金沙江下游多种面雨量集成预报方法的对比分析

集成方法有利于提高降水要素预报的准确性和可预报性。本文基于格点实况资料和智能网格预报、西南区域数值预报、ECMWF模式预报、GRAPES模式预报产品，以面雨量为研究对象，采用多元回归法、BP神经网络法、评分权重法、加权集成预报法和算术平均法，得到集成面雨量预报，再运用平均绝对误差、模糊评分、正确率、TS评分、偏差分析等方法，对2020年4—10月金沙江下游面雨量预报效果进行对比分析。结果表明：多元回归集成法和BP神经网络法的预报效果总体上优于其他几种集成方法。在考虑流域面雨量的预报量级时，下游可以采用预报量级较小的模式和集成方法。集成后偏差百分比均有降低，且多元回归法和BP神经网络法对预报量级较小的模式有矫正作用。在面雨量有无、小雨和中雨预报中，多元回归法集成效果较好，在大雨量级预报中，BP神经网络法集成效果较好。这些结论可为流域面雨量预报提供参考借鉴。     

重庆市区雾的天气特征分析及预报方法研究

分析了重庆市区雾的特点、天气特征及温、湿等气象要素垂直分布特征,利用重庆站的观测资料选取适当的诊断因子,采用动态学习率BP算法的人工神经网络对重庆市区能见度进行了拟合和预报检验。研究表明:55年以来,重庆年雾日数总体呈逐年下降趋势,同时轻雾日数急剧上升,这种变化可能主要与城市热岛效应增强和空气污染状况加重有关;发展成熟的辐射雾大多具有逆温的稳定结构,雾顶上下温度、湿度存在明显跃变特征;神经网络模型具有较强的自适应学习和非线性映射能力,对能见度为0~1 km雾的报出率为83%,Ts评分达到69%,平均预报误差为0.384 km。除常规气象要素外,通过M指数、Ri数、凝结核、辐射状况和其他物理量的引入,以及对因子网络输入值的技术处理,明显提高了神经网络对雾尤其浓雾的预报能力,其对能见度在0.4 km以下浓雾预报的Ts评分可达89.5%。模型结果对重庆市区雾的预报具有良好的参考价值。     

基于ECMWF细网格产品的吉林省东南部冬季气温BP-MOS预报方法

基于ECMWF细网格模式输出产品和BP神经网络预报方法建立一种优化的BP模型,对吉林省东南部山区(白山地区、通化地区)未来24h的日最高和最低气温进行预测,并对比该方法、ECMWF细网格的2m温度输出产品以及线性MOS方法的预报效果.结果表明:在建立预报方法时,考虑预报因子对气温影响的累积过程,并对其进行优化处理,有利于提高预报水平;通过比较各预报方法的预报准确率(TT)、系统偏差(MBE)、平均绝对误差(MAE),最终得出对预报因子进行优化处理的BP神经网络法预报效果最好.     

皖江中东部雾的客观预报自动化业务系统

介绍了一种雾的客观预报方法，并研制了一个与ＳＴＹＳ接口的雾的客观预报自动化业务系统，对雾有一定的预报能力。     

人工智能技术的热带气旋预报综述(之一)——BP神经网络和集成方法的热带气旋预报研究和业务应用

热带气旋路径和强度的客观定量预报是当前热带气旋预报研究和业务预报工作中的重点和难点。简要介绍了有关热带气旋业务预报技术的研究现状,综述了人工智能神经网络技术方法中最广泛使用的BP神经网络模型、进化计算遗传算法—神经网络的热带气旋路径、强度集成预报方法,以及近十多年来,这些方法在实际业务预报中与国内外主要数值预报模式及其他客观预报方法预报性能的对比分析,指出了人工智能神经网络和遗传神经网络集成预报方法等在热带气旋强度、路径预报中的优势和不足,并提出了未来更深入研究需要关注的重点关键问题。     

重庆能见度特征分析及其与颗粒物浓度和气象影响因子的关系

利用重庆地区能见度及温、压、湿、风等气象资料和大气颗粒物浓度数据，对重庆能见度特征及其影响因子进行分析，采用神经网络方法建立能见度预报模型，分析比较了引入PM2.5浓度因子对预报模型的影响效果。发现：重庆地区能见度分布呈现西低东高以及长江沿线较低的分布特征；雾发生时的平均能见度低于降水时能见度也远低于剔除雨、雾后的能见度，表明低能见度受大气中水汽影响更大；雾在冬季比例明显增加，使得平均能见度在冬季明显降低，而6月和10月降水增多是导致这两个月平均能见度出现明显降低的重要原因；能见度日变化呈现单峰型，雾和降水高发时段与平均能见度低值区重叠，是造成夜间能见度低的一个重要原因；大气湿度、温度及颗粒物浓度都是影响能见度的重要因子，当相对湿度小于70%时能见度随PM2.5增加明显降低，当相对湿度大于70%时PM2.5对能见度的影响降低；在能见度的客观预报模型中引入PM2.5浓度因子的预报效果好于不引入该因子的效果，特别是秋冬季的预报效果改善明显。     

基于BP神经网络的地质灾害细网格预报模型

地质灾害成因复杂,其中以气象因素、地质地貌因素引发的地质灾害最为常见.以金华地区为例,通过对金华市地质地貌条件及其对地质灾害点的调查,将全区划分为4个地质灾害隐患风险等级的网格区域.在此基础上利用金华中尺度气象资料,采用BP神经网络模型,建立地质灾害细网格预报模型,对该模型进行模拟和预报试验.结果表明,合理的隐患风险等级分区能使预报模型更符合科学规律,而采用分布较细的中尺度资料作为预报因子能进一步提高预报精度.模型的预报结果达到一定的可信度,为防灾减灾工作提供了科学依据.     

基于数值预报产品的地面气温BP-MOS预报方法

在山东省临沂市气象局开发的"中尺度数值预报业务系统"的基础上,利用高分辨率的数值预报产品和地面气温观测资料,建立了地面气温的BP神经网络方法预报模型。检验结果表明BP神经网络模型的气温预报准确率高于逐步回归模型和MM5模式输出的气温预报准确率,可应用于实际预报业务中来制作气温的精细化预报。     

改进BP神经网络在城市环境大气污染分季节预报中的应用

用泛化改进后的BP神经网络模型，选用2001、2002、2003年的气象因子和环境监测浓度资料按年度、季节分别建立预报模型，对贵阳市城市大气污染浓度进行预报。该方法除弥补了统计方法的预报精度不高和数值预报模式的方法复杂难实现的不足之处外，还很好地解决了未改进的BP网络训练误差很小时，一个新的输入与对应的目标输出具有较大误差的问题。     

湖州市大雾天气的成因分析及预报研究

提出了一个大雾预报的天气学模型，为了建立客观预报模式，必须把相应环流背景信息进行量化处理。该模式应用多种物理量来描述大雾发生前天气形势的变化，既便于量化又很容易实现预报的客观化和自动化。给出了物理量转换、组合计算方法，定向输送量概念对背景场的动态量化十分有效。应用BP神经网络建立大雾预报模型，选取的预报因子、预报指标可以较完整地描述形成大雾的整个背景场，包含的信息量大，业务应用效果明显。用于建模的神经网络设计和参数化方案对其他预报系统建设有借鉴意义。     

合肥市大雾预报方法建立与应用

利用主成分分析方法对2012～2014年合肥市高速公路沿线交通气象站日最低能见度资料统计出的大雾观测样本进行研究，应用因子荷载点聚图将合肥市县大雾划分为2个区：中南区、北区。基于PP法统计ECMWF模式输出产品与大雾之间的相关性，全市和分片分别确定与大雾密切相关的高影响因子，利用等级分类和逐步回归建立大雾预报模型。在回归结果的判定阈值和消空指标选定的情况下，通过研发的大雾天气精细化预报系统每日定时输出合肥市大雾预报格点产品。经过前期业务化运行和预报效果检验表明：数值模式产品释用方法在有无大雾预报技巧方面较WRF模式明显占优，技巧评分大幅提升，而两类典型大雾天气过程预报效果检验则可以更直观地看出数值模式产品释用的预报方法效果要更好。     

A THREE-DIMENSIONAL NUMERICAL STUDY ON DENSE FOG OVER MOUNTAINOUS AREAS

Using the non-hydrostatic meso-scale model MM5v3, dense fog that occurred from March 7 to March 8, 2001 over the Mts. Nanling area was studied. With integrated field experiments and observations, the occurrence, development and lift mechanism of fog were analyzed. The results indicate that before the coming of stratiform clouds, southerly warm and wet air ascended along mountainside and cooling condensation formed mountain fog. Then fog was formed by the stratiform on cloud-contacting mountaintop. A front inversion layer accelerated the development and extended the duration of the lower cloud and fog. The intensity, occurrence time, mass content and the variation of temperature and relative humidity of the fog agreed with those of the observation. It showed that the meso-scale model has the potential to forecast mountain fog.     

北京市一次大雾过程边界层结构的模拟研究

利用一个包括土壤 植被 大气相互作用的一维边界层模式 ,对 1999年 11月发生在北京的一次大雾过程的边界层特征进行了数值模拟。通过与相应时段边界层观测资料的对比表明 ,模式能较好地模拟出雾的大气边界层结构特征 ,以及雾的发生、消散时间和持续过程。由于模式中包括了辐射和平流物理过程 ,因此 ,模拟结果进一步证实相应的雾属于平流辐射雾。另外 ,对模式模拟结果的不足之处和可能原因也进行了分析。     

基于城市交通脆弱性核算的大雾灾害风险评估

选用大雾观测资料测算城市地区的雾灾危险性指数,以规则网格作为评估单元,逐网格计算网格区域内的路网密度,以此作为雾灾的空间脆弱性指标,并针对重点设施的分布情况对脆弱性指数进行空间叠加订正;选用网格内的人口密度作为雾灾的易损性指标;危险性、脆弱性及易损性3项指标按5:2:1的分配比例综合测算雾灾的风险指数。实例研究选用北京地区1996年1月—2006年12月的大雾资料,按空间网格化方法对大雾灾害风险进行评估,结果表明:北京地区雾灾脆弱性指数的高值区域与高速路及环城路延伸方向一致,城市中心为人口集中分布地区,其综合风险指数高,与高速路段、环城路及机场等地段均为雾灾的高风险区域,北京东南部地区年平均雾日数相对较多,危险性指数值也较高,是雾灾较高风险区域。     

Multidecadal simulation of coastal fog with a regional climate model

In order to model stratocumulus clouds and coastal fog, we have coupled the University of Washington boundary layer model to the regional climate model, RegCM (RegCM-UW). By comparing fog occurrences observed at various coastal airports in the western United States, we show that RegCM-UW has success at modeling the spatial and temporal (diurnal, seasonal, and interannual) climatology of northern California coastal fog. The quality of the modeled fog estimate depends on whether coast-adjacent ocean or land grid cells are used; for the model runs shown here, the oceanic grid cells seem to be most appropriate. The interannual variability of oceanic northern California summertime fog, from a multi-decadal simulation, has a high and statistically significant correlation with the observed interannual variability (r = 0.72), which indicates that RegCM-UW is capable of investigating the response of fog to long-term climatological forcing. While RegCM-UW has a number of aspects that would benefit from further investigation and development, RegCM-UW is a new tool for investigating the climatology of coastal fog and the physical processes that govern it. We expect that with appropriate physical parameterizations and moderate horizontal resolution, other climate models should be capable of simulating coastal fog. The source code for RegCM-UW is publicly available, under the GNU license, through the International Centre for Theoretical Physics.     

重庆雾的三维数值模拟

利用复杂地形上的三维雾模式模拟了重庆冬季雾的形成、发展及演变过程，模拟结果与实况基本吻合。一系列敏感性数值试验的结果使我们对重庆雾的影响因子有了进一步的了解。     

THE IMPACT OF MOUNTAIN TO BASIN WINDS ON THE DIURNAL VARIATION IN FOG OVER THE SICHUAN BASIN, CHINA

There is an increased demand for the accurate prediction of fog events in the Sichuan Basin (SCB) using numerical methods. A dense fog event that occurred over the SCB on 22 December 2016 was investigated. The results show that this dense fog event was influenced by the southwest of a low pressure with a weak horizontal pressure gradient and high relative humidity. This fog event showed typical diurnal variations. The fog began to form at 1800 UTC on 21 December 2016 (0200 local standard time on 22 December 2016) and dissipated at 0600 UTC on 22 December 2016 (1400 local standard time on 22 December 2016). The Weather Research and Forecasting model was able to partially reproduce the main features of this fog event and the diurnal variation in the local mountain to basin winds. The simulated horizontal visibility and liquid water content were used to characterize the fog. The mountain to basin winds had an important role in the diurnal variation of the fog event. The positive feedback mechanism between the fog and mountain to basin winds was good for the formation and maintain of the fog during the night. During the day, the mountain to basin wind displayed a transition from downslope flows to upslope flows. Water vapor evaporated easily from the warm, strong upslope winds, which resulted in the dissipation of fog during the day. The topography surrounding the SCB favored the lifting and condensation of air parcels in the lower troposphere as a result of the low height of the lifting condensation level.     

一次山地浓雾的三维数值研究

利用非静力平衡中尺度数值模式MM5v3对发生在2001年3月7—8日的南岭山地浓雾进行数值研究，结合外场综合观测资料，分析浓雾的发生、发展和消散机制。结果表明，在大范围的主体层状云系到来之前，低层的偏南暖湿气流沿山坡爬升冷却凝结形成爬坡雾，提早了地面浓雾出现的时间；大范围层状云系在山头接地形成了地面的浓雾。随后锋面逆温促进了低云和浓雾的长期维持与发展。模拟雾的强度、出现时间、含水量和温度、湿度随时间的演变与实测基本吻合。表明了中尺度模式预报山地浓雾的潜在能力。     

Fog Prediction for Road Traffic Safety in a Coastal Desert Region

Modern weather prediction models use relatively high grid resolutions as well as sophisticated parametrization schemes for microphysical and other subgrid-scale atmospheric processes. Nonetheless, with these models it remains a difficult task to perform successful numerical fog forecasts since many factors controlling a particular fog event are not yet sufficiently simulated. Here we describe our efforts to create a mechanism that produces successful predictions of fog in the territory located on the north coast of the Arabian Peninsula. Our approach consists in the coupling of the one-dimensional PAFOG fog model with the three-dimensional WRF 3.0 (Weather Research and Forecast) modelling system. The proposed method allows us to construct an efficient operative road traffic warning system for the occurrence of fog in the investigated region. In total 84 historical situations were studied during the period 2008?C2009. Moreover, results of operative day-by-day fog forecasting during January and February 2010 are presented. For the investigated arid and hot climate region the land-sea breeze circulation seems to be the major factor affecting the diurnal variations of the meteorological conditions, frequently resulting in the formation of fog.     

A CASE STUDY OF NUMERICAL SIMULATION OF SEA FOG ON THE SOUTHERN CHINA COAST

This study uses numerical simulations to examine a case of sea fog that was observed from 20 to 22 March2011 on the southern China coast. The observation dataset includes observatory data, cloud-top temperature from MODIS, GPS sonde, and data from the Integrated Observation Platform for Marine Meteorology(IOPMM). The simulations are based on the Weather Research and Forecasting(WRF) model with four distinct parameter settings.Both the observations and simulations focus on the characteristics of the fog extent, boundary layer structure, and meteorological elements near the air-sea interface. Our main results are as follows:(1) The extent of mesoscale sea fog can be well simulated when the sea surface temperature has at least 0.5 ×0.5 horizontal resolution.(2) To accurately model the vertical structure of the sea fog, particularly the surface-based inversion, vertical levels must be added in the boundary layer.(3) When these model conditions are met, the simulations faithfully reproduce the measured downward shortwave radiation, downward longwave radiation, and surface sensible heat flux during the sea fog period.