Study on Fog Climate Characteristic and Forecasting Method in East of Hexi Corridor

Using fog meteorological data of five stations of Wuwei in east of Hexi Corridor from 1961 to 2008,geography distribution and climate characteristic of fog were analyzed with statistical method.The results showed that fog had the obvious region characteristic,fog days were more in mountainous area than Sichuan area and were more in south than north.Fog assumed reducing tendency year by year.Fog occurring frequency was the highest from July to October in one year.Fog occurring centralized time was form 20:00 to next day 08:00 in one day.Selecting of ECMWF numerical forecast grid field,factor was initially elected with Press criterion,factor was selected with stepwise regression forecast method.The fog forecasting equation was built with optimal subset regression.The overall situation and the most superior significance equations of fog forecasting were ascertained finally for spring,summer and autumn.Fitting rate three seasonal forecasting equation were 85.5%,82.1% and 81.2% respectively,which would provide objective and effective instruction products for forecasting service.

Interpretation of EC Fine Grid Numerical Prediction Products in the Forecast of Frozen Fog in Urumqi Airport

In this paper, Urumqi Airport time-lapse ground man-made observation data from November 2015 to February 2017, European fine grid (0.25 × 0.25) initial field (20 o’clock) and the forecast field within 24 hours were utilized. From November 2015 to February 2016, the relevant materials were used as research samples (a total of 948 times), and from November 2016 to February 2017 as test samples (a total of 922 times), statistical methods were used to establish the scoring standards. And each relevant element was scored. After the score, the score level range was delineated, and the visibility forecast was performed according to the scope. The conclusions are as follows: 1) European fine grid forecast products are with good correspondence with the visibility of this field are 850 hPa and 2 m high temperature inversion, 850 hPa relative humidity and 850 hPa wind field over the field. 2) Through the statistical analysis of scores, it is defined that the score below 400 is level 4, the score above 1000 is level 1, the difference is significant, and the forecast indication is strong. Level 2 and level 3 are more evenly distributed, with no more concentrated fractions. 3) Applying the test sample to test the above indicators. The forecast accuracy of level 1 is 61.2%, and the forecast accuracy of level 4 is 97.2%, so level 1 and level 4 are expected to obtain better forecast results, which is of practical application value.

Climate Features of Regional Fog in Liaoxi Area and Forecast Analysis

By using the fog data from 1995 to 2004 of four selected observation stations,the weather features of foggy days in Liaoxi area have been studied in this paper.The favorable surface and upper circulation for fog and its frequency have also been concluded from the statistic.In this paper,the forecasting index of fog,proposed on the basis of the condition and mechanism of the fog occurrence,has been tested by the 10-year analysis.Another test conducted by using the data of 1st July-31st December,2004 also gives a good result which has a vacancy rate of 22.2% and a miss rate of 5.1%.

Analysis on Change Characteristics and Forecast Factors of the Fog in Beibei District of Chongqing

[Objective] The research aimed to analyze change characteristics and forecast factors of the fog in Beibei District of Chongqing from 1953 to 2010. [Method] By observation data of the fog in Beibei District from 1953 to 2010, interdeoadal, interannual, seasonal and monthly varia- tion characteristics of the fog days and formation-dispersion time of the fog were conducted statistical analysis. Meteorological conditions and fore- cast factors of the fog were also analyzed. [Result] Distribution of the fog days in Beibei District had obvious interdecadal characteristics. Fog days was at its maximum in the 1980s while minimum in the 1960s. Fog duration presented slow increase trend. Interannual characteristic of the fog days overall presented increase trend, and it had 9-year periodic oscillation characteristic. Fog mainly concentrated in autumn and winter. Fog was mainly formed at night （20：00 -08：00） and dispersed in the daytime （08：00 -13：00）. Meteorological conditions which affected heavy fog in Beibei District were water vapor and stratification, wind field, temperature, relative humidity and so on. [ Conclusion] The research provided theoretical basis for scientific predication and forecast of the fog in Beibei District.

Primary Discussion on the Characteristics and Forecasting Methods of Dense Fog in Xuzhou City

[Objective] The study aimed to discuss the characteristics and forecasting methods of dense fog in Xuzhou City. [Method] Based on the data of dense fog in Xuzhou City from 1960 to 2009, the characteristics and forming conditions of dense fog in the region were analyzed, and then its forecasting methods were introduced, finally corresponding disaster prevention measures were put forward. [ Result] Dense fog might ap- pear in each season, its frequency of occurrence was the highest in December, namely 16.4% ; it was the lowest in June （2.2%）, and the fog las- ted for a short time and was thin. Heavy fog occurred more frequently in winter half year than summer half year, and the frequency of occurrence from October to next February was about 66.7%. In addition, dense fog mostly generated from late midnight to morning, while it appeared less in the afternoon. It shows that dense fog in Xuzhou City is mainly radiation fog instead of advection fog, but the two kinds of fog appeared simultane- ously sometimes. [ Conclusion] The research could provide scientific forecasting methods for the precise prediction of dense fog in Xuzhou City.

Characteristics Analysis and Forecast of Fogin Changzhi Region

By analyzing meteorological data during 2000 -2009, the characteristics of fog in Changzhi region and weather situation of fog weather occurrence were summarized, and forecast equation was established.

Weather Typing and Dissipation Forecast of Fog in Haizhou Bay

[ Objective] The research aimed to study weather typing and dissipation forecast of the fog in Haizhou Bay. [ Method] Based on the me- teorological observation data of three representative stations in Lianyungang, we analyzed weather situation before fog occurrence as well as the meteorological elements of coastal fog in Haizhou Bay, and established dissipation rating forecast equation of the fog. [ Result] From the surface weather chart, the fog in Haizhou Bay was divided into four types： low-pressure inverted trough type, prefrontal warm-zone type, high-pressure rear type and high-pressure bottom type. FOg formation was closely related to stratification stability, temperature, relative humidity, wind direction and wind velocity. By using multiple linear regression method, dissipation rating prediction equation of the fog was established. Via test, prediction was correct basically, and it reached 77% that forecast rating error was below level 0.5.[Conclusion] The research could provide favorable reference for forecast and warninq of the fo_q in Haizhou Bay.

Analysis of Climatic Characteristics of Fog and Haze Weather in Shaoyang in the Past 60 Years and Its Forecast

Based on conventional observation data and NCEP reanalysis data at 10 national basic stations and reference stations of Shaoyang City during 1951-2014,300 cases of typical regional dense fog process appeared in the history were selected. From meteorological factors and weather situation,temporal-spatial distribution characteristics and trend change characteristics of dense fog in Shaoyang were analyzed. The results showed that（ 1） temporal-spatial distribution of dense fog in Shaoyang region was uneven,and interannual variability of fog days had large volatility and bad periodicity; dense fog days in Shaoyang region was obviously more in winter half year and less in summer half year. Dense fog was the most in November and the least in July. Dense fog mostly concentrated during 03： 00-09： 00; appearance time mostly concentrated during 05： 00-07： 30,and dissipation time mostly appeared after 08： 30. Dense fog appeared early and dissipated late in winter half year,and vice verse in summer half year.（ 2） Seen from meteorological factors,ground and 850 h Pa of wind velocity was generally 0-3 m/s,which was all small. Moreover,there existed temperature inversion from ground to 850 h Pa. Relative humidity on dense fog day was larger,and precipitation or cloudy day mostly appeared in prior day.（ 3） There were four kinds of ground weather situation forming dense fog： uniform pressure field type,cold and high pressure bottom type,cold and high pressure rear type,frontal type. Based on grasping change characteristics,rule and formation reason of dense fog,some forecast focus was found.

MM5与MM5-PAFOG模式区域雾预报效果评估比较

利用MM5和PAFOG模式对2010年10-11月中国东部地区的雾进行逐日预报,包括基于MM5模式结果的多要素诊断预报和PAFOG模式预报能见度的定量预报,应用TS/ETS评分方法和区域内所有测站每3h(或6h)一次的常规观测资料,根据该两方法的预报结果分别对中国东部地区和安徽进行了效果评估。结果表明:(1)两种预报方法对区域雾都有一定的预报能力,但都存在空报率较高的明显不足。基于MM5模式的多要素诊断预报,得到中国东部地区的TS/ETS评分为0.075/0.065,命中率(HR)为0.296;安徽的TS/ETS评分为0.094/0.081,HR为0.378。(2)PAFOG模式对中国东部地区的TS/ETS评分为0.038/0.027,HR为0.388;安徽的TS/ETS评分为0.053/0.038,HR为0.52。与MM5模式的诊断结果相比,TS/ETS评分较低,HR较高。(3)按日统计结果表明,MM5模式在整个中国东部地区的预报效果优于PAFOG模式,但在内陆省份(安徽),PAFOG模式的预报效果略优于MM5模式。(4)效果评估时若考虑过去1h内的天气现象记录,两种方法的预报效果都有明显改善。

PREDICTION OF SEA FOG OF GUANGDONG COASTLAND USING THE VARIABLE FACTORS OUTPUT BY GRAPES MODEL

By analyzing the NCEP 1°×1° reanalysis (2004–2008), a number of predictors (factors of variables) are established with the output from the GRAPES model and with reference to the sea fog data from observational stations (2004–2008) and field observations (2006–2008). Based on the criteria and conditions for sea fog appearance at the stations of Zhanjiang, Zhuhai and Shantou, a Model Output Statistics (MOS) scheme for distinguishing and forecasting 24-h sea fog is established and put into use for three representative coastal areas of Guangdong. As shown in an assessment of the forecasts for Zhanjiang and Shantou (March of 2008) and Zhuhai (April of 2008), the scheme was quite capable of forecasting sea fog on the coast of the province, with the accuracy ranging from 84% to 90%, the threat score from 0.40 to 0.50 and the Heidke skill from 0.52 to 0.56.

Characteristics Analysis and the Early-warning Service System of Heavy Fog in Chizhou City

By analyzing the heavy fog data in Chizhou City in recent 50 years(1959-2007),the general rules of meteorological elements variations were found when the heavy fog happened.The meteorological elements included the temperature,humidity,wind direction,wind speed,air pressure and so on.The conceptual models of high-altitude and ground situation were established when the heavy fog happened in Chizhou City.Based on considering sufficiently the special geographical environment in Chizhou City,we found the key factors which affected the local heavy fog via the relative analyses.By using the statistical forecast methods which included the second-level judgment method and regression method of event probability and so on,the forecast mode equation of heavy fog was established.Moreover,the objective forecast system of heavy fog in Chizhou City was also manufactured.It provided the basis and platform which could be referred for the heavy fog forecast,service and the release of early-warning signal.

Evaluation of the Global and Regional Assimilation and Prediction System for Predicting Sea Fog over the South China Sea

In the South China Sea, sea fog brings severe disasters every year, but forecasters have yet to implement an effective seafog forecast. To address this issue, we test a liquid-water-content-only(LWC-only) operational sea-fog prediction method based on a regional mesoscale numerical model with a horizontal resolution of about 3 km, the Global and Regional Assimilation and Prediction System(GRAPES), hereafter GRAPES-3 km. GRAPES-3 km models the LWC over the sea, from which we infer the visibility that is then used to identify fog. We test the GRAPES-3 km here against measurements in 2016 and 2017 from coastal-station observations, as well as from buoy data, data from the Integrated Observation Platform for Marine Meteorology, and retrieved fog and cloud patterns from Himawari-8 satellite data. For two cases that we examine in detail, the forecast region of sea fog overlaps well with the multi-observational data within 72 h. Considering forecasting for0–24 h, GRAPES-3 km has a 2-year-average equitable threat score(ETS) of 0.20 and a Heidke skill score(HSS) of 0.335,which is about 5.6%(ETS) and 6.4%(HSS) better than our previous method(GRAPES-MOS). Moreover, the stations near the particularly foggy region around the Leizhou Peninsula have relatively high forecast scores compared to other sea areas.Overall, the results show that GRAPES-3 km can roughly predict the formation, evolution, and dissipation of sea fog on the southern China coast.

Case Study of Fog Predictability for an Event with Cold-Front Synoptic Pattern

Fog has recently become a frequent high-impact weather phenomenon along the coastal regions of North China. Accurate fog forecasting remains challenging due to limited understanding of the predictability and mechanism of fog formation associated with synoptic-scale circulation. One frequent synoptic pattern of fog formation in this area is associated with cold front passage(cold-front synoptic pattern, CFSP). This paper explored the predictability of a typical CFSP fog event from the perspective of analyzing key characteristics of synoptic-scale circulation determining fog forecasting performance and the possible mechanism. The event was ensemble forecasted with the Weather Research and Forecasting model. Two groups of ensemble members with good and bad forecasting performance were selected and composited. Results showed that the predictability of this case was largely determined by the simulated strengths of the cold-front circulation(i.e., trough and ridge and the associated surface high). The bad-performing members tended to have a weaker ridge behind a stronger trough, and associated higher pressure over land and a weaker surface high over the sea, leading to an adverse impact on strength and direction of steering flows that inhibit warm moist advection and enhance cold dry advection transported to the focus region. Associated with this cold dry advection, adverse synoptic conditions of stratification and moisture for fog formation were produced, consequently causing failure of fog forecasting in the focus region. This study highlights the importance of accurate synoptic-scale information for improved CFSP fog forecasting, and enhances understanding of fog predictability from perspective of synoptic-scale circulation.

我国海雾监测预报业务现状及未来展望

海雾因海面低层大气中水蒸气凝结而成,是海上一种常见的天气现象。海雾发生时能见度较低,对港口作业、海上航运、渔业及海上油气生产等造成严重影响。我国近海发生的船舶碰撞或触礁事故中,有50%~70%与海雾有关。本文回顾了近20年我国在海雾监测预报业务方面的进展,分析了地基海雾观测、卫星遥感、人工智能应用及海雾数值预报等对国省海雾监测预报业务的支撑,探讨了海雾在精细能见度监测预报及检验评估等方面存在的问题,并从开展海雾多平台协同观测,加强海雾机理、数值预报模式及客观预报方法研究和应用等角度对未来海雾业务发展提出了若干展望。

基于数值模式与客观方法的辽宁地区空气质量预报检验分析

为检验数值模式和客观方法的空气质量产品预报能力,基于中国气象科学研究院(简称气科院,下同)CUACE模式、中国气象局沈阳大气环境研究所(简称沈阳大气所,下同)CUACE模式和中央气象台(简称中央台,下同)空气质量客观预报方法的产品,利用辽宁地区14个地市大气污染物质量浓度地面观测资料,对2019年1月至2021年4月各家预报产品在辽宁地区的预报效果进行检验。结果表明:中央台的环境空气质量指数(Air Quality Index,AQI)预报偏大,气科院和沈阳大气所偏小;各家的PM_(2.5)和PM_(10)预报均偏小,但中央台的误差最小;各家的O_(3)预报均偏大,气科院的预报误差最小。各家产品对PM_(2.5)和O_(3)浓度变化趋势的预报能力较高,对AQI范围的预报能力是强于AQI等级的。各家产品预报的PM_(2.5)质量浓度离散度和预报偏差最小,TS评分最高;大气污染物浓度和AQI在辽宁东南部的预报可靠性最高、中部地区最差。三家产品相比,中央台在辽宁地区的预报能力最强,对AQI、大气污染物浓度和首要污染物的预报TS评分均为最高,特别是在有(或无明显首要污染物)特定大气污染物对应的季节,预报更具有指导性。沈阳大气所的本地化CUACE模式对于大气污染物浓度和AQI的预报能力显著提高。

长江口一次持续性强浓雾过程特征和成因分析

使用船载观测资料、FNL再分析资料和葵花八号云图资料,对2022年3月11—14日发生于长江口的持续性浓雾过程进行研究分析,并归纳总结了预报的实用性方法。结果表明:本次浓雾过程包含辐射雾和锋面平流雾,持续时间长,影响范围广,雾区在锋面的前后部都相继出现,且锋面移动往往影响着雾的移动。在雾的生消和维持过程中,气温和相对湿度通常呈负相关,温度露点差和能见度呈正相关,当温度露点差大于3℃时,长江口也会有雾生成,但能见度不低于3 km。雾生时段通常盛行偏南风和偏北风,偏西风和西北风则会导致相对湿度迅速下降,不利于雾的形成和维持。可见光云图上雾的特征明显,颜色纹理均匀,边界清晰整齐,外围无丝状或纤维状云系扩散,而红外云图上对应处是一片灰暗区,只有边界隐约可见。

基于LightGBM的豫南浓雾客观预报研究

准确预报浓雾天气(能见度小于等于500 m)对保障人民生命安全和减少经济损失具有重要意义。利用2019—2021年豫南地区31个国家级气象站地面观测资料、环境监测站数据及欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)的ERA5再分析资料分析该地区浓雾的空间分布和物理量特征并选取30个浓雾预报因子,基于LightGBM(Light Gradient Boost⁃ing Machine)机器学习方法训练出豫南地区能见度分级预报(Visibility Classification Forecast,VCF)模型。通过输入ECMWF模式每日08:00(北京,下同)起报的预报场数据和08:00 PM2.5质量浓度监测,得到豫南国家站逐3 h能见度分级预报产品。通过对2022年1—3月豫南17个浓雾日的预报检验显示,VCF模型各项评分总体来看优于ECMWF模式直接输出的能见度预报,基于该模型生成的20:00—20:00豫南浓雾预报产品可为当日夜间到次日上午的浓雾落区潜势预报提供重要参考。

基于神经网络的单站雾预报试验

采集大连某机场2004—2007年大雾、轻雾和无雾天气事件共186例,选取雾天气事件前期(前一日08:00,14:00,20:00(北京时)实测资料)的温、压、湿、风等要素指标为预报因子,基于学习向量量化神经网络(learning vector quantization,LVQ),采用逐级预报思想建立起某机场雾天气事件的预报模型。在网络训练过程中,动态调整网络神经元比例参数,提高模型的预报能力;采用根据检验准确率适时终止训练的"先停止"技术,有效提高了模型的泛化能力。预报试验表明:无论是拟合率还是独立预报准确率,模型均已达到较高水准,具有实际应用意义。

用GRAPES模式输出变量因子作广东沿海海雾预报

通过分析5年(2004—2008年)NCEP的1°×1°再分析气象资料,结合2004—2008年的台站观测资料和2006—2008年海雾野外试验的观测资料建立预报变量因子,利用GRAPES模式得到并输出变量因子。结合NCEP资料分析海雾出现的各种判据和条件,选取湛江、珠海、汕头3站为代表,建立了广东沿海自西向东各地区的海雾MOS判别预报方法,实现了24h的海雾判别预报。对2008年3月湛江和汕头、4月珠海的预报检验表明,该海雾MOS判别预报方法对广东沿海海雾具有一定的预报能力,预报准确率为84%～90%,Ts评分为0.40～0.53,Hss评分为0.52～0.56。

低能见度浓雾监测、临近预报的实例分析与认识

依据在沪宁高速公路上对低能见度浓雾的监测资料,结合四年来的预报实践,阐述了对浓雾突发性的认识和预报工作中值得关注的问题。本文揭示了低能见度浓雾的形成不是一次完成的,在浓雾过程形成前有一个"象鼻形"的先期振荡,它持续时间短,能见度值还不太低,但具有普遍性,对后期浓雾的形成和预报有相当的应用价值。