Skills of different mesoscale models over Indian region during monsoon season: Forecast errors

Performance of four mesoscale models namely, the MM5, ETA, RSM and WRF, run at NCMRWF for short range weather forecasting has been examined during monsoon-2006. Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind, temperature, specific humidity, geopotential height, rainfall, systematic errors, root mean square errors and specific events like the monsoon depressions.It is very difficult to address the question of which model performs best over the Indian region? An honest answer is ‘none’. Perhaps an ensemble approach would be the best. However, if we must make a final verdict, it can be stated that in general, (i) the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and, the MM5 is able to produce best All India rainfall forecasts in day-3, but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India, (ii) the MM5 is able to produce least RMSE of wind and geopotential fields at most of the time, and (iii) the RSM is able to produce least errors in the day-1 forecasts of the tracks, while the ETA model produces least errors in the day-3 forecasts.     

西风与季风扰动对黑河流域降水影响的数值模拟

利用中尺度天气模式WRF V2.2进行了两组风场的敏感性试验,分别模拟了西风与季风变化对黑河流域降水的影响.通过对大气环流、水汽输送、水汽辐合以及垂直上升运动的分析得出以下结论:西风与季风对黑河流域降水的影响方式不同,西风带直接作用于黑河流域,影响其降水,而季风则是通过对西风的调整间接影响黑河流域降水;西风与季风变化对黑河流域降水的影响范围不同,西风增强后,黑河流域南部山区降水落区西移,降水增加,最大值中心偏西北;季风增强后,黑河流域南部山区降水落区向东南移,降水增加,最大值中心偏东南.与其它量相比,黑河流域降水与垂直速度的对应关系最好.     

Impact of modified physics in limited area model forecasts

A number of physical factors have been introduced to improve limited area model forecasts. The factors include land surface fluxes, shallow convection and radiation. The model including these additional physical factors (modified physics) is run for five cases of monsoon depression which made landfall over the Indian coast, and the results are compared with those of the control run. The forecasts are verified by computing the root mean square and mean errors. The differences in these skill scores between the two model runs are tested for their statistical significance. It is found that the modified physics has a statistically significant effect on the model skill with the maximum impact on the mean sea level pressure and the temperature. Detailed analyses of mean sea level pressure, wind, rainfall and temperature further confirm that the modified physics has maximum impact on mean sea level pressure and temperature and marginal impact on wind and rainfall. Furthermore, analyses of some model parameters related to physics at a grid point for one case of depression were done. The results show that the inclusion of the land surface physics, shallow convection and radiative processes have produced a better precipitation forecast over the grid point.     

The Indian summer monsoon as revealed by the NCMRWF system

In this study, we present the mean seasonal features of the Indian summer monsoon circulation in the National Centre for Medium Range Weather Forecasting (NCMRWF) global data assimilation and forecast system. The large-scale budgets of heat and moisture are examined in the analyzed and model atmosphere. The daily operational analyses and forecasts (day 1 through day 5) produced for the summer seasons comprising June, July and August of 1995 and 1993 have been considered for the purpose. The principal aim of the study is two-fold. Primarily, to comprehend the influence of the systematic errors over the Indian summer monsoon, secondarily, to analyze the performance of the model in capturing the interseasonal variability. The heat and moisture balances show reduction in the influx of heat and moisture in the model forecasts compared to the analyzed atmosphere over the monsoon domain. Consequently, the diabatic heating also indicates reducing trend with increase in the forecast period. In effect, the strength of Indian summer monsoon, which essentially depends on these parameters, weakens considerably in the model forecasts. Despite producing feeble monsoon circulation, the model captures interseasonal variability realistically. Although, 1995 and 1993 are fairly normal monsoon seasons, the former received more rainfall compared to the latter in certain pockets of the monsoon domain. This is clearly indicated by the analyzed and model atmosphere in terms of energetics.     

Simulation of a flood producing rainfall event of 29 July 2010 over north-west Pakistan using WRF-ARW model

Simulation of a flood producing rainfall event of 29 July 2010 over north-west Pakistan has been carried out using the Weather Research and Forecasting (WRF) model. This extraordinary rainfall event was localized over north-west Pakistan and recorded 274 mm of rainfall at Peshawar (34.02°N, 71.58°E), within a span of 24 h on that eventful day where monthly July normal rainfall is only 46.1 mm. The WRF model was run with the triple-nested domains of 27, 9, and 3 km horizontal resolution using Kain–Fritsch cumulus parameterization scheme having YSU planetary boundary layer. The model performance was evaluated by examining the different simulated parameters. The model-derived rainfall was compared with Pakistan Meteorological Department–observed rainfall. The model suggested that this flood producing heavy rainfall event over north-west region of Pakistan might be the result of an interaction of active monsoon flow with upper air westerly trough (mid-latitude). The north-west Pakistan was the meeting point of the southeasterly flow from the Bay of Bengal following monsoon trough and southwesterly flow from the Arabian Sea which helped to transport high magnitude of moisture. The vertical profile of the humidity showed that moisture content was reached up to upper troposphere during their mature stage (monsoon system usually did not extent up to that level) like a narrow vertical column where high amounts of rainfall were recorded. The other favourable conditions were strong vertical wind shear, low-level convergence and upper level divergence, and strong vorticity field which demarked the area of heavy rainfall. The WRF model might be able to simulate the flood producing rainfall event over north-west Pakistan and associated dynamical features reasonably well, though there were some spatial and temporal biases in the simulated rainfall pattern.     

Quantitative precipitation forecasting over Narmada Catchment

Quantitative precipitation forecasting (QPF) has been attempted over the Narmada Catchment following a statistical approach. The catchment has been divided into five sub-regions for the development of QPF models with a maximum lead-time of 24 hours. For this purpose the data of daily rainfall from 56 raingauge stations, twice daily observations on different surface meteorological parameters from 28 meteorological observatories and upper air data from 11 aerological stations for the nine monsoon seasons of 1972–1980 have been utilized. The horizontal divergence, relative vorticity, vertical velocity and moisture divergence are computed using the kinematic method at different pressure levels and used as independent variables along with the rainfall and surface meteorological parameters. Multiple linear regression equations have been developed using the stepwise procedure separately with actual and square root and log-transformed rainfall using 8-year data (1972–1979). When these equations were verified with an independent data for the monsoon season of 1980, it was found that the transformed rainfall equations fared much better compared to the actual rainfall equations. The performance of the forecasts of QPF model compared to the climatological and persistence forecasts has been assessed by computing the verification scores using the forecasts for the monsoon season of 1980.     

A new ensemble-based data assimilation algorithm to improve track prediction of tropical cyclones

This paper proposes a new ensemble-based algorithm that assimilates the vertical rain structure retrieved from microwave radiometer and radar measurements in a regional weather forecast model, by employing a Bayesian framework. The goal of the study is to evaluate the capability of the proposed technique to improve track prediction of tropical cyclones that originate in the North Indian Ocean. For this purpose, the tropical cyclone Jal has been analyzed by the community mesoscale weather model, weather research and forecasting (WRF). The ensembles of prognostic variables such as perturbation potential temperature (θ, k), perturbation geopotential (?, m²/s²), meridional (U) and zonal velocities (V) and water vapor mixing ratio (q _v , kg/kg) are generated by the empirical orthogonal function technique. An over pass of the tropical rainfall-measuring mission (TRMM) satellite occurred on 06th NOV 0730 UTC over the system, and the observations from the radiometer and radar on board the satellite(1B11 data products) are inverted using a combined in-home radiometer-radar retrieval technique to estimate the vertical rain structure, namely the cloud liquid water, cloud ice, precipitation water and precipitation ice. Each ensemble is input as a possible set of initial conditions to the WRF model from 00 UTC which was marched in time till 06th NOV 0730 UTC. The above-mentioned hydrometeors from the cloud water and rain water mixing ratios are then estimated for all the ensembles. The Bayesian filter framework technique is then used to determine the conditional probabilities of all the candidates in the ensemble by comparing the retrieved hydrometeors through measured TRMM radiances with the model simulated hydrometeors. Based on the posterior probability density function, the initial conditions at 06 00 UTC are then corrected using a linear weighted average of initial ensembles for the all prognostic variables. With these weighted average initial conditions, the WRF model has been run up to 08th Nov 06 UTC and the predictions are then compared with observations and the control run. An ensemble independence study was conducted on the basis of which, an optimum of 25 ensembles is arrived at. With the optimum ensemble size, the sensitivity of prognostic variables was also analyzed. The model simulated track when compared with that obtained with the corrected set of initial conditions gives better results than the control run. The algorithm can improve track prediction up to 35 % for a 24 h forecast and up to 12 % for a 54 h forecast.     

Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India

The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs (1982–2008) indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among these three schemes, SVD based MME has more skill than other MME schemes as well as member models.     

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.     

Current status of multimodel superensemble and operational NWP forecast of the Indian summer monsoon

In the last thirty years great strides have been made by large-scale operational numerical weather prediction models towards improving skills for the medium range time-scale of 7 days. This paper illustrates the use of these current forecasts towards the construction of a consensus multimodel forecast product called the superensemble. This procedure utilizes 120 of the recent-past forecasts from these models to arrive at the training phase statistics. These statistics are described by roughly 10⁷ weights. Use of these weights provides the possibility for real-time medium range forecasts with the superensemble. We show the recent status of this procedure towards real-time forecasts for the Asian summer monsoon. The member models of our suite include ECMWF, NCEP/EMC, JMA, NOGAPS (US Navy), BMRC, RPN (Canada) and an FSU global spectral forecast model. We show in this paper the skill scores for day 1 through day 6 of forecasts from standard variables such as winds, temperature, 500 hPa geopotential height, sea level pressure and precipitation. In all cases we noted that the superensemble carries a higher skill compared to each of the member models and their ensemble mean. The skill matrices we use include the RMS errors, the anomaly correlations and equitable threat scores. For many of these forecasts the improvements of skill for the superensemble over the best model was found to be quite substantial. This real-time product is being provided to many interested research groups. The FSU multimodel superensemble, in real-time, stands out for providing the least errors among all of the operational large scale models.     

Systematic errors in the medium range prediction of the Asian summer monsoon circulation

The present study describes an analysis of Asian summer monsoon forecasts with an operational general circulation model (GCM) of the European Centre for Medium Range Weather Forecasts (ECMWF), U.K. An attempt is made to examine the influence of improved treatment of physical processes on the reduction of systematic errors. As some of the major changes in the parameterization of physical processes, such as modification to the infrared radiation scheme, deep cumulus convection scheme, introduction of the shallow convection scheme etc., were introduced during 1985–88, a thorough systematic error analysis of the ECMWF monsoon forecasts is carried out for a period prior to the incorporation of such changes i.e. summer monsoon season (June–August) of 1984, and for the corresponding period after relevant changes were implemented (summer monsoon season of 1988). Monsoon forecasts of the ECMWF demonstrate an increasing trend of forecast skill after the implementation of the major changes in parameterizations of radiation, convection and land-surface processes. Further, the upper level flow is found to be more predictable than that of the lower level and wind forecasts display a better skill than temperature. Apart from this, a notable increase in the magnitudes of persistence error statistics indicates that the monsoon circulation in the analysed fields became more intense with the introduction of changes in the operational forecasting system. Although, considerable reduction in systematic errors of the Asian summer monsoon forecasts is observed (up to day-5) with the introduction of major changes in the treatment of physical processes, the nature of errors remain unchanged (by day-10). The forecast errors of temperature and moisture in the middle troposphere are also reduced due to the changes in treatment of longwave radiation. Moreover, the introduction of shallow convection helped it further by enhancing the vertical transports of heat and moisture from the lower troposphere. Though, the hydrological cycle in the operational forecasts appears to have enhanced with the major modifications and improvements to the physical parameterization schemes, certain regional peculiarities have developed in the simulated rainfall distribution over the monsoon region. Hence, this study suggests further attempts to improve the formulations of physical processes for further reduction of systematic forecast errors.     

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.     

Impact of cloud microphysics and cumulus parameterization on simulation of heavy rainfall event during 7–9 October 2007 over Bangladesh

In the present study, the Advanced Research WRF (ARW) version 3.2.1 has been used to simulate the heavy rainfall event that occurred between 7 and 9 October 2007 in the southern part of Bangladesh. Weather Research and Forecast (WRF–ARW version) modelling system with six different microphysics (MP) schemes and two different cumulus parameterization (CP) schemes in a nested configuration was chosen for simulating the event. The model domains consist of outer and inner domains having 9 and 3 km horizontal resolution, respectively with 28 vertical sigma levels. The impacts of cloud microphysical processes by means of precipitation, wind and reflectivity, kinematic and thermodynamic characteristics of the event have been studied. Sensitivity experiments have been conducted with the WRF model to test the impact of microphysical and cumulus parameterization schemes in capturing the extreme weather event. NCEP FNL data were used for the initial and boundary condition. The model ran for 72 h using initial data at 0000 UTC of 7 October 2007. The simulated rainfall shows that WSM6–KF combination gives better results for all combinations and after that Lin–KF combination. WSM3–KF has simulated, less area average rainfall out of all MP schemes that were coupled with KF scheme. The sharp peak of relative humidity up to 300 hPa has been simulated along the vertical line where maximum updraft has been found for all MPs coupled with KF and BMJ schemes. The simulated rain water and cloud water mixing ratio were maximum at the position where the vertical velocity and reflectivity has also been maximum. The production of rain water mixing ratio depends on MP schemes as well as CP schemes. Rainfall depends on rain water mixing ratio between 950 and 500 hPa. Rain water mixing ratio above 500 hPa level has no effect on surface rain.     

Anomalous behaviour of the Indian summer monsoon 2009

The Indian subcontinent witnessed a severe monsoon drought in the year 2009. India as a whole received 77% of its long period average during summer monsoon season (1 June to 30 September) of 2009, which is the third highest deficient all India monsoon season rainfall year during the period 1901–2009. Therefore, an attempt is made in this paper to study the characteristic features of summer monsoon rainfall of 2009 over the country and to investigate some of the possible causes behind the anomalous behaviour of the monsoon.     

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.     

Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models.     

Evaluation of operational tropical cyclone intensity forecasts over north Indian Ocean issued by India Meteorological Department

India Meteorological Department (IMD) introduced the objective tropical cyclone (TC) intensity forecast valid for next 24 h over the north Indian Ocean (NIO) in 2003 and extended up to 72 h in 2009. In this study, an attempt is made to evaluate the TC intensity forecast issued by IMD during 2005–2011 (7 years) by calculating the absolute error (AE), root mean square error (RMSE) and skill in intensity forecast in terms of maximum sustained surface wind (MSW). The accuracy of TC intensity forecast has been analysed with respect to basin of formation (Bay of Bengal, Arabian Sea and NIO as whole), season of formation (pre-monsoon and post-monsoon seasons), intensity of TCs (cyclonic storm and severe cyclonic storm or higher intensities) and type of track of TCs (climatological/straight moving and recurving/looping type). The study shows that the average AE (RMSE) in intensity forecast is about 11(14), 14(19) and 20(26) knots, respectively, for 24-, 48- and 72-h forecasts over the NIO as a whole during 2009–2011. The skill of intensity forecast is about 44 %(48 %), 60 %(58 %) and 60 %(65 %) for 24-, 48- and 72-h forecasts during 2009–2011 with respect to AE (RMSE). There is no significant improvement in terms of reduction in AE and RMSE of MSW forecast over the NIO like that over the northwest Pacific and northern Atlantic Oceans during 2005–2011. However, the skill in intensity forecast compared to persistence method has significantly improved by about 6 %(10 %) and 9 %(8 %) per year, respectively, for 12- and 24-h forecasts considering the AE (RMSE) during 2005–2011. There is also significant increasing trend in percentage of 24-h intensity forecasts with error of 10 knots or less during 2005–2011.     

Case studies of seasonal rainfall forecasts for Hong Kong and its vicinity using a regional climate model

Seasonal climate forecasts are one of the most promising tools for providing early warnings for natural hazards such as floods and droughts. Using two case studies, this paper documents the skill of a regional climate model in the seasonal forecasting of below normal rainfall in southern China during the rainy seasons of July–August–September 2003 and April–May–June 2004. The regional model is based on the Regional Spectral Model of the National Centers for Environmental Prediction of the United States. It is the first time that the model has been applied to a region dominated by the East Asian Monsoon. The article shows that the regional climate model, when being forced by reasonably good forecasts from a global model, can generate useful seasonal rainfall forecasts for the region, where it is dominated by the East Asia monsoon. The spatial details of the dry conditions obtained from the regional climate model forecast are also found to be comparable with the observed distribution.     

A comprehensive study of surface and upper-air characteristics over two stations on the west coast of India during the occurrence of a cyclonic storm

While qualitative information from meteorological satellites has long been recognized as critical for monitoring weather events such as tropical cyclone activity, quantitative data are required to improve the numerical prediction of these events. In this paper, the sea surface winds from QuikSCAT, cloud motion vectors and water vapor winds from KALPANA-1 are assimilated using three-dimensional variational assimilation technique within Weather Research Forecasting (WRF) modeling system. Further, the sensitivity experiments are also carried out using the available cumulus convective parameterizations in WRF modeling system. The model performance is evaluated using available observations, and both qualitative and quantitative analyses are carried out while analyzing the surface and upper-air characteristics over Mumbai (previously Bombay) and Goa during the occurrence of the tropical cyclone PHYAN at the west coast of Indian subcontinent. The model-predicted surface and upper-air characteristics show improvements in most of the situations with the use of the satellite-derived winds from QuikSCAT and KALPANA-1. Some of the model results are also found to be better in sensitivity experiments using cumulus convection schemes as compared to the CONTROL simulation.