Verification of SPE probability forecasts at the Space Environment Prediction Center (SEPC)

In space weather forecasting, forecast verification is necessary so that the forecast quality can be assessed. This paper provides an example of how to choose and devise verification methods and techniques according to different space weather forecast products. Solar proton events (SPEs) are hazardous space weather events, and forecasting them is one of the major tasks of the Space Environment Prediction Center (SEPC) at the National Space Science Center of the Chinese Academy of Sciences. Through analyzing SPE occurrence characteristics, SPE forecast properties, and verification requirements at SEPC, verification methods for SPE probability forecasts are identified, and verification results obtained. Overall, SPE probability forecasts at SEPC exhibit good accuracy, reliability, and discrimination. Compared with climatology and persistence forecasts, the SPE forecasts are more accurate. However, the forecasts for SPE onset days are substantially underestimated and need to be considerably improved.     

A mathematical formulation for optimal control of air pollution

In recent years the urban air pollutions caused by emissions from industry, transportation and energy consuming for daily living are getting worse in many major cities of China. Peoples awareness on air quality is increasing. Routine monitoring and foreca…     

The Impact of Weather Forecasts of Various Lead Times on Snowmaking Decisions Made for the 2010 Vancouver Olympic Winter Games

The Vancouver 2010 Winter Olympics were held from 12 to 28 February 2010, and the Paralympic events followed 2 weeks later. During the Games, the weather posed a grave threat to the viability of one venue and created significant complications for the event schedule at others. Forecasts of weather with lead times ranging from minutes to days helped organizers minimize disruptions to sporting events and helped ensure all medal events were successfully completed. Of comparable importance, however, were the scenarios and forecasts of probable weather for the winter in advance of the Games. Forecasts of mild conditions at the time of the Games helped the Games' organizers mitigate what would have been very serious potential consequences for at least one venue. Snowmaking was one strategy employed well in advance of the Games to prepare for the expected conditions. This short study will focus on how operational decisions were made by the Games' organizers on the basis of both climatological and snowmaking forecasts during the pre-Games winter. An attempt will be made to quantify, economically, the value of some of the snowmaking forecasts made for the Games' operators. The results obtained indicate that although the economic value of the snowmaking forecast was difficult to determine, the Games' organizers valued the forecast information greatly. This suggests that further development of probabilistic forecasts for applications like pre-Games snowmaking would be worthwhile.     

Extending Data Worth Analyses to Select Multiple Observations Targeting Multiple Forecasts

Hydrological models are often set up to provide specific forecasts of interest. Owing to the inherent uncertainty in data used to derive model structure and used to constrain parameter variations, the model forecasts will be uncertain. Additional data collection is often performed to minimize this forecast uncertainty. Given our common financial restrictions, it is critical that we identify data with maximal information content with respect to forecast of interest. In practice, this often devolves to qualitative decisions based on expert opinion. However, there is no assurance that this will lead to optimal design, especially for complex hydrogeological problems. Specifically, these complexities include considerations of multiple forecasts, shared information among potential observations, information content of existing data, and the assumptions and simplifications underlying model construction. In the present study, we extend previous data worth analyses to include: simultaneous selection of multiple new measurements and consideration of multiple forecasts of interest. We show how the suggested approach can be used to optimize data collection. This can be used in a manner that suggests specific measurement sets or that produces probability maps indicating areas likely to be informative for specific forecasts. Moreover, we provide examples documenting that sequential measurement election approaches often lead to suboptimal designs and that estimates of data covariance should be included when selecting future measurement sets.     

Study of statistically correcting model CMAQ-MOS for forecasting regional air quality

Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area".     

Development,implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System

The NOAA Great Lakes Operational Forecast System (GLOFS) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water temperature and currents, and two-dimensional forecasts of water levels of the Great Lakes. This system, originally called the Great Lakes forecasting system (GLFS), was developed at The Ohio State University and NOAA’s Great Lakes Environmental Research Laboratory (GLERL) in 1989. In 1996, a workstation version of the GLFS was ported to GLERL to generate semi-operational nowcasts and forecasts daily. In 2004, GLFS went through rigorous skill assessment and was transitioned to the National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS since September 30, 2005. Hindcast, nowcast, and forecast evaluations using the NOS-developed skill assessment software tool indicated both surface water levels and temperature predictions passed the NOS specified criteria at a majority of the validation locations with relatively low root mean square error (4–8 cm for water levels and 0.5 to 1°C for surface water temperatures). The difficulty of accurately simulating seiches generated by storms (in particular in shallow lakes like Lake Erie) remains a major source of error in water level prediction and should be addressed in future improvements of the forecast system.     

大别山库区降水预报性能评估及应用对策

对降水预报进行性能评估及应用对策研究可以更好地发挥降水预报在水库调度中的决策支持作用.基于大别山库区近10 a汛期(2007—2016年5月1日—9月30日)24~168 h共7个预见期降水预报和地面降水观测资料,采用正确率、TS评分、概率统计、ROC曲线以及CTS等方法评估大别山库区降水预报性能,并以响洪甸水库为重点研究区域分析降水预报在水库调度中的应用对策.结果表明:1)大别山库区各量级的降水预报都有正预报技巧;24~72 h预见期降水预报的TS评分较高且空报率、漏报率也较低,具有较高的预报性能;但96 h及以上预见期降水预报性能明显下降,中雨以上量级空报率、漏报率较大,特别是对大暴雨及其以上量级的降水预报性能显著下降.2)大别山库区预报降水量级与实况降水量级基本符合,预报降水量级大于等于实况降水量级的概率超过75%;虽然降水预报量级上呈现出过度预报的现象,但降水过程预报对水库调度仍有较好的应用价值,应用时要考虑到降水预报量级可能存在偏差.3)转折性天气预报96 h及以上预见期CTS评分较低,但72 h以内预见期的性能明显改进,尤其是24 h预见期CTS评分也提高到了38.2%;水库调度可从长预见期的降水预报获取降水过程及其可能发生转折的信息,根据短预见期的降水预报进行调度方案调整.     

Conditional Probabilities for Large Events Estimated by Small Earthquake Rate

We examined forecasting quiescence and activation models to obtain the conditional probability that a large earthquake will occur in a specific time period on different scales in Taiwan. The basic idea of the quiescence and activation models is to use earthquakes that have magnitudes larger than the completeness magnitude to compute the expected properties of large earthquakes. We calculated the probability time series for the whole Taiwan region and for three subareas of Taiwan—the western, eastern, and northeastern Taiwan regions—using 40 years of data from the Central Weather Bureau catalog. In the probability time series for the eastern and northeastern Taiwan regions, a high probability value is usually yielded in cluster events such as events with foreshocks and events that all occur in a short time period. In addition to the time series, we produced probability maps by calculating the conditional probability for every grid point at the time just before a large earthquake. The probability maps show that high probability values are yielded around the epicenter before a large earthquake. The receiver operating characteristic (ROC) curves of the probability maps demonstrate that the probability maps are not random forecasts, but also suggest that lowering the magnitude of a forecasted large earthquake may not improve the forecast method itself. From both the probability time series and probability maps, it can be observed that the probability obtained from the quiescence model increases before a large earthquake and the probability obtained from the activation model increases as the large earthquakes occur. The results lead us to conclude that the quiescence model has better forecast potential than the activation model.     

An overview of techniques for seasonal forecasting

A brief review of the state of seasonal forecasting at the end of the twentieth century is given. The physical basis of seasonal predictability is examined, and the implications of this for forecast strategies considered. The range of methods used for seasonal forecasting is described, with its division into empirical and numerical strategies, and methods for creating multi-model forecasts are discussed. Numerical prediction of climate anomalies is a new and emerging field of human endeavour, and some of its particular challenges are highlighted. Finally, the importance of the development of applications of seasonal forecasts is stressed, and the non-trivial nature of this task is noted.     

Hazard area and recurrence rate time series for determining the probability of volcanic disruption of the proposed high-level radioactive waste repository at Yucca Mountain, Nevada, USA

The post-12-Ma volcanism at Yucca Mountain (YM), Nevada, a potential site for an underground geologic repository of high-level radioactive waste in the USA, is assumed to follow a Poisson process and is characterized by a sequence of empirical recurrence rate time series. The last ten time series are used as a prediction set to check the predictive ability of the candidate model produced by a training sample using autoregressive integrated moving average modeling techniques. The model is used to forecast future recurrence rates that, in turn, are used to develop a continuous mean function of the volcanic process, which is not only required to evaluate the probability of site disruption by volcanic activity but accommodates a long period of compliance. At the model validation stage, our candidate model forecasts a mean number of 6.196 eruptions for the prediction set which accounts for seven volcanic events of the 33 post-12-Ma eruptions at the YM site. For a full-scaled forecasting, our fitted model predicts a waning volcanism producing only 3.296 new eruptions in the next million years. We then present the site disruption probability as the chance that a new eruption will occur in the “hazard area” based on a model developed for licensing commercial space launch and reentry operations in the space transportation industry. The results of the site disruption probability and sensitivity analysis are summarized with a numerical table generated from a simple equation sufficient for practical use. We also produce three-dimensional plots to visualize the nonlinearity of the intensity function associated with the underlying model of a nonhomogeneous Poisson process and emphasize that the interpretation of site disruption probability should always be accompanied by a compliance period.     

Simplified methods for real-time prediction of storm surge uncertainty: The city of Venice case study

Providing reliable and accurate storm surge forecasts is important for a wide range of problems related to coastal environments. In order to adequately support decision-making processes, it also become increasingly important to be able to estimate the uncertainty associated with the storm surge forecast. The procedure commonly adopted to do this uses the results of a hydrodynamic model forced by a set of different meteorological forecasts; however, this approach requires a considerable, if not prohibitive, computational cost for real-time application. In the present paper we present two simplified methods for estimating the uncertainty affecting storm surge prediction with moderate computational effort. In the first approach we use a computationally fast, statistical tidal model instead of a hydrodynamic numerical model to estimate storm surge uncertainty. The second approach is based on the observation that the uncertainty in the sea level forecast mainly stems from the uncertainty affecting the meteorological fields; this has led to the idea to estimate forecast uncertainty via a linear combination of suitable meteorological variances, directly extracted from the meteorological fields. The proposed methods were applied to estimate the uncertainty in the storm surge forecast in the Venice Lagoon. The results clearly show that the uncertainty estimated through a linear combination of suitable meteorological variances nicely matches the one obtained using the deterministic approach and overcomes some intrinsic limitations in the use of a statistical tidal model.     

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.     

Assimilating Doppler radar observations with an ensemble Kalman filter for convection-permitting prediction of convective development in a heavy rainfall event during the pre-summer rainy season of South China

This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of convection evolution in a high-impact heavy-rainfall event over coastal areas of South China during the pre-summer rainy season. An ensemble of 40 deterministic forecast experiments(40 DADF) with data assimilation(DA) is conducted, in which the DA starts at the same time but lasts for different time spans(up to 2 h) and with different time intervals of 6, 12, 24, and 30 min. The reference experiment is conducted without DA(NODA).To show more clearly the impact of radar DA on mesoscale convective system(MCS)forecasts, two sets of 60-member ensemble experiments(NODA EF and exp37 EF) are performed using the same 60-member perturbed-ensemble initial fields but with the radar DA being conducted every 6 min in the exp37 EF experiments from 0200 to0400 BST. It is found that the DA experiments generally improve the convection prediction. The 40 DADF experiments can forecast a heavy-rain-producing MCS over land and an MCS over the ocean with high probability, despite slight displacement errors. The exp37 EF improves the probability forecast of inland and offshore MCSs more than does NODA EF. Compared with the experiments using the longer DA time intervals, assimilating the radial-velocity observations at 6-min intervals tends to produce better forecasts. The experiment with the longest DA time span and shortest time interval shows the best performance.However, a shorter DA time interval(e.g., 12 min) or a longer DA time span does not always help. The experiment with the shortest DA time interval and maximum DA window shows the best performance, as it corrects errors in the simulated convection evolution over both the inland and offshore areas. An improved representation of the initial state leads to dynamic and thermodynamic conditions that are more conducive to earlier initiation of the inland MCS and longer maintenance of the offshore MCS.     

High-resolution ensemble forecasting for the Gulf of Mexico eddies and fronts

High-resolution models and realistic boundary conditions are necessary to reproduce the mesoscale dynamics of the Gulf of Mexico (GOM). In order to achieve this, we use a nested configuration of the Hybrid Coordinate Ocean Model (HYCOM), where the Atlantic TOPAZ system provides lateral boundary conditions to a high-resolution (5 km) model of the GOM . However, such models cannot provide accurate forecasts of mesoscale variability, such as eddy shedding event, without data assimilation. Eddy shedding events involve the rapid growth of nonlinear instabilities that are difficult to forecast. The known sources of error are the initial state, the atmospheric condition, and the lateral boundary condition. We present here the benefit of using a small ensemble forecast (10 members) for providing confidence indices for the prediction, while using a data assimilation scheme based on optimal interpolation. Our set of initial states is provided by using different values of a data assimilation parameter, while the atmospheric and lateral boundary conditions are perturbed randomly. Changes in the data assimilation parameter appear to control the main position of the large features of the GOM in the initial state, whereas changes in the boundary conditions (lateral and atmospheric) appears to control the propagation of cyclonic eddies at their boundary. The ensemble forecast is tested for the shedding of Eddy Yankee (2006). The Loop Current and eddy fronts observed from ocean color and altimetry are almost always within the estimated positions from the ensemble forecast. The ensemble spread is correlated both in space and time to the forecast error, which implies that confidence indices can be provided in addition to the forecast. Finally, the ensemble forecast permits the optimization of a data assimilation parameter for best performance at a given forecast horizon.     

An evaluation of numerical weather prediction based rainfall forecasts

ABSTRACT

Assessment of forecast precipitation is required before it can be used as input to hydrological models. Using radar observations in southeastern Australia, forecast rainfall from the Australian Community Climate Earth-System Simulator (ACCESS) was evaluated for 2010 and 2011. Radar rain intensities were first calibrated to gauge rainfall data from four research rainfall stations at hourly time steps. It is shown that the Australian ACCESS model (ACCESS-A) overestimated rainfall in low precipitation areas and underestimated elevated accumulations in high rainfall areas. The forecast errors were found to be dependent on the rainfall magnitude. Since the cumulative rainfall observations varied across the area and through the year, the relative error (RE) in the forecasts varied considerably with space and time, such that there was no consistent bias across the study area. Moreover, further analysis indicated that both location and magnitude errors were the main sources of forecast uncertainties on hourly accumulations, while magnitude was the dominant error on the daily time scale. Consequently, the precipitation output from ACCESS-A may not be useful for direct application in hydrological modelling, and pre-processing approaches such as bias correction or exceedance probability correction will likely be necessary for application of the numerical weather prediction (NWP) outputs.

EDITOR M.C. Acreman ASSOCIATE EDITOR A. Viglione     

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.     

Un exemple de collaboration internationale en hydrologie

Summary

Forecasts of the discharge of the Rhine at Basel have been studied in Zurich since 1954.

In this programme have cooperated hydro-power companies of Switzerland, Germany and France, French shipping-firms, and the Dutch water management authorities. For the Netherlands the forecast of the summer yield of the Rhine is of great concern, since it is the main resource of fresh water.

The studies have resulted in:

— since 1955, annual forecasts of the yield from snowmelt for periods of one to eight months beginning with March, and monthly and bi-monthly forecasts in winter

— since 1960, forecasts issued 2 to 5 times per week for the next three days, from October to March.

The forecast method is the least squares multiple regression, based on the observations of more than 50 years.

For the short term forecasts, the significant predictors are discharge and rainfall, eventually the snow cover percentage on the Swiss midland plateau. The largest errors of forecast were encountered in thawing conditions. Meteorological forecasts are provided by the Swiss Meteorological Institute.

For the long range forecasts, the alpine snow pack (in practice represented by the cumulative winter precipitations) and the levels of the Swiss lakes play an important role.

The forecasts are used by the hydro-power plants in their operation programmes for maintenance planning, and for proper timing of construction work.

In the future, the probability graduation of these forecasts will also be introduced in the optimisation of power production according to the methods of operations research.     

A variational method for correcting non-systematic errors in numerical weather prediction

A variational method based on previous numerical forecasts is developed to estimate and correct non-systematic component of numerical weather forecast error. In the method, it is assumed that the error is linearly dependent on some combination of the forecast fields, and three types of forecast combination are applied to identifying the forecasting error: 1) the forecasts at the ending time, 2) the combination of initial fields and the forecasts at the ending time, and 3) the combination of the forecasts at...     

Stochastic rainfall-runoff forecasting: parameter estimation,multi-step prediction,and evaluation of overflow risk

Probabilistic runoff forecasts generated by stochastic greybox models can be notably useful for the improvement of the decision-making process in real-time control setups for urban drainage systems because the prediction risk relationships in these systems are often highly nonlinear. To date, research has primarily focused on one-step-ahead flow predictions for identifying, estimating, and evaluating greybox models. For control purposes, however, stochastic predictions are required for longer forecast horizons and for the prediction of runoff volumes, rather than flows. This article therefore analyzes the quality of multistep ahead forecasts of runoff volume and considers new estimation methods based on scoring rules for k-step-ahead predictions. The study shows that the score-based methods are, in principle, suitable for the estimation of model parameters and can therefore help the identification of models for cases with noisy in-sewer observations. For the prediction of the overflow risk, no improvement was demonstrated through the application of stochastic forecasts instead of point predictions, although this result is thought to be caused by the notably simplified setup used in this analysis. In conclusion, further research must focus on the development of model structures that allow the proper separation of dry and wet weather uncertainties and simulate runoff uncertainties depending on the rainfall input.