BAYESIAN MODELS OF FORECASTED TIME SERIES1

Bayesian Processor of Forecasts (BPF) combines a prior distribution, which describes the natural uncertainty about the realization of a hydrologic process, with a likelihood function, which describes the uncertainty in categorical forecasts of that process, and outputs a posterior distribution of the process, conditional upon the forecasts. The posterior distribution provides a means of incorporating uncertain forecasts into optimal decision models. We present fundamentals of building BPF for time series. They include a general formulation, stochastic independence assumptions and their interpretation, computationally tractable models for forecasts of an independent process and a first-order Markov process, and parametric representations for normal-linear processes. An example is shown of an application to the annual time series of seasonal snowmelt runoff volume forecasts.     

Brazil's Electric Power Choices and Their Corresponding Carbon Emissions Implications

This study analyzes the options for meeting power demand in the Brazilianpower sector through the year 2015. Three policy cases are constructedto test economic and environmental policy measures against a baseline:advanced technologies scenario, environmental control scenario and carbon(C) elimination scenario. Least-cost modeling simulated these scenarios throughchanges in emissions fees and caps, costs for advanced technologies,demand side efficiency, and clean energy supplies. Results show that, in theabsence of alternative policies, new additions to Brazil's electric powersector will shift rapidly from hydroelectricity to combined-cycle natural gasplants. When the cost of environmental impacts are incorporated in theprice of power, the least-cost mix of electric power generation technologycould change in other ways. In all scenarios, energy efficiency andcogeneration play an important role in the least-cost power solution. Savingelectricity through increased efficiency offsets the needs for new supply andhas enormous potential in Brazil's industrial sector. Efficiency also reducesthe environmental burden associated with electricity production andtransmission, without compromising the quality of the services demandedby end users. Interesting enough, carbon dioxide (CO₂) emissions will remainrelatively low under almost every conceivable scenario.     

Developing Subseasonal to Seasonal Climate Forecast Products for Hydrology and Water Management

We describe a new effort to enhance climate forecast relevance and usability through the development of a system for evaluating and displaying real‐time subseasonal to seasonal (S2S) climate forecasts on a watershed scale. Water managers may not use climate forecasts to their full potential due to perceived low skill, mismatched spatial and temporal resolutions, or lack of knowledge or tools to ingest data. Most forecasts are disseminated as large‐domain maps or gridded datasets and may be systematically biased relative to watershed climatologies. Forecasts presented on a watershed scale allow water managers to view forecasts for their specific basins, thereby increasing the usability and relevance of climate forecasts. This paper describes the formulation of S2S climate forecast products based on the Climate Forecast System version 2 (CFSv2) and the North American Multi‐Model Ensemble (NMME). Forecast products include bi‐weekly CFSv2 forecasts, and monthly and seasonal NMME forecasts. Precipitation and temperature forecasts are aggregated spatially to a United States Geological Survey (USGS) hydrologic unit code 4 (HUC‐4) watershed scale. Forecast verification reveals appreciable skill in the first two bi‐weekly periods (Weeks 1–2 and 2–3) from CFSv2, and usable skill in NMME Month 1 forecast with varying skills at longer lead times dependent on the season. Application of a bias‐correction technique (quantile mapping) eliminates forecast bias in the CFSv2 reforecasts, without adding significantly to correlation skill.     

基于BP神经网络的污染物浓度多模式集成预报

基于中国气象局雾-霾数值预报系统CUACE、北京区域环境气象数值预报系统BREMPS和华东区域大气环境数值预报系统WRF-Chem三个环境气象模式预报产品,利用BP神经网络方法建立多模式集成预报模型.首先通过实验得到BP神经网络的训练函数、隐含层节点数和训练样本长度分别为贝叶斯归一化训练函数trainbr、10和50.随后选取北京、天津和石家庄站点的预报结果检验该模型的预报性能.结果表明：（1）相对于单模式,BP神经网络集成预报的3~72h逐3h污染物浓度和观测之间的归一化平均偏差从-100%~200%降低到-20%~20%,污染物浓度和观测的均方根误差比各单模式降低15%以上,相关系数从0.1~0.8提升到0.3~0.85之间,说明其预报结果优于各单模式.（2）2016年AQI等级评估表明,集成模型预报的北京轻度和中度污染的TS评分分别比CUACE提高22%和10%,在天津重度污染的空报率和漏报率分别降低31%和25%.（3）2016年12月份的重污染过程评估发现,集成模型预报的PM_2.5浓度的演变趋势和实况基本相符.     

Timing of hurricane emergency actions

Emergency actions to prepare for hurricanes often require more time than is available from official public warnings. This means that the preparedness official must decide not onlywhat to do butwhen to do it. The action decision system, described here, developed for use in the State of Florida, reformats the hurricane track forecast, a 72-h projection, prepared at the National Hurricane Center in Miami, to specify the probability of a strike at each of 12 vulnerable coastal communities, and then normalizes the value in terms of a composite of probabilities computed for historic hurricanes that struck the respective communities. The normalization, a ratio of the two probability values, current and historic, expressed as a percentage is defined as the level of risk. When this level reaches or exceeds 100% the risk level is defined as critical and the system recommends that emergency actions to prepare begin immediately.The system is founded upon individual hurricane climatologies and decision procedures that are tailored for use at each community. The action recommendations generated by the risk analyses with a 93% level of confidence relieve the preparedness official of the need to make meteorological decisions in timing evacuations and other critical measures, even when these must begin before official hurricane warnings are received.The work that provided the basis for this publication was supported by funding under a Cooperative Agreement with the Federal Emergency Management Agency. The substance and findings of the work are dedicated to the public. The authors are solely responsible for the accuracy of the statements and interpretations contained in the publication. Such interpretations do not necessarily reflect the views of the government.     

Overview and Forecast on Forestry Productions Worldwide

Our world is largely dependent upon the forestry productions. Through the exploitation of forest reserves, we manufacture various industrial products, furniture, and obtain fuel and energy. Forestry productions should be conducted without large-scale deforestation and environmental degradation. In present study we perform a review and forecast analysis on forestry productions worldwide, with the objectives of providing an insight into the trend for several types of forestry productions in the future, and providing referential data for sustainable forestry productions and environmental management. Polynomial functions are used to fit trajectories of forestry productions since 1961 and forecasts during the coming 20 years are given in detail. If the past pattern continues, world fibreboard production would dramatically grow and reach 224,300,000 ± 44,400,000 m³ by the year 2020, an increase up to 240.7 to 408.9% as compared to the present level. Roundwood production of the world would change by −55.5 to 70.4% and reach 3,526,600,000 ± 2,066,800,000 m³ by 2020. In 2020 world production of sawlogs and veneer logs would change by −100 to 164.6% and reach 1,212,900,000 ± 1,242,600,000 m³. Global wood fuel production would change by −68.9 to 1.4% and reach 1,130,900,000 ± 600,800,000 m³ by 2020. Forestry productions in developed countries would largely surpass productions in developing countries in the near future. World forestry production grew since 1961 excluding wood fuel. Roundwood and wood fuel account for the critical proportions in the forestry productions. Wood fuel production has being declined and rapid growing of roundwood production has slowed in recent years. Widespread use of regenerative wood substitutes and worldwide afforestation against deforestation will be among the most effective ways to reduce deforestation and environment degradation associated with forestry productions.     

Estimating contribution of wildland fires to ambient ozone levels in National Parks in the Sierra Nevada, California

Data from four continuous ozone and weather monitoring sites operated by the National Park Service in Sierra Nevada, California, are used to develop an ozone forecasting model and to estimate the contribution of wildland fires on ambient ozone levels. The analyses of weather and ozone data pointed to the transport of ozone precursors from the Central Valley as an important source of pollution in these National Parks. Comparisons of forecasted and observed values demonstrated that accurate forecasts of next-day hourly ozone levels may be achieved by using a time series model with historic averages, expected local weather and modeled PM values as explanatory variables. Results on fire smoke influence indicated occurrence of significant increases in average ozone levels with increasing fire activity. The overall effect on diurnal ozone values, however, was small when compared with the amount of variability attributed to sources other than fire.     

A Framework to Develop Nationwide Flooding Extents Using Climate Models and Assess Forecast Potential for Flood Resilience

The methods used to simulate flood inundation extents can be significantly improved by high‐resolution spatial data captured over a large area. This paper presents a hydraulic analysis methodology and framework to estimate national‐level floodplain changes likely to be generated by climate change. The hydraulic analysis was performed using existing published Federal Emergency Management Agency 100‐year floodplains and estimated 100‐ and 10‐year return period peak flow discharges. The discharges were estimated using climate variables from global climate models for two future growth scenarios: Representative Concentration Pathways 2.6 and 8.5. River channel dimensions were developed based on existing regional United States Geological Survey publications relating bankfull discharges with channel characteristics. Mathematic relationships for channel bankfull topwidth, depth, and side slope to contributing drainage area measured at model cross sections were developed. The proposed framework can be utilized at a national level to identify critical areas for flood risk assessment. Existing hydraulic models at these “hot spots” could be repurposed for near–real‐time flood forecasting operations. Revitalizing these models for use in simulating flood scenarios in near–real time through the use of meteorological forecasts could provide useful information for first responders of flood emergencies.     

The potential of statistical state space models in urban ozone forecasting

State space models for tropospheric urban ozone prediction are introduced and compared with linear regression models. The linear and non-linear state space models make accurate short-term predictions of the ozone dynamics. The average prediction error one hour in advance is 7 μg/m³ and increases logarithmically with time until it reaches 26 μg/m³ after 30 days. For a given sequence of solar radiation inputs, predictions converge exponentially with a time scale of 8 hours, so that the model is insensitive to perturbations of more than 150 μg/m³ O₃. The slow increase of the prediction error in addition to the uniqueness of the prediction are encouraging for applications of state space models in forecasting ozone levels when coupled with a model that predicts total radiation. Since a radiation prediction model will be more accurate during cloud-free conditions, in addition to the fact that the state space models perform better during the summer months, state space models are suitable for applications in sunny environments.     

Using Instream Water Temperature Forecasts for Fisheries Management: An Application in the Pacific Northwest1

Huang, Biao, Christian Langpap, and Richard M. Adams, 2011. Using Instream Water Temperature Forecasts for Fisheries Management: An Application in the Pacific Northwest. Journal of the American Water Resources Association (JAWRA) 47(4):861‐876. DOI: 10.1111/j.1752‐1688.2011.00562.x Abstract: Water temperature is an important factor affecting aquatic life within the stream environment. Cold water species, such as salmonids, are particularly susceptible to elevated water temperatures. This paper examines the potential usefulness of short‐term (7 to 10 days) water temperature forecasts for salmonid management. Forecasts may be valuable if they allow the water resource manager to make better water allocation decisions. This study considers two applications: water releases from Lewiston Dam for management of adult Chinook salmon (Oncorhynchus tshawytscha) in the Klamath River and leasing water from agriculture for management of steelhead trout (Oncorhynchus mykiss) in the John Day River. We incorporate biophysical models and water temperature distribution data into a Bayesian framework to simulate changes in fish populations and the corresponding opportunity cost of water under different levels of temperature forecast reliability. Simulation results indicate that use of the forecasts results in increased fish production and that marginal costs decline as forecast reliability increases, suggesting that provision and use of such stream temperature forecasts would have potential value to society.