A New Integrated Weighted Model in SNOW-V10: Verification of Categorical Variables

This paper presents the verification results for nowcasts of seven categorical variables from an integrated weighted model (INTW) and the underlying numerical weather prediction (NWP) models. Nowcasting, or short range forecasting (0–6 h), over complex terrain with sufficient accuracy is highly desirable but a very challenging task. A weighting, evaluation, bias correction and integration system (WEBIS) for generating nowcasts by integrating NWP forecasts and high frequency observations was used during the Vancouver 2010 Olympic and Paralympic Winter Games as part of the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) project. Forecast data from Canadian high-resolution deterministic NWP system with three nested grids (at 15-, 2.5- and 1-km horizontal grid-spacing) were selected as background gridded data for generating the integrated nowcasts. Seven forecast variables of temperature, relative humidity, wind speed, wind gust, visibility, ceiling and precipitation rate are treated as categorical variables for verifying the integrated weighted forecasts. By analyzing the verification of forecasts from INTW and the NWP models among 15 sites, the integrated weighted model was found to produce more accurate forecasts for the 7 selected forecast variables, regardless of location. This is based on the multi-categorical Heidke skill scores for the test period 12 February to 21 March 2010.     

Wave measurement and modeling in Chesapeake Bay

Three recently measured wind and wave data sets in the northern part of Chesapeake Bay (CB) are presented. Two of the three data sets were collected in late 1995. The third one was collected in July of 1998. The analyzed wind and wave data show that waves were dominated by locally generated, fetch limited young wind seas. Significant wave heights were highly correlated to the local driving wind speeds and the response time of the waves to the winds was about 1 h. We also tested two very different numerical wave models, Simulation of WAves Nearshore (SWAN) and Great Lakes Environmental Research Laboratory (GLERL), to hind-cast the wave conditions against the data sets. Time series model–data comparisons made using SWAN and GLERL showed that both models behaved well in response to a suddenly changing wind. In general, both SWAN and GLERL over-predicted significant wave height; SWAN over-predicted more than GLERL did. SWAN had a larger scatter index and a smaller correlation coefficient for wave height than GLERL had. In addition, both models slightly under-predicted the peak period with a fairly large scatter and low correlation coefficient. SWAN predicted mean wave direction better than GLERL did. Directional wave spectral comparisons between SWAN predictions and the data support these statistical comparisons. The GLERL model was much more computationally efficient for wind wave forecasts in CB. SWAN and GLERL predicted different wave height field distributions for the same winds in deeper water areas of the Bay where data were not available, however. These differences are as yet unresolved.     

Quantitative assessment of short‐term rainfall forecasts from radar nowcasts and MM5 forecasts

Short‐term Quantitative Precipitation Forecasts (QPFs) can be achieved from numerical weather prediction (NWP) models or radar nowcasting, that is the extrapolation of the precipitation at a future time from consecutive radar scans. Hybrid forecasts obtained by merging rainfall forecasts from radar nowcasting and NWP models are potentially more skilful than either radar nowcasts or NWP rainfall forecasts alone. This paper provides an assessment of deterministic and probabilistic high‐resolution QPFs achieved by implementing the Short‐term Ensemble Prediction System developed by the UK Met Office. Both radar nowcasts and hybrid forecasts have been performed. The results show that the performance of both deterministic nowcasts and deterministic hybrid forecasts decreases with increasing rainfall intensity and spatial resolution. The results also show that the blending with the NWP forecasts improves the performance of the forecasting system. Probabilistic hybrid forecasts have been obtained through the modelling of a stochastic noise component to produce a number of equally likely ensemble members, and the comparative assessment of deterministic and probabilistic hybrid forecasts shows that the probabilistic forecasting system is characterised by a higher discrimination accuracy than the deterministic one. Copyright © 2011 John Wiley & Sons, Ltd.     

Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models

Weather radar has a potential to provide accurate short‐term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro‐meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)‐TN] and the empirically based scheme (ERF‐EM), and explores which one exhibits higher predictive skill. The ERF‐TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so‐called ERF‐EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF‐EM achieves better performance than the ERF‐TN at 1‐h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd.     

Evaluation of Short-Range Forecasts from a Mesoscale Model Over the Indian Region During Monsoon 2006

This study examines the short-range forecast accuracy of the Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) as applied to the July 2006 episode of the Indian summer monsoon (ISM) and the model's sensitivity to the choice of different cumulus parameterization schemes (CPSs), namely Betts-Miller, Grell (GR) and Kain-Fritsch (KF). The results showed that MM5 day 1 (0–24 h prediction) and day 2 (24–48 h prediction) forecasts using all three CPSs overpredicted monsoon rainfall over the Indian landmass, with the larger overprediction seen in the day 2 forecasts. Among the CPSs, the rainfall distribution over the Indian landmass was better simulated in forecasts using the KF scheme. The KF scheme showed better skill in predicting the area of rainfall for most of the rainfall thresholds. The root mean square error (RMSE) in day 1 and day 2 rainfall forecasts using different CPSs showed that rainfall simulated using the KF scheme agreed better with the observed rainfall. As compared to other CPSs, simulation using the GR scheme showed larger RMSE in wind speed prediction at 850 and 200 hPa over the Indian landmass. MM5 24-h temperature forecasts at 850 hPa with all the CPSs showed a warm bias of the order of 1 K over the Indian landmass and the bias doubled in 48-h model forecasts. The mean error in temperature prediction at 850 hPa over the Indian region using the KF scheme was comparatively smaller for all the forecast intervals. The model with all the CPSs overpredicted humidity at 850 hPa. The improved prediction by MM5 with the KF scheme is well complemented by the smaller error shown by the KF scheme in vertical distribution of heat and mean moist static energy in the lower troposphere. In this study, the KF scheme which explicitly resolve the downdrafts in the cloud column tended to produce more realistic precipitation forecasts as compared to other schemes which did not explicitly incorporate downdraft effects. This is an important result especially given that the area covered by monsoon-precipitating systems is largely from stratiform-type clouds which are associated with strong downdrafts in the lower levels. This result is useful for improving the treatment of cumulus convection in numerical models over the ISM region.     

Coherence between atmospheric teleconnections,Great Lakes water levels,and regional climate

We investigated the frequency domain relationships between four atmospheric teleconnections (Trans-Niño Index TNI, Pacific Decadal Oscillation PDO, Northern Annular Mode/Arctic Oscillation Index NAM/AO, and Pacific/North American PNA pattern) and water levels in the Great Lakes from 1948 to 2002 by quantifying the coherence between these time series. The levels in all Great Lakes are significantly correlated with the TNI in the frequency range (3–7)⁻¹ cycles year⁻¹, and with the PDO in interdecadal frequencies. The levels in Lakes Superior, Michigan, and Erie are significantly correlated with the PNA pattern in interdecadal frequencies, and the levels in all Great Lakes are significantly correlated with the NAM/AO in interannual frequencies.     

Forecast-skill-based simulation of streamflow forecasts

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

Artificial neural networks as routine for error correction with an application in Singapore regional model

This research presents an error correction scheme based on artificial neural networks, and demonstrates its application on water level forecast for the Singapore water. The error correction scheme combines the numerical model outputs with the in situ measurements on a two-step basis: (1) predicting the model errors at the measurement stations and (2) distributing the predicted errors to the nonmeasurement stations. Artificial neural networks are used in both error prediction and error distribution as the mapping function approximators. The efficiency of this scheme is tested on six water level stations in the Singapore regional model domain with four prediction horizons. The results show that this error correction scheme produces high-precision forecasts, and improves the forecast accuracy at both measurement and nonmeasurement stations.     

Comparing quantitative precipitation forecast methods for prediction of sewer flows in a small urban area

Abstract

Due to the relatively small spatial scale, as well as rapid response, of urban drainage systems, the use of quantitative rainfall forecasts for providing quantitative flow and depth predictions is a challenging task. Such predictions are important when consideration is given to urban pluvial flooding and receiving water quality, and it is worthwhile to investigate the potential for improved forecasting. In this study, three quantitative precipitation forecast methods of increasing complexity were compared and used to create quantitative forecasts of sewer flows 0–3 h ahead in the centre of a small town in the north of England. The HyRaTrac radar nowcast model was employed, as well as two different versions of the more complex STEPS model. The STEPS model was used as a deterministic nowcasting system, and was also blended with the Numerical Weather Prediction (NWP) model MM5 to investigate the potential of increasing forecast lead-times (LTs) using high-resolution NWP. Predictive LTs between 15 and 90 min gave acceptable results, but were a function of the event type. It was concluded that higher resolution rainfall estimation as well as nowcasts are needed for prediction of both local pluvial flooding and combined sewer overflow spill events.

Editor D. Koutsoyiannis; Guest editor R.J. Moore     

A New Integrated Weighted Model in SNOW-V10: Verification of Continuous Variables

This paper presents the verification results of nowcasts of four continuous variables generated from an integrated weighted model and underlying Numerical Weather Prediction (NWP) models. Real-time monitoring of fast changing weather conditions and the provision of short term forecasts, or nowcasts, in complex terrain within coastal regions is challenging to do with sufficient accuracy. A recently developed weighting, evaluation, bias correction and integration system was used in the Science of Nowcasting Olympic Weather for Vancouver 2010 project to generate integrated weighted forecasts (INTW) out to 6 h. INTW forecasts were generated with in situ observation data and background gridded forecasting data from Canadian high-resolution deterministic NWP system with three nested grids at 15-, 2.5- and 1-km horizontal grid-spacing configurations. In this paper, the four variables of temperature, relative humidity, wind speed and wind gust are treated as continuous variables for verifying the INTW forecasts. Fifteen sites were selected for the comparison of the model performances. The results of the study show that integrating surface observation data with the NWP forecasts produce better statistical scores than using either the NWP forecasts or an objective analysis of observed data alone. Overall, integrated observation and NWP forecasts improved forecast accuracy for the four continuous variables. The mean absolute errors from the INTW forecasts for the entire test period (12 February to 21 March 2010) are smaller than those from NWP forecasts with three configurations. The INTW is the best and most consistent performer among all models regardless of location and variable analyzed.     

Rainfall and streamflow response to El Niño Southern Oscillation: a case study in a semiarid catchment,Australia

Abstract

This paper aims to compare the shift in frequency distribution and skill of seasonal climate forecasting of both streamflow and rainfall in eastern Australia based on the Southern Oscillation Index (SOI) Phase system. Recent advances in seasonal forecasting of climate variables have highlighted opportunities for improving decision making in natural resources management. Forecasting of rainfall probabilities for different regions in Australia is available, but the use of similar forecasts for water resource supply has not been developed. The use of streamflow forecasts may provide better information for decision-making in irrigation supply and flow management for improved ecological outcomes. To examine the relative efficacy of seasonal forecasting of streamflow and rainfall, the shift in probability distributions and the forecast skill were evaluated using the Wilcoxon rank-sum test and the linear error in probability space (LEPS) skill score, respectively, at three river gauging stations in the Border Rivers Catchment of the Murray-Darling Basin in eastern Australia. A comparison of rainfall and streamflow distributions confirms higher statistical significance in the shift of streamflow distribution than that in rainfall distribution. Moreover, streamflow distribution showed greater skill of forecasting with 0–3 month lead time, compared to rainfall distribution.     

Bayesian analyses of seasonal runoff forecasts

Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to theex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971–1988.     

The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

A data based mechanistic approach to nonlinear flood routing and adaptive flood level forecasting

Operational flood forecasting requires accurate forecasts with a suitable lead time, in order to be able to issue appropriate warnings and take appropriate emergency actions. Recent improvements in both flood plain characterization and computational capabilities have made the use of distributed flood inundation models more common. However, problems remain with the application of such models. There are still uncertainties associated with the identifiability of parameters; with the computational burden of calculating distributed estimates of predictive uncertainty; and with the adaptive use of such models for operational, real-time flood inundation forecasting. Moreover, the application of distributed models is complex, costly and requires high degrees of skill. This paper presents an alternative to distributed inundation models for real-time flood forecasting that provides fast and accurate, medium to short-term forecasts. The Data Based Mechanistic (DBM) methodology exploits a State Dependent Parameter (SDP) modelling approach to derive a nonlinear dependence between the water levels measured at gauging stations along the river. The transformation of water levels depends on the relative geometry of the channel cross-sections, without the need to apply rating curve transformations to the discharge. The relationship obtained is used to transform water levels as an input to a linear, on-line, real-time and adaptive stochastic DBM model. The approach provides an estimate of the prediction uncertainties, including allowing for heterescadasticity of the multi-step-ahead forecasting errors. The approach is illustrated using an 80 km reach of the River Severn, in the UK.     

The predictability of near-coastal currents using a baroclinic unstructured grid model

A limited domain, coastal ocean forecast system consisting of an unstructured grid model, a meteorological model, a regional ocean model, and a global tidal database is designed to be globally relocatable. For such a system to be viable, the predictability of coastal currents must be well understood with error sources clearly identified. To this end, the coastal forecast system is applied at the mouth of Chesapeake Bay in response to a Navy exercise. Two-day forecasts are produced for a 10-day period from 4 to 14 June 2010 and compared to real-time observations. Interplay between the temporal frequency of the regional model boundary forcing and the application of external tides to the coastal model impacts the tidal characteristics of the coastal current, even contributing a small phase error. Frequencies of at least 3 h are needed to resolve the tidal signal within the regional model; otherwise, externally applied tides from a database are needed to capture the tidal variability. Spatial resolution of the regional model (3 vs 1 km) does not impact skill of the current prediction. Tidal response of the system indicates excellent representation of the dominant M ₂ tide for water level and currents. Diurnal tides, especially K ₁, are amplified unrealistically with the application of coarse 27-km winds. Higher-resolution winds reduce current forecast error with the exception of wind originating from the SSW, SSE, and E. These winds run shore parallel and are subject to strong interaction with the shoreline that is poorly represented even by the 3-km wind fields. The vertical distribution of currents is also well predicted by the coastal model. Spatial and temporal resolution of the wind forcing including areas close to the shoreline is the most critical component for accurate current forecasts. Additionally, it is demonstrated that wind resolution plays a large role in establishing realistic thermal and density structures in upwelling prone regions.     

The effect of density stratification on the prediction of global storm surges

With the long-term goal of developing an operational forecast system for total water level, we conduct a hindcast study of global storm surges for Fall 2014 using a baroclinic ocean model based on the NEMO framework. The model has 19 vertical levels, a horizontal resolution of 1/12°, and is forced by hourly forecasts of atmospheric wind and air pressure. Our first objective is to evaluate the model’s ability to predict hourly sea levels recorded by a global array of 257 tide gauges. It is shown that the model can provide reasonable predictions of surges for the whole test period at tide gauges with relatively large tidal residuals (i.e., gauges where the standard deviation of observed sea level, after removal of the tide, exceeds 5 cm). Our second objective is to quantify the effect of density stratification on the prediction of global surges. It is found that the inclusion of density stratification increases the overall predictive skill at almost all tide gauges. The increase in skill for the instantaneous peak surge is smaller. The location for which the increase in overall skill is largest (east coast of South Africa) is discussed in detail and physical reasons for the improvement are given.     

Targeted GOES Satellite Observations to Improve Hurricane Track Forecast: A Case Study of Hurricane Floyd

This study performs a set of Observing System Simulation Experiments (OSSEs) using Geostationary Operational Environmental Satellite (GOES) soundings. The primary objective of the OSSEs is to demonstrate that targeted observations can improve forecast accuracy by enhancing the initial conditions and mitigating their uncertainties. Hurricane Floyd (1999) is chosen as a study case. The main reason for choosing hurricane Floyd as a test case is that the movement of the storm was dictated by a mid-level complex polar jet steering flow region. This well-defined feature allowed us to examine the inaccuracy of analysis over the steering flow area using GOES soundings as targeted observations and its impact on the forecast track error. The set of experiments starts from a baseline forecast of hurricane Floyd using the Operational Multiscale Environment model with Grid Adaptivity (OMEGA). From GOES satellite soundings, atmospheric vertical profiles were extracted to simulate targeted observations. These data extracts were assimilated in the initial conditions to simulate new forecasts of hurricane Floyd which were then compared against both the baseline track and observed track. It was found that targeted observations in a forecast sensitive area can help to reduce hurricane forecast track error. Assimilation of only the subset of data (about 50 soundings) from the subjectively chosen fully sampled target region produced a considerable reduction of the track forecast errors (about 30%) within the first critical three days of the forecast.     

Forecast of Low Visibility and Fog from NCEP: Current Status and Efforts

Based on the visibility analysis data during November 2009 through April 2010 over North America from the Aviation Digital Database Service (ADDS), the performance of low visibility/fog predictions from the current operational 12?km-NAM, 13?km-RUC and 32?km-WRF-NMM models at the National Centers for Environmental Prediction (NCEP) was evaluated. The evaluation shows that the performance of the low visibility/fog forecasts from these models is still poor in comparison to those of precipitation forecasts from the same models. In order to improve the skill of the low visibility/fog prediction, three efforts have been made at NCEP, including application of a rule-based fog detection scheme, extension of the NCEP Short Range Ensemble Forecast System (SREF) to fog ensemble probabilistic forecasts, and a combination of these two applications. How to apply these techniques in fog prediction is described and evaluated with the same visibility analysis data over the same period of time. The evaluation results demonstrate that using the multi-rule-based fog detection scheme significantly improves the fog forecast skill for all three models relative to visibility-diagnosed fog prediction, and with a combination of both rule-based fog detection and the ensemble technique, the performance skill of fog forecasting can be further raised.     

A One-dimensional Ensemble Forecast and Assimilation System for Fog Prediction

A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.