Weather Research And Forecasting Research Articles

Influence of Assimilation of NEXRAD-Derived 2D Inner-Core Structure Data from Single Radar on Numerical Simulations of Hurricane Charley (2004) near Its Landfall

This study presents the first research that assimilates the ground-based NEXRAD observations-derived two-dimensional (2D), azimuthally averaged radar radial velocity and reflectivity within 60 km of radius from the hurricane center to examine their influence on the analysis and prediction of a hurricane near and after its landfall. The mesoscale community Weather Research and Forecasting (WRF) model and its four-dimensional variational (4D-VAR) data assimilation system are utilized to conduct data assimilation experiments for Hurricane Charley (2004). Results show that assimilation of the radar inner-core data leads to better forecasts of hurricane tracks, intensity, and precipitation. The improved forecast outcomes imply that the changes in dynamical, thermal, and moisture structures from data assimilations made more reasonable conditions for the hurricane development near and after its landfall. Overall results indicate that the assimilation of the radar-derived 2D inner-core structure could be a feasible way to utilize the radar data for improved hurricane prediction.

An Assessment of the Coupled Weather Research and Forecasting Hydrological Model on Streamflow Simulations over the Source Region of the Yellow River

The Source Region of the Yellow River (SRYR), renowned as the “Water Tower of the Yellow River”, serves as an important water conservation domain in the upper reaches of the Yellow River, significantly influencing water resources within the basin. Based on the Weather Research and Forecasting (WRF) Model Hydrological modeling system (WRF-Hydro), the key variables of the atmosphere–land–hydrology coupling processes over the SRYR during the 2013 rainy season are analyzed. The investigation involves a comparative analysis between the coupled WRF-Hydro and the standalone WRF simulations, focusing on the hydrological response to the atmosphere. The results reveal the WRF-Hydro model’s proficiency in depicting streamflow variations over the SRYR, yielding Nash Efficiency Coefficient (NSE) values of 0.44 and 0.61 during the calibration and validation periods, respectively. Compared to the standalone WRF simulations, the coupled WRF-Hydro model demonstrates enhanced performance in soil heat flux simulations, reducing the Root Mean Square Error (RMSE) of surface soil temperature by 0.96 K and of soil moisture by 0.01 m3/m3. Furthermore, the coupled model adeptly captures the streamflow variation characteristics with an NSE of 0.33. This underscores the significant potential of the coupled WRF-Hydro model for describing atmosphere–land–hydrology coupling processes in regions characterized by cold climates and intricate topography.

Heat stress resilience assessment of urban form from physical space dimension: A case study of Guangdong-Hong Kong-Macao Greater Bay Area

Improving urban resilience is crucial to counteract rising high-temperature disasters. This study constructed a framework for a process-based heat stress resilience (HSR_phy) assessment from the urban physical space dimension. Based on the universal thermal climate index (UTCI), the Weather Research and Forecasting (WRF) model, and the Local Climate Zone (LCZ) map generated through WUDAPT, an HSR_phy evaluation method with high spatiotemporal accuracy is proposed. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was studied for heat stress resilience patterns in July 2021. Boosted regression tree (BRT) models analyzed LCZ landscape patterns' impacts on HSR_phy. The results indicated that HSR_phy during the daytime was significantly lower than that at night. The HSR_phy of GBA showed the characteristics of low inland and high periphery. Significant differences in HSR_phy existed among most LCZs. LCZA, LCZC, and LCZ9 showed strong HSR_phy, whereas LCZ2, LCZ8, and LCZ7 were opposite. At the 1500 m scale, the Percentage of Landscape (PLAND), Landscape Shape Index (LSI), and Shannon's Diversity Index (SHDI) of LCZ4, LCZ5, and LCZ6 were the key factors affecting HSR_phy. Our research demonstrates how to quantify heat stress resilience and impact factors of urban physical spaces to provide a basis for developing climate-sensitive urban design strategies.

Does a Scale‐Aware Convective Parameterization Scheme Improve the Simulation of Heavy Rainfall Events?

AbstractPrecipitation predictability using the non‐scale‐aware and scale‐aware convective parameterization schemes (CPSs) was investigated to assess the necessity of the CPSs within the gray‐zone. This study evaluates the performance of the Weather Research and Forecasting (WRF) model's CPS for 135 heavy rainfall events (HREs) over the Korean Peninsula for 10 years (i.e., 2011–2020). We tested the Kain–Fritsch (KF) scheme (non‐scale‐aware) and Multi‐scale Kain–Fritsch (MSKF) scheme (scale‐aware) in the WRF model. The MSKF scheme shows an overall improved performance of precipitation simulation compared to the KF scheme, but the precipitation forecast performance of CPS depends on the characteristics of HREs. When the HREs are characterized by synoptic‐scale atmospheric conditions with strong winds and large‐scale water vapor transport, the forecast performance of both CPSs is similar because a cloud microphysics scheme can explicitly resolve most of the precipitation. However, in the case of HREs with weak synoptic forcing conditions (e.g., moisture transport and winds) related to the localized and meso‐scale HREs, the MSKF scheme can improve overall simulated precipitation by increasing grid‐scale precipitation and reducing the overestimation of subgrid‐scale precipitation simulated in the KF scheme. Therefore, using the scale‐aware CPS in the gray‐zone can provide more accurate precipitation forecasts regardless of the environmental condition of the HREs.

Investigating the intensity of urban heat island and the impacts of local climate using verified WRF data: A case study of Rasht, Northern Iran

Due to the complicated and uncertain dynamics of the urban heat island (UHI) in humid subtropical areas, the diurnal, seasonal and long-term variations of the UHI were investigated in Rasht, Iran. Besides, the study utilizes the heat and moisture transfer concepts to analyze the impacts of daytime rainfall duration on UHI. Meteorological data at five virtual stations was acquired by verified Weather Research and Forecasting (WRF) model. Overall, the nighttime UHI peaks in summer (3.4 °C) and is lowest in fall (1.4 °C) over 2017–2019. Moreover, the maximum and minimum daytime UHI occurred in winter (1.2 °C) and summer (0.6 °C), respectively. In hot seasons, an increase in daytime precipitation duration initially leads to an increase in daytime UHI, followed by a subsequent decrease. Conversely, in cold seasons, daytime UHI shows a decreasing trend regardless of rain conditions. Additionally, extended daytime precipitation attenuates nocturnal UHI in hot seasons, while no significant influence is observed in cold seasons. An increase in midday cloud cover during hot seasons reduces the nighttime UHI. The nighttime UHI is inversely proportional to the third root of wind speed, in low cloudiness conditions of hot seasons. Furthermore, the Ladsat-5/7/8 imageries revealed an increasing trend in the surface UHI (SUHI) during hot seasons over the past two decades, while SUHI in cold seasons remains insignificant. The article reports the most influential parameters on daytime, nighttime and seasonal UHI and the best mitigation policies for the humid subtropical climate, particularly in hot seasons.

Intercomparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1–CMAQ v5.3.1, WRF–Chem v4.1.1, and WRF v3.7.1–CHIMERE v2020r1) in eastern China

Abstract. Two-way coupled meteorology and air quality models, which account for aerosol–radiation–cloud interactions, have been employed to simulate meteorology and air quality more realistically. Although numerous related studies have been conducted, none have compared the performances of multiple two-way coupled models in simulating meteorology and air quality over eastern China. Thus, we systematically evaluated annual and seasonal meteorological and air quality variables simulated by three open-source, widely utilized two-way coupled models (Weather Research and Forecasting (WRF)–Community Multiscale Air Quality (WRF–CMAQ), WRF coupled with chemistry (WRF–Chem), and WRF coupled with a regional chemistry-transport model named CHIMERE (WRF–CHIMERE)) by validating their results with surface and satellite observations for eastern China in 2017. Although we have made every effort to evaluate these three coupled models by using configurations that are as consistent as possible, there are still unavoidable differences between them in their treatments of physical and chemical processes. Our thorough evaluations revealed that all three two-way coupled models captured the annual and seasonal spatiotemporal characteristics of meteorology and air quality reasonably well. Notably, the role of the aerosol–cloud interaction (ACI) in improving the models' performances was limited compared to that of the aerosol–radiation interaction (ARI). The sources of uncertainties and bias in the different ACI schemes in the two-way coupled models were identified. With sufficient computational resources, these models can provide more accurate air quality forecasting to support atmospheric environment management and deliver timely warnings of heavy air pollution events. Finally, we propose potential improvements to two-way coupled models for future research.

Impact of cumulus parameterization schemes on summer extreme precipitation simulation in the Yellow River Basin: the 2018 case

ABSTRACT This study uses the Weather Research and Forecasting (WRF) model with five different cumulus parameterization schemes (CPSs) at a resolution of 30 km to simulate the summer (June, July, and August) extreme precipitation event in the Yellow River Basin (YRB) during 2018. The goal of this study is to investigate the sensitivity of extreme precipitation simulation in the YRB during the summer of 2018 to CPSs in the WRF model. The results show that all five CPSs were capable of approximately simulating the direction of the rain bands in the YRB during the summer of 2018, but the simulation results of all CPSs tended to overestimate the value of precipitation amount. Upon further evaluation using seven different methods, it was found that the Betts–Miller–Janjic scheme provided the best simulation of this event. The complex orography of the YRB has a significant influence on moisture transport. The WRF model may have overestimated the moisture flux, which could have contributed to the overestimation of precipitation. The summer extreme precipitation event in the YRB during 2018 may have been influenced by an influx of excessive moisture from the western boundary.

Sensitivity analysis of cumulus and microphysics schemes in the WRF model in simulating Extreme Rainfall Events over the hilly terrain of Nagaland

A comprehensive study is carried out to investigate the sensitivity of various combinations between cumulus (CU) and microphysics (MP) schemes using the Weather Research and Forecasting (WRF) model for simulating Extreme Rainfall Events (EREs) over the hilly terrain of Nagaland (25.1 oN – 27.2 oN; 93.2 oE– 95.3 °E). The study explores fifty-four distinct simulation combinations resulting from six CU and nine MP schemes. The simulated rainfall is compared with ground-based rain gauge network and Global Precipitation Measurement (GPM) satellite observations. The investigation focuses primarily on finding the optimal CU-MP scheme combination based on spatiotemporal precision achieved by the identified schemes. The C14 (New Simplified Arakawa Schubert) cumulus scheme is found to be the optimum individual CU scheme. Similarly, the M14 (WDM5) microphysics scheme performs as the optimum individual MP scheme. The study highlights the significance of evaluating the performance of combined schemes over individual ones. Among the fifty-four combinations, sensitivity analysis identified six top-performing combinations, out of which the C11-M8 (Multiscale Kain Fritsch - New Thompson) combination stands out to be optimum with a robust correlation coefficient (CC) of 0.75, lower standard deviation (STD) of 0.76 mm, and RMSD of 0.51 mm. A variation in lead/lag time is found to vary from (−) 3.2 to (+) 5.0 h, with a median value of (+) 1.3 h, suggesting that, on average, the model tends to predict the occurrence of peak rainfall events slightly earlier than observed one. The absolute difference in latitudes (longitudes) of peak rainfall variation ranges from 0.42o - 0.8o (0 o - 0.51o) with a median value of 0.66o (0.37o), indicating that the model could better represent the longitudinal peak rainfall positions with notable accuracy. The spatial correlation coefficient (SCC) ranges between 0.31 and 0.68 with a median value of 0.50. From the overall analysis, it is also found that double moment MP schemes (New Thompson, Morrison (2 M), WDM5, WDM6) consistently perform better than the single moment MP schemes (Kessler, Lin et al., WSM3, WSM5, WSM6), whether in combination with CU schemes or individually. Spatiotemporal variations observed across different cases underscore the pivotal role of accurate topography representation in complex terrains, emphasizing the significance of terrain data in shaping rainfall patterns.

Wind Speed Ramp Rate Predictions Using Wind Farm SCADA Data Assimilation and a WRF Ensemble

This study presents a novel method for improving wind power ramp events forecasts up to six hours ahead by utilizing data assimilation of SCADA measurements with an ensemble of Weather Research and Forecasting (WRF) models estimates. Leveraging data from nine wind farms in France and Belgium, the approach aims to improve WRF model predictions for wind speed and ramp event timing. The methodology employs grid and observational nudging techniques, enhancing model accuracy by incorporating real-time observational data. Key findings demonstrate that nudging significantly reduces Mean Absolute Error (MAE), decreases the Time Distortion Index (TDI), and increases the Probability of Detection (POD) of ramp events. Nudged ensemble members outperform non-nudged counterparts, exhibiting better accuracy in identifying true ramp events and reducing false alarms. MAE, TDI and POD improvements are as high as 3.7%, 8.5% and 37%, respectively. The study also explores the benefits of an ensemble approach, highlighting improved accuracy in predicting ramp rate magnitudes and providing valuable insights for grid stability management. This research contributes to wind power forecasting, showcasing the importance of integrating SCADA data into predictive models.

Comprehensive onshore wind energy assessment in Malawi based on the WRF downscaling with ERA5 reanalysis data, optimal site selection, and energy production

Malawi, despite its minimal CO2 emissions, faces huge climate impacts and a low electrification rate. Recognizing these challenges, this study presents a comprehensive and nationwide assessment of Malawi's wind energy potential, leveraging the high-resolution capabilities of the Weather Research and Forecasting (WRF) model. Unlike most site-specific studies, this study covers the entire country, factoring in different turbine hub heights. Through sensitivity analysis, the optimal configuration for the WRF model in Malawi was determined and ERA5 reanalysis data was used for initial and boundary conditions. The study's findings were validated using MERRA-2 reanalysis and actual observational datasets. A combination of GIS and the fuzzy analytic hierarchy process (AHP) was employed, integrating key factors like land use and topographical complexity to determine optimal wind farm sites. Notably, the wind suitability map uncovered significant potential for wind farm development. Furthermore, in the evaluation of a 50 MW wind farm, 80 m hub heights consistently outperformed 100 m elevations, both technically and economically. An 80 m wind farm at Site A can potentially power up to 32,931 urban and 84,475 rural households. Economically, 80 m hub heights showcased lower levelized cost of electricity (LCOE) values and faster payback periods than 100 m ones. Furthermore, these proposed wind projects can markedly reduce CO2 emissions, positioning Malawi in tandem with global sustainability aspirations. Overall, this study provides a key foundation for Malawi's renewable energy strategies, highlighting the potential for improved electrification and better climate change mitigation outcomes.

Hyper-local source strength retrieval and apportionment of black carbon in an urban area

Neighborhood-scale air pollution hotspots have recently been identified through detailed field campaigns, including the 100x100 Black Carbon Experiment which took place in West Oakland, CA, in 2017. Here, high-resolution nested atmospheric simulations are used together with a Bayesian inversion framework to estimate source apportionment at the hyper-local scale for a neighborhood in West Oakland. Forward simulations are performed with the Weather Research and Forecasting (WRF) model using 6 grid nests from 11.25 km to 2 m horizontal resolution. On the finest grid, building geometries are resolved using the immersed boundary method. Seven point sources and four line sources at known locations are included in the forward simulation for two 1-h periods during the 2017 field campaign. Data from 12 black carbon sensors are used to perform source inversion using a Markov Chain Monte Carlo approach, which provides a probability distribution for each of the 11 source strengths. From this, a most-likely plume can be created using the peaks of the distributions, and source apportionment can be estimated for each sensor. In addition, a composite plume can be constructed to indicate 90% confidence that concentrations are above or below a specified value. With this probabilistic analysis, it is possible to determine that more than half of the neighborhood has black carbon concentrations of higher than 0.4 μg/m3, with some areas higher than 3 μg/m3 during the time periods studied.

On the Sensitivity of Large-Eddy Simulations of the Atmospheric Boundary Layer Coupled with Realistic Large-Scale Dynamics

Abstract We present a new ensemble of 36 numerical experiments aimed at comprehensively gauging the sensitivity of nested large-eddy simulations (LES) driven by large-scale dynamics. Specifically, we explore 36 multiscale configurations of the Weather Research and Forecasting (WRF) Model to simulate the boundary layer flow over the complex topography at the Perdigão field site, with five nested domains discretized at horizontal resolutions ranging from 11.25 km to 30 m. Each ensemble member has a unique combination of the following input factors: (i) large-scale initial and boundary conditions, (ii) subgrid turbulence modeling in the gray zone of turbulence, (iii) subgrid-scale (SGS) models in LES, and (iv) topography and land-cover datasets. We probe their relative importance for LES calculations of velocity, temperature, and moisture fields. Variance decomposition analysis unravels large sensitivities to topography and land-use datasets and very weak sensitivity to the LES SGS model. Discrepancies within ensemble members can be as large as 2.5 m s−1 for the time-averaged near-surface wind speed on the ridge and as large as 10 m s−1 without time averaging. At specific time points, a large fraction of this sensitivity can be explained by the different turbulence models in the gray zone domains. We implement a horizontal momentum and moisture budget routine in WRF to further elucidate the mechanisms behind the observed sensitivity, paving the way for an increased understanding of the tangible effects of the gray zone of turbulence problem. Significance Statement Several science and engineering applications, including wind turbine siting and operations, weather prediction, and downscaling of climate projections, call for high-resolution numerical simulations of the lowest part of the atmosphere. Recent studies have highlighted that such high-resolution simulations, coupled with large-scale models, are challenging and require several important assumptions. With a new set of numerical experiments, we evaluate and compare the significance of different assumptions and outstanding challenges in multiscale modeling (i.e., coupling large-scale models and high-resolution atmospheric simulations). The ultimate goal of this analysis is to put each individual assumption into the wider perspective of a realistic problem and quantify its relative importance compared to other important modeling choices.

Improving Weather Forecasts for Sailing Events Using a Combination of a Numerical Forecast Model and Machine Learning Postprocessing

Accurate predictions of wind and other weather phenomena are essential for making informed strategic and tactical decisions in sailing. Sailors worldwide utilize current state-of-the-art forecasts, yet such forecasts are often insufficient because they do not offer the high temporal and geographic resolution required by sailors. This paper examines wind forecasting in competitive sailing and demonstrates that traditional wind forecasts can be improved for sailing events by using an integration of traditional numerical modeling and machine learning (ML) methods. Our primary objective is to provide practical and more precise wind forecasts that will give sailors a competitive edge. As a case study, we demonstrate the capabilities of our proposed methods to improve wind forecasting at Lake Kinneret, a popular sailing site. The lake wind pattern is highly influenced by the area’s topographic features and is characterized by unique local and mesoscale phenomena at different times of the day. In this research, we simulate the Kinneret wind during the summers of 2015–2021 in up to one-kilometer resolution using the Weather Research and Forecasting (WRF) atmospheric model. The results are used as input for convolutional neural network (CNN) and multilayer perceptron (MLP) ML models to postprocess and improve the WRF model accuracy. These advanced ML models are trained using training datasets based on the WRF data as well as real data measured by the meteorological service, and subsequently, a validation process of the trained ML model is performed on unseen datasets against site-specific meteorological service observations. Through our experimental analysis, we demonstrate the limitations of the WRF model. It uncovers notable biases in wind direction and velocity, particularly a persistent northern bias in direction and an overestimation of wind strength. Despite its inherent limitations, this study demonstrates that the integration of ML models can potentially improve wind forecasting due to the remarkable prediction accuracy rate achieved by the CNN model, surpassing 95%, while achieving partial success for the MLP model. Furthermore, a successful CNN-based preliminary forecast was effectively generated, suggesting its potential contribution to the future development of a user-friendly tool for sailors.

Improving the lightning forecast with the WRF model and lightning data assimilation: Results of a two-seasons numerical experiment over Italy

We show, for the first time over Italy and over part of the central Mediterranean Basin, the impact of lightning data assimilation (LDA) on the strokes forecast for a long period. We use the Weather Research and Forecasting (WRF) model coupled with the Dynamic Lightning Scheme (DLS) at convection allowing horizontal resolution (3 km). We carried out a two-seasons experiment (summer 2020 and fall 2021) providing the forecast of lightning and precipitation for the next 6 h (nowcasting), considering two sub-periods (0-3 h and 3-6 h) for verification. The LDA is done through a nudging scheme that increases the water vapor mass in the mixed-phase region based on observed flash density rates and simulated graupel mixing ratio. No changes are made to the model run if spurious convection is predicted or no flashes are observed. LDA can trigger convection missed by the control forecast, without LDA, and/or can redistribute the strokes predicted to be more consistent with observations. LDA has a positive impact on strokes forecast, improving correct forecasts and reducing false alarms. This improvement is however confined to the first three-hours of forecast with negligible to negative impact for longer time ranges, in line with other studies. The improvement pattern is different in summer and fall, depending on the convection development.The analysis of the Fraction Skill Score shows the usefulness of the forecast for practical purposes, considering the current areas used by the Civil Protection Department to issue meteorological alerts for intense convective events over Italy. Finally, it is shown that the forecast at the short-range (0−3h) using LDA can improve the strokes forecast issued on the previous day, not using LDA, and the methodology of this paper can be applied to issue warnings and alerts as the storm is approaching.A brief examination of rainfall forecast shows positive impact of LDA at the short-range (0-3 h), with neutral impact for longer time ranges. The different impact of LDA on the strokes and precipitation forecasts is also highlighted.

Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling

Spring consecutive rainfall events (CREs) are key triggers of geological hazards in the Three Gorges Reservoir area (TGR), China. However, previous projections of CREs based on the direct outputs of global climate models (GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF (Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6 (Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6, indicating larger uncertainties in the CREs projected by MIROC6.

Towards urban wind utilization: The spatial characteristics of wind energy in urban areas

Urban wind energy is gaining increasing recognition for its environmentally friendly nature, particularly in the context of energy shortages. Predicting wind speed in urban environments is challenging due to the varying roughness and drag caused by obstacles on the ground. The complexity of urban morphology significantly disrupts the wind field and hampers the utilization of wind energy. This study aimed to investigate the diverse impacts of highly heterogeneous urban environment on the urban wind energy by innovatively establishing spatially varying power law wind profiles. Firstly, long-term LIDAR observations were conducted to measure the vertical wind profile in the study area. Then, a high-resolution LCZ map was created and integrated into the Weather Research and Forecasting (WRF) model to reproduce the urban wind field, which was validated using the observational data. Finally, the spatial characteristics of the urban wind field and wind energy were analyzed based on 3 representative points and the urban LCZ categories. The results show that the average power law exponents of each LCZ classification ranges from 0.29 to 0.75, with the average power law exponent of the high-volume building area being larger than 0.6. The highest wind power density in the study area can reach 343.3W/m2 at 200m height. Further, significant negative correlation coefficients were found between wind power density and building height, as well as building density, with values of approximately −0.6 and −0.35, respectively. Overall, the high-volume-ratio built-up areas with higher roughness had a notable impact on the wind speed by increasing the power law exponents. These spatially varying wind profiles are expected to provide technical support for the utilization of urban wind energy.

A study of evaporation duct characteristics in the South China Sea during the Winter of 2017

The evaporation duct (ED) near the sea surface is the most frequent type of duct that traps and redirects electromagnetic waves over longer or shorter distances. This study uses radiosonde data from the South China Sea Two-Island Monsoon Experiment (SCSTIMX) in 2017 and the Weather Research and Forecasting (WRF) model to study the ED and ED height (EDH). We enhanced the vertical resolution to 26 layers in the lowest 134 m. The simulations indicate that EDs were prevalent throughout the South China Sea (SCS) area in winter 2017, where the prevailing surface winds are strong northeasterly. The EDH exhibits diurnal variations and significant variability across the SCS. The WRF simulations of ED and EDH generally agree with observations of the trajectory of Taiwan-O.R.1. Our results indicate that the WRF model can simulate the pattern of ED and EDH over SCS. Results reveal that Yonsei University (YSU) boundary layer (BL) parameterization more accurately simulates the vertical temperature and humidity variations in the lower atmosphere, with stronger winds and higher EDH in most areas. The YSU and the Medium Range Forecast Model (MRF) BL schemes show similar ED patterns to the Taiwan-O.R.1 SCS radiosonde observations but consistently underestimate the EDH. While strong surface winds in the winter SCS may contribute to a higher EDH, the strong surface wind-induced vertical mixing also modifies sea surface air temperature, relative humidity, and vapor pressure, affecting the vertical refractivity. Our results indicate that sea surface air temperature is a more reliable factor affecting EDH in the simulations.

Evaluating Ice Phase Microphysics in the Simulation of a Snowstorm Over Northern China

AbstractThe complexity of ice particles in the atmosphere makes it difficult to model microphysical growth processes accurately. In this study, we simulated a snowfall case over Northern China Plain using two different microphysics schemes, that is, Thompson and Morrison schemes, in the Advanced Research WRF (Weather Research and Forecasting) model. Both schemes are able to reproduce the event, albeit with a slightly weaker precipitation compared with the surface observation. However, the radar reflectivity factor in Morrison simulation is higher than the radar observation to ∼10 dBZ. Further analysis reveals that such stronger radar reflectivity in the Morrison simulation might be caused by larger collection efficiency, which would lead to more active self‐aggregation process in prediction of snow number concentration and then larger snow particle size. Sensitivity tests show that using an alternative formula of collection efficiency produces smaller radar reflectivity that is in better agreement with observations. This study highlights the accurate representation of self‐aggregation process and underscores the needs of further improvement of ice microphysics schemes for the better snowfall simulations.

A Numerical Study of Clear-Air Turbulence over North China on 6 June 2017

On 6 June 2017, four severe clear-air turbulence (CAT) events were observed over northern China within 3 h. These events mainly occurred at altitudes between 8.1 and 9.5 km. The characteristics and possible mechanisms of the CAT events in the different regions are investigated here using the weather research and forecasting (WRF) model. The simulated wind and temperature fields in a 27 km coarse domain were found to be in good agreement with those of the ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) and the observed soundings of operational radiosondes over northern China. In terms of synoptic features, the region where the turbulence occurred is characterized by a southwest–northeast upper-level jet stream. The upper-level jet stream observed at an altitude of 10.4 km consistently moved eastwards, with a maximum wind speed of 61.7 m/s. Simultaneously, the upper-level front–jet system on the cyclonic shear side of the upper-level jet stream also exhibited an eastward motion. The developed upper-level front–jet system induced significant vertical wind shear (VWS) and tropopause folding in the vicinity of these CAT events. Despite the high stability resulting from tropopause folding, the presence of strong VWS (1.90 × 10−2 s−1–2.55 × 10−2 s−1) led to a low Richardson number (Ri) (0.24–0.88) and caused Kelvin–Helmholtz instability (KHI), which ultimately induced CAT. Although a standard numerical weather forecast resolution of tens of kilometers is adequate to capture turbulence for most CAT events, it is still necessary to use high-resolution numerical simulations (such as 3 km) to calculate more accurate CAT indices (such as Ri) for CAT prediction in some specific cases.

A New Climatology of Vegetation and Land Cover Information for South America

Accurate information on vegetation and land cover is crucial for numerical forecasting models in South America. This data aids in generating more realistic forecasts, serving as a tool for decision-making to reduce environmental impacts. Regular updates are necessary to ensure the data remains representative of local conditions. In this study, we assessed the suitability of ‘Catchment Land Surface Models-Fortuna 2.5’ (CLSM), Noah, and Weather Research and Forecasting (WRF) for the region. The evaluation revealed significant changes in the distribution of land cover classes. Consequently, it is crucial to adjust this parameter during model initialization. The new land cover classifications demonstrated an overall accuracy greater than 80%, providing an improved alternative. Concerning vegetation information, outdated climatic series for Leaf Area Index (LAI) and Greenness Vegetation Fraction (GVF) were observed, with notable differences between series, especially for LAI. While some land covers exhibited good performance for GVF, the Forest class showed limitations. In conclusion, updating this information in models across South America is essential to minimize errors and enhance forecast accuracy.