Mesoscale Numerical Weather Prediction Model Research Articles

The Influence of Typhoon Khanun on the Return Migration of Nilaparvata lugens (Stål) in Eastern China

Migratory insects adapt to and exploit the atmospheric environment to complete their migration and maintain their population. However, little is known about the mechanism of insect migration under the influence of extreme weather conditions such as typhoons. A case study was conducted to investigate the effect of typhoon Khanun, which made landfall in the eastern China in Sept. 2005, on the migration of brown planthopper, Nilaparvata lugens (Stål). The migration pathways of N. lugens were reconstructed for the period under the influence of the typhoon by calculating trajectories using the MM5, a mesoscale numerical weather prediction model, and migration events were examined in 7 counties of the Yangtze River Delta region with ancillary information. The light trap catches and field observations indicated that the migration peak of N. lugens coincided with the period when the typhoon made landfall in this region. The trajectory analyses revealed that most emigrations from this region during this period were hampered or ended in short distances. The sources of the light-trap catches were mainly located the nearby regions of each station (i.e. mostly less than 100 km away, with a few exceeding 200 km but all less than 300 km). This disrupted emigration was very different from the usual N. lugens migration which would bring them to Hunan, Jiangxi, and southern Anhui from this region at this time of year. This study revealed that the return migration of N. lugens was suppressed by the typhoon Khanun, leading to populations remaining high in the Yangtze River Delta and exacerbating later outbreaks.

Aviation Model: A Fine-Scale Numerical Weather Prediction System for Aviation Applications at the Hong Kong International Airport

The Hong Kong Observatory (HKO) is planning to implement a fine-resolution Numerical Weather Prediction (NWP) model for supporting the aviation weather applications at the Hong Kong International Airport (HKIA). This new NWP model system, called Aviation Model (AVM), is configured at a horizontal grid spacing of 600 m and 200 m. It is based on the WRF-ARW (Advance Research WRF) model that can have sufficient computation efficiency in order to produce hourly updated forecasts up to 9 hours ahead on a future high performance computer system with theoretical peak performance of around 10 TFLOPS. AVM will be nested inside the operational mesoscale NWP model of HKO with horizontal resolution of 2 km. In this paper, initial numerical experiment results in forecast of windshear events due to seabreeze and terrain effect are discussed. The simulation of sea-breeze-related windshear is quite successful, and the headwind change observed from flight data could be reproduced in the model forecast. Some impacts of physical processes on generating the fine-scale wind circulation and development of significant convection are illustrated. The paper also discusses the limitations in the current model setup and proposes methods for the future development of AVM.

Evaluation of four numerical wind flow models for wind resource mapping

A wide range of numerical wind flow models are available to simulate atmospheric flows. For wind resource mapping, the traditional approach has been to rely on linear Jackson–Hunt type wind flow models. Mesoscale numerical weather prediction (NWP) models coupled to linear wind flow models have been in use since the end of the 1990s. In the last few years, computational fluid dynamics (CFD) methods, in particular Reynolds-averaged Navier–Stokes (RANS) models, have entered the mainstream, whereas more advanced CFD models such as large-eddy simulations (LES) have been explored in research but remain computationally intensive. The present study aims to evaluate the ability of four numerical models to predict the variation in mean wind speed across sites with a wide range of terrain complexities, surface characteristics and wind climates. The four are (1) Jackson–Hunt type model, (2) CFD/RANS model, (3) coupled NWP and mass-consistent model and (4) coupled NWP and LES model. The wind flow model predictions are compared against high-quality observations from a total of 26 meteorological masts in four project areas. The coupled NWP model and NWP-LES model produced the lowest root mean square error (RMSE) as measured between the predicted and observed mean wind speeds. The RMSE for the linear Jackson-Hunt type model was 29% greater than the coupled NWP models and for the RANS model 58% greater than the coupled NWP models. The key advantage of the coupled NWP models appears to be their ability to simulate the unsteadiness of the flow as well as phenomena due to atmospheric stability and other thermal effects. Copyright © 2012 John Wiley & Sons, Ltd.

Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model

Abstract A new wind farm parameterization has been developed for the mesoscale numerical weather prediction model, the Weather Research and Forecasting model (WRF). The effects of wind turbines are represented by imposing a momentum sink on the mean flow; transferring kinetic energy into electricity and turbulent kinetic energy (TKE). The parameterization improves upon previous models, basing the atmospheric drag of turbines on the thrust coefficient of a modern commercial turbine. In addition, the source of TKE varies with wind speed, reflecting the amount of energy extracted from the atmosphere by the turbines that does not produce electrical energy. Analyses of idealized simulations of a large offshore wind farm are presented to highlight the perturbation induced by the wind farm and its interaction with the atmospheric boundary layer (BL). A wind speed deficit extended throughout the depth of the neutral boundary layer, above and downstream from the farm, with a long wake of 60-km e-folding distance. Within the farm the wind speed deficit reached a maximum reduction of 16%. A maximum increase of TKE, by nearly a factor of 7, was located within the farm. The increase in TKE extended to the top of the BL above the farm due to vertical transport and wind shear, significantly enhancing turbulent momentum fluxes. The TKE increased by a factor of 2 near the surface within the farm. Near-surface winds accelerated by up to 11%. These results are consistent with the few results available from observations and large-eddy simulations, indicating this parameterization provides a reasonable means of exploring potential downwind impacts of large wind farms.

Exploring the effect of data assimilation by WRF‐3DVar for numerical rainfall prediction with different types of storm events

AbstractThe mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high‐resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real‐time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper‐air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three‐dimensional variational data‐assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km2) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one‐dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two‐dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases. Copyright © 2012 John Wiley & Sons, Ltd.

Research on Wind Power Prediction by Combining Mesoscale Numerical Model with Neural Network Model

Effective wind power prediction on wind farm can not only guarantee safe operation of wind farm, but also increase wind power storage and utilization efficiency. This research combines mesoscale numerical weather prediction model with BP neural network model for the use of wind power prediction. WRF model is used to recalculate the meteorological elements of trial wind farm from Jun. 2008 to Jun. 2009, and the accuracy check result shows that the correlation coefficient between predicted value and corresponding measured value of wind speed reaches 0.72. Predictions accuracy of wind direction, air temperature, humidity and air pressure are also precise, which meets the requirement of building BP neural network prediction model. The BP neural network prediction models of output power of 40 wind turbines are established on trial wind farm one by one, to analyze the influence of data normalization method and neuron number at the hidden layer on prediction accuracy. The prediction test every 10 minutes, with the actual effect of 24 hours, is done for 26 days, and prediction accuracy test is conducted by using independent samples. The result shows that relative root mean square error of the output power of the single wind turbine from 24.8% to 32.6%, and the correlation coefficient between predicted value and measured value is from 0.45 to 0.68; relative root mean square error of the whole wind farm is 21.5%, and the correlation coefficient between predicted value and measured value is 0.74.

Radar data assimilation of the GRAPES model and experimental results in a typhoon case

Constructing β-mesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction (NWP) model. Without vertical velocity matching the β-mesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrometeors. In this study, a method, basing on the three-dimensional variational (3DVAR) assimilation technique, was developed to obtain reasonable structures of β-mesoscale weather systems by assimilating radar data in a next-generation NWP system named GRAPES (the Global and Regional Assimilation and Prediction System) of China. Single-point testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity (taking Richardson’s equation as the observational operator) can greatly improve the vertical motion. Experiments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further in-depth study.

An Investigation of Sea Surface Temperature Influence on Microwave Refractivity: The Wallops-2000 Experiment

AbstractThe Wallops-2000 experiment took place in April and May 2000 in the vicinity of Wallops Island, Virginia, to collect high-resolution measurements of microwave propagation and coincident meteorological parameters in a complex coastal environment. These data are used in conjunction with a mesoscale numerical weather prediction model to examine the impact of sea surface temperature (SST) on microwave ducting. Analysis of time series of meteorological fields at the location of an instrumented buoy indicates reliable forecast skill. Statistics from vertical profiles and of derived ducting characteristics (duct frequency, duct strength, duct-base height, and duct thickness) show that the model reproduced observed duct characteristics with modest accuracy, allowing for a 3–6-h error in synoptic airmass transitions. In addition to the control run, two experiments are conducted to examine the impact of SST on ducting. In one experiment a climatological SST field is used, and in the other a diurnal variation in SST is imposed. The higher SST in the diurnally varying simulations promotes stronger turbulent mixing, deep boundary layers, and small vertical gradients in mixing ratio in comparison with the control, which lead to reduced duct frequency and strength in many cases. The study further reveals that, while advection of large-scale air masses (vertical and horizontal) plays a crucial role in determining whether an environment is favorable for microwave ducting, diurnal variations in SST can be influential in determining the onset of ducting and the frequency of surface-based ducting in coastal regions.

Application of an adaptive radiative transfer scheme in a mesoscale numerical weather prediction model

AbstractThe computational burden of radiative transfer parametrization is considerable, and hence operational atmospheric models use various sampling, coarsening and interpolation techniques to reduce this load; this, however, introduces new errors. An adaptive radiative transfer scheme takes advantage of the spatial and temporal correlations in the optical characteristics of the atmosphere to make the parametrization computationally more efficient. The adaptive scheme employed here generalizes the accurate radiation computations made in a fraction of the spatial and temporal space to the rest of the field. In this study, a previously developed scheme has been extended to atmospheric heating rates and implemented in the numerical weather prediction model COSMO. The performance of the adaptive scheme is compared with the performance of the currently operational COSMO‐DE radiation configuration, in which radiation computations are performed quarter‐hourly on 2 × 2 averaged atmospheric columns. The reference for both schemes is a set of frequent radiation computations for the full grid. We show that the adaptive scheme is able to reduce the sampling errors in the radiation surface fluxes by 15–25% and to conserve the spatial variability, in contrast to the operational scheme. Deviations in the heating‐rate profiles are reduced for larger averaging scales. Physical relationships between the radiative quantities and cloud water or rain rates are better captured. We demonstrate that these improvements also lead to improvements with respect to the dynamical development of the model simulation, showing a smaller divergence from the reference model run. Copyright © 2011 Royal Meteorological Society

The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations

The local ensemble transform Kalman filter (LETKF) is implemented with the Weather Research and Forecasting (WRF) model, and real observations are assimilated to assess the newly-developed WRF-LETKF system. The WRF model is a widely-used mesoscale numerical weather prediction model, and the LETKF is an ensemble Kalman filter (EnKF) algorithm particularly efficient in parallel computer architecture. This study aims to provide the basis of future research on mesoscale data assimilation using the WRF-LETKF system, an additional testbed to the existing EnKF systems with the WRF model used in the previous studies. The particular LETKF system adopted in this study is based on the system initially developed in 2004 and has been continuously improved through theoretical studies and wide applications to many kinds of dynamical models including realistic geophysical models. Most recent and important improvements include an adaptive covariance inflation scheme which considers the spatial and temporal inhomogeneity of inflation parameters. Experiments show that the LETKF successfully assimilates real observations and that adaptive inflation is advantageous. Additional experiments with various ensemble sizes show that using more ensemble members improves the analyses consistently.

An event of tail strike of an aircraft due to terrain‐induced wind shear at the Hong Kong International Airport

AbstractIn the morning of 5 March 2010, an aircraft experienced tail strike when it departed from the south runway of the Hong Kong International Airport to the west. This paper documents the meteorological conditions in the tail strike event, and discusses the alerting and the forecasting aspects of the event. Based on the analysis of the aircraft manufacturer, the tail strike is believed to arise from significant wind shear (headwind loss) experienced by the aircraft. By analysing the surface and the LIDAR observations, it appears that the significant wind shear is related to disruption of the background southwesterly flow by the complex terrain near the airport. From the surface observations, there is a microscale cyclone at the western part of the south runway. The radial velocity image of the LIDAR also depicts an area of reverse flow in that region against the background southerly flow. The alerting of the wind shear based on the two wind shear alerting algorithms developed by the Hong Kong Observatory is discussed, and it turns out that the anemometer‐based wind shear algorithm could successfully capture this wind shear occurring close to the ground. On the other hand, due to geometrical constraint, the LIDAR does not pick up the headwind loss experienced by the aircraft. The forecasting of this wind shear event is considered by performing high resolution (down to a horizontal resolution of 50 m) using a mesoscale numerical weather prediction model. The simulated headwind profile at the event time is generally consistent with the aircraft data, though the headwind loss in the simulation is smaller than the reality. Together with the forecast wind gusts, it may be possible to alert the aviation weather forecaster at an earlier time about the occurrence of terrain‐induced wind shear over the south runway in this particular event. Copyright © 2011 Royal Meteorological Society

Multi-wavelength radar target detection in an extreme advection duct event

Near sea surface radio frequency (RF) refraction is four dimensional (4D) and can significantly impact the performance of radar systems. The refractivity field is dictated by the vertical thermodynamic structure of the constantly evolving marine atmospheric boundary layer (MABL). Logistical and budgetary restraints on meteorological measurements over water to capture the spatio-temporal structure of refractivity fields influencing radar performance have limited the knowledge of how and why radar performance is azimuth, range, and time dependent. Rapidly increasing computer processing speeds and decreasing memory capacity costs have supported the horizontal and vertical resolution requirements for mesoscale numerical weather prediction (NWP) models to resolve the thermodynamic structure in the MABL. Once modeled, refractivity structure is easily calculated from the thermodynamic structure. Mesoscale NWP models coupled with modern parabolic equation radar performance models can support the prediction of 4D radar performance in challenging non-homogeneous, near surface refractivity fields at the time and location of the modeler's choice. The NWP modeling presented in this paper demonstrates how large-scale offshore flow of warm and dry air over colder seas produces strong near surface RF trapping. Large land-sea temperature differences can produce near shore sea breezes and surface-based ducts. This paper describes modeled radar performance in such a complex ducting structure over the Persian Gulf during large-scale northwest atmospheric flow. The refractivity field was resolved by the Coupled Ocean/ Atmosphere Mesoscale Prediction System (COAMPS® is a registered trademark of the Naval Research Laboratory) and the notional radar performance was modeled by the advanced refractive effects prediction system (AREPS). The results indicate strong spatial and wavelength-dependent enhancements and degradations in radar performance relative to a standard atmosphere.

Processing of Indian Doppler Weather Radar data for mesoscale applications

This paper demonstrates the usefulness of Indian Doppler Weather Radar (DWR) data for nowcasting applications, and assimilation into a mesoscale Numerical Weather Prediction (NWP) model. Warning Decision Support System Integrated Information (WDSS-II) developed by National Severe Storm Laboratory (NSSL) and Advanced Regional Prediction System (ARPS) developed at the Centre for Analysis and Prediction, University of Oklahoma are used for this purpose. The study reveals that the WDSS-II software is capable of detecting and removing anomalous propagation echoes from the Indian DWR data. The software can be used to track storm cells and mesocyclones through successive scans. Radar reflectivity mosaics are created for a land-falling tropical cyclone—Khaimuk of 14 November 2008 over the Bay of Bengal using observations from three DWR stations, namely, Visakhapatnam, Machilipatnam and Chennai. Assimilation of the quality-controlled radar data (DWR, Chennai) of the WDSS-II software in a very high-resolution NWP model (ARPS) has a positive impact for improving mesoscale prediction. This has been demonstrated for a land-falling tropical cyclone Nisha of 27 November 2008 of Tamil Nadu coast. This paper also discusses the optimum scan strategy and networking considerations. This work illustrates an important step of transforming research to operation.

Coupled weather research and forecasting–stochastic time-inverted lagrangian transport (WRF–STILT) model

This paper describes the coupling between a mesoscale numerical weather prediction model, the Weather Research and Forecasting (WRF) model, and a Lagrangian Particle Dispersion Model, the Stochastic Time-Inverted Lagrangian Transport (STILT) model. The primary motivation for developing this coupled model has been to reduce transport errors in continental-scale top–down estimates of terrestrial greenhouse gas fluxes. Examples of the model’s application are shown here for backward trajectory computations originating at CO2 measurement sites in North America. Owing to its unique features, including meteorological realism and large support base, good mass conservation properties, and a realistic treatment of convection within STILT, the WRF–STILT model offers an attractive tool for a wide range of applications, including inverse flux estimates, flight planning, satellite validation, emergency response and source attribution, air quality, and planetary exploration.

Impact of coupling a microscale computational fluid dynamics model with a mesoscale model on urban scale contaminant transport and dispersion

Results are presented from a study designed to evaluate the impact upon urban area transport and dispersion (T&D) modeling accuracy by coupling a microscale computational fluid dynamics (CFD) model with a mesoscale numerical weather prediction (NWP) model. The CFD model taking part in the evaluation was the CFD-Urban model while the NWP model was the Weather Research and Forecasting (WRF) model. The following two different approaches of supplying initial and boundary conditions to drive CFD-Urban were evaluated by comparing the resulting tracer gas transport fields to field data: (i) using observation obtained from a single sounding site during the URBAN 2000 field experiment and (ii) using WRF output in quasi-steady mode. The WRF and the CFD-Urban model results were evaluated against data obtained from the Intensive Observation Period (IOP) 10 during the URBAN 2000 field experiment. It was found that the CFD-Urban T&D prediction was significantly improved when using wind fields produced by downscaling WRF output as initial and boundary conditions. One key reason for such success is that the turning of lower boundary layer wind and pressure gradient are well represented in the time-varying three-dimensional WRF fields.

Synthesis of mesoscale numerical weather prediction and empirical site-specific radar clutter models

A littoral clutter modelling approach that derives three-dimensional refractivity profiles from mesoscale numerical weather prediction (MSNWP), applies them to parabolic equation (PE) propagation models, and combines them with empirical surface clutter reflectivity models to generate a more realistic model of surface clutter, is described. The MSNWP model is COAMPS®, coupled atmospheric mesoscale numerical prediction system. This study updates a paper for the IEEE Radar Conference in 2008: overwater discrete clutter, oil rigs, have been added to the model; new model comparisons with recorded data have been executed; and an evaporative duct model was appended below the refractivity profiles Burk et al. developed the approach presented here (i.e. COAMPS® provides atmospheric data for propagation models, which in turn provide data for clutter models). The current work advances this technology into the littorals. It has been tested over many cases of atmospheric conditions in several geographic areas. This synthesis of MSNWP and radar clutter models can provide clutter and propagation forecasts for military planners, retroactive prediction of propagation for clutter test data analysis and realistic models for radar system design and performance analysis.

Assimilation of rain-affected radiances with an adjoint model

The ability to assimilate microwave radiance observations of the Earth's atmosphere affected by precipitation is investigated with an emphasis on channels that are sensitive to frozen hydrometeors. Mesoscale numerical weather prediction and radiative transfer models, as well as their corresponding adjoint models are utilized in sensitivity and data assimilation experiments. Special Sensor Microwave / Imager observations of Hurricane Bonnie (1998) are compared with model results that are transferred to radiance space with the radiative transfer model. Sensitivity results indicate that the model error in radiance space in areas of precipitation at the initial time is most dependent on the initial hydrometeor fields. At later forecast times, the model error is more sensitive to initial conventional model variables such as water vapor and temperature. When radiance data is assimilated, the model fields have better agreement with the observations compared to a control experiment for all observed channels at the initial time. However, at the next observation time 12 h later, the quantitative error measurements for the control and post- assimilation forecasts are approximately the same value. Although this study demonstrates the ability to assimilate observations sensitive to atmospheric ice (as well as liquid) concentrations in a variational framework, important aspects such a background error correlation and bias have been ignored for simplification. More observations in a more complex data assimilation system will be needed in order to fully maximize the forecast impact of these observations.

Sources of water vapour contributing to the Elbe flood in August 2002—A tagging study in a mesoscale model

AbstractIn this study we investigate the contribution of various moisture sources to the Elbe flood that occurred in Central Europe during August 2002. An 8‐day simulation with the mesoscale numerical weather prediction model CHRM, including newly implemented water vapour tracers, has been performed. According to the simulation, rather than drawing moisture from one single dominant source region, water vapour from widely separated moisture sources contributed to the extreme precipitation in the most affected area, notably at distinct, subsequent periods of time, and each in significant amounts. These moisture sources include the Atlantic and Mediterranean ocean areas inside the model domain, evapotranspiration from land areas, and long‐range advection from subtropical areas outside the model domain.The results highlight the importance of the concurrent upper‐level circulation and the mesoscale flow structures associated with the cyclone for producing extreme precipitation in parts of Germany, Austria, and the Czech Republic during that period. Furthermore, the numerical and technical problems of implementing water vapour tracers into a limited‐area model are discussed, including conservative tracer advection, initialization, boundary treatment, and the handling of precipitation parametrizations. An evaluation of the consistency of the method in terms of water vapour, cloud water, and precipitation is provided, with generally satisfying results. The model with its detailed water vapour tracer implementation can now be used for further case‐studies and climatological simulations, and serve as a reference for evaluating the performance of other moisture tracking methods, such as those based on backward trajectories. Copyright © 2009 Royal Meteorological Society

Effect of the soil wetness state on the stomatal ozone fluxes over Hungary

A coupled Eulerian photochemical reaction-transport model and a detailed ozone dry deposition model have been utilised for the estimation of stomatal ozone fluxes over Hungary. Ozone concentrations were modelled on an unstructured triangular grid using a method of lines approach to the solution of the reaction-diffusion-advection equations describing ozone formation, transport and deposition. The model domain covers Central-Europe including Hungary, which was located at the centre of the domain and covered by a high resolution nested grid. The dry deposition velocity of ozone was calculated based on the aerodynamic, quasi-laminar boundary layer and canopy resistance. The effect of soil water content on the stomatal ozone flux was analysed. The stomatal ozone flux calculations were performed for two cases, with and without taking into account the effect of the soil moisture stress on the ozone deposition. The meteorological data were generated by the ALADIN meso-scale limited area numerical weather prediction model. It was found that soil water deficiency can strongly reduce the stomatal conductance and hence the ozone flux through it.

Modeling and Simulation of Notional Future Radar in Non‐Standard Propagation Environments Facilitated by Mesoscale Numerical Weather Prediction Modeling

AbstractNormal near surface radio‐frequency (RF) propagation in the littorals across the land–sea boundary is rare due to the land–sea temperature difference, coastline shape, ground cover, urban density, coastal topography, and soil moisture content. The resulting frequent existence of coastal non‐standard vertical profiles of refractivity and the resulting RF propagation has a profound impact on the performance of Navy ship‐borne radars operating within 100 nm of the shore. In addition, these non‐standard RF propagation conditions are spatio‐temporally inhomogeneous. These spatial and time dependent propagation conditions and the resulting radar engineering implications cannot be revealed by a single vertical profile of refractivity taken near the ship borne radar. The results from single profile analysis techniques provide no spatiotemporal information and may lead to overly conservative radar design. Mesoscale numerical weather prediction (NWP) is a rapidly maturing technology with a strong operational Navy history that can provide a vertical profile of refractivity every 1 km in the battle space and every hour, up to 48 h, in the future. The Sensor Division at NSWCDD has applied mesoscale NWP for the last 2 years to better understand the performance of prototype radar in realistic four‐dimensional (4D) coastal environments. Modern RF parabolic equation models are designed to model specific radar designs and to employ 3D refractivity fields from mesoscale NWP models. This allows for a radar design to be tested in realistic littoral non‐standard atmospheres produced by stable internal boundary layers, sea breeze events, and the more rare sub‐refractive events. Mesoscale NWP is currently qualitative for this purpose, but a research and development program focused on sea testing of prototype radars is described with the purpose of developing a more quantitative mesoscale NWP technology to support radar acquisition, testing, and operations.