Weather Research And Forecasting Research Articles

DeepFore: A Deep Reinforcement Learning Approach for Power Forecasting in Renewable Energy Systems

An open network known as the “energy internet” links every component of the whole energy supply chains, from the generations. Due to their ability to mimic regional flow dynamics that have an impact on wind farm production, regional meteorological models are increasingly being used as a general tool for wind resource forecasting. In this study, higher vertical and horizontal resolutions WRF (weather research and forecasting) paradigm simulation are used to anticipate and validate production for a genuine onshore wind farm. This paper proposed a DeepFore which is a power forecasting system for hybrid renewable energy systems. Initially, the dataset is generated by the hybrid system. This data is preprocessed to improve the quality of the data by incorporating, filtering and outlier detection techniques. Then, this enriched data is fed into K++ means clustering algorithm to separate the normal data from faulty data. With the normal and original data, Teaching-Learning based optimization algorithm attempts to realize the optimal features which are important for forecasting. Finally, Deep SARSA which is deep reinforcement learning algorithm is incorporated to determine the power generated by the hybrid system. Better winds energy prediction estimations enable more efficient utilization of the produced electricity, according to computational models.

The impact of WRF vertical resolution on the simulated thermal-dynamic structures and intensity of Typhoon Lekima

This study utilizes the Weather Research and Forecasting (WRF) to comparatively analyze the impact of three vertical grid resolution (VGR) enhancement schemes on the simulation of super typhoon Lekima under two different horizontal resolutions. The relationship between structural changes and typhoon intensity is explored from the perspective of the simulated three-dimensional thermodynamic and dynamic structure of the typhoon. The main conclusions obtained are as follows: Typhoon track simulation is not sensitive to the setting of VGR, while intensity simulation is greatly affected by it. Increasing VGR in the lower layer can enhance the simulated intensity of the typhoon, but when VGR enhancement is made in the middle layer, the simulated typhoon intensity decreases. For VGR enhancement in the upper levels, it is only when coupled with a simultaneous increase in horizontal resolution that the simulated typhoon intensity is enhanced. Different VGR enhancement schemes result in significant differences in the simulated thermodynamic and dynamic structures of the typhoon, which is a crucial factor causing variations in simulated typhoon intensity. In terms of dynamics, increasing VGR in the lower layer reduces the hydrostatic stability of the lower troposphere, which enhances convection and improves its symmetry. This leads to strengthened inflow and outflow activities of the typhoon, resulting in a stronger simulated typhoon with a tighter and straighter eyewall. In terms of thermal structure, increasing VGR in both the lower and upper levels enhances the warm core of the typhoon, thereby increasing its simulated intensity. However, the warm core simulated with upper layer enhancement extends to a higher altitude. This may be related to two different heating mechanisms, where lower layer VGR enhancement has a more pronounced effect on changes in boundary layer latent heat flux, while upper layer VGR enhancement promotes more sinking of high-entropy air from higher levels.

Effects of land use and land cover change under shared socioeconomic pathways on future climate in the Yellow River basin, China

The Yellow River basin of China has experienced significant land use and land cover change (LUCC) due to excessive exploitation of nature resources, ecological degradation, and rapid urbanization, which plays an important role in the regional climate. It is crucial to explore the climate patterns of the basin under different future development scenarios to mitigate climate issues and achieve “carbon peaking and carbon neutrality goals”. Here we utilized land use and land cover (LULC) data and projections of future climate under two shared socioeconomic path – representative concentration path (SSP245 and SSP585) scenarios. Using the Weather Research and Forecasting (WRF) model, we simulated four future spatial variation patterns of temperature and precipitation in the basin. Results indicated that under the SSP245 and SSP585 scenarios, the temperature is projected to increase by 0.18 °C and 0.46 °C, respectively, while precipitation is expected to rise by 32.21 mm and 134.24 mm, respectively. The impact of LUCC was found to be relatively minor and mainly concentrated in the middle reaches of the basin. It resulted in a slight increase in temperature in both scenarios and an increase in precipitation in SSP245, but a decrease in precipitation in SSP585. Changes in farmland and urban area exhibited a certain warming effect in both scenarios, with urban areas having a greater influence, leading to a temperature increase by 0.25 °C and 0.28 °C, respectively. Forest, grassland, and bare areas had a lesser impact on temperature and showed different trends under two scenarios. Regarding precipitation, forests and urban areas had a greater influence in both SSP245 and SSP585 scenarios. This study identified the significant role of LUCC under different development scenarios in shaping future temperature and precipitation changes, providing valuable insights for effectively addressing climate issues in the Yellow River basin. It also highlights the need for clear policy recommendations and identifies institutions or agencies responsible for implementing such recommendations.

Investigating the spatio–temporal characteristics of lower atmospheric ducts across the China seas by performing a long–term simulation using the WRF model

In this work, a diagnostic scheme for lower atmospheric ducts was established based on the Weather Research and Forecasting (WRF) model. More specifically, a 10-year simulation test was conducted for the China seas to investigate the spatio-temporal characteristics of the lower atmospheric ducts phenomenon. Compared with the sounding data, the long-term simulations showed a high temporal correlation and the root mean square error of the modified atmospheric refractivity remained between 4 M and 7 M. Based on the simulations, significant regional differences in the occurrence probability of lower atmospheric ducts were detected from south to north. Among them, the surface ducts near the sea surface exhibited the highest occurrence probability, with higher probabilities being recorded in autumn and winter, and the probability gradually increased with the decreasing latitude. The spatio-temporal characteristics of duct height, thickness, and strength were generally consistent. In the seas at mid-latitudes, strong ducts mostly occurred in the spring and autumn, with the single-layer ducts being predominant and the first layer duct showing stronger characteristics than the second layer. In the lower latitude regions, the situation was exactly the opposite. The first duct layer, which existed throughout the year, exhibited weaker characteristics with less pronounced seasonal variations. On the other hand, the second duct layer demonstrated stronger features.

Comprehensive study of thunderstorm indices threshold favorable for thunderstorms during monsoon season using WRF–ARW model and ERA5 over India

IntroductionThe current research investigates into the application of various thunderstorm indices to predict severe thunderstorm occurrences during the monsoon season across four distinct regions in India. Methods: The study assesses the prediction model’s efficacy using various skill scores and the Weather Research and Forecasting (WRF) model has been integrated for 30 h with double moment microphysics scheme NSSL-17 which accurately reproduces vertical and meteorological measures.ObjectiveFurthermore, it investigates fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for forecasting severe thunderstorms.ResultsThe results indicate that combining thunderstorm indices with skill scores, such as the Heidke Skill Score and True Skill Statistic, enhances the accuracy of severe thunderstorm predictions in the Indian monsoon season. The accurate predictions rely on determining optimal thresholds for each index. The study emphasizes the importance of using multiple indices rather relying solely on single measure for predicting severe thunderstorms. Advanced indices like the Energy Helicity Index (EHI) and Supercell Composite Parameter (SCP) perform well in forecasting extreme severe thunderstormsdue to their strong reliance on wind shears. The EHI (> 1), and SCP (≥ 3.5), STP (≥ 1.2) along with low SRH at 3 km (100 m2/s2), indicated no evidence of helicity or tornado activity during the event. On the other hand, the CAPE, K Index, and VT Index demonstrate robust predictive capabilities for non-severe category thunderstorms.ConclusionsIntegrating numerous thunderstorm indices improves meteorologists’ forecasts, ensuring public safety.Based on this work, future research can improve severe weather forecasting models’ accuracy and reliability.

Improving GNSS PPP Performance in the South China Under Different Weather Conditions by Using the Weather Research and Forecasting (WRF) Model‐Derived Wet Delay Corrections

AbstractAtmospheric wet delay caused by Precipitable Water Vapor (PWV) significantly impacts the performance of many geodetic surveying systems such as Global Navigation Satellite System (GNSS). In this study, we use wet delay corrections forecast by the Weather Research and Forecasting (WRF) model to enhance GNSS Precise Point Positioning (PPP) during two observation periods with two different weather conditions, that is, period 1: March 01 to 14, 2020 (average PWV: 23.5 kg/m2) and period 2: June 02 to 15, 2020 (flooding weather with average PWV: 55.6 kg/m2), over the South China. PWV data from 277 to 263 GNSS stations are assimilated into WRF model to enhance the WRF water vapor forecasting capability for period 1 and period 2, respectively. Wet delay corrections from two different WRF configurations, that is, WRF no data assimilation and WRF with assimilation of GNSS PWV, are used to augment the PPP. Totally, eight WRF‐enhanced PPP schemes are tested. The results show that WRF‐enhanced PPP schemes generally have a better positioning performance in the up component than traditional PPP. After using WRF wet delay corrections, for static mode, the vertical positioning accuracy is improved by 14.6% and 33.7% for period 1 and period 2, respectively. The corresponding convergence time are reduced by 41.8% and 25.0% for period 1 and period 2, respectively. For kinematic mode, the positioning accuracy improvements in the up component reach 13.8% and 19.0% for period 1 and period 2, respectively. The kinematic PPP convergence time is reduced by up to 8.2% for period 1.

Effects of Background Synoptic Environment in Controlling South China Sea Tropical Cyclone Intensity and Size Changes in Pseudo–Global Warming Experiments

Abstract Assessing how global warming affects tropical cyclones (TC) is immensely important for climate change adaptation and hazard mitigation. However, projected intensity and size change can vary greatly among different individual storms, even under the same forcing in pseudo–global warming (PGW) experiments. Here we hypothesize that these variations are related to the historical environment in which each TC was embedded. Twenty-five TCs in the South China Sea (SCS) region were simulated using the Weather Research and Forecasting (WRF) Model. Their changes in the near (2036–65) and far future (2075–99) following the representative concentration pathways 8.5 (RCP8.5) and 4.5 (RCP4.5) under phase 5 of the Coupled Model Intercomparison Project (CMIP5) were investigated by the PGW technique. The mean changes in TC intensity and gale-force wind radius (R17) in the SCS were +6.4% and +1.5% for a 2°C warming, respectively. Multiple linear regression and stepwise regression analysis revealed that storm intensity variations were positively correlated with historical sea surface temperature and negatively with outer (i.e., outside the TC’s R17) atmospheric instability, while the R17 variations correlated positively with outer midtropospheric relative humidity (RH) and surface outer wind speed (OWS). Ertel potential vorticity (EPV) diagnostics further showed a moister SCS background can cause stronger diabatic heating and EPV production at the spiral rainbands under PGW, which increases R17. Additionally, stronger background absolute angular momentum (AAM) promoted stronger AAM influx, leading to larger R17. Implications were drawn to explain the uncertainties in projected TC intensity and size due to natural variability. Significance Statement Changes in the intensity and size of individual tropical cyclones in future climates are highly variable. This study aims to understand the environmental factors influencing these variations. We selected 25 storms that entered the South China Sea region and modeled them in both present and future climates. The mean intensity and size were projected to increase by 6.4% and 1.5% for a 2°C warming, respectively. We found that tropical cyclones embedded in historically warmer oceans and more stable atmospheres intensified more than the others, while historically wetter and windier environments promoted larger size growth. Our results suggest that certain tropical cyclones can exhibit a greater increase in intensity and size in a warmer climate under favorable environmental conditions.

Identifying wind regimes near Kuwait using self-organizing maps

Optimization of wind energy integration requires knowing the relationship between weather patterns and winds they cause. For a region with less-studied weather such as the Middle East, climatology becomes more vital. The Shagaya Renewable Energy Park in development in Kuwait experiences regional wind regimes that affect wind power production. Weather Research and Forecasting (WRF) model output allowed investigation into the weather regimes most likely to impact Shagaya Park. The self-organizing maps (SOMs) machine-learning method clustered the WRF output into six primary weather regimes experienced by the Middle East. According to the wind regimes mapped by the SOM, two of the six regimes have average wind speeds of approximately 9.9 and 8.6 m s−1 at 80 m near Shagaya Park, as well as wind speed and estimated wind power distributions that are more favorable to wind power production in Kuwait. One regime depicts a strong northwesterly wind called the summer shamal, and the second is associated with strong westerlies. Regimes less favorable for Kuwaiti wind power production are represented by the remaining four SOM nodes: local weak southeasterlies, an African nocturnal low-level jet, a daytime planetary boundary layer, and local northwesterlies from autumn to spring. The remaining four SOM nodes have average wind speeds of 5.7–7.2 m s−1 and wind speed and estimated wind power distributions which indicate regimes less favorable for wind power production in Kuwait.

Attributing Extreme Precipitation Characteristics in South China Pearl River Delta Region to Anthropogenic Influences Based on Pseudo Global Warming

AbstractIn the context of human‐induced warming climate, the atmosphere is expected to hold a greater amount of water vapor, leading to heavier precipitation on a global scale. However, the extent to which changes in extreme rainfall can be attributed to human influences varies at regional scales. Here we conduct attribution analyses on 40 extreme precipitation events in different seasons during 1998–2018 over the Pearl River Delta (PRD), by using the Weather Research and Forecasting (WRF) model and applying the pseudo global warming (PGW) method. The model was integrated with the factual and counterfactual conditions separately, with the latter derived from differences between the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical and historical‐natural runs. By comparing parallel experiments, extreme daily rainfall (>95th percentile) in PRD enhanced by 8%–9.5% for 0.9–1.1 K near‐surface warming (nearly Clausius‐Clapeyron, or CC scaling) in the May‐to‐September (MJJAS) and 12.4% at most for 0.6–0.8 K warming (∼2 × CC rate) in non‐MJJAS seasons; the probability of those extremes increased by 10%–30% during MJJAS (20%–40% in other seasons). While moisture‐related thermodynamic effects play a similar role in modulating rainfall, the wind circulation‐related dynamic effects act differently in different seasons. Changes in MJJAS extremes are related to stronger low‐level southerly winds, while non‐MJJAS rainfall is exacerbated by strengthened low‐level wind convergence and updrafts. Moisture budget analysis suggests that thermodynamic effects associated with the increased moisture amount account for the mean rainfall increase, whereas dynamic effects related to wind circulation changes are responsible for extreme precipitation, regardless of seasons.

Combining Cn2 models to forecast the optical turbulence at Paranal

ABSTRACT We applied a combination of models to improve the forecasts of refractive index structure coefficient ($C^{2}_{n}$) profiles and seeing at Paranal using high-temporal and spatial resolution simulations with the Weather Research and Forecasting model. We assessed our method with Stereo−SCIntillation Detection And Ranging observations from several nights between 2016 and 2018. The combined approach consists of the turbulent kinetic energy-based model to estimate the $C^{2}_{n}$ profile within the boundary layer and another model for the free atmosphere. We tested the Dewan, Jackson−Dewan, and Gladstone models. The implementation of the combined method gives better results than those obtained using each model separately for the whole atmospheric column. However, a much better agreement with observations is obtained when we use a calibration method to improve the results. Calibrated seeing forecasts at Paranal showed a root mean squared error of 0.30 arcsec and a bias around −0.1 arcsec for all the nights of 2017 and 2018, which are similar to previous results obtained at Paranal during the same nights. Due to its performance and rapid execution, the proposed methodology could be implemented as an operational tool to forecast the $C^{2}_{n}$ profiles and the seeing at Paranal and potentially over other astronomical sites around the world.

Assessing spatiotemporal characteristics of atmospheric water cycle processes over the Tibetan Plateau using the WRF model and finer box model

The Tibetan Plateau (TP) is the highest and one of the most extensive plateaus in the world and serves as a hotspot of climate change. In the context of climate warming, changes in evapotranspiration (ET) and external water vapor transport have a significant impact on assessing atmospheric water cycle processes over the TP. By using the Weather Research and Forecasting (WRF) model for long-term simulations and the finer box model for the calculation of water vapor along the boundary of the TP, the external atmospheric water vapor transport and its spatiotemporal characteristics over the TP are finely described. The simulated precipitation and ET are well-simulated compared with observation. Research results show that: (1) The total water path on the TP decreases from southeast to northwest. Water vapor is mainly transported into the TP from the western and southern boundaries. The net water vapor flux transported from the western boundary to the TP by westerly wind is negative, while the net water vapor flux transported from the southern boundary to the TP by southerly wind is positive. (2) In spring and winter, water vapor is mainly transported into the TP by mid-latitude westerlies from the western boundary. In summer, water vapor transport controlled by mid-latitude westerlies weakens, and water vapor is mainly transported into the TP from the southern boundary. In autumn, water vapor controlled by mid-latitude westerlies gradually strengthens, and water vapor is mainly transported into the TP from the western boundary. In addition, the ratio of ET to precipitation on the TP is about 0.48, and the moisture recycling is about 0.37. Water vapor mainly comes from external water vapor transport.

Projection of Non-Industrial Electricity Consumption in China’s Pearl River Delta under Global Warming Scenarios

Global warming is a global issue closely linked to sustainability, and power systems around the world are facing immense pressure due to global warming. The purpose of this study is to investigate the impact of global warming on non-industrial electricity consumption in China’s Pearl River Delta. The Weather Research and Forecasting (WRF) model is employed to dynamically downscale and simulate summer climate change characteristics during historical periods and future warming scenarios of 1.5/2 °C. Then, in order to dynamically investigate the changes in non-industrial electricity consumption in cities after warming, we developed a non-industrial electricity consumption estimation model based on degree days and GDP. The regression model can well reproduce non-industrial electricity consumption in summer. Under future warming scenarios of 1.5/2 °C, the results indicate an annual growth trend in non-industrial electricity consumption due to global warming. Under a 1.5 °C warming scenario, non-industrial electricity consumption in both Guangzhou and Zhuhai increases, with Guangzhou experiencing a larger increase of about 10 terawatt-hours (TWh) compared to the historical period. However, under a 2 °C warming scenario, non-industrial electricity consumption in both cities slightly decreases compared to the 1.5 °C warming scenario, with a maximum decrease of 874 million kilowatt-hours.

Observational Evaluation of Estimated Air Exchange Flux Between Atmospheric Boundary Layer and Free Troposphere With WRF Model

AbstractVertical exchange between the atmospheric boundary layer (ABL) and free troposphere (FT) is a key link in coupling the earth's surface and upper atmosphere. This process is usually quantified by numerical simulations, while its reliability is not well assessed until now. Using space‐time intensified ABL observations, we evaluate the ABL‐FT air mass exchange flux derived from the Weather Research and Forecast (WRF) model. A six‐site sounding experiment is conducted in the North China Plain during the wintertime of 2019. The measured data is processed to provide enough information to derive the vertical exchange flux corresponding to the model‐based result, so that a systematic comparison is conducted. Three physical processes involved in ABL‐FT vertical exchange are quantitatively evaluated, that is, temporal variation of ABL height, advection across the inclined ABL top, and vertical motion at the ABL‐FT interface. Results show that the model‐based and observation‐based fluxes are generally agreed in temporal evolution (R = 0.67, p < 0.01), both characterized by 4–6 days periodicity and diurnal cycle. Their relative mean error was about 45% during the whole study period, mainly stemming from the vertical motion term and the advection crossing term. The model inaccuracy in representing these relevant processes at the ABL top is largely responsible for the discrepancy. Besides, the difference may also be attributed to the observational uncertainty (∼22%) that is caused by the measurement's difficulties in determining ABL spatial variation and acquiring vertical velocity. Through this study, the credibility and limitation of the WRF model in deriving ABL‐FT exchange flux are quantified.

Wind power forecasting based on a machine learning model: considering a coastal wind farm in Zhejiang as an example

ABSTRACT The unpredictability and instability of wind have hindered the development and utilization of wind power. To harness wind energy and ensure a secure and stable power grid after wind power integration, precise predictions of wind power generation are imperative. Here, we apply one-year data from a coastal wind farm in Zhejiang to train a Random Forest (RF) model for predicting wind power generation. The results indicate that the RF model (mean bias (MB) 1.33) outperforms traditional linear models (MB 87.70). An evaluation of prediction-measurement bias shows a strong agreement between the RF model’s predictions and actual measurements (MB 1.33), especially when wind speeds exceed 5.7 m/s. While the Weather Research and Forecasting (WRF) model yields less accurate results (R2 0.65) compared to using measured wind velocity data (R2 0.97) for wind power prediction, it remains acceptable as it can capture and forecast peak generating power, meeting daily management requirements. Therefore, our machine learning-based approach offers practical guidance for reducing wind power generation uncertainties. Enhancing wind velocity forecasting through the WRF model could further improve the RF model’s accuracy in predicting wind power generation. Our study also verified the future application of RF model in coastal areas of China.

Impacts of vegetation dynamics on hydrological simulations under drought conditions in a humid river basin in Southern China

AbstractVegetation plays an essential role in the atmospheric and hydrological processes, and vegetation responds differently to climate change in various regions, especially in extreme climates. Therefore, the use of static prescribed vegetation information from past years in numerical models can be a source of biases in hydrological simulations. However, previous studies have mainly focused on the effects of vegetation dynamics on hydrological processes in arid and semi‐arid regions. It remains unclear how static or dynamic vegetation affects hydrological simulations in humid regions, especially under drought conditions. In this study, the Weather Research and Forecasting (WRF) model coupled with Noah‐MP was used to assess the impact of vegetation dynamics on hydrological simulations in the East River basin (ERb) of China, which is a major water source for several major cities in the Pearl River Delta. The model was run with prescribed and dynamic vegetation conditions, respectively. Our model validation based on observed 2‐m temperature (T2) and Leaf Area Index (LAI) showed that the model performance was improved when vegetation dynamics were considered. Our simulations with static or dynamic vegetation showed the impacts of vegetation dynamics on hydrological simulations under droughts. The model with vegetation dynamics simulated a wetter condition with higher soil moisture and runoff and lower T2, compared with the simulations of static vegetation. The results suggested that ignoring vegetation dynamics may overestimate the severity of drought in this humid basin, unlike arid and semi‐arid regions. Therefore, consideration of vegetation dynamics in this humid basin will deepen our research on different types of zones and serve as a reference for other humid regions.

Assessing the Atmospheric Response to Subgrid Surface Heterogeneity in the Single‐Column Community Earth System Model, Version 2 (CESM2)

AbstractLand‐atmosphere interactions are central to the evolution of the atmospheric boundary layer and the subsequent formation of clouds and precipitation. Existing global climate models represent these connections with bulk approximations on coarse spatial scales, but observations suggest that small‐scale variations in surface characteristics and co‐located turbulent and momentum fluxes can significantly impact the atmosphere. Recent model development efforts have attempted to capture this phenomenon by coupling existing representations of subgrid‐scale (SGS) heterogeneity between land and atmosphere models. Such approaches are in their infancy and it is not yet clear if they can produce a realistic atmospheric response to surface heterogeneity. Here, we implement a parameterization to capture the effects of SGS heterogeneity in the Community Earth System Model (CESM2), and compare single‐column simulations against high‐resolution Weather Research and Forecasting (WRF) large‐eddy simulations (LESs), which we use as a proxy for observations. The CESM2 experiments increase the temperature and humidity variances in the lowest atmospheric levels, but the response is weaker than in WRF‐LES. In part, this is attributed to an underestimate of surface heterogeneity in the land model due to a lack of SGS meteorology, a separation between deep and shallow convection schemes in the atmosphere, and a lack of explicitly represented mesoscale secondary circulations. These results highlight the complex processes involved in capturing the effects of SGS heterogeneity and suggest the need for parameterizations that communicate their influence not only at the surface but also vertically.

Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields

The accurate prediction of small-scale three-dimensional wind fields is of great practical significance for aviation safety, wind power generation, and related fields. This study proposes a novel method for predicting small-scale three-dimensional wind fields by combining the mesoscale Weather Research and Forecasting (WRF) model with Computational Fluid Dynamics (CFD). The method consists of three components: the WRF module, the hybrid neural network prediction module, and the CFD module. First, mesoscale meteorological fields are simulated using the WRF module to establish a historical inflow boundary dataset for the CFD domain. Next, deep separable convolutions are incorporated, and convolutional long short-term memory (ConvLSTM) is combined with a deep separable convolution-gated recurrent unit (DSConvGRU) to construct a hybrid neural network prediction module named ConvLSTM-DSConvGRU. This module is employed for predicting inflow boundary data. Finally, the predicted inflow boundary conditions drive the CFD module to predict small-scale three-dimensional wind fields. The effectiveness of the WRF and CFD downscaling coupling method was validated using observed data from meteorological stations within the simulated domain, along with statistical indicators of errors. Additionally, a comparative evaluation was conducted between the proposed hybrid network model and the four commonly used spatiotemporal prediction models to assess its prediction performance. The results demonstrate that our proposed wind field prediction method achieves accurate simulation and short-term prediction of small-scale three-dimensional wind fields, and the hybrid network model exhibits comprehensive advantages in terms of model complexity and prediction accuracy.

A novel framework to investigate wind-driven snow redistribution over an Alpine glacier: combination of high-resolution terrestrial laser scans and large-eddy simulations

Abstract. Wind-driven snow redistribution affects the glacier mass balance by eroding or depositing mass from or to different parts of the glacier’s surface. High-resolution observations are used to test the ability of large-eddy simulations as a tool for distributed mass balance modeling. We present a case study of observed and simulated snow redistribution over Hintereisferner glacier (Ötztal Alps, Austria) between 6 and 9 February 2021. Observations consist of three high-resolution digital elevation models (Δx=1 m) derived from terrestrial laser scans taken shortly before, directly after, and 15 h after snowfall. The scans are complemented by datasets from three on-site weather stations. After the snowfall event, we observed a snowpack decrease of 0.08 m on average over the glacier. The decrease in the snow depth can be attributed to post-snowfall compaction and the wind-driven redistribution of snow. Simulations were performed with the Weather Research and Forecasting (WRF) model at Δx=48 m with a newly implemented snow drift module. The spatial patterns of the simulated snow redistribution agree well with the observed generalized patterns. Snow redistribution contributed −0.026 m to the surface elevation decrease over the glacier surface on 8 February, resulting in a mass loss of −3.9 kg m−2, which is on the same order of magnitude as the observations. With the single case study we cannot yet extrapolate the impact of post-snowfall events on the seasonal glacier mass balance, but the study shows that the snow drift module in WRF is a powerful tool to improve knowledge on wind-driven snow redistribution patterns over glaciers.

Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas

There is an urgent need to develop accurate predictions of power production, wake losses and array–array interactions from multi-GW offshore wind farms in order to enable developments that maximize power benefits, minimize levelized cost of energy and reduce investment uncertainty. New, climatologically representative simulations with the Weather Research and Forecasting (WRF) model are presented and analyzed to address these research needs with a specific focus on offshore wind energy lease areas along the U.S. east coast. These, uniquely detailed, simulations are designed to quantify important sources of wake-loss projection uncertainty. They sample across different wind turbine deployment scenarios and thus span the range of plausible installed capacity densities (ICDs) and also include two wind farm parameterizations (WFPs; Fitch and explicit wake parameterization (EWP)) and consider the precise WRF model release used. System-wide mean capacity factors for ICDs of 3.5 to 6.0 MWkm−2 range from 39 to 45% based on output from Fitch and 50 to 55% from EWP. Wake losses are 27–37% (Fitch) and 11–19% (EWP). The discrepancy in CF and wake losses from the two WFPs derives from two linked effects. First, EWP generates a weaker ‘deep array effect’ within the largest wind farm cluster (area of 3675 km2), though both parameterizations indicate substantial within-array wake losses. If 15 MW wind turbines are deployed at an ICD of 6 MWkm−2 the most heavily waked wind turbines generate an average of only 32–35% of the power of those that experience the freestream (undisturbed) flow. Nevertheless, there is no evidence for saturation of the resource. The wind power density (electrical power generation per unit of surface area) increases with ICD and lies between 2 and 3 Wm−2. Second, EWP also systematically generates smaller whole wind farm wakes. Sampling across all offshore wind energy lease areas and the range of ICD considered, the whole wind farm wake extent for a velocity deficit of 5% is 1.18 to 1.38 times larger in simulations with Fitch. Over three-quarters of the variability in normalized wake extents is attributable to variations in freestream wind speeds, turbulent kinetic energy and boundary layer depth. These dependencies on meteorological parameters allow for the development of computationally efficient emulators of wake extents from Fitch and EWP.

The impacts of hail microphysics on maximum potential intensity of idealized tropical cyclone

Maximum potential intensity (MPI), which a TC may reach in certain environment conditions, can be affected by microphysical processes. Latent heat released in the process of TC development plays a significant role in it. However, the impacts of hail added both to single-moment and double-moment microphysics parameterization scheme on the MPI remain unclear. In this study, high-resolution sensitivity experiments are conducted in the Weather Research and Forecasting (WRF) model by using four bulk microphysics schemes belonging to a family, namely, WRF Single-Moment 6-Class (WSM6) scheme, WRF Double-Moment 6-Class (WDM6) scheme, WRF Single-Moment 7-Class (WSM7) scheme, WRF Double-Moment 7-Class (WDM7) scheme. Results show that SM schemes simulate the greater MPI than DM schemes. Adding hail in SM scheme increases the MPI while in DM scheme makes less difference. There is a close relationship between the MPI and the radial peak location and intensity of latent heat. The closer the latent heat peak is to the TC center and the greater the peak intensity is, the greater the MPI can be achieved. Though the presence of hail plays a cooling effect thermally, it may affect the TC structures due to the larger sedimentation speed. WSM7 scheme including hail microphysics simulates the TC with smaller size and eye wall inclination, and thus the latent heating efficiency in the eye wall is higher, which is more conducive to TC intensification. However, the larger content of hail resulting from the accretion of liquid water in WDM7 scheme brings a stronger cooling effect and probably offsets the dynamic advantage.