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.     

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.     

Quantitative assessment of short‐term rainfall forecasts from radar nowcasts and MM5 forecasts

Short‐term Quantitative Precipitation Forecasts (QPFs) can be achieved from numerical weather prediction (NWP) models or radar nowcasting, that is the extrapolation of the precipitation at a future time from consecutive radar scans. Hybrid forecasts obtained by merging rainfall forecasts from radar nowcasting and NWP models are potentially more skilful than either radar nowcasts or NWP rainfall forecasts alone. This paper provides an assessment of deterministic and probabilistic high‐resolution QPFs achieved by implementing the Short‐term Ensemble Prediction System developed by the UK Met Office. Both radar nowcasts and hybrid forecasts have been performed. The results show that the performance of both deterministic nowcasts and deterministic hybrid forecasts decreases with increasing rainfall intensity and spatial resolution. The results also show that the blending with the NWP forecasts improves the performance of the forecasting system. Probabilistic hybrid forecasts have been obtained through the modelling of a stochastic noise component to produce a number of equally likely ensemble members, and the comparative assessment of deterministic and probabilistic hybrid forecasts shows that the probabilistic forecasting system is characterised by a higher discrimination accuracy than the deterministic one. Copyright © 2011 John Wiley & Sons, Ltd.     

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.     

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.     

Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10): a World Weather Research Programme Project

A World Weather Research Programme (WWRP) project entitled the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) was developed to be associated with the Vancouver 2010 Olympic and Paralympic Winter Games conducted between 12 February and 21 March 2010. The SNOW-V10 international team augmented the instrumentation associated with the Winter Games and several new numerical weather forecasting and nowcasting models were added. Both the additional observational and model data were available to the forecasters in real time. This was an excellent opportunity to demonstrate existing capability in nowcasting and to develop better techniques for short term (0–6 h) nowcasts of winter weather in complex terrain. Better techniques to forecast visibility, low cloud, wind gusts, precipitation rate and type were evaluated. The weather during the games was exceptionally variable with many periods of low visibility, low ceilings and precipitation in the form of both snow and rain. The data collected should improve our understanding of many physical phenomena such as the diabatic effects due to melting snow, wind flow around and over terrain, diurnal flow reversal in valleys associated with daytime heating, and precipitation reductions and increases due to local terrain. Many studies related to these phenomena are described in the Special Issue on SNOW-V10 for which this paper was written. Numerical weather prediction and nowcast models have been evaluated against the unique observational data set now available. It is anticipated that the data set and the knowledge learned as a result of SNOW-V10 will become a resource for other World Meteorological Organization member states who are interested in improving forecasts of winter weather.     

Improvement of Local Categorical Precipitation Forecasts from an NWP Model by Various Statistical Postprocessing Methods

Several statistical postprocessing methods are applied to results from a numerical weather prediction (NWP) model to test the potential for increasing the accuracy of its local precipitation forecasts. Categorical (Yes/No) forecasts for 12hr precipitation sums equalling or exceeding 0.1, 2.0 and 5.0 mm are selected for improvement. The two 12hr periods 0600-1800 UTC and 1800-0600 UTC are treated separately based on NWP model initial times 0000 UTC and 1200 UTC, respectively. Input data are taken from three successive summer seasons, April-September, 1994-96. The forecasts are prepared and verified for five synoptic stations, four located in the western Czech Republic, and one in Germany near the Czech-German border. Two approaches to statistical postprocessing are tested. The first uses Model Output Statistics (MOS) and the second modifies the MOS approach by applying a successive learning technique (SLT). For each approach several statistical models for the relationship between NWP model predictors and predictand were studied. An independent data set is used for forecast verification with the skill measured by a True Skill Score. The results of the statistical postprocessing are compared with the direct model precipitation forecasts from gridpoints nearest the stations, and they show that both postprocessing approaches provide substantially better forecasts than the direct NWP model output. The relative improvement increases with increasing precipitation amount and there is no significant difference in performance between the two 12hr periods. The skill of the SLT does not depend significantly on the size of the initial learning sample, but its results are nevertheless comparable with the results obtained from the MOS approach, which requires larger developmental samples.     

Comparing quantitative precipitation forecast methods for prediction of sewer flows in a small urban area

Abstract

Due to the relatively small spatial scale, as well as rapid response, of urban drainage systems, the use of quantitative rainfall forecasts for providing quantitative flow and depth predictions is a challenging task. Such predictions are important when consideration is given to urban pluvial flooding and receiving water quality, and it is worthwhile to investigate the potential for improved forecasting. In this study, three quantitative precipitation forecast methods of increasing complexity were compared and used to create quantitative forecasts of sewer flows 0–3 h ahead in the centre of a small town in the north of England. The HyRaTrac radar nowcast model was employed, as well as two different versions of the more complex STEPS model. The STEPS model was used as a deterministic nowcasting system, and was also blended with the Numerical Weather Prediction (NWP) model MM5 to investigate the potential of increasing forecast lead-times (LTs) using high-resolution NWP. Predictive LTs between 15 and 90 min gave acceptable results, but were a function of the event type. It was concluded that higher resolution rainfall estimation as well as nowcasts are needed for prediction of both local pluvial flooding and combined sewer overflow spill events.

Editor D. Koutsoyiannis; Guest editor R.J. Moore     

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.     

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%).     

Exploring the effect of data assimilation by WRF‐3DVar for numerical rainfall prediction with different types of storm events

The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high‐resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real‐time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper‐air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three‐dimensional variational data‐assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km²) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one‐dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two‐dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases. Copyright © 2012 John Wiley & Sons, Ltd.     

Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models

Weather radar has a potential to provide accurate short‐term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro‐meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)‐TN] and the empirically based scheme (ERF‐EM), and explores which one exhibits higher predictive skill. The ERF‐TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so‐called ERF‐EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF‐EM achieves better performance than the ERF‐TN at 1‐h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Using Lightning Data to Better Understand and Predict Flash Floods in the Mediterranean

One of the costliest natural hazards around the globe is flash floods, resulting from localized intense convective precipitation over short periods of time. Since intense convective rainfall (especially over the continents) is well correlated with lightning activity in these storms, a European Union FP6 FLASH project was realized from 2006 to 2010, focusing on using lightning observations to better understand and predict convective storms that result in flash floods. As part of the project, 23 case studies of flash floods in the Mediterranean region were examined. For the analysis of these storms, lightning data were used together with rainfall estimates in order to understand the storms?? development and electrification processes. In addition, these case studies were simulated using mesoscale meteorological models to better understand the local and synoptic conditions leading to such intense and damaging storms. As part of this project, tools for short-term predictions (nowcasts) of intense convection across the Mediterranean and Europe, and long-term forecasts (a few days) of the likelihood of intense convection, were developed and employed. The project also focused on educational outreach through a special Web site http://flashproject.org supplying real-time lightning observations, real-time experimental nowcasts, medium-range weather forecasts and educational materials. While flash floods and intense thunderstorms cannot be prevented, long-range regional lightning networks can supply valuable data, in real time, for warning the public, end-users and stakeholders of imminent intense rainfall and possible flash floods.     

Development,implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System

The NOAA Great Lakes Operational Forecast System (GLOFS) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water temperature and currents, and two-dimensional forecasts of water levels of the Great Lakes. This system, originally called the Great Lakes forecasting system (GLFS), was developed at The Ohio State University and NOAA’s Great Lakes Environmental Research Laboratory (GLERL) in 1989. In 1996, a workstation version of the GLFS was ported to GLERL to generate semi-operational nowcasts and forecasts daily. In 2004, GLFS went through rigorous skill assessment and was transitioned to the National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS since September 30, 2005. Hindcast, nowcast, and forecast evaluations using the NOS-developed skill assessment software tool indicated both surface water levels and temperature predictions passed the NOS specified criteria at a majority of the validation locations with relatively low root mean square error (4–8 cm for water levels and 0.5 to 1°C for surface water temperatures). The difficulty of accurately simulating seiches generated by storms (in particular in shallow lakes like Lake Erie) remains a major source of error in water level prediction and should be addressed in future improvements of the forecast system.     

Development and evaluation of a storm surge warning system in Taiwan

An operational storm surge forecasting system aimed at providing warning information for storm surges has been developed and evaluated using four typhoon events. The warning system triggered by typhoon forecasts from Taiwan Cooperative Precipitation Ensemble Forecast Experiment (TAPEX) has been executed with two storm surge forecasting scenarios with and without tides. Three numerical experiments applying different meteorological inputs have been designed to assess the impact of typhoon forcing on storm surges. One uses synthetic wind fields, and the others use realistic wind fields with and without adjustments to the initial wind fields for the background circulation. Local observations from Central Weather Bureau (CWB) weather stations and tide gauge stations are used to evaluate the wind fields and storm surges from our numerical experiments. The comparison results show that the accuracy of the storm surge forecast is dominated by the track, the intensity, and the driving flow of a typhoon. When the structure of a typhoon is disturbed by Taiwan’s topography, using meteorological inputs from real wind fields can result in a better typhoon simulation than using inputs from synthetic wind fields. The driving flow also determines the impact of topography on typhoon movement. For quickly moving typhoons, storm forcing from TAPEX is reliable when a typhoon is strong enough to be relatively unaffected by environmental flows; otherwise, storm forcing from a sophisticated typhoon initialization scheme that better simulates the typhoon and environmental flows results in a more accurate prediction of storm surges. Therefore, when a typhoon moves slowly and interacts more with the topography and environmental flows, incorporating realistic wind fields with adjustments to the initial wind fields for the background circulation in the warning system will obtain better predictions for a typhoon and its resultant storm surges.     

Assimilation and High Resolution Forecasts of Surface and Near Surface Conditions for the 2010 Vancouver Winter Olympic and Paralympic Games

A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today’s optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf’s initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.     

GPS大气掩星技术在全球气候变化研究中的应用

人类活动引起全球变暖,衡量全球气候变化的指标有陆地、大气和海洋温度,水汽含量等等.研究对流层底层大气温度和水汽含量变化的传统方法是用数值天气预报模型和微波声纳,尚未实现用全球均匀覆盖的数据来做精确的定量研究.和GNSS系列卫星计划比较,最近发射的COSMIC卫星气象探测数据的空间、时间以及垂直分辨率都大大提高.采用COSMIC数据可以改进和量化南极洲的大气压力模型,并综合GNSS系列卫星测量的水汽和温度剖面研究全球气候变化.用一维协方差算法估计南极洲及附近海洋的大气压、温度和湿度剖面.把COSMIC卫星密集测量期间演算得到的大气折射率和GNSS系列卫星的结果进行比较.再和独立测量数据进行比较,包括南极洲自动气象观测站资料,数值天气预报模型资料,多种测高卫星水汽资料和海洋表面温度资料以及区域GPS水汽图.上述工作将改进发展中的气象遥感技术并应用于天气预报和空间天气预报及全球气候变化研究.     

Very short-term spatio-temporal wind power prediction using a censored Gaussian field

Wind power is a renewable energy resource, that has relatively cheap installation costs and it is highly possible that will become the main energy resource in the near future. Wind power needs to be integrated efficiently into electricity grids, and to optimize the power dispatch, techniques to predict the level of wind power and the associated variability are critical. Ideally, one would like to obtain reliable probability density forecasts for the wind power distributions. We aim at contributing to the literature of wind power prediction by developing and analysing a spatio-temporal methodology for wind power production, that is tested on wind power data from Denmark. We use anisotropic spatio-temporal correlation models to account for the propagation of weather fronts, and a transformed latent Gaussian field model to accommodate the probability masses that occur in wind power distribution due to chains of zeros. We apply the model to generate multi-step ahead probability predictions for wind power generated at both locations where wind farms already exist but also to nearby locations.