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

    NASA Astrophysics Data System (ADS)

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

    2016-07-01

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

  4. A gene-wavelet model for long lead time drought forecasting

    NASA Astrophysics Data System (ADS)

    Danandeh Mehr, Ali; Kahya, Ercan; Özger, Mehmet

    2014-09-01

    Drought forecasting is an essential ingredient for drought risk and sustainable water resources management. Due to increasing water demand and looming climate change, precise drought forecasting models have recently been receiving much attention. Beginning with a brief discussion of different drought forecasting models, this study presents a new hybrid gene-wavelet model, namely wavelet-linear genetic programing (WLGP), for long lead-time drought forecasting. The idea of WLGP is to detect and optimize the number of significant spectral bands of predictors in order to forecast the original predictand (drought index) directly. Using the observed El Niño-Southern Oscillation indicator (NINO 3.4 index) and Palmer's modified drought index (PMDI) as predictors and future PMDI as predictand, we proposed the WLGP model to forecast drought conditions in the State of Texas with 3, 6, and 12-month lead times. We compared the efficiency of the model with those of a classic linear genetic programing model developed in this study, a neuro-wavelet (WANN), and a fuzzy-wavelet (WFL) drought forecasting models formerly presented in the relevant literature. Our results demonstrated that the classic linear genetic programing model is unable to learn the non-linearity of drought phenomenon in the lead times longer than 3 months; however, the WLGP can be effectively used to forecast drought conditions having 3, 6, and 12-month lead times. Genetic-based sensitivity analysis among the input spectral bands showed that NINO 3.4 index has strong potential effect in drought forecasting of the study area with 6-12-month lead times.

  5. 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.

  6. 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.

  7. Trend Analysis of Long Lead-Time Snowpack Forecasts using Data Segregated by Phases of the Pacific Decadal Oscillation

    NASA Astrophysics Data System (ADS)

    Anderson, S.; Kunkel, M. L.; Holbrook, V. P.; Timilsena, J.; Blestrud, D.; Parkinson, S.

    2013-12-01

    Water planners in the western United States are challenged with managing resources for various uses, including hydropower. In southern Idaho, water planners rely heavily on snowpack forecasts to determine future hydropower availability and estimate the need for other generation sources. The development of improved snowpack forecast models in the Snake River Basin is an ongoing challenge. Previously, we examined the ability of sea surface temperatures (SSTs) and atmospheric pressure to long lead-time forecasts snowpack using six-month predictor periods with mixed results. Our current analysis uses three-month predictor periods and segregates data based on the phases of the Pacific Decadal Oscillation (PDO). Data from cold-phase PDO years (1950 - 1976, 1999 - present) were used with singular value decomposition to identify regions of SSTs and 500-millibar heights (500-Mb) that were teleconnected with snowpack in western Idaho. The identified regions were used in a non-parametric forecasting model to produce long lead-time snowpack forecasts. Forecasts using data segregated by cold phase PDO proved more accurate than forecasts using data that spanned warm and cold phases of the PDO. Trend analysis was performed to determine forecast accuracy relative to both a climatological forecast and the observed snowpack. While forecasting specific values of snowpack remains a challenge, strong trends emerge which are useful in predicting the relative volume of snowpack anticipated for a given region.

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

    NASA Astrophysics Data System (ADS)

    Barros, A. P.; Yoo, J.

    2004-05-01

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

  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. 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

  13. Using oceanic-atmospheric oscillations for long lead-time streamflow forecasting in the Upper Colorado River Basin

    NASA Astrophysics Data System (ADS)

    Kalra, A.; Ahmad, S.

    2007-12-01

    In the recent past, oceanic-atmospheric oscillations have been used successfully for long lead-time streamflow forecasting. Herein, we present a data-driven model, Support Vector Machine (SVM) for the long lead-time streamflow forecast incorporating oceanic-atmospheric oscillations. The SVM is based on Statistical Learning Theory that uses a hypothesis space of linear functions based on Kernel approach and can be used to predict a quantity forward in time based on training that uses past data. The principal strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problems. The SVMs are considered superior to the Artificial Neural Networks (ANNs) due to the tendency of formulating a quadratic optimization problem which ensures a global optimum that is found missing in the traditional ANN approach. The SVM model was applied to four unimpaired gages in the Upper Colorado River Basin (UCRB). The streamflow data for the selected gages was used from 1906¡§C2004. Annual oceanic-atmospheric indexes comprising of Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Nino-Southern Oscillations (ENSO) for a period of 1906¡§C2001 were used to generate streamflow volumes for three years ahead. The SVM model was trained with 86 years of data (1906¡§C1991) and tested for 10 years of data (1992-2001). The testing criteria used for the model effectiveness was based on correlation coefficient r, root means square error (RMSE) and nash sutcliffe efficiency coefficient e. Predictions during the testing phase showed a good agreement with measured streamflow volumes for the selected gages in UCRB. Rigorous sensitivity analysis was performed to evaluate the effect of individual oscillation. The results indicated a strong signal for NAO and ENSO indexes as compared to PDO and AMO indexes for the long lead-time streamflow forecast. The oceanic

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  16. 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

  17. 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

  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

  19. 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.

  20. Evaluation of seasonal forecast skill over China

    NASA Astrophysics Data System (ADS)

    Roads, John O.; Chen, Shyh-Chin

    2003-06-01

    Since Sept. 26, 1997, the Scripps Experimental Climate Prediction Center (ECPC) has been making experimental, near real-time seasonal global forecasts. Images of these forecasts, at daily to seasonal time scales, are provided on the World Wide Web, and experimental digital forecast products are made available to international collaborators. Over Asia, these experimental forecasts are now being used to drive regional prediction and various application models at National Taiwan University (NTU) and the Hong Kong Observatory. Roads et al. [Bull. Am. Meteorol. Soc. 82 (2001) 639] and Terra Chen et al. [Atmos. Oceanogr. Sci. 12 (2003a) 377] previously discussed the basic forecast and analysis system. The purpose of this paper is to discuss specific synoptic characteristics of recent seasonal forecasts as a guide to further application and development.

  1. 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.

  2. Seasonal hydrological ensemble forecasts over Europe

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  3. A multisite seasonal ensemble streamflow forecasting technique

    NASA Astrophysics Data System (ADS)

    Bracken, Cameron; Rajagopalan, Balaji; Prairie, James

    2010-03-01

    We present a technique for providing seasonal ensemble streamflow forecasts at several locations simultaneously on a river network. The framework is an integration of two recent approaches: the nonparametric multimodel ensemble forecast technique and the nonparametric space-time disaggregation technique. The four main components of the proposed framework are as follows: (1) an index gauge streamflow is constructed as the sum of flows at all the desired spatial locations; (2) potential predictors of the spring season (April-July) streamflow at this index gauge are identified from the large-scale ocean-atmosphere-land system, including snow water equivalent; (3) the multimodel ensemble forecast approach is used to generate the ensemble flow forecast at the index gauge; and (4) the ensembles are disaggregated using a nonparametric space-time disaggregation technique resulting in forecast ensembles at the desired locations and for all the months within the season. We demonstrate the utility of this technique in skillful forecast of spring seasonal streamflows at four locations in the Upper Colorado River Basin at different lead times. Where applicable, we compare the forecasts to the Colorado Basin River Forecast Center's Ensemble Streamflow Prediction (ESP) and the National Resource Conservation Service "coordinated" forecast, which is a combination of the ESP, Statistical Water Supply, a principal component regression technique, and modeler knowledge. We find that overall, the proposed method is equally skillful to existing operational models while tending to better predict wet years. The forecasts from this approach can be a valuable input for efficient planning and management of water resources in the basin.

  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. 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.

  6. 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

  7. 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...

  8. Tropical ocean initialisation strategies for seasonal forecasting

    NASA Astrophysics Data System (ADS)

    Mulholland, David; Haines, Keith

    2016-04-01

    Operational seasonal ENSO forecasts show substantial skill in tropical regions, but are sensitive to the initialisation procedure used in the ocean. Due to errors in wind stress forcing and in modelling the vertical transfer of momentum, a bias correction method is often used during ocean data assimilation in order to assimilate hydrographic data, e.g. from the TOGA/TAO array. While this improves the ocean state, particularly the circulation, during the analysis, it leads to an inconsistency at the beginning of a coupled forecast, since the bias correction term is generally not retained during the forecast itself. We present results from a number of ensemble simulations carried out with the European Centre for Medium-range Weather Forecasts (ECMWF) coupled forecast system, comparing different initialisation strategies for the equatorial ocean. Rapid adjustments in the ocean at the beginning of the forecast are found to induce additional variability in the thermocline. We then show that this spurious variability can be substantially reduced by persisting or more slowly adjusting the bias correction term during the first month, and that this leads to significant improvements in ENSO SST forecast skill, at lead times of 3-7 months. The results highlight the importance of ocean initialisation in maximising the skill of ENSO predictions.

  9. 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.

  10. A multiple model assessment of seasonal climate forecast skill for applications

    NASA Astrophysics Data System (ADS)

    Lavers, David; Luo, Lifeng; Wood, Eric F.

    2009-12-01

    Skilful seasonal climate forecasts have potential to affect decision making in agriculture, health and water management. Organizations such as the National Oceanic and Atmospheric Administration (NOAA) are currently planning to move towards a climate services paradigm, which will rest heavily on skilful forecasts at seasonal (1 to 9 months) timescales from coupled atmosphere-land-ocean models. We present a careful analysis of the predictive skill of temperature and precipitation from eight seasonal climate forecast models with the joint distribution of observations and forecasts. Using the correlation coefficient, a shift in the conditional distribution of the observations given a forecast can be detected, which determines the usefulness of the forecast for applications. Results suggest there is a deficiency of skill in the forecasts beyond month-1, with precipitation having a more pronounced drop in skill than temperature. At long lead times only the equatorial Pacific Ocean exhibits significant skill. This could have an influence on the planned use of seasonal forecasts in climate services and these results may also be seen as a benchmark of current climate prediction capability using (dynamic) couple models.

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

    NASA Astrophysics Data System (ADS)

    Spirig, Christoph; Bhend, Jonas; Liniger, Mark

    2016-04-01

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

  12. 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 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.

  13. 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.

  14. In Brief: Atlantic seasonal hurricane forecast

    NASA Astrophysics Data System (ADS)

    Showstack, Randy

    2007-12-01

    Two hurricane forecasters are predicting that 2008 will be an above-average Atlantic basin tropical cyclone season with an above-average probability of a major hurricane making landfall in the United States. During 2008, there could be about seven hurricanes (the annual average is 5.9) and 13 named storms (the average is 9.6), according to a 7 December report by Philip Klotzbach, research scientist at Colorado State University in Fort Collins, and William Gray, university professor emeritus of atmospheric sciences. The forecasters indicate that they believe the Atlantic basin is in an active hurricane cycle that is associated with a strong thermohaline circulation and an active phase of the Atlantic Multidecadal Oscillation. The report notes that, ``real-time operational early December forecasts have not shown forecast skill over climatology during this 16-year period [1992-2007]. This has occurred despite the fact that the skill over the hindcast period...showed appreciable skill.'' For more information, visit the Web site: http://hurricane.atmos.colostate.edu/Forecasts/2007/dec2007/dec2007.pdf.

  15. 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

  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. 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

  18. Seasonal UK Drought Forecasting using Statistical Methods

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

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

    NASA Astrophysics Data System (ADS)

    Yuan, Xing

    2016-06-01

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

  20. Forecasts of seasonal streamflow in West-Central Florida using multiple climate predictors

    NASA Astrophysics Data System (ADS)

    Risko, Susan L.; Martinez, Christopher J.

    2014-11-01

    Large-scale climate can provide predictive information for streamflow forecasts in many parts of the world. However, the optimal selection of predictors can be problematic when focusing on a localized region. This work evaluated multiple gridded climate datasets in order to determine optimal predictors of seasonal streamflow in West-Central Florida. Using persistence in streamflow, existing indices of climate, and sea surface temperature (SST) expansion coefficient time-series from singular value decomposition (SVD) analysis, this work developed probability of exceedance streamflow forecasts for multiple stations, seasons, and lead-times. Forecasts were found to be generally skillful between the September-November and April-June seasons with this range narrowing as lead time increased and skill was mainly related to the impact of the El Niño-Southern Oscillation (ENSO) on the region. Using multiple indices of ENSO that were determined by correlation and composite analyses to track its evolution from the west Pacific at long lead-times to the east Pacific at short lead-times was not found to appreciably improve forecasts over using the Niño 3.4 index alone. Using SST expansion coefficient time-series from SVD analysis was found to capture the evolution of ENSO from west to east and to provide skillful forecasts of streamflow at earlier leads (up to 7 months in advance) compared to that found by pre-defined indices, indicating the importance of predictor selection in achieving optimal forecast skill.

  1. 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

  2. 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.

  3. 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

  4. Seasonal forecast skill of Arctic sea ice area in a dynamical forecast system

    NASA Astrophysics Data System (ADS)

    Sigmond, M.; Fyfe, J. C.; Flato, G. M.; Kharin, V. V.; Merryfield, W. J.

    2013-02-01

    AbstractWe assess the <span class="hlt">seasonal</span> <span class="hlt">forecast</span> skill of pan-Arctic sea ice area in a dynamical <span class="hlt">forecast</span> system that includes interactive atmosphere, ocean, and sea ice components. <span class="hlt">Forecast</span> skill is quantified by the correlation skill score computed from 12 month ensemble <span class="hlt">forecasts</span> initialized in each month between January 1979 to December 2009. We find that <span class="hlt">forecast</span> skill is substantial for all lead times and predicted <span class="hlt">seasons</span> except spring but is mainly due to the strong downward trend in observations for lead times of about 4 months and longer. Skill is higher when evaluated against an observation-based dataset with larger trends. The <span class="hlt">forecast</span> skill when linear trends are removed from the <span class="hlt">forecasts</span> and verifying observations is small and generally not statistically significant at lead times greater than 2 to 3 months, except for January/February when <span class="hlt">forecast</span> skill is moderately high up to an 11 month lead time. For short lead times, high trend-independent <span class="hlt">forecast</span> skill is found for October, while low skill is found for November/December. This is consistent with the <span class="hlt">seasonal</span> variation of observed lag correlations. For most predicted months and lead times, trend-independent <span class="hlt">forecast</span> skill exceeds that of an anomaly persistence <span class="hlt">forecast</span>, highlighting the potential for dynamical <span class="hlt">forecast</span> systems to provide valuable <span class="hlt">seasonal</span> predictions of Arctic sea ice.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.4273C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.4273C"><span id="translatedtitle">Skill of a global <span class="hlt">seasonal</span> ensemble streamflow <span class="hlt">forecasting</span> system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Candogan Yossef, Naze; Winsemius, Hessel; Weerts, Albrecht; van Beek, Rens; Bierkens, Marc</p> <p>2013-04-01</p> <p><span class="hlt">Forecasting</span> of water availability and scarcity is a prerequisite for managing the risks and opportunities caused by the inter-annual variability of streamflow. Reliable <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> are necessary to prepare for an appropriate response in disaster relief, management of hydropower reservoirs, water supply, agriculture and navigation. <span class="hlt">Seasonal</span> hydrological <span class="hlt">forecasting</span> on a global scale could be valuable especially for developing regions of the world, where effective hydrological <span class="hlt">forecasting</span> systems are scarce. In this study, we investigate the <span class="hlt">forecasting</span> skill of the global <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasting</span> 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 <span class="hlt">forecasting</span> 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 <span class="hlt">Forecasts</span> (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 <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> with PCR-GLOBWB or comparable models are positive. However this assessment did not include actual meteorological <span class="hlt">forecasts</span>. Thus the meteorological forcing errors were not assessed. Yet, in a <span class="hlt">forecasting</span> setup, the predictive skill of a hydrological <span class="hlt">forecasting</span> system is affected by errors due to uncertainty from numerical weather prediction models. For the assessment in retroactive <span class="hlt">forecasting</span> mode, the model is forced with actual ensemble <span class="hlt">forecasts</span> from the <span class="hlt">seasonal</span> <span class="hlt">forecast</span> archives of ECMWF. Skill is assessed at 78 stations on large river basins across the globe, for all the months of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PApGe.172.1699D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PApGe.172.1699D"><span id="translatedtitle">Validation of <span class="hlt">Seasonal</span> <span class="hlt">Forecast</span> of Indian Summer Monsoon Rainfall</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Das, Sukanta Kumar; Deb, Sanjib Kumar; Kishtawal, C. M.; Pal, Pradip Kumar</p> <p>2015-06-01</p> <p>The experimental <span class="hlt">seasonal</span> <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span>, 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> in operational mode, it is required that the overall validation or correctness of the <span class="hlt">forecast</span> system is quantified and that the scope is assessed for further improvements of the <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> has been evaluated by assuming the control simulation as the model reference. The <span class="hlt">forecast</span> improvement factor shows positive improvements, with higher values for the recent <span class="hlt">forecasted</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecast</span> made during 2013. Further, the bias score of different phases of experiment revealed the fact that the ISM rainfall <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> over the last 5 years, viz. 2009-2013, due to several important modifications that have been implemented in the <span class="hlt">forecast</span> system. The validation exercise has also pointed out a number of shortcomings in the <span class="hlt">forecast</span> system; these will be addressed in the upcoming years of experiments to improve the quality of the ISM prediction.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.5946S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.5946S"><span id="translatedtitle">The Canadian coupled multi-<span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sebatian Fontecilla, Juan</p> <p>2013-04-01</p> <p>The Canadian coupled multi-<span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system Since a year now, the Meteorological Service of Canada has its first coupled operational multi-<span class="hlt">seasonal</span> <span class="hlt">forecasting</span> 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 <span class="hlt">forecasting</span> system used for <span class="hlt">forecasts</span> of months 1 to 4, and the CCA statistical <span class="hlt">forecasting</span> system used for <span class="hlt">forecasts</span> of months 4 to 12. The coupled atmosphere-ocean-sea ice system combines ensemble <span class="hlt">forecasts</span> 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 <span class="hlt">Forecasting</span> Project (CHFP2) will be described briefly. <span class="hlt">Forecast</span> skill improvements will be shown. The implementation of this new system permits the issuance of ENSO and arctic sea ice <span class="hlt">forecasts</span>, 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H51N..01B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H51N..01B"><span id="translatedtitle">Why large-scale <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> are feasible</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bierkens, M. F.; Candogan Yossef, N.; Van Beek, L. P.</p> <p>2011-12-01</p> <p><span class="hlt">Seasonal</span> <span class="hlt">forecasts</span> of precipitation and temperature, using either statistical or dynamic prediction, have been around for almost 2 decades. The skill of these <span class="hlt">forecasts</span> differ both in space and time, with highest skill in areas heavily influenced by SST anomalies such as El Nino or areas where land surface properties have a major impact on e.g. Monsoon strength, such as the vegetation cover of the Sahel region or the snow cover of the Tibetan plateau. However, the skill of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> is limited in most regions, with anomaly correlation coefficients varying between 0.2 and 0.5 for 1-3 month precipitation totals. This raises the question whether <span class="hlt">seasonal</span> hydrological <span class="hlt">forecasting</span> is feasible. Here, we make the case that it is. Using the example of statistical <span class="hlt">forecasts</span> of NAO-strength and related precipitation anomalies over Europe, we show that the skill of large-scale streamflow <span class="hlt">forecasts</span> is generally much higher than the precipitation <span class="hlt">forecasts</span> itself, provided that the initial state of the system is accurately estimated. In the latter case, even the precipitation climatology can produce skillful results. This is due to the inertia of the hydrological system rooted in the storage of soil moisture, groundwater and snow pack, as corroborated by a recent study using snow observations for <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasting</span> in the Western US. These examples seem to suggest that for accurate <span class="hlt">seasonal</span> hydrological <span class="hlt">forecasting</span>, correct state estimation is more important than accurate <span class="hlt">seasonal</span> meteorological <span class="hlt">forecasts</span>. However, large-scale estimation of hydrological states is difficult and validation of large-scale hydrological models often reveals large biases in e.g. streamflow estimates. Fortunately, as shown with a validation study of the global model PCR-GLOBWB, these biases are of less importance when <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> are evaluated in terms of their ability to reproduce anomalous flows and extreme events, i.e. by anomaly correlations or categorical quantile</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H41A1148C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H41A1148C"><span id="translatedtitle"><span class="hlt">Seasonal</span> Runoff <span class="hlt">Forecasts</span> Based on the Climate <span class="hlt">Forecast</span> System Version 2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, L.; Mo, K. C.; Shukla, S.; Lettenmaier, D. P.</p> <p>2012-12-01</p> <p><span class="hlt">Seasonal</span> runoff <span class="hlt">forecasts</span> are needed for many hydroclimatological applications, such as drought outlook, agricultural planning, <span class="hlt">seasonal</span> hydrologic prediction, and multi-purpose reservoir management. Recently, NOAA National Centers for Environmental Prediction (NCEP) has transitioned to their second generation of the Climate <span class="hlt">Forecast</span> System (CFSv2) in operation. CFSv2 is a coupled ocean-atmosphere-land model with advanced physics, increased resolution, refined initialization, and improved land surface model, and provides <span class="hlt">forecasts</span> up to nine months in advance. Information on the accuracy and skill of the CFSv2 <span class="hlt">forecasts</span> is sought for the daily operation of many applications. In this study, we conduct an assessment of the prediction skill of <span class="hlt">seasonal</span> runoff <span class="hlt">forecasts</span> from CFSv2 using its retrospective <span class="hlt">forecasts</span> from 1982 to 2009. <span class="hlt">Forecast</span> skill of spatially aggregated cumulative runoff (CR) from direct CFSv2 <span class="hlt">forecasts</span> and those obtained from the Variable Infiltration Capacity (VIC) model driven by daily precipitation, temperature, and wind <span class="hlt">forecasts</span> from CFSv2 (i.e., hydroclimate <span class="hlt">forecasts</span>) are compared with <span class="hlt">forecasts</span> based on the ensemble streamflow prediction (ESP) technique. All <span class="hlt">forecasts</span> are verified against historical VIC simulations with input forcing of precipitation and temperature derived from a set of 2131 high-quality index stations selected from the National Climatic Data Center's (NCDC's) Cooperative Observer stations across the contiguous United States. The monthly CR is spatially aggregated to 48 sub-regions created by merging the 221 U.S. Geological Survey (USGS) hydrologic sub-regions in order to evaluate regional characteristics. Preliminary results suggest that <span class="hlt">forecast</span> skill of CR is <span class="hlt">seasonally</span> and regionally dependent. Direct runoff <span class="hlt">forecasts</span> from CFSv2 have the lowest skill on average, indicating limited use for hydrological drought prediction. Month-1 CR prediction from hydroclimate <span class="hlt">forecasts</span> is superior than that from the other two <span class="hlt">forecast</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.6153W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.6153W"><span id="translatedtitle">Toward <span class="hlt">Seasonal</span> <span class="hlt">Forecasting</span> of Global Droughts: Evaluation over USA and Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, Eric; Yuan, Xing; Roundy, Joshua; Sheffield, Justin; Pan, Ming</p> <p>2013-04-01</p> <p> in different <span class="hlt">seasons</span> for different basins. The R2 of drought severity accumulated over USA is higher during winter, and climate models present added value especially at <span class="hlt">long</span> <span class="hlt">leads</span>. 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 <span class="hlt">forecasts</span> over Africa starting on the 1st of each calendar month during 1982-2007. Our CHM-based <span class="hlt">seasonal</span> hydrologic <span class="hlt">forecasts</span> are now being analyzed for its skill in predicting short-term soil moisture droughts over Africa. Besides relying on a single <span class="hlt">seasonal</span> climate model or a single drought index, preliminary <span class="hlt">forecast</span> results will be presented using multiple <span class="hlt">seasonal</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003AGUFM.H12B0971W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003AGUFM.H12B0971W&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Seasonal</span> Stream Flow <span class="hlt">Forecasting</span> and Decision Support in Central Texas</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Watkins, D. W.; Nykanen, D. K.; Mahmoud, M.; Wei, W.</p> <p>2003-12-01</p> <p>A decision support model based on stream flow ensemble <span class="hlt">forecasts</span> has been developed for the Lower Colorado River Authority in Central Texas, and predictive skill is added to climatology-based <span class="hlt">forecasts</span> by conditioning the ensembles on observable climate indicators. These indicators include stream flow (persistence), soil moisture, and large-scale recurrent patterns such as the El Nino-Southern Oscillation, Pacific Decadal Oscillation, and the North Atlantic Oscillation. In the absence of historical soil moisture measurements, the Variable Infiltration Capacity (VIC) Retrospective Land Surface Data Set is applied. Strong correlation between observed runoff volumes and runoff volumes simulated by the (uncalibrated) VIC model indicates the viability of this approach. Following correlation analysis to screen potential predictors, a Bayesian procedure for updating ensemble probabilities is outlined, and various skill scores are reviewed for evaluating <span class="hlt">forecast</span> performance. Verification of the ensemble <span class="hlt">forecasts</span> using a resampling procedure indicates a small but potentially significant improvement in <span class="hlt">forecast</span> skill over climatology that could be exploited in <span class="hlt">seasonal</span> water management decisions. Future work involves evaluation of <span class="hlt">seasonal</span> soil moisture <span class="hlt">forecasts</span>, further evaluation of annual flow <span class="hlt">forecasts</span>, incorporation of climate <span class="hlt">forecasts</span> in reservoir operating rules, and estimation of the value of the <span class="hlt">forecasts</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..17.8687Z&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..17.8687Z&link_type=ABSTRACT"><span id="translatedtitle">Application and verification of ECMWF <span class="hlt">seasonal</span> <span class="hlt">forecast</span> for wind energy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Žagar, Mark; Marić, Tomislav; Qvist, Martin; Gulstad, Line</p> <p>2015-04-01</p> <p>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 <span class="hlt">forecasting</span> 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 <span class="hlt">seasonal</span> and intra-annual variations and their geographical distribution. The <span class="hlt">seasonal</span> power <span class="hlt">forecast</span> presented here is designed to bridge this gap. The <span class="hlt">seasonal</span> power production <span class="hlt">forecast</span> is based on the ECMWF <span class="hlt">seasonal</span> weather <span class="hlt">forecast</span> and the Vestas' high-resolution, mesoscale weather library. The <span class="hlt">seasonal</span> weather <span class="hlt">forecast</span> 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 <span class="hlt">forecasting</span> systems. The turbine power output is non-linearly related to the wind speed, which has important implications for the wind power <span class="hlt">forecast</span>. 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 <span class="hlt">forecasted</span> power</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.2386S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.2386S&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasting</span> of discharge for the Raccoon River, Iowa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Slater, Louise; Villarini, Gabriele; Bradley, Allen; Vecchi, Gabriel</p> <p>2016-04-01</p> <p>The state of Iowa (central United States) is regularly afflicted by severe natural hazards such as the 2008/2013 floods and the 2012 drought. To improve preparedness for these catastrophic events and allow Iowans to make more informed decisions about the most suitable water management strategies, we have developed a framework for medium to long range probabilistic <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasting</span> for the Raccoon River at Van Meter, a 8900-km2 catchment located in central-western Iowa. Our flow <span class="hlt">forecasts</span> use statistical models to predict <span class="hlt">seasonal</span> discharge for low to high flows, with lead <span class="hlt">forecasting</span> times ranging from one to ten months. Historical measurements of daily discharge are obtained from the U.S. Geological Survey (USGS) at the Van Meter stream gage, and used to compute quantile time series from minimum to maximum <span class="hlt">seasonal</span> flow. The model is forced with basin-averaged total <span class="hlt">seasonal</span> precipitation records from the PRISM Climate Group and annual row crop production acreage from the U.S. Department of Agriculture's National Agricultural Statistics Services database. For the <span class="hlt">forecasts</span>, we use corn and soybean production from the previous year (persistence <span class="hlt">forecast</span>) as a proxy for the impacts of agricultural practices on streamflow. The monthly precipitation <span class="hlt">forecasts</span> are provided by eight Global Climate Models (GCMs) from the North American Multi-Model Ensemble (NMME), with lead times ranging from 0.5 to 11.5 months, and a resolution of 1 decimal degree. Additionally, precipitation from the month preceding each <span class="hlt">season</span> is used to characterize antecedent soil moisture conditions. The accuracy of our modelled (1927-2015) and <span class="hlt">forecasted</span> (2001-2015) discharge values is assessed by comparison with the observed USGS data. We explore the sensitivity of <span class="hlt">forecast</span> skill over the full range of lead times, flow quantiles, <span class="hlt">forecast</span> <span class="hlt">seasons</span>, and with each GCM. <span class="hlt">Forecast</span> skill is also examined using different formulations of the statistical models, as well as NMME <span class="hlt">forecast</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006513','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006513"><span id="translatedtitle">Evaluating Downscaling Methods for <span class="hlt">Seasonal</span> Climate <span class="hlt">Forecasts</span> over East Africa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris</p> <p>2013-01-01</p> <p>The U.S. National Multi-Model Ensemble <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system is providing hindcast and real-time data streams to be used in assessing and improving <span class="hlt">seasonal</span> 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 <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> of temperature and precipitation over East Africa. The current <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system provides monthly averaged <span class="hlt">forecast</span> 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 <span class="hlt">seasons</span>. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006440','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006440"><span id="translatedtitle">Evaluating Downscaling Methods for <span class="hlt">Seasonal</span> Climate <span class="hlt">Forecasts</span> over East Africa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Robertson, Franklin R.; Roberts, J. Brent; Bosilovich, Michael; Lyon, Bradfield</p> <p>2013-01-01</p> <p>The U.S. National Multi-Model Ensemble <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system is providing hindcast and real-time data streams to be used in assessing and improving <span class="hlt">seasonal</span> 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 <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> of temperature and precipitation over East Africa. The current <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system provides monthly averaged <span class="hlt">forecast</span> 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 <span class="hlt">seasons</span>. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013HESSD..1014747W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013HESSD..1014747W"><span id="translatedtitle">The potential value of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> in a changing climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Winsemius, H. C.; Dutra, E.; Engelbrecht, F. A.; Archer Van Garderen, E.; Wetterhall, F.; Pappenberger, F.; Werner, M. G. F.</p> <p>2013-12-01</p> <p>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 <span class="hlt">seasonal</span> rainfall, anomalous onsets and lengths of the rainy <span class="hlt">season</span> 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 <span class="hlt">forecasting</span> 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 <span class="hlt">seasonal</span> meteorological <span class="hlt">forecasts</span> of the European Centre for Medium-range Weather <span class="hlt">Forecasts</span> (ECMWF) <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> 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, <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecast</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1715278C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1715278C"><span id="translatedtitle">Linking <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> with crop models in Iberian Peninsula</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Capa, Mirian; Ines, Amor; Baethgen, Walter; Rodriguez-Fonseca, Belen; Han, Eunjin; Ruiz-Ramos, Margarita</p> <p>2015-04-01</p> <p>Translating <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> into agricultural production <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> rainfall <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> rainfall <span class="hlt">forecast</span>. This study evaluates two methods for disaggregating <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> into daily weather data: 1) a stochastic weather generator (CondWG), and 2) a <span class="hlt">forecast</span> tercile resampler (FResampler). Both methods were used to generate 100 (with FResampler) and 110 (with CondWG) weather series/sequences for three scenarios of <span class="hlt">seasonal</span> rainfall <span class="hlt">forecasts</span>. 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 <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> was accomplished to estimate production risks under different climate scenarios. This methodology allows farmers to assess the benefits and risks of a <span class="hlt">seasonal</span> weather <span class="hlt">forecast</span> in IP prior to the crop growing <span class="hlt">season</span>. 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H11A0850L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H11A0850L"><span id="translatedtitle">Sources of Errors in Developing Monthly to <span class="hlt">Seasonal</span> Nutrient <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Libera, D.; Arumugam, S.</p> <p>2014-12-01</p> <p>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 <span class="hlt">seasonal</span> nutrient <span class="hlt">forecasts</span> that can be used to control nonpoint reduction strategies. Given that the <span class="hlt">seasonal</span> nutrients are developed using large-scale climate <span class="hlt">forecasts</span>, 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 <span class="hlt">seasonal</span> nutrient <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> from the ECHAM4.5 model and NOAA NCEP Climate <span class="hlt">Forecast</span> System (CFS) will be downscaled and disaggregated for developing nutrient <span class="hlt">forecasts</span> from the SWAT model and the LOADEST model. Using both the canonical correlation model and the bi-variate copula based bias-correction procedures, the <span class="hlt">forecasted</span> 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 <span class="hlt">seasonal</span> nutrient <span class="hlt">forecasts</span> using climate information.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4431683','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4431683"><span id="translatedtitle"><span class="hlt">Forecasting</span> the 2013–2014 Influenza <span class="hlt">Season</span> Using Wikipedia</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.</p> <p>2015-01-01</p> <p>Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, <span class="hlt">forecasting</span> their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), <span class="hlt">seasonal</span> influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and <span class="hlt">forecasting</span> 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 <span class="hlt">forecast</span> for <span class="hlt">seasonal</span> influenza. The methods are applied to the 2013-2014 influenza <span class="hlt">season</span> but are sufficiently general to <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">season</span>, our <span class="hlt">forecasting</span> method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the <span class="hlt">forecast</span>. 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 <span class="hlt">season</span> has passed. PMID:25974758</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25974758','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25974758"><span id="translatedtitle"><span class="hlt">Forecasting</span> the 2013-2014 influenza <span class="hlt">season</span> using Wikipedia.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hickmann, Kyle S; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M; Deshpande, Alina; Del Valle, Sara Y</p> <p>2015-05-01</p> <p>Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, <span class="hlt">forecasting</span> their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), <span class="hlt">seasonal</span> influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and <span class="hlt">forecasting</span> 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 <span class="hlt">forecast</span> for <span class="hlt">seasonal</span> influenza. The methods are applied to the 2013-2014 influenza <span class="hlt">season</span> but are sufficiently general to <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">season</span>, our <span class="hlt">forecasting</span> method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the <span class="hlt">forecast</span>. 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 <span class="hlt">season</span> has passed. PMID:25974758</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1214725','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1214725"><span id="translatedtitle"><span class="hlt">Forecasting</span> the 2013–2014 influenza <span class="hlt">season</span> using Wikipedia</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.; Salathé, Marcel</p> <p>2015-05-14</p> <p>Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, <span class="hlt">forecasting</span> their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), <span class="hlt">seasonal</span> influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and <span class="hlt">forecasting</span> 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 <span class="hlt">forecast</span> for <span class="hlt">seasonal</span> influenza. The methods are applied to the 2013-2014 influenza <span class="hlt">season</span> but are sufficiently general to <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">season</span>, our <span class="hlt">forecasting</span> method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the <span class="hlt">forecast</span>. 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 <span class="hlt">season</span> has passed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/pages/biblio/1214725-forecasting-influenza-season-using-wikipedia','SCIGOV-DOEP'); return false;" href="http://www.osti.gov/pages/biblio/1214725-forecasting-influenza-season-using-wikipedia"><span id="translatedtitle"><span class="hlt">Forecasting</span> the 2013–2014 influenza <span class="hlt">season</span> using Wikipedia</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGESBeta</a></p> <p>Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Del Valle, Sara Y.; Salathé, Marcel</p> <p>2015-05-14</p> <p>Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, <span class="hlt">forecasting</span> their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), <span class="hlt">seasonal</span> influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and <span class="hlt">forecasting</span> 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 <span class="hlt">forecast</span> for <span class="hlt">seasonal</span> influenza. The methods are appliedmore » to the 2013-2014 influenza <span class="hlt">season</span> but are sufficiently general to <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">season</span>, our <span class="hlt">forecasting</span> method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the <span class="hlt">forecast</span>. 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 <span class="hlt">season</span> has passed.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMPA13A2193G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMPA13A2193G"><span id="translatedtitle">Using NMME in Region-Specific Operational <span class="hlt">Seasonal</span> Climate <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gronewold, A.; Bolinger, R. A.; Fry, L. M.; Kompoltowicz, K.</p> <p>2015-12-01</p> <p>The National Oceanic and Atmospheric Administration's Climate Prediction Center (NOAA/CPC) provides access to a suite of real-time monthly climate <span class="hlt">forecasts</span> that comprise the North American Multi-Model Ensemble (NMME) in an attempt to meet increasing demands for monthly to <span class="hlt">seasonal</span> climate prediction. While the graphical map <span class="hlt">forecasts</span> of the NMME are informative, there is a need to provide decision-makers with probabilistic <span class="hlt">forecasts</span> specific to their region of interest. Here, we demonstrate the potential application of the NMME to address regional climate projection needs by developing new <span class="hlt">forecasts</span> of temperature and precipitation for the North American Great Lakes, the largest system of lakes on Earth. Regional opertional water budget <span class="hlt">forecasts</span> rely on these outlooks to initiate monthly <span class="hlt">forecasts</span> not only of the water budget, but of monthly lake water levels as well. More specifically, we present an alternative for improving existing operational protocols that currently involve a relatively time-consuming and subjective procedure based on interpreting the maps of the NMME. In addition, all <span class="hlt">forecasts</span> are currently presented in the NMME in a probabilistic format, with equal weighting given to each member of the ensemble. In our new evolution of this product, we provide historical context for the <span class="hlt">forecasts</span> by superimposing them (in an on-line graphical user interface) with the historical range of observations. Implementation of this new tool has already led to noticeable advantages in regional water budget <span class="hlt">forecasting</span>, and has the potential to be transferred to other regional decision-making authorities as well.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.3192B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.3192B"><span id="translatedtitle">Towards reliable <span class="hlt">seasonal</span> ensemble streamflow <span class="hlt">forecasts</span> for ephemeral rivers</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bennett, James; Wang, Qj; Li, Ming; Robertson, David</p> <p>2016-04-01</p> <p>Despite their inherently variable nature, ephemeral rivers are an important water resource in many dry regions. Water managers are likely benefit considerably from even mildly skilful ensemble <span class="hlt">forecasts</span> of streamflow in ephemeral rivers. As with any ensemble <span class="hlt">forecast</span>, <span class="hlt">forecast</span> uncertainty - i.e., the spread of the ensemble - must be reliably quantified to allow users of the <span class="hlt">forecasts</span> to make well-founded decisions. Correctly quantifying uncertainty in ephemeral rivers is particularly challenging because of the high incidence of zero flows, which are difficult to handle with conventional statistical techniques. Here we apply a <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasting</span> system, the model for generating <span class="hlt">Forecast</span> Guided Stochastic Scenarios (FoGSS), to 26 Australian ephemeral rivers. FoGSS uses post-processed ensemble rainfall <span class="hlt">forecasts</span> from a coupled ocean-atmosphere prediction system to force an initialised monthly rainfall runoff model, and then applies a staged hydrological error model to describe and propagate hydrological uncertainty in the <span class="hlt">forecast</span>. FoGSS produces 12-month streamflow <span class="hlt">forecasts</span>; as <span class="hlt">forecast</span> skill declines with lead time, the <span class="hlt">forecasts</span> are designed to transit seamlessly to stochastic scenarios. The ensemble rainfall <span class="hlt">forecasts</span> used in FoGSS are known to be unbiased and reliable, and we concentrate here on the hydrological error model. The FoGSS error model has several features that make it well suited to <span class="hlt">forecasting</span> ephemeral rivers. First, FoGSS models the error after data is transformed with a log-sinh transformation. The log-sinh transformation is able to normalise even highly skewed data and homogenise its variance, allowing us to assume that errors are Gaussian. Second, FoGSS handles zero values using data censoring. Data censoring allows streamflow in ephemeral rivers to be treated as a continuous variable, rather than having to model the occurrence of non-zero values and the distribution of non-zero values separately. This greatly simplifies parameter</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20020054243&hterms=soil+study+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dsoil%2Bstudy%2Bmodel','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20020054243&hterms=soil+study+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dsoil%2Bstudy%2Bmodel"><span id="translatedtitle">The Impact of Soil Moisture Initialization On <span class="hlt">Seasonal</span> Precipitation <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koster, R. D.; Suarez, M. J.; Tyahla, L.; Houser, Paul (Technical Monitor)</p> <p>2002-01-01</p> <p>Some studies suggest that the proper initialization of soil moisture in a <span class="hlt">forecasting</span> model may contribute significantly to the accurate prediction of <span class="hlt">seasonal</span> 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 <span class="hlt">forecasted</span> <span class="hlt">season</span>, 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span>. Initial soil moisture conditions for the <span class="hlt">forecasts</span> are established by forcing the land surface model with realistic precipitation prior to the start of the <span class="hlt">forecast</span> period. As expected, the impacts on <span class="hlt">forecasted</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.H23F1685M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.H23F1685M"><span id="translatedtitle">Hydrological <span class="hlt">Forecasting</span> in Mexico: Extending the University of Washington West-wide <span class="hlt">Seasonal</span> Hydrologic <span class="hlt">Forecast</span> System</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Munoz-Arriola, F.; Thomas, G.; Wood, A.; Wagner-Gomez, A.; Lobato-Sanchez, R.; Lettenmaier, D. P.</p> <p>2007-12-01</p> <p>Hydrologic <span class="hlt">forecasting</span> in areas constrained by the availability of hydrometeorological records is a notable challenge in water resource management. Techniques from the University of Washington West-wide <span class="hlt">Seasonal</span> Hydrologic <span class="hlt">Forecast</span> system www.hydro.washington.edu/<span class="hlt">forecast</span>/westwide) for generating daily nowcasts in areas with sparse and time-varying station coverage have been extended from the western U.S. into Mexico. The primary <span class="hlt">forecasting</span> approaches consist of ensembles based on the NWS ensemble streamflow prediction method (ESP; essentially resampling of climatology) and on NCEP Coupled <span class="hlt">Forecast</span> System (CFS) outputs. These in turn are used to force the Variable Infiltration Capacity (VIC) macroscale hydrology model to produce streamflow ensembles. The initial hydrologic state utilized in the <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> is generated by VIC using daily real-time hydrologic nowcasts, produced using forcings derived via an 'index-station percentile' approach from meteorological station data accessed in real time from Servicio Meteorológico Nacional (SMN). One-year lead time streamflow <span class="hlt">forecasts</span> at monthly time step are produced at a set of major river locations in Mexico. As a case study, the streamflow <span class="hlt">forecasts</span>, along with <span class="hlt">forecasts</span> of reservoir evaporation, are used as input to the Simulation-Optimization (SIMOP) model of the Rio Yaqui system, one of the major agricultural production centers of Mexico. This is the first step in an eventual planned water management implementation over all of Mexico.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ESASP.686E.452S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ESASP.686E.452S"><span id="translatedtitle"><span class="hlt">Forecasting</span> <span class="hlt">Seasonal</span> Water Needs Under Current and Future Climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Spisni, A.; Pratizzoli, W.; Tomei, F.; Mariani, M. C.; Villani, G.; Pavan, V.; Tomozeiu, R.; Marletto, V.</p> <p>2010-12-01</p> <p>This work outlines the complex strategy being developed at ARPA-SIMC for the integrated exploitation of remote sensing, soil water modelling, <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> 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 <span class="hlt">season</span>, 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 <span class="hlt">season</span>. Downscaled <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003PhDT........88W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003PhDT........88W"><span id="translatedtitle">Using climate model ensemble <span class="hlt">forecasts</span> for <span class="hlt">seasonal</span> hydrologic prediction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, Andrew Whitaker</p> <p></p> <p><span class="hlt">Seasonal</span> hydrologic <span class="hlt">forecasting</span> has long played an invaluable role in the development and use of water resources. Despite notable advances in the science and practice of climate prediction, current approaches of hydrologists and water managers largely fail to incorporate <span class="hlt">seasonal</span> climate <span class="hlt">forecast</span> information that has become operationally available during the last decade. This study is motivated by the view that a combination of hydrologic and climate prediction methods affords a new opportunity to improve hydrologic <span class="hlt">forecast</span> skill. A relatively direct statistical approach for achieving this combination (i.e., downscaling) was formulated that used ensemble climate model <span class="hlt">forecasts</span> with a six month lead time produced by the NCEP/CPC Global Spectral Model (GSM) as input to the macroscale Variable Infiltration Capacity hydrologic model to produce ensemble runoff and streamflow <span class="hlt">forecasts</span>. The approach involved the bias correction of climate model precipitation and temperature fields, and spatial and temporal disaggregation from monthly climate model scale (about 2 degrees latitude by longitude) fields to daily hydrology model scale (1/8 degrees) inputs. A qualitative evaluation of the approach in the eastern U.S. suggested that it was successful in translating climate <span class="hlt">forecast</span> signals to local hydrologic variables and streamflow, but that the dominant influence on <span class="hlt">forecast</span> results tended to be persistence in initial hydrologic conditions. The suitability of the statistical downscaling approach for supporting hydrologic simulation was then assessed (using a continuous retrospective 20-year climate simulation from the DOE Parallel Climate Model) relative to dynamical downscaling via a regional, meso-scale climate model. The statistical approach generally outperformed the dynamical approach, in that the dynamical approach alone required additional bias-correction to reproduce the retrospective hydrology as well as the statistical approach. Finally, using 21 years of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.6336R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.6336R"><span id="translatedtitle"><span class="hlt">Seasonal</span> statistical-dynamical <span class="hlt">forecasts</span> of droughts over Western Iberia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ribeiro, Andreia; Pires, Carlos</p> <p>2015-04-01</p> <p>The Standard Precipitation Index (SPI) has been used here as a drought predictand in order to assess <span class="hlt">seasonal</span> drought predictability over the western Iberia. Hybrid (statistical-dynamical) long-range <span class="hlt">forecasts</span> of the drought index SPI are estimated with lead-times up to 6 months, over the period of 1987-2008. Operational <span class="hlt">forecasts</span> of geopotential height and total precipitation from the UK Met Office operational <span class="hlt">forecasting</span> system are considered. Past ERA-Interim reanalysis data, prior to the <span class="hlt">forecast</span> 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 <span class="hlt">forecasted</span> and observational large-scale fields are subjected to a Principal Component Analysis (PCA) and <span class="hlt">forecasted</span> 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 <span class="hlt">forecasts</span> 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 <span class="hlt">season</span>, and most of the predictive power is on the large-scale fields and at the shorter lead-times. The hybridization improves <span class="hlt">forecasting</span> drought skill in comparison to purely dynamical <span class="hlt">forecasts</span>, 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26460115','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26460115"><span id="translatedtitle">Communicating uncertainty in <span class="hlt">seasonal</span> and interannual climate <span class="hlt">forecasts</span> in Europe.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Taylor, Andrea L; Dessai, Suraje; de Bruin, Wändi Bruine</p> <p>2015-11-28</p> <p>Across Europe, organizations in different sectors are sensitive to climate variability and change, at a range of temporal scales from the <span class="hlt">seasonal</span> to the interannual to the multi-decadal. Climate <span class="hlt">forecast</span> providers face the challenge of communicating the uncertainty inherent in these <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> and interannual climate <span class="hlt">forecasts</span>. We find that while many participating organizations perform their own 'in house' risk analysis most require some form of processing and interpretation by <span class="hlt">forecast</span> providers. However, we also find that while users tend to perceive <span class="hlt">seasonal</span> and interannual <span class="hlt">forecasts</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4608030','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4608030"><span id="translatedtitle">Communicating uncertainty in <span class="hlt">seasonal</span> and interannual climate <span class="hlt">forecasts</span> in Europe</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Taylor, Andrea L.; Dessai, Suraje; de Bruin, Wändi Bruine</p> <p>2015-01-01</p> <p>Across Europe, organizations in different sectors are sensitive to climate variability and change, at a range of temporal scales from the <span class="hlt">seasonal</span> to the interannual to the multi-decadal. Climate <span class="hlt">forecast</span> providers face the challenge of communicating the uncertainty inherent in these <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> and interannual climate <span class="hlt">forecasts</span>. We find that while many participating organizations perform their own ‘in house’ risk analysis most require some form of processing and interpretation by <span class="hlt">forecast</span> providers. However, we also find that while users tend to perceive <span class="hlt">seasonal</span> and interannual <span class="hlt">forecasts</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GeoRL..43..377M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GeoRL..43..377M"><span id="translatedtitle">Improved <span class="hlt">seasonal</span> drought <span class="hlt">forecasts</span> using reference evapotranspiration anomalies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McEvoy, Daniel J.; Huntington, Justin L.; Mejia, John F.; Hobbins, Michael T.</p> <p>2016-01-01</p> <p>A novel contiguous United States (CONUS) wide evaluation of reference evapotranspiration (ET0; a formulation of evaporative demand) anomalies is performed using the Climate <span class="hlt">Forecast</span> 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 <span class="hlt">forecast</span> skill of ET0 was found up to leads of 5 months and was consistently better than precipitation skill over most of CONUS. <span class="hlt">Forecasts</span> of ET0 during drought events revealed high categorical skill for notable warm-<span class="hlt">season</span> 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 <span class="hlt">forecasts</span> were initialized during moderate-to-strong El Niño-Southern Oscillation events. Our findings suggest that ET0 anomaly <span class="hlt">forecasts</span> can improve and complement existing <span class="hlt">seasonal</span> drought <span class="hlt">forecasts</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1982JApMe..21..510B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1982JApMe..21..510B"><span id="translatedtitle">The Value of <span class="hlt">Seasonal</span> Climate <span class="hlt">Forecasts</span> in Managing Energy Resources.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brown Weiss, Edith</p> <p>1982-04-01</p> <p>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 <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> would only have limited value in fine tuning the management of energy supply, even if the <span class="hlt">forecasts</span> were more reliable and detailed than at present.On the other hand, reliable <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span>, and greater incentive to conserve. The use of <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> is not likely to be constrained by fear of legal action by those claiming to be injured by a possible incorrect <span class="hlt">forecast</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H31F1498M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H31F1498M"><span id="translatedtitle">Optimization of Evaporative Demand Models for <span class="hlt">Seasonal</span> Drought <span class="hlt">Forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McEvoy, D.; Huntington, J. L.; Hobbins, M.</p> <p>2015-12-01</p> <p>Providing reliable <span class="hlt">seasonal</span> drought <span class="hlt">forecasts</span> continues to pose a major challenge for scientists, end-users, and the water resources and agricultural communities. Precipitation (Prcp) <span class="hlt">forecasts</span> beyond weather time scales are largely unreliable, so exploring new avenues to improve <span class="hlt">seasonal</span> drought prediction is necessary to move towards applications and decision-making based on <span class="hlt">seasonal</span> <span class="hlt">forecasts</span>. A recent study has shown that evaporative demand (E0) anomaly <span class="hlt">forecasts</span> from the Climate <span class="hlt">Forecast</span> System Version 2 (CFSv2) are consistently more skillful than Prcp anomaly <span class="hlt">forecasts</span> during drought events over CONUS, and E0 drought <span class="hlt">forecasts</span> may be particularly useful during the growing <span class="hlt">season</span> in the farming belts of the central and Midwestern CONUS. For this recent study, we used CFSv2 reforecasts to assess the skill of E0 and of its individual drivers (temperature, humidity, wind speed, and solar radiation), using the American Society for Civil Engineers Standardized Reference Evapotranspiration (ET0) Equation. Moderate skill was found in ET0, temperature, and humidity, with lesser skill in solar radiation, and no skill in wind. Therefore, <span class="hlt">forecasts</span> of E0 based on models with no wind or solar radiation inputs may prove to be more skillful than the ASCE ET0. For this presentation we evaluate CFSv2 E0 reforecasts (1982-2009) from three different E0 models: (1) ASCE ET0; (2) Hargreaves and Samani (ET-HS), which is estimated from maximum and minimum temperature alone; and (3) Valiantzas (ET-V), which is a modified version of the Penman method for use when wind speed data are not available (or of poor quality) and is driven only by temperature, humidity, and solar radiation. The University of Idaho's gridded meteorological data (METDATA) were used as observations to evaluate CFSv2 and also to determine if ET0, ET-HS, and ET-V identify similar historical drought periods. We focus specifically on CFSv2 lead times of one, two, and three months, and <span class="hlt">season</span> one <span class="hlt">forecasts</span>; which are</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140012052','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140012052"><span id="translatedtitle">An Assessment of the Skill of GEOS-5 <span class="hlt">Seasonal</span> <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ham, Yoo-Geun; Schubert, Siegfried D.; Rienecker, Michele M.</p> <p>2013-01-01</p> <p>The <span class="hlt">seasonal</span> <span class="hlt">forecast</span> skill of the NASA Global Modeling and Assimilation Office coupled global climate model (CGCM) is evaluated based on an ensemble of 9-month lead <span class="hlt">forecasts</span> 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) <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> of the developing phase of ENSO from boreal spring to summer. The skill of ENSO <span class="hlt">forecasts</span> initiated during the boreal winter <span class="hlt">season</span>, 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 <span class="hlt">forecasts</span> starting in boreal spring and summer, which causes large RMSE in the <span class="hlt">forecast</span>. For V2, the ENSO amplitude is slightly stronger than that in observations and V1 for <span class="hlt">forecasts</span> starting in boreal winter <span class="hlt">season</span>. 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 <span class="hlt">forecasts</span> that are initiated during boreal winter <span class="hlt">season</span>, exhibit a slower phase transition of El Nino, which is consistent with larger amplitude of ENSO after the ENSO peak <span class="hlt">season</span>. 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC51G..07K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC51G..07K"><span id="translatedtitle">Can Abrupt <span class="hlt">Seasonal</span> Transitions be Predicted in Climate <span class="hlt">Forecasts</span>?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kirtman, B. P.</p> <p>2014-12-01</p> <p>There is on ongoing debate in the <span class="hlt">seasonal</span> prediction community as to whether high frequency weather statistics in climate <span class="hlt">forecasts</span> have any inherent predictability, and ultimately prediction skill. The North American Multi-Model Ensemble (NMME) <span class="hlt">seasonal</span>-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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> since August 2011 that adhere to the CPC operational schedule. In addition to the monthly data, daily output from some of the retrospective <span class="hlt">forecasts</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFM.A43E3320R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFM.A43E3320R&link_type=ABSTRACT"><span id="translatedtitle">NMME Monthly / <span class="hlt">Seasonal</span> <span class="hlt">Forecasts</span> for NASA SERVIR Applications Science</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Robertson, F. R.; Roberts, J. B.</p> <p>2014-12-01</p> <p>This work details use of the North American Multi-Model Ensemble (NMME) experimental <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> models to monitor and <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of <span class="hlt">seasonal</span> 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 <span class="hlt">forecasting</span>. The NMME <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150002527','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150002527"><span id="translatedtitle">NMME Monthly / <span class="hlt">Seasonal</span> <span class="hlt">Forecasts</span> for NASA SERVIR Applications Science</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Robertson, Franklin R.; Roberts, Jason B.</p> <p>2014-01-01</p> <p>This work details use of the North American Multi-Model Ensemble (NMME) experimental <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> models to monitor and <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> projections enable scientists, educators, project managers and policy implementers in developing countries to better use probabilistic outlooks of <span class="hlt">seasonal</span> 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 <span class="hlt">forecasting</span>. The NMME <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014GeoRL..41.7566D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014GeoRL..41.7566D"><span id="translatedtitle">Will Arctic sea ice thickness initialization improve <span class="hlt">seasonal</span> <span class="hlt">forecast</span> skill?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Day, J. J.; Hawkins, E.; Tietsche, S.</p> <p>2014-11-01</p> <p>Arctic sea ice thickness is thought to be an important predictor of Arctic sea ice extent. However, coupled <span class="hlt">seasonal</span> <span class="hlt">forecast</span> systems do not generally use sea ice thickness observations in their initialization and are therefore missing a potentially important source of additional skill. To investigate how large this source is, a set of ensemble potential predictability experiments with a global climate model, initialized with and without knowledge of the sea ice thickness initial state, have been run. These experiments show that accurate knowledge of the sea ice thickness field is crucially important for sea ice concentration and extent <span class="hlt">forecasts</span> up to 8 months ahead, especially in summer. Perturbing sea ice thickness also has a significant impact on the <span class="hlt">forecast</span> error in Arctic 2 m temperature a few months ahead. These results suggest that advancing capabilities to observe and assimilate sea ice thickness into coupled <span class="hlt">forecast</span> systems could significantly increase skill.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.8950W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.8950W&link_type=ABSTRACT"><span id="translatedtitle">The value of the North American Multi Model Ensemble phase 2 for sub-<span class="hlt">seasonal</span> hydrological <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wanders, Niko; Wood, Eric</p> <p>2016-04-01</p> <p> from 30 days to 12 days (2.5 month lead) and an increased predictability of the high flow <span class="hlt">season</span> from 45 days to 20 days (3-4 months lead). Additionally, we show that snow accumulation and melt onset in the Northern hemisphere can be <span class="hlt">forecasted</span> with an uncertainty of 10 days (2.5 months lead). Both the overall skill, and the skill found in these last two examples, indicates that the new NMME-2 <span class="hlt">forecast</span> dataset is valuable for sub-<span class="hlt">seasonal</span> <span class="hlt">forecast</span> applications. The high temporal resolution (daily), <span class="hlt">long</span> <span class="hlt">leads</span> (one year leads) and large hindcast archive enable new sub-<span class="hlt">seasonal</span> <span class="hlt">forecasting</span> applications to be explored. We show that the NMME-2 has a large potential for sub-<span class="hlt">seasonal</span> hydrological <span class="hlt">forecasting</span> and other potential hydrological applications (e.g. reservoir management), which could benefit from these new <span class="hlt">forecasts</span>.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1816629B&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1816629B&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Forecasting</span> droughts in West Africa: Operational practice and refined <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bliefernicht, Jan; Siegmund, Jonatan; Seidel, Jochen; Arnold, Hanna; Waongo, Moussa; Laux, Patrick; Kunstmann, Harald</p> <p>2016-04-01</p> <p>Precipitation <span class="hlt">forecasts</span> for the upcoming rainy <span class="hlt">seasons</span> are one of the most important sources of information for an early warning of droughts and water scarcity in West Africa. The meteorological services in West Africa perform <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> within the framework of PRESAO (the West African climate outlook forum) since the end of the 1990s. Various sources of information and statistical techniques are used by the individual services to provide a harmonized <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> for decision makers in West Africa. In this study, we present a detailed overview of the operational practice in West Africa including a first statistical assessment of the performance of the precipitation <span class="hlt">forecasts</span> for drought situations for the past 18 years (1998 to 2015). In addition, a long-term hindcasts (1982 to 2009) and a semi-operational experiment for the rainy <span class="hlt">season</span> 2013 using statistical and/or dynamical downscaling are performed to refine the precipitation <span class="hlt">forecasts</span> from the Climate <span class="hlt">Forecast</span> System Version 2 (CFSv2), a global ensemble prediction system. This information is post-processed to provide user-oriented precipitation indices such as the onset of the rainy <span class="hlt">season</span> for supporting water and land use management for rain-fed agriculture. The evaluation of the individual techniques is performed focusing on water-scarce regions of the Volta basin in Burkina Faso and Ghana. The <span class="hlt">forecasts</span> of the individual techniques are compared to state-of-the-art global observed precipitation products and a novel precipitation database based on long-term daily rain-gage measurements provided by the national meteorological services. The statistical assessment of the PRESAO <span class="hlt">forecasts</span> indicates skillful <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> for many locations in the Volta basin, particularly for years with water deficits. The operational experiment for the rainy <span class="hlt">season</span> 2013 illustrates the high potential of a physically-based downscaling for this region but still shows</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016GeoRL..43.5124K&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016GeoRL..43.5124K&link_type=ABSTRACT"><span id="translatedtitle">Skill improvement of dynamical <span class="hlt">seasonal</span> Arctic sea ice <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Krikken, Folmer; Schmeits, Maurice; Vlot, Willem; Guemas, Virginie; Hazeleger, Wilco</p> <p>2016-05-01</p> <p>We explore the error and improve the skill of the outcome from dynamical <span class="hlt">seasonal</span> Arctic sea ice reforecasts using different bias correction and ensemble calibration methods. These reforecasts consist of a five-member ensemble from 1979 to 2012 using the general circulation model EC-Earth. The raw model reforecasts show large biases in Arctic sea ice area, mainly due to a differently simulated <span class="hlt">seasonal</span> cycle and long term trend compared to observations. This translates very quickly (1-3 months) into large biases. We find that (heteroscedastic) extended logistic regressions are viable ensemble calibration methods, as the <span class="hlt">forecast</span> skill is improved compared to standard bias correction methods. Analysis of regional skill of Arctic sea ice shows that the Northeast Passage and the Kara and Barents Sea are most predictable. These results show the importance of reducing model error and the potential for ensemble calibration in improving skill of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> of Arctic sea ice.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015GSL.....2....9G&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015GSL.....2....9G&link_type=ABSTRACT"><span id="translatedtitle">Benchmark analysis of <span class="hlt">forecasted</span> <span class="hlt">seasonal</span> temperature over different climatic areas</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Giunta, G.; Salerno, R.; Ceppi, A.; Ercolani, G.; Mancini, M.</p> <p>2015-12-01</p> <p>From a long-term perspective, an improvement of <span class="hlt">seasonal</span> <span class="hlt">forecasting</span>, 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 <span class="hlt">forecasts</span> over Italy in the year 2010, comparing the eni-kassandra meteo <span class="hlt">forecast</span> (e-kmf®) model, the Climate <span class="hlt">Forecast</span> 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 <span class="hlt">forecasts</span> compared to climatology and the CFS-NCEP model. By using the reliable high-performance <span class="hlt">forecast</span> system, it is possible to optimize the natural gas portfolio and management operations, thereby obtaining a competitive advantage in the European energy market.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003BAMS...84.1783B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003BAMS...84.1783B"><span id="translatedtitle">Multimodel Ensembling in <span class="hlt">Seasonal</span> Climate <span class="hlt">Forecasting</span> at IRI.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barnston, Anthony G.; Mason, Simon J.; Goddard, Lisa; Dewitt, David G.; Zebiak, Stephen E.</p> <p>2003-12-01</p> <p>The International Research Institute (IRI) for Climate Prediction <span class="hlt">seasonal</span> <span class="hlt">forecast</span> system is based largely on the predictions of ensembles of several atmospheric general circulation models (AGCMs) forced by two versions of an SST prediction—one consisting of persisted SST anomalies from the current observations and one of evolving SST anomalies as predicted by a set of dynamical and statistical SST prediction models. Recently, an objective multimodel ensembling procedure has replaced a more laborious and subjective weighting of the predictions of the several AGCMs. Here the skills of the multimodel predictions produced retrospectively over the first 4 years of IRI <span class="hlt">forecasts</span> are examined and compared with the skills of the more subjectively derived <span class="hlt">forecasts</span> actually issued. The multimodel ensemble predictions are generally found to be an acceptable replacement, although the precipitation <span class="hlt">forecasts</span> do benefit from inclusion of empirical <span class="hlt">forecast</span> tools. Planned pattern-level model output statistics (MOS) corrections for systematic biases in the AGCM <span class="hlt">forecasts</span> may render them more sufficient in their own right.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1712453P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1712453P"><span id="translatedtitle">Ensemble <span class="hlt">seasonal</span> hydrological <span class="hlt">forecasting</span> at the pan-European scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pechlivanidis, Ilias; Spångmyr, Henrik; Bosshard, Thomas; Gustafsson, David; Olsson, Jonas</p> <p>2015-04-01</p> <p>Recent advances in understanding and <span class="hlt">forecasting</span> 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 <span class="hlt">seasonal</span> 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 <span class="hlt">seasonal</span> 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-<span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> timescales. <span class="hlt">Seasonal</span> re-<span class="hlt">forecasts</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1814052A&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1814052A&link_type=ABSTRACT"><span id="translatedtitle">Dynamically downscaled multi-model ensemble <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> over Ethiopia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Asharaf, Shakeel; Fröhlich, Kristina; Fernandez, Jesus; Cardoso, Rita; Nikulin, Grigory; Früh, Barbara</p> <p>2016-04-01</p> <p>Truthful and reliable <span class="hlt">seasonal</span> rainfall predictions have an important social and economic value for the east African countries as their economy is highly dependent on rain-fed agriculture and pastoral systems. Only June to September (JJAS) <span class="hlt">seasonal</span> rainfall accounts to more than 80% crop production in Ethiopia. Hence, <span class="hlt">seasonal</span> foresting is a crucial concern for the region. The European Provision of Regional Impact Assessment on a <span class="hlt">seasonal</span> to decadal timescale (EUPORIAS) project offers a common framework to understand hindcast uncertainties through the use of multi-model and multi-member simulations over east Africa. Under this program, the participating regional climate models (RCMs) were driven by the atmospheric-only version of the ECEARTH global climate model, which provides hindcasts of a five-months period (May to September) from 1991-2012. In this study the RCMs downscaled rainfall is evaluated with respect to the observed JJAS rainfall over Ethiopia. Both deterministic and probabilistic based <span class="hlt">forecast</span> skills are assessed. Our preliminary results show the potential usefulness of multi-model ensemble simulations in <span class="hlt">forecasting</span> the <span class="hlt">seasonal</span> rainfall over the region.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.9387V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.9387V"><span id="translatedtitle">Use of <span class="hlt">Seasonal</span> <span class="hlt">Forecasts</span> for Dams Management in Spain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Voces, Jose; Sanchez, Eroteida; Navascues, Beatriz; Rodriguez-Camino, Ernesto</p> <p>2016-04-01</p> <p>This presentation describes the potential use of <span class="hlt">seasonal</span> climate predictions for water management in Spain. Given the low skill provided by <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> systems based on the current operational models, and after analyzing the response to the climate drivers at different time scales for selected river basins in Spain, a two steps empirical <span class="hlt">forecasting</span> technique has been developed and tested as a pilot study in the framework of the Euporias project. In the first step the winter North Atlantic Oscillation index is <span class="hlt">forecasted</span> from the observed Eurasian Snow Advance Index in October taking advantage of the high correlation between them. Then, a synthetic probability distribution function is generated to sample the NAO index in order to assess the <span class="hlt">forecasting</span> uncertainty. In the second step, a KNN algorithm is used to retrieve a set of reservoir inflows associated with each of the NAO index samples. The final result is an ensemble of possible scenarios (reservoir inflows) which are used as input for the water allocation decision support models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.8445H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.8445H"><span id="translatedtitle">The new Met Office strategy for <span class="hlt">seasonal</span> <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hewson, T. D.</p> <p>2012-04-01</p> <p>In October 2011 the Met Office began issuing a new-format UK <span class="hlt">seasonal</span> <span class="hlt">forecast</span>, 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 <span class="hlt">forecasting</span> 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 <span class="hlt">forecasts</span> for the last 3 winters. The new structure 3-month outlook will then be described and presented. Previously, our <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> pdfs (probability density functions) are displayed alongside climatological pdfs. To generate the <span class="hlt">forecast</span> pdf we take the bias-corrected GLOSEA4 output (42 members), and then incorporate, via expert team, all other relevant information. Firstly model <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> pdfs accordingly. The full modification process will be illustrated</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012APJAS..48..205J&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012APJAS..48..205J&link_type=ABSTRACT"><span id="translatedtitle">Bayesian regression model for <span class="hlt">seasonal</span> <span class="hlt">forecast</span> of precipitation over Korea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jo, Seongil; Lim, Yaeji; Lee, Jaeyong; Kang, Hyun-Suk; Oh, Hee-Seok</p> <p>2012-08-01</p> <p>In this paper, we apply three different Bayesian methods to the <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> of the precipitation in a region around Korea (32.5°N-42.5°N, 122.5°E-132.5°E). We focus on the precipitation of summer <span class="hlt">season</span> (June-July-August; JJA) for the period of 1979-2007 using the precipitation produced by the Global Data Assimilation and Prediction System (GDAPS) as predictors. Through cross-validation, we demonstrate improvement for <span class="hlt">seasonal</span> <span class="hlt">forecast</span> of precipitation in terms of root mean squared error (RMSE) and linear error in probability space score (LEPS). The proposed methods yield RMSE of 1.09 and LEPS of 0.31 between the predicted and observed precipitations, while the prediction using GDAPS output only produces RMSE of 1.20 and LEPS of 0.33 for CPC Merged Analyzed Precipitation (CMAP) data. For station-measured precipitation data, the RMSE and LEPS of the proposed Bayesian methods are 0.53 and 0.29, while GDAPS output is 0.66 and 0.33, respectively. The methods seem to capture the spatial pattern of the observed precipitation. The Bayesian paradigm incorporates the model uncertainty as an integral part of modeling in a natural way. We provide a probabilistic <span class="hlt">forecast</span> integrating model uncertainty.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1812384C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1812384C&link_type=ABSTRACT"><span id="translatedtitle">FORWINE - Statistical Downscaling of <span class="hlt">Seasonal</span> <span class="hlt">forecasts</span> for wine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cardoso, Rita M.; Soares, Pedro M. M.; Miranda, Pedro M. A.</p> <p>2016-04-01</p> <p>The most renowned viticulture regions in the Iberian Peninsula have a long standing tradition in winemaking and are considered world-class grapevine (Vitis Vinifera L.) producing regions. Portugal is the 11th wine producer in the world, with internationally acclaimed wines, such as Port wine, and vineyards across the whole territory. Climate is widely acknowledged of one of the most important factors for grapevine development and growth (Fraga et al. 2014a and b; Jackson et al. 1993; Keller 2010). During the growing <span class="hlt">season</span> (April-October in the Northern Hemisphere) of this perennial and deciduous crop, the climatic conditions are responsible for numerous morphologically and physiological changes. Anomalously low February-March mean temperature, anomalously high May mean temperature and anomalously high March precipitation tend to be favourable to wine production in the Douro Valley. <span class="hlt">Seasonal</span> <span class="hlt">forecast</span> of precipitation and temperature tailored to fit critical thresholds, for crucial <span class="hlt">seasons</span>, can be used to inform management practices (viz. phytosanitary measures, land operations, marketing campaigns) and develop a wine production <span class="hlt">forecast</span>. Statistical downscaling of precipitation, maximum, minimum temperatures is used to model wine production following Santos et al. (2013) and to calculate bioclimatic indices. The skill of the ensemble <span class="hlt">forecast</span> is evaluated through anomaly correlation, ROC area, spread-error ratio and CRPS</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016JESS..125..231P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016JESS..125..231P&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasting</span> of tropical cyclogenesis over the North Indian Ocean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pattanaik, D. R.; Mohapatra, M.</p> <p>2016-03-01</p> <p>Over the North Indian Ocean (NIO) and particularly over the Bay of Bengal (BoB), the post-monsoon <span class="hlt">season</span> 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 <span class="hlt">season</span> is found to be associated with variability of previous large-scale features during monsoon <span class="hlt">season</span> from June to September, which is used to develop <span class="hlt">seasonal</span> <span class="hlt">forecast</span> 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 <span class="hlt">season</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> is found to be having much better <span class="hlt">forecast</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.4410K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.4410K"><span id="translatedtitle">Improvements of <span class="hlt">seasonal</span> weather <span class="hlt">forecasts</span> using optimal combination of multimodel hydrodynamical <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khan, V.; Tischenko, V.; Kryjov, V.; Vilfand, R.</p> <p>2009-04-01</p> <p>The main objective of the present study is to improve <span class="hlt">seasonal</span> weather <span class="hlt">forecasting</span> 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 <span class="hlt">forecasts</span> have been observed over tropical zones, while the <span class="hlt">forecast</span> assessments were low over North Eurasian region. Although performance of basic methods of complexation demonstrated advantage of the multimodel <span class="hlt">forecast</span> over individual <span class="hlt">forecasts</span> constituting the ensemble, the prognostic ability of complexated <span class="hlt">forecast</span> 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 <span class="hlt">season</span> 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 <span class="hlt">forecasts</span>. This work is partially supported by RFBR grant N 07-05-00740, 07-05-00240.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=206135','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=206135"><span id="translatedtitle">Reductions in <span class="hlt">seasonal</span> climate <span class="hlt">forecast</span> dependability as a result of downscaling.</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>NOAA's Climate Prediction Center issues <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> predicting total precipitation and average air temperature for three-month periods out to a year in advance. The utility of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> for agricultural applications depends on several <span class="hlt">forecast</span> characteristics, including depe...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H41K1401D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H41K1401D"><span id="translatedtitle"><span class="hlt">Seasonal</span> Water Resources Management and Probabilistic Operations <span class="hlt">Forecast</span> in the San Juan Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Daugherty, L.; Zagona, E. A.; Rajagopalan, B.; Grantz, K.; Miller, W. P.; Werner, K.</p> <p>2013-12-01</p> <p> within the NWS Community Hydrologic Prediction System (CHPS) to produce an ensemble streamflow <span class="hlt">forecast</span>. 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 <span class="hlt">forecasting</span> 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 <span class="hlt">forecasts</span> may produce increased skill especially at <span class="hlt">long</span> <span class="hlt">lead</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=ARIMA&pg=2&id=ED326579','ERIC'); return false;" href="http://eric.ed.gov/?q=ARIMA&pg=2&id=ED326579"><span id="translatedtitle">Effects of <span class="hlt">Forecasts</span> on the Revisions of Concurrent <span class="hlt">Seasonally</span> Adjusted Data Using the X-11 <span class="hlt">Seasonal</span> Adjustment Procedure.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Bobbitt, Larry; Otto, Mark</p> <p></p> <p>Three Autoregressive Integrated Moving Averages (ARIMA) <span class="hlt">forecast</span> procedures for Census Bureau X-11 concurrent <span class="hlt">seasonal</span> adjustment were empirically tested. Forty time series from three Census Bureau economic divisions (business, construction, and industry) were analyzed. <span class="hlt">Forecasts</span> were obtained from fitted <span class="hlt">seasonal</span> ARIMA models augmented with…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011EOSTr..92..186S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011EOSTr..92..186S"><span id="translatedtitle">Above-normal Atlantic basin hurricane <span class="hlt">season</span> <span class="hlt">forecast</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Showstack, Randy</p> <p>2011-05-01</p> <p>Between three and six major hurricanes with winds of 111 miles per hour and greater could whip across the Atlantic basin during what is <span class="hlt">forecast</span> to be an above-normal 2011 hurricane <span class="hlt">season</span>, 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 <span class="hlt">season</span>, 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.4577L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.4577L"><span id="translatedtitle">Application of <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> for the drought <span class="hlt">forecasting</span> in Catalonia (Spain)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Llasat, Maria-Carmen; Zaragoza, Albert; Aznar, Blanca; Cabot, Jordi</p> <p>2010-05-01</p> <p>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 <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> to improve the water management in Catalonia, particularly in drought conditions. A <span class="hlt">seasonal</span> 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 <span class="hlt">forecasting</span> of water volume into reservoirs. The advantage of this methodology is that it only requires <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> 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 <span class="hlt">forecasting</span> is focused on this region (Zaragoza, 2008).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015WRR....51.1797S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015WRR....51.1797S"><span id="translatedtitle">Model averaging methods to merge operational statistical and dynamic <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> in Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schepen, Andrew; Wang, Q. J.</p> <p>2015-03-01</p> <p>The Australian Bureau of Meteorology produces statistical and dynamic <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span>. The statistical and dynamic <span class="hlt">forecasts</span> are similarly reliable in ensemble spread; however, skill varies by catchment and <span class="hlt">season</span>. Therefore, it may be possible to optimize <span class="hlt">forecasting</span> skill by weighting and merging statistical and dynamic <span class="hlt">forecasts</span>. Two model averaging methods are evaluated for merging <span class="hlt">forecasts</span> for 12 locations. The first method, Bayesian model averaging (BMA), applies averaging to <span class="hlt">forecast</span> probability densities (and thus cumulative probabilities) for a given <span class="hlt">forecast</span> variable value. The second method, quantile model averaging (QMA), applies averaging to <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> skill across catchments and <span class="hlt">seasons</span>. However, when both the statistical and dynamical <span class="hlt">forecasting</span> approaches are skillful but produce, on special occasions, very different event <span class="hlt">forecasts</span>, the BMA merged <span class="hlt">forecasts</span> for these events can have unusually wide and bimodal distributions. In contrast, the distributions of the QMA merged <span class="hlt">forecasts</span> for these events are narrower, unimodal and generally more smoothly shaped, and are potentially more easily communicated to and interpreted by the <span class="hlt">forecast</span> users. Such special occasions are found to be rare. However, every <span class="hlt">forecast</span> counts in an operational service, and therefore the occasional contrast in merged <span class="hlt">forecasts</span> between the two methods may be more significant than the indifference shown by the overall skill and reliability performance.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.6517C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.6517C"><span id="translatedtitle">Evaluation of ensemble <span class="hlt">forecast</span> uncertainty using a new proper score: application to medium-range and <span class="hlt">seasonal</span> <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Christensen, Hannah; Moroz, Irene; Palmer, Tim</p> <p>2015-04-01</p> <p><span class="hlt">Forecast</span> verification is important across scientific disciplines as it provides a framework for evaluating the performance of a <span class="hlt">forecasting</span> 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 <span class="hlt">forecasts</span>. In order to be useful, a skill score must be proper -- it must encourage honesty in the <span class="hlt">forecaster</span>, and reward <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span>. It is formulated with respect to the moments of the <span class="hlt">forecast</span>. The ES is confirmed to be a proper score, and is therefore sensitive to both resolution and reliability. The ES is tested on <span class="hlt">forecasts</span> made using the Lorenz '96 system, and found to be useful for summarising the skill of the <span class="hlt">forecasts</span>. The European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared to a perfect statistical probabilistic <span class="hlt">forecast</span> -- the ECMWF high resolution deterministic <span class="hlt">forecast</span> dressed with the observed error distribution. This generates a <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> and the statically reliable dressed deterministic <span class="hlt">forecasts</span>. Other skill scores are tested and found to be comparatively insensitive to this desirable <span class="hlt">forecast</span> quality. The ES is used to evaluate <span class="hlt">seasonal</span> range ensemble <span class="hlt">forecasts</span> made with the ECMWF System 4. The ensemble <span class="hlt">forecasts</span> are found to be skilful when compared with climatological or persistence <span class="hlt">forecasts</span>, though this skill is dependent on region and time of year.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.7934C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.7934C&link_type=ABSTRACT"><span id="translatedtitle">Bias correcting precipitation <span class="hlt">forecasts</span> for extended-range skilful <span class="hlt">seasonal</span> streamflow predictions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian</p> <p>2016-04-01</p> <p>Meteorological centres make sustained efforts to provide <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> that are increasingly skilful, which has the potential to also benefit streamflow <span class="hlt">forecasting</span>. <span class="hlt">Seasonal</span> streamflow <span class="hlt">forecasts</span> can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of <span class="hlt">seasonal</span> precipitation and streamflow <span class="hlt">forecasts</span> in France in order to provide insights into the way bias correcting <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> can contribute to maintain skill of <span class="hlt">seasonal</span> flow predictions at extended lead times. First, we evaluate the skill of raw (i.e., without bias correction) <span class="hlt">seasonal</span> precipitation ensemble <span class="hlt">forecasts</span> for streamflow <span class="hlt">forecasting</span> in sixteen French catchments. A lumped daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. A reference prediction system based on historic observed precipitation and watershed initial conditions at the time of <span class="hlt">forecast</span> (i.e., ESP method) is used as benchmark. In a second step, we apply eight variants of bias correction approaches to the precipitation <span class="hlt">forecasts</span> prior to generating the flow <span class="hlt">forecasts</span>. The approaches were based on the linear scaling and the distribution mapping methods. The skill of the ensemble <span class="hlt">forecasts</span> is assessed in reliability, sharpness, accuracy, and overall performance. The results show that, in most catchments, raw <span class="hlt">seasonal</span> precipitation and streamflow <span class="hlt">forecasts</span> are often more skilful than the conventional ESP method in terms of sharpness. However, reliability is an attribute that is not significantly improved. <span class="hlt">Forecast</span> skill is generally improved when applying bias correction. Two bias correction methods showed the best performance for the studied catchments, with, however, each method being more successful in improving specific attributes of <span class="hlt">forecast</span> quality: the simple linear scaling of monthly values contributed mainly to increase <span class="hlt">forecast</span></p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1713027C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1713027C"><span id="translatedtitle">Assessing the skill of <span class="hlt">seasonal</span> precipitation and streamflow <span class="hlt">forecasts</span> in sixteen French catchments</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Crochemore, Louise; Ramos, Maria-Helena; Pappenberger, Florian</p> <p>2015-04-01</p> <p>Meteorological centres make sustained efforts to provide <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> that are increasingly skilful. Streamflow <span class="hlt">forecasting</span> is one of the many applications than can benefit from these efforts. <span class="hlt">Seasonal</span> flow <span class="hlt">forecasts</span> generated using <span class="hlt">seasonal</span> ensemble precipitation <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> precipitation and streamflow <span class="hlt">forecasts</span> in France. First, we evaluated the skill of ECMWF SYS4 <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> for streamflow <span class="hlt">forecasting</span> in sixteen French catchments. Daily flow <span class="hlt">forecasts</span> were produced using raw <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> as input to the GR6J hydrological model. Ensemble <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> prior to generating the flow <span class="hlt">forecasts</span>. The approaches were based on the linear scaling and the distribution mapping methods. The skill of the ensemble <span class="hlt">forecasts</span> was assessed in accuracy (MAE), reliability (PIT Diagram) and overall performance (CRPS). The results show that, in most catchments, raw <span class="hlt">seasonal</span> precipitation and streamflow <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span>. Reliability is the only attribute that is not significantly improved. The skill of the <span class="hlt">forecasts</span> 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).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013ClDy...41.1969O&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013ClDy...41.1969O&link_type=ABSTRACT"><span id="translatedtitle">Impact of snow initialization on sub-<span class="hlt">seasonal</span> <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Orsolini, Y. J.; Senan, R.; Balsamo, G.; Doblas-Reyes, F. J.; Vitart, F.; Weisheimer, A.; Carrasco, A.; Benestad, R. E.</p> <p>2013-10-01</p> <p>The influence of the snowpack on wintertime atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on <span class="hlt">seasonal</span> 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 <span class="hlt">Forecasts</span> (ECMWF) ensemble <span class="hlt">forecast</span> system to investigate the impact of accurate snow initialisation. Pairs of 2-month ensemble <span class="hlt">forecasts</span> 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 Arctic—cold continent" difference means that the <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHyd..530..815M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..530..815M"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasting</span> of groundwater levels in principal aquifers of the United Kingdom</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mackay, J. D.; Jackson, C. R.; Brookshaw, A.; Scaife, A. A.; Cook, J.; Ward, R. S.</p> <p>2015-11-01</p> <p>To date, the majority of hydrological <span class="hlt">forecasting</span> studies have focussed on using medium-range (3-15 days) weather <span class="hlt">forecasts</span> to drive hydrological models and make predictions of future river flows. With recent developments in <span class="hlt">seasonal</span> (1-3 months) weather <span class="hlt">forecast</span> skill, such as those from the latest version of the UK Met Office global <span class="hlt">seasonal</span> <span class="hlt">forecast</span> system (GloSea5), there is now an opportunity to use similar methodologies to <span class="hlt">forecast</span> groundwater levels in more slowly responding aquifers on <span class="hlt">seasonal</span> timescales. This study uses <span class="hlt">seasonal</span> rainfall <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> have skill; outperforming a persistence <span class="hlt">forecast</span> and demonstrating reliability, resolution and discrimination. However, there is currently little to gain from using <span class="hlt">seasonal</span> rainfall <span class="hlt">forecasts</span> over using site climatology for this type of application. Furthermore, the <span class="hlt">forecasts</span> are not able to capture extreme groundwater levels, primarily because of inadequacies in the driving rainfall <span class="hlt">forecasts</span>. The findings also show that the origin of <span class="hlt">forecast</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.5580S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.5580S"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">Forecasting</span> of Fires across Southern Borneo, 1997-2010</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Spessa, Allan; Field, Robert; Kaiser, Johannes; Langner, Andreas; Moore, Jonathan; Pappenberger, Florian; Siegert, Florian; Weber, Ulrich</p> <p>2014-05-01</p> <p> 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 <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> 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 <span class="hlt">seasonal</span> rainfall <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016OcMod.100...20V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016OcMod.100...20V"><span id="translatedtitle">Downscaling and extrapolating dynamic <span class="hlt">seasonal</span> marine <span class="hlt">forecasts</span> for coastal ocean users</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vanhatalo, Jarno; Hobday, Alistair J.; Little, L. Richard; Spillman, Claire M.</p> <p>2016-04-01</p> <p>Marine weather and climate <span class="hlt">forecasts</span> are essential in planning strategies and activities on a range of temporal and spatial scales. However, <span class="hlt">seasonal</span> dynamical <span class="hlt">forecast</span> models, that provide <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> covering coastal waters. Here, we have developed a method to combine observational data with dynamical <span class="hlt">forecasts</span> from POAMA (Predictive Ocean Atmosphere Model for Australia; Australian Bureau of Meteorology) in order to produce <span class="hlt">seasonal</span> downscaled, corrected <span class="hlt">forecasts</span>, extrapolated to include inshore regions that POAMA does not cover. We demonstrate the method in <span class="hlt">forecasting</span> 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 <span class="hlt">forecast</span> is approximately 1° × 0.25°. We use data and model hindcasts for the period 1994-2010 for <span class="hlt">forecast</span> validation. The predictive performance of our statistical downscaling model improves on the original POAMA <span class="hlt">forecast</span>. Additionally, this statistical downscaling model extrapolates <span class="hlt">forecasts</span> to coastal regions not covered by POAMA and its <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> at <span class="hlt">seasonal</span> timescales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1816370O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1816370O"><span id="translatedtitle">Usefulness of ECMWF system-4 ensemble <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> for East Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ogutu, Geoffrey; Franssen, Wietse; Supit, Iwan; Omondi, Philip; Hutjes, Ronald</p> <p>2016-04-01</p> <p>This study evaluates whether European Centre for Medium-Range Weather <span class="hlt">Forecast</span> (ECMWF) system-4 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> can potentially be used as input for impact analysis over East Africa. To be of any use, these <span class="hlt">forecasts</span> should have skill. We used the 15-member ensemble runs and tested potential <span class="hlt">forecast</span> skill of precipitation (tp), near surface air temperature (tas) and surface downwelling shortwave radiation (rsds) for future use in impact models. Probabilistic measures verified the ECMWF ensemble <span class="hlt">forecasts</span> against the WATCH Forcing Data methodology applied to ERA-Interim data (WFDEI) climatology for the period 1981-2010. The Ranked Probability Skill Score (RPSS) assesses the overall <span class="hlt">forecast</span> skill, whereas the Relative Operating Curve Skill Score (ROCSS) analyses skill of the <span class="hlt">forecasted</span> tercile at both grid cell and over sub-regions with homogeneous rainfall characteristics. The results show that predictability of the three variables varies with <span class="hlt">season</span>, location and <span class="hlt">forecast</span> month (lead-time) before start of the <span class="hlt">seasons</span>. Quantile-quantile bias correction clears biases in the raw <span class="hlt">forecasts</span> but does not improve probabilistic skills. The October-December (OND) tp <span class="hlt">forecasts</span> show skill over a larger area up to lead-time of three months compared to the March-May (MAM) and June-August (JJA) <span class="hlt">seasons</span>. Temperature <span class="hlt">forecasts</span> are skillful up to a minimum three months lead-time in all <span class="hlt">seasons</span>, while the rsds is less skillful. ROCSS analyses indicate high skill in simulation of upper- and lower-tercile <span class="hlt">forecasts</span> compared to simulation of the middle-terciles. Upper- and lower-tercile precipitation <span class="hlt">forecasts</span> are 20-80% better than climatology over a larger area at 0-3 month lead-time; tas <span class="hlt">forecasts</span> are 40-100% better at shorter lead-times while rsds <span class="hlt">forecasts</span> are less skillful in all <span class="hlt">seasons</span>. The <span class="hlt">forecast</span> system captures manifestations of strong El Niño and La Niña years in terms of precipitation but the skill scores are region dependent.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H52A..05S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H52A..05S"><span id="translatedtitle"><span class="hlt">Seasonal</span> Scale Water Deficit <span class="hlt">Forecasting</span> in East Africa and the Middle East Region Using the NMME Models <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shukla, S.; Funk, C. C.; Zaitchik, B. F.; Narapusetty, B.; Arsenault, K. R.; Peters-Lidard, C. D.</p> <p>2015-12-01</p> <p>In this presentation we report on our ongoing efforts to provide <span class="hlt">seasonal</span> scale water deficit <span class="hlt">forecasts</span> in East Africa and the Middle East regions. First, we report on the skill of the <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> from the North American Multimodel Ensemble (NMME) models over this region. We evaluated deterministic (anomaly correlation), categorical (the equitable threat score) and probabilistic (the ranked probabilistic skill score) skill of the NMME models <span class="hlt">forecasts</span> over the hindcast period of 1982-2010, focusing on the primary rainy <span class="hlt">seasons</span> of March-May (MAM), July-September (JAS) and October-December (OND). We also examined the potential predictability of the NMME models using the anomaly correlation between the ensemble mean <span class="hlt">forecasts</span> from a given model against a single ensemble member of the same model (homogenous predictability) and rest of the models (heterogeneous predictability), and observations (<span class="hlt">forecast</span> skill). Overall, we found precipitation <span class="hlt">forecast</span> skill in this region to be sparse and limited (up to three month of lead) to some locations and <span class="hlt">seasons</span>, and temperature <span class="hlt">forecast</span> skill to be much more skillful than the precipitation <span class="hlt">forecast</span> skill. Highest level of skill exists over equatorial East Africa (OND <span class="hlt">season</span>) and over parts of northern Ethiopia and southern Sudan (JAS <span class="hlt">season</span>). Categorical and probabilistic <span class="hlt">forecast</span> skills are also higher in those regions. We found the homogeneous predictability to be greater than the <span class="hlt">forecast</span> skill indicating potential for <span class="hlt">forecast</span> skill improvement. In the rest of the presentation we describe implementation and evaluation of a hybrid approach (that combines statistical and dynamical approaches) of downscaling climate <span class="hlt">forecasts</span> to improve the precipitation <span class="hlt">forecast</span> skill in this region. For this part of the analysis we mainly focus on two of the NMME models (NASA's GMAO and NCEP's CFSv2). Past research on a hybrid approach focusing only over equatorial East Africa has shown promising results. We found that MAM</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.H31A0823P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.H31A0823P"><span id="translatedtitle">Verification and error sources of the California <span class="hlt">Seasonal</span> Hydrologic <span class="hlt">Forecast</span> (Cali<span class="hlt">Forecast</span>) System over the Feather River Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Park, G.; Imam, B.; Sorooshian, S.</p> <p>2008-12-01</p> <p>Operational water resource planning and management heavily rely on the <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> of reservoir. The California Hydrologic <span class="hlt">Forecast</span> System, a regional implementation of the West-Wide <span class="hlt">Seasonal</span> Hydrologic <span class="hlt">forecast</span> System over the state of California at the University of California-Irvine in a 1/8th degree resolution, provides probabilistic <span class="hlt">forecasts</span> in the form of ensemble streamflow predictions (ESP) to facilitate our need in the state of California. Similar to any other hydrologic <span class="hlt">forecast</span> systems, Cali<span class="hlt">Forecast</span>, however, contains significant <span class="hlt">forecast</span> errors and uncertainties that are propagated from many sources. These within the Cali<span class="hlt">Forecast</span> 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 <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> skills.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002AGUSMGC21A..05K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002AGUSMGC21A..05K"><span id="translatedtitle">Estimating the Potential Use of <span class="hlt">Seasonal</span> <span class="hlt">Forecasts</span> by Commercial Farmers in South Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Klopper, E.</p> <p>2002-05-01</p> <p>Currently the South African Weather Service compiles <span class="hlt">seasonal</span> rainfall and temperature outlooks by combining output from various empirical and dynamical models. In this paper the quality of <span class="hlt">forecasts</span> are evaluated during the December-January-February <span class="hlt">season</span> over a retro-active period from 1991/92 to 1999/2000. Furthermore, the usefulness of these <span class="hlt">forecasts</span> are evaluated in real-life situations. With <span class="hlt">seasonal</span> <span class="hlt">forecast</span> information at hand, individuals and organizations do not need to rely only on climatic averages or traditional <span class="hlt">forecasts</span> to make their preparations anymore. The case-study method, as a tool for evaluating situations in real-life context, has been employed here to test the hypothesis that <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> are useful and have value when applied by commercial farmers. To assess the potential usefulness of climate <span class="hlt">forecasts</span>, a basic understanding is needed of how users perceive the <span class="hlt">forecasts</span>, how their decisions are influenced by the <span class="hlt">forecast</span> information and the specific attributes required by the users. The value of climate <span class="hlt">forecasts</span> depends partly on the relevance of the information to users' decisions and also on the way in which it is presented. In an attempt to understand and assess this, individual commercial farmers were presented with retro-active <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> produced for the summer rainfall <span class="hlt">seasons</span> of South Africa during the period 1991/92 to 1999/2000. The potential uptake and benefits of this information in planning and decision-making processes of these farmers are presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRD..12011809M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..12011809M"><span id="translatedtitle">Decomposition of sources of errors in <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasting</span> over the U.S. Sunbelt</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mazrooei, Amirhossein; Sinha, Tushar; Sankarasubramanian, A.; Kumar, Sujay; Peters-Lidard, Christa D.</p> <p>2015-12-01</p> <p><span class="hlt">Seasonal</span> streamflow <span class="hlt">forecasts</span>, 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 <span class="hlt">forecasts</span> pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate <span class="hlt">forecasts</span>. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate <span class="hlt">forecasts</span>, and downscaling/disaggregation techniques in developing <span class="hlt">seasonal</span> streamflow <span class="hlt">forecast</span>. For this purpose, three month ahead <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> and NLDAS-2 hourly climatology to develop retrospective <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> over a period of 20 years (1991-2010). Finally, the performance of LSMs in <span class="hlt">forecasting</span> streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing <span class="hlt">seasonal</span> streamflow <span class="hlt">forecast</span>. Our results indicate that the most dominant source of errors during winter and fall <span class="hlt">seasons</span> is the errors due to ECHAM4.5 precipitation <span class="hlt">forecasts</span>, while temporal disaggregation scheme contributes to maximum errors during summer <span class="hlt">season</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1712437C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1712437C"><span id="translatedtitle">Tests of oceanic stochastic parameterisation in a <span class="hlt">seasonal</span> <span class="hlt">forecast</span> system.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cooper, Fenwick; Andrejczuk, Miroslaw; Juricke, Stephan; Zanna, Laure; Palmer, Tim</p> <p>2015-04-01</p> <p>Over <span class="hlt">seasonal</span> time scales, our aim is to compare the relative impact of ocean initial condition and model uncertainty, upon the ocean <span class="hlt">forecast</span> skill and reliability. Over <span class="hlt">seasonal</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AGUFM.H34F..07J&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AGUFM.H34F..07J&link_type=ABSTRACT"><span id="translatedtitle">The Influence of <span class="hlt">Seasonal</span> <span class="hlt">Forecast</span> Accuracy on Farmer Behavior: An Agent-Based Modeling Approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jacobi, J. H.; Nay, J.; Gilligan, J. M.</p> <p>2013-12-01</p> <p><span class="hlt">Seasonal</span> climates dictate the livelihoods of farmers in developing countries. While farmers in developed countries often have <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> on which to base their cropping decisions, developing world farmers usually make plans for the <span class="hlt">season</span> without such information. Climate change increases the <span class="hlt">seasonal</span> uncertainty, making things more difficult for farmers. Providing <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> to farmers. The accuracy of these <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> rainfall <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span>. Farmers can either choose to grow paddy rice or plant a cash crop. Prospect theory is used to evaluate outcomes of the growing <span class="hlt">season</span>; the farmer's memory of the level of success under a certain set of conditions affects next <span class="hlt">season</span>'s decision. Results from this study have implications for policy makers and <span class="hlt">seasonal</span> <span class="hlt">forecasters</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H11F0916R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H11F0916R"><span id="translatedtitle">Streamflow <span class="hlt">forecasts</span> on <span class="hlt">seasonal</span> and interannual time scales for reservoir management</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Robertson, A. W.; Lu, M.; Lall, U.</p> <p>2014-12-01</p> <p><span class="hlt">Seasonal</span> climate <span class="hlt">forecasts</span> are beginning to be complemented by improved <span class="hlt">forecasting</span> capabilities at both sub-<span class="hlt">seasonal</span> and interannual annual timescales, with the future prospect of seamless climate <span class="hlt">forecasts</span> for water system operations. While <span class="hlt">seasonal</span> 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 <span class="hlt">forecasts</span> in terms of the kinds of management decisions that can be informed. Here we present an example of combining <span class="hlt">season</span> and year-ahead streamflow <span class="hlt">forecasts</span> as input to a multi-use reservoir optimization model, applied to the Bhakra Dam in NW India. Bi-timescale <span class="hlt">forecasts</span> are made with a <span class="hlt">seasonal</span> periodic autoregressive (PAR) model with exogenous climate-<span class="hlt">forecast</span> inputs, together with an annual PAR model fit to observed flows used as a baseline for year-ahead <span class="hlt">forecasts</span>. 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 <span class="hlt">seasonal</span> predictors based on climate model output and data and show that, with the choice of a good start date, even <span class="hlt">forecasts</span> with relatively low skill can have value.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.3311B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.3311B"><span id="translatedtitle">Daily calibration of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> to derive impact-relevant climate indices</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bhend, Jonas; Spirig, Christoph; Mahlstein, Irina; Liniger, Mark</p> <p>2015-04-01</p> <p>Climate information indices (CIIs) are impact-relevant quantities derived from basic meteorological variables such as rainfall or temperature. The computation of CIIs often involves absolute thresholds such as for the number of frost days per <span class="hlt">seasons</span> or <span class="hlt">seasonal</span> degree days. The dependence on absolute thresholds poses challenges in a <span class="hlt">forecasting</span> context where such indices have to be derived from daily time series of <span class="hlt">forecasting</span> systems with sometimes considerable systematic, time and location dependent biases. In order to reduce the effect of such biases on the skill of CII <span class="hlt">forecasts</span>, the daily time series need to be calibrated before computing the CIIs. Here we analyze the performance and the effect of several bias correction and calibration methods on the skill of <span class="hlt">seasonal</span> CII <span class="hlt">forecasts</span> derived from the calibrated daily series. We use <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> from the European Centre of Medium-Range Weather <span class="hlt">Forecasts</span>' (ECMWF) System 4 for the period 1981-2012, with a focus on the winter <span class="hlt">season</span>. The <span class="hlt">forecasts</span> are verified against CIIs derived from the ERA-INTERIM reanalysis. We find that bias correction and calibration approaches have a positive effect on the skill of CII <span class="hlt">forecasts</span>. For CIIs involving a moderate non-linearity (e.g. <span class="hlt">seasonal</span> heating degree days), all correction and calibration methods result in similar skill. For CIIs with pronounced threshold dependency (e.g. <span class="hlt">seasonal</span> frost days), skill is more sensitive to the choice of calibration method. However, the analyzed daily correction and calibration methods do not achieve to produce reliable <span class="hlt">forecasts</span> of <span class="hlt">seasonally</span> aggregated CIIs. Hence, an additional re-calibration of the CII <span class="hlt">forecasts</span> is necessary to get well calibrated CII <span class="hlt">forecasts</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1815197K&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1815197K&link_type=ABSTRACT"><span id="translatedtitle">Skill improvement of <span class="hlt">seasonal</span> Arctic sea ice <span class="hlt">forecasts</span> using bias-correction and ensemble calibration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Krikken, Folmer; Hazeleger, Wilco; Vlot, Willem; Schmeits, Maurice; Guemas, Virginie</p> <p>2016-04-01</p> <p>We explore the standard error and skill of dynamical <span class="hlt">seasonal</span> sea ice <span class="hlt">forecasts</span> of the Arctic using different bias-correction and ensemble calibration methods. The latter is often used in weather <span class="hlt">forecasting</span>, but so far has not been applied to Arctic sea ice <span class="hlt">forecasts</span>. We use <span class="hlt">seasonal</span> predictions of Arctic sea ice of a 5-member ensemble <span class="hlt">forecast</span> using the fully coupled GCM EC-Earth, with model initial states obtained by nudging towards ORAS4 and ERA-Interim. The raw model <span class="hlt">forecasts</span> contain large biases in total sea ice area, especially during the summer months. This is mainly caused by a difference in average <span class="hlt">seasonal</span> cycle between EC-Earth and observations, which translates directly into the <span class="hlt">forecasts</span> yielding large biases. Further errors are introduced by the differences in long term trend between the observed sea ice, and the uninitialised EC-earth simulation. We find that extended logistic regression (ELR) and heteroscedastic extended logistic regression (HELR) both prove viable ensemble calibration methods, and improve the <span class="hlt">forecasts</span> substantially compared to standard bias correction techniques. No clear distinction between ELR and HELR is found. <span class="hlt">Forecasts</span> starting in May have higher skill (CRPSS > 0 up to 5 months lead time) than <span class="hlt">forecasts</span> starting in August (2-3 months) and November (2-3 months), with trend-corrected climatology as reference. Analysis of regional skill in the Arctic shows distinct differences, where mainly the Arctic ocean and the Kara and Barents sea prove to be one of the more predictable regions with skilful <span class="hlt">forecasts</span> starting in May up to 5-6 months lead time. Again, <span class="hlt">forecasts</span> starting in August and November show much lower regional skill. Overall, it is still difficult to beat relative simple statistical <span class="hlt">forecasts</span>, but by using ELR and HELR we are getting reasonably close to skilful <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> up to 12 months lead time. These results show there is large potential, and need, for using ensemble calibration in <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ClDy...47..919P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ClDy...47..919P&link_type=ABSTRACT"><span id="translatedtitle">Impact of land-surface initialization on sub-<span class="hlt">seasonal</span> to <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> over Europe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prodhomme, Chloé; Doblas-Reyes, Francisco; Bellprat, Omar; Dutra, Emanuel</p> <p>2016-08-01</p> <p>Land surfaces and soil conditions are key sources of climate predictability at the <span class="hlt">seasonal</span> time scale. In order to estimate how the initialization of the land surface affects the predictability at <span class="hlt">seasonal</span> time scale, we run two sets of <span class="hlt">seasonal</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AdSR...12...31H&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AdSR...12...31H&link_type=ABSTRACT"><span id="translatedtitle">The verification of <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> for early warning in Zambia and Malawi</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hyvärinen, O.; Mtilatila, L.; Pilli-Sihvola, K.; Venäläinen, A.; Gregow, H.</p> <p>2015-04-01</p> <p>We assess the probabilistic <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> issued by Regional Climate Outlook Forum (RCOF) for the area of two southern African countries, Malawi and Zambia from 2002 to 2013. The <span class="hlt">forecasts</span>, issued in August, are of rainy <span class="hlt">season</span> rainfall accumulations in three categories (above normal, normal, and below normal), for early <span class="hlt">season</span> (October-December) and late <span class="hlt">season</span> (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 <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> are rather reliable or well-calibrated, but have a very low resolution; that is, they are not able to discriminate different events. The <span class="hlt">forecasts</span> also lack sharpness as <span class="hlt">forecasts</span> for one category are rarely higher than 40 % or less than 25 %. However, these results might be unnecessarily pessimistic, because <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> have gone through much development during the period when the <span class="hlt">forecasts</span> verified in this paper were issued, and <span class="hlt">forecasts</span> using current methodology might have performed better.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.9292B&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.9292B&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">Forecasts</span> of Climate Indices: Impact of Definition and Spatial Aggregation on Predictive Skill</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bhend, Jonas; Mahlstein, Irina; Liniger, Mark</p> <p>2016-04-01</p> <p><span class="hlt">Seasonal</span> <span class="hlt">forecasting</span> models are increasingly being used to <span class="hlt">forecast</span> application-relevant aspects. A simple way to make such user-oriented predictions are application-specific climate indices. Little is known, however, on how the predictive skill of <span class="hlt">forecasts</span> of such climate indices relates to the predictive skill in <span class="hlt">forecasting</span> <span class="hlt">seasonal</span> mean conditions. Here we analyse <span class="hlt">forecasts</span> of two types of indices derived from daily precipitation and temperature: counts of events such as the number of dry days and accumulated threshold exceedances such as degree days. We find that the predictive skill of <span class="hlt">forecasts</span> of heating and cooling degree days and of consecutive dry days is generally lower than the skill of <span class="hlt">seasonal</span> mean temperature and rainfall <span class="hlt">forecasts</span> respectively. By use of a toy model we demonstrate that this reduction in skill is more pronounced for skilful <span class="hlt">forecasts</span> and climate indices with a threshold in the tail of the statistical distribution. We further analyse the impact of spatial aggregation and find that aggregation generally improves the predictive skill. Using appropriate covariates for weighting - for example population density to derive a proxy for the national energy demand for heating - the usefulness of <span class="hlt">forecasts</span> of climate indices can be further enhanced while retaining predictive skill. We conclude that processing of direct model output to derive climate indices in combination with spatial aggregation can be used to render still skilful and even more useful <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> of user-relevant quantities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150002555','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150002555"><span id="translatedtitle"><span class="hlt">Seasonal</span> Drought Prediction in East Africa: Can National Multi-Model Ensemble <span class="hlt">Forecasts</span> Help?</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew</p> <p>2014-01-01</p> <p>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 <span class="hlt">forecasts</span> at <span class="hlt">seasonal</span> 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 <span class="hlt">seasonal</span> drought prediction in this region faces several challenges. Lack of skillful <span class="hlt">seasonal</span> rainfall <span class="hlt">forecasts</span>; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of <span class="hlt">seasonal</span> scale dynamical climate <span class="hlt">forecasts</span>. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate <span class="hlt">forecast</span> system. The NMME incorporates climate <span class="hlt">forecasts</span> from 6+ fully coupled dynamical models resulting in 100+ ensemble member <span class="hlt">forecasts</span>. Recent studies have indicated that in general NMME offers improvement over <span class="hlt">forecasts</span> from any single model. However thus far the skill of NMME for <span class="hlt">forecasting</span> 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 <span class="hlt">forecasting</span> rainfall at <span class="hlt">seasonal</span> scale in East Africa for all three of the prominent <span class="hlt">seasons</span> 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 <span class="hlt">forecasts</span>; to improve rainfall <span class="hlt">forecast</span> skill in the region when raw NMME <span class="hlt">forecasts</span> lack in skill.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150002966','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150002966"><span id="translatedtitle"><span class="hlt">Seasonal</span> Drought Prediction in East Africa: Can National Multi-Model Ensemble <span class="hlt">Forecasts</span> Help?</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew</p> <p>2015-01-01</p> <p>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 <span class="hlt">forecasts</span> at <span class="hlt">seasonal</span> 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 <span class="hlt">seasonal</span> drought prediction in this region faces several challenges. Lack of skillful <span class="hlt">seasonal</span> rainfall <span class="hlt">forecasts</span>; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of <span class="hlt">seasonal</span> scale dynamical climate <span class="hlt">forecasts</span>. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate <span class="hlt">forecast</span> system. The NMME incorporates climate <span class="hlt">forecasts</span> from 6+ fully coupled dynamical models resulting in 100+ ensemble member <span class="hlt">forecasts</span>. Recent studies have indicated that in general NMME offers improvement over <span class="hlt">forecasts</span> from any single model. However thus far the skill of NMME for <span class="hlt">forecasting</span> 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 <span class="hlt">forecasting</span> rainfall at <span class="hlt">seasonal</span> scale in East Africa for all three of the prominent <span class="hlt">seasons</span> 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 <span class="hlt">forecasts</span>; to improve rainfall <span class="hlt">forecast</span> skill in the region when raw NMME <span class="hlt">forecasts</span> lack in skill.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.7634C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.7634C"><span id="translatedtitle">Effect of realistic vegetation variability on <span class="hlt">seasonal</span> <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Catalano, Franco; Alessandri, Andrea; De Felice, Matteo; Doblas-Reyes, Francisco J.</p> <p>2014-05-01</p> <p>A real predictability hindcast experiment with prescribed Leaf Area Index (LAI) has been performed using the state-of-the-art Earth System Model EC-Earth. LAI input to the climate model has been prescribed using a novel observational dataset based on the third generation GIMMS and MODIS satellite data. The LAI dataset has been pre-processed (monthly averaged, interpolated, gap-filled) to use it in the land surface scheme of EC-Earth (HTESSEL). The vegetation density is modeled by an exponential dependence on LAI, based on the Lambert-Beer formulation. Retrospective hindcasts have been performed with the following model setup: 7 months <span class="hlt">forecast</span> length, 2 start dates (1st May and 1st November), 10 members, 28 years (1982-2009). Initial conditions were produced at IC3, based on ERA-40/ERA-Interim (atmosphere and land-surface) and NEMOVAR-ORAS4 (ocean and sea-ice) data. Model resolution is T159L62 for the atmosphere and the ORCA1 grid for the ocean. The effect of the realistic LAI prescribed from observation is evaluated with respect to a control experiment where LAI does not vary. Results of the retrospective hindcast experiment demonstrate that a realistic representation of vegetation has a significant effect on evaporation, temperature and precipitation. An improvement of model sensitivity to vegetation variability on the <span class="hlt">seasonal</span> scale is also evidenced, especially during boreal winter. This may be attributed in particular to the effect of the high vegetation component on the snow cover.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESSD..11.5377D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESSD..11.5377D"><span id="translatedtitle">The skill of <span class="hlt">seasonal</span> ensemble low flow <span class="hlt">forecasts</span> for four different hydrological models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Demirel, M. C.; Booij, M. J.; Hoekstra, A. Y.</p> <p>2014-05-01</p> <p>This paper investigates the skill of 90 day low flow <span class="hlt">forecasts</span> using two conceptual hydrological models and two data-driven models based on Artificial Neural Networks (ANNs) for the Moselle River. One data-driven model, ANN-Indicator (ANN-I), requires historical inputs on precipitation (P), potential evapotranspiration (PET), groundwater (G) and observed discharge (Q), whereas the other data-driven model, ANN-Ensemble (ANN-E), and the two conceptual models, HBV and GR4J, use <span class="hlt">forecasted</span> meteorological inputs (P and PET), whereby we employ ensemble <span class="hlt">seasonal</span> meteorological <span class="hlt">forecasts</span>. We compared low flow <span class="hlt">forecasts</span> without any meteorological <span class="hlt">forecasts</span> as input (ANN-I) and five different cases of <span class="hlt">seasonal</span> meteorological forcing: (1) ensemble P and PET <span class="hlt">forecasts</span>; (2) ensemble P <span class="hlt">forecasts</span> and observed climate mean PET; (3) observed climate mean P and ensemble PET <span class="hlt">forecasts</span>; (4) observed climate mean P and PET and (5) zero P and ensemble PET <span class="hlt">forecasts</span> as input for the other three models (GR4J, HBV and ANN-E). The ensemble P and PET <span class="hlt">forecasts</span>, each consisting of 40 members, reveal the <span class="hlt">forecast</span> 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 four models are compared based on their skill of low flow <span class="hlt">forecasts</span> for varying lead times up to 90 days. Before <span class="hlt">forecasting</span>, 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 low flows using ensemble <span class="hlt">seasonal</span> meteorological forcing. The largest range for 90 day low flow <span class="hlt">forecasts</span> is found for the GR4J model when using ensemble <span class="hlt">seasonal</span> meteorological <span class="hlt">forecasts</span> as input. GR4J, HBV and ANN-E under-predicted 90 day ahead low flows in the very dry year 2003 without precipitation data, whereas ANN</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H32C..06S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H32C..06S"><span id="translatedtitle">Skill Assessment of National Multi-Model Ensemble <span class="hlt">Forecasts</span> for <span class="hlt">Seasonal</span> Drought Prediction in East Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shukla, S.; Hoell, A.; Roberts, J. B.; Funk, C. C.; Robertson, F. R.</p> <p>2014-12-01</p> <p>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 <span class="hlt">forecasts</span> at a <span class="hlt">seasonal</span> 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, <span class="hlt">seasonal</span> drought prediction in this region faces several challenges including lack of skillful <span class="hlt">seasonal</span> rainfall <span class="hlt">forecasts</span>. The National Multi-model Ensemble (NMME); a state-of-the-art dynamical climate <span class="hlt">forecast</span> system is potentially a promising tool for drought prediction in this region. The NMME incorporates climate <span class="hlt">forecasts</span> from 6+ fully coupled dynamical models resulting in 100+ <span class="hlt">forecasts</span> ensemble members. Recent studies have indicated that in general NMME offers improvement over <span class="hlt">forecasts</span> from any of the individual model. However, thus far the skill of NMME for <span class="hlt">forecasting</span> 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 <span class="hlt">forecasting</span> rainfall at <span class="hlt">seasonal</span> scale in East Africa for all three of the prominent <span class="hlt">seasons</span> 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 <span class="hlt">forecasts</span> to improve rainfall <span class="hlt">forecast</span> skill in the region when raw NMME <span class="hlt">forecasts</span> skill is lacking. This approach uses constructed analog method to improve NMME's March-April-May rainfall <span class="hlt">forecast</span> skill in East Africa.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H11F0923M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H11F0923M"><span id="translatedtitle">Decomposition of Sources of Errors in <span class="hlt">Seasonal</span> Streamflow <span class="hlt">Forecasting</span> over the US Sunbelt</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mazrooei, A.; Sinha, T.; Kumar, S.; Peters-Lidard, C. D.; Arumugam, S.</p> <p>2014-12-01</p> <p>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 <span class="hlt">seasonal</span> streamflow based on climate <span class="hlt">forecasts</span>. However, various sources of uncertainty in <span class="hlt">forecasting</span> streamflow pose significant challenges to utilize streamflow <span class="hlt">forecasts</span> in real time operations. In this study we systematically decompose various sources of errors in developing <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> from multiple Land Surface Models (LSMs) forced with downscaled and disaggregated climate <span class="hlt">forecasts</span>. The objectives of this study are: 1) Quantifying various sources of errors arising from each LSM, climate <span class="hlt">forecasts</span>, and downscaling/disaggregation techniques employed in developing streamflow <span class="hlt">forecasts</span>, and 2) Comparing the performance and the skill of different LSMs in streamflow <span class="hlt">forecasting</span> over selected target basins in the study area. First, three-month ahead precipitation <span class="hlt">forecasts</span> from ECHAM4.5 GCM for each <span class="hlt">season</span> 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 <span class="hlt">forecasts</span> and climatological forcings to develop retrospective <span class="hlt">seasonal</span> streamflow <span class="hlt">forecast</span> over the period of 20 years (1991-2010). Finally, the performance of different LSMs in <span class="hlt">forecasting</span> streamflow under different schemes were analyzed to quantify sources of errors and to validate <span class="hlt">forecasted</span> streamflow.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014EGUGA..1610155C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014EGUGA..1610155C&link_type=ABSTRACT"><span id="translatedtitle">Using <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> in a drought <span class="hlt">forecasting</span> system for water management: case-study of the Arzal dam in Brittany</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Crochemore, Louise; Ramos, Maria-Helena; Perrin, Charles; Penasso, Aldo</p> <p>2014-05-01</p> <p>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 <span class="hlt">season</span> 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 <span class="hlt">forecasts</span> 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 <span class="hlt">forecasting</span> system developed to <span class="hlt">forecast</span> low flows upstream the Arzal dam and based on a lumped hydrological model. Medium-range meteorological <span class="hlt">forecasts</span> from the ECMWF ensemble prediction system (51 scenarios up to 9 days ahead) are combined with <span class="hlt">seasonal</span> meteorological <span class="hlt">forecasts</span> also from ECMWF to provide extended streamflow <span class="hlt">forecasts</span> for the summer period. The performance of the <span class="hlt">forecasts</span> obtained by this method is compared with the performance of two benchmarks: (i) flow <span class="hlt">forecasts</span> obtained using an ensemble of past observed precipitation series as precipitation scenarios, i.e. without any use of <span class="hlt">forecasts</span> from meteorological models and (ii) flow <span class="hlt">forecasts</span> obtained using the <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> only, i.e. without medium-term information. First, the performance of ensemble <span class="hlt">forecasts</span> is evaluated and compared by means of probabilistic scores. Then, a risk</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014JMS...139..261M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014JMS...139..261M&link_type=ABSTRACT"><span id="translatedtitle">Modelling dinoflagellates as an approach to the <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> of bioluminescence in the North Atlantic</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Marcinko, Charlotte L. J.; Martin, Adrian P.; Allen, John T.</p> <p>2014-11-01</p> <p>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 <span class="hlt">forecasting</span> surface ocean bioluminescence in the open ocean, a simple ecological model is developed which simulates <span class="hlt">seasonal</span> changes in dinoflagellate abundance. How <span class="hlt">forecasting</span> <span class="hlt">seasonal</span> 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 <span class="hlt">forecasting</span> BPOT through explicitly modelling the population dynamics of a prolific bioluminescent phylum. The model developed here offers a promising platform for the future operational <span class="hlt">forecasting</span> of the broad temporal changes in bioluminescence within the North Atlantic. Such <span class="hlt">forecasting</span> of <span class="hlt">seasonal</span> patterns could provide valuable information for the targeting of scientific field campaigns.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013HESS...17.2359D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013HESS...17.2359D"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasts</span> of droughts in African basins using the Standardized Precipitation Index</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dutra, E.; Di Giuseppe, F.; Wetterhall, F.; Pappenberger, F.</p> <p>2013-06-01</p> <p>Vast parts of Africa rely on the rainy <span class="hlt">season</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> 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 <span class="hlt">forecasted</span> fields as provided by the ECMWF <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system. The <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the <span class="hlt">seasonal</span> <span class="hlt">forecast</span> can be used for monitoring (first month of <span class="hlt">forecast</span>). Furthermore</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H21C1193S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H21C1193S"><span id="translatedtitle">Decomposition of Sources of Errors in <span class="hlt">Seasonal</span> Streamflow <span class="hlt">Forecasts</span> in a Rainfall-Runoff Dominated Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sinha, T.; Arumugam, S.</p> <p>2012-12-01</p> <p><span class="hlt">Seasonal</span> streamflow <span class="hlt">forecasts</span> contingent on climate <span class="hlt">forecasts</span> can be effectively utilized in updating water management plans and optimize generation of hydroelectric power. Streamflow in the rainfall-runoff dominated basins critically depend on <span class="hlt">forecasted</span> precipitation in contrast to snow dominated basins, where initial hydrological conditions (IHCs) are more important. Since precipitation <span class="hlt">forecasts</span> from Atmosphere-Ocean-General Circulation Models are available at coarse scale (~2.8° by 2.8°), spatial and temporal downscaling of such <span class="hlt">forecasts</span> are required to implement land surface models, which typically runs on finer spatial and temporal scales. Consequently, multiple sources are introduced at various stages in predicting <span class="hlt">seasonal</span> streamflow. Therefore, in this study, we addresses the following science questions: 1) How do we attribute the errors in monthly streamflow <span class="hlt">forecasts</span> to various sources - (i) model errors, (ii) spatio-temporal downscaling, (iii) imprecise initial conditions, iv) no <span class="hlt">forecasts</span>, and (iv) imprecise <span class="hlt">forecasts</span>? and 2) How does monthly streamflow <span class="hlt">forecast</span> errors propagate with different lead time over various <span class="hlt">seasons</span>? In this study, the Variable Infiltration Capacity (VIC) model is calibrated over Apalachicola River at Chattahoochee, FL in the southeastern US and implemented with observed 1/8° daily forcings to estimate reference streamflow during 1981 to 2010. The VIC model is then forced with different schemes under updated IHCs prior to <span class="hlt">forecasting</span> period to estimate relative mean square errors due to: a) temporally disaggregation, b) spatial downscaling, c) Reverse Ensemble Streamflow Prediction (imprecise IHCs), d) ESP (no <span class="hlt">forecasts</span>), and e) ECHAM4.5 precipitation <span class="hlt">forecasts</span>. Finally, error propagation under different schemes are analyzed with different lead time over different <span class="hlt">seasons</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.4481W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.4481W"><span id="translatedtitle">On the impact of stochastic parametrisations in the ECMWF <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic</p> <p>2014-05-01</p> <p><span class="hlt">Seasonal</span> climate predictions several months ahead based on dynamical atmosphere-ocean GCMs are part of the routinely operational <span class="hlt">forecasts</span> issued by the European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> (ECMWF). Here, the <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system is a seamless extension of ECMWF's medium-range ensemble weather <span class="hlt">forecasting</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span>. 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> has improved due to the stochastic parametrisations. Both schemes substantially increase the ensemble spread for El Niño SST <span class="hlt">forecasts</span> and thus make the ensemble <span class="hlt">forecasting</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=climate+AND+area&pg=2&id=EJ923200','ERIC'); return false;" href="http://eric.ed.gov/?q=climate+AND+area&pg=2&id=EJ923200"><span id="translatedtitle">Constraints and Suggestions in Adopting <span class="hlt">Seasonal</span> Climate <span class="hlt">Forecasts</span> by Farmers in South India</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Shankar, K. Ravi; Nagasree, K.; Venkateswarlu, B.; Maraty, Pochaiah</p> <p>2011-01-01</p> <p>The main objective of this study was to determine constraints and suggestions of farmers towards adopting <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span>. It addresses the question: Which forms of providing <span class="hlt">forecasts</span> will be helpful to farmers in agricultural decision making? For the study, farmers were selected from Andhra Pradesh state of South India. One hundred…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=213342','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=213342"><span id="translatedtitle">Reductions in <span class="hlt">seasonal</span> climate <span class="hlt">forecast</span> dependability as a result of downscaling</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>This research determines whether NOAA/CPC <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> dependability and a factor accounting for l...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1814745F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1814745F"><span id="translatedtitle">Modelled <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> of snow water equivalent and runoff in alpine catchments</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Förster, Kristian; Hanzer, Florian; Schöber, Johannes; Huttenlau, Matthias; Achleitner, Stefan; Strasser, Ulrich</p> <p>2016-04-01</p> <p><span class="hlt">Seasonal</span> <span class="hlt">forecasts</span> of water balance components are becoming increasingly important for hydrological applications. These <span class="hlt">forecasts</span> are typically derived from coupled atmosphere-ocean climate models, which enable physically based <span class="hlt">seasonal</span> <span class="hlt">forecasts</span>. In mountainous regions, however, topography is complex whilst typical spatial resolutions of the climate models are still comparably coarse, i.e in the data, ridges and valleys are not represented with sufficient accuracy. Therefore, <span class="hlt">seasonal</span> predictions of atmospheric variables require consideration of representative gradients. We present first results of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> and re-<span class="hlt">forecasts</span> processed by the NCEP (National Centers for Environmental Prediction) Climate <span class="hlt">Forecast</span> System version 2 (CFSv2). These are prepared for monthly time steps in order to be used for ensemble runs of water balance simulation using the Alpine Water balance And Runoff Estimation model (AWARE). This model has been designed for monthly <span class="hlt">seasonal</span> predictions in ice- and snowmelt dominated catchments. The study area is the Inn catchment in Tyrol/Austria, including its headwaters in Switzerland. Results are evaluated for both anomalies of meteorological input data (temperature and precipitation), as well as balance components including snow water equivalent and runoff, both simulated with AWARE. Based on model skill evaluations derived from <span class="hlt">forecasts</span> and observations, the model chain CFSv2 - AWARE proves helpful to analyse possible future hydrological system states of mountainous catchments with emphasis on spatio-temporal snow cover evolution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015ERL....10d4005M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015ERL....10d4005M&link_type=ABSTRACT"><span id="translatedtitle">Demonstration of successful malaria <span class="hlt">forecasts</span> for Botswana using an operational <span class="hlt">seasonal</span> climate model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>MacLeod, Dave A.; Jones, Anne; Di Giuseppe, Francesca; Caminade, Cyril; Morse, Andrew P.</p> <p>2015-04-01</p> <p>The severity and timing of <span class="hlt">seasonal</span> malaria epidemics is strongly linked with temperature and rainfall. Advance warning of meteorological conditions from <span class="hlt">seasonal</span> 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 <span class="hlt">seasonal</span> climate model from the European Centre for Medium-Range Weather <span class="hlt">Forecasts</span>. 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 <span class="hlt">forecasts</span>. <span class="hlt">Forecast</span> skill is demonstrated for upper tercile malaria incidence for the Botswana malaria <span class="hlt">season</span> (January-May), using <span class="hlt">forecasts</span> issued at the start of November; the <span class="hlt">forecast</span> system anticipates six out of the seven upper tercile malaria <span class="hlt">seasons</span> in the observational period. The length of the validation time series gives confidence in the conclusion that it is possible to make reliable <span class="hlt">forecasts</span> of <span class="hlt">seasonal</span> malaria risk, forming a key part of a health early warning system for Botswana and contributing to efforts to adapt to climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H11F0915A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H11F0915A"><span id="translatedtitle">Improved Water and Energy Management Utilizing <span class="hlt">Seasonal</span> to Interannual Hydroclimatic <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Arumugam, S.; Lall, U.</p> <p>2014-12-01</p> <p><span class="hlt">Seasonal</span> to interannual climate <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> to allocate water for uses with pre-specified reliabilities. To ensure that the system needs are not compromised due to <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span>, (c) significance of streamflow <span class="hlt">forecasts</span> in promoting interbasin transfers and (d) scope for developing power demand <span class="hlt">forecasts</span> utilizing temperature <span class="hlt">forecasts</span>. Potential for developing <span class="hlt">seasonal</span> nutrient <span class="hlt">forecasts</span> using climate <span class="hlt">forecasts</span> 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 <span class="hlt">Forecasts</span> and Water Management" will also be summarized.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H11F0920W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H11F0920W"><span id="translatedtitle">Assessing the skill of <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> of streamflow and drought in the Limpopo basin, Southern Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Werner, M.; Trambauer, P.; Winsemius, H.; Maskey, S.; Dutra, E. N.; Pappenberger, F.</p> <p>2014-12-01</p> <p>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 <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> streamflow and derived drought indices in the basin. The first applies the ECMWF <span class="hlt">seasonal</span> <span class="hlt">forecast</span> system (S4), an operational global atmospheric model that provides <span class="hlt">seasonal</span> ensemble <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> skill over the wet <span class="hlt">season</span> which is the most relevant to water users in the basin. Comparison of the skill of the three <span class="hlt">forecasting</span> approaches in <span class="hlt">forecasting</span> drought indices and streamflow shows that S4 is moderately skilful at the lead times up to 3-5 months. The ESP <span class="hlt">forecasts</span> are skilful when compared to climatology, but only for the short lead times, and the skill decays rapidly with lead time. <span class="hlt">Forecasts</span> based on the conditional ESP ensemble have improved skill when compared to ESP, though S4 <span class="hlt">forecasts</span> remain superior. Through exploring drought indices that are used by reservoir operators in determining curtailments to water users we show how the <span class="hlt">forecasts</span> can be meaningful to reservoir operators and irrigators in the basin.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1510526B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1510526B"><span id="translatedtitle">Recalibration of CFS <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> using statistical techniques for bias correction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bliefernicht, Jan; Laux, Patrick; Siegmund, Jonatan; Kunstmann, Harald</p> <p>2013-04-01</p> <p>The development and application of statistical techniques with a special focus on a recalibration of meteorological or hydrological <span class="hlt">forecasts</span> to eliminate the bias between <span class="hlt">forecasts</span> and observations has received a great deal of attention in recent years. One reason is that retrospective <span class="hlt">forecasts</span> are nowadays available which allows for a proper training and validation of this kind of techniques. The objective of this presentation is to propose several statistical techniques with different degree of complexity and to evaluate and compare their performance for a recalibration of <span class="hlt">seasonal</span> ensemble <span class="hlt">forecasts</span> of monthly precipitation. The techniques selected in this study range from straightforward normal score and quantile-quantile transformation, local scaling, to more sophisticated and novel statistical techniques such as Copula-based methodology recently proposed by Laux et al. (2011). The <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> are derived from the Climate <span class="hlt">Forecast</span> System Version 2. This version is the current coupled ocean-atmosphere general circulation model of the U.S. National Centers for Environmental Prediction used to provide <span class="hlt">forecasts</span> up to nine months. The CFS precipitation <span class="hlt">forecasts</span> are compared to monthly precipitation observations from the Global Precipitation Climatology Centre. The statistical techniques are tested for semi-arid regions in West Africa and the Indian subcontinent focusing on large-scale river basins such as the Ganges and the Volta basin. In both regions <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> are a crucial source of information for the prediction of hydro-meteorological extremes, in particular for droughts. The evaluation is done using retrospective CFS ensemble <span class="hlt">forecast</span> from 1982 to 2009. The training of the statistical techniques is done in a cross-validation mode. The outcome of this investigation illustrates large systematic differences between <span class="hlt">forecasts</span> and observations, in particular for the Volta basin in West Africa. The selection of straightforward</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70155250','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70155250"><span id="translatedtitle">A <span class="hlt">seasonal</span> agricultural drought <span class="hlt">forecast</span> system for food-insecure regions of East Africa</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Shukla, Shraddhanand; McNally, Amy; Husak, Gregory; Funk, Christopher C.</p> <p>2014-01-01</p> <p> 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 <span class="hlt">seasonal</span> agricultural drought <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> agricultural drought <span class="hlt">forecast</span> system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this <span class="hlt">forecast</span> system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing <span class="hlt">season</span>. 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 <span class="hlt">season</span> our <span class="hlt">forecast</span> system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming <span class="hlt">season</span>. 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 <span class="hlt">forecasting</span> system with hindcast runs (1993–2012). We found that initializing SM <span class="hlt">forecasts</span> with start-of-<span class="hlt">season</span> (5 March) SM conditions resulted in useful SM <span class="hlt">forecast</span> skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the <span class="hlt">forecast</span> was initialized with mid-<span class="hlt">season</span> (i.e. 5 April) SM conditions the skill until the end-of-<span class="hlt">season</span> improved. This shows that early-<span class="hlt">season</span> rainfall</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESSD..11.3049S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESSD..11.3049S"><span id="translatedtitle">A <span class="hlt">seasonal</span> agricultural drought <span class="hlt">forecast</span> system for food-insecure regions of East Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shukla, S.; McNally, A.; Husak, G.; Funk, C.</p> <p>2014-03-01</p> <p>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 <span class="hlt">seasonal</span> agricultural drought <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> agricultural drought <span class="hlt">forecast</span> system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's science team. We evaluate this <span class="hlt">forecast</span> system for a region of equatorial EA (2° S to 8° N, and 36° to 46° E) for the March-April-May growing <span class="hlt">season</span>. 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 <span class="hlt">season</span> our <span class="hlt">forecast</span> system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios for the upcoming <span class="hlt">season</span>. 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 <span class="hlt">forecasting</span> system with hindcast runs (1993-2012). We found that initializing SM <span class="hlt">forecasts</span> with start-of-<span class="hlt">season</span> (5 March) SM conditions resulted in useful SM <span class="hlt">forecast</span> skill (> 0.5 correlation) at 1-month, and in some cases at 3 month lead times. Similarly, when the <span class="hlt">forecast</span> was initialized with mid-<span class="hlt">season</span> (i.e. 5 April) SM conditions the skill until the end-of-<span class="hlt">season</span> improved. This shows that early-<span class="hlt">season</span> rainfall is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMGC11D1032C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMGC11D1032C"><span id="translatedtitle"><span class="hlt">Seasonal</span> Rainfall <span class="hlt">Forecasting</span> Using SST Dipoles with Application to the Southeast US</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, C.; Georgakakos, A. P.</p> <p>2012-12-01</p> <p>Advances in <span class="hlt">seasonal</span> 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 <span class="hlt">forecasting</span> method for <span class="hlt">seasonal</span> rainfall. The <span class="hlt">forecasting</span> process first identifies relevant SST dipole predictors for <span class="hlt">seasonal</span> rainfall through an optimization algorithm based on the Gerrity Skill Score. The resulting <span class="hlt">forecasts</span> are cross-validated, and a composite of the most significant SST dipole predictors are identified to generate rainfall <span class="hlt">forecasts</span> in each <span class="hlt">season</span>. Finally, for a target year, ensemble year-round prediction traces as well as uncertainty intervals can be produced by superimposing hindcasting errors on <span class="hlt">seasonal</span> rainfall predictions. These <span class="hlt">forecasts</span> 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 <span class="hlt">forecasting</span> methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESS...18.3907S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESS...18.3907S"><span id="translatedtitle">A <span class="hlt">seasonal</span> agricultural drought <span class="hlt">forecast</span> system for food-insecure regions of East Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shukla, S.; McNally, A.; Husak, G.; Funk, C.</p> <p>2014-10-01</p> <p>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 <span class="hlt">seasonal</span> agricultural drought <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> agricultural drought <span class="hlt">forecast</span> system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's (FEWS NET) science team. We evaluate this <span class="hlt">forecast</span> system for a region of equatorial EA (2° S-8° N, 36-46° E) for the March-April-May (MAM) growing <span class="hlt">season</span>. 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 <span class="hlt">forecast</span> system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios describing the upcoming <span class="hlt">season</span>. 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 <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> with start-of-<span class="hlt">season</span> (SOS) (5 March) SM conditions resulted in useful SM <span class="hlt">forecast</span> skill (> 0.5 correlation) at 1-month and, in some cases, 3-month lead times. Similarly, when the <span class="hlt">forecast</span> was initialized with midseason (i.e., 5</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H11F1121C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H11F1121C"><span id="translatedtitle">Incorporating <span class="hlt">Seasonal</span> <span class="hlt">Forecast</span> of Inflow into Existing Water Resource Management at Ubolratana Dam, Thailand</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chatikavanij, V.; Block, P. J.; Lall, U.</p> <p>2011-12-01</p> <p><span class="hlt">Seasonal</span> <span class="hlt">forecast</span> of streamflow, coupled with dynamic reservoir operation policy, has been shown to improve water allocation and flood control. By considering current and expected future reservoir level, water managers can make more adaptive and informed judgments about reservoir operation and allocation policy. While <span class="hlt">seasonal</span> <span class="hlt">forecast</span> application has shown substantial positive results, its adoption is still lagging due to the difficulties in integrating <span class="hlt">forecast</span> into the current reservoir management system. This project offers a simple framework for incorporating <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> into existing operation to optimize reservoir management decisions at the Ubolratana Dam in the Northeastern region of Thailand. The framework allows for the retention of existing reservoir policy while altering the application of the upper rule curve based on <span class="hlt">forecast</span> information. Our objective is to maximize water release for hydropower generation, minimize spill and shortages while meeting demands for irrigation and water quality control during the peak summer monsoon <span class="hlt">season</span>. Climate variables, including sea-surface temperature and sea-level pressure in March-May, are used to develop streamflow <span class="hlt">forecast</span> ensembles for September-November. We use a dynamically linked system of <span class="hlt">forecast</span> and reservoir management models to specify operating rules for end of the month storage levels at the Ubolratana Reservoir. Benefits and reliability based on the <span class="hlt">forecast</span> ensembles are compared with historical operations and a climatological-based approach. <span class="hlt">Forecast</span> ensembles are also divided into terciles to evaluate performance during dry and wet years, of particular interest to water managers. Results demonstrate that the dynamic operations contingent on <span class="hlt">forecasts</span> may increase water releases for hydropower, decrease spill and deficit, and improve reliability compared with the status quo.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AdAtS..30.1343S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AdAtS..30.1343S&link_type=ABSTRACT"><span id="translatedtitle">Statistical guidance on <span class="hlt">seasonal</span> <span class="hlt">forecast</span> of Korean dust days over South Korea in the springtime</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sohn, Keon Tae</p> <p>2013-09-01</p> <p>This study aimed to develop the <span class="hlt">seasonal</span> <span class="hlt">forecast</span> models of Korean dust days over South Korea in the springtime. <span class="hlt">Forecast</span> mode was a ternary <span class="hlt">forecast</span> (below normal, normal, above normal) which was classified based on the mean and the standard deviation of Korean dust days for a period of 30 years (1981-2010). In this study, we used three kinds of monthly data: the Korean dust days observed in South Korea, the National Center for Environmental Prediction in National Center for Atmospheric Research (NCEP/NCAR) reanalysis data for meteorological factors over source regions of Asian dust, and the large-scale climate indices offered from the Climate Diagnostic Center and Climate Prediction Center in NOAA. <span class="hlt">Forecast</span> guidance consisted of two components; ordinal logistic regression model to generate trinomial distributions, and conversion algorithm to generate ternary <span class="hlt">forecast</span> by two thresholds. <span class="hlt">Forecast</span> guidance was proposed for each month separately and its predictability was evaluated based on skill scores.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.2456S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.2456S"><span id="translatedtitle"><span class="hlt">Seasonal</span> drought <span class="hlt">forecast</span> system for food-insecure regions of East Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shukla, Shraddhanand; McNally, Amy; Husak, Greg; Funk, Chris</p> <p>2014-05-01</p> <p>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 <span class="hlt">seasonal</span> agricultural drought <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> drought <span class="hlt">forecast</span> system that is being used for providing <span class="hlt">seasonal</span> 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 <span class="hlt">season</span> 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 <span class="hlt">Forecast</span> System Reanalysis), state-of-the-art dynamical climate <span class="hlt">forecast</span> system (NCEP's Climate <span class="hlt">Forecast</span> System Verison-2) and large scale land surface models (e.g. Variable Infiltration Capacity, NASA's Land Information System) to provide <span class="hlt">forecasts</span> of <span class="hlt">seasonal</span> rainfall, soil moisture and Water Requirement Satisfaction Index (WRSI) throughout the <span class="hlt">season</span> - with an emphasis on times when water is the most critical: start of <span class="hlt">season</span>/planting and the mid-<span class="hlt">season</span>/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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFM.H11F0928N&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFM.H11F0928N&link_type=ABSTRACT"><span id="translatedtitle">Evaluating National Weather Service <span class="hlt">Seasonal</span> <span class="hlt">Forecast</span> Products in Reservoir Operation Case Studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nielson, A.; Guihan, R.; Polebistki, A.; Palmer, R. N.; Werner, K.; Wood, A. W.</p> <p>2014-12-01</p> <p><span class="hlt">Forecasts</span> 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 <span class="hlt">forecast</span> products are most informative and useful for optimized water management. This study incorporates several reforecast products provided by the Colorado Basin River <span class="hlt">Forecast</span> Center (CBRFC) which allows a complete retrospective analysis of climate <span class="hlt">forecasts</span>, resulting in an evaluation of each product's skill in the context of water resources management. The accuracy and value of <span class="hlt">forecasts</span> generated from the Climate <span class="hlt">Forecast</span> 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 <span class="hlt">forecasts</span> rather than using all of the historic climate record as being equally probable. The role of <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span>. 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 <span class="hlt">seasonal</span> operational nuances, it is difficult to identify <span class="hlt">forecast</span> improvements in meaningful ways. These metrics of system performance are compared using the different <span class="hlt">forecast</span> products to evaluate the potential benefits of using CFSv2 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> in systems decision making.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140005780','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140005780"><span id="translatedtitle">Evaluating NMME <span class="hlt">Seasonal</span> <span class="hlt">Forecast</span> Skill for use in NASA SERVIR Hub Regions</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Roberts, J. Brent; Roberts, Franklin R.</p> <p>2013-01-01</p> <p>The U.S. National Multi-Model Ensemble <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system is providing hindcast and real-time data streams to be used in assessing and improving <span class="hlt">seasonal</span> predictive capacity. The coupled <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for <span class="hlt">seasonal</span> <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> as a function of <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.2661F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.2661F"><span id="translatedtitle">Application of Medium and <span class="hlt">Seasonal</span> Flood <span class="hlt">Forecasts</span> for Agriculture Damage Assessment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fakhruddin, Shamsul; Ballio, Francesco; Menoni, Scira</p> <p>2015-04-01</p> <p>Early warning is a key element for disaster risk reduction. In recent decades, major advancements have been made in medium range and <span class="hlt">seasonal</span> flood <span class="hlt">forecasting</span>. 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 <span class="hlt">seasonal</span> (20-25 days) flood <span class="hlt">forecasting</span> model has been developed for Thailand and Bangladesh. It provides 51 sets of discharge ensemble <span class="hlt">forecasts</span> of 1-10 days with significant persistence and high certainty and qualitative outlooks for 20-25 days. This type of <span class="hlt">forecast</span> could assist farmers and other stakeholders for differential preparedness activities. These ensembles probabilistic flood <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> flood <span class="hlt">forecasts</span> in a way that is not commonly practiced globally today.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1510168S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1510168S"><span id="translatedtitle">Statistical evaluation of CFS <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> for large-scale droughts in Africa and India</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Siegmund, Jonatan; Bliefernicht, Jan; Laux, Patrick; Kunstmann, Harald</p> <p>2013-04-01</p> <p>Monthly and <span class="hlt">seasonal</span> meteorological <span class="hlt">forecasts</span> 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 <span class="hlt">forecasting</span> 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 <span class="hlt">Forecast</span> System (CFS) Version 2, became operational providing real-time ensemble <span class="hlt">forecasts</span> up to nine months. However, a comprehensive analysis of the CFS <span class="hlt">forecast</span> for the prediction of droughts in water stress regions has not yet been performed. In this study we evaluate the CFS precipitation <span class="hlt">forecasts</span> for large-scale droughts that occurred during the rainy <span class="hlt">season</span> 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 <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> demonstrates a low accuracy. Furthermore, the <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> of monthly precipitation are characterized by a large over- and underestimation during the rainy <span class="hlt">season</span> depending on the target region. In this presentation, the following issues are highlighted: (i) The performance of the CFS precipitation <span class="hlt">forecast</span> for individual events such as the severe India drought in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESS...18.1525W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESS...18.1525W"><span id="translatedtitle">The potential value of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> in a changing climate in southern Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Winsemius, H. C.; Dutra, E.; Engelbrecht, F. A.; Archer Van Garderen, E.; Wetterhall, F.; Pappenberger, F.; Werner, M. G. F.</p> <p>2014-04-01</p> <p>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 <span class="hlt">seasonal</span> rainfall, anomalous onsets and lengths of the rainy <span class="hlt">season</span> 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 <span class="hlt">forecasting</span> 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 <span class="hlt">seasonal</span> meteorological <span class="hlt">forecasts</span> of the European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> (ECMWF) <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> 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, <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1049659','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/1049659"><span id="translatedtitle">Value of medium range weather <span class="hlt">forecasts</span> in the improvement of <span class="hlt">seasonal</span> hydrologic prediction skill</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Shukla, Shraddhanand; Voisin, Nathalie; Lettenmaier, D. P.</p> <p>2012-08-15</p> <p>We investigated the contribution of medium range weather <span class="hlt">forecasts</span> with lead times up to 14 days to <span class="hlt">seasonal</span> 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 <span class="hlt">forecasts</span> of monthly runoff and soil moisture (SM) at lead-1 (first month of the <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day <span class="hlt">forecast</span>) or by a deterministic 14-day weather <span class="hlt">forecast</span>. We used Spearman rank correlations of <span class="hlt">forecasts</span> and observations as the <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> skill at lead-1 to -3 months can be obtained by exploiting medium range weather <span class="hlt">forecast</span> skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather <span class="hlt">forecasts</span>, for runoff (SM) <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span>, although some improvement in SM was achieved at lead-2.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H12A..01B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H12A..01B"><span id="translatedtitle">Conditional Monthly Weather Resampling Procedure for Operational <span class="hlt">Seasonal</span> Water Resources <span class="hlt">Forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.</p> <p>2013-12-01</p> <p>To provide reliable and accurate <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> 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 <span class="hlt">forecaster</span> may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP <span class="hlt">forecast</span>, 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 <span class="hlt">forecast</span>. 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> system for the Columbia River basin operated by the Bonneville Power Administration. The <span class="hlt">forecast</span> skill of the k-nn resampler was tested against the original ESP for three basins at the long-range <span class="hlt">seasonal</span> time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> at the <span class="hlt">seasonal</span> time scale. Further improvement is possible by fine tuning the method and selecting the most</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUSM.U34B..04S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUSM.U34B..04S"><span id="translatedtitle">The Utility of <span class="hlt">Seasonal</span> Climate <span class="hlt">Forecasts</span>: Understanding Argentine Farmers' Attribute Priorities and Trade-Offs</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Seipt, E. C.; Easterling, W. E.</p> <p>2007-05-01</p> <p>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. <span class="hlt">Seasonal</span> climate <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> climate <span class="hlt">forecast</span> (i.e., mode of distribution, spatial resolution, lead time, and <span class="hlt">forecast</span> performance) when judging its utility. A conjoint analysis evaluation decomposes holistic evaluations of <span class="hlt">forecasts</span> into the part-worth utilities associated with their different attributes. Part-worth utilities combine to reveal the structure of farmers' <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> utility. Attribute trade-off values suggest that advances in spatial resolution, <span class="hlt">forecast</span> performance, and/or product dissemination via the Internet offer the greatest potential for increasing the utility of future <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> for farmers in the Pampas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUSM.A43B..04D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUSM.A43B..04D"><span id="translatedtitle">Pros and Cons of 1-tiered versus 2-tiered <span class="hlt">Seasonal</span> <span class="hlt">Forecast</span> Systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dewitt, D. G.; Goddard, L.; Li, S.</p> <p>2005-05-01</p> <p>All reasonable <span class="hlt">seasonal</span> <span class="hlt">forecast</span> systems have advantages and disadvantages.Some advantages/disadvantages may be theoretical; others may be practical. A clear understanding of where the limitations of a particular <span class="hlt">forecast</span> system lie is helpful in making the most of the tool(s) in hand. In this presentation we examine the good, the bad and the ugly in both 1-tiered <span class="hlt">forecast</span> systems (i.e. coupled ocean-atmosphere general circulation models or CGCMs) and 2-tiered <span class="hlt">forecast</span> systems (i.e. atmospheric general circulation models or AGCMs). AGCMs are potentially hindered by unphysical air-sea fluxes in the mid-latitudes and warm pool regions, where the observations suggest that the atmosphere forces changes in the ocean, rather than the other way around. In CGCMs the ocean and atmosphere evolve harmoniously, but that is no guarantee that their air-sea fluxes are correct.And indeed, CGCMs have problems with climate drift and large-scale systematic biases because of difficulties in getting the proper air-sea fluxes. To what extent these physical limitations limit skill in <span class="hlt">seasonal</span> climate prediction will be presented. Suggestions will be offered for how one might capitalize on the strengths of both types of dynamical <span class="hlt">forecast</span> systems, while minimizing the weaknesses, in constructing a <span class="hlt">seasonal</span> climate <span class="hlt">forecast</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A51H3141D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A51H3141D"><span id="translatedtitle">Application of <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> in agricultural crop monitoring in Brazil</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>de Avila, A. M. H.; Pereira, V. R.; Lopes, F. A.</p> <p>2014-12-01</p> <p>This work is investigating the contribution of <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span>. 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 <span class="hlt">seasonal</span> <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> averages and the observed precipitation standard deviation. The Skill Score Climatology (SSC) was used to compare the accuracy between the <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> model. This behavior indicates that the climatological analysis is more accurate to predict the monthly climate than the ETA model <span class="hlt">forecast</span>. Also the consecutive negative bias was observed in some locations that can be corrected removing the systematic error.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1817111O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1817111O"><span id="translatedtitle">Probabilistic maize yield simulation over East Africa using ensemble <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ogutu, Geoffrey; Supit, Iwan; Hutjes, Ronald</p> <p>2016-04-01</p> <p><span class="hlt">Seasonal</span> climate variability influences crop yields, especially in areas where rain fed agriculture is widely practiced such as in the East African region. Assuming that <span class="hlt">seasonal</span> climate prediction skill would translate to similarly skillful prediction of impacts, an ensemble <span class="hlt">seasonal</span> climate hindcast (ECMWF system4 EPS) for the period 1981 to 2010 at different initialization dates (lead months) before sowing is used to drive a crop simulation model: the World Food Studies (WOFOST) model, implemented for a single <span class="hlt">season</span> Maize crop. The water-limited yield predictions were assessed against reference yields produced by the same crop model forced by the WATCH Forcing Data ERA-Interim (WFDEI) at grid point level. We focus on the main sowing dates of June/July (Northern region), March (Equatorial East Africa) and November (Southern region). Deviation of yields from the mean over the study period is used to indicate regions in which probabilistic yield <span class="hlt">forecasts</span> would be useful while the Relative Operating Curve Skill Score (ROCSS) indicates prediction skill of above normal, near normal and below normal yield prediction. Equatorial regions of East Africa and coastal Kenya with sowing date in March show a mean deviation of ≥ 500 Kg/ha. Here probabilistic yield <span class="hlt">forecasts</span> can potentially be useful as opposed to the northern and southern regions with less deviation. The high deviation in this region may also be due to the existence of more than one cropping <span class="hlt">season</span> corresponding to the bi-modal rainfall regime since the model only simulates a single <span class="hlt">season</span>. A positive ROCSS over a large extent of the equatorial region show predictability skill of all the tercile <span class="hlt">forecasts</span> simulated by <span class="hlt">forecasts</span> initialized at the start of sowing date (March i.e. lead 0 <span class="hlt">forecasts</span>) and no predictability at longer lead months. Over Ethiopia in the northern region of East Africa, November harvests with a sowing date of June show predictability of the upper, lower and middle terciles at lead-0</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012GeoRL..3923707J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012GeoRL..3923707J"><span id="translatedtitle">Skill of ENSEMBLES <span class="hlt">seasonal</span> re-<span class="hlt">forecasts</span> for malaria prediction in West Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jones, A. E.; Morse, A. P.</p> <p>2012-12-01</p> <p>This study examines the performance of malaria-relevant climate variables from the ENSEMBLES <span class="hlt">seasonal</span> ensemble re-<span class="hlt">forecasts</span> for sub-Saharan West Africa, using a dynamic malaria model to transform temperature and rainfall <span class="hlt">forecasts</span> into simulated malaria incidence and verifying these <span class="hlt">forecasts</span> against simulations obtained by driving the malaria model with General Circulation Model-derived reanalysis. Two subregions of <span class="hlt">forecast</span> skill are identified: the highlands of Cameroon, where low temperatures limit simulated malaria during the <span class="hlt">forecast</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.1724P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.1724P"><span id="translatedtitle">Identification of the drivers controlling the <span class="hlt">seasonal</span> hydrological <span class="hlt">forecasting</span> skill in Europe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pechlivanidis, Ilias; Spångmyr, Henrik; Bosshard, Thomas</p> <p>2016-04-01</p> <p>: Recent advances in understanding and <span class="hlt">forecasting</span> of climate have led into skilful meteorological predictions, which can consequently increase the confidence of hydrological prognosis. There is currently a need to understand the large European river systems and make practical use of <span class="hlt">seasonal</span> hydrological <span class="hlt">forecasts</span>. In here, we analyse the <span class="hlt">seasonal</span> predictive skill along Europe's hydro-climatic gradient using the pan-European E-HYPE v3.0 multi-basin hydrological model. Both model state initialisation (level in surface water, i.e. reservoirs, lakes and wetlands, soil moisture, snow depth) and provision of climatology are based on forcing input derived from the WFDEI product for the period 1979-2010. An ensemble of re-<span class="hlt">forecast</span> forcing data (daily mean precipitation and temperature for the period 1981-2010) from ECMWF's System 4 (15 members initialised every month) are firstly bias corrected using a modified version of the Distribution Based Scaling (DBS) method to account for drifting conditioning the bias correction on the lead month, and further used to drive E-HYPE. The predictive skill of river runoff based on ECMWF and climatology for the European basins is assessed on monthly timescales. <span class="hlt">Seasonal</span> re-<span class="hlt">forecasts</span> are evaluated with respect to their accuracy against impact variables, i.e. streamflow, at different space and time-scales; the value of the predictions is assessed using various (deterministic and probabilistic) performance metrics. We analyse the skill across the about 35000 subbasins which represent various climatologies, soil-types, land uses, altitudes and basin scales within Europe. We finally use the Classification and Regression Trees (CART) analysis to link the gain in the <span class="hlt">seasonal</span> skill to physiographic-climatic characteristics and meteorological skill, in order to suggest possible model improvements. Keywords: <span class="hlt">Seasonal</span> hydrological <span class="hlt">forecasting</span>; E-HYPE; ensemble <span class="hlt">seasonal</span> <span class="hlt">forecasts</span>; pan-European scale</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014ClDy...42.1449L&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014ClDy...42.1449L&link_type=ABSTRACT"><span id="translatedtitle">Global <span class="hlt">seasonal</span> climate predictability in a two tiered <span class="hlt">forecast</span> system. Part II: boreal winter and spring <span class="hlt">seasons</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Haiqin; Misra, Vasubandhu</p> <p>2014-03-01</p> <p>We examine the Florida Climate Institute-Florida State University <span class="hlt">Seasonal</span> Hindcast (FISH50) skill at a relatively high (50 km grid) resolution two tiered Atmospheric General Circulation Model (AGCM) for boreal winter and spring <span class="hlt">seasons</span> at zero and one <span class="hlt">season</span> lead respectively. The AGCM in FISH50 is forced with bias corrected <span class="hlt">forecast</span> sea surface temperature averaged from two dynamical coupled ocean-atmosphere models. The comparison of the hindcast skills of precipitation and surface temperature from FISH50 with the coupled ocean-atmosphere models reveals that the probabilistic skill is nearly comparable in the two types of <span class="hlt">forecast</span> systems (with some improvements in FISH50 outside of the global tropics). Furthermore the drop in skill in going from zero lead (boreal winter) to one <span class="hlt">season</span> lead (boreal spring) is also similar in FISH50 and the coupled ocean-atmosphere models. Both the <span class="hlt">forecast</span> systems also show that surface temperature hindcasts have more skill than the precipitation hindcasts and that land based precipitation hindcasts have slightly lower skill than the corresponding hindcasts over the ocean.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013EGUGA..15.1084C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013EGUGA..15.1084C&link_type=ABSTRACT"><span id="translatedtitle">An analysis of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> from POAMA and SCOPIC in the Pacific region</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cottrill, Andrew; Charles, Andrew; Kuleshov, Yuriy</p> <p>2013-04-01</p> <p>The Australian Bureau of Meteorology (BoM), as part of the Pacific Island Climate Prediction Project (PI-CPP), has developed <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> for ten National Meteorological Services (NMS) in the Pacific region for nearly a decade, to improve <span class="hlt">seasonal</span> <span class="hlt">forecast</span> services to local communities and industry. As part of this project, a new statistical model called SCOPIC (<span class="hlt">Seasonal</span> Climate Outlooks for Pacific Island Countries) was developed to provide partner countries with the ability to produce their own <span class="hlt">seasonal</span> climate outlooks. In 2010, as part of the Pacific Adaptation Strategy and Assistance Programme (PASAP), the BoM developed a <span class="hlt">seasonal</span> outlook portal for Pacific NMS as an alternative source of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">forecasting</span> research in Australia. However, no formal assessment of the skill of the two <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> 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 <span class="hlt">forecasts</span> of many climate variables at all locations across the globe and also at various lead times. Here we demonstrate the skill</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011AGUFM.H42A..05B&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011AGUFM.H42A..05B&link_type=ABSTRACT"><span id="translatedtitle">Two-stage <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> to guide water resources decisions and water rights allocation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Block, P. J.; Gonzalez, E.; Bonnafous, L.</p> <p>2011-12-01</p> <p>Decision-making in water resources is inherently uncertain producing copious risks, ranging from operational (present) to planning (<span class="hlt">season</span>-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 <span class="hlt">seasonal</span> <span class="hlt">forecast</span> skill and advocate for integration to reduce risk, however only minimal adoption is evident. Impediments are well defined, yet tailoring <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> streamflow <span class="hlt">forecast</span> at leads of one and two <span class="hlt">seasons</span> 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 <span class="hlt">forecast</span> to a simple reservoir decision model also allows water managers to select a level of confidence in the <span class="hlt">forecast</span> information.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.3868S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.3868S"><span id="translatedtitle"><span class="hlt">Seasonal</span> UK river flow <span class="hlt">forecasts</span> based on persistence and historical analogy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Svensson, Cecilia</p> <p>2014-05-01</p> <p>A range of methods for <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> of river flows and groundwater levels for application nationwide is currently being developed for the United Kingdom (http://www.hydoutuk.net/). These methods include modelling approaches using either <span class="hlt">seasonal</span> rainfall <span class="hlt">forecasts</span> or historical rainfall series as input. Regression-based models for river flow <span class="hlt">forecasting</span> using large-scale forcings, such as sea surface temperatures and climate indices, as predictors are also under development. The present study outlines river flow <span class="hlt">forecasting</span> methods based on persistence and historical flow analogues. The underlying assumption for the latter is that sequences of river flow in the historical record that are similar to the recent past will provide valuable information on what flows will occur in the near future. <span class="hlt">Forecasts</span> are made for the coming one and three months, using either persistence or one of two historical analogue methods. A weighted mean of the five most similar analogues is used as one <span class="hlt">forecast</span> method, and an alternative is to shift this <span class="hlt">forecast</span> to fit with the observed flow in the last month of observations. For each calendar month, catchment and <span class="hlt">forecast</span> duration, the one of the three methods that has performed best in the past is selected for making the <span class="hlt">forecast</span>. Here, performance is measured by the correlation between the hindcasts and the observed records. The <span class="hlt">forecasts</span> based on persistence of the previous month's flow generally outperform the analogues approach, particularly for slowly responding catchments with large underground water storage in aquifers. These are mainly located in the southeast of the country. Historical analogues make a useful contribution to the <span class="hlt">forecasts</span> in the northwest. The one-month <span class="hlt">forecasts</span> are better from May to October than during the rest of the year, and are better in the southeast than in the northwest. The three-month <span class="hlt">forecasts</span> are poor in the northwest for most of the year. Overall, <span class="hlt">forecasts</span> with significant (at the 10</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PIAHS.369..115G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PIAHS.369..115G"><span id="translatedtitle">Ensemble <span class="hlt">seasonal</span> <span class="hlt">forecast</span> of extreme water inflow into a large reservoir</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gelfan, A. N.; Motovilov, Yu. G.; Moreido, V. M.</p> <p>2015-06-01</p> <p>An approach to <span class="hlt">seasonal</span> ensemble <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> (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 <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> date. A spatially distributed stochastic weather generator was used to produce time-series of daily weather scenarios for the <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> technique was verified on the basis of the hindcast simulations for 29 spring and summer <span class="hlt">seasons</span> 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 <span class="hlt">forecast</span> freshet/low-water events of the pre-assigned severity categories.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.3292S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.3292S"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasts</span> of impact-relevant climate information indices developed as part of the EUPORIAS project</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Spirig, Christoph; Bhend, Jonas</p> <p>2015-04-01</p> <p>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 <span class="hlt">Seasonal</span>-to-decadal timescale) we have developed examples of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> of CIIs. We present <span class="hlt">forecasts</span> and analyses of the skill of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> skill over Europe, highlighting the challenge for providing added-value services to stakeholders operating in Europe. The reasons for the lack of <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> and prospect for improvements by investing in post-processing. Furthermore, those cases for which CII <span class="hlt">forecasts</span> showed similar skill values as those of the underlying meteorological variables, <span class="hlt">forecasts</span> of CIIs provide added value from a user perspective.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H53E0988D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H53E0988D"><span id="translatedtitle">Can Regional Climate Models Improve Warm <span class="hlt">Season</span> <span class="hlt">Forecasts</span> in the North American Monsoon Region?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dominguez, F.; Castro, C. L.</p> <p>2009-12-01</p> <p>The goal of this work is to improve warm <span class="hlt">season</span> <span class="hlt">forecasts</span> in the North American Monsoon Region. To do this, we are dynamically downscaling warm <span class="hlt">season</span> CFS (Climate <span class="hlt">Forecast</span> System) reforecasts from 1982-2005 for the contiguous U.S. using the Weather Research and <span class="hlt">Forecasting</span> (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. <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span>. 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 <span class="hlt">forecast</span> tropical Pacific SSTs and large-scale teleconnection patterns, at least as evaluated for the winter <span class="hlt">season</span>; 2) has greater skill in <span class="hlt">forecasting</span> 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 <span class="hlt">season</span> 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 <span class="hlt">forecasting</span> of the warm <span class="hlt">season</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ERL.....7a5602S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ERL.....7a5602S"><span id="translatedtitle">What is the current state of scientific knowledge with regard to <span class="hlt">seasonal</span> and decadal <span class="hlt">forecasting</span>?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Smith, Doug M.; Scaife, Adam A.; Kirtman, Ben P.</p> <p>2012-03-01</p> <p>Environmental factors, such as the frequency, intensity and duration of extreme weather events, are important drivers of migration and displacement of people. There is therefore a growing need for regional climate predictions for the coming <span class="hlt">seasons</span> to decades. This paper reviews the current state of the art of <span class="hlt">seasonal</span> to decadal climate prediction, focusing on the potential sources of skill, <span class="hlt">forecasting</span> techniques, current capability and future prospects.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=272196','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=272196"><span id="translatedtitle">Generating synthetic daily precipitation realizations for <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>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 <span class="hlt">seasonal</span> climate outlook p...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.H51B0356B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.H51B0356B"><span id="translatedtitle">A multi-model hydrologic ensemble for <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasting</span> in the western U.S.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bohn, T. J.; Wood, A. W.; Akanda, A.; Lettenmaier, D. P.</p> <p>2005-12-01</p> <p>Since 2003, the Variable Infiltration Capacity (VIC) macroscale hydrology model has been applied in real time over the western U.S. for experimental ensemble hydrologic prediction at lead times of six months to a year. VIC hydrologic initial conditions are produced from gridded station observations during a two-year runup period prior to the <span class="hlt">forecast</span> date; and hydrologic <span class="hlt">forecast</span> ensembles are driven by climate <span class="hlt">forecasts</span> from several sources, including NCEP and NASA climate model outputs, CPC official <span class="hlt">seasonal</span> outlooks and, as a baseline <span class="hlt">forecast</span>, Extended Streamflow Prediction (ESP). We are now in the process of expanding this approach to include <span class="hlt">forecasts</span> made from a Bayesian combination of the results from a suite of land surface models. Our initial set of LSMs includes VIC, the NWS grid-based Sacramento model (HL-RMS) and the NCEP NOAH model. All three LSMs are implemented on the 1/8 degree grid used by the North American Land Data Assimilation System (N-LDAS). Here we present preliminary results from several river basins in the Western US, focusing on both retrospective deterministic simulations and retrospective ESP-based ensemble <span class="hlt">forecasts</span> and <span class="hlt">forecast</span> error properties. We compare linear regression and Bayesian methods of combining model results, and investigate <span class="hlt">seasonal</span> and geographic variations in <span class="hlt">forecast</span> skill. Our data set includes 20+ years of 1-year, ESP-based, 25-member ensemble <span class="hlt">forecasts</span> for each model, using both April 1 and October 1 as starting dates, from several basins including the Salmon River, ID, the Feather River, CA, and the San Juan River, UT.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.4815S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4815S"><span id="translatedtitle">Parametrisation of initial conditions for <span class="hlt">seasonal</span> stream flow <span class="hlt">forecasting</span> in the Swiss Rhine basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schick, Simon; Rössler, Ole; Weingartner, Rolf</p> <p>2016-04-01</p> <p>Current climate <span class="hlt">forecast</span> models show - to the best of our knowledge - low skill in <span class="hlt">forecasting</span> climate variability in Central Europe at <span class="hlt">seasonal</span> lead times. When it comes to <span class="hlt">seasonal</span> stream flow <span class="hlt">forecasting</span>, initial conditions thus play an important role. Here, initial conditions refer to the catchments moisture at the date of <span class="hlt">forecast</span>, i.e. snow depth, stream flow and lake level, soil moisture content, and groundwater level. The parametrisation of these initial conditions can take place at various spatial and temporal scales. Examples are the grid size of a distributed model or the time aggregation of predictors in statistical models. Therefore, the present study aims to investigate the extent to which the parametrisation of initial conditions at different spatial scales leads to differences in <span class="hlt">forecast</span> errors. To do so, we conduct a <span class="hlt">forecast</span> experiment for the Swiss Rhine at Basel, which covers parts of Germany, Austria, and Switzerland and is southerly bounded by the Alps. <span class="hlt">Seasonal</span> mean stream flow is defined for the time aggregation of 30, 60, and 90 days and <span class="hlt">forecasted</span> at 24 dates within the calendar year, i.e. at the 1st and 16th day of each month. A regression model is employed due to the various anthropogenic effects on the basins hydrology, which often are not quantifiable but might be grasped by a simple black box model. Furthermore, the pool of candidate predictors consists of antecedent temperature, precipitation, and stream flow only. This pragmatic approach follows the fact that observations of variables relevant for hydrological storages are either scarce in space or time (soil moisture, groundwater level), restricted to certain <span class="hlt">seasons</span> (snow depth), or regions (lake levels, snow depth). For a systematic evaluation, we therefore focus on the comprehensive archives of meteorological observations and reanalyses to estimate the initial conditions via climate variability prior to the date of <span class="hlt">forecast</span>. The experiment itself is based on four different</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.H41E1121A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H41E1121A"><span id="translatedtitle">Probabilistic Water quality trading model conditioned on <span class="hlt">season</span>-ahead nutrient load <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Arumugam, S.; Oh, J.</p> <p>2010-12-01</p> <p>Successful water quality trading programs in the country rely on expected point and nonpoint nutrient loadings from multiple sources. Pollutant sources, through nutrient transactions, are in pursuit of minimum allocation strategies that can keep both the loadings and the associated concentrations under the target limit. It is well established in the hydroclimatic literature that interannual variability in <span class="hlt">seasonal</span> streamflow could be explained partially using SST conditions. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. We intend to bridge these two findings to develop probabilistic nutrient loading model for supporting water quality trading in the Tar River basin, NC. Utilizing the precipitation <span class="hlt">forecasts</span> derived from ECHAM4.5 General Circulation Model, we develop <span class="hlt">season</span>-ahead <span class="hlt">forecasts</span> of total nitrogen (TN) and total phosphorus (TP) by forcing the calibrated water quality model with <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span>. Based on the <span class="hlt">season</span>-head loadings, the probability of violation of desired nutrient concentration for the currently allowed loadings is also estimated. Through retrospective analyses using <span class="hlt">forecasted</span> streamflow and the associated loadings, the probabilistic water quality trading model estimates the nutrient reduction strategies that can ensure the net loadings from both sources being below the target loadings. Challenges in applying the proposed framework for actual trading are also discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Climate&pg=5&id=EJ1106865','ERIC'); return false;" href="http://eric.ed.gov/?q=Climate&pg=5&id=EJ1106865"><span id="translatedtitle">Developing the Capacity of Farmers to Understand and Apply <span class="hlt">Seasonal</span> Climate <span class="hlt">Forecasts</span> through Collaborative Learning Processes</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Cliffe, Neil; Stone, Roger; Coutts, Jeff; Reardon-Smith, Kathryn; Mushtaq, Shahbaz</p> <p>2016-01-01</p> <p>Purpose: This paper documents and evaluates collaborative learning processes aimed at developing farmer's knowledge, skills and aspirations to use <span class="hlt">seasonal</span> climate <span class="hlt">forecasting</span> (SCF). Methodology: Thirteen workshops conducted in 2012 engaged over 200 stakeholders across Australian sugar production regions. Workshop design promoted participant…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26032315','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26032315"><span id="translatedtitle">Arctic sea ice trends, variability and implications for <span class="hlt">seasonal</span> ice <span class="hlt">forecasting</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Serreze, Mark C; Stroeve, Julienne</p> <p>2015-07-13</p> <p>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 <span class="hlt">season</span>, 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 <span class="hlt">seasonal</span> ice <span class="hlt">forecasting</span>. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled <span class="hlt">forecast</span> systems have improved <span class="hlt">forecast</span> skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability. PMID:26032315</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4455712','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4455712"><span id="translatedtitle">Arctic sea ice trends, variability and implications for <span class="hlt">seasonal</span> ice <span class="hlt">forecasting</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Serreze, Mark C.; Stroeve, Julienne</p> <p>2015-01-01</p> <p>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 <span class="hlt">season</span>, 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 <span class="hlt">seasonal</span> ice <span class="hlt">forecasting</span>. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled <span class="hlt">forecast</span> systems have improved <span class="hlt">forecast</span> skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability. PMID:26032315</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H51T..06D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H51T..06D"><span id="translatedtitle">Towards Optimization of Reservoir Operations for Hydropower Production in East Africa: Application of <span class="hlt">Seasonal</span> Climate <span class="hlt">Forecasts</span> and Remote Sensing Products</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Demissie, S. S.; Gebremichael, M.; Hopson, T. M.; Riddle, E. E.; Yeh, W. W. G.</p> <p>2015-12-01</p> <p>Hydroelectric generation and interconnections are the major priority areas of infrastructure development in Africa. A number of hydropower projects are currently being developed in East Africa in order to meet the energy demands of the fast growing economy in sustainable and climate-resilient manner. However, the performance efficiency of existing hydropower systems in Africa is much lower (about 30% in some cases) than their design capacity. This study proposes a decision support system (DSS) that integrates climate <span class="hlt">forecasts</span> and remote sensing products into modeling and optimization of the hydropower systems in order to achieve reliable reservoir operations and enhance hydropower production efficiency. The DSS has three main components; climate system, hydrologic and water resources system, and optimization system. The climate system comprises of tools and interfaces for accessing, customizing and integrating climate <span class="hlt">forecasts</span> and remote sensing data. The North America Multi-Model Ensemble (NMME) <span class="hlt">seasonal</span> retrospective <span class="hlt">forecasts</span> for the East Africa Power Pool (EAPP) region are compared with the TRMM rainfall estimates and the CPC unified gauged rainfall data. The errors of the NMME <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> have portrayed significant spatial and temporal variability in the EAPP region. The root mean square errors of the <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> are relatively higher for wetter locations and months. However, the skills of the NMME <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> are not significantly depreciating with lead time for the study region. The <span class="hlt">seasonal</span> <span class="hlt">forecast</span> errors vary from one model to another. Here, we present the skills of NMME <span class="hlt">seasonal</span> <span class="hlt">forecasts</span>, the physical factors and mechanisms that affect the skills. In addition, we discuss our methodology that derives the best <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> for the study region from the NMME <span class="hlt">seasonal</span> <span class="hlt">forecasts</span>, and show how the climate <span class="hlt">forecast</span> errors propagate through hydrologic models into hydrological <span class="hlt">forecasting</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1817150I&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1817150I&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">Forecasts</span> for Northern Hemisphere Winter 2015/16</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ineson, Sarah; Scaife, Adam; Comer, Ruth; Dunstone, Nick; Fereday, David; Folland, Chris; Gordon, Margaret; Karpechko, Alexey; Knight, Jeff; MacLachlan, Craig; Smith, Doug; Walker, Brent</p> <p>2016-04-01</p> <p>The northern winter of 2015/16 gave rise to the strongest El Niño event since 1997/8. Central and eastern Pacific sea surface temperature anomalies exceeded three degrees and closely resembled the strong El Niño in winter of 1982/3. A second feature of this winter was a strong westerly phase of the Quasi-Biennial Oscillation and very strong winds in the stratospheric polar night jet. At the surface, intense extratropical circulation anomalies occurred in both the North Pacific and North Atlantic that were consistent with known teleconnections to the observed phases of ENSO and the QBO. The North Atlantic Oscillation was very positive in the early winter period (Nov-Dec) and was more blocked in the late winter. Initialised climate predictions were able to capture these signals at <span class="hlt">seasonal</span> lead times. This case study adds to the evidence that north Atlantic circulation exhibits predictability on <span class="hlt">seasonal</span> timescales, and in this case we show that even aspects of the detailed pattern and sub-<span class="hlt">seasonal</span> evolution were predicted, providing warning of increased risk of extreme events such as the intense rainfall which caused extreme flooding in the UK in December.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140011277','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011277"><span id="translatedtitle">Soil Moisture Initialization Error and Subgrid Variability of Precipitation in <span class="hlt">Seasonal</span> Streamflow <span class="hlt">Forecasting</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koster, Randal D.; Walker, Gregory K.; Mahanama, Sarith P.; Reichle, Rolf H.</p> <p>2013-01-01</p> <p>Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the <span class="hlt">forecasting</span> of large-scale <span class="hlt">seasonal</span> streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow <span class="hlt">forecasts</span>, and (ii) the extent to which a realistic increase in the spatial resolution of <span class="hlt">forecasted</span> precipitation would improve streamflow <span class="hlt">forecasts</span>. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in streamflow <span class="hlt">forecast</span> skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70059149','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70059149"><span id="translatedtitle">Improving groundwater predictions utilizing <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> from general circulation models forced with sea surface temperature <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Almanaseer, Naser; Sankarasubramanian, A.; Bales, Jerad</p> <p>2014-01-01</p> <p>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 <span class="hlt">forecasts</span> based on the precipitation <span class="hlt">forecasts</span> from ECHAM 4.5 General Circulation Model Forced with Sea Surface Temperature <span class="hlt">forecasts</span>. 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 <span class="hlt">seasonal</span> 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 <span class="hlt">forecasts</span> in improving monthly and <span class="hlt">seasonal</span> 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 <span class="hlt">forecasts</span> based on leave-five-out cross-validation. Results from the research reported in this paper show that using</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H43P..06B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H43P..06B"><span id="translatedtitle">A Simple Bayesian Climate Index Weighting Method for <span class="hlt">Seasonal</span> Ensemble <span class="hlt">Forecasting</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bradley, A.; Habib, M. A.; Schwartz, S. S.</p> <p>2014-12-01</p> <p>Climate information — in the form of a measure of climate state or a climate <span class="hlt">forecast</span> — can be an important predictor of future hydrologic conditions. For instance, streamflow variability for many locations around the globe is related to large-scale atmospheric oscillations, like the El Nino Southern Oscillation (ENSO) or the Pacific/Decadal Oscillation (PDO). Furthermore, climate <span class="hlt">forecast</span> models are growing more skillful in their predictions of future climate variables on <span class="hlt">seasonal</span> time scales. Finding effective ways to translate this climate information into improved hydrometeorological predictions is an area of ongoing research. In ensemble streamflow <span class="hlt">forecasting</span>, where historical weather inputs or streamflow observations are used to generate the ensemble, climate index weighting is one way to represent the influence of current climate information. Using a climate index, each <span class="hlt">forecast</span> variable member of the ensemble is selectively weighted to reflect climate conditions at the time of the <span class="hlt">forecast</span>. A simple Bayesian climate index weighting of ensemble <span class="hlt">forecasts</span> is presented. The original hydrologic ensemble members define a sample of the prior distribution; the relationship between the climate index and the ensemble member <span class="hlt">forecast</span> variable is used to estimate a likelihood function. Given an observation of the climate index at the time of the <span class="hlt">forecast</span>, the estimated likelihood function is then used to assign weights to each ensemble member. The weighted ensemble <span class="hlt">forecast</span> is then used to estimate the posterior distribution of the <span class="hlt">forecast</span> variable conditioned on the climate index. The proposed approach has several advantages over traditional climate index weighting methods. The weights assigned to the ensemble members accomplish the updating of the (prior) ensemble <span class="hlt">forecast</span> distribution based on Bayes' Theorem, so the method is theoretically sound. The method also automatically adapts to the strength of the relationship between the climate index and the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ThApC.117...41A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ThApC.117...41A"><span id="translatedtitle">Prediction of the Caspian Sea level using ECMWF <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> and reanalysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Arpe, K.; Leroy, S. A. G.; Wetterhall, F.; Khan, V.; Hagemann, S.; Lahijani, H.</p> <p>2014-07-01</p> <p>The hydrological budget of the Caspian Sea (CS) is investigated using the European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> interim reanalysis (ERAi) and <span class="hlt">seasonal</span> <span class="hlt">forecast</span> (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 <span class="hlt">forecasts</span> of the CSL a few months ahead, and using the FCSTs of precipitation improves the CSL <span class="hlt">forecasts</span>. The evaporation in the <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> FCSTs to 1 year, considerable <span class="hlt">forecast</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H33M..06T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H33M..06T"><span id="translatedtitle">Development of an Operational Hydrological Monitoring and <span class="hlt">Seasonal</span> <span class="hlt">Forecast</span> System for China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tang, Q.; Zhang, X.</p> <p>2014-12-01</p> <p>Hydrological monitoring and <span class="hlt">forecast</span> are critical for disaster mitigation and water resources management. Although large investments have been made in climate <span class="hlt">forecasting</span> and in related monitoring of land surface conditions, the experimental streamflow monitoring and <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecast</span>. The performance of hydrological monitoring and skill of <span class="hlt">seasonal</span> streamflow prediction were assessed. The potential and challenges of using the operational monitoring and <span class="hlt">forecast</span> system for improved flooding and drought management are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014WRR....50.6592S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014WRR....50.6592S"><span id="translatedtitle">Assessing the value of <span class="hlt">seasonal</span> climate <span class="hlt">forecast</span> information through an end-to-end <span class="hlt">forecasting</span> framework: Application to U.S. 2012 drought in central Illinois</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shafiee-Jood, Majid; Cai, Ximing; Chen, Ligang; Liang, Xin-Zhong; Kumar, Praveen</p> <p>2014-08-01</p> <p>This study proposes an end-to-end <span class="hlt">forecasting</span> framework to incorporate operational <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> to help farmers improve their decisions prior to the crop growth <span class="hlt">season</span>, 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">Forecasting</span> model (CWRF) driven by two operational general circulation models (GCMs) is used to provide the <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecast</span>. The proposed framework is applied to the Salt Creek watershed in Illinois that experienced an extreme drought event during 2012 crop growth <span class="hlt">season</span>. The results show that the <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span>, 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 <span class="hlt">forecast</span> value.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A31I..04M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A31I..04M"><span id="translatedtitle">The impact of quantified land surface uncertainties on <span class="hlt">seasonal</span> <span class="hlt">forecast</span> skill</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>MacLeod, D.</p> <p>2015-12-01</p> <p>The land surface is a key component in <span class="hlt">seasonal</span> <span class="hlt">forecasting</span>, and well-represented soil moisture is particularly important for the simulation of heatwaves. Methods to represent uncertainties in the atmosphere of climate models have been shown to improve <span class="hlt">forecasts</span>. However these methods have not yet been applied to the land surface component of climate models. We consider three methods of incorporating uncertainties into CHTESSEL, the land surface model of the ECMWF <span class="hlt">forecasting</span> system. These methods are: stochastic perturbation of soil moisture tendencies, static and then stochastic perturbation of key soil parameters. We present analysis of the results of fully coupled <span class="hlt">seasonal</span> hindcasts with each method applied. We find significant improvement for extreme events, particularly in terms of <span class="hlt">forecast</span> reliability of upper and lower quintile soil moisture. These improvements also propagate into the atmosphere, impacting the reliability of <span class="hlt">seasonal</span>-average predictions of latent and sensible heat flux anomalies and air temperature. This improvement is consistent over the hindcast, and also for particular cases such as the 2003 European summer (MacLeod et al 2015). We also present work with an uncoupled version of CHTESSEL. Extending the method of Wood & Lettenmaier (2008), we quantify the global evolution over <span class="hlt">forecast</span> lead-time of the relative magnitudes of initial condition, forcing and parameter uncertainty in the land surface. Among other things this gives some indication of where predictability from initial conditions is more persistent, and where uncertainty in land surface parameters has the largest impact on simulated soil moisture. MacLeod DA, CLoke, HL, Pappenberger F and Weisheimer AF (2015), Improved <span class="hlt">seasonal</span> prediction of the hot summer of 2003 through better representation of uncertainty in the land surface, QJRMSWood, AW, and Lettenmaier DP (2008), An ensemble approach for attribution of hydrologic prediction uncertainty, GRL</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120003387','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120003387"><span id="translatedtitle"><span class="hlt">Forecasting</span> Cool <span class="hlt">Season</span> Daily Peak Winds at Kennedy Space Center and Cape Canaveral Air Force Station</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barrett, Joe, III; Short, David; Roeder, William</p> <p>2008-01-01</p> <p>The expected peak wind speed for the day is an important element in the daily 24-Hour and Weekly Planning <span class="hlt">Forecasts</span> 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 <span class="hlt">forecasters</span> have indicated that peak wind speeds are a challenging parameter to <span class="hlt">forecast</span> during the cool <span class="hlt">season</span> (October-April). The 45 WS requested that the Applied Meteorology Unit (AMU) develop a tool to help them <span class="hlt">forecast</span> the speed and timing of the daily peak and average wind, from the surface to 300 ft on KSC/CCAFS during the cool <span class="hlt">season</span>. The tool must only use data available by 1200 UTC to support the issue time of the Planning <span class="hlt">Forecasts</span>. 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 <span class="hlt">season</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC31A1007N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC31A1007N"><span id="translatedtitle">The use of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> in a crop failure early warning system for West Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nicklin, K. J.; Challinor, A.; Tompkins, A.</p> <p>2011-12-01</p> <p><span class="hlt">Seasonal</span> 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 <span class="hlt">forecasting</span> system uses observed weather to calculate a water satisfaction index, which is then related to expected crop yield (Traore et al, 2006). <span class="hlt">Seasonal</span> climate <span class="hlt">forecasts</span> may be able to increase the lead-time of yield <span class="hlt">forecasts</span> and reduce the humanitarian impact of drought. This study assesses the potential for a crop failure early warning system, which uses dynamic <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> weather <span class="hlt">forecasts</span> (Challinor et al, 2004). GLAM is modified for use in West Africa by allowing multiple planting dates each <span class="hlt">season</span>, 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 <span class="hlt">forecast</span> date, followed by ECMWF system 3 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> until harvest. The variation of skill with <span class="hlt">forecast</span> date</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H43A1171W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H43A1171W"><span id="translatedtitle">A <span class="hlt">seasonal</span> climate and streamflow <span class="hlt">forecasting</span> testbed for the Colorado River Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, A. W.; Werner, K.; Schmidt, M.</p> <p>2011-12-01</p> <p>CBRFC, NIDIS, USBR, and others have documented a consistent need for climate <span class="hlt">forecasts</span> from one <span class="hlt">season</span> to two years lead time to support a variety of applications, and particularly for streamflow <span class="hlt">forecasting</span> for water, energy and agricultural management. The Colorado River basin presents a challenge due to the limited <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">forecasting</span> community.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20040081366&hterms=skills+drive&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dskills%2Bdrive','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20040081366&hterms=skills+drive&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dskills%2Bdrive"><span id="translatedtitle">Alternative Approaches to Land Initialization for <span class="hlt">Seasonal</span> Precipitation and Temperature <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koster, Randal; Suarez, Max; Liu, Ping; Jambor, Urszula</p> <p>2004-01-01</p> <p>The <span class="hlt">seasonal</span> prediction system of the NASA Global Modeling and Assimilation Office is used to generate ensembles of summer <span class="hlt">forecasts</span> utilizing realistic soil moisture initialization. To derive the realistic land states, we drive offline the system's land model with realistic meteorological forcing over the period 1979-1993 (in cooperation with the Global Land Data Assimilation System project at GSFC) and then extract the state variables' values on the chosen <span class="hlt">forecast</span> start dates. A parallel series of <span class="hlt">forecast</span> ensembles is performed with a random (though climatologically consistent) set of land initial conditions; by comparing the two sets of ensembles, we can isolate the impact of land initialization on <span class="hlt">forecast</span> skill from that of the imposed SSTs. The base initialization experiment is supplemented with several <span class="hlt">forecast</span> ensembles that use alternative initialization techniques. One ensemble addresses the impact of minimizing climate drift in the system through the scaling of the initial conditions, and another is designed to isolate the importance of the precipitation signal from that of all other signals in the antecedent offline forcing. A third ensemble includes a more realistic initialization of the atmosphere along with the land initialization. The impact of each variation on <span class="hlt">forecast</span> skill is quantified.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.553D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.553D"><span id="translatedtitle">Downscaling of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> and possible application to hydro-power production <span class="hlt">forecasts</span> in France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dubus, L.; Berthelot, M.; Qu, Z.; Gailhard, J.</p> <p>2009-09-01</p> <p>Managing the power generation system at the scale of a country is a very complex problem which involves in particular climatic variables at different space and time scales. Air temperature and precipitation are among the most important ones, as they explain respectively an important part of the demand variability and the hydro power production capacity. If direct GCMs <span class="hlt">forecasts</span> of local variables are not very skilful, specially over mid-latitudes, large scale fields such as geopotential height or mean sea level pressure show some positive skill over the North Atlantic / european region, that can be used to make local predictions of surface variables, using downscaling technics. In this study, we evaluated the 2m temperature and precipitation hindcasts of the DEMETER and ENSEMBLES systems on a number of hydrological basins in France. We used the University of Cantabria web portal for statistical downscaling, developed in the ENSEMBLES project, to downscale the most predictable large scale fields, and compared direct raw hindcasts with indirect downscaled hindcasts. Both direct and indirect hindcasts are then used in an hydrolocial model to evaluate their respective interest for hydro-power production <span class="hlt">forecasts</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130012589','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130012589"><span id="translatedtitle">Weather Research and <span class="hlt">Forecasting</span> Model Sensitivity Comparisons for Warm <span class="hlt">Season</span> Convective Initiation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.</p> <p>2007-01-01</p> <p>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 <span class="hlt">forecasters</span> at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical <span class="hlt">forecast</span> information as Terminal Aerodrome <span class="hlt">Forecasts</span> (TAF5), Spot <span class="hlt">Forecasts</span> for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these <span class="hlt">forecasting</span> challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather <span class="hlt">forecasts</span> for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model <span class="hlt">forecasts</span> to aid in their decision making is crucial. This study specifically addresses the skill of different model configurations in <span class="hlt">forecasting</span> warm <span class="hlt">season</span> 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 <span class="hlt">season</span> convective patterns seen over the Florida peninsula. However, warm <span class="hlt">season</span> convection remains one of the most poorly <span class="hlt">forecast</span> meteorological parameters. To determine which</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1158496','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/1158496"><span id="translatedtitle">Joint <span class="hlt">Seasonal</span> ARMA Approach for Modeling of Load <span class="hlt">Forecast</span> Errors in Planning Studies</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.; Diao, Ruisheng; Lu, Ning</p> <p>2014-04-14</p> <p>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 <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load <span class="hlt">forecast</span> error series for all BAs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007EOSTr..88R.278.','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007EOSTr..88R.278."><span id="translatedtitle">In Brief: U.K. Met Office <span class="hlt">forecast</span> for Atlantic hurricane <span class="hlt">season</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p></p> <p>2007-07-01</p> <p>GloSea, the U.K. Meteorological Office's computer model of the global atmosphere-ocean system, has predicted a cooling trend in sea surface temperatures in the tropical North Atlantic that will result in a less active hurricane <span class="hlt">season</span>. The Met Office has predicted that there is a 70% chance of a less active hurricane <span class="hlt">season</span> in the North Atlantic this year, with only 7-13 named storms occurring within the remaining five months of the <span class="hlt">season</span> (July through November). There have already been two named storms this year-Andrea and Barry. From 1990-2005, there were an average of 12.4 storms during July-November. The U.K. Met Office <span class="hlt">forecast</span> contrasts with NOAA's, which was released in May and predicted a busier <span class="hlt">season</span> than average, with 13-17 named storms.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1982JApMe..21.1798G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1982JApMe..21.1798G"><span id="translatedtitle"><span class="hlt">Seasonal</span> Climate <span class="hlt">Forecasts</span> and Water Management for Steam-Electric Generation.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Greis, Noel P.</p> <p>1982-12-01</p> <p>A water demand model for electricity production is presented which estimates the variability of water demand for energy production as a function of climate, especially temperature. The model incorporates the effects of temperature on both consumer energy demand levels and process evaporation for steam-electric cooling. The weather-sensitive analysis of water use contained herein is motivated by two factors. First, the electric power industry is using an increasingly large quantity of water, primarily for cooling. The extent of this use is highly dependent on weather conditions. Second, the current state-of-the-art of <span class="hlt">seasonal</span> climate <span class="hlt">forecasting</span>, especially temperature, continues to advance. Whether or not <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> can be of beneficial use in water management in the electric power industry becomes an important question in the face of a prolonged water shortage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/5643882','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/5643882"><span id="translatedtitle"><span class="hlt">Seasonal</span> Climate <span class="hlt">Forecasts</span> and Water Management for Steam-Electric Generation</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Greis, N.P.</p> <p>1982-12-01</p> <p>A water demand model for electricity production is presented which estimates the variablility of water demand for energy production as a function of climate, especially temperature. The model incorporates the effects of temperature on both consumer energy demand levels and process evaporation for steam-electric cooling. The weather-sensitive analysis of water use contained herein is motivated by two factors. First, the electric power industry is using an increasingly large quantity of water, primarily for cooling. The extent of this use is highly dependent on weather conditions. Second, the current state-of-the-art of <span class="hlt">seasonal</span> climate <span class="hlt">forecasting</span>, especially temperature, continues to advance. Whether or not <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> can be of beneficial use in water management in the electric power industry becomes an important question in the face of a prolonged water shortage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AGUFM.H21A1015W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AGUFM.H21A1015W&link_type=ABSTRACT"><span id="translatedtitle">Client-Friendly <span class="hlt">Forecasting</span>: <span class="hlt">Seasonal</span> Runoff Predictions Using Out-of-the-Box Indices</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weil, P.</p> <p>2013-12-01</p> <p>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 <span class="hlt">forecasts</span> of <span class="hlt">seasonal</span> runoff. The methodology described here creates a simple-to-use model that utilizes easily accessed data to make <span class="hlt">forecasts</span> of April through September runoff months before the runoff <span class="hlt">season</span> begins. For this project, <span class="hlt">forecasting</span> 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 <span class="hlt">forecasts</span>, 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 <span class="hlt">forecasting</span> 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 <span class="hlt">forecasting</span> model product is highly portable, applicable to many major river systems and easily explained to a non-technical audience.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24842026','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24842026"><span id="translatedtitle">Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic</p> <p>2014-06-28</p> <p>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 <span class="hlt">Forecasts</span> 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 <span class="hlt">forecast</span> mode, and the latest <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system--System 4--has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather <span class="hlt">forecasts</span>. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on <span class="hlt">seasonal</span> time scales during the retrospective <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4024238','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4024238"><span id="translatedtitle">Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Weisheimer, Antje; Corti, Susanna; Palmer, Tim; Vitart, Frederic</p> <p>2014-01-01</p> <p>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 <span class="hlt">Forecasts</span> 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 <span class="hlt">forecast</span> mode, and the latest <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather <span class="hlt">forecasts</span>. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on <span class="hlt">seasonal</span> time scales during the retrospective <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC12C..07D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC12C..07D"><span id="translatedtitle">How can monthly to <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> help to better manage power systems? (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dubus, L.; Troccoli, A.</p> <p>2013-12-01</p> <p>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 <span class="hlt">forecast</span> climate on monthly to <span class="hlt">seasonal</span> time scales. Several studies have already demonstrated the effectiveness of using these <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span>. 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFMNH41A1233L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFMNH41A1233L"><span id="translatedtitle">Global <span class="hlt">Seasonal</span> <span class="hlt">Forecasting</span> of Thermal Stress Conducive to Mass Coral Bleaching Events</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, G.; Eakin, C.; Matrosova, L.; Penland, M. C.; Webb, R. S.; Pulwarty, R. S.; Lynds, S.; Christensen, T.; Heron, S. F.; Morgan, J.; Parker, B. A.; Skirving, W. J.; Strong, A. E.</p> <p>2009-12-01</p> <p>As a consequence of climate change, coral bleaching on global and regional scales has been increasing in both frequency and severity over the past decades. In July 2008, NOAA Coral Reef Watch launched a new <span class="hlt">seasonal</span> prediction tool for coral bleaching conditions to augment its real-time satellite monitoring. A model predicting thermal stress from two weeks to three months in the future was developed collaboratively by our team to provide outlooks of the risk of coral bleaching well in advance of such events. Our predictive system is built on sea surface temperature <span class="hlt">forecasts</span> provided by NOAA’s Linear Inverse Model (LIM) that has successfully produced experimental predictions of tropical Pacific and Atlantic sea surface temperature anomalies for decades. The outlook worked well for the 2008 boreal bleaching <span class="hlt">season</span> and the 2009 austral bleaching <span class="hlt">season</span>. The outlook for 2009 boreal bleaching <span class="hlt">season</span> showed a potential for significant bleaching in the eastern Caribbean region and its evaluation will be conducted as in-situ observations become available. Natural resource managers in the Caribbean have felt sufficient confidence in the outlook that they have taken steps to prepare for the possible bleaching event in 2009. Next steps for improving the outlook system include developing a similar outlook product based on the NOAA National Center for Environmental Predictions’ Climate <span class="hlt">Forecast</span> System (CFS) operational model, collaborating with Australian colleagues to implement a similar system with the Bureau of Meteorology’s Predictive Ocean Atmosphere Model for Australia (POAMA), and then potentially do the same for other climate models. Such <span class="hlt">forecasting</span> tools provide critical and timely decision support for coral reef managers and scientists worldwide. Preliminary results indicate the <span class="hlt">forecast</span> compares favorably with satellite observations of actual thermal stress and is thus useful for coral reef management.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESS...19..275D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESS...19..275D"><span id="translatedtitle">The skill of <span class="hlt">seasonal</span> ensemble low-flow <span class="hlt">forecasts</span> in the Moselle River for three different hydrological models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Demirel, M. C.; Booij, M. J.; Hoekstra, A. Y.</p> <p>2015-01-01</p> <p>This paper investigates the skill of 90-day low-flow <span class="hlt">forecasts</span> 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 <span class="hlt">forecasted</span> meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble <span class="hlt">seasonal</span> meteorological <span class="hlt">forecasts</span>. We compared low-flow <span class="hlt">forecasts</span> for five different cases of <span class="hlt">seasonal</span> meteorological forcing: (1) ensemble P and PET <span class="hlt">forecasts</span>; (2) ensemble P <span class="hlt">forecasts</span> and observed climate mean PET; (3) observed climate mean P and ensemble PET <span class="hlt">forecasts</span>; (4) observed climate mean P and PET and (5) zero P and ensemble PET <span class="hlt">forecasts</span> as input for the models. The ensemble P and PET <span class="hlt">forecasts</span>, each consisting of 40 members, reveal the <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> for varying lead times up to 90 days. Before <span class="hlt">forecasting</span>, 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 <span class="hlt">seasonal</span> meteorological forcing. The largest range for 90-day low-flow <span class="hlt">forecasts</span> is found for the GR4J model when using ensemble <span class="hlt">seasonal</span> meteorological <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P <span class="hlt">forecasts</span> has a larger effect on <span class="hlt">seasonal</span> low-flow <span class="hlt">forecasts</span> than the uncertainty from ensemble PET</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120003374','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120003374"><span id="translatedtitle">Weather Research and <span class="hlt">Forecasting</span> Model Sensitivity Comparisons for Warm <span class="hlt">Season</span> Convective Initiation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela R.</p> <p>2007-01-01</p> <p>This report describes the work done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting warm <span class="hlt">season</span> convection over East-Central Florida. The Weather Research and <span class="hlt">Forecasting</span> 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 <span class="hlt">forecasters</span>, such as determining which configuration options are best to address specific <span class="hlt">forecast</span> concerns. This project assessed three different model intializations available to determine which configuration best predicts warm <span class="hlt">season</span> convective initiation in East-Central Florida. The project also examined the use of one- and two-way nesting in predicting warm <span class="hlt">season</span> convection.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.A13A0223K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.A13A0223K"><span id="translatedtitle">Dynamical-statistical <span class="hlt">Forecasting</span> of <span class="hlt">Seasonal</span> Air Temperature Over European Part of Russia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khan, V.</p> <p>2008-12-01</p> <p>The aim of the present study is to improve prediction of <span class="hlt">seasonal</span> 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 <span class="hlt">forecasting</span> approach. Consistent spatial patterns between <span class="hlt">forecasted</span> 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 <span class="hlt">forecast</span> skill compared to separate model <span class="hlt">forecasts</span> and multi-model mean <span class="hlt">forecasts</span>. This study has been supported by RFBR grants 07-05-00740, 07-05-13591.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.4644B&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.4644B&link_type=ABSTRACT"><span id="translatedtitle">Subseasonal-to-<span class="hlt">seasonal</span> (S2S) <span class="hlt">forecasts</span> with CNRM-CM: model evaluation and perspectives</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Batté, Lauriane; Ardilouze, Constantin; Chevallier, Matthieu; Déqué, Michel</p> <p>2016-04-01</p> <p>Météo-France takes part in the WWRP/Thorpex-WCRP joint project S2S (Robertson et al. 2015) since May 2015 and thus provides sub-<span class="hlt">seasonal</span> ensemble <span class="hlt">forecasts</span> run with the CNRM-CM coupled model (Voldoire et al. 2013) on the 1st of each month up to 61 days. After describing the current setup, this presentation provides an analysis of the CNRM-CM model ensemble hindcast available on the S2S database, which spans 22 years, by assessing <span class="hlt">forecast</span> quality and model skill for key variables (e.g. 500 hPa geopotential height, near-surface temperature, sea ice extent) and relevant phenomena at the S2S scale (MJO, NAO). We focus on <span class="hlt">forecast</span> weeks 2-4 and show that the model exhibits limited but reasonable skill at these time scales. We also examine the case of the July 2015 real-time <span class="hlt">forecast</span>, focusing on the western Europe heat wave. Prospects for the increased frequency of real-time S2S <span class="hlt">forecasts</span> and multi-model assessments using other systems of the S2S database will also be presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013WRR....49.4997T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013WRR....49.4997T"><span id="translatedtitle">Incorporating probabilistic <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> into river management using a risk-based framework</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Towler, Erin; Roberts, Mike; Rajagopalan, Balaji; Sojda, Richard S.</p> <p>2013-08-01</p> <p>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 <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> model, (3) generate climate <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> are used in conjunction with the GLMs, resulting in <span class="hlt">season</span>-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 <span class="hlt">forecasts</span> can be beneficial to planning, especially in wet years, but historical precipitation <span class="hlt">forecasts</span> are quite conservative (i.e., not very "sharp"). Synthetic <span class="hlt">forecasts</span> 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.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.A21C0643D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.A21C0643D"><span id="translatedtitle">Evaluation of Real-time Hurricane <span class="hlt">Forecasts</span> Using the Advanced Hurricane WRF Model for the 2007 Atlantic Hurricane <span class="hlt">Season</span>.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Done, J. M.</p> <p>2007-12-01</p> <p>Real-time <span class="hlt">forecasts</span> have been conducted with the Advanced Hurricane WRF Model (AHW) for named storms of the 2007 Atlantic hurricane <span class="hlt">season</span>. Taking advantage of increased computational power over previous years, 5- day <span class="hlt">forecasts</span> are conducted daily using three domains; two nests of 4km and 1.3km grid-spacing track the vortex within a fixed parent domain of 12km grid-spacing. In this presentation, <span class="hlt">forecast</span> accuracy in terms of track and intensity will be presented. The quality of the <span class="hlt">forecast</span> storm intensity can vary dramatically between storms, and sometimes between successive <span class="hlt">forecasts</span> of a given storm. This variability in model performance is explored by analyzing the statistics of the observed and model storm intensities for the 2007 hurricane <span class="hlt">season</span>. Conditions under which the model performs poorly are identified and a series of sensitivity simulations highlight aspects of the modeling system to which the <span class="hlt">forecast</span> intensity is most sensitive.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ThApC.tmp...23X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.tmp...23X"><span id="translatedtitle">Probabilistic <span class="hlt">forecasting</span> of <span class="hlt">seasonal</span> drought behaviors in the Huai River basin, China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xiao, Mingzhong; Zhang, Qiang; Singh, Vijay P.; Chen, Xiaohong</p> <p>2016-01-01</p> <p>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 <span class="hlt">seasonal</span> drought <span class="hlt">forecasting</span> 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 <span class="hlt">forecast</span> the <span class="hlt">seasonal</span> drought. Results indicated that the uncertainty related to the predicted <span class="hlt">seasonal</span> drought is larger when more severe droughts occurred in the previous <span class="hlt">seasons</span>, and the severe drought which occurs in summer and autumn will be more likely to be persistent in the next <span class="hlt">season</span> while the severe drought in winter and spring will be more likely to be recovered in the subsequent <span class="hlt">season</span>. Furthermore, given the different drought statuses in the previous <span class="hlt">season</span>, 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130009987','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130009987"><span id="translatedtitle">Weather Research and <span class="hlt">Forecasting</span> Model Sensitivity Comparisons for Warm <span class="hlt">Season</span> Convective Initiation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Watson, Leela R.; Hoeth, Brian; Blottman, Peter F.</p> <p>2007-01-01</p> <p> 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 <span class="hlt">forecast</span> concerns. The goal of this project is to assess the different configurations available and to determine which configuration will best predict warm <span class="hlt">season</span> 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 <span class="hlt">forecast</span>. 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 <span class="hlt">forecast</span> 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 <span class="hlt">season</span> convective initiation over East-Central Florida.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002EGSGA..27.1600D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27.1600D"><span id="translatedtitle">Perun: The System For <span class="hlt">Seasonal</span> Crop Yield <span class="hlt">Forecasting</span> Based On The Crop Model and Weather Generator</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dubrovsky, M.; Zalud, Z.; Trnka, M.; Haberle, J.; Pesice, P.</p> <p></p> <p>The main purpose of the computer system PERUN, which is now being developed, is the probabilistic <span class="hlt">seasonal</span> crop yield <span class="hlt">forecasting</span>. The crop yields (winter wheat and spring barley in the first step) are simulated by crop model WOFOST. The input daily weather series consist of observed data, which are available in the date of <span class="hlt">forecast</span> issuance, and synthetic data, which follow up with the observed data till the end of the crop model simulation. The synthetic weather series are generated by stochastic generator Met&Roll conditionally on the <span class="hlt">seasonal</span> weather <span class="hlt">forecast</span>. The probabilis- tic <span class="hlt">forecast</span> is based on multiple crop model runs. To provide the six daily weather characteristics required for crop model simulation (precipitation, solar radiation, max- imum and minimum temperatures, air humidity, wind speed), the previous WGEN- like four-variate version of Met&Roll generator was supplemented by a new module. This module adds wind speed and air humidity (necessary to calculate evapotranspi- ration) using the nearest neighbours resampling from the observed data. Because of the problems with availability and/or accuracy of wind and humidity data, the source code of the WOFOST model was modified and allows now to switch between Penman and Makkink methods of calculating the evapotranspiration (the daily values of wind speed and humidity are not required in the Makkink method). The contribution will address following items: 1) Structure of the PERUN system: components and their inputs and outputs. Modifications to WOFOST crop model and Met&Roll generator will be discussed. 2) Validation of the WOFOST crop model. The accuracy obtained using the Penman and Makkink methods will be compared. 3) Demonstration of the <span class="hlt">forecast</span> accuracy in dependence on the date of issuance. Acknowledgement: The system PERUN is being developed within the frame of project QC1316 sponsored by the Czech National Agency for Agricultural Research (NAZV).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4895184','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4895184"><span id="translatedtitle">Experiments with <span class="hlt">Seasonal</span> <span class="hlt">Forecasts</span> of ocean conditions for the Northern region of the California Current upwelling system</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.</p> <p>2016-01-01</p> <p>Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a <span class="hlt">seasonal</span> <span class="hlt">forecast</span> system is experimented with here. JISAO’s <span class="hlt">Seasonal</span> Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and <span class="hlt">forecasts</span> from NOAA’s Climate <span class="hlt">Forecast</span> System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and <span class="hlt">forecast</span> comparisons with observations. Results indicate J-SCOPE <span class="hlt">forecasts</span> have measurable skill on <span class="hlt">seasonal</span> timescales. Experiments suggest that <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on <span class="hlt">seasonal</span> timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates <span class="hlt">seasonal</span> conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders. PMID:27273473</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27273473','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27273473"><span id="translatedtitle">Experiments with <span class="hlt">Seasonal</span> <span class="hlt">Forecasts</span> of ocean conditions for the Northern region of the California Current upwelling system.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Siedlecki, Samantha A; Kaplan, Isaac C; Hermann, Albert J; Nguyen, Thanh Tam; Bond, Nicholas A; Newton, Jan A; Williams, Gregory D; Peterson, William T; Alin, Simone R; Feely, Richard A</p> <p>2016-01-01</p> <p>Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a <span class="hlt">seasonal</span> <span class="hlt">forecast</span> system is experimented with here. JISAO's <span class="hlt">Seasonal</span> Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and <span class="hlt">forecasts</span> from NOAA's Climate <span class="hlt">Forecast</span> System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and <span class="hlt">forecast</span> comparisons with observations. Results indicate J-SCOPE <span class="hlt">forecasts</span> have measurable skill on <span class="hlt">seasonal</span> timescales. Experiments suggest that <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on <span class="hlt">seasonal</span> timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates <span class="hlt">seasonal</span> conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders. PMID:27273473</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016NatSR...627203S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016NatSR...627203S&link_type=ABSTRACT"><span id="translatedtitle">Experiments with <span class="hlt">Seasonal</span> <span class="hlt">Forecasts</span> of ocean conditions for the Northern region of the California Current upwelling system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Siedlecki, Samantha A.; Kaplan, Isaac C.; Hermann, Albert J.; Nguyen, Thanh Tam; Bond, Nicholas A.; Newton, Jan A.; Williams, Gregory D.; Peterson, William T.; Alin, Simone R.; Feely, Richard A.</p> <p>2016-06-01</p> <p>Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a <span class="hlt">seasonal</span> <span class="hlt">forecast</span> system is experimented with here. JISAO’s <span class="hlt">Seasonal</span> Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and <span class="hlt">forecasts</span> from NOAA’s Climate <span class="hlt">Forecast</span> System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and <span class="hlt">forecast</span> comparisons with observations. Results indicate J-SCOPE <span class="hlt">forecasts</span> have measurable skill on <span class="hlt">seasonal</span> timescales. Experiments suggest that <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on <span class="hlt">seasonal</span> timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates <span class="hlt">seasonal</span> conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016NatGe...9..389M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NatGe...9..389M"><span id="translatedtitle"><span class="hlt">Long-lead</span> predictions of eastern United States hot days from Pacific sea surface temperatures</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McKinnon, K. A.; Rhines, A.; Tingley, M. P.; Huybers, P.</p> <p>2016-05-01</p> <p><span class="hlt">Seasonal</span> <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> persistence of these anomalies suggests potential for <span class="hlt">long-lead</span> 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 <span class="hlt">seasonal</span> prediction of summer precipitation deficits and high temperature anomalies in the eastern US.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016AdSR...13...51V&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016AdSR...13...51V&link_type=ABSTRACT"><span id="translatedtitle">How <span class="hlt">seasonal</span> <span class="hlt">forecast</span> could help a decision maker: an example of climate service for water resource management</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Viel, Christian; Beaulant, Anne-Lise; Soubeyroux, Jean-Michel; Céron, Jean-Pierre</p> <p>2016-04-01</p> <p>The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at <span class="hlt">seasonal</span> 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 <span class="hlt">season</span>. It is based on a <span class="hlt">forecasting</span> system, built on a refined hydrological suite, forced by a coupled <span class="hlt">seasonal</span> <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span>, with a <span class="hlt">forecast</span> A and with a <span class="hlt">forecast</span> B. One of <span class="hlt">forecast</span> A and B really contained <span class="hlt">seasonal</span> <span class="hlt">forecast</span>, the other only contained random <span class="hlt">forecasts</span> taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on <span class="hlt">seasonal</span> <span class="hlt">forecast</span> in comparison to a classical approach based on climatology, and to EPTG SGL current practice.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PIAHS.370..229W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PIAHS.370..229W"><span id="translatedtitle">Using subseasonal-to-<span class="hlt">seasonal</span> (S2S) extreme rainfall <span class="hlt">forecasts</span> for extended-range flood prediction in Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>White, C. J.; Franks, S. W.; McEvoy, D.</p> <p>2015-06-01</p> <p>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 <span class="hlt">forecasts</span> of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, <span class="hlt">seasonal</span>). Making improvements to extended-range rainfall and flood <span class="hlt">forecast</span> models, assessing <span class="hlt">forecast</span> skill and uncertainty, and exploring how to apply flood <span class="hlt">forecasts</span> and communicate their benefits to decision-makers are significant challenges facing the <span class="hlt">forecasting</span> 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 <span class="hlt">forecasts</span> on the extended-range "subseasonal-to-<span class="hlt">seasonal</span>" (S2S) <span class="hlt">forecasting</span> timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood <span class="hlt">forecasts</span> and warnings, and the growing adoption of "climate services". The paper also demonstrates how <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span>, 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1113063C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1113063C"><span id="translatedtitle"><span class="hlt">Seasonal</span> streamflow <span class="hlt">forecasts</span> in a semi-arid Andean watershed using remotely sensed snow cover data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cartes, M.; McPhee, J.; Vargas, X.</p> <p>2009-04-01</p> <p><span class="hlt">Forecasts</span> of monthly streamflow during the snowmelt <span class="hlt">season</span> 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 <span class="hlt">forecasts</span>, 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> 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 <span class="hlt">seasonal</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005TellA..57..409D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005TellA..57..409D"><span id="translatedtitle">Statistical and dynamical downscaling of precipitation over Spain from DEMETER <span class="hlt">seasonal</span> <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Díez, E.; Primo, C.; García-Moya, J. A.; Gutiérrez, J. M.; Orfila, B.</p> <p>2005-05-01</p> <p>Statistical and dynamical downscaling methods are tested and compared for downscaling <span class="hlt">seasonal</span> precipitation <span class="hlt">forecasts</span> over Spain from two DEMETER models: the European Centre for Medium-Range Weather <span class="hlt">Forecasts</span> (ECMWF) and the UK Meteorological Office (UKMO). The statistical method considered is a particular implementation of the standard analogue technique, based on close neighbours of the predicted atmospheric geopotential and humidity fields. Dynamical downscaling is performed using the Rossby Centre Climate Atmospheric model, which has been nested to the ECMWF model output, and run in climate mode for six months. We first check the performance of the direct output models in the period 1986 1997 and compare it with the results obtained applying the analogue method. We have found that the direct outputs underestimate the precipitation amount and that the statistical downscaling method improves the results as the skill of the direct <span class="hlt">forecast</span> increases. The highest skills relative operating characteristic skill areas (RSAs) above 0.6 are associated with early and late spring, summer and autumn <span class="hlt">seasons</span> at zero- and one-month lead times. On the other hand, models have poor skill during winter with the exception of the El Niño period (1986 1988), especially in the south of Spain. In this case, high RSAs and economic values have been found. We also compare statistical and dynamical downscaling during four <span class="hlt">seasons</span>, obtaining no concluding result. Both methods outperform direct output from DEMETER models, but depending on the <span class="hlt">season</span> and on the region of Spain one method is better than the other. Moreover, we have seen that dynamical and statistical methods can be used in combination, yielding the best skill scores in some cases of the study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ERL....11f4006H&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ERL....11f4006H&link_type=ABSTRACT"><span id="translatedtitle">Real-time extreme weather event attribution with <span class="hlt">forecast</span> <span class="hlt">seasonal</span> SSTs</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Haustein, K.; Otto, F. E. L.; Uhe, P.; Schaller, N.; Allen, M. R.; Hermanson, L.; Christidis, N.; McLean, P.; Cullen, H.</p> <p>2016-06-01</p> <p>Within the last decade, extreme weather event attribution has emerged as a new field of science and garnered increasing attention from the wider scientific community and the public. Numerous methods have been put forward to determine the contribution of anthropogenic climate change to individual extreme weather events. So far nearly all such analyses were done months after an event has happened. Here we present a new method which can assess the fraction of attributable risk of a severe weather event due to an external driver in real-time. The method builds on a large ensemble of atmosphere-only general circulation model simulations forced by <span class="hlt">seasonal</span> <span class="hlt">forecast</span> sea surface temperatures (SSTs). Taking the England 2013/14 winter floods as an example, we demonstrate that the change in risk for heavy rainfall during the England floods due to anthropogenic climate change, is of similar magnitude using either observed or <span class="hlt">seasonal</span> <span class="hlt">forecast</span> SSTs. Testing the dynamic response of the model to the anomalous ocean state for January 2014, we find that observed SSTs are required to establish a discernible link between a particular SST pattern and an atmospheric response such as a shift in the jetstream in the model. For extreme events occurring under strongly anomalous SST patterns associated with known low-frequency climate modes, however, <span class="hlt">forecast</span> SSTs can provide sufficient guidance to determine the dynamic contribution to the event.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015WRR....51.3543H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015WRR....51.3543H"><span id="translatedtitle">Long-range <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasting</span> over the Iberian Peninsula using large-scale atmospheric and oceanic information</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hidalgo-Muñoz, J. M.; Gámiz-Fortis, S. R.; Castro-Díez, Y.; Argüeso, D.; Esteban-Parra, M. J.</p> <p>2015-05-01</p> <p>Identifying the relationship between large-scale climate signals and <span class="hlt">seasonal</span> streamflow may provide a valuable tool for long-range <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> in regions under water stress, such as the Iberian Peninsula (IP). The skill of the main teleconnection indices as predictors of <span class="hlt">seasonal</span> 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 <span class="hlt">forecasting</span> scenarios, from one to four <span class="hlt">seasons</span> in advance. The correlation coefficient (RHO), Root Mean Square Error Skill Score (RMSESS), and the Gerrity Skill Score (GSS) were used to evaluate the <span class="hlt">forecasting</span> skill. For autumn streamflow, good <span class="hlt">forecasting</span> 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 <span class="hlt">forecasting</span> 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 <span class="hlt">seasons</span> in advance. For winter streamflow, fair <span class="hlt">forecasting</span> skill was found for one <span class="hlt">season</span> in advance in 168 stations, with the Snow Advance Index as the main predictor. Finally, <span class="hlt">forecasting</span> was poorer for spring streamflow than for autumn and winter, since only 16 stations showed fair <span class="hlt">forecasting</span> skill in with one <span class="hlt">season</span> in advance, particularly in north-western of IP.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.1886D&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.1886D&link_type=ABSTRACT"><span id="translatedtitle">Overcoming data scarcity: <span class="hlt">Seasonal</span> <span class="hlt">forecasting</span> of reservoir inflows using public domain resources in Central Asia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dixon, Samuel G.; Wilby, Robert L.</p> <p>2016-04-01</p> <p>Management of large hydropower reservoirs can be politically and strategically problematic. Traditional flow <span class="hlt">forecasting</span> techniques rely on accurate ground based observations, a requirement not met in many areas of the globe (Artan et al., 2007). In particular, access to real-time observational data in transnational river basins is often not possible. In these regions, novel techniques are required to combat the challenges of flow <span class="hlt">forecasting</span> for efficient reservoir management. Near real time remotely sensed information regarding flow predictors (e.g. satellite precipitation estimates) could combat data availability issues, improving the utility of <span class="hlt">seasonal</span> reservoir inflow <span class="hlt">forecasts</span>. This study investigates the potential for river flow <span class="hlt">forecasting</span> using public domain resources, including satellite and re-analysis precipitation as well as climate indices for several strategically important reservoirs throughout Central Asia (including Toktogul, Andijan, Kayrakkum and Nurek). Using reservoir inflows from 2001-2010, parsimonious numerical models were created for each study site using selected significant predictors for lead times of 1-3 months as well half year averages. Preliminary investigation has shown that parsimonious statistical models can explain over 80% of the variance in monthly inflows with three month lead to the Toktogul reservoir, Kyrgyzstan (Dixon and Wilby, 2015). Such findings show promise for improving the safety and efficiency of reservoir operations as well as reducing risks emerging from climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H41I..02C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H41I..02C"><span id="translatedtitle"><span class="hlt">Seasonal</span> Rainfall <span class="hlt">Forecasting</span> Using Sea Surface Temperatures with an application in the Southeast US</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, C.; Georgakakos, A. P.</p> <p>2011-12-01</p> <p>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 <span class="hlt">forecast</span> <span class="hlt">seasonal</span> 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 <span class="hlt">seasonal</span> 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), <span class="hlt">forecasting</span> is carried out using regression models. The dipole teleconnection method is applied to the <span class="hlt">forecasting</span> of <span class="hlt">seasonal</span> 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 <span class="hlt">seasons</span> for the southeast. Hindcasts of four-<span class="hlt">season</span> rainfall generated by the dipole model show that the strongest predictability exists in fall and winter. The best <span class="hlt">forecast</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFM.H11F0927B&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFM.H11F0927B&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Season</span>-ahead Drought <span class="hlt">Forecast</span> Models for the Lower Colorado River Authority in Texas</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Block, P. J.; Zimmerman, B.; Grzegorzewski, M.; Watkins, D. W., Jr.; Anderson, R.</p> <p>2014-12-01</p> <p>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 <span class="hlt">season</span>-ahead statistical streamflow and precipitation <span class="hlt">forecast</span> models for integration into LCRA decision support models. Optimal <span class="hlt">forecast</span> lead time, predictive skill, form, and communication are all considered.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1815456T&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1815456T&link_type=ABSTRACT"><span id="translatedtitle">High-resolution wave <span class="hlt">forecasting</span> system for the <span class="hlt">seasonally</span> ice-covered Baltic Sea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tuomi, Laura; Lehtiranta, Jonni</p> <p>2016-04-01</p> <p>When <span class="hlt">forecasting</span> surface waves in <span class="hlt">seasonally</span> ice-covered seas, the inclusion of ice conditions in the modelling is important. The ice cover affects the propagation and also changes the fetch over which the waves grow. In wave models the ice conditions are often still given as a boundary condition and handled by excluding areas where the ice concentration exceeds a certain threshold value. The ice data used are typically based on satellite analysis or expert analysis of local Ice Services who combine data from different sources. This type of data is sufficiently accurate to evaluate the near-real time ice concentrations, but when making <span class="hlt">forecasts</span> it is also important to account for the possible changes in ice conditions. For example in a case of a high wind situation, there can be rapid changes in the ice field, when the wind and waves may push the ice towards shores and cause fragmentation of ice field. To enhance handling of ice conditions in the Baltic Sea wave <span class="hlt">forecasts</span>, utilisation of ice model data was studied. Ice concentration, thickness produced by FMI's operational ice model HELMI were used to provide ice data to wave model as follows: Wave model grid points where the ice concentration was more than or equal to 70% and the ice thickness more than1 cm, were excluded from calculations. Ice concentrations smaller than that were taken into account as additional grid obstructions by decreasing the wave energy passed from one grid cell to another. A challenge in evaluating wave <span class="hlt">forecast</span> accuracy in partly ice covered areas it that there's typically no wave buoy data available, since the buoys have to be recovered well before the sea area freezes. To evaluate the accuracy of wave <span class="hlt">forecast</span> in partially ice covered areas, significant wave heights from altimeter's ERS2, Envisat, Jason-1 and Jason-2 were extracted from Ifremer database. Results showed that the more frequent update of the ice data was found to improve the wave <span class="hlt">forecast</span> especially during high wind</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy...46.2305P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...46.2305P"><span id="translatedtitle"><span class="hlt">Seasonal</span> prediction of Indian summer monsoon rainfall in NCEP CFSv2: <span class="hlt">forecast</span> and predictability error</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pokhrel, Samir; Saha, Subodh Kumar; Dhakate, Ashish; Rahman, Hasibur; Chaudhari, Hemantkumar S.; Salunke, Kiran; Hazra, Anupam; Sujith, K.; Sikka, D. R.</p> <p>2016-04-01</p> <p>A detailed analysis of sensitivity to the initial condition for the simulation of the Indian summer monsoon using retrospective <span class="hlt">forecast</span> by the latest version of the Climate <span class="hlt">Forecast</span> 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 <span class="hlt">seasonal</span> 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 <span class="hlt">forecast</span> time decreases (5 month lead to 0 month lead time i.e. L5-L0). In general spatial correlation (bias) increases (decreases) as <span class="hlt">forecast</span> 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) <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> 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 <span class="hlt">forecast</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17..900S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17..900S"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasts</span> of hydrological drought in the Limpopo basin: Getting the most out of a bouquet of methods.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Seibert, Mathias; Trambauer, Patricia</p> <p>2015-04-01</p> <p>Droughts are a widespread natural hazard with large socio-economical and environmental impacts. Preparedness to droughts can be enhanced by <span class="hlt">seasonal</span> 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. <span class="hlt">Seasonal</span> streamflow <span class="hlt">forecast</span> systems have been dominated by statistical methods in the past. Recently, dynamic physics-based <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> from global climate models have become available operationally. In combination with hydrological models these modern <span class="hlt">forecast</span> systems have the potential to replace the <span class="hlt">seasonal</span> statistical <span class="hlt">forecasts</span>. However, at lead times exceeding 6 months statistical methods might still be useful. In this study we present a <span class="hlt">forecast</span> scheme for streamflow in the Limpopo basin in Southern Africa, combining statistical methods at longer lead times with a dynamical <span class="hlt">forecast</span>, a distributed hydrological model forced with the ECMWF <span class="hlt">seasonal</span> <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span>, has its greatest advantage. The model provides a great array of information regarding the catchment status and therefore can be used to <span class="hlt">forecast</span> 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.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1992JApMe..31..964W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1992JApMe..31..964W"><span id="translatedtitle">Some Aspects of <span class="hlt">Forecasting</span> Severe Thunderstorms during Cool-<span class="hlt">Season</span> Return-Flow Episodes.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weiss, Steven J.</p> <p>1992-08-01</p> <p>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 <span class="hlt">season</span>.To gain insight into the short-range skill in <span class="hlt">forecasting</span> 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-<span class="hlt">season</span> cases that preceded severe local storm outbreaks. The NGM and AVN provided useful guidance in <span class="hlt">forecasting</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> of stability and the resultant operational severe local storm <span class="hlt">forecasts</span> 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 <span class="hlt">Forecast</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003BAMS...84.1741T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003BAMS...84.1741T"><span id="translatedtitle">Climate Research and <span class="hlt">Seasonal</span> <span class="hlt">Forecasting</span> for West Africans: Perceptions, Dissemination, and Use?.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tarhule, Aondover; Lamb, Peter J.</p> <p>2003-12-01</p> <p>Beginning in response to the disastrous drought of 1968 73, considerable research and monitoring have focused on the characteristics, causes, predictability, and impacts of West African Soudano Sahel (10° 18°N) rainfall variability and drought. While these efforts have generated substantial information on a range of these topics, very little is known of the extent to which communities, activities at risk, and policy makers are aware of, have access to, or use such information. This situation has prevailed despite Glantz&;s provocative BAMS paper on the use and value of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> for the Sahel more than a quarter century ago. We now provide a systematic reevaluation of these issues based on questionnaire responses of 566 participants (in 13 communities) and 26 organizations in Burkina Faso, Mali, Niger, and Nigeria. The results reveal that rural inhabitants have limited access to climate information, with nongovernmental organizations (NGOs) being the most important source. Moreover, the pathways for information flow are generally weakly connected and informal. As a result, utilization of the results of climate research is very low to nonexistent, even by organizations responsible for managing the effects of climate variability. Similarly, few people have access to <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span>, although the vast majority expressed a willingness to use such information when it becomes available. Those respondents with access expressed great enthusiasm and satisfaction with <span class="hlt">seasonal</span> <span class="hlt">forecasts</span>. The results suggest that inhabitants of the Soudano Sahel savanna are keen for changes that improve their ability to cope with climate variability, but the lack of information on alternative courses of action is a major constraint. Our study, thus, essentially leaves unchanged both Glantz&;s negative “tentative conclusion” and more positive “preliminary assessment” of 25 years ago. Specifically, while many of the infrastructural deficiencies and socioeconomic</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000050469','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000050469"><span id="translatedtitle">The Impact of ENSO on Extratropical Low Frequency Noise in <span class="hlt">Seasonal</span> <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Schubert, Siegfried D.; Suarez, Max J.; Chang, Yehui; Branstator, Grant</p> <p>2000-01-01</p> <p>This study examines the uncertainty in <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> mean noise (intra-ensemble variability). The North Pacific, in particular, shows extensive regions where the 1989 <span class="hlt">seasonal</span> mean noise kinetic energy (SKE), which is dominated by a "PNA-like" spatial structure, is more than twice that of the 1983 <span class="hlt">forecasts</span>. The larger SKE in 1989 is associated with a larger than normal barotropic conversion of kinetic energy from the mean Pacific jet to the <span class="hlt">seasonal</span> 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 <span class="hlt">seasonal</span> 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 <span class="hlt">seasonal</span> AGCM <span class="hlt">forecasts</span>. 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1816912H&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1816912H&link_type=ABSTRACT"><span id="translatedtitle">Project Ukko - Design of a climate service visualisation interface for <span class="hlt">seasonal</span> wind <span class="hlt">forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hemment, Drew; Stefaner, Moritz; Makri, Stephann; Buontempo, Carlo; Christel, Isadora; Torralba-Fernandez, Veronica; Gonzalez-Reviriego, Nube; Doblas-Reyes, Francisco; de Matos, Paula; Dykes, Jason</p> <p>2016-04-01</p> <p>Project Ukko is a prototype climate service to visually communicate probabilistic <span class="hlt">seasonal</span> wind <span class="hlt">forecasts</span> for the energy sector. In Project Ukko, an interactive visualisation enhances the accessibility and readability to the latests advances in <span class="hlt">seasonal</span> wind speed predictions developed as part of the RESILIENCE prototype of the EUPORIAS (EC FP7) project. Climate services provide made-to-measure climate information, tailored to the specific requirements of different users and industries. In the wind energy sector, understanding of wind conditions in the next few months has high economic value, for instance, for the energy traders. Current energy practices use retrospective climatology, but access to reliable <span class="hlt">seasonal</span> predictions based in the recent advances in global climate models has potential to improve their resilience to climate variability and change. Despite their potential benefits, a barrier to the development of commercially viable services is the complexity of the probabilistic <span class="hlt">forecast</span> information, and the challenge of communicating complex and uncertain information to decision makers in industry. Project Ukko consists of an interactive climate service interface for wind energy users to explore probabilistic wind speed predictions for the coming <span class="hlt">season</span>. This interface enables fast visual detection and exploration of interesting features and regions likely to experience unusual changes in wind speed in the coming months.The aim is not only to support users to better understand the future variability in wind power resources, but also to bridge the gap between practitioners' traditional approach and the advanced prediction systems developed by the climate science community. Project Ukko is presented as a case study of cross-disciplinary collaboration between climate science and design, for the development of climate services that are useful, usable and effective for industry users. The presentation will reflect on the challenge of developing a climate</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002EGSGA..27.2284M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27.2284M"><span id="translatedtitle">Probabilistic Prediction of European Winter Temperature and Their Application Using The Ecmwf <span class="hlt">Seasonal</span> <span class="hlt">Forecast</span> System 1 and 2</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Müller, W.; Appenzeller, Ch.</p> <p></p> <p>There is rising interest in economical applications of <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span>, for example for weather risk and weather derivative markets. However <span class="hlt">seasonal</span> <span class="hlt">forecast</span>- ing based on coupled atmosphere -ocean models is a complex task. As a consequence of the chaotic nature of the climate system <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">forecasts</span> on different drift correction methods. The <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> skill, especially over Europe. As an economic application we evaluate the skill of three month averaged heating degree days <span class="hlt">forecasts</span> over Europe.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJBm..tmp...21F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJBm..tmp...21F"><span id="translatedtitle">Regional <span class="hlt">forecast</span> model for the Olea pollen <span class="hlt">season</span> in Extremadura (SW Spain)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fernández-Rodríguez, Santiago; Durán-Barroso, Pablo; Silva-Palacios, Inmaculada; Tormo-Molina, Rafael; Maya-Manzano, José María; Gonzalo-Garijo, Ángela</p> <p>2016-02-01</p> <p>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 <span class="hlt">season</span> lasted an average of 34 days, from May 4th to June 7th. The model proposed to <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> Olea airborne pollen concentration.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H33D1647S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H33D1647S"><span id="translatedtitle">Value versus Accuracy: application of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> to a hydro-economic optimization model for the Sudanese Blue Nile</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Satti, S.; Zaitchik, B. F.; Siddiqui, S.; Badr, H. S.; Shukla, S.; Peters-Lidard, C. D.</p> <p>2015-12-01</p> <p>The unpredictable nature of precipitation within the East African (EA) region makes it one of the most vulnerable, food insecure regions in the world. There is a vital need for <span class="hlt">forecasts</span> to inform decision makers, both local and regional, and to help formulate the region's climate change adaptation strategies. Here, we present a suite of different <span class="hlt">seasonal</span> <span class="hlt">forecast</span> models, both statistical and dynamical, for the EA region. Objective regionalization is performed for EA on the basis of interannual variability in precipitation in both observations and models. This regionalization is applied as the basis for calculating a number of standard skill scores to evaluate each model's <span class="hlt">forecast</span> accuracy. A dynamically linked Land Surface Model (LSM) is then applied to determine <span class="hlt">forecasted</span> flows, which drive the Sudanese Hydroeconomic Optimization Model (SHOM). SHOM combines hydrologic, agronomic and economic inputs to determine the optimal decisions that maximize economic benefits along the Sudanese Blue Nile. This modeling sequence is designed to derive the potential added value of information of each <span class="hlt">forecasting</span> model to agriculture and hydropower management. A rank of each model's <span class="hlt">forecasting</span> skill score along with its added value of information is analyzed in order compare the performance of each <span class="hlt">forecast</span>. This research aims to improve understanding of how characteristics of accuracy, lead time, and uncertainty of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> influence their utility to water resources decision makers who utilize them.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4750006','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4750006"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasting</span> of lightning and thunderstorm activity in tropical and temperate regions of the world</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Dowdy, Andrew J.</p> <p>2016-01-01</p> <p>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 <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> of lightning and thunderstorm activity in various regions throughout the world. To investigate this possibility, <span class="hlt">seasonal</span> 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 <span class="hlt">season</span>, 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 <span class="hlt">seasons</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26865431','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26865431"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasting</span> of lightning and thunderstorm activity in tropical and temperate regions of the world.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Dowdy, Andrew J</p> <p>2016-01-01</p> <p>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 <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> of lightning and thunderstorm activity in various regions throughout the world. To investigate this possibility, <span class="hlt">seasonal</span> 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 <span class="hlt">season</span>, 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 <span class="hlt">seasons</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016NatSR...620874D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NatSR...620874D"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasting</span> of lightning and thunderstorm activity in tropical and temperate regions of the world</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dowdy, Andrew J.</p> <p>2016-02-01</p> <p>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 <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> of lightning and thunderstorm activity in various regions throughout the world. To investigate this possibility, <span class="hlt">seasonal</span> 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 <span class="hlt">season</span>, 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 <span class="hlt">seasons</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy..tmp...49C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy..tmp...49C"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasting</span> of intense tropical cyclones over the North Atlantic and the western North Pacific basins</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Choi, Woosuk; Ho, Chang-Hoi; Jin, Chun-Sil; Kim, Jinwon; Feng, Song; Park, Doo-Sun R.; Schemm, Jae-Kyung E.</p> <p>2016-02-01</p> <p>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 <span class="hlt">forecasting</span> the <span class="hlt">seasonal</span> activities of intense TCs in the two basins. Cross-validation of the model skill for 1982-2013 as well as verification of a <span class="hlt">forecast</span> for the 2014 TC <span class="hlt">season</span> suggest that our intense TC model is applicable to operational uses.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1513329P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1513329P"><span id="translatedtitle">Towards <span class="hlt">seasonal</span> hydrological <span class="hlt">forecasting</span> in mountain catchments: preliminary results from the APRIL project</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pistocchi, Alberto; Mazzoli, Paolo; Bagli, Stefano; Notarnicola, Claudia; Pasolli, Luca</p> <p>2013-04-01</p> <p>The APRIL project aims at addressing the long term quantitative prediction of monthly discharge from mountain catchments and setting up a system which can then be used operationally. More specifically, its objectives are: - To investigate the potential of EO products (snow cover extent, vegetation and soil moisture statust) and weather/climatic variables for the prediction of water streamflow from mountain catchments - To develop a robust methodology for the long term quantitative <span class="hlt">forecast</span> of montly discharge from EO and weather/climatic data - To build a fully operational system for <span class="hlt">seasonal</span> hydrological <span class="hlt">forecasting</span>. This contribution illustrates the general concept of the project as well as some preliminary results. Water discharge in mountain catchments is physically related to antecedent snow cover and climatology (precipitation, temperature). Other factors may play a role, such as vegetation/soil status and topography. Historical discharge measurements and earth observation (EO) data are a valuable source for inferring the quantitative relationship between the discharge and its predictors using appropriate techniques. The prediction is based on the Support Vector Regression (SVR)technique, a state of the art machine learning regression method with good intrinsic generalization ability and robustness. In the contribution we present and discuss results of a preliminary analysis on water discharge prediction ( with lead time of 1 to 3 months) in South Tyrol, Italy. Despite the use of a limited set of predictors (among which mainly snow cover area), the results are encouraging. The analysis is in the process of being extended at different spatial scales, which will give the possibility to investigate different aspects of the problem and develop different prediction systems; by updating on the current developments, the contribution discusses also perspectives and current limitations towards the set up of a fully operational <span class="hlt">seasonal</span> hydrological <span class="hlt">forecasting</span> system</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016RvGeo..54..336K&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016RvGeo..54..336K&link_type=ABSTRACT"><span id="translatedtitle">A review of multimodel superensemble <span class="hlt">forecasting</span> for weather, <span class="hlt">seasonal</span> climate, and hurricanes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Krishnamurti, T. N.; Kumar, V.; Simon, A.; Bhardwaj, A.; Ghosh, T.; Ross, R.</p> <p>2016-06-01</p> <p>This review provides a summary of work in the area of ensemble <span class="hlt">forecasts</span> for weather, climate, oceans, and hurricanes. This includes a combination of multiple <span class="hlt">forecast</span> model results that does not dwell on the ensemble mean but uses a unique collective bias reduction procedure. A theoretical framework for this procedure is provided, utilizing a suite of models that is constructed from the well-known Lorenz low-order nonlinear system. A tutorial that includes a walk-through table and illustrates the inner workings of the multimodel superensemble's principle is provided. Systematic errors in a single deterministic model arise from a host of features that range from the model's initial state (data assimilation), resolution, representation of physics, dynamics, and ocean processes, local aspects of orography, water bodies, and details of the land surface. Models, in their diversity of representation of such features, end up leaving unique signatures of systematic errors. The multimodel superensemble utilizes as many as 10 million weights to take into account the bias errors arising from these diverse features of multimodels. The design of a single deterministic <span class="hlt">forecast</span> models that utilizes multiple features from the use of the large volume of weights is provided here. This has led to a better understanding of the error growths and the collective bias reductions for several of the physical parameterizations within diverse models, such as cumulus convection, planetary boundary layer physics, and radiative transfer. A number of examples for weather, <span class="hlt">seasonal</span> climate, hurricanes and sub surface oceanic <span class="hlt">forecast</span> skills of member models, the ensemble mean, and the superensemble are provided.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.5946G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.5946G"><span id="translatedtitle">Verification of <span class="hlt">seasonal</span> hydrological <span class="hlt">forecasts</span> for Europe with real and pseudo observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Greuell, Wouter; Biemans, Hester; Franssen, Wietse; Hutjes, Ronald</p> <p>2016-04-01</p> <p>Within the framework of the EU-project EUPORIAS, <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> of hydrological variables are produced for Europe with three hydrological models. We evaluated the skill of the <span class="hlt">forecasts</span> made by two of the models, VIC and LPJmL, by analysing the model output from hindcasts (1981-2010), which were forced with ECMWF's System4 <span class="hlt">seasonal</span> 15 output. The forcing was bias-corrected with a quantile-quantile method using the WFDEI data as reference. The latter data set was also employed as forcing for a so-called baseline simulation, which generated the initial conditions for the hindcast runs. Also, all output fields from the baseline run (e.g. runoff, discharge, evapotranspiration and soil moisture) served as so-called pseudo-observations for verification of the hindcasts. An asset of these pseudo-observations is the completeness of its spatial coverage. Probabilistic skill was determined with several metrics (correlation coefficient, ROC area and RPSS). For a lead time of two months we found several regions with significant skill that persisted during at least three target months, e.g. the southern part of the Mediterranean regions from June to August and southern Fennoscandia from April to June. These findings are hardly sensitive to the type of verification metric. We also found that for lead times beyond the first month, significant skill is mostly due to the initial conditions. Tailored experiments demonstrate the relative importance of snow and soil moisture. The hindcasts were also evaluated with real discharge observations (GRDC and EWA). Significant skill remains in specific regions and <span class="hlt">seasons</span> but the skill found when using the pseudo-observations also diminishes in a number of cases. We analyse the differences between using pseudo and real observations and we discuss the advantages and limitations of both types of observations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.1244B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.1244B"><span id="translatedtitle">Assessment and limits of the existent <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> as support for the decision making process in the Sahel</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bacci, Maurizio; Genesio, Lorenzo; di Vecchia, Andrea; Tarchiani, Vieri; Vignaroli, Patrizio</p> <p>2010-05-01</p> <p>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 <span class="hlt">season</span> 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 <span class="hlt">season</span> 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 <span class="hlt">forecast</span> models. Nowadays, despite the general awareness on their potential role in food crises prevention, <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> 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 <span class="hlt">season</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> and in their tailoring to users. The aim of this paper is contributing to the scientific debate on <span class="hlt">Seasonal</span> <span class="hlt">Forecast</span> proposing possible orientations for models further development and the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMGC11D1024F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMGC11D1024F"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasts</span> of the exceptional boreal winters of 2009/10 and 2010/11</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fereday, D.; Maidens, A.; Knight, J.; Scaife, A. A.; Arribas, A.; MacLachlan, C.; Peterson, D.</p> <p>2012-12-01</p> <p>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 <span class="hlt">seasonal</span> <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> 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 <span class="hlt">forecast</span> 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 <span class="hlt">forecast</span> for December is examined using ensembles of atmosphere only runs, forced with SST fields as <span class="hlt">forecast</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.1218D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.1218D"><span id="translatedtitle"><span class="hlt">Seasonal</span> <span class="hlt">forecasting</span> for water resource management: the example of CNR Genissiat dam on the Rhone River in France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dommanget, Etienne; Bellier, Joseph; Ben Daoud, Aurélien; Graff, Benjamin</p> <p>2014-05-01</p> <p>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 <span class="hlt">seasonal</span> <span class="hlt">forecasting</span>. Indeed, <span class="hlt">seasonal</span> <span class="hlt">forecasting</span> 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. <span class="hlt">Seasonal</span> <span class="hlt">forecasting</span> 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 <span class="hlt">forecasting</span> 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. <span class="hlt">Forecasts</span> are based on hydrological statistical models. Discharge on the main Rhone River tributaries upstream Genissiat dam are <span class="hlt">forecasted</span> 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 <span class="hlt">season</span>, 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 <span class="hlt">forecasters</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GeoRL..43..852B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GeoRL..43..852B"><span id="translatedtitle"><span class="hlt">Seasonal</span> climate <span class="hlt">forecasts</span> significantly affected by observational uncertainty of Arctic sea ice concentration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bunzel, Felix; Notz, Dirk; Baehr, Johanna; Müller, Wolfgang A.; Fröhlich, Kristina</p> <p>2016-01-01</p> <p>We investigate how observational uncertainty in satellite-retrieved sea ice concentrations affects <span class="hlt">seasonal</span> 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 <span class="hlt">forecasts</span> of teleconnections that depend on northern hemispheric climate indices.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20120013669&hterms=seasons&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dseasons','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20120013669&hterms=seasons&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dseasons"><span id="translatedtitle"><span class="hlt">Forecasting</span> Fire <span class="hlt">Season</span> Severity in South America Using Sea Surface Temperature Anomalies</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chen, Yang; Randerson, James T.; Morton, Douglas C.; DeFries, Ruth S.; Collatz, G. James; Kasibhatla, Prasad S.; Giglio, Louis; Jin, Yufang; Marlier, Miriam E.</p> <p>2011-01-01</p> <p>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 <span class="hlt">forecast</span> regional fire <span class="hlt">season</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFM.H21H..04L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFM.H21H..04L"><span id="translatedtitle"><span class="hlt">Seasonal</span> to Interannual Hydroclimatic Prediction: From Identification of Dynamics to Multi-Attribute <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lall, U.</p> <p>2004-12-01</p> <p>Dynamical and Statistical Models for <span class="hlt">seasonal</span> to interannual <span class="hlt">forecasts</span> of key hydroclimatic state variables have been explored in recent years. Many authors report success based on typical performance metrics. Thus, a casual external observer may feel that we are at the verge of a breakthrough in hydrologic prediction, and hence in water resource management. This talk explores this notion, with particular regard to the multi-scale (time and space) nature of hydrologic fluxes, and of the management variables and styles that the water resources community has become accustomed to. A conceptual framework for the nascent predictive science of hydroclimatology is developed and exemplified. Aspects of the dynamics that need to be understood, and a unifying estimation/inference framework are proposed.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUSM.H23F..06L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUSM.H23F..06L"><span id="translatedtitle"><span class="hlt">Seasonal</span> to Interannual Hydroclimatic Prediction: From Identification of Dynamics to Multi-Attribute <span class="hlt">Forecasts</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lall, U.</p> <p>2004-05-01</p> <p>Dynamical and Statistical Models for <span class="hlt">seasonal</span> to interannual <span class="hlt">forecasts</span> of key hydroclimatic state variables have been explored in recent years. Many authors report success based on typical performance metrics. Thus, a casual external observer may feel that we are at the verge of a breakthrough in hydrologic prediction, and hence in water resource management. This talk explores this notion, with particular regard to the multi-scale (time and space) nature of hydrologic fluxes, and of the management variables and styles that the water resources community has become accustomed to. A conceptual framework for the nascent predictive science of hydroclimatology is developed and exemplified. Aspects of the dynamics that need to be understood, and a unifying estimation/inference framework are proposed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/22076373','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/22076373"><span id="translatedtitle"><span class="hlt">Forecasting</span> fire <span class="hlt">season</span> severity in South America using sea surface temperature anomalies.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Chen, Yang; Randerson, James T; Morton, Douglas C; DeFries, Ruth S; Collatz, G James; Kasibhatla, Prasad S; Giglio, Louis; Jin, Yufang; Marlier, Miriam E</p> <p>2011-11-11</p> <p>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 <span class="hlt">forecast</span> regional fire <span class="hlt">season</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.H53A1650L&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.H53A1650L&link_type=ABSTRACT"><span id="translatedtitle">Optimization of precipitation and streamflow <span class="hlt">forecasts</span> in the southwest Contiguous US for warm <span class="hlt">season</span> convection</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lahmers, T.; Castro, C. L.; Gupta, H. V.; Gochis, D. J.; ElSaadani, M.</p> <p>2015-12-01</p> <p>Warm <span class="hlt">season</span> convection associated with the North American Monsoon (NAM) provides an important source of precipitation for much of the Southwest Contiguous US (CONUS) and Northwest Mexico. Convection associated with the NAM can also result in flash flooding, a hazard to metropolitan areas such as Tucson and Phoenix, as well as rural areas where washouts of main roads can sever critical transportation infrastructure. In order to mitigate the effects of this problem, the National Oceanic and Atmospheric Administration (NOAA) National Water Center (NWC) is developing a national distributed hydrologic model using the WRF-Hydro framework with forcing from the High Resolution Rapid Refresh (HRRR) mesoscale atmospheric model. We aim to improve this National hydrologic and atmospheric modeling framework through the calibration of the WRF-Hydro model for the southwest CONUS and the optimization of planetary boundary layer and cloud microphysics schemes for the Weather Research and <span class="hlt">Forecasting</span> (WRF) model in the same region. The WRF-Hydro model, with a similar structure as the national configuration used by the NWC, has been set up for the Gila River basin in southern Arizona. We demonstrate the utility of the model for <span class="hlt">forecasting</span> high impact precipitation events in catchments with limited human modification. The WRF-Hydro model is spun up using past precipitation from the NCEP Stage-IV records and TRMM estimates. Atmospheric forcing for WRF-Hydro comes from the NASA Phase 2 North American Land Data Assimilation (NLDAS-2) dataset. WRF-Hydro is forced for selected high-impact events using a 3-km grid resolution Advanced Research WRF (WRF-ARW) atmospheric simulation. WRF-ARW is forced with the operational National Center for Environmental Prediction (NCEP) Global <span class="hlt">Forecasting</span> System (GFS) operational model. This methodology demonstrates the modeling framework that will be used for future parameter calibration of WRF-Hydro and optimization of WRF-ARW.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140011342','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011342"><span id="translatedtitle">Prediction of the Arctic Oscillation in Boreal Winter by Dynamical <span class="hlt">Seasonal</span> <span class="hlt">Forecasting</span> Systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kang, Daehyun; Lee, Myong-In; Im, Jungho; Kim, Daehyun; Kim, Hye-Mi; Kang, Hyun-Suk; Shubert, Siegfried D.; Arriba, Albertom; MacLachlan, Craig</p> <p>2013-01-01</p> <p>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 <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> time scale. It is also found that the deterministic and probabilistic <span class="hlt">forecast</span> skill of the AO in the recent period (1997-2010) is higher than that in the earlier period (1983-1996).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140017691','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140017691"><span id="translatedtitle">Prediction of the Arctic Oscillation in Boreal Winter by Dynamical <span class="hlt">Seasonal</span> <span class="hlt">Forecasting</span> Systems</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Kang, Daehyun; Lee, Myong-In; Im, Jungho; Kim, Daehyun; Kim, Hye-Mi; Kang, Hyun-Suk; Schubert, Siegfried D.; Arribas, Alberto; MacLachlan, Craig</p> <p>2014-01-01</p> <p>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 <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> time scales. It is also found that the skill of AO <span class="hlt">forecasts</span> during the recent period (1997-2010) is higher than that of the earlier period (1983-1996).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AGUFM.H13K..08K&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AGUFM.H13K..08K&link_type=ABSTRACT"><span id="translatedtitle">Assessing the potential skill of <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasting</span> for the River Rhine and the Upper Danube Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Klein, B.; Meissner, D.; Gerl, N.; Hemri, S.; Gneiting, T. J.</p> <p>2013-12-01</p> <p>Reliable <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> <span class="hlt">forecast</span> products there is no operational <span class="hlt">forecasting</span> 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 <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasts</span> 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 <span class="hlt">forecasting</span> 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 <span class="hlt">seasonal</span> weather <span class="hlt">forecasts</span> 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 <span class="hlt">seasonal</span> streamflow <span class="hlt">forecasting</span> 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</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC31A1004F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC31A1004F"><span id="translatedtitle">Incorporating SST <span class="hlt">seasonal</span> <span class="hlt">forecast</span> into drought and fire predictions in western Amazon</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fernandes, K.; Baethgen, W.; Bernardes, S.; DeFries, R. S.; DeWitt, D. G.; Goddard, L. M.; Lavado, W.; Lee, D.; Padoch, C.; Pinedo-Vasquez, M.; Uriarte, M.</p> <p>2011-12-01</p> <p>The prevailing wet climate in western Amazon is not favorable to the natural occurrence of fires. In the last decade, however, the region has experienced some of the most catastrophic fires in the history of Amazonia. In 2005 over 300,000 ha of burned rain forest in the Brazilian state of Acre and around 22,000 ha in the province of Coronel Portillo in Peru. In 2010 another severe drought prompted the Bolivian government to declare a state of emergency due to widespread fires and one major Amazon tributary, the Negro River, registered its lowest water lever in over 100 years. Fire dynamics in humid tropical forests are complex and involve a swath of socio-economic aspects, including replacement of forests by crops and pastures, fires for agricultural maintenance, timber extraction and infrastructure development all of which result in greater vulnerability of the natural system to fires. Despite the importance of these effects at fine spatial scales, we find that precipitation anomalies are the main drivers of interannual fire variability at large spatial and temporal scales in western Amazonia. Using real-time SST <span class="hlt">forecasts</span> for the north tropical Atlantic sector we are able to predict precipitation and fire anomalies during the dry <span class="hlt">season</span> months. The 2010 positive fire anomalies predicted by the 2010 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> for MJJ, JJA, and JAS are in agreement with the negative predicted 2010 JAS SPI and observed precipitation anomalies estimated by TRMM. Our results show that ECHAM-GML MJJ SST can be used to predict western Amazon JAS precipitation and fire anomalies as early as April, information that can be regionally used as an early warning system product.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ERL....11d5001C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ERL....11d5001C"><span id="translatedtitle">How much global burned area can be <span class="hlt">forecast</span> on <span class="hlt">seasonal</span> time scales using sea surface temperatures?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Yang; Morton, Douglas C.; Andela, Niels; Giglio, Louis; Randerson, James T.</p> <p>2016-04-01</p> <p>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 <span class="hlt">seasonal</span> timescales because fuel characteristics respond to the cumulative effects of climate prior to the onset of the fire <span class="hlt">season</span>. 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 <span class="hlt">forecast</span> 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 <span class="hlt">forecasts</span> may be possible with the aim of improving ecosystem management.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.8108L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.8108L"><span id="translatedtitle">Evaluation of an early warning system for heat wave related mortality in Europe: implications for sub-<span class="hlt">seasonal-to-seasonal</span> <span class="hlt">forecasting</span> and climate services</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lowe, Rachel; García-Díez, Markel; Ballester, Joan; Creswick, James; Robine, Jean-Marie; Herrmann, François R.; Rodó, Xavier</p> <p>2016-04-01</p> <p>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-<span class="hlt">seasonal-to-seasonal</span> (S2S) climate <span class="hlt">forecasts</span> of extreme temperatures could allow for better resource management within heat-health action plans, to protect vulnerable populations and ensure access to preventive measures well in advance. The objective of this study is to assess the extent to which S2S climate <span class="hlt">forecasts</span> could be incorporated into heat-health action plans, to support timely public health decision-making ahead of imminent heat wave events in Europe. <span class="hlt">Forecasts</span> 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 <span class="hlt">forecasts</span> up to several months ahead of the 2003 heat wave event in Europe. Results were compared to mortality predictions inferred from using observed apparent temperature data in the mortality model. In general, we found a decreasing transition in skill between excellent predictions when using observed temperature, to predictions with no skill when using <span class="hlt">forecast</span> 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 <span class="hlt">seasonal</span> climate <span class="hlt">forecasts</span> 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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013EGUGA..1510035D&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013EGUGA..1510035D&link_type=ABSTRACT"><span id="translatedtitle">Calibration of <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> over Euro-Mediterranean region: improve climate information for the applications in the energy sector</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>De Felice, Matteo; Alessandri, Andrea; Catalano, Franco</p> <p>2013-04-01</p> <p>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 <span class="hlt">forecast</span> energy demand and renewable output. On time-scales longer than two weeks (<span class="hlt">seasonal</span>), 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 <span class="hlt">forecast</span>, 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 <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> main modes of temperature anomaly and the main modes of reanalysis on Euro-Mediterranean domain. Then, <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> are calibrated by means of a cross-validation procedure with the aim of optimize climate information over Italy. Calibrated <span class="hlt">seasonal</span> <span class="hlt">forecasts</span> are used as predictor for electricity demand <span class="hlt">forecast</span> on Italy during the summer (JJA) in the period 1990-2009. Finally, a comparison with the results obtained with not calibrated climate <span class="hlt">forecasts</span> is performed. The proposed calibration procedure led to an improvements of electricity demand <span class="hlt">forecast</span> 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</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <center> <div class="footer-extlink text-muted"><small>Some links on this page may take you to non-federal websites. Their policies may differ from this site.</small> </div> </center> <div id="footer-wrapper"> <div class="footer-content"> <div id="footerOSTI" class=""> <div class="row"> <div class="col-md-4 text-center col-md-push-4 footer-content-center"><small><a href="http://www.science.gov/disclaimer.html">Privacy and Security</a></small> <div class="visible-sm visible-xs push_footer"></div> </div> <div class="col-md-4 text-center col-md-pull-4 footer-content-left"> <img src="http://www.osti.gov/images/DOE_SC31.png" alt="U.S. Department of Energy" usemap="#doe" height="31" width="177"><map style="display:none;" name="doe" id="doe"><area shape="rect" coords="1,3,107,30" href="http://www.energy.gov" alt="U.S. Deparment of Energy"><area shape="rect" coords="114,3,165,30" href="http://www.science.energy.gov" alt="Office of Science"></map> <a ref="http://www.osti.gov" style="margin-left: 15px;"><img src="http://www.osti.gov/images/footerimages/ostigov53.png" alt="Office of Scientific and Technical Information" height="31" width="53"></a> <div class="visible-sm visible-xs push_footer"></div> </div> <div class="col-md-4 text-center footer-content-right"> <a href="http://www.osti.gov/nle"><img src="http://www.osti.gov/images/footerimages/NLElogo31.png" alt="National Library of Energy" height="31" width="79"></a> <a href="http://www.science.gov"><img src="http://www.osti.gov/images/footerimages/scigov77.png" alt="science.gov" height="31" width="98"></a> <a href="http://worldwidescience.org"><img src="http://www.osti.gov/images/footerimages/wws82.png" alt="WorldWideScience.org" height="31" width="90"></a> </div> </div> </div> </div> </div> <p><br></p> </div><!-- container --> </body> </html>