Science.gov

Sample records for long-lead seasonal forecast

  1. Effective use of general circulation model outputs for forecasting monthly rainfalls to long lead times

    NASA Astrophysics Data System (ADS)

    Hawthorne, Sandra; Wang, Q. J.; Schepen, Andrew; Robertson, David

    2013-09-01

    Long lead rainfall forecasts are highly valuable for planning and management of water resources and agriculture. In this study, we establish multiple statistical calibration and bridging models that use general circulation model (GCM) outputs as predictors to produce monthly rainfall forecasts for Australia with lead times up to 8 months. The statistical calibration models make use of raw forecasts of rainfall from a coupled GCM, and the statistical bridging models make use of sea surface temperature (SST) forecasts of the GCM. The forecasts from the multiple models are merged through Bayesian model averaging to take advantage of the strengths of individual models. The skill of monthly rainfall forecasts is generally low. Compared to forecasting seasonal rainfall totals, it is more challenging to forecast monthly rainfall. However, there are regions and months for which forecasts are skillful. In particular, there are months of the year for which forecasts can be skillfully made at long lead times. This is most evident for the period of November and December. Using GCM forecasts of SST through bridging clearly improves monthly rainfall forecasts. For lead time 0, the improvement is particularly evident for February to March, July and October to December. For longer lead times, the benefit of bridging is more apparent. As lead time increases, bridging is able to maintain forecast skill much better than when only calibration is applied.

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

    NASA Astrophysics Data System (ADS)

    Madadgar, S.; Moradkhani, H.

    2013-12-01

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

  3. Using CPC long lead climate outlooks for ensemble streamflow forecasting

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Lettenmaier, D. P.

    2006-05-01

    We describe a method for implementing the NWS NCEP Climate Prediction Center's probabilistic climate outlooks within an experimental ensemble hydrologic forecast system for the western US. The CPC 3-month average probability of exceedence (POE) statistical climate outlooks are transformed into a 30-member ensemble of precipitation and temperature sequences using a resampling technique called the Schaake Shuffle. Subsequently, these traces are statistical disaggregated to a monthly timestep, spatially disaggregated to 1/8 degree latitude by longitude resolution (from the climate division scale), and finally temporally disaggregated (via another resampling approach) to a daily timestep. The final ensemble of daily sequences is used to drive the Variable Infiltration Capacity hydrologic model in simulations of 1-year lead hydrologic forecasts. This talk focuses on the performance of the spatial and temporal disaggregation approaches.

  4. Long lead statistical forecasts of area burned in western U.S. wildfires by ecosystem province

    USGS Publications Warehouse

    Westerling, A.L.; Gershunov, A.; Cayan, D.R.; Barnett, T.P.

    2002-01-01

    A statistical forecast methodology exploits large-scale patterns in monthly U.S. Climatological Division Palmer Drought Severity Index (PDSI) values over a wide region and several seasons to predict area burned in western U.S. wildfires by ecosystem province a season in advance. The forecast model, which is based on canonical correlations, indicates that a few characteristic patterns determine predicted wildfire season area burned. Strong negative associations between anomalous soil moisture (inferred from PDSI) immediately prior to the fire season and area burned dominate in most higher elevation forested provinces, while strong positive associations between anomalous soil moisture a year prior to the fire season and area burned dominate in desert and shrub and grassland provinces. In much of the western U.S., above- and below-normal fire season forecasts were successful 57% of the time or better, as compared with a 33% skill for a random guess, and with a low probability of being surprised by a fire season at the opposite extreme of that forecast.

  5. Water quality in the Schuylkill River, Pennsylvania: the potential for long-lead forecasts

    NASA Astrophysics Data System (ADS)

    Block, P. J.; Peralez, J.

    2012-12-01

    Prior analysis of pathogen levels in the Schuylkill River has led to a categorical daily forecast of water quality (denoted as red, yellow, or green flag days.) The forecast, available to the public online through the Philadelphia Water Department, is predominantly based on the local precipitation forecast. In this study, we explore the feasibility of extending the forecast to the seasonal scale by associating large-scale climate drivers with local precipitation and water quality parameter levels. This advance information is relevant for recreational activities, ecosystem health, and water treatment (energy, chemicals), as the Schuylkill provides 40% of Philadelphia's water supply. Preliminary results indicate skillful prediction of average summertime water quality parameters and characteristics, including chloride, coliform, turbidity, alkalinity, and others, using season-ahead oceanic and atmospheric variables, predominantly from the North Atlantic. Water quality parameter trends, including historic land use changes along the river, association with climatic variables, and prediction models will be presented.

  6. 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.

  7. Ensemble forecasts of monthly catchment rainfall out to long lead times by post-processing coupled general circulation model output

    NASA Astrophysics Data System (ADS)

    Schepen, Andrew; Wang, Q. J.

    2014-11-01

    Monthly streamflow forecasts with long lead time are being sought by water managers in Australia. In this study, we take a first step towards a monthly streamflow modelling approach by harnessing a coupled ocean-atmosphere general circulation model (CGCM) to produce monthly rainfall forecasts for three catchments across Australia. Bayesian methodologies are employed to produce forecasts based on CGCM raw rainfall forecasts and also CGCM sea surface temperature forecasts. The Schaake Shuffle is used to connect forecast ensemble members of individual months to form ensemble monthly time series forecasts. Monthly forecasts and three-monthly forecasts of rainfall are assessed for lead times of 0-6 months, based on leave-one-year-out cross-validation for 1980-2010. The approach is shown to produce well-calibrated ensemble forecasts that source skill from both the atmospheric and ocean modules of the CGCM. Although skill is generally low, moderate skill scores are observed in some catchments for lead times of up to 6 months. In months and catchments where there is limited skill, the forecasts revert to climatology. Thus the forecasts developed can be considered suitable for continuously forecasting time series of streamflow to long lead times, when coupled with a suitable monthly hydrological model.

  8. 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)

  9. Seasonal Climate Forecasts and Adoption by Agriculture

    NASA Astrophysics Data System (ADS)

    Garbrecht, Jurgen; Meinke, Holger; Sivakumar, Mannava V. K.; Motha, Raymond P.; Salinger, Michael J.

    2005-06-01

    Recent advances in atmospheric and ocean sciences and a better understanding of the global climate have led to skillful climate forecasts at seasonal to interannual timescales, even in midlatitudes. These scientific advances and forecasting capabilities have opened the door to practical applications that benefit society. The benefits include the reduction of weather/climate related risks and vulnerability, increased economic opportunities, enhanced food security, mitigation of adverse climate impacts, protection of environmental quality, and so forth. Agriculture in particular can benefit substantially from accurate long-lead seasonal climate forecasts. Indeed, agricultural production very much depends on weather, climate, and water availability, and unexpected departures from anticipated climate conditions can thwart the best laid management plans. Timely climate forecasts offer means to reduce losses in drought years, increase profitability in good years, deal more effectively with climate variability, and choose from targeted risk-management strategies. In addition to benefiting farmers, forecasts can also help marketing systems and downstream users prepare for anticipated production outcomes and associated consequences.

  10. 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 scales, it is very difficult to improve on the use of climatological forecasts. However, results presented regionally and globally pinpoint several regions in the world where drought onset forecasting is feasible and skilful.

  11. Long lead-time flood forecasting using data-driven modeling approaches

    NASA Astrophysics Data System (ADS)

    Bhatia, N.; He, J.; Srivastav, R. K.

    2014-12-01

    In spite of numerous structure measures being taken for floods, accurate flood forecasting is essential to condense the damages in hazardous areas considerably. The need of producing more accurate flow forecasts motivates the researchers to develop advanced innovative methods. In this study, it is proposed to develop a hybrid neural network model to exploit the strengths of artificial neural networks (ANNs). The proposed model has two components: i.) Dual - ANN model developed using river flows; and ii.) Multiple Linear Regression (MLR) model trained on meteorological data (Rainfall and Snow on ground). Potential model inputs that best represent the process of river basin were selected in stepwise manner by identifying input-output relationship using a linear approach, Partial Correlation Input Selection (PCIS) combined with Akaike Information Criterion (AIC) technique. The presented hybrid model was compared with three conventional methods: i) Feed-forward artificial neural network (FF-ANN) using daily river flows; ii) FF-ANN applied on decomposed river flows (low flow, rising limb and falling limb of hydrograph); and iii) Recursive method for daily river flows with lead-time of 7 days. The applicability of the presented model is illustrated through daily river flow data of Bow River, Canada. Data from 1912 to 1976 were used to train the models while data from 1977 to 2006 were used to validate the models. The results of the study indicate that the proposed model is robust enough to capture the non-linear nature of hydrograph and proves to be highly promising to forecast peak flows (extreme values) well in advance (higher lead time).

  12. 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

    El Niño (EN) is a dominant feature of climate variability on inter-annual time scales driving changes in the climate throughout the globe, and having wide-spread natural and socio-economic consequences. In this sense, its forecast is an important task, and predictions are issued on a regular basis by a wide array of prediction schemes and climate centres around the world. This study explores a novel method for EN forecasting. In the state-of-the-art the advantageous statistical technique of unobserved components time series modeling, also known as structural time series modeling, has not been applied. Therefore, we have developed such a model where the statistical analysis, including parameter estimation and forecasting, is based on state space methods, and includes the celebrated Kalman filter. The distinguishing feature of this dynamic model is the decomposition of a time series into a range of stochastically time-varying components such as level (or trend), seasonal, cycles of different frequencies, irregular, and regression effects incorporated as explanatory covariates. These components are modeled separately and ultimately combined in a single forecasting scheme. Customary statistical models for EN prediction essentially use SST and wind stress in the equatorial Pacific. In addition to these, we introduce a new domain of regression variables accounting for the state of the subsurface ocean temperature in the western and central equatorial Pacific, motivated by our analysis, as well as by recent and classical research, showing that subsurface processes and heat accumulation there are fundamental for the genesis of EN. 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 1996-2015. Retrospective forecasts of these 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.

  13. On the reliability of seasonal climate forecasts

    PubMed Central

    Weisheimer, A.; Palmer, T. N.

    2014-01-01

    Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time. PMID:24789559

  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 anomalies and extreme events. The prospects for seasonal/monthly forecasting of hydrological extremes are therefore positive. Next, the true skill of the global hydrological forecasting system FEWS-World is assessed in retroactive forecasting mode, using seasonal meteorological forecasts subject to uncertainty from numerical weather prediction (NWP) models. The system is forced with ensemble seasonal meteorological forecasts from the seasonal forecast archives of ECMWF. We assess the skill of FEWS-World in forecasting monthly anomalies and extreme events on a range of different lead-times by applying verification measures for ensemble forecasts. Although forecasting skill decreases with increasing lead time, the value of forecasts does not necessarily do so. The real value of a forecast is to be determined on the basis of the benefits and costs of possible actions that can be taken in response to a forecast, provided that information on forecast reliability is properly communicated. A preliminary investigation of the forecast requirements and response options of several sectors over lead times from short-range through medium-range to monthly and seasonal show that most sectors benefit from seasonal forecasts to prepare for appropriate response.

  15. Seasonal hydrological ensemble forecasts over Europe

    NASA Astrophysics Data System (ADS)

    Arnal, Louise; Wetterhall, Fredrik; Pappenberger, Florian

    2015-04-01

    Seasonal forecasts have an important socio-economic value in hydro-meteorological forecasting. The applications are for example hydropower management, spring flood prediction and water resources management. The latter includes prediction of low flows, primordial for navigation, water quality assessment, droughts and agricultural water needs. Traditionally, seasonal hydrological forecasts are done using the observed discharge from previous years, so called Ensemble Streamflow Prediction (ESP). With the recent increasing development of seasonal meteorological forecasts, the incentive for developing and improving seasonal hydrological forecasts is great. In this study, a seasonal hydrological forecast, driven by the ECMWF's System 4 (SEA), was compared with an ESP of modelled discharge using observations. The hydrological model used for both forecasts was the LISFLOOD model, run over a European domain with a spatial resolution of 5 km. The forecasts were produced from 1990 until the present time, with a daily time step. They were issued once a month with a lead time of seven months. The SEA forecasts are constituted of 15 ensemble members, extended to 51 members every three months. The ESP forecasts comprise 20 ensembles and served as a benchmark for this comparative study. The forecast systems were compared using a diverse set of verification metrics, such as continuous ranked probability scores, ROC curves, anomaly correlation coefficients and Nash-Sutcliffe efficiency coefficients. These metrics were computed over several time-scales, ranging from a weekly to a six-months basis, for each season. The evaluation enabled the investigation of several aspects of seasonal forecasting, such as limits of predictability, timing of high and low flows, as well as exceedance of percentiles. The analysis aimed at exploring the spatial distribution and timely evolution of the limits of predictability.

  16. Statistical calibration of seasonal ensemble streamflow forecasts

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Wiley, M.; Nijssen, B.

    2008-12-01

    Model-based streamflow forecast ensembles routinely exhibit errors due to input forecast uncertainty, initial condition uncertainty and hydrologic modeling error, and such errors can undermine the suitability of raw model forecast output for use in follow-on applications such as reservoir management. This presentation evaluates the use of quantile regression for modeling and correcting streamflow prediction errors at seasonal lead times. Quantile regression has been used rarely in the hydrologic forecasting area, yet its insensitivity to outliers and avoidance of parametric assumptions about forecast errors makes it suitable for representing quantiles of datasets (such as streamflow) that often have skewed or otherwise irregular distributions. For illustration, a local linear quantile regression framework is applied to seasonal streamflow hindcasts in the Pacific Northwest and elsewhere in the western US, and found to reduce forecast bias and improve reliability, albeit with a slight loss of forecast resolution.

  17. CFSv2-based seasonal hydroclimatic forecasts over conterminous United States

    NASA Astrophysics Data System (ADS)

    Yuan, X.; Wood, E. F.; Roundy, J. K.; Pan, M.

    2012-12-01

    There is a long history of debate on the usefulness of climate model-based seasonal hydroclimatic (e.g., precipitation, streamflow and drought) forecasts as compared to traditional approaches such as Ensemble Streamflow Prediction (ESP). The rationales of ESP method are that most of the hydrologic predictability comes from initial conditions especially in some snowmelt dominant regimes over the western U.S., and the skill of climate forecast models diminishes in the first month or even the first two weeks due to chaotic nature of the climate system. Nevertheless, the gradual improvement of climate models in last decade, with skill of dynamical models now exceeding that of statistical models in El Niño-Southern Oscillation (ENSO) prediction, motivates our revisitation of their values in seasonal hydroclimatic forecasting. In this presentation, we compare climate model-based forecasts with ESP through 27-year comprehensive hydrologic hindcasts over CONUS at 1/8 degree resolution. The climate models used here are the NCEP's Climate Forecast System version 2 (CFSv2), and its previous version CFSv1. The climatic forcings downscaled from climate models by using Bayesian method, or randomly selected from history through ESP procedure, are used to drive Variable Infiltration Capacity (VIC) land surface model to provide six month hydrologic forecasts with 20 ensemble members for each calendar month during 1982-2008. Analysis for large river basin averaged precipitation shows that CFSv2 has significant higher predictability than ESP over entire CONUS for month-1 precipitation, and moderate enhanced predictability over some basins in specific seasons for long lead forecasts. The streamflow forecasts are validated at 2273 USGS gauges which almost cover the whole eastern U.S. and western coast area. We find that more than 50% of the forecasts from climate model-based approaches are more skillful than ESP in terms of the Ranked Probability Skill Score (RPSS), but again significant improvements (more than 60-70%) occur in the first month. The predictability and forecast skill conditioned on ENSO signals, wet/dry events and size of basins are also being investigated. The soil moisture drought analysis indicates that climate model-based forecasts tend to produce more reasonable frequency of short-term drought (1-6 months) occurrence than ESP over central and eastern U.S. The severity and area of drought from ensemble mean forecast and the ensemble characteristics will be analyzed. In addition, we would like to share some of our recent activities on the clustering of multiple climate models and post-processsing streamflow forecasts in the context of climate model-based seasonal hydroclimatic prediction.

  18. 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 lead-time forecasts. Incorporating Pacific oceanic-atmospheric climatic variability in forecast models can lead to improved forecasts of snowpack and precipitation.

  19. Impact of Seasonal Forecasts on Agriculture

    NASA Astrophysics Data System (ADS)

    Aldor-Noiman, S. C.

    2014-12-01

    More extreme and volatile weather conditions are a threat to U.S. agricultural productivity today, as multiple environmental conditions during the growing season impact crop yields. That's why farmers' agronomic management decisions are dominated by consideration for near, medium and seasonal forecasts of climate. The Climate Corporation aims to help farmers around the world protect and improve their farming operations by providing agronomic decision support tools that leverage forecasts on multiple timescales to provide valuable insights directly to farmers. In this talk, we will discuss the impact of accurate seasonal forecasts on major decisions growers face each season. We will also discuss assessment and evaluation of seasonal forecasts in the context of agricultural applications.

  20. On the reliability of ECMWF's seasonal forecasts

    NASA Astrophysics Data System (ADS)

    Weisheimer, Antje; Palmer, Tim

    2014-05-01

    Seasonal climate forecasts are being used increasingly across a range of application sectors including climate services. A recent UK governmental report asked: How good are seasonal climate forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal climate forecasts are made from ensembles of integrations of numerical models of climate. We argue that "goodness" should be assessed primarily in terms of the probabilistic reliability of these ensemble-based forecasts and that a "5" should be reserved for systems which are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts. A wide range of "goodness" rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching "5" across all regions and variables in 30 years time.

  1. Empirical seasonal forecasts of the NAO

    NASA Astrophysics Data System (ADS)

    Sanchezgomez, E.; Ortizbevia, M.

    2003-04-01

    We present here seasonal forecasts of the North Atlantic Oscillation (NAO) issued from ocean predictors with an empirical procedure. The Singular Values Decomposition (SVD) of the cross-correlation matrix between predictor and predictand fields at the lag used for the forecast lead is at the core of the empirical model. The main predictor field are sea surface temperature anomalies, although sea ice cover anomalies are also used. Forecasts are issued in probabilistic form. The model is an improvement over a previous version (1), where Sea Level Pressure Anomalies were first forecast, and the NAO Index built from this forecast field. Both correlation skill between forecast and observed field, and number of forecasts that hit the correct NAO sign, are used to assess the forecast performance , usually above those values found in the case of forecasts issued assuming persistence. For certain seasons and/or leads, values of the skill are above the .7 usefulness treshold. References (1) SanchezGomez, E. and Ortiz Bevia M., 2002, Estimacion de la evolucion pluviometrica de la Espana Seca atendiendo a diversos pronosticos empiricos de la NAO, in 'El Agua y el Clima', Publicaciones de la AEC, Serie A, N 3, pp 63-73, Palma de Mallorca, Spain

  2. Towards custom made seasonal/decadal forecasting

    NASA Astrophysics Data System (ADS)

    Mahlstein, Irina; Spirig, Christoph; Liniger, Mark

    2014-05-01

    Climate indices offer the possibility to deliver information to the end user that can be easily applied to their field of work. For instance, a 3-monthly mean average temperature does not say much about the Heating Degree Days of a season, or how many frost days there are to be expected. Hence, delivering aggregated climate information can be more useful to the consumer than just raw data. In order to ensure that the end-users actually get what they need, the providers need to know what exactly they need to deliver. Hence, the specific user-needs have to be identified. In the framework of EUPORIAS, interviews with the end-user were conducted in order to learn more about the types of information that are needed. But also to investigate what knowledge exists among the users about seasonal/decadal forecasting and in what way uncertainties are taken into account. It is important that we gain better knowledge of how forecasts/predictions are applied by the end-user to their specific situation and business. EUPORIAS, which is embedded in the framework of EU FP7, aims exactly to improve that knowledge and deliver very specific forecasts that are custom made. Here we present examples of seasonal forecasts and their skill of several climate impact indices with direct relevance for specific economic sectors, such as energy. The results are compared to the visualization of conventional depiction of seasonal forecasts, such as 3 monthly average temperature tercile probabilities and the differences are highlighted.

  3. 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

  4. 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. PMID:16433097

  5. TEMPORAL DISAGGREGATION OF PROBABILISTIC SEASONAL CLIMATE FORECASTS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Seasonal climate forecasts are issued by NOAA/CPC for average temperature and total precipitation over 3-month overlapping periods covering the coming year. Many crop and hydrologic models employ weather generators based on monthly statistics to produce stochastic realizations of daily weather (e.g...

  6. Potential for malaria seasonal forecasting in Africa

    NASA Astrophysics Data System (ADS)

    Tompkins, Adrian; Di Giuseppe, Francesca; Colon-Gonzalez, Felipe; Namanya, Didas; Friday, Agabe

    2014-05-01

    As monthly and seasonal dynamical prediction systems have improved their skill in the tropics over recent years, there is now the potential to use these forecasts to drive dynamical malaria modelling systems to provide early warnings in epidemic and meso-endemic regions. We outline a new pilot operational system that has been developed at ECMWF and ICTP. It uses a precipitation bias correction methodology to seamlessly join the monthly ensemble prediction system (EPS) and seasonal (system 4) forecast systems of ECMWF together. The resulting temperature and rainfall forecasts for Africa are then used to drive the recently developed ICTP malaria model known as VECTRI. The resulting coupled system of ECMWF climate forecasts and VECTRI thus produces predictions of malaria prevalence rates and transmission intensity across Africa. The forecasts are filtered to highlight the regions and months in which the system has particular value due to high year to year variability. In addition to epidemic areas, these also include meso and hyper-endemic regions which undergo considerable variability in the onset months. We demonstrate the limits of the forecast skill as a function of lead-time, showing that for many areas the dynamical system can add one to two months additional warning time to a system based on environmental monitoring. We then evaluate the past forecasts against district level case data in Uganda and show that when interventions can be discounted, the system can show significant skill at predicting interannual variability in transmission intensity up to 3 or 4 months ahead at the district scale. The prospects for a operational implementation will be briefly discussed.

  7. 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.

    2014-08-01

    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 up-to-date and long series observations on burnt area and 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 area with several months lead time explaining at least 70% of the variance between rainfall and with burnt area. Results are strongly influenced by El Nio 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.

  8. 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.

  9. 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 help evaluate the role of using climate information in improving Q and G predictions. In conclusion, results show that incorporating precipitation forecasts can significantly improve the skill of the developed prediction models at seasonal and monthly time scales and can extend the prediction up to six months ahead.

  10. 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 find a negative relationship between these controls that weakens the forecast skills, nevertheless there is a middle ground between both controls in several catchments, as shown by our results.

  11. DEPENDABILITY AND EFFECTIVENESS OF SEASONAL FORECASTS FOR AGRICULTURAL APPLICATIONS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    NOAA's Climate Prediction Center issues seasonal climate forecasts predicting total precipitation and average air temperature for three-month periods out to a year in advance. The utility of these seasonal forecasts for agricultural applications will depend on forecast characterisitcs, including de...

  12. Seasonal forecasting of high wind speeds over Western Europe

    NASA Astrophysics Data System (ADS)

    Palutikof, J. P.; Holt, T.

    2003-04-01

    As financial losses associated with extreme weather events escalate, there is interest from end users in the forestry and insurance industries, for example, in the development of seasonal forecasting models with a long lead time. This study uses exceedences of the 90th, 95th, and 99th percentiles of daily maximum wind speed over the period 1958 to present to derive predictands of winter wind extremes. The source data is the 6-hourly NCEP Reanalysis gridded surface wind field. Predictor variables include principal components of Atlantic sea surface temperature and several indices of climate variability, including the NAO and SOI. Lead times of up to a year are considered, in monthly increments. Three regression techniques are evaluated; multiple linear regression (MLR), principal component regression (PCR), and partial least squares regression (PLS). PCR and PLS proved considerably superior to MLR with much lower standard errors. PLS was chosen to formulate the predictive model since it offers more flexibility in experimental design and gave slightly better results than PCR. The results indicate that winter windiness can be predicted with considerable skill one year ahead for much of coastal Europe, but that this deteriorates rapidly in the hinterland. The experiment succeeded in highlighting PLS as a very useful method for developing more precise forecasting models, and in identifying areas of high predictability.

  13. 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. PMID:24552508

  14. Improved seasonal forecast using ozone hole variability?

    NASA Astrophysics Data System (ADS)

    Son, Seok-Woo; Purich, Ariaan; Hendon, Harry H.; Kim, Baek-Min; Polvani, Lorenzo M.

    2013-12-01

    Southern Hemisphere (SH) climate change has been partly attributed to Antarctic ozone depletion in the literatures. Here we show that the ozone hole has affected not only the long-term climate change but also the interannual variability of SH surface climate. A significant negative correlation is observed between September ozone concentration and the October southern annular mode index, resulting in systematic variations in precipitation and surface air temperature throughout the SH. This time-lagged relationship is comparable to and independent of that associated with El Nio-Southern Oscillation and the Indian Ocean Dipole Mode, suggesting that SH seasonal forecasts could be improved by considering Antarctic stratospheric variability.

  15. Does improved SSTA prediction ensure better seasonal rainfall forecasts?

    NASA Astrophysics Data System (ADS)

    Khan, Mohammad Zaved Kaiser; Sharma, Ashish; Mehrotra, Rajeshwar; Schepen, Andrew; Wang, Q. J.

    2015-05-01

    Seasonal rainfall forecasts in Australia are issued based on concurrent sea surface temperature anomalies (SSTAs) using a Bayesian model averaging (BMA) approach. The SSTA fields are derived from the Predictive Ocean-Atmosphere Model for Australia (POAMA) initialized in the preceding season. This study investigates the merits of the rainfall forecasted using POAMA SSTAs in contrast to that forecasted using a multimodel combination of SSTAs derived using five existing models. In addition, seasonal rainfall forecasts derived from multimodel and POAMA SSTA fields are subsequently combined to obtain a single weighted forecast over Australia. These three forecasts are compared against "idealized" forecasts where observed SSTAs are used instead of those predicted. The results indicate that while seasonal rainfall forecasts derived using multimodel-based SSTA indices offer improvements in selected seasons over a majority of grid cells in comparison to the case where a single SSTA model is used in two seasons, these improvements are not as significant as the improvements in the SSTA field that drive the rainfall forecasting model. The forecasts derived from the combination of multimodel and POAMA SSTA indices forecasts are found to offer greater improvements over the multimodel or the POAMA forecasts for a majority of grid cells in all seasons. It is also observed that these combined forecasts are touching the upper limits of forecastability, which are reached when observed SSTAs are used to forecast the rainfall. This suggests that further improvements in rainfall forecasting are only possible through the use of an improved forecasting algorithm, and not the driver (SSTA) information used in the current study.

  16. 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.

  17. Climate informed long term seasonal forecasts of hydroenergy inflow for the Brazilian hydropower system

    NASA Astrophysics Data System (ADS)

    Lima, Carlos H. R.; Lall, Upmanu

    2010-02-01

    SummaryEfficient management of water and energy is an important goal of sustainable development for any nation. Streamflow forecasts, have been used in complex optimization models to maximize water use efficiency and electrical energy production. In this paper we develop a statistical model for the long term forecasts of hydroenergy inflow into the Brazilian hydropower system, which consists of more than 70 hydropower reservoirs. At present, the planning of reservoir operation and energy production in Brazil is made with no reliable long term (one season or longer lead times) streamflow forecasts. Here we use the NINO3 index and the main modes of the tropical Pacific thermocline structure as climate predictors in order to achieve skillfull forecasts at long leads. Cross-validated results show that about 50% of the total hydroenergy inflow can be predicted with moderate accuracy up to 20 month lead time.

  18. Evaluating and Intercomparison of Statistical and Probabilistic Seasonal Streamflow Forecasting Procedures - A Western US Case Study

    NASA Astrophysics Data System (ADS)

    Meier, M. J.; Moradkhani, H.

    2008-12-01

    Over the past decade the western region of the US has experienced explosive population growth, which has created a demand for not only more municipal water supply, but also an increase in water for hydropower, recreation, and aquatic habitat and restoration. In order to provide an uninterrupted, dependable water supply to meet all of these downstream needs, many water resource managers will rely on seasonal streamflow forecasts issued by the National Weather Service (NWS) and the National Resources Conservation Service (NRCS) for planning and decision making purposes. These forecasts are possible in this region because much of the streamflow in this area is a result of the collection of seasonal snowpack over the winter months and the melting of this snowpack over the spring and summer. This pattern of snow accumulation and melt makes it possible for a forecaster to develop water supply forecasts with lead-times of several months. The primary goal of this research is to compare and analyze the results from four different procedures used for producing long-lead streamflow forecasts. The models and techniques covered in this work include Principal Component Analysis (PCA), z-score regression, Independent Component Analysis (ICA), and the Ensemble Streamflow Prediction (ESP) System. These procedures were tested on a few watersheds in the Pacific Northwest and the Colorado River Basin. This work investigates the merits and disadvantages of each procedure, and useful recommendations are made for providing more accurate, reliable and skillful seasonal forecasts.

  19. 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 of antecedent moisture conditions (soil moisture or baseflows) grow in stature, relative to snowpacks, in partial compensation. Forecast skills, e.g., of March forecasts for April-July water supplies from those parts of the region that yield the majority of the runoff, decline by an average of about 15% of captured variance in response to the imposed warming.

  20. 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.

  1. Setup of the GLOWASIS seasonal global water scarcity forecasting system

    NASA Astrophysics Data System (ADS)

    Winsemius, H.; Weerts, A.; Candogan, N.; Dutra, E.; van Beek, R.; Wisser, D.; Westerhoff, R.

    2011-12-01

    The EU-FP7 project "Global Water Scarcity Information Service" (GLOWASIS) is aimed at pre-validating a GMES Global Service for Water Scarcity Information. This includes improving and piloting our ability to forecast water scarcity at global scale. Here, we present first results of the GLOWASIS seasonal global water scarcity forecasting system. This forecasting system provides seasonal probabilistic forecasts of water scarcity indicators over the whole globe. The system is built within the data and model integration shell Delft-FEWS. The GLOWASIS system integrates reanalysis data from the European Centre for Medium-ranged Weather Forecasts (ECMWF), ECMWF seasonal probabilistic forecasts, information on water demand and use, the global hydrological model PCRGLOB-WB and user interfacing. The system can provide a forecast each month with a lead time of 6 months with daily time steps. Given the large amounts of data and computation time required to run a full forecast ensemble, the system is set up to run ensembles over multiple cores. A large number of hindcasts are made with the system. These hindcasts are used to demonstrate which water scarcity indicators are useful to forecast at seasonal time scales, where these indicators may provide satisfactory skill and with which lead time they can be meaningfully forecasted. Further investigation will focus on improvement of skill by means of data assimilation of remotely sensed data sources such as soil moisture, snow water equivalent and water levels, and by better parameterisation of the hydrological model

  2. Skill of a global seasonal ensemble streamflow forecasting system

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

    Forecasting of water availability and scarcity is a prerequisite for managing the risks and opportunities caused by the inter-annual variability of streamflow. Reliable seasonal streamflow forecasts are necessary to prepare for an appropriate response in disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation. Seasonal hydrological forecasting on a global scale could be valuable especially for developing regions of the world, where effective hydrological forecasting systems are scarce. In this study, we investigate the forecasting skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCR-GLOBWB. FEWS-World has been setup within the European Commission 7th Framework Programme project Global Water Scarcity Information Service (GLOWASIS). Skill is assessed in historical simulation mode as well as retroactive forecasting mode. The assessment in historical simulation mode used 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). We assessed the skill of the global hydrological model PCR-GLOBWB in reproducing past discharge extremes in 20 large rivers of the world. This preliminary assessment concluded that the prospects for seasonal forecasting with PCR-GLOBWB or comparable models are positive. However this assessment did not include actual meteorological forecasts. Thus the meteorological forcing errors were not assessed. Yet, in a forecasting setup, the predictive skill of a hydrological forecasting system is affected by errors due to uncertainty from numerical weather prediction models. For the assessment in retroactive forecasting mode, the model is forced with actual ensemble forecasts from the seasonal forecast archives of ECMWF. Skill is assessed at 78 stations on large river basins across the globe, for all the months of the year and for lead times up to 6 months. The forecasted discharges are compared with observed monthly streamflow records using the ensemble verification measures Brier Skill Score (BSS) and Continuous Ranked Probability Score (CRPS). 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.

  3. Downscaled seasonal forecasts using an ensemble of regional models

    NASA Astrophysics Data System (ADS)

    Arritt, R.

    2012-04-01

    The Multi-Regional climate model Ensemble Downscaling (MRED) project is a multi-institutional effort to evaluate the usefulness of dynamically downscaled global seasonal forecasts. Seven regional climate models have downscaled 10-member ensembles from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) for each winter season (December-April) of 1982-2003. The target region for downscaling is the continental United States. MRED investigators also have developed methods and metrics for analysis of downscaled forecasts. These include an Added Value Index that quantifies skill improvement of a downscaled forecast compared to the corresponding global forecast. Results show that added value from downscaling depends on location, forecast variable, and lead time. Locations with added value are generally in the western United States, and added value tends to be greater for precipitation than for temperature. Downscaled forecasts have greatest skill for seasonal precipitation anomalies in strong El Niño events such as 1982-83 and 1997-98. In most circumstances area averaged seasonal precipitation for the regional models closely tracks the corresponding results for the global model, though with an offset that varies considerably amongst the regional models. There is large spread amongst the 15 CFS ensemble members and this carries through to the corresponding downscaled forecasts. Because of the strong dependence of downscaled results on the global model, future experiments should test the use of multiple global models downscaled by multiple regional models.

  4. 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.

  5. Seasonal drought predictability and forecast skill over China

    NASA Astrophysics Data System (ADS)

    Ma, Feng; Yuan, Xing; Ye, Aizhong

    2015-08-01

    Under a changing environment, seasonal droughts have been exacerbated with devastating impacts. However, the understanding of drought mechanism and predictability is limited. Based on the hindcasts from multiple climate models, the predictability and forecast skill for drought over China are investigated. The 3 month standardized precipitation index is used as the drought index, and the predictability is quantified by using a perfect model assumption. Ensemble hindcasts from multiple climate models are assessed individually, and the grand multimodel ensemble is also evaluated. Drought forecast skill for model ensemble mean is higher than individual ensemble members, and North American Multimodel Ensemble grand ensemble performs the best. Predictability is higher than forecast skill, indicating the room for improving drought forecast. Drought predictability and forecast skill are positively correlated in general, but they vary depending on seasons, regions, and forecast leads. Higher drought predictability and forecast skill are found over regimes where ENSO has significant impact. For the ENSO-affected regimes, both drought predictability and forecast skill in ENSO years are higher than that in neutral years. This study suggests that predictability not only provides a measure for selecting climate models for ensemble drought forecast in ENSO-affected regimes but also serves as an indicator for forecast skill especially when in situ and/or remote sensing measurements for the hindcast verifications are considered unreliable.

  6. The Canadian coupled multi-seasonal forecasting system

    NASA Astrophysics Data System (ADS)

    Sebatian Fontecilla, Juan

    2013-04-01

    The Canadian coupled multi-seasonal forecasting system Since a year now, the Meteorological Service of Canada has its first coupled operational multi-seasonal forecasting system. The Canadian Meteorological Centre (CMC) in collaboration with the Canadian Centre for Climate Modeling and Analysis (CCCma) has implemented a one-tier climate prediction system which has replaced the old two-tier 4 model forecasting system used for forecasts of months 1 to 4, and the CCA statistical forecasting system used for forecasts of months 4 to 12. The coupled atmosphere-ocean-sea ice system combines ensemble forecasts from the CanCM3 and CanCM4 versions of CCCma's coupled global climate model and provide dynamical atmospheric, oceanic and sea ice predictions for lead times out to 12 months. This system, developed under the second Coupled Historical Forecasting Project (CHFP2) will be described briefly. Forecast skill improvements will be shown. The implementation of this new system permits the issuance of ENSO and arctic sea ice forecasts, which were not possible before. The predictive skill of NINO3.4 index from this new coupled system will compared against the skill from other centers.

  7. Experimental Real-time Seasonal Hydrologic Forecasting

    NASA Astrophysics Data System (ADS)

    Wood, A.; Lettenmaier, D. P.

    We describe an experimental end-to-end streamflow forecasting approach that uses National Centers for Environmental Prediction (NCEP) Global Spectral Model (GSM) ensemble climate forecasts to drive the Variable Infiltration Capacity (VIC) macroscale hydrologic model over large continental river basins. The method was initially tested over the East Coast U.S. during the summer 2000 drought, and sub- sequently over a western U.S. domain that includes the Columbia, Colorado, and Sacramento-San Joaquin Rivers, beginning in March 2001. The linkage of the cli- mate and hydrologic models provides a mechanism for exploiting potential forecast skill that results from either (or the combination of) climate forecasts, or knowledge of hydrologic initial conditions (soil moisture, snow), for streamflow forecasts with lead times of up to six months. Each month, GSM precipitation and temperature ensemble forecasts are adjusted to remove climate model bias, and the bias-adjusted ensem- bles are then used to drive the hydrologic model, which in turn produces ensemble streamflow forecasts with six-month lead times. Initial hydrologic conditions for the forecasts are estimated by driving the VIC model with observed meteorological data (gridded product derived from NCDC cooperator stations) and updated for the most recent three months using archived real-time Land Data Assimilation System (LDAS) gridded forcings. In the East Coast application, the climate model forecasts mostly tended toward climate normals during the test periods, and anomalous initial hydro- logic states dominated most of the hydrologic forecasts. Critically low soil moisture in the spring of 2000 persisted into summer, despite a return to more normal atmospheric conditions during the summer. In the Pacific Northwest, the summer 2001 forecast simulations predicted, among other effects, the severe deficit in snowpack, runoff and soil moisture; hence summer streamflow, starting in March. In this case, prediction skill derived primarily from hydrologic persistence associated with the initial states of snowpack during the spring. A retrospective analysis of a year with significant cli- mate forecast anomalies over the western U.S. (the 1997-98 ENSO event), however, showed that streamflow prediction skill can result from the climate forecast signal as well.

  8. Improving statistical forecasts of seasonal streamflows using hydrological model output

    NASA Astrophysics Data System (ADS)

    Robertson, D. E.; Pokhrel, P.; Wang, Q. J.

    2013-02-01

    Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

  9. Improving statistical forecasts of seasonal streamflows using hydrological model output

    NASA Astrophysics Data System (ADS)

    Robertson, D. E.; Pokhrel, P.; Wang, Q. J.

    2012-07-01

    Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record.

  10. 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.

  11. 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.

  12. 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

    Extreme hydrologic events in the form of droughts are significant sources of social and economic damage. In the United States according to the National Climatic Data Center, the losses from drought exceed US210 billion during 1980-2011, and account for about 24% of all losses from major weather disasters. Internationally, especially for the developing world, drought has had devastating impacts on local populations through food insecurity and famine. Providing reliable drought forecasts with sufficient early warning will help the governments to move from the management of drought crises to the management of drought risk. After working on drought monitoring and forecasting over the USA for over 10 years, the Princeton land surface hydrology group is now developing a global drought monitoring and forecasting system using a dynamical seasonal climate-hydrologic LSM-model (CHM) approach. Currently there is an active debate on the merits of the CHM-based seasonal hydrologic forecasts as compared to Ensemble Streamflow Prediction (ESP). We use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2) and its previous version CFSv1, to investigate the value of seasonal climate model forecasts by conducting a set of 27-year seasonal hydrologic hindcasts over the USA. Through Bayesian downscaling, climate models have higher squared correlation (R2) and smaller error than ESP for monthly precipitation averaged over major river basins across the USA, and the forecasts conditional on ENSO show further improvements (out to four months) over river basins in the southern USA. All three approaches have plausible predictions of soil moisture drought frequency over central USA out to six months because of strong soil moisture memory, and seasonal climate models provide better results over central and eastern USA. The R2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur 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.

  13. Seasonal Flood Forecasts and Dynamic Flood Risk Management

    NASA Astrophysics Data System (ADS)

    Arumugam, S.; Lall, U.

    2004-05-01

    Recent developments in predicting seasonal flood peaks/volumes conditioned on ocean, atmospheric and land surface conditions offer the scope for dynamic flood risk management. We address a deficiency of the traditional assumption that flood series are stationary, independent and identically distributed (iid). In this study, we evaluate a semi-parametric methodology based on local likelihood estimation for estimating the flood quantiles based on climatic indices for a seasonal storage reservoir. The utility of this methodology for estimating long-term slow variation of flood potential and for reconstructing past flood series using a small set of climate and other predictors is also explored. Conditioned on the seasonal flood forecasts and streamflow forecasts, the flood rule curve and the operating rules of the reservoir are modified to evaluate the additional benefits the seasonal flood forecasts offer in improving the reservoir operation over the currently pursued approach of constant/static flood rule curve. The benefits of seasonal flood forecasts in relation to the seasonal streamflow forecasts are quantified and their potential utility in flood premium and insurance setting is also discussed.

  14. 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 to the wind speed anomalies. On the other hand, in some cases and areas where turbines operate close to, or above the rated power, the sensitivity of power forecast is reduced. Thus, the seasonal power forecasting system requires good knowledge of the changes in frequency of events with sufficient wind speeds to have acceptable skill. The scientific background for the Vestas seasonal power forecasting system is described and the relationship between predicted monthly wind speed anomalies and observed wind energy production are investigated for a number of operating wind farms in different climate zones. Current challenges will be discussed and some future research and development areas identified.

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

    NASA Astrophysics Data System (ADS)

    Roberts, J. B.; Robertson, F. R.; Bosilovich, M. G.; Lyon, B.

    2013-12-01

    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.

  16. 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

  17. 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.

  18. 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 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.

  19. 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 Support System for Agrotechnology Transfer (DSSAT).Version 4.5 [CD-ROM].University of Hawaii, Honolulu, Hawaii. Ritchie, J.T., Otter, S., 1985. Description and performanceof CERES-Wheat: a user-oriented wheat yield model. In: ARS Wheat Yield Project. ARS-38.Natl Tech Info Serv, Springfield, Missouri, pp. 159-175.

  20. 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 system demonstrates that there is a potential to adapt to this change by utilizing the weather forecasts given that both indicators can be skilfully predicted for the December-to-February season, at least two months ahead of the wet season. This is particularly the case for predicting above-normal and below-normal conditions. The frequency of heat stress conditions shows better predictability than the frequency of dry spells. Although results are promising for end users on the ground, forecasts alone are insufficient to ensure appropriate response. Sufficient support for appropriate measures must be in place, and forecasts must be communicated in a context-specific, accessible and understandable format.

  1. 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.

  2. 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.

  3. 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

  4. Forecasting the 2013–2014 influenza season using Wikipedia

    DOE PAGESBeta

    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 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

  5. 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. PMID:25974758

  6. 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.

  7. 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.

  8. 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.

  9. Seasonal statistical-dynamical forecasts of droughts over Western Iberia

    NASA Astrophysics Data System (ADS)

    Ribeiro, Andreia; Pires, Carlos

    2015-04-01

    The Standard Precipitation Index (SPI) has been used here as a drought predictand in order to assess seasonal drought predictability over the western Iberia. Hybrid (statistical-dynamical) long-range forecasts of the drought index SPI are estimated with lead-times up to 6 months, over the period of 1987-2008. Operational forecasts of geopotential height and total precipitation from the UK Met Office operational forecasting system are considered. Past ERA-Interim reanalysis data, prior to the forecast launching, are used for the purpose of build a set of SPI predictors, integrating recent past observations. Then, a two-step hybridization procedure is adopted: in the first-step both forecasted and observational large-scale fields are subjected to a Principal Component Analysis (PCA) and forecasted PCs and persistent PCs are used as predictors. The second hybridization step consists on a statistical/hybrid downscaling to the regional scale based on regression techniques, after the selection of the statistically significant predictors. The large-scale filter predictors from past observations and operational forecasts are used to downscale SPI and the advantage of combining predictors with both dynamical and statistical background in the prediction of drought conditions at different lags is evaluated. The SPI estimations and the added value of combining dynamical and statistical methods are evaluated in cross-validation mode. Results show that winter is the most predictable season, and most of the predictive power is on the large-scale fields and at the shorter lead-times. The hybridization improves forecasting drought skill in comparison to purely dynamical forecasts, since the persistence of large-scale patterns displays the main role in the long-range predictability of precipitation. These findings provide clues about the predictability of the SPI, particularly in Portugal, and may contribute to the predictability of crops yields and to some guidance on users (such as farmers) decision making process.

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

    PubMed Central

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

    2015-01-01

    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. PMID:26460115

  11. A robust multimodel framework for ensemble seasonal hydroclimatic forecasts

    NASA Astrophysics Data System (ADS)

    Mendoza, Pablo A.; Rajagopalan, Balaji; Clark, Martyn P.; Cortés, Gonzalo; McPhee, James

    2014-07-01

    We provide a framework for careful analysis of the different methodological choices we make when constructing multimodel ensemble seasonal forecasts of hydroclimatic variables. Specifically, we focus on three common modeling decisions: (i) number of models, (ii) multimodel combination approach, and (iii) lead time for prediction. The analysis scheme includes a multimodel ensemble forecasting algorithm based on nonparametric regression, a set of alternatives for the options previously pointed, and a selection of probabilistic verification methods for ensemble forecast evaluation. The usefulness of this framework is tested through an example application aimed to generate spring/summer streamflow forecasts at multiple locations in Central Chile. Results demonstrate the high impact that subjectivity in decision-making may have on the quality of ensemble seasonal hydroclimatic forecasts. In particular, we note that the probabilistic verification criteria may lead to different choices regarding the number of models or the multimodel combination method. We also illustrate how this objective analysis scheme may lead to results that are extremely relevant for the case study presented here, such as skillful seasonal streamflow predictions for very dry conditions.

  12. A Robust Multimodel Framework for Ensemble Seasonal Hydroclimatic Forecasts

    NASA Astrophysics Data System (ADS)

    Mendoza, P. A.; Rajagopalan, B.; Clark, M. P.; Cortés, G.; McPhee, J. P.

    2014-12-01

    We provide a framework for careful analysis of the different methodological choices we make when constructing multimodel ensemble seasonal forecasts of hydroclimatic variables. Specifically, we focus on three common modeling decisions: (i) number of models, (ii) multimodel combination approach, and (iii) lead time for prediction. The analysis scheme includes a multimodel ensemble forecasting algorithm based on nonparametric regression, a set of alternatives for the options previously pointed, and a selection of probabilistic verification methods for ensemble forecast evaluation. The usefulness of this framework is tested through an example application aimed to generate spring/summer streamflow forecasts at multiple locations in Central Chile. Results demonstrate the high impact that subjectivity in decision-making may have on the quality of ensemble seasonal hydroclimatic forecasts. In particular, we note that the probabilistic verification criteria may lead to different choices regarding the number of models or the multimodel combination method. We also illustrate how this objective analysis scheme may lead to results that are extremely relevant for the case study presented here, such as skillful seasonal streamflow predictions for very dry conditions.

  13. 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. PMID:26460115

  14. The Value of Seasonal Climate Forecasts in Managing Energy Resources.

    NASA Astrophysics Data System (ADS)

    Brown Weiss, Edith

    1982-04-01

    Research and interviews with officials of the United States energy industry and a systems analysis of decision making in a natural gas utility lead to the conclusion that seasonal climate forecasts would only have limited value in fine tuning the management of energy supply, even if the forecasts were more reliable and detailed than at present.On the other hand, reliable forecasts could be useful to state and local governments both as a signal to adopt long-term measures to increase the efficiency of energy use and to initiate short-term measures to reduce energy demand in anticipation of a weather-induced energy crisis.To be useful for these purposes, state governments would need better data on energy demand patterns and available energy supplies, staff competent to interpret climate forecasts, and greater incentive to conserve. The use of seasonal climate forecasts is not likely to be constrained by fear of legal action by those claiming to be injured by a possible incorrect forecast.

  15. Improved seasonal drought forecasts using reference evapotranspiration anomalies

    NASA Astrophysics Data System (ADS)

    McEvoy, Daniel J.; Huntington, Justin L.; Mejia, John F.; Hobbins, Michael T.

    2016-01-01

    A novel contiguous United States (CONUS) wide evaluation of reference evapotranspiration (ET0; a formulation of evaporative demand) anomalies is performed using the Climate Forecast System version 2 (CFSv2) reforecast data for 1982-2009. This evaluation was motivated by recent research showing ET0 anomalies can accurately represent drought through exploitation of the complementary relationship between actual evapotranspiration and ET0. Moderate forecast skill of ET0 was found up to leads of 5 months and was consistently better than precipitation skill over most of CONUS. Forecasts of ET0 during drought events revealed high categorical skill for notable warm-season droughts of 1988 and 1999 in the central and northeast CONUS, with precipitation skill being much lower or absent. Increased ET0 skill was found in several climate regions when CFSv2 forecasts were initialized during moderate-to-strong El Niño-Southern Oscillation events. Our findings suggest that ET0 anomaly forecasts can improve and complement existing seasonal drought forecasts.

  16. 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 transition of the equatorial zonal current from westerly to easterly.

  17. Can Abrupt Seasonal Transitions be Predicted in Climate Forecasts?

    NASA Astrophysics Data System (ADS)

    Kirtman, B. P.

    2014-12-01

    There is on ongoing debate in the seasonal prediction community as to whether high frequency weather statistics in climate forecasts have any inherent predictability, and ultimately prediction skill. The North American Multi-Model Ensemble (NMME) seasonal-to-interannual prediction experiment is the ideal test-bed to evaluate the predictability and prediction of weather within climate. NMME is multi-institutional multi-agency system to improve operational monthly and seasonal forecasts based on the prediction systems developed at the major US climate modeling centers (NOAA/EMC, NOAA/GFDL, NCAR, NASA) and Canada. Although currently in an experimental stage, the NMME prediction system has been providing routine real-time monthly and seasonal forecasts since August 2011 that adhere to the CPC operational schedule. In addition to the monthly data, daily output from some of the retrospective forecasts are now being archived. Based on the NMME daily output this talk evaluates the predictability and prediction of two aspects of weather within climate: (i) monsoon onset in India and in South West North America and (ii) onset of spring severe weather in the mid-west US. The analysis estimates predictability by examining how well the individual models "predict" themselves and how well they "predict" other models. Prediction quality is assessed based on comparisons with observational estimates.

  18. 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.

  19. 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.

  20. 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.

  1. 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 regimes several months ahead. This suggests that in some special occasions like this one, a multimodel approach is not necessarily better than an especially skillful model.

  2. Impact of snow initialization on sub-seasonal forecasts

    NASA Astrophysics Data System (ADS)

    Orsolini, Yvan; Balsamo, Giancarlo; Weisheimer, Antje; Vitart, Frederic; Senan, Retish; Doblas-Reyes, Francisco; Benestad, Rasmus

    2014-05-01

    The influence of the snowpack on atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on sub-seasonal to seasonal predictability. Many observational and model studies have indicated that the autumn Eurasian snow cover in particular, influences circulation patterns over the North Pacific and North Atlantic. We have performed a suite of coupled simulations with the ECMWF ensemble forecast system to investigate the impact of accurate snow initialisation on sub-seasonal forecasts, following the GLACE2 methodology but focusing on the impact of snow rather than soil moisture. Pairs of two-month ensemble forecasts were run over the years 2004 to 2009, with either realistic initialization of snow variables, or else with "scrambled" snow initial conditions. Initially, the presence of a thicker snowpack cools surface temperature over the continental land masses of Eurasia and North America. At a longer lead of 30-day, it causes a warming over the Arctic and the high latitudes of Eurasia due to an intensification and westward expansion of the Siberian High. This "warm Arctic - cold continent" difference means that the forecasts of near-surface temperature with the more realistic snow initialization are in closer agreement with re-analyses over Eurasia, reducing a cold model bias over the Arctic and a warm model bias over mid-latitudes. We next focus on the cold winter of 2009/10 characterized by an exceptional negative NAO phase, and show that snow initialization reinforces the initial NAO anomaly. The impact of the snowpack over the Himalaya-Tibet region in the springtime is also an area where better snow initialization could improve forecast, e.g. of the Indian summer monsoon. Some preliminary results are shown using the same approach as used for the autumn case.

  3. Ensemble seasonal hydrological forecasting at the pan-European scale

    NASA Astrophysics Data System (ADS)

    Pechlivanidis, Ilias; Spångmyr, Henrik; Bosshard, Thomas; Gustafsson, David; Olsson, Jonas

    2015-04-01

    Recent advances in understanding and forecasting of climate and climate change have resulted into skillful and useful meteorological predictions, which can consequently increase the confidence of hydrological prognosis and awareness from an end-user perspective. However, the majority of seasonal impact modelling has commonly been conducted at only one or a limited number of basins limiting the need to understand large systems which are heavily influenced by human activities. In here, we complement the "deep" knowledge from basin based modelling using large scale multi-basin modelling, which is capable of representing human influences (i.e. irrigation, reservoirs and groundwater use). We analyse the seasonal predictive skill along Europe's hydro-climatic gradient using the pan-European E-HYPE v3.0 multi-basin hydrological model. Forcing data (mean daily precipitation and temperature) are derived from the WFDEI product for the period 1979-2010 and used to initialise the hydrological model (level in surface water, i.e. reservoirs, lakes and wetlands, soil moisture, snow depth). 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 for a number of European basins is assessed on seasonal timescales. Seasonal re-forecasts are evaluated with respect to their accuracy against observed impact variables, i.e. streamflow, at different space and time-scales; the value of the predictions are assessed using various performance metrics. Verification points (around 2600 stations) are used to represent various climatologies, soil-types, land uses, altitudes and basin scales within Europe. We finally identify regions of similar hydrological forecasting skill and link this to physiographic-climatic characteristics and meteorological skill, in order to suggest possible model improvements. This can assist on developing optimal geographical forecasting units, as a function of model physics and stakeholder needs. Keywords Hydrological modelling, E-HYPE, ensemble seasonal forecasts, pan-European scale, skill scores

  4. 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.

  5. The new Met Office strategy for seasonal forecasts

    NASA Astrophysics Data System (ADS)

    Hewson, T. D.

    2012-04-01

    In October 2011 the Met Office began issuing a new-format UK seasonal forecast, called "The 3-month Outlook". Government interest in a UK-relevant product had been heightened by infrastructure issues arising during the severe cold of previous winters. At the same time there was evidence that the Met Office's "GLOSEA4" long range forecasting system exhibited some hindcast skill for the UK, that was comparable to its hindcast skill for the larger (and therefore less useful) 'northern Europe' region. Also, the NAO- and AO- signals prevailing in the previous two winters had been highlighted by the GLOSEA4 model well in advance. This presentation will initially give a brief overview of GLOSEA4, describing key features such as evolving sea-ice, a well-resolved stratosphere, and the perturbation strategy. Skill measures will be shown, along with forecasts for the last 3 winters. The new structure 3-month outlook will then be described and presented. Previously, our seasonal forecasts had been based on a tercile approach. The new format outlook aims to substantially improve upon this by illustrating graphically, and with text, the full range of possible outcomes, and by placing those outcomes in the context of climatology. In one key component the forecast pdfs (probability density functions) are displayed alongside climatological pdfs. To generate the forecast pdf we take the bias-corrected GLOSEA4 output (42 members), and then incorporate, via expert team, all other relevant information. Firstly model forecasts from other centres are examined. Then external 'forcing factors', such as solar, and the state of the land-ocean-ice system, are referenced, assessing how well the models represent their influence, and bringing in statistical relationships where appropriate. The expert team thereby decides upon any changes to the GLOSEA4 data, employing an interactive tool to shift, expand or contract the forecast pdfs accordingly. The full modification process will be illustrated during the presentation. Another key component of the 3-month outlook is the focus it places on potential hazards and impacts. To date specific references have been made to snow and ice disruption, to replenishment expectation for regions suffering water supply shortages, and to windstorm frequency. This aspect will be discussed, showing also some subjective verification. In future we hope to extend the 3-month outlook framework to other parts of the world, notably Africa, a region where the Met Office, with DfID support, is working collaboratively to improve real-time long range forecasts. Brief reference will also be made to such activities.

  6. Using Seasonal Forecasts for medium-term Electricity Demand Forecasting on Italy

    NASA Astrophysics Data System (ADS)

    De Felice, M.; Alessandri, A.; Ruti, P.

    2012-12-01

    Electricity demand forecast is an essential tool for energy management and operation scheduling for electric utilities. In power engineering, medium-term forecasting is defined as the prediction up to 12 months ahead, and commonly is performed considering weather climatology and not actual forecasts. This work aims to analyze the predictability of electricity demand on seasonal time scale, considering seasonal samples, i.e. average on three months. Electricity demand data has been provided by Italian Transmission System Operator for eight different geographical areas, in Fig. 1 for each area is shown the average yearly demand anomaly for each season. This work uses data for each summer during 1990-2010 and all the datasets have been pre-processed to remove trends and reduce the influence of calendar and economic effects. The choice of focusing this research on the summer period is due to the critical peaks of demand that power grid is subject during hot days. Weather data have been included considering observations provided by ECMWF ERA-INTERIM reanalyses. Primitive variables (2-metres temperature, pressure, etc) and derived variables (cooling and heating degree days) have been averaged for summer months. A particular attention has been given to the influence of persistence of positive temperature anomaly and a derived variable which count the number of consecutive days of extreme-days has been used. Electricity demand forecast has been performed using linear and nonlinear regression methods and stepwise model selection procedures have been used to perform a variable selection with respect to performance measures. Significance tests on multiple linear regression showed the importance of cooling degree days during summer in the North-East and South of Italy with an increase of statistical significance after 2003, a result consistent with the diffusion of air condition and ventilation equipment in the last decade. Finally, using seasonal climate forecasts we evaluate the performances of electricity demand forecast performed with predicted variables on Italian regions with encouraging results on the South of Italy. This work gives an initial assessment on the predictability of electricity demand on seasonal time scale, evaluating the relevance of climate information provided by seasonal forecasts for electricity management during high-demand periods.;

  7. Implementing Probabilistic Climate Outlooks Within a Seasonal Hydrologic Forecast System

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Lettenmaier, D. P.

    2004-12-01

    We describe a method for implementing the Climate Prediction Center's probabilistic climate outlooks within an experimental ensemble hydrologic forecast system. The forecast system has been developed for the portion of the U.S. and the Canadian portion of the Columbia River basin west of the Continental Divide. Forecasts are made at lead times of six months to a year, using the Variable Infiltration Capacity (VIC) macroscale hydrology model implemented at 1/8 degree spatial resolution. Ensemble forecasts are derived from historical resampling, global climate model ensemble forecasts, and most recently the CPC Official Seasonal Outlooks, formulated as probability of exceedence (POE) distributions for temperature and precipitation, using the climate division as the spatial unit. Our current approach for downscaling the CPC POE forecasts involves a resampling step (called the "Schaake Shuffle", as described in a recent paper by M. Clark and others) that is applied to create a 13-member ensemble (of monthly timeseries of precipitation and temperature for each Climate Division) that exhibits plausible spatial and temporal structure. A subsequent resampling step is applied to disaggregate timeseries from a monthly to a daily timestep, and from the climate division to the 1/8 degree hydrologic model grid scale. In this talk, we address two questions: 1) whether the downscaling approach reproduces the observational mean and variability for streamflow, and for spatial temperature and precipitation fields; and 2) whether bias-correction of the climate division POE forecasts is a necessary step in the downscaling process. These questions were investigated through the application of the downscaling approach to the CPC retrospective observational climate division dataset, and via comparisons with a climate division unit dataset aggregated from the authors' PRISM-corrected retrospective climatology.

  8. Improvements of seasonal weather forecasts using optimal combination of multimodel hydrodynamical forecasts

    NASA Astrophysics Data System (ADS)

    Khan, V.; Tischenko, V.; Kryjov, V.; Vilfand, R.

    2009-04-01

    The main objective of the present study is to improve seasonal weather forecasting applying statistical analysis to hydrodynamical model outputs from Russian and foreign GCM models. Quantitative estimates of the ability of the models to reproduce the temporal and spatial variability of the meteorological fields were obtained. Reasonable skill scores of forecasts have been observed over tropical zones, while the forecast assessments were low over North Eurasian region. Although performance of basic methods of complexation demonstrated advantage of the multimodel forecast over individual forecasts constituting the ensemble, the prognostic ability of complexated forecast is still not enough high in high latitudes regions. In attempt to increase the predictability, a new statistical approach based on "predictant-predictors" system was elaborated. H-500 data from model set were used as predictors, and T850 - as a predictant. Correlation analysis between the local Т850 and the global H-500 from different models was appplyed to identify informative geographical regions of H-500 for each model. Compact representation of the H-500 predictor data was done using EOF analysis. Two best-predictor models from extended predictor dataset were identified at concrete prognostic season after applying stepwise multiple regression procedure. Evaluation of the statistical approach on dependent and cross-valiadated datasets demonstrates high skill score for dependent and cross-validated datasets. However the method has some deficiencies related with instability of found equations and needs more test experiments. Preliminary results of this study figure out that adaptive statistical methods for optimal complexation of hydrodynamical models can be useful tool to improve long-range forecasts. This work is partially supported by RFBR grant N 07-05-00740, 07-05-00240.

  9. 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.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    NOAA's Climate Prediction Center issues seasonal climate forecasts predicting total precipitation and average air temperature for three-month periods out to a year in advance. The utility of seasonal forecasts for agricultural applications depends on several forecast characteristics, including depe...

  11. 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

    Projections of reservoir conditions and operations of major water resources systems in the Colorado River Basin are generated each month for a 2-year period by the Bureau of Reclamation (Reclamation) using the 24-Month Study (24MS) model. This is a monthly timestep deterministic model that incorporates a single streamflow forecast trace produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), resulting in the most probable reservoir operations projection. Using an Extended Streamflow Prediction (ESP) method and a physically based hydrologic model, the CBRFC produces an ensemble of streamflow forecasts by sampling historical weather sequences conditioned on 3-7 month seasonal climate forecasts starting from the model's current initial conditions. Using the 24MS model with the most probable forecast from the ESP ensemble, Reclamation manually inputs projected operations, adjusting the operations to meet system objectives. The result is a single most probable operations forecast that does not quantify the uncertainty associated with the ensemble flow projections. In addition, the variability in the ESP method is limited by the flows that result from the historical meteorological record. This research addresses these shortcomings by using an alternative method of generating an ensemble of forecasts with greater variability and applies these to a rulebased operations model to produce a probabilistic projection of operations. To accomplish this, we combined an enhanced ESP with a probabilistic version of the 24MS model known as the Mid-Term Operations Model (MTOM). The MTOM has captured the operating policies in a set of rules that are designed to meet system objectives for a wide range of hydrologic conditions, thus can be used to simulate operations for many hydrologic scenarios. For each year, stochastic weather sequences are generated conditioned on probabilistic seasonal climate forecasts which are coupled with the SAC-SMA model 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.

  12. Oceanic Stochastic Parameterizations in a Seasonal Forecast System

    NASA Astrophysics Data System (ADS)

    Andrejczuk, M.; Cooper, F. C.; Juricke, S.; Palmer, T. N.; Weisheimer, A.; Zanna, L.

    2016-05-01

    We study the impact of three stochastic parametrizations in the ocean component of a coupled model, on forecast reliability over seasonal timescales. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the Stochastically Perturbed Parametrization Tendency (SPPT) scheme - which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely the western boundary currents and the Southern Ocean. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error. Whilst there are good grounds for implementing stochastic schemes in ocean models, our results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.

  13. A New Seasonal Forecasting System For The Ethiopian Summer Rains

    NASA Astrophysics Data System (ADS)

    Gissila, T.; Slingo, J. M.; Grimes, D. I. F.; Black, E. C. L.

    We present a new seasonal forecasting system for the June-September rains in Ethiopia. It has previously been found that the total June-September rainfall over the whole country is difficult to predict using statistical methods. Detailed study of all available data shows the rainfall seasonality varies greatly from one region to another, which would explain why the total June-September rainfall over all regions is a diffi- cult property to forecast. In addition, the correlation between rainfall and the Southern Oscillation Index varies spatially, with a strong teleconnection present only in some regions. This study accounts for the spatial variability in rainfall by grouping the rain gauge stations into 4 geographical clusters based on seasonality and cross-correlation of rainfall anomalies. Linear regression equations are then developed separately for each cluster. The variables we use for the regressions are SST anomalies in the pre- ceding March, April and May of: the tropical West Indian Ocean, tropical East Indian Ocean and Nino 3.4. Formal skill-testing of the equations shows that the new fore- casting scheme is more effective than either climatology or persistence - the methods currently used by the Ethiopian Meteorological Office.

  14. 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).

  15. Effects of Forecasts on the Revisions of Concurrent Seasonally Adjusted Data Using the X-11 Seasonal Adjustment Procedure.

    ERIC Educational Resources Information Center

    Bobbitt, Larry; Otto, Mark

    Three Autoregressive Integrated Moving Averages (ARIMA) forecast procedures for Census Bureau X-11 concurrent seasonal adjustment were empirically tested. Forty time series from three Census Bureau economic divisions (business, construction, and industry) were analyzed. Forecasts were obtained from fitted seasonal ARIMA models augmented with…

  16. Above-normal Atlantic basin hurricane season forecast

    NASA Astrophysics Data System (ADS)

    Showstack, Randy

    2011-05-01

    Between three and six major hurricanes with winds of 111 miles per hour and greater could whip across the Atlantic basin during what is forecast to be an above-normal 2011 hurricane season, according to the U.S. National Oceanic and Atmospheric Administration's Climate Prediction Center (NOAA CPC). Including those, there could be a total of 6-10 hurricanes with winds of 74 miles per hour or greater and 12-18 named storms with winds of 39 miles per hour or greater during the hurricane season, which officially begins on 1 June and lasts for 6 months, NOAA administrator Jane Lubchenco said at a 19 May briefing. There is a 70% likelihood for these ranges occurring, according to NOAA.

  17. Model averaging methods to merge operational statistical and dynamic seasonal streamflow forecasts in Australia

    NASA Astrophysics Data System (ADS)

    Schepen, Andrew; Wang, Q. J.

    2015-03-01

    The Australian Bureau of Meteorology produces statistical and dynamic seasonal streamflow forecasts. The statistical and dynamic forecasts are similarly reliable in ensemble spread; however, skill varies by catchment and season. Therefore, it may be possible to optimize forecasting skill by weighting and merging statistical and dynamic forecasts. Two model averaging methods are evaluated for merging forecasts for 12 locations. The first method, Bayesian model averaging (BMA), applies averaging to forecast probability densities (and thus cumulative probabilities) for a given forecast variable value. The second method, quantile model averaging (QMA), applies averaging to forecast variable values (quantiles) for a given cumulative probability (quantile fraction). BMA and QMA are found to perform similarly in terms of overall skill scores and reliability in ensemble spread. Both methods improve forecast skill across catchments and seasons. However, when both the statistical and dynamical forecasting approaches are skillful but produce, on special occasions, very different event forecasts, the BMA merged forecasts for these events can have unusually wide and bimodal distributions. In contrast, the distributions of the QMA merged forecasts for these events are narrower, unimodal and generally more smoothly shaped, and are potentially more easily communicated to and interpreted by the forecast users. Such special occasions are found to be rare. However, every forecast counts in an operational service, and therefore the occasional contrast in merged forecasts between the two methods may be more significant than the indifference shown by the overall skill and reliability performance.

  18. Evaluation of ensemble forecast uncertainty using a new proper score: application to medium-range and seasonal forecasts

    NASA Astrophysics Data System (ADS)

    Christensen, Hannah; Moroz, Irene; Palmer, Tim

    2015-04-01

    Forecast verification is important across scientific disciplines as it provides a framework for evaluating the performance of a forecasting system. In the atmospheric sciences, probabilistic skill scores are often used for verification as they provide a way of unambiguously ranking the performance of different probabilistic forecasts. In order to be useful, a skill score must be proper -- it must encourage honesty in the forecaster, and reward forecasts which are reliable and which have good resolution. A new score, the Error-spread Score (ES), is proposed which is particularly suitable for evaluation of ensemble forecasts. It is formulated with respect to the moments of the forecast. The ES is confirmed to be a proper score, and is therefore sensitive to both resolution and reliability. The ES is tested on forecasts made using the Lorenz '96 system, and found to be useful for summarising the skill of the forecasts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared to a perfect statistical probabilistic forecast -- the ECMWF high resolution deterministic forecast dressed with the observed error distribution. This generates a forecast that is perfectly reliable if considered over all time, but which does not vary from day to day with the predictability of the atmospheric flow. The ES distinguishes between the dynamically reliable EPS forecasts and the statically reliable dressed deterministic forecasts. Other skill scores are tested and found to be comparatively insensitive to this desirable forecast quality. The ES is used to evaluate seasonal range ensemble forecasts made with the ECMWF System 4. The ensemble forecasts are found to be skilful when compared with climatological or persistence forecasts, though this skill is dependent on region and time of year.

  19. 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).

  20. Impact of snow initialization on sub-seasonal forecasts

    NASA Astrophysics Data System (ADS)

    Orsolini, Y. J.; Senan, R.; Balsamo, G.; Doblas-Reyes, F. J.; Vitart, F.; Weisheimer, A.; Carrasco, A.; Benestad, R. E.

    2013-10-01

    The influence of the snowpack on wintertime atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on seasonal predictability. Many observational and model studies have indicated that the autumn Eurasian snow cover in particular, influences circulation patterns over the North Pacific and North Atlantic. We have performed a suite of coupled atmosphere-ocean simulations with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast system to investigate the impact of accurate snow initialisation. Pairs of 2-month ensemble forecasts were started every 15 days from the 15th of October through the 1st of December in the years 2004-2009, with either realistic initialization of snow variables based on re-analyses, or else with "scrambled" snow initial conditions from an alternate autumn date and year. Initially, in the first 15 days, the presence of a thicker snowpack cools surface temperature over the continental land masses of Eurasia and North America. At a longer lead of 30-day, it causes a warming over the Arctic and the high latitudes of Eurasia due to an intensification and westward expansion of the Siberian High. It also causes a cooling over the mid-latitudes of Eurasia, and lowers sea level pressures over the Arctic. This "warm Arcticcold continent" difference means that the forecasts of near-surface temperature with the more realistic snow initialization are in closer agreement with re-analyses, reducing a cold model bias over the Arctic and a warm model bias over mid-latitudes. The impact of realistic snow initialization upon the forecast skill in snow depth and near-surface temperature is estimated for various lead times. Following a modest skill improvement in the first 15 days over snow-covered land, we also find a forecast skill improvement up to the 30-day lead time over parts of the Arctic and the Northern Pacific, which can be attributed to the realistic snow initialization over the land masses.

  1. Seasonal forecasting of groundwater levels in principal aquifers of the United Kingdom

    NASA Astrophysics Data System (ADS)

    Mackay, J. D.; Jackson, C. R.; Brookshaw, A.; Scaife, A. A.; Cook, J.; Ward, R. S.

    2015-11-01

    To date, the majority of hydrological forecasting studies have focussed on using medium-range (3-15 days) weather forecasts to drive hydrological models and make predictions of future river flows. With recent developments in seasonal (1-3 months) weather forecast skill, such as those from the latest version of the UK Met Office global seasonal forecast system (GloSea5), there is now an opportunity to use similar methodologies to forecast groundwater levels in more slowly responding aquifers on seasonal timescales. This study uses seasonal rainfall forecasts and a lumped groundwater model to simulate groundwater levels at 21 locations in the United Kingdom up to three months into the future. The results indicate that the forecasts have skill; outperforming a persistence forecast and demonstrating reliability, resolution and discrimination. However, there is currently little to gain from using seasonal rainfall forecasts over using site climatology for this type of application. Furthermore, the forecasts are not able to capture extreme groundwater levels, primarily because of inadequacies in the driving rainfall forecasts. The findings also show that the origin of forecast skill, be it from the meteorological input, groundwater model or initial condition, is site specific and related to the groundwater response characteristics to rainfall and antecedent hydro-meteorological conditions.

  2. Model averaging methods to merge statistical and dynamic seasonal streamflow forecasts in Australia

    NASA Astrophysics Data System (ADS)

    Schepen, A.; Wang, Q. J.

    2014-12-01

    The Australian Bureau of Meteorology operates a statistical seasonal streamflow forecasting service. It has also developed a dynamic seasonal streamflow forecasting approach. The two approaches produce similarly reliable forecasts in terms of ensemble spread but can differ in forecast skill depending on catchment and season. Therefore, it may be possible to augment the skill of the existing service by objectively weighting and merging the forecasts. Bayesian model averaging (BMA) is first applied to merge statistical and dynamic forecasts for 12 locations using leave-five-years-out cross-validation. It is seen that the BMA merged forecasts can sometimes be too uncertain, as shown by ensemble spreads that are unrealistically wide and even bi-modal. The BMA method applies averaging to forecast probability densities (and thus cumulative probabilities) for a given forecast variable value. An alternative approach is quantile model averaging (QMA), whereby forecast variable values (quantiles) are averaged for a given cumulative probability (quantile fraction). For the 12 locations, QMA is compared to BMA. BMA and QMA perform similarly in terms of forecast accuracy skill scores and reliability in terms of ensemble spread. Both methods improve forecast skill across catchments and seasons by combining the different strengths of the statistical and dynamic approaches. A major advantage of QMA over BMA is that it always produces reasonably well defined forecast distributions, even in the special cases where BMA does not. Optimally estimated QMA weights and BMA weights are similar; however, BMA weights are more efficiently estimated.

  3. 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.

  4. 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

    Wildfire is a fundamental Earth System process, affecting almost all biogeochemical cycles, and all vegetated biomes. Fires are naturally rare in humid tropical forests, and tropical trees are generally killed by even low-intensity fires. However, fire activity in the tropics has increased markedly over the past 15-20 years, especially in Indonesia, Amazonia, and more recently, central Africa also. Since fire is the prime tool for clearing land in the tropics, it not surprising that the increase in fire activity is strongly associated with increased levels of deforestation, which is driven mainly by world-wide demand for timber and agricultural commodities. The consequences of deforestation fires for biodiversity conservation and emissions of greenhouse gases and aerosols are enormous. For example, carbon emissions from tropical biomass burning are around 20% of annual average global fossil fuel emissions. The destructive fires in Indonesia during the exceptionally strong El Niño-induced drought in late 1997 and early 1998 rank as some of the largest peak emissions events in recorded history. Past studies estimate about 1Gt of carbon was released to the atmosphere from the Indonesian fires in 1997 (which were mostly concentrated in carbon-rich forested peatlands). This amount is equivalent to about 14% of the average global annual fossil fuel emissions released during the 1990s. While not as large as the 1997-98 events, significant emissions from biomass burning have also been recorded in other (less severe) El Niño years across Indonesia, in particular, 2002, 2004, 2006 and 2009-2010. Recent climate modelling studies indicate that the frequency of El Niño events may increase under future climate change, affecting many tropical countries, including Indonesia. An increased drought frequency plus a projected increase in population and land use pressures in Indonesia, imply there will be even more fires and emissions in future across the region. However, while 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.

  5. Verification and error sources of the California Seasonal Hydrologic Forecast (CaliForecast) System over the Feather River Basin

    NASA Astrophysics Data System (ADS)

    Park, G.; Imam, B.; Sorooshian, S.

    2008-12-01

    Operational water resource planning and management heavily rely on the seasonal streamflow forecasts of reservoir. The California Hydrologic Forecast System, a regional implementation of the West-Wide Seasonal Hydrologic forecast System over the state of California at the University of California-Irvine in a 1/8th degree resolution, provides probabilistic forecasts in the form of ensemble streamflow predictions (ESP) to facilitate our need in the state of California. Similar to any other hydrologic forecast systems, CaliForecast, however, contains significant forecast errors and uncertainties that are propagated from many sources. These within the CaliForecast system, includes uncertainty associated with the interpolation techniques (Index station method) for the precipitation input, validity of ESP with respect to the climate change, efficiency of snow assimilation scheme, error in naturalized streamflow, and many others. This presentation will attempt to verify the ESP forecasts over the Feather River Basin that is a major tributary to the Sacramento River Basin, provide understanding of error sources using existing verification metrics, and finally suggest next steps towards improving forecast skills.

  6. MEASURES OF THE USEFULNESS OF SEASONAL PRECIPITATION FORECASTS FOR AGRICULTURAL APPLICATIONS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    NOAA's Climate Prediction Center produces experimental climate forecasts that predict total precipitation over three-month periods out to a year in advance. The utility of these seasonal forecasts for agricultural applications will depend on a number of forecast characteristics, including dependabi...

  7. 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.

  8. 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.

  9. Tests of oceanic stochastic parameterisation in a seasonal forecast system.

    NASA Astrophysics Data System (ADS)

    Cooper, Fenwick; Andrejczuk, Miroslaw; Juricke, Stephan; Zanna, Laure; Palmer, Tim

    2015-04-01

    Over seasonal time scales, our aim is to compare the relative impact of ocean initial condition and model uncertainty, upon the ocean forecast skill and reliability. Over seasonal timescales we compare four oceanic stochastic parameterisation schemes applied in a 1x1 degree ocean model (NEMO) with a fully coupled T159 atmosphere (ECMWF IFS). The relative impacts upon the ocean of the resulting eddy induced activity, wind forcing and typical initial condition perturbations are quantified. Following the historical success of stochastic parameterisation in the atmosphere, two of the parameterisations tested were multiplicitave in nature: A stochastic variation of the Gent-McWilliams scheme and a stochastic diffusion scheme. We also consider a surface flux parameterisation (similar to that introduced by Williams, 2012), and stochastic perturbation of the equation of state (similar to that introduced by Brankart, 2013). The amplitude of the stochastic term in the Williams (2012) scheme was set to the physically reasonable amplitude considered in that paper. The amplitude of the stochastic term in each of the other schemes was increased to the limits of model stability. As expected, variability was increased. Up to 1 month after initialisation, ensemble spread induced by stochastic parameterisation is greater than that induced by the atmosphere, whilst being smaller than the initial condition perturbations currently used at ECMWF. After 1 month, the wind forcing becomes the dominant source of model ocean variability, even at depth.

  10. 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.

  11. Combining the strengths of statistical and dynamical modeling approaches for forecasting Australian seasonal rainfall

    NASA Astrophysics Data System (ADS)

    Schepen, Andrew; Wang, Q. J.; Robertson, David E.

    2012-10-01

    Forecasting rainfall at the seasonal time scale is highly challenging. Seasonal rainfall forecasts are typically made using statistical or dynamical models. The two types of models have different strengths, and their combination has the potential to increase forecast skill. In this study, statistical-dynamical forecasts of Australian seasonal rainfall are assessed. Statistical rainfall forecasts are made based on observed relationships with lagged climate indices. Dynamical forecasts are made by calibrating raw outputs from multiple general circulation models. The statistical and dynamical forecasts are then merged using a Bayesian model averaging (BMA) method. The skill and reliability of the forecasts is assessed through cross-validation for the period 1980-2010. We confirm that the dynamical and statistical groups of models give skill in different locations and seasons and the merged statistical-dynamical forecasts represent a significant improvement in terms of maximizing spatial and temporal coverage of skillfulness. We find that the merged statistical-dynamical forecasts are reliable in representing forecast uncertainty.

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

    NASA Astrophysics Data System (ADS)

    Hyvrinen, O.; Mtilatila, L.; Pilli-Sihvola, K.; Venlinen, 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.

  13. Use of Seasonal Climate Forecasts in Rangeland-Based Livestock Operations in West Texas.

    NASA Astrophysics Data System (ADS)

    Jochec, Kristi G.; Mjelde, James W.; Lee, Andrew C.; Conner, J. Richard

    2001-09-01

    The potential for west Texas ranchers to increase the profitability of their enterprises by becoming more proactive in their management practices by using seasonal climate forecasts is investigated using a focus group and ecological-economic modeling. The focus group felt forecasts could potentially be used in making decisions concerning stocking rates, brush control, and deer herd management. Further, the focus group raised concerns about the potential misuse of `value-added' forage forecasts. These concerns necessitate a revisiting of the value-added concept by the climate-forecasting community. Using only stocking-rate decisions, the potential value of seasonal forage forecasts is estimated. As expected, the economic results suggest the value of the forecasts depends on the restocking and destocking price along with other economic factors. More important, the economic results and focus-group reactions to these results suggest the need for multiyear modeling when examining the potential impact of using improved climate forecasts.

  14. 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.

  15. 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.

  16. Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States

    NASA Astrophysics Data System (ADS)

    Yoon, Jin-Ho; Ruby Leung, L.; Correia, James, Jr.

    2012-11-01

    This study compares two approaches, dynamical and statistical downscaling, for their potential to improve regional seasonal forecasts for the United States (U.S.) during the cold season. In the MultiRCM Ensemble Downscaling (MRED) project, seven regional climate models (RCMs) are used to dynamically downscale the Climate Forecast System (CFS) seasonal prediction over the conterminous U.S. out to 5 months for the period of 1982-2003. The simulations cover December to April of next year with 10 ensemble members from each RCM with different initial and boundary conditions from the corresponding ensemble members. These dynamically downscaled forecasts are compared with statistically downscaled forecasts produced by two bias correction methods applied to both the CFS and RCM forecasts. Results of the comparison suggest that the RCMs add value in seasonal prediction application, but the improvements largely depend on location, forecast lead time, variables, and skill metrics used for evaluation. Generally, more improvements are found over the Northwest and North Central U.S. for the shorter lead times. The comparison results also suggest a hybrid forecast system that combines both dynamical and statistical downscaling methods have the potential to maximize prediction skill.

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

    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.

  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 ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.

  19. REGIONAL UTILITY OF NOAA/CPC SEASONAL CLIMATE PRECIPITATION FORECASTS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Experimental climate forecasts for 3-month total precipitation are issued monthly by the NOAA Climate Prediction Center (CPC) for lead times from 0.5 to 12.5 months. Among these forecasts, the CPC probability of exceedance maps and graphs for 102 forecast divisions covering the contiguous United St...

  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-Thermalito reservoir system as a surrogate for alternate reservoir configurations. Specifically, we explore the sensitivity of reservoir operation performance to the ratio of reservoir mean inflow volume to reservoir capacity and downstream demand requirements.

  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. 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-based visualisation tool was set up to assess whether the different ensembles can forecast past drought risks. The tool is designed to support decision-making in situations that may potentially be a source of conflict between stakeholders and users by characterising future events in relation to past observed extremes. Results are presented and the capacity of the system to provide useful information in an operational context is discussed. This study is carried out within the Interreg IVB DROP Project (Benefit of governance in DROught adaPtation).

  3. 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.

  4. 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.

  5. 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, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

  6. Impact of atmosphere and land surface initial conditions on seasonal forecast of global surface temperature

    NASA Astrophysics Data System (ADS)

    Materia, Stefano; Borrelli, Andrea; Bellucci, Alessio; Alessandri, Andrea; Di Pietro, Pierluigi; Athanasiadis, Panagiotis; Navarra, Antonio; Gualdi, Silvio

    2014-05-01

    The impact of land surface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, mitigating the coupling shock and possibly increasing the model predictive skill in the ocean. In fact, in a few regions characterized by strong air-sea coupling, the atmosphere initial condition affects the forecast skill for several months. In particular, the ENSO region, the eastern tropical Atlantic and the North Pacific benefit significantly from the atmosphere initialization. On mainland, the impact of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of land surface initialization affects the forecast skill in the following lead seasons. The winter forecast in the high latitude plains of Siberia and Canada benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region, in central Asia and Australia. However, initialization through land surface reanalysis does not systematically guarantee an enhancement of the predictive skill: the quality of the forecast is sometimes higher for the non-constrained model. Overall, the introduction of a realistic initialization of land surface and atmosphere substantially increases skill and accuracy. However, further developments in the operating procedure for land surface initialization are required for more accurate seasonal forecasts.

  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. On the impact of stochastic parametrisations in the ECMWF seasonal forecasting system

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

    Seasonal climate predictions several months ahead based on dynamical atmosphere-ocean GCMs are part of the routinely operational forecasts issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). Here, the seasonal forecasting system is a seamless extension of ECMWF's medium-range ensemble weather forecasting system for the atmosphere coupled to a state-of-the-art ocean model. Model uncertainty in the atmosphere is represented by two schemes, the Stochastically Perturbed Physical Tendency (SPPT) scheme and the Stochastic Kinetic Energy Backscatter (SKEB) scheme. This contributions looks at the impact of these two stochastic parametrisation schemes on the model performance for seasonal forecasts. It is found that these schemes reduce long-standing model biases in the Indonesian warm pool area dominated by intense convection. The simulation of MJO events in the seasonal forecasts has improved due to the stochastic parametrisations. Both schemes substantially increase the ensemble spread for El Niño SST forecasts and thus make the ensemble forecasting system better calibrated. In addition, the stochastic parametrisations also have a positive effect on the simulation of atmospheric quasi-stationary circulation regimes over the extratropical Pacific-North America region.

  10. 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…

  11. 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.

  12. Formulation and results from ensemble forecasting using Multimodels for Hurricanes, Global Weather and Seasonal Climate

    NASA Astrophysics Data System (ADS)

    Krishnamurthi, T. N.

    2006-11-01

    This paper carries a short review of a multimodel/multianalysis superensemble for weather and seasonal climate forecasts. This model was first developed by the authors in 1999 at Florida State University. This entails a large number of forecasts using these multimodels from past data sets, that is called a training phase of the superensemble. During this training phase statistical relation among the model forecasts and the observed fields is obtained using multiple regression methods. This training phase requires roughly 4 months of past daily forecasts for numerical weather prediction (NWP), approximately 6 years of past seasonal forecast and about 60 past hurricane/typhoon/tropical cyclone forecasts from each of the participating member models. The training phase is followed by a forecast phase where the member model forecasts (into the future) use the aforementioned statistics to construct multimodel superensemble forecasts. Our focus on NWP has been to examine the performance of the multimodel superensemble forecast against those of the member models, their ensemble mean and the bias removed ensemble means. We have noted an invariable much superior performance of the multimodel superensemble. We have noted that roughly a minimal number of 7 to 8 models are needed to carry out this exercise. We were also able to improve the database and the statistics of the training phase by rejecting poorer forecast days and optimizing the number of training days. The common metrics for forecast evaluation include root mean square error, anomaly correlation and equitable threat scores. Great impact on real time and experimental forecasts from the superensemble were noted for precipitation, sea level pressure, temperature and 500 hPa geopotential height fields. The improvements in forecasting heavy rains by the multimodel/multianalysis superensemble are found to provide useful guidance in flood events. In hurricane forecasts improvements in track position forecasts of the order of 100 to 250 km were noted in one to three day forecasts. Intensity forecast for hurricanes shows only a marginal improvement. The seasonal climate forecasts show a lower performance from the member models compared to climatology, the multimodel superensemble appears to have skill somewhat above that of climatology.

  13. 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.

  14. 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.

  15. Assessing the skill of seasonal forecasting of streamflow and drought in the Limpopo basin, Southern Africa

    NASA Astrophysics Data System (ADS)

    Werner, M.; Trambauer, P.; Winsemius, H.; Maskey, S.; Dutra, E. N.; Pappenberger, F.

    2014-12-01

    The semi-arid Limpopo Basin in Southern Africa experiences frequent droughts, leading to shortfall of water resources available to irrigation, drinking water supply and environmental flow requirements. In this paper we explore three approaches to seasonal forecasting streamflow and derived drought indices in the basin. The first applies the ECMWF seasonal forecast system (S4), an operational global atmospheric model that provides seasonal ensemble forecast with a lead time of six months. We apply a 30 year hindcast set available for S4 in forcing a 0.05O distributed hydrological model for the basin. The second approach uses the Ensemble Streamflow Prediction (ESP) method. This develops a forecast ensemble of six months lead time based on resampling historic meteorological data over the basin, and we again use this ensemble to force the hydrological model. The third approach again applies the ESP method, but we now use the ENSO index to condition the sampling probabilities. We focus on comparing forecast skill over the wet season which is the most relevant to water users in the basin. Comparison of the skill of the three forecasting approaches in forecasting drought indices and streamflow shows that S4 is moderately skilful at the lead times up to 3-5 months. The ESP forecasts are skilful when compared to climatology, but only for the short lead times, and the skill decays rapidly with lead time. Forecasts based on the conditional ESP ensemble have improved skill when compared to ESP, though S4 forecasts remain superior. Through exploring drought indices that are used by reservoir operators in determining curtailments to water users we show how the forecasts can be meaningful to reservoir operators and irrigators in the basin.

  16. How good do seasonal streamflow forecasts need to be to improve reservoir operation?

    NASA Astrophysics Data System (ADS)

    Anghileri, D.; Voisin, N.; Pianosi, F.; Castelletti, A.; Nijssen, B.; Lettenmaier, D. P.

    2013-12-01

    Reservoir operating rules inform release decisions based on competing demands, priorities, available storage, and reservoir characteristics. Reservoir inflow forecasts over a range of forecast lead times (from days or less to seasons or longer) can improve release decisions and lead to more efficient use of reservoir storage. However, the utility or value of inflow forecasts is directly related to forecast quality. Through a case study of the Oroville-Thermalito reservoir complex in the Feather River Basin, California, we explore the effect of forecast quality on optimal release strategies. Streamflow in the Upper Feather Basin is strongly seasonal signal, with most of the flow occurring during the winter (mostly from rainfall at lower elevations) and spring (from melt of the previous winter's snow accumulation). Accurate prediction of the volume and timing of snowmelt (which is possible via various means, including monitoring of accumulated winter precipitation, and measurements of high elevation snowpack) has the potential to improve reservoir operation. In this study, we use Deterministic Dynamic Programming to optimize medium-range and seasonal reservoir operation based on different forecasts of reservoir inflows. We determine maximum reservoir performance by forcing the optimization with observed inflows, which is equivalent to a perfect forecast. The forecast quality is then progressively degraded to allow forecast skill to be related 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, including streamflow and snow data assimilation, while inflow forecasts are based on seasonal climate forecasts. Although the specific forecast skill level is specific to the Feather River basin, the methodology should be transferable to other systems, especially elsewhere in the western U.S., and other locations with strongly seasonal runoff regimes. We assess the transferability of the case study results to other systems using alternative reservoir characteristics of the Oroville-Thermalito reservoir system as a surrogate for alternate reservoir configurations. Specifically, we explore the sensitivity of reservoir operation performance to the ratio of reservoir mean inflow volume to reservoir capacity and downstream demand requirements.

  17. 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 is critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.

  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.

    2014-03-01

    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 critical for end-of-season outcomes. Finally we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years. This means that this system might be particularity useful for identifying the events that present the greatest risk to the region.

  19. 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. PMID:21431557

  20. Seasonal Rainfall Forecasting Using SST Dipoles with Application to the Southeast US

    NASA Astrophysics Data System (ADS)

    Chen, C.; Georgakakos, A. P.

    2012-12-01

    Advances in seasonal climate predictions have demonstrated considerable benefits to several societal activities, including agriculture, health care, and water resources management. Such climate predictions can rely on empirical approaches based upon the projections of major teleconnection indices (e.g., ENSO) or linear combinations of selected predictor fields [e.g., sea surface temperatures (SST)]. This experience demonstrates that improved prediction skill would lead to higher operational utility and more effective water resources applications. To this end, this study introduces a new forecasting method for seasonal rainfall. The forecasting process first identifies relevant SST dipole predictors for seasonal rainfall through an optimization algorithm based on the Gerrity Skill Score. The resulting forecasts are cross-validated, and a composite of the most significant SST dipole predictors are identified to generate rainfall forecasts in each season. Finally, for a target year, ensemble year-round prediction traces as well as uncertainty intervals can be produced by superimposing hindcasting errors on seasonal rainfall predictions. These forecasts are then used within hydrologic models to drive water resources planning and management models and processes. Applications to the southeast US show significant improvements over existing forecasting methods.

  1. 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 April) SM conditions, the skill of forecasting SM estimates until the end-of-season improved (correlation > 0.5 over several grid cells). We also found these SM forecasts to be more skillful than the ones generated using the Ensemble Streamflow Prediction (ESP) method, which derives its hydrologic forecast skill solely from the knowledge of the initial hydrologic conditions. Finally, we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years (when standardized anomaly of MAM precipitation is below 0). This indicates that this system might be particularity useful for identifying drought events in this region and can support decision-making for mitigation or humanitarian assistance.

  2. 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.

  3. 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.

  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. 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.

  6. 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.

  7. 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 2007 and the Sahel drought in 1983; (ii) The CFS forecast performance for predicting areal monthly precipitation of a river basin for different lead times using a set of verification measures to determine bias, accuracy and skill; (iii) The value of the CFS forecast if the monthly areal information is used for a warning of dry months during the rainy season.

  8. 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.

  9. 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 skill analysis of the seasonal forecast system demonstrates that there is a potential to adapt to this change by utilizing the weather forecasts, given that both indicators can be skilfully predicted for the December-February season, at least 2 months ahead of the wet season. This is particularly the case for predicting above-normal and below-normal conditions. The frequency of heat stress conditions shows better predictability than the frequency of dry spells. Although results are promising for end users on the ground, forecasts alone are insufficient to ensure appropriate response. Sufficient support for appropriate measures must be in place, and forecasts must be communicated in a context-specific, accessible and understandable format.

  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. 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 informative climate indices for the region of interest.

  12. The Utility of Seasonal Climate Forecasts: Understanding Argentine Farmers' Attribute Priorities and Trade-Offs

    NASA Astrophysics Data System (ADS)

    Seipt, E. C.; Easterling, W. E.

    2007-05-01

    A distinct El Niño - Southern Oscillation (ENSO) signal and its impacts have been confirmed in the Argentine Pampas, and precipitation variability is currently recognized as the region's most marked ENSO-driven influence. In the Pampas, precipitation is also a major limiting factor for agricultural production given spatial differences in soil water storage capacities and the region's relatively minimal use of irrigation. Seasonal climate forecasts that provide advanced knowledge of expected ENSO-driven precipitation anomalies may benefit farm management decision-making by helping to either mitigate potentially negative consequences or to take advantage of potentially positive influences. To be useful and applicable, however, these forecasts must suit the decisions that they are meant to inform. In this research, a case study is presented that investigates how farmers in the Pampas prioritize and trade off specific attributes of a seasonal climate forecast (i.e., mode of distribution, spatial resolution, lead time, and forecast performance) when judging its utility. A conjoint analysis evaluation decomposes holistic evaluations of forecasts into the part-worth utilities associated with their different attributes. Part-worth utilities combine to reveal the structure of farmers' forecast utility preferences - a model of the decision-making process. Utility preference structures are analyzed to compute the importance value of each attribute and to determine the trade-offs that farmers find acceptable between different attributes. Analysis indicates that, on average, spatial resolution is the most influential attribute in determining climate forecast utility. Attribute trade-off values suggest that advances in spatial resolution, forecast performance, and/or product dissemination via the Internet offer the greatest potential for increasing the utility of future seasonal climate forecasts for farmers in the Pampas.

  13. Skill of ENSEMBLES seasonal re-forecasts for malaria prediction in West Africa

    NASA Astrophysics Data System (ADS)

    Jones, A. E.; Morse, A. P.

    2012-12-01

    This study examines the performance of malaria-relevant climate variables from the ENSEMBLES seasonal ensemble re-forecasts for sub-Saharan West Africa, using a dynamic malaria model to transform temperature and rainfall forecasts into simulated malaria incidence and verifying these forecasts against simulations obtained by driving the malaria model with General Circulation Model-derived reanalysis. Two subregions of forecast skill are identified: the highlands of Cameroon, where low temperatures limit simulated malaria during the forecast period and interannual variability in simulated malaria is closely linked to variability in temperature, and northern Nigeria/southern Niger, where simulated malaria variability is strongly associated with rainfall variability during the peak rain months.

  14. 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.

  15. 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 of each system based on hindcast and real time skill using hit rate, ROC score, LEPS and other verification techniques to show the forecast skill of each system. As expected, both systems show good skill in El Niño and La Niña years and lower skill in neutral years, when regional teleconnections of tropical SST are lower. In some countries, SCOPIC has higher skill where local orography is important on rainfall i.e. Papua New Guinea and parts of Fiji. SCOPIC also has a longer period of observations at many stations that can be used to train the model, compared to the hindcast period used by POAMA. However, POAMA has good skill across most of the equatorial Pacific and parts of the southwest Pacific, especially in the austral summer, which is the wet season in the southwest Pacific. In the real time forecasts, POAMA has improved skill of 8-10% over SCOPIC using tercile forecasts based on scoring using consistent, near-consistent or inconsistent forecasts. This study highlights some aspects of higher and lower skill from both seasonal forecast systems and both can assist Pacific Island countries with seasonal forecasts and prepare them for climate change and extremes associated with the ENSO.

  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. Ensemble seasonal forecast of extreme water inflow into a large reservoir

    NASA Astrophysics Data System (ADS)

    Gelfan, A. N.; Motovilov, Yu. G.; Moreido, V. M.

    2015-06-01

    An approach to seasonal ensemble forecast of unregulated water inflow into a large reservoir was developed. The approach is founded on a physically-based semi-distributed hydrological model ECOMAG driven by Monte-Carlo generated ensembles of weather scenarios for a specified lead-time of the forecast (3 months ahead in this study). Case study was carried out for the Cheboksary reservoir (catchment area is 374 000 km2) located on the middle Volga River. Initial watershed conditions on the forecast date (1 March for spring freshet and 1 June for summer low-water period) were simulated by the hydrological model forced by daily meteorological observations several months prior to the forecast date. A spatially distributed stochastic weather generator was used to produce time-series of daily weather scenarios for the forecast lead-time. Ensemble of daily water inflow into the reservoir was obtained by driving the ECOMAG model with the generated weather time-series. The proposed ensemble forecast technique was verified on the basis of the hindcast simulations for 29 spring and summer seasons beginning from 1982 (the year of the reservoir filling to capacity) to 2010. The verification criteria were used in order to evaluate an ability of the proposed technique to forecast freshet/low-water events of the pre-assigned severity categories.

  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. Can Regional Climate Models Improve Warm Season Forecasts in the North American Monsoon Region?

    NASA Astrophysics Data System (ADS)

    Dominguez, F.; Castro, C. L.

    2009-12-01

    The goal of this work is to improve warm season forecasts in the North American Monsoon Region. To do this, we are dynamically downscaling warm season CFS (Climate Forecast System) reforecasts from 1982-2005 for the contiguous U.S. using the Weather Research and Forecasting (WRF) regional climate model. CFS is the global coupled ocean-atmosphere model used by the Climate Prediction Center (CPC), a branch of the National Center for Environmental Prediction (NCEP), to provide official U.S. seasonal climate forecasts. Recently, NCEP has produced a comprehensive long-term retrospective ensemble CFS reforecasts for the years 1980-2005. These reforecasts show that CFS model 1) has an ability to forecast tropical Pacific SSTs and large-scale teleconnection patterns, at least as evaluated for the winter season; 2) has greater skill in forecasting winter than summer climate; and 3) demonstrates an increase in skill when a greater number of ensembles members are used. The decrease in CFS skill during the warm season is due to the fact that the physical mechanisms of rainfall at this time are more related to mesoscale processes, such as the diurnal cycle of convection, low-level moisture transport, propagation and organization of convection, and surface moisture recycling. In general, these are poorly represented in global atmospheric models. Preliminary simulations for years with extreme summer climate conditions in the western and central U.S. (specifically 1988 and 1993) show that CFS-WRF simulations can provide a more realistic representation of convective rainfall processes. Thus a RCM can potentially add significant value in climate forecasting of the warm season provided the downscaling methodology incorporates the following: 1) spectral nudging to preserve the variability in the large scale circulation while still permitting the development of smaller-scale variability in the RCM; and 2) use of realistic soil moisture initial condition, in this case provided by the North American Regional Reanalysis. With these conditions, downscaled CFS-WRF reforecast simulations can produce realistic continental-scale patterns of warm season precipitation. This includes a reasonable representation of the North American monsoon in the southwest U.S. and northwest Mexico, which is notoriously difficult to represent in a global atmospheric model. We anticipate that this research will help lead the way toward substantially improved real time operational forecasts of North American summer climate with a RCM.

  20. Calibration and combination of dynamical seasonal forecasts to enhance the value of predicted probabilities for managing risk

    NASA Astrophysics Data System (ADS)

    Dutton, John A.; James, Richard P.; Ross, Jeremy D.

    2013-06-01

    Seasonal probability forecasts produced with numerical dynamics on supercomputers offer great potential value in managing risk and opportunity created by seasonal variability. The skill and reliability of contemporary forecast systems can be increased by calibration methods that use the historical performance of the forecast system to improve the ongoing real-time forecasts. Two calibration methods are applied to seasonal surface temperature forecasts of the US National Weather Service, the European Centre for Medium Range Weather Forecasts, and to a World Climate Service multi-model ensemble created by combining those two forecasts with Bayesian methods. As expected, the multi-model is somewhat more skillful and more reliable than the original models taken alone. The potential value of the multimodel in decision making is illustrated with the profits achieved in simulated trading of a weather derivative. In addition to examining the seasonal models, the article demonstrates that calibrated probability forecasts of weekly average temperatures for leads of 2-4 weeks are also skillful and reliable. The conversion of ensemble forecasts into probability distributions of impact variables is illustrated with degree days derived from the temperature forecasts. Some issues related to loss of stationarity owing to long-term warming are considered. The main conclusion of the article is that properly calibrated probabilistic forecasts possess sufficient skill and reliability to contribute to effective decisions in government and business activities that are sensitive to intraseasonal and seasonal climate variability.

  1. ADAPTING SEASONAL PRECIPITATION FORECASTS FOR AGRICULTURAL AND NATURAL RESOURCE MANAGEMENT: ASSESSMENT OF UTILITY, DOWNSCALING, AND INITIAL STRATEGY FOR IMPLEMENTATION

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The NOAA/Climate Prediction Center seasonal precipitation forecasts offer potentially important information for practical applications. However, given the nature of the forecasts, it is not obvious to agricultural and natural resource managers when or how the forecast information could or should be...

  2. 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...

  3. 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.

  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. PMID:26032315

  5. 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 precipitation forecasts in climate models improves the ability to predict the interannual variability of winter and spring streamflow and groundwater levels over the basin. However, significant conditional bias exists in all the three modeling schemes, which indicates the need to consider improved modeling schemes as well as the availability of longer time-series of observed hydroclimatic information over the basin.

  6. 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.

  7. Risk and Reward: Adaptive Reservoir Operations using Seasonal Climate Forecasts

    NASA Astrophysics Data System (ADS)

    Brown, C.; Souza Filho, F. A.

    2007-12-01

    A typical water manager's objectives consist of retaining enough water in the reservoir to meet urban demand over the decision period, and releasing remaining water to agriculture in accordance with demand. The goal is to release as much water as possible, but not so much that the urban demand is not met. As a result, there are tradeoffs between releasing more water from the reservoir, and improving the yield. The decision hinges on the assumption the water manager holds regarding the future inflows to the reservoir and the risk of an urban water shortfall that the manager is willing to bear. Here we explore the effects of the future inflows to the reservoir that are assumed in making reservoir release decisions. With a conservative assumption of inflows, the reservoir releases will be limited by the continuous need to conserve water for the future. This results in very high reservoir levels that are prepared to be drawn down when the chosen drought scenario occurs. An alternative to these approaches is one that embraces the known year to year variability in the probability of drought at a particular location. We term these approaches "dynamic" risk management and provide an example using real interannual streamflow forecasts applied to reservoir release decision making for Oros Reservoir, NE Brazil.

  8. 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.

  9. Can improved SSTA prediction be translated into better seasonal rainfall forecast?

    NASA Astrophysics Data System (ADS)

    Khan, M. Z. K.; Sharma, A.; Mehrotra, R.; Schepen, A.; Wang, Q.

    2014-12-01

    Seasonal rainfall forecasts are predicted throughout Australia based on concurrent sea surface temperature anomalies (SSTAs) fields coming from the main dynamical Australian model, the Predictive Ocean-Atmosphere Model for Australia (POAMA). In this study, we derive SSTA fields using a multi-model combination approach by including information from five additional models. The combination takes into account the cross-dependence between all the models while combining individual SSTA fields. The resulting SSTA fields are then used to derive SSTA indices to issue seasonal rainfall forecasts rainfall over a 2.5 degree grid using a Bayesian Model Averaging approach currently in use operationally. These forecasts are compared with those derived from a single SSTA model in three settings: (i) 2.5 degree gridded rainfall over Australia; (ii) only grid cells where one of the models (multi-model or single model) had skill (measured in terms of mean squared error relative to climatology) ; and (iii) only grid cells where both of the models had skill. The results indicate that the forecasts derived using multi-model based SSTA indices offer clear improvements over the case where a single SSTA model is used in three seasons, ASO (August, September and October), FMA (February, March and April), and MJJ (May, June and July). A further assessment over grid locations where predictability is significantly better than climatology indicates consistent improvements when the multi-model SSTA fields are used.

  10. Development of an Operational Hydrological Monitoring and Seasonal Forecast System for China

    NASA Astrophysics Data System (ADS)

    Tang, Q.; Zhang, X.

    2014-12-01

    Hydrological monitoring and forecast are critical for disaster mitigation and water resources management. Although large investments have been made in climate forecasting and in related monitoring of land surface conditions, the experimental streamflow monitoring and forecast system is yet to be developed for China. We propose a frame to collect near-real-time meteorological forcings from various sources, to apply land surface hydrological model to simulate hydrological states and fluxes, and to generate ensemble seasonal forecasts of river discharge and soil moisture over China. A retrospective land surface hydrologic fluxes and states dataset with a 0.25° spatial resolution and a 3-hourly time step was developed using the Variable Infiltration Capacity (VIC) model as driven by gridded observation-based meteorological forcings in 1952-2012. The VIC simulations were carefully calibrated against the available streamflow observations and the simulated river discharge matched well with the observed monthly streamflow at the large river basins in China. The Tropical Rainfall Measuring Mission (TRMM) based near-real-time satellite precipitation product was adjusted at each grid to match the daily precipitation distribution with the ground observations during the period of 2000-2010. The adjusted satellite precipitation was used to simulate hydrological states and fluxes in a near-real-time manner and to provide initial hydrological conditions for seasonal forecast. The performance of hydrological monitoring and skill of seasonal streamflow prediction were assessed. The potential and challenges of using the operational monitoring and forecast system for improved flooding and drought management are discussed.

  11. Prediction of the Caspian Sea level using ECMWF seasonal forecasts and reanalysis

    NASA Astrophysics Data System (ADS)

    Arpe, K.; Leroy, S. A. G.; Wetterhall, F.; Khan, V.; Hagemann, S.; Lahijani, H.

    2014-07-01

    The hydrological budget of the Caspian Sea (CS) is investigated using the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERAi) and seasonal forecast (FCST) data with the aim of predicting the Caspian Sea Level (CSL) some months ahead. Precipitation and evaporation are used. After precipitation events over the Volga River, the discharge (Volga River discharge (VRD)) follows with delays, which are parameterized. The components of the water budget from ERAi and FCSTs are integrated to obtain time series of the CSL. Observations of the CSL and the VRD are used for comparison and tuning. The quality of ERAi data is sufficiently good to calculate the time variability of the CSL with a satisfactory accuracy. Already the storage of water within the Volga Basin allows forecasts of the CSL a few months ahead, and using the FCSTs of precipitation improves the CSL forecasts. The evaporation in the seasonal forecasts is deficient due to unrealistic sea surface temperatures over the CS. Impacts of different water budget terms on the CSL variability are shown by a variety of validation tools. The importance of precipitation anomalies over the catchment of the Volga River is confirmed, but also impacts from the two southern rivers (Sefidrud and Kura River) and the evaporation over the CS become obvious for some periods. When pushing the FCSTs beyond the limits of the seasonal FCSTs to 1 year, considerable forecast skill can still be found. Validating only FCSTs by the present approach, which show the same trend as one based on a statistical method, significantly enhances the skill scores.

  12. 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 precipitation at the SLF, and 6) strongest wind in the lowest 3000 ft. The forecast tool was developed as a graphical user interface with Microsoft Excel to help the forecaster enter the variables, and run the appropriate regression equations. Based on the forecaster's input and regression equations, a forecast of the day's peak and average wind is generated and displayed. The application also outputs the probability that the peak wind speed will be ^ 35 kt, 50 kt, and 60 kt.

  13. A seasonal climate and streamflow forecasting testbed for the Colorado River Basin

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Werner, K.; Schmidt, M.

    2011-12-01

    CBRFC, NIDIS, USBR, and others have documented a consistent need for climate forecasts from one season to two years lead time to support a variety of applications, and particularly for streamflow forecasting for water, energy and agricultural management. The Colorado River basin presents a challenge due to the limited forecast skill that can be harnessed from traditional sources (e.g., ENSO) even at shorter lead times for runoff-generating headwaters in the upper basin. Nonetheless, management and planning objectives related to the larger reservoirs that USBR manages make use of predictions out to two full years. To facilitate intercomparison of research results toward improving climate and flow prediction at these lead times, CBRFC has formed a tested that targets CBRFC's USBR-oriented predictions in the Colorado River basin. The testbed contains climate and flow hindcasts for eight critical watersheds, defining the current state of the practice to support basin water management. These forecasts include those derived from the CFS and GFS, and from the CPC objective consolidation. The testbed environment also illustrates pathways for transfer of promising methods into the operational forecast environment, and define the constraints applicable to those pathways. This presentation describes the testbed and the skill of the hindcasts it currently contains, and invites additional contributions from the climate and flow forecasting community.

  14. The use of seasonal forecasts in a crop failure early warning system for West Africa

    NASA Astrophysics Data System (ADS)

    Nicklin, K. J.; Challinor, A.; Tompkins, A.

    2011-12-01

    Seasonal rainfall in semi-arid West Africa is highly variable. Farming systems in the region are heavily dependent on the monsoon rains leading to large variability in crop yields and a population that is vulnerable to drought. The existing crop yield forecasting system uses observed weather to calculate a water satisfaction index, which is then related to expected crop yield (Traore et al, 2006). Seasonal climate forecasts may be able to increase the lead-time of yield forecasts and reduce the humanitarian impact of drought. This study assesses the potential for a crop failure early warning system, which uses dynamic seasonal forecasts and a process-based crop model. Two sets of simulations are presented. In the first, the crop model is driven with observed weather as a control run. Observed rainfall is provided by the GPCP 1DD data set, whilst observed temperature and solar radiation data are given by the ERA-Interim reanalysis. The crop model used is the groundnut version of the General Large Area Model for annual crops (GLAM), which has been designed to operate on the grids used by seasonal weather forecasts (Challinor et al, 2004). GLAM is modified for use in West Africa by allowing multiple planting dates each season, replanting failed crops and producing parameter sets for Spanish- and Virginia- type West African groundnut. Crop yields are simulated for three different assumptions concerning the distribution and relative abundance of Spanish- and Virginia- type groundnut. Model performance varies with location, but overall shows positive skill in reproducing observed crop failure. The results for the three assumptions are similar, suggesting that the performance of the system is limited by something other than information on the type of groundnut grown. In the second set of simulations the crop model is driven with observed weather up to the forecast date, followed by ECMWF system 3 seasonal forecasts until harvest. The variation of skill with forecast date is assessed along with the extent to which forecasts can be improved by bias correction of the rainfall data. Two forms of bias correction are applied: a novel method of spatially bias correcting daily data, and statistical bias correction of the frequency and intensity distribution. Results are presented using both observed yields and the control run as the reference for verification. The potential for current dynamic seasonal forecasts to form part of an operational system giving timely and accurate warnings of crop failure is discussed. Traore S.B. et al., 2006. A Review of Agrometeorological Monitoring Tools and Methods Used in the West African Sahel. In: Motha R.P. et al., Strengthening Operational Agrometeorological Services at the National Level. Technical Bulletin WAOB-2006-1 and AGM-9, WMO/TD No. 1277. Pages 209-220. www.wamis.org/agm/pubs/agm9/WMO-TD1277.pdf Challinor A.J. et al., 2004. Design and optimisation of a large-area process based model for annual crops. Agric. For. Meteorol. 124, 99-120.

  15. 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 configuration options are best to address this specific forecast concern, the Weather Research and Forecasting (WRF) model, which has two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM) was employed. In addition to the two dynamical cores, there are also two options for a "hot-start" initialization of the WRF model - the Local Analysis and Prediction System (LAPS; McGinley 1995) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS; Brewster 1996). Both LAPS and ADAS are 3- dimensional weather analysis systems that integrate multiple meteorological data sources into one consistent analysis over the user's domain of interest. This allows mesoscale models to benefit from the addition of highresolution data sources. Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and MLB with considerable flexibility as well as challenges. It is the goal of this study to assess the different configurations available and to determine which configuration will best predict warm season convective initiation.

  16. Probability-weighted historical patterns for downscaling seasonal forecasts using minimum relative entropy

    NASA Astrophysics Data System (ADS)

    Weijs, S. V.; van de Giesen, N.

    2012-04-01

    Seasonal forecasts based on large scale circulation patterns provide weak predictive power for climatic deviations with lead times of several months. For example, the ENSO and PDO indices from November to February contain some information about the climate in the Pacific Northwest of the USA from April to August. These forecasts are probabilistic and large scale, often concerning regional averages over several months. To be useful in planning for, for example, management of hydropower reservoirs, these forecasts need to be downscaled to finer space and time resolution, providing reasonable realizations of spatial and temporal variability and correlations. Especially for planning of multiple-reservoir systems, which are interconnected through both the hydrological network and the power-grid, it is important to have correct statistical properties at smaller scales, while still benefitting from the information about the large scales. One method to achieve this is to use the forecast to obtain a probability weighted ensemble of historical patterns, e.g. fields or time series of observations. These ensembles, having non-uniform weights, can be used in weighted Monte-Carlo simulations for risk analysis and planning of reservoir operations. We present a method to determine the weights based on moments of the large-scale forecast, while ensuring the deviations from uniform weights do not introduce more information than is warranted by the forecast. This is ensured by minimizing the relative entropy from the original uniform distribution of weights to the new weights, while simultaneously satisfying the constraints posed by the moments of the large-scale forecast information. We analyze the results of the minimum relative entropy update and compare it to other methods for obtaining weighted ensemble forecasts. This analysis from an information-theoretical perspective shows how the new method balances information and lost uncertainty, while other methods may not use all information or create false certainty.

  17. 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.

  18. Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

    SciTech Connect

    Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.; Diao, Ruisheng; Lu, Ning

    2014-04-14

    To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation. We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.

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

    PubMed

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

    2014-06-28

    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-latitude circulation regimes over the Pacific-North America region. PMID:24842026

  20. 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-latitude circulation regimes over the Pacific–North America region. PMID:24842026

  1. Client-Friendly Forecasting: Seasonal Runoff Predictions Using Out-of-the-Box Indices

    NASA Astrophysics Data System (ADS)

    Weil, P.

    2013-12-01

    For more than a century, statistical relationships have been recognized between atmospheric conditions at locations separated by thousands of miles, referred to as teleconnections. Some of the recognized teleconnections provide useful information about expected hydrologic conditions, so certain records of atmospheric conditions are quantified and published as hydroclimate indices. Certain hydroclimate indices can serve as strong leading indicators of climate patterns over North America and can be used to make skillful forecasts of seasonal runoff. The methodology described here creates a simple-to-use model that utilizes easily accessed data to make forecasts of April through September runoff months before the runoff season begins. For this project, forecasting models were developed for two snowmelt-driven river systems in Colorado and Wyoming. In addition to the global hydroclimate indices, the methodology uses several local hydrologic variables including the previous year's drought severity, headwater snow water equivalent and the reservoir contents for the major reservoirs in each basin. To improve the skill of the forecasts, logistic regression is used to develop a model that provides the likelihood that a year will fall into the upper, middle or lower tercile of historical flows. Categorical forecasting has two major advantages over modeling of specific flow amounts: (1) with less prediction outcomes models tend to have better predictive skill and (2) categorical models are very useful to clients and agencies with specific flow thresholds that dictate major changes in water resources management. The resulting methodology and functional forecasting model product is highly portable, applicable to many major river systems and easily explained to a non-technical audience.

  2. Season-ahead water quality forecasts for the Schuylkill River, Pennsylvania

    NASA Astrophysics Data System (ADS)

    Block, P. J.; Leung, K.

    2013-12-01

    Anticipating and preparing for elevated water quality parameter levels in critical water sources, using weather forecasts, is not uncommon. In this study, we explore the feasibility of extending this prediction scale to a season-ahead for the Schuylkill River in Philadelphia, utilizing both statistical and dynamical prediction models, to characterize the season. This advance information has relevance for recreational activities, ecosystem health, and water treatment, as the Schuylkill provides 40% of Philadelphia's water supply. The statistical model associates large-scale climate drivers with streamflow and water quality parameter levels; numerous variables from NOAA's CFSv2 model are evaluated for the dynamical approach. A multi-model combination is also assessed. Results indicate moderately skillful prediction of average summertime total coliform and wintertime turbidity, using season-ahead oceanic and atmospheric variables, predominantly from the North Atlantic Ocean. Models predicting the number of elevated turbidity events across the wintertime season are also explored.

  3. 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.

  4. The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models

    NASA Astrophysics Data System (ADS)

    Demirel, M. C.; Booij, M. J.; Hoekstra, A. Y.

    2015-01-01

    This paper investigates the skill of 90-day low-flow forecasts using two conceptual hydrological models and one data-driven model based on Artificial Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV, GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low-flow forecasts for five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the models. The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the models are compared based on their skill of low-flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90-day-ahead low flows in the very dry year 2003 without precipitation data. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.

  5. 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.

  6. Multi-Model Seasonal Forecasting at Environment Canada: Past, Present and Future

    NASA Astrophysics Data System (ADS)

    Merryfield, W. J.

    2014-12-01

    Typically, a given operational or modeling center produces seasonal forecasts using a single model version that represents that center's current state of model development. By contrast, Environment Canada (EC) has employed multi-model ensembles to produce its dynamical seasonal forecasts since their inception in 1995. Successive versions of this forecasting system have drawn upon "models of opportunity" that were developed for other purposes by EC's climate and meteorological modeling centers. This talk will describe the origins of this single-center/multi-model approach, and its evolution through configurations consisting of two and subsequently four 2-tier models in the 1990s and 2000s, to the current Canadian Seasonal to Interannual Prediction System (CanSIPS). The latter is based on two global climate models, CanCM3 and CanCM4, that employ common ocean, sea ice and land components coupled to two versions of CCCma's atmospheric model. Lessons learned concerning the efficacy of this multi-model approach will be discussed, focusing on the relative merits of adding more ensemble members versus adding more models, and on methods for combination and calibration. Current efforts aimed at improving the ensemble of prediction models contributing to CanSIPS and the techniques used to initialize them will then be summarized.

  7. Dynamical-statistical Forecasting of Seasonal Air Temperature Over European Part of Russia

    NASA Astrophysics Data System (ADS)

    Khan, V.

    2008-12-01

    The aim of the present study is to improve prediction of seasonal surface air temperature using outputs of 7 GCMs from Russia, Korea, USA, and Japan. Geographical regions in European part of Russia with identical pattern of variability of monthly air temperature were identified using one objective classification method. Averaged over each identified regions air temperatures from NCEP/DOE reanalysis dataset were used as a predictant matrix. Modified "Perfect Prognosis" method was served as forecasting approach. Consistent spatial patterns between forecasted by different models H-500 fields and smoothed reanalysis air temperature were found. EOF analysis was applied to these informative areas separately for negative and positive correlation patterns. The 1st EOF for each model corresponded to maximal ~80% explained variance of H-500, and the convergence of EOFs for positive correlation areas was higher then that for negative. Predictor data set was created selecting only 1st EOFs of H-500 of 7 input models. Stepwise multiple regression technique allowed selecting optimal two predictor variables. The results demonstrated improved forecast skill compared to separate model forecasts and multi-model mean forecasts. This study has been supported by RFBR grants 07-05-00740, 07-05-13591.

  8. Incorporating probabilistic seasonal climate forecasts into river management using a risk-based framework

    NASA Astrophysics Data System (ADS)

    Towler, Erin; Roberts, Mike; Rajagopalan, Balaji; Sojda, Richard S.

    2013-08-01

    Despite the influence of hydroclimate on river ecosystems, most efforts to date have focused on using climate information to predict streamflow for water supply. However, as water demands intensify and river systems are increasingly stressed, research is needed to explicitly integrate climate into streamflow forecasts that are relevant to river ecosystem management. To this end, we present a five step risk-based framework: (1) define risk tolerance, (2) develop a streamflow forecast model, (3) generate climate forecast ensembles, (4) estimate streamflow ensembles and associated risk, and (5) manage for climate risk. The framework is successfully demonstrated for an unregulated watershed in southwest Montana, where the combination of recent drought and water withdrawals has made it challenging to maintain flows needed for healthy fisheries. We put forth a generalized linear modeling (GLM) approach to develop a suite of tools that skillfully model decision-relevant low flow characteristics in terms of climate predictors. Probabilistic precipitation forecasts are used in conjunction with the GLMs, resulting in season-ahead prediction ensembles that provide the full risk profile. These tools are embedded in an end-to-end risk management framework that directly supports proactive fish conservation efforts. Results show that the use of forecasts can be beneficial to planning, especially in wet years, but historical precipitation forecasts are quite conservative (i.e., not very "sharp"). Synthetic forecasts show that a modest "sharpening" can strongly impact risk and improve skill. We emphasize that use in management depends on defining relevant environmental flows and risk tolerance, requiring local stakeholder involvement.

  9. 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 is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision at the Shuttle Landing Facility. 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 (TAFs), 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. Both the SMG and the MLB are currently implementing the Weather Research and Forecasting Environmental Modeling System (WRF EMS) software into their operations. The WRF EMS software allows users to employ both 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). Having a series of initialization options and WRF cores, as well as many 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.

  10. Probabilistic forecasting of seasonal drought behaviors in the Huai River basin, China

    NASA Astrophysics Data System (ADS)

    Xiao, Mingzhong; Zhang, Qiang; Singh, Vijay P.; Chen, Xiaohong

    2016-01-01

    The Huai River basin is one of the major supplier of agricultural products in China, and droughts have critical impacts on agricultural development. Good knowledge of drought behaviors is of great importance in the planning and management of agricultural activities in the Huai River basin. With the copula functions to model the persistence property of drought, the probabilistic seasonal drought forecasting models have been built in the Huai River basin. In this study, droughts were monitored by the Standardized Precipitation Evapotranspiration Index (SPEI) with the time scales of 3, 6, and 9 months, and their composite occurrence probability has been used to forecast the seasonal drought. Results indicated that the uncertainty related to the predicted seasonal drought is larger when more severe droughts occurred in the previous seasons, and the severe drought which occurs in summer and autumn will be more likely to be persistent in the next season while the severe drought in winter and spring will be more likely to be recovered in the subsequent season. Furthermore, given the different drought statuses in the previous season, spatial patterns of the predicted drought events with the largest occurrence probability have also been investigated, and results indicate that the Huai River basin is vulnerable to the extreme drought in most parts of the basin, e.g., the severe drought in winter will be more likely to be persistent in spring in the central part of the southern Huai River basin. Such persistent drought events pose serious challenges for planning and management of agricultural irrigation, then results of the study will be valuable for the planning of crop cultivation or mitigation of the losses caused by drought in the Huai River basin, China.

  11. 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.

  12. 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.

  13. 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.

  14. Long-range seasonal streamflow forecasting over the Iberian Peninsula using large-scale atmospheric and oceanic information

    NASA Astrophysics Data System (ADS)

    Hidalgo-Muñoz, J. M.; Gámiz-Fortis, S. R.; Castro-Díez, Y.; Argüeso, D.; Esteban-Parra, M. J.

    2015-05-01

    Identifying the relationship between large-scale climate signals and seasonal streamflow may provide a valuable tool for long-range seasonal forecasting in regions under water stress, such as the Iberian Peninsula (IP). The skill of the main teleconnection indices as predictors of seasonal streamflow in the IP was evaluated. The streamflow database used was composed of 382 stations, covering the period 1975-2008. Predictions were made using a leave-one-out cross-validation approach based on multiple linear regression, combining Variance Inflation Factor and Stepwise Backward selection to avoid multicollinearity and select the best subset of predictors. Predictions were made for four forecasting scenarios, from one to four seasons in advance. The correlation coefficient (RHO), Root Mean Square Error Skill Score (RMSESS), and the Gerrity Skill Score (GSS) were used to evaluate the forecasting skill. For autumn streamflow, good forecasting skill (RHO>0.5, RMSESS>20%, GSS>0.4) was found for a third of the stations located in the Mediterranean Andalusian Basin, the North Atlantic Oscillation of the previous winter being the main predictor. Also, fair forecasting skill (RHO>0.44, RMSESS>10%, GSS>0.2) was found in stations in the northwestern IP (16 of these located in the Douro and Tagus Basins) with two seasons in advance. For winter streamflow, fair forecasting skill was found for one season in advance in 168 stations, with the Snow Advance Index as the main predictor. Finally, forecasting was poorer for spring streamflow than for autumn and winter, since only 16 stations showed fair forecasting skill in with one season in advance, particularly in north-western of IP.

  15. 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 methods and may support better water resources management by providing advance information on emerging droughts. The value of the dipole method in water management is currently being evaluated through coupling with detailed hydrologic and water resources models.

  16. Climate and Salmon Restoration in the Columbia River Basin: The Role and Usability of Seasonal Forecasts.

    NASA Astrophysics Data System (ADS)

    Pulwarty, Roger S.; Redmond, Kelly T.

    1997-03-01

    The Pacific Northwest is dependent on the vast and complex Columbia River system for power production, irrigation, navigation, flood control, recreation, municipal and industrial water supplies, and fish and wildlife habitat. In recent years Pacific salmon populations in this region, a highly valued cultural and economic resource, have declined precipitously. Since 1980, regional entities have embarked on the largest effort at ecosystem management undertaken to date in the United States, primarily aimed at balancing hydropower demands with salmon restoration activities. It has become increasingly clear that climatically driven fluctuations in the freshwater and marine environments occupied by these fish are an important influence on population variability. It is also clear that there are significant prospects of climate predictability that may prove advantageous in managing the water resources shared by the long cast of regional interests. The main thrusts of this study are 1) to describe the climate and management environments of the Columbia River basin, 2) to assess the present degree of use and benefits of available climate information, 3) to identify new roles and applications made possible by recent advances in climate forecasting, and 4) to understand, from the point of view of present and potential users in specific contexts of salmon management, what information might be needed, for what uses, and when, where, and how it should be provided. Interviews were carried out with 32 individuals in 19 organizations involved in salmon management decisions. Primary needs were in forecasting runoff volume and timing, river transit times, and stream temperatures, as much as a year or more in advance. Most respondents desired an accuracy of 75% for a seasonal forecast. Despite the significant influence of precipitation and its subsequent hydrologic impacts on the regional economy, no specific use of the present climate forecasts was uncovered. Understanding the limitations to information use forms a major component of this study. The complexity of the management environment, the lack of well-defined linkages among potential users and forecasters, and the lack of supplementary background information relating to the forecasts pose substantial barriers to future use of forecasts. Recommendations to address these problems are offered. The use of climate information and forecasts to reduce the uncertainty inherent in managing large systems for diverse needs bears significant promise.

  17. 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.

  18. 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, CPC Merged Analysis of Precipitation and the India Meteorological Department precipitation dataset respectively for L3. Despite significant El-Niño Southern Oscillation (ENSO) spring predictability barrier at L3, the ISMR skill score is highest at L3. Further, large scale zonal wind shear (Webster-Yang index) and SST over Niño3.4 region is best at L1 and L0. This implies that predictability aspect of ISMR is controlled by factors other than ENSO and Indian Ocean Dipole. Also, the model error (forecast error) outruns the error acquired by the inadequacies in the initial conditions (predictability error). Thus model deficiency is having more serious consequences as compared to the initial condition error for the seasonal forecast. All the model parameters show the increase in the predictability error as the lead decreases over the equatorial eastern Pacific basin and peaks at L2, then it further decreases. The dynamical consistency of both the forecast and the predictability error among all the variables indicates that these biases are purely systematic in nature and improvement of the physical processes in the CFSv2 may enhance the overall predictability.

  19. 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.

    2015-06-01

    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, CPC Merged Analysis of Precipitation and the India Meteorological Department precipitation dataset respectively for L3. Despite significant El-Niño Southern Oscillation (ENSO) spring predictability barrier at L3, the ISMR skill score is highest at L3. Further, large scale zonal wind shear (Webster-Yang index) and SST over Niño3.4 region is best at L1 and L0. This implies that predictability aspect of ISMR is controlled by factors other than ENSO and Indian Ocean Dipole. Also, the model error (forecast error) outruns the error acquired by the inadequacies in the initial conditions (predictability error). Thus model deficiency is having more serious consequences as compared to the initial condition error for the seasonal forecast. All the model parameters show the increase in the predictability error as the lead decreases over the equatorial eastern Pacific basin and peaks at L2, then it further decreases. The dynamical consistency of both the forecast and the predictability error among all the variables indicates that these biases are purely systematic in nature and improvement of the physical processes in the CFSv2 may enhance the overall predictability.

  20. Seasonal forecasts of hydrological drought in the Limpopo basin: Getting the most out of a bouquet of methods.

    NASA Astrophysics Data System (ADS)

    Seibert, Mathias; Trambauer, Patricia

    2015-04-01

    Droughts are a widespread natural hazard with large socio-economical and environmental impacts. Preparedness to droughts can be enhanced by seasonal drought early warning. When warned several months ahead of a drought event water managers can trigger action plans to mitigate drought and reduce the risk for severe impacts. Seasonal streamflow forecast systems have been dominated by statistical methods in the past. Recently, dynamic physics-based seasonal forecasts from global climate models have become available operationally. In combination with hydrological models these modern forecast systems have the potential to replace the seasonal statistical forecasts. However, at lead times exceeding 6 months statistical methods might still be useful. In this study we present a forecast scheme for streamflow in the Limpopo basin in Southern Africa, combining statistical methods at longer lead times with a dynamical forecast, a distributed hydrological model forced with the ECMWF seasonal forecast system S4, for shorter lead times ( <6 months). The statistical model is set up and tailor-made specifically for drought early warning at a station of interest. The main advantage is that the approach is simple and requires little infrastructure once the model is set up. However, the model does not provide any more information than what it was set up for. This is where the second approach, the dynamical forecast, has its greatest advantage. The model provides a great array of information regarding the catchment status and therefore can be used to forecast a variety of indicators. The skill of the presented systems is higher than climatology (ROC > 0.5) for both methods. There was a large difference in predictability between stations. Yet, the skill in several stations was good. We show that the presented approach is feasible and can provide useful information for drought early warning systems and water managers.

  1. Some Aspects of Forecasting Severe Thunderstorms during Cool-Season Return-Flow Episodes.

    NASA Astrophysics Data System (ADS)

    Weiss, Steven J.

    1992-08-01

    Historically, the Gulf of Mexico has been considered a primary source of water vapor that influences the weather for much of the United States east of the Rocky Mountains. Although severe thunderstorms and tornadoes occur most frequently during the spring and summer months, the periodic transport of Gulf moisture inland ahead of traveling baroclinic waves can result in significant severe-weather episodes during the cool season.To gain insight into the short-range skill in forecasting surface synoptic patterns associated with moisture return from the Gulf, operational numerical weather prediction models from the National Meteorological Center were examined. Sea level pressure fields from the Limited-Area Fine-Mesh Model (LFM), Nested Grid Model (NGM), and the aviation (AVN) run of the Global Spectral Model, valid 48 h after initial data time, were evaluated for three cool-season cases that preceded severe local storm outbreaks. The NGM and AVN provided useful guidance in forecasting the onset of return flow along the Gulf coast. There was a slight tendency for these models to be slightly slow in the development of return flow. In contrast the LFM typically overforecasts the occurrence of return flow and tends to `open the Gulf' from west to east too quickly.Although the low-level synoptic pattern may be forecast correctly, the overall prediction process is hampered by a data void over the Gulf. It is hypothesized that when the return-flow moisture is located over the Gulf, model forecasts of stability and the resultant operational severe local storm forecasts are less skillful compared to situations when the moisture has spread inland already. This hypothesis is tested by examining the performance of the initial second-day (day 2) severe thunderstorm outlook issued by the National Severe Storms Forecast Center during the Gulf of Mexico Experiment (GUFMEX) in early 1988.It has been found that characteristically different air masses were present along the Gulf coast prior to the issuance of outlooks that accurately predicted the occurrence of severe thunderstorms versus outlooks that did not verify well. Unstable air masses with ample low-level moisture were in place along the coast prior to the issuance of the `good' day 2 outlooks, whereas relatively dry, stable air masses were present before the issuance of `false-alarm' outlooks. In the latter cases, large errors in the NGM 48-h lifted-index predictions were located north of the Gulf coast.

  2. 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.

  3. Probabilistic Prediction of European Winter Temperature and Their Application Using The Ecmwf Seasonal Forecast System 1 and 2

    NASA Astrophysics Data System (ADS)

    Müller, W.; Appenzeller, Ch.

    There is rising interest in economical applications of seasonal climate forecasts, for example for weather risk and weather derivative markets. However seasonal forecast- ing based on coupled atmosphere -ocean models is a complex task. As a consequence of the chaotic nature of the climate system seasonal forecasts can not be calculated in a deterministic sense. They need to be calculated in a probabilistic way by using an ensemble of model runs with slightly different initial conditions. Here we use the ECMWF experimental ensemble prediction system 1 and 2 to explore the sensitivity of mid-latitude winter mean temperature forecasts on different drift correction methods. The forecast quality is quantified in a probabilistic framework using ranked proba- bility skill scores (RPSS). It is shown that a drift correction method that accounts for system 1 decadal climate variability (such as the NAO) has a positive, but weak impact on the forecast skill, especially over Europe. As an economic application we evaluate the skill of three month averaged heating degree days forecasts over Europe.

  4. 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. Linear calculations that include forcing from the AGCM variance of the tropical forcing and submonthly transients show a small impact on the variability over the Pacific/North American region compared with that of the base state differences.

  5. 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

    2016-02-01

    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.

  6. Seasonal forecasting of intense tropical cyclones over the North Atlantic and the western North Pacific basins

    NASA Astrophysics Data System (ADS)

    Choi, Woosuk; Ho, Chang-Hoi; Jin, Chun-Sil; Kim, Jinwon; Feng, Song; Park, Doo-Sun R.; Schemm, Jae-Kyung E.

    2016-02-01

    Intense tropical cyclones (TCs) accompanying torrential rain and powerful wind gusts often cause substantial socio-economic losses in the regions around their landfall. This study analyzes intense TCs in the North Atlantic (NA) and the western North Pacific (WNP) basins during the period 1982-2013. Different intensity criteria are used to define intense TCs for these two basins, category 1 and above for NA and category 3 and above for WNP, because the number of TCs in the NA basin is much smaller than that in the WNP basin. Using a fuzzy clustering method, intense TC tracks in the NA and the WNP basins are classified into two and three representative patterns, respectively. On the basis of the clustering results, a track-pattern-based model is then developed for forecasting the seasonal activities of intense TCs in the two basins. Cross-validation of the model skill for 1982-2013 as well as verification of a forecast for the 2014 TC season suggest that our intense TC model is applicable to operational uses.

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

    PubMed

    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

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

    NASA Astrophysics Data System (ADS)

    Dowdy, Andrew J.

    2016-02-01

    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.

  9. 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

  10. Impact of realistic soil moisture initialization on seasonal forecasting of continental near surface variables.

    NASA Astrophysics Data System (ADS)

    Boisserie, M.; Cocke, S.; Shin, D. W.

    2009-04-01

    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 have 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 this, we use a technique named the Precipitation Assimilation Reanalysis (PAR) and described in Nunes and Cocke (2004). This technique consists of modifying the vertical profile of humidity as a function of the observed and predicted model rain rates. In the second experiment, the model is initialized without precipitation assimilation. For each experiment, ten sets of seasonal forecasts of the coupled land-atmosphere Florida State University/Center for Ocean and Atmosphere Predictions Studies (FSU/COAPS) model were generated, starting from the boreal summer of each year between 1986 and 1995. For each forecast, ten ensembles are produced by starting the forecast from the 1st and the 15th of each month from April to August. The results of these experiments show, first, that the PAR technique greatly improves the temporal and spatial variability of soil moisture throughout the precipitation assimilation. Second, using these realistic soil moisture initial conditions, we demonstrate an increase in the seasonal forecasting skills for the continental precipitation and temperature over the "hot spots" - identified by the GLACE-1 multi-model study as regions where the evaporation from soil moisture has a strong effect on precipitation (Koster et al. 2004) - of the central United States. The results of this study will be also involved in the GLACE-2 international multi-model experiment.

  11. Assessment and limits of the existent seasonal forecasts as support for the decision making process in the Sahel

    NASA Astrophysics Data System (ADS)

    Bacci, Maurizio; Genesio, Lorenzo; di Vecchia, Andrea; Tarchiani, Vieri; Vignaroli, Patrizio

    2010-05-01

    The economy of West Africa sahelian countries is based on the primary sector and the population's food security is strictly linked to rainfed crops production. The sahelian countries constitute a belt from Senegal to Chad characterized by an unimodal rainfall distribution during the summer months. The rainfall spatial and time distribution are very variable: dry spells and shifts in the rainy season onset are very frequent. The famines that stroke the region in the past demonstrate that in these ecosystems drought represents a key factor for the food security. In particular, anomalies in precipitation amount and distribution represent the major cause of losses in rainfed agriculture. The early availability of information on the development of the rainy season is essential for decision makers to assess the level of risk in terms of spatial extension and intensity, to take consequent decision on the mobilization of national/international stocks and to provide information for farmers orienting their choices for risk reduction. Addressing these needs, since late 90's the scientific community begins to develop long term meteorological forecast models. Nowadays, despite the general awareness on their potential role in food crises prevention, seasonal forecasts are still under exploited at regional/national level. Indeed, the major constraints to their operational use are (i) the reduced skill in intercepting key aspects of the agricultural season such as starting and ending date and presence of dry spells, and (ii) the difficulty of decision makers of understanding and consequently handling the level of uncertainty of the predictive information. Today the growing demand for early information to support decision-making requires an improvement in the suitability of seasonal forecasts and in their tailoring to users. The aim of this paper is contributing to the scientific debate on Seasonal Forecast proposing possible orientations for models further development and the production of new outputs translating climate forecast in impact forecast. The paper provides an assessment of the existing seasonal forecast products available for West African and highlighting the relevance of information for decision-making process. Moreover, it underlines the actual limits of the seasonal forecast products and the improvements needed to achieve more useful information for the end-user. The operational framework is the Food Crises Prevention Calendar (CPC) , which characterizes the crisis level in order to identify the appropriate information for decision makers in terms of timing, content and format. The study intend to differentiate Regional and National needs, evidencing that working at different scales is not only a resolution problem.

  12. Seasonal forecasts of the exceptional boreal winters of 2009/10 and 2010/11

    NASA Astrophysics Data System (ADS)

    Fereday, D.; Maidens, A.; Knight, J.; Scaife, A. A.; Arribas, A.; MacLachlan, C.; Peterson, D.

    2012-12-01

    The northern hemisphere winters of 2009/10 and 2010/11 were exceptional, with extremes of both atmospheric circulation and temperature. This presentation examines the causes and predictability of these extreme winters within the UK Met Office seasonal forecast system GloSea4. In winter 2009/10 the North Atlantic Oscillation (NAO) index was the lowest on record for over a century, contributing to cold conditions over large areas of Eurasia and North America. The then-operational version of GloSea4 used a "low top" model and successfully predicted a negative NAO in forecasts produced in September-November 2009. GloSea4 was later changed to use a "high top" model, which better simulates sudden stratospheric warmings. These events are shown to play an influential role in surface conditions, producing a stronger sea level pressure signal in the high top model and further improving retrospective predictions of the 2009/10 winter. Early winter 2010/11 also saw record-breaking cold anomalies over much of northern Europe, once again associated with a very negative NAO index. The negative winter NAO signal was forecast with near unanimity by the 11 WMO Global Producing Centres (including GloSea4) in September. Different potential mechanisms have been identified as driving the NAO, including El Niño-Southern Oscillation teleconnections, autumn Eurasian snow cover, Arctic ice extent and North Atlantic sea surface temperature (SST) anomalies. The representation of these mechanisms in GloSea4 is assessed using hindcasts for the period 1989-2009. The November 2010 forecast for December is examined using ensembles of atmosphere only runs, forced with SST fields as forecast in November 2010. We relax possible forcings (the strong La Niña in November 2010, the Arctic ice field, and the North Atlantic SST tripole) back to climatology to see which forcings produce the cold signal over Europe in December, and conclude that the main driver was the North Atlantic SST tripole.

  13. 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 seasonal forecasts and their confidence interval. After a presentation of CNR methodology, results for the years 2011 and 2013 will illustrate CNR's seasonal forecasting models ability. These years are of particular interest regarding water resource management seeing that they are, respectively, unusually dry and snowy. Model performances will be assessed in comparison with historical climatology thanks to CRPS skill score.

  14. Forecasting fire season severity in South America using sea surface temperature anomalies.

    PubMed

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

    2011-11-11

    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 Niño 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. PMID:22076373

  15. 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.

  16. Seasonal climate forecasts significantly affected by observational uncertainty of Arctic sea ice concentration

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

    We investigate how observational uncertainty in satellite-retrieved sea ice concentrations affects seasonal climate predictions. To do so, we initialize hindcast simulations with the Max Planck Institute Earth System Model every 1 May and 1 November from 1981 to 2011 with two different sea ice concentration data sets, one based on the NASA Team and one on the Bootstrap algorithm. 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 differences in sea ice area 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 initialization during winter. This implies that the observational uncertainty also affects forecasts of teleconnections that depend on northern hemispheric climate indices.

  17. 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).

  18. 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).

  19. 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 the two basins. For the River Rhine with a catchment area of approx. 185.000 km2 the semi-distributed HBV model with a time-step of one day and for the Upper Danube Basin (102.000 km2) the water balance model COSERO with a time-step of 1 month are applied. As observed meteorological input the ERA Interim dataset is used, which is statistically downscaled from its relatively coarse grid resolution to the subbasins of the models. As meteorological forecast input two different approaches are used for the 30-year hindcast period in this study: (a) the Extended Streamflow Prediction ESP - a resampling approach of historical meteorology - which is applied to asses the potential predictability arising from the initial conditions and (b) the ensemble re-forecasts of the ECMWF seasonal forecast system 4 - a global coupled ocean-atmosphere general circulation model - used to quantify the potential benefit of numerical weather forecasts. Bayesian Model Averaging BMA and Ensemble-Model Output statistics EMOS are applied to the generated seasonal ensemble streamflow forecasts for calibration and the estimation of the predictive probability density function. Different skill measures are used to verify the potential skill of the seasonal forecasts of the different methods.

  20. 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.

  1. 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 order to have a better understanding of the effects of the calibration procedure. This approach makes a step further on the use of seasonal climate forecasts for applications on energy sectors, coping with the necessity of extract the informative signal for a specific domain. McNeil, Michael A., and Virginie E. Letschert. "Future air conditioning energy consumption in developing countries and what can be done about it: the potential of efficiency in the residential sector." ECEEE Summer Study. Côte d'Azur, France (2007). Navarra, A., and J. Tribbia. "The Coupled Manifold." Journal of the atmospheric sciences 62.2 (2005): 310-330.

  2. 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.

  3. Dynamical Downscaling NCEP Global Climate Forecast System (CFS) Seasonal Predictions Using Regional Atmospheric Modeling System (RAMS)

    NASA Astrophysics Data System (ADS)

    Lu, L.; Zheng, Y.; Pielke, R. A.

    2009-12-01

    As part of the NOAA CPPA-sponsored MRED project, the state-of-the-art Regional Atmospheric Modeling System (RAMS) version 6.0 is used to dynamically and progressively downscale NCEP global Climate Forecast System (CFS, at 100s-km grid increment) seasonal predictions to a regional domain that covers the conterminous United States at 30-km grid increment. The first set of RCM prediction experiment focuses on the winter seasons, during which the precipitation is largely dependent on synoptic-scale mid-latitude storms and orographic dominant mesoscale processes. Our first suite of numerical experiment includes one ensemble member for each year from 1982 through 2008, with all the simulations starting on December 1 and ending on April 30. Driven by the same atmospheric and SST forcings, RAMS will be compared with other RCMs, and evaluated against observations and reanalysis (NARR) to see if the simulations capture the climatology and interannual variability of temperature and precipitation distributions. The overall strengths and weaknesses of the modeling systems will be identified, as well as the consistent model biases. In addition, we will analyze the changes in kinetic energy spectra before and after the spectral nudging algorithm is implemented. The results show that with the spectral nudging scheme, RAMS can better preserve large-scale kinetic energy than standard boundary forcing method, and allow more large-scale energy to cascade to smaller scales.

  4. Maximization of seasonal forecasts performance combining Grand Multi-Model Ensembles

    NASA Astrophysics Data System (ADS)

    Alessandri, Andrea; De Felice, Matteo; Catalano, Franco; Lee, Doo Young; Yoo, Jin Ho; Lee, June-Yi; Wang, Bin

    2014-05-01

    Multi-Model Ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model errors. Previous works suggested that the potential benefit that can be expected by using a MME amplify with the increase of the independence of the contributing Seasonal Prediction Systems. In this work we combine the two Multi Model Ensemble (MME) Seasonal Prediction Systems (SPSs) independently developed by the European (ENSEMBLES) and by the Asian-Pacific (CliPAS/APCC) communities. To this aim, all the possible multi-model combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from CliPAS/APCC have been evaluated. The grand ENSEMBLES-CliPAS/APCC Multi-Model enhances significantly the skill compared to previous estimates from the contributing MMEs. The combinations of SPSs maximizing the skill that is currently attainable for specific predictands/phenomena is evaluated. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and CliPAS/APCC models and that only a limited number of SPSs is required to obtain the maximum performance. The number and selection of models that perform better is usually different depending on the region/phenomenon under consideration. As an example for the tropical Pacific, the maximum performance is obtained with only the combination of 5-to-6 SPSs from the grand ENSEMBLES-CliPAS/APCC MME. With particular focus over Tropical Pacific, the relationship between performance and bias of the grand-MME combinations is evaluated. The skill of the grand-MME combinations over Euro-Mediterranean and East-Asia regions is further evaluated as a function of the capability of the selected contributing SPSs to forecast anomalies of the Polar/Siberian highs during winter and of the Asian summer monsoon precipitation during summer. Our results indicate that, combining SPSs from independent MME sources is a good strategy to go beyond current limitation in seasonal predictions. At the same time our results suggest that overconfidence may be still limiting performance of MME forecasts currently available from the European and the Asian-Pacific communities.

  5. 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.

  6. Tropical cyclone count predictability on seasonal time-scale using dynamical climate forecasts

    NASA Astrophysics Data System (ADS)

    Alessandri, A.; Gualdi, S.; Scoccimarro, E.; Masina, S.; di Pietro, P.; Navarra, A.

    2009-04-01

    This study investigates the predictability of Tropical Cyclone (TC) seasonal count anomalies using the first version of the CMCC-INGV Seasonal Prediction System (CSPS-v1). The skill in reproducing the observed (National Hurricane Center and U.S Joint Typhoon Warning Center) TC counts has been evaluated after the application of a TC location and tracking detection method to the CSPS-v1 retrospective forecasts. The CSPS-v1 displays a good skill in predicting the observed TC count anomalies, particularly over the tropical Pacific and Atlantic Oceans. The simulated TC activity exhibits realistic seasonal modulation, geographical distribution and interannual variability, thus indicating that the model is able to reproduce the major basic mechanisms that link the TCs occurrence with the large scale circulation. Further, TC count anomalies prediction has been found to be sensitive to the inclusion of the assimilation of in situ temperature and salinity data in the estimation of the ocean Initial Conditions (ICs). Using the ocean ICs thus improved, we performed an additional set of ensemble hindcasts taking the same initial conditions for all the coupled model components but the ocean. Our results indicate that the assimilation significantly improves the prediction of the TC count anomalies over Eastern Pacific and Indian Ocean during Boreal summer. During the Austral counterpart, we evidenced a significant progress in the prediction over the area surrounding Australia. The improved prediction skill of the anomalous TC activity appears to be linked to both the improved interannual anomalies and the reduced mean bias of the predicted Sea Surface Temperatures.

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

    NASA Astrophysics Data System (ADS)

    Schick, Simon; Rssler, 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 subbasins per region. The results are finally discussed with respect to the runoff regimes as defined in the Hydrological Atlas of Switzerland.

  8. Statistical Climate Prediction for the Interior Southwestern U.S.: Lessons Learned From six Years of Experimental Seasonal Forecasts

    NASA Astrophysics Data System (ADS)

    Wolter, K.

    2006-05-01

    New and improved "climate divisions" can be used for the statistical prediction of climate anomalies (here: precipitation) in the interior southwestern U.S. This presentation gives a brief survey of the employed forecast technique (stepwise multiple regressions in "ensemble" mode) as well as different predictors that were found useful. Experimental climate (precipitation) forecasts were first issued in late 2001, and are updated monthly on the internet. Since the original data training period ended in September 1999, there are now six years of verification data available. Seasonal forecast skill for the interior southwestern U.S. appears to be linked not only to ENSO (and its various 'flavors'), but also to SST regions further afield (Indian Ocean) as well as closer to the U.S. (eastern subtropical Pacific and Caribbean). Other useful predictors include northern hemispheric teleconnection patterns, and antecedent regional precipitation anomalies. Cross-validated skill shows large spatial and temporal variation. It is largest in the winter season, and lowest in the spring, consistent with operational forecast skill by the Climate Prediction Center. However, independent verification for the last six years shows the opposite to be the case - perhaps attesting to rather unusual conditions during this period, such as the absence of strong ENSO events. Nevertheless, two regions of high forecast skill in winter are arguably very important for the interior Southwest: the mountains of northern Utah and Colorado, both regions NOT well predicted by CPC.

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

    PubMed Central

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

    2016-01-01

    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 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. PMID:26861369

  10. 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

    2016-02-01

    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 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. PMID:26861369

  11. 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 temperature and rainfall at a city level. Table 1. Cross correlation of climate variables with dengue cases in Chennai ** p<0.01,*p<0.05

  12. 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.

  13. DOES DOWNSCALING IN SPACE AND TIME DEGRADE THE DEPENDABILITY OF SEASONAL CLIMATE FORECASTS?

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center issues total precipitation and average temperature forecasts for 3-month periods for relatively large areas (forecast divisions are about 9,000 sq. km.). Incorporation of the climate forecasts into farm level decis...

  14. 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.

  15. Seasonal volume forecast of the Diamante River, Argentina, based on El Niño observations and predictions

    NASA Astrophysics Data System (ADS)

    Berri, Guillermo J.; Flamenco, Eduardo A.

    1999-12-01

    A statistically significant relationship is found between the seasonal volume October-March of the Diamante River (central Andes mountains of Argentina) and the sea surface temperature (SST) in the Niño3 region of the Pacific Ocean (5°S-5°N, 9O°W-150°W), during the previous March-April and simultaneous November-December. The Niño3 SST anomalies are positively correlated with seasonal volume anomalies. A multiple linear regression model for October-March water volume predictions is developed, making use of Niño3 anomalies observed during March-April and six-month November-December Niño3 forecast produced in May. A cross-validation analysis of three-category (tercile) forecasts for the period 1980-1994 reveals a 73% model skill, equal to that of the snowpack thickness model in use. The results of this study are of practical application to hydrologic forecast for water resources management and hydroelectricity generation. This methodology would allow the water resources administrator to have available every year in May a forecast of the water available in the system during the following melting period October-March, before the wintertime snowfalls.

  16. Stochastic atmospheric perturbations in the EC-Earth3 global coupled model: impact of SPPT on seasonal forecast quality

    NASA Astrophysics Data System (ADS)

    Batté, Lauriane; Doblas-Reyes, Francisco J.

    2015-12-01

    Atmospheric model uncertainties at a seasonal time scale can be addressed by introducing stochastic perturbations in the model formulation. In this paper the stochastically perturbed parameterization tendencies (SPPT) technique is activated in the atmospheric component of the EC-Earth global coupled model and the impact on seasonal forecast quality is assessed, both at a global scale and focusing on the Tropical Pacific region. Re-forecasts for winter and summer seasons using two different settings for the perturbation patterns are evaluated and compared to a reference experiment without stochastic perturbations. We find that SPPT tends to increase the systematic error of the model sea-surface temperature over most regions of the globe, whereas the impact on precipitation and sea-level pressure is less clear. In terms of ensemble spread, larger-scale perturbation patterns lead to a greater increase in spread and in the model spread-skill ratio in a system that is overconfident. Over the Tropical Pacific, improvements in the representation of key processes associated with ENSO are highlighted. The evaluation of probabilistic re-forecasts shows that SPPT improves their reliability. Finally, we discuss the limitations to this study and future prospects with EC-Earth.

  17. 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 Nino3.4 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 Nino-Southern Oscillation.

  18. 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 forecasts are bias corrected, downscaled and used as inputs to the VIC LSM as well as forecasts based on ESP and CPC official seasonal outlook. For Africa, data from a combination of remote sensing (TMPA-based precipitation, land cover characteristics) and GFS analysis fields (temperature and wind) are used to monitor drought using our soil moisture drought index as well as 1, 3 and and 6-month SPI. River discharge is also estimated at over 900 locations. Seasonal forecasts have been developed using CFSv2 climate forecasts following the approaches used over CONUS. We will discuss the performance of the system to evaluate the depiction of drought over various scales, from regional to the African continent, and over a number of years to capture multiple drought events. Furthermore, the hindcasts from the seasonal drought forecast system are analyzed to assess the ability of seasonal climate models to detect drought on-set and its recovery. Finally, we will discuss whether our ADM provides a pathway to a Global Drought Information System, a goal of the WCRP Drought Task Force.

  19. 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 compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e peak wind speed, average wind speed, and the probability the peak wind will meet or exceed the 25-, 35- and 50-kt thresholds. The user can display the Day-1 through Day-3 peak wind forecasts, and separate forecasts are made for precipitation and non-precipitation days. The MIDDS GUI uses data from the NAM and Global Forecast System (GFS), instead of the MesoNAM. It can display Day-1 and Day-2 forecasts using NAM data, and Day-1 through Day-5 forecasts using GFS data. The timing of the peak wind is not displayed, since the independent verification showed that none of the forecast methods performed significantly better than climatology. The forecaster should use the climatological timing of the peak wind (2248 UTC) as a first guess and then adjust it based on the movement of weather features.

  20. Long-lead ENSO predictability from CMIP5 decadal hindcasts

    NASA Astrophysics Data System (ADS)

    Gonzalez, Paula L. M.; Goddard, Lisa

    2015-07-01

    Using decadal prediction experiments from the WCRP/CMIP5 suite that were initialized every year from 1960-onward, we explore long-lead predictability of ENSO events. Both deterministic and probabilistic skill metrics are used to assess the ability of these decadal prediction systems to reproduce ENSO variability as represented by the NINO3.4 index (EN3.4). Several individual systems as well as the multi-model mean can predict ENSO events 3-4 years in advance, though not for every event during the hindcast period. This long-lead skill is beyond the previously documented predictability limits of initialized prediction systems. As part of the analysis, skill in reproducing the annual cycle of EN3.4, and the annual cycle of its interannual variability is examined. Most of the prediction systems reproduce the seasonal cycle of EN3.4, but are less able to capture the timing and magnitude of the variability. However, for the prediction systems used here, the fidelity of annual cycle characteristics does not appear to be related to the system's ability to predict ENSO events. In addition, the performance of the multi-model ensemble mean is explored and compared to the multi-model mean based solely on the most skillful systems; the latter is found to yield better results for the deterministic metrics. Finally, an analysis of the near-surface temperature and precipitation teleconnections reveals that the ability of the systems to detect ENSO events far in advance could translate into predictive skill over land for several lead years, though with reduced amplitudes compared to observations.

  1. Long-lead ENSO predictability from CMIP5 decadal hindcasts

    NASA Astrophysics Data System (ADS)

    Gonzalez, Paula L. M.; Goddard, Lisa

    2016-05-01

    Using decadal prediction experiments from the WCRP/CMIP5 suite that were initialized every year from 1960-onward, we explore long-lead predictability of ENSO events. Both deterministic and probabilistic skill metrics are used to assess the ability of these decadal prediction systems to reproduce ENSO variability as represented by the NINO3.4 index (EN3.4). Several individual systems as well as the multi-model mean can predict ENSO events 3-4 years in advance, though not for every event during the hindcast period. This long-lead skill is beyond the previously documented predictability limits of initialized prediction systems. As part of the analysis, skill in reproducing the annual cycle of EN3.4, and the annual cycle of its interannual variability is examined. Most of the prediction systems reproduce the seasonal cycle of EN3.4, but are less able to capture the timing and magnitude of the variability. However, for the prediction systems used here, the fidelity of annual cycle characteristics does not appear to be related to the system's ability to predict ENSO events. In addition, the performance of the multi-model ensemble mean is explored and compared to the multi-model mean based solely on the most skillful systems; the latter is found to yield better results for the deterministic metrics. Finally, an analysis of the near-surface temperature and precipitation teleconnections reveals that the ability of the systems to detect ENSO events far in advance could translate into predictive skill over land for several lead years, though with reduced amplitudes compared to observations.

  2. ASSESSMENT OF SEASONAL FORECASTS OF LA NINA ASSOCIATED DROUGHT IN THE SOUTHWEST

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Experimental climate forecasts for 3-month total precipitation are issued monthly by the NOAA Climate Prediction Center (CPC), for lead times from 0.5 to 12.5 months. Among these forecasts, the CPC Probability of Exceedance maps and graphs present information on expected shifts in the probability d...

  3. 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.

  4. 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.

  5. 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.

    2015-09-01

    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 mainly by forecast signals, with high forecast noise also playing a role. Actual skills, though lower than potential predictability, likewise show a positive influence of realistic land initialization.

  6. 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

    2016-02-01

    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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Use of Data to Improve Seasonal-to-Interannual Forecasts Simulated by Intermediate Coupled Models

    NASA Technical Reports Server (NTRS)

    Perigaud, C.; Cassou, C.; Dewitte, B.; Fu, L-L.; Neelin, J.

    1999-01-01

    This paper provides a detailed illustration that it can be much more beneficial for ENSO forecasting to use data to improve the model parameterizations rather than to modify the initial conditions to gain in consistency with the simulated coupled system.

  12. Statistical-Dynamical Seasonal Forecasts of Central-Southwest Asian Winter Precipitation.

    NASA Astrophysics Data System (ADS)

    Tippett, Michael K.; Goddard, Lisa; Barnston, Anthony G.

    2005-06-01

    Interannual precipitation variability in central-southwest (CSW) Asia has been associated with East Asian jet stream variability and western Pacific tropical convection. However, atmospheric general circulation models (AGCMs) forced by observed sea surface temperature (SST) poorly simulate the region's interannual precipitation variability. The statistical-dynamical approach uses statistical methods to correct systematic deficiencies in the response of AGCMs to SST forcing. Statistical correction methods linking model-simulated Indo-west Pacific precipitation and observed CSW Asia precipitation result in modest, but statistically significant, cross-validated simulation skill in the northeast part of the domain for the period from 1951 to 1998. The statistical-dynamical method is also applied to recent (winter 1998/99 to 2002/03) multimodel, two-tier December-March precipitation forecasts initiated in October. This period includes 4 yr (winter of 1998/99 to 2001/02) of severe drought. Tercile probability forecasts are produced using ensemble-mean forecasts and forecast error estimates. The statistical-dynamical forecasts show enhanced probability of below-normal precipitation for the four drought years and capture the return to normal conditions in part of the region during the winter of 2002/03.May Kabul be without gold, but not without snow.—Traditional Afghan proverb

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

    NASA Astrophysics Data System (ADS)

    Moron, Vincent; Barbero, Renaud; Robertson, Andrew W.

    2015-07-01

    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 score. In general, ANFIS models show superior results in terms of correlation coefficient for the overall case study. As a pioneer study, it is proposed that ANFIS is a promising tool for the purpose of seasonal predictions in Australia as they produce comparable accuracy using minimal inputs, require less development time and they are less complex compared to dynamic models.

  14. 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 score. In general, ANFIS models show superior results in terms of correlation coefficient for the overall case study. As a pioneer study, it is proposed that ANFIS is a promising tool for the purpose of seasonal predictions in Australia as they produce comparable accuracy using minimal inputs, require less development time and they are less complex compared to dynamic models.

  15. 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.

  16. Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers

    PubMed Central

    Axelsen, Jacob Bock; Yaari, Rami; Grenfell, Bryan T.; Stone, Lewi

    2014-01-01

    Human influenza occurs annually in most temperate climatic zones of the world, with epidemics peaking in the cold winter months. Considerable debate surrounds the relative role of epidemic dynamics, viral evolution, and climatic drivers in driving year-to-year variability of outbreaks. The ultimate test of understanding is prediction; however, existing influenza models rarely forecast beyond a single year at best. Here, we use a simple epidemiological model to reveal multiannual predictability based on high-quality influenza surveillance data for Israel; the model fit is corroborated by simple metapopulation comparisons within Israel. Successful forecasts are driven by temperature, humidity, antigenic drift, and immunity loss. Essentially, influenza dynamics are a balance between large perturbations following significant antigenic jumps, interspersed with nonlinear epidemic dynamics tuned by climatic forcing. PMID:24979763

  17. 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 precipitation forecasts of a global ensemble prediction system, the Climate Forecast System Version 2. In addition, an event-based comparison is performed for the year 2013 using the precipitation forecasts from a regional ensemble prediction system which refines the CFS2 forecasts by using the Weather and Research Forecasting model. In this poster presentation a detailed overview about the various techniques is given including first outcomes of this investigation.

  18. 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 tree-based models were built and county-level, within-sample coefficients of determination (R2) of 0.93 were found for both crops. Limiting the models by systematically removing late season data showed the model performance to remain strong even at mid-season and still viable even earlier. Finally, the 2006-2011 based models were used to predict out-of-sample for the 2012 season, which ended up having an anomalous drought, yet results compared reasonably well with official statistics (R2 = 0.77 and 0.71, for corn and soybeans respectively). Yield model use and results for the 2013 crop growing season, which is currently underway at the time of writing, will also be presented.

  19. 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.

    2016-02-01

    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.

  20. Accounting for model uncertainty in EC-Earth3: impact of SPPT on seasonal forecast quality

    NASA Astrophysics Data System (ADS)

    Batté, Lauriane; Doblas-Reyes, Francisco J.

    2014-05-01

    In recent years, stochastic parameterization has emerged as a new method to take into account atmospheric model uncertainty in monthly to decadal climate predictions. One straightforward method consists in applying in-run multiplicative noise to the physical tendencies computed by the atmospheric model. The stochastically perturbed parameterization tendency scheme (SPPT; Palmer et al. 2009) designed at ECMWF introduces univariate Gaussian perturbations to the wind, humidity and temperature tendencies. This method along with a stochastic backscatter algorithm showed promising results at a monthly-to-seasonal scale with the ECMWF coupled model when compared to multi-model ensemble techniques (Weisheimer et al., 2011). SPPT was implemented in the IFS atmospheric component of the EC-Earth3 Earth system model. Two sets of space and time scales for the perturbation patterns were tested in EC-Earth3 to represent uncertainties at the seasonal time scale. The impact of these perturbations is analyzed in terms of systematic error, spread-to-skill ratio, anomaly correlation of the ensemble mean as well as probabilistic skill. Results depend on the variable and region studied. Over the Tropical Pacific, both of the SPPT settings tested result in a reduction of the systematic error and an increase in ensemble spread for sea-surface temperature, and these improvements translate into enhanced probabilistic skill.

  1. The ECOMS User Data Gateway: homogeneous seasonal-to-decadal forecast data access for end-users

    NASA Astrophysics Data System (ADS)

    Magario, Maria Eugenia; Cofio, Antonio; Bedia, Joaquin; Vega, Manuel; Fernndez, Jess; Manzanas, Rodrigo; Gutirrez, Jose Manuel

    2014-05-01

    The European Climate Observations, Modelling and Services initiative (ECOMS), coordinates three ongoing European FP7 projects: EUPORIAS, SPECS and NACLIM. These projects gather a research community of data providers and consumers, including end-users, which have specific needs regarding data access. In many cases, the required datasets (predictions/hindcasts from different models, reanalysis, etc.) are provided from different servers with different data and metadata formats. This impedes in many cases the accomplishment of comprehensive studies comparing several models/predictions for uncertainty assessment. The ECOMS User Data Gateway (ECOMS UDG) provides a homogeneous access point to collections of impact-relevant variables. The aim of ECOMS UDG is to gather different data sources (including third-party) with different terms of use in a single data server, so that users can access all the data and metadata they typically need (seasonal forecasts, reanalysis and observations) in a homogeneous and simple way, without worrying about the inherent complexities of data access, download and post-processing of the variables stored in distributed databases and data servers, such as the Earth System Grid Federation (ESGF) database or a variety of massive archive systems at different institutions. Currently, the ECOMS UDG collects seasonal forecast products from two different sources: System4, provided by the ECMWF, and CFSv2, provided by the NCEP, focusing on a reduced number of fields identified by end-user requirements. Typically these fields are considered at surface (precipitation, temperature...) but also at pressure levels (geopotential or temperature). Also the user requirements for temporal aggregation (mean, minimum...) and frequency (daily, monthly, ...) are met. All datasets are catalogued by a THREDDS data server using remote data access services (OPeNDAP). The UDG also provides tools to allow a user-friendly data exploration (bias correction, downscaling, verification) and access, without worrying about the fragmentation of the datasets into files and accessing only the subset of data they need. As different use policies apply to the different datasets, a fine-grained user authorization scheme has been implemented to allow them. The user may accept the usage terms and conditions as a required step to apply for the dataset access role. More details: http://meteo.unican.es/ecoms-udg

  2. Role of upper ocean processes in the seasonal SST evolution over tropical Indian Ocean in climate forecasting system

    NASA Astrophysics Data System (ADS)

    Chowdary, Jasti S.; Parekh, Anant; Ojha, Sayantani; Gnanaseelan, C.

    2015-11-01

    In this study role of upper ocean processes in the evolution of sea surface temperature (SST) seasonal variations over the tropical Indian Ocean (TIO) is investigated in climate forecast system version1 (CFSv1) and version2 (CFSv2). Analysis reveals that CFSv2 could capture seasonal evolution of SST, wind speed and mixed layer depth better than CFSv1 with some biases. Discrepancy in reproducing the evolution of seasonal SST in coupled models leads to bias in the spatial and temporal distribution of precipitation. This has motivated to carry out mixed layer heat budget analysis in determining seasonal evolution of TIO SST. Spatial pattern of mixed layer heat budget from observations and models suggest that the processes responsible for SST tendency differ from region to region over the TIO. Further it is found that models underestimated SST tendency compared to the observations. Misrepresentation of advective processes and heat flux (HF) over the TIO is mainly responsible for the distortion of seasonal SST change in the coupled models. Sub-regional heat budget analysis reveals that CFSv1 is unable to reproduce the annual cycle of mixed layer temperature (MLT) tendency over the Arabian Sea, while CFSv2 captured the annual cycle of SST with systematic cold bias. Misrepresentation of the annual cycles of net HF and horizontal advection (Hadv) are accountable for the low rate of change of MLT during most of the year. Hadv during summer season is underestimated by 50 and 25 % respectively in CFSv1 and CFSv2. Further, CFSv1 fails to simulate MLT tendency due to improper evolution of HF annual cycle over the Bay of Bengal. Though annual cycle of HF in CFSv2 is well represented over the Bay of Bengal, its contribution to MLT change is underestimated compared to observations. Over the southern TIO region, MLT tendency is dominated by HF and Hadv terms in both observations and models. Contribution of HF to the annual cycle of MLT tendency is underestimated in CFSv1 whereas it is overestimated in CFSv2. Contribution of Hadv to MLT change is underestimated by about 50 % in CFSv1 and 10-20 % in CFSv2 over southern TIO. These errors in HF and Hadv are associated with biases in HF components and surface wind representation. Evolution of lead-lag relationship between HF and MLT/SST in both the observations and models suggest the importance of HF in SST evolution over the TIO region. Over all, CFSv2 produced better SST seasonal/annual cycle in spite of having cold bias. This improvement in CFSv2 may be attributed to better cloud-aerosol-radiation physics, which reduces radiation biases. Updated land-surface, ocean and sea ice processes and ocean component may be responsible for improved circulation and annual cycle of ocean-atmospheric components (winds and ocean circulation). However, there is a requirement for improved parameterization of turbulent HF and radiation estimates in CFSv2 to reduce the cold SST bias.

  3. Validation and development of existing and new RAOB-based warm-season convective wind forecasting tools for Cape Canaveral Air Force Station and Kennedy Space Center

    NASA Astrophysics Data System (ADS)

    McCue, Mitchell Hollis

    Using a 15-year (1995 to 2009) climatology of 1500 UTC warm-season (May through September) rawinsonde observation (RAOB) data from the Cape Canaveral Air Force Station (CCAFS) Skid Strip (KXMR) and 5 minute wind data from 36 wind towers on CCAFS and Kennedy Space Center (KSC), several convective wind forecasting techniques currently employed by the 45th Weather Squadron (45 WS) were evaluated. Present forecasting methods under evaluation include examining the vertical equivalent potential temperature (theta e) profile, vertical profiles of wind spend and direction, and several wet downburst forecasting indices. Although previous research found that currently used wet downburst forecasting methods showed little promise for forecasting convective winds, it was carried out with a very small sample, limiting the reliability of the results. Evaluation versus a larger 15-year dataset was performed to truly assess the forecasting utility of these methods in the central Florida warm-season convective environment. In addition, several new predictive analytic based forecast methods for predicting the occurrence of warm-season convection and its associated wind gusts were developed and validated. This research was performed in order to help the 45 WS better forecast not only which days are more likely to produce convective wind gusts, but also to better predict which days are more likely to yield warning criteria wind events of 35 knots or greater, should convection be forecasted. Convective wind forecasting is a very challenging problem that requires new statistically based modeling techniques since conventional meteorologically based methods do not perform well. New predictive analytic based forecasting methods were constructed using R statistical software and incorporate several techniques including multiple linear regression, logistic regression, multinomial logistic regression, classification and regression trees (CART), and ensemble CART using bootstrapping. All of these techniques except the ensemble CART methods were built with data from the 1995 to 2007 warm-seasons and validated with a separate independent dataset from the 2008 and 2009 warm-seasons. Ensemble CART models were built using randomly selected data from the 1995 to 2009 RAOB dataset and validated with data not used in constructing the models. Three different ensemble CART algorithms including the random forests, bagging, and boosting algorithms were tested to find the best performing model. Quantitative verification results suggest that the presently used convection and wet downburst forecasting techniques do not show much operational promise. As such, it is not recommended that the 45 WS use vertical profiles of thetae, wind speed, or wind direction to make specific predictions for which days are likely to produce convection or warning threshold wind gusts. None of the wet downburst indices used displayed much potential either. Although, the linear regression based predictive analytic models do not perform too well, CART based models perform better, especially those that utilize a binary response variable. Of the new techniques, the ensemble CART models displayed the most promise with the boosting algorithm showing nearly perfect results for predicting which days would produce convection and which days would produce warning threshold winds should convection be predicted.

  4. Skills of yearly prediction of the early-season rainfall over southern China by the NCEP climate forecast system

    NASA Astrophysics Data System (ADS)

    Zhao, Siyu; Yang, Song; Deng, Yi; Li, Qiaoping

    2015-11-01

    The prominent rainfall over southern China from April to June, usually characterized by a rain belt aligned in a southwest-northeast direction, is referred to here as the early-season rainfall (ESR). The predictability of the ESR is studied by analyzing the 45-day hindcast made with the US National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) and several observational data sets. Skills of predicting the ESR and the associated atmospheric circulation and surface air temperature (SAT) patterns in each year are assessed with multiple methods. Results show that the ESR can be well predicted by the CFSv2 in advance by 20 days in some years including 2005 and 2006, while the lead time of skillful prediction is limited to around 1 week in some other years such as 2001 and 2010. More accurate predictions of the ESR seem to be related to the higher skills of CFSv2 in predicting the dominant modes of the rainfall variability. Moreover, atmospheric circulation patterns associated with the ESR and the SAT over the western Pacific warm pool in the preceding winter can be important signals for ESR prediction since in the years with good skills of ESR prediction, the CFSv2 also predicted these signals successfully. An overestimate (underestimate) of the SAT may lead to large biases in the predicted atmospheric circulation and subsequently result in an underestimate (overestimate) southern China ESR.

  5. Toward a reliable prediction of seasonal forecast uncertainty: Addressing model and initial condition uncertainty with ensemble data assimilation and Sequential Bayesian Combination

    NASA Astrophysics Data System (ADS)

    DeChant, Caleb M.; Moradkhani, Hamid

    2014-11-01

    Uncertainties are an unfortunate yet inevitable part of any forecasting system. Within the context of seasonal hydrologic predictions, these uncertainties can be attributed to three causes: imperfect characterization of initial conditions, an incomplete knowledge of future climate and errors within computational models. This study proposes a method to account for all threes sources of uncertainty, providing a framework to reduce uncertainty and accurately convey persistent predictive uncertainty. In currently available forecast products, only a partial accounting of uncertainty is performed, with the focus primarily on meteorological forcing. For example, the Ensemble Streamflow Prediction (ESP) technique uses meteorological climatology to estimate total uncertainty, thus ignoring initial condition and modeling uncertainty. In order to manage all three sources of uncertainty, this study combines ESP with ensemble data assimilation, to quantify initial condition uncertainty, and Sequential Bayesian Combination, to quantify model errors. This gives a more complete description of seasonal hydrologic forecasting uncertainty. Results from this experiment suggest that the proposed method increases the reliability of probabilistic forecasts, particularly with respect to the tails of the predictive distribution.

  6. Impact on Hurricane Track and Intensity Forecasts of GPS Dropwindsonde Observations from the First-Season Flights of the NOAA Gulfstream-IV Jet Aircraft.

    NASA Astrophysics Data System (ADS)

    Aberson, Sim D.; Franklin, James L.

    1999-03-01

    In 1997, the Tropical Prediction Center (TPC) began operational Gulfstream-IV jet aircraft missions to improve the numerical guidance for hurricanes threatening the continental United States, Puerto Rico, and the Virgin Islands. During these missions, the new generation of Global Positioning System dropwindsondes were released from the aircraft at 150-200-km intervals along the flight track in the environment of the tropical cyclone to obtain profiles of wind, temperature, and humidity from flight level to the surface. The observations were ingested into the global model at the National Centers for Environmental Prediction, which subsequently serves as initial and boundary conditions to other numerical tropical cyclone models. Because of a lack of tropical cyclone activity in the Atlantic basin, only five such missions were conducted during the inaugural 1997 hurricane season.Due to logistical constraints, sampling in all quadrants of the storm environment was accomplished in only one of the five cases during 1997. Nonetheless, the dropwindsonde observations improved mean track forecasts from the Geophysical Fluid Dynamics Laboratory hurricane model by as much as 32%, and the intensity forecasts by as much as 20% during the hurricane watch period (within 48 h of projected landfall). Forecasts from another dynamical tropical cyclone model (VICBAR) also showed modest improvements with the dropwindsonde observations. These improvements, if confirmed by a larger sample, represent a large step toward the forecast accuracy goals of TPC. The forecast track improvements are as large as those accumulated over the past 20-25 years, and those for forecast intensity provide further evidence that better synoptic-scale data can lead to more skillful dynamical tropical cyclone intensity forecasts.

  7. 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

    Capturing climatic variations in boreal winter to spring (December-May) is essential for properly predicting droughts in South Korea. This study investigates the variability and predictability of the South Korean climate during this extended season, based on observations from 60 station locations and multi-model ensemble (MME) hindcast experiments (1983/1984-2005/2006) archived at the APEC Climate Center (APCC). Multivariate empirical orthogonal function (EOF) analysis results based on observations show that the first two leading modes of winter-to-spring precipitation and temperature variability, which together account for ~80 % of the total variance, are characterized by regional-scale anomalies covering the whole South Korean territory. These modes were also closely related to some of the recurrent large-scale circulation changes in the northern hemisphere during the same season. Consistent with the above, examination of the standardized precipitation evapotranspiration index (SPEI) indicates that drought conditions in South Korea tend to be accompanied by regional-to-continental-scale circulation anomalies over East Asia to the western north Pacific. Motivated by the aforementioned findings on the spatial-temporal coherence among station-scale precipitation and temperature anomalies, a new bivariate and pattern-based downscaling method was developed. The novelty of this method is that precipitation and temperature data were first filtered using multivariate EOFs to enhance their spatial-temporal coherence, before being linked to large-scale circulation variables using canonical correlation analysis (CCA). To test its applicability and to investigate its related potential predictability, a perfect empirical model was first constructed with observed datasets as predictors. Next, a model output statistics (MOS)-type hybrid dynamical-statistical model was developed, using products from nine one-tier climate models as inputs. It was found that, with model sea-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.

  8. 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

    2015-07-01

    Capturing climatic variations in boreal winter to spring (December-May) is essential for properly predicting droughts in South Korea. This study investigates the variability and predictability of the South Korean climate during this extended season, based on observations from 60 station locations and multi-model ensemble (MME) hindcast experiments (1983/1984-2005/2006) archived at the APEC Climate Center (APCC). Multivariate empirical orthogonal function (EOF) analysis results based on observations show that the first two leading modes of winter-to-spring precipitation and temperature variability, which together account for ~80 % of the total variance, are characterized by regional-scale anomalies covering the whole South Korean territory. These modes were also closely related to some of the recurrent large-scale circulation changes in the northern hemisphere during the same season. Consistent with the above, examination of the standardized precipitation evapotranspiration index (SPEI) indicates that drought conditions in South Korea tend to be accompanied by regional-to-continental-scale circulation anomalies over East Asia to the western north Pacific. Motivated by the aforementioned findings on the spatial-temporal coherence among station-scale precipitation and temperature anomalies, a new bivariate and pattern-based downscaling method was developed. The novelty of this method is that precipitation and temperature data were first filtered using multivariate EOFs to enhance their spatial-temporal coherence, before being linked to large-scale circulation variables using canonical correlation analysis (CCA). To test its applicability and to investigate its related potential predictability, a perfect empirical model was first constructed with observed datasets as predictors. Next, a model output statistics (MOS)-type hybrid dynamical-statistical model was developed, using products from nine one-tier climate models as inputs. It was found that, with model sea-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.

  9. 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 influenced by the teleconnection patterns that appear in the North Atlantic region. References: Lorenzo, M.N., I. Iglesias, J.J. Taboada and M. Gómez-Gesteira (2009): Relationship between monthly rainfall in NW Iberian Peninsula and North Atlantic sea surface temperature. International Journal of Climatology. DOI:10.1002/joc.1959. Philips I.D. and G.R. McGregor (2002): The relationship between monthly and seasonal southwest England rainfall anomalies and concurrent North Atlantic sea surface temperatures. International Journal of Climatology, 22: 197-217.

  10. Forecasting U.S. hurricanes 6 months in advance

    NASA Astrophysics Data System (ADS)

    Elsner, J. B.; Murnane, R. J.; Jagger, T. H.

    2006-05-01

    Katrina is a grim reminder of the serious social and economic threat that hurricanes pose to the United States. Recent advances in hurricane climate science provide skillful forecasts of the U.S. hurricane threat at (or near) the start of the season. Predictions of hurricane landfalls at longer lead times (forecast horizons) for the complete hurricane season would greatly benefit risk managers and others interested in acting on these forecasts. Here we show a model that provides a 6-month forecast horizon for annual hurricane counts along the U.S. coastline during the June through November hurricane season using the North Atlantic Oscillation (NAO) and Atlantic sea-surface temperature (SST) as predictors. Forecast skill exceeds that of climatology. The long-lead skill is linked to the persistence of Atlantic SST and to teleconnections between North Atlantic sea-level pressures and precipitation variability over North America and Europe. The model is developed using Bayesian regression and therefore incorporates the full set of Atlantic hurricane data extending back to 1851.

  11. 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.

  12. 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 investigated over lead times from short-range through medium-range to monthly and seasonal. These sectors are disaster management agencies and aid organizations, dam operators, hydro-power companies, irrigation agencies and agricultural sector, water supply companies, tourism sector and wildlife agencies, navigation sector, insurance companies. Most sectors benefit from seasonal forecasts in managing the risks and opportunities brought by the inter-annual variability of streamflow. Many sectors need forecasts on a lead time of several months to prepare for appropriate response, and others can make use of seasonal hydrological forecasts for planning purposes. A global forecasting system would be valuable for those parts of the world where there are no available local systems, provided that information on forecast reliability is properly communicated. The availability of a forecast along with an indication of its reliability offers water managers the choice of using the forecasts in decision-making. The real value of a forecast is specific to the user. It is to be determined on the basis of the the user's requirements, and by evaluating the benefits and costs of possible actions that can be taken in response to the forecast.

  13. Thermal instabilities in micro-SQUIDs with long leads

    NASA Astrophysics Data System (ADS)

    Kumar, Nikhil; Fournier, T.; Courtois, H.; Winkelmann, C. B.; Gupta, Anjan K.

    2015-03-01

    We report on Nb film based micron-size superconducting quantum interference devices (μ-SQUIDs) with long leads and with constrictions as weak-links (WLs). The current-voltage characteristics (IVCs) of these devices are hysteretic at low temperatures (T) with two re-trapping currents, Ir1 and Ir2, arising from thermal instabilities, and a critical-current (Ic) . We see oscillations with magnetic flux in Ic, but not in Ir1 and Ir2, in both the hysteretic and non-hysteretic regimes. We describe a one dimensional model for thermal instability in long-leads to understand re-trapping currents and its T-dependence. The critical-current shows a marked change in behaviour at the crossover temperature when it meets the lower re-trapping current and above this temperature the critical current subsists due to proximity superconductivity. The thermal stability with respect to phase-slips evolves with temperature and only above the crossover temperature the weak-link can cope with the heat generated by phase-slips giving a non-hysteretic behaviour. Keeping other device dimensions same the WLs' dimensions give a control on the hysteresis-free T-range. NK acknowledges the financial support from CSIR, India as well as CNRS-Institute Néel, Grenoble, France.

  14. 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 this, we use a technique named the Precipitation Assimilation Reanalysis (PAR) described in Nunes and Cocke (2004). This technique consists of modifying the vertical profile of humidity as a function of the observed and predicted model rain rates. In the second experiment, the model is initialized without precipitation assimilation. For each experiment, ten sets of seasonal forecasts of the coupled land-atmosphere Florida State University/Center for Ocean and Atmosphere Predictions Studies (FSU/COAPS) model were generated, starting from the boreal summer of each year between 1986 and 1995. For each forecast, ten ensembles are produced by starting the forecast from the 1st and the 15th of each month from April to August. The results of these experiments show, first, that the PAR technique greatly improves the temporal and spatial variability of out model soil moisture estimate. Second, using these realistic soil moisture initial conditions, we found a significant increase in the air temperature seasonal forecasting skills. However, not significant increase has been found in the precipitation seasonal forecasting skills. The results of this study are involved in the GLACE-2 international multi-model experiment.

  15. 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 for the FASST land-surface model and WRF-ARW to work together as fully coupled models. Changes had to be made to the WRF-ARW build mechanisms (Chapter 1, section a) so that FASST would be recognized as a new option that could be chosen through the namelist and compiled with other modules. Similarly, FASST had to be altered to no longer read meteorological data from a file, but accept input from WRF-ARW at each time step in a way that did not alter the integrity or run-time processes of the model. Several icing events were available to test the newly coupled model as well as the performance of other available land-surface models from the WRF-ARW. A variation of event intensities and durations from these events were chosen to give a broader view of the land-surface models' abilities to accurately predict icing in complex terrain. Non- icing events were also used in testing to ensure the land-surface models were not predicting ice in the events where none occurred. When compared to the other land-surface models and observations FASST showed a warm bias in several regions. As the forecasts progressed, FASST appeared to attempt to correct this bias and performed similarly to the other land-surface models and at times better than these land-surface models in areas of the domain not affected by this bias. To correct this warm bias, future investigation should be conducted into the reasoning behind this warm bias, including but not limited to: FASST operation and elevation modeling, WRF-ARW variables and forecasting methods, as well as allowing for spin-up prior to forecast times. Following the correction to the warm bias, FASST can be parallelized to allow for operational forecast performance and included in the WRF-ARW forecasting suite for future software releases. (Abstract shortened by UMI.).

  16. 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

    2016-04-01

    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.

  17. 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 forecasters used to predict the location of SLD icing conditions during the winter of 1997-1998. The objectives of this report are to: (1) assess the tools used to forecast in-flight icing. (2) assess the success/failure rate of the forecasts, and (3) discuss suggested changes to forecast techniques.

  18. 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.

  19. 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 particular, the short-term forecasts picked 10 days ahead of time the double flooding peak. In 2007, the system forecast the commencement and retreat of the July- August floods allowing for the first time for the Bangladesh Disaster Management Committee to act proactively rather than reactively. As a result, many thousands of villagers were evacuated out of harms way. The forecasting system will be discussed in some detail together with examples of forecasts made during the last 5 years. Most importantly, we see the method we have developed as a template for flood forecasting in the developing world where modern technology from the United States and Europe interfaces, interacts and supports local infrastructure.

  20. Assessing the vulnerability of economic sectors to climate variability to improve the usability of seasonal to decadal climate forecasts in Europe - a preliminary concept

    NASA Astrophysics Data System (ADS)

    Funk, Daniel

    2015-04-01

    Climate variability poses major challenges for decision-makers in climate-sensitive sectors. Seasonal to decadal (S2D) forecasts provide potential value for management decisions especially in the context of climate change where information from present or past climatology loses significance. However, usable and decision-relevant tailored climate forecasts are still sparse for Europe and successful examples of application require elaborate and individual producer-user interaction. The assessment of sector-specific vulnerabilities to critical climate conditions at specific temporal scale will be a great step forward to increase the usability and efficiency of climate forecasts. A concept for a sector-specific vulnerability assessment (VA) to climate variability is presented. The focus of this VA is on the provision of usable vulnerability information which can be directly incorporated in decision-making processes. This is done by developing sector-specific climate-impact-decision-pathways and the identification of their specific time frames using data from both bottom-up and top-down approaches. The structure of common VA's for climate change related issues is adopted which envisages the determination of exposure, sensitivity and coping capacity. However, the application of the common vulnerability components within the context of climate service application poses some fundamental considerations: Exposure - the effect of climate events on the system of concern may be modified and delayed due to interconnected systems (e.g. catchment). The critical time-frame of a climate event or event sequence is dependent on system-internal thresholds and initial conditions. But also on decision-making processes which require specific lead times of climate information to initiate respective coping measures. Sensitivity - in organizational systems climate may pose only one of many factors relevant for decision making. The scope of "sensitivity" in this concept comprises both the potential physical response of the system of concern as well as the criticality of climate-related decision-making processes. Coping capacity - in an operational context coping capacity can only reduce vulnerability if it can be applied purposeful. With respect to climate vulnerabilities this refers to the availability of suitable, usable and skillful climate information. The focus for this concept is on existing S2D climate service products and their match with user needs. The outputs of the VA are climate-impact-decision-pathways which characterize critical climate conditions, estimate the role of climate in decision-making processes and evaluate the availability and potential usability of S2D climate forecast products. A classification scheme is developed for each component of the impact-pathway to assess its specific significance. The systemic character of these schemes enables a broad application of this VA across sectors where quantitative data is limited. This concept is developed and will be tested within the context of the EU-FP7 project "European Provision Of Regional Impacts Assessments on Seasonal and Decadal Timescales" EUPORIAS.

  1. 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...

  2. 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.

  3. 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.

  4. Dynamical Downscaling NCEP Global Climate Forecast System (CFS) Seasonal Predictions Using Regional Atmospheric Modeling System (RAMS) - Evaluation with North American Regional Reanalysis

    NASA Astrophysics Data System (ADS)

    Lu, L.; Zheng, Y.; Pielke, R. A.; Dynamical Downscaling Using Rams

    2010-12-01

    As part of the NOAA CPPA-sponsored MRED project, the state-of-the-art Regional Atmospheric Modeling System (RAMS) version 6.0 is used to dynamically and progressively downscale NCEP global Climate Forecast System (CFS, at 100s-km grid increment) seasonal predictions to a regional domain that covers the conterminous United States at 30-km grid increment. The project’s first stage focuses on the winter seasons, during which the precipitation is largely dependent on synoptic-scale mid-latitude storms and orographic dominant mesoscale processes. Ten ensemble experiments for each year from 1982 through 2003 have been performed, starting on November 21 through 25, and November 29 through December 3 respectively, and ending on April 30. Driven by the CFS atmospheric and SST forcings, RAMS is evaluated against observations and North American Regional Reanalysis (NARR) to see if the simulations capture the climatology and interannual variability of temperature and precipitation distributions, as well as the energy and water cycles. The results show that large interannual variations exist in each of the downscaled variables, and there are also pronounced differences between each of these variables. The spatial correlation coefficients between NARR and RAMS simulations are high for surface air temperature and specific humidity, surface pressure, geopotential heights, surface downwelling short- and long- wave radiation, latent heat fluxes, but low for precipitation and sensible heat fluxes. Meridional and zonal moisture fluxes and geopotential height at 850 hPa have the largest interannual variability. The annual-mean domain-averaged surface temperature, surface specific humidity, and precipitation shows that RAMS simulated a warmer and drier near surface climate, and rained more for all the years compare to NARR products. RAMS-simulated spatial patterns of 200 mb geopotential height, surface specific humidity, and surface air temperature mostly resemble NARR product, while the warmer temperature bias is a direct result of overestimation of surface temperature around Gulf of Mexico and southeast of the model domain. In addition, we analyzed the changes in kinetic energy spectra before and after the spectral nudging algorithm was implemented. The results show that with the spectral nudging scheme, RAMS can better preserve large-scale kinetic energy than standard boundary forcing method, and allow more large-scale energy to cascade to smaller scales. The extent that the spectral nudging constrains the evolution of the smaller scale features, and preserve the large-scale forcing is quantified by comparing the results with and without the nudging scheme.

  5. 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.

  6. 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.

  7. Using Climate Forecasts for Drought Management

    NASA Astrophysics Data System (ADS)

    Steinemann, Anne C.

    2006-10-01

    Drought hazards, and the ability to mitigate them with advance warning, offer potentially valuable applications of climate forecast products. Yet the value is often untapped, owing to the gap between climate science and societal decisions. This study bridged that gap; it determined forecast needs among water managers, translated forecasts to meet those needs, and shaped drought decision making to take advantage of forecasts. NOAA Climate Prediction Center (CPC) seasonal precipitation outlooks were converted into a forecast precipitation index (FPI) tailored for water managers in the southeastern United States. The FPI expresses forecasts as a departure from the climatological normal and is consistent with other drought indicators. Evaluations of CPC seasonal forecasts issued during 1995 2000 demonstrated positive skill for drought seasons in the Southeast. In addition, using evaluation criteria of water managers, 88% of forecasts for drought seasons would have appropriately prompted drought responses. Encouraged by these evaluations, and the understandability of the FPI, state water managers started using the forecasts in 2001 for deciding whether to pay farmers to suspend irrigation. Economic benefits of this forecast information were estimated at $100 $350 million in a state-declared drought year (2001, 2002) and $5 $30 million in the other years (2003, 2004). This study provides four main contributions: 1) an investigation of the needs and potential benefits of seasonal forecast information for water management, 2) a method for translating the CPC forecasts into a format needed by water managers, 3) the integration of forecast information into agency decision making, and 4) the economic valuation of that forecast information.

  8. 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-scale details consistent with its grid resolution. SPORT is conducting parallel WRF EMS runs identical to the operational runs at NWS MIA except for the use of MODIS SST composites in place of the RTG product as the initial and boundary conditions over water, The MODIS SST composites for initializing the SPORT WRF runs are generated on a 2-km grid four times daily at 0400, 0700, 1600, and 1900 UTC, based on the times of the overhead passes of the Aqua and Terra satellites. The incorporation of the MODIS SST data into the SPORT WRF runs is staggered such that SSTs are updated with a new composite every six hours in each of the WRF runs. From mid-February to July 2007, over 500 parallel WRF simulations have been collected for analysis and verification. This paper will present verification results comparing the NWS MIA operational WRF runs to the SPORT experimental runs, and highlight any substantial differences noted in the predicted mesoscale phenomena for specific cases.

  9. Seasonality of infectious diseases.

    PubMed

    Fisman, David N

    2007-01-01

    Seasonality, a periodic surge in disease incidence corresponding to seasons or other calendar periods, characterizes many infectious diseases of public health importance. The recognition of seasonal patterns in infectious disease occurrence dates back at least as far as the Hippocratic era, but mechanisms underlying seasonality of person-to-person transmitted diseases are not well understood. Improved understanding will enhance understanding of host-pathogen interactions and will improve the accuracy of public health surveillance and forecasting systems. Insight into seasonal disease patterns may be gained through the use of autocorrelation methods or construction of periodograms, while seasonal oscillation of infectious diseases can be easily simulated using simple transmission models. Models demonstrate that small seasonal changes in host or pathogen factors may be sufficient to create large seasonal surges in disease incidence, which may be important particularly in the context of global climate change. Seasonality represents a rich area for future research. PMID:17222079

  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. Application of a New Hybrid Model with Seasonal Auto-Regressive Integrated Moving Average (ARIMA) and Nonlinear Auto-Regressive Neural Network (NARNN) in Forecasting Incidence Cases of HFMD in Shenzhen, China

    PubMed Central

    Tan, Li; Jiang, Hongbo; Wang, Ying; Wei, Sheng; Nie, Shaofa

    2014-01-01

    Background Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic. Method In this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012. Results The best-fitted hybrid model was combined with seasonal ARIMA and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively −965.03, −1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend. Conclusion The model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information. PMID:24893000

  12. Forecasting global ENSO-related climate anomalies

    NASA Astrophysics Data System (ADS)

    Barnett, T. P.; Bengtsson, L.; Arpe, K.; Flügel, M.; Graham, N.; Latif, M.; Ritchie, J.; Roeckner, E.; Schlese, U.; Schulzweida, U.; Tyree, M.

    1994-08-01

    Long-range global climate forecasts have been made by use of a model for predicting a tropical Pacific sea surface temperature (SST) in tandem with an atmospheric general circulation model. The SST is predicted first at long lead times into the future. These ocean forecasts are then used to force the atmospheric model and so produce climate forecasts at lead times of the SST forecasts. Prediction of the wintertime 500mb height, surface air temperature and precipitation for seven large climatic events of the 1970 1990s by this two-tiered technique agree well in general with observations over many regions of the globe. The levels of agreement are high enough in some regions to have practical utility.

  13. Using Climate Forecast Information in Water Resource Planning: Opportunities and Challenges in the Yakima River Basin, Washington, USA

    NASA Astrophysics Data System (ADS)

    Vano, J. A.; Steinemann, A. C.

    2007-12-01

    Drought is a costly natural hazard, particularly in the Yakima River basin, whose irrigated crops represent the largest agricultural value in the state of Washington. Like many basins throughout the West, the Yakima has experienced an increasing demand for water for irrigation, environmental flows, and hydropower production. These demands, coupled with shifts in hydrologic timing and variations in water supply, including droughts in 2001 and 2005, have resulted in a narrowing margin within which the Yakima's multi-reservoir system must be managed. Because the region is vulnerable to variations in seasonal water supplies, it has the potential to benefit from information on current and future hydroclimatic conditions, especially information to help prepare for and mitigate drought impacts. To better connect climate forecast information with water management decisions, the University of Washington is working directly with individual stakeholders and public agencies within the Yakima water-user community to understand current and potential uses of medium- and long-lead climate forecast information, and to assess its economic value. Through this case study, we provide concrete examples of the opportunities and challenges in working towards the adoption of decision-focused, understandable forecast information. We emphasize (1) the scope and diversity of decisions water managers make throughout the year, (2) the accuracy and usefulness of forecast products, (3) the limitations of forecast information, (4) the potential for new decision-focused products, and (5) generalizable guidelines on how to effectively communicate and facilitate the adoption of climate forecast information. An ultimate goal is to find ways to develop interactions that provide decision makers with relevant science-based indicators they can use to promote sustainable water resource planning.

  14. Reliable probabilistic forecasts from an ensemble reservoir inflow forecasting system

    NASA Astrophysics Data System (ADS)

    Bourdin, Dominique R.; Nipen, Thomas N.; Stull, Roland B.

    2014-04-01

    This paper describes a probabilistic reservoir inflow forecasting system that explicitly attempts to sample from major sources of uncertainty in the modeling chain. Uncertainty in hydrologic forecasts arises due to errors in the hydrologic models themselves, their parameterizations, and in the initial and boundary conditions (e.g., meteorological observations or forecasts) used to drive the forecasts. The Member-to-Member (M2M) ensemble presented herein uses individual members of a numerical weather model ensemble to drive two different distributed hydrologic models, each of which is calibrated using three different objective functions. An ensemble of deterministic hydrologic states is generated by spinning up the daily simulated state using each model and parameterization. To produce probabilistic forecasts, uncertainty models are used to fit probability distribution functions (PDF) to the bias-corrected ensemble. The parameters of the distribution are estimated based on statistical properties of the ensemble and past verifying observations. The uncertainty model is able to produce reliable probability forecasts by matching the shape of the PDF to the shape of the empirical distribution of forecast errors. This shape is found to vary seasonally in the case-study watershed. We present an "intelligent" adaptation to a Probability Integral Transform (PIT)-based probability calibration scheme that relabels raw cumulative probabilities into calibrated cumulative probabilities based on recent past forecast performance. As expected, the intelligent scheme, which applies calibration corrections only when probability forecasts are deemed sufficiently unreliable, improves reliability without the inflation of ignorance exhibited in certain cases by the original PIT-based scheme.

  15. Forecasting Future Social Needs

    ERIC Educational Resources Information Center

    Abt, Clark C.

    1971-01-01

    Describes briefly why social forecasting is easier than technological forecasting, offers four approaches to social forecasting (judgment, extrapolation, speculation, analysis), and suggests a procedure recommended for social forecasting. (CJ)

  16. 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 clothing or…

  17. 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.

  18. Forecasting Influenza Epidemics in Hong Kong

    PubMed Central

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

    2015-01-01

    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. PMID:26226185

  19. 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 Apple Computer, Inc. Fourth Dimension is a registered trademark of Acius, Inc.

  20. NOAA'S CLIMATE PRECIPITATION FORECASTS: INITIAL ASSESSMENT OF UTILITY FOR AGRICULTURAL APPLICATIONS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Experimental seasonal precipitation climate forecasts are issued by the National Oceanic and Atmospheric Administration's Climate Prediction Center. These forecasts specify the probabilities for total precipitation relative to climatological distributions, for 13 overlapping 3-month periods coverin...

  1. Solar Cycle 23 forecast update

    NASA Astrophysics Data System (ADS)

    Schatten, Kenneth; Hoyt, Douglas

    Solar activity, although virtually impossible to forecast a month in advance, has succumbed to scientific methods on long time scales, much as climate or seasonal weather predictions are simpler than weekly weather forecasting. Moderately accurate solar activity forecasts on decadal time scales now seem possible. The methods that work fall into a class of prediction techniques called “precursor methods.” We utilize solar, interplanetary field, and geomagnetic precursors to update our cycle 23 prediction to provide a mean smoothed sunspot number of 153 ± 30 and mean smoothed Fl0.7 cm Radio flux of 200 ± 30. This is comparable to, but somewhat smaller than, the NOAA SEC panel findings that the next solar cycle would peak at a sunspot number near 160 ± 30. This paper also provides some discussion relating solar and interplanetary field components to serve as a bridge in interplanetary space, helping to forge Sun-Earth connections.

  2. A simulation of variability of ENSO forecast skill

    SciTech Connect

    Davey, M.K.; Anderson, D.L.T.; Lawrence, S.

    1996-01-01

    In many prediction schemes, the skill of long-range forecasts of ENSO events depends on the time of year. Such variability could be directly due to seasonal changes in the basic ocean-atmosphere system or due to the state of ENSO itself. A highly idealized delayed oscillator model with seasonally varying internal parameters is used here to simulate such behavior. The skill of the artificial forecasts shows dependence on both seasonal and ENSO phase. Experiments with ENSO phase-locked to the seasonal cycle, but with no seasonal variation of model parameters, show that the ENSO cycle alone can induce variability in skill. Inclusion of seasonal parameters enhances seasonal skill dependence. It is suggested that the seasonal skill variations found in practice are due to a combination of seasonal changes in the basic state and the phase-locking of the ENSO and annual cycles. 16 refs., 8 figs., 1 tab.

  3. Development of Quantitative Precipitation Forecast (QPF) Confidence Factor Using Short Range Ensemble Forecasts

    NASA Astrophysics Data System (ADS)

    Im, J.; Danaher, E.; Brill, K.

    2004-05-01

    Hydrometeorological Prediction Center (HPC) produces a suit of deterministic quantitative precipitation forecast (QPF). The verification statistics shows a steady but gradual improvement. While these forecasts have proven to be useful as they are, they offer no information concerning the uncertainties of individual forecasts. The uncertainty in manually derived HPC QPF can be related directly to the inherent uncertainty in model predictions that is the basis for all HPC forecasts. The availability of ensemble forecasts has allowed forecasters to better assess the uncertainty of model forecast. This study is an attempt to objectively quantify the level of confidence that is justified in a particular HPC QPF by relating errors in HPC QPF to ensemble forecast spread. The first step was to seek out a relationship between the desired quantity, the absolute error (AE) of the HPC QPF, and a known quantity such as the spread from the short range ensemble forecast. Our study indicates that the AE of HPC QPF is highly correlated with ensemble QPF spread. Using the regression model equations derived at each horizontal grid point for each season, we predict an AE of HPC QPF associated with an individual ensemble QPF spread and 95% confidence interval (CI) of the AE. Based on the AE CI forecast and the QPF itself, we also predict 95% CI of the QPF. Currently the CI forecasts are processed for the continental US twice (00z and 12z) per day. The verifications for these CI forecasts have been (and will be) performed for a variety of seasons, geographical regions, various CI ranges, and QPF categories as well as overall regime.

  4. Toward Improving Streamflow Forecasts Using SNODAS Products

    NASA Astrophysics Data System (ADS)

    Barth, C.; Boyle, D. P.; Lamorey, G. W.; Bassett, S. D.

    2007-12-01

    As part of the Water 2025 initiative, researchers at the Desert Research Institute in collaboration with the U.S. Bureau of Reclamation are developing and improving water decision support system (DSS) tools to make seasonal streamflow forecasts for management and operations of water resources in the mountainous western United States. Streamflow forecasts in these areas may have errors that are directly related to uncertainties resulting from the lack of direct high resolution snow water equivalent (SWE) measurements. The purpose of this study is to investigate the possibility of improving the accuracy of streamflow forecasts through the use of Snow Data Assimilation System (SNODAS) products, which are high-resolution daily estimates of snow cover and associated hydrologic variables such as SWE and snowmelt runoff that are available for the coterminous United States. To evaluate the benefit of incorporating the SNODAS product into streamflow forecasts, a variety of Ensemble Streamflow Predictions (ESP) are generated using the Precipitation-Runoff Modeling System (PRMS). A series of manual and automatic calibrations of PRMS to different combinations of measured (streamflow) and estimated (SNODAS SWE) hydrologic variables is performed for several watersheds at various scales of spatial resolution. This study, which is embedded in the constant effort to improve streamflow forecasts and hence water operations DSS, shows the potential of using a product such as SNODAS SWE estimates to decrease parameter uncertainty related to snow variables and enhance forecast skills early in the forecast season.

  5. 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 allocation are provided.

  6. Verification of precipitation forecasts from two numerical weather prediction models in the Middle Atlantic Region of the USA: A precursory analysis to hydrologic forecasting

    NASA Astrophysics Data System (ADS)

    Siddique, Ridwan; Mejia, Alfonso; Brown, James; Reed, Seann; Ahnert, Peter

    2015-10-01

    Accurate precipitation forecasts are required for accurate flood forecasting. The structures of different precipitation forecasting systems are constantly evolving, with improvements in forecasting techniques, increases in spatial and temporal resolution, improvements in model physics and numerical techniques, and better understanding of, and accounting for, predictive uncertainty. Hence, routine verification is necessary to understand the quality of forecasts as inputs to hydrologic modeling. In this study, we verify precipitation forecasts from the National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2), as well as the 21-member Short Range Ensemble Forecast (SREF) system. Specifically, basin averaged precipitation forecasts are verified for different basin sizes (spatial scales) in the operating domain of the Middle Atlantic River Forecast Center (MARFC), using multi-sensor precipitation estimates (MPEs) as the observed data. The quality of the ensemble forecasts is evaluated conditionally upon precipitation amounts, forecast lead times, accumulation periods, and seasonality using different verification metrics. Overall, both GEFSRv2 and SREF tend to overforecast light to moderate precipitation and underforecast heavy precipitation. In addition, precipitation forecasts from both systems become increasingly reliable with increasing basin size and decreasing precipitation threshold, and the 24-hourly forecasts show slightly better skill than the 6-hourly forecasts. Both systems show a strong seasonal trend, characterized by better skill during the cool season than the warm season. Ultimately, the verification results lead to guidance on the expected quality of the precipitation forecasts, together with an assessment of their relative quality and unique information content, which is useful and necessary for their application in hydrologic forecasting.

  7. 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 bad air quality warning and forecast air quality forecasts. There is a difficulty presented with evaluating the economic utility of the forecasts. All may not comply and even with a low compliance rate of 5% and 72% as the average probability of detection of poor air quality days by the air quality models, we estimate that the forecasting program saves 412 lives or 412 million dollars per year for the region. The totals we found are great or greater than other typical yearly meteorological hazard programs such as tornado or hurricane forecasting and it is clear that the economic value of air quality forecasting in the Baltimore/Washington region is vital.

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

    NASA Astrophysics Data System (ADS)

    Block, P. J.

    2014-12-01

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

  9. Impact of hindcast length on estimates of seasonal climate predictability

    NASA Astrophysics Data System (ADS)

    Shi, W.; Schaller, N.; MacLeod, D.; Palmer, T. N.; Weisheimer, A.

    2015-03-01

    It has recently been argued that single-model seasonal forecast ensembles are overdispersive, implying that the real world is more predictable than indicated by estimates of so-called perfect model predictability, particularly over the North Atlantic. However, such estimates are based on relatively short forecast data sets comprising just 20 years of seasonal predictions. Here we study longer 40 year seasonal forecast data sets from multimodel seasonal forecast ensemble projects and show that sampling uncertainty due to the length of the hindcast periods is large. The skill of forecasting the North Atlantic Oscillation during winter varies within the 40 year data sets with high levels of skill found for some subperiods. It is demonstrated that while 20 year estimates of seasonal reliability can show evidence of overdispersive behavior, the 40 year estimates are more stable and show no evidence of overdispersion. Instead, the predominant feature on these longer time scales is underdispersion, particularly in the tropics.

  10. 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.

  11. Forecasting phenology under global warming

    PubMed Central

    Ibáñez, Inés; Primack, Richard B.; Miller-Rushing, Abraham J.; Ellwood, Elizabeth; Higuchi, Hiroyoshi; Lee, Sang Don; Kobori, Hiromi; Silander, John A.

    2010-01-01

    As a consequence of warming temperatures around the world, spring and autumn phenologies have been shifting, with corresponding changes in the length of the growing season. Our understanding of the spatial and interspecific variation of these changes, however, is limited. Not all species are responding similarly, and there is significant spatial variation in responses even within species. This spatial and interspecific variation complicates efforts to predict phenological responses to ongoing climate change, but must be incorporated in order to build reliable forecasts. Here, we use a long-term dataset (1953–2005) of plant phenological events in spring (flowering and leaf out) and autumn (leaf colouring and leaf fall) throughout Japan and South Korea to build forecasts that account for these sources of variability. Specifically, we used hierarchical models to incorporate the spatial variability in phenological responses to temperature to then forecast species' overall and site-specific responses to global warming. We found that for most species, spring phenology is advancing and autumn phenology is getting later, with the timing of events changing more quickly in autumn compared with the spring. Temporal trends and phenological responses to temperature in East Asia contrasted with results from comparable studies in Europe, where spring events are changing more rapidly than are autumn events. Our results emphasize the need to study multiple species at many sites to understand and forecast regional changes in phenology. PMID:20819816

  12. Role of climate forecasts and initial conditions in developing streamflow and soil moisture forecasts in a rainfall-runoff regime

    NASA Astrophysics Data System (ADS)

    Sinha, T.; Sankarasubramanian, A.

    2013-02-01

    Skillful seasonal streamflow forecasts obtained from climate and land surface conditions could significantly improve water and energy management. Since climate forecasts are updated on a monthly basis, we evaluate the potential in developing operational monthly streamflow forecasts on a continuous basis throughout the year. Further, basins in the rainfall-runoff regime critically depend on the forecasted precipitation in the upcoming months as opposed to snowmelt regimes where initial hydrological conditions (IHC) play a critical role. The goal of this study is to quantify the role of updated monthly precipitation forecasts and IHC in forecasting 6-month lead monthly streamflow and soil moisture for a rainfall-runoff mechanism dominated basin - Apalachicola River at Chattahoochee, FL. The Variable Infiltration Capacity (VIC) land surface model is implemented with two forcings: (a) updated monthly precipitation forecasts from ECHAM4.5 Atmospheric General Circulation Model (AGCM) forced with sea surface temperature forecasts and (b) daily climatological ensembles. The difference in skill between the above two quantifies the improvements that could be attainable using the AGCM forecasts. Monthly retrospective streamflow forecasts are developed from 1981 to 2010 and streamflow forecasts estimated from the VIC model are also compared with those predicted by using the principal component regression (PCR) model. The mean square error (MSE) in predicting monthly streamflows, using the VIC model, are compared with the MSE of streamflow climatology under ENSO (El Niño Southern Oscilation) conditions as well as under normal years. Results indicate that VIC forecasts obtained using ECHAM4.5 are significantly better than VIC forecasts obtained using climatological ensembles and PCR models over 2-6 month lead time during winter and spring seasons in capturing streamflow variability and reduced mean square errors. However, at 1-month lead time, streamflow utilizing the climatological forcing scheme outperformed ECHAM4.5 based streamflow forecasts during winter and spring, indicating a dominant role of IHCs up to a 1-month lead time. During ENSO years, streamflow forecasts exhibit better skill even up to a six-month lead time. Comparisons of the seasonal soil moisture forecasts, developed using ECHAM4.5 forcings, with seasonal streamflows also show significant skill, up to a 6-month lead time, in the four seasons.

  13. 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. Copyright ?? 2010 Royal Meteorological Society.

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

    NASA Astrophysics Data System (ADS)

    Thiboult, A.; Anctil, F.; Boucher, M.-A.

    2015-07-01

    Seeking for 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, multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims at untangling the sources of uncertainty by studying the combination of these tools and assessing their 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 stage in the forecasting process by using different means. Their combination outperforms any of the tool used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial condition 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 improve the general forecasting performance and prevents 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 Ensemble Kalman Filter tuning to avoid overlapping in error deciphering.

  15. 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.

  16. 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.

  17. Global Forecasting of Coral Bleaching Events

    NASA Astrophysics Data System (ADS)

    Eakin, C. M.; Liu, G.; Matrosova, L. E.; Penland, M. C.; Gledhill, D. K.; Webb, R. S.; Christensen, T. R.; Heron, S. F.; Morgan, J. A.; Parker, B. A.; Skirving, W. J.; Strong, A. E.

    2009-05-01

    In July 2008, NOAA Coral Reef Watch launched a new seasonal prediction tool for coral bleaching conditions to augment its real-time satellite monitoring. A model of thermal stress from two to 16 weeks in the future was developed through collaboration with the Physical Sciences Division of the NOAA Earth System Research Laboratory, to forecast the risk of coral bleaching well in advance. The system is built on sea surface temperature forecasts provided by NOAA's Linear Inverse Model (LIM) that has successfully produced predictions of tropical Pacific and Atlantic SST anomalies. This presentation will outline this product, and compare the forecast with satellite observations of actual thermal stress. Such forecasting tools provide critical and timely decision support for coral reef managers and scientists worldwide.

  18. Objective Lightning Probability Forecast Tool Phase II

    NASA Technical Reports Server (NTRS)

    Lambert, Winnie

    2007-01-01

    This presentation describes the improvement of a set of lightning probability forecast equations that are used by the 45th Weather Squadron forecasters for their daily 1100 UTC (0700 EDT) weather briefing during the warm season months of May-September. This information is used for general scheduling of operations at Cape Canaveral Air Force Station and Kennedy Space Center. Forecasters at the Spaceflight Meteorology Group also make thunderstorm forecasts during Shuttle flight operations. Five modifications were made by the Applied Meteorology Unit: increased the period of record from 15 to 17 years, changed the method of calculating the flow regime of the day, calculated a new optimal layer relative humidity, used a new smoothing technique for the daily climatology, and used a new valid area. The test results indicated that the modified equations showed and increase in skill over the current equations, good reliability, and an ability to distinguish between lightning and non-lightning days.

  19. EARLY FORECASTS OF SNOWMELT RUNOFF USING SNOTEL DATA IN THE UPPER RIO GRANDE BASIN

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In order to improve early forecasts of water availability from the Upper Rio Grande Basin in Colorado, we studied the accuracy of 1- to 6-month advance forecasts of snowmelt runoff based upon SNOTEL data. Equations were developed to forecast seasonal runoff volume, peak daily flow rate, and date of...

  20. The Forecast Paradigm is Changing - What Role Will the Forecaster Play?

    NASA Astrophysics Data System (ADS)

    Reynolds, D. W.

    2004-12-01

    The use of Numerical Weather Prediction (NWP) is now the fundamental starting point for all weather forecasts with valid times beyond 12 hours and in many cases 6 hours. This is a testament to the skill of numerical predictions today. Forecasters are inherently deterministic in there thinking and have been taught to provide specific forecast parameters or a small range in the forecast parameter with little emphasis as to quantifying the inherent uncertainty associated with any forecast. As such one of the main forecaster challenges today is to chose the "right" model for a given weather event. Most weather forecast offices have access to a multitude of numerical model solutions, some times dozens, especially when ensemble members are considered. For a forecaster at a Weather Forecast Office (WFO), it usually means choosing a model that has had the best track record over the most recent series of weather events. Some of the real challenges for the forecaster is maintaining an awareness of model bias since models change on the order of yearly instead of every 2 to 5 years as was true in the past. The forecaster must also be aware that depictions of mesoscale phenomena by high resolution numerical guidance may in fact simulate previously observed local phenomena but in fact be totally in error as the larger scale forcing may be in error. This is becoming a very big challenge for the WFO forecaster in the era of producing digital forecasts (Glahn and Ruth, WRF 2002). Here the forecaster is asked to produce 2.5 to 5 km sensible weather grids over their forecast area of responsibility at temporal frequencies of from 1 hour to 12-hour durations out through 7 days. There is no attempt to denote either the reliability of these forecasts or quantify the uncertainty associated with the forecasts. In fact each forecaster is required to collaborate with his/her neighboring offices to adjust the numbers to reduce incoherency of the particular forecast parameter. This philosophy flies directly in the face of two recent US Weather Research Program position papers on warm and cool season QPF studies, which proposes that probabilistic QPFs be the focus of the next 5 to 10 years and that ensembles should play a key role in this transition. We appear to have reached a plateau in our skill level in such forecasts as QPF that we may see only very minor improvements if the deterministic approach is continued. It must be recognized that if the forecast process transitions to a more probabilistic methodology, that the role of the forecaster, both at national centers and at the local office will change. In the digital forecast era, when statistical post-processing of ensemble output will provide both deterministic and reliable probabilistic forecasts, there is a question as to exactly what role, if any, the forecaster will have in producing value added sensible weather grids beyond forecast valid times of say 12 hours for use by the general public and for input to hydrologic models. This paper will try to provoke some thoughts on these issues.

  1. 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.

  2. A framework for analyzing seasonal prediction through canonical event analysis

    NASA Astrophysics Data System (ADS)

    Wood, Eric; Roundy, Joshua; Yuan, Xing

    2014-05-01

    Hydrologic extremes in the form of wet and dry periods (flood and drought prone periods) have large impacts on society. The ability to predict such periods allows for preparations that can reduce the severity of these events on society. Such preparations require predictions at a seasonal timescales. While seasonal predictions from global climate models can provide forecasts at such timescales; their skill varies seasonally and spatially, which severely limits their practical use. A better understanding of when and where climate models are skillful is assessed through hindcasts, which are usually limited to less than 30 years and are therefore prone to randomness and uncertainty. Until now, most hindcast analyses used to assess seasonal forecast skill have focused on a single temporal or spatial resolution ? often the model resolution ? even though it?s recognized that the fidelity of forecast skill deceases with lead time. In this work, we analyze ?canonical? forecast events, which are defined as space-time averaged forecasts that range from 2-week forecasts at leads 0 to 7 months to single 8 month seasonal average forecast (52 events), and spatially from forecasts for a single grid to an average forecast for an 8x8 grid area (9 events). Together this results in 468 canonical events per forecast, and 5612 events per year. To better understand the seasonal space-time skill, a probabilistic predictability metric based on model skill was developed across temporal and spatial scales; i.e. for the canonical events. This probabilistic predictability metric is demonstrated using the 28 year hindcast data set from NCEP?s Climate Forecast System version 2 for forecast of precipitation and daily maximum and minimum temperature. Additionally, the attribution of this skill to the El-Nino Southern Oscillation (ENSO) over the contiguous United States is also explored. The results show clear seasonal and spatial patterns of predictability that vary with each forecast variable and provide a better understanding of when and where to have confidence in model predictions. The influence of ENSO on the predictability was the strongest in the cold season, but varied with season and variable. The implications, limitations and extensions of this work for making seasonal predictions more useful are discussed, as well as extensions to multi-model seasonal forecasts systems where the approach may provide insights into how different forecasts may be weighted.

  3. Thoughts on assessing decadal-scale precipitation variations as surrogate forecasts

    Technology Transfer Automated Retrieval System (TEKTRAN)

    For much of the United States, in particular regions without significant ENSO impacts or strong decadal trends, seasonal precipitation forecasts have very limited skill, and forecasts for non-climatological conditions are infrequent. The low predictability for seasonal to interannual precipitation ...

  4. Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004)

    NASA Astrophysics Data System (ADS)

    Wang, Bin; Lee, June-Yi; Kang, In-Sik; Shukla, J.; Park, C.-K.; Kumar, A.; Schemm, J.; Cocke, S.; Kug, J.-S.; Luo, J.-J.; Zhou, T.; Wang, B.; Fu, X.; Yun, W.-T.; Alves, O.; Jin, E. K.; Kinter, J.; Kirtman, B.; Krishnamurti, T.; Lau, N. C.; Lau, W.; Liu, P.; Pegion, P.; Rosati, T.; Schubert, S.; Stern, W.; Suarez, M.; Yamagata, T.

    2009-07-01

    We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980-2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981-2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial-temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80-90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.

  5. Integrating climate forecasts and natural gas supply information into a natural gas purchasing decision

    NASA Astrophysics Data System (ADS)

    Changnon, David; Ritsche, Michael; Elyea, Karen; Shelton, Steve; Schramm, Kevin

    2000-09-01

    This paper illustrates a key lesson related to most uses of long-range climate forecast information, namely that effective weather-related decision-making requires understanding and integration of weather information with other, often complex factors. Northern Illinois University's heating plant manager and staff meteorologist, along with a group of meteorology students, worked together to assess different types of available information that could be used in an autumn natural gas purchasing decision. Weather information assessed included the impact of ENSO events on winters in northern Illinois and the Climate Prediction Center's (CPC) long-range climate outlooks. Non-weather factors, such as the cost and available supplies of natural gas prior to the heating season, contribute to the complexity of the natural gas purchase decision. A decision tree was developed and it incorporated three parts: (a) natural gas supply levels, (b) the CPC long-lead climate outlooks for the region, and (c) an ENSO model developed for DeKalb. The results were used to decide in autumn whether to lock in a price or ride the market each winter. The decision tree was tested for the period 1995-99, and returned a cost-effective decision in three of the four winters.

  6. Activity of long-lead burst neurons in pontine reticular formation during head-unrestrained gaze shifts

    PubMed Central

    Freedman, Edward G.

    2013-01-01

    Primates explore a visual scene through a succession of saccades. Much of what is known about the neural circuitry that generates these movements has come from neurophysiological studies using subjects with their heads restrained. Horizontal saccades and the horizontal components of oblique saccades are associated with high-frequency bursts of spikes in medium-lead burst neurons (MLBs) and long-lead burst neurons (LLBNs) in the paramedian pontine reticular formation. For LLBNs, the high-frequency burst is preceded by a low-frequency prelude that begins 12–150 ms before saccade onset. In terms of the lead time between the onset of prelude activity and saccade onset, the anatomical projections, and the movement field characteristics, LLBNs are a heterogeneous group of neurons. Whether this heterogeneity is endemic of multiple functional subclasses is an open question. One possibility is that some may carry signals related to head movement. We recorded from LLBNs while monkeys performed head-unrestrained gaze shifts, during which the kinematics of the eye and head components were dissociable. Many cells had peak firing rates that never exceeded 200 spikes/s for gaze shifts of any vector. The activity of these low-frequency cells often persisted beyond the end of the gaze shift and was usually related to head-movement kinematics. A subset was tested during head-unrestrained pursuit and showed clear modulation in the absence of saccades. These “low-frequency” cells were intermingled with MLBs and traditional LLBNs and may represent a separate functional class carrying signals related to head movement. PMID:24174648

  7. 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 flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases. PMID:23457520

  8. Evaluation of Experimental Models for Tropical Cyclone Forecasting in Support of the NOAA Hurricane Forecast Improvement Project (HFIP)

    NASA Astrophysics Data System (ADS)

    Kucera, P. A.; Brown, B.; Williams, C.; Nance, L. B.

    2014-12-01

    The Tropical Cyclone Modeling Team (TCMT) in NCAR's Joint Numerical Testbed (JNT) Program focuses on the verification of experimental forecasts of tropical cyclones (TCs). Activities of the team include the development of new verification methods and tools for TC forecasts and the design and implementation of diagnostic verification experiments to evaluate the performance of tropical cyclone forecast models. For the NOAA Hurricane Forecast Improvement Project (HFIP), the TCMT has designed and conducted verification studies involving various deterministic, ensemble, and statistical regional and global forecast models that participate in the annual HFIP real-time forecast Demonstration experiment. The HFIP Demonstration experiment is conducted during the months of August through October each year. The TCMT has applied new and established statistical approaches to provide statistically meaningful diagnostic evaluations of TC forecasts for storms observed during the Demonstration period. For this study, the TCMT has conducted evaluation of operational and experimental forecast performance for the 2012-2014 hurricane seasons in the North Atlantic and Eastern Pacific Oceans. This presentation will provide an overview of the Demonstration experiment along with a summary of results from the experimental model forecasts for the Atlantic and Eastern Pacific Ocean basins with the goal of documenting potential improvements to hurricane forecasts in comparison to operational baselines.

  9. Role of climate forecasts and initial land-surface conditions in developing operational streamflow and soil moisture forecasts in a rainfall-runoff regime: skill assessment

    NASA Astrophysics Data System (ADS)

    Sinha, T.; Sankarasubramanian, A.

    2012-04-01

    Skillful seasonal streamflow forecasts obtained from climate and land surface conditions could significantly improve water and energy management. Since climate forecasts are updated on monthly basis, we evaluate the potential in developing operational monthly streamflow forecasts on a continuous basis throughout the year. Further, basins in the rainfall-runoff regime critically depend on the forecasted precipitation in the upcoming months as opposed to snowmelt regimes where initial hydrological conditions (IHC) play a critical role. The goal of this study is to quantify the role of monthly updated precipitation forecasts and IHC in forecasting 6-month lead monthly streamflow for a rainfall-runoff mechanism dominated basin - Apalachicola River at Chattahoochee, FL. The Variable Infiltration Capacity (VIC) land surface model is implemented with two forcings: (a) monthly updated precipitation forecasts from ECHAM4.5 Atmospheric General Circulation Model (AGCM) forced with sea surface temperature forecasts and (b) daily climatological ensemble. The difference in skill between the above two quantifies the improvements that could be attainable using the AGCM forecasts. Monthly retrospective streamflow forecasts are developed from 1981 to 2010 and streamflow forecasts estimated from the VIC model are also compared with those predicted by using the principal component regression (PCR) model. Mean square error (MSE) in predicting monthly streamflow using the above VIC model are compared with the MSE of streamflow climatology under ENSO conditions as well as under normal years. Results indicate that VIC forecasts, at 1-2 month lead time, obtained using ECHAM4.5 are significantly better than VIC forecasts obtained using climatological ensemble over all the seasons except forecasts issued in fall and the PCR models perform better during the fall months. Over longer lead times (3-6 months), VIC forecasts derived using ECHAM4.5 forcings alone performed better compared to the MSE of streamflow climatology during winter and spring seasons. During ENSO years, streamflow forecasts exhibit better skill even up to six month lead time. Comparison of the seasonal soil moisture forecasts developed using ECHAM4.5 forcings with seasonal streamflow also show significant skill at 1-3 month lead time over the all four seasons.

  10. A Variational Ensemble Streamflow Prediction Assessment Approach for Quantifying Streamflow Forecast Skill Elasticity

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Hopson, T. M.; Newman, A. J.; Brekke, L. D.; Arnold, J. R.; Clark, M. P.

    2014-12-01

    Water resources decision-making commonly depends on monthly to seasonal streamflow forecasts, among other kinds of information. The skill of such predictions derives from the ability to estimate a watershed's initial moisture and energy conditions and to forecast future weather and climate. We investigate the first two sources of predictability in an idealized experiment using calibrated hydrologic simulation models for 424-watersheds that span the continental US. Earlier work in this area outlined an ensemble-based strategy for attributing streamflow forecast uncertainty between two endpoints representing zero and perfect information about future forcings (ie, the National Weather Service ensemble streamflow prediction, or ESP approach) and initial conditions. This study expands this approach to characterize the influence of varying levels of uncertainty in each area on streamflow prediction uncertainty, and, importantly, we calculate derivatives in predictability throughout the initial conditions and future forcing space to illustrate how science investments can improve streamflow forecasts. Ensemble hindcasts are initialized on a monthly basis for each basin's periods of record, and forecasts of streamflow for the 1, 3 and 6 month periods following the initialization are evaluated. Regional and seasonal variations in watershed hydroclimatology largely determine the relative importance of initial hydrologic conditions and seasonal climate forecasts, leading to striking differences between rainfall driven and snowmelt driven watersheds. We use the flow forecast skill derivatives - elasticities relative to skill in either predictability source - to characterize the regional, seasonal and predictand variations in flow forecast skill dependencies. The resulting analysis provides insights on the relative benefits of investments toward improving watershed monitoring (through modeling and measurement) versus improved climate forecasting. Among other key findings, the results suggest that climate forecast and initial condition skill improvements can be amplified in streamflow prediction skill, which means that climate forecasts may have greater benefit for monthly-to-seasonal flow forecasting than is apparent from climate forecast skill considerations alone.

  11. Verification of Medium Range Probabilistic Rainfall Forecasts Over India

    NASA Astrophysics Data System (ADS)

    Dube, Anumeha; Ashrit, Raghavendra; Singh, Harvir; Iyengar, Gopal; Rajagopal, E. N.

    2016-03-01

    Forecasting rainfall in the tropics is a challenging task further hampered by the uncertainty in the numerical weather prediction models. Ensemble prediction systems (EPSs) provide an efficient way of handling the inherent uncertainty of these models. Verification of forecasts obtained from an EPS is a necessity, to build confidence in using these forecasts. This study deals with the verification of the probabilistic rainfall forecast obtained from the National Centre for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Forecast system (NGEFS) for three monsoon seasons, i.e., JJAS 2012, 2013 and 2014. Verification is done based on the Brier Score (BS) and its components (reliability, resolution and uncertainty), Brier Skill Score (BSS), reliability diagram, relative operating characteristic (ROC) curve and area under the ROC (AROC) curve. Three observation data sets are used (namely, NMSG, CPC-RFE2.0 and TRMM) for verification of forecasts and the statistics are compared. BS values for verification of NGEFS forecasts using NMSG data are the lowest, indicating that the forecasts have a better match with these observations as compared to both TRMM and CPC-RFE2.0. This is further strengthened by lower reliability, higher resolution and BSS values for verification against this data set. The ROC curve shows that lower rainfall amounts have a higher hit rate, which implies that the model has better skill in predicting these rainfall amounts. The reliability plots show that the events with lower probabilities were under forecasted and those with higher probabilities were over forecasted. From the current study it can be concluded that even though NGEFS is a coarse resolution EPS, the probabilistic forecast has good skill. This in turn leads to an increased confidence in issuing operational probabilistic forecasts based on NGEFS.

  12. Influenza Forecasting with Google Flu Trends

    PubMed Central

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

    2013-01-01

    Objective We sought to develop a practical influenza forecast model, based on real-time, geographically focused, and easy to access data, to provide individual medical centers with advanced warning of the number of influenza cases, thus allowing sufficient time to implement an intervention. Secondly, we evaluated how the addition of a real-time influenza surveillance system, Google Flu Trends, would impact the forecasting capabilities of this model. Introduction Each year, influenza results in increased Emergency Department crowding which can be mitigated through early detection linked to an appropriate response. Although current surveillance systems, such as Google Flu Trends, yield near real-time influenza surveillance, few demonstrate ability to forecast impending influenza cases. Methods Forecasting models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004 – 2011) divided into training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear, and autoregressive 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. Models were developed and evaluated through statistical measures of global deviance and log-likelihood ratio tests. An additional measure of forecast confidence, defined as the percentage of forecast values, during an influenza peak, that are within 7 influenza cases of the actual data, was examined to demonstrate practical utility of the model. Results A generalized autoregressive Poisson (GARMA) forecast model integrating previous influenza cases with Google Flu Trends information provided the most accurate influenza case predictions. Google Flu Trend data was the only source of external information providing significant forecast improvements (p = 0.00002). The final model, a GARMA intercept model with the addition of Google Flu Trends, predicted weekly influenza cases during 4 out-of-sample outbreaks within 7 cases for 80% of estimates (Figure 1). 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 flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

  13. The HFIP High Resolution Hurricane Forecast Test

    NASA Astrophysics Data System (ADS)

    Nance, L. B.; Bernardet, L.; Bao, S.; Brown, B.; Carson, L.; Fowler, T.; Halley Gotway, J.; Harrop, C.; Szoke, E.; Tollerud, E. I.; Wolff, J.; Yuan, H.

    2010-12-01

    Tropical cyclones are a serious concern for the nation, causing significant risk to life, property and economic vitality. The National Oceanic and Atmospheric Administration (NOAA) National Weather Service has a mission of issuing tropical cyclone forecasts and warnings, aimed at protecting life and property and enhancing the national economy. In the last 10 years, the errors in hurricane track forecasts have been reduced by about 50% through improved model guidance, enhanced observations, and forecaster expertise. However, little progress has been made during this period toward reducing forecasted intensity errors. To address this shortcoming, NOAA established the Hurricane Forecast Improvement Project (HFIP) in 2007. HFIP is a 10-year plan to improve one to five day tropical cyclone forecasts, with a focus on rapid intensity change. Recent research suggests that prediction models with grid spacing less than 1 km in the inner core of the hurricane may provide a substantial improvement in intensity forecasts. The 2008-09 staging of the High Resolution Hurricane (HRH) Test focused on quantifying the impact of increased horizontal resolution in numerical models on hurricane intensity forecasts. The primary goal of this test was an evaluation of the effect of increasing horizontal resolution within a given model across a variety of storms with different intensity, location and structure. The test focused on 69 retrospectives cases from the 2005 and 2007 hurricane seasons. Six modeling groups participated in the HRH test utilizing a variety of models, including three configurations of the Weather Research and Forecasting (WRF) model, the operational GFDL model, the Navy’s tropical cyclone model, and a model developed at the University of Wisconsin-Madison (UWM). The Development Testbed Center (DTC) was tasked with providing objective verification statistics for a variety of metrics. This presentation provides an overview of the HRH Test and a summary of the standard verification results, as well as results obtained by applying new verification tools developed at the DTC that assess changes in forecast skill for Rapid Intensification (RI) and Rapid Weakening (RW) events and forecast consistency.

  14. 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.

  15. 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.

  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. 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.

  18. Improvement of ECMWF monthly forecasts of precipitation over France with an analog method

    NASA Astrophysics Data System (ADS)

    Berthelot, M.; Dubus, L.; Gailhard, J.

    2010-09-01

    Optimal operation of hydro-power plants requires accurate forecasts of precipitation which are then integrated into hydrological models to forecast river flows and water volumes in reservoirs. Precipitation is a difficult parameter to forecast, especially at long lead times (monthly and over) one of the reasons being the too coarse resolution of numerical weather prediction systems. In this study, we evaluate ECMWF's monthly forecasts of precipitation over 9 important basins in France. The deterministic approach shows that forecasts are useless over week 1. Using the probabilistic approach allows to get useful information for some events (lower and upper terciles for instance), up to week 3, but the overall scores are quite low, and hardly better than climatological scores. In a second step, EDF's analog method, currently used in operations for D+7 forecasts, has been adapted to ECWMF's monthly forecasts. It uses Z700 and Z1000 fields over North Atlantic and Europe to get local precipitations. For the nine catchments studied here and for the four weeks, results show an overall improvement of analog precipitation forecasts compared to raw forecasts. Improvement is also identified with respect to climatology in more than half of the catchments. The prediction skill is mostly pronounced for extreme events (low and heavy precipitations). The analog method thus presents significant performance, suited for operational use. Improvements can also be expected with some optimization of the method (mix of predictors, new similarity criterion…)

  19. Insolation Forecasting Using Weather Forecast with Weather Change Patterns

    NASA Astrophysics Data System (ADS)

    Shimada, Takae; Kurokawa, Kosuke

    This paper reports an insolation forecasting method for photovoltaic power predictions. The method of proposal forecasts the global irradiance every one hour by using weather forecast every three hours. The weather forecast is classified into 14 kinds of weather change categories by weather change patterns, and the insolation forecasting accuracy is improved. The forecasting accuracy was examined based on the measurement data and the weather forecast announced in Tokyo. In the result, using weather change patterns decrease mean absolute error ratio of hourly forecasting from 32.6 to 30.2% and the error ratio of daily forecasting from 24.7 to 23.5%. The result also shows the possibility that the error ratio of hourly forecasting is decreased to 24.5% and the error ratio of daily forecasting is decreased to 17.8% in Tokyo when the weather forecast accuracy is improved.

  20. Evaluation and economic value of winter weather forecasts

    NASA Astrophysics Data System (ADS)

    Snyder, Derrick W.

    State and local highway agencies spend millions of dollars each year to deploy winter operation teams to plow snow and de-ice roadways. Accurate and timely weather forecast information is critical for effective decision making. Students from Purdue University partnered with the Indiana Department of Transportation to create an experimental winter weather forecast service for the 2012-2013 winter season in Indiana to assist in achieving these goals. One forecast product, an hourly timeline of winter weather hazards produced daily, was evaluated for quality and economic value. Verification of the forecasts was performed with data from the Rapid Refresh numerical weather model. Two objective verification criteria were developed to evaluate the performance of the timeline forecasts. Using both criteria, the timeline forecasts had issues with reliability and discrimination, systematically over-forecasting the amount of winter weather that was observed while also missing significant winter weather events. Despite these quality issues, the forecasts still showed significant, but varied, economic value compared to climatology. Economic value of the forecasts was estimated to be 29.5 million or 4.1 million, depending on the verification criteria used. Limitations of this valuation system are discussed and a framework is developed for more thorough studies in the future.

  1. 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.

  2. Investigation of Geophysical Variables for Drought Forecasting

    NASA Astrophysics Data System (ADS)

    Opoku-Ankomah, Yaw

    1993-12-01

    Drought occurrence in Ghana and New South Wales (NSW), Australia can bring huge economic losses. However, if drought could be predicted it might be possible to mitigate its effects. Hence, with the aim of forecasting drought, relationships between geophysical variables and rainfalls in NSW and Ghana have been investigated. The geophysical variables studied include the southern oscillation index (SOI), sea-surface temperatures (SSTs) and pressures. The degree of association between the variables and the rainfalls was assessed by the simple linear correlation coefficient. Further, principal component (PC) analysis was used to identify the major spatial patterns of NSW rainfalls and SSTs. The amplitudes associated with the PCs were then used in the correlations. In NSW good relationships were observed between rainfall and the SOI but plots of running correlation coefficients revealed temporal instabilities in the relationships. The changes in the relationships vary spatially across the state. In some seasons there was a decline in the relationships whereas in other seasons there was an improvement. In some seasons very little change was observed. These changes limit the possible use of the relationships for forecasting rainfall and drought in NSW. In Ghana the rainfalls in July to September were strongly related to SSTs in the eastern Atlantic. The relationships were stable and forecasting of drought and rainfall appears promising. The widely reported relationship of the Sahel rainfall and SSTs in the same locality as used in this study is quite different from that found for Ghana which is immediately to the south of the Sahel.

  3. 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 again by six years, from October 1996 to April 2002, by interpolating 1000-ft sounding data to 100-ft increments. The Phase II developmental data set included observations for the cool season months of October 1996 to February 2007. The AMU calculated 68 candidate predictors from the XMR soundings, to include 19 stability parameters, 48 wind speed parameters and one wind shear parameter. Each day in the data set was stratified by synoptic weather pattern, low-level wind direction, precipitation and Richardson Number, for a total of 60 stratification methods. Linear regression equations, using the 68 predictors and 60 stratification methods, were created for the tool's three forecast parameters: the highest peak wind speed of the day (PWSD), 5-minute average speed at the same time (A WSD), and timing of the PWSD. For PWSD and A WSD, 30 Phase II methods were selected for evaluation in the verification data set. For timing of the PWSD, 12 Phase\\I methods were selected for evaluation. The verification data set contained observations for the cool season months of March 2007 to April 2009. The data set was used to compare the Phase I and II forecast methods to climatology, model forecast winds and wind advisories issued by the 45 WS. The model forecast winds were derived from the 0000 and 1200 UTC runs of the 12-km North American Mesoscale (MesoNAM) model. The forecast methods that performed the best in the verification data set were selected for the Phase II version of the tool. For PWSD and A WSD, linear regression equations based on MesoNAM forecasts performed significantly better than the Phase I and II methods. For timing of the PWSD, none of the methods performed significantly bener than climatology. The AMU then developed the Microsoft Excel and MIDDS GUls. The GUIs display the forecasts for PWSD, AWSD and the probability the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. Since none of the prediction methods for timing of the PWSD performed significantly better thanlimatology, the tool no longer displays this predictand. The Excel and MIDDS GUIs display forecasts for Day-I to Day-3 and Day-I to Day-5, respectively. The Excel GUI uses MesoNAM forecasts as input, while the MIDDS GUI uses input from the MesoNAM and Global Forecast System model. Based on feedback from the 45 WS, the AMU added the daily average wind speed from 30 ft to 60 ft to the tool, which is one of the parameters in the 24-Hour and Weekly Planning Forecasts issued by the 45 WS. In addition, the AMU expanded the MIDDS GUI to include forecasts out to Day-7.

  4. 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 of forecasts for irrigation scheduling in a more effective way.

  5. Assessment of Tropical Cyclone Track Forecast Errors using GDAPS (UM)

    NASA Astrophysics Data System (ADS)

    Kim, D.; Kim, J.; Chang, K.; Byun, K.; Lee, J.

    2013-12-01

    After the Joint Typhoon Warning Center (JTWC) began issuing official five-day tropical cyclone (TC) forecasts in 2003, the Korea Meteorological Administration (KMA) started issuing official five-day forecasts of TCs in May 2012 after 2 year of beta test. Forming a selective consensus (SCON) by proper removal of a likely erroneous track forecast is hypothesized to be more accurate than the non-selective consensus (NCON) of all model tracks that are used for the five-day forecasts. Conceptual models describing large track error mechanisms, which are related to known tropical cyclone motion processes being misrepresented in the dynamical models, are applied to forecasts during the 2012 western North Pacific typhoon season by the Global Data Assimilation and Prediction System (GDAPS (UM N512 L70)) which is KMA's main operational model. GDAPS (UM) is one of consensus members used in making KMA's five-day forecasts and thus analysis of its track error tendencies would be useful for forming a SCON forecast. All 72-h track errors greater than 320 km are examined on the basis of the approach developed by Carr and Elsberry (2000a, b). Tropical-influenced error sources caused 37% (47 times / 126 erroneous forecasts) of the GDAPS (UM) large track forecast errors primarily because an incorrect beta effect-related process depicted by the model contributed to the erroneous forecasts. Midlatitude-influenced error sources accounted for 63% (79 times / 126 error cases) in the GDAPS (UM) erroneous forecasts mainly due to an incorrect forecast of the midlatitude system evolutions. It is proposed that KMA will be able to issue more reliable TC track information if a likely model track error is recognized by optimum use of conceptual models by Carr and Elsberry (2000a, b) and a selective consensus track is then the basis for an improved warning.

  6. Impact of hindcast length on estimates of seasonal climate predictability

    PubMed Central

    Shi, W; Schaller, N; MacLeod, D; Palmer, T N; Weisheimer, A

    2015-01-01

    It has recently been argued that single-model seasonal forecast ensembles are overdispersive, implying that the real world is more predictable than indicated by estimates of so-called perfect model predictability, particularly over the North Atlantic. However, such estimates are based on relatively short forecast data sets comprising just 20 years of seasonal predictions. Here we study longer 40 year seasonal forecast data sets from multimodel seasonal forecast ensemble projects and show that sampling uncertainty due to the length of the hindcast periods is large. The skill of forecasting the North Atlantic Oscillation during winter varies within the 40 year data sets with high levels of skill found for some subperiods. It is demonstrated that while 20 year estimates of seasonal reliability can show evidence of overdispersive behavior, the 40 year estimates are more stable and show no evidence of overdispersion. Instead, the predominant feature on these longer time scales is underdispersion, particularly in the tropics. Key Points Predictions can appear overdispersive due to hindcast length sampling error Longer hindcasts are more robust and underdispersive, especially in the tropics Twenty hindcasts are an inadequate sample size to assess seasonal forecast skill PMID:26074651

  7. 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.

  8. 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…

  9. 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

  10. 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 parameterizations are needed to ensure that we derive maximum benefits from these observations.

  11. The role of retrospective weather forecasts in developing daily forecasts of nutrient loadings over the southeast US

    NASA Astrophysics Data System (ADS)

    Oh, J.; Sinha, T.; Sankarasubramanian, A.

    2014-08-01

    It is well known in the hydrometeorology literature that developing real-time daily streamflow forecasts in a given season significantly depends on the skill of daily precipitation forecasts over the watershed. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. The intent of this study is to bridge these two findings so that daily nutrient loadings and the associated concentration could be predicted using daily precipitation forecasts and previously observed streamflow as surrogates of antecedent land surface conditions. By selecting 18 relatively undeveloped basins in the southeast US (SEUS), we evaluate the skill in predicting observed total nitrogen (TN) loadings in the Water Quality Network (WQN) by first developing the daily streamflow forecasts using the retrospective weather forecasts based on K-nearest neighbor (K-NN) resampling approach and then forcing the forecasted streamflow with a nutrient load estimation (LOADEST) model to obtain daily TN forecasts. Skill in developing forecasts of streamflow, TN loadings and the associated concentration were computed using rank correlation and RMSE (root mean square error), by comparing the respective forecast values with the WQN observations for the selected 18 Hydro-Climatic Data Network (HCDN) stations. The forecasted daily streamflow and TN loadings and their concentration have statistically significant skill in predicting the respective daily observations in the WQN database at all 18 stations over the SEUS. Only two stations showed statistically insignificant relationships in predicting the observed nitrogen concentration. We also found that the skill in predicting the observed TN loadings increases with the increase in drainage area, which indicates that the large-scale precipitation reforecasts correlate better with precipitation and streamflow over large watersheds. To overcome the limited samplings of TN in the WQN data, we extended the analyses by developing retrospective daily streamflow forecasts over the period 1979-2012 using reforecasts based on the K-NN resampling approach. Based on the coefficient of determination (R2Q-daily) of the daily streamflow forecasts, we computed the potential skill (R2TN-daily) in developing daily nutrient forecasts based on the R2 of the LOADEST model for each station. The analyses showed that the forecasting skills of TN loadings are relatively better in the winter and spring months, while skills are inferior during summer months. Despite these limitations, there is potential in utilizing the daily streamflow forecasts derived from real-time weather forecasts for developing daily nutrient forecasts, which could be employed for various adaptive nutrient management strategies for ensuring better water quality.

  12. 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.

  13. 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. PMID:25215714

  14. 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.

  15. SSUSI Aurora Forecast Model

    NASA Astrophysics Data System (ADS)

    Hsieh, S. W.; Zhang, Y.; Schaefer, R. K.; Romeo, G.; Paxton, L.

    2013-12-01

    A new capability has been developed at JHU/APL for forecasting the global aurora quantities based on the DMSP SSUSI data and the TIMED/GUVI Global Aurora Model. The SSUSI Aurora Forecast Model predicts the electron energy flux, mean energy, and equatorward boundary in the auroral oval for up to 1 day or 15 DMSP orbits in advance. In our presentation, we will demonstrate this newly implemented capability and its results. The future improvement plan will be discussed too.

  16. Constraints on Rational Model Weighting, Blending and Selecting when Constructing Probability Forecasts given Multiple Models

    NASA Astrophysics Data System (ADS)

    Higgins, S. M. W.; Du, H. L.; Smith, L. A.

    2012-04-01

    Ensemble forecasting on a lead time of seconds over several years generates a large forecast-outcome archive, which can be used to evaluate and weight "models". Challenges which arise as the archive becomes smaller are investigated: in weather forecasting one typically has only thousands of forecasts however those launched 6 hours apart are not independent of each other, nor is it justified to mix seasons with different dynamics. Seasonal forecasts, as from ENSEMBLES and DEMETER, typically have less than 64 unique launch dates; decadal forecasts less than eight, and long range climate forecasts arguably none. It is argued that one does not weight "models" so much as entire ensemble prediction systems (EPSs), and that the marginal value of an EPS will depend on the other members in the mix. The impact of using different skill scores is examined in the limits of both very large forecast-outcome archives (thereby evaluating the efficiency of the skill score) and in very small forecast-outcome archives (illustrating fundamental limitations due to sampling fluctuations and memory in the physical system being forecast). It is shown that blending with climatology (J. Bröcker and L.A. Smith, Tellus A, 60(4), 663-678, (2008)) tends to increase the robustness of the results; also a new kernel dressing methodology (simply insuring that the expected probability mass tends to lie outside the range of the ensemble) is illustrated. Fair comparisons using seasonal forecasts from the ENSEMBLES project are used to illustrate the importance of these results with fairly small archives. The robustness of these results across the range of small, moderate and huge archives is demonstrated using imperfect models of perfectly known nonlinear (chaotic) dynamical systems. The implications these results hold for distinguishing the skill of a forecast from its value to a user of the forecast are discussed.

  17. 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://www.ecmwf.int/en/research/projects/tigge) to prolong the lead time of the forecasts downstream. Both hydrological models show different performances when forced with raw and de-biased ECMWF ensembles. This work was partly supported by the project "Stochastic flood forecasting system (The River Vistula reach from Zawichost to Warsaw)" carried out by the Institute of Geophysics, Polish Academy of Sciences by order of the National Science Centre (contract No. 2011/01/B/ST10/06866). The rainfall and flow data were provided by the Institute of Meteorology and Water Management (IMGW), Poland.

  18. 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 variability period from January 1, 2011 to April 30, 2011 identified by "P1"; - a medium variability period from May 1, 2011 to June 30, 2011 identified by "P2"; - a low variability period from July 1, 2011 to August 15, 2011 identified by "P3". Conclusions The application of evolutionary forecasting methods enhances the forecasting skill, with or without exogenous variables. Stochastic learning offers many advantages over purely deterministic models. Stochastic learning methods on their own are competitive in several time horizons of interest, and can be hybridized with exogenous data to provide continuous real-time improvement over physical models at all time scales. The GA/ANN method yields optimal forecasts for different quality levels of telemetry, and is robust over a wide range of time horizons.Forecast Horizon: 1 hour (all values in kW, except for R2) for Non-Exogenous InputsStatistical error metrics for the 1-hour ahead forecasts for several stochastic methodologies, and for different solar variability seasons (P1-P3).

  19. A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

    SciTech Connect

    Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

    2013-12-18

    This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.

  20. 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.

  1. 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 result we hope to evaluate how weather can influence the intensity of the disease. Thus, the knowledge of the weather forecast for several days will help us to predict a state of health. The person will be able to take some proactive actions to avoid the anticipated worsening of his health. Literature 1. A. N. Bagrov, F. L. Bykov, V. A. Gordin. Complex Forecast of Surface Meteorological Parameters. Meteorology and Hydrology, 2014, N 5, 5-16 (Russian), 283-291 (English). 2. Bykov, Ph.L., Gordin, V.A., Objective Analysis of the Structure of Three-Dimensional Atmospheric Fronts. Izvestia of Russian Academy of Sciences. Ser. The Physics of Atmosphere and Ocean, 48 (2) (2012), 172-188 (Russian), 152-168 (English), http://dx.doi.org/10.1134/S0001433812020053 3. V.A.Gordin. Mathematical Problems and Methods in Hydrodynamical Weather Forecasting. Amsterdam etc.: Gordon & Breach Publ. House, 2000. 4. V.A.Gordin. Mathematics, Computer, Weather Forecasting, and Other Mathematical Physics' Scenarios. Moscow, Fizmatlit, 2010, 2012 (Russian).

  2. Status Report: Assessing decadal precipitation variations as surrogate forecasts

    Technology Transfer Automated Retrieval System (TEKTRAN)

    To date, seasonal climate forecasts for precipitation lack useful skill for much of the contiguous U.S. This leaves major agricultural areas with questionable climatic guidance for decision support. This presentation will report on work in progress intended to produce monthly, location-specific pr...

  3. Development and application of an atmospheric-hydrologic-hydraulic flood forecasting model driven by TIGGE ensemble forecasts

    NASA Astrophysics Data System (ADS)

    Bao, Hongjun; Zhao, Linna

    2012-02-01

    A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction (NWP) information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centers for Environmental Prediction (NCEP). The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, and show that TIGGE ensemble forecast data are a promising tool for forecasting of flood inundation, comparable with that driven by raingauge observations.

  4. 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-GFS accounts for the majority of skill in the CNRFC basins. This is associated with the greater predictability of large storms in the North Coast Ranges during the winter months. In CBRFC, much of the skill in the streamflow forecasts originates from the hydrologic modeling and the EnsPost, particularly during the snowmelt period. In AB- and MA-RFCs, the contributions from the MEFP and the EnsPost are more variable. This paper summarizes the verification results, describes the expected performance and limitations of the HEFS for short- to medium-range streamflow forecasting, and provides recommendations for future research.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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 appropriately correct for, conditions that are undetected by the GFS. The calibration of the MEFP to provide reliable and skillful forecasts of a range of precipitation amounts (not only large amounts) is a secondary factor responsible for these Type-II conditional biases. Interpretation of the verification results leads to guidance on the expected performance and limitations of the MEFP, together with recommendations on future enhancements.

  10. Forecast Impact Simulation Studies

    NASA Technical Reports Server (NTRS)

    Atlas, R.; Kalnay, E.; Baker, W. E.; Susskind, J.; Reuter, D.; Halem, M.

    1985-01-01

    A series of simulation experiments is being conducted as a cooperative effort between the European Center for Medium Range Weather Forecasts (ECMWF), the National Meteorological Center (NMC) and the Goddard Laboratory for Atmospheres (GLA), to provide a quantitative assessment of the potential impact of proposed observing systems on large scale numerical weather prediction. For these studies an advanced analysis/forecast simulation system was developed which provides for a more realistic assessment of the impact of proposed observing systems than was possible in earlier studies. This system consists of four elements: (1) An atmospheric model integration to provide a complete record of the true state of the atmosphere (called nature); (2) a conventional data assimilation cycle that is used as the control experiment; (3) a satellite data assimilation that differs from the control in also including fabricated satellite data incorporated in the forecast-analysis cycle; and (4) forecasts produced from both control and satellite initial conditions. Comparison of these forecasts with nature provides an assessment of the impact of satellite data.

  11. Earthquake Prediction and Forecasting

    NASA Astrophysics Data System (ADS)

    Jackson, David D.

    Prospects for earthquake prediction and forecasting, and even their definitions, are actively debated. Here, "forecasting" means estimating the future earthquake rate as a function of location, time, and magnitude. Forecasting becomes "prediction" when we identify special conditions that make the immediate probability much higher than usual and high enough to justify exceptional action. Proposed precursors run from aeronomy to zoology, but no identified phenomenon consistently precedes earthquakes. The reported prediction of the 1975 Haicheng, China earthquake is often proclaimed as the most successful, but the success is questionable. An earthquake predicted to occur near Parkfield, California in 19885 years has not happened. Why is prediction so hard? Earthquakes start in a tiny volume deep within an opaque medium; we do not know their boundary conditions, initial conditions, or material properties well; and earthquake precursors, if any, hide amongst unrelated anomalies. Earthquakes cluster in space and time, and following a quake earthquake probability spikes. Aftershocks illustrate this clustering, and later earthquakes may even surpass earlier ones in size. However, the main shock in a cluster usually comes first and causes the most damage. Specific models help reveal the physics and allow intelligent disaster response. Modeling stresses from past earthquakes may improve forecasts, but this approach has not yet been validated prospectively. Reliable prediction of individual quakes is not realistic in the foreseeable future, but probabilistic forecasting provides valuable information for reducing risk. Recent studies are also leading to exciting discoveries about earthquakes.

  12. Hybrid deterministic - stochastic model for forecasting of monthly river flows

    NASA Astrophysics Data System (ADS)

    Svetlíková, D.; Szolgay, J.; Kohnová, S.; Komorníková, M.; Szökeová, D.

    2009-04-01

    Flows of the Váh River and its tributaries in the Tatry alpine mountain region in Slovakia are predominantly fed by snowmelt during the spring period and convective precipitation in the summer. Therefore their regime properties exhibit clear seasonal patterns. Moreover left and right side tributaries of the Váh River spring in different physiographic conditions in the High and Low Tatry Mountains. This provides intuitive justification for the application of nonlinear two-regime models for modelling and forecasting of monthly time series of these rivers. In the poster the forecasting performance of several linear and nonlinear time series models is compared with respect to their capabilities of forecasting monthly flows into the Liptovská Mara reservoir. ARMA and SETAR regime switching models were identified for each tributary respectively and forecasts of the tributary flows were composed through a simple water balance model into the forecast of the overall reservoir inflow. The combined hybrid (deterministic-stochastic) forecast, which preserves both the specific regime of the tributaries and the water balance in the catchments, was compared against different forecasts set up for the overall reservoir inflow.

  13. Validation of WRF forecasts for the Chajnantor region

    NASA Astrophysics Data System (ADS)

    Pozo, Diana R.; Marín, J. C.; Illanes, L.; Curé, M.; Rabanus, D.

    2016-03-01

    This study assesses the performance of the Weather Research and Forecasting (WRF) model to represent the near-surface weather conditions and the precipitable water vapor (PWV) in the Chajnantor plateau, in the North of Chile, from April to December 2007. The WRF model shows a very good performance forecasting the near-surface temperature and zonal wind component, although it overestimates the 2m water vapor mixing ratio and underestimates the 10m meridional wind component. The model represents very well the seasonal, intraseasonal and the diurnal variation of PWV. However, the PWV errors increase after the 12 hours of simulation. Errors in the simulations are larger than 1.5 mm only during 10 % of the study period, they do not exceed 0.5 mm during 65 % of the time and they are below 0.25 mm more than 45 % of the time, which emphasizes the good performance of the model to forecast the PWV over the region. The misrepresentation of the near-surface humidity in the region by the WRF model may have a negative impact on the PWV forecasts. Thus, having accurate forecasts of humidity near the surface may result in more accurate PWV forecasts. Overall, results from this, as well as recent studies, supports the use of the WRF model to provide accurate weather forecasts for the region, particularly for the PWV, which can be of great benefit for astronomers in the planning of their scientific operations and observing time.

  14. Validation of WRF forecasts for the Chajnantor region

    NASA Astrophysics Data System (ADS)

    Pozo, Diana; Marín, J. C.; Illanes, L.; Curé, M.; Rabanus, D.

    2016-06-01

    This study assesses the performance of the Weather Research and Forecasting (WRF) model to represent the near-surface weather conditions and the precipitable water vapour (PWV) in the Chajnantor plateau, in the north of Chile, from 2007 April to December. The WRF model shows a very good performance forecasting the near-surface temperature and zonal wind component, although it overestimates the 2 m water vapour mixing ratio and underestimates the 10 m meridional wind component. The model represents very well the seasonal, intraseasonal and the diurnal variation of PWV. However, the PWV errors increase after the 12 h of simulation. Errors in the simulations are larger than 1.5 mm only during 10 per cent of the study period, they do not exceed 0.5 mm during 65 per cent of the time and they are below 0.25 mm more than 45 per cent of the time, which emphasizes the good performance of the model to forecast the PWV over the region. The misrepresentation of the near-surface humidity in the region by the WRF model may have a negative impact on the PWV forecasts. Thus, having accurate forecasts of humidity near the surface may result in more accurate PWV forecasts. Overall, results from this, as well as recent studies, supports the use of the WRF model to provide accurate weather forecasts for the region, particularly for the PWV, which can be of great benefit for astronomers in the planning of their scientific operations and observing time.

  15. 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.

  16. 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. PMID:23456373

  17. Norway and Cuba Continue Collaborating to Build Capacity to Improve Weather Forecasting

    NASA Astrophysics Data System (ADS)

    Antuña, Juan Carlos; Kalnay, Eugenia; Mesquita, Michel D. S.

    2014-06-01

    The Future of Climate Extremes in the Caribbean Extreme Cuban Climate (XCUBE) project, which is funded by the Norwegian Directorate for Civil Protection as part of an assignment for the Norwegian Ministry of Foreign Affairs to support scientific cooperation between Norway and Cuba, carried out a training workshop on seasonal forecasting, reanalysis data, and weather research and forecasting (WRF). The workshop was a follow-up to the XCUBE workshop conducted in Havana in 2013 and provided Cuban scientists with access to expertise on seasonal forecasting, the WRF model developed by the National Center for Atmospheric Research (NCAR) and the community, data assimilation, and reanalysis.

  18. Seasonal predictions of agro-meteorological drought indicators for the Limpopo basinSeasonal predictions of agro-meteorological drought indicators for the Limpopo basin

    NASA Astrophysics Data System (ADS)

    Wetterhall, Fredrik; Winsemius, Hessel; Dutra, Emanuel; Werner, Micha; Pappenberger, Florian

    2014-05-01

    The rainfall in Southern Africa has a large interannual variability, which can cause rain-fed agriculture to fail. The staple crop maize is especially sensitive to dry spells during the early growing season. An early prediction of the probability of dry spells and below normal precipitation can potentially mitigate damages through water management. This paper investigates how well ECMWF's seasonal forecasts predict dry spells over the Limpopo basin during the rainy season December-February (DJF) with lead times from 1 to 5 months. Seasonal forecasts were evaluated against ERA-Interim reanalysis data which in turn was corrected with GPCP (EGPCP) to match monthly precipitation totals. The seasonal forecasts were also bias-corrected with the EGPCP using quantile matching as well as post-processed using a precipitation threshold to define a dry day as well as spatial filtering. The results indicate that the forecasts show skill in predicting dry spells in comparison with a "climatological ensemble" based on previous years. Quantile matching in combination with a precipitation threshold improved the skill of the forecast, whereas a spatial filter had no effect. The skill in prediction of dry spell was largest over the most drought-sensitive region. Seasonal forecasts have potential to be used in a probabilistic forecast system for drought-sensitive crops, however these should be used with caution given the large uncertainties.

  19. Forecasters of earthquakes

    NASA Astrophysics Data System (ADS)

    Maximova, Lyudmila

    1987-07-01

    For the first time Soviet scientists have set up a bioseismological proving ground which will stage a systematic extensive experiment of using birds, ants, mountain rodents including marmots, which can dig holes in the Earth's interior to a depth of 50 meters, for the purpose of earthquake forecasting. Biologists have accumulated extensive experimental data on the impact of various electromagnetic fields, including fields of weak intensity, on living organisms. As far as mammals are concerned, electromagnetic waves with frequencies close to the brain's biorhythms have the strongest effect. How these observations can be used to forecast earthquakes is discussed.

  20. US industrial battery forecast

    SciTech Connect

    Hollingsworth, V. III

    1996-09-01

    Last year was strong year for the US industrial battery market with growth in all segments. Sales of industrial batteries in North America grew 19.2% in 1995, exceeding last year`s forecasted growth rate of 11.6%. The results of the recently completed BCI Membership Survey forecast 1996 sales to be up 10.5%, and to continue to increase at a 10.4% compound annual rate through the year 2000. This year`s survey includes further detail on the stationary battery market with the inclusion of less than 25 Ampere-Hour batteries for the first time.

  1. Route based forecasting

    NASA Astrophysics Data System (ADS)

    Zuurendonk, I. W.; Wokke, M. J. J.

    2009-09-01

    Road surface temperatures can differ several degrees on a very short distance due to local effects. In order to get more insight in the local temperature differences and to develop safer gritting routes, Meteogroup has developed a system for route based temperature forecasting. The standard version of the road model is addressed to forecast road surface temperature and condition for a specific location. This model consists of two parts. First a physical part, based on the energy balance equations. The second part of the model performs a statistical correction on the calculated physical road surface temperature. The road model is able to create a forecast for one specific location. From infrared measurements, we know that large local differences in road surface temperature exist on a route. Differences can be up to 5 degrees Celsius over a distance of several hundreds of meters. Based on those measurements, the idea came up to develop a system that forecasts road surface temperature and condition for an entire route: route based forecasting. The route is split up in sections with equal properties. For each section a temperature and condition will be calculated. The main factors that influence the road surface temperature are modelled in this forecasting system: The local weather conditions: temperature, dew point temperature, wind, precipitation, weather type, cloudiness. The sky view: A very sheltered place will receive less radiation during daytime and emit less radiation during nighttime. For a very open spot, the effects are reversed. The solar view: A road section with trees on the southern side, will receive less solar radiation during daytime than a section with tress on the southern side. The route based forecast shows by means of a clear Google Maps presentation which sections will be slippery at what time of the coming night. The final goal of this type of forecast, is to make dynamical gritting possible: a variable salt amount and a different gritting route. This will contribute to safety on the roads (colder spots will be treated earlier) and also it is financially interesting (less salt necessary and fewer kilometers to drive).

  2. 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

    2016-01-01

    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. PMID:26910315

  3. 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.

  4. 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 experimental forecast and model development program that focuses on one of the key sources of untapped predictability, namely the MJO. The tangible outcomes of the meeting included: 1) the development of a recommended framework for a set of multi-year ensembles of 45-day hindcasts to be carried out by a number of GCMs so that they can be analyzed in regards to their representations of subseasonal variability, predictability and forecast skill, 2) an assessment of the present status of GCM representations of the MJO and recommendations for future steps to take in order to remedy the remaining shortcomings in these representations, and 3) a final implementation plan for a multi-institute/multi-nation Experimental MJO Prediction Program.

  5. Survival Sales Forecasting.

    ERIC Educational Resources Information Center

    Paradiso, James; Stair, Kenneth

    Intended to provide insight into the dynamics of demand analysis, this paper presents an eight-step method for forecasting sales. Focusing on sales levels that must be achieved to enjoy targeted profits in favor of the usual approach of emphasizing how much will be sold within a given period, a sample situation is provided to illustrate this…

  6. Forecasting Mass Communication.

    ERIC Educational Resources Information Center

    Dailey, Joseph M.

    In sorting through predictions about future communications, it should be kept in mind that if one can think of a communication technology in the future, then that communication technology will stand a very good chance of becoming a reality. In other words, the forecasting of invention is not separate from invention itself. Secondly, the inventions…

  7. Improving operational plume forecasts

    NASA Astrophysics Data System (ADS)

    Balcerak, Ernie

    2012-04-01

    Forecasting how plumes of particles, such as radioactive particles from a nuclear disaster, will be transported and dispersed in the atmosphere is an important but computationally challenging task. During the Fukushima nuclear disaster in Japan, operational plume forecasts were produced each day, but as the emissions continued, previous emissions were not included in the simulations used for forecasts because it became impractical to rerun the simulations each day from the beginning of the accident. Draxler and Rolph examine whether it is possible to improve plume simulation speed and flexibility as conditions and input data change. The authors use a method known as a transfer coefficient matrix approach that allows them to simulate many radionuclides using only a few generic species for the computation. Their simulations work faster by dividing the computation into separate independent segments in such a way that the most computationally time consuming pieces of the calculation need to be done only once. This makes it possible to provide real-time operational plume forecasts by continuously updating the previous simulations as new data become available. They tested their method using data from the Fukushima incident to show that it performed well. (Journal of Geophysical Research-Atmospheres, doi:10.1029/2011JD017205, 2012)

  8. The Latest Forecast.

    ERIC Educational Resources Information Center

    Laurence, David

    2002-01-01

    Discusses the "latest forecast" for the future of English departments. Addresses departmental and institutional staffing practices, employment opportunities for PhDs, the acceleration of change in the institution, and the general state of the study and teaching of English. (RS)

  9. 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.

  10. Short-term Ensemble Flood Forecasting Experiments in Brazil

    NASA Astrophysics Data System (ADS)

    Collischonn, Walter; Meller, Adalberto; Fan, Fernando; Moreira, Demerval; Dias, Pedro; Buarque, Diogo; Bravo, Juan

    2013-04-01

    Flood Forecasting and issuing early warnings to communities under risk can help reduce the impacts of those events. However, to be effective, warnings should be given several hours in advance. The best solution to extend the lead time is possibly the use of rainfall-runoff models with input given by rainfall and streamflow observations and by forecasts of future precipitation derived from numerical weather prediction (NWP) models. Recent studies showed that probabilistic or ensemble flood forecasts produced using ensemble precipitation forecasts as input data outperform deterministic flood forecasts in several cases in Europe and the United States, and ensemble flood forecasting systems are increasingly becoming operational in these regions. In Brazil, on the other hand, operational flood warning systems are rare, and often based on simplified river routing or linear transfer function models. However, a large number of global and regional meteorological models is operationally run covering most of the country, and forecasts of those models are available for recent years. We used this available data to conduct experiments of short term ensemble flood forecasting in the Paraopeba River basin (12 thousand km2), located in Southeastern Brazil. Streamflow forecasts were produced using the MGB-IPH hydrological model, using a simple empirical state updating method and using an ensemble of precipitation forecasts generated by several models, with different initial conditions and parameterizations, from several weather forecasting centers. A single deterministic streamflow forecast, based on a quantitative precipitation forecast derived from the optimal combination of several outputs of NWP models was used as a reference to assess the performance of the ensemble streamflow forecasts. Flood forecasts experiments were performed for three rainy seasons (austral summer) between 2008-2011. The results for predictions of dichotomous events, which mean exceeding or not flood warning thresholds, showed that the upper quantiles of the ensemble (e.g. 80th and 90th quantiles) over performed the deterministic forecast and even the ensemble mean. In most cases we observed an increase in the proportion of correctly forecasted events while keeping false alarm rates at low levels. This benefit was generally higher for higher flow thresholds and for longer lead times, which are the most important situations for flood impact mitigation. In parallel with the ensemble forecasts studies, a forecasting system platform fully coupled to a GIS tool (Mapwindow GIS) is being developed, which facilitates the system operation and interpretation of results. Currently, this system is being tested, however using only deterministic precipitation forecasts, in two large scale river basins in Brazil: the So Francisco River upstream of Pirapora (60 thousand km2) and the Tocantins River (300 thousand km2). Results obtained in the Paraopeba River are now motivating the incorporation of NWP ensemble outputs in these systems to make probabilistic predictions.

  11. A Modeling Framework for Improved Agricultural Water Supply Forecasting

    NASA Astrophysics Data System (ADS)

    Leavesley, G. H.; David, O.; Garen, D. C.; Lea, J.; Marron, J. K.; Pagano, T. C.; Perkins, T. R.; Strobel, M. L.

    2008-12-01

    The National Water and Climate Center (NWCC) of the USDA Natural Resources Conservation Service is moving to augment seasonal, regression-equation based water supply forecasts with distributed-parameter, physical process models enabling daily, weekly, and seasonal forecasting using an Ensemble Streamflow Prediction (ESP) methodology. This effort involves the development and implementation of a modeling framework, and associated models and tools, to provide timely forecasts for use by the agricultural community in the western United States where snowmelt is a major source of water supply. The framework selected to support this integration is the USDA Object Modeling System (OMS). OMS is a Java-based modular modeling framework for model development, testing, and deployment. It consists of a library of stand-alone science, control, and database components (modules), and a means to assemble selected components into a modeling package that is customized to the problem, data constraints, and scale of application. The framework is supported by utility modules that provide a variety of data management, land unit delineation and parameterization, sensitivity analysis, calibration, statistical analysis, and visualization capabilities. OMS uses an open source software approach to enable all members of the scientific community to collaboratively work on addressing the many complex issues associated with the design, development, and application of distributed hydrological and environmental models. A long-term goal in the development of these water-supply forecasting capabilities is the implementation of an ensemble modeling approach. This would provide forecasts using the results of multiple hydrologic models run on each basin.

  12. 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)

  13. Evolving forecasting classifications and applications in health forecasting

    PubMed Central

    Soyiri, Ireneous N; Reidpath, Daniel D

    2012-01-01

    Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation. PMID:22615533

  14. A global approach to defining flood seasons

    NASA Astrophysics Data System (ADS)

    Lee, D.; Ward, P.; Block, P.

    2015-04-01

    Globally, flood catastrophes lead all natural hazards in terms of impacts on society, causing billions of dollars of damages annually. While short-term flood warning systems are improving in number and sophistication, forecasting systems on the order of months to seasons are a rarity, yet may lead to further disaster preparedness. To lay the groundwork for prediction, dominant flood seasons must be adequately defined. A global approach is adopted here, using the PCR-GLOBWB model to define spatial and temporal characteristics of major flood seasons globally. The main flood season is identified using a volume-based threshold technique. In comparison with observations, 40% (50%) of locations at a station (sub-basin) scale have identical peak months and 81% (89%) are within 1 month, indicating strong agreement between model and observed flood seasons. Model defined flood seasons are additionally found to well represent actual flood records from the Dartmouth Flood Observatory, further substantiating the models ability to reproduce the appropriate flood season. Minor flood seasons are also defined for regions with bi-modal streamflow climatology. Properly defining flood seasons can lead to prediction through association of streamflow with local and large-scale hydroclimatic indicators, and eventual integration into early warning systems for informed advanced planning and management. This is especially attractive for regions with limited observations and/or little capacity to develop early warning flood systems.

  15. 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.

  16. 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.

  17. 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/

  18. Forecasting Electricity Prices in an Optimization Hydrothermal Problem

    NASA Astrophysics Data System (ADS)

    Matías, J. M.; Bayón, L.; Suárez, P.; Argüelles, A.; Taboada, J.

    2007-12-01

    This paper presents an economic dispatch algorithm in a hydrothermal system within the framework of a competitive and deregulated electricity market. The optimization problem of one firm is described, whose objective function can be defined as its profit maximization. Since next-day price forecasting is an aspect crucial, this paper proposes an efficient yet highly accurate next-day price new forecasting method using a functional time series approach trying to exploit the daily seasonal structure of the series of prices. For the optimization problem, an optimal control technique is applied and Pontryagin's theorem is employed.

  19. Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin

    NASA Astrophysics Data System (ADS)

    Wetterhall, F.; Winsemius, H. C.; Dutra, E.; Werner, M.; Pappenberger, F.

    2014-01-01

    The rainfall in Southern Africa has a large interannual variability, which can cause rain-fed agriculture to fail. The staple crop maize is especially sensitive to dry spells during the early growing season. An early prediction of the probability of dry spells and below normal precipitation can potentially mitigate damages through water management. This paper investigates how well ECMWF's seasonal forecasts predict dry spells over the Limpopo basin during the rainy season December-February (DJF) with lead times from 1 to 5 months. The seasonal forecasts were evaluated against ERA-Interim reanalysis data which in turn was corrected with GPCP (EGPCP) to match monthly precipitation totals. The seasonal forecasts were also bias-corrected with the EGPCP using quantile matching as well as post-processed using a precipitation threshold to define a dry day as well as spatial filtering. The results indicate that the forecasts show skill in predicting dry spells in comparison with a "climatological ensemble" based on previous years. Quantile matching in combination with a precipitation threshold improved the skill of the forecast, whereas a spatial filter had no effect. The skill in prediction of dry spells was largest over the most drought-sensitive region. Seasonal forecasts have potential to be used in a probabilistic forecast system for drought-sensitive crops, though these should be used with caution given the large uncertainties.