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.     

Markov chain model to study the occurrence of pre-monsoon thunderstorms over Bhubaneswar,India

The present work deals with pre-monsoon thunderstorms over Bhubaneswar belonging to the state of Orissa, India. A Markovian approach has been adopted to discern the probabilistic behavior of the time series of the occurrence and non-occurrence of this hazardous weather event by introducing a dichotomy within the time series. After a painstaking analysis through chi-square tests, we have identified serial independence in a few years and first-order two-state Markovian dependence in a few years (2000, 2001, 2004 and 2006). Finally, for the years of first-order two-state Markovian dependence, it has been observed that the probability of occurrence or non-occurrence of thunderstorm gets higher if the state of the previous day is similar to that of the current day. Furthermore, the probability of getting non-thunderstorm day followed by non-thunderstorm day is higher than the probability of getting thunderstorm day followed by thunderstorm day. It has been also observed that the unconditional climatological probability of the occurrence of severe pre-monsoon thunderstorm implied by the Markov chain is closely in agreement with the observed relative frequencies. However, it could be revealed that Markov chain cannot, in general, be suggested as a predictive tool for pre-monsoon thunderstorms under study without investigating the serial dependence inherent in the time series.     

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 weather research and forecasting (WRF) model over Guyana

In the present study, diagnostic studies were undertaken using station-based rainfall data sets of selected stations of Guyana to understand the variability of rainfall. The multidecadal variation in rainfall of coastal station Georgetown and inland station Timehri has shown that the rainfall variability was less during the May–July (20–30%) of primary wet season compared to the December--January (60–70%) of second wet season. The rainfall analysis of Georgetown based on data series from 1916 to 2007 shows that El Niño/La Niña has direct relation with monthly mean rainfall of Guyana. The impact is more predominant during the second wet season December--January. A high-resolution Weather Research and Forecasting model was made operational to generate real-time forecasts up to 84 h based on 00 UTC global forecast system (GFS), NCEP initial condition. The model real-time rainfall forecast during July 2010 evaluation has shown a reasonable skill of the forecast model in predicting the heavy rainfall events and major circulation features for day-to-day operational forecast guidance. In addition to the operational experimental forecast, as part of model validation, a few sensitivity experiments are also conducted with the combination of two cloud cumulus (Kain--Fritsch (KF) and Betts–Miller–Janjic (BMJ)) and three microphysical schemes (Ferrier et al. WSM-3 simple ice scheme and Lin et al.) for heavy rainfall event occurred during 28–30 May 2010 over coastal Guyana and tropical Hurricane ‘EARL’ formed during 25 August–04 September 2010 over east Caribbean Sea. It was observed that there are major differences in the simulations of heavy rainfall event among the cumulus schemes, in spite of using the same initial and boundary conditions and model configuration. Overall, it was observed that the combination of BMJ and WSM-3 has shown qualitatively close to the observed heavy rainfall event even though the predicted amounts are less. In the case of tropical Hurricane ‘EARL’, the forecast track in all the six experiments based on 00 UTC of 28 August 2010 initial conditions for the forecast up to 84 h has shown that the combination of KF cumulus and Ferrier microphysics scheme has shown less track errors compared to other combinations. The overall average position errors for all the six experiments taken together work out to 103 km in 24, 199 km in 48, 197 km in 72 and 174 km in 84 h.     

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.     

区域气候模式在中国西北地区气温和降水长时间序列模拟的误差分析

气温和降水微小的变化都可能引起中国西北地区脆弱生态环境空间格局剧烈变化.由于西北地区气象站点分布稀疏,站点观测结果难于完全代表西北地区复杂地貌的气象要素空间分布格局.区域气候模式模拟可以弥补现有观测资料的不足,但模式模拟存在的误差,往往制约着西北区域气候变化评估工作.本文在CN05格点数据和站点数据基础上,从空间格局、极值模拟等方面对高分辨率WRF模式数据气温和降水模拟精度进行评估,假定偏差恒定基础上,使用分类回归树模型建立偏差预测模型.结果表明:CN05数据和WRF模式多年年平均气温和多年平均年降水数据空间格局基本一致,但WRF模式结果空间格局更加细致.WRF模式结果在山区和湖泊地区降水偏高估,气温和降水距平在新疆地区相对于其他地区变化较为一致;95%分位数的降水在甘肃和新疆地区模拟结果好于宁夏、青海和陕西;5%和95%分位数气温,WRF模式大部分站点表现为暖偏差,而CN05数据表现为冷偏差;不同的地貌单元区域气温和降水的偏差时间序列存在一定的相关性.在西北五省区气温和降水的分类树模型中,由于高程和地形复杂度对偏差预测模型影响较大,无法在整个西北地区建立统一的误差预测模型.     

Probing for overshooting as extreme event of thunderstorms

Thunderstorm overshooting is rare but not an unusual phenomenon in a metropolitan of India, Kolkata (22.57° N; 88.36° E) during the pre-monsoon months (April–May). An attempt is made in this study to identify the important parameters differentiating the thunderstorms in overshooting and non-overshooting categories through data analytics from 2000 to 2015. The present investigation on parametric classification would facilitate in estimating the predictability of thunderstorms with overshooting which subsequently might assist in operational forecast of thunderstorm severity over Kolkata. The altitudes of lifting condensation level (LCL), wind shear, bulk Richardson number (BRN), gust speed, boundary layer characteristics and their correlation with thunderstorm cloud top height (CTH) and also their variation and distribution during overshooting (OTS) and non-overshooting (TS) thunderstorms are analyzed in this study. The result depicts that over Kolkata the intensity of storms during OTS is higher than during TS though the frequency of OTS is less than that of TS. The results further show that the potential temperature (θ), equivalent potential temperature (θ_e), mixing ratio (e_s) in the boundary layer, convective available potential energy, convective inhibition energy, BRN and gust speed play significant roles in regulating the CTH during OTS and TS thunderstorms over Kolkata.

     

Hydrologic models coupled with 2D hydrodynamic model for high-resolution urban flood simulation

Natural Hazards - Floods are the most frequent natural disaster and pose a very challenging threat to many cities worldwide. Understanding the flood dynamic is essential for developing strategies...     

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.     

Satellite-based technique for nowcasting of thunderstorms over Indian region

India experiences severe thunderstorms during the months, March–June. But these systems are not predicted well, mainly due to the absence of mesoscale observational network over Indian region and the expert system. As these are short lived systems, the nowcast is attempted worldwide based on satellite and radar observations. Due to inadequate radar network, satellite plays the dominant role for nowcast of these thunderstorms. In this study, a nowcast based algorithm ForTracc developed by Vila et al. (Weather Forecast 23:233–245, 2008) has been examined over the Indian region using Infrared Channel \((10.8~\upmu \hbox {m})\) of INSAT-3D for prediction of Mesoscale Convective Systems (MCS). In this technique, the current location and intensity in terms of Cloud Top Brightness Temperature (CTBT) of the MCS are extrapolated. The purpose of this study is to validate this satellite-based nowcasting technique for Convective Cloud Clusters that helps in optimum utilization of satellite data and improve the nowcasting. The model could predict reasonably the minimum CTBT of the convective cell with average absolute error (AAE) of \({<}7\hbox { K}\) for different lead periods (30–180 min). However, it was underestimated for all the lead periods of forecasts. The AAE in the forecasts of size of the cluster varies from about \(3\times 10^{4}\hbox { km}^{2}\) for 30-min forecast to \(7\times 10^{4}\hbox { km}^{2}\) for 120-min forecast. The mean absolute error in prediction of size is above 31–38% of actual size for different lead periods of forecasts from 30 to 180 min. There is over estimation in prediction of size for 30 and 60 min forecasts (17% and 2.6% of actual size of the cluster, respectively) and underestimation in 90 to 180-min forecasts (–2.4% to –28%). The direct position error (DPE) based on the location of minimum CTBT ranges from 70 to 144 km for 30–180-min forecast respectively.     

Impact of INSAT-3D radiance data assimilation using WRF 3DVAR on simulation of Indian summer monsoon and high-resolution rainfall forecast over hilly terrain

Natural Hazards - This study describes the impact of assimilation of INSAT-3D radiances data from both imager and sounder for Indian summer monsoon simulation and rainfall forecast over a hilly...     

Application of the WRF model rainfall product for the localized flood hazard modeling in a data-scarce environment

Natural Hazards - Urban flood hazard model needs rainfall with high spatial and temporal&nbsp;resolutions for flood hazard analysis to better simulate flood dynamics in complex urban...     

Sensitivity of simulated cyclone Gonu intensity and track to variety of parameterizations: Advanced hurricane WRF model application

Domain configuration and several physical parameterization settings such as planetary boundary layer, cumulus convection, and ocean–atmosphere surface flux parameterizations can play significant roles in numerical prediction of tropical cyclones. The present study focuses to improve the prediction of the TC Gonu by investigating the sensitivity of simulations to mentioned configurations with the Advanced Hurricane WRF model. The experiments for domain design sensitivity with 27 km resolution has been shown moving the domains towards the east improve the results, due to better account for the large-scale process. The fixed and movable nests on a 9-km grid were considered separately within the coarse domain and their results showed that despite salient improvement in simulated intensity, an accuracy reduction in simulated track was observed. Increasing horizontal resolution to 3 km incredibly reduced the simulated intensity accuracy when compared to 27 km resolution. Thereafter, different initial conditions were experimented and the results have shown that the cyclone of 1000 hPa sea level pressure is the best simulation initial condition in predicting the track and intensity for cyclone Gonu. The sensitivity of simulations to ocean–atmosphere surface-flux parameterizations on a 9-km grid showed the combination of ‘Donelan scheme’ for momentum exchanges along with ‘Large and Pond scheme’ for heat and moisture exchanges provide the best prediction for cyclone Gonu intensity. The combination of YSU and MYJ PBL scheme with KF convection for prediction of track and the combination of YSU PBL scheme with KF convection for prediction of intensity are found to have better performance than the other combinations. These 22 sensitivity experiments also implicitly lead us to the conclusion that each particular forecast aspect of TC (e.g., track, intensity, etc.) will require its own special design.     

Analysis of stability parameters in relation to precipitation associated with pre-monsoon thunderstorms over Kolkata,India

The upper air RS/RW (Radio Sonde/Radio Wind) observations at Kolkata (22.65N, 88.45E) during pre-monsoon season March–May, 2005–2012 is used to compute some important dynamic/thermodynamic parameters and are analysed in relation to the precipitation associated with the thunderstorms over Kolkata, India. For this purpose, the pre-monsoon thunderstorms are classified as light precipitation (LP), moderate precipitation (MP) and heavy precipitation (HP) thunderstorms based on the magnitude of associated precipitation. Richardson number in non-uniformly saturated (R _i *) and saturated atmosphere (R _i ); vertical shear of horizontal wind in 0–3, 0–6 and 3–7 km atmospheric layers; energy-helicity index (EHI) and vorticity generation parameter (VGP) are considered for the analysis. The instability measured in terms of Richardson number in non-uniformly saturated atmosphere ( \(R_{i}^{\mathrm {\ast } })\) well indicate the occurrence of thunderstorms about 2 hours in advance. Moderate vertical wind shear in lower troposphere (0–3 km) and weak shear in middle troposphere (3–7 km) leads to heavy precipitation thunderstorms. The wind shear in 3–7 km atmospheric layers, EHI and VGP are good predictors of precipitation associated with thunderstorm. Lower tropospheric wind shear and Richardson number is a poor discriminator of the three classified thunderstorms.     

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.     

The effectiveness of high-resolution LiDAR data combined with PSInSAR data in landslide study

The spatial resolution of digital elevation models (DEMs) is an important factor for reliable landslide studies. Multi-interferometric techniques such as persistent scatterer interferometric synthetic aperture radar (PSInSAR) are used to evaluate the landslide state of activity and its ground deformation velocity, which is commonly measured along the satellite line of sight (LOS). In order to compare velocities measured by different satellites in different periods, their values can be projected along the steepest slope direction, which is the most probable direction of real movement. In order to achieve this result, DEM-derived products are needed. In this paper, the effectiveness of different DEM resolutions was evaluated in order to project ground deformation velocities measured by means of PSInSAR technique in two different case studies in the Messina Province (Sicily, southern Italy): San Fratello and Giampilieri. Three DEMs were used: (i) a 20-m resolution DEM of the Italian Military Geographic Institute (IGM), (ii) a 2-m resolution DEM derived from airborne laser scanning (ALS) light detection and ranging (LiDAR) data for the San Fratello 2010 landslide, and (iii) a 1-m resolution DEM derived from ALS LiDAR data for the area of Giampilieri. The evaluation of the applied method effectiveness was performed by comparing the DEMs elevation with those of each single permanent scatterer (PS) and projecting the measured velocities along the steepest slope direction. Results highlight that the higher DEM resolution is more suitable for this type of analysis; in particular, the PS located nearby the watershed divides is affected by geometrical problems when their velocities are projected along the steepest slope.     

Development and parameterisation of a complex hydrogeological model based on high-resolution direct-push data

Ongoing developments in geological and hydrogeological investigation techniques, especially direct-push methods, have led to an increase in the quality, density and spatial resolution of data available from such investigations. This has created new challenges in the development of numerical models in terms of accurately and efficiently translating detailed and complex conceptual models into effective numerical models. Suitable geometrical and numerical modelling tools are essential in order to meet these challenges. This paper describes the development of a three-dimensional hydrogeological flow model for a contaminated site near Berlin, Germany, based on high-resolution geological data obtained principally using direct-push methods. The available data were first interpreted to construct a detailed GIS-based geological model, which formed the basis of the conceptual site model. The conceptual model was then translated into a geometrical model, which was used to create a finite element numerical model. An innovative geometry object-based approach enabled the complex structural details of the conceptual model to be accurately reproduced in the numerical model domain. The resulting three-dimensional steady-state unconfined flow model was successfully calibrated using external automated calibration software, whereby parameter values for groundwater recharge and hydraulic conductivity were determined.