Nonhydrostatic Mesoscale Model Research Articles

Impact of urban land use on mean and heavy rainfall during the Indian summer monsoon

Abstract. Northern India has undergone intense urbanization since the middle of the 20th century. The impact of such drastic land-use change on the regional weather and climate remains to be assessed. In this work, we study the impact of the modification of land use – from vegetation to urban – on the Indian summer monsoon rainfall as well as on other meteorological variables. We use the regional Meso-scale Non-Hydrostatic (Meso-NH) model coupled with an urban module (the Town Energy Balance model) to perform monthlong sensitivity simulations centered around Kolkata, the most urbanized area in northeastern India. Paired simulations, one with and another without urban settings, have been performed to identify the impacts related to urbanization through both thermodynamic and kinetic effects. We find that the perturbation induced by urban land use enhances the mean rainfall over the model domain, principally by intensifying the convective activity through thermodynamic perturbation, leading to a 14.4 % increase in the monthly mean rainfall. The urban area also induces a 15.0 % rainfall increase during two modeled periods of heavy precipitation caused by low-pressure systems. In addition, the modeling results demonstrate that the urban area not only generally acts as a rainfall enhancer, particularly during nighttime, but also induces the generation of a specific storm in one modeled case that would not have formed in the absence of the urban area. The initiation of this storm over the city was primarily due to the urban terrain's disturbance of the near-surface wind flow, leading to a surge in dynamically produced turbulent kinetic energy (TKE). The thermal production of TKE over the nighttime urban boundary layer, on the other hand, serves as a contributing factor to the storm formation.

Verification of temperature, wind and precipitation fields for the high-resolution WRF NMM model over the complex terrain of Montenegro.

The necessity of setting up high-resolution models is essential to timely forecast dangerous meteorological phenomena. This study presents a verification of the numerical Weather Research and Forecasting non-hydrostatic Mesoscale Model (WRF NMM) for weather prediction using the High-Performance Computing (HPC) cluster over the complex relief of Montenegro. Verification was performed comparing WRF NMM predicted values and measured values for temperature, wind and precipitation for six Montenegrin weather stations in a five-year period using statistical parameters. The difficult task of adjusting the model over the complex Montenegrin terrain is caused by a rapid altitude change in in the coastal area, numerous karst fields, basins, river valleys and canyons, large areas of artificial lakes on a relatively small terrain. Based on the obtained verification results, the results of the model vary during time of day, the season of the year, the altitude of the station for which the model results were verified, as well as the surrounding relief for them. The results show the best performance in the central region and show deviations for some metrological measures in some periods of the year. This study can give recommendations on how to adapt a numerical model to a real situation in order to produce better weather forecast for the public.

A HWRF-POM-TC coupled model forecast performance over North Indian Ocean: VSCS TITLI & VSCS LUBAN

The HWRF-POM-TC coupled model is run operationally at India Meteorological Department (IMD). This study is first attempt to assess the IMD's operational HWRF-POM-TC (Atmosphere-Ocean) coupled model forecast performance over North Indian Ocean (NIO). The two cyclonic storms one each in Arabian Sea and Bay of Bengal were examined. Among them, VSCS LUBAN formed over Arabian Sea (AS) and was followed by the formation of VSCS TITLI over Bay of Bengal (BoB). It constituted a rare case whereby two VSCS have formed in the north Indian Ocean (NIO) simultaneously.The HWRF-POM-TC modeling system, which was developed at National Centers for Environmental Prediction (NCEP) based on Non-hydrostatic Mesoscale Model (NMM) dynamic core, was customized for NIO conditions. For the two storms, VSCS LUBAN & VSCS TITLI, 28 and 15 consecutive 6-hourly HWRF model runs were performed. The HWRF-POM-TC coupled model showed great skill in forecasting of Track and Intensity for examined cyclones. The result shows that the model predicted the intensification and landfall of VSCS Luban & Titli in agreement with the best track data as made available by Cyclone Warning Division (CWD), India Meteorological Department which is also recognized as Regional Specialized Meteorological Center (RSMC) by WMO for NIO.

Recent developments in the data assimilation of AROME/HU numerical weather prediction model

A local three-dimensional variational data assimilation (DA) system was implemented operationally in AROME/HU (Application of Research to Operations at Mesoscale) non-hydrostatic mesoscale model at the Hungarian Meteorological Service (OMSZ) in 2013. In the first version, rapid update cycling (RUC) approach was employed with 3-hour frequency in local upper-air DA using conventional observations only. Optimal interpolation method was adopted for the surface data assimilation later in 2016. This paper describes the current developments showing the impact of more conventional and remote-sensing observations assimilated in this system, which reveals the benefit of additional local high-resolution observations. Furthermore, it is shown that an hourly assimilation-forecast cycle outperforms the 3-hourly updated system in our DA. Besides the upper-air assimilation developments, a simplified extended Kalman filter (SEKF) was also tested for surface data assimilation, showing promising performance on both the analyses and the forecasts of AROME/HU system.

Short‐term wind speed forecasting using S‐transform with compactly supported kernel

AbstractThis paper presents a modified S‐transform (ST) based on a compactly supported kernel. A version of Cheriet‐Belochrani (CB) kernel is chosen for this purpose. It is shown that the proposed modified S‐transform (CBST) offers better frequency resolution than the traditional ST. It is used to decompose the wind speed time series into frequency‐based subseries. Further, artificial neural network (ANN) is applied to each of the subseries for an hour ahead prediction. Finally, forecast for the original wind speed series is obtained by combining the prediction result of all the subseries. Initially, increasing the number of subseries results in a decrease in prediction error. However, when the number of subseries is sufficiently large, no significant change in prediction error is observed if the number is further increased. It is also observed that, for a model based on neural‐network, involving decomposition of wind speed time series, the proposed model offers low prediction error. A comparative study with the methods based on wavelet transform (WT) and empirical mode decomposition (EMD) demonstrates the effectiveness of the proposed method. For this study, we have used simulated wind speed data generated by nonhydrostatic mesoscale model and data recorded using anemometer and LiDAR instrument at different heights to evaluate the short‐term forecasting results.

Impact of Model Resolution and Initial/Boundary Conditions in Forecasting Flood-Causing Precipitations

In late summer and autumn Mediterranean coastal regions are quite regularly affected by small-scale, flood-producing convective systems. The complexity of mesoscale triggering mechanisms, related to low-level temperature gradients, moisture convergence, and topographic effects contributes to limit the predictability of such phenomena. In the present work, a severe convection episode associated to a flash flood occurred in Cannes (southern France) in October 2015, is investigated by means of numerical simulations with a state-of-the-art nonhydrostatic mesoscale model. In the modelling configuration operational at the University of Genoa precipitation maxima were underestimated and misplaced. The impact of model resolution as well as initial and boundary conditions on the quantitative precipitation forecasts is analyzed and discussed. In particular, the effect of ingesting a high-resolution satellite-derived sea surface temperature field is proven to be beneficial in terms of precipitation intensity and localization, especially when also associated with the most accurate lateral boundary conditions.

Evaluation of Convection-Permitting Precipitation Forecast Products Using WRF, NMMB, and FV3 for the 2016–17 NOAA Hydrometeorology Testbed Flash Flood and Intense Rainfall Experiments

Abstract During the summers of 2016 and 2017, the Center for Analysis and Prediction of Storms (CAPS) ran real-time storm-scale ensemble forecasts (SSEFs) in support of the Hydrometeorology Testbed (HMT) Flash Flood and Intense Rainfall (FFaIR) experiment. These forecasts, using WRF-ARW and Nonhydrostatic Mesoscale Model on the B-grid (NMMB) in 2016, and WRF-ARW and GFDL Finite Volume Cubed-Sphere Dynamical Core (FV3) in 2017, covered the contiguous United States at 3-km horizontal grid spacing, and supported the generation and evaluation of precipitation forecast products, including ensemble probabilistic products. Forecasts of 3-h precipitation accumulation are evaluated. Overall, the SSEF produces skillful 3-h accumulated precipitation forecasts, with ARW members generally outperforming NMMB members and the single FV3 member run in 2017 outperforming ARW members; these differences are significant at some forecast hours. Statistically significant differences exist in the performance, in terms of bias and ETS, among subensembles of members sharing common microphysics and PBL schemes. Year-to-year consistency is higher for PBL subensembles than for microphysical subensembles. Probability-matched (PM) ensemble mean forecasts outperform individual members, while the simple ensemble mean exhibits substantial bias. A newly developed localized probability-matched (LPM) ensemble mean product was produced in 2017; compared to the simple ensemble mean and the conventional PM mean, the LPM mean exhibits improved retention of small-scale structures, evident in both 2D forecast fields and variance spectra. Probabilistic forecasts of precipitation exceeding flash flood guidance (FFG) or thresholds associated with recurrence intervals (RI) ranging from 10 to 100 years show utility in predicting regions of flooding threat, but generally overpredict the occurrence of such events; however, they may still be useful in subjective flash flood risk assessment.

Forecasting of poor visibility episodes in the vicinity of Tenerife Norte Airport

Aviation safety is a priority that may be compromised by adverse weather conditions. This is the case for poor visibility in the vicinity of airports, which can pose a risk during takeoff and landing. For airports that are prone to fog because of their location, an accurate forecast of poor-visibility episodes is vital. However, the forecasting of low clouds is still a challenge in numerical weather prediction, especially when an airport is near complex terrain for which the use of non-hydrostatic mesoscale models is mandatory. All these factors are present at Tenerife Norte Airport, which is commonly affected by poor visibility from low clouds related to persistent trade winds and moist flows from the Atlantic Ocean.In this paper, several methods for estimating visibility based on mesoscale model outputs are tested. Use of the HARMONIE-AROME model is encouraged because of its excellent performance in the detection of poor-visibility episodes (False Alarm Ratio = 0.34–0.38; Frequency Of Misses = 0.22–0.38, depending on the model version and method used). In addition, the use of satellite application facilities is proposed for the nowcasting of low clouds affecting the airport area. Specifically, we used products that estimate cloud type, cloud top altitude, and integrated water vapor content in the boundary layer. Finally, an application is presented for the monitoring of weather conditions in real time to estimate poor-visibility risk.

Banded Convective Activity Associated With Mesoscale Gravity Waves Over Southern China

AbstractBanded convective activity that occurred near the south coast of China on 30 January 2018 was investigated through convection‐allowing simulations using a nonhydrostatic mesoscale model. The simulations capture reasonably well the observed characteristics of this event. The convective bands are found to be closely related to an episode of mesoscale gravity waves propagating northeastward with a wave speed of around 12 m/s and a primary wavelength of about ~40–50 km. Further analyses and sensitivity experiments reveal that the environment provides a wave duct for these gravity waves, with a thick low‐level stable layer below 850 hPa capped by a low‐stability reflecting layer with a critical level. The strength and depth of the low‐level stable layer determine the intrinsic phase speed and wavelength of the ducted gravity waves. In the sensitivity tests that the stable layer depth is reduced, the wave characteristics change according to what are predicted with the wave duct theory. The convective bands collocate and propagate in phase with the peak updraft regions of the gravity waves, suggesting strong interactions of convection and gravity waves, in which the ducted gravity waves can trigger and modulate convection, while latent heating from convection enhances the waves. In essence, both wave ducting and wave‐convection interaction are jointly responsible for the banded convective activity.

Heavy Rainfall Associated with Double Low-Level Jets over Southern China. Part II: Convection Initiation

Abstract Heavy rainfall that occurred at the south coast of China on 10–11 May 2014 was associated with a synoptic-system-related low-level jet (SLLJ) and a boundary layer jet (BLJ). To clarify the role of the double low-level jets in convection initiation (CI), we perform convective-permitting simulations using a nonhydrostatic mesoscale model. The simulations reproduce the occurrence location and mesoscale evolution of new convective cells as well as their small-scale wavelike structures at the elevated layers, which are generally consistent with radar observations despite some differences in their orientation. The nighttime BLJ over the northern South China Sea strengthens the convergence at ~950 hPa near the coast where the BLJ’s northern terminus reaches the coastal terrain. Meanwhile, the SLLJ to the south of the inland cold front provides divergence at ~700 hPa near the SLLJ’s entrance region. Such low-level convergence and midlevel divergence collectively produce strong mesoscale lifting for CI at the coast. In addition to the enhanced mesoscale lifting, the double low-level jets also provide favorable conditions for the superimposed small-scale disturbances that can serve as effective moistening mechanisms of the lower troposphere during CI. In a sensitivity experiment with coastal terrain removed, CI still occurs near the coast but is delayed and weaker compared to the control run. This latter experiment suggests that double low-level jets and their coupling indeed exert key effects on CI, while the BLJ colliding with terrain may enhance coastal convergence for amplifying CI. These findings provide new insights into the occurrence of coastal heavy rainfall in the warm sector far ahead of the fronts.

Dynamical downscaling the impact of spring Western US land surface temperature on the 2015 flood extremes at the Southern Great Plains: effect of domain choice, dynamic cores and land surface parameterization

Recent studies have shown that spring land surface temperature (LST) and subsurface temperature (SUBT) over the high elevation areas in the western US (WUS) have significant impacts on the downstream summer droughts/floods in North America. In this paper, both the National Centers for Environmental Prediction—Global Forecast System (NCEP-GFS) general circulation model (GCM) and the weather research and forecasting (WRF) regional climate model (RCM) are employed, where RCM scenarios utilized initial and lateral boundary conditions derived from the corresponding NCEP-GFS scenarios. Here we use a late spring flood in the US Southern Great Plains (SGP) case to examine whether simulation of the LST/SUBT downstream effects is sensitive to the domain size choice, change in dynamical cores within the same model, as well as to the representation of surface processes parameterizations. Although all RCM experiments with different settings simulate reasonably geographical patterns of observed LST and precipitation anomalies, we found that the choice of the domain size is crucial for proper downscaling the LST/SUBT downstream effects to accurately produce the observed precipitation/LST anomalies over the SGP/WUS, respectively, along with the associated large-scale features. The southern boundary location has been identified to be crucial in producing the SGP Low Level Jet strength, which in turn brings more moisture from the Gulf of Mexico to the SGP and thereby resulting in a better simulation of the precipitation anomaly in that area. The sensitivity of the simulation of the LST/SUBT downstream effect to dynamical cores is assessed by inter-comparing the Non-hydrostatic Mesoscale Model (NMM) and the Advanced Research WRF dynamic cores. We find NMM was better at generating the large-scale eastward wave train, a crucial process associated with the LST/SUBT downstream effect. Meanwhile, this study also shows that the LST/SUBT downstream effects were not significantly dependent on the surface process parameterizations, although the Simplified Simple Biosphere model version 3 (SSiB3) highlighted a better performance over SSiB2.

Improving empirical storm damage models by coupling with high-resolution gust speed data

Empirical forest storm damage models can assist in identifying the key factors of the occurrence of storm damage in order to develop locally adapted measures to minimize damage in forests. Yet, there is a significant lack of knowledge in these models concerning the correlation between storm damage and high-impact near-surface airflow. To improve our understanding in this field, we built Random Forests (RF) and Generalized Linear Models (GLM) for evaluating the association between high resolution gust speed data and long-term, multi-event forest storm damage data from long-term permanent forest growth and yield plots. The tested gust speed data were derived from two different gust speed models: a numerical non-hydrostatic mesoscale model and a statistical model.In all RF and GLM models gust speed was a statistically significant predictor. The performance of the evaluated empirical models was very high (area under the receiver operating characteristic curve values AUC = 0.86–0.99). Depending on the type of model, the relative importance of gust speed was moderate to very high (up to 35%). However, starting from models using all significant predictors and excluding gust speed, the performance loss was almost negligible in all models. Furthermore, modeling long-term storm damage for each storm event individually performed better compared to modeling average long-term, event-unspecific storm damage.Our results demonstrate that empirical storm damage models using only gust speed as a predictor can reach moderate (GLM) to very high (RF) performance, even without any other information on terrain and forest attributes. However, if detailed terrain and forest data are available, empirical storm damage models may have such a high performance that adding gust speed data improves them very little. The correlation between gust speed and storm damage in the coupled modeling system is a fundamental first step in being able to evaluate potential changes of forest storm damage in a changing climate with potentially changing wind regimes. Additionally, further improvements could be achieved by improved representation of airflow in complex forest.

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.

An analysis of the synoptic and dynamical characteristics of hurricane Sandy (2012)

Hurricane Sandy affected the Caribbean Islands and the Northeastern United States in October 2012 and caused 233 fatalities, severe rainfalls, floods, electricity blackouts, and 75 billion U.S. dollars in damages. In this study, the synoptic and dynamical characteristics that led to the formation of the hurricane are investigated. The system was driven by the interaction between the polar jet displacement and the subtropical jet stream. In particular, Sandy was initially formed as a tropical depression system over the Caribbean Sea and the unusually warm sea drove its intensification. The interaction between a rapidly approaching trough from the northwest and the stagnant ridge over the Atlantic Ocean drove Sandy to the northeast coast of United States. To better understand the dynamical characteristics and the mechanisms that triggered Sandy, a non-hydrostatic mesoscale model has been used. Model results indicate that the surface heat fluxes and the moisture advection enhanced the convective available potential energy, increased the low-level convective instability, and finally deepened the hurricane. Moreover, the upper air conditions triggered the low-level frontogenesis and increased the asymmetry of the system which finally affected its trajectory.

Application of the COSMO-CLM mesoscale model to assess the effects of forest cover changes on regional weather conditions in the European part of Russia

The influence of forest cover in the west of the European part of Russia (55°–59° N, 28°–37° E) on regional weather conditions is analyzed via the climatic version of the COSMO nonhydrostatic mesoscale model (COSMO-CLM). The simulations are performed with a model grid spacing of 13.2 km for the warm season of 2010. The analysis demonstrates that the changes in forest cover in the selected model domain may lead to substantial variations in spatial precipitation and surface temperature patterns. At the same time, precipitation changes are evident within the entire area of the East European Plain.

Modelling Airport Pollutants Dispersion at High Resolution

Local air quality is a major concern for the population regularly exposed to high levels of air pollution. Due mainly to its aircraft engine activities during taxiing and take-off, the airport is often submitted to heterogeneous but important concentrations of NO x and Particulate Matter (PM). The study suggests an innovative approach to determining the air traffic impact on air quality at the scale of the airport, its runways, and its terminals, to be able to locate the persistent high-concentration spots, for example. The pollutant concentrations at 10 m resolution and 1 s time step are calculated in order to identify the most affected areas of an airport platform and their contributors. A real day of air traffic on a regional airport is simulated, using observations and aircraft trajectories data from radar streams. In order to estimate the aircraft emissions, the Air Transport Systems Evaluation Infrastructure (IESTA) is used. Regarding local air quality, IESTA relies on the non-hydrostatic meso-scale atmospheric model Meso-NH using its grid-nesting capabilities with three domains. The detailed cartography of the airport distinguishes between grassland, parking, and terminals, allowing the computation of exchanges of heat, water, and momentum between the different types of surfaces and the atmosphere as well as the interactions with the building using a drag force. The dynamic parameters like wind, temperature, turbulent kinetic energy, and pollutants concentration are computed at 10 m resolution over the 2 km × 4 km airport domain. The pollutants are considered in this preliminary study as passive tracers, without chemical reactions. This study aims at proving the feasibility of high-scale modelling over an airport with state-of-the-art physical models in order to better understand the repartition of pollutants over an airport, taking into account advection and turbulence in interactions with buildings and regional trends, emissions, Auxiliary Power Units (APU), taxiing, parking, take off. All these processes drive the model at each time step and are not averaged over one hour or more like in Gaussian or Lagrangian ones. This study is investigating the feasibility of high spatio-temporal air quality modelling for research purposes but not for operational forecasting.

A Multimodel Intercomparison of an Intense Typhoon in Future, Warmer Climates by Four 5-km-Mesh Models

AbstractIntense tropical cyclones (TCs) sometimes cause huge disasters, so it is imperative to explore the impacts of climate change on such TCs. Therefore, the authors conducted numerical simulations of the most destructive historical TC in Japanese history, Typhoon Vera (1959), in the current climate and a global warming climate. The authors used four nonhydrostatic models with a horizontal resolution of 5 km: the cloud-resolving storm simulator, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, the Japan Meteorological Agency (JMA) operational nonhydrostatic mesoscale model, and the Weather Research and Forecasting Model. Initial and boundary conditions for the control simulation were provided by the Japanese 55-year Reanalysis dataset. Changes between the periods of 1979–2003 and 2075–99 were estimated from climate runs of a 20-km-mesh atmospheric general circulation model, and these changes were added to the initial and boundary conditions of the control simulation to produce the future climate conditions.Although the representation of inner-core structures varies largely between the models, all models project an increase in the maximum intensity of future typhoons. It is found that structural changes only appeared around the storm center with sudden changes in precipitation and near-surface wind speeds as the radius of maximum wind speed (RMW) contracted. In the future climate, the water vapor mixing ratio in the lower troposphere increased by 3–4 g kg−1. The increased water vapor allowed the eyewall updrafts to form continuously inside the RMW and contributed to rapid condensation in the taller and more intense updrafts.

Forecasting surface-layer atmospheric parameters at the Large Binocular Telescope site

In this paper, we quantify the performance of an automated weather forecast system implemented on the Large Binocular Telescope (LBT) site at Mt Graham (Arizona) in forecasting the main atmospheric parameters close to the ground. The system employs a mesoscale non-hydrostatic numerical model (Meso-Nh). To validate the model, we compare the forecasts of wind speed, wind direction, temperature and relative humidity close to the ground with the respective values measured by instrumentation installed on the telescope dome. The study is performed over a large sample of nights uniformly distributed over 2 yr. The quantitative analysis is done using classical statistical operators [bias, root-mean-square error (RMSE) and σ] and contingency tables, which allows us to extract complementary key information, such as the percentage of correct detections (PC) and the probability of obtaining a correct detection within a defined interval of values (POD). The results of our study indicate that the model performance in forecasting the atmospheric parameters we have just cited are very good, in some cases excellent: RMSE for temperature is below 1°C, for relative humidity it is 14 per cent and for the wind speed it is around 2.5 m s−1. The relative error of the RMSE for wind direction varies from 9 to 17 per cent depending on the wind speed conditions. This work is performed in the context of the ALTA (Advanced LBT Turbulence and Atmosphere) Center project, whose final goal is to provide forecasts of all the atmospheric parameters and the optical turbulence to support LBT observations, adaptive optics facilities and interferometric facilities.

The Tornadic Supercell on the Kanto Plain on 6 May 2012: Polarimetric Radar and Surface Data Assimilation with EnKF and Ensemble-Based Sensitivity Analysis

A tornadic supercell and associated low-level mesocyclone (LMC) observed on the Kanto Plain, Japan, on 6 May 2012 were predicted with a nonhydrostatic mesoscale model with a horizontal resolution of 350 m through assimilation of surface meteorological data (horizontal wind, temperature, and relative humidity) of high spatial density and C-band Doppler radar data (radial velocity and rainwater estimated from reflectivity and specific differential phase) with a local ensemble transform Kalman filter. With assimilation of both surface and radar data, a strong LMC was successfully predicted near the path of the actual tornado. When either surface or radar data were not assimilated, however, the LMC was not predicted. Therefore, both surface and radar data were essential for successful LMC forecasts. The factors controlling the strength of the predicted LMC, defined as a low-level maximum vertical vorticity, were clarified by an ensemble-based sensitivity analysis (ESA), which is a new approach for analyzing LMC intensification. The ESA showed that the strength of the LMC was sensitive to low-level convergence forward of the storm and to low-level relative humidity in the rear of the storm. Therefore, the correction of these low-level variables by assimilation of dense observations was found to be particularly important for forecasting and monitoring the LMC in the present case.

Simulation of Storm Surges in the Bay of Bengal Using One-Way Coupling Between NMM-WRF and IITD Storm Surge Model

ABSTRACTThe storm surge associated with severe tropical cyclones (TCs) in the Bay of Bengal (BoB) is a serious concern along the coastal regions of India, Bangladesh, Myanmar, and Sri Lanka. It is one of the most hazardous elements associated with landfalling TCs other than strong winds and heavy precipitation and about 75% of the casualities in this region are attributed to storm surges. Therefore, it is highly essential to predict the storm surges with greater accuracy at least 2 days in advance for effective evacuation. In the present study, an attempt is made to simulate the storm surges associated with severe TCs in the BoB using one-way coupling of the Non-hydrostatic Mesoscale Model core of Weather Research and Forecasting (NMM-WRF) system with the two-dimensional finite-difference storm surge model developed at the Indian Institute of Technology Delhi (IITD). The NMM-WRF model simulated track, pressure drop, and radius of maximum wind are used to calculate the wind-stress through Jelesnianski wind formulation. The results are compared with the observed/estimated values as provided by the operational/meteorological agencies of India, Bangladesh, and Myanmar. This study suggests that using simulated surface meteorological fields of a high-resolution mesoscale model, the storm surge can be predicted at least 2 days in advance of the actual landfall of TCs with reasonable accuracy. This approach will be helpful in providing disastrous storm warning well in advance in a coastal region, which will help with rapid evacuation from the vulnerable coastal region, relocation as well as protection of valuables, disaster mitigation, and coastal zone management.