Fuzzy computing based rainfall–runoff model for real time flood forecasting

This paper analyses the skills of fuzzy computing based rainfall–runoff model in real time flood forecasting. The potential of fuzzy computing has been demonstrated by developing a model for forecasting the river flow of Narmada basin in India. This work has demonstrated that fuzzy models can take advantage of their capability to simulate the unknown relationships between a set of relevant hydrological data such as rainfall and river flow. Many combinations of input variables were presented to the model with varying structures as a sensitivity study to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff. The most appropriate set of input variables was determined, and the study suggests that the river flow of Narmada behaves more like an autoregressive process. As the precipitation is weighted only a little by the model, the last time‐steps of measured runoff are dominating the forecast. Thus a forecast based on expected rainfall becomes very inaccurate. Although good results for one‐step‐ahead forecasts are received, the accuracy deteriorates as the lead time increases. Using the one‐step‐ahead forecast model recursively to predict flows at higher lead time, however, produces better results as opposed to different independent fuzzy models to forecast flows at various lead times. Copyright © 2004 John Wiley & Sons, Ltd.     

A three-dimensional model comparison of PSC processing during the Arctic winters of 1991/1992 and 1992/1993

A three-dimensional transport model has been used to compare and contrast the extent of processing by polar stratospheric clouds during the northern hemisphere winters of 1991/1992 and 1992/1993. The model has also been used to compare the potential for ozone loss between these two winters. The TOMCAT off-line model is forced using meteorological analyses from the ECMWF. During winter 1992/1993 polar stratospheric clouds (PSCs) in the model persisted into late February/early March, which is much later than in 1991/1992. This persistence of PSCs should have resulted in much more ozone loss in the later winter. Interestingly, however, the extent of PSC processing and ozone loss was greater in January 1992 than January 1993. In January 1992 PSCs occurred at the edge of a distorted polar vortex whilst in January 1993 the PSCs were located at the centre of a much more zonally symmetrical vortex. In March 1993, distortions of the vortex led to the tearing off of vortex air and its mixing into midlatitudes.     

The species pattern of Baltic phytoplankton in autumn 1991

Species pattern of phytoplankton in the surface waters (0–1 m) of the Baltic Sea was studied in the autumn 1991 during 25th cruise of the R/V “Akademik Mstislav Keldysh”. The samples were analysed by light and scanning electron microscopy. Results of this work show that sharp changes in the Central Baltic phytoplankton community have not yet begun. In coastal eutrophicated waters the growth of biomass and decrease of phytoplankton diversity are more significant.     

Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self‐organizing map

During typhoons or storms, accurate forecasts of hourly streamflow are necessary for flood warning and mitigation. However, hourly streamflow is difficult to forecast because of the complex physical process and the high variability in time. Furthermore, under the global warming scenario, events with extreme streamflow may occur that leads to more difficulties in forecasting streamflows. Hence, to obtain more accurate hourly streamflow forecasts, an improved streamflow forecasting model is proposed in this paper. The computational kernel of the proposed model is developed on the basis of support vector machine (SVM). Additionally, self‐organizing map (SOM) is used to analyse observed data to extract data with specific properties, which are capable of providing valuable information for streamflow forecasting. After reprocessing, these extracted data and the observed data are used to construct the SVM‐based model. An application is conducted to clearly demonstrate the advantage of the proposed model. The comparison between the proposed model and the conventional SVM model, which is constructed without SOM, is performed. The results indicate that the proposed model is better performed than the conventional SVM model. Moreover, as regards the extreme events, the result shows that the proposed model reduces the forecasting error, especially the error of peak streamflow. It is confirmed that because of the use of data extracted by SOM, the improved forecasting performance is obtained. The proposed model, which can produce accurate forecasts, is expected to be useful to support flood warning systems. Copyright © 2012 John Wiley & Sons, Ltd.     

The inclusion of surface synoptic data in very short range predictions of convective events

Summary Predictor vectors, including upper air as well as surface data, were used for categorical forecasting convective events over a subregion of the Czech territory, and the effect of including surface variables in the predictor vector was examined. While upper air data were considered as Perfect Prognosis, the surface data were successively included according to the time of their origin. The forecasting technique was based on linear multiple regression with learning, and the accuracy of the forecast was measured by the Critical Success Index. The input data from the three May-September periods in 1989–91 were used, and the first year served as the learning set. The aerological data from TEMP 12 UTC, simulating Perfect Prognosis, were the source of the upper air predictors. The performance of all, upper air, surface and combined, predictors were evaluated and compared. It turned out that the improvement of prediction accuracy due to the inclusion of surface variables was not negligible. Significant improvements were made in the forecasts of thunderstorm occurrence between 18 and 24 UTC.     

Updating Real-Time Flood Forecasting Using a Fuzzy Rule-Based Model/Mise à Jour de Prévision de Crue en Temps Réel Grâce à un Modèle à Base de Règles Floues

Abstract

An updating technique is a tool to update the forecasts of mathematical flood forecasting model based on data observed in real time, and is an important element in a flood forecasting model. An error prediction model based on a fuzzy rule-based method was proposed as the updating technique in this work to improve one- to four-hour-ahead flood forecasts by a model that is composed of the grey rainfall model, the grey rainfall—runoff model and the modified Muskingum flow routing model. The coefficient of efficiency with respect to a benchmark is applied to test the applicability of the proposed fuzzy rule-based method. The analysis reveals that the fuzzy rule-based method can improve flood forecasts one to four hours ahead. The proposed updating technique can mitigate the problem of the phase lag in forecast hydrographs, and especially in forecast hydrographs with longer lead times.     

Stationary magnetospheric convection on November 24, 1981. 1. A case study of “pressure gradient/minimum-B” auroral arc generation

We present two case studies in the night and evening sides of the auroral oval, based on plasma and field measurements made at low altitudes by the AUREOL-3 satellite, during a long period of stationary magnetospheric convection (SMC) on November 24, 1981. The basic feature of both oval crossings was an evident double oval pattern, including (1) a weak arc-type structure at the equatorial edge of the oval/polar edge of the diffuse auroral band, collocated with an upward field-aligned current (FAC) sheet of ≈1.0 μA m⁻², (2) an intermediate region of weaker precipitation within the oval, (3) a more intense auroral band at the polar oval boundary, and (4) polar diffuse auroral zone near the polar cap boundary. These measurements are compared with the published magnetospheric data during this SMC period, accumulated by Yahnin et al. and Sergeev et al., including a semi-empirical radial magnetic field profile B_Z in the near-Earth neutral sheet, with a minimum at about 10–14 R_E. Such a radial B_Z profile appears to be very similar to that assumed in the “minimum B/cross-tail line current” model by Galperin et al. (GVZ92) as the “root of the arc”, or the arc generic region. This model considers a FAC generator mechanism by Grad-Vasyliunas-Boström-Tverskoy operating in the region of a narrow magnetic field minimum in the near-Earth neutral sheet, together with the concept of ion non-adiabatic scattering in the “wall region”. The generated upward FAC branch of the double sheet current structure feeds the steady auroral arc/inverted-V at the equatorial border of the oval. When the semi-empirical B_Z profile is introduced in the GVZ92 model, a good agreement is found between the modelled current and the measured characteristics of the FACs associated with the equatorial arc. Thus the main predictions of the GVZ92 model concerning the “minimum-B” region are consistent with these data, while some small-scale features are not reproduced. Implications of the GVZ92 model are discussed, particularly concerning the necessary conditions for a substorm onset that were not fulfilled during the SMC period.     

A Monte Carlo study of rainfall forecasting with a stochastic model

A procedure for short-term rainfall forecasting in real-time is developed and a study of the role of sampling on forecast ability is conducted. Ground level rainfall fields are forecasted using a stochastic space-time rainfall model in state-space form. Updating of the rainfall field in real-time is accomplished using a distributed parameter Kalman filter to optimally combine measurement information and forecast model estimates. The influence of sampling density on forecast accuracy is evaluated using a series of a simulated rainfall events generated with the same stochastic rainfall model. Sampling was conducted at five different network spatial densities. The results quantify the influence of sampling network density on real-time rainfall field forecasting. Statistical analyses of the rainfall field residuals illustrate improvement in one hour lead time forecasts at higher measurement densities.     

Assessing the skill of the UK Hydrological Outlook

ABSTRACT

The UK Hydrological Outlook (UKHO) is a seasonal forecast of future river flows and groundwater levels. The UKHO contains both presentations of outputs from models simulating future conditions and a high-level summary. The summary is produced by an expert panel of forecasters that considers the model outputs together with other recent hydrological and meteorological information. Whilst the skill and uncertainty of the individual models have been explored and published, this study sets out to establish the performance of the high-level summary, and presents such an assessment of the river flow forecasts at the 1-month timescale. Both qualitative and quantitative assessments are presented and compared with two naïve forecasting methods. The UKHO summary is found to have a similar Gerrity skill score to a “same as last month” forecast, an outcome that generates suggestions for improvements in how the different model outputs should be considered and presented in the high-level summary.     

PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments

The Process Modelling and Artificial Intelligence for Online Flood Forecasting (PAI-OFF) methodology combines the reliability of physically based, hydrologic/hydraulic modelling with the operational advantages of artificial intelligence. These operational advantages are extremely low computation times and straightforward operation. The basic principle of the methodology is to portray process models by means of ANN. We propose to train ANN flood forecasting models with synthetic data that reflects the possible range of storm events. To this end, establishing PAI-OFF requires first setting up a physically based hydrologic model of the considered catchment and – optionally, if backwater effects have a significant impact on the flow regime – a hydrodynamic flood routing model of the river reach in question. Both models are subsequently used for simulating all meaningful and flood relevant storm scenarios which are obtained from a catchment specific meteorological data analysis. This provides a database of corresponding input/output vectors which is then completed by generally available hydrological and meteorological data for characterizing the catchment state prior to each storm event. This database subsequently serves for training both a polynomial neural network (PoNN) – portraying the rainfall–runoff process – and a multilayer neural network (MLFN), which mirrors the hydrodynamic flood wave propagation in the river. These two ANN models replace the hydrological and hydrodynamic model in the operational mode. After presenting the theory, we apply PAI-OFF – essentially consisting of the coupled “hydrologic” PoNN and “hydrodynamic” MLFN – to the Freiberger Mulde catchment in the Erzgebirge (Ore-mountains) in East Germany (3000 km²). Both the demonstrated computational efficiency and the prediction reliability underline the potential of the new PAI-OFF methodology for online flood forecasting.     

Adaptive Blending of Model and Observations for Automated Short-Range Forecasting: Examples from the Vancouver 2010 Olympic and Paralympic Winter Games

An automated short-range forecasting system, adaptive blending of observations and model (ABOM), was tested in real time during the 2010 Vancouver Olympic and Paralympic Winter Games in British Columbia. Data at 1-min time resolution were available from a newly established, dense network of surface observation stations. Climatological data were not available at these new stations. This, combined with output from new high-resolution numerical models, provided a unique and exciting setting to test nowcasting systems in mountainous terrain during winter weather conditions. The ABOM method blends extrapolations in time of recent local observations with numerical weather predictions (NWP) model predictions to generate short-range point forecasts of surface variables out to 6 h. The relative weights of the model forecast and the observation extrapolation are based on performance over recent history. The average performance of ABOM nowcasts during February and March 2010 was evaluated using standard scores and thresholds important for Olympic events. Significant improvements over the model forecasts alone were obtained for continuous variables such as temperature, relative humidity and wind speed. The small improvements to forecasts of variables such as visibility and ceiling, subject to discontinuous changes, are attributed to the persistence component of ABOM.     

An RBF‐based model with an information processor for forecasting hourly reservoir inflow during typhoons

Inflow forecasting is essential for decision making on reservoir operation during typhoons. In this paper, a radial basis function (RBF)‐based model with an information processor is proposed for more accurate forecasts of hourly reservoir inflow. Firstly, based on the multilayer perceptron neural (MLP) network, an information processor is developed to pre‐process the typhoon information (namely, typhoon characteristics and rainfall) and to produce forecasts of rainfall. The forecasted rainfall and the observed inflow are then used as input to the RBF‐based model, which is a nonlinear function approximator, to produce forecasts of hourly inflow. For parameter estimation of the RBF‐based model, the fully‐supervised learning algorithm is used. Actual applications of the proposed model are performed to yield 1‐ to 6‐h ahead forecasts of inflow. To assess the improvement due to the use of the typhoon information processor, models without the typhoon information processor are constructed and compared with the proposed model. The results show that the proposed model performs the best and is capable of providing improved forecasts of hourly inflow, especially for long lead‐time. In conclusion, the proposed model with a typhoon information processor can extract useful information from typhoon characteristics and rainfall, and consequently improve the forecasting performance. Copyright © 2009 John Wiley & Sons, Ltd.     

Evaluation of monthly precipitation forecasting skill of the National Multi‐model Ensemble in the summer season

As a test bed, the National Multi‐model Ensemble (NMME) comprises seven climate models from different sources, including the National Oceanic and Atmospheric Administration, the National Aeronautics and Space Administration, the National Center for Atmospheric Research and the International Research Institute for Climate and Society. It provides 89 ensemble members of precipitation forecasts at different lead times. Precipitation forecasting from climate models has been applied to provide streamflow forecasts, and its utility in water resource system operation has been demonstrated in the literature. In this study, 1‐month‐ahead precipitation forecasts from NMME are evaluated for 945 grid points of 1°‐by‐1° resolution over the continental USA using mean square error and rank probability score. The temporal and spatial variabilities of the forecasting skill over different months of the summer season are discussed. The relation between forecasting uncertainty and observed precipitation is investigated. Such analyses have implications for monthly operational forecasts and water resource management at the watershed scale. Copyright © 2013 John Wiley & Sons, Ltd.     

Ionospheric measurements during the CRISTA/MAHRSI campaign: their implications and comparison with previous campaigns

The CRISTA/MAHRSI experiment on board a space shuttle was accompanied by a broad campaign of rocket, balloon and ground-based measurements. Supporting lower ionospheric ground-based measurements were run in Europe and Eastern Asia between 1 October–30 November, 1994. Results of comparisons with long ionospheric data series together with short-term comparisons inside the interval October-November, 1994, showed that the upper middle atmosphere (h =80–100 km) at middle latitudes of the Northern Hemisphere in the interval of the CRISTA/MAHRSI experiment (4–12 November, 1994) was very close to its expected climatological state. In other words, the average results of the experiment can be used as climatological data, at least for the given area/altitudes. The role of solar/geomagnetic and “meteorological” control of the lower ionosphere is investigated and compared with the results of MAP/WINE, MAC/SINE and DYANA campaigns. The effects of both solar/geomagnetic and global meteorological factors on the lower ionosphere are found to be weak during autumn 1994 compared to those in MAP/WINE and DYANA winters, and they are even slightly weaker than those in MAP/SINE summer. The comparison of the four campaigns suggests the following overall pattern: in winter the lower ionosphere at northern middle latitudes appears to be fairly well “meteorologically” controlled with a very weak solar influence. In summer, solar influence is somewhat stronger and dominates the weak “meteorological” influence, but the overall solar/meteorological control is weaker than in winter. In autumn we find the weakest overall solar/meteorological control, local effects evidently dominate.     

Revisiting the 1669 Etnean eruptive crisis using a cellular automata model and implications for volcanic hazard in the Catania area

Cellular Automata provide an alternative approach to standard numerical methods for modelling some complex natural systems, the behaviour of which can be described in terms of local interactions of their constituent parts. SCIARA is a 2-D Cellular Automata model which simulates lava flows. It was tested on, validated by, and improved on several Etnean lava events such as the 1986–1987 eruption and the first and last phase of the 1991–1993 event. With respect to forecasting the surface covered by the lava flows, the best results were acceptable. The model has been used to determine hazard zones in the inhabited areas of Nicolosi, Pedara, S. Alfio and Zafferana (Sicily, Italy). The main goal of the current work in the Etnean area from Nicolosi to Catania has been the verification of the volcanic hazard effects of an eruptive crisis similar to the event that occurred in 1669. The simulation uses the volcanic data of the 1669 eruption with present-day morphology. Catania has been affected by some historical Etnean events, the most famous one being the 1669 eruption, involving 1 km³ of lava erupted over the course of 120 days. The simulation of ephemeral vents and the use of different histories within the experiments have been crucial in the determination of a new hazard area for Catania. In fact, during the simulation the city was never affected without the introduction of ephemeral vents, proving the fact that lava tubes played a fundamental role in the 1669 Catania lava crisis.     

Integrating hydrometeorological information for rainfall‐runoff modelling by artificial neural networks

The major purpose of this study is to effectively construct artificial neural networks‐based multistep ahead flood forecasting by using hydrometeorological and numerical weather prediction (NWP) information. To achieve this goal, we first compare three mean areal precipitation forecasts: radar/NWP multisource‐derived forecasts (P_r), NWP precipitation forecasts (P_n), and improved precipitation forecasts (P_m) by merging P_r and P_n. The analysis shows that the accuracy of P_m is higher than that of P_r and P_n. The analysis also indicates that the NWP precipitation forecasts do provide relative effectiveness to the merging procedure, particularly for forecast lead time of 4–6 h. In sum, the merged products performed well and captured the main tendency of rainfall pattern. Subsequently, a recurrent neural network (RNN)‐based multistep ahead flood forecasting techniques is produced by feeding in the merged precipitation. The evaluation of 1–6‐h flood forecasting schemes strongly shows that the proposed hydrological model provides accurate and stable flood forecasts in comparison with a conventional case, and significantly improves the peak flow forecasts and the time‐lag problem. An important finding is the hydrologic model responses which do not seem to be sensitive to precipitation predictions in lead times of 1–3 h, whereas the runoff forecasts are highly dependent on predicted precipitation information for longer lead times (4–6 h). Overall, the results demonstrate that accurate and consistent multistep ahead flood forecasting can be obtained by integrating predicted precipitation information into ANNs modelling. Copyright © 2009 John Wiley & Sons, Ltd.     

Estimates of surface drifter trajectories in the equatorial Atlantic: a multi-model ensemble approach

We compared the estimates of surface drifter trajectories from 1 to 7?days in the equatorial Atlantic over an 18-month period with five eddying ocean general circulation model (OGCM) reanalyses and one observational product. The cumulative distribution of trajectory error was estimated using over 7,000?days of drifter trajectories. The observational product had smaller errors than any of the individual OGCM reanalyses. Three strategies for improving trajectory estimates using the ensemble of five operational ocean analysis and forecasting products were explored: two methods using a multi-model ensemble estimate and also spatial low-pass filtering. The results were insensitive to the method used to create the ensemble estimates, and by most measures, the results were better than the observational product. Comparison of relative skill of the various OGCM reanalyses suggested promising avenues for exploration for further improvements: forcing with higher frequency wind stress and quality control of input data. One of the lowest horizontal resolution OGCMs, with 1/4° longitude horizontal resolution, made the best trajectory estimates. The individual OGCMs were dominated by errors at spatial scales smaller than about 100 to 200?km, i.e., less than the local deformation radius. But buried in those errors were valuable signals that could be retrieved by combining all the OGCM velocity fields to produce a multi-model ensemble-based estimate. This estimate had skill down to spatial scales about 75?km. Results from this study are consistent with previous work showing that ensemble-mean forecast skill is superior to individual forecasts.     

A new methodology for the extension of the impact of data assimilation on ocean wave prediction

It is a common fact that the majority of today's wave assimilation platforms have a limited, in time, ability of affecting the final wave prediction, especially that of long-period forecasting systems. This is mainly due to the fact that after “closing” the assimilation window, i.e., the time that the available observations are assimilated into the wave model, the latter continues to run without any external information. Therefore, if a systematic divergence from the observations occurs, only a limited portion of the forecasting period will be improved. A way of dealing with this drawback is proposed in this study: A combination of two different statistical tools—Kolmogorov–Zurbenko and Kalman filters—is employed so as to eliminate any systematic error of (a first run of) the wave model results. Then, the obtained forecasts are used as artificial observations that can be assimilated to a follow-up model simulation inside the forecasting period. The method was successfully applied to an open sea area (Pacific Ocean) for significant wave height forecasts using the wave model WAM and six different buoys as observational stations. The results were encouraging and led to the extension of the assimilation impact to the entire forecasting period as well as to a significant reduction of the forecast bias.