Weather Research Forecasting Research Articles

Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines

Abstract. Energy scenarios require realistic estimates of technical wind energy potentials – estimates for how much electricity can be generated by wind turbines given a certain level of deployed capacity within a region. These are typically obtained using observed wind speeds, neglecting the depletion of the wind energy resource with increasing deployment at the regional scale. Here, we use the kinetic energy budget of the atmosphere (KEBA) approach to evaluate the importance of this resource depletion effect for the technical potential of Kansas, USA. To do so, we first apply the KEBA approach to a previously conducted set of numerical simulations with the Weather Research Forecasting (WRF) model. This set simulated the resource depletion effect for a range of different levels of wind turbine deployments within Kansas, which we use to test the KEBA approach. We specifically test the approach for nighttime and daytime conditions to capture the different mixing regimes of the atmospheric boundary layer. We find that KEBA can adequately capture the effect for both settings. We then extend our analysis by using ERA-5 forcing to the climatological scale. We find that this resource depletion effect increases almost linearly with the level of wind turbine deployment. Compared to previously published estimates for the technical potential for Kansas, the resource depletion effect lowers capacity factors by a third to a half. Since this resource depletion effect increases with installed capacity within a region, it is policy relevant even at relatively low deployment levels.

The Evaluation of Global and Regional Applications of Model for Prediction Across Scales-Atmosphere (MPAS) Against Weather Research Forecast (WRF) Model over California for a Winter (2013 DISCOVER-AQ) and Summer (2016 CABOTS) Episode

The Model for Prediction Across Scales-Atmosphere (MPAS) was used to simulate meteorological conditions for a two-week winter episode during 10–23 January 2013, and a two-week summer episode during 18–31 July 2016, using both as a global model and a regional model with a focus on California. The results of both global and regional applications of MPAS were compared against the surface and upper air rawinsonde observations while the variations of characteristic meteorological variables and modeling errors were evaluated in space, time, and statistical sense. The results of the Advanced Weather Research and Forecast (WRF-ARW, hereafter WRF) model simulations for the same episodes were also used to evaluate the results of both applications of MPAS. The temporal analyses performed at surface stations indicate that both global and regional applications of MPAS and WRF model predict the diurnal evolution of characteristic meteorological parameters reasonably well in both winter and summer episodes studied here. The average diurnal bias in predicting 2 m temperature by MPAS and WRF are about the same with a maximum of 2 °C in winter and 1 °C in summer while that of 2 m mixing ratio is within 1 g/kg for all three modeling applications. The rawinsonde profiles of temperature, dew point temperature, and wind direction agree reasonably well with observations while wind speed is underestimated by all three applications. The comparisons of the spatial distribution of anomaly correlation and mean bias errors calculated from each model results for 2 m temperature, 2 m water vapor mixing ratio, 10 m wind speed and wind direction indicate that all three models have similar magnitudes of agreement with observations as well as errors away from observations throughout California.

Evaluating the performance and detection efficiency of Weather Research Forecasting model with lightning parameterization schemes for identifying lightning hotspots over Northeast region in India

Evaluating the performance and detection efficiency of Weather Research Forecasting model with lightning parameterization schemes for identifying lightning hotspots over Northeast region in India

Numerical study for atmospheric transport of radioactive materials for hypothetical severe nuclear accident under different meteorological conditions

A study for atmospheric transport is essential for the consequence assessment of severe nuclear accidents since radionuclides could be released from the nuclear facility into the atmosphere and cause radioactive pollution in the environment. Atmospheric transport behaviors are strongly related with meteorological conditions, which can obviously influence the transport and diffusion characteristics of radioactive materials in the atmosphere; thus, it is meaningful to investigate the coupling effects between meteorological processes and transport behaviors of radioactive materials. To evaluate the influence of meteorological conditions on atmospheric transport, meteorological parameters for different seasons were first acquired by the weather research forecast model. Furthermore, atmospheric transport behaviors of radioactive materials were simulated by the meso-scale numerical model under different meteorological conditions, and numerical analyses were conducted toward transport and deposition behaviors of radioactive materials. In addition, the influence of FDDA (four-dimensional data assimilation) on meteorological parameters and atmospheric transport behaviors was researched. The present study is important for strengthening consequence assessment for severe nuclear accident and made it possible to apply the data assimilation technology in further research works.

Applying double cropping and interactive irrigation in the North China Plain using WRF4.5

Abstract. Irrigated cultivation exerts a significant influence on the local climate and the hydrological cycle. The North China Plain (NCP) is known for its intricate agricultural system, marked by expansive cropland, high productivity, compact rotation, a semi-arid climate, and intensive irrigation practices. As a result, there has been considerable attention on the potential impact of this intensive irrigated agriculture on the local climate. However, studying the irrigation impact in this region has been challenging due to the lack of an accurate simulation of crop phenology and irrigation practices within the climate model. By incorporating double cropping with interactive irrigation, our study extends the capabilities of the Weather Research Forecast (WRF) model, which has previously demonstrated commendable performance in simulating single-cropping scenarios. This allows for two-way feedback between irrigated crops and climate, further enabling the inclusion of irrigation feedback from both ground and vegetation perspectives. The improved crop modeling system shows significant enhancement in capturing vegetation and irrigation patterns, which is evidenced by its ability to identify crop stages, estimate field biomass, predict crop yield, and project monthly leaf area index. The improved simulation of large-scale irrigated crops in the NCP can further enhance our understanding of the intricate relationship between agricultural development and climate change.

Long-range transport impact of a severe dust storm over the Yangtze River Basin region and its modeling sensitivity to dust emission scheme

Dust storms frequently occur in the spring over East Asia, affecting southern Mongolia and northeast China. An exceptionally powerful dust storm occurred in East Asia from March 14th to March 18th, 2021. To investigate the ability of climate models to simulate severe dust storms, we employed the Weather Research Forecast model coupled with chemistry (WRF-Chem) with Goddard Chemistry Aerosol Radiation and Transport (GOCART), SHAO and KOK dust emission schemes to simulate the evolution of this event and to characterize dust aerosols long-range transport. Results showed that this storm was caused by a Mongolian cyclone depicted at 850 hPa with a cold front, which swept dust aerosols southeastward across southern Mongolia to northeast China. Model evaluation with satellite-retrieved Aerosol Optical Depth (AOD) and observational PM10 concentrations revealed that the SHAO dust emission scheme performed better in simulating spatial and temporal distribution of AOD over the Gobi Desert (GD) but overestimated them over Taklimakan Desert (TD). In contrast, the GOCART dust emission scheme underestimated AOD over most areas except the Taklimakan Desert; the KOK dust emission scheme overestimated them over both TD and GD. Overall, the SHAO dust emission scheme had good performance in simulating the surface PM10 concentrations over the Yangtze River Basin (YRB) and depicted an increase in PM10 concentrations, which denoted the transport of dust aerosols over YRB. As a better description of the physical process of dust emission flux over the eastern region of the Gobi Desert, the SHAO dust emission scheme depicted well the spatial and temporal evolution of dust plume episodes, especially over northeast China and YRB. The significant differences in dust emission fluxes between the three dust emission schemes were attributed to the sensitivity of threshold friction velocity and erodibility factor. Thereunto, the SHAO dust emission scheme incorporated well soil particle size distribution and threshold velocity to produce the dust emission flux, which allowed higher dust emission flux over the eastern GD and under strong southwesterly transported dust aerosols to East Central China's region of North China Plain (NCP) and YRB. This study will help further the simulation of extreme dust events more accurately and the exploration of climate change's effect on dust events over East Asia.

Performance of weather research forecasting model for seasonal prediction of precipitation over Indonesian maritime continent

The precipitation over the Indonesian Maritime Continent (IMC) is one of the most challenging atmospheric parameters to predict accurately. The Weather Research Forecasting (WRF) model used in this study produces overestimated predictions of precipitation intensity compared to satellite data. Therefore, it is necessary to modify the model output to minimize the bias between predictions and satellite observations. The modification includes adjusting the dynamic model parameters and settings to better reflect atmospheric conditions in the IMC region, applying bias correction techniques through a linear scaling method, aligning the monthly average of the model's output with observational data, and conducting statistical analysis in several areas within the IMC. This procedure has significantly reduced the biases and is considered acceptable for each satellite area. Based on the results of the statistical analysis and by applying the precipitation threshold criteria, the accuracy of the predictions in each observation area is quite good, ranging from 0.59 to 1.0. Precipitation with a threshold of 50 mm/day or higher exhibits good accuracy, with a minimum value of 0.59 for RI and a maximum value of 0.97 for RIII and RV. On the other hand, precipitation with a threshold of 20 mm/day or higher demonstrates very good accuracy, with values of 0.97 for RI and 1.00 for RIII to RVI.

A Study on the Possible Brown Ocean Effect: Impacts of an Antecedent Tropical Cyclone on the Rapid Intensification of the 1999 Odisha Supercyclone

AbstractOdhisa, a state of India, bore the disastrous consequences of two consequent Tropical Cyclones (TCs), TC 04B and TC 05B (Odisha 1999 supercyclone), which formed over the Bay of Bengal and experienced landfall in October 1999, with a time gap of fewer than two weeks, over the same region. It is suggested that the first TC, TC 04B, provided an “ocean‐like situation” over the coastal land region, thus delivering the appropriate land conditions that would facilitate the intensification of the following second tropical cyclone, TC 05B; a clear illustration of the “Brown Ocean effect.” Two Weather Research Forecasting (WRF) simulations were conducted, with the control and experimental runs differing solely in the following aspect: the initial cyclonic vortex corresponding to the first TC at the initial time was removed in the experimental run, whereas it was retained in the control run. Both simulations were analyzed to reveal the “Brown Ocean Effect” role. The experimental run result indicates that the minimum central sea level pressure of the second TC was 35 hPa higher than the second TC simulation in the control run. The heavy rainfall associated with TC 04B led to increased soil moisture conditions, providing the second TC (TC 05B) with the necessary conditions for its intensification by the “Brown Ocean Effect.” The results of this study appear to strongly suggest that the “Brown Ocean Effect” could provide one of the main reasons for the extraordinary intensities associated with the 1999 Odisha supercyclone.

Aerosol-induced closure of marine cloud cells: enhanced effects in the presence of precipitation

Abstract. The Weather Research Forecasting (WRF) version 4.3 model is configured within a Lagrangian framework to quantify the impact of aerosols on evolving cloud fields. Kilometer-scale simulations utilizing meteorological boundary conditions are based on 10 case study days offering diverse meteorology during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA). Measurements from aircraft, the ground-based Atmosphere Radiation Measurement (ARM) site at Graciosa Island in the Azores, and A-Train and geostationary satellites are utilized for validation, demonstrating good agreement with the WRF-simulated cloud and aerosol properties. Higher aerosol concentration leads to suppressed drizzle and increased cloud water content in all case study days. These changes lead to larger radiative cooling rates at cloud top, enhanced vertical velocity variance, and increased vertical and horizontal wind speed near the base of the lower-tropospheric inversion. As a result, marine cloud cell area expands, narrowing the gap between shallow clouds and increasing cloud optical thickness, liquid water content, and the top-of-atmosphere outgoing shortwave flux. While similar aerosol effects are observed in lightly to non-raining clouds, they tend to be smaller by comparison. These simulations show a relationship between cloud cell area expansion and the radiative adjustments caused by liquid water path and cloud fraction changes. The adjustments are positive and scale as 74 % and 51 %, respectively, relative to the Twomey effect. While higher-resolution large-eddy simulations may provide improved representation of cloud-top mixing processes, these results emphasize the importance of addressing mesoscale cloud-state transitions in the quantification of aerosol radiative forcing that cannot be attained from traditional climate models.

An evaluation of the reliability of the Weather Research Forecasting (WRF) model in predicting wind data: a case study of Burundi

BackgroundThe Weather Research and Forecasting (WRF) Model is an exceptional software for mesoscale climate modeling. It is extensively used to simulate key meteorological variables, including temperature, rainfall, and wind.MethodsThis study thoroughly examined the effectiveness of the WRF model in generating precise wind data for assessing the potential of wind power in Burundi. A meticulous evaluation of various combinations of model physics parameterization schemes was conducted to ensure accuracy.By comparing the simulated data with measurements from four meteorological stations and utilizing statistical metrics such as root-mean-square error (RMSE) and bias, the accuracy of the WRF model was determined.Results The findings of the study uncovered that utilizing WRF Single-Moment 3-Class (WSM3) for microphysics, Grell-Devenyi ensemble for cumulus physics, and Yonsei University for planetary boundary layer yields highly accurate wind data results for Burundi.Furthermore, the WRF model was utilized to create detailed seasonal and annual mean wind maps with a high resolution. ConclusionThese maps demonstrated that the western part of Burundi experiences higher wind speeds (ranging from 4 to 9.7 m/s) during the dry seasons revealing the potential for wind energy harvesting in the different areas of Burundi.

Impact of aerosol actinic radiative effect on ozone during haze pollution in the Pearl River Delta region

Large amounts of aerosol in the atmosphere can cause significant attenuation of photochemical radiation and have a substantial impact on ozone and other secondary pollutants involved in photochemical reactions. This study examines the impact of aerosol actinic radiative effect on ozone and secondary pollutants during typical haze processes in the Pearl River Delta (PRD) region. The investigation employed lidar data and Weather Research Forecast–Community Multiscale Air Quality (WRF-CMAQ) modeling system. The results revealed that the aerosol caused a significant decrease in ground-level photolysis rates of NO2 and ozone by 22% and 29%, respectively, when haze pollution was severe. The actinic radiative effect of aerosol caused an average reduction of more than 10 ppb in ground level ozone concentration in the PRD, with the core area experiencing a maximum reduction of 30 ppb. Additionally, the core area witnessed a maximum reduction of 2 ppb in peroxyacetyl nitrate (PAN) and 2 μg/m3 in PM2.5. Due to reduced photolysis rates, nitrogen oxides increased by more than 5 ppb on average, reaching up to 10 ppb, while volatile organic compounds (VOCs) increased by a maximum of 10 ppb in the core region. Temporally, the aerosol actinic radiative effect was most pronounced in the morning and less significant in the afternoon.

Machine Learning Classification of Air Quality Monitoring Stations to Achieve Ambient NO2 Objectives Using Emission Scenarios and Chemical Transport Model

Exceeding the latest Canadian Ambient Air Quality Standards (CAAQS) for NO2 concentration in Canadian cities motivates discussions on new policies and directives. Advanced air quality modeling and machine learning clustering algorithms integrating the Weather Research Forecast (WRF) and Community Multiscale Air Quality (CMAQ) models were used to classify air quality monitoring (AQM) stations based on pollution concentration modeling results. The sensitivity of ambient NO2 to the primary anthropogenic emission sources was investigated in Alberta, Canada. Emissions from two main sources, upstream oil and gas (UOG) and transportation sources, have been identified as the reason Alberta fails to meet the newly adopted NO2 CAAQS objectives. The air quality model was validated with ground-level observation data and the atmospheric model accurately replicates spatiotemporal NO2 variations. Despite contributing 62% of Alberta’s total NOx emissions, UOG influences ambient NO2 concentrations modestly in urban areas (<10%) but significantly affects rural regions. In contrast, transportation emission sources, responsible for 23% of NOx emissions, dominate ambient NO2 levels (up to 63%) in large cities. The discrepancy of emission contribution and ground-level concentrations, obtained from the chemical transport model, was resolved using a k-prototypes clustering algorithm to propose a new approach for categorizing AQM stations which led to improving the conventional classifications. The new approach considered the sensitivity of NO2 to emission reduction scenarios and provided an improved classification to be used for emission reduction interventions. Based on the updated classification, one set of AQM stations clearly showed sensitivity to NO2 emission reduction in the transportation sector despite their lower contributions to overall emissions. These stations were categorized as population exposure stations in large cities.

Determination of major drive of ozone formation and improvement of O3 prediction in typical North China Plain based on interpretable random forest model

O3 pollution in China has become prominent in recent years, and it has become one of the most challenging issues in air pollution control. We used data on atmospheric pollutants and meteorology from 2019 to 2021 to build an interpretable random forest (RF) model, applying this model to predict O3 concentration in 2022 in five cities in the Southwest North China Plain. The model was also used to identify and explain the influence of various factors on O3 formation. The correlation coefficient R2 between the predicted O3 concentration and observed O3 concentration was 0.82, the MAE was 15.15 μg/m3, and the RMSE was 20.29 μg/m3, indicating that the model can effectively predict O3 concentration in the studying area. The results of correlation analysis, feature importance, and the driving factor analysis from SHapley Additive exPlanations (SHAP) model indicated that temperature (T), NO2, and relative humidity (RH) are the top three features affecting O3 prediction, while the weights of wind speed and wind direction were relatively low. Thus, O3 in the southwestern North China Plain may mainly come from the formation of local photochemical activities. The dominant factors behind O3 also varied in different seasons. In spring and autumn, O3 pollution is more likely to occur under high NO2 concentration and high-temperature conditions, while in summer, it is more likely to occur under high-temperature and precipitation-free weather. In winter, NO2 is the dominant factor in O3 formation. Finally, the interpretable RF model is used to predict future O3 concentration based on features provided by Community Multiscale Air Quality (CMAQ) and Weather Research & Forecast (WRF) model, and the simulation performance of CMAQ on O3 concentration is enhanced to a certain extent, improving the prediction of future O3 pollution situations and guiding pollution control.

Influence of convective parameterization on the simulation of tropical cyclones over the South West Indian Ocean: A case study of tropical cyclone Idai (2019)

Proper configuration of the physical parameterization scheme is important for accurate outcomes of mesoscale weather forecasting model. This study evaluates the performance of four distinct cumulus parameterization schemes: the Kain-Fritsch (KF), Grell 3D ensemble (GR3D), Modified Tiedtke (MTIEDKE) and Modified Kain-Fritsch (MKF) schemes, in simulating Tropical Cyclone (TC) Idai (2019) in the Southern Indian Ocean. Utilizing the Weather Research Forecasting (WRF) model and TC best-track and satellite precipitation datasets, the research assesses the models' efficacy in replicating Idai's observed track and intensity. Results indicate that the KF, MTIEDKE, and MKF schemes reasonably capture Idai's southwestward track, with MKF demonstrating superior accuracy in reproducing observed intensity evolution. Conversely, the GR3D simulation exhibits a northwestward deviation, leading to notable track and intensity errors. Further investigations suggest that the smaller track and intensity differences in the MKF simulation are linked to the more accurate simulation of rainfall in both the surrounding environment and the TC inner-core region. The two factors exert strong influences on the large-scale steering flow and TC secondary circulation, respectively. The findings underscore the importance of selecting appropriate convective parameterization schemes for precise tropical cyclone simulations in the Southern Indian Ocean. This research contributes valuable insights into the intricate interaction between convective processes and atmospheric dynamics, offering implications for enhancing tropical cyclone prediction models in the region.

Dynamical and Microphysical Aspects of Two Distinct Precipitation Systems in the Himalayas With 206.5 MHz Radar and WRF Model

AbstractThe dynamical and microphysical aspects of two different precipitating systems have been investigated using the ARIES Stratosphere‐Troposphere Radar (ASTRad) facility and further substantiated by Weather Research Forecasting (WRF) model over Manora Peak. The first event (Case‐I) is associated with the southwest Indian summer monsoon that occurred on 4 August 2020, with a vertical extension of 10–12 km and leads to liquid phase precipitation. The second event (Case‐II) linked to the winter western disturbance occurred on 5 February 2021. This precipitating system was developed with a vertical extension of 6–7 km, resulting in both liquid and solid phase precipitation. Such distinct vertical extension of the systems is found to be associated with the thermodynamical conditions and prevailed large‐scale circulations. By analyzing the vertical structure of these systems using three Doppler moments estimated from the ASTRad (equivalent Reflectivity dBZe, Doppler velocity, and Spectral width), maximum dBZe (∼60 dB) is observed in Case‐II, while higher spectral width (&gt;2 m s−1) is associated to Case‐I. The microphysical processes assessed by the WRF model pointed out that Case‐I involved snow accretion on supercooled droplets, leading to graupel and raindrop formation, while in Case–II, solid and liquid precipitation resulted from ice processes, including accretion or autoconversion. These findings highlight the significance of integrating radar and modeling data to understand the dynamical and microphysical evolution of precipitation under the influence of orography in the Himalayan region.

Multi-scale simulation of typhoon wind field at building scale utilizing mesoscale model with nested large eddy simulation

Based on the mesoscale WRF (Weather Research Forecast) and LES (Large Eddy Simulation), an integrated numerical simulation framework aiming to resolve typhoon wind fields around urban building blocks has been developed. The holistic multi-scale simulation spans four scales: macroscale (∼1000 km), mesoscale (∼100 km), microscale (∼1 km), and building scale (0.1–100m). By interactively sharing the meteorological data among different scales, this simulation framework addressed the challenge of downscaling typhoon effects in urban environments. Typhoon weather reanalysis and urban wind field simulation were conducted by the WRF model with nested computational domains incorporating high-resolution topography data. For the downscaled LES model with urban buildings, the wind profiles produced by the WRF model were used at the interface with provided turbulent fluctuations in the flow. K11 building is a 270-m-tall building located in a densely built-up area of Hong Kong. Wind velocity and pressure fields surrounding the K11 building in Hong Kong during Typhoon Kammuri were obtained by the proposed framework, and the simulation results were validated by comparing with the meteorological data and full-scale measurements at the top of the K11 building. The interactions of the wind fields and building clusters resulting in vortex shedding, separation, and channeling effects have been resolved at high resolutions. Probabilistic distributions and higher-order statistics of wind pressure, i.e., skewness and kurtosis, were presented to highlight non-Gaussian characteristics of the simulated local wind pressure on the K11 building. Based on the simulated wind pressure, wind-induced vibration of the K11 building during the typhoon was analyzed and compared to the measurements.

INVESTIGATION OF THE EFFECT OF SPACE VARYING WIND INPUT ON HYDRODYNAMIC MODELING IN MANILA BAY AND LAGUNA LAKE

Abstract. Surface water currents and waves are largely driven by wind forcing by inducing shear stress to the water surface. The circulations formed carry with it various materials and pollutants all over a large body of water like the Manila Bay and Laguna Lake. Considering the spatial and temporal variability of wind over the Bay and the Lake is essential to model the hydrodynamics of water more accurately. This study simulated bay hydrodynamics using high resolution space- and time-varying wind data from Weather Research Forecasting (WRF) as input and compared the results with using uniform-directed wind. The downscaled winds from WRF demonstrated patterns in agreement with the country’s seasonal monsoon wind dynamics. Its effect on the bay hydrodynamics is more pronounced during the dry season as it changes the flow direction of water at the bay mouth. On the other hand, the hydrodynamic difference in circulation patterns for the lake was accentuated with a more defined clockwise gyre circulation during the wet season where prevailing winds are stronger. Thus, it is imperative to incorporate spatially varying wind input to accurately characterize the bay water movement. Results also showed that global weather inputs can be used in areas with scarce monitoring stations as long as they are validated.

Assessing the role of cold front passage and synoptic patterns on air pollution in the Korean Peninsula

Various numerical experiments using WRF (Weather Research & Forecasting Model) and CMAQ (Community Multiscale Air Quality Modeling System) were performed to analyze the phenomenon of rapidly high concentration PM2.5 after the passage of a cold front in an area with limited local emissions. The episode period was from January 14 to 23, 2018, and analysis was conducted by dividing it into two stages according to the characteristics of changes in PM2.5 concentrations during the period. Through the analysis of observational data during the episode period, we confirmed meteorological impacts (decrease in temperature, increase in wind speed and relative humidity) and an increase in air pollution (PM10 and PM2.5) attributed to the passage of a cold front. Using CMAQ's IPR (Integrated Process Rate) analysis, the contribution of the horizontal advection process was observed in transporting PM2.5 to Gangneung at higher altitudes, and the PM2.5 concentrations at the surface increased because the vertical advection process was influenced by the terrain. Notably, in Stage 2 (64 μg·m−3), a higher contribution of the vertical advection process compared to Stage 1 (35 μg·m−3) was observed, which is attributed to the differences in synoptic patterns following the passage of the cold front. During Stage 2, following the cold front, atmospheric stability (dominance of high-pressure system) led to air subsidence and the presence of a temperature inversion layer, creating favorable meteorological conditions for the accumulation of air pollutants. This study offers the mechanisms of air pollution over the Korean Peninsula under non-stationary meteorological conditions, particularly in relation to the passage of the cold front (low-pressure system). Notably, the influence of a cold front can vary according to the synoptic patterns that develop following its passage.

Sensitivity analysis of the WRF simulated planetary boundary layer height to synoptic conditions over eastern China

Accurately representing the planetary boundary layer height (PBLH) and its thermal/dynamic structures within is essential for simulating meteorological and environmental conditions in numerical models. However, how accurately model reproduces PBL processes under diverse synoptic conditions remains under-explored. Using the Weather Research Forecasting model that configured with the widely-used YSU boundary layer scheme, the present study conducted a nearly three-year PBL simulation to examine its sensitivity to synoptic conditions in eastern China. Nine types of synoptic patterns were identified by combining T-mode principal component analysis with K-means clustering. The simulated PBLH varied significantly with synoptic patterns, with the PBLH magnitude over land following the order: quasi-summer monsoon > transition > quasi-winter monsoon. In terms of simulation biases, the model presented an overall overestimation of PBLH over most land and an underestimation over the ocean compared with two spaceborne lidars (Cloud Aerosol Transport System; Cloud Aerosol Lidar with Orthogonal Polarization) and radiosondes; The overestimation and underestimation were amplified under quasi-summer monsoon types. As the synoptic patterns were characterized by different levels of pressure, wind, temperature, and humidity. This study further explored the relationship between model biases and meteorological factors. The results showed that the simulated PBLH biases in unstable conditions were linearly dependent on thermal factors, and showed less relationship with dynamical factors. The negative PBLH biases under warm, dry, and unstable conditions can be attributed to the model's underestimation of virtual potential temperature at lowest model level. While the positive PBLH biases under cold, wet, and stable conditions were caused by warm bias of virtual potential temperature profile at lower levels.

Predicting infrasonic wind noise levels using local topographic features

This presentation describes a method for predicting RMS wind noise levels within a user-specified infrasonic frequency band (e.g., 1–10 Hz) using local topographic features. This capability is especially valuable when performing site selection for infrasound sensor array deployment. The method is based on building models using measured infrasound data from known sensor site locations and the local topographic features corresponding to the site. The presented results correspond to using features such relative terrain elevation and relative vegetation height obtained from publicly available sources. Mean wind speed and direction are also included as predictive parameters and these can be from data measured at the infrasound sensor site or from estimates obtained from a tool such as the Weather Research Forecast (WRF) model as was utilized for this investigation. A novel feature of the approach is that the terrain specification is not uniquely site specific but also depends on wind direction. Essentially, the topographic features are specified relative to wind direction and not in absolute coordinates. This enables a much richer set of samples from which to build the predictive models. While many options are potentially available for model building, this work focused on the use of multiple-layer artificial neural networks as a basis for regression. The results presented correspond to data from several infrasound sensor sites in the U.S Array Project.