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1.
One very specific operational requirement of the Tropical Cyclone (TC) Programme of the Regional Specialized Meteorological Centre, New Delhi is to provide 12-hourly forecasts valid up to 48 h (preferably 72 h) on the intensity of cyclones over the southern Indian Seas. In this paper, a simple empirical model for predicting the intensity of TCs occurring in the Bay of Bengal is proposed. The model parameter has been determined from a database assembled on 30 recent cyclones, and the model itself is based on the assumption that a TC intensifies exponentially. A method for correcting the forecast during subsequent observation hours (6- or 12-h intervals) is also presented. The results show that the forecast skill for forecasts of up to 48 h is reasonably good. The absolute mean errors are less than 12 knots for 48-h forecasts, with the forecast skill decreasing with time. With the incorporation of a correction procedure based on the latest observations, some improvement in the forecast skill can be obtained. The model is expected to be useful to operational forecasters.  相似文献   

2.
An attempt is made in this study to develop a model to forecast the cyclonic depressions leading to cyclonic storms over North Indian Ocean (NIO) with 3 days lead time. A multilayer perceptron (MLP) model is developed for the purpose and the forecast quality of the model is compared with other neural network and multiple linear regression models to assess the forecast skill and performances of the MLP model. The input matrix of the model is prepared with the data of cloud coverage, cloud top temperature, cloud top pressure, cloud optical depth, cloud water path collected from remotely sensed moderate resolution imaging spectro-radiometer (MODIS), and sea surface temperature. The input data are collected 3 days before the cyclogenesis over NIO. The target output is the central pressure, pressure drop, wind speed, and sea surface temperature associated with cyclogenesis over NIO. The models are trained with the data and records from 1998 to 2008. The result of the study reveals that the forecast error with MLP model varies between 0 and 7.2 % for target outputs. The errors with MLP are less than radial basis function network, generalized regression neural network, linear neural network where the errors vary between 0 and 8.4 %, 0.3 and 24.8 %, and 0.3 and 32.4 %, respectively. The forecast with conventional statistical multiple linear regression model, on the other hand, generates error values between 15.9 and 32.4 %. The performances of the models are validated for the cyclonic storms of 2009, 2010, and 2011. The forecast errors with MLP model during validation are also observed to be minimum.  相似文献   

3.
The output from Global Forecasting System (GFS) T574L64 operational at India Meteorological Department (IMD), New Delhi is used for obtaining location specific quantitative forecast of maximum and minimum temperatures over India in the medium range time scale. In this study, a statistical bias correction algorithm has been introduced to reduce the systematic bias in the 24–120 hour GFS model location specific forecast of maximum and minimum temperatures for 98 selected synoptic stations, representing different geographical regions of India. The statistical bias correction algorithm used for minimizing the bias of the next forecast is Decaying Weighted Mean (DWM), as it is suitable for small samples. The main objective of this study is to evaluate the skill of Direct Model Output (DMO) and Bias Corrected (BC) GFS for location specific forecast of maximum and minimum temperatures over India. The performance skill of 24–120 hour DMO and BC forecast of GFS model is evaluated for all the 98 synoptic stations during summer (May-August 2012) and winter (November 2012–February 2013) seasons using different statistical evaluation skill measures. The magnitude of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for BC GFS forecast is lower than DMO during both summer and winter seasons. The BC GFS forecasts have higher skill score as compared to GFS DMO over most of the stations in all day-1 to day-5 forecasts during both summer and winter seasons. It is concluded from the study that the skill of GFS statistical BC forecast improves over the GFS DMO remarkably and hence can be used as an operational weather forecasting system for location specific forecast over India.  相似文献   

4.
Seasonal forecasting of tropical cyclogenesis over the North Indian Ocean   总被引：1，自引：0，他引：1  
Over the North Indian Ocean (NIO) and particularly over the Bay of Bengal (BoB), the post-monsoon season from October to December (OND) are known to produce tropical cyclones, which cause damage to life and property over India and many neighbouring countries. The variability of frequency of cyclonic disturbances (CDs) during OND season is found to be associated with variability of previous large-scale features during monsoon season from June to September, which is used to develop seasonal forecast model of CDs frequency over the BoB and NIO based on principal component regression (PCR). Six dynamical/thermodynamical parameters during previous June–August, viz., (i) sea surface temperature (SST) over the equatorial central Pacific, (ii) sea level pressure (SLP) over the southeastern equatorial Indian Ocean, (iii) meridional wind over the eastern equatorial Indian Ocean at 850 hPa, (iv) strength of upper level easterly, (v) strength of monsoon westerly over North Indian Ocean at 850 hPa, and (vi) SST over the northwest Pacific having significant and stable relationship with CDs over BoB in subsequent OND season are used in PCR model for a training period of 40 years (1971–2010) and the latest four years (2011–2014) are used for validation. The PCR model indicates highly significant correlation coefficient of 0.77 (0.76) between forecast and observed frequency of CD over the BoB (NIO) for the whole period of 44 years and is associated with the root mean square error and mean absolute error ≤ 1 CD. With respect to the category forecast of CD frequency over BoB and NIO, the Hit score is found to be about 63% and the Relative Operating Curves (ROC) for above and below normal forecast is found to be having much better forecast skill than the climatology. The PCR model performs very well, particularly for the above and below normal CD year over the BoB and the NIO, during the test period from 2011 to 2014.  相似文献   

5.
The convection and planetary boundary layer (PBL) processes play significant role in the genesis and intensification of tropical cyclones (TCs). Several convection and PBL parameterization schemes incorporate these processes in the numerical weather prediction models. Therefore, a systematic intercomparison of performance of parameterization schemes is essential to customize a model. In this context, six combinations of physical parameterization schemes (2 PBL Schemes, YSU and MYJ, and 3 convection schemes, KF, BM, and GD) of WRF-ARW model are employed to obtain the optimum combination for the prediction of TCs over North Indian Ocean. Five cyclones are studied for sensitivity experiments and the out-coming combination is tested on real-time prediction of TCs during 2008. The tracks are also compared with those provided by the operational centers like NCEP, ECMWF, UKMO, NCMRWF, and IMD. It is found that the combination of YSU PBL scheme with KF convection scheme (YKF) provides a better prediction of intensity, track, and rainfall consistently. The average RMSE of intensity (13?hPa in CSLP and 11?m?s?1 in 10-m wind), mean track, and landfall errors is found to be least with YKF combination. The equitable threat score (ETS) of YKF combination is more than 0.2 for the prediction of 24-h accumulated rainfall up to 125?mm. The vertical structural characteristics of cyclone inner core also recommend the YKF combination for Indian seas cyclones. In the real-time prediction of 2008 TCs, the 72-, 48-, and 24-h mean track errors are 172, 129, and 155?km and the mean landfall errors are 125, 73, and 66?km, respectively. Compared with the track of leading operational agencies, the WRF model is competing in 24?h (116?km error) and 72?h (166?km) but superior in 48-h (119?km) track forecast.  相似文献   

6.
An accurate tropical cyclone track and intensity forecast is very important for disaster management. Specialized numerical prediction models have been recently used to provide high-resolution temporal and special forecasts. Hurricane Weather Research and Forecast (HWRF) model is one of the emerging numerical models for tropical cyclone forecasting. This study evaluates the performance of HWRF model during the post monsoon tropical cyclone Nilofar on the north Indian Ocean basin. The evaluation uses the best track data provided by the Indian Meteorological Department (IMD) and the Joint Typhoon Warning Centre (JTWC). Cyclone track, central pressure, and wind speed are covered on this evaluation. Generally, HWRF was able to predict the Nilofar track with track error less than 230 km within the first 66 h of forecast time span. HWRF predicted more intense tropical cyclone. It predicted the lowest central pressure to be 922 hPa while it reached 950 hPa according to IMD and 937 hPa according to JTWC. Wind forecast was better as it predicted maximum wind speed of 122 kt while it reached 110 and 115 kt according to IMD and JTWC, respectively.  相似文献   

7.
India Meteorological Department has the responsibility of monitoring and prediction of cyclonic disturbances (CDs) including tropical cyclone (TC) and depression, collection, processing and archival of all data pertaining to CDs and preparation of best track data over the North Indian Ocean (NIO). The process of post-season analysis of CDs to determine the best estimate of a CD??s position and intensity along with other characteristics during its lifetime is described as ??best tracking??. The best tracking procedure has undergone several changes world-over including NIO due to change in definition and classification of TCs, monitoring and analysis tools and procedure and physical understanding of TCs. There have been a few attempts to document the temporal changes in the best track procedure including changes in observational network, monitoring technique, area of responsibility for monitoring, terminology and classification of the TCs over the NIO. Hence, a study has been undertaken to review the temporal variations in all the above aspects of best tracking procedure and its impact on quality of best track parameters over the NIO. The problems and prospective with the best track data over the (NIO) have been presented and discussed. Based on quality and availability, the whole period of best track information may be broadly classified into four phases, viz. (i) pre-1877, (ii) 1877?C1890, (iii) 1891?C1960 and (iv) 1961?C2010. The period of 1961?C2010 may be further classified into (a) 1961?C1973, (b) 1974?C1990 and (c) 1991?C2010. As optimum observational network including satellite leading to better estimation of location and intensity without missing of CDs was available since 1961, the climatology of genesis, location, intensity, movement (track) and landfall can be best represented based on the data set of 1961?C2010. The best track parameters need to be reanalysed since 1891, based on the present criteria/classification of CDs to develop a digital data set of every six hourly position, intensity and other characteristics throughout the life period of each recorded CD over the NIO to meet the world standard. At least attempt should be made from 1974 when all types of major data including satellite, radar, surface and upper air observations are available for best track analysis. The reanalysis of best track parameters can help in better understanding and prediction of CDs and address the issues related to climate change aspects over the NIO region.  相似文献   

8.
The Advanced Research WRF (ARW) model is used to simulate Very Severe Cyclonic Storms (VSCS) Hudhud (7–13 October, 2014), Phailin (8–14 October, 2013) and Lehar (24–29 November, 2013) to investigate the sensitivity to microphysical schemes on the skill of forecasting track and intensity of the tropical cyclones for high-resolution (9 and 3 km) 120-hr model integration. For cloud resolving grid scale (<5 km) cloud microphysics plays an important role. The performance of the Goddard, Thompson, LIN and NSSL schemes are evaluated and compared with observations and a CONTROL forecast. This study is aimed to investigate the sensitivity to microphysics on the track and intensity with explicitly resolved convection scheme. It shows that the Goddard one-moment bulk liquid-ice microphysical scheme provided the highest skill on the track whereas for intensity both Thompson and Goddard microphysical schemes perform better. The Thompson scheme indicates the highest skill in intensity at 48, 96 and 120 hr, whereas at 24 and 72 hr, the Goddard scheme provides the highest skill in intensity. It is known that higher resolution domain produces better intensity and structure of the cyclones and it is desirable to resolve the convection with sufficiently high resolution and with the use of explicit cloud physics. This study suggests that the Goddard cumulus ensemble microphysical scheme is suitable for high resolution ARW simulation for TC’s track and intensity over the BoB. Although the present study is based on only three cyclones, it could be useful for planning real-time predictions using ARW modelling system.  相似文献   

9.
The genesis of tropical cyclones (TCs) over Indian seas comprising of Bay of Bengal (BoB) and Arabian Sea (AS) is highly seasonal with primary maximum in postmonsoon season (mid-September to December) and secondary maximum during premonsoon season (April and May). The present study is focused to demonstrate changes in genesis and intensity of TCs over Indian seas in warming environment. For this purpose, observational data of TCs, obtained from the India Meteorological Department (IMD), are analyzed. The sea surface temperature (SST), surface wind speed, and potential evaporation factor (PEF), obtained from the International Comprehensive Ocean Atmosphere Data Set (ICOADS), are also analyzed to examine the possible linkage with variations in TC activities over Indian seas. The study period has been divided into two epochs: past cooling period (PCP, period up to 1950) and current warming period (CWP, period after 1950) based on SST anomaly (became positive from 1950) over the BoB and AS. The study reveals that the number of severe cyclones (SCS) increases significantly (statistically significant at 99% confidence level) by about 41% during CWP though no such significant change is observed in cyclonic disturbances (CDs) and cyclones (CS) over Indian seas. It is also observed that the rate of dissipation of CS and SCS over Indian seas has been decreasing considerably by about 63 and 71%, respectively, during CWP. The analysis shows that the BoB contributes about 75% in each category of TCs and remaining 25% by the AS towards total of Indian seas. A detailed examination on genesis and intensity of TC over both the basins and the seasons illustrates that significant enhancement of SCS by about 65% during CWP is confined to the postmonsoon season of the BoB. Further, the BoB is sub-divided into northern, central, and southern sectors and the AS into western and eastern sectors based on genesis of TCs and SST gradient. Results show that in postmonsoon season during CWP, the number of SCS increases significantly by about 71% in southern BoB and 300% over western AS.  相似文献   

10.
In this study, the impact of four-dimensional data assimilation (FDDA) analysis nudging is examined on the prediction of tropical cyclones (TC) in the Bay of Bengal to determine the optimum period and timescale of nudging. Six TCs (SIDR: November 13–16, 2007; NARGIS: April 29–May 02, 2008; NISHA: November 25–28, 2008; AILA: May 23–26, 2009; LAILA: May 18–21, 2010; JAL: November 04–07, 2010) were simulated with a doubly nested Weather Research and Forecasting (WRF) model with a horizontal resolution of 9 km in the inner domain. In the control run for each cyclone, the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis and forecasts at 0.5° resolution are used for initial and boundary conditions. In the FDDA experiments available surface, upper air observations obtained from NCEP Atmospheric Data Project (ADP) data sets were used for assimilation after merging with the first guess through objective analysis procedure. Analysis nudging experiments with different nudging periods (6, 12, 18, and 24 h) indicated a period of 18 or 24 h of nudging during the pre-forecast stage provides maximum impact on simulations in terms of minimum track and intensity forecasts. To determine the optimum timescale of nudging, two cyclone cases (NARGIS: April 28–May 02, 2008; NISHA: November 25–28, 2008) were simulated varying the inverse timescales as 1.0e?4 to 5.0e?4 s?1 in steps of 1.0e?4 s?1. A positive impact of assimilation is found on the simulated characteristics with a nudging coefficient of either 3.0e?4 or 4.0e?4 s?1 which corresponds to a timescale of about 1 h for nudging dynamic (u,v) and thermodynamical (t,q) fields.  相似文献   

11.
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.  相似文献   

12.
The genesis potential parameter (GPP) consists of two dynamical variables low-level relative vorticity and vertical wind shear, and two thermodynamic variables middle tropospheric relative humidity and instability are analysed during pre-cyclone watch period over the Bay of Bengal. The pre-cyclone watch period is taken as the period prior to 72-h from the formation of a Depression. The GPP values for 30 tropical disturbances that formed over the Bay of Bengal during the period 2001–2010 are analysed. An independent evaluation of the parameter and possible applications to operational forecasting are presented using data from the year 1998 to 1999. The variables of GPP are calculated using the ECMWF interim reanalysis 1.5° × 1.5° resolution data, averaged within an area of 5° × 5° box on the centre of tropical disturbances and also over the 5° × 5° boxes over the adjacent surrounding areas. The results show that maximum value is observed over the genesis region at 48- and 24-h lead time for both the cases of cyclones and Depressions. The threshold value of GPP is found to be 9.3, 6.3 and 2.7 during pre-cyclone watch period at 24-, 48- and 72-h lead time, respectively. A distinction in GPP values above threshold value for cyclonic and a Depression system is also observed for the cyclogenesis region in 69, 75 and 75 % of cases at 72-, 48- and 24-h lead time, respectively. However, the individual case studies show that the GPP could indicate the genesis of a tropical cyclone with a 2-day lead time. The mean GPP values are 11.8, 8.5 and 3.8 for cyclonic systems and 6.9, 4.2 and 1.6 for Depression systems over an area of a box 5° × 5° on the systems at 24-, 48- and 72-h lead time, respectively, from the stage of Depression. The result of the study is found to be providing probable area of genesis and intensification of a tropical disturbance at a 2 day lead time from the stage Depression.  相似文献   

13.
The northeast monsoon rainfall (NEMR) contributes about 20–40 % of annual rainfall over the North Indian Ocean (NIO). In the present study, the relationship between the NEMR and near-surface atmospheric wind convergence (NSAWC) over the NIO is demonstrated using high-resolution multisatellite data. The rainfall product from the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis and near-surface wind product from the Cross-Calibration Multi-Platform available at 0.25° × 0.25° spatial resolution are used for the study. Large-scale NSAWC and divergence maps over the tropical Indian Ocean are generated at monthly scale from the wind product for the period of 1988–2010. A preliminary analysis is carried out for two consecutive anomalous Indian Ocean Dipole (IOD) years 2005 (negative) and 2006 (positive). The distinct spatial patterns of rainfall rate and NSAWC fields over the NIO clearly show the evolution of the anomalous IOD events in the south eastern equatorial Indian Ocean (EEIO). The spatially averaged time-series of pentad NSAWC over the south EEIO box suggests that the variability occurs in phase with rainfall rate during both the northeast monsoon years. Furthermore, the scatter plot between area-averaged pentad rainfall and convergence over the south EEIO box for the period of 1998–2010 shows statistically significant linear correlation which reveals that NSAWC plays a key role in regulating the NEMR.  相似文献   

14.
The roles of vortex initialization and model spin-up in tropical cyclone (TC) prediction using Advanced Research Weather Research and Forecasting (ARW) Model are studied through a case study of NARGIS (2008) cyclone over Bay of Bengal. ARW model is designed to have three two-way interactive nested domains, and a suite of 36 numerical experiments are performed with three values of maximum wind (MW), four of radius of maximum wind (RMW), and three of α and one experiment without vortex initialization. The results indicate that vortex initialization is important toward realistic representation of initial structure and location of cyclone vortex. Model spin-up during the first 18–24 h of model integration lead to faster intensification than of the real atmosphere, thus a weaker initial vortex evolved more realistically. Three experiments from vortex initialization produced MW and RMW nearer to the observations, but none of these produced a good prediction due to unrealistic intensification during model spin-up. A weaker vortex with intensity less than 50 % than observations produced the best forecast in terms of intensity, track, and landfall. The results suggest that slightly larger (~30 %) RMW than observations with α as ?0.5 (for 81 km model resolution) that produces weaker vortex is to be implemented in the design of bogus vortex. This study assesses the merits of TC bogus scheme in ARW model, illustrates the need for vortex initialization, and analyzes the spin-up problem in cold-start model simulations of TC prediction.  相似文献   

15.
The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T??number. The result of the study reveals that the forecast error with MLP model is minimum (4.70?%) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62?%. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8?%, respectively. The models provide the forecast beyond 72?h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models.  相似文献   

16.
Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications.  相似文献   

17.
Prediction of heavy rainfall events due to severe convective storms in terms of their spatial and temporal scales is a challenging task for an operational forecaster. The present study is about a record-breaking heavy rainfall event observed in Pune (18°31′N, 73°55′E) on October 4, 2010. The day witnessed highest 24-h accumulated precipitation of 181.3 mm and caused flash floods in the city. The WRF model-based real-time weather system, operating daily at Centre for Development of Advanced Computing using PARAM Yuva supercomputer showed the signature of this convective event 4-h before, but failed to capture the actual peak rainfall and its location with reference to the city’s observational network. To investigate further, five numerical experiments were conducted to check the impact of assimilation of observations in the WRF model forecast. First, a control experiment was conducted with initialization using National Centre for Environmental Prediction (NCEP)’s Global Forecast System 0.5° data, while surface observational data from NCEP Prepbufr system were assimilated in the second experiment (VARSFC). In the third experiment (VARAMV), NCEP Prepbufr atmospheric motion vectors were assimilated. Fourth experiment (VARPRO) was assimilated with conventional soundings data, and all the available NCEP Prepbufr observations were assimilated in the fifth experiment (VARALL). Model runs were compared with observations from automated weather stations (AWS), synoptic charts of Indian Meteorological Department (IMD). Comparison of 24-h accumulated rainfall with IMD AWS 24-h gridded data showed that the fifth experiment (VARALL) produced better picture of heavy rainfall, maximum up to 251 mm/day toward the southern side, 31 km away from Pune’s IMD observatory. It was noticed that the effect of soundings observations experiment (VARPRO) caused heavy precipitation of 210 mm toward the southern side 49 km away from Pune. The wind analysis at 850 and 200 hPa indicated that the surface and atmospheric motion vector observations (VARAMV) helped in shifting its peak rainfall toward Pune, IMD observatory by 18 km, though VARALL over-predicted rainfall by 60 mm than the observed.  相似文献   

18.
An objective NWP-based cyclone prediction system (CPS) was implemented for the operational cyclone forecasting work over the Indian seas. The method comprises of five forecast components, namely (a) Cyclone Genesis Potential Parameter (GPP), (b) Multi-Model Ensemble (MME) technique for cyclone track prediction, (c) cyclone intensity prediction, (d) rapid intensification, and (e) predicting decaying intensity after the landfall. GPP is derived based on dynamical and thermodynamical parameters from the model output of IMD operational Global Forecast System. The MME technique for the cyclone track prediction is based on multiple linear regression technique. The predictor selected for the MME are forecast latitude and longitude positions of cyclone at 12-hr intervals up to 120 hours forecasts from five NWP models namely, IMD-GFS, IMD-WRF, NCEP-GFS, UKMO, and JMA. A statistical cyclone intensity prediction (SCIP) model for predicting 12 hourly cyclone intensity (up to 72 hours) is developed applying multiple linear regression technique. Various dynamical and thermodynamical parameters as predictors are derived from the model outputs of IMD operational Global Forecast System and these parameters are also used for the prediction of rapid intensification. For forecast of inland wind after the landfall of a cyclone, an empirical technique is developed. This paper briefly describes the forecast system CPS and evaluates the performance skill for two recent cyclones Viyaru (non-intensifying) and Phailin (rapid intensifying), converse in nature in terms of track and intensity formed over Bay of Bengal in 2013. The evaluation of performance shows that the GPP analysis at early stages of development of a low pressure system indicated the potential of the system for further intensification. The 12-hourly track forecast by MME, intensity forecast by SCIP model and rapid intensification forecasts are found to be consistent and very useful to the operational forecasters. The error statistics of the decay model shows that the model was able to predict the decaying intensity after landfall with reasonable accuracy. The performance statistics demonstrates the potential of the system for improving operational cyclone forecast service over the Indian seas.  相似文献   

19.
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.  相似文献   

20.
Indian region is severely affected by the tropical cyclones (TCs) due to the long coast line of about 7500 km. Hence, whenever any low level circulation (LLC) forms over the Indian Seas, the prediction of its intensification into a TC is very essential for the management of TC disaster. Satellite Application Centre (SAC) of Indian Space Research Organization (ISRO), Ahmedabad, has developed a technique to predict TCs based on scatterometer-derived winds from the polar orbiting satellite, QuikSCAT and Oceansat-II. The India Meteorological Department (IMD) has acquired the technique and verified it for the years 2010–2013 for operational use. The model is based on the concept of analogs of the sea surface wind distribution at the stage of LLC or vortex (T1.0) as per Dvorak’s classifications, which eventually leads to cyclogenesis (T2.5). The results indicate that the developed model could predict cyclogenesis with a probability of detection of 61% and critical success index of 0.29. However, it shows high over-prediction of the model is better over the Bay of Bengal than over Arabian Sea and during post-monsoon season (September–December) than in pre-monsoon season (March–June).  相似文献   

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