Performance of operational systems with respect to water level forecasts in the Gulf of Finland

This paper is devoted to the validation of water level forecasts in the Gulf of Finland. Daily forecasts produced by four setups of operational, three-dimensional Baltic Sea oceanographic models are analyzed using statistical means and are compared with water level observations at three Finnish stations located on the northern coast of the Gulf of Finland. The overall conclusion is that the operational systems were skillful in forecasting water level variations during the study period from November 1, 2003, to January 31, 2005. The factors causing differences between the water level forecasts of different models are discussed as well. An important task of operational sea level forecasting services is to provide accurate and early information about extreme water levels, both positive and negative surges. During the study period, two major winter storms occurred which caused coastal flooding in the region. According to our analysis, the operational models forecast the rise of water levels during these events rather successfully. Nowadays, operational forecasts can provide early warnings of extreme water levels at least 1 day in advance, which may be regarded as a minimum requirement for an operational forecasting system. The paper concludes that the models generally performed very well, with over 93% of the hourly water level forecasts found to be within the range of ±15 cm of the observed water levels, and with the timing of the water level peaks accurately predicted. Further discussion and studies dealing with the assessment of the skills of both operational meteorological and oceanographic forecasts, especially in connection with rare surge events, will be necessary. Skill assessment of operational oceanographic models would be relatively easy if acceptable error limits or a quality system was developed for the Baltic Sea operational models.     

Hindcast of oil-spill pollution during the Lebanon crisis in the Eastern Mediterranean, July-August 2006

MOON (Mediterranean Operational Oceanography Network http://www.moon-oceanforecasting.eu) provides near-real-time information on oil-spill detection (ocean color and SAR) and predictions [ocean forecasts (MFS and CYCOFOS) and oil-spill predictions (MEDSLIK)]. We employ this system to study the Lebanese oil-pollution crisis in summer 2006 and thus to assist regional and local decision makers in Europe, regionally and locally. The MEDSLIK oil-spill predictions obtained using CYCOFOS high-resolution ocean fields are compared with those obtained using lower-resolution MFS hydrodynamics, and both are validated against satellite observations. The predicted beached oil distributions along the Lebanese and Syrian coasts are compared with in situ observations.The oil-spill predictions are able to simulate the northward movement of the oil spill, with the CYCOFOS predictions being in better agreement with satellite observations. Among the free MEDSLIK parameters tested in the sensitivity experiments, the drift factor appears to be the most relevant to improve the quality of the results.     

Validation of the Flood‐PROOFS probabilistic forecasting system

Probabilistic hydrometeorological forecasting systems are becoming more and more an operational tool used by civil protection centres for issuing flood alerts. One of the most important requests of decision makers is related to the reliability of such systems and to the validation of their predictive performances. For these reasons, this work is devoted to the validation of a probabilistic flood forecasting system called Flood‐PRObabilistic Operational Forecasting System (Flood‐PROOFS). The system is operational in real time, since 2008, in Valle d'Aosta, an alpine Region of northern Italy. It is used by the Civil Protection regional service to issue warnings and by the local water company to protect its facilities. The system manages and uses both real‐time meteorological and satellite data and real‐time data on the operation of the control structures in dam and river, managed by the water company. It has proven a useful tool for flood forecasting and for managing complex situations, facilitating the dialogue between civil protection and the water company during crisis periods. The system uses both a limited area model forecast and a forecast issued by regional expert meteorologists. The main outputs are deterministic and probabilistic discharge forecasts in different outlet areas of the river network. The performance of the system has been evaluated on a 25 months period with different statistical methods such as Brier score and Rank histograms. The results highlight good performances of the system as support system for emitting warnings, but there is a lack of statistics especially for huge discharge events. Copyright © 2013 John Wiley & Sons, Ltd.     

Future developments in modelling and monitoring of volcanic ash clouds: outcomes from the first IAVCEI-WMO workshop on Ash Dispersal Forecast and Civil Aviation

As a result of the serious consequences of the 2010 Eyjafjallajökull eruption (Iceland) on civil aviation, 52 volcanologists, meteorologists, atmospheric dispersion modellers and space and ground-based monitoring specialists from 12 different countries (including representatives from 6 Volcanic Ash Advisory Centres and related institutions) gathered to discuss the needs of the ash dispersal modelling community, investigate new data-acquisition strategies (i.e. quantitative measurements and observations) and discuss how to improve communication between the research community and institutions with an operational mandate. Based on a dedicated benchmark exercise and on 3 days of in-depth discussion, recommendations have been made for future model improvements, new strategies of ash cloud forecasting, multidisciplinary data acquisition and more efficient communication between different communities. Issues addressed in the workshop include ash dispersal modelling, uncertainty, ensemble forecasting, combining dispersal models and observations, sensitivity analysis, model variability, data acquisition, pre-eruption forecasting, first simulation and data assimilation, research priorities and new communication strategies to improve information flow and operational routines. As a main conclusion, model developers, meteorologists, volcanologists and stakeholders need to work closely together to develop new and improved strategies for ash dispersal forecasting and, in particular, to: (1) improve the definition of the source term, (2) design models and forecasting strategies that can better characterize uncertainties, (3) explore and identify the best ensemble strategies that can be adapted to ash dispersal forecasting, (4) identify optimized strategies for the combination of models and observations and (5) implement new critical operational strategies.     

Hydrological Outlook UK: an operational streamflow and groundwater level forecasting system at monthly to seasonal time scales

This paper describes the development of the first operational seasonal hydrological forecasting service for the UK, the Hydrological Outlook UK (HOUK). Since June 2013, this service has delivered monthly forecasts of streamflow and groundwater levels, with an emphasis on forecasting hydrological conditions over the next three months, accompanied by outlooks over longer time horizons. This system is based on three complementary approaches combined to produce the outlooks: (i) national-scale modelling of streamflow and groundwater levels based on dynamic seasonal rainfall forecasts, (ii) catchment-scale modelling where streamflow and groundwater level models are driven by historical meteorological forcings (i.e. the Ensemble Streamflow Prediction, ESP, approach), and (iii) a catchment-scale statistical method based on persistence and historical analogues. This paper provides the background to the Hydrological Outlook, describes the various component methods in detail and then considers the impact and usefulness of the product. As an example of a multi-method, operational seasonal hydrological forecasting system, it is hoped that this overview provides useful information and context for other forecasting initiatives around the world.     

利用地震海洋学方法估算南海中尺度涡的地转流速

中尺度涡是重要的海洋学现象,它在很大程度上影响着海洋内部的能量传递过程.由于传统海洋学观测手段的固有局限性,一直以来对中尺度涡观测和研究的程度都比较低.地震海洋学的诞生和发展为海洋学观测提供了一个全新的手段.对南海的历史地震数据重新处理后,我们首次在本研究海域的地震剖面上看到了透镜状结构.它位于南海西南次海盆(~113.6°E,11.4°N),中心深度约为450 m,中心厚度约为300 m,半径约为55~65 km,具有典型的中尺度涡特征,综合解释为反气旋.我们利用地震海洋学方法估算了地转剪切,结合来自于卫星高度数据的海表面地转流速度进一步得到了绝对流速的垂向剖面.结果显示,流速的最大值约为0.7 m/s,出现在400~450 m处,对应于涡旋的中心深度;西北部分为正,东南部分为负,整体呈现出顺时针的转动方向,说明了它是一个反气旋结构.     

Application of the geomorphological instantaneous unit hydrograph theory to development of forecasting models in Poland

ABSTRACT

Geomorphological instantaneous unit hydrograph (GIUH) theory has been applied for the estimation of the parameters of two conceptual models: a linear cascade model and a Laurenson-type model. Conceptual models, especially the linear cascade model, are more convenient for operational forecasting than the original GIUH model. A single linear reservoir model is suggested, with limited storage to represent the subsurface flow component. Subsurface flow is significant in Polish mountainous river catchments. Preliminary results of applying the model to operational flood forecasting are described.     

Scientific management of Mediterranean coastal zone: a hybrid ocean forecasting system for oil spill and search and rescue operations

The oil spill from Prestige tanker showed the importance of scientifically based protocols to minimize the impacts on the environment. In this work, we describe a new forecasting system to predict oil spill trajectories and their potential impacts on the coastal zone. The system is formed of three main interconnected modules that address different capabilities: (1) an operational circulation sub-system that includes nested models at different scales, data collection with near-real time assimilation, new tools for initialization or assimilation based on genetic algorithms and feature-oriented strategic sampling; (2) an oil spill coastal sub-system that allows simulation of the trajectories and fate of spilled oil together with evaluation of coastal zone vulnerability using environmental sensitivity indexes; (3) a risk management sub-system for decision support based on GIS technology. The system is applied to the Mediterranean Sea where surface currents are highly variable in space and time, and interactions between local, sub-basin and basin scale increase the non-linear interactions effects which need to be adequately resolved at each one of the intervening scales. Besides the Mediterranean Sea is a complex reduced scale ocean representing a real scientific and technological challenge for operational oceanography and particularly for oil spill response and search and rescue operations.     

Uncertainties in real‐time flood forecasting with neural networks

Although artificial neural networks (ANNs) have been applied in rainfall runoff modelling for many years, there are still many important issues unsolved that have prevented this powerful non‐linear tool from wide applications in operational flood forecasting activities. This paper describes three ANN configurations and it is found that a dedicated ANN for each lead‐time step has the best performance and a multiple output form has the worst result. The most popular form with multiple inputs and single output has the average performance. In comparison with a linear transfer function (TF) model, it is found that ANN models are uncompetitive against the TF model in short‐range predictions and should not be used in operational flood forecasting owing to their complicated calibration process. For longer range predictions, ANN models have an improved chance to perform better than the TF model; however, this is highly dependent on the training data arrangement and there are undesirable uncertainties involved, as demonstrated by bootstrap analysis in the study. To tackle the uncertainty issue, two novel approaches are proposed: distance analysis and response analysis. Instead of discarding the training data after the model's calibration, the data should be retained as an integral part of the model during its prediction stage and the uncertainty for each prediction could be judged in real time by measuring the distances against the training data. The response analysis is based on an extension of the traditional unit hydrograph concept and has a very useful potential to reveal the hydrological characteristics of ANN models, hence improving user confidence in using them in real time. Copyright © 2006 John Wiley & Sons, Ltd.     

Improving NWP through radar rainfall-driven land surface parameters: A case study on convective precipitation forecasting

In this study we investigate the effect of forcing the land surface scheme of an atmospheric mesoscale model with radar rainfall data instead of the model-generated rainfall fields. The goal is to provide improved surface conditions for the atmospheric model in order to achieve accurate simulations of the mesoscale circulations that can significantly affect the timing, distribution and intensity of convective precipitation. The performance of the approach is evaluated in a set of numerical experiments on the basis of a 2-day-long mesoscale convective system that occurred over the US Great Plains in July 2004. The experimental design includes multiple runs covering a variety of forcing periods. Continuous data integration was initially used to investigate the sensitivity of the model’s performance in varying soil state conditions, while shorter time windows prior to the storm event were utilized to assess the effectiveness of the procedure for improving convective precipitation forecasting. Results indicate that continuous integration of radar rainfall data brings the simulated precipitation fields closer to the observed ones, as compared to the control simulation. The precipitation forecasts (up to 48 h) appear improved also in the cases of shorter integration periods (24 and 36 h), making this technique potentially useful for operational settings of weather forecasting systems. A physical interpretation of the results is provided on the basis of surface moisture and energy exchange.     

Predicting ground-level ozone concentrations by adaptive Bayesian model averaging of statistical seasonal models

While seasonal time-varying models should generally be used to predict the daily concentration of ground-level ozone given its strong seasonal cycles, the sudden switching of models according to their designated period in an annual operational forecasting system may affect their performance, especially during the season’s transitional period in which the starting date and duration time can vary from year to year. This paper studies the effectiveness of an adaptive Bayesian Model Averaging scheme with the support of a transitional prediction model in solving the problem. The scheme continuously evaluates the probabilities of all the ozone prediction models (ozone season, nonozone season, and the transitional period) in a forecasting system, which are then used to provide a weighted average forecast. The scheme has been adopted in predicting the daily maximum of 8-h averaged ozone concentration in Macau for a period of 2 years (2008 and 2009), with results proved to be satisfactory.     

Efficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea

Forecasting the state of large marine ecosystems is important for many economic and public health applications. However, advanced three-dimensional (3D) ecosystem models, such as the European Regional Seas Ecosystem Model (ERSEM), are computationally expensive, especially when implemented within an ensemble data assimilation system requiring several parallel integrations. As an alternative to 3D ecological forecasting systems, we propose to implement a set of regional one-dimensional (1D) water-column ecological models that run at a fraction of the computational cost. The 1D model domains are determined using a Gaussian mixture model (GMM)-based clustering method and satellite chlorophyll-a (Chl-a) data. Regionally averaged Chl-a data is assimilated into the 1D models using the singular evolutive interpolated Kalman (SEIK) filter. To laterally exchange information between subregions and improve the forecasting skills, we introduce a new correction step to the assimilation scheme, in which we assimilate a statistical forecast of future Chl-a observations based on information from neighbouring regions. We apply this approach to the Red Sea and show that the assimilative 1D ecological models can forecast surface Chl-a concentration with high accuracy. The statistical assimilation step further improves the forecasting skill by as much as 50%. This general approach of clustering large marine areas and running several interacting 1D ecological models is very flexible. It allows many combinations of clustering, filtering and regression technics to be used and can be applied to build efficient forecasting systems in other large marine ecosystems.     

A high-resolution operational forecast system for oil spill response in Belfast Lough

This paper presents a high-resolution operational forecast system for providing support to oil spill response in Belfast Lough. The system comprises an operational oceanographic module coupled to an oil spill forecast module that is integrated in a user-friendly web application. The oceanographic module is based on Delft3D model which uses daily boundary conditions and meteorological forcing obtained from COPERNICUS and from the UK Meteorological Office. Downscaled currents and meteorological forecasts are used to provide short-term oil spill fate and trajectory predictions at local scales. Both components of the system are calibrated and validated with observational data, including ADCP data, sea level, temperature and salinity measurements and drifting buoys released in the study area. The transport model is calibrated using a novel methodology to obtain the model coefficients that optimize the numerical simulations. The results obtained show the good performance of the system and its capability for oil spill forecast.     

Forecasting the summer rainfall in North China using the year-to-year increment approach

A new approach to forecasting the year-to-year increment of rainfall in North China in July–August (JA) is proposed. DY is defined as the difference of a variable between the current year and the preceding year (year-to-year increment). NR denotes the seasonal mean precipitation rate over North China in JA. After analyzing the atmospheric circulation anomalies associated with the DY of NR, five key predictors for the DY of NR have been identified. The prediction model for the DY of NR is established by using multi-linear regression method and the NR is obtained (the current forecasted DY of NR added to the preceding observed NR). The prediction model shows a high correlation coefficient (0.8) between the simulated and the observed DY of NR throughout period 1965–1999, with an average relative root mean square error of 19% for the percentage of precipitation rate anomaly over North China. The prediction model makes a hindcast for 2000–2007, with an average relative root mean square error of 21% for the percentage of precipitation rate anomaly over North China. The model reproduces the downward trend of the percentage of precipitation rate anomaly over North China during 1965–2006. Because the current operational prediction models of the summer precipitation have average forecast scores of 60%–70%, it has been more difficult to forecast the summer rainfall over North China. Thus this new approach for predicting the year-to-year increment of the summer precipitation (and hence the summer precipitation itself) has the potential to significantly improve operational forecasting skill for summer precipitation. Supported by National Basic Research Program of China (Grant No. 2009CB421406), National Natural Science Foundation of China (Grant Nos. 40631005, 40775049) and Excellent Ph. D Dissertation in Chinese Academy of Sciences     

Improved water-level forecasting for the Northwest European Shelf and North Sea through direct modelling of tide, surge and non-linear interaction

In real-time operational coastal forecasting systems for the northwest European shelf, the representation accuracy of tide–surge models commonly suffers from insufficiently accurate tidal representation, especially in shallow near-shore areas with complex bathymetry and geometry. Therefore, in conventional operational systems, the surge component from numerical model simulations is used, while the harmonically predicted tide, accurately known from harmonic analysis of tide gauge measurements, is added to forecast the full water-level signal at tide gauge locations. Although there are errors associated with this so-called astronomical correction (e.g. because of the assumption of linearity of tide and surge), for current operational models, astronomical correction has nevertheless been shown to increase the representation accuracy of the full water-level signal. The simulated modulation of the surge through non-linear tide–surge interaction is affected by the poor representation of the tide signal in the tide–surge model, which astronomical correction does not improve. Furthermore, astronomical correction can only be applied to locations where the astronomic tide is known through a harmonic analysis of in situ measurements at tide gauge stations. This provides a strong motivation to improve both tide and surge representation of numerical models used in forecasting. In the present paper, we propose a new generation tide–surge model for the northwest European Shelf (DCSMv6). This is the first application on this scale in which the tidal representation is such that astronomical correction no longer improves the accuracy of the total water-level representation and where, consequently, the straightforward direct model forecasting of total water levels is better. The methodology applied to improve both tide and surge representation of the model is discussed, with emphasis on the use of satellite altimeter data and data assimilation techniques for reducing parameter uncertainty. Historic DCSMv6 model simulations are compared against shelf wide observations for a full calendar year. For a selection of stations, these results are compared to those with astronomical correction, which confirms that the tide representation in coastal regions has sufficient accuracy, and that forecasting total water levels directly yields superior results.     

Developing a Web-based flood forecasting system for reservoirs with J2EE / Développement sur Internet avec J2EE d’un système de prévision de crue pour barrages

Abstract

Abstract A flood forecasting system is a crucial component in flood mitigation. For certain important large-scale reservoirs, cooperation and communication among federal, state, and local stakeholders are required when heavy flood events are encountered. The Web-based environment is emerging as a very important development and delivery platform for real-time flood forecasting systems. In this paper, the findings of a case study are presented of the development of a Web-based flood forecasting system for reservoirs using Java 2 platform Enterprise Edition (J2EE). J2EE of Sun Microsystems is chosen as the development solution for the Web-based flood forecasting system, Weblogic 6.0 of BEA as the container provider, and JBuilder 7.0 of Borland as the development tool. One of the key objectives in this project is to establish a collaborative platform for flood forecasting via Web technology in order to render hydrological models and data available to stakeholders and experts involved and thus offer an efficient medium for transferring and sharing information, knowledge and experiences among them. Compared with general Web-based query systems and traditional flood forecasting systems, the Web-based flood forecasting system is more focused on the on-line analysis of model-based forecasting of floods and provides opportunities for improving the transfer of information and knowledge from the hydrological scientists and managers to decision makers. Finally, a prototype system is used to demonstrate the system application.     

Operational earthquake forecasting in Europe: progress,despite challenges

Is Europe-wide operational earthquake forecasting (OEF) possible? We discuss the myriad problems that prevent it today, many of which relate to heterogeneities in earthquake recording, processing, and reporting. We contemplate the difficulty of building models that cross political boundaries, and we consider the prospect of European OEF in light of recent efforts to harmonize long-term seismic hazard assessment among several nations. Emphasizing the Strategies and Tools for Real-time Earthquake Risk Reduction (REAKT) project, we report achievements related to short-term seismicity forecasting in Iceland and Italy that could apply elsewhere in Europe. In Iceland, collaboration fostered by REAKT resulted in a revised earthquake catalog and a prototype OEF system. We report results from an experiment conducted with this prototype; these results suggest ensemble models provide an information gain, updating models more frequently improves their forecast skill, and that OEF is computationally feasible. In Italy, REAKT supported the creation of an ensemble model that now issues weekly hazard forecasts. We present examples of these forecasts, highlighting the problem that OEF often yields low probabilities, which are difficult to interpret and convert into actionable decisions. Motivated by such low hazard probabilities, we highlight Europe’s pioneering efforts in operational earthquake loss forecasting and mention solutions to problems that currently prevent OEF at the European scale.     

The SOLAR2000 empirical solar irradiance model and forecast tool

SOLAR2000 is a collaborative project for accurately characterizing solar irradiance variability across the spectrum. A new image- and full-disk proxy empirical solar irradiance model, SOLAR2000, is being developed that is valid in the spectral range of 1–1,000,000 nm for historical modeling and forecasting throughout the solar system. The overarching scientific goal behind SOLAR2000 is to understand how the Sun varies spectrally and through time from X-ray through infrared wavelengths. This will contribute to answering key scientific questions and will aid national programmatic goals related to solar irradiance specification. SOLAR2000 is designed to be a fundamental energy input into planetary atmosphere models, a comparative model with numerical/first principles solar models, and a tool to model or predict the solar radiation component of the space environment. It is compliant with the developing International Standards Organization (ISO) solar irradiance standard. SOLAR2000 captures the essence of historically measured solar irradiances and this expands our knowledge about the quiet and variable Sun including its historical envelope of variability. The implementation of the SOLAR2000 is described, including the development of a new EUV proxy, E10.7, which has the same units as the commonly used F10.7. SOLAR2000 also provides an operational forecasting and global specification capability for solar irradiances and information can be accessed at the website address of http://www.spacenvironment.net.     

FerryBox - Application of continuous water quality observations along transects in the North Sea

In 2002, the Helmholtz-Zentrum Geesthacht, Germany, started to use FerryBox-automated monitoring systems on Ships of Opportunity to continuously record standard oceanographic, biological and chemical in situ data in the North Sea. The present study summarises the operational experience gathered since the beginning of this deployment and reflects on the potential and limits of FerryBox systems as a monitoring tool. One part relates to the instrumental performance, constancy of shipping services, and the availability and quality of the recorded in situ data. The other considers integration of the FerryBox observations in scientific applications and routine monitoring campaigns. Examples are presented that highlight the added value of the recorded data for the study of both long- and short-term variability in water mass stability, plankton communities and surface water productivity in the North Sea. Through the assessment of technical and scientific performance, it is evident that FerryBoxes have become a valuable tool in marine research that helps to fill gaps in coastal and open ocean operational observation networks.     

Operational snow modeling: Addressing the challenges of an energy balance model for National Weather Service forecasts

Prediction of snowmelt has become a critical issue in much of the western United States given the increasing demand for water supply, changing snow cover patterns, and the subsequent requirement of optimal reservoir operation. The increasing importance of hydrologic predictions necessitates that traditional forecasting systems be re-evaluated periodically to assure continued evolution of the operational systems given scientific advancements in hydrology. The National Weather Service (NWS) SNOW17, a conceptually based model used for operational prediction of snowmelt, has been relatively unchanged for decades. In this study, the Snow–Atmosphere–Soil Transfer (SAST) model, which employs the energy balance method, is evaluated against the SNOW17 for the simulation of seasonal snowpack (both accumulation and melt) and basin discharge. We investigate model performance over a 13-year period using data from two basins within the Reynolds Creek Experimental Watershed located in southwestern Idaho. Both models are coupled to the NWS runoff model [SACramento Soil Moisture Accounting model (SACSMA)] to simulate basin streamflow. Results indicate that while in many years simulated snowpack and streamflow are similar between the two modeling systems, the SAST more often overestimates SWE during the spring due to a lack of mid-winter melt in the model. The SAST also had more rapid spring melt rates than the SNOW17, leading to larger errors in the timing and amount of discharge on average. In general, the simpler SNOW17 performed consistently well, and in several years, better than, the SAST model. Input requirements and related uncertainties, and to a lesser extent calibration, are likely to be primary factors affecting the implementation of an energy balance model in operational streamflow prediction.