Tropical cyclone prediction over Bay of Bengal: a comparison of the performance of NCEP operational HWRF, NCAR ARW, and MM5 models

Much progress has been made in the area of tropical cyclone prediction using high-resolution mesoscale models based on community models developed at National Centers for Environmental Predication (NCEP) and National Center for Atmospheric Research (NCAR). While most of these model research and development activities are focused on predicting hurricanes in the Atlantic and Eastern Pacific domains, there has been much interest in using these models for tropical cyclone prediction in the North Indian Ocean region, particularly for Bay of Bengal storms that are known historically causing severe damage to life and property. In this study, the advanced operational hurricane modeling system developed at NCEP, known as the Hurricane Weather Research and Forecast (HWRF) model, is used to simulate two recent Bay of Bengal tropical cyclones??Nargis of November 2007 and Sidr of April 2008. The advanced NCEP operational vortex initialization procedure is adapted for simulating these Bay of Bengal tropical cyclones. Two additional regional models, the NCAR Advanced Research WRF and NCAR/Penn State University Mesoscale Model version 5 (MM5) are also used in simulating these storms. Results from these experiments highlight the superior performance of HWRF model over other models in predicting the Bay of Bengal cyclones. These results also suggest the need for a sophisticated vortex initialization procedure in conjunction with a model designed exclusively for tropical cyclone prediction for operational considerations.     

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

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

Performance evaluation of HWRF model during post monsoon tropical cyclone Nilofar

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.     

A multiple scale modeling system for coastal hurricane wind damage mitigation

Hurricane wind damage constitutes the largest percentage of catastrophic insured losses in the US. Yet the complicated wind structures and their changes are not fully understood and, thus, have not been considered in current risk catastrophic models. To obtain realistic landfall hurricane surface winds, a large eddy simulation (LES) framework in a weather forecasting mode has been developed from a multiple nested Weather Research & Forecasting (WRF) model to explicitly simulate a spectrum of scales from large-scale background flow, hurricane vortex, mesoscale organizations, down to fine-scale turbulent eddies in a unified system. The unique WRF-LES enables the high resolution data to be generated in a realistic environment as a hurricane evolves. In this paper, a simulation of the landfalling Hurricane Katrina is presented to demonstrate various features of the WRF-LES. It shows that the localized damaging winds are caused by the large eddy circulations generated in the hurricane boundary layer. With a sufficient computational power, WRF-LES has the potential to be developed into the next generation operational public wind-field model for hurricane wind damage mitigation.     

Simulation of very severe cyclone Mala over Bay of Bengal with HWRF modeling system

Tropical cyclone is one of the most devastating weather phenomena all over the world. The Environmental Modeling Center (EMC) of the National Center for Environmental Prediction (NCEP) has developed a sophisticated mesoscale model known as Hurricane Weather Research and Forecasting (HWRF) system for tropical cyclone studies. The state-of-the-art HWRF model (atmospheric component) has been used in simulating most of the features our present study of a very severe tropical cyclone ??Mala??, which developed on April 26 over the Bay of Bengal and crossed the Arakan coast of Myanmar on April 29, 2006. The initial and lateral boundary conditions are obtained from Global Forecast System (GFS) analysis and forecast fields of the NCEP, respectively. The performance of the model is evaluated with simulation of cyclone Mala with six different initial conditions at an interval of 12?h each from 00 UTC 25 April 2006 to 12 UTC 27 April 2006. The best result in terms of track and intensity forecast as obtained from different initial conditions is further investigated for large-scale fields and structure of the cyclone. For this purpose, a number of important predicted fields?? viz. central pressure/pressure drop, winds, precipitation, etc. are verified against observations/verification analysis. Also, some of the simulated diagnostic fields such as relative vorticity, pressure vertical velocity, heat fluxes, precipitation rate, and moisture convergences are investigated for understanding of the characteristics of the cyclone in more detail. The vector displacement errors in track forecasts are calculated with the estimated best track provided by the India Meteorological Department (IMD). The results indicate that the model is able to capture most of the features of cyclone Mala with reasonable accuracy.     

Experimental superensemble forecasts of tropical cyclones over the Bay of Bengal

This study entails the implementation of an experimental real time forecast capability for tropical cyclones over the Bay of Bengal basin of North Indian Ocean. This work is being built on the experience gained from a number of recent studies using the concept of superensemble developed at the Florida State University (FSU). Real time hurricane forecasts are one of the major components of superensemble modeling at FSU. The superensemble approach of training followed by real time forecasts produces the best forecasts for tracks and intensity (up to 5 days) of Atlantic hurricanes and Pacific typhoons. Improvements in track forecasts of about 25–35% compared to current operational forecast models has been noted over the Atlantic Ocean basin. The intensity forecasts for hurricanes are only marginally better than the best models. In this paper, we address tropical cyclone forecasts over the Bay of Bengal for the years 1996–2000. The main result from this study is that the position and intensity errors for tropical cyclone forecasts over the Bay of Bengal from the multimodel superensemble are generally less than those of all of the participating models during 1- to 3-day forecasts. Some of the major tropical cyclones, such as the November 1996 Andhra Pradesh cyclone and October 1999 Orissa super cyclone were well handled by this superensemble approach. A conclusion from this study is that the proposed approach may be a viable way to construct improved forecasts of Bay of Bengal tropical cyclone positions and intensity.     

An HWRF-based ensemble assessment of the land surface feedback on the post-landfall intensification of Tropical Storm Fay (2008)

While tropical cyclones (TCs) usually decay after landfall, Tropical Storm Fay (2008) initially developed a storm central eye over South Florida by anomalous intensification overland. Unique to the Florida peninsula are Lake Okeechobee and the Everglades, which may have provided a surface feedback as the TC tracked near these features around the time of peak intensity. Analysis is done with the use of an ensemble model-based approach with the Developmental Testbed Center (DTC) version of the Hurricane WRF (HWRF) model using an outer domain and a storm-centered moving nest with 27- and 9-km grid spacing, respectively. Choice of land surface parameterization and small-scale surface features may influence TC structure, dictate the rate of TC decay, and even the anomalous intensification after landfall in model experiments. Results indicate that the HWRF model track and intensity forecasts are sensitive to three features in the model framework: land surface parameterization, initial boundary conditions, and the choice of planetary boundary layer (PBL) scheme. Land surface parameterizations such as the Geophysical Fluid Dynamics Laboratory (GFDL) Slab and Noah land surface models (LSMs) dominate the changes in storm track, while initial conditions and PBL schemes cause the largest changes in the TC intensity overland. Land surface heterogeneity in Florida from removing surface features in model simulations shows a small role in the forecast intensity change with no substantial alterations to TC track.     

Impact of gridpoint statistical interpolation scheme over Indian region

An analysis system experiment was conducted for the month of June 2008 with Gridpoint Statistical Interpolation (GSI) analysis scheme using NCMRWF’s (National Centre for Medium Range Weather Forecasting) T254L64 model. Global analyses were carried out for all days of the month and respective forecast runs are made up to 120-hr. These analyses and forecasts are inter-compared with the operational T254L64 model outputs which uses Spectral Statistical Interpolation (SSI) analysis scheme. The prime objective of this study is to assess the impact of GSI analysis scheme with special emphasis on Indian summer monsoon as compared to SSI.     

One step back for a leap forward: toward operational measurements of elements at risk

This study presents the evaluation of 1 year of operational lightning forecasts provided for Europe, using the Weather Research and Forecasting model coupled with a cloud-top height-based lightning parameterization scheme. Three different convective parameterization schemes were employed for parameterizing sub-grid cloud-top heights and consequently driving the lightning scheme. Triggering of the lightning scheme was controlled by means of a model-resolved microphysics-based masking filter, while the formulation for deriving lightning flash rates was also modified, assuming a single “marine” equation instead of the original equations discriminating between continental and marine lightning. Gridded lightning observations were used for evaluating model performance on a dichotomous decision basis. Analysis showed that the lightning scheme is sensitive to the parameterization of convection. In particular, the Kain–Fritsch convective scheme was found to outperform the Grell–Devenyi and Grell–Freitas schemes, showing a statistically significant better performance with respect to lightning prediction. This was most evident during the warm season, while smaller differences among the schemes were recorded during the cold season. Further, for all examined convective schemes, it was found that the application of the masking filter is desirable for improving model performance in terms of lightning forecasting. Last, the reported results revealed that the refinement of the formulation of the lightning parameterization scheme, adhering to a “global” marine equation instead of distinguishing between land and sea lightning, may be necessary in order to obtain reliable lightning forecasts.     

Numerical simulation of the heavy rainfall caused by a convection band over Korea: a case study on the comparison of WRF and CReSS

Natural Hazards - This study investigates the capability of two numerical models, namely the Weather Research and Forecasting (WRF) and Cloud Resolving Storm Simulator (CReSS), to simulate the...     

Simulation Study on Precipitation Recycling Ratio in the Tibetan Plateau from 1982 to 2005

A dynamical downscaling approach using a regional climate model WRF (Weather Research and Forecasting Model Vision 3.5) driven by a global climate model CCSM4 (The Community Climate System Model Version 4) was adopted, and the downscaling results for the historical period (1982-2005) were evaluated for annual mean precipitation rate and evaporation rate over the Tibetan Plateau (TP). Furthermore, the spatial distribution and seasonal variation characteristics of Precipitation Recycling Ratio (PRR) simulated by CCSM4 and WRF were analyzed with the QIBT (Quasi-isentropic Back-trajectory method). The results show that the historical spatial distributions of annual mean precipitation rate and evaporation rate over the TP were found to better reproduce in the dynamical downscaling modeling compared to its coarse-resolution forcing. The PRR of the TP is 32% simulated by WRF, with a higher PRR in the wet season and a lower PRR in the dry season for the river basins in the northern TP, but the opposite seasonal variation was found for the river basins in the southern TP. In addition, the different land covers over the TP are more precisely represented in the WRF model, the PRR of grassland, shrubland and sparsely vegetation is higher than that of other land cover types.     

Atmospheric mesoscale conditions during the Boothbay meteotsunami: a numerical sensitivity study using a high-resolution mesoscale model

The article aims to test the sensitivity of high-resolution mesoscale atmospheric model to fairly reproduce atmospheric processes that were present during the Boothbay Harbor meteotsunami on 28 October 2008. The simulations were performed by the Weather and Research Forecasting (WRF) model at 1-km horizontal grid spacing by varying initial conditions (ICs) and lateral boundary conditions (LBCs), nesting strategy, simulation lead time and microphysics and convective parameterizations. It seems that the simulations that used higher-resolution IC and LBC were more successful in reproduction of precipitation zone and surface pressure oscillations caused by internal gravity waves observed during the event. The results were very sensitive to the simulation lead time and to the choice of convective parameterization, while the choice of microphysics parameterization and the type of nesting strategy (one-way or two-way) was less important for reproducibility of the event. The success of the WRF model appears limited to very short-range forecasting, most advanced parameterizations, and very high-resolution grid spacing; therefore, the applicability of present atmospheric mesoscale models to future operational meteotsunami warning systems still has a lot of room for improvements.     

The role of anomalous soil moisture on the inland reintensification of Tropical Storm Erin (2007)

Prior research on tropical storm systems that have made landfall and undergone a period of sustainability or reintensification has been linked to the synoptic environment at the time the storm restrengthened. Tropical Storm (TS) Erin is an interesting case study in that it did not take on hurricane-like structure nor reach hurricane intensity until it moved through west-central Oklahoma on August 19, 2007. This study seeks to examine the possible impact of anomalously wet soils across much of Oklahoma on the reintensification of TS Erin during the early morning hours of August 19, 2007. To determine the degree to which the antecedent soil state impacted TS Erin??s inland evolution and reintensification, analyses of the synoptic environment and the mesoscale environment/boundary layer environment are undertaken using operational and research datasets such as upper air soundings, surface soil moisture and temperature data, and multiple products from the Storm Prediction Center (SPC) mesoanalysis archive. This observational assessment is complemented with numerical experiments using the Weather Research and Forecast Model, Advanced Research Version 3.2 (WRF-ARW) to further study the role of soil moisture availability and surface fluxes that may have led to the boundary layer feedback and inland reintensification. Observational analysis and model results indicate that anomalously wet conditions over the central Oklahoma region may have helped develop a regional boundary layer feedback that appears to have contributed to the inland reintensification of TS Erin. Thus, the anomalously wet land surface had a positive role in TS Erin reintensifying over Oklahoma during the early morning hours of August 19, 2007.     

Implications of vortex initialization and model spin-up in tropical cyclone prediction using Advanced Research Weather Research and Forecasting Model

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.     

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.     

Sensitivity of HF radar-derived surface current self-organizing maps to various processing procedures and mesoscale wind forcing

We performed a number of sensitivity experiments by applying a mapping technique, self-organizing maps (SOM) method, to the surface current data measured by high-frequency (HF) radars in the northern Adriatic and surface winds modelled by two state-of-the-art mesoscale meteorological models, the Aladin (Aire Limitée Adaptation Dynamique Développement InterNational) and the Weather and Research Forecasting models. Surface current data used for the SOM training were collected during a period in which radar coverage was the highest: between February and November 2008. Different pre-processing techniques, such as removal of tides and low-pass filtering, were applied to the data in order to test the sensitivity of characteristic patterns and the connectivity between different SOM solutions. Topographic error did not exceed 15 %, indicating the applicability of the SOM method to the data. The largest difference has been obtained when comparing SOM patterns originating from unprocessed and low-pass filtered data. Introduction of modelled winds in joint SOM analyses stabilized the solutions, while sensitivity to wind forcing coming from the two different meteorological models was found to be small. Such a low sensitivity is considered to be favourable for creation of an operational ocean forecasting system based on neural networks, HF radar measurements and numerical weather prediction mesoscale models.     

Real-time prediction of a severe cyclone ��Jal�� over Bay of Bengal using a high-resolution mesoscale model WRF (ARW)

Real-time predictions for the JAL severe cyclone formed in November 2010 over Bay of Bengal using a high-resolution Weather Research and Forecasting (WRF ARW) mesoscale model are presented. The predictions are evaluated with different initial conditions and assimilation of observations. The model is configured with two-way interactive nested domains and with fine resolution of 9?km for the region covering the Bay of Bengal. Simulations are performed with NCEP GFS 0.5° analysis and forecasts for initial/boundary conditions. To examine the impact of initial conditions on the forecasts, eleven real-time numerical experiments are conducted with model integration starting at 00, 06, 12, 18 UTC 4 Nov, 5?Nov and 00, 06, 12 UTC 6 Nov and all ending at 00 UTC 8 Nov. Results indicated that experiments starting prior to 18 UTC 04 Nov produced faster moving cyclones with higher intensity relative to the IMD estimates. The experiments with initial time at 18 UTC 04 Nov, 00 UTC 05 Nov and with integration length of 78?h and 72?h produced best prediction comparable with IMD estimates of the cyclone track and intensity parameters. To study the impact of observational assimilation on the model predictions FDDA, grid nudging is performed separately using (1) land-based automated weather stations (FDDAAWS), (2) MODIS temperature and humidity profiles (FDDAMODIS), and (3) ASCAT and OCEANSAT wind vectors (FDDAASCAT). These experiments reduced the pre-deepening period of the storm by 12?h and produced an early intensification. While the assimilation of AWS data has shown meagre impact on intensity, the assimilation of scatterometer winds produced an intermittent drop in intensity in the peak stage. The experiments FDDAMODIS and FDDAQSCAT produced minimum error in track and intensity estimates for a 90-h prediction of the storm.     

Impact of period and timescale of FDDA analysis nudging on the numerical simulation of tropical cyclones in the Bay of Bengal

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.     

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.     

A numerical study on rapid intensification of Hurricane Charley (2004) near landfall

The rapid intensification of Hurricane Charley (2004) near landfall is studied using the fifth-generation Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) Mesoscale Model (MM5) and its adjoint system for both vortex initialization and forecasts. A significant improvement in both track and intensity forecasts is achieved after an ill-defined storm vortex, derived from large-scale analysis, in the initial condition is replaced by the vortex generated by a four-dimensional data variational (4D-Var) hurricane initialization scheme. Results from numerical experiments suggest that both the inclusion of the upper-level trough and the use of high horizontal resolution (6 km) are important for numerical simulations to capture the observed rapid intensification as well as the size reduction during the rapid intensification of Hurricane Charley. The approach of the upper-level trough significantly enhanced the upper-level divergence and vertical motion within simulated hurricanes. Small-scale features that are not resolvable at 18 km resolution are important to the rapid intensification and shrinking of Hurricane Charley (2004). Numerical results from this study further confirm that the theoretical relationship between the intensification and shrinking of tropical cyclones based on the angular momentum conservation and the cyclostrophic approximation can be applied to the azimuthal mean flows.