Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

STREAM TEMPERATURE ESTIMATION FROM AIR TEMPERATURE1

ABSTRACT: Air temperatures are sometimes used as substitutes for stream temperatures. To examine the errors associated with this procedure, linear relationships between stream temperatures, T, and air temperatures, T_a, recorded for 11 streams in the central U.S. (Mississippi River basin) were analyzed. Weather stations were an average 42 miles (range 0 to 144 miles) from the rivers. The general equations, T_w= 5.0 + 0.75 T_a and T_w= 2.9 + 0.86 T_a with temperatures in °C, were derived for daily and weekly water temperatures, respectively, for the 11 streams studied. The simulations had a standard deviation between measurements and predictions of 2.7°C (daily) and 2.1°C (weekly). Equations derived for each specific stream individually gave lower standard deviations, i.e., 2.1°C and 1.4°C, respectively. Small, shallow streams had smaller deviations than large, deep rivers. The measured water temperatures follow the air temperatures closely with some time lag. time lags ranged from hours to days, increasing with stream depth. Taking into account these time lags improved the daily temperature predictions slightly. Periods of ice cover were excluded from the analysis.     

METEOROLOGICAL EXPERIMENTS FOR AUTOMATED SATELLITES

Four satellites have been used in meteorology. They are TIROS, ESSA, Nimbus, and Applications Technology Satellite (ATS). The first three operate in the orbital altitudes of about 1000 to 1200 km while the fourth, ATS, is at geosynchronous altitude of 36,000 km. Cloud cover is being observed operationally from low orbit and experimentally from synchronous altitude. Wind velocity has been inferred from the frequent cloud cover pictures taken by ATS and satellite-balloon systems are being developed which will locate and track constant-density level balloons for determining wind flow. Spectrometers and radiometers operating in the electromagnetic spectral region from the ultraviolet to the microwave region are being developed to quantitatively measure temperature, water vapor, density, and wind profiles. These will furnish data for the development and testing of atmospheric models for numerical prediction. In addition, experiments are being developed to measure ozone and other constituents of the air, solar energy, surface conditions, heat balance and other atmospheric attributes which affect the structure and dynamics of the atmosphere.     

EFFECTS OF THE URBAN ENVIRONMENT ON HEAVY RAINFALL DISTRIBUTION1

ABSTRACT: A network of 225 recording raingages was operated over an area of 5200 km² in the St. Louis region during 1971-1975, in conjunction with an extensive investigation of urban effects on precipitation. Study of urban-induced effects on the frequency of heavy rainstorms has revealed a pronounced increase in the occurrence of storms producing 25 mm (1 inch) or more of rain. The increase is greatest in an area that is frequently in the path of storms passing across two urban-industrial regions. Analyses of raincells (rain intensity centers) within heavy convective storms shows a pronounced increase in water yield from cells exposed to potential urban effects, compared with those exposed only to the surrounding rural environment. Naturally-occurring heavy cells tend to undergo the greatest enhancement from urban exposure. Other analyses indicate an above-average frequency of excessive rain rates for periods of five minutes to two hours downwind of the urban-industrial complex. It is concluded that urban-induced intensification of short-duration rainstorms is sufficient to merit inclusion in the design and operation of urban-area hydrologic systems that control the flow of surplus storm water.     

黑龙江省影响水稻安全生产的气象要素

在分析水稻安全生产条件的基础上，探讨了有关气象因素及灾害对水稻生长发育和产量的影响。重点分析了地区冷害的发生状况及干湿条件，在充分考虑黑龙江省地区气候特征、农业生产区域及品种熟性的基础上，确定了影响水稻安全生产的灾害和气象指标。利用多年气象资料，计算了黑龙江省各地的冷害发生机率及稻田干燥度指数等，通过指标对比分析，初步提出了黑龙江省水稻安全生产的区域划分，为水稻生产的防灾减灾提供了科学的气象依据。     

环境评价污染气象观测与分析的几个问题

就实际环评工作中污染气象观测与分析中资料的选取与分析,地面常规气象资料的统计与分析,大气边界层观测与分析等几个问题提出了看法和建议。     

CAUSES OF PEAK FLOWS IN NORTHWESTERN MONTANA AND NORTHEASTERN IDAHO1

ABSTRACT: Both catchment experiments and a review of hydrologic processes suggest a varying effect of forest harvest on the magnitude of peak flows according to the cause of those peak flows. In northwestern Montana and Northeastern Idaho, annual maximum flows can result from spring snowmelt, rain, mid-winter rain-on-snow, or rain-on-spring-snowmelt. Meteorologic and physical data were used to determine the cause of annual maximum flows in six basins which had the necessary data and were smaller than 150 mi². Rain-on-spring-snowmelt was the most frequent cause of annual maximum flows in all six basins, although there was a strong gradient in the magnitude and cause of peak flows from southwest to northeast. Less frequent mid-winter rain-on-snow events caused the largest flows on record in four basins. Mid-winter rain-on-snow should be distinguished from rain-on-spring-snowmelt because of differences in seasonal timing, the relative contributions of rain vs. snowmelt, and the projected effects of forest harvest. The effects of mixed flood populations on the flood-frequency curve varied from basin to basin. Annual maximum daily flows could not be reliably predicted from rainfall and snowmelt data.     

2015年10月南京霾污染过程分析

为探讨南京秋季霾污染过程发生的主要影响因素,利用南京信息工程大学太阳光度计观测霾污染发生天气下AOD（aerosol optical depth,气溶胶光学厚度）数据,计算AE_440-1020（?ngstr?m Exponent,波长指数）以及a₂（光谱曲率）,结合CALIPSO（Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations）卫星气溶胶组分分析以及MODIS（Moderate Resolution Imaging Spectroradiometer）火点数据,对2015年10月南京霾污染过程进行分析.结果表明:2015年10月出现的两次霾污染过程期间南京地区AE_440-1020均高于1.0并且a₂呈负值,其中10月16日AQI达到峰值（201）,AOD₅₀₀达1.51,AE_440-1020达1.37,a₂达-0.77;这两次较为严重的霾污染过程均主要由人为因素（工业污染、城市建设、生物质燃烧、汽车尾气排放等）产生的细粒子所致.后向轨迹分析发现,2015年10月16日南京地区霾污染天气发生的主要原因是区域型污染,同时受长距离输送影响,大量携带人为因素产生的细粒子以及少量沙尘等污染物的空气团途经内蒙古、山东等地到达南京,加剧了当日的污染程度;2015年10月23日南京地区霾污染天气的发生则主要受长距离输送影响,同时也受到区域型污染影响,加剧了当日的污染程度.研究显示,在稳定的气象条件下,较高的相对湿度、较低的地表风速、低混合层高度以及贴地逆温的出现是诱发霾污染天气产生的有利气象条件.      

2008年秋季珠江三角洲污染气象分析

利用2008年10月15日至11月20日珠江三角洲从化、广州、开平三地的加强观测资料,分析了加强观测期间珠江三角洲的污染气象特征.结果表明,观测期间,造成珠江三角洲空气污染的天气形势可分为冷锋前部型、高压底部型、高压脊控制型3类.冷锋前部型易造成珠江三角洲中部和东北部污染;高压底部型易造成珠江三角洲西南部污染;高压脊控制型易造成珠江三角洲局地性污染.珠江三角洲城市群污染与盛行东北风且日平均风速小于2m/s有密切关系,大部分时间珠江三角洲西南部出现污染,是由于污染物沿着主导风向输送并累积造成.