Assessing the performance of WRF model in predicting high-impact weather conditions over Central and Western Africa: an ensemble-based approach

Natural Hazards - For numerical weather prediction over a particular region, it is important to know the best combination of physical parameterizations available in the considered modelling frame...     

Development of extended WRF variational data assimilation system (WRFDA) for WRF non-hydrostatic mesoscale model

The paper intends to present the development of the extended weather research forecasting data assimilation (WRFDA) system in the framework of the non-hydrostatic mesoscale model core of weather research forecasting system (WRF-NMM), as an imperative aspect of numerical modeling studies. Though originally the WRFDA provides improved initial conditions for advanced research WRF, we have successfully developed a unified WRFDA utility that can be used by the WRF-NMM core, as well. After critical evaluation, it has been strategized to develop a code to merge WRFDA framework and WRF-NMM output. In this paper, we have provided a few selected implementations and initial results through single observation test, and background error statistics like eigenvalues, eigenvector and length scale among others, which showcase the successful development of extended WRFDA code for WRF-NMM model. Furthermore, the extended WRFDA system is applied for the forecast of three severe cyclonic storms: Nargis (27 April–3 May 2008), Aila (23–26 May 2009) and Jal (4–8 November 2010) formed over the Bay of Bengal. Model results are compared and contrasted within the analysis fields and later on with high-resolution model forecasts. The mean initial position error is reduced by 33% with WRFDA as compared to GFS analysis. The vector displacement errors in track forecast are reduced by 33, 31, 30 and 20% to 24, 48, 72 and 96 hr forecasts respectively, in data assimilation experiments as compared to control run. The model diagnostics indicates successful implementation of WRFDA within the WRF-NMM system.     

A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea

Accurate wind modeling is important for wind resources assessment and wind power forecasting.To improve the WRF model configuration for the offshore wind modeling over the Baltic Sea,this study per-formed a sensitivity study of the WRF model to multiple model configurations,including domain setup,grid resolution,sea surface temperature,land surface data,and atmosphere-wave coupling.The simu-lated offshore wind was evaluated against LiDAR observations under different wind directions,atmo-spheric stabilities,and sea status.Generally,the simulated wind profiles matched observations,despite systematic underestimations.Strengthening the forcing from the reanalysis data through reducing the number of nested domains played the largest role in improving wind modeling.Atmosphere-wave cou-pling further improved the simulated wind,especially under the growing and mature sea conditions.Increasing the vertical resolution,and updating the sea surface temperature and the land surface infor-mation only had a slight impact,mainly visible during very stable conditions.Increasing the horizontal resolution also only had a slight impact,most visible during unstable conditions.Our study can help to improve the wind resources assessment and wind power forecasting over the Baltic Sea.     

Performance of nested WRF model in typhoon simulations over West Pacific and South China Sea

Forecasting skill of weather research and forecasting (WRF) model in simulating typhoons over the West Pacific and South China Sea with different trajectories has been studied in terms of track direction and intensity. Four distinct types of typhoons are chosen for this study in such a way that one of them turns toward left during its motion and had landfall, while the second took a right turn before landfall. The third typhoon followed almost a straight line path during its course of motion, while the fourth typhoon tracked toward the coast and just before landfall, ceased its motion and travelled in reverse direction. WRF model has been nested in one way with a coarse resolution of 9?km and a fine resolution of 3?km for this study, and the experiments are performed with National Center for Environmental Prediction-Global Forecasting System (NCEP-GFS) analyses and forecast fields. The model has been integrated up to 96?h and the simulation results are compared with observed and analyzed fields. The results show that the WRF model could satisfactorily simulate the typhoons in terms of time and location of landfall, mean sea-level pressure, maximum wind speed, etc. Results also show that the sensitivity of model resolution is less in predicting the track, while the fine-resolution model component predicted slightly better in terms of central pressure drop and maximum wind. In the case of typhoon motion speed, the coarse-resolution component of the model predicted the landfall time ahead of the actual, whereas the finer one produced either very close to the best track or lagging little behind the best track though the difference in forecast between the model components is minimal. The general tendency of track error forecast is that it increases almost linearly up to 48?h of model simulations and then it diverges quickly. The results also show that the salient features of typhoons such as warm central core, radial increase of wind speed, etc. are simulated well by both the coarse and fine domains of the WRF model.     

The WRF model performance for the simulation of heavy precipitating events over Ahmedabad during August 2006

The summer monsoon season of the year 2006 was highlighted by an unprecedented number of monsoon lows over the central and the western parts of India, particularly giving widespread rainfall over Gujarat and Rajasthan. Ahmedabad had received 540.2mm of rainfall in the month of August 2006 against the climatological mean of 219.8mm. The two spells of very heavy rainfall of 108.4mm and 97.7mm were recorded on 8 and 12 August 2006 respectively. Due to meteorological complexities involved in replicating the rainfall occurrences over a region, the Weather Research and Forecast (WRF-ARW version) modeling system with two different cumulus schemes in a nested configuration is chosen for simulating these events. The spatial distributions of large-scale circulation and moisture fields have been simulated reasonably well in this model, though there are some spatial biases in the simulated rainfall pattern. The rainfall amount over Ahmedabad has been underestimated by both the cumulus parameterization schemes. The quantitative validation of the simulated rainfall is done by calculating the categorical skill scores like frequency bias, threat scores (TS) and equitable threat scores (ETS). In this case the KF scheme has outperformed the GD scheme for the low precipitation threshold.     

Evaluation of the Hurricane Weather Research and Forecasting (HWRF) model for tropical cyclone forecasts over the North Indian Ocean (NIO)

Natural Hazards - The Hurricane Weather Research and Forecast (HWRF) model, which was operational at the US National Centers for Environmental Prediction, was ported in India Meteorological...     

陕西省地质灾害-气象预报预警系统研制及应用

为了提高地质灾害-气象预报预警工作的自动化程度和产品的质量,文章从计算机系统制作的角度探讨了地质灾害-气象预报预警的方法,论述了陕西省地质灾害-气象预报预警系统的运行环境、软件功能、空间数据库、模型方法等内容,并介绍了陕西省汛期地质灾害-气象预报预警的应用实例。     

Improvements in medium range weather forecasting system of India

Medium range weather forecasts are being generated in real time using Global Data Assimilation Forecasting System (GDAFS) at NCMRWF since 1994. The system has been continuously upgraded in terms of data usage, assimilation and forecasting system. Recently this system was upgraded to a horizontal resolution of T574 (about 22 km) with 64 levels in vertical. The assimilation scheme of this upgraded system is based on the latest Grid Statistical Interpolation (GSI) scheme and it has the provision to use most of available meteorological and oceanographic satellite datasets besides conventional meteorological observations. The new system has an improved procedure for relocating tropical cyclone to its observed position with the correct intensity. All these modifications have resulted in improvement of skill of medium range forecasts by about 1 day.     

Dynamic weather forecaster: results of the testing of a collaborative, on-line educational platform for weather forecasting

The online Dynamic Weather Forecaster is an open, collaborative application available now to high-school and college instructors across the United States who would like to easily incorporate weather forecasting in their instruction. The application consists of a set of 13 questions that allow students to submit forecasts that cover most of the parameters used by professional weather forecasters. Submissions are automatically validated against weather parameters and graded. We tested the impact of the application on the learning of 199 undergraduate students in an introductory meteorology course in spring 2008. Students who begin forecasting early in the semester and continue to do so throughout the semester are statistically significantly more successful in the course than students who start late or complete a low number of forecasts. College, year in school, and gender were not significant predictors of success. Students found the application easy to use, and 92.3% of them found it at least somewhat helpful as they learned about the weather. Through the use of the DWF, students also experience first-hand that uncertainty is a critical part of weather forecasting and of scientific studies in general. With sufficient interest from potential users outside the USA, the DWF platform could easily be expanded to include global weather data.     

Scale matching of multiscale digital elevation model (DEM) data and the Weather Research and Forecasting (WRF) model: a case study of meteorological simulation in Hong Kong

It is becoming easier to combine geographical data and dynamic models to provide information for problem solving and geographical cognition. However, the scale dependencies of the data, model, and process can confuse the results. This study extends traditional scale research in static geographical patterns to dynamic processes and focuses on the combined scale effect of multiscale geographical data and dynamic models. The capacity for topographical expression under the combined scale effect was investigated by taking multiscale topographical data and meteorological processes in Hong Kong as a case study. A meteorological simulation of the combined scale effect was evaluated against data from Hong Kong Observatory stations. The experiments showed that (1) a digital elevation model (DEM) using 3 arc sec data with a 1 km resolution Weather Research and Forecasting (WRF) model gives better topographical expression and meteorological reproduction in Hong Kong; (2) a fine-scale model is sensitive to the resolution of the DEM data, whereas a coarse-scale model is less sensitive to it; (3) better topographical expression alone does not improve weather process simulation; and (4) uncertainty arising from a scale mismatch between the DEM data and the dynamic model may account for 38 % of the variance in certain meteorological variables (e.g., temperature). This case study gives a clear explanation of the significance and implementation of scale matching for multiscale geographical data and dynamic models.     

Numerical simulation of cloud burst event on August 05, 2010, over Leh using WRF mesoscale model

Localized deep cumulus convective clouds have a capability of giving enormous amount of rainfall over a limited horizontal area, within a short span of time. Such types of extreme rainfall events are most common over the high elevated areas of Northern India during the Southwest monsoon season which causes widespread damage to the property and lives. Therefore, it is necessary to predict such extreme events accurately to avoid damage associated with them. The numerical mesoscale model Weather Research and Forecasting has been used to simulate the cloud burst event of Leh on August 05, 2010, so as to capture the main characteristics of the various parameters associated with this localized mesoscale phenomenon. The model has been integrated with four nested domains keeping Leh and its adjoining area as center. Two cloud microphysics parameterization schemes namely WSM3 and WSM6 have been used for the sensitivity experiments and results have been analyzed to examine the performance of both the schemes in capturing such extreme localized heavy rainfall events. Results show that the WSM6 microphysics was able to simulate the precipitation near to the observation. WSM3 microphysics simulated the location of the circulation near to the observation. In addition, the results also show that the maximum magnitudes of meridional and vertical wind as simulated with WSM3 microphysics are 12 and 4 m/s, respectively.     

Efficient execution of the WRF model and other HPC applications in the cloud

There are many scientific applications that have high performance computing (HPC) demands. Such demands are traditionally supported by cluster- or Grid-based systems. Cloud computing, which has experienced a tremendous growth, emerged as an approach to provide on-demand access to computing resources. The cloud computing paradigm offers a number of advantages over other distributed platforms. For example, the access to resources is flexible and cost-effective since it is not necessary to invest a large amount of money on a computing infrastructure nor pay salaries for maintenance functions. Therefore, the possibility of using cloud computing for running high performance computing applications is attractive. However, it has been shown elsewhere that current cloud computing platforms are not suitable for running some of these kinds of applications since the performance offered is very poor. The reason is mainly the overhead from virtualisation which is extensively used by most cloud computing platforms as a means to optimise resource usage. Furthermore, running HPC applications in current cloud platforms is a complex task that in many cases requires configuring a cluster of virtual machines (VMs). In this paper, we present a lightweight virtualisation approach for efficiently running the Weather Research and Forecasting (WRF) model (a computing- and communication-intensive application) in a cloud computing environment. Our approach also provides a higher-level programming model that automates the process of configuring a cluster of VMs. We assume such a cloud environment can be shared with other types of HPC applications such as mpiBLAST (an embarrassingly parallel application), and MiniFE (a memory-intensive application). Our experimental results show that lightweight virtualisation imposes about 5 % overhead and it substantially outperforms traditional heavyweight virtualisation such as KVM.     

Mechanism and forecasting method of persistent extreme weather events: review and prospect

Persistent extreme weather is of high disaster causing capability, represents a great threat to the safety of both people and property and results in substantial economic losses. However, the underlying mechanism of such high impact weather remains unclear, and related forecasting methods are quite under studied currently. Based on the comprehensive reviews of the relevant studies about persistent extreme weather, the prediction of such events within the period during 1～2 weeks in advance is believed to be a significant scientific issue. For this scientific problem, the studies of atmospheric low frequency process, the interaction between multi scale systems, the forcing of complicated underlying surface and sea land atmosphere interactions are necessary to be performed. These multi perspective studies will favor the final establishment of the corresponding forecasting theory and method based on the combination of dynamical prediction and statistical predication. It is hoped that the deficiencies in systematic studies about persistent extreme weather may be made up through pertinent studies, which will prolong the time length of forecasting and increase the prediction precision of such high impact events.     

新一代中尺度预报模式（WRF）国内应用进展

随着中尺度大气模式的不断发展,新一代中尺度天气研究和预报模式WRF因其完全开放、可移植性强、更新快等特点在国内外得到了广泛应用。从物理参数化方案研究,实时个例模拟研究及与中尺度大气模式MM5的对比研究3个方面介绍近10年来WRF模式在国内的发展和应用概况,阐明WRF模式在中尺度模拟中的普适性和优越性,展望WRF模式在国...     

估算冻结(融化)深度方法的比较及在中国地区的修正和应用

利用中国地区的冻土观测资料计算比较了常用冻土活动层深度估算的几种方法,并对影响估算的主要因子进行了讨论.结果表明:考虑了土壤水分变化、土壤水分相变潜热变化、积雪和植被效应的指数与实际观测值之间有较好的拟合;在采用分区插值方法对温度进行处理,并应用数字化高程、数字化植被和土壤数据的基础上,采用和观测值相关较高的Kudryavtsev指数计算、以ArcGIS为工具获取的中国地区冻土分布更为合理.     

High resolution numerical weather prediction over the Indian subcontinent

In this study, the Florida State University Global Spectral Model (FSUGSM), in association with a high-resolution nested regional spectral model (FSUNRSM), is used for short-range weather forecasts over the Indian domain. Three-day forecasts for each day of August 1998 were performed using different versions of the FSUGSM and FSUNRSM and were compared with the observed fields (analysis) obtained from the European Center for Medium Range Weather Forecasts (ECMWF). The impact of physical initialization (a procedure that assimilates observed rain rates into the model atmosphere through a set of reverse algorithms) on rainfall forecasts was examined in detail. A very high nowcasting skill for precipitation is obtained through the use of high-resolution physical initialization applied at the regional model level. Higher skills in wind and precipitation forecasts over the Indian summer monsoon region are achieved using this version of the regional model with physical initialization. A relatively new concept, called the ‘multimodel/multianalysis superensemble’ is described in this paper and is applied for the wind and precipitation forecasts over the Indian subcontinent. Large improvement in forecast skills of wind at 850 hPa level over the Indian subcontinent is shown possible through the use of the multimodel superensemble. The multianalysis superensemble approach that uses the latest satellite data from the Tropical Rainfall Measuring Mission (TRMM) and the Defense Meteorological Satellite Program (DMSP) has shown significant improvement in the skills of precipitation forecasts over the Indian monsoon region.