastal Kenya with sowing date in March show a mean deviation of ≥ 500 Kg/ha. Here probabilistic yield forecasts can potentially be useful as opposed to the northern and southern regions with less deviation. The high deviation in this region may also be due to the existence of more than one cropping season corresponding to the bi-modal rainfall regime since the model only simulates a single season. A positive ROCSS over a large extent of the equatorial region show predictability skill of all the tercile forecasts simulated by forecasts initialized at the start of sowing date (March i.e. lead 0 forecasts) and no predictability at longer lead months. Over Ethiopia in the northern region of East Africa, November harvests with a sowing date of June show predictability of the upper, lower and middle terciles at lead-0

  13. Identification of the drivers controlling the seasonal hydrological forecasting skill in Europe

    NASA Astrophysics Data System (ADS)

    Pechlivanidis, Ilias; Spångmyr, Henrik; Bosshard, Thomas

    2016-04-01

    : Recent advances in understanding and forecasting of climate have led into skilful meteorological predictions, which can consequently increase the confidence of hydrological prognosis. There is currently a need to understand the large European river systems and make practical use of seasonal hydrological forecasts. In here, we analyse the seasonal predictive skill along Europe's hydro-climatic gradient using the pan-European E-HYPE v3.0 multi-basin hydrological model. Both model state initialisation (level in surface water, i.e. reservoirs, lakes and wetlands, soil moisture, snow depth) and provision of climatology are based on forcing input derived from the WFDEI product for the period 1979-2010. An ensemble of re-forecast forcing data (daily mean precipitation and temperature for the period 1981-2010) from ECMWF's System 4 (15 members initialised every month) are firstly bias corrected using a modified version of the Distribution Based Scaling (DBS) method to account for drifting conditioning the bias correction on the lead month, and further used to drive E-HYPE. The predictive skill of river runoff based on ECMWF and climatology for the European basins is assessed on monthly timescales. Seasonal re-forecasts are evaluated with respect to their accuracy against impact variables, i.e. streamflow, at different space and time-scales; the value of the predictions is assessed using various (deterministic and probabilistic) performance metrics. We analyse the skill across the about 35000 subbasins which represent various climatologies, soil-types, land uses, altitudes and basin scales within Europe. We finally use the Classification and Regression Trees (CART) analysis to link the gain in the seasonal skill to physiographic-climatic characteristics and meteorological skill, in order to suggest possible model improvements. Keywords: Seasonal hydrological forecasting; E-HYPE; ensemble seasonal forecasts; pan-European scale

  14. An analysis of seasonal forecasts from POAMA and SCOPIC in the Pacific region

    NASA Astrophysics Data System (ADS)

    Cottrill, Andrew; Charles, Andrew; Kuleshov, Yuriy

    2013-04-01

    The Australian Bureau of Meteorology (BoM), as part of the Pacific Island Climate Prediction Project (PI-CPP), has developed seasonal forecasts for ten National Meteorological Services (NMS) in the Pacific region for nearly a decade, to improve seasonal forecast services to local communities and industry. As part of this project, a new statistical model called SCOPIC (Seasonal Climate Outlooks for Pacific Island Countries) was developed to provide partner countries with the ability to produce their own seasonal climate outlooks. In 2010, as part of the Pacific Adaptation Strategy and Assistance Programme (PASAP), the BoM developed a seasonal outlook portal for Pacific NMS as an alternative source of seasonal forecasts based on the Bureau's dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia). This dynamical model is a coupled ocean-atmosphere model, which has been developed by the Bureau for over ten years for forecasting research in Australia. However, no formal assessment of the skill of the two forecast systems (POAMA and SCOPIC) has been carried out using a number of skill metrics for the Pacific region. Although the skill of POAMA in the Australian region is now well documented, the forecast skill is even higher in the Pacific region due to its proximity to the tropical ocean, where the El Niño-Southern Oscillation (ENSO) provides the main source of tropical climate variability and predictability on seasonal time scales. The statistical model (SCOPIC) uses discriminant analysis (multiple linear regression) and the relationships of sea surface temperatures (SST) or the Southern Oscillation Index (predictors) and monthly rainfall (predictands) to predict rainfall at various lead times. In contrast, POAMA uses the current state of the climate (initial ocean and atmospheric conditions) and model physics to predict forecasts of many climate variables at all locations across the globe and also at various lead times. Here we demonstrate the skill

  15. Applying Seasonal Climate Forecasts to Project Streamflows and Water Storage of Reservoirs

    NASA Astrophysics Data System (ADS)

    Lin, Hsuan-Te

    2016-04-01

    It is important to estimate available water in advance for water resources management. The purpose of this study is to apply seasonal climate forecasts to project streamflows and the storage of reservoirs with the lead time of three months, which can further be used to analyzes drought risk and even to develop drought early warning system (DEWS). The Central Weather Bureau of Taiwan has developed a two-tier dynamical climate forecast system (CWB-2tier-GFS-T42L18), which combines two atmospheric general circulation models with two global sea surface temperature forecasts. The CWB system can be used to forecast temperature and precipitation with the lead time of three months. The climatic conditions are classified into three categories (Below Normal, Normal, and Above Normal). This research generates weather data based on the projected seasonal climate to input a hydrological model to estimate streamflows. The hydrological component of GWLF model is used to simulate streamflows. Furthermore, the simulated streamflows are used to calculate the inflow and storage of Baoshan Reservoir and Baoshan Second Reservoir. The reliability of using seasonal climate forecast to project streamflows and available water of reservoirs will be verified. Keywords: Seasonal Climate, Water Resources, Hydrology, Reservoir, Drought

  16. Two-stage seasonal streamflow forecasts to guide water resources decisions and water rights allocation

    NASA Astrophysics Data System (ADS)

    Block, P. J.; Gonzalez, E.; Bonnafous, L.

    2011-12-01

    Decision-making in water resources is inherently uncertain producing copious risks, ranging from operational (present) to planning (season-ahead) to design/adaptation (decadal) time-scales. These risks include human activity and climate variability/change. As the risks in designing and operating water systems and allocating available supplies vary systematically in time, prospects for predicting and managing such risks become increasingly attractive. Considerable effort has been undertaken to improve seasonal forecast skill and advocate for integration to reduce risk, however only minimal adoption is evident. Impediments are well defined, yet tailoring forecast products and allowing for flexible adoption assist in overcoming some obstacles. The semi-arid Elqui River basin in Chile is contending with increasing levels of water stress and demand coupled with insufficient investment in infrastructure, taxing its ability to meet agriculture, hydropower, and environmental requirements. The basin is fed from a retreating glacier, with allocation principles founded on a system of water rights and markets. A two-stage seasonal streamflow forecast at leads of one and two seasons prescribes the probability of reductions in the value of each water right, allowing water managers to inform their constituents in advance. A tool linking the streamflow forecast to a simple reservoir decision model also allows water managers to select a level of confidence in the forecast information.

  17. The effect of seasonality and stationarity on specific humidity forecast over Nigeria

    NASA Astrophysics Data System (ADS)

    Adeyemi, B.; Akerele, M. I.

    2017-04-01

    This paper investigates the importance of seasonality and stationarity of climatological data to effective forecast of a meteorological parameter - the specific humidity, 'q'. This was done by computing q using monthly datasets of temperature and relative humidity from twenty carefully selected stations for twenty-six (26) years (1970-1995). The data were retrieved from the archives of the Nigerian Meteorological Agencies. The data were analysed to examine its seasonality as well as stationarity which are considered to be the basic requirements for good and accurate forecasting in climate studies. The stations were grouped into four climatic regions namely: the Sahel, Guinea Savanna, Rainforest and Derived Savanna regions. It was found that stations from the Guinea Savanna and Sahelian regions show more seasonality and stationarity compared to stations in Rainforest and Derived Savanna regions. Forecast was then carried out using three techniques which are: the ordinary Autoregressive model, Harmonic Analysis and Exponential Smoothing modelling techniques. These were analysed and evaluated using series of statistical error analysis methods. Exponential smoothing technique, which incorporates the seasonal components of the data into its model, was found to give the most accurate and reliable results with minimal statistical errors, re-iterating the importance of seasonality and stationarity in effective climate forecast.

  18. Seasonal forecasts of impact-relevant climate information indices developed as part of the EUPORIAS project

    NASA Astrophysics Data System (ADS)

    Spirig, Christoph; Bhend, Jonas

    2015-04-01

    Climate information indices (CIIs) represent a way to communicate climate conditions to specific sectors and the public. As such, CIIs provide actionable information to stakeholders in an efficient way. Due to their non-linear nature, such CIIs can behave differently than the underlying variables, such as temperature. At the same time, CIIs do not involve impact models with different sources of uncertainties. As part of the EU project EUPORIAS (EUropean Provision Of Regional Impact Assessment on a Seasonal-to-decadal timescale) we have developed examples of seasonal forecasts of CIIs. We present forecasts and analyses of the skill of seasonal forecasts for CIIs that are relevant to a variety of economic sectors and a range of stakeholders: heating and cooling degree days as proxies for energy demand, various precipitation and drought-related measures relevant to agriculture and hydrology, a wild fire index, a climate-driven mortality index and wind-related indices tailored to renewable energy producers. Common to all examples is the finding of limited forecast skill over Europe, highlighting the challenge for providing added-value services to stakeholders operating in Europe. The reasons for the lack of forecast skill vary: often we find little skill in the underlying variable(s) precisely in those areas that are relevant for the CII, in other cases the nature of the CII is particularly demanding for predictions, as seen in the case of counting measures such as frost days or cool nights. On the other hand, several results suggest there may be some predictability in sub-regions for certain indices. Several of the exemplary analyses show potential for skillful forecasts and prospect for improvements by investing in post-processing. Furthermore, those cases for which CII forecasts showed similar skill values as those of the underlying meteorological variables, forecasts of CIIs provide added value from a user perspective.

  19. What is the current state of scientific knowledge with regard to seasonal and decadal forecasting?

    NASA Astrophysics Data System (ADS)

    Smith, Doug M.; Scaife, Adam A.; Kirtman, Ben P.

    2012-03-01

    Environmental factors, such as the frequency, intensity and duration of extreme weather events, are important drivers of migration and displacement of people. There is therefore a growing need for regional climate predictions for the coming seasons to decades. This paper reviews the current state of the art of seasonal to decadal climate prediction, focusing on the potential sources of skill, forecasting techniques, current capability and future prospects.

  20. Using NPMR to forecast fire season severity for the state of Oregon

    NASA Astrophysics Data System (ADS)

    Lintz, H. E.; Short, K.; Saltenberger, J. F.; Yost, A.; Mote, P.; Wood, A. W.

    2014-12-01

    We demonstrate that objective prediction of fire season severity in Oregon is possible and promising. The causes of fire season severity are various but seasonal climate plays a dominant role in the Pacific Northwest. Seasonal climate prediction has been gaining momentum in recent decades, and skillful forecasting occurs when perturbed boundary conditions alter weather regimes regionally. Regimes most conducive to fire season severity in Oregon are anomalously warm, dry seasons with pronounced dry lightning activity. Here, we used Non-Parametric Multiplicative Regression, a forecasting algorithm well suited to automatically accommodating complex interactions, to identify meaningful interactions and predict fire season severity as a function of sea surface temperature anomalies and atmospheric modes. We find the severity of a fire season can be forecast several months ahead of time in March with a cross-validated R-squared of 0.53. A cross-validated R-squared is more conservative than an R-squared as it is a measure of model fit to previously unseen data. The model we developed predicts fire season severity as the result of a complex interaction between specific modes the Pacific Decadal Oscillation, Pacific North American Pattern, and the Madden Julian Oscillation. Modes were derived using Variational Mode Decomposition (VMD). VMD is a new algorithm in signal processing that improves upon the limitations of Empirical Mode Decomposition (EMD) to decompose a signal into different modes of unknown but separate spectral bands. Prediction of fire season severity from atmosheric and boundary conditions can help budgetary planning and reduce the number of stand replacing fires and related economic losses.

  1. Parametrisation of initial conditions for seasonal stream flow forecasting in the Swiss Rhine basin

    NASA Astrophysics Data System (ADS)

    Schick, Simon; Rössler, Ole; Weingartner, Rolf

    2016-04-01

    Current climate forecast models show - to the best of our knowledge - low skill in forecasting climate variability in Central Europe at seasonal lead times. When it comes to seasonal stream flow forecasting, initial conditions thus play an important role. Here, initial conditions refer to the catchments moisture at the date of forecast, i.e. snow depth, stream flow and lake level, soil moisture content, and groundwater level. The parametrisation of these initial conditions can take place at various spatial and temporal scales. Examples are the grid size of a distributed model or the time aggregation of predictors in statistical models. Therefore, the present study aims to investigate the extent to which the parametrisation of initial conditions at different spatial scales leads to differences in forecast errors. To do so, we conduct a forecast experiment for the Swiss Rhine at Basel, which covers parts of Germany, Austria, and Switzerland and is southerly bounded by the Alps. Seasonal mean stream flow is defined for the time aggregation of 30, 60, and 90 days and forecasted at 24 dates within the calendar year, i.e. at the 1st and 16th day of each month. A regression model is employed due to the various anthropogenic effects on the basins hydrology, which often are not quantifiable but might be grasped by a simple black box model. Furthermore, the pool of candidate predictors consists of antecedent temperature, precipitation, and stream flow only. This pragmatic approach follows the fact that observations of variables relevant for hydrological storages are either scarce in space or time (soil moisture, groundwater level), restricted to certain seasons (snow depth), or regions (lake levels, snow depth). For a systematic evaluation, we therefore focus on the comprehensive archives of meteorological observations and reanalyses to estimate the initial conditions via climate variability prior to the date of forecast. The experiment itself is based on four different

  2. Towards Optimization of Reservoir Operations for Hydropower Production in East Africa: Application of Seasonal Climate Forecasts and Remote Sensing Products

    NASA Astrophysics Data System (ADS)

    Demissie, S. S.; Gebremichael, M.; Hopson, T. M.; Riddle, E. E.; Yeh, W. W. G.

    2015-12-01

    Hydroelectric generation and interconnections are the major priority areas of infrastructure development in Africa. A number of hydropower projects are currently being developed in East Africa in order to meet the energy demands of the fast growing economy in sustainable and climate-resilient manner. However, the performance efficiency of existing hydropower systems in Africa is much lower (about 30% in some cases) than their design capacity. This study proposes a decision support system (DSS) that integrates climate forecasts and remote sensing products into modeling and optimization of the hydropower systems in order to achieve reliable reservoir operations and enhance hydropower production efficiency. The DSS has three main components; climate system, hydrologic and water resources system, and optimization system. The climate system comprises of tools and interfaces for accessing, customizing and integrating climate forecasts and remote sensing data. The North America Multi-Model Ensemble (NMME) seasonal retrospective forecasts for the East Africa Power Pool (EAPP) region are compared with the TRMM rainfall estimates and the CPC unified gauged rainfall data. The errors of the NMME seasonal forecasts have portrayed significant spatial and temporal variability in the EAPP region. The root mean square errors of the seasonal forecasts are relatively higher for wetter locations and months. However, the skills of the NMME seasonal forecasts are not significantly depreciating with lead time for the study region. The seasonal forecast errors vary from one model to another. Here, we present the skills of NMME seasonal forecasts, the physical factors and mechanisms that affect the skills. In addition, we discuss our methodology that derives the best seasonal forecasts for the study region from the NMME seasonal forecasts, and show how the climate forecast errors propagate through hydrologic models into hydrological forecasting.

  3. Arctic sea ice trends, variability and implications for seasonal ice forecasting

    PubMed Central

    Serreze, Mark C.; Stroeve, Julienne

    2015-01-01

    September Arctic sea ice extent over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal ice forecasting. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability. PMID:26032315

  4. Arctic sea ice trends, variability and implications for seasonal ice forecasting.

    PubMed

    Serreze, Mark C; Stroeve, Julienne

    2015-07-13

    September Arctic sea ice extent over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal ice forecasting. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability.

  5. Generating synthetic daily precipitation realizations for seasonal precipitation forecasts

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Synthetic weather generation models that depend on statistics of past weather observations are often limited in their applications to issues that depend upon historical weather characteristics. Enhancing these models to take advantage of increasingly available and skillful seasonal climate outlook p...

  6. Developing the Capacity of Farmers to Understand and Apply Seasonal Climate Forecasts through Collaborative Learning Processes

    ERIC Educational Resources Information Center

    Cliffe, Neil; Stone, Roger; Coutts, Jeff; Reardon-Smith, Kathryn; Mushtaq, Shahbaz

    2016-01-01

    Purpose: This paper documents and evaluates collaborative learning processes aimed at developing farmer's knowledge, skills and aspirations to use seasonal climate forecasting (SCF). Methodology: Thirteen workshops conducted in 2012 engaged over 200 stakeholders across Australian sugar production regions. Workshop design promoted participant…

  7. Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

    NASA Technical Reports Server (NTRS)

    Koster, Randal D.; Walker, Gregory K.; Mahanama, Sarith P.; Reichle, Rolf H.

    2013-01-01

    Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, e.g., through satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale streamflow forecasts only when evaporation variance is significant relative to the precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface modeling system as a tool for addressing the science of hydrological prediction.

  8. Seasonal forecasting of groundwater levels in natural aquifers in the United Kingdom

    NASA Astrophysics Data System (ADS)

    Mackay, Jonathan; Jackson, Christopher; Pachocka, Magdalena; Brookshaw, Anca; Scaife, Adam

    2014-05-01

    Groundwater aquifers comprise the world's largest freshwater resource and provide resilience to climate extremes which could become more frequent under future climate changes. Prolonged dry conditions can induce groundwater drought, often characterised by significantly low groundwater levels which may persist for months to years. In contrast, lasting wet conditions can result in anomalously high groundwater levels which result in flooding, potentially at large economic cost. Using computational models to produce groundwater level forecasts allows appropriate management strategies to be considered in advance of extreme events. The majority of groundwater level forecasting studies to date use data-based models, which exploit the long response time of groundwater levels to meteorological drivers and make forecasts based only on the current state of the system. Instead, seasonal meteorological forecasts can be used to drive hydrological models and simulate groundwater levels months into the future. Such approaches have not been used in the past due to a lack of skill in these long-range forecast products. However systems such as the latest version of the Met Office Global Seasonal Forecast System (GloSea5) are now showing increased skill up to a 3-month lead time. We demonstrate the first groundwater level ensemble forecasting system using a multi-member ensemble of hindcasts from GloSea5 between 1996 and 2009 to force 21 simple lumped conceptual groundwater models covering most of the UK's major aquifers. We present the results from this hindcasting study and demonstrate that the system can be used to forecast groundwater levels with some skill up to three months into the future.

  9. Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods

    NASA Astrophysics Data System (ADS)

    Seibert, Mathias; Merz, Bruno; Apel, Heiko

    2017-03-01

    The Limpopo Basin in southern Africa is prone to droughts which affect the livelihood of millions of people in South Africa, Botswana, Zimbabwe and Mozambique. Seasonal drought early warning is thus vital for the whole region. In this study, the predictability of hydrological droughts during the main runoff period from December to May is assessed using statistical approaches. Three methods (multiple linear models, artificial neural networks, random forest regression trees) are compared in terms of their ability to forecast streamflow with up to 12 months of lead time. The following four main findings result from the study. 1. There are stations in the basin at which standardised streamflow is predictable with lead times up to 12 months. The results show high inter-station differences of forecast skill but reach a coefficient of determination as high as 0.73 (cross validated). 2. A large range of potential predictors is considered in this study, comprising well-established climate indices, customised teleconnection indices derived from sea surface temperatures and antecedent streamflow as a proxy of catchment conditions. El Niño and customised indices, representing sea surface temperature in the Atlantic and Indian oceans, prove to be important teleconnection predictors for the region. Antecedent streamflow is a strong predictor in small catchments (with median 42 % explained variance), whereas teleconnections exert a stronger influence in large catchments. 3. Multiple linear models show the best forecast skill in this study and the greatest robustness compared to artificial neural networks and random forest regression trees, despite their capabilities to represent nonlinear relationships. 4. Employed in early warning, the models can be used to forecast a specific drought level. Even if the coefficient of determination is low, the forecast models have a skill better than a climatological forecast, which is shown by analysis of receiver operating characteristics

  10. Using meteorological data to forecast seasonal runoff on the River Jhelum, Pakistan

    NASA Astrophysics Data System (ADS)

    Archer, D. R.; Fowler, H. J.

    2008-10-01

    SummaryThe upper River Jhelum, which drains the southern slopes of the Himalaya and Pir Panjal, provides water for power and irrigated agriculture, the mainstay of the national economy of Pakistan. Seasonal forecasts of spring and summer flow provide the opportunity for planning and would confer significant national benefits. In this mountainous region, runoff from snowmelt and glacier-melt provides the dominant contribution to river flows during the spring and summer seasons although monsoon rainfall may also influence peak flows. Estimates of runoff in the Jhelum and its main tributaries can be made using precipitation measurements from valley stations; producing correlation coefficients of >0.7 between winter precipitation and spring and summer runoff. This study investigates the links between climate and runoff for eight gauging stations in the Jhelum catchment but then concentrates on seasonal forecasting of spring and summer inflows to Mangla Dam which is a major controlling structure contributing to the Indus Basin Irrigation System. Observed climatic variables, precipitation and temperature, from valley stations are used to forecast summer season flows at stations upstream from the reservoir with a lead time of up to three months based on multiple linear regression models built using data from 1965 to 1979. The analysis demonstrates that good forecasts within 15% of observed flows for 92% of years (ROC score = 0.77) can be achieved for summer season flows from April to September over the 1980-1991 validation period. For spring flows from April to June, excellent forecasts can be provided within 15% of observed flows for 83% of years, with a ROC score of 0.93. These provide a useful basis for practical water management.

  11. Improving groundwater predictions utilizing seasonal precipitation forecasts from general circulation models forced with sea surface temperature forecasts

    USGS Publications Warehouse

    Almanaseer, Naser; Sankarasubramanian, A.; Bales, Jerad

    2014-01-01

    Recent studies have found a significant association between climatic variability and basin hydroclimatology, particularly groundwater levels, over the southeast United States. The research reported in this paper evaluates the potential in developing 6-month-ahead groundwater-level forecasts based on the precipitation forecasts from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature forecasts. Ten groundwater wells and nine streamgauges from the USGS Groundwater Climate Response Network and Hydro-Climatic Data Network were selected to represent groundwater and surface water flows, respectively, having minimal anthropogenic influences within the Flint River Basin in Georgia, United States. The writers employ two low-dimensional models [principle component regression (PCR) and canonical correlation analysis (CCA)] for predicting groundwater and streamflow at both seasonal and monthly timescales. Three modeling schemes are considered at the beginning of January to predict winter (January, February, and March) and spring (April, May, and June) streamflow and groundwater for the selected sites within the Flint River Basin. The first scheme (model 1) is a null model and is developed using PCR for every streamflow and groundwater site using previous 3-month observations (October, November, and December) available at that particular site as predictors. Modeling schemes 2 and 3 are developed using PCR and CCA, respectively, to evaluate the role of precipitation forecasts in improving monthly and seasonal groundwater predictions. Modeling scheme 3, which employs a CCA approach, is developed for each site by considering observed groundwater levels from nearby sites as predictands. The performance of these three schemes is evaluated using two metrics (correlation coefficient and relative RMS error) by developing groundwater-level forecasts based on leave-five-out cross-validation. Results from the research reported in this paper show that using

  12. Seasonal Forecasts for Northern Hemisphere Winter 2015/16

    NASA Astrophysics Data System (ADS)

    Ineson, Sarah; Scaife, Adam; Comer, Ruth; Dunstone, Nick; Fereday, David; Folland, Chris; Gordon, Margaret; Karpechko, Alexey; Knight, Jeff; MacLachlan, Craig; Smith, Doug; Walker, Brent

    2016-04-01

    The northern winter of 2015/16 gave rise to the strongest El Niño event since 1997/8. Central and eastern Pacific sea surface temperature anomalies exceeded three degrees and closely resembled the strong El Niño in winter of 1982/3. A second feature of this winter was a strong westerly phase of the Quasi-Biennial Oscillation and very strong winds in the stratospheric polar night jet. At the surface, intense extratropical circulation anomalies occurred in both the North Pacific and North Atlantic that were consistent with known teleconnections to the observed phases of ENSO and the QBO. The North Atlantic Oscillation was very positive in the early winter period (Nov-Dec) and was more blocked in the late winter. Initialised climate predictions were able to capture these signals at seasonal lead times. This case study adds to the evidence that north Atlantic circulation exhibits predictability on seasonal timescales, and in this case we show that even aspects of the detailed pattern and sub-seasonal evolution were predicted, providing warning of increased risk of extreme events such as the intense rainfall which caused extreme flooding in the UK in December.

  13. A Simple Bayesian Climate Index Weighting Method for Seasonal Ensemble Forecasting

    NASA Astrophysics Data System (ADS)

    Bradley, A.; Habib, M. A.; Schwartz, S. S.

    2014-12-01

    Climate information — in the form of a measure of climate state or a climate forecast — can be an important predictor of future hydrologic conditions. For instance, streamflow variability for many locations around the globe is related to large-scale atmospheric oscillations, like the El Nino Southern Oscillation (ENSO) or the Pacific/Decadal Oscillation (PDO). Furthermore, climate forecast models are growing more skillful in their predictions of future climate variables on seasonal time scales. Finding effective ways to translate this climate information into improved hydrometeorological predictions is an area of ongoing research. In ensemble streamflow forecasting, where historical weather inputs or streamflow observations are used to generate the ensemble, climate index weighting is one way to represent the influence of current climate information. Using a climate index, each forecast variable member of the ensemble is selectively weighted to reflect climate conditions at the time of the forecast. A simple Bayesian climate index weighting of ensemble forecasts is presented. The original hydrologic ensemble members define a sample of the prior distribution; the relationship between the climate index and the ensemble member forecast variable is used to estimate a likelihood function. Given an observation of the climate index at the time of the forecast, the estimated likelihood function is then used to assign weights to each ensemble member. The weighted ensemble forecast is then used to estimate the posterior distribution of the forecast variable conditioned on the climate index. The proposed approach has several advantages over traditional climate index weighting methods. The weights assigned to the ensemble members accomplish the updating of the (prior) ensemble forecast distribution based on Bayes' Theorem, so the method is theoretically sound. The method also automatically adapts to the strength of the relationship between the climate index and the

  14. Impact of data assimilation on high-resolution rainfall forecasts: A spatial, seasonal, and category analysis

    NASA Astrophysics Data System (ADS)

    V, Rakesh; Goswami, Prashant

    2015-01-01

    a limited area model (LAM), the impact of data assimilation is likely to depend on the background state through lateral boundary forcing; this may introduce certain seasonality in the impact of data assimilation on rainfall forecasting. It is also likely that the impact of data assimilation on forecasts will have certain spatial variability. Finally, owing to the convective nature of rainfall and the roles of parameterization scheme, the impact of data assimilation may depend on the category (intensity) of rainfall. Here these aspects for rainfall forecasts at high resolution were examined. Using a LAM (An advanced version of Weather Research and Forecasting Model), we have carried out twin simulations with and without data assimilation; the simulations without data assimilation are used as the benchmark for assessing the impact of data assimilation. Analysis of simulations for 40 sample days distributed over the years 2012-2014 over Karnataka (southern state in India) is carried out to estimate impact of data assimilation. Various statistical measures show that data assimilation improved the rainfall prediction in most cases; however, there is also strong seasonality and location dependence in impact of data assimilation. Our results also show that improvement due to data assimilation is higher/lower for lower/higher rainfall categories. Analysis shows that the cases where the initial states with data assimilation depart strongly from the first guess generally result in less or even negative impact. It is pointed out that the results have important implications in design of observation system and assessment of impact of forecasts.

  15. Seasonal Sensitivity on COBEL-ISBA Local Forecast System for Fog and Low Clouds

    NASA Astrophysics Data System (ADS)

    Roquelaure, Stevie; Bergot, Thierry

    2007-06-01

    Skillful low visibility forecasts are essential for air-traffic managers to effectively regulate traffic and to optimize air-traffic control at international airports. For this purpose, the COBEL-ISBA local numerical forecast system has been implemented at Paris CDG international airport. This local approach is robust owing to the assimilation of detailed local observations. However, even with dedicated observations and initialization, uncertainties remain in both initial conditions and mesoscale forcings. The goal of the research presented here is to address the sensitivity of COBEL-ISBA forecast to initial conditions and mesoscale forcings during the winter season 2002 2003. The main sources of uncertainty of COBEL-ISBA input parameters have been estimated and the evaluation of parameter uncertainty on the forecasts has been studied. A budget strategy is applied during the winter season to quantify COBEL-ISBA sensitivity. This study is the first step toward building a local ensemble prediction system based on COBEL-ISBA. The conclusions of this work point out the potential for COBEL-ISBA ensemble forecasting and quantify sources of uncertainty that lead to dispersion.

  16. Assessing the value of seasonal climate forecast information through an end-to-end forecasting framework: Application to U.S. 2012 drought in central Illinois

    NASA Astrophysics Data System (ADS)

    Shafiee-Jood, Majid; Cai, Ximing; Chen, Ligang; Liang, Xin-Zhong; Kumar, Praveen

    2014-08-01

    This study proposes an end-to-end forecasting framework to incorporate operational seasonal climate forecasts to help farmers improve their decisions prior to the crop growth season, which are vulnerable to unanticipated drought conditions. The framework couples a crop growth model with a decision-making model for rainfed agriculture and translates probabilistic seasonal forecasts into more user-related information that can be used to support farmers' decisions on crop type and some market choices (e.g., contracts with ethanol refinery). The regional Climate-Weather Research and Forecasting model (CWRF) driven by two operational general circulation models (GCMs) is used to provide the seasonal forecasts of weather parameters. To better assess the developed framework, CWRF is also driven by observational reanalysis data, which theoretically can be considered as the best seasonal forecast. The proposed framework is applied to the Salt Creek watershed in Illinois that experienced an extreme drought event during 2012 crop growth season. The results show that the forecasts cannot capture the 2012 drought condition in Salt Creek and therefore the suggested decisions can make farmers worse off if the suggestions are adopted. Alternatively, the optimal decisions based on reanalysis-based CWRF forecasts, which can capture the 2012 drought conditions, make farmers better off by suggesting "no-contract" with ethanol refineries. This study suggests that the conventional metric used for ex ante value assessment is not capable of providing meaningful information in the case of extreme drought. Also, it is observed that institutional interventions (e.g., crop insurance) highly influences farmers' decisions and, thereby, the assessment of forecast value.

  17. The impact of quantified land surface uncertainties on seasonal forecast skill

    NASA Astrophysics Data System (ADS)

    MacLeod, D.

    2015-12-01

    The land surface is a key component in seasonal forecasting, and well-represented soil moisture is particularly important for the simulation of heatwaves. Methods to represent uncertainties in the atmosphere of climate models have been shown to improve forecasts. However these methods have not yet been applied to the land surface component of climate models. We consider three methods of incorporating uncertainties into CHTESSEL, the land surface model of the ECMWF forecasting system. These methods are: stochastic perturbation of soil moisture tendencies, static and then stochastic perturbation of key soil parameters. We present analysis of the results of fully coupled seasonal hindcasts with each method applied. We find significant improvement for extreme events, particularly in terms of forecast reliability of upper and lower quintile soil moisture. These improvements also propagate into the atmosphere, impacting the reliability of seasonal-average predictions of latent and sensible heat flux anomalies and air temperature. This improvement is consistent over the hindcast, and also for particular cases such as the 2003 European summer (MacLeod et al 2015). We also present work with an uncoupled version of CHTESSEL. Extending the method of Wood & Lettenmaier (2008), we quantify the global evolution over forecast lead-time of the relative magnitudes of initial condition, forcing and parameter uncertainty in the land surface. Among other things this gives some indication of where predictability from initial conditions is more persistent, and where uncertainty in land surface parameters has the largest impact on simulated soil moisture. MacLeod DA, CLoke, HL, Pappenberger F and Weisheimer AF (2015), Improved seasonal prediction of the hot summer of 2003 through better representation of uncertainty in the land surface, QJRMSWood, AW, and Lettenmaier DP (2008), An ensemble approach for attribution of hydrologic prediction uncertainty, GRL

  18. Forecasting Cool Season Daily Peak Winds at Kennedy Space Center and Cape Canaveral Air Force Station

    NASA Technical Reports Server (NTRS)

    Barrett, Joe, III; Short, David; Roeder, William

    2008-01-01

    The expected peak wind speed for the day is an important element in the daily 24-Hour and Weekly Planning Forecasts issued by the 45th Weather Squadron (45 WS) for planning operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The morning outlook for peak speeds also begins the warning decision process for gusts ^ 35 kt, ^ 50 kt, and ^ 60 kt from the surface to 300 ft. The 45 WS forecasters have indicated that peak wind speeds are a challenging parameter to forecast during the cool season (October-April). The 45 WS requested that the Applied Meteorology Unit (AMU) develop a tool to help them forecast the speed and timing of the daily peak and average wind, from the surface to 300 ft on KSC/CCAFS during the cool season. The tool must only use data available by 1200 UTC to support the issue time of the Planning Forecasts. Based on observations from the KSC/CCAFS wind tower network, surface observations from the Shuttle Landing Facility (SLF), and CCAFS upper-air soundings from the cool season months of October 2002 to February 2007, the AMU created multiple linear regression equations to predict the timing and speed of the daily peak wind speed, as well as the background average wind speed. Several possible predictors were evaluated, including persistence, the temperature inversion depth, strength, and wind speed at the top of the inversion, wind gust factor (ratio of peak wind speed to average wind speed), synoptic weather pattern, occurrence of precipitation at the SLF, and strongest wind in the lowest 3000 ft, 4000 ft, or 5000 ft. Six synoptic patterns were identified: 1) surface high near or over FL, 2) surface high north or east of FL, 3) surface high south or west of FL, 4) surface front approaching FL, 5) surface front across central FL, and 6) surface front across south FL. The following six predictors were selected: 1) inversion depth, 2) inversion strength, 3) wind gust factor, 4) synoptic weather pattern, 5) occurrence of

  19. Alternative Approaches to Land Initialization for Seasonal Precipitation and Temperature Forecasts

    NASA Technical Reports Server (NTRS)

    Koster, Randal; Suarez, Max; Liu, Ping; Jambor, Urszula

    2004-01-01

    The seasonal prediction system of the NASA Global Modeling and Assimilation Office is used to generate ensembles of summer forecasts utilizing realistic soil moisture initialization. To derive the realistic land states, we drive offline the system's land model with realistic meteorological forcing over the period 1979-1993 (in cooperation with the Global Land Data Assimilation System project at GSFC) and then extract the state variables' values on the chosen forecast start dates. A parallel series of forecast ensembles is performed with a random (though climatologically consistent) set of land initial conditions; by comparing the two sets of ensembles, we can isolate the impact of land initialization on forecast skill from that of the imposed SSTs. The base initialization experiment is supplemented with several forecast ensembles that use alternative initialization techniques. One ensemble addresses the impact of minimizing climate drift in the system through the scaling of the initial conditions, and another is designed to isolate the importance of the precipitation signal from that of all other signals in the antecedent offline forcing. A third ensemble includes a more realistic initialization of the atmosphere along with the land initialization. The impact of each variation on forecast skill is quantified.

  20. Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

    NASA Technical Reports Server (NTRS)

    Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.

    2007-01-01

    Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation at the Shuttle Landing Facility is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAF5), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. This study specifically addresses the skill of different model configurations in forecasting warm season convective initiation. Numerous factors influence the development of convection over the Florida peninsula. These factors include sea breezes, river and lake breezes, the prevailing low-level flow, and convergent flow due to convex coastlines that enhance the sea breeze. The interaction of these processes produces the warm season convective patterns seen over the Florida peninsula. However, warm season convection remains one of the most poorly forecast meteorological parameters. To determine which

  1. How to Generate More Reliable Seasonal Drought Forecasts and Analyze Drought Recovery Under Uncertainties?

    NASA Astrophysics Data System (ADS)

    Moradkhani, H.; DeChant, C. M.

    2014-12-01

    Forecasting of drought is vital for resource management and planning. Both societal and agricultural requirements for water weigh heavily on the natural environment, which may become scarce in the event of drought. Although drought forecasts are an important tool for managing water in hydrologic systems, these forecasts are plagued by uncertainties, owing to the complexities of water dynamics and the spatial heterogeneities of pertinent variables. Due to these uncertainties, it is necessary to frame forecasts in a probabilistic manner. While it is common to focus on the meteorological aspects of uncertainty (e.g. precipitation, temperature), this presentation will discuss the impacts of initial condition and model uncertainty on drought, and particularly ways in which these uncertainties may be reduced. In order to reduce these uncertainties, the recently developed Particle Filter with Sequential Bayesian Combination (PF-SBC) algorithm is applied to drought forecasting within the Upper Colorado River Basin (UCRB). It will be shown that this methodology has the potential to increase the reliability of seasonal hydrologic drought forecasts. In addition, the recovery of drought will be examined with initialization of forecasts through the Particle Filter. From these forecasts, it is found that drought recovery is a longer process than suggested in recent literature. Drought in land surface variables (snow, soil moisture) is shown to be persistent up to a year in certain locations, depending on the intensity of the drought. Location within the UCRB appears to be a driving factor in the ability of the land surface to recover from drought, allowing for differentiation between drought prone and drought resistant regions.

  2. Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system

    PubMed Central

    Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic

    2014-01-01

    The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid

  3. Seasonal Climate Forecasts and Water Management for Steam-Electric Generation

    SciTech Connect

    Greis, N.P.

    1982-12-01

    A water demand model for electricity production is presented which estimates the variablility of water demand for energy production as a function of climate, especially temperature. The model incorporates the effects of temperature on both consumer energy demand levels and process evaporation for steam-electric cooling. The weather-sensitive analysis of water use contained herein is motivated by two factors. First, the electric power industry is using an increasingly large quantity of water, primarily for cooling. The extent of this use is highly dependent on weather conditions. Second, the current state-of-the-art of seasonal climate forecasting, especially temperature, continues to advance. Whether or not seasonal forecasts can be of beneficial use in water management in the electric power industry becomes an important question in the face of a prolonged water shortage.

  4. Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features.

    PubMed

    Navares, Ricardo; Aznarte, José Luis

    2017-04-01

    In this paper, we approach the problem of predicting the concentrations of Poaceae pollen which define the main pollination season in the city of Madrid. A classification-based approach, based on a computational intelligence model (random forests), is applied to forecast the dates in which risk concentration levels are to be observed. Unlike previous works, the proposal extends the range of forecasting horizons up to 6 months ahead. Furthermore, the proposed model allows to determine the most influential factors for each horizon, making no assumptions about the significance of the weather features. The performace of the proposed model proves it as a successful tool for allergy patients in preventing and minimizing the exposure to risky pollen concentrations and for researchers to gain a deeper insight on the factors driving the pollination season.

  5. Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features

    NASA Astrophysics Data System (ADS)

    Navares, Ricardo; Aznarte, José Luis

    2016-09-01

    In this paper, we approach the problem of predicting the concentrations of Poaceae pollen which define the main pollination season in the city of Madrid. A classification-based approach, based on a computational intelligence model (random forests), is applied to forecast the dates in which risk concentration levels are to be observed. Unlike previous works, the proposal extends the range of forecasting horizons up to 6 months ahead. Furthermore, the proposed model allows to determine the most influential factors for each horizon, making no assumptions about the significance of the weather features. The performace of the proposed model proves it as a successful tool for allergy patients in preventing and minimizing the exposure to risky pollen concentrations and for researchers to gain a deeper insight on the factors driving the pollination season.

  6. Developing a U.S. Research Agenda to Advance Subseasonal to Seasonal Forecasting

    NASA Astrophysics Data System (ADS)

    Shafer, M.; Ban, R. J.; Bitz, C. M.; Brown, A.; Chassignet, E.; Dunlea, E.; Dutton, J. A.; Hallberg, R.; Kamrath, A.; Kleist, D. T.; Lermusiaux, P. F. J.; Lin, H.; Macalady, A.; Mengelt, C.; Myers, L.; Pullen, J.; Sandgathe, S. A.; Waliser, D. E.; Zhang, C.

    2015-12-01

    A National Academies of Sciences, Engineering, and Medicine committee was tasked with developing a strategy to increase the nation's scientific capability for research on sub-seasonal to seasonal prediction of weather and climate over the coming decade. The Committee's report (released in the fall of 2015) discusses the advancement of S2S prediction skill for weather and ocean forecasts through various mechanisms, including improvements in coupled modeling systems, key observations, data assimilation techniques, and computational and data storage. Further, the report discusses the identification of potential sources of predictability and process studies for incorporating new sources of predictability. Key elements of a long-term research agenda also include understanding the needs of decision makers who use S2S forecasting information and exploring approaches to the communication of S2S prediction information in a way that is useful to and understandable by those decision makers.

  7. How can monthly to seasonal forecasts help to better manage power systems? (Invited)

    NASA Astrophysics Data System (ADS)

    Dubus, L.; Troccoli, A.

    2013-12-01

    The energy industry increasingly depends on weather and climate, at all space and time scales. This is especially true in countries with volunteer renewable energies development policies. There is no doubt that Energy and Meteorology is a burgeoning inter-sectoral discipline. It is also clear that the catalyst for the stronger interaction between these two sectors is the renewed and fervent interest in renewable energies, especially wind and solar power. Recent progress in meteorology has led to a marked increase in the knowledge of the climate system and in the ability to forecast climate on monthly to seasonal time scales. Several studies have already demonstrated the effectiveness of using these forecasts for energy operations, for instance for hydro-power applications. However, it is also obvious that scientific progress on its own is not sufficient to increase the value of weather forecasts. The process of integration of new meteorological products into operational tools and decision making processes is not straightforward but it is at least as important as the scientific discovery. In turn, such integration requires effective communication between users and providers of these products. We will present some important aspects of energy systems in which monthly to seasonal forecasts can bring useful, if not vital, information, and we will give some examples of encouraging energy/meteorology collaborations. We will also provide some suggestions for a strengthened collaboration into the future.

  8. Regional seasonal forecasts of the Arctic sea ice in two coupled climate models

    NASA Astrophysics Data System (ADS)

    Chevallier, Matthieu; Guémas, Virginie; Salas y Mélia, David; Doblas-Reyes, Francisco

    2015-04-01

    The predictive capabilities of two state-of-the-art coupled atmosphere-ocean global climate models (CNRM-CM5.1 and EC-Earth v2.3) in seasonal forecasting of the Arctic sea ice will be presented with a focus on regional skill. 5-month hindcasts of September sea ice area in the Arctic peripherial seas (Barents-Kara seas, Laptev-East Siberian seas, Chukchi sea and Beaufort sea) and March sea ice area in the marginal ice zones (Barents, Greenland, Labrador, Bering and Okhotsk sea) have been produced over the period 1990-2009. Systems mainly differ with respect to the initialization strategy, the ensemble generation techniques and the sea ice components. Predictive skill, assessed in terms of actual and potential predictability, is comparable in the two systems for both summer and winter hindcasts. Most interestingly, the multi-model prediction is often better than individual predictions in several sub-basins, including the Barents sea in the winter and most shelf seas in the summer. Systematic biases are also reduced using the multi-model predictions. Results from this study show that a regional zoom of global seasonal forecasts could be useful for operational needs. This study also show that the multi-model approach may be the step forward in producing accurate and reliable seasonal forecasts based on coupled global climate models.

  9. Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

    NASA Technical Reports Server (NTRS)

    Watson, Leela R.

    2007-01-01

    This report describes the work done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting warm season convection over East-Central Florida. The Weather Research and Forecasting Environmental Modeling System (WRF EMS) software allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Besides model core and initialization options, the WRF model can be run with one- or two-way nesting. Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. This project assessed three different model intializations available to determine which configuration best predicts warm season convective initiation in East-Central Florida. The project also examined the use of one- and two-way nesting in predicting warm season convection.

  10. Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

    NASA Technical Reports Server (NTRS)

    Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.

    2007-01-01

    options within each core, provides SMG and NWS MLB with a lot of flexibility. It also creates challenges, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and to determine which configuration will best predict warm season convective initiation in East-Central Florida. Four different combinations of WRF initializations will be run (ADAS-ARW, ADAS-NMM, LAPS-ARW, and LAPS-NMM) at a 4-km resolution over the Florida peninsula and adjacent coastal waters. Five candidate convective initiation days using three different flow regimes over East-Central Florida will be examined, as well as two null cases (non-convection days). Each model run will be integrated 12 hours with three runs per day, at 0900, 1200, and 1500 UTe. ADAS analyses will be generated every 30 minutes using Level II Weather Surveillance Radar-1988 Doppler (WSR-88D) data from all Florida radars to verify the convection forecast. These analyses will be run on the same domain as the four model configurations. To quantify model performance, model output will be subjectively compared to the ADAS analyses of convection to determine forecast accuracy. In addition, a subjective comparison of the performance of the ARW using a high-resolution local grid with 2-way nesting, I-way nesting, and no nesting will be made for select convective initiation cases. The inner grid will cover the East-Central Florida region at a resolution of 1.33 km. The authors will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for predicting warm season convective initiation over East-Central Florida.

  11. Long-lead predictions of eastern United States hot days from Pacific sea surface temperatures

    NASA Astrophysics Data System (ADS)

    McKinnon, K. A.; Rhines, A.; Tingley, M. P.; Huybers, P.

    2016-05-01

    Seasonal forecast models exhibit only modest skill in predicting extreme summer temperatures across the eastern US. Anomalies in sea surface temperature and monthly-resolution rainfall have, however, been correlated with hot days in the US, and seasonal persistence of these anomalies suggests potential for long-lead predictability. Here we present a clustering analysis of daily maximum summer temperatures from US weather stations between 1982-2015 and identify a region spanning most of the eastern US where hot weather events tend to occur synchronously. We then show that an evolving pattern of sea surface temperature anomalies, termed the Pacific Extreme Pattern, provides for skillful prediction of hot weather within this region as much as 50 days in advance. Skill is demonstrated using out-of-sample predictions between 1950 and 2015. Rainfall deficits over the eastern US are also associated with the occurrence of the Pacific Extreme Pattern and are demonstrated to offer complementary skill in predicting high temperatures. The Pacific Extreme Pattern appears to provide a cohesive framework for improving seasonal prediction of summer precipitation deficits and high temperature anomalies in the eastern US.

  12. Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system

    NASA Astrophysics Data System (ADS)

    Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.

    2016-06-01

    Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA’s Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders.

  13. Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system

    PubMed Central

    Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.

    2016-01-01

    Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA’s Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders. PMID:27273473

  14. Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia

    NASA Astrophysics Data System (ADS)

    White, C. J.; Franks, S. W.; McEvoy, D.

    2015-06-01

    Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S) forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR) activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.

  15. How seasonal forecast could help a decision maker: an example of climate service for water resource management

    NASA Astrophysics Data System (ADS)

    Viel, Christian; Beaulant, Anne-Lise; Soubeyroux, Jean-Michel; Céron, Jean-Pierre

    2016-04-01

    The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

  16. Seasonal streamflow forecasts in a semi-arid Andean watershed using remotely sensed snow cover data

    NASA Astrophysics Data System (ADS)

    Cartes, M.; McPhee, J.; Vargas, X.

    2009-04-01

    Forecasts of monthly streamflow during the snowmelt season are highly relevant for real-time decision making such as hydropower production scheduling, irrigation planning, and water transfers in market-driven water resource systems. The Chilean water bureau issues such forecasts, for a number of snowmelt-driven watersheds in northern and central Chile, based on measurements from a sparse network of snow course stations. This research aims at improving the accuracy of the government-issued seasonal forecasts by combining streamflow data and remotely sensed snow cover information through a recurrent neural network (RNN). The snow cover area (SCA) obtained from MODIS-Surface Reflectance product (MOD09) and the Normalized Differentiation Snow Index (NDSI), from 2000-2008 period, allow us to understand the variation of the snowmelt and accumulation processes in six different basins located in central Chile (32,5° - 34,5° south latitude; 69,5° -70,5° west longitude). For the three basins located at higher altitudes (> 1800 m.s.l.), after applying a cross-correlation procedure we determined a strong relation (r > 0.7) between SCA and the seasonal hydrograph, lagged around 4 months. The basin SCA, the NDSI at specific points inside the basin and past basin streamflow data are input to the RNN for recognizing the pattern variation of seasonal hydrograph through supervised learning. The determination coefficients for the validation period (r2 > 0.6) indicate a good support for the application of this methodology in normal-humid hydrological years. Particularly for the dryer years we obtain a considerable overestimation (around 30%) of the monthly snowmelt runoff. These results are limited by the availability of data for different types (dry, normal or humid) of hydrological years.

  17. Real-time extreme weather event attribution with forecast seasonal SSTs

    NASA Astrophysics Data System (ADS)

    Haustein, K.; Otto, F. E. L.; Uhe, P.; Schaller, N.; Allen, M. R.; Hermanson, L.; Christidis, N.; McLean, P.; Cullen, H.

    2016-06-01

    Within the last decade, extreme weather event attribution has emerged as a new field of science and garnered increasing attention from the wider scientific community and the public. Numerous methods have been put forward to determine the contribution of anthropogenic climate change to individual extreme weather events. So far nearly all such analyses were done months after an event has happened. Here we present a new method which can assess the fraction of attributable risk of a severe weather event due to an external driver in real-time. The method builds on a large ensemble of atmosphere-only general circulation model simulations forced by seasonal forecast sea surface temperatures (SSTs). Taking the England 2013/14 winter floods as an example, we demonstrate that the change in risk for heavy rainfall during the England floods due to anthropogenic climate change, is of similar magnitude using either observed or seasonal forecast SSTs. Testing the dynamic response of the model to the anomalous ocean state for January 2014, we find that observed SSTs are required to establish a discernible link between a particular SST pattern and an atmospheric response such as a shift in the jetstream in the model. For extreme events occurring under strongly anomalous SST patterns associated with known low-frequency climate modes, however, forecast SSTs can provide sufficient guidance to determine the dynamic contribution to the event.

  18. Comparison of cross-validation and bootstrap aggregating for building a seasonal streamflow forecast model

    NASA Astrophysics Data System (ADS)

    Schick, Simon; Rössler, Ole; Weingartner, Rolf

    2016-10-01

    Based on a hindcast experiment for the period 1982-2013 in 66 sub-catchments of the Swiss Rhine, the present study compares two approaches of building a regression model for seasonal streamflow forecasting. The first approach selects a single "best guess" model, which is tested by leave-one-out cross-validation. The second approach implements the idea of bootstrap aggregating, where bootstrap replicates are employed to select several models, and out-of-bag predictions provide model testing. The target value is mean streamflow for durations of 30, 60 and 90 days, starting with the 1st and 16th day of every month. Compared to the best guess model, bootstrap aggregating reduces the mean squared error of the streamflow forecast by seven percent on average. Thus, if resampling is anyway part of the model building procedure, bootstrap aggregating seems to be a useful strategy in statistical seasonal streamflow forecasting. Since the improved accuracy comes at the cost of a less interpretable model, the approach might be best suited for pure prediction tasks, e.g. as in operational applications.

  19. Overcoming data scarcity: Seasonal forecasting of reservoir inflows using public domain resources in Central Asia

    NASA Astrophysics Data System (ADS)

    Dixon, Samuel G.; Wilby, Robert L.

    2016-04-01

    Management of large hydropower reservoirs can be politically and strategically problematic. Traditional flow forecasting techniques rely on accurate ground based observations, a requirement not met in many areas of the globe (Artan et al., 2007). In particular, access to real-time observational data in transnational river basins is often not possible. In these regions, novel techniques are required to combat the challenges of flow forecasting for efficient reservoir management. Near real time remotely sensed information regarding flow predictors (e.g. satellite precipitation estimates) could combat data availability issues, improving the utility of seasonal reservoir inflow forecasts. This study investigates the potential for river flow forecasting using public domain resources, including satellite and re-analysis precipitation as well as climate indices for several strategically important reservoirs throughout Central Asia (including Toktogul, Andijan, Kayrakkum and Nurek). Using reservoir inflows from 2001-2010, parsimonious numerical models were created for each study site using selected significant predictors for lead times of 1-3 months as well half year averages. Preliminary investigation has shown that parsimonious statistical models can explain over 80% of the variance in monthly inflows with three month lead to the Toktogul reservoir, Kyrgyzstan (Dixon and Wilby, 2015). Such findings show promise for improving the safety and efficiency of reservoir operations as well as reducing risks emerging from climate change.

  20. Seasonal Hydrometeorological Ensemble Prediction System; Forecast of Irrigation Potentials in Denmark

    NASA Astrophysics Data System (ADS)

    Lucatero, D.; Jensen, K. H.; Madsen, H.; Refsgaard, J. C.; Kidmose, J.

    2015-12-01

    The European Center for Medium Weather Forecast seasonal ensemble prediction system (ECMWF-SEPS) of weather variables such as precipitation, temperature and evapotranspiration is used as input to an integrated surface-subsurface hydrological model based on the MIKE SHE system to generate probabilistic forecasts of the irrigation requirements in the Skjern river catchment in Denmark. We demonstrate the usability of the ECMWF-SEPS and discuss the challenges and areas of opportunities when issuing forecasts generated with this methodology. A simple bias-correction and downscaling technique, namely linear scaling, is applied to the raw inputs to remove the bias intrinsic in ensemble prediction systems and to downscale the data to a scale appropriate for hydrological modelling. The forecasts of the meteorological variables are analysed for accuracy and reliability by comparing them to meteorological observations. Additionally, weather ensembles will be generated using the nearest-neighbour resampling technique with the purpose of exploring additional possibilities of hydrometeorological system input for complementing situations where the SEPS is lacking skill.

  1. Seasonal Rainfall Forecasting Using Sea Surface Temperatures with an application in the Southeast US

    NASA Astrophysics Data System (ADS)

    Chen, C.; Georgakakos, A. P.

    2011-12-01

    Sea surface temperatures (SSTs) are a key determinant of atmospheric circulation patterns and the prevalence of regional climatic conditions. This has been the motivation for various teleconnection methods that aim to forecast seasonal rainfall. Among such methods are linear projections based upon teleconnection gross indices (e.g., NINO3.4 and NAO index) or leading empirical orthogonal functions (EOFs). However, these methods deteriorate drastically if neither the predefined indices nor EOFs can account for climatic variability in the region of interest. This study introduces a method seeking to identify SST "dipoles" able to explain as much of seasonal rainfall variance as possible. A SST dipole is defined as the difference or addition of the average SST anomalies over two oceanic areas of appropriate size and geographic location (poles). The new method is based on an optimization algorithm that shifts through all possible dipole configurations and identifies the one with the strongest teleconnection strength (measured by the Gerrity Skill Score) with an external rainfall (or other climatic) series. After the determination of the best dipole(s), forecasting is carried out using regression models. The dipole teleconnection method is applied to the forecasting of seasonal rainfall on the Apalachicola/Chattahoochee/Flint (ACF) River basin in the southeast US. The monthly rainfall climatology shows that winter (December - February) and summer (June - August) are most significant rainfall seasons for the southeast. Hindcasts of four-season rainfall generated by the dipole model show that the strongest predictability exists in fall and winter. The best forecast lead time is three months, explaining up to 64% of the observed winter rainfall variance. Spring and summer rainfall predictability is less, with the identified dipoles accounting for 25% to 42% of the variance. These preliminary results indicate that the dipole method compares favorably with other existing

  2. High-resolution wave forecasting system for the seasonally ice-covered Baltic Sea

    NASA Astrophysics Data System (ADS)

    Tuomi, Laura; Lehtiranta, Jonni

    2016-04-01

    When forecasting surface waves in seasonally ice-covered seas, the inclusion of ice conditions in the modelling is important. The ice cover affects the propagation and also changes the fetch over which the waves grow. In wave models the ice conditions are often still given as a boundary condition and handled by excluding areas where the ice concentration exceeds a certain threshold value. The ice data used are typically based on satellite analysis or expert analysis of local Ice Services who combine data from different sources. This type of data is sufficiently accurate to evaluate the near-real time ice concentrations, but when making forecasts it is also important to account for the possible changes in ice conditions. For example in a case of a high wind situation, there can be rapid changes in the ice field, when the wind and waves may push the ice towards shores and cause fragmentation of ice field. To enhance handling of ice conditions in the Baltic Sea wave forecasts, utilisation of ice model data was studied. Ice concentration, thickness produced by FMI's operational ice model HELMI were used to provide ice data to wave model as follows: Wave model grid points where the ice concentration was more than or equal to 70% and the ice thickness more than1 cm, were excluded from calculations. Ice concentrations smaller than that were taken into account as additional grid obstructions by decreasing the wave energy passed from one grid cell to another. A challenge in evaluating wave forecast accuracy in partly ice covered areas it that there's typically no wave buoy data available, since the buoys have to be recovered well before the sea area freezes. To evaluate the accuracy of wave forecast in partially ice covered areas, significant wave heights from altimeter's ERS2, Envisat, Jason-1 and Jason-2 were extracted from Ifremer database. Results showed that the more frequent update of the ice data was found to improve the wave forecast especially during high wind

  3. Season-ahead Drought Forecast Models for the Lower Colorado River Authority in Texas

    NASA Astrophysics Data System (ADS)

    Block, P. J.; Zimmerman, B.; Grzegorzewski, M.; Watkins, D. W., Jr.; Anderson, R.

    2014-12-01

    The Lower Colorado River Authority (LCRA) in Austin, Texas, manages the Highland Lakes reservoir system in Central Texas, a series of six lakes on the Lower Colorado River. This system provides water to approximately 1.1 million people in Central Texas, supplies hydropower to a 55-county area, supports rice farming along the Texas Gulf Coast, and sustains in-stream flows in the Lower Colorado River and freshwater inflows to Matagorda Bay. The current, prolonged drought conditions are severely taxing the LCRA's system, making allocation and management decisions exceptionally challenging, and affecting the ability of constituents to conduct proper planning. In this work, we further develop and evaluate season-ahead statistical streamflow and precipitation forecast models for integration into LCRA decision support models. Optimal forecast lead time, predictive skill, form, and communication are all considered.

  4. Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error

    NASA Astrophysics Data System (ADS)

    Pokhrel, Samir; Saha, Subodh Kumar; Dhakate, Ashish; Rahman, Hasibur; Chaudhari, Hemantkumar S.; Salunke, Kiran; Hazra, Anupam; Sujith, K.; Sikka, D. R.

    2016-04-01

    A detailed analysis of sensitivity to the initial condition for the simulation of the Indian summer monsoon using retrospective forecast by the latest version of the Climate Forecast System version-2 (CFSv2) is carried out. This study primarily focuses on the tropical region of Indian and Pacific Ocean basin, with special emphasis on the Indian land region. The simulated seasonal mean and the inter-annual standard deviations of rainfall, upper and lower level atmospheric circulations and Sea Surface Temperature (SST) tend to be more skillful as the lead forecast time decreases (5 month lead to 0 month lead time i.e. L5-L0). In general spatial correlation (bias) increases (decreases) as forecast lead time decreases. This is further substantiated by their averaged value over the selected study regions over the Indian and Pacific Ocean basins. The tendency of increase (decrease) of model bias with increasing (decreasing) forecast lead time also indicates the dynamical drift of the model. Large scale lower level circulation (850 hPa) shows enhancement of anomalous westerlies (easterlies) over the tropical region of the Indian Ocean (Western Pacific Ocean), which indicates the enhancement of model error with the decrease in lead time. At the upper level circulation (200 hPa) biases in both tropical easterly jet and subtropical westerlies jet tend to decrease as the lead time decreases. Despite enhancement of the prediction skill, mean SST bias seems to be insensitive to the initialization. All these biases are significant and together they make CFSv2 vulnerable to seasonal uncertainties in all the lead times. Overall the zeroth lead (L0) seems to have the best skill, however, in case of Indian summer monsoon rainfall (ISMR), the 3 month lead forecast time (L3) has the maximum ISMR prediction skill. This is valid using different independent datasets, wherein these maximum skill scores are 0.64, 0.42 and 0.57 with respect to the Global Precipitation Climatology Project

  5. Predictability of the Seasonal Climate Associated with ENSO in NCEP Climate Forecast System

    NASA Astrophysics Data System (ADS)

    Zhang, Q.

    2005-05-01

    The predictability of seasonal climate associated with ENSO is studied for NCEP Climate Forecast System (CFS) 23-year retrospective forecasts. Warm-minus-cold composites of the lead 1-6 month sea surface temperature (SST) anomalies show an ENSO-like horse-shoes pattern in the tropical Pacific, comparable with observation. There is a corresponding increased precipitation band along the equator near the dateline extending eastward to the South American coast, as well as the less precipitation over the Maritime Continents and off-equatorial western Pacific. Extended empirical orthogonal function (EEOF) analysis of the SST anomaly recovers ENSO -like dominant mode in the tropics for all seasons. Identification of patterns that optimize the signal-to-noise ratio is obtained by linear regression of the ensemble means on the principal component (PC) time series of SST. The optimized height patterns for boreal winter and spring are similar, although the winter response over the northern extratropics is somewhat weaker. Some subtle changes in amplitude are found in difference of leading initial conditions. The signal-to-noise ratio is significantly greater than unity in the Tropics (all seasons), the northern Pacific and continental North America subtropics (boreal winter and spring), and the southern Pacific subtropics (boreal fall).

  6. Analysis and downscaling multi-model seasonal forecasts in Peru using self-organizing maps

    NASA Astrophysics Data System (ADS)

    Gutiérrez, J. M.; Cano, R.; Cofiño, A. S.; Sordo, C.

    2005-05-01

    We present an application of self-organizing maps (SOMs) for analysing multi-model ensemble seasonal forecasts from the DEMETER project in the tropical area of Northern Peru. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors (cluster centroids). Moreover, it has outstanding analysis and visualization properties, because the reference vectors can be projected into a two-dimensional lattice, preserving their high-dimensional topology.In the first part of the paper, the SOM is applied to analyse both atmospheric patterns over Peru and local precipitation observations at two nearby stations. First, the method is applied to cluster the ERA40 reanalysis patterns on the area of study (Northern Peru), obtaining a two-dimensional lattice which represents the climatology. Then, each particular phenomenon or event (e.g. El Niño or La Niña) is shown to define a probability density function (PDF) on the lattice, which represents its characteristic 'location' within the climatology. On the other hand, the climatological lattice is also used to represent the local precipitation regime associated with a given station. For instance, we show that the precipitation regime is strongly associated with El Niño events for one station, whereas it is more uniform for the other.The second part of the paper is devoted to downscaling seasonal ensemble forecasts from the multi-model DEMETER ensemble to local stations. To this aim, the PDF generated on the lattice by the patterns predicted for a particular season is combined with the local precipitation lattice for a given station. Thus, a probabilistic or numeric local forecast is easily obtained from the resulting PDF. Moreover, a measure of predictability for the downscaled forecast can be computed in terms of the entropy of the ensemble PDF. We present some evidence that accurate local predictions for accumulated seasonal

  7. The Impact of ENSO on Extratropical Low Frequency Noise in Seasonal Forecasts

    NASA Technical Reports Server (NTRS)

    Schubert, Siegfried D.; Suarez, Max J.; Chang, Yehui; Branstator, Grant

    2000-01-01

    This study examines the uncertainty in forecasts of the January-February-March (JFM) mean extratropical circulation, and how that uncertainty is modulated by the El Nino/Southern Oscillation (ENSO). The analysis is based on ensembles of hindcasts made with an Atmospheric General Circulation Model (AGCM) forced with sea surface temperatures observed during; the 1983 El Nino and 1989 La Nina events. The AGCM produces pronounced interannual differences in the magnitude of the extratropical seasonal mean noise (intra-ensemble variability). The North Pacific, in particular, shows extensive regions where the 1989 seasonal mean noise kinetic energy (SKE), which is dominated by a "PNA-like" spatial structure, is more than twice that of the 1983 forecasts. The larger SKE in 1989 is associated with a larger than normal barotropic conversion of kinetic energy from the mean Pacific jet to the seasonal mean noise. The generation of SKE due to sub-monthly transients also shows substantial interannual differences, though these are much smaller than the differences in the mean flow conversions. An analysis of the Generation of monthly mean noise kinetic energy (NIKE) and its variability suggests that the seasonal mean noise is predominantly a statistical residue of variability resulting from dynamical processes operating on monthly and shorter times scales. A stochastically-forced barotropic model (linearized about the AGCM's 1983 and 1989 base states) is used to further assess the role of the basic state, submonthly transients, and tropical forcing, in modulating the uncertainties in the seasonal AGCM forecasts. When forced globally with spatially-white noise, the linear model generates much larger variance for the 1989 base state, consistent with the AGCM results. The extratropical variability for the 1989 base state is dominanted by a single eigenmode, and is strongly coupled with forcing over tropical western Pacific and the Indian Ocean, again consistent with the AGCM results

  8. Value versus Accuracy: application of seasonal forecasts to a hydro-economic optimization model for the Sudanese Blue Nile

    NASA Astrophysics Data System (ADS)

    Satti, S.; Zaitchik, B. F.; Siddiqui, S.; Badr, H. S.; Shukla, S.; Peters-Lidard, C. D.

    2015-12-01

    The unpredictable nature of precipitation within the East African (EA) region makes it one of the most vulnerable, food insecure regions in the world. There is a vital need for forecasts to inform decision makers, both local and regional, and to help formulate the region's climate change adaptation strategies. Here, we present a suite of different seasonal forecast models, both statistical and dynamical, for the EA region. Objective regionalization is performed for EA on the basis of interannual variability in precipitation in both observations and models. This regionalization is applied as the basis for calculating a number of standard skill scores to evaluate each model's forecast accuracy. A dynamically linked Land Surface Model (LSM) is then applied to determine forecasted flows, which drive the Sudanese Hydroeconomic Optimization Model (SHOM). SHOM combines hydrologic, agronomic and economic inputs to determine the optimal decisions that maximize economic benefits along the Sudanese Blue Nile. This modeling sequence is designed to derive the potential added value of information of each forecasting model to agriculture and hydropower management. A rank of each model's forecasting skill score along with its added value of information is analyzed in order compare the performance of each forecast. This research aims to improve understanding of how characteristics of accuracy, lead time, and uncertainty of seasonal forecasts influence their utility to water resources decision makers who utilize them.

  9. Project Ukko - Design of a climate service visualisation interface for seasonal wind forecasts

    NASA Astrophysics Data System (ADS)

    Hemment, Drew; Stefaner, Moritz; Makri, Stephann; Buontempo, Carlo; Christel, Isadora; Torralba-Fernandez, Veronica; Gonzalez-Reviriego, Nube; Doblas-Reyes, Francisco; de Matos, Paula; Dykes, Jason

    2016-04-01

    Project Ukko is a prototype climate service to visually communicate probabilistic seasonal wind forecasts for the energy sector. In Project Ukko, an interactive visualisation enhances the accessibility and readability to the latests advances in seasonal wind speed predictions developed as part of the RESILIENCE prototype of the EUPORIAS (EC FP7) project. Climate services provide made-to-measure climate information, tailored to the specific requirements of different users and industries. In the wind energy sector, understanding of wind conditions in the next few months has high economic value, for instance, for the energy traders. Current energy practices use retrospective climatology, but access to reliable seasonal predictions based in the recent advances in global climate models has potential to improve their resilience to climate variability and change. Despite their potential benefits, a barrier to the development of commercially viable services is the complexity of the probabilistic forecast information, and the challenge of communicating complex and uncertain information to decision makers in industry. Project Ukko consists of an interactive climate service interface for wind energy users to explore probabilistic wind speed predictions for the coming season. This interface enables fast visual detection and exploration of interesting features and regions likely to experience unusual changes in wind speed in the coming months.The aim is not only to support users to better understand the future variability in wind power resources, but also to bridge the gap between practitioners' traditional approach and the advanced prediction systems developed by the climate science community. Project Ukko is presented as a case study of cross-disciplinary collaboration between climate science and design, for the development of climate services that are useful, usable and effective for industry users. The presentation will reflect on the challenge of developing a climate

  10. Regional forecast model for the Olea pollen season in Extremadura (SW Spain).

    PubMed

    Fernández-Rodríguez, Santiago; Durán-Barroso, Pablo; Silva-Palacios, Inmaculada; Tormo-Molina, Rafael; Maya-Manzano, José María; Gonzalo-Garijo, Ángela

    2016-10-01

    The olive tree (Olea europaea) is a predominantly Mediterranean anemophilous species. The pollen allergens from this tree are an important cause of allergic problems. Olea pollen may be relevant in relation to climate change, due to the fact that its flowering phenology is related to meteorological parameters. This study aims to investigate airborne Olea pollen data from a city on the SW Iberian Peninsula, to analyse the trends in these data and their relationships with meteorological parameters using time series analysis. Aerobiological sampling was conducted from 1994 to 2013 in Badajoz (SW Spain) using a 7-day Hirst-type volumetric sampler. The main Olea pollen season lasted an average of 34 days, from May 4th to June 7th. The model proposed to forecast airborne pollen concentrations, described by one equation. This expression is composed of two terms: the first term represents the resilience of the pollen concentration trend in the air according to the average concentration of the previous 10 days; the second term was obtained from considering the actual pollen concentration value, which is calculated based on the most representative meteorological variables multiplied by a fitting coefficient. Due to the allergenic characteristics of this pollen type, it should be necessary to forecast its short-term prevalence using a long record of data in a city with a Mediterranean climate. The model obtained provides a suitable level of confidence to forecast Olea airborne pollen concentration.

  11. Regional forecast model for the Olea pollen season in Extremadura (SW Spain)

    NASA Astrophysics Data System (ADS)

    Fernández-Rodríguez, Santiago; Durán-Barroso, Pablo; Silva-Palacios, Inmaculada; Tormo-Molina, Rafael; Maya-Manzano, José María; Gonzalo-Garijo, Ángela

    2016-10-01

    The olive tree ( Olea europaea) is a predominantly Mediterranean anemophilous species. The pollen allergens from this tree are an important cause of allergic problems. Olea pollen may be relevant in relation to climate change, due to the fact that its flowering phenology is related to meteorological parameters. This study aims to investigate airborne Olea pollen data from a city on the SW Iberian Peninsula, to analyse the trends in these data and their relationships with meteorological parameters using time series analysis. Aerobiological sampling was conducted from 1994 to 2013 in Badajoz (SW Spain) using a 7-day Hirst-type volumetric sampler. The main Olea pollen season lasted an average of 34 days, from May 4th to June 7th. The model proposed to forecast airborne pollen concentrations, described by one equation. This expression is composed of two terms: the first term represents the resilience of the pollen concentration trend in the air according to the average concentration of the previous 10 days; the second term was obtained from considering the actual pollen concentration value, which is calculated based on the most representative meteorological variables multiplied by a fitting coefficient. Due to the allergenic characteristics of this pollen type, it should be necessary to forecast its short-term prevalence using a long record of data in a city with a Mediterranean climate. The model obtained provides a suitable level of confidence to forecast Olea airborne pollen concentration.

  12. Seasonal forecasting of lightning and thunderstorm activity in tropical and temperate regions of the world.

    PubMed

    Dowdy, Andrew J

    2016-02-11

    Thunderstorms are convective systems characterised by the occurrence of lightning. Lightning and thunderstorm activity has been increasingly studied in recent years in relation to the El Niño/Southern Oscillation (ENSO) and various other large-scale modes of atmospheric and oceanic variability. Large-scale modes of variability can sometimes be predictable several months in advance, suggesting potential for seasonal forecasting of lightning and thunderstorm activity in various regions throughout the world. To investigate this possibility, seasonal lightning activity in the world's tropical and temperate regions is examined here in relation to numerous different large-scale modes of variability. Of the seven modes of variability examined, ENSO has the strongest relationship with lightning activity during each individual season, with relatively little relationship for the other modes of variability. A measure of ENSO variability (the NINO3.4 index) is significantly correlated to local lightning activity at 53% of locations for one or more seasons throughout the year. Variations in atmospheric parameters commonly associated with thunderstorm activity are found to provide a plausible physical explanation for the variations in lightning activity associated with ENSO. It is demonstrated that there is potential for accurately predicting lightning and thunderstorm activity several months in advance in various regions throughout the world.

  13. Seasonal forecasting of lightning and thunderstorm activity in tropical and temperate regions of the world

    PubMed Central

    Dowdy, Andrew J.

    2016-01-01

    Thunderstorms are convective systems characterised by the occurrence of lightning. Lightning and thunderstorm activity has been increasingly studied in recent years in relation to the El Niño/Southern Oscillation (ENSO) and various other large-scale modes of atmospheric and oceanic variability. Large-scale modes of variability can sometimes be predictable several months in advance, suggesting potential for seasonal forecasting of lightning and thunderstorm activity in various regions throughout the world. To investigate this possibility, seasonal lightning activity in the world’s tropical and temperate regions is examined here in relation to numerous different large-scale modes of variability. Of the seven modes of variability examined, ENSO has the strongest relationship with lightning activity during each individual season, with relatively little relationship for the other modes of variability. A measure of ENSO variability (the NINO3.4 index) is significantly correlated to local lightning activity at 53% of locations for one or more seasons throughout the year. Variations in atmospheric parameters commonly associated with thunderstorm activity are found to provide a plausible physical explanation for the variations in lightning activity associated with ENSO. It is demonstrated that there is potential for accurately predicting lightning and thunderstorm activity several months in advance in various regions throughout the world. PMID:26865431

  14. A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes

    NASA Astrophysics Data System (ADS)

    Krishnamurti, T. N.; Kumar, V.; Simon, A.; Bhardwaj, A.; Ghosh, T.; Ross, R.

    2016-06-01

    This review provides a summary of work in the area of ensemble forecasts for weather, climate, oceans, and hurricanes. This includes a combination of multiple forecast model results that does not dwell on the ensemble mean but uses a unique collective bias reduction procedure. A theoretical framework for this procedure is provided, utilizing a suite of models that is constructed from the well-known Lorenz low-order nonlinear system. A tutorial that includes a walk-through table and illustrates the inner workings of the multimodel superensemble's principle is provided. Systematic errors in a single deterministic model arise from a host of features that range from the model's initial state (data assimilation), resolution, representation of physics, dynamics, and ocean processes, local aspects of orography, water bodies, and details of the land surface. Models, in their diversity of representation of such features, end up leaving unique signatures of systematic errors. The multimodel superensemble utilizes as many as 10 million weights to take into account the bias errors arising from these diverse features of multimodels. The design of a single deterministic forecast models that utilizes multiple features from the use of the large volume of weights is provided here. This has led to a better understanding of the error growths and the collective bias reductions for several of the physical parameterizations within diverse models, such as cumulus convection, planetary boundary layer physics, and radiative transfer. A number of examples for weather, seasonal climate, hurricanes and sub surface oceanic forecast skills of member models, the ensemble mean, and the superensemble are provided.

  15. Seasonal forecasting for water resource management: the example of CNR Genissiat dam on the Rhone River in France

    NASA Astrophysics Data System (ADS)

    Dommanget, Etienne; Bellier, Joseph; Ben Daoud, Aurélien; Graff, Benjamin

    2014-05-01

    Compagnie Nationale du Rhône (CNR) has been granted the concession to operate the Rhone River from the Swiss border to the Mediterranean Sea since 1933 and carries out three interdependent missions: navigation, irrigation and hydropower production. Nowadays, CNR generates one quarter of France's hydropower electricity. The convergence of public and private interests around optimizing the management of water resources throughout the French Rhone valley led CNR to develop hydrological models dedicated to discharge seasonal forecasting. Indeed, seasonal forecasting is a major issue for CNR and water resource management, in order to optimize long-term investments of the produced electricity, plan dam maintenance operations and anticipate low water period. Seasonal forecasting models have been developed on the Genissiat dam. With an installed capacity of 420MW, Genissiat dam is the first of the 19 CNR's hydropower plants. Discharge forecasting at Genissiat dam is strategic since its inflows contributes to 20% of the total Rhone average discharge and consequently to 40% of the total Rhone hydropower production. Forecasts are based on hydrological statistical models. Discharge on the main Rhone River tributaries upstream Genissiat dam are forecasted from 1 to 6 months ahead thanks to multiple linear regressions. Inputs data of these regressions are identified depending on river hydrological regimes and periods of the year. For the melting season, from spring to summer, snow water equivalent (SWE) data are of major importance. SWE data are calculated from Crocus model (Météo France) and SLF's model (Switzerland). CNR hydro-meteorological forecasters assessed meteorological trends regarding precipitations for the next coming months. These trends are used to generate stochastically precipitation scenarios in order to complement regression data set. This probabilistic approach build a decision-making supports for CNR's water resource management team and provides them with

  16. Assessing the potential skill of seasonal streamflow forecasting for the River Rhine and the Upper Danube Basin

    NASA Astrophysics Data System (ADS)

    Klein, B.; Meissner, D.; Gerl, N.; Hemri, S.; Gneiting, T. J.

    2013-12-01

    Reliable seasonal streamflow forecasts could be a valuable tool for the medium-term to long-term planning of many users of the water sector. Especially for the optimization of hydropower generation and the water-related logistic transportation chain the knowledge about the possible future evolution of streamflows within the next 1 to 6 months would be an important additional information in the decision process. Although there is a strong need for seasonal forecast products there is no operational forecasting system available for the large rivers in Germany. One of the main reasons is that the long-term meteorological predictability, especially for precipitation, is quite limited over Central Europe. Potential gain of predictability in the hydrological system that makes us believe that skillful seasonal streamflow forecasts in Central Europe are not out of reach is the hydrological memory and the delayed and damped system response of river basins. Natural (like snow pack, groundwater, soil moisture) as well as man-made reservoirs and dams have a large influence on the future runoff. In hydrological forecasting this memory is represented by the initial conditions of the hydrological model. In addition the streamflow at a gauge is an integrated system response with the meteorological variables as system input. If there is at least some valuable information in the numeric seasonal weather forecasts about the future evolution of precipitation and temperature as the main drivers of the hydrological processes, it could be possibly assessed through spatial (considering larger catchments) and temporal aggregation (e.g. monthly mean runoff values instead of daily values). In this contribution the potential skill of seasonal streamflow forecasting is evaluated for River Rhine and the Upper Danube Basin (up to the gauge Vienna). Different spatial and temporal scales are considered as well as different meteorological forcings. Two different hydrological models are applied in

  17. Optimization of precipitation and streamflow forecasts in the southwest Contiguous US for warm season convection

    NASA Astrophysics Data System (ADS)

    Lahmers, T.; Castro, C. L.; Gupta, H. V.; Gochis, D. J.; ElSaadani, M.

    2015-12-01

    Warm season convection associated with the North American Monsoon (NAM) provides an important source of precipitation for much of the Southwest Contiguous US (CONUS) and Northwest Mexico. Convection associated with the NAM can also result in flash flooding, a hazard to metropolitan areas such as Tucson and Phoenix, as well as rural areas where washouts of main roads can sever critical transportation infrastructure. In order to mitigate the effects of this problem, the National Oceanic and Atmospheric Administration (NOAA) National Water Center (NWC) is developing a national distributed hydrologic model using the WRF-Hydro framework with forcing from the High Resolution Rapid Refresh (HRRR) mesoscale atmospheric model. We aim to improve this National hydrologic and atmospheric modeling framework through the calibration of the WRF-Hydro model for the southwest CONUS and the optimization of planetary boundary layer and cloud microphysics schemes for the Weather Research and Forecasting (WRF) model in the same region. The WRF-Hydro model, with a similar structure as the national configuration used by the NWC, has been set up for the Gila River basin in southern Arizona. We demonstrate the utility of the model for forecasting high impact precipitation events in catchments with limited human modification. The WRF-Hydro model is spun up using past precipitation from the NCEP Stage-IV records and TRMM estimates. Atmospheric forcing for WRF-Hydro comes from the NASA Phase 2 North American Land Data Assimilation (NLDAS-2) dataset. WRF-Hydro is forced for selected high-impact events using a 3-km grid resolution Advanced Research WRF (WRF-ARW) atmospheric simulation. WRF-ARW is forced with the operational National Center for Environmental Prediction (NCEP) Global Forecasting System (GFS) operational model. This methodology demonstrates the modeling framework that will be used for future parameter calibration of WRF-Hydro and optimization of WRF-ARW.

  18. Forecasting Fire Season Severity in South America Using Sea Surface Temperature Anomalies

    NASA Technical Reports Server (NTRS)

    Chen, Yang; Randerson, James T.; Morton, Douglas C.; DeFries, Ruth S.; Collatz, G. James; Kasibhatla, Prasad S.; Giglio, Louis; Jin, Yufang; Marlier, Miriam E.

    2011-01-01

    Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. We investigated the relationship between year-to-year changes in fire activity in South America and sea surface temperatures. We found that the Oceanic Ni o Index was correlated with interannual fire activity in the eastern Amazon, whereas the Atlantic Multidecadal Oscillation index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model to forecast regional fire season severity with lead times of 3 to 5 months. Our approach may contribute to the development of an early warning system for anticipating the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for climate and air quality.

  19. Prediction of the Arctic Oscillation in Boreal Winter by Dynamical Seasonal Forecasting Systems

    NASA Technical Reports Server (NTRS)

    Kang, Daehyun; Lee, Myong-In; Im, Jungho; Kim, Daehyun; Kim, Hye-Mi; Kang, Hyun-Suk; Schubert, Siegfried D.; Arribas, Alberto; MacLachlan, Craig

    2014-01-01

    This study assesses the skill of boreal winter Arctic Oscillation (AO) predictions with state-of-the-art dynamical ensemble prediction systems (EPSs): GloSea4, CFSv2, GEOS-5, CanCM3, CanCM4, and CM2.1. Long-term reforecasts with the EPSs are used to evaluate how well they represent the AO and to assess the skill of both deterministic and probabilistic forecasts of the AO. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper level wind, and precipitation associated with the different phases of the AO. The results demonstrate that most EPSs improve upon persistence skill scores for lead times up to 2 months in boreal winter, suggesting some potential for skillful prediction of the AO and its associated climate anomalies at seasonal time scales. It is also found that the skill of AO forecasts during the recent period (1997-2010) is higher than that of the earlier period (1983-1996).

  20. Prediction of the Arctic Oscillation in Boreal Winter by Dynamical Seasonal Forecasting Systems

    NASA Technical Reports Server (NTRS)

    Kang, Daehyun; Lee, Myong-In; Im, Jungho; Kim, Daehyun; Kim, Hye-Mi; Kang, Hyun-Suk; Shubert, Siegfried D.; Arriba, Albertom; MacLachlan, Craig

    2013-01-01

    This study assesses the prediction skill of the boreal winter Arctic Oscillation (AO) in the state-of-the-art dynamical ensemble prediction systems (EPSs): the UKMO GloSea4, the NCEP CFSv2, and the NASA GEOS-5. Long-term reforecasts made with the EPSs are used to evaluate representations of the AO, and to examine skill scores for the deterministic and probabilistic forecast of the AO index. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper-level wind, and precipitation according to the AO phase. Results demonstrate that all EPSs have better prediction skill than the persistence prediction for lead times up to 3-month, suggesting a great potential for skillful prediction of the AO and the associated climate anomalies in seasonal time scale. It is also found that the deterministic and probabilistic forecast skill of the AO in the recent period (1997-2010) is higher than that in the earlier period (1983-1996).

  1. How Much Global Burned Area Can Be Forecast on Seasonal Time Scales Using Sea Surface Temperatures?

    NASA Technical Reports Server (NTRS)

    Chen, Yang; Morton, Douglas C.; Andela, Niels; Giglio, Louis; Randerson, James T.

    2016-01-01

    Large-scale sea surface temperature (SST) patterns influence the interannual variability of burned area in many regions by means of climate controls on fuel continuity, amount, and moisture content. Some of the variability in burned area is predictable on seasonal timescales because fuel characteristics respond to the cumulative effects of climate prior to the onset of the fire season. Here we systematically evaluated the degree to which annual burned area from the Global Fire Emissions Database version 4 with small fires (GFED4s) can be predicted using SSTs from 14 different ocean regions. We found that about 48 of global burned area can be forecast with a correlation coefficient that is significant at a p < 0.01 level using a single ocean climate index (OCI) 3 or more months prior to the month of peak burning. Continental regions where burned area had a higher degree of predictability included equatorial Asia, where 92% of the burned area exceeded the correlation threshold, and Central America, where 86% of the burned area exceeded this threshold. Pacific Ocean indices describing the El Nino-Southern Oscillation were more important than indices from other ocean basins, accounting for about 1/3 of the total predictable global burned area. A model that combined two indices from different oceans considerably improved model performance, suggesting that fires in many regions respond to forcing from more than one ocean basin. Using OCI-burned area relationships and a clustering algorithm, we identified 12 hotspot regions in which fires had a consistent response to SST patterns. Annual burned area in these regions can be predicted with moderate confidence levels, suggesting operational forecasts may be possible with the aim of improving ecosystem management.

  2. How much global burned area can be forecast on seasonal time scales using sea surface temperatures?

    NASA Astrophysics Data System (ADS)

    Chen, Yang; Morton, Douglas C.; Andela, Niels; Giglio, Louis; Randerson, James T.

    2016-04-01

    Large-scale sea surface temperature (SST) patterns influence the interannual variability of burned area in many regions by means of climate controls on fuel continuity, amount, and moisture content. Some of the variability in burned area is predictable on seasonal timescales because fuel characteristics respond to the cumulative effects of climate prior to the onset of the fire season. Here we systematically evaluated the degree to which annual burned area from the Global Fire Emissions Database version 4 with small fires (GFED4s) can be predicted using SSTs from 14 different ocean regions. We found that about 48% of global burned area can be forecast with a correlation coefficient that is significant at a p < 0.01 level using a single ocean climate index (OCI) 3 or more months prior to the month of peak burning. Continental regions where burned area had a higher degree of predictability included equatorial Asia, where 92% of the burned area exceeded the correlation threshold, and Central America, where 86% of the burned area exceeded this threshold. Pacific Ocean indices describing the El Niño-Southern Oscillation were more important than indices from other ocean basins, accounting for about 1/3 of the total predictable global burned area. A model that combined two indices from different oceans considerably improved model performance, suggesting that fires in many regions respond to forcing from more than one ocean basin. Using OCI—burned area relationships and a clustering algorithm, we identified 12 hotspot regions in which fires had a consistent response to SST patterns. Annual burned area in these regions can be predicted with moderate confidence levels, suggesting operational forecasts may be possible with the aim of improving ecosystem management.

  3. Calibration of seasonal forecasts over Euro-Mediterranean region: improve climate information for the applications in the energy sector

    NASA Astrophysics Data System (ADS)

    De Felice, Matteo; Alessandri, Andrea; Catalano, Franco

    2013-04-01

    Accurate and reliable climate information, calibrated for the specific geographic domain, are critical for an effective planning of operations in industrial sectors, and more in general, for all the human activities. The connection between climate and energy sector became particularly evident in the last decade, due to the diffusion of renewable energy sources and the consequent attention on the socio-economical effects of extreme climate events .The energy sector needs reliable climate information in order to plan effectively power plants operations and forecast energy demand and renewable output. On time-scales longer than two weeks (seasonal), it is of critical importance the optimization of global climate information on the local domains needed by specific applications. An application that is distinctly linked with climate is electricity demand forecast, in fact, especially during cold/hot periods, the electricity usage patterns are influenced by the use of electric heating/cooling equipments which diffusion is steadily increasing worldwide [McNeil & Letschert, 2007]. Following an approach similar to [Navarra & Tribbia, 2005], we find a linear relationship between seasonal forecasts main modes of temperature anomaly and the main modes of reanalysis on Euro-Mediterranean domain. Then, seasonal forecasts are calibrated by means of a cross-validation procedure with the aim of optimize climate information over Italy. Calibrated seasonal forecasts are used as predictor for electricity demand forecast on Italy during the summer (JJA) in the period 1990-2009. Finally, a comparison with the results obtained with not calibrated climate forecasts is performed. The proposed calibration procedure led to an improvements of electricity demand forecast performance with more evident effects on the North of Italy, reducing the overall RMSE of 10% (from 1.09 to 0.98). Furthermore, main principal components are visualized and put in relation with electricity demand patterns in

  4. Evaluation of an early warning system for heat wave related mortality in Europe: implications for sub-seasonal-to-seasonal forecasting and climate services

    NASA Astrophysics Data System (ADS)

    Lowe, Rachel; García-Díez, Markel; Ballester, Joan; Creswick, James; Robine, Jean-Marie; Herrmann, François R.; Rodó, Xavier

    2016-04-01

    Heat waves have been responsible for more fatalities in Europe over the past decades than any other extreme weather event. However, temperature-related illnesses and deaths are largely preventable. Reliable sub-seasonal-to-seasonal (S2S) climate forecasts of extreme temperatures could allow for better resource management within heat-health action plans, to protect vulnerable populations and ensure access to preventive measures well in advance. The objective of this study is to assess the extent to which S2S climate forecasts could be incorporated into heat-health action plans, to support timely public health decision-making ahead of imminent heat wave events in Europe. Forecasts of apparent temperature at different lead times (e.g. 1 day, 4 days, 8 days, up to 3 months) were used in a mortality model to produce probabilistic mortality forecasts up to several months ahead of the 2003 heat wave event in Europe. Results were compared to mortality predictions inferred from using observed apparent temperature data in the mortality model. In general, we found a decreasing transition in skill between excellent predictions when using observed temperature, to predictions with no skill when using forecast temperature with lead times greater than one week. However, even at lead-times up to three months, there were some regions in Spain and the United Kingdom where excess mortality was detected with some certainty. This suggests that in some areas of Europe, there is potential for seasonal climate forecasts to be incorporated in localised heat-health action plans. In general, these results show that the performance of this climate service framework is not limited by the mortality model itself, but rather by the predictability of the climate variables, at S2S time scales, over Europe.

  5. The value of seasonal forecasting and crop mix adaptation to climate variability for agriculture under climate change

    NASA Astrophysics Data System (ADS)

    Choi, H. S.; Schneider, U.; Schmid, E.; Held, H.

    2012-04-01

    Changes to climate variability and frequency of extreme weather events are expected to impose damages to the agricultural sector. Seasonal forecasting and long range prediction skills have received attention as an option to adapt to climate change because seasonal climate and yield predictions could improve farmers' management decisions. The value of seasonal forecasting skill is assessed with a crop mix adaptation option in Spain where drought conditions are prevalent. Yield impacts of climate are simulated for six crops (wheat, barely, cotton, potato, corn and rice) with the EPIC (Environmental Policy Integrated Climate) model. Daily weather data over the period 1961 to 1990 are used and are generated by the regional climate model REMO as reference period for climate projection. Climate information and its consequent yield variability information are given to the stochastic agricultural sector model to calculate the value of climate information in the agricultural market. Expected consumers' market surplus and producers' revenue is compared with and without employing climate forecast information. We find that seasonal forecasting benefits not only consumers but also producers if the latter adopt a strategic crop mix. This mix differs from historical crop mixes by having higher shares of crops which fare relatively well under climate change. The corresponding value of information is highly sensitive to farmers' crop mix choices.

  6. Extending to seasonal scales the current usage of short range weather forecasts and climate projections for water management in Spain

    NASA Astrophysics Data System (ADS)

    Rodriguez-Camino, Ernesto; Voces, José; Sánchez, Eroteida; Navascues, Beatriz; Pouget, Laurent; Roldan, Tamara; Gómez, Manuel; Cabello, Angels; Comas, Pau; Pastor, Fernando; Concepción García-Gómez, M.°; José Gil, Juan; Gil, Delfina; Galván, Rogelio; Solera, Abel

    2016-04-01

    This presentation, first, briefly describes the current use of weather forecasts and climate projections delivered by AEMET for water management in Spain. The potential use of seasonal climate predictions for water -in particular dams- management is then discussed more in-depth, using a pilot experience carried out by a multidisciplinary group coordinated by AEMET and DG for Water of Spain. This initiative is being developed in the framework of the national implementation of the GFCS and the European project, EUPORIAS. Among the main components of this experience there are meteorological and hydrological observations, and an empirical seasonal forecasting technique that provides an ensemble of water reservoir inflows. These forecasted inflows feed a prediction model for the dam state that has been adapted for this purpose. The full system is being tested retrospectively, over several decades, for selected water reservoirs located in different Spanish river basins. The assessment includes an objective verification of the probabilistic seasonal forecasts using standard metrics, and the evaluation of the potential social and economic benefits, with special attention to drought and flooding conditions. The methodology of implementation of these seasonal predictions in the decision making process is being developed in close collaboration with final users participating in this pilot experience.

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

    USGS Publications Warehouse

    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.

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

    NASA Astrophysics Data System (ADS)

    Shukla, Shraddhanand; Roberts, Jason; Hoell, Andrew; Funk, Christopher C.; Robertson, Franklin; Kirtman, Ben

    2016-07-01

    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.

  9. Statistical Downscaling of Seasonal Forecasts and Climate Change Scenarios using Generalized Linear Modeling Approach for Stochastic Weather Generators

    NASA Astrophysics Data System (ADS)

    Kim, Y.; Katz, R. W.; Rajagopalan, B.; Podesta, G. P.

    2009-12-01

    Climate forecasts and climate change scenarios are typically provided in the form of monthly or seasonally aggregated totals or means. But time series of daily weather (e.g., precipitation amount, minimum and maximum temperature) are commonly required for use in agricultural decision-making. Stochastic weather generators constitute one technique to temporally downscale such climate information. The recently introduced approach for stochastic weather generators, based generalized linear modeling (GLM), is convenient for this purpose, especially with covariates to account for seasonality and teleconnections (e.g., with the El Niño phenomenon). Yet one important limitation of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of seasonally aggregated variables. To reduce this “overdispersion” phenomenon, we incorporate time series of seasonal total precipitation and seasonal mean minimum and maximum temperature in the GLM weather generator as covariates. These seasonal time series are smoothed using locally weighted scatterplot smoothing (LOESS) to avoid introducing underdispersion. Because the aggregate variables appear explicitly in the weather generator, downscaling to daily sequences can be readily implemented. The proposed method is applied to time series of daily weather at Pergamino and Pilar in the Argentine Pampas. Seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society (IRI) are used as prototypes. In conjunction with the GLM weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts (the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and

  10. A Distributed Modeling System for Short-Term to Seasonal Ensemble Streamflow Forecasting in Snowmelt Dominated Basins

    SciTech Connect

    Wigmosta, Mark S.; Gill, Muhammad K.; Coleman, Andre M.; Prasad, Rajiv; Vail, Lance W.

    2007-12-01

    This paper describes a distributed modeling system for short-term to seasonal water supply forecasts with the ability to utilize remotely-sensed snow cover products and real-time streamflow measurements. Spatial variability in basin characteristics and meteorology is represented using a raster-based computational grid. Canopy interception, snow accumulation and melt, and simplified soil water movement are simulated in each computational unit. The model is run at a daily time step with surface runoff and subsurface flow aggregated at the basin scale. This approach allows the model to be updated with spatial snow cover and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in weather forecasts, model parameters, and observations used for updating. Model inflow forecasts for the Dworshak Reservoir in northern Idaho are compared to observations and to April-July volumetric forecasts issued by the Natural Resource Conservation Service (NRCS) for Water Years 2000 – 2006. October 1 volumetric forecasts are superior to those issued by the NRCS, while March 1 forecasts are comparable. The ensemble spread brackets the observed April-July volumetric inflows in all years. Short-term (one and three day) forecasts also show excellent agreement with observations.

  11. Evaluation of an Early-Warning System for Heat Wave-Related Mortality in Europe: Implications for Sub-seasonal to Seasonal Forecasting and Climate Services.

    PubMed

    Lowe, Rachel; García-Díez, Markel; Ballester, Joan; Creswick, James; Robine, Jean-Marie; Herrmann, François R; Rodó, Xavier

    2016-02-06

    Heat waves have been responsible for more fatalities in Europe over the past decades than any other extreme weather event. However, temperature-related illnesses and deaths are largely preventable. Reliable sub-seasonal-to-seasonal (S2S) climate forecasts of extreme temperatures could allow for better short-to-medium-term resource management within heat-health action plans, to protect vulnerable populations and ensure access to preventive measures well in advance. The objective of this study is to assess the extent to which S2S climate forecasts could be incorporated into heat-health action plans, to support timely public health decision-making ahead of imminent heat wave events in Europe. Forecasts of apparent temperature at different lead times (e.g., 1 day, 4 days, 8 days, up to 3 months) were used in a mortality model to produce probabilistic mortality forecasts up to several months ahead of the 2003 heat wave event in Europe. Results were compared to mortality predictions, inferred using observed apparent temperature data in the mortality model. In general, we found a decreasing transition in skill between excellent predictions when using observed temperature, to predictions with no skill when using forecast temperature with lead times greater than one week. However, even at lead-times up to three months, there were some regions in Spain and the United Kingdom where excess mortality was detected with some certainty. This suggests that in some areas of Europe, there is potential for S2S climate forecasts to be incorporated in localised heat-health action plans. In general, these results show that the performance of this climate service framework is not limited by the mortality model itself, but rather by the predictability of the climate variables, at S2S time scales, over Europe.

  12. Observational uncertainty of Arctic sea-ice concentration significantly affects seasonal climate forecasts

    NASA Astrophysics Data System (ADS)

    Bunzel, Felix; Notz, Dirk; Baehr, Johanna; Müller, Wolfgang; Fröhlich, Kristina

    2016-04-01

    We examine how the choice of a particular satellite-retrieved sea-ice concentration dataset used for initialising seasonal climate forecasts impacts the prediction skill of Arctic sea-ice area and Northern hemispheric 2-meter air temperatures. To do so, we performed two assimilation runs with the Max Planck Institute Earth System Model (MPI-ESM) from 1979 to 2012, where atmospheric and oceanic parameters as well as sea-ice concentration were assimilated using Newtonian relaxation. The two assimilation runs differ only in the sea-ice concentration dataset used for assimilating sea ice. In the first run, we use sea-ice concentrations as derived by the NASA-Team algorithm, while in the second run we use sea-ice concentrations as derived from the Bootstrap algorithm. A major difference between these two sea-ice concentration data products involves the treatment of melt ponds. While for both products melt ponds appear as open water in the raw satellite data, the Bootstrap algorithm more strongly attempts to offset this systematic bias by synthetically increasing the retrieved ice concentration during summer months. For each year of the two assimilation runs we performed a 10-member ensemble of hindcast experiments starting on 1 May and 1 November with a hindcast length of 6 months. For hindcasts started in November, initial differences in Arctic sea-ice area and surface temperature decrease rapidly throughout the freezing period. For hindcasts started in May, initial sea-ice area differences increase over time. By the end of the melting period, this causes significant differences in 2-meter air temperature of regionally more than 3°C. Hindcast skill for surface temperatures over Europe and North America is higher with Bootstrap initialization during summer and with NASA Team initialisation during winter. This implies that the choice of the sea-ice data product and, thus, the observational uncertainty also affects forecasts of teleconnections that depend on Northern

  13. A similarity based approach to identify homogeneous regions for seasonal forecasting

    NASA Astrophysics Data System (ADS)

    Schick, Simon; Rössler, Ole; Weingartner, Rolf

    2015-04-01

    Seasonal runoff forecasting using statistical models is challenged by a large number of candidate predictors and a general weak predictor-predictand relationship. As the area of the target basin increases, often also the available data sets do, thus reinforcing the predictor selection challenge. We propose an approach which follows the idea of 'divide and conquer' as developed in computational sciences and machine learning: First, the macroscale target basin is partitioned into homogeneous regions using all its gauged mesoscale subbasins. Second, one representative subbasin per homogeneous region is identified, for which models are fitted and applied. Third, the resulting forecasts are combined at the scale of the macroscale target basin. This approach requires a suitable method to identify homogeneous regions and representative subbasins. We suggest a way based on hydrological similarity, as catchment similarity estimated with respect to physiographic-climatic descriptors does not necessarily imply similar runoff response. Each descriptor is derived from daily runoff series and aimed to reflect a specific catchment characteristic: autocorrelation coefficient, parameters of fitted Gamma distribution and low/high flow indices (based on daily runoff values) fluctuation of the standard deviation within the yearly cycle (based on weekly runoff values) dominant harmonics obtained from the discrete Fourier transform (based on monthly runoff values) long term trend (based on yearly runoff values) Where necessary, the runoff series first need to be standardized, aggregated, detrended or deseasonalized. As a preliminary study we present the results of a cluster analysis for the Swiss Rhine River as macroscale target basin, which leads to about 40 mesoscale subbasins with runoff series for the period 1991-2010. Problems we have to address include the choice of a clustering algorithm, the identification of an appropriate number of regions and the selection of representative

  14. Climate variables as predictors for seasonal forecast of dengue occurrence in Chennai, Tamil Nadu

    NASA Astrophysics Data System (ADS)

    Subash Kumar, D. D.; Andimuthu, R.

    2013-12-01

    Background Dengue is a recently emerging vector borne diseases in Chennai. As per the WHO report in 2011 dengue is one of eight climate sensitive disease of this century. Objective Therefore an attempt has been made to explore the influence of climate parameters on dengue occurrence and use for forecasting. Methodology Time series analysis has been applied to predict the number of dengue cases in Chennai, a metropolitan city which is the capital of Tamil Nadu, India. Cross correlation of the climate variables with dengue cases revealed that the most influential parameters were monthly relative humidity, minimum temperature at 4 months lag and rainfall at one month lag (Table 1). However due to intercorrelation of relative humidity and rainfall was high and therefore for predictive purpose the rainfall at one month lag was used for the model development. Autoregressive Integrated Moving Average (ARIMA) models have been applied to forecast the occurrence of dengue. Results and Discussion The best fit model was ARIMA (1,0,1). It was seen that the monthly minimum temperature at four months lag (β= 3.612, p = 0.02) and rainfall at one month lag (β= 0.032, p = 0.017) were associated with dengue occurrence and they had a very significant effect. Mean Relative Humidity had a directly significant positive correlation at 99% confidence level, but the lagged effect was not prominent. The model predicted dengue cases showed significantly high correlation of 0.814(Figure 1) with the observed cases. The RMSE of the model was 18.564 and MAE was 12.114. The model is limited by the scarcity of the dataset. Inclusion of socioeconomic conditions and population offset are further needed to be incorporated for effective results. Conclusion Thus it could be claimed that the change in climatic parameters is definitely influential in increasing the number of dengue occurrence in Chennai. The climate variables therefore can be used for seasonal forecasting of dengue with rise in minimum

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

    NASA Astrophysics Data System (ADS)

    Brak, B.; Challinor, A.

    2011-12-01

    skill in seasonal weather forecasting in West Africa (Senegal) sufficient to predict the occurrence of high (median) aflatoxin concentrations in groundnut at harvest and after some period of storage? For multiple locations in Senegal, aflatoxin contamination (AC) indices estimated using observed weather data from 1999-2010 were compared with AC indices based on gridded seasonal weather forecasts for the same location and year. Pearson correlation coefficients for ACobs and ACpred indices were calculated using all locations combined and, if sufficient weather years without missing values were available, for individual locations to test for regional differences in skill.

  16. Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station (CCAFS)

    NASA Technical Reports Server (NTRS)

    Barrett, Joe H., III; Roeder, William P.

    2010-01-01

    Peak wind speed is important element in 24-Hour and Weekly Planning Forecasts issued by 45th Weather Squadron (45 WS). Forecasts issued for planning operations at KSC/CCAFS. 45 WS wind advisories issued for wind gusts greater than or equal to 25 kt. 35 kt and 50 kt from surface to 300 ft. AMU developed cool-season (Oct - Apr) tool to help 45 WS forecast: daily peak wind speed, 5-minute average speed at time of peak wind, and probability peak speed greater than or equal to 25 kt, 35 kt, 50 kt. AMU tool also forecasts daily average wind speed from 30 ft to 60 ft. Phase I and II tools delivered as a Microsoft Excel graphical user interface (GUI). Phase II tool also delivered as Meteorological Interactive Data Display System (MIDDS) GUI. Phase I and II forecast methods were compared to climatology, 45 WS wind advisories and North American Mesoscale model (MesoNAM) forecasts in a verification data set.

  17. Modelling of Peach Tree (Prunus persica) Full Blooming Dates Using APCC MME Seasonal Forecasts

    NASA Astrophysics Data System (ADS)

    Chun, Jong; Kim, Sung; Lee, Hyojin; Han, Hyun-Hee; Son, In-Chang; Cho, Kyung Hwa

    2016-04-01

    Changbangjosaeng and Youmyeong cultivars at the Suwon site. The values of the goodness-of-fit measures using the selected synthetic daily maximum and minimum temperatures reflecting APCC MME seasonal datasets and selected were worse than those using those collected from the Suwon station. It is concluded that further work was recommended that the predictability of APCC MME seasonal forecasts should be improved to reduce the prediction errors of full blooming dates of peach trees.

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

    NASA Astrophysics Data System (ADS)

    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

  19. Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City

    NASA Astrophysics Data System (ADS)

    Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.

    2016-10-01

    Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.

  20. Ensemble Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts

    NASA Technical Reports Server (NTRS)

    Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.

    2001-01-01

    This paper presents preliminary results of an ensemble canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.

  1. Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1(m)

    NASA Astrophysics Data System (ADS)

    Liu, Xiangwen; Wu, Tongwen; Yang, Song; Jie, Weihua; Nie, Suping; Li, Qiaoping; Cheng, Yanjie; Liang, Xiaoyun

    2015-08-01

    This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC CSM1.1(m). For the South and Southeast Asian summer monsoon, reasonable skill is found in the model's forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Ni˜no3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and El Ni˜no-Southern Oscillation.

  2. Impacts of intra-seasonal agricultural decision-making and forecast information on maize production in Zambia

    NASA Astrophysics Data System (ADS)

    Tian, D.; Estes, L. D.; Evans, T. P.; Caylor, K. K.; Sheffield, J.; Wood, E. F.

    2015-12-01

    Maize is the most important food staple in sub-Saharan Africa. Climate change and rainfall variability pose great risks on maize production in this region. Intra-seasonal adaptive management combined with more skillful weather forecasts has the potential to improve the resilience of agricultural systems. Our aim is to understand the extent to which within-season agricultural management decisions can mitigate weather risks to maize production, and the degree to which this mitigation varies as a function of when the decision is made and the trajectory of weather. Using Zambia as a test case, we conducted crop-modeling experiments to determine which crop and water management decisions (typical of smallholder farmers) are most effective in mitigating rainfall-driven yield reductions under three precipitation scenarios (below normal, normal, and above normal). Yields were simulated using the DSSAT CERES-Maize model driven by an ensemble of historical weather data. Potential maize yields under different management options were simulated from different forecast points during the growing season, starting at planting and then in successive two-week intervals through the grain-filling period. The yield distributions were constructed as a function of the weather conditions and the management options, with results indicating which decision options provide the most mitigation in relation to a) the particular point in the growing season at which they are made, and b) the potential rainfall scenario. This study will help to understand how smallholder farmers in semi-arid systems may increase their resilience to highly variable weather by using typical within-season management options, and which decisions are most robust to forecast uncertainty.

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

    NASA Astrophysics Data System (ADS)

    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

  4. Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

    NASA Technical Reports Server (NTRS)

    Barrett, Joe H., III; Roeder, William P.

    2010-01-01

    The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were

  5. Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts

    NASA Astrophysics Data System (ADS)

    Tommasi, Desiree; Stock, Charles A.; Hobday, Alistair J.; Methot, Rick; Kaplan, Isaac C.; Eveson, J. Paige; Holsman, Kirstin; Miller, Timothy J.; Gaichas, Sarah; Gehlen, Marion; Pershing, Andrew; Vecchi, Gabriel A.; Msadek, Rym; Delworth, Tom; Eakin, C. Mark; Haltuch, Melissa A.; Séférian, Roland; Spillman, Claire M.; Hartog, Jason R.; Siedlecki, Samantha; Samhouri, Jameal F.; Muhling, Barbara; Asch, Rebecca G.; Pinsky, Malin L.; Saba, Vincent S.; Kapnick, Sarah B.; Gaitan, Carlos F.; Rykaczewski, Ryan R.; Alexander, Michael A.; Xue, Yan; Pegion, Kathleen V.; Lynch, Patrick; Payne, Mark R.; Kristiansen, Trond; Lehodey, Patrick; Werner, Francisco E.

    2017-03-01

    Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.

  6. News Media Coverage of Seasonal Forecasts: The Case of Winter 1982-83.

    NASA Astrophysics Data System (ADS)

    Riebsame, William E.

    1983-12-01

    Public interest in the 1982-83 winter forecast was heightened by antecedent press coverage of the possible effects of a solar luminosity decline and the El Chichon eruption on the climate. During the late summer and fall of 1982, several private climatologists and "folk forecasters" issued statements calling for an exceptionally cold winter, especially in the eastern United States. The National Weather Service (NWS) forecast, issued on 29 November, contradicted these early, dire predictions by calling for warmer-than-normal winter temperatures in the East. The NWS forecast was carried by slightly more than half of the U.S. daily newspapers, but by only a few weekly newspapers. The reporting was generally quite accurate, although some problems emerged in headlining and in using maps along with stories. The apparent controversy between official and private forecasts was mentioned in virtually every news article. Communication problems such as those surrounding the winter forecast should be of as great a concern to forecasters as is basic accuracy: both affect forecast usefulness.

  7. Influence of snow and soil moisture initialization on sub-seasonal predictability and forecast skill in boreal spring

    NASA Astrophysics Data System (ADS)

    Thomas, Jaison Ambadan; Berg, Aaron A.; Merryfield, William J.

    2016-07-01

    This study examines the influence of snow and soil moisture initialization on sub-seasonal potential and actual prediction skill of Canadian Climate Model version 3 (CanCM3) predictions of springtime (April-May) near surface air temperature. Four series of ten-member ensemble forecasts, initialized on 1st April where each series use different land surface initialization, were performed for the 20 year period 1986-2005. Potential predictability of temperature for extratropical Northern Hemisphere land is assessed using synthetic truth and signal-to-noise methods, and compared with actual prediction skills determined through validation against an ensemble mean of six reanalysis products. These metrics are computed for the forecasted 15 days averaged values of temperature at 15, 30 and 45 days lead times. Three of the four land surface initializations considered are intended to be realistic. These are obtained from the Canadian LAand Surface Scheme (CLASS) land surface component of the climate model driven off line with bias-corrected meteorological fields, with and without rescaling to the climate model's land climatology, and from climate model runs where the atmospheric component is constrained by reanalysis fields. A fourth land surface initialization that is intended to be unrealistic consists of a "scrambled" version of that obtained from rescaled offline-driven CLASS, in which each ensemble member is assigned values from a year other than the one being forecasted. Comparisons of forecasts using the scrambled and corresponding realistic land initializations indicate that the latter show higher potential predictability overall especially over North America and parts of Eurasia at all lead times. The higher potential predictability is primarily attributed to correct initialization of land surface variables, in particular the snow water equivalent, and the frozen and liquid components of soil moisture. Our results also indicate that predictability is governed

  8. Model Forecast Skill and Sensitivity to Initial Conditions in the Seasonal Sea Ice Outlook

    NASA Technical Reports Server (NTRS)

    Blanchard-Wrigglesworth, E.; Cullather, R. I.; Wang, W.; Zhang, J.; Bitz, C. M.

    2015-01-01

    We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed -1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

  9. Model forecast skill and sensitivity to initial conditions in the seasonal Sea Ice Outlook

    NASA Astrophysics Data System (ADS)

    Blanchard-Wrigglesworth, E.; Cullather, R. I.; Wang, W.; Zhang, J.; Bitz, C. M.

    2015-10-01

    We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed -1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

  10. A Drought Early Warning System Using System Dynamics Model and Seasonal Climate Forecasts: a case study in Hsinchu, Taiwan.

    NASA Astrophysics Data System (ADS)

    Tien, Yu-Chuan; Tung, Ching-Ping; Liu, Tzu-Ming; Lin, Chia-Yu

    2016-04-01

    In the last twenty years, Hsinchu, a county of Taiwan, has experienced a tremendous growth in water demand due to the development of Hsinchu Science Park. In order to fulfill the water demand, the government has built the new reservoir, Baoshan second reservoir. However, short term droughts still happen. One of the reasons is that the water level of the reservoirs in Hsinchu cannot be reasonably forecasted, which sometimes even underestimates the severity of drought. The purpose of this study is to build a drought early warning system that projects the water levels of two important reservoirs, Baoshan and Baoshan second reservoir, and also the spatial distribution of water shortagewith the lead time of three months. Furthermore, this study also attempts to assist the government to improve water resources management. Hence, a system dynamics model of Touchien River, which is the most important river for public water supply in Hsinchu, is developed. The model consists of several important subsystems, including two reservoirs, water treatment plants and agricultural irrigation districts. Using the upstream flow generated by seasonal weather forecasting data, the model is able to simulate the storage of the two reservoirs and the distribution of water shortage. Moreover, the model can also provide the information under certain emergency scenarios, such as the accident or failure of a water treatment plant. At last, the performance of the proposed method and the original water resource management method that the government used were also compared. Keyword: Water Resource Management, Hydrology, Seasonal Climate Forecast, Reservoir, Early Warning, Drought

  11. Impact of springtime Himalayan-Tibetan Plateau snowpack on the onset of the Indian summer monsoon in coupled seasonal forecasts

    NASA Astrophysics Data System (ADS)

    Senan, Retish; Orsolini, Yvan J.; Weisheimer, Antje; Vitart, Frédéric; Balsamo, Gianpaolo; Stockdale, Timothy N.; Dutra, Emanuel; Doblas-Reyes, Francisco J.; Basang, Droma

    2016-11-01

    The springtime snowpack over the Himalayan-Tibetan Plateau (HTP) region and Eurasia has long been suggested to be an influential factor on the onset of the Indian summer monsoon. To assess the impact of realistic initialization of springtime snow over HTP on the onset of the Indian summer monsoon, we examine a suite of coupled ocean-atmosphere 4-month ensemble reforecasts made at the European Centre for Medium-Range Weather Forecasts, using their Seasonal Forecasting System 4. The reforecasts were initialized on 1 April every year for the period 1981-2010. In these seasonal reforecasts, the snow is initialized "realistically" with ERA-Interim/Land Reanalysis. In addition, we carried out an additional set of forecasts, identical in all aspects except that initial conditions for snow-related land surface variables over the HTP region are randomized. We show that high snow depth over HTP influences the meridional tropospheric temperature gradient reversal that marks the monsoon onset. Composite difference based on a normalized HTP snow index reveal that, in high snow years, (1) the onset is delayed by about 8 days, and (2) negative precipitation anomalies and warm surface conditions prevail over India. We show that about half of this delay can be attributed to the realistic initialization of snow over the HTP region. We further demonstrate that high April snow depths over HTP are not uniquely influenced by El Nino-Southern Oscillation, the Indian Ocean Dipole or the North Atlantic Oscillation.

  12. A statistically based seasonal precipitation forecast model with automatic predictor selection and its application to central and south Asia

    NASA Astrophysics Data System (ADS)

    Gerlitz, Lars; Vorogushyn, Sergiy; Apel, Heiko; Gafurov, Abror; Unger-Shayesteh, Katy; Merz, Bruno

    2016-11-01

    The study presents a statistically based seasonal precipitation forecast model, which automatically identifies suitable predictors from globally gridded sea surface temperature (SST) and climate variables by means of an extensive data-mining procedure and explicitly avoids the utilization of typical large-scale climate indices. This leads to an enhanced flexibility of the model and enables its automatic calibration for any target area without any prior assumption concerning adequate predictor variables. Potential predictor variables are derived by means of a cell-wise correlation analysis of precipitation anomalies with gridded global climate variables under consideration of varying lead times. Significantly correlated grid cells are subsequently aggregated to predictor regions by means of a variability-based cluster analysis. Finally, for every month and lead time, an individual random-forest-based forecast model is constructed, by means of the preliminary generated predictor variables. Monthly predictions are aggregated to running 3-month periods in order to generate a seasonal precipitation forecast. The model is applied and evaluated for selected target regions in central and south Asia. Particularly for winter and spring in westerly-dominated central Asia, correlation coefficients between forecasted and observed precipitation reach values up to 0.48, although the variability of precipitation rates is strongly underestimated. Likewise, for the monsoonal precipitation amounts in the south Asian target area, correlations of up to 0.5 were detected. The skill of the model for the dry winter season over south Asia is found to be low. A sensitivity analysis with well-known climate indices, such as the El Niño- Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) and the East Atlantic (EA) pattern, reveals the major large-scale controlling mechanisms of the seasonal precipitation climate for each target area. For the central Asian target areas, both

  13. A Statistical Seasonal Forecast for the Apalachicola-Chattahoochee-Flint (ACF) and Alabama-Coosa-Tallapoosa (ACT) River Basins

    NASA Astrophysics Data System (ADS)

    Maleski, J.; Martinez, C. J.

    2013-12-01

    Seasonal hydrological predictions are an important tool for water management, and the El Niño connection is a well-known and widely used statistical predictor for rainfall over the southeast. In order to improve the seasonal hydrological forecast a regional focus in the ACT-ACF river basin was used and an increased number of regional predictors relevant to the region were evaluated for the model. The region chosen for this study is of particular interest because of ongoing water conflicts between Georgia, Alabama, and Florida in these river basins. It is hoped that better forecast models will aid in water management and planning for this area. A multiple regression model and a support vector regression model with multiple predictors were used to predict seasonal precipitation and temperature in the ACT-ACF river basins. Models were considered using a full time series (1895-2012) and a shorter time series (1945-2012). Predictors considered for the models were: Southern Oscillation Index (SOI), Atlantic Multidecadal Oscillation (AMO), Pacific decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and Bermuda High Index (BHI). These predictors were historically correlated with 21 weather stations over the time period (1895-2012) in the ACF and ACT area and evaluated in each model. Models are validated using leave-one-out cross-validation. Skill scores are calculated for each model relative to the climate normal.

  14. An industry perspective on the use of seasonal forecasts and weather information for evaluating sensitivities in traded commodity supply chains

    NASA Astrophysics Data System (ADS)

    Domeisen, Daniela; Slavov, Georgi

    2015-04-01

    Weather information on seasonal timescales is crucial to various end users, from the level of subsistence farming to the government level. Also the financial industry is ever more aware of and interested in the benefits that early and correctly interpreted forecast information provides. Straight forward and often cited applications include the estimation of rainfall and temperature anomalies for drought - prone agricultural areas producing traded commodities, as well as some of the rather direct impacts of weather on energy production. Governments, weather services, as well as both academia and private companies are working on tailoring climate and weather information to a growing number of customers. However, also other large markets, such as coal, iron ore, and gas, are crucially dependent on seasonal weather information and forecasts, while the needs are again very dependent on the direction of the predicted signal. So far, relatively few providers in climate services address these industries. All of these commodities show a strong seasonal and weather dependence, and an unusual winter or summer can crucially impact their demand and supply. To name a few impacts, gas is crucially driven by heating demand, iron ore excavation is dependent on the available water resources, and coal mining is dependent on winter temperatures and rainfall. This contribution will illustrate and provide an inside view of the type of climate and weather information needed for the various large commodity industries.

  15. Sources of interannual yield variability in JULES-crop and implications for forcing with seasonal weather forecasts

    NASA Astrophysics Data System (ADS)

    Williams, K. E.; Falloon, P. D.

    2015-06-01

    JULES-crop is a parametrisation of crops in the Joint UK Land Environment Simulator (JULES). We investigate the sources of the interannual variability in the modelled maize yield, using global runs driven by reanalysis data, with a view to understanding the impact of various approximations in the driving data and initialisation. The standard forcing dataset for JULES consists of a combination of meteorological variables describing precipitation, radiation, temperature, pressure, specific humidity and wind, at subdaily time resolution. We find that the main characteristics of the modelled yield can be reproduced with a subset of these variables and using daily forcing, with internal disaggregation to the model timestep. This has implications in particular for the use of the model with seasonal forcing data, which may not have been provided at subdaily resolution for all required driving variables. We also investigate the effect on annual yield of initialising the model with climatology on the sowing date. This approximation has the potential to considerably simplify the use of the model with seasonal forecasts, since obtaining observations or reanalysis output for all the initialisation variables required by JULES for the start date of the seasonal forecast would present significant practical challenges.

  16. Sources of interannual yield variability in JULES-crop and implications for forcing with seasonal weather forecasts

    NASA Astrophysics Data System (ADS)

    Williams, K. E.; Falloon, P. D.

    2015-12-01

    JULES-crop is a parametrisation of crops in the Joint UK Land Environment Simulator (JULES). We investigate the sources of the interannual variability in the modelled maize yield, using global runs driven by reanalysis data, with a view to understanding the impact of various approximations in the driving data and initialisation. The standard forcing data set for JULES consists of a combination of meteorological variables describing precipitation, radiation, temperature, pressure, specific humidity and wind, at subdaily time resolution. We find that the main characteristics of the modelled yield can be reproduced with a subset of these variables and using daily forcing, with internal disaggregation to the model time step. This has implications in particular for the use of the model with seasonal forcing data, which may not have been provided at subdaily resolution for all required driving variables. We also investigate the effect on annual yield of initialising the model with climatology on the sowing date. This approximation has the potential to considerably simplify the use of the model with seasonal forecasts, since obtaining observations or reanalysis output for all the initialisation variables required by JULES for the start date of the seasonal forecast would present significant practical challenges.

  17. A simple method to forecast the frequency of depressions and cyclones over Bay of Bengal during summer monsoon season

    NASA Astrophysics Data System (ADS)

    Sadhuram, Y.; Maneesha, K.; Suneeta, P.

    2017-02-01

    In this study, an attempt has been made to develop a simple multiple regression model to forecast the total number of depressions and cyclones (TNDC) over Bay of Bengal during summer monsoon (June-September) season using the data for the period, 1995-2016. Four potential predictors (zonal wind speed at 850 hPa in May and April SST in the North Australia-Indonesia region, 05°S-15°S; 120°E-160°E; March NINO 3.4 SST and geopotential height at 200 hPa in the region, 0°N-10°N; 80°E-100°E) have been identified to forecast TNDC. A remarkably high multiple correlation coefficient of 0.92 has been observed with the TNDC which explains 85% variability. The methodology has been tested for the recent 5 years (2012-2016) and found a good agreement between the observed and forecast values of TNDC except in 2015 in which the observed and predicted TNDC were 2 and 0, respectively. It is interesting to see high and significant correlations between the above predictors and the genesis potential parameter (GPP) during summer monsoon season. This GPP depends on the relative vorticity at 850 hPa, mid troposphere relative humidity, thermal instability between 850 and 500 hPa, and vertical wind shear between 200 and 850 hPa. It is inferred that the above predictors are influencing the environmental conditions over Bay of Bengal which, in turn, influencing the genesis of cyclones during summer monsoon season. The impact of ENSO (El-Nino-Southern Oscillation) and La-Nina in TNDC is examined and found that the vertical wind shear and relative vorticity are high and the GPP was almost double in ENSO compared with that in La-Nina which favoured high (low) TNDC under ENSO (La-Nina).

  18. Exploiting the atmosphere's memory for monthly, seasonal and interannual temperature forecasting using Scaling LInear Macroweather Model (SLIMM)

    NASA Astrophysics Data System (ADS)

    Del Rio Amador, Lenin; Lovejoy, Shaun

    2016-04-01

    . The corresponding space-time model (the ScaLIng Macroweather Model (SLIMM) is thus only multifractal in space where the spatial intermittency is associated with different climate zones. SLIMM exploits the power law (scaling) behavior in time of the temperature field and uses the long historical memory of the temperature series to improve the skill. The only model parameter is the fluctuation scaling exponent, H (usually in the range -0.5 - 0), which is directly related to the skill and can be obtained from the data. The results predicted analytically by the model have been tested by performing actual hindcasts in different 5° x 5° regions covering the planet using ERA-Interim, 20CRv2 and NCEP/NCAR reanalysis as reference datasets. We report maps of theoretical skill predicted by the model and we compare it with actual skill based on hindcasts for monthly, seasonal and annual resolutions. We also present maps of calibrated probability hindcasts with their respective validations. Comparisons between our results using SLIMM, some other stochastic autoregressive model, and hindcasts from the Canadian Seasonal to Interannual Prediction System (CanSIPS) and the National Centers for Environmental Prediction (NCEP)'s model CFSv2, are also shown. For seasonal temperature forecasts, SLIMM outperforms the GCM based forecasts in over 90% of the earth's surface. SLIMM forecasts can be accessed online through the site: http://www.to_be_announced.mcgill.ca.

  19. Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model

    NASA Astrophysics Data System (ADS)

    Bennett, James C.; Wang, Q. J.; Li, Ming; Robertson, David E.; Schepen, Andrew

    2016-10-01

    We present a new streamflow forecasting system called forecast guided stochastic scenarios (FoGSS). FoGSS makes use of ensemble seasonal precipitation forecasts from a coupled ocean-atmosphere general circulation model (CGCM). The CGCM forecasts are post-processed with the method of calibration, bridging and merging (CBaM) to produce ensemble precipitation forecasts over river catchments. CBaM corrects biases and removes noise from the CGCM forecasts, and produces highly reliable ensemble precipitation forecasts. The post-processed CGCM forecasts are used to force the Wapaba monthly rainfall-runoff model. Uncertainty in the hydrological modeling is accounted for with a three-stage error model. Stage 1 applies the log-sinh transformation to normalize residuals and homogenize their variance; Stage 2 applies a conditional bias-correction to correct biases and help remove negative forecast skill; Stage 3 applies an autoregressive model to improve forecast accuracy at short lead-times and propagate uncertainty through the forecast. FoGSS generates ensemble forecasts in the form of time series for the coming 12 months. In a case study of two catchments, FoGSS produces reliable forecasts at all lead-times. Forecast skill with respect to climatology is evident to lead-times of about 3 months. At longer lead-times, forecast skill approximates that of climatology forecasts; that is, forecasts become like stochastic scenarios. Because forecast skill is virtually never negative at long lead-times, forecasts of accumulated volumes can be skillful. Forecasts of accumulated 12 month streamflow volumes are significantly skillful in several instances, and ensembles of accumulated volumes are reliable. We conclude that FoGSS forecasts could be highly useful to water managers.

  20. An Assessment of Melting Season Streamflow Forecasts using EPS for a Snow Dominated Basin in Turkey

    NASA Astrophysics Data System (ADS)

    Ertaş, Cansaran; Şensoy, Aynur; Akkol, Bulut; Şorman, Arda; Uysal, Gökçen; Çoşkun, Cihan

    2016-04-01

    In many mountainous regions, snowmelt makes significant contribution to streamflow, particularly during spring and summer months. Therefore, runoff modeling and forecasting during spring and early summer is important in terms of energy and water resources management. In this study, the Upper Euphrates Basin (10,275 km2 area and elevation range of 1125-3500 m) located at the headwater of Euphrates River, one of Turkey's most important rivers, is selected as the application area. In this region, snowmelt runoff constitutes approximately 2/3 in volume of the total yearly runoff. The aim of the study is to make a forward-oriented, medium-range flow forecasting using Ensemble Prediction System (EPS) which is a pioneer study for Turkey. Conceptual hydrological model HBV, which has a common usage in the literature, is chosen to predict streamflows. According to preliminary results, Nash-Sutcliffe model efficiencies are 0.85 for calibration (2001-2008) and 0.71 for validation (2009-2014) respectively. After calibrating/validating the hydrologic model, EPS data including 51 different combinations produced by ECMWF is used as probability based weather forecasts. Melting period during March-June of 2009-2015 is chosen as the forecast period. The probabilistic skill of EPS based hydrological model results are analyzed to verify the ensemble forecasts.

  1. A Climate Service Prototype for the Hydropower Industry: Using a Multi-Model Approach to Improve Seasonal Forecasts of the Spring Flood Period in Sweden.

    NASA Astrophysics Data System (ADS)

    Foster, K. L.; Uvo, C. B.; Olsson, J.; Bosshard, T.; Berg, P.; Södling, J.

    2015-12-01

    The Ångerman River is Sweden's third largest river with over 50 regulation reservoirs, 42 hydropower stations and accounts for nearly 20% of the country's hydropower production. In seasonally snow covered regions, such as the Ångerman river system, the winter precipitation is often temporarily stored in the snow pack during the colder months and released over a relatively short period of intense flows during in the warmer months. These spring flood events dominate the hydrology in these regions and as such reliable hydrological forecasts of these events are in great demand. However, it has been shown that the spread in the forecast error of operational hydrological forecasts in Sweden have not changed significantly over the last 25 years (Arheimer et al., 2012). Building on previous work by Foster et al. (2011) and Olsson et al. (2015), a multi-model hydrological seasonal forecast prototype has been developed for the Ångerman river system and applied to the spring flood season. The prototype employs different model-chain approaches: (1) a climatological forecast, where historical observations for the same period are selected to run the hydrological model, (2) a reduced historical ensemble, where analogue years from the historical observations dataset are selected to run the hydrological model, (3) using bias corrected meteorological seasonal forecasts from the ECMWF to force the hydrological model, and (4) statistical downscaling large-scale circulation variables from ECMWF seasonal forecasts directly to accumulated discharge. These different chains are combined into a multi-model ensemble forecast. The different approaches and the multi-model prototype are evaluated against the state-of-the-art operational system for the spring flood season for the period 1981-2014 in the Ångerman river system. References Arheimer, B., Lindström, G., and Olsson, J.: A systematic review of sensitivities in the Swedish flood-forecasting system, Atmos. Res., 100, 275-284, 2011

  2. Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals

    NASA Astrophysics Data System (ADS)

    Mekanik, F.; Imteaz, M. A.; Talei, A.

    2016-05-01

    Accurate seasonal rainfall forecasting is an important step in the development of reliable runoff forecast models. The large scale climate modes affecting rainfall in Australia have recently been proven useful in rainfall prediction problems. In this study, adaptive network-based fuzzy inference systems (ANFIS) models are developed for the first time for southeast Australia in order to forecast spring rainfall. The models are applied in east, center and west Victoria as case studies. Large scale climate signals comprising El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Inter-decadal Pacific Ocean (IPO) are selected as rainfall predictors. Eight models are developed based on single climate modes (ENSO, IOD, and IPO) and combined climate modes (ENSO-IPO and ENSO-IOD). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (r) and root mean square error in probability (RMSEP) skill score are used to evaluate the performance of the proposed models. The predictions demonstrate that ANFIS models based on individual IOD index perform superior in terms of RMSE, MAE and r to the models based on individual ENSO indices. It is further discovered that IPO is not an effective predictor for the region and the combined ENSO-IOD and ENSO-IPO predictors did not improve the predictions. In order to evaluate the effectiveness of the proposed models a comparison is conducted between ANFIS models and the conventional Artificial Neural Network (ANN), the Predictive Ocean Atmosphere Model for Australia (POAMA) and climatology forecasts. POAMA is the official dynamic model used by the Australian Bureau of Meteorology. The ANFIS predictions certify a superior performance for most of the region compared to ANN and climatology forecasts. POAMA performs better in regards to RMSE and MAE in east and part of central Victoria, however, compared to ANFIS it shows weaker results in west Victoria in terms of prediction errors and RMSEP skill

  3. Limitations of Snow-Water Equivalent and Precipitation Data in Seasonal Flow Forecasting in the Upper Klamath River Basin of Oregon and California

    NASA Astrophysics Data System (ADS)

    Risley, J. C.; Roehl, E. A.

    2005-12-01

    Water managers in the upper Klamath Basin, located in south-central Oregon and northeastern California, rely on accurate forecasts of spring and summer streamflow to optimally allocate increasingly limited water supplies for various demands that include irrigation for agriculture, habitat for endangered fishes, and hydropower production. Federal agencies make forecasts on the 1st of each month, from January through May, of the total volume of water expected to pass a stream gage or flow into a reservoir during an entire summer irrigation season. Often the forecasts are based on output from flow forecast models that use real-time snow-water equivalent (SWE) and precipitation data as input. Because the January and February forecasts are significantly less accurate than those made in the spring, we were interested in quantifying the limitations of real-time SWE and precipitation data in forecasting future flows. Using over 20 years of daily SWE, precipitation, and flow time-series from five sites in the upper Klamath Basin, we first decomposed the flow records into annual periodic, long-term climatic, and chaotic traces. Being the component of the original flow records with their seasonality and long-term trends removed, the chaotic traces were then lag correlated with SWE and precipitation records. After a 120 day lag (approximately 4 months), all of the correlation coefficients between the chaotic flow traces and the SWE and precipitation records were less than 0.4. From this, we could infer that SWE and precipitation data older than 120 days provide forecast models only with information regarding the annual seasonal and long-term climate patterns and do not provide information that is unique and specific for the upcoming irrigation season. These results also support the need to find new climate variables, such as mid-oceanic indicators, to improve forecast model accuracy rather than using just real-time SWE and precipitation conditions.

  4. Randomly correcting model errors in the ARPEGE-Climate v6.1 component of CNRM-CM: applications for seasonal forecasts

    NASA Astrophysics Data System (ADS)

    Batté, Lauriane; Déqué, Michel

    2016-06-01

    Stochastic methods are increasingly used in global coupled model climate forecasting systems to account for model uncertainties. In this paper, we describe in more detail the stochastic dynamics technique introduced by Batté and Déqué (2012) in the ARPEGE-Climate atmospheric model. We present new results with an updated version of CNRM-CM using ARPEGE-Climate v6.1, and show that the technique can be used both as a means of analyzing model error statistics and accounting for model inadequacies in a seasonal forecasting framework.The perturbations are designed as corrections of model drift errors estimated from a preliminary weakly nudged re-forecast run over an extended reference period of 34 boreal winter seasons. A detailed statistical analysis of these corrections is provided, and shows that they are mainly made of intra-month variance, thereby justifying their use as in-run perturbations of the model in seasonal forecasts. However, the interannual and systematic error correction terms cannot be neglected. Time correlation of the errors is limited, but some consistency is found between the errors of up to 3 consecutive days.These findings encourage us to test several settings of the random draws of perturbations in seasonal forecast mode. Perturbations are drawn randomly but consistently for all three prognostic variables perturbed. We explore the impact of using monthly mean perturbations throughout a given forecast month in a first ensemble re-forecast (SMM, for stochastic monthly means), and test the use of 5-day sequences of perturbations in a second ensemble re-forecast (S5D, for stochastic 5-day sequences). Both experiments are compared in the light of a REF reference ensemble with initial perturbations only. Results in terms of forecast quality are contrasted depending on the region and variable of interest, but very few areas exhibit a clear degradation of forecasting skill with the introduction of stochastic dynamics. We highlight some positive

  5. Seasonal precipitation forecasts for selected regions in West Africa using circulation type classifications in combination with further statistical approaches - Conceptual framework and first results

    NASA Astrophysics Data System (ADS)

    Bliefernicht, Jan; Laux, Patrik; Waongo, Moussa; Kunstmann, Harald

    2015-04-01

    Providing valuable forecasts of the seasonal precipitation amount for the upcoming rainy season is one of the big challenges for the national weather services in West Africa. Every year a harmonized forecast of the seasonal precipitation amount for the West African region is issued by the national weather services within the PRESAO framework. The PREASO forecast is based on various statistical approaches ranging from a simple subjective analog method based on the experiences of a meteorological expert to objective regression-based approaches by using various sources of input information such as predicted monsoon winds or observed sea surface temperature anomalies close to the West African coastline. The objective of this study is to perform an evaluation of these techniques for selected West African regions and to introduce classification techniques in the current operational practices and to combine these approaches with further techniques for an additional refinement of the forecasting procedure. We use a fuzzy-rule based technique for a classification of (sub-) monthly large-scale atmospheric and oceanic patterns which are combined to further statistical approaches such as an analog method and a data depth approach for the prediction of the (sub-) seasonal precipitation amounts and additional precipitation indices. The study regions are located from the Edges of the Sahel region in the North of Burkina Faso to the coastline of Ghana. A novel precipitation archive based on daily observations provided by the meteorological services of Burkina Faso and Ghana is the basis for the predictands and is used as reference for model evaluation. The performance of the approach is evaluated over a long period (e.g. 50 years) using cross-validation techniques and sophisticated verification measures for an evaluation of a probability forecast. The precipitation forecast of the classification techniques are also compared to the techniques of the PREASAO community, the

  6. Comparison of Multiple Quantitative Precipitation Estimates for Warm-Season Flood Forecasting in the Colorado Front Range

    NASA Astrophysics Data System (ADS)

    Moreno, H. A.; Vivoni, E. R.; Gochis, D. J.

    2010-12-01

    Quantitative Precipitation Estimates (QPEs) from ground and satellite platforms can potentially serve as input to hydrologic models used for flood forecasting in mountainous watersheds. This work compares the impact of ten different high-resolution (4-km and hourly) precipitation products on flood forecast skill in a large region of the Colorado Front Range. These products range from radar fields (Level II, Stage III and IV) to satellite estimates (HydroEstimator, AutoEstimator, Blend, GMSRA, PERSIANN-CCS). We examine QPE skill relative to ground rain gauges to detect error characteristics during the 2004 summer season which exhibited above-average precipitation accumulations in the region. We then quantify flood forecast skill by using the TIN-based Real time Integrated Basin Simulator (tRIBS) as an analysis tool in four mountain basins. The structural features of radar and satellite precipitation products determine the timing and magnitude of simulated summer floods in the study basins. Use of ground-based radar and multi-sensor satellite estimates minimize streamflow differences at the outlet locations compared to satellite-only QPEs which tend to underestimate total rainfall volumes, resulting in significant hydrologic response uncertainties. Given the generally low rainfall estimates from satellite-only products, a mean field bias correction is applied to all products and results are compared against non-corrected precipitation products. An exploratory analysis is conducted to assess precipitation volume differences between the bias-corrected and raw satellite products. Probability density functions of the differences allow examining the links between QPE bias, the diurnal precipitation cycle and topographic position. Analysis of the spatiotemporal precipitation and streamflow patterns help identify benefits and shortcomings of high-resolution QPEs for summer storms in mountainous areas.

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

    PubMed Central

    Azeez, Adeboye; Obaromi, Davies; Odeyemi, Akinwumi; Ndege, James; Muntabayi, Ruffin

    2016-01-01

    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

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

    NASA Astrophysics Data System (ADS)

    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

  9. Using a model and forecasted weather to predict forage and livestock production for making stocking decisions in the coming growing season

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Forecasting peak standing crop (PSC) for the coming grazing season can help ranchers make appropriate stocking decisions to reduce enterprise risks. Previously developed PSC predictors were based on short-term experimental data (<15 yr) and limited stocking rates (SR) without including the effect of...

  10. Balance characteristics of multivariate background error covariance for rainy and dry seasons and their impact on precipitation forecasts of two rainfall events

    NASA Astrophysics Data System (ADS)

    Chen, Yaodeng; Xia, Xue; Min, Jinzhong; Huang, Xiang-Yu; Rizvi, Syed R. H.

    2016-10-01

    Atmospheric moisture content or humidity is an important analysis variable of any meteorological data assimilation system. The humidity analysis can be univariate, using humidity background (normally short-range numerical forecasts) and humidity observations. However, more and more data assimilation systems are multivariate, analyzing humidity together with wind, temperature and pressure. Background error covariances, with unbalanced velocity potential and humidity in the multivariate formulation, are generated from weather research and forecasting model forecasts, collected over a summer rainy season and a winter dry season. The unbalanced velocity potential and humidity related correlations are shown to be significantly larger, indicating more important roles unbalanced velocity potential and humidity play, in the rainy season than that in the dry season. Three cycling data assimilation experiments of two rainfall events in the middle and lower reaches of the Yangtze River are carried out. The experiments differ in the formulation of the background error covariances. Results indicate that only including unbalanced velocity potential in the multivariate background error covariance improves wind analyses, but has little impact on temperature and humidity analyses. In contrast, further including humidity in the multivariate background error covariance although has a slight negative effect on wind analyses and a neutral effect on temperature analyses, but significantly improves humidity analyses, leading to precipitation forecasts more consistent with China Hourly Merged Precipitation Analysis.

  11. Forecasting corn and soybean yields in the United States utilizing pre- and within-season remotely sensed variables

    NASA Astrophysics Data System (ADS)

    Johnson, D. M.

    2013-12-01

    Four timely and broadly available remotely sensed datasets were assessed for inclusion into county-level corn and soybean yield forecasting efforts focused on the Corn Belt region of the central United States (US). Those datasets were the (1) Normalized Difference Vegetation Index (NDVI) as derived from the Terra satellite's Moderate Resolution Imaging Spectroradiometer (MODIS), (2) daytime and (3) nighttime land surface temperature (LST) as derived from Aqua satellite's MODIS, and (4) precipitation from the National Weather Service (NWS) Nexrad-based gridded data product. The originating MODIS data utilized were the globally produced 8-day, clear sky composited science products (MOD09Q1 and MYD11A2), while the US-wide NWS data were manipulated to mesh with the MODIS imagery both spatially and temporally by regridding and summing the otherwise daily measurements. The crop growing seasons of 2006 - 2011 were analyzed with each year bounded by 32 8-day periods from mid-February through late October. Land cover classifications known as the Cropland Data Layer as produced annually by the National Agricultural Statistics Service (NASS) were used to isolate the input dataset pixels as to corn and soybeans for each of the corresponding years. The relevant pixels were then averaged by crop and time period to produce a county-level estimate of NDVI, the LSTs, and precipitation. They in turn were related to official annual NASS county level yield statistics. For the Corn Belt region as a whole, both corn and soybean yields were found to be positively correlated with NDVI in the middle of the summer and negatively correlated to daytime LST at that same time. Nighttime LST and precipitation showed no correlations to yield, regardless of the time prior or during the growing season. There was also slight suggestion of low NDVI and high daytime LST in the spring being positively related to final yields, again for both crops. Taking only NDVI and daytime LST as inputs, regression

  12. A Stormy Forecast for the Upcoming Dust Storm Season on Mars

    NASA Astrophysics Data System (ADS)

    Shirley, J. H.; Mischna, M. A.

    2015-12-01

    The dust storm season on Mars is centered on the time of Mars' perihelion, which occurs late in the southern spring. The dust storm season of the current Mars year (MY 33) will begin on 1 June 2016 (Ls=160°) and will end on 28 March 2017 (Ls=340°). Mars will reach perihelion in late October 2016, about a month after the planned landing of the Mars InSight Lander. Spectacular global-scale dust storms (GDS) occur during the dust storm season in some Mars years but not in others. In a prior study (Shirley, 2015; Icarus 251, 128-144), systematic relationships were found linking the occurrence of historic global-scale dust storms on Mars with the variability of the orbital angular momentum of the planet with respect to the solar system barycenter. Conditions favorable to the occurrence of a future GDS in the current Mars year were noted in that study. A physical model that may account for the observed relationships has subsequently emerged. In this model, a weak coupling of the orbital and rotational motions of extended bodies is effected through a modulation of circulatory flows of atmospheres. The formal derivation yields a coupling term, which in turn prescribes an acceleration field that may constructively or destructively interfere with large-scale atmospheric motions driven by other means. This coupling term has been incorporated within the MarsWRF Global Circulation Model, and the GCM thus modified has undergone extensive validation and testing. The GCM "retrodicts" accelerations and circulation patterns favorable for the occurrence of southern summer solstice season global-scale dust storms in all of the prior years (n=7) in which such storms are known to have occurred (Mischna and Shirley, this meeting). The forcing function for the upcoming dust storm season exhibits amplitude and phasing that largely replicates the dynamical conditions prevailing prior to and during the MY 9 GDS of 1971. We conclude that a global-scale dust storm is likely to occur during

  13. Multi-model ensemble forecasting of North Atlantic tropical cyclone activity

    NASA Astrophysics Data System (ADS)

    Villarini, Gabriele; Luitel, Beda; Vecchi, Gabriel A.; Ghosh, Joyee

    2016-09-01

    North Atlantic tropical cyclones (TCs) and hurricanes are responsible for a large number of fatalities and economic damage. Skillful seasonal predictions of the North Atlantic TC activity can provide basic information critical to our improved preparedness. This study focuses on the development of statistical-dynamical seasonal forecasting systems for different quantities related to the frequency and intensity of North Atlantic TCs. These models use only tropical Atlantic and tropical mean sea surface temperatures (SSTs) to describe the variability exhibited by the observational records because they reflect the importance of both local and non-local effects on the genesis and development of TCs in the North Atlantic basin. A set of retrospective forecasts of SSTs by six experimental seasonal-to-interannual prediction systems from the North American Multi-Model Ensemble are used as covariates. The retrospective forecasts are performed over the period 1982-2015. The skill of these statistical-dynamical models is quantified for different quantities (basin-wide number of tropical storms and hurricanes, power dissipation index and accumulated cyclone energy) for forecasts initialized as early as November of the year prior to the season to forecast. The results of this work show that it is possible to obtain skillful retrospective forecasts of North Atlantic TC activity with a long lead time. Moreover, probabilistic forecasts of North Atlantic TC activity for the 2016 season are provided.

  14. Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based downscaling

    NASA Astrophysics Data System (ADS)

    Sohn, Soo-Jin; Tam, Chi-Yung

    2016-05-01

    -level pressure (SLP) and 500 hPa geopotential height (Z500) as predictors, statistically downscaled MME (DMME) precipitation and temperature predictions were substantially improved compared to those based on raw MME outputs. Limitations and possible causes of error of such a dynamical-statistical model, in the current framework of dynamical seasonal climate predictions, were also discussed. Finally, the method was used to construct a dynamical-statistical system for 6 month-lead drought predictions for 60 stations in South Korea. DMME was found to give reasonably skillful long-lead forecasts of SPEI for winter to spring. Moreover, DMME-based products clearly outperform the raw MME predictions, especially during extreme wet years. Our results could lead to more reliable climatic extreme predictions for policymakers and stakeholders in the water management sector, and for better mitigation and climate adaptations.

  15. Development of an integrated method for long-term water quality prediction using seasonal climate forecast

    NASA Astrophysics Data System (ADS)

    Cho, Jaepil; Shin, Chang-Min; Choi, Hwan-Kyu; Kim, Kyong-Hyeon; Choi, Ji-Yong

    2016-10-01

    The APEC Climate Center (APCC) produces climate prediction information utilizing a multi-climate model ensemble (MME) technique. In this study, four different downscaling methods, in accordance with the degree of utilizing the seasonal climate prediction information, were developed in order to improve predictability and to refine the spatial scale. These methods include: (1) the Simple Bias Correction (SBC) method, which directly uses APCC's dynamic prediction data with a 3 to 6 month lead time; (2) the Moving Window Regression (MWR) method, which indirectly utilizes dynamic prediction data; (3) the Climate Index Regression (CIR) method, which predominantly uses observation-based climate indices; and (4) the Integrated Time Regression (ITR) method, which uses predictors selected from both CIR and MWR. Then, a sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create daily weather inputs to the Soil and Water Assessment Tool (SWAT) model. Long-term predictability of water quality within the Wecheon watershed of the Nakdong River Basin was evaluated. According to the Korean Ministry of Environment's Provisions of Water Quality Prediction and Response Measures, modeling-based predictability was evaluated by using 3-month lead prediction data issued in February, May, August, and November as model input of SWAT. Finally, an integrated approach, which takes into account various climate information and downscaling methods for water quality prediction, was presented. This integrated approach can be used to prevent potential problems caused by extreme climate in advance.

  16. Impact of seasonal forecast use on agricultural income in a system with varying crop costs and returns: an empirically-grounded simulation

    NASA Astrophysics Data System (ADS)

    Gunda, T.; Bazuin, J. T.; Nay, J.; Yeung, K. L.

    2017-03-01

    Access to seasonal climate forecasts can benefit farmers by allowing them to make more informed decisions about their farming practices. However, it is unclear whether farmers realize these benefits when crop choices available to farmers have different and variable costs and returns; multiple countries have programs that incentivize production of certain crops while other crops are subject to market fluctuations. We hypothesize that the benefits of forecasts on farmer livelihoods will be moderated by the combined impact of differing crop economics and changing climate. Drawing upon methods and insights from both physical and social sciences, we develop a model of farmer decision-making to evaluate this hypothesis. The model dynamics are explored using empirical data from Sri Lanka; primary sources include survey and interview information as well as game-based experiments conducted with farmers in the field. Our simulations show that a farmer using seasonal forecasts has more diversified crop selections, which drive increases in average agricultural income. Increases in income are particularly notable under a drier climate scenario, when a farmer using seasonal forecasts is more likely to plant onions, a crop with higher possible returns. Our results indicate that, when water resources are scarce (i.e. drier climate scenario), farmer incomes could become stratified, potentially compounding existing disparities in farmers’ financial and technical abilities to use forecasts to inform their crop selections. This analysis highlights that while programs that promote production of certain crops may ensure food security in the short-term, the long-term implications of these dynamics need careful evaluation.

  17. ECOMS-UDG. A User-friendly Data access Gateway to seasonal forecast datasets allowing R-based remote data access, visualization-validation, bias correction and downscaling

    NASA Astrophysics Data System (ADS)

    Santiago Cofiño, Antonio; Gutiérrez, José Manuel; Fernández, Jesús; Bedia, Joaquín; Vega, Manuel; Herrera, Sixto; Frías, María Dolores; Iturbide, Maialen; Magariño, Maria Eugenia; Manzanas, Rodrigo

    2016-04-01

    Seasonal forecasting data from state-or-the-art forecasting systems (e.g. NCEP/CFSv2 or ECMWF/System4) can be obtained directly from the data providers, but the resulting formats, aggregations and vocabularies may not be homogeneous across datasets, requiring some post processing. Moreover, different data policies hold for the various datasets - which are freely available only in some cases - and therefore data access may not be straightforward. Thus, obtaining seasonal climate forecast data is typically a time consuming task. The ECOMS-UDG (User Data Gateway for the ECOMS initiative) has been developed building in the ​User Data Gateway (UDG, http://meteo.unican.es/udg-wiki) in order to facilitate seasonal (re)forecast data access to end users. The required variables have been downloaded from data providers and stored locally in a THREDDS data server implementing fine-grained user authorization. Thus, users can efficiently retrieve the subsets that best suits their particular research aims (typically surface variables for certain regions, periods and/or ensemble members) from a large volume of information. Moreover, an interface layer developed in R allows remote data exploration, access (including homogenization, collocation and sub-setting) and the integration of ECOMS-UDG with a number of R packages developed in the framework of ECOMS for forecast visualization, validation, bias correction and downscaling. This unique framework oriented to climate services allows users from different sectors to easily access seasonal forecasting data (typically surface variables), calibrating and/or downscaling (using upper air information from large scale predictors) this data at local level and validating the different results (using observations). The documentation delivered with the packages includes worked examples showing that the whole visualization, bias correction and/or downscaling tasks requires only a few lines of code and are fully reproducible and adaptable to

  18. Predictive influence of sea surface temperature on teleconnection patterns in North Atlantic. A case study on winter seasonal forecast in NW Iberian Peninsula.

    NASA Astrophysics Data System (ADS)

    Iglesias, I.; Lorenzo, M. N.; Taboada, J. J.; Gómez-Gesteira, M.; Ramos, A. M.

    2010-05-01

    Seasonal forecast in medium latitudes is a research field not too much developed, but it is likely to improve considerable as the dynamics of atmosphere and ocean as a coupled system are better understood. The aim of this work is to study the relationship between the global sea surface temperature anomalies (SSTA) and the most important teleconnection patterns which affect the North Atlantic area: North Atlantic Oscillation (NAO), East Atlantic pattern (EA), Scandinavia pattern (SCA), East Atlantic/Western Russia pattern (EA/WR) and Europe Polar/Eurasia pattern (POL). The relationship between SSTA and those patterns will be explored in autumn and winter, the seasons with the highest quantity of rainfall in the area under study. These teleconnection patterns have a relationship with climate characteristics in Europe. Therefore, any forecast skill over teleconnection patterns will mean a forecast skill on climate. The SST data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA. The teleconnection indices were taken from the Climate Prediction Center of the NOAA between 1950 and 2006. Monthly precipitation and temperature data from 1951-2006 for two locations at NW Iberian Peninsula were obtained from the database of MeteoGalicia, the forecast center of the Regional Government of Galicia. The methodology used in this work is the same one used in Phillips and McGregor, 2002 and Lorenzo el al., 2009. Results show that SST anomalies in certain areas of the world ocean have a great potential to improve seasonal climate forecast in the mid-latitudes. A potential predictability for NAO and EA patterns in winter and for SCA and EA patterns in autumn was obtained. The value of those kind of correlations have been studied for a particular region, located at the NW part of the Iberian Peninsula, highlighting the possibility of perform a climate forecast for autumn and winter. This work could serve like a reference for many other regions in Europe, whose climate is

  19. Value assessment of a global hydrological forecasting system

    NASA Astrophysics Data System (ADS)

    Candogan Yossef, N.; Winsemius, H.; van Beek, L. P. H.; van Beek, E.; Bierkens, M. F. P.

    2012-04-01

    The inter-annual variability in streamflow presents risks and opportunities in the management of water resources systems. Reliable hydrological forecasts, effective communication and proper response allow several sectors to make more informed management decisions. In many developing regions of the world, there are no efficient hydrological forecasting systems. A global forecasting system which indicates increased probabilities of streamflow excesses or shortages over long lead-times can be of great value for these regions. FEWS-World system is developed for this purpose. It is based on the Delft-FEWS (flood early warning system) developed by Deltares and incorporates the global hydrological model PCR-GLOBWB. This study investigates the skill and value of FEWS-World. Skill is defined as the ability of the system to forecast discharge extremes; and value as its usefulness for possible users and ultimately for affected populations. Skill is assessed in historical simulation mode as well as retroactive forecasting mode. For the assessment in historical simulation mode a meteorological forcing based on observations from the Climate Research Unit of the University of East Anglia and the ERA-40 reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF) was used. For the assessment in retroactive forecasting mode the model was forced with ensemble forecasts from the seasonal forecast archives of ECMWF. The eventual goal is to transfer FEWS-World to operational forecasting mode, where the system will use operational seasonal forecasts from ECMWF. The results will be disseminated on the internet, and hopefully provide information that is valuable for users in data and model-poor regions of the world. The results of the preliminary assessment show that although forecasting skill decreases with increasing lead time, the value of forecasts does not necessarily decrease. The forecast requirements and response options of several water related sectors was

  20. Forecasting and prediction of scorpion sting cases in Biskra province, Algeria, using a seasonal autoregressive integrated moving average model

    PubMed Central

    2016-01-01

    OBJECTIVES The aims of this study were to highlight some epidemiological aspects of scorpion envenomations, to analyse and interpret the available data for Biskra province, Algeria, and to develop a forecasting model for scorpion sting cases in Biskra province, which records the highest number of scorpion stings in Algeria. METHODS In addition to analysing the epidemiological profile of scorpion stings that occurred throughout the year 2013, we used the Box-Jenkins approach to fit a seasonal autoregressive integrated moving average (SARIMA) model to the monthly recorded scorpion sting cases in Biskra from 2000 to 2012. RESULTS The epidemiological analysis revealed that scorpion stings were reported continuously throughout the year, with peaks in the summer months. The most affected age group was 15 to 49 years old, with a male predominance. The most prone human body areas were the upper and lower limbs. The majority of cases (95.9%) were classified as mild envenomations. The time series analysis showed that a (5,1,0)×(0,1,1)12 SARIMA model offered the best fit to the scorpion sting surveillance data. This model was used to predict scorpion sting cases for the year 2013, and the fitted data showed considerable agreement with the actual data. CONCLUSIONS SARIMA models are useful for monitoring scorpion sting cases, and provide an estimate of the variability to be expected in future scorpion sting cases. This knowledge is helpful in predicting whether an unusual situation is developing or not, and could therefore assist decision-makers in strengthening the province’s prevention and control measures and in initiating rapid response measures. PMID:27866407

  1. Winter temperatures over the Korean Peninsula and East Asia: development of a new index and its application to seasonal forecast

    NASA Astrophysics Data System (ADS)

    Kim, Seon Tae; Sohn, Soo-Jin; Kug, Jong-Seong

    2016-10-01

    This study proposes a new index for monitoring and predicting winter temperatures of the Korean Peninsula based on the dominant atmospheric winter teleconnection patterns. The utilization of this index is further extended to the East Asian Winter Monsoon (EAWM) index because the new index is found to well represent the main feature of the EAWM circulation. Among the teleconnection patterns, the East Atlantic (EA) and Western Pacific (WP) patterns are found to be most strongly correlated with winter temperatures via their partial association with changes in sea level pressure (SLP) around the Korean Peninsula, i.e., the EA and WP patterns are associated with SLP variation over the Siberian High region and the Kuroshio extension region to the east of Japan, respectively. On the basis of this relationship, the two regions representing the northwest-to-southeast SLP gradients are determined to define the new index. It is found that the new index can represent the Korean winter temperatures consistently well regardless of their considerable decadal changes. When compared with the existing SLP-based EAWM indices, the new index shows the best performance in delineating winter air temperatures, not only in the Korean Peninsula but also in the entire East Asian region. We also assess the prediction skill of the new index with seasonal coupled forecast models of the APEC Climate Center of Korea and its capability to predict winter temperatures. This assessment shows that the new index has potential for operationally predicting and monitoring winter temperatures in Korea and the whole of East Asia.

  2. Fishing Forecasts

    NASA Technical Reports Server (NTRS)

    1988-01-01

    ROFFS stands for Roffer's Ocean Fishing Forecasting Service, Inc. Roffer combines satellite and computer technology with oceanographic information from several sources to produce frequently updated charts sometimes as often as 30 times a day showing clues to the location of marlin, sailfish, tuna, swordfish and a variety of other types. Also provides customized forecasts for racing boats and the shipping industry along with seasonal forecasts that allow the marine industry to formulate fishing strategies based on foreknowledge of the arrival and departure times of different fish. Roffs service exemplifies the potential for benefits to marine industries from satellite observations. Most notable results are reduced search time and substantial fuel savings.

  3. An assessment of Indian monsoon seasonal forecasts and mechanisms underlying monsoon interannual variability in the Met Office GloSea5-GC2 system

    NASA Astrophysics Data System (ADS)

    Johnson, Stephanie J.; Turner, Andrew; Woolnough, Steven; Martin, Gill; MacLachlan, Craig

    2017-03-01

    We assess Indian summer monsoon seasonal forecasts in GloSea5-GC2, the Met Office fully coupled subseasonal to seasonal ensemble forecasting system. Using several metrics, GloSea5-GC2 shows similar skill to other state-of-the-art seasonal forecast systems. The prediction skill of the large-scale South Asian monsoon circulation is higher than that of Indian monsoon rainfall. Using multiple linear regression analysis we evaluate relationships between Indian monsoon rainfall and five possible drivers of monsoon interannual variability. Over the time period studied (1992-2011), the El Niño-Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) are the most important of these drivers in both observations and GloSea5-GC2. Our analysis indicates that ENSO and its teleconnection with Indian rainfall are well represented in GloSea5-GC2. However, the relationship between the IOD and Indian rainfall anomalies is too weak in GloSea5-GC2, which may be limiting the prediction skill of the local monsoon circulation and Indian rainfall. We show that this weak relationship likely results from a coupled mean state bias that limits the impact of anomalous wind forcing on SST variability, resulting in erroneous IOD SST anomalies. Known difficulties in representing convective precipitation over India may also play a role. Since Indian rainfall responds weakly to the IOD, it responds more consistently to ENSO than in observations. Our assessment identifies specific coupled biases that are likely limiting GloSea5-GC2 Indian summer monsoon seasonal prediction skill, providing targets for model improvement.

  4. GFS water vapor forecast error evaluated over the 2009-2010 West Coast cool season using the MET/MODE object analyses package

    NASA Astrophysics Data System (ADS)

    Clark, W. L.; Sukovich, E.; Tollerud, E. I.; Jensen, T.; Yuan, H.; Wick, G. A.; Bullock, R.; Hmt-Dtc Collaboration Project

    2010-12-01

    Research over the last decade and a half confirms that the vast majority of West Coast cool-season extreme precipitation events are due to the landfall of intense wind-driven streams of concentrated water vapor associated with extratropical cyclones called atmospheric rivers (ARs). Accurate prediction of the effects of ARs as they come ashore depends on accurate numeric modeling of integrated water vapor (IWV) over the Northeast Pacific (NEP). Quantifying the uncertainty in this forecast field is an important step toward understanding the causes of uncertainty in West Coast extreme event forecasts. To this end GFS (Global Forecast System) model output obtained in real time of the fields needed to calculate IWV were archived and analyzed. GFS was used because it is well known, it covers our area of interest, and the output is readily available to the community. To estimate forecast uncertainties we used an object-based method that allows quantitative comparisons of object location, size, shape, and intensity. In particular, we used MODE, the Method for Object-based Diagnostic Evaluation. MODE is an object-based verification tool from the MET (Model Evaluation Tools) package developed and supported by the Developmental Testbed Center (DTC). This package of verification tools is readily available and intended to provide the community with a common software package incorporating the latest advances in forecast verification. We describe results from two studies conducted as part of the Hydrometeorology Testbed (HMT)—DTC collaboration project. The studies are based upon Northeast Pacific (NEP) data collected during the 2009-2010 cool season. In the first study we focus on verifying GFS-analysis IWV against satellite-observed IWV throughout the NEP. Specifically, IWV GFS analysis objects are compared with 12-hour composite, satellite-derived Special Sensor Microwave/Imager (SSM/I) observational objects. Then we incorporate MODE object attributes related to object

  5. The Year Without a Ski Season: An Analysis of the Winter of 2015 for Three Ski Resorts in Western Canada Using Historical and Simulation Model Forecasted Climate Data

    NASA Astrophysics Data System (ADS)

    Pidwirny, M. J.; Goode, J. D.; Pedersen, S.

    2015-12-01

    The winter of 2015 will go down as "the year without a ski season" for many ski resorts located close to the west coast of Canada and the USA. During this winter season, a large area of the eastern North Pacific Ocean had extremely high sea surface temperatures. These high sea surface temperatures influenced weather patterns on the west coast of North America producing very mild temperatures inland. Further, in alpine environments precipitation that normally arrives in the form of snow instead fell as rain. This research examines the climate characteristics of the winter of 2015 in greater detail for three ski resorts in British Columbia, Canada: Mount Washington, Cypress Mountain and Hemlock Valley. For these resorts, historical (1901 to 2013) and IPCC AR5 climate model forecasted climate data (RCP8.5 for 2025, 2055, and 2085) was generated for the variable winter degree days < 0°C (a measure of winter season coldness) using the spatially interpolated climate database ClimateBC. A value for winter degree days < 0°C was also estimated from recorded climate data at nearby meteorological stations for comparative analysis. For all three resorts, the winter of 2015 proved to be warmer than any individual year in the period 1901 to 2013. Interpolations involving the multi-model ensemble forecast means suggest that the climate associated with winter of 2015 will become the average normal for these resorts in only 35 to 45 years under the RCP8.5 emission scenario.

  6. Fusing enhanced radar precipitation, in-situ hydrometeorological measurements and airborne LIDAR snowpack estimates in a hyper-resolution hydrologic model to improve seasonal water supply forecasts

    NASA Astrophysics Data System (ADS)

    Gochis, D. J.; Busto, J.; Howard, K.; Mickey, J.; Deems, J. S.; Painter, T. H.; Richardson, M.; Dugger, A. L.; Karsten, L. R.; Tang, L.

    2015-12-01

    Scarcity of spatially- and temporally-continuous observations of precipitation and snowpack conditions in remote mountain watersheds results in fundamental limitations in water supply forecasting. These limitationsin observational capabilities can result in strong biases in total snowmelt-driven runoff amount, the elevational distribution of runoff, river basin tributary contributions to total basin runoff and, equally important for water management, the timing of runoff. The Upper Rio Grande River basin in Colorado and New Mexico is one basin where observational deficiencies are hypothesized to have significant adverse impacts on estimates of snowpack melt-out rates and on water supply forecasts. We present findings from a coordinated observational-modeling study within Upper Rio Grande River basin whose aim was to quanitfy the impact enhanced precipitation, meteorological and snowpack measurements on the simulation and prediction of snowmelt driven streamflow. The Rio Grande SNOwpack and streamFLOW (RIO-SNO-FLOW) Prediction Project conducted enhanced observing activities during the 2014-2015 water year. Measurements from a gap-filling, polarimetric radar (NOXP) and in-situ meteorological and snowpack measurement stations were assimilated into the WRF-Hydro modeling framework to provide continuous analyses of snowpack and streamflow conditions. Airborne lidar estimates of snowpack conditions from the NASA Airborne Snow Observatory during mid-April and mid-May were used as additional independent validations against the various model simulations and forecasts of snowpack conditions during the melt-out season. Uncalibrated WRF-Hydro model performance from simulations and forecasts driven by enhanced observational analyses were compared against results driven by currently operational data inputs. Precipitation estimates from the NOXP research radar validate significantly better against independent in situ observations of precipitation and snow-pack increases

  7. Generation of a Realistic Soil Moisture Initialization System and its Potential Impact on Short-to-Seasonal Forecasting of Near Surface Variables

    NASA Astrophysics Data System (ADS)

    Boisserie, M.; Cocke, S.; O'Brien, J. J.

    2009-12-01

    Although the amount of water contained in the soil seems insignificant when compared to the total amount of water on a global-scale, soil moisture is widely recognized as a crucial variable for climate studies. It plays a key role in regulating the interaction between the atmosphere and the land-surface by controlling the repartition between the surface latent and sensible heat fluxes. In addition, the persistence of soil moisture anomalies provides one of the most important components of memory for the climate system. Several studies have shown that, during the boreal summer in mid-latitudes, the soil moisture role in controlling the continental precipitation variability may be more important than that of the sea surface temperature (Koster et al. 2000, Hong and Kalnay 2000, Koster et al. 2000, Kumar and Hoerling 1995, Trenberth et al. 1998, Shukla 1998). Although all of the above studies have demonstrated the strong sensitivity of seasonal forecasts to the soil moisture initial conditions, they relied on extreme or idealized soil moisture levels. The question of whether realistic soil moisture initial conditions lead to improved seasonal predictions has not been adequately addressed. Progress in addressing this question has been hampered by the lack of long-term reliable observation-based global soil moisture data sets. Since precipitation strongly affects the soil moisture characteristics at the surface and in depth, an alternative to this issue is to assimilate precipitation. Because precipitation is a diagnostic variable, most of the current reanalyses do not directly assimilate it into their models (M. Bosilovitch, 2008). In this study, an effective technique that directly assimilates the precipitation is used. We examine two experiments. In the first experiment, the model is initialized by directly assimilating a global, 3-hourly, 1.0° precipitation dataset, provided by Sheffield et al. (2006), in a continuous assimilation period of a couple of months. For

  8. Coupling fast all-season soil strength land surface model with weather research and forecasting model to assess low-level icing in complex terrain

    NASA Astrophysics Data System (ADS)

    Sines, Taleena R.

    Icing poses as a severe hazard to aircraft safety with financial resources and even human lives hanging in the balance when the decision to ground a flight must be made. When analyzing the effects of ice on aviation, a chief cause for danger is the disruption of smooth airflow, which increases the drag force on the aircraft therefore decreasing its ability to create lift. The Weather Research and Forecast (WRF) model Advanced Research WRF (WRF-ARW) is a collaboratively created, flexible model designed to run on distributed computing systems for a variety of applications including forecasting research, parameterization research, and real-time numerical weather prediction. Land-surface models, one of the physics options available in the WRF-ARW, output surface heat and moisture flux given radiation, precipitation, and surface properties such as soil type. The Fast All-Season Soil STrength (FASST) land-surface model was developed by the U.S. Army ERDC-CRREL in Hanover, New Hampshire. Designed to use both meteorological and terrain data, the model calculates heat and moisture within the surface layer as well as the exchange of these parameters between the soil, surface elements (such as snow and vegetation), and atmosphere. Focusing on the Presidential Mountain Range of New Hampshire under the NASA Experimental Program to Stimulate Competitive Research (EPSCoR) Icing Assessments in Cold and Alpine Environments project, one of the main goals is to create a customized, high resolution model to predict and assess ice accretion in complex terrain. The purpose of this research is to couple the FASST land-surface model with the WRF to improve icing forecasts in complex terrain. Coupling FASST with the WRF-ARW may improve icing forecasts because of its sophisticated approach to handling processes such as meltwater, freezing, thawing, and others that would affect the water and energy budget and in turn affect icing forecasts. Several transformations had to take place in order

  9. An evaluation of fall speed characteristics in bin and bulk microphysical schemes and use of bin fall speeds to improve forecasts of warm-season rainfall

    NASA Astrophysics Data System (ADS)

    Aligo, Eric A.

    2011-12-01

    As computer power increases and model grid spacing decreases, more emphasis will be put on model microphysics to produce accurate forecasts of rainfall including that from warm-season mesoscale convective systems (MCSs). Some believe bin microphysical schemes are far superior to the commonly used bulk microphysical schemes because of their ability to more accurately depict certain processes like sedimentation. However, bin schemes are computationally inefficient and there are no plans in the near future to implement such schemes operationally. Instead, this study proposes to use a technique in Weather Research and Forecast (WRF) Advanced Research WRF (ARW) simulations that attempts to improve bulk microphysical forecasts of warm-season MCSs by harnessing the intrinsic characteristics of bin fall speed distributions that are important for the sedimentation process provided the fall speed characteristics in bin schemes differ from those in commonly used bulk schemes. Fall speed distributions of rain, snow, graupel and cloud ice were compared between a bin scheme and three bulk schemes, and were found to be different between the different schemes. The microphysical processes that contributed the largest to the microphysical budget in the bin scheme often occurred with the slower fall speeds, but the opposite was true for the bulk schemes. There was evidence of size-sorting in the bin and Thompson bulk schemes, a naturally occurring phenomenon. This feature was not simulated in the WSM6 and Lin schemes and can be attributed to those schemes being single moment and the Thompson scheme being double moment in ice and rain. Since the characteristics of the bin fall speeds were different from those in the bulk schemes, bin fall speeds were used to modify bulk scheme fall speeds using a probability matching technique that was developed to improve the prediction of warm-season MCSs. The sensitivity of different convective morphologies to the fall speed modifications was also

  10. Comparison of Statistical Downscaling Methods for Seasonal Precipitation Prediction: An Application Toward a Fire and Haze Early Warning System for Southeast Asia

    NASA Astrophysics Data System (ADS)

    Cho, J.; Lee, H.; Lee, E.; Field, R. D.; Hameed, S. N.; Foo, K. K.; Albar, I.; Sopaheluwakan, A.

    2014-12-01

    Smoke haze from forest fires is among Southeast Asia's most serious environmental problems and there is a clear need for a long-lead fire and haze early warning system (EWS) for the regions. The seasonal forecast supplied by the APEC Climate Center (APCC) is one of available information can be used to predict drought conditions triggering forest fires in the region. The objective of this study is to assess the skill of the current and downscaled products of APCC's seasonal forecast of 6-month lead-time for predicting ASO precipitation over the fire-prone regions. First, seasonal forecast skill by six individual models (MSC_CANCM3, MSC_CANCM4, NASA, NCEP, PNU, POAMA) and simple composite model (SCM) ensemble was assessed by considering available each ensemble members. Second, three different statistical downscaling methods including simple bias-correction (SBC), moving window regression (MWReg), and climate index regression (CIReg) were applied and the forecast sill were compared. Both current and downscaled seasonal forecast showed higher predictability over Sumatra regions compared to the Kalimantan regions. Statistical downscaling of forecasts showed the skill improvement over the Kalimantan region where current APCC's forecast shows low predictability. Study also shows that temporal correlation coefficient (TCC) between observed and forecasted ASO precipitation increases as lead-time decrease.

  11. Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1) for operational seasonal forecasting

    NASA Astrophysics Data System (ADS)

    Takaya, Yuhei; Yasuda, Tamaki; Fujii, Yosuke; Matsumoto, Satoshi; Soga, Taizo; Mori, Hirotoshi; Hirai, Masayuki; Ishikawa, Ichiro; Sato, Hitoshi; Shimpo, Akihiko; Kamachi, Masafumi; Ose, Tomoaki

    2017-01-01

    This paper describes the operational seasonal prediction system of the Japan Meteorological Agency (JMA), the Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1), which was in operation at JMA during the period between February 2010 and May 2015. The predictive skill of the system was assessed with a set of retrospective seasonal predictions (reforecasts) covering 30 years (1981-2010). JMA/MRI-CPS1 showed reasonable predictive skill for the El Niño-Southern Oscillation, comparable to the skills of other state-of-the-art systems. The one-tiered approach adopted in JMA/MRI-CPS1 improved its overall predictive skills for atmospheric predictions over those of the two-tiered approach of the previous uncoupled system. For 3-month predictions with a 1-month lead, JMA/MRI-CPS1 showed statistically significant skills in predicting 500-hPa geopotential height and 2-m temperature in East Asia in most seasons; thus, it is capable of providing skillful seasonal predictions for that region. Furthermore, JMA/MRI-CPS1 was superior overall to the previous system for atmospheric predictions with longer (4-month) lead times. In particular, JMA/MRI-CPS1 was much better able to predict the Asian Summer Monsoon than the previous two-tiered system. This enhanced performance was attributed to the system's ability to represent atmosphere-ocean coupled variability over the Indian Ocean and the western North Pacific from boreal winter to summer following winter El Niño events, which in turn influences the East Asian summer climate through the Pacific-Japan teleconnection pattern. These substantial improvements obtained by using an atmosphere-ocean coupled general circulation model underpin its success in providing more skillful seasonal forecasts on an operational basis.

  12. Evaluation of NCAR Icing/SLD Forecasts, Tools and Techniques Used During The 1998 NASA SLD Flight Season

    NASA Technical Reports Server (NTRS)

    Bernstein, Ben C.

    2001-01-01

    Supercooled Large Droplet (SLD) icing conditions were implicated in at least one recent aircraft crash, and have been associated with other aircraft incidents. Inflight encounters with SLD can result in ice accreting on unprotected areas of the wing where it can not be removed. Because this ice can adversely affect flight characteristics of some aircraft, there has been concern about flight safety in these conditions. The FAA held a conference on in-flight icing in 1996 where the state of knowledge concerning SLD was explored. One outcome of these meetings was an identified need to acquire SLD flight research data, particularly in the Great Lakes Region. The flight research data was needed by the FAA to develop a better understanding of the meteorological characteristics associated with SLD and facilitate an assessment of existing aircraft icing certification regulations with respect to SLD. In response to this need, NASA, the Federal Aviation Administration (FAA), and the National Center for Atmospheric Research (NCAR) conducted a cooperative icing flight research program to acquire SLD flight research data. The NASA Glenn Research Center's Twin Otter icing research aircraft was flown throughout the Great Lakes region during the winters of 1996-97 and 1997-98 to acquire SLD icing and meteorological data. The NASA Twin Otter was instrumented to measure cloud microphysical properties (particle size, LWC (Liquid Water Content), temperature, etc.), capture images of wing and tail ice accretion, and then record the resultant effect on aircraft performance due to the ice accretion. A satellite telephone link enabled the researchers onboard the Twin Otter to communicate with NCAR meteorologists. who provided real-time guidance into SLD icing conditions. NCAR meteorologists also provided preflight SLD weather forecasts that were used to plan the research flights, and served as on-board researchers. This document contains an evaluation of the tools and techniques NCAR

  13. Filling a capability and decision support gap between weather and seasonal forecasts: The role of USCLIVAR and initiating efforts of its MJO Working Group

    NASA Astrophysics Data System (ADS)

    Waliser, D. E.; Sperber, K. R.

    2015-12-01

    A decade ago there was a clear gap in our operational environmental prediction capabilities, and associated decision support guidance, at lead times beyond ten days and less than one month. Recognizing the need for making advances in the scientific understanding, model fidelity and prediction capabilities that could fill this gap, US CLIVAR formed the Madden-Julian Oscillation Working Group (MJOWG; 2006-09), as the MJO represented an obvious, unexploited source of predictability at these time scales. The MJOWG focused on advancements on two fronts, experimental to operational prediction of the MJO and weather/climate model experimentation, diagnostics and performance metrics. The former led to a multi-national operational MJO prediction capability hosted at NCEP/NOAA. The successful efforts of the MJOWG, and remaining work to do to further these objectives, led to the subsequent formation of the WCRP-WWRP MJO Task Force (MJOTF; 2010-present). Expanding the MJOWG efforts, the MJOTF advanced experimental to operational predictions of the boreal summer Intraseasonal Oscillation (BSISO). The multi-national operational BSISO predictions have been available since 2013 and are hosted by the APEC Climate Center (APCC), providing guidance on BSISO-driven onsets and breaks of the Asian summer monsoon. The success of these initial R2O MJO/BSISO prediction efforts led, in part, to the formation of the WCRP-WWRP Subseasonal to Seasonal (S2S) Prediction Project and North America Multi-Model Ensemble (NMME) designed to improve the skill of S2S forecasts and advance their utilization by the applications community. The above developments, projects and capabilities, including key multi-model research experimentation, will be discussed, highlighting the role of US CLIVAR, and concluding with a summary of the outcomes and recommendations of the 2015 National Academy of Science report on Developing a U.S. Research Agenda to Advance Subseasonal to Seasonal Forecasting.

  14. 24 CFR 242.48 - Insured advances for certain equipment and long lead items.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... 24 Housing and Urban Development 2 2012-04-01 2012-04-01 false Insured advances for certain equipment and long lead items. 242.48 Section 242.48 Housing and Urban Development Regulations Relating to... for certain equipment and long lead items. The Commissioner may allow advances for certain pieces...

  15. Forecasting Flooding in the Brahmaputra and Ganges Delta of Bangladesh on Short (1-10 days), Medium (20-30 days) and Seasonal Time Scales (1-6 months)

    NASA Astrophysics Data System (ADS)

    Webster, P. J.; Hoyos, C. D.; Hopson, T. M.; Chang, H.; Jian, J.

    2007-12-01

    Following the devastating flood years of 1998 during which 60% of Bangladesh was under water for a period of 3 months, the Climate Forecast Applications in Bangladesh (CFAB) project was formed with funding by USAID and NSF which eventually resulted in a joint project with the European Centre for Medium Range Weather Forecasting (ECMWF), the Asian Disaster Preparedness Centre (ADPC) and the Bangladesh Flood Forecasting and Warning Centre. The project was organized and developed through the Georgia Institute of Technology. The aim of CFAB was to develop innovative methods of extending the warning of flooding in Bangladesh noting that there was a unique problem: India provided no upstream discharge data to Bangladesh so that before CFAB the maximum lead time of a forecast was that given by measuring river discharge at the India-Bangladesh border: no lead-time at the border and 2 days in the southern parts of the country. Given that the Brahmaputra and Ganges catchment areas had to be regarded as essentially unguaged, it was clear that innovative techniques had to be developed. On of the basic criterion was that the system should provide probabilistic forecasts in order for the Bangladeshis to assess risk. A three-tier system was developed to allow strategic and tactical decisions to be made for agricultural purposes and disaster mitigation: seasonal (1-6 months: strategic), medium range (20-30 days: strategic/tactical) and short range (1-10 days: tactical). The system that has been developed brings together for the first time operational meteorological forecasts (ensemble forecasts from ECMWF), with satellite and discharge data and a suite of hydrological models. In addition, with ADPC and FFWC we have developed an in-country forecast dispersion system that allows a rapid dissemination. The system has proven to be rather successful, especially in the short range. The flooding events of 2004 were forecast with all forecasting tiers at the respective lead time. In

  16. Enabling Philippine Farmers to Adapt to Climate Variability Using Seasonal Climate and Weather Forecast with a Crop Simulation Model in an SMS-based Farmer Decision Support System

    NASA Astrophysics Data System (ADS)

    Ebardaloza, J. B. R.; Trogo, R.; Sabido, D. J.; Tongson, E.; Bagtasa, G.; Balderama, O. F.

    2015-12-01

    Corn farms in the Philippines are rainfed farms, hence, it is of utmost importance to choose the start of planting date so that the critical growth stages that are in need of water will fall on dates when there is rain. Most farmers in the Philippines use superstitions and traditions as basis for farming decisions such as when to start planting [1]. Before climate change, superstitions like planting after a feast day of a saint has worked for them but with the recent progression of climate change, farmers now recognize that there is a need for technological intervention [1]. The application discussed in this paper presents a solution that makes use of meteorological station sensors, localized seasonal climate forecast, localized weather forecast and a crop simulation model to provide recommendations to farmers based on the crop cultivar, soil type and fertilizer type used by farmers. It is critical that the recommendations given to farmers are not generic as each farmer would have different needs based on their cultivar, soil, fertilizer, planting schedule and even location [2]. This application allows the farmer to inquire about whether it will rain in the next seven days, the best date to start planting based on the potential yield upon harvest, when to apply fertilizer and by how much, when to water and by how much. Short messaging service (SMS) is the medium chosen for this application because while mobile penetration in the Philippines is as high as 101%, the smart phone penetration is only at 15% [3]. SMS has been selected as it has been identified as the most effective way of reaching farmers with timely agricultural information and knowledge [4,5]. The recommendations while derived from making use of Automated Weather Station (AWS) sensor data, Weather Research Forecasting (WRF) models and DSSAT 4.5 [9], are translated into the local language of the farmers and in a format that is easily understood as recommended in [6,7,8]. A pilot study has been started

  17. The sensitivity of warm season quantitative precipitation forecasts to modifications of the Kain-Fritch Convective Parameterization Scheme

    NASA Astrophysics Data System (ADS)

    Mazarakis, N.; Kotroni, V.; Lagouvardos, K.; Argiriou, A.

    2009-09-01

    The sensitivity of quantitative precipitation forecasts to various modifications of the Kain-Fritsch (KF) convective parameterization scheme (CPS) is examined for twenty selected cases characterized by intense convective activity and widespread precipitation over Greece, during the warm period of years 2005-2007. Namely, the study is conducted using MM5 model. The modifications to the KF CPS, each designed to test model sensitivity to the convective scheme formulation, are discussed. The modifications include: (a) the maximization of the convective scheme precipitation efficiency, (b) the change of the convective time step, (c) the force of the convective scheme to produce more/less cloud material, (d) the alteration of the vertical profile of updraft mass flux detrainment. One hundred forty numerical simulations have been carried out on two nested domains, with horizontal grid increments of 24 and 8 km respectively. The simulated precipitation from the 8-km grid is verified against raingauge measurements. Model results using the aforementioned modifications of the convective scheme does not show significant improvements in 6-h precipitations totals compared to simulations generated using the unmodified convective scheme. In general, skill scores among the cases and the precipitation thresholds vary widely.

  18. Evolution of dynamic and thermodynamic fields during the Indian summer monsoon onset in the initialised atmosphere-ocean seasonal forecasting model of the UK Met Office

    NASA Astrophysics Data System (ADS)

    Menon, Arathy; Turner, Andrew; Martin, Gill

    2016-04-01

    The onset of the Indian summer monsoon has significant influence on the agricultural planning that affects food production and the gross domestic product of the country. Hence understanding and prediction of the monsoon onset is of paramount importance. Here we use hindcast simulations from the Met Office fully coupled atmosphere-ocean Global Seasonal Forecast System 5 (GloSea5) to study the monsoon onset over India. The GloSea5 hindcast simulations are produced for three different start dates in late April or early May prior to the monsoon season and the atmosphere and ocean components are both initialized. Rather than focus on skill metrics of the performance at simulating the onset timing, we use common objective indices of the onset circulation and wind shears in the meridional (Wang-Fan) and vertical (Webster-Yang) directions to determine the monsoon onset over India. We find that the dynamic indices obtained from GloSea5 ensemble mean are consistent with those from the ERA-Interim reanalysis dataset. GloSea5 is also very effective in capturing the spatial pattern of the monsoon rainfall progression following the onset. We next analyse the composite evolution of various dynamic and thermodynamic fields associated with these indices, focusing on recent findings suggesting the importance of dry air incursions above the surface from the northwest. We further extend our analysis by looking at the physical mechanisms leading to onset in the GloSea5 simulations, and examine case studies comparing late and early onset years in both the model hindcasts and reanalysis data.

  19. Extreme precipitation event over North China in August 2010: observations, monthly forecasting, and link to intra-seasonal variability of the Silk-Road wave-train across Eurasia

    NASA Astrophysics Data System (ADS)

    Orsolini, Yvan; Zhang, Ling; Peters, Dieter; Fraedrich, Klaus

    2014-05-01

    Forecast of regional precipitation events at the sub-seasonal timescale remains a big challenge for operational global prediction systems. Over the Far East in summer, climate and precipitation are strongly influenced by the fluctuating western Pacific subtropical high (WPSH) and strong precipitation is often associated with southeasterly low-level wind that brings moist-laden air from the southern China seas. The WPSH variability is partly influenced by quasi-stationary wave-trains propagating eastwards from Europe across Asia along the two westerly jets: the Silk-Road wave-train along the Asian jet at mid-latitudes and, on a more northern route, the polar wave-train along the sub-polar jet. While the Silk-Road wave-train appears as a robust, internal mode of variability in seasonal predictions models, its predictability is very low on the sub-seasonal to seasonal time scale. A case in point is the unusual summer of 2010, when China experienced its worst seasonal flooding for a decade, triggered by unusually prolonged and severe monsoonal rains. In addition that summer was also characterized by record-breaking heat wave over Eastern Europe and Russia as well as catastrophic monsoonal floods in Pakistan 2010. The impact of the latter circulation anomalies on the precipitation further east over China, has been little explored. Here, we examine the role and the actual predictability of the Silk-Road wave-train, and its impact on precipitation over Northeastern China throughout August 2010, using the high-resolution IFS forecast model of ECMWF, realistic initialized and run in an ensemble mode. We demonstrate that the forecast failure with regard to flooding and extreme precipitation over Northeastern China in August 2010 is linked to the failure to represent intra-seasonal variations of the Silk-Road wave-train and the associated intensification of the WPSH.

  20. Forecasting forecast skill

    NASA Technical Reports Server (NTRS)

    Kalnay, Eugenia; Dalcher, Amnon

    1987-01-01

    It is shown that it is possible to predict the skill of numerical weather forecasts - a quantity which is variable from day to day and region to region. This has been accomplished using as predictor the dispersion (measured by the average correlation) between members of an ensemble of forecasts started from five different analyses. The analyses had been previously derived for satellite-data-impact studies and included, in the Northern Hemisphere, moderate perturbations associated with the use of different observing systems. When the Northern Hemisphere was used as a verification region, the prediction of skill was rather poor. This is due to the fact that such a large area usually contains regions with excellent forecasts as well as regions with poor forecasts, and does not allow for discrimination between them. However, when regional verifications were used, the ensemble forecast dispersion provided a very good prediction of the quality of the individual forecasts.

  1. Statistical-dynamical seasonal forecast of western North Pacific and East Asia landfalling tropical cyclones using the high-resolution GFDL FLOR coupled model

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Villarini, Gabriele; Vecchi, Gabriel A.; Murakami, Hiroyuki; Gudgel, Richard

    2016-06-01

    This study examines the seasonal prediction of western North Pacific [WNP) and East Asia landfalling tropical cyclones (TCs) using the Geophysical Fluid Dynamics Laboratory(GFDL) Forecast-oriented Low Ocean Resolution version of CM2.5 with Flux Adjustment (FLOR-FA) and finite-mixture-model (FMM)-based statistical cluster analysis. Using the FMM-based cluster analysis, seven clusters are identified from the historical and FLOR-FA-predicted TC tracks for the period 1980-2013. FLOR-FA has significant skill in predicting year-to-year variations in the frequency of TCs within clusters 1 (recurving TCs) and 5 (straight-moving TCs). By building Poisson regression models for each cluster using key predictors (i.e., sea surface temperature, 500 hPa geopotential height, and zonal vertical wind shear), the predictive skill for almost all the clusters at all initialization months improves with respect to the dynamic prediction. The prediction of total WNP TC frequency made by combining hybrid predictions for each of the seven clusters in the hybrid model shows skill higher than what achieved using the TC frequency directly from FLOR-FA initialized from March to July. However, the hybrid predictions for total WNP TC frequency initialized from January to February exhibit lower skill than FLOR-FA. The prediction of TC landfall over East Asia made by combining the hybrid models of TC frequency in each cluster and its landfall rate over East Asia also outperforms FLOR-FA for all initialization months January through July.

  2. The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season

    PubMed Central

    Pavlovic, Radenko; Chen, Jack; Anderson, Kerry; Moran, Michael D.; Beaulieu, Paul-André; Davignon, Didier; Cousineau, Sophie

    2016-01-01

    ABSTRACT Environment and Climate Change Canada’s FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2–July 15, and August 15–31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of –7.3 µg m−3 and 3.1 µg m−3), it showed better forecast skill than the RAQDPS (MB of –11.7 µg m−3 and –5.8 µg m−3) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m−3 also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR

  3. Forecasting droughts in East Africa

    NASA Astrophysics Data System (ADS)

    Mwangi, E.; Wetterhall, F.; Dutra, E.; Di Giuseppe, F.; Pappenberger, F.

    2014-02-01

    The humanitarian crises caused by the recent droughts (2008-2009 and 2010-2011) in East Africa have illustrated that the ability to make accurate drought forecasts with sufficient lead time is essential. The use of dynamical model precipitation forecasts in combination with drought indices, such as the Standardized Precipitation Index (SPI), can potentially lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for March-May and October-December rain seasons when evaluated against measurements from the available in situ stations from East Africa. The forecast for October-December rain season has higher skill than for the March-May season. ECMWF forecasts add value to the consensus forecasts produced during the Greater Horn of Africa Climate Outlook Forum (GHACOF), which is the present operational product for precipitation forecast over East Africa. Complementing the original ECMWF precipitation forecasts with SPI provides additional information on the spatial extent and intensity of the drought event.

  4. Wheat yield forecasts using LANDSAT data

    NASA Technical Reports Server (NTRS)

    Colwell, J. E.; Rice, D. P.; Nalepka, R. F.

    1977-01-01

    Several considerations of winter wheat yield prediction using LANDSAT data were discussed. In addition, a simple technique which permits direct early season forecasts of wheat production was described.

  5. Forecasting droughts in East Africa

    NASA Astrophysics Data System (ADS)

    Mwangi, Emmah; Wetterhall, Fredrik; Dutra, Emanuel; Di Giuseppe, Francesca; Pappenberger, Florian

    2014-05-01

    The humanitarian crisis caused by the recent droughts (2008-2009 and 2010-2011) in East Africa have illustrated that the ability to make accurate drought predictions with sufficient lead time is essential. The use of dynamical model forecasts in combination with drought indices, such as the Standardized Precipitation Index (SPI), can potentially to lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for both rain seasons when evaluated against measurements from the available in-situ stations from East Africa. The forecast for October-December rain season has higher skill than for the March-May season. ECMWF forecasts add value to the statistical forecasts produced during the Greater Horn of Africa Climate Outlook Forums (GHACOF), which is the present operational product. Complementing the raw precipitation forecasts with SPI provides additional information on the spatial extent and intensity of the drought event.

  6. The Impact of Ocean Data Assimilation on Seasonal-to-Interannual Forecasts: A Case Study of the 2006 El Nino Event

    NASA Technical Reports Server (NTRS)

    Yang, Shu-Chih; Rienecker, Michele; Keppenne, Christian

    2010-01-01

    This study investigates the impact of four different ocean analyses on coupled forecasts of the 2006 El Nino event. Forecasts initialized in June 2006 using ocean analyses from an assimilation that uses flow-dependent background error covariances are compared with those using static error covariances that are not flow dependent. The flow-dependent error covariances reflect the error structures related to the background ENSO instability and are generated by the coupled breeding method. The ocean analyses used in this study result from the assimilation of temperature and salinity, with the salinity data available from Argo floats. Of the analyses, the one using information from the coupled bred vectors (BV) replicates the observed equatorial long wave propagation best and exhibits more warming features leading to the 2006 El Nino event. The forecasts initialized from the BV-based analysis agree best with the observations in terms of the growth of the warm anomaly through two warming phases. This better performance is related to the impact of the salinity analysis on the state evolution in the equatorial thermocline. The early warming is traced back to salinity differences in the upper ocean of the equatorial central Pacific, while the second warming, corresponding to the mature phase, is associated with the effect of the salinity assimilation on the depth of the thermocline in the western equatorial Pacific. The series of forecast experiments conducted here show that the structure of the salinity in the initial conditions is important to the forecasts of the extension of the warm pool and the evolution of the 2006 El Ni o event.

  7. Airborne Snow Observatory: measuring basin-wide seasonal snowpack with LiDAR and an imaging spectrometer to improve runoff forecasting and reservoir operation (Invited)

    NASA Astrophysics Data System (ADS)

    McGurk, B. J.; Painter, T. H.

    2013-12-01

    The Airborne Snow Observatory (ASO) NASA-JPL demonstration mission collected detailed snow information for portions of the Tuolumne Basin in California and the Uncompahgre Basin in Colorado in spring of 2013. The ASO uses an imaging spectrometer and LiDAR sensors mounted in an aircraft to collect snow depth and extent data, and snow albedo. By combining ground and modeled density fields, the ~weekly flights over the Tuolumne produced both basin-wide and detailed sub-basin snow water equivalent (SWE) estimates that were used in a hydrologic simulation model to improve the accuracy and timing of runoff forecasting tools used to manage Hetch Hetchy Reservoir, the source of 85% of the water supply for 2.5 million people on the San Francisco Peninsula. The USGS PRMS simulation model was calibrated to the 459 square mile basin and was updated with both weather forecast data and distributed snow information from ASO flights to inform the reservoir operators of predicted inflow volumes and timing. Information produced by the ASO data collection was used to update distributed SWE and albedo state variables in the PRMS model and improved inflow forecasts for Hetch Hetchy. Data from operational ASO programs is expected to improve the ability of reservoir operators to more efficiently allocate the last half of the recession limb of snowmelt inflow and be more assured of meeting operational mandates. This presentation will provide results from the project after its first year.

  8. Forecasting droughts in East Africa

    NASA Astrophysics Data System (ADS)

    Mwangi, E.; Wetterhall, F.; Dutra, E.; Di Giuseppe, F.; Pappenberger, F.

    2013-08-01

    The humanitarian crisis caused by the recent droughts (2008-2009 and 2010-2011) in the East African region have illustrated that the ability to make accurate drought predictions with adequate lead time is essential. The use of dynamical model forecasts and drought indices, such as Standardized Precipitation Index (SPI), promises to lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for both rain seasons when evaluated against measurements from the available in-situ stations from East Africa. The October-December rain season has higher skill that the March-May season. ECMWF forecasts add value to the statistical forecasts produced during the Greater Horn of Africa Climate Outlook Forums (GHACOF) which is the present operational product. Complementing the raw precipitation forecasts with SPI provides additional information on the spatial extend and intensity of the drought event.

  9. A Multi-Season Study of the Effects of MODIS Sea-Surface Temperatures on Operational WRF Forecasts at NWS Miami, FL

    NASA Technical Reports Server (NTRS)

    Case, Jonathan L.; Santos, Pablo; Lazarus, Steven M.; Splitt, Michael E.; Haines, Stephanie L.; Dembek, Scott R.; Lapenta, William M.

    2008-01-01

    Studies at the Short-term Prediction Research and Transition (SPORT) Center have suggested that the use of Moderate Resolution Imaging Spectroradiometer (MODIS) sea-surface temperature (SST) composites in regional weather forecast models can have a significant positive impact on short-term numerical weather prediction in coastal regions. Recent work by LaCasse et al (2007, Monthly Weather Review) highlights lower atmospheric differences in regional numerical simulations over the Florida offshore waters using 2-km SST composites derived from the MODIS instrument aboard the polar-orbiting Aqua and Terra Earth Observing System satellites. To help quantify the value of this impact on NWS Weather Forecast Offices (WFOs), the SPORT Center and the NWS WFO at Miami, FL (MIA) are collaborating on a project to investigate the impact of using the high-resolution MODIS SST fields within the Weather Research and Forecasting (WRF) prediction system. The project's goal is to determine whether more accurate specification of the lower-boundary forcing within WRF will result in improved land/sea fluxes and hence, more accurate evolution of coastal mesoscale circulations and the associated sensible weather elements. The NWS MIA is currently running WRF in real-time to support daily forecast operations, using the National Centers for Environmental Prediction Nonhydrostatic Mesoscale Model dynamical core within the NWS Science and Training Resource Center's Environmental Modeling System (EMS) software. Twenty-seven hour forecasts are run dally initialized at 0300, 0900, 1500, and 2100 UTC on a domain with 4-km grid spacing covering the southern half of Florida and adjacent waters of the Gulf of Mexico and Atlantic Ocean. Each model run is initialized using the Local Analysis and Prediction System (LAPS) analyses available in AWIPS. The SSTs are initialized with the NCEP Real-Time Global (RTG) analyses at 1/12deg resolution (approx.9 km); however, the RTG product does not exhibit fine

  10. Hydrologic Forecasting and Hydropower Production

    NASA Astrophysics Data System (ADS)

    Wigmosta, M. S.; Voisin, N.; Lettenmaier, D. P.; Coleman, A.; Mishra, V.; Schaner, N. A.

    2011-12-01

    Hydroelectric power production is one of many competing demands for available water along with other priority uses such as irrigation, thermoelectric cooling, municipal, recreation, and environmental performance. Increasingly, hydroelectric generation is being used to offset the intermittent nature of some renewable energy sources such as wind-generated power. An accurate forecast of the magnitude and timing of water supply assists managers in integrated planning and operations to balance competing water uses against current and future supply while protecting against the possibility of water or energy shortages and excesses with real-time actions. We present a medium-range to seasonal ensemble streamflow forecasting system where uncertainty in forecasts is addressed explicitly. The integrated forecast system makes use of remotely-sensed data and automated spatial and temporal data assimilation. Remotely-sensed snow cover, observed snow water equivalent, and observed streamflow data are used to update the hydrologic model state prior to the forecast. In forecast mode, the hydrology model is forced by calibrated ensemble weather/climate forecasts. This system will be fully integrated into a water optimization toolset to inform reservoir and power operations, and guide environmental performance decision making. This flow forecast system development is carried out in agreement with the National Weather Service so that the system can later be incorporated into the NOAA eXperimental Ensemble Forecast Service (XEFS).

  11. Forecasting Skill

    DTIC Science & Technology

    1981-01-01

    and in synoptic meteorology, many feel the improvements in forecasting the weather (clouds, winds , precipitation, and obstructions to vision) have...and maximum temperature) are each awarded marks 2, 1, or 0 according to whether the forecast is correct, 8 - *- -**■*- ———"—- - -■ t0m 1 MM—IB I...rate of improve- ment of 10% as roughly comparable to the improvement rate obtained by the numerical models. The following types of forecasts seem to

  12. Forecasting global atmospheric CO2

    NASA Astrophysics Data System (ADS)

    Agustí-Panareda, A.; Massart, S.; Chevallier, F.; Boussetta, S.; Balsamo, G.; Beljaars, A.; Ciais, P.; Deutscher, N. M.; Engelen, R.; Jones, L.; Kivi, R.; Paris, J.-D.; Peuch, V.-H.; Sherlock, V.; Vermeulen, A. T.; Wennberg, P. O.; Wunch, D.

    2014-11-01

    A new global atmospheric carbon dioxide (CO2) real-time forecast is now available as part of the pre-operational Monitoring of Atmospheric Composition and Climate - Interim Implementation (MACC-II) service using the infrastructure of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). One of the strengths of the CO2 forecasting system is that the land surface, including vegetation CO2 fluxes, is modelled online within the IFS. Other CO2 fluxes are prescribed from inventories and from off-line statistical and physical models. The CO2 forecast also benefits from the transport modelling from a state-of-the-art numerical weather prediction (NWP) system initialized daily with a wealth of meteorological observations. This paper describes the capability of the forecast in modelling the variability of CO2 on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO2 diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO2 forecast also has high skill in simulating day-to-day synoptic variability. In the atmospheric boundary layer, this skill is significantly enhanced by modelling the day-to-day variability of the CO2 fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO2 fluxes also lead to accumulating errors in the CO2 forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO2 fluxes compared to total optimized fluxes and the atmospheric CO2 compared to observations. The largest biases in the atmospheric CO2 forecast are found in spring, corresponding to the onset of the growing season in the Northern Hemisphere. In the future, the forecast will be re-initialized regularly with atmospheric CO2 analyses based on the assimilation of CO2 products retrieved from satellite measurements and

  13. Forecasting global atmospheric CO2

    NASA Astrophysics Data System (ADS)

    Agustí-Panareda, A.; Massart, S.; Chevallier, F.; Boussetta, S.; Balsamo, G.; Beljaars, A.; Ciais, P.; Deutscher, N. M.; Engelen, R.; Jones, L.; Kivi, R.; Paris, J.-D.; Peuch, V.-H.; Sherlock, V.; Vermeulen, A. T.; Wennberg, P. O.; Wunch, D.

    2014-05-01

    A new global atmospheric carbon dioxide (CO2) real-time forecast is now available as part of the pre-operational Monitoring of Atmospheric Composition and Climate - Interim Implementation (MACC-II) service using the infrastructure of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). One of the strengths of the CO2 forecasting system is that the land surface, including vegetation CO2 fluxes, is modelled online within the IFS. Other CO2 fluxes are prescribed from inventories and from off-line statistical and physical models. The CO2 forecast also benefits from the transport modelling from a state-of-the-art numerical weather prediction (NWP) system initialized daily with a wealth of meteorological observations. This paper describes the capability of the forecast in modelling the variability of CO2 on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO2 diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO2 forecast also has high skill in simulating day-to-day synoptic variability. In the atmospheric boundary layer, this skill is significantly enhanced by modelling the day-to-day variability of the CO2 fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO2 fluxes also lead to accumulating errors in the CO2 forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO2 fluxes compared to total optimized fluxes and the atmospheric CO2 compared to observations. The largest biases in the atmospheric CO2 forecast are found in spring, corresponding to the onset of the growing season in the Northern Hemisphere. In the future, the forecast will be re-initialized regularly with atmospheric CO2 analyses based on the assimilation of CO2 satellite retrievals, as they become available in

  14. Assessing the effects of forecasted climate change on the growing season length of European temperate trees species along an altitudinal gradient.

    NASA Astrophysics Data System (ADS)

    Vitasse, Y.; Delpierre, N.; François, C.; Dufrêne, E.; Chuine, I.; Delzon, S.

    2009-04-01

    Modelling the phenology of trees is crucial to assess the impact of climate change on the length of the growing season and the productivity of terrestrial ecosystems. A lot of models have been proposed to predict bud burst in the literature, but little has been made to predict leaf senescence and therefore to investigate how growing season length will be modified by climate change. The first aim of this study is to assess through a comparison of phenological models (i) the role of chilling and forcing temperatures on leaf flushing and (ii) the role of autumn temperature and daylength on leaf senescence for six dominant tree species in Europe (4 deciduous and 2 evergreens). We further use the best model for each species to predict shifts in growing season length for the 21st century. Models testing and validation were done for each species on two to three years of phenological observations (flushing and senescence) acquired along a 1500 m altitudinal gradient in the Pyrénées mountains (France). Leaf flushing phenological models were based both on forcing temperature-based (F-models) and on chilling/forcing temperature-based models (CF-models). Leaf senescence phenological models were based on photoperiod-sensitive cold-degree day sums. We showed that most flushing models are able to accurately predict the observed flushing dates for all the species studied. However, the structural difference between F and CF models appears to influence the 21st century predicted trends in leaf flushing, especially for beech. Because global warming could lead to insufficient chilling at the rear edge of species distribution, we therefore recommend considering results from F-models with caution. The prediction of leaf senescence appears more challenging, as the proposed model is better than the null model for only two out of four deciduous species (beech and oak). When used in a prospective analysis under moderate warming (ARPEGE climate model, A1b IPCC scenario) for the 21st

  15. Uses and Applications of Climate Forecasts for Power Utilities.

    NASA Astrophysics Data System (ADS)

    Changnon, Stanley A.; Changnon, Joyce M.; Changnon, David

    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.

  16. Forecasting Infectious Disease Outbreaks

    NASA Astrophysics Data System (ADS)

    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.

  17. Forecasting Influenza Epidemics in Hong Kong.

    PubMed

    Yang, Wan; Cowling, Benjamin J; Lau, Eric H Y; Shaman, Jeffrey

    2015-07-01

    Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.

  18. Forecasting Earthquakes

    NASA Technical Reports Server (NTRS)

    1994-01-01

    In this video there are scenes of damage from the Northridge Earthquake and interviews with Dr. Andrea Donnelan, Geophysics at JPL, and Dr. Jim Dolan, earthquake geologist from Cal. Tech. The interviews discuss earthquake forecasting by tracking changes in the earth's crust using antenna receiving signals from a series of satellites called the Global Positioning System (GPS).

  19. Reasonable Forecasts

    ERIC Educational Resources Information Center

    Taylor, Kelley R.

    2010-01-01

    This article presents a sample legal battle that illustrates school officials' "reasonable forecasts" of substantial disruption in the school environment. In 2006, two students from a Texas high school came to school carrying purses decorated with images of the Confederate flag. The school district has a zero-tolerance policy for…

  20. Turbulence forecasting

    NASA Technical Reports Server (NTRS)

    Chandler, C. L.

    1987-01-01

    In order to forecast turbulence, one needs to have an understanding of the cause of turbulence. Therefore, an attempt is made to show the atmospheric structure that often results when aircraft encounter moderate or greater turbulence. The analysis is based on thousands of hours of observations of flights over the past 39 years of aviation meteorology.

  1. TRAVEL FORECASTER

    NASA Technical Reports Server (NTRS)

    Mauldin, L. E.

    1994-01-01

    Business travel planning within an organization is often a time-consuming task. Travel Forecaster is a menu-driven, easy-to-use program which plans, forecasts cost, and tracks actual vs. planned cost for business-related travel of a division or branch of an organization and compiles this information into a database to aid the travel planner. The program's ability to handle multiple trip entries makes it a valuable time-saving device. Travel Forecaster takes full advantage of relational data base properties so that information that remains constant, such as per diem rates and airline fares (which are unique for each city), needs entering only once. A typical entry would include selection with the mouse of the traveler's name and destination city from pop-up lists, and typed entries for number of travel days and purpose of the trip. Multiple persons can be selected from the pop-up lists and multiple trips are accommodated by entering the number of days by each appropriate month on the entry form. An estimated travel cost is not required of the user as it is calculated by a Fourth Dimension formula. With this information, the program can produce output of trips by month with subtotal and total cost for either organization or sub-entity of an organization; or produce outputs of trips by month with subtotal and total cost for international-only travel. It will also provide monthly and cumulative formats of planned vs. actual outputs in data or graph form. Travel Forecaster users can do custom queries to search and sort information in the database, and it can create custom reports with the user-friendly report generator. Travel Forecaster 1.1 is a database program for use with Fourth Dimension Runtime 2.1.1. It requires a Macintosh Plus running System 6.0.3 or later, 2Mb of RAM and a hard disk. The standard distribution medium for this package is one 3.5 inch 800K Macintosh format diskette. Travel Forecaster was developed in 1991. Macintosh is a registered trademark of

  2. Forecaster's dilemma: Extreme events and forecast evaluation

    NASA Astrophysics Data System (ADS)

    Lerch, Sebastian; Thorarinsdottir, Thordis; Ravazzolo, Francesco; Gneiting, Tilmann

    2015-04-01

    In discussions of the quality of forecasts in the media and public, attention often focuses on the predictive performance in the case of extreme events. Intuitively, accurate predictions on the subset of extreme events seem to suggest better predictive ability. However, it can be demonstrated that restricting conventional forecast verification methods to subsets of observations might have unexpected and undesired effects and may discredit even the most skillful forecasters. Hand-picking extreme events is incompatible with the theoretical assumptions of established forecast verification methods, thus confronting forecasters with what we refer to as the forecaster's dilemma. For probabilistic forecasts, weighted proper scoring rules provide suitable alternatives for forecast evaluation with an emphasis on extreme events. Using theoretical arguments, simulation experiments and a case study on probabilistic forecasts of wind speed over Germany, we illustrate the forecaster's dilemma and the use of weighted proper scoring rules.

  3. Evaluation and first forecasts of the German Climate Forecast System 1 (GCFS1)

    NASA Astrophysics Data System (ADS)

    Fröhlich, Kristina; Baehr, Johanna; Müller, Wolfgang; Bunzel, Felix; Pohlmann, Holger; Dobrynin, Mikhail

    2016-04-01

    We present the near-operational seasonal forecast system GCFS1 (German Climate Forecast System version 1), based on the CMIP5 version of the global coupled climate model MPI-ESM-LR. For GCFS1 we also present a detailed assessment on the predictive skill of the model. GCFS1 has been developed in cooperation between the Max Planck Institute for Meteorology, University of Hamburg and German Meteorological Service (DWD), the forecasts are conducted by DWD. The system is running at ECMWF with a re-forecast ensemble of 15 member and a forecast ensemble of 30 member. The re-forecasts are initialised with full field nudging in the atmosphere (using ERA Interim), in the ocean (using ORAS4) and in the sea-ice component (using NSIDC sea-ice concentration). For the initialization of the forecasts analyses from the ECMWF NWP model and recent ORAS4 analyses are taken. The ensemble perturbations are, for both re-forecasts and forecasts, generated through bred vectors in the ocean which provide initial perturbations for the ensemble in combination with simple physics perturbations in the atmosphere. Evaluation of the re-forecasted climatologies will be presented for different variables, start dates and regions. The first winter forecast during the strong El Niño phase is also subject of evaluation.

  4. An Optimization of Inventory Demand Forecasting in University Healthcare Centre

    NASA Astrophysics Data System (ADS)

    Bon, A. T.; Ng, T. K.

    2017-01-01

    Healthcare industry becomes an important field for human beings nowadays as it concerns about one’s health. With that, forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. Hence, a case study was conducted in University Health Centre to collect historical demand data of Panadol 650mg for 68 months from January 2009 until August 2014. The aim of the research is to optimize the overall inventory demand through forecasting techniques. Quantitative forecasting or time series forecasting model was used in the case study to forecast future data as a function of past data. Furthermore, the data pattern needs to be identified first before applying the forecasting techniques. Trend is the data pattern and then ten forecasting techniques are applied using Risk Simulator Software. Lastly, the best forecasting techniques will be find out with the least forecasting error. Among the ten forecasting techniques include single moving average, single exponential smoothing, double moving average, double exponential smoothing, regression, Holt-Winter’s additive, Seasonal additive, Holt-Winter’s multiplicative, seasonal multiplicative and Autoregressive Integrated Moving Average (ARIMA). According to the forecasting accuracy measurement, the best forecasting technique is regression analysis.

  5. Mesoscale model forecast verification during monsoon 2008

    NASA Astrophysics Data System (ADS)

    Ashrit, Raghavendra; Mohandas, Saji

    2010-08-01

    There have been very few mesoscale modelling studies of the Indian monsoon, with focus on the verification and intercomparison of the operational real time forecasts. With the exception of Das et al (2008), most of the studies in the literature are either the case studies of tropical cyclones and thunderstorms or the sensitivity studies involving physical parameterization or climate simulation studies. Almost all the studies are based on either National Center for Environmental Prediction (NCEP), USA, final analysis fields (NCEP FNL) or the reanalysis data used as initial and lateral boundary conditions for driving the mesoscale model. Here we present a mesoscale model forecast verification and intercomparison study over India involving three mesoscale models: (i) the Weather Research and Forecast (WRF) model developed at the National Center for Atmospheric Research (NCAR), USA, (ii) the MM5 model developed by NCAR, and (iii) the Eta model of the NCEP, USA. The analysis is carried out for the monsoon season, June to September 2008. This study is unique since it is based entirely on the real time global model forecasts of the National Centre for Medium Range Weather Forecasting (NCMRWF) T254 global analysis and forecast system. Based on the evaluation and intercomparison of the mesoscale model forecasts, we recommend the best model for operational real-time forecasts over the Indian region. Although the forecast mean 850 hPa circulation shows realistic monsoon flow and the monsoon trough, the systematic errors over the Arabian Sea indicate an easterly bias to the north (of mean flow) and westerly bias to the south (of mean flow). This suggests that the forecasts feature a southward shift in the monsoon current. The systematic error in the 850 hPa temperature indicates that largely the WRF model forecasts feature warm bias and the MM5 model forecasts feature cold bias. Features common to all the three models include warm bias over northwest India and cold bias over

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

    NASA Astrophysics Data System (ADS)

    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

  7. A Simulation Optimization Approach to Epidemic Forecasting

    PubMed Central

    Nsoesie, Elaine O.; Beckman, Richard J.; Shashaani, Sara; Nagaraj, Kalyani S.; Marathe, Madhav V.

    2013-01-01

    Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individual-based model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area. PMID:23826222

  8. Skilful seasonal predictions for the European energy industry

    NASA Astrophysics Data System (ADS)

    Clark, Robin T.; Bett, Philip E.; Thornton, Hazel E.; Scaife, Adam A.

    2017-02-01

    We assess the utility of seasonal forecasts for the energy industry by showing how recently-established predictability of the North Atlantic Oscillation (NAO) in winter allows predictability of near-surface wind speed and air temperature and therefore energy supply and demand respectively. Our seasonal prediction system (GloSea5) successfully reproduces the influence of the NAO on European climate, leading to skilful forecasts of wind speed and wind power and hence wind driven energy supply. Temperature is skilfully forecast using the observed temperature-NAO relationship and the NAO forecast. Using the correlation between forecast NAO and observed GB electricity demand, we demonstrate that skilful predictions of winter demand are also achievable on seasonal timescales well in advance of the season. Finally, good reliability of probabilistic forecasts of above/below-average wind speed and temperature is also demonstrated.

  9. Wind speed forecasting in the central California wind resource area

    SciTech Connect

    McCarthy, E.F.

    1997-12-31

    A wind speed forecasting program was implemented in the summer seasons of 1985 - 87 in the Central California Wind Resource Area (WRA). The forecasting program is designed to use either meteorological observations from the WRA and local upper air observations or upper air observations alone to predict the daily average windspeed at two locations. Forecasts are made each morning at 6 AM and are valid for a 24 hour period. Ease of use is a hallmark of the program as the daily forecast can be made using data entered into a programmable HP calculator. The forecasting program was the first step in a process to examine whether the electrical energy output of an entire wind power generation facility or defined subsections of the same facility could be predicted up to 24 hours in advance. Analysis of the results of the summer season program using standard forecast verification techniques show the program has skill over persistence and climatology.

  10. The Economic Value of Air Quality Forecasting

    NASA Astrophysics Data System (ADS)

    Anderson-Sumo, Tasha

    Both long-term and daily air quality forecasts provide an essential component to human health and impact costs. According the American Lung Association, the estimated current annual cost of air pollution related illness in the United States, adjusted for inflation (3% per year), is approximately $152 billion. Many of the risks such as hospital visits and morality are associated with poor air quality days (where the Air Quality Index is greater than 100). Groups such as sensitive groups become more susceptible to the resulting conditions and more accurate forecasts would help to take more appropriate precautions. This research focuses on evaluating the utility of air quality forecasting in terms of its potential impacts by building on air quality forecasting and economical metrics. Our analysis includes data collected during the summertime ozone seasons between 2010 and 2012 from air quality models for the Washington, DC/Baltimore, MD region. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our collection of data included available air quality model forecasts of ozone and particulate matter data from the U.S. Environmental Protection Agency (EPA)'s AIRNOW as well as observational data of ozone and particulate matter from Clean Air Partners. We evaluated the performance of the air quality forecasts with that of the observational data and found that the forecast models perform well for the Baltimore/Washington region and the time interval observed. We estimate the potential amount for the Baltimore/Washington region accrues to a savings of up to 5,905 lives and 5.9 billion dollars per year. This total assumes perfect compliance with

  11. Forecast Mekong

    USGS Publications Warehouse

    Turnipseed, D. Phil

    2011-01-01

    Forecast Mekong is part of the U.S. Department of State's Lower Mekong Initiative, which was launched in 2009 by Secretary Hillary Clinton and the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam to enhance partnerships between the U.S. and the Lower Mekong River countries in the areas of environment, health, education, and infrastructure. The U.S. Geological Survey (USGS) is working in close cooperation with the U.S. Department of State to use research and data from the Lower Mekong Basin to provide hands-on results that will help decision makers in Lower Mekong River countries in the planning and design for restoration, conservation, and management efforts in the basin.

  12. A seasonal hydrologic ensemble prediction system for water resource management

    NASA Astrophysics Data System (ADS)

    Luo, L.; Wood, E. F.

    2006-12-01

    A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

  13. Potential application of wavelet neural network ensemble to forecast streamflow for flood management

    NASA Astrophysics Data System (ADS)

    Kasiviswanathan, K. S.; He, Jianxun; Sudheer, K. P.; Tay, Joo-Hwa

    2016-05-01

    Streamflow forecasting, especially the long lead-time forecasting, is still a very challenging task in hydrologic modeling. This could be due to the fact that the forecast accuracy measured in terms of both the amplitude and phase or temporal errors and the forecast precision/reliability quantified in terms of the uncertainty significantly deteriorate with the increase of the lead-time. In the model performance evaluation, the conventional error metrics, which primarily quantify the amplitude error and do not explicitly account for the phase error, have been commonly adopted. For the long lead-time forecasting, the wavelet based neural network (WNN) among a variety of advanced soft computing methods has been shown to be promising in the literature. This paper presented and compared WNN and artificial neural network (ANN), both of which were combined with the ensemble method using block bootstrap sampling (BB), in terms of the forecast accuracy and precision at various lead-times on the Bow River, Alberta, Canada. Apart from conventional model performance metrics, a new index, called percent volumetric error, was proposed, especially for quantifying the phase error. The uncertainty metrics including percentage of coverage and average width were used to evaluate the precision of the modeling approaches. The results obtained demonstrate that the WNN-BB consistently outperforms the ANN-BB in both the categories of the forecast accuracy and precision, especially in the long lead-time forecasting. The findings strongly suggest that the WNN-BB is a robust modeling approach for streamflow forecasting and thus would aid in flood management.

  14. Accounting for three sources of uncertainty in ensemble hydrological forecasting

    NASA Astrophysics Data System (ADS)

    Thiboult, Antoine; Anctil, François; Boucher, Marie-Amélie

    2016-05-01

    Seeking more accuracy and reliability, the hydrometeorological community has developed several tools to decipher the different sources of uncertainty in relevant modeling processes. Among them, the ensemble Kalman filter (EnKF), multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims to untangle the sources of uncertainty by studying the combination of these tools and assessing their respective contribution to the overall forecast quality. Each of these components is able to capture a certain aspect of the total uncertainty and improve the forecast at different stages in the forecasting process by using different means. Their combination outperforms any of the tools used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial conditions uncertainty is dominant. However, it fails to maintain the required dispersion throughout the entire forecast horizon and needs to be supported by a multimodel approach to take into account structural uncertainty. Moreover, the multimodel approach contributes to improving the general forecasting performance and prevents this performance from falling into the model selection pitfall since models differ strongly in their ability. Finally, the use of probabilistic meteorological forcing was found to contribute mostly to long lead time reliability. Particular attention needs to be paid to the combination of the tools, especially in the EnKF tuning to avoid overlapping in error deciphering.

  15. Severe Weather Forecast Decision Aid

    NASA Technical Reports Server (NTRS)

    Bauman, William H., III; Wheeler, Mark M.; Short, David A.

    2005-01-01

    This report presents a 15-year climatological study of severe weather events and related severe weather atmospheric parameters. Data sources included local forecast rules, archived sounding data, Cloud-to-Ground Lightning Surveillance System (CGLSS) data, surface and upper air maps, and two severe weather event databases covering east-central Florida. The local forecast rules were used to set threat assessment thresholds for stability parameters that were derived from the sounding data. The severe weather events databases were used to identify days with reported severe weather and the CGLSS data was used to differentiate between lightning and non-lightning days. These data sets provided the foundation for analyzing the stability parameters and synoptic patterns that were used to develop an objective tool to aid in forecasting severe weather events. The period of record for the analysis was May - September, 1989 - 2003. The results indicate that there are certain synoptic patterns more prevalent on days with severe weather and some of the stability parameters are better predictors of severe weather days based on locally tuned threat values. The results also revealed the stability parameters that did not display any skill related to severe weather days. An interactive web-based Severe Weather Decision Aid was developed to assist the duty forecaster by providing a level of objective guidance based on the analysis of the stability parameters, CGLSS data, and synoptic-scale dynamics. The tool will be tested and evaluated during the 2005 warm season.

  16. The Forecast Interpretation Tool—a Monte Carlo technique for blending climatic distributions with probabilistic forecasts

    USGS Publications Warehouse

    Husak, G.J.; Michaelsen, J.; Kyriakidis, P.; Verdin, J.P.; Funk, C.; Galu, G.

    2011-01-01

    Probabilistic forecasts are produced from a variety of outlets to help predict rainfall, and other meteorological events, for periods of 1 month or more. Such forecasts are expressed as probabilities of a rainfall event, e.g. being in the upper, middle, or lower third of the relevant distribution of rainfall in the region. The impact of these forecasts on the expectation for the event is not always clear or easily conveyed. This article proposes a technique based on Monte Carlo simulation for adjusting existing climatologic statistical parameters to match forecast information, resulting in new parameters defining the probability of events for the forecast interval. The resulting parameters are shown to approximate the forecasts with reasonable accuracy. To show the value of the technique as an application for seasonal rainfall, it is used with consensus forecast developed for the Greater Horn of Africa for the 2009 March-April-May season. An alternative, analytical approach is also proposed, and discussed in comparison to the first simulation-based technique.

  17. An integrated two-stage support vector machine approach to forecast inundation maps during typhoons

    NASA Astrophysics Data System (ADS)

    Jhong, Bing-Chen; Wang, Jhih-Huang; Lin, Gwo-Fong

    2017-04-01

    During typhoons, accurate forecasts of hourly inundation depths are essential for inundation warning and mitigation. Due to the lack of observed data of inundation maps, sufficient observed data are not available for developing inundation forecasting models. In this paper, the inundation depths, which are simulated and validated by a physically based two-dimensional model (FLO-2D), are used as a database for inundation forecasting. A two-stage inundation forecasting approach based on Support Vector Machine (SVM) is proposed to yield 1- to 6-h lead-time inundation maps during typhoons. In the first stage (point forecasting), the proposed approach not only considers the rainfall intensity and inundation depth as model input but also simultaneously considers cumulative rainfall and forecasted inundation depths. In the second stage (spatial expansion), the geographic information of inundation grids and the inundation forecasts of reference points are used to yield inundation maps. The results clearly indicate that the proposed approach effectively improves the forecasting performance and decreases the negative impact of increasing forecast lead time. Moreover, the proposed approach is capable of providing accurate inundation maps for 1- to 6-h lead times. In conclusion, the proposed two-stage forecasting approach is suitable and useful for improving the inundation forecasting during typhoons, especially for long lead times.

  18. Improved Anvil Forecasting

    NASA Technical Reports Server (NTRS)

    Lambert, Winifred C.

    2000-01-01

    This report describes the outcome of Phase 1 of the AMU's Improved Anvil Forecasting task. Forecasters in the 45th Weather Squadron and the Spaceflight Meteorology Group have found that anvil forecasting is a difficult task when predicting LCC and FR violations. The purpose of this task is to determine the technical feasibility of creating an anvil-forecasting tool. Work on this study was separated into three steps: literature search, forecaster discussions, and determination of technical feasibility. The literature search revealed no existing anvil-forecasting techniques. However, there appears to be growing interest in anvils in recent years. If this interest continues to grow, more information will be available to aid in developing a reliable anvil-forecasting tool. The forecaster discussion step revealed an array of methods on how better forecasting techniques could be developed. The forecasters have ideas based on sound meteorological principles and personal experience in forecasting and analyzing anvils. Based on the information gathered in the discussions with the forecasters, the conclusion of this report is that it is technically feasible at this time to develop an anvil forecasting technique that will significantly contribute to the confidence in anvil forecasts.

  19. Value of long-term streamflow forecast to reservoir operations for water supply in snow-dominated catchments

    SciTech Connect

    Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea F.; Pianosi, Francesca; Nijssen, B.; Lettenmaier, Dennis P.

    2016-04-12

    In this study, we develop a forecast-based adaptive control framework for Oroville reservoir, California, to assess the value of seasonal and inter-annual forecasts for reservoir operation.We use an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity hydrology model. The optimal sequence of daily release decisions from the reservoir is then determined by Model Predictive Control, a flexible and adaptive optimization scheme.We assess the forecast value by comparing system performance based on the ESP forecasts with that based on climatology and a perfect forecast. In addition, we evaluate system performance based on a synthetic forecast, which is designed to isolate the contribution of seasonal and inter-annual forecast skill to the overall value of the ESP forecasts.Using the same ESP forecasts, we generalize our results by evaluating forecast value as a function of forecast skill, reservoir features, and demand. Our results show that perfect forecasts are valuable when the water demand is high and the reservoir is sufficiently large to allow for annual carry-over. Conversely, ESP forecast value is highest when the reservoir can shift water on a seasonal basis.On average, for the system evaluated here, the overall ESP value is 35% less than the perfect forecast value. The inter-annual component of the ESP forecast contributes 20-60% of the total forecast value. Improvements in the seasonal component of the ESP forecast would increase the overall ESP forecast value between 15 and 20%.

  20. Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model

    NASA Astrophysics Data System (ADS)

    Li, Ji; Chen, Yangbo; Wang, Huanyu; Qin, Jianming; Li, Jie; Chiao, Sen

    2017-03-01

    Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. The latest numerical weather forecast model could provide 1-15-day quantitative precipitation forecasting products in grid format, and by coupling this product with a distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe model with the Weather Research and Forecasting quantitative precipitation forecast (WRF QPF) for large watershed flood forecasting in southern China. The QPF of WRF products has three lead times, including 24, 48 and 72 h, with the grid resolution being 20 km  × 20 km. The Liuxihe model is set up with freely downloaded terrain property; the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with the WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post-process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also. This suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of the WRF QPF decreases, as does the flood forecasting capability. Flood forecasting products produced by coupling the Liuxihe model with the WRF QPF provide a good reference for large watershed flood warning due to its long lead time and rational results.

  1. Seasonal drought ensemble predictions based on multiple climate models in the upper Han River Basin, China

    NASA Astrophysics Data System (ADS)

    Ma, Feng; Ye, Aizhong; Duan, Qingyun

    2017-03-01

    An experimental seasonal drought forecasting system is developed based on 29-year (1982-2010) seasonal meteorological hindcasts generated by the climate models from the North American Multi-Model Ensemble (NMME) project. This system made use of a bias correction and spatial downscaling method, and a distributed time-variant gain model (DTVGM) hydrologic model. DTVGM was calibrated using observed daily hydrological data and its streamflow simulations achieved Nash-Sutcliffe efficiency values of 0.727 and 0.724 during calibration (1978-1995) and validation (1996-2005) periods, respectively, at the Danjiangkou reservoir station. The experimental seasonal drought forecasting system (known as NMME-DTVGM) is used to generate seasonal drought forecasts. The forecasts were evaluated against the reference forecasts (i.e., persistence forecast and climatological forecast). The NMME-DTVGM drought forecasts have higher detectability and accuracy and lower false alarm rate than the reference forecasts at different lead times (from 1 to 4 months) during the cold-dry season. No apparent advantage is shown in drought predictions during spring and summer seasons because of a long memory of the initial conditions in spring and a lower predictive skill for precipitation in summer. Overall, the NMME-based seasonal drought forecasting system has meaningful skill in predicting drought several months in advance, which can provide critical information for drought preparedness and response planning as well as the sustainable practice of water resource conservation over the basin.

  2. Streamflow forecast uncertainty evolution and its effect on real-time reservoir operation

    NASA Astrophysics Data System (ADS)

    Chen, Lu; Singh, Vijay P.; Lu, Weiwei; Zhang, Junhong; Zhou, Jianzhong; Guo, Shenglian

    2016-09-01

    When employing streamflow forecasting in practical applications, such as reservoir operation, one important issue is to deal with the uncertainty involved in forecasting. Traditional studies dealing with the uncertainty in streamflow forecasting have been limited in describing the evolution of forecast uncertainty. This paper proposes a copula-based uncertainty evolution (CUE) model to describe the evolution of streamflow forecast uncertainty. The generated forecast uncertainty series fits the observed series well in terms of observed mean, standard deviation and skewness. Daily flow with forecast uncertainty are simulated and used to determine the effect of forecast uncertainty on real-time reservoir operation of the Three Gorges Reservoir (TGR), China. Results show that using the forecast inflow coupled with the pre-release module for reservoir operation of TGR in flood season cannot increase the flood risk.

  3. Considering Seasons.

    ERIC Educational Resources Information Center

    Gonzalez-Mena, Janet

    1994-01-01

    Argues that the traditional way that the four seasons are taught is culturally biased and does not reflect the actual seasons in many parts of the United States and other nations. Suggests that early childhood programs should take into account the diversity of seasonal transitions. (MDM)

  4. Stochastic demographic forecasting.

    PubMed

    Lee, R D

    1992-11-01

    "This paper describes a particular approach to stochastic population forecasting, which is implemented for the U.S.A. through 2065. Statistical time series methods are combined with demographic models to produce plausible long run forecasts of vital rates, with probability distributions. The resulting mortality forecasts imply gains in future life expectancy that are roughly twice as large as those forecast by the Office of the Social Security Actuary.... Resulting stochastic forecasts of the elderly population, elderly dependency ratios, and payroll tax rates for health, education and pensions are presented."

  5. Forecast-skill-based simulation of streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Zhao, Tongtiegang; Zhao, Jianshi

    2014-09-01

    Streamflow forecasts are updated periodically in real time, thereby facilitating forecast evolution. This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts. The proposed model applies stochastic models that deal with streamflow variability to generate streamflow scenarios, which represent cases without forecast skill of future streamflow. The model then employs a coefficient of prediction to determine forecast skill and to quantify the streamflow variability ratio explained by the forecast. By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast. Simulated forecast uncertainty increases with increasing lead time; and simulated uncertainty during a specific future period decreases over time. We combine the statistical model with an optimization model and design a hypothetical case study of reservoir operation. The results indicate the significance of forecast skill in forecast-based reservoir operation. Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level. Moreover, an effective forecast horizon exists beyond which more forecast information does not contribute to reservoir operation and higher forecast skill results in longer effective forecast horizon. The results illustrate that the statistical model is efficient in simulating forecast evolution and facilitates analysis of forecast-based decision making.

  6. A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly

    NASA Technical Reports Server (NTRS)

    Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Guilong

    2001-01-01

    This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.

  7. Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts

    NASA Technical Reports Server (NTRS)

    Koster, R. D.; Walker, G. K.

    2015-01-01

    The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.

  8. Assessing hurricane season

    NASA Astrophysics Data System (ADS)

    Showstack, Randy

    2011-12-01

    With the official conclusion of the Atlantic hurricane season on 29 November, Irene was the only hurricane to strike the United States this year and the first one since Hurricane Ike made landfall in Texas in 2008, according to the National Oceanic and Atmospheric Administration (NOAA). Irene “broke the ‘hurricane amnesia’ that can develop when so much time lapses between landfalling storms,” indicated Jack Hayes, director of NOAA's National Weather Service. “This season is a reminder that storms can hit any part of our coast and that all regions need to be prepared each and every season.” During the season, there were 19 tropical storms, including 7 that became hurricanes; 3 of those were major hurricanes, of category 3 or above. The activity level was in line with NOAA predictions. The agency stated that Hurricane Irene was an example of improved accuracy in forecasting storm tracks: NOAA National Hurricane Center had accurately predicted the hurricane's landfall in North Carolina and its path northward more than 4 days in advance.

  9. Influenza Forecasting with Google Flu Trends

    PubMed Central

    Dugas, Andrea Freyer; Jalalpour, Mehdi; Gel, Yulia; Levin, Scott; Torcaso, Fred; Igusa, Takeru; Rothman, Richard E.

    2013-01-01

    Background We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. Methods Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004–2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. Results A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. Conclusions Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and

  10. Evaluation of Canadian Seasonal to Interannual Prediction System: seasonal hindcasts of the recent past climate.

    NASA Astrophysics Data System (ADS)

    Markovic, M.

    2015-12-01

    Canadian Seasonal to Interannual Prediction System (CanSIPS) has been operationally active within the Meteorological Service of Canada since the year of 2011. This coupled (atmosphere-land-ocean) system is in charge of producing seasonal forecasts of near surface temperature and precipitation for the following 12 months with respect to the forecast onset. CanSIPS comprises two coupled atmosphere-land-ocean models: CanCM3 and CanCM4 developed in Canadian Centre for Climate Modelling and Analysis. Each model produces ten-member ensemble forecasts which generate twenty member ensemble predictions. In this work we evaluate seasonal hindcasts of the recent past climate (1981-2010) simulated by the CanSIPS system. The importance of such evaluation stems from the fact that seasonal hindcasts can be used to calibrate the results of the seasonal predictions. Calibrated forecasts have in general more skill compared to the raw predictions. Moreover, verification of seasonal hindcasts enables an estimation of the expected performance of the prediction system over various regions and seasons (i.e. expected skill maps). Evaluation will be presented against reanalysis data. Near surface temperature and precipitation will be assessed over different geographical locations and various lead times.

  11. Real-time forecasts of dengue epidemics

    NASA Astrophysics Data System (ADS)

    Yamana, T. K.; Shaman, J. L.

    2015-12-01

    Dengue is a mosquito-borne viral disease prevalent in the tropics and subtropics, with an estimated 2.5 billion people at risk of transmission. In many areas with endemic dengue, disease transmission is seasonal but prone to high inter-annual variability with occasional severe epidemics. Predicting and preparing for periods of higher than average transmission is a significant public health challenge. Here we present a model of dengue transmission and a framework for optimizing model simulations with real-time observational data of dengue cases and environmental variables in order to generate ensemble-based forecasts of the timing and severity of disease outbreaks. The model-inference system is validated using synthetic data and dengue outbreak records. Retrospective forecasts are generated for a number of locations and the accuracy of these forecasts is quantified.

  12. Assessing methods for developing crop forecasting in the Iberian Peninsula

    NASA Astrophysics Data System (ADS)

    Ines, A. V. M.; Capa Morocho, M. I.; Baethgen, W.; Rodriguez-Fonseca, B.; Han, E.; Ruiz Ramos, M.

    2015-12-01

    Seasonal climate prediction may allow predicting crop yield to reduce the vulnerability of agricultural production to climate variability and its extremes. It has been already demonstrated that seasonal climate predictions at European (or Iberian) scale from ensembles of global coupled climate models have some skill (Palmer et al., 2004). The limited predictability that exhibits the atmosphere in mid-latitudes, and therefore de Iberian Peninsula (PI), can be managed by a probabilistic approach based in terciles. This study presents an application for the IP of two methods for linking tercile-based seasonal climate forecasts with crop models to improve crop predictability. Two methods were evaluated and applied for disaggregating seasonal rainfall forecasts into daily weather realizations: 1) a stochastic weather generator and 2) a forecast tercile resampler. Both methods were evaluated in a case study where the impacts of two seasonal rainfall forecasts (wet and dry forecast for 1998 and 2015 respectively) on rainfed wheat yield and irrigation requirements of maize in IP were analyzed. Simulated wheat yield and irrigation requirements of maize were computed with the crop models CERES-wheat and CERES-maize which are included in Decision Support System for Agrotechnology Transfer (DSSAT v.4.5, Hoogenboom et al., 2010). Simulations were run at several locations in Spain where the crop model was calibrated and validated with independent field data. These methodologies would allow quantifying the benefits and risks of a seasonal climate forecast to potential users as farmers, agroindustry and insurance companies in the IP. Therefore, we would be able to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse ones. ReferencesPalmer, T. et al., 2004. Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bulletin of the

  13. Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling

    SciTech Connect

    Cai, Ximing; Hejazi, Mohamad I.; Wang, Dingbao

    2011-09-29

    This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration's (NOAA's) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1-7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years under the various forecast horizons. To mitigate drought effect on crop yield through irrigation, medium- or long-term climate predictions likely play a more important role than short-term forecasts. NOAA's imperfect 1-week forecast is still valuable in terms of both profit gain and water saving. Compared with the no-rain forecast case, the short-term imperfect forecasts could lead to additional 2.4-8.5% gain in profit and 11.0-26.9% water saving. However, the performance of the imperfect forecast is only slightly better than the ensemble weather forecast based on historical data and slightly inferior to the perfect forecast. It seems that the 1-week forecast horizon is too limited to evaluate the role of the various forecast scenarios for irrigation scheduling, which is actually a seasonal decision issue. For irrigation scheduling, both the forecast quality and the length of forecast time horizon matter. Thus, longer forecasts might be necessary to evaluate the role

  14. Metrics for the Evaluation the Utility of Air Quality Forecasting

    NASA Astrophysics Data System (ADS)

    Sumo, T. M.; Stockwell, W. R.

    2013-12-01

    Global warming is expected to lead to higher levels of air pollution and therefore the forecasting of both long-term and daily air quality is an important component for the assessment of the costs of climate change and its impact on human health. Some of the risks associated with poor air quality days (where the Air Pollution Index is greater than 100), include hospital visits and mortality. Accurate air quality forecasting has the potential to allow sensitive groups to take appropriate precautions. This research builds metrics for evaluating the utility of air quality forecasting in terms of its potential impacts. Our analysis of air quality models focuses on the Washington, DC/Baltimore, MD region over the summertime ozone seasons between 2010 and 2012. The metrics that are relevant to our analysis include: (1) The number of times that a high ozone or particulate matter (PM) episode is correctly forecasted, (2) the number of times that high ozone or PM episode is forecasted when it does not occur and (3) the number of times when the air quality forecast predicts a cleaner air episode when the air was observed to have high ozone or PM. Our evaluation of the performance of air quality forecasts include those forecasts of ozone and particulate matter and data available from the U.S. Environmental Protection Agency (EPA)'s AIRNOW. We also examined observational ozone and particulate matter data available from Clean Air Partners. Overall the forecast models perform well for our region and time interval.

  15. Objective Lightning Probability Forecasts for East-Central Florida Airports

    NASA Technical Reports Server (NTRS)

    Crawford, Winfred C.

    2013-01-01

    The forecasters at the National Weather Service in Melbourne, FL, (NWS MLB) identified a need to make more accurate lightning forecasts to help alleviate delays due to thunderstorms in the vicinity of several commercial airports in central Florida at which they are responsible for issuing terminal aerodrome forecasts. Such forecasts would also provide safer ground operations around terminals, and would be of value to Center Weather Service Units serving air traffic controllers in Florida. To improve the forecast, the AMU was tasked to develop an objective lightning probability forecast tool for the airports using data from the National Lightning Detection Network (NLDN). The resulting forecast tool is similar to that developed by the AMU to support space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) for use by the 45th Weather Squadron (45 WS) in previous tasks (Lambert and Wheeler 2005, Lambert 2007). The lightning probability forecasts are valid for the time periods and areas needed by the NWS MLB forecasters in the warm season months, defined in this task as May-September.

  16. Weather forecasting expert system study

    NASA Technical Reports Server (NTRS)

    1985-01-01

    Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.

  17. Advanced Climate Analysis and Long Range Forecasting

    DTIC Science & Technology

    2014-09-30

    1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Advanced Climate Analysis and Long Range Forecasting...project is to improve the long range and climate support provided by the U.S. Naval Oceanography Enterprise (NOe) for planning, conducting, and...months, several seasons, several years). The primary transition focus is on improving the long range and climate support capabilities of the Fleet

  18. Future freeze forecasting

    NASA Technical Reports Server (NTRS)

    Bartholic, J. F.; Sutherland, R. A.

    1979-01-01

    Real time GOES thermal data acquisition, an energy balance minimum temperature prediction model and a statistical model are incorporated into a minicomputer system. These components make up the operational "Satellite Freeze Forecast System" being used to aid NOAA, NWS forecasters in developing their freeze forecasts. The general concept of the system is presented in this paper. Specific detailed aspects of the system can be found in the reference cited.

  19. Forecaster priorities for improving probabilistic flood forecasts

    NASA Astrophysics Data System (ADS)

    Wetterhall, Fredrik; Pappenberger, Florian; Alfieri, Lorenzo; Cloke, Hannah; Thielen, Jutta

    2014-05-01

    Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

  20. A successful forecast of an El Nino winter

    SciTech Connect

    Kerr, R.A.

    1992-01-24

    This year, for the first time, weather forecasters used signs of a warming in the tropical Pacific as the basis for a long-range prediction of winter weather patterns across the United States. Now forecasters are talking about the next step: stretching the lead time for such forecasts by a year or more. That seems feasible because although this Pacific warming was unmistakable by the time forecasters at the National Weather Service's Climate Analysis Center (CAC) in Camp Springs, Maryland, issued their winter forecast, the El Nino itself had been predicted almost 2 years in advance by a computer model. Next time around, the CAC may well be listening to the modelers and predicting El Nino-related patterns of warmth and flooding seasons in advance.

  1. A recurrent support vector regression model in rainfall forecasting

    NASA Astrophysics Data System (ADS)

    Pai, Ping-Feng; Hong, Wei-Chiang

    2007-03-01

    To minimize potential loss of life and property caused by rainfall during typhoon seasons, precise rainfall forecasts have been one of the key subjects in hydrological research. However, rainfall forecast is made difficult by some very complicated and unforeseen physical factors associated with rainfall. Recently, support vector regression (SVR) models and recurrent SVR (RSVR) models have been successfully employed to solve time-series problems in some fields. Nevertheless, the use of RSVR models in rainfall forecasting has not been investigated widely. This study attempts to improve the forecasting accuracy of rainfall by taking advantage of the unique strength of the SVR model, genetic algorithms, and the recurrent network architecture. The performance of genetic algorithms with different mutation rates and crossover rates in SVR parameter selection is examined. Simulation results identify the RSVR with genetic algorithms model as being an effective means of forecasting rainfall amount. Copyright

  2. Weather assessment and forecasting

    NASA Technical Reports Server (NTRS)

    1977-01-01

    Data management program activities centered around the analyses of selected far-term Office of Applications (OA) objectives, with the intent of determining if significant data-related problems would be encountered and if so what alternative solutions would be possible. Three far-term (1985 and beyond) OA objectives selected for analyses as having potential significant data problems were large-scale weather forecasting, local weather and severe storms forecasting, and global marine weather forecasting. An overview of general weather forecasting activities and their implications upon the ground based data system is provided. Selected topics were specifically oriented to the use of satellites.

  3. Changing Seasons

    ERIC Educational Resources Information Center

    Karolak, Eric

    2011-01-01

    In some ways, there is a season of change at the national level in early childhood. Some things are wrapping up while some developments aim to prepare the "field" for improvements in the next year and beyond, just as a garden plot is readied for the next planting season. Change is in the air, and there's hope of renewal, but what changes and how…

  4. Peak Wind Tool for General Forecasting

    NASA Technical Reports Server (NTRS)

    Barrett, Joe H., III

    2010-01-01

    The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded

  5. Some Advances in Downscaling Probabilistic Climate Forecasts for Agricultural Decision Support

    NASA Astrophysics Data System (ADS)

    Han, E.; Ines, A.

    2015-12-01

    Seasonal climate forecasts, commonly provided in tercile-probabilities format (below-, near- and above-normal), need to be translated into more meaningful information for decision support of practitioners in agriculture. In this paper, we will present two new novel approaches to temporally downscale probabilistic seasonal climate forecasts: one non-parametric and another parametric method. First, the non-parametric downscaling approach called FResampler1 uses the concept of 'conditional block sampling' of weather data to create daily weather realizations of a tercile-based seasonal climate forecasts. FResampler1 randomly draws time series of daily weather parameters (e.g., rainfall, maximum and minimum temperature and solar radiation) from historical records, for the season of interest from years that belong to a certain rainfall tercile category (e.g., being below-, near- and above-normal). In this way, FResampler1 preserves the covariance between rainfall and other weather parameters as if conditionally sampling maximum and minimum temperature and solar radiation if that day is wet or dry. The second approach called predictWTD is a parametric method based on a conditional stochastic weather generator. The tercile-based seasonal climate forecast is converted into a theoretical forecast cumulative probability curve. Then the deviates for each percentile is converted into rainfall amount or frequency or intensity to downscale the 'full' distribution of probabilistic seasonal climate forecasts. Those seasonal deviates are then disaggregated on a monthly basis and used to constrain the downscaling of forecast realizations at different percentile values of the theoretical forecast curve. As well as the theoretical basis of the approaches we will discuss sensitivity analysis (length of data and size of samples) of them. In addition their potential applications for managing climate-related risks in agriculture will be shown through a couple of case studies based on

  6. Forecasting Future Trends in Education

    ERIC Educational Resources Information Center

    Collazo, Andres; And Others

    1977-01-01

    Describes a forecasting model sensitive to the major factors influencing educational outcomes, presents several forecasts based on alternative sets of assumptions, and discusses the implications of these forecasts, including ways to subvert them. (Author/JG)

  7. Seasonal predictability of the North Atlantic Oscillation

    NASA Astrophysics Data System (ADS)

    Vellinga, Michael; Scaife, Adam

    2015-04-01

    Until recently, long-range forecast systems showed only modest levels of skill in predicting surface winter climate around the Atlantic Basin and associated fluctuations in the North Atlantic Oscillation at seasonal lead times. Here we use a new forecast system to assess seasonal predictability of winter North Atlantic climate. We demonstrate that key aspects of European and North American winter climate and the surface North Atlantic Oscillation are highly predictable months ahead. We demonstrate high levels of prediction skill in retrospective forecasts of the surface North Atlantic Oscillation, winter storminess, near-surface temperature, and wind speed, all of which have high value for planning and adaptation to extreme winter conditions. Analysis of forecast ensembles suggests that while useful levels of seasonal forecast skill have now been achieved, key sources of predictability are still only partially represented and there is further untapped predictability. This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.

  8. Aviation Forecasting in ICAO

    NASA Technical Reports Server (NTRS)

    Mcmahon, J.

    1972-01-01

    Opinions or plans of qualified experts in the field are used for forecasting future requirements for air navigational facilities and services of international civil aviation. ICAO periodically collects information from Stators and operates on anticipated future operations, consolidates this information, and forecasts the future level of activity at different airports.

  9. Role of Satellite Rainfall Information in Improving Understanding of the Dynamical Link Between the Tropics and Extratropics Prospects of Improved Forecasts of Weather and Short-Term Climate Variability on Sub-Seasonal Time Scales

    NASA Technical Reports Server (NTRS)

    Hou, Arthur Y.

    2002-01-01

    The tropics and extratropics are two dynamically distinct regimes. The coupling between these two regimes often defies simple analytical treatment. Progress in understanding of the dynamical interaction between the tropics and extratropics relies on better observational descriptions to guide theoretical development. However, global analyses currently contain significant errors in primary hydrological variables such as precipitation, evaporation, moisture, and clouds, especially in the tropics. Tropical analyses have been shown to be sensitive to parameterized precipitation processes, which are less than perfect, leading to order-one discrepancies between estimates produced by different data assimilation systems. One strategy for improvement is to assimilate rainfall observations to constrain the analysis and reduce uncertainties in variables physically linked to precipitation. At the Data Assimilation Office at the NASA Goddard Space Flight Center, we have been exploring the use of tropical rain rates derived from the TRMM Microwave Imager (TMI) and the Special Sensor Microwave/ Imager (SSM/I) instruments in global data assimilation. Results show that assimilating these data improves not only rainfall and moisture fields but also related climate parameters such as clouds and radiation, as well as the large-scale circulation and short-range forecasts. These studies suggest that assimilation of microwave rainfall observations from space has the potential to significantly improve the quality of 4-D assimilated datasets for climate investigations (Hou et al. 2001). In the next few years, there will be a gradual increase in microwave rain products available from operational and research satellites, culminating to a target constellation of 9 satellites to provide global rain measurements every 3 hours with the proposed Global Precipitation Measurement (GPM) mission in 2007. Continued improvements in assimilation methodology, rainfall error estimates, and model

  10. Verification of ECMWF monthly forecasts for the use in hydrological predictions

    NASA Astrophysics Data System (ADS)

    Monhart, Samuel; Spirig, Christoph; Bhend, Jonas; Liniger, Mark A.; Bogner, Konrad; Schär, Christoph

    2016-04-01

    In recent years, sub-seasonal forecasts have received increasing attention. These forecasts with a time horizon of 2 to 6 weeks bridge the gap between operational weather forecasts and seasonal predictions. Different sectors (e.g. agriculture, energy, warnings systems) show high demand in seamless forecasts from days to seasons. Within the HEPS4Power project we aim at developing a hydrometeorological end-to-end ensemble prediction system for several catchments in the Swiss Alps. In order to use the monthly forecast in hydrological modeling, we first explore the performance of the meteorological forecasts separately. This framework allows also an assessment of different bias correction and downscaling techniques. Such a post-processing will be important to couple the hydrological model to the meteorological model data. We verified the ECMWF extended-range forecast against approximately 1000 observational time series of ECA&D across Europe. To do so, we made use of 20 years of hindcasts of the forecasting system that was operational from May 2014 to April 2015 (cycle 40r1), yielding an analysis period of May 1995 to June 2014. This unique data set is large enough to stratify the performance of the monthly forecasting system with season and region. Weekly temperature and precipitation of both raw hindcasts and post-processed hindcasts were analyzed. Various skill metrics (RPSS, CRPSS, ROCSS) characterizing different aspects of forecast quality were computed using climatology as a benchmark. Overall, skillful forecasts were found in some regions and seasons up to three weeks of lead time in case of temperature and up to two weeks for precipitation, respectively. Bias-corrections allowed to enhance forecast skill in the first two weeks for most of the stations. Spatial and seasonal differences in skill were found both for temperature and precipitation, with winter forecasts generally being better than those of other seasons. Geographically, forecasts tend to show higher

  11. Phantosmia as a meteorological forecaster

    NASA Astrophysics Data System (ADS)

    Aiello, S. R.; Hirsch, A. R.

    2013-09-01

    In normosmics, olfactory ability has been found to vary with ambient humidity, barometric pressure, and season. While hallucinated sensations of phantom pain associated with changes in weather have been described, a linkage to chemosensory hallucinations has heretofore not been reported. A 64-year-old white male with Parkinson's disease presents with 5 years of phantosmia of a smoky burnt wood which changed to onion-gas and then to a noxious skunk-onion excrement odor. Absent upon waking it increases over the day and persists for hours. When severe, there appears a phantom taste with the same qualities as the odor. It is exacerbated by factors that manipulate intranasal pressure, such as coughing. When eating or sniffing, the actual flavors replace the phantosmia. Since onset, he noted the intensity and frequency of the phantosmia forecasted the weather. Two to 3 h before a storm, the phantosmia intensifies from a level 0 to a 7-10, which persists through the entire thunderstorm. Twenty years prior, he reported the ability to forecast the weather, based on pain in a torn meniscus, which vanished after surgical repair. Extensive olfactory testing demonstrates underlying hyposmia. Possible mechanisms for such chemosensory-meteorological linkage includes: air pressure induced synesthesia, disinhibition of spontaneous olfactory discharge, exacerbation of ectopic discharge, affect mediated somatic sensory amplification, and misattribution error with expectation and recall bias. This is the first reported case of weather-induced exacerbation of phantosmia. Further investigation of the connection between chemosensory complaints and ambient weather is warranted.

  12. Seasonal Variations in Mars Atmospheric Predictability

    NASA Astrophysics Data System (ADS)

    Greybush, S. J.; Hoffman, M. J.; Kalnay, E.; Wilson, R.; Hoffman, R. N.; Miyoshi, T.; Ide, K.

    2011-12-01

    The seasonal dependence of the predictability of the Martian atmosphere is examined using a Numerical Weather Prediction system, spacecraft observations, and the bred vector technique. Mars atmosphere analyses, created by assimilating Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) temperature profiles, provide the initial conditions for forecasts generated by the GFDL Mars Global Circulation Model (MGCM). Comparing longer term (1-10 sol) forecasts with observations and the latest analyses from data assimilation reveal the duration of time where Martian numerical weather prediction forecasts initialized with observations provide superior guidance than freely running model simulations; initial results in the northern hemisphere autumn indicate a predictability horizon of more than 5 sols (Greybush, 2011). In this study we examine the seasonal variation in predictability at several different times of year, which exhibit significant differences in circulation patterns, dust opacities, and the strength and location of traveling waves. The impacts of using a fixed dust distribution versus a seasonally varying dust distribution based on Thermal Emission Spectrometer (TES) observations, as well as the role of empirical bias correction, are assessed. The bred vector technique explores the locations, seasonality, and physical origins of dynamical instabilities that result in the growth of forecast errors. The application of a kinetic energy equation illuminates the role of barotropic and baroclinic processes.

  13. Improving Global Forecast System of extreme precipitation events with regional statistical model: Application of quantile-based probabilistic forecasts

    NASA Astrophysics Data System (ADS)

    Shastri, Hiteshri; Ghosh, Subimal; Karmakar, Subhankar

    2017-02-01

    Forecasting of extreme precipitation events at a regional scale is of high importance due to their severe impacts on society. The impacts are stronger in urban regions due to high flood potential as well high population density leading to high vulnerability. Although significant scientific improvements took place in the global models for weather forecasting, they are still not adequate at a regional scale (e.g., for an urban region) with high false alarms and low detection. There has been a need to improve the weather forecast skill at a local scale with probabilistic outcome. Here we develop a methodology with quantile regression, where the reliably simulated variables from Global Forecast System are used as predictors and different quantiles of rainfall are generated corresponding to that set of predictors. We apply this method to a flood-prone coastal city of India, Mumbai, which has experienced severe floods in recent years. We find significant improvements in the forecast with high detection and skill scores. We apply the methodology to 10 ensemble members of Global Ensemble Forecast System and find a reduction in ensemble uncertainty of precipitation across realizations with respect to that of original precipitation forecasts. We validate our model for the monsoon season of 2006 and 2007, which are independent of the training/calibration data set used in the study. We find promising results and emphasize to implement such data-driven methods for a better probabilistic forecast at an urban scale primarily for an early flood warning.

  14. Ensemble stream flow predictions using the ECMWF forecasts

    NASA Astrophysics Data System (ADS)

    Kiczko, Adam; Romanowicz, Renata; Osuch, Marzena; Pappenberger, Florian; Karamuz, Emilia

    2015-04-01

    Floods and low flows in rivers are seasonal phenomena that can cause several problems to society. To anticipate high and low flow events, flow forecasts are crucial. They are of particular importance in mountainous catchments, where the lead time of forecasts is usually short. In order to prolong the forecast lead-time, numerical weather predictions (NWPs) are used as a hydrological model driving force. The forecasted flow is commonly given as one value, even though it is uncertain. There is an increasing interest in accounting for the uncertainty in flood early warning and decision support systems. When NWP are given in the form of ensembles, such as the ECMWF forecasts, the uncertainty of these forecasts can be accounted for. Apart from the forecast uncertainty the uncertainty related to the hydrological model used also plays an important role in the uncertainty of the final flow prediction. The aim of this study is the development of a stream flow prediction system for the Biała Tarnowska, a mountainous catchment in the south of Poland. We apply two different hydrological models. One is a conceptual HBV model for rainfall-flow predictions, applied within a Generalised Likelihood Uncertainty Estimation (GLUE) framework, the second is a data-based DBM model, adjusted for Polish conditions by adding the Soil Moisture Accounting (SMA) and snow-melt modules. Both models provide the uncertainty of the predictions, but the DBM approach is much more numerically efficient, therefore more suitable for the real-time forecasting.. The ECMWF forecasts require bias reduction in order to correspond to observations. Therefore we applied Quantile Mapping with and without seasonal adjustment for bias correction. Up to seven-days ahead forecast skills are compared using the Relative Operation Characteristic (ROC) graphs, for the flood warning and flood alarm flow value thresholds. The ECMWF forecasts are obtained from the project TIGGE (http

  15. Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison: Preprint

    SciTech Connect

    Zhang, J.; Hodge, B. M.; Gomez-Lazaro, E.; Lovholm, A. L.; Berge, E.; Miettinen, J.; Holttinen, H.; Cutululis, N.; Litong-Palima, M.; Sorensen, P.; Dobschinski, J.

    2013-10-01

    One of the critical challenges of wind power integration is the variable and uncertain nature of the resource. This paper investigates the variability and uncertainty in wind forecasting for multiple power systems in six countries. An extensive comparison of wind forecasting is performed among the six power systems by analyzing the following scenarios: (i) wind forecast errors throughout a year; (ii) forecast errors at a specific time of day throughout a year; (iii) forecast errors at peak and off-peak hours of a day; (iv) forecast errors in different seasons; (v) extreme forecasts with large overforecast or underforecast errors; and (vi) forecast errors when wind power generation is at different percentages of the total wind capacity. The kernel density estimation method is adopted to characterize the distribution of forecast errors. The results show that the level of uncertainty and the forecast error distribution vary among different power systems and scenarios. In addition, for most power systems, (i) there is a tendency to underforecast in winter; and (ii) the forecasts in winter generally have more uncertainty than the forecasts in summer.

  16. Statistical Correction for Short-term Wind Forecasting

    NASA Astrophysics Data System (ADS)

    Jiang, J.; Tambke, J.; Heinemann, D.

    2009-04-01

    The uncertainty of wind speed forecasts from Numerical Weather Predictions (NWP) is a main source of errors in wind power forecasts. Despite increased accuracy of wind power forecasts in the last years, large wind power forecast errors still occur due to local weather and/or synoptic systems that either develop locally or that are phase-shifted relative to the forecast. The main error sources of NWP are, amongst others, inaccurate initial and/or lateral boundary conditions and inadequate representation of local forcing. Wind measurements in the WMEP program and the wind field forecasts of DWD-LM(German Weather Service - Local Model) in 2004 and from Sept. to Dec. of 2006 are used for correcting DWD-LM wind forecasts. In order to remove systematic error, phase error, or local-scale error in wind forecasts, three kinds of methods, bias correction, phase correction and Cressman technique are applied. Using bias increments changing with wind direction, the wind forecasts are modified. After bias correction, RMSE ( Root Mean Square Error ) of corrected forecasts decrease 0.1m/s to 0.4m/s. Diurnal or seasonal change of bias is also important. In addition, with optimum phase-shift vectors, the phase-shift correction is applied. It reduces the RMSE about 0.1m/s to 0.5m/s at different time steps. Moreover, statistical Cressman correction increments were obtained by Cressman technique and used to correct wind forecasts. After Cressman correction, the RMSE of wind forecasts decreases 0.1m/s to 0.2m/s. The results show that bias correction, phase correction and Cressman scheme do, to some extent, improve wind speed forecasts. That contributes to the improvement of wind power forecasts. As part of the new EU-project "SafeWind", we are continuing that study with NWP-data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and wind speed observations over Europe.

  17. Translating the potential of hydrological forecasts into improved decision making in African regions

    NASA Astrophysics Data System (ADS)

    Sheffield, J.; He, X.; Wanders, N.; Wood, E. F.; Ali, A.; Olang, L.; Estes, L. D.; Caylor, K. K.; Evans, T. P.

    2015-12-01

    Hydrological forecasts at local scale and seasonal time scales have the potential to inform decision-making by individuals and institutions to improve management of water resources and enhance food security. Much progress has been made in recent years in understanding climate variability and its predictability over African regions. However, there remain many challenges in translating large-scale evaluations and forecasts into locally relevant information. This is hampered by lack of on the ground data of hydrological and agricultural states, and the generally low skill of climate forecasts at time scales beyond one or two weeks. Additionally, the uptake of forecasts is not prevalent because of lack of capacity, and institutional and cultural barriers to using new and uncertain information. New technologies for monitoring and forecasting relevant hydrological variables, and novel approaches to understanding how this information may be used within decision making processes, have the potential to make substantial progress in addressing these challenges. We present a quasi-operational drought and flood monitoring and forecasting system and its use in understanding the potential of hydrological forecasts for improved decision-making. The system monitors in near real-time the terrestrial water cycle for the African continent based on remote sensing data and land surface hydrological modeling. The monitoring forms initial conditions for hydrological forecasts at short time scale, aimed at flood forecasting, and seasonal scale aimed at drought and crop yield forecasts. The flood forecasts are driven by precipitation and temperature forecasts from the Global Forecast System (GFS). The drought forecasts are driven by climate forecasts from the North American Multi-Model Ensemble (NMME). The seasonal forecast skill is modest and seasonally/regionally dependent with part of the skill coming from persistence in initial land surface conditions. We discuss the use of the system

  18. Statistical evaluation of forecasts.

    PubMed

    Mader, Malenka; Mader, Wolfgang; Gluckman, Bruce J; Timmer, Jens; Schelter, Björn

    2014-08-01

    Reliable forecasts of extreme but rare events, such as earthquakes, financial crashes, and epileptic seizures, would render interventions and precautions possible. Therefore, forecasting methods have been developed which intend to raise an alarm if an extreme event is about to occur. In order to statistically validate the performance of a prediction system, it must be compared to the performance of a random predictor, which raises alarms independent of the events. Such a random predictor can be obtained by bootstrapping or analytically. We propose an analytic statistical framework which, in contrast to conventional methods, allows for validating independently the sensitivity and specificity of a forecasting method. Moreover, our method accounts for the periods during which an event has to remain absent or occur after a respective forecast.

  19. Statistical evaluation of forecasts

    NASA Astrophysics Data System (ADS)

    Mader, Malenka; Mader, Wolfgang; Gluckman, Bruce J.; Timmer, Jens; Schelter, Björn

    2014-08-01

    Reliable forecasts of extreme but rare events, such as earthquakes, financial crashes, and epileptic seizures, would render interventions and precautions possible. Therefore, forecasting methods have been developed which intend to raise an alarm if an extreme event is about to occur. In order to statistically validate the performance of a prediction system, it must be compared to the performance of a random predictor, which raises alarms independent of the events. Such a random predictor can be obtained by bootstrapping or analytically. We propose an analytic statistical framework which, in contrast to conventional methods, allows for validating independently the sensitivity and specificity of a forecasting method. Moreover, our method accounts for the periods during which an event has to remain absent or occur after a respective forecast.

  20. Evolutionary Forecast Engines for Solar Meteorology

    NASA Astrophysics Data System (ADS)

    Coimbra, C. F.

    2012-12-01

    A detailed comparison of non-stationary regression and stochastic learning methods based on k-Nearest Neighbor (kNN), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) approaches is carried out in order to develop high-fidelity solar forecast engines for several time horizons of interest. A hybrid GA/ANN method emerges as the most robust stochastic learning candidate. The GA/ANN approach In general the following decisions need to be made when creating an ANN-based solar forecast model: the ANN architecture: number of layers, numbers of neurons per layer; the preprocessing scheme; the fraction and distribution between training and testing data, and the meteorological and radiometric inputs. ANNs are very well suited to handle multivariate forecasting models due to their overall flexibility and nonlinear pattern recognition abilities. However, the forecasting skill of ANNs depends on a new set of parameters to be optimized within the context of the forecast model, which is the selection of input variables that most directly impact the fidelity of the forecasts. In a data rich scenario where irradiation, meteorological, and cloud cover data are available, it is not always evident which variables to include in the model a priori. New variables can also arise from data preprocessing such as smoothing or spectral decomposition. One way to avoid time-consuming trial-and-error approaches that have limited chance to result in optimal ANN topology and input selection is to couple the ANN with some optimization algorithm that scans the solution space and "evolves" the ANN structure. Genetic Algorithms (GAs) are well suited for this task. Results and Discussion The models built upon the historical data of 2009 and 2010 are applied to the 2011 data without modifications or retraining. We consider 3 solar variability seasons or periods, which are subsets of the total error evaluation data set. The 3 periods are defined based on the solar variability study as: - a high

  1. Precipitation and temperature ensemble forecasts from single-value forecasts

    NASA Astrophysics Data System (ADS)

    Schaake, J.; Demargne, J.; Hartman, R.; Mullusky, M.; Welles, E.; Wu, L.; Herr, H.; Fan, X.; Seo, D. J.

    2007-04-01

    A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004). The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved. Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods. The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other hydrological variables that reflect

  2. Calls Forecast for the Moscow Ambulance Service. The Impact of Weather Forecast

    NASA Astrophysics Data System (ADS)

    Gordin, Vladimir; Bykov, Philipp

    2015-04-01

    We use the known statistics of the calls for the current and previous days to predict them for tomorrow and for the following days. We assume that this algorithm will work operatively, will cyclically update the available information and will move the horizon of the forecast. Sure, the accuracy of such forecasts depends on their lead time, and from a choice of some group of diagnoses. For comparison we used the error of the inertial forecast (tomorrow there will be the same number of calls as today). Our technology has demonstrated accuracy that is approximately two times better compared to the inertial forecast. We obtained the following result: the number of calls depends on the actual weather in the city as well as on its rate of change. We were interested in the accuracy of the forecast for 12-hour sum of the calls in real situations. We evaluate the impact of the meteorological errors [1] on the forecast errors of the number of Ambulance calls. The weather and the Ambulance calls number both have seasonal tendencies. Therefore, if we have medical information from one city only, we should separate the impacts of such predictors as "annual variations in the number of calls" and "weather". We need to consider the seasonal tendencies (associated, e. g. with the seasonal migration of the population) and the impact of the air temperature simultaneously, rather than sequentially. We forecasted separately the number of calls with diagnoses of cardiovascular group, where it was demonstrated the advantage of the forecasting method, when we use the maximum daily air temperature as a predictor. We have a chance to evaluate statistically the influence of meteorological factors on the dynamics of medical problems. In some cases it may be useful for understanding of the physiology of disease and possible treatment options. We can assimilate some personal archives of medical parameters for the individuals with concrete diseases and the relative meteorological archive. As a

  3. Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 2. Streamflow verification

    NASA Astrophysics Data System (ADS)

    Brown, James D.; He, Minxue; Regonda, Satish; Wu, Limin; Lee, Haksu; Seo, Dong-Jun

    2014-11-01

    Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas-Red Basin RFC, the Colorado Basin RFC, the California-Nevada RFC and the Middle Atlantic RFC. In a companion paper, temperature and precipitation hindcasts were produced with the Meteorological Ensemble Forecast Processor (MEFP) and verified against observed temperature and precipitation, respectively. Inputs to the MEFP comprised raw precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (MEFP-GFS) and a conditional or "resampled" climatology (MEFP-CLIM). For this paper, streamflow hindcasts were produced with the Community Hydrologic Prediction System and were bias-corrected with the Ensemble Post-processor (EnsPost). In order to separate the meteorological and hydrologic uncertainties, the raw streamflow forecasts were verified against simulated streamflows, as well as observed flows. Also, when verifying the bias-corrected streamflow forecasts, the total skill was decomposed into contributions from the MEFP-GFS and the EnsPost. In general, the streamflow forecasts are substantially more skillful when using the MEFP-GFS together with the EnsPost than using the MEFP with resampled climatology alone. However, both the raw and bias-corrected streamflow forecasts have lower biases, stronger correlations and are more skillful in CB- and CN-RFCs than AB- and MA-RFCs. In addition, there are strong variations in forecast quality with streamflow amount, forecast lead time, season and aggregation period. The relative importance of the meteorological and hydrologic uncertainties also varies between basins and is modulated by the same controls on forecast quality. For example, the MEFP

  4. Sub-seasonal Modulation of Indian Summer Monsoon Seasonal Predictability

    NASA Astrophysics Data System (ADS)

    Robertson, A. W.; Moron, V.; Pai, D. S.

    2015-12-01

    inherently unpredictable. We conclude that the scope for skillful sub-seasonal to seasonal forecasts of Indian summer monsoon rainfall is likely to be much greater during the onset and decay phases, rather than during the core of the monsoon.

  5. A quality assessment of the MARS crop yield forecasting system for the European Union

    NASA Astrophysics Data System (ADS)

    van der Velde, Marijn; Bareuth, Bettina

    2015-04-01

    Timely information on crop production forecasts can become of increasing importance as commodity markets are more and more interconnected. Impacts across large crop production areas due to (e.g.) extreme weather and pest outbreaks can create ripple effects that may affect food prices and availability elsewhere. The MARS Unit (Monitoring Agricultural ResourceS), DG Joint Research Centre, European Commission, has been providing forecasts of European crop production levels since 1993. The operational crop production forecasting is carried out with the MARS Crop Yield Forecasting System (M-CYFS). The M-CYFS is used to monitor crop growth development, evaluate short-term effects of anomalous meteorological events, and provide monthly forecasts of crop yield at national and European Union level. The crop production forecasts are published in the so-called MARS bulletins. Forecasting crop yield over large areas in the operational context requires quality benchmarks. Here we present an analysis of the accuracy and skill of past crop yield forecasts of the main crops (e.g. soft wheat, grain maize), throughout the growing season, and specifically for the final forecast before harvest. Two simple benchmarks to assess the skill of the forecasts were defined as comparing the forecasts to 1) a forecast equal to the average yield and 2) a forecast using a linear trend established through the crop yield time-series. These reveal a variability in performance as a function of crop and Member State. In terms of production, the yield forecasts of 67% of the EU-28 soft wheat production and 80% of the EU-28 maize production have been forecast superior to both benchmarks during the 1993-2013 period. In a changing and increasingly variable climate crop yield forecasts can become increasingly valuable - provided they are used wisely. We end our presentation by discussing research activities that could contribute to this goal.

  6. Characterizing the Behavior of NOAA's Hydrologic Ensemble Forecast Service in California

    NASA Astrophysics Data System (ADS)

    He, M.; Whitin, B.; Brown, J.; Fickenscher, P.; Henkel, A.; Talanki, S.; Hartman, R.

    2014-12-01

    The National Oceanic and Atmospheric Administration (NOAA)'s National Weather Service (NWS) is implementing the Hydrologic Ensemble Forecast Service (HEFS) across the operating areas of the 13 NWS River Forecast Centers (RFCs). As the implementation progresses, hindcasting and validation is necessary to understand the strengths and weaknesses of the HEFS and to guide its operational use. Particularly in regions such as California that encompass a broad range of elevation, temperature, and precipitation gradients, the quality of the HEFS forecasts will vary geographically, and it is important to understand the degrees and controls on forecast quality in this context. This study aims to develop a comprehensive understanding of the quality of HEFS forecasts in California, with the aim of guiding and enhancing the implementation of the HEFS, as well as informing end-users about the expected quality of the HEFS forecasts. The HEFS was calibrated with temperature and precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environmental Prediction. Also, in order to determine forecast skill and to benchmark the HEFS against a simpler forecasting system, the HEFS was calibrated with a conditional ("resampled") climatology. The calibrated HEFS was used to generate retrospective forecasts of precipitation, temperature, and streamflow for a 25-year (1985-2009) period for six basins in the state. The forecast horizon was 1-14 days. The retrospective forecasts were verified conditionally on forecast lead time, magnitude, and season. Preliminary results indicate that HEFS forecasts are much more skillful when forced by inputs from the GEFS, rather than resampled climatology. However, there are noticeable differences in forecast quality among basins. These observations demonstrate the applicability of HEFS in a wide hydroclimatic gradient within California, while highlighting the difficulty in generalizing its behavior across the state.

  7. An overview of health forecasting.

    PubMed

    Soyiri, Ireneous N; Reidpath, Daniel D

    2013-01-01

    Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.

  8. Earthquake Forecasting, Validation and Verification

    NASA Astrophysics Data System (ADS)

    Rundle, J.; Holliday, J.; Turcotte, D.; Donnellan, A.; Tiampo, K.; Klein, B.

    2009-05-01

    Techniques for earthquake forecasting are in development using both seismicity data mining methods, as well as numerical simulations. The former rely on the development of methods to recognize patterns in data, while the latter rely on the use of dynamical models that attempt to faithfully replicate the actual fault systems. Testing such forecasts is necessary not only to determine forecast quality, but also to improve forecasts. A large number of techniques to validate and verify forecasts have been developed for weather and financial applications. Many of these have been elaborated in public locations, including, for example, the URL as listed below. Typically, the goal is to test for forecast resolution, reliability and sharpness. A good forecast is characterized by consistency, quality and value. Most, if not all of these forecast verification procedures can be readily applied to earthquake forecasts as well. In this talk, we discuss both methods of forecasting, as well as validation and verification using a number of these standard methods. We show how these test methods might be useful for both fault-based forecasting, a group of forecast methods that includes the WGCEP and simulator-based renewal models, and grid-based forecasting, which includes the Relative Intensity, Pattern Informatics, and smoothed seismicity methods. We find that applying these standard methods of forecast verification is straightforward. Judgments about the quality of a given forecast method can often depend on the test applied, as well as on the preconceptions and biases of the persons conducting the tests.

  9. Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

    NASA Astrophysics Data System (ADS)

    Hebert, David A.; Allard, Richard A.; Metzger, E. Joseph; Posey, Pamela G.; Preller, Ruth H.; Wallcraft, Alan J.; Phelps, Michael W.; Smedstad, Ole Martin

    2015-12-01

    In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1-7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short-term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

  10. Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 1. Experimental design and forcing verification

    NASA Astrophysics Data System (ADS)

    Brown, James D.; Wu, Limin; He, Minxue; Regonda, Satish; Lee, Haksu; Seo, Dong-Jun

    2014-11-01

    Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas-Red Basin RFC, the Colorado Basin RFC, the California-Nevada RFC, and the Middle Atlantic RFC. Precipitation and temperature forecasts were produced with the HEFS Meteorological Ensemble Forecast Processor (MEFP). Inputs to the MEFP comprised "raw" precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (GFS) and a climatological ensemble, which involved resampling historical observations in a moving window around the forecast valid date ("resampled climatology"). In both cases, the forecast horizon was 1-14 days. This paper outlines the hindcasting and verification strategy, and then focuses on the quality of the temperature and precipitation forecasts from the MEFP. A companion paper focuses on the quality of the streamflow forecasts from the HEFS. In general, the precipitation forecasts are more skillful than resampled climatology during the first week, but comprise little or no skill during the second week. In contrast, the temperature forecasts improve upon resampled climatology at all forecast lead times. However, there are notable differences among RFCs and for different seasons, aggregation periods and magnitudes of the observed and forecast variables, both for precipitation and temperature. For example, the MEFP-GFS precipitation forecasts show the highest correlations and greatest skill in the California Nevada RFC, particularly during the wet season (November-April). While generally reliable, the MEFP forecasts typically underestimate the largest observed precipitation amounts (a Type-II conditional bias). As a statistical technique, the MEFP cannot detect, and thus

  11. CFSv2 Driven Seasonal Drought Prediction over the United States

    NASA Astrophysics Data System (ADS)

    Luo, L.; Tang, W.; Tan, P.; Lin, Z.

    2012-12-01

    Skillful seasonal hydrologic predictions are useful in managing water resources, preparing for droughts and their impacts, energy planning, and many other related sectors. Earlier studies have established a VIC-based seasonal hydrological ensemble prediction system for the United States. In this study, we focused on upgrading and improving the system for predicting droughts in the US in the following ways: 1) Seasonal climate forecast from NCEP Climate Forecast System (CFS) has been replaced by the second version of CFS (CFSv2) as the major driving climate forecast for downscaling; 2) daily temperature and precipitation fields are now used instead of monthly average fields; 3) The downscaling procedure now produces posterior distributions of temperature and precipitation at multiple spatial and temporal scales. Because of these upgrades, we expect improved forecast skills and the capability to produce more frequent updates and seasonal drought predictions over the US. The performance of the new system is evaluated with both realtime drought forecast and retrospective forecast of historical events.

  12. Forecasting vegetation greenness with satellite and climate data

    USGS Publications Warehouse

    Ji, Lei; Peters, Albert J.

    2004-01-01

    A new and unique vegetation greenness forecast (VGF) model was designed to predict future vegetation conditions to three months through the use of current and historical climate data and satellite imagery. The VGF model is implemented through a seasonality-adjusted autoregressive distributed-lag function, based on our finding that the normalized difference vegetation index is highly correlated with lagged precipitation and temperature. Accurate forecasts were obtained from the VGF model in Nebraska grassland and cropland. The regression R2 values range from 0.97-0.80 for 2-12 week forecasts, with higher R2 associated with a shorter prediction. An important application would be to produce real-time forecasts of greenness images.

  13. Proceedings: Eleventh forecasting symposium. Forecasting in a competitive electricity market

    SciTech Connect

    Vogt, T.; Ignelzi, P.

    1998-10-01

    EPRI`s Eleventh Forecasting Symposium: ``Forecasting in a Competitive Electricity Market`` was held in Arlington, Virginia, in November 1997. This proceedings documents the symposium`s wide variety of topics, ranging from very-short-term operations issues to mid-term market planning issues. Speakers described the forecasting practices of other industries, predicted forecasting directions in the electric power industry; related their experiences with new forecasting approaches; and suggested further enhancements to forecasting methods, tools, and data. The objectives of the symposium were to explore the expanding roles of forecasting in a competitive market, to exchange information about forecasting techniques under development, and to discuss the forecasting techniques currently used by the electric power industry in and outside the US and in other industries. The 30 papers are arranged under the following topical sections: restructuring and regulatory issues--implications for forecasting; forecasting experiences in other industries; operations-related forecasting; data warehousing and database marketing; forecasting and risk management; understanding and predicting market prices; forecasting methods for the new environment; predicting customer response; and symposium wrap-up.

  14. Forecasting residual herbicide concentrations in soil

    NASA Astrophysics Data System (ADS)

    McGrath, Gavan; Scanlan, Craig; van Zwieten, Lukas; Rose, Mick; Rose, Terry

    2016-04-01

    High concentrations of herbicides remaining in soil at the time of planting can adversely impact agricultural production and lead to off-site impacts in streams and groundwater. Being able to forecast the likelihood of residual concentrations at specific times in the future offers the potential to improve environmental and economic outcomes. Here we develop a solution for the full transient probability density function for herbicide concentrations in soil as a function of rainfall variability. Quasi-analytical solutions that account for rainfall seasonality are also demonstrated. In addition, new rapid and relatively cost-effective bioassays to quantify herbicide concentrations in near real-time, offers opportunities for data assimilation approaches to improve forecast risks.

  15. Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

    NASA Technical Reports Server (NTRS)

    Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long

    2001-01-01

    This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

  16. Medium Range Flood Forecasting for Agriculture Damage Reduction

    NASA Astrophysics Data System (ADS)

    Fakhruddin, S. H. M.

    2014-12-01

    Early warning is a key element for disaster risk reduction. In recent decades, major advancements have been made in medium range and seasonal flood forecasting. This progress provides a great opportunity to reduce agriculture damage and improve advisories for early action and planning for flood hazards. This approach can facilitate proactive rather than reactive management of the adverse consequences of floods. In the agricultural sector, for instance, farmers can take a diversity of options such as changing cropping patterns, applying fertilizer, irrigating and changing planting timing. An experimental medium range (1-10 day) flood forecasting model has been developed for Bangladesh and Thailand. It provides 51 sets of discharge ensemble forecasts of 1-10 days with significant persistence and high certainty. This type of forecast could assist farmers and other stakeholders for differential preparedness activities. These ensembles probabilistic flood forecasts have been customized based on user-needs for community-level application focused on agriculture system. The vulnerabilities of agriculture system were calculated based on exposure, sensitivity and adaptive capacity. Indicators for risk and vulnerability assessment were conducted through community consultations. The forecast lead time requirement, user-needs, impacts and management options for crops were identified through focus group discussions, informal interviews and community surveys. This paper illustrates potential applications of such ensembles for probabilistic medium range flood forecasts in a way that is not commonly practiced globally today.

  17. The Making of National Seasonal Wildfire Outlooks

    NASA Astrophysics Data System (ADS)

    Garfin, G. M.; Brown, T. J.

    2015-12-01

    Bridging the gap between research-based experiments and fully operational products has been likened to crossing the valley of death. In this talk, we document the development of pre-season fire potential outlooks, informed by seasonal climate predictions, through a long-term collaboration between NOAA RISA teams, the Program for Climate, Ecosystem and Fire Applications (Desert Research Institute), the National Interagency Fire Center's Predictive Services program and multiple collaborators. To transition experimental outlooks into a sustained, monthly operational product, we co-developed a temporary institution, the National Seasonal Assessment Workshops, as a platform for cross-disciplinary knowledge exchange, training, and experimentation in consensus forecast processes and product development. In our retrospective evaluation of the process, we identified several factors that supported the transition from research to operations. These include: the development of new institutions; focus on a geographic scale commensurate with the needs of federal and state land management agencies; participatory and deliberative engagements; cooperation by many partners with perspectives on the connections between climate and wildland fire management; and iterative engagement sustained by funding and human resource commitments from the key partners. Through co-production of the outlooks and the institution, we created a cross-disciplinary community of practice, thus, increasing the capacity of fire management practitioners to use climate information in decision making. This experiment in developing a collaborative climate service was not an unqualified success. For example, while practitioners almost always *consult* official probabilistic climate forecasts, based on the output from dynamical and statistical models, they sometimes *act* on information from self-constructed forecasts, based on analysis of analogue years. We recommend research to further examine the distribution and

  18. Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil.

    PubMed

    Lowe, Rachel; Coelho, Caio As; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

    2016-02-24

    Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.

  19. Phantosmia as a meteorological forecaster.

    PubMed

    Aiello, S R; Hirsch, A R

    2013-09-01

    In normosmics, olfactory ability has been found to vary with ambient humidity, barometric pressure, and season. While hallucinated sensations of phantom pain associated with changes in weather have been described, a linkage to chemosensory hallucinations has heretofore not been reported. A 64-year-old white male with Parkinson's disease presents with 5 years of phantosmia of a smoky burnt wood which changed to onion-gas and then to a noxious skunk-onion excrement odor. Absent upon waking it increases over the day and persists for hours. When severe, there appears a phantom taste with the same qualities as the odor. It is exacerbated by factors that manipulate intranasal pressure, such as coughing. When eating or sniffing, the actual flavors replace the phantosmia. Since onset, he noted the intensity and frequency of the phantosmia forecasted the weather. Two to 3 h before a storm, the phantosmia intensifies from a level 0 to a 7-10, which persists through the entire thunderstorm. Twenty years prior, he reported the ability to forecast the weather, based on pain in a torn meniscus, which vanished after surgical repair. Extensive olfactory testing demonstrates underlying hyposmia. Possible mechanisms for such chemosensory-meteorological linkage includes: air pressure induced synesthesia, disinhibition of spontaneous olfactory discharge, exacerbation of ectopic discharge, affect mediated somatic sensory amplification, and misattribution error with expectation and recall bias. This is the first reported case of weather-induced exacerbation of phantosmia. Further investigation of the connection between chemosensory complaints and ambient weather is warranted.

  20. A human judgment approach to epidemiological forecasting

    PubMed Central

    Farrow, David C.; Brooks, Logan C.; Rosenfeld, Roni

    2017-01-01

    Infectious diseases impose considerable burden on society, despite significant advances in technology and medicine over the past century. Advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic. Historically, such a capability has lagged for many reasons, including in particular the uncertainty in the current state of the system and in the understanding of the processes that drive epidemic trajectories. Presently we have access to data, models, and computational resources that enable the development of epidemiological forecasting systems. Indeed, several recent challenges hosted by the U.S. government have fostered an open and collaborative environment for the development of these technologies. The primary focus of these challenges has been to develop statistical and computational methods for epidemiological forecasting, but here we consider a serious alternative based on collective human judgment. We created the web-based “Epicast” forecasting system which collects and aggregates epidemic predictions made in real-time by human participants, and with these forecasts we ask two questions: how accurate is human judgment, and how do these forecasts compare to their more computational, data-driven alternatives? To address the former, we assess by a variety of metrics how accurately humans are able to predict influenza and chikungunya trajectories. As for the latter, we show that real-time, combined human predictions of the 2014–2015 and 2015–2016 U.S. flu seasons are often more accurate than the same predictions made by several statistical systems, especially for short-term targets. We conclude that there is valuable predictive power in collective human judgment, and we discuss the benefits and drawbacks of this approach. PMID:28282375

  1. A Review of Real-Time Markov Model ENSO Forecast in 1996-2015: Why did it Forecast a Strong El Nino since March 2015?

    NASA Astrophysics Data System (ADS)

    Xue, Y.

    2015-12-01

    The Markov model for real time ENSO forecast at Climate Prediction Center of National Centers for Environmental Prediction (NCEP) is based on observed sea surface temperature, sea level from the NCEP ocean reanalysis, and pseudo wind stress from the Florida State University in 1980-1995. The Markov model is constructed in a reduced multivariate EOF (MEOF) space with 3 MEOFs. The cross-validated hindcast skill of NINO3.4 in 1980-1995 is competitive among dynamical and statistical models. The model was implemented into operation at CPC in early 2000s since it successfully forecasted the El Nino in winter 1997/98 starting from November 1996 initial conditions (I.C.). In this study, we assessed the real time forecast skill of ENSO by the Markov model in 1996-2015 and compared it with that of other operational forecast models. It is found that the Markov model has lower forecast skill of ENSO in the 2000s than that in the 1980s and 1990s, which is common among ENSO forecast models. The lower forecast skill of the Markov model in the 2000s can be attributed to weak precursor of positive heat content anomaly in the equatorial Pacific and a shorter lead time of the precursor relative to NINO3.4, both of which is related to the decadal change of ENSO. However, out of surprise, the Markov model successfully forecasted the El Nino in winter 2014/15 starting from February 2014 I.C.. In addition, the Markov model forecasted the continuation of the El Nino into the spring/summer/fall of 2015. Starting from March 2015 I.C., the Markov model forecasted a strong El Nino in winter 2015/16. This surprising long-lead forecast skill can be attributed to the positive second principal component (PC) of MEOF that leads NINO3.4 by 6-9 months, a precursor commonly seen in the 1980s and 1990s. This provided us confidence in the model forecast of a strong El Nino in winter 2015/16 that is highly consistent with the ensemble forecast of dynamical models.

  2. Assessing the skill of seasonal rainfall outlooks for the Caribbean

    NASA Astrophysics Data System (ADS)

    Bedward, Shanice; Van Meerbeeck, Cedric

    2013-04-01

    The Caribbean's island and low lying coastal nations make the region highly vulnerable to water-related natural hazards, many originating from seasonal rainfall variability. To help mitigate this risk, Global Producing Centres (GPCs) such as the International Research Institute for Climate and Society (IRI) publish global seasonal rainfall probability forecasts each month. However, the Caribbean's geography warrants the production of downscaled forecasts, such as done by the Caribbean Institute for Meteorology and Hydrology (CIMH). Yet, even CIMH's prediction system, which balances GPC forecasts with regional climatological expertise, is perceived to show limited reliability. To find out to what extent this results from inherently low predictability and what model improvements should be made, we compared the forecasting skills of the IRI and CIMH prediction systems from 2000 to 2012. Specifically, we calculated the commonly used Ranked Probability Skill Score (RPSS) and Heidke Skill Score (HSS) to distinguish which system, season and sub-region are more accurately forecasted. If scores above 0.2 and 0.5 represent good and very good forecasts, respectively, the CIMH prediction system produced ~1/3 good and ~1/10 very good forecasts or ~1.5 times as many as IRI's. The most accurately forecasted season was Jan-Feb-Mar by IRI (~1/3% good or very good forecasts), compared to Sep-Oct-Nov by CIMH (~2/3 good or very good forecasts). By contrast, Apr-May-Jun was less well predicted by both systems. Broken down by sub-region, the Lesser Antilles were best predicted with an average RPSS score of nearly 0.1 by CIMH and 0.05 by IRI whereas less skill was found for the Greater Antilles and Guianas and virtually no skill for Belize in either system. Though consistent with a greater predictability of seasonal rainfall in the Lesser Antilles, such scores point to forecasting accuracy well below a previously estimated 30% inherent predictability. Thus, there is much space for system

  3. Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments

    NASA Astrophysics Data System (ADS)

    Anghileri, D.; Voisin, N.; Castelletti, A.; Pianosi, F.; Nijssen, B.; Lettenmaier, D. P.

    2016-06-01

    We present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations. Our framework is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons. We quantify and bound the contribution of seasonal and inter-annual forecast components to optimal, adaptive reservoir operation. The framework uses an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity (VIC) hydrology model. We determine the optimal sequence of daily release decisions using the Model Predictive Control (MPC) optimization scheme. We then assess the forecast value by comparing system performance based on the ESP forecasts with the performances based on climatology and perfect forecasts. We distinguish among the relative contributions of the seasonal component of the forecast versus the inter-annual component by evaluating system performance based on hybrid forecasts, which are designed to isolate the two contributions. As an illustration, we first apply the forecast-based adaptive management framework to a specific case study, i.e., Oroville Reservoir in California, and we then modify the characteristics of the reservoir and the demand to demonstrate the transferability of the findings to other reservoir systems. Results from numerical experiments show that, on average, the overall ESP value in informing reservoir operation is 35% less than the perfect forecast value and the inter-annual component of the ESP forecast contributes 20-60% of the total forecast value.

  4. Operational Space Weather Needs - Perspectives from SEASONS 2014

    NASA Astrophysics Data System (ADS)

    Comberiate, J.; Kelly, M. A.; Paxton, L. J.; Schaefer, R. K.; Bust, G. S.; Sotirelis, T.; Fox, N. J.

    2014-12-01

    A key challenge for the operational space weather community is the gap between the latest scientific data, models, methods, and indices and those that are currently used in operational systems. The November 2014 SEASONS (Space Environment Applications, Systems, and Operations for National Security) Workshop at JHU/APL in Laurel, Maryland, brings together representatives from the operational and scientific communities. The theme of SEASONS 2014 is "Beyond Climatology," with a focus on how space weather events threaten operational assets and disrupt missions. Here we present perspectives from SEASONS 2014 on new observations, models in development, and forecasting methods that are of interest to the operational space weather community. Highlighted topics include ionospheric data assimilation and forecasting models, HF propagation models, radiation belt observations, and energetic particle modeling. The SEASONS 2014 web site can be found at https://secwww.jhuapl.edu/SEASONS/

  5. Influenza Forecasting in Human Populations: A Scoping Review

    PubMed Central

    Chretien, Jean-Paul; George, Dylan; Shaman, Jeffrey; Chitale, Rohit A.; McKenzie, F. Ellis

    2014-01-01

    Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms “influenza AND (forecast* OR predict*)”, excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials. PMID:24714027

  6. Influenza forecasting in human populations: a scoping review.

    PubMed

    Chretien, Jean-Paul; George, Dylan; Shaman, Jeffrey; Chitale, Rohit A; McKenzie, F Ellis

    2014-01-01

    Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms "influenza AND (forecast* OR predict*)", excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials.

  7. Diagnostic studies of ensemble forecast "jumps"

    NASA Astrophysics Data System (ADS)

    Magnusson, Linus; Hewson, Tim; Ferranti, Laura; Rodwell, Mark

    2016-04-01

    During 2015 we saw exceptional consistency in successive seasonal forecasts produced at ECMWF, for the winter period 2015/16, right across the globe. This winter was characterised by a well-predicted and unusually strong El Nino, and some have ascribed the consistency to that. For most of December this consistency was mirrored in the (separate) ECMWF monthly forecast system, which correctly predicted anomalously strong (mild) zonal flow, over the North Atlantic and western Eurasia, even in forecasts for weeks 3 and 4. In monthly forecasts in general these weeks are often devoid of strong signals. However in late December and early January strong signals, even in week 2, proved to be incorrect, most notably over the North Atlantic and Eurasian sectors. Indeed on at least two occasions the outcome was beyond the ensemble forecast range over Scandinavia. In one of these conditions flipped from extreme mild to extreme cold as a high latitude block developed. Temperature prediction is very important to many customers, notably those dealing with renewable energy, because cold weather causes increased demand but also tends to coincide with reduced wind power production. So understandably jumps can cause consternation amongst some customer groups, and are very difficult to handle operationally. This presentation will discuss the results of initial diagnostic investigations into what caused the "ensemble jumps", particularly at the week two lead, though reference will also be made to a related shorter range (day 3) jump that was important for flooding over the UK. Initial results suggest that an inability of the ECMWF model to correctly represent convective outbreaks over North America (that for winter-time were quite extreme) played an important role. Significantly, during this period, an unusually large amount of upper air data over North America was rejected or ascribed low weight. These results bear similarities to previous diagnostic studies at ECMWF, wherein major

  8. Modeling, Simulation, and Forecasting of Subseasonal Variability

    NASA Technical Reports Server (NTRS)

    Waliser, Duane; Schubert, Siegfried; Kumar, Arun; Weickmann, Klaus; Dole, Randall

    2003-01-01

    A planning workshop on "Modeling, Simulation and Forecasting of Subseasonal Variability" was held in June 2003. This workshop was the first of a number of meetings planned to follow the NASA-sponsored workshop entitled "Prospects For Improved Forecasts Of Weather And Short-Term Climate Variability On Sub-Seasonal Time Scales" that was held April 2002. The 2002 workshop highlighted a number of key sources of unrealized predictability on subseasonal time scales including tropical heating, soil wetness, the Madden Julian Oscillation (MJO) [a.k.a Intraseasonal Oscillation (ISO)], the Arctic Oscillation (AO) and the Pacific/North American (PNA) pattern. The overarching objective of the 2003 follow-up workshop was to proceed with a number of recommendations made from the 2002 workshop, as well as to set an agenda and collate efforts in the areas of modeling, simulation and forecasting intraseasonal and short-term climate variability. More specifically, the aims of the 2003 workshop were to: 1) develop a baseline of the "state of the art" in subseasonal prediction capabilities, 2) implement a program to carry out experimental subseasonal forecasts, and 3) develop strategies for tapping the above sources of predictability by focusing research, model development, and the development/acquisition of new observations on the subseasonal problem. The workshop was held over two days and was attended by over 80 scientists, modelers, forecasters and agency personnel. The agenda of the workshop focused on issues related to the MJO and tropicalextratropical interactions as they relate to the subseasonal simulation and prediction problem. This included the development of plans for a coordinated set of GCM hindcast experiments to assess current model subseasonal prediction capabilities and shortcomings, an emphasis on developing a strategy to rectify shortcomings associated with tropical intraseasonal variability, namely diabatic processes, and continuing the implementation of an

  9. On forecasting mortality.

    PubMed

    Olshansky, S J

    1988-01-01

    Official forecasts of mortality made by the U.S. Office of the Actuary throughout this century have consistently underestimated observed mortality declines. This is due, in part, to their reliance on the static extrapolation of past trends, an atheoretical statistical method that pays scant attention to the behavioral, medical, and social factors contributing to mortality change. A "multiple cause-delay model" more realistically portrays the effects on mortality of the presence of more favorable risk factors at the population level. Such revised assumptions produce large increases in forecasts of the size of the elderly population, and have a dramatic impact on related estimates of population morbidity, disability, and health care costs.

  10. Experimental Forecasts of Wildfire Pollution at the Canadian Meteorological Centre

    NASA Astrophysics Data System (ADS)

    Pavlovic, Radenko; Beaulieu, Paul-Andre; Chen, Jack; Landry, Hugo; Cousineau, Sophie; Moran, Michael

    2016-04-01

    Environment and Climate Change Canada's Canadian Meteorological Centre Operations division (CMCO) has been running an experimental North American air quality forecast system with near-real-time wildfire emissions since 2014. This system, named FireWork, also takes anthropogenic and other natural emission sources into account. FireWork 48-hour forecasts are provided to CMCO forecasters and external partners in Canada and the U.S. twice daily during the wildfire season. This system has proven to be very useful in capturing short- and long-range smoke transport from wildfires over North America. Several upgrades to the FireWork system have been made since 2014 to accommodate the needs of operational AQ forecasters and to improve system performance. In this talk we will present performance statistics and some case studies for the 2014 and 2015 wildfire seasons. We will also describe current limitations of the FireWork system and ongoing and future work planned for this air quality forecast system.

  11. Visualizing and verifying probabilistic forecasts of the Madden-Julian Oscillation

    NASA Astrophysics Data System (ADS)

    Marshall, Andrew G.; Hendon, Harry H.; Hudson, Debra

    2016-12-01

    We describe a new approach for presenting probabilistic forecasts of the Madden-Julian Oscillation (MJO) based on the community standard Real-time Multivariate MJO (RMM) index, using forecasts from version 2 of the Predictive Ocean Atmosphere Model for Australia. This new display overcomes the difficulty of interpreting a dispersive ensemble plume and directly quantifies the probability for the MJO to occur in each of its eight RMM-defined phases as well as the weak phase. Beyond monitoring and interpreting predictions of the MJO, this new approach also provides a basis for forecast verification using probability-based skill scores. Here we present a clear and concise quantitative summary of this innovative method for accessing probability of the state of the MJO in an ensemble forecast. This new method compliments the traditional MJO ensemble forecast display and verification and will benefit global forecasting centers, international MJO working groups, and the World Meteorological Organization Subseasonal to Seasonal Project.

  12. Using climate regionalization to understand Climate Forecast System Version 2 (CFSv2) precipitation performance for the Conterminous United States (CONUS)

    NASA Astrophysics Data System (ADS)

    Regonda, Satish K.; Zaitchik, Benjamin F.; Badr, Hamada S.; Rodell, Matthew

    2016-06-01

    Dynamically based seasonal forecasts are prone to systematic spatial biases due to imperfections in the underlying global climate model (GCM). This can result in low-forecast skill when the GCM misplaces teleconnections or fails to resolve geographic barriers, even if the prediction of large-scale dynamics is accurate. To characterize and address this issue, this study applies objective climate regionalization to identify discrepancies between the Climate Forecast System Version 2 (CFSv2) and precipitation observations across the Contiguous United States (CONUS). Regionalization shows that CFSv2 1 month forecasts capture the general spatial character of warm season precipitation variability but that forecast regions systematically differ from observation in some transition zones. CFSv2 predictive skill for these misclassified areas is systematically reduced relative to correctly regionalized areas and CONUS as a whole. In these incorrectly regionalized areas, higher skill can be obtained by using a regional-scale forecast in place of the local grid cell prediction.

  13. Using Climate Regionalization to Understand Climate Forecast System Version 2 (CFSv2) Precipitation Performance for the Conterminous United States (CONUS)

    NASA Technical Reports Server (NTRS)

    Regonda, Satish K.; Zaitchik, Benjamin F.; Badr, Hamada S.; Rodell, Matthew

    2016-01-01

    Dynamically based seasonal forecasts are prone to systematic spatial biases due to imperfections in the underlying global climate model (GCM). This can result in low-forecast skill when the GCM misplaces teleconnections or fails to resolve geographic barriers, even if the prediction of large-scale dynamics is accurate. To characterize and address this issue, this study applies objective climate regionalization to identify discrepancies between the Climate Forecast SystemVersion 2 (CFSv2) and precipitation observations across the Contiguous United States (CONUS). Regionalization shows that CFSv2 1 month forecasts capture the general spatial character of warm season precipitation variability but that forecast regions systematically differ from observation in some transition zones. CFSv2 predictive skill for these misclassified areas is systematically reduced relative to correctly regionalized areas and CONUS as a whole. In these incorrectly regionalized areas, higher skill can be obtained by using a regional-scale forecast in place of the local grid cell prediction.

  14. A global empirical system for probabilistic seasonal climate prediction

    NASA Astrophysics Data System (ADS)

    Eden, J. M.; van Oldenborgh, G. J.; Hawkins, E.; Suckling, E. B.

    2015-12-01

    Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

  15. An empirical system for probabilistic seasonal climate prediction

    NASA Astrophysics Data System (ADS)

    Eden, Jonathan; van Oldenborgh, Geert Jan; Hawkins, Ed; Suckling, Emma

    2016-04-01

    Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

  16. Forecasting Methods for Institutional Research.

    ERIC Educational Resources Information Center

    Jennings, Linda W.; Young, Dean M.

    1988-01-01

    Increasing demands for accurate forecasts in such areas as student enrollment, energy expenditures, and facility capacity are placing new demands on the institutional researcher. A variety of forecasting models and methods are available, all to be used with caution in long-range forecasting. (Author/MSE)

  17. Corporate Forecasting: Promise and Reality

    ERIC Educational Resources Information Center

    Wheelwright, Steven C.; Clarke, Darral G.

    1976-01-01

    Discusses a survey of forecast preparers and users in 127 major companies in an attempt to assess underlying problems and identify areas for improvement. Concludes that forecasting responsibilities and tasks must be better defined and that forecast preparers and users must become better informed about one another's roles. (Author/JG)

  18. Federal Forecasters Directory, 1995.

    ERIC Educational Resources Information Center

    National Center for Education Statistics (ED), Washington, DC.

    This directory lists employees of the federal government who are involved in forecasting for policy formation and trend prediction purposes. Job title, agency, business address, phone or e-mail number, and specialty areas are listed for each employee. Employees are listed for the following agencies: (1) Bureau of the Census; (2) Bureau of Economic…

  19. Developing air quality forecasts

    NASA Astrophysics Data System (ADS)

    Lee, Pius; Saylor, Rick; Meagher, James

    2012-05-01

    Third International Workshop on Air Quality Forecasting Research; Potomac, Maryland, 29 November to 1 December 2011 Elevated concentrations of both near-surface ozone (O3) and fine particulate matter smaller than 2.5 micrometers in diameter have been implicated in increased mortality and other human health impacts. In light of these known influences on human health, many governments around the world have instituted air quality forecasting systems to provide their citizens with advance warning of impending poor air quality so that they can take actions to limit exposure. In an effort to improve the performance of air quality forecasting systems and provide a forum for the exchange of the latest research in air quality modeling, the International Workshop on Air Quality Forecasting Research (IWAQFR) was established in 2009 and is cosponsored by the U.S. National Oceanic and Atmospheric Administration (NOAA), Environment Canada (EC), and the World Meteorological Organization (WMO). The steering committee for IWAQFR's establishment was composed of Véronique Bouchet, Mike Howe, and Craig Stoud (EC); Greg Carmichael (University of Iowa); Paula Davidson and Jim Meagher (NOAA); and Liisa Jalkanen (WMO). The most recent workshop took place in Maryland.

  20. From Oceans to Farms: The Value of a Novel Statistical Climate Forecast for Agricultural Management.

    NASA Astrophysics Data System (ADS)

    McIntosh, Peter C.; Ash, Andrew J.; Stafford Smith, Mark

    2005-10-01

    The economic value of seasonal climate forecasting is assessed using a whole-of-chain analysis. The entire system, from sea surface temperature (SST) through pasture growth and animal production to economic and resource outcomes, is examined. A novel statistical forecast method is developed using the partial least squares spatial correlation technique with near-global SST. This method permits forecasts to be tailored for particular regions and industries. The method is used to forecast plant growth days rather than rainfall. Forecast skill is measured by performing a series of retrospective forecasts (hindcasts) over the previous century. The hindcasts are cross-validated to guard against the possibility of artificial skill, so there is no skill at predicting random time series. The hindcast skill is shown to be a good estimator of the true forecast skill obtained when only data from previous years are used in developing the forecast.Forecasts of plant growth, reduced to three categories, are used in several agricultural examples in Australia. For the northeast Queensland grazing industry, the economic value of this forecast is shown to be greater than that of a Southern Oscillation index (SOI) based forecast and to match or exceed the value of a “perfect” category rainfall forecast. Reasons for the latter surprising result are given. Resource degradation, in this case measured by soil loss, is shown to remain insignificant despite increasing production from the land. Two further examples in Queensland, one for the cotton industry and one for wheat, are illustrated in less depth. The value of a forecast is again shown to match or exceed that obtained using the SOI, although further investigation of the decision-making responses to forecasts is needed to extract the maximum benefit for these industries.

  1. Do quantitative decadal forecasts from GCMs provide decision relevant skill?

    NASA Astrophysics Data System (ADS)

    Suckling, E. B.; Smith, L. A.

    2012-04-01

    forecasts up to ten years ahead (decadal forecasts) are considered, both on global and regional spatial scales for surface air temperature. Such decadal forecasts are not only important in terms of providing information on the impacts of near-term climate change, but also from the perspective of climate model validation, as hindcast experiments and a sufficient database of historical observations allow standard forecast verification methods to be used. Simulation models from the ENSEMBLES hindcast experiment [3] are evaluated and contrasted with static forecasts of the observed climatology, persistence forecasts and against simple statistical models, called dynamic climatology (DC). It is argued that DC is a more apropriate benchmark in the case of a non-stationary climate. It is found that the ENSEMBLES models do not demonstrate a significant increase in skill relative to the empirical models even at global scales over any lead time up to a decade ahead. It is suggested that the contsruction and co-evaluation with the data-based models become a regular component of the reporting of large simulation model forecasts. The methodology presented may easily be adapted to other forecasting experiments and is expected to influence the design of future experiments. The inclusion of comparisons with dynamic climatology and other data-based approaches provide important information to both scientists and decision makers on which aspects of state-of-the-art simulation forecasts are likely to be fit for purpose. [1] J. Bröcker and L. A. Smith. From ensemble forecasts to predictive distributions, Tellus A, 60(4), 663-678 (2007). [2] J. Bröcker and L. A. Smith. Scoring probabilistic forecasts: The importance of being proper, Weather and Forecasting, 22, 382-388 (2006). [3] F. J. Doblas-Reyes, A. Weisheimer, T. N. Palmer, J. M. Murphy and D. Smith. Forecast quality asessment of the ENSEMBLES seasonal-to-decadal stream 2 hindcasts, ECMWF Technical Memorandum, 621 (2010).

  2. Forecasting distribution of numbers of large fires

    USGS Publications Warehouse

    Eidenshink, Jeffery C.; Preisler, Haiganoush K.; Howard, Stephen; Burgan, Robert E.

    2014-01-01

    Systems to estimate forest fire potential commonly utilize one or more indexes that relate to expected fire behavior; however they indicate neither the chance that a large fire will occur, nor the expected number of large fires. That is, they do not quantify the probabilistic nature of fire danger. In this work we use large fire occurrence information from the Monitoring Trends in Burn Severity project, and satellite and surface observations of fuel conditions in the form of the Fire Potential Index, to estimate two aspects of fire danger: 1) the probability that a 1 acre ignition will result in a 100+ acre fire, and 2) the probabilities of having at least 1, 2, 3, or 4 large fires within a Predictive Services Area in the forthcoming week. These statistical processes are the main thrust of the paper and are used to produce two daily national forecasts that are available from the U.S. Geological Survey, Earth Resources Observation and Science Center and via the Wildland Fire Assessment System. A validation study of our forecasts for the 2013 fire season demonstrated good agreement between observed and forecasted values.

  3. A survey on wind power ramp forecasting.

    SciTech Connect

    Ferreira, C.; Gama, J.; Matias, L.; Botterud, A.; Wang, J.

    2011-02-23

    The increasing use of wind power as a source of electricity poses new challenges with regard to both power production and load balance in the electricity grid. This new source of energy is volatile and highly variable. The only way to integrate such power into the grid is to develop reliable and accurate wind power forecasting systems. Electricity generated from wind power can be highly variable at several different timescales: sub-hourly, hourly, daily, and seasonally. Wind energy, like other electricity sources, must be scheduled. Although wind power forecasting methods are used, the ability to predict wind plant output remains relatively low for short-term operation. Because instantaneous electrical generation and consumption must remain in balance to maintain grid stability, wind power's variability can present substantial challenges when large amounts of wind power are incorporated into a grid system. A critical issue is ramp events, which are sudden and large changes (increases or decreases) in wind power. This report presents an overview of current ramp definitions and state-of-the-art approaches in ramp event forecasting.

  4. Ionospheric ion temperature forecasting in multiples of 27 days

    NASA Astrophysics Data System (ADS)

    Sojka, Jan J.; Schunk, Robert W.; Nicolls, Michael J.

    2014-03-01

    The ionospheric variability found at auroral locations is usually assumed to be unpredictable. The magnetosphere, which drives this ionospheric variability via storms and substorms, is at best only qualitatively describable. In this study we demonstrate that over a 3 year period, ionospheric variability observed from Poker Flat, Alaska, has, in fact, a high degree of long-term predictability. The observations used in this study are (a) the solar wind high speed stream velocity measured by the NASA Advanced Composition Explorer satellite, used to define the corotating interaction region (CIR), and (b) the ion temperature at 300 km altitude measured by the National Science Foundation Poker Flat Incoherent Scatter Radar over Poker Flat, Alaska. After determining a seasonal and diurnal climatology for the ion temperature, we show that the residual ion temperature heating events occur synchronously with CIR-geospace interactions. Furthermore, we demonstrate examples of ion temperature forecasting at 27, 54, and 81 days. A rudimentary operational forecasting scenario is described for forecasting recurrence 27 days ahead for the CIR-generated geomagnetic storms. These forecasts apply specifically to satellite tracking operations (thermospheric drag) and emergency HF-radio communications (ionospheric modifications) in the polar regions. The forecast is based on present-day solar and solar wind observations that can be used to uniquely identify the coronal hole and its CIR. From this CIR epoch, a 27 day forecast is then made.

  5. Real time drought forecasting system for irrigation management

    NASA Astrophysics Data System (ADS)

    Ceppi, A.; Ravazzani, G.; Corbari, C.; Salerno, R.; Meucci, S.; Mancini, M.

    2013-12-01

    In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in European areas traditionally rich of water such as the Po Valley in northern Italy. In dry periods problems of water shortage can be enhanced by conflictual uses of water such as irrigation, industrial and power production (hydroelectric and thermoelectric). Further, over the last decade the social perspective about this issue is increasing due to possible impacts of climate change and global warming scenarios which come out from the fourth IPCC Report. The increased frequency of drought periods has stimulated the improvement of irrigation and water management. In this study we show the development and implementation of the real-time drought forecasting system PRE.G.I., an Italian acronym that stands for "Hydro-Meteorological forecast for irrigation management". The system is based on ensemble prediction (20 members) at long-range (30 days) with hydrological simulations of water balance to forecast the soil water content over a maize field. The hydrological model was validated against measurements of latent heat flux acquired by an eddy-covariance station, and soil moisture measured by TDR probes. Reliability of the forecasting system and its benefits were assessed on the growing season of 2012. Obtained results show how the proposed drought forecasting system is able to have a high reliability of forecast at least for a fortnight as lead time.

  6. Forecast for the Remainder of the Leonid Storm Season

    NASA Technical Reports Server (NTRS)

    Jenniskens, Peter; DeVincenzi, Donald L. (Technical Monitor)

    2001-01-01

    The dust trails of comet 55P/Tempel-Tuttle lead to Leonid storms on Earth, threatening satellites in orbit. We present a new model that accounts in detail for the observed properties of dust tails evolved by the comet at previous oppositions. The prediction model shows the 1767-dust trail closer to Earth's orbit in 2001 than originally thought; increasing expected peak rates for North America observers. Predictions for the 2002 storms are less affected. We demonstrate that the observed shower profiles can be understood as a projection of the comet lightcurve.

  7. Probabilistic seasonal Forecasts to deterministic Farm Leve Decisions: Innovative Approach

    NASA Astrophysics Data System (ADS)

    Mwangi, M. W.

    2015-12-01

    Climate change and vulnerability are major challenges in ensuring household food security. Climate information services have the potential to cushion rural households from extreme climate risks. However, most the probabilistic nature of climate information products is not easily understood by majority of smallholder farmers. Despite the probabilistic nature, climate information have proved to be a valuable climate risk adaptation strategy at the farm level. This calls for innovative ways to help farmers understand and apply climate information services to inform their farm level decisions. The study endeavored to co-design and test appropriate innovation systems for climate information services uptake and scale up necessary for achieving climate risk development. In addition it also determined the conditions necessary to support the effective performance of the proposed innovation system. Data and information sources included systematic literature review, secondary sources, government statistics, focused group discussions, household surveys and semi-structured interviews. Data wasanalyzed using both quantitative and qualitative data analysis techniques. Quantitative data was analyzed using the Statistical Package for Social Sciences (SPSS) software. Qualitative data was analyzed using qualitative techniques, which involved establishing the categories and themes, relationships/patterns and conclusions in line with the study objectives. Sustainable livelihood, reduced household poverty and climate change resilience were the impact that resulted from the study.

  8. Measuring the benefits of climate forecasts in predicting PV power production

    NASA Astrophysics Data System (ADS)

    De Felice, Matteo; Alessandri, Andrea; Pollino, Maurizio

    2016-04-01

    Surface solar radiation is an important variable to model and predict solar power output. Having accurate forecast may be of potential use for planning and operational tasks, both at short- and long-time scales. This study examines the predictability of seasonal surface solar radiation comparing ECMWF System4 Seasonal operational forecasts the SARAH Satellite Dataset on the period 1984-2007. This work tries to answer the following question: how useful are climate forecasts in predicting seasonal PV production? The "information layer" provided by climate information is overlapped with 1) the information about the land cover (CLC2006) to consider the potential amount of land available for PV panels and 2) the information about the solar power installed capacity for European region in order to define the areas where an improved forecast could have a bigger impact. All the information layers are summarised by using a simple scalar index (Index of Opportunity) computed for all the European regions for the four seasons. The results are very interesting, in fact the potential benefits of climate forecasts are not (only) related to their statistica