An Experimental High-Resolution Forecast System During the Vancouver 2010 Winter Olympic and Paralympic Games

Environment Canada ran an experimental numerical weather prediction (NWP) system during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM–LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP system has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the forecasts from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best forecast accuracy. Appreciable errors were noted in all models for the forecasts of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic system was skillful at forecasting meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic forecasters in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow.     

A New Integrated Weighted Model in SNOW-V10: Verification of Categorical Variables

This paper presents the verification results for nowcasts of seven categorical variables from an integrated weighted model (INTW) and the underlying numerical weather prediction (NWP) models. Nowcasting, or short range forecasting (0–6 h), over complex terrain with sufficient accuracy is highly desirable but a very challenging task. A weighting, evaluation, bias correction and integration system (WEBIS) for generating nowcasts by integrating NWP forecasts and high frequency observations was used during the Vancouver 2010 Olympic and Paralympic Winter Games as part of the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) project. Forecast data from Canadian high-resolution deterministic NWP system with three nested grids (at 15-, 2.5- and 1-km horizontal grid-spacing) were selected as background gridded data for generating the integrated nowcasts. Seven forecast variables of temperature, relative humidity, wind speed, wind gust, visibility, ceiling and precipitation rate are treated as categorical variables for verifying the integrated weighted forecasts. By analyzing the verification of forecasts from INTW and the NWP models among 15 sites, the integrated weighted model was found to produce more accurate forecasts for the 7 selected forecast variables, regardless of location. This is based on the multi-categorical Heidke skill scores for the test period 12 February to 21 March 2010.     

A Comparative Study of Radiation Fog and Quasi-Fog Formation Processes During the ParisFog Field Experiment 2007

Fog is an atmospheric phenomenon that has important environmental consequences related to visibility, air quality and climate change on local and regional scales. The formation of radiation fog results from a complex balance between surface radiative cooling, turbulent mixing in the surface layer, aerosol growth by deliquescence and activation of fog droplets. During the ParisFog field experiment, out of 16 events forecasted for radiation fog, activated fog materialized in seven events, while in five other events the visibility dropped to 1–2 km but haze particle size remained below the critical size of activation. To better understand the conditions that lead to or do not lead to sustained fog droplet activation, we performed a comparative study of dynamic, thermal, radiative and microphysical processes occurring between sunset and fog (or quasi-fog) onset. We selected two radiation fog events and two quasi-radiation fog events that occurred under similar large-scale conditions for this comparative study. We identified that aerosol growth by deliquescence and droplet activation actually occurred in both quasi-fog events, but only during <1 h. Based on ParisFog measurements, we found that the main factors limiting sustained activation of droplets at fog onset in the Paris metropolitan area are (1) lack of mixing in the surface layer (typically wind speed <0.5 ms^?1), (2) relative humidity exceeding 90 % throughout the residual layer, (3) low cooling rate in the surface layer (typically less than ?1 °C per hour on average) due to weak radiative cooling (0 to ?30 Wm^?2) and near zero sensible heat fluxes, and (4) a combination of the three factors listed above during the critical phase of droplet activation preventing the transfer of cooling from the surface to the liquid layer. In addition, we found some evidence of contrasted aerosol growth by deliquescence under high relative humidity conditions in the four events, possibly associated with the chemical nature of the aerosols, which could be another factor impacting droplet activation.     

Adaptive Blending of Model and Observations for Automated Short-Range Forecasting: Examples from the Vancouver 2010 Olympic and Paralympic Winter Games

An automated short-range forecasting system, adaptive blending of observations and model (ABOM), was tested in real time during the 2010 Vancouver Olympic and Paralympic Winter Games in British Columbia. Data at 1-min time resolution were available from a newly established, dense network of surface observation stations. Climatological data were not available at these new stations. This, combined with output from new high-resolution numerical models, provided a unique and exciting setting to test nowcasting systems in mountainous terrain during winter weather conditions. The ABOM method blends extrapolations in time of recent local observations with numerical weather predictions (NWP) model predictions to generate short-range point forecasts of surface variables out to 6 h. The relative weights of the model forecast and the observation extrapolation are based on performance over recent history. The average performance of ABOM nowcasts during February and March 2010 was evaluated using standard scores and thresholds important for Olympic events. Significant improvements over the model forecasts alone were obtained for continuous variables such as temperature, relative humidity and wind speed. The small improvements to forecasts of variables such as visibility and ceiling, subject to discontinuous changes, are attributed to the persistence component of ABOM.     

Low Visibility Formation and Forecasting on the Northern Coast of Brazil

Visibility analysis and forecast at the Maceio International Airport in the Brazilian Northeast (NEB) was the principal goal of this investigation. Surface meteorological data of the Maceio International Airport were used for low visibility frequency study. Low visibility in NEB was provoked more frequently by light fog (LF) formation (1,098 or 92 h month^?1 on average). Haze and fog were very rare (81 h and one event per year, respectively on average). Light fog with a visibility less than 2 km usually was detected together with rain or drizzle. Low visibility was observed more frequently at night and during the rainy season. Applications of the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model for light fog forecast were tested. Thermodynamic processes were studied by vertical profile, elaborated by: (1) National Centers for Environmental Prediction (NCEP) reanalysis data for Maceio (because of some radiosonde absence) and (2) forecast vertical temperature and humidity profiles were produced, using Air Parcels Trajectories of the HYSPLIT model at the pattern levels. The synoptic situations before and during low visibility phenomena were analyzed using different products of NCEP reanalysis, the high resolution (10 km) ETA model and infrared satellite images. Wave disturbance in the trade winds field, localized on the northwest periphery of the South Atlantic subtropical High, usually accompanied the phenomena. A humidity advection, weak ascendant movement and thermal inversion absence at the low levels were created by these waves. The middle level’s descendent movement provoked the humidity accumulation at levels below. Satisfactory results of the HYSPLIT model applications for light fog forecast were obtained with 12 h antecedence. In particular, stable level forecast by the ETA model was forecast satisfactorily with 12 h antecedence; vertical movements were predicted better with up to 48 h antecedence. The PSU/NCAR mesoscale model (MM5) and PAFOG models were tested for analysis and forecast of an intensive fog event. Intensive fog provoked a fatal accident of a small airplane near the Maceio Airport in 2007. These fog formation processes were studied by NCEP reanalysis data, the high resolution regional model MM5, and satellite and radar data. Fog formation was simulated by PAFOG model and satisfactory results were obtained with 10 h antecedence.     

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.     

Analysis of long-term variations of fog and haze in China in recent 50 years and their relations with atmospheric humidity

Our analysis of fog and haze observations from the surface weather stations in China in recent 50 years(from 1961 to 2011)shows that the number of fog days has experienced two-stage variations,with an increasing trend before 1980 and a decreasing trend after 1990.Especially,an obvious decreasing trend after 1990 can be clearly seen,which is consistent with the decreasing trend of the surface relative humidity.However,the number of haze days has demonstrated an increasing trend.As such,the role of reduction of atmospheric relative humidity in the transition process from fog into haze has been further investigated.It is estimated that the mean relative humidity of haze days is about 69%,lower than previously estimated,which implies that it is more difficult for the haze particles to transform into fog drops.This is possibly one of the major environmental factors leading to the reduction of number of fog days.The threshold of the relative humidity for transition from fog into haze is about82%,also lower than previously estimated.Thus,the reduction of the surface relative humidity in China mainly due to the increase of the surface temperature and the saturation specific humidity may exert an obvious impact on the environmental conditions for the formations of fog and haze.In addition,our investigation of the relationship between haze and visibility reveals that with the increase of haze days,the visibility has declined markedly.Since 1961,the mean visibility has dropped from 4–10to 2–4 km,about a half of the previous horizontal distance of visibility.     

A One-dimensional Ensemble Forecast and Assimilation System for Fog Prediction

A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.     

Integration of Local Observations into the One Dimensional Fog Model PAFOG

The numerical prediction of fog requires a very high vertical resolution of the atmosphere. Owing to a prohibitive computational effort of high resolution three dimensional models, operational fog forecast is usually done by means of one dimensional fog models. An important condition for a successful fog forecast with one dimensional models consists of the proper integration of observational data into the numerical simulations. The goal of the present study is to introduce new methods for the consideration of these data in the one dimensional radiation fog model PAFOG. First, it will be shown how PAFOG may be initialized with observed visibilities. Second, a nudging scheme will be presented for the inclusion of measured temperature and humidity profiles in the PAFOG simulations. The new features of PAFOG have been tested by comparing the model results with observations of the German Meteorological Service. A case study will be presented that reveals the importance of including local observations in the model calculations. Numerical results obtained with the modified PAFOG model show a distinct improvement of fog forecasts regarding the times of fog formation, dissipation as well as the vertical extent of the investigated fog events. However, model results also reveal that a further improvement of PAFOG might be possible if several empirical model parameters are optimized. This tuning can only be realized by comprehensive comparisons of model simulations with corresponding fog observations.     

MOS based forecast of 6-hourly area precipitation

A statistical post-processing methodology for application to numerical weather prediction (NWP) model outputs for precipitation forecast is proposed. The post-processing is based on the model output statistics approach. The statistical relationships are described by the multiple linear regression model, which is complemented by an iteration procedure to further correct the regression outputs. Prognostic fields of the ALADIN/LACE (Aire Limitée Adaptation Dynamique Développement InterNational/Limited Area Modelling in Central Europe) NWP model are used for the forecast of 6-hourly areal precipitation amounts at 15 river basins. The NWP model integration starts at 00UTC and forecasts are calculated for lead times of +12, +18, +24 and +30 hours. The post-processing models are developed separately for each lead time and for separate warm (April to September) and cool (October to March) seasons. The forecasts are focused on large precipitation amounts. Using all the combinations, data from four years (1999–2002) are divided into calibration data (3 years), where the models are developed, and verification data. The models are evaluated by examining the root-mean-square error (RMSE), bias, and correlation coefficient (CC) on the verification data samples. The results show that the additional iteration procedure increases the forecast accuracy for a given range of precipitation amounts and simultaneously does not deteriorate the bias, a situation which can arise when negative regression outputs are set to zero. The post-processing method improves the forecast of the NWP model in terms of RMSE and CC. For large precipitation amounts during the summer season, the decrease of RMSE reaches 10% to 20% depending upon the applied method of verification. For the cool season, the decrease is somewhat smaller (7% to 15%).     

Statistical Postprocessing of NWP Model Outputs Applied to Maximum and Minimum Temperature Forecasts

Statistical postprocessing of NWP model outputs is applied to maximum and minimum temperature forecasts. Two approaches to its application are effected to local short-range weather forecasts of minimum and maximum temperatures: Model Output Statistics and modified Perfect Prognosis. The modified Perfect Prognosis method is restricted to the first step of PP because of the significant difference between the horizontal resolution of the available objective analyses and the NWP model outputs. The modified Perfect Prognosis method uses actual data from the objective analysis related to the forecast period instead of the NWP forecast. The results are compared with a simple statistical prognostic model, which does not utilize the NWP model outputs, and with simple reference methods. The forecast is verified using ground station measurements from stations providing SYNOP reports. The results show that the predictive accuracy of the Model Output Statistics method is not very different from that of the modified Perfect Prognosis method, and both are significantly more accurate than the direct predictions of the NWP model. The results have confirmed that statistical postprocessing is able to make localized predictions even if lowresolution data are used.     

A New Integrated Weighted Model in SNOW-V10: Verification of Continuous Variables

This paper presents the verification results of nowcasts of four continuous variables generated from an integrated weighted model and underlying Numerical Weather Prediction (NWP) models. Real-time monitoring of fast changing weather conditions and the provision of short term forecasts, or nowcasts, in complex terrain within coastal regions is challenging to do with sufficient accuracy. A recently developed weighting, evaluation, bias correction and integration system was used in the Science of Nowcasting Olympic Weather for Vancouver 2010 project to generate integrated weighted forecasts (INTW) out to 6 h. INTW forecasts were generated with in situ observation data and background gridded forecasting data from Canadian high-resolution deterministic NWP system with three nested grids at 15-, 2.5- and 1-km horizontal grid-spacing configurations. In this paper, the four variables of temperature, relative humidity, wind speed and wind gust are treated as continuous variables for verifying the INTW forecasts. Fifteen sites were selected for the comparison of the model performances. The results of the study show that integrating surface observation data with the NWP forecasts produce better statistical scores than using either the NWP forecasts or an objective analysis of observed data alone. Overall, integrated observation and NWP forecasts improved forecast accuracy for the four continuous variables. The mean absolute errors from the INTW forecasts for the entire test period (12 February to 21 March 2010) are smaller than those from NWP forecasts with three configurations. The INTW is the best and most consistent performer among all models regardless of location and variable analyzed.     

A Review of some Recent Radiation Fog Prediction Studies and the Results of Integrating a Simple Numerical Model to Predict Radiation Fog over Brunei

— Several radiation fog studies with emphasis on numerical simulation and prediction are reviewed. One of the earliest attempts started with a given surface diurnal variation of temperature and water vapor, and concluded by forecasting the onset of saturation at various levels; thus fog, by examining the spread of temperature and moisture in the vertical. The one-dimensional (1-D) models are still popular. Some of the recent numerical simulations use more than 100 levels in the vertical and treat various kinds of vegetation, aerosols, and soils with moisture contents. Some also employ a mesoscale model in conjunction with a 1-D model to consider the advective effects. In the following a simple 1-D numerical model was used to predict the onset of fog at Brunei, based on a desktop computer and routine surface observations of dry bulb temperature (T), dewpoint temperature (T _d ), and wind speed at 1800 Local Time (LT). Optimism exists in improved predictions of fog and stratus as 1-D models incorporate many physical processes, and mesoscale models continue to improve in predicting advection and cloud cover.     

Microphysical Observations and Mesoscale Model Simulation of a Warm Fog Case during FRAM Project

The objective of this work is to apply a new microphysical parameterization for fog visibility for potential use in numerical weather forecast simulations, and to compare the results with ground-based observations. The observations from the Fog Remote Sensing And Modeling (FRAM) field which took place during the winter of 2005 – 2006 over southern Ontario, Canada (Phase I) were used in the analysis. The liquid water content (LWC), droplet number concentration (N_d), and temperature (T) were obtained from the fog measuring device (FMD) spectra and Rosemount probe, correspondingly. The visibility (Vis) from a visibility meter, liquid water path from microwave radiometers (MWR), and inferred fog properties such as mean volume diameter, LWC, and N_d were also used in the analysis. The results showed that Vis is nonlinearly related to both LWC and N_d. Comparisons between newly derived parameterizations and the ones already in use as a function of LWC suggested that if models can predict the total N_d and LWC at each time step using a detailed microphysics parameterization, Vis can then be calculated for warm fog conditions. Using outputs from the Canadian Mesoscale Compressible Community (MC2) model, being tested with a new multi-moment bulk microphysical scheme, the new Vis parameterization resulted in more accurate Vis values where the correction reached up to 20 –50%.     

Nowcasting with INCA During SNOW-V10

A Central-European nowcasting system which has been developed for use in mountainous terrain is tested in the Whistler/Vancouver area as part of the SNOW-V10 experiment. The integrated nowcasting through comprehensive analysis system provides hourly updated gridded forecasts of temperature, humidity, and wind, as well as precipitation forecasts which are updated every 15 min. It is based on numerical weather prediction (NWP) output and real-time surface weather station and radar data. Verification of temperature, relative humidity, and wind against surface stations shows that forecast errors are significantly reduced in the nowcasting range compared to those of the driving NWP model. The main contribution to the improvement comes from the implicit bias correction due to use of the latest observations. Relative humidity shows the longest lasting effect, with >50 % reduction of mean absolute error up to +4 h. For temperature and wind speed this percentage is reached after +2 and +3 h, respectively. Two cases of precipitation nowcasting are discussed and verified qualitatively.     

北京城市下垫面对雾影响的数值模拟研究

为了探究北京城市下垫面对雾天气过程的影响,为北京地区雾数值预报水平的提高提供理论基础和科学依据,选取2011年10月29日北京地区雾天个例进行了数值模拟试验,通过对WRF/Noah/UCM模式系统中城市冠层参数的调整,显著改善了模式对此次雾天气过程的模拟效果.使用参数调整后的模式系统通过敏感性试验分析研究了北京城市下垫面对雾发生、发展和消散过程的影响.结果表明:参数调整后的WRF/Noah/UCM模式系统能够与实际观测较相符地模拟此次发生在北京地区的雾天气过程,北京城市下垫面主要通过对温度的改变对雾的形成、发展和消散产生显著影响,使雾不易在城市及其附近形成和发展,延后城市地区雾的形成,但城市的存在也使得城市地区及其附近雾不易消散,相较于没有城市时消散时间延后.     

Implementation of a Single-Column Model for Fog and Low Cloud Forecasting at Central-Spanish Airports

Operations at Central-Spanish airports are often, especially in winter, affected by visibility reduction. The Instituto Nacional de Meteorología (INM), the Spanish Weather Service, has developed a single-column model (SCM) in order to improve short-term forecasts of fog, visibility and low-clouds. The SCM, called H1D, is a one-dimensional version of the HIRLAM limited-area model. It is operationally run for three airports in the region: Madrid-Barajas, Almagro and Albacete-Los Llanos. Since SCMs cannot deal with horizontal heterogeneity, the terms that depend on the horizontal structure of the atmosphere are estimated from the outputs of the three-dimensional (3-D) model and introduced into the SCM as external forcings. The systematic analysis of the meteorological situations has evidenced the existence of a close relationship between fog formation and the presence of drainage winds in the region. Since the 3-D model docs not have the necessary resolution to correctly simulate the main features of the drainage flow caused by the complex topography in the proximity of Madrid-Barajas, it cannot provide the SCM with the correct forcings. This problem has been partially overcome through the introduction of a module that, under certain conditions, substitutes the values computed from the 3-D model outputs by others that are based on a conceptual model of the phenomenon and have been empirically derived from climatological knowledge. This module improves the H1D verification scores for the basic meteorological variables—wind, temperature and humidity—and reduces the false alarm rate in fog forecast.     

A Study of Fog Characteristics Using a Coupled WRF–COBEL Model Over Thessaloniki Airport, Greece

An attempt is made to couple the one dimensional COBEL-ISBA (Code de Brouillard à l’échelle Locale-Interactions Soil Biosphere Atmosphere) model with the WRF (Weather Research and Forecasting)–ARW (Advanced Research WRF) numerical weather prediction model to study a fog event that formed on 20 January 2008 over Thessaloniki Airport, Greece. It is the first time that the coupling of COBEL and WRF models is achieved and applied to a fog event over an airport. At first, the performance of the integrated WRF–COBEL system is investigated, by validating it against the available surface observations. The temperature and humidity vertical profiles were used for initializing the model. The performance of WRF–COBEL is considered successful, since it realistically simulated the fog onset and dissipation better than the WRF alone. The COBEL’s sensitivity to initial conditions such as temperature and specific humidity perturbations was also tested. It is found that a small increase of temperature (~1°C) counteracts fog development and results in less fog density. On the other hand, a small decrease of temperature results in much denser fog formation. It is concluded that the integrated model approach for aviation applications can be useful to study fog impact on local traffic and aviation.     

Initialisation of Land Surface Variables for Numerical Weather Prediction

Land surface processes and their initialisation are of crucial importance for Numerical Weather Prediction (NWP). Current land data assimilation systems used to initialise NWP models include snow depth analysis, soil moisture analysis, soil temperature and snow temperature analysis. This paper gives a review of different approaches used in NWP to initialise land surface variables. It discusses the observation availability and quality, and it addresses the combined use of conventional observations and satellite data. Based on results from the European Centre for Medium-Range Weather Forecasts (ECMWF), results from different soil moisture and snow depth data assimilation schemes are shown. Both surface fields and low-level atmospheric variables are highly sensitive to the soil moisture and snow initialisation methods. Recent developments of ECMWF in soil moisture and snow data assimilation improved surface and atmospheric forecast performance.     

A novel method for distinguishing fog and haze based on PM2.5, visibility,and relative humidity

Haze and fog are both low visibility events, but with different physical properties. Haze is caused by the increase of aerosol loading or the hygroscopic growth of aerosol at high relative humidity, whereas visibility degradation in fog is due to the light scattering of fog droplets, which are transited from aerosols via activation. Based on the difference of physical properties between haze and fog, this study presents a novel method to distinguish haze and fog using real time measurements of PM2.5, visibility, and relative humidity. In this method, a criterion can be developed based on the local historical data of particle number size distributions and aerosol hygroscopicity. Low visibility events can be classified into haze and fog according to this criterion.