Forecasting Mesoscale Model Research Articles

Simulated Influence of Mountainous Wind Farms Operations on Local Climate

Renewable energy sources, especially wind power, were believed to be able to slow down global warming; however, evidence in recent years shows that wind farms may also induce climate change. With the rapid development of wind power industry, the number of wind farms installed in mountains has gradually increased. Therefore, it is necessary to study the impact of wind farms in mountainous areas on local climate. The Suizhou and Dawu wind farms in northern Hubei Province were chosen for the present study on the impact of wind farm operations on the local climate in mountainous areas. The mesoscale meteorological numerical model Weather Research and Forecasting Model (WRF) and the Fitch model, together with turbulence correction factor, were used to simulate wind farm operations and study their effects on local climate. The results showed the characteristics of wind speed attenuation in mountainous wind farms: the amplitude and range of wind speed attenuation were stronger in the nighttime than in the daytime, and stronger in summer than in winter. The surface temperature increased and became more significant in summer. However, a cooling variation was observed above the surface warming center. The height of this center was higher in the daytime than it was in the nighttime. The latent heat flux in the wind farms decreased at night, accompanied by an increase in sensible heat flux. However, these changes were not significant. Some differences were observed between the impact of wind farms on the climate in the plains and the mountains. Such differences are more likely to be related to complex terrain conditions, climate conditions, and the density of wind turbines. The present study may provide support for the development and construction of wind farms in mountainous areas.

Diagnostic Analysis of Multimodel Rainstorm Forecast for Cases Based on MODE Method

It is very important to analyze the performance of the model rainstorm forecast for understanding the intensity and position deviation of the model precipitation and improving the forecast ability. This paper uses classical scoring and the MODE (Method for Object-Based Diagnostic Evaluation) method to evaluate the forecast performance of different products. The forecast and observation data used in the article mainly include CMA (China Meteorological Administration) multi-source merged precipitation analysis, the precipitation forecast of ECMWF (European Centre for Medium-Range Weather Forecasts), CMA-Meso (Mesoscale model forecast of CMA) and SCMOC (National Meteorological Center grid precipitation forecast guidance product) data. At the same time, the possible correction method of heavy rainfall area is explored by using the high and low-level circulation configuration of the ECMWF model. The main conclusions are as follows: ① MODE spatial verification shows that the number, intensity, area and location of ECMWF rainstorm precipitation objects match the observed precipitation best, which is obviously better than SCMOC and CMA-Meso precipitation forecasts. There are significantly fewer SCMOC rainstorm precipitation objects, and the area of each single precipitation object is significantly larger, which often fails to report the small area objects of convective precipitation. ② The reason for the high TS score of SCMOC is that it reduces the number of small area rainstorm objects and avoids the “double punishment” phenomenon caused by position forecast error, which leads to the failure of SCMOC in local rainstorm forecasts. ③ Analyzing the relationship between the circulation situation of the ECMWF model and the location of rainstorm forecasts by the model, it is found that the location of the rainstorm area is consistent with the upper circulation system, especially with the strong rising area of the vertical velocity of 700 hPa and the high value area of the specific humidity of 850 hPa. When the rainstorm area coincides with the upper air system but is not consistent with the ground convergence area and the high value area of velocity potential, the rainstorm location often has a large deviation. The location of the surface convergence area can be used as a reference to improve the performance of the rainstorm forecast.

Improved Representation of Horizontal Variability and Turbulence in Mesoscale Simulations of an Extended Cold-Air Pool Event

Abstract Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models in part because CAPs occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effects of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) Model. Model runs with a one-dimensional (1D) PBL scheme and Smagorinsky-like horizontal diffusion are compared with runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3-km/750-m horizontal grid spacing over a 10-day case study in the Columbia River basin, and results are compared with observations from the Second Wind Forecast Improvement Project. Using event-averaged error metrics, potential temperature and wind speed errors are shown to decrease both with increased horizontal grid resolution and with improved treatment of horizontal diffusion over steep terrain. The 3D PBL scheme further reduces errors relative to a standard 1D PBL approach. Error reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Last, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event. The sensitivity of turbulence predictions to the master length-scale formulation in the 3D PBL parameterization is also explored. Significance Statement In this article, we demonstrate how a new framework for modeling atmospheric turbulence improves cold pool predictions, using a case study from January 2017 in the Columbia River basin (U.S. Pacific Northwest). Cold pools are regions of cold, stagnant air that form within valleys or basins, and improved forecasts could help to mitigate the risks they pose to air quality, transportation, and wind energy production. For the chosen case study, our tests show a reduction in temperature and wind speed errors by up to a factor of 2–3 relative to standard model options. These results strongly motivate continued development of the framework as well as its application to other complex weather events.

The Flash Flood Guidance System Implementation Worldwide: A Successful Multidecadal Research-to-Operations Effort

Abstract At the beginning of the twenty-first century a research-to-operations program was initiated to design and develop operational systems to support local forecasters in their challenging task to provide advance warning for flash floods worldwide. Twenty-some years later, the Flash Flood Guidance System with global coverage provides real-time assessment and guidance products to more than 60 countries, serving nearly 3 billion people. The implementation domains cover a wide range of hydroclimatological, geomorphological, and land-use regimes worldwide. This flexible and evolving system combines meteorology and hydrology data and concepts as well as supports product utility for flash flood disaster mitigation on very large scales with high spatial and temporal resolution. Through quality control procedures, it integrates remotely sensed data of land surface precipitation and of land surface properties from geostationary and polar-orbiting satellite platforms, reflectivity data from a variety of weather radar systems, and asynchronous precipitation data from ground-based automated precipitation gauges in order to produce assessments and short-term forecasts that support forecasters and disaster managers in real time. For each region, it also integrates mesoscale meteorological model forecasts with land surface model response to produce longer-term guidance products. It contains components and interfaces that allow real-time forecaster adjustments to products based on local last-minute field information and relevant forecaster experience. Assessments of utility for flash flood warning operations by national forecasting agencies worldwide are positive. The article exemplifies the process of realization and evolution of the Flash Flood Guidance System (FFGS) from research in interdisciplinary fields to operations in diverse environments, and discusses lessons learned.

Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

Low-visibility conditions are a weather hazard that affects all forms of transport, and accurate forecasting of their spatial coverage is still a challenge for meteorologists, particularly over a large domain. Current predictions of visibility are based on physical parametrizations in mesoscale models and are thus limited with respect to accuracy. This paper examines the use of supervised machine-learning regression techniques (tree-based ensemble, feed-forward neural network and generalized linear methods) to diagnose visibility from operational mesoscale model forecasts over a large domain. To achieve this, hourly forecasts of meteorological parameters in the lower levels of the atmosphere have been used. In the short-range forecasting framework, the machine-learning algorithms were developed to provide hourly forecasts up to 24 h. To assess the performance of the developed models, hourly observed data, collected at 36 synoptic land stations over the northern part of Morocco, have been used. This region is characterized by a heterogeneous topography. The tree-based ensemble methods have shown some improvement in visibility forecasting in comparison with the operational visibility diagnostic scheme based on Kunkel’s formula and also with persistence. It is also found that this machine-learning technique performs better when the forecast depends on multiple predictors instead of only a few with very high importance. In addition, their performance is very sensitive to the disproportionality of data availability between daytime and night-time. Furthermore, it is found that the performance decreases when principal components are used instead of raw correlated data.

Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula

A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed’s Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes.

Atmospheric Retrievals and Assessment for Microwave Observations from Chinese FY-3C Satellite during Hurricane Matthew

The evolution process of hurricane Matthew (NO. 8, 2016) was simulated using the mesoscale Weather Research and Forecasting (WRF) model at temporal resolution of 5 min and spatial resolution of 15 km. The atmospheric temperature and humidity profiles were retrieved accordingly for diagnostic analysis of the short-term heavy rainstorm. The satellite-based microwave observations from Microwave Humidity and Temperature Sounder (MWHTS) instrument on board the FY-3C polar-orbiting satellite were matched with the WRF grid points. In particular, the in-orbit calibration and data quality control are detailed, and an innovative method combining artificial neural network (ANN) and 1-D variational approach is presented to derive the high-performance retrieval profiles. Results show that the root-mean-square errors of the retrieved temperature and water vapor density profiles are 0.75 K and 0.41 g/m3, respectively. In addition, this study used both the retrievals and radiance from MWHTS as input to the WRF Data Assimilation (WRFDA) model to forecast the track and intensity of hurricane Matthew. The forecast results were cross-compared with the best track to verify the radiance quality and performance of the retrievals, especially for the 118 GHz channel, which was firstly used in meteorological satellite.

The Northern California Wildfires of 8–9 October 2017: The Role of a Major Downslope Wind Event

AbstractA series of large wildfires began over the terrain north of San Francisco, California, during the evening of 8 October 2017 and spread across nearly 250,000 acres, including areas near the towns of Santa Rosa and Napa. These “Wine Country” wildfires were the most destructive in California history, with 44 deaths; the loss of 9,000 buildings; damage to approximately 21,000 structures; $10 billion of insured losses; and substantially greater total economic loss.This paper describes the synoptic and mesoscale conditions that were associated with the wildfires, with strong, easterly “Diablo” winds playing a central role in both initiating and supporting the fires. The climatological conditions preceding the fires are reviewed, including near-normal precipitation and above-normal temperatures during the summer, as well as much above-normal precipitation the previous winter, which led to abundant dry grass that provided fuel for the wind-driven fires.High-resolution meteorological modeling realistically simulated the strong winds associated with this event. Importantly, operational mesoscale forecast models provided excellent forecasts of the high winds several days in advance. It appears that a vulnerable power system, urbanization of fire-prone areas, flammable invasive species, and poor communication of dangerous conditions contributed to this catastrophic event. The potential for mitigating or preventing such destructive wildfires using skillful weather prediction is examined, as well as the possible role of global warming.

Semi‐idealized urban heat advection simulations using the Weather Research and Forecasting mesoscale model

Urban heat advection (UHA) can extend the climatic impact of a city into the surrounding countryside. This may lead to an intensification of already well‐documented urban heat island (UHI) impacts on health and infrastructure, and challenge the representativeness of long‐term reference temperature records taken near urban areas. However, previous UHA studies have been unable to accurately quantify surface‐level UHA due to challenges arising from complex urban land‐use patterns. To address this, the numerical Weather Research and Forecasting (WRF) mesoscale model coupled with the Building Energy Parameterization urban canopy scheme is used to simulate meteorological fields for idealized land‐use cases. Hypothetical square cities (up to 32 km in size) are simulated for a year's period. A time‐mean 2‐m temperature field (representing the canopy UHI) shows that the mean UHI intensity (up to 4.3 °C [SD 1.7 °C]), wind speed <3.9 m/s) is linearly related to the logarithm of city size. This finding, entirely derived from numerical modelling, is consistent with the log‐linear relationships previously found in the observational data of many cities in the world. A UHA methodology was then applied to the temperature fields to separate UHA from the UHI, with up to 2.9 °C (SD 1.7 °C) of UHA found downwind of the largest city size. For this hypothetical city size, an UHA intensity of 0.5 °C is found up to 24‐km downwind from the urban boundary. In addition, the UHA‐distance profiles along the central horizontal transect for various urban sizes are found to follow a scaling rule as a good approximation. As a result, the findings of this paper can be used as a starting point for climate impact assessments for areas surrounding urban areas without the need for complex, computation‐intensive simulations.

Mechanism Study of Tropical Cyclone Impact on East Asian Subtropical Upper-Level Jet: a Numerical Case Investigation

In the case study of this paper, sensitivity experiments are carried out using the mesoscale non-hydrostatic Weather Research and Forecasting (WRF) model to investigate the impact of tropical cyclone (TC) Soudelor (2003) on the East Asian subtropical upper-level jet (EASJ) before TC Soudelor transformed into an extratropical cyclone. The physical mechanism for changes in the EASJ intensity and position caused by TC Soudelor is explored. Results indicate that TC Soudelor would warm the air in the middle and upper troposphere over the Japan Sea and the adjacent areas through stimulating northward propagating teleconnection pattern as well as releasing large amounts of latent heat, which led to increase (decrease) the meridional air temperature gradient to the south (north) below the EASJ axis. As a result, the geopotential height abnormally increased in the upper troposphere, resulting in an anomalous anticyclonic circulation belt along the EASJ axis. Correspondingly, the westerly winds to the north (south) of the EASJ axis intensified (weakened) and the EASJ axis shifted northward by one degree. The case study also suggests that before the extratropical cyclone transition of TC Soudelor, the TC activities had exerted significant impacts on the EASJ through thermodynamic processes.

A Simple Model for the Anomalous Counterclockwise Turning of the Surface Wind with Time over the Great Plains of the United States

AbstractA recent study found that surface hodographs over the Great Plains of the United States turn in a counterclockwise direction with time. This observed turning is opposite of the clockwise turning observed (and expected, based on theory) at higher altitudes. Using a mesoscale forecast model, the same study shows that it has the same hodograph behavior as found in the observations. The study further shows that the reason for this anomalous counterclockwise turning is the decoupling of the surface layer from the boundary layer after sunset and its recoupling after sunrise. The present paper presents a simple model for this behavior by extending a recent analytical model for the diurnal oscillation to include the surface-layer effect. In addition, selected solution features are analyzed in terms of several of the nondimensional input parameters.

A wind field downscaling strategy based on domain segmentation and transfer functions

AbstractThis paper presents a novel methodology for mesoscale‐to‐microscale downscaling of near‐surface wind fields. The model chain consists on the Weather Research and Forecast mesoscale model and the Alya‐CFDWind microscale model (assuming neutral stability). The downscaling methodology combines precomputed microscale simulations with a mesoscale forecast using a domain segmentation technique and transfer functions. As a result, the downscaled wind field preserves the mesoscale pattern but, at the same time, incorporates local mesoscale subgrid terrain effects, particularly at valleys and channelling zones. The methodology has been validated over a 9‐month period on a very complex terrain site instrumented with a dense observational network of meteorological masts. With respect to mesoscale results, the global skills of the downscaled wind at masts improve for wind direction and remain similar for wind velocity. However, a substantial improvement occurs under stable and neutral conditions and for high wind velocity regimes.

Evaluation of the WRF-UCM mesoscale model and ECMWF global operational forecasts over the Paris region in the prospect of tracer atmospheric transport modeling

The quantification of CO2 emissions from cities using atmospheric measurements requires accurate knowledge of the atmospheric transport. Complex urban terrains significantly modify surface roughness, augment surface energy budgets, and create heat islands, all of which lead to lower horizontal winds and enhanced convection over urban areas. The question remains whether these processes should be included in atmospheric transport models that are used for city scale CO2 inversion, and whether they need to be tailored on a city basis. In this study, we use the WRF model over Paris to address the following research question: does WRF runs at a 3 km resolution, including urban effects and the assimilation of local weather data, perform better than ECMWF forecasts that give fields at 16 km resolution? The analysis of model performances focuses on three variables: air temperature, wind and the planetary boundary layer (PBL) height. The results show that the use of objective analysis and nudging tools are required to obtain good agreements between WRF simulated fields with observations. Surface temperature is well reproduced by both WRF and ECMWF forecasts, with correlation coefficients with hourly observations larger than 0.92 and MBEs within 1°C over one month. Wind speed correlations with hourly observations are similar for WRF (range 0.76~0.85 across stations) and ECMWF (0.79~0.84), but the associated RMSEs and MBEs are better for ECMWF. Conversely, WRF outperforms ECMWF forecasts for its description of wind direction, horizontal and vertical gradients. Sensitivity tests with different WRF physics schemes show that the wind speed and the PBL height are strongly influenced by PBL schemes. The marginal advantage of WRF over ECMWF for the desired application is sufficient to motive additional testing with prescribed CO2 flux maps for comparing modeled CO2 concentrations with available observations in an urban environment.

WRF Hub-Height Wind Forecast Sensitivity to PBL Scheme, Grid Length, and Initial Condition Choice in Complex Terrain

Abstract This study evaluates the sensitivity of wind turbine hub-height wind speed forecasts to the planetary boundary layer (PBL) scheme, grid length, and initial condition selection in the Weather Research and Forecasting (WRF) Model over complex terrain. Eight PBL schemes available for the WRF-ARW dynamical core were tested with initial conditions sources from the North American Mesoscale (NAM) model and Global Forecast System (GFS) to produce short-term wind speed forecasts. The largest improvements in forecast accuracy primarily depended on the grid length or PBL scheme choice, although the most important factor varied by location, season, time of day, and bias-correction application. Aggregated over all locations, the Asymmetric Convective Model, version 2 (ACM2) PBL scheme provided the best forecast accuracy, particularly for the 12-km grid length. Other PBL schemes and grid lengths, however, did perform better than the ACM2 scheme for individual seasons or locations.

Evaluation of High-Frequency Channels for Deep-Space Data Transmission Using Radiometeorological Model Forecast

The aim of this paper is to investigate the usability of high-frequency channels for deep-space (DS) transmissions exploiting radiometeorological forecast modeling. A previously developed model chain for DS link-budget optimization, based on numerical weather forecasts (WFs), is adopted. The latter, already tested at Ka-band, exploits the combination of a high-resolution mesoscale forecast model and a radiative transfer model to predict the atmospheric scenario and optimize received data volume (DV) during DS transmissions. To shift available Ka-band results to other frequencies, we apply frequency-scaling laws to extrapolate forecast path attenuation, link parameters, and maximum allowed bit-rate for data transmission. Exploiting the available WF-based methodology, we compute DV return for DS missions operating at $X$ -, $K$ -, Ka-, $Q$ -, and $W$ -bands in order to make a comparative study of the behavior of DS transmission-channels at these frequencies. Results show that, in terms of received DV, an innovative WF-based approach is more convenient than traditional methodologies and exhibits a trend similar to the benchmark (ideal case). Increasing link frequency, received DV increases up to $Q$ -band. From $Q$ - to $W$ -band, despite received DV does not increase significantly, lost data remain under reasonable values, thus making the $W$ -band suitable if coupled with a WF-based technique.

ПРАКТИЧНІ АСПЕКТИ ЗАСТОСУВАННЯ МЕТЕОРОЛОГІЧНИХ МОДЕЛЕЙ ПРОГНОЗУ ПОГОДИ ДЛЯ ПОТРЕБ АГРАРНОГО СЕКТОРУ ЕКОНОМІКИ

Different aspects of the application of short-term and long-term meteorological forecasting in the work of agricultural companies of different scales were investigated in this paper. Systematic assessment and consideration of weather factors in the work of agricultural companies of various scales is very important on the way to effective management and increase profits, especially in today's climate change. The use of existing global meteorological models does not always allow to obtain a detailed and qualitative forecast for individual areas, which is important for assessing the impact of weather on crops. The advantages of using the mesoscale Weather Research and Forecasting Model (WRF) for agricultural companies are considered. The results of short-term and medium- term WRF modeling can be used by agricultural campaigns to assess the potential negative impact of weather conditions on crops, as well as in preventive decisions to reduce the negative impact of extreme weather events on crops. The problem of the need for significant computing resources for mesoscale modeling can be solved by using commercial Cloud platforms, which are more cost-effective than purchasing high-performance computer systems (HPC). The scheme of application of short-term and medium-term weather forecasting in agricultural companies by realization of mesoscale modeling with Weather Research and Forecasting Model (WRF) and with application of cloud technologies was developed. The result of the proposed scheme are graphic products of meteorological quantities and weather maps of the modeling area (where crops are located), which is a ready product for analysis by agricultural companies for further agricultural and technical decision making process.

분산 컴퓨팅 기술을 이용한 고해상도 강수량 예측

분산컴퓨팅은 네트워크로 연결된 여러 컴퓨터들의 연산 능력을 이용하여 거대 계산 문제를 해결하려는 분산처리 모델이다. 인터넷에 연결된 수많은 컴퓨팅 자원들의 참여를 통해 대규모의 계산이 필요한 기상, 바이오, 천문학, 암호학 등과 같은 다양한 분야의 어플리케이션 들을 병렬로 처리할 수 있다. 본 논문에서는 기상 분야의 고해상도 강수량 예측 어플리케이션 수행을 위해 인터넷 분산컴퓨팅 모델을 구성하여 성능을 분석하였다. 한반도의 강수량 예측을 위해서 중규모 예측 모형인 QPM(Quantitative Precipitation Model)을 적용하였으며 이 모형은 한반도의 지형을 격자 간격 27km로 나누고 각 격자의 특성인 고도, 기온, 강수, 강도 습도 등을 기반으로 강수량을 예측하게 된다. 그러나 QPM의 격자 간격에 따른 분석은 모형 구축에 많은 시간이 소요 되고 한번에 수행되어야 할 계산량이 많아 효율성이 저하된다. 따라서 이 모형을 기반으로 하여 3km 간격의 상세 지형을 반영하는 모형으로 소규모 지형 효과를 표현함으로써 상세 지역에서의 강수량 산출과 지형에 따른 강수량의 분포 파악이 용이해지며 계산 효율성을 개선시킬 수 있다. 이렇게 상세지역으로 세분화 된 모형은 병렬처리가 필요하며 계산노드의 수가 많아질수록 그 효율은 선형적으로 증가된다. 이 모형은 <TEX>$20{\times}20$</TEX>의 아격자 도메인의 분산된 단위작업들로 나뉘어 분산되고 네트워크로 연결된 컴퓨팅 자원에서 수행되게 된다. Distributed Computing attempts to harness a massive computing power using a great numbers of idle PCs resource distributed linked to the internet and processes a variety of applications parallel way such as bio, climate, cryptology, and astronomy. In this paper, we develop internet-distributed computing environment, so that we can analyze High Resolution Rainfall Prediction application in meteorological field. For analyze the rainfall forecast in Korea peninsula, we used QPM(Quantitative Precipitation Model) that is a mesoscale forecasting model. It needs to a lot of time to construct model which consisted of 27KM grid spacing, also the efficiency is degraded. On the other hand, based on this model it is easy to understand the distribution of rainfall calculated in accordance with the detailed topography of the area represented by a small terrain model reflecting the effects 3km radius of detail and terrain can improve the computational efficiency. The model is broken down into detailed area greater the required parallelism and increases the number of compute nodes that efficiency is increased linearly.. This model is distributed divided in two sub-grid distributed units of work to be done in the domain of <TEX>$20{\times}20$</TEX> is networked computing resources.

Mitigating Excessive Drying from the Use of Observations in Mesoscale Modeling

AbstractObservations can be used to enhance mesoscale model forecasts by both improving the initial conditions and adjusting the model state toward the observations during its forward integration. Since observations are not available at the same density as the model grid points, spatial and temporal error correlations must be specified in order to effectively assimilate the observations into the model. Overdrying is found while applying the Weather Research and Forecasting Model to five cases centered over Southern California. This overdrying is caused by incorporation of observations in the initial conditions both at a discrete time and over a preforecast data assimilation period. Nonphysical dryness results from the assumption that model moisture error at the observation location is correlated with model moisture error at other locations in the model without regard to the relative magnitude of the moisture at those two locations. Modifying the model and its preprocessor to remove this assumption greatly reduces the occurrence of excessive model dryness without degrading the overall model performance in predicting moisture.

Assessment of Planetary Boundary-Layer Schemes in the Weather Research and Forecasting Mesoscale Model Using MATERHORN Field Data

The study was aimed at understanding the deficiencies of numerical mesoscale models by comparing predictions with a new high-resolution meteorological dataset collected during the Mountain Terrain Atmospheric Modelling and Observations (MATERHORN) Program. The simulations focussed on the stable boundary layer (SBL), the predictions of which continue to be challenging. High resolution numerical simulations (0.5-km horizontal grid size) were conducted to investigate the efficacy of six planetary boundary-layer (PBL) parametrizations available in the advanced research version of the Weather Research and Forecasting model. One of the commonly used PBL schemes was modified to include eddy diffusivities that account for enhanced momentum transport compared to heat transport in the SBL, representing internal wave dynamics. All of the tested PBL schemes, including the modified scheme, showed a positive surface temperature bias. None of the PBL schemes was found to be superior in predicting the vertical wind and temperature profiles over the lowest 500 m, however two of the schemes appeared superior in capturing the lower PBL structure. The lowest model layers appear to have a significant impact on the predictions aloft. Regions of sporadic flow interactions delineated by the MATERHORN observations were poorly predicted, given such interactions are not represented in typical PBL schemes.

A Review of Planetary Boundary Layer Parameterization Schemes and Their Sensitivity in Simulating Southeastern U.S. Cold Season Severe Weather Environments

AbstractThe representation of turbulent mixing within the lower troposphere is needed to accurately portray the vertical thermodynamic and kinematic profiles of the atmosphere in mesoscale model forecasts. For mesoscale models, turbulence is mostly a subgrid-scale process, but its presence in the planetary boundary layer (PBL) can directly modulate a simulation’s depiction of mass fields relevant for forecast problems. The primary goal of this work is to review the various parameterization schemes that the Weather Research and Forecasting Model employs in its depiction of turbulent mixing (PBL schemes) in general, and is followed by an application to a severe weather environment. Each scheme represents mixing on a local and/or nonlocal basis. Local schemes only consider immediately adjacent vertical levels in the model, whereas nonlocal schemes can consider a deeper layer covering multiple levels in representing the effects of vertical mixing through the PBL. As an application, a pair of cold season severe weather events that occurred in the southeastern United States are examined. Such cases highlight the ambiguities of classically defined PBL schemes in a cold season severe weather environment, though characteristics of the PBL schemes are apparent in this case. Low-level lapse rates and storm-relative helicity are typically steeper and slightly smaller for nonlocal than local schemes, respectively. Nonlocal mixing is necessary to more accurately forecast the lower-tropospheric lapse rates within the warm sector of these events. While all schemes yield overestimations of mixed-layer convective available potential energy (MLCAPE), nonlocal schemes more strongly overestimate MLCAPE than do local schemes.