Variability Of Wind Field Research Articles

Influence of Wind Speed on the Motion Characteristics of Peach Leaves (Prunus persica)

Air-assisted sprayers are widely used in orchards due to their efficiency in enhancing droplet penetration and deposition. These sprayers disperse droplets through a high-velocity airflow, which agitates the leaves and aids in canopy penetration. This study involved controlled experiments to simulate leaf movement during field spraying, with a focus on the dynamics of peach tree leaves (Prunus persica) in varying wind fields. An experimental setup consisting of a wind-conveying system, a measurement system, and a fixed system was designed. The moving speeds of the wind field (0.75 m/s, 0.5 m/s, and 1.0 m/s) and wind velocities (ranging from 2 m/s to 8 m/s) were varied. Key parameters, including leaf tip displacement, angular velocity, and twisting amplitude, were measured using high-speed cameras and motion analysis software. The results indicate that, at a constant wind velocity, increasing the wind field’s moving speed resulted in a reduced range of motion, decelerated angular velocity, and decreased twisting amplitude of the leaves. Notably, at a wind field speed of 8 m/s and a moving speed of 1.0 m/s, the twisting duration of the leaves was only 67% of that observed at a moving speed of 0.5 m/s. These findings suggest that wind speed and field motion characteristics play a crucial role in leaf dynamics, informing the design of air-assisted spraying systems.

Wind speed effect on infrared-image-based coal and gangue recognition with liquid intervention in LTCC

The commitment to achieving carbon neutrality by 2060 makes the clean transition of fossil energy production inevitable in China. As the primary fossil energy source, China produced approximately 4.66 billion tons of raw coal in 2023. Among them, in the fields of mining engineering and environmental science, the accurate recognition of gangue (one of the environmental pollutants produced by coal combustion is black-gray solid waste) from coal is crucial because it is a key step in reducing resource waste and environmental pollution. Hence, coal and gangue automatic recognition with high accuracy is very important for intelligent longwall top coal caving (LTCC) mining. A novel approach of “liquid intervention + infrared detection” is introduced to enhance the recognition accuracy when the exterior color of coal and gangue is similar. This study focuses on the infrared image recognition of coal and gangue after liquid intervention under varying wind speed conditions, and optimizes the image processing path. The effect of varying wind speed conditions on the recognition accuracy of coal and gangue after liquid intervention was analyzed, and the optimal wind speed range was determined. The morphological evolution characteristics of droplets under varying wind field conditions were studied by COMSOL two-phase flow interface. The results show that with the increase of wind speed, the surface contour of coal is gradually clear, and the overall recognition accuracy is also improved. When the high wind speed is 1.55 m/s, the recognition accuracy reaches 99.89%, and the recognition accuracy of other wind speed regions is also above 90% (within 7s). In addition, the numerical simulation can maintain the volume fraction of the droplet in a short time under the constant flow wind flow field, thus inhibiting the phenomenon that the recognition accuracy decreases with time. The simulation results are in good agreement with the physical experimental phenomena, which proves the reliability of the numerical simulation. This study is of great significance to the recognition of coal gangue types with low identification characteristics in LTCC, so as to ensure the primary economic benefits, ecological environment safety and sustainable development of coal mines.

Review of Wind Field Characteristics of Downbursts and Wind Effects on Structures under Their Action

Downbursts belong to sudden, local, and strong convection weather, which present significant destruction for structures. At any given time, there are approximately 2000 thunderstorms occurring on the Earth. Many studies have investigated the effects of downbursts on different structures. However, the extensive range of varying wind field parameters and the diverse representations of wind speeds render the study of structural wind effects complex and challenging under downbursts. This study firstly reviews the research of wind field properties of downbursts according to four common approaches, and the major findings, advantages, and disadvantages of which are concluded. Then, failure analysis of transmission line systems under stationary and moving downbursts is explored. The article also reviews the wind pressure on the roof of different kinds of low-rise buildings, and some dominant parameters, namely roof slope, distance of building from downburst center, wind direction angle, and so on, are discussed. Moreover, the wind effects caused by downbursts on high-rise buildings and some specialized structures are also considered because more and more wind hazards are related to downbursts. Finally, the limitations of the current study are pointed out, and recommendations for further research are given for the accurate assessment of the effects of wind on buildings, with a view to providing safer and more economical wind-resistant design solutions for structures.

Intelligent evaluation of interference effects between tall buildings based on wind tunnel experiments and explainable machine learning

The interference effects of taller high-rise buildings significantly impact the nearby shorter high-rise buildings in dense urban areas, potentially leading to severe wind-induced disasters. This study aims to develop a universal intelligent assessment method to predict the aerodynamic forces and wind-induced responses of buildings, considering various interfering and coupling factors. A database containing 61440 samples is established through a series of synchronous pressure measurement wind tunnel tests. The aerodynamic forces and wind-induced responses were predicted using six machine learning models under varying wind fields, interfering building locations, interfering building sizes, and dynamic characteristics of the principal building. The performance of the models was enhanced through Bayesian hyperparameter optimization and evaluated using the R2 and NRMSE. Additionally, the SHapley Additive exPlanations (SHAP) method was applied to evaluate the impact of each factor on the aerodynamic force and wind-induced response coefficients. Eight specific cases were examined using local SHAP explanations to explore the influence of seven input variables on maximum wind-induced response coefficients. Results indicated that the XGB model exhibited superior predictive performance, with R2 exceeding 0.99 in both training and testing sets. The SHAP analysis revealed significant impacts from wind direction angle, reduced wind speed, and damping ratio on wind-induced response. Consequently, irrelevant or minimally impactful input variables were removed from the XGB models. The R2 and NRMSE variation rate with reduced inputs for the optimized XGB model was less than 0.45 %. The graphical user interface (GUI) was designed can effectively guide intelligent urban planning and building design.

Investigating Wind Characteristics and Temporal Variations in the Lower Troposphere over the Northeastern Qinghai–Tibet Plateau Using a Doppler LiDAR

Knowledge of wind field characteristics and variation principles in complex topographical regions is of great importance for the development of numerical prediction models, aviation safety support, and wind energy utilization. However, there has been limited research focused on the lower-tropospheric wind fields in the Qinghai-Tibet Plateau. This paper aims to study the wind characteristics, vertical distributions, and temporal variations in the northeast of the plateau by analyzing a four-year continuous dataset collected from a Doppler wind LiDAR deployed in Xining, Qinghai Province of China. The results indicate that the prevailing horizontal wind direction in the low levels is primarily influenced by the mountain-valley wind circulation. However, as the altitude increases, the prevailing winds are predominantly affected by the westerlies. From a diurnal perspective, noticeable transition processes between up-valley and down-valley winds can be observed. The west-northwest wind (down-valley wind) dominates from late night to morning, while the east-southeast wind (up-valley wind) prevails from afternoon to early evening. The vertical winds in the low levels exhibit a downward motion during the daytime and an upward motion during the nighttime. In this plateau valley, the wind shear exponent is found to be highest in spring and lowest in winter, and it is generally lower during the daytime compared to the nighttime.

Interannual Variations in the Summer Coastal Upwelling in the Northeastern South China Sea

This study scrutinizes interannual (2003–2023) variations in coastal upwelling along the Guangdong Province during summers (June–August) in the northeastern South China Sea (NESCS) by comprehensively applying the moderate-resolution imaging spectroradiometer (MODIS) remote sensing sea surface temperature (SST) and chlorophyll concentration (CHL) data and the model reanalysis product. The results show that SST and upwelling intensity in the sea area have significant (p < 0.05) rising trends in the last 21 years. The CHL shows an upward but insignificant trend, which is affected simultaneously by the rise in SST and the enhancement of upwelling. Further analysis reveals that the interannual variations in upwelling are robustly related to the wind fields’ variations in the coastal region. A clockwise/counter-clockwise anomaly in the wind field centered on the NESCS facilitates alongshore/onshore winds near the Guangdong coast, which can strengthen/weaken coastal upwelling. Based on the correlation between wind field variations and large-scale climate factors, long-term variations in the upwelling intensity can be primarily predicted by the Oceanic Niño Index.

Wind Profile Retrieval Based on LSTM Algorithm and Mobile Observation of Brightness Temperature over the Tibetan Plateau

Stationary or mobile microwave radiometers (MRs) can measure atmospheric temperature, relative humidity, and water vapor density profiles with high spatio-temporal resolution, but cannot obtain the vertical variations of wind field. Based on a dataset of brightness temperatures (TBs) measured with a mobile MR over the Three-River-Source Region of the Tibetan Plateau from 18 to 30 July 2021, we develop a direct retrieval method for the wind profile (WP) based on the Long Short-Term Memory (LSTM) network technique, and obtain the reliable dynamic variation characteristics of the WP in the region. Furthermore, the ground-based radiative transfer model for TOVS (RTTOV-gb) was employed to validate the reliability of the TB observation, and we analyzed the impact of weather conditions, altitude, observational mode, and TB diurnal variation on the accuracy of the TB measurement and the retrieval of the WP. Results show that the TB from the mobile observation (MOTB) on clear and cloudy days are close to those of the simulated TB with the RTTOV-gb model, while TB measurements on rainy days are far larger than the modeled TBs. When compared with radiosonde observations, the WPs retrieved with the LSTM algorithm are better than the ERA5 reanalysis data, especially below 350 hPa, where the root mean square errors for both wind speed and wind direction are smaller than those of ERA5. The major factors influencing WP retrieval include the weather conditions, altitude, observational mode, and TB diurnal variation. Under clear-sky and cloudy conditions, the LSTM retrieval method can reproduce the spatio-temporal evolution of wind field and vertical wind shear characteristics. The findings of this study help to improve our understanding of meso-scale atmospheric dynamic structures, characteristics of vertical wind shear, atmospheric boundary layer turbulence, and enhance the assessment and forecasting accuracy of wind energy resources.

Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding

Abstract. Tropical-cyclone impacts can have devastating effects on the population, infrastructure, and natural habitats. However, predicting these impacts is difficult due to the inherent uncertainties in the storm track and intensity. In addition, due to computational constraints, both the relevant ocean physics and the uncertainties in meteorological forcing are only partly accounted for. This paper presents a new method, called the Tropical Cyclone Forecasting Framework (TC-FF), to probabilistically forecast compound flooding induced by tropical cyclones, considering uncertainties in track, forward speed, and wind speed and/or intensity. The open-source method accounts for all major relevant physical drivers, including tide, surge, and rainfall, and considers TC uncertainties through Gaussian error distributions and autoregressive techniques. The tool creates temporally and spatially varying wind fields to force a computationally efficient compound-flood model, allowing for the computation of probabilistic wind and flood hazard maps for any oceanic basin in the world as it does not require detailed information on the distribution of historical errors. A comparison of TC-FF and JTWC operational ensembles, both based on DeMaria et al. (2009), revealed minor differences of <10 %, suggesting that TC-FF can be employed as an alternative, for example, in data-scarce environments. The method was applied to Cyclone Idai in Mozambique. The underlying physical model showed reliable skill in terms of tidal propagation, reproducing the storm surge generation during landfall and flooding near the city of Beira (success index of 0.59). The method was successfully applied to forecasting the impact of Idai with different lead times. The case study analyzed needed at least 200 ensemble members to get reliable water levels and flood results 3 d before landfall (<1 % flood probability error and <20 cm sampling errors). Results showed the sensitivity of forecasting, especially with increasing lead times, highlighting the importance of accounting for cyclone variability in decision-making and risk management.

Quantifying Changes in the Florida Synoptic-Scale Sea-Breeze Regime Climatology

Abstract Florida’s summertime precipitation patterns are in part influenced by convergence between the synoptic-scale wind and local sea-breeze fronts that form along the east and west coasts of the peninsula. While the National Weather Service previously defined nine sea-breeze regimes resulting from variations in the synoptic-scale vector wind field near Tampa, Florida, these regimes were developed using a shorter 18-yr period and examined primarily for the purposes of short-term weather prediction. This study employs reanalysis data to develop a full 30-yr climatology of the Florida sea-breeze regime distribution and analyze the composite mean atmospheric conditions associated with each regime. Further, given that 1) the synoptic-scale wind primarily varies as a result of movement in the western ridge of the North Atlantic subtropical high (NASH), and 2) previous studies suggest long-term shifts in the mean position of the NASH western ridge, this study also examines variability and trends in the sea-breeze regime distribution and its relationship to rainy-day frequency over a longer 60-yr period. Results indicate that synoptic-scale flow from the west through southwest, which enhances precipitation probabilities along the eastern half of the peninsula, has increased in frequency, while flow from the east through northeast has decreased in frequency. These changes in the sea-breeze regime distribution may be partially responsible for increases in rainy-day frequency during June–August over northeastern Florida, though results suggest that other factors likely contribute to interannual variability in precipitation across the southern peninsula.

Estimating intraevent and interevent variability and spatial correlation of tropical cyclone wind fields and their use for the risk assessment of a portfolio of structures

Often the tropical cyclone (TC) wind hazard assessment requires the use of the TC wind field model. While theoretical models typically predict the observed wind field well, there can be a spatially varying residual correlation that impacts the damage assessment of communities or groups of structures. In this study, we focus on developing spatial correlation models and assessing the intraevent and interevent variability of the TC wind field using the H*Wind dataset and two widely used wind field models - the vertically averaged boundary layer slab model and the gradient wind field-based model. Our models and the statistics of the interevent and intraevent variability are integrated into a framework for evaluating the wind-induced damage of a portfolio of structures. The framework includes simulating TC tracks and wind fields, considering interevent and intraevent variabilities, and assessing peak linear elastic and nonlinear responses. Numerical examples illustrating the use of this framework are provided, indicating that realistic spatial correlation of the TC wind field needs to be considered to assess the correlation coefficient of the damage factor of a pair of spatially distributed structures and the probability distribution of the damage cost of a portfolio of structures.

IPC‐based robust disturbance accommodating control for load mitigation and speed regulation of wind turbines

SummaryOver the past few decades, global demand for renewable energy has been rising steadily. To meet this demand, there has been an exponential growth in size of wind turbines (WTs) to capture more energy from wind. Consequent increase in weight and flexibility of WT components has led to increased structural loading, affecting reliability of these wind energy conversion systems. Spatio‐temporal variation of rotor effective wind field acts as a disturbance to a WT system, hence, necessitating controllers that can cancel this disturbance. Additionally, assumptions made in extracting linear models for controller design lead to modeling errors resulting from changing operating conditions. Previous attempts have proposed robust controllers incorporating wind disturbance models. However, these controllers have been evaluated on smaller WTs, which experience lower structural loading than larger ones. Additionally, a majority these controllers are based on collective pitch control (CPC), hence do not address loading in the blades. To address these challenges, this contribution proposes an independent pitch‐based robust disturbance accommodating controller (IPC‐RDAC) for reducing structural loads and regulating generator speed in utility‐scale WTs. The proposed controller is designed using ‐synthesis approach and is evaluated on the 5 MW National Renewable Energy Laboratory (NREL) reference WT. Its performance is evaluated against a gain‐scheduled proportional integral (GSPI)‐based reference open‐source controller (ROSCO) and a CPC‐based RDAC (CPC‐RDAC) controller, developed previously by the authors. Simulation results for various wind conditions show that the proposed controller offers improved performance in blade and tower load mitigation, as well a generator speed regulation.

Variation of Local Wind Fields under the Background of Climate Change and Its Impact on Algal Blooms in Lake Taihu, China

Global climate change can greatly promote the continuing expansion of algal blooms in eutrophic inland lakes. Wind fields, an important climate factor, provide an external driving force for the movement of algal blooms. Based on algal bloom satellite imageries and wind observation data from 2003 to 2022, this study explored a quantitative assessment of the variations in surface wind fields and their impacts on the algal blooms in Lake Taihu, China. The results indicate that the mean wind speed at different time scales in the Lake Taihu area presents a continuous descending tendency in recent decades, which is the probable cause for the increasing frequency and severity of algal blooms in the lake. Wind fields affect the formation, location, and severity of algal blooms in diverse and complex ways. The area and frequency of algal blooms in Lake Taihu increase with the decrease in wind speed. The 6 h mean wind speed before 12:00 LT (Local Time) on the day of the algal bloom occurrence generally follows a Gaussian distribution, with a wind speed range of (0.6 m/s, 3.4 m/s) at the 95.5% confidence level. Accordingly, the wind speeds of 0.6 m/s and 3.4 m/s are identified to be the lower and upper critical wind speed indicators suitable for the formation of algal blooms, respectively. Another meaningful finding is that the outbreak of large-scale algal blooms requires stricter wind speed conditions, with a significantly lower wind speed threshold of around 2 m/s. Our study also demonstrates that the dominant wind direction of southeast in the region may be an important cause of the continuous water-quality decline and the high frequency and severity of algal blooms in the northwest waters of the lake. These findings will contribute to further studies on the dynamic mechanism of algal blooms and provide support for water environment management and algal bloom prevention and control.

Niepewność pomiarów średniej temperatury promieniowania za pomocą termometrów kulistych = Uncertainty of mean radiant-temperature measurements using globe thermometers

In the analysis of the human heat balance, the biggest problem is to precisely determine the amount of energy absorbed by a human organism under dynamically changing solar conditions and while being surrounded by many different surfaces that can absorb, emit or reflect solar radiation in different ways. Therefore, in thermal comfort studies some simplifications of the radiation field around human body are necessary and thus the concept of mean radiant temperature (Mrt) is most often used. Mrt values can be derived either from direct instrumental measurements or calculated by one of the microclimate models. The most accurate way to determine the Tmrt outdoors is by integral radiation measurements and the calculation of angular factors (i.e. the proportion of radiation received by the human body from different directions). This method involves simultaneous measurements of long- and short-wave radiation flux densities in six directions, which requires three sets of net radiometers. Although this method of Mrt determination describes the radiation environment accurately and independently of the variable wind field, it requires the use of expensive and complex measurement devices. Cheap and easy to operate alternative offers globe thermometer. In steady state conditions, readings from the air temperature sensor inside the globe thermometer (tg) will reflect the convective and radiative heat exchange around the globe thermometer. Knowing tg, as well as globe emissivity and diameter, the Mrt may be calculated. In this research three different globe thermometers were used to determine Mrt: 15-cm standard globe thermometer (SGT) and two 4-cm custom made acrylic globe thermometers (AGT) painted in black and grey. The measurements were conducted in the summer in Warsaw, on two selected green areas, under sunny and warm weather conditions. In each location Mrt measurements were taken in two spots – in the shade of trees and in the open grassy spaces, with high SVF. Our results demonstrate, that globe thermometers, although being relatively cheap and less complex instruments for determining Mrt, can be used to a limited extend, due to high instability of their indications under significant radiative loads.

Where and how to cool through blue infrastructure? Large lake groups to ameliorate urban overheating in a typical inland multi-lake megacity

Large lake groups have been recognized as important blue infrastructure to ameliorate urban overheating in inland megacities. Previous studies have evaluated climate regulation capabilities of large lakes on cities. However, how the specific location of lake groups affects thermal environments across extensive built-up areas through thermal and aerodynamic mechanisms remains unclear. Therefore, we conducted sensitivity experiments using the Weather Research and Forecasting (WRF) model to explore the intensity and spatial variation of air temperatures and wind fields influenced by lake groups—located in downtown areas, inner suburbs, and outer suburbs—under typical heatwave conditions in Wuhan. Results indicate that compared to similar-sized suburban lake groups, downtown lake groups exhibit higher and lower daytime lake-land differences in sensible and latent heat fluxes, respectively, resulting in greater lake cooling intensities (LCIs). Also, upwind lake groups cool more built-up areas since lake breeze circulations (LBCs) promote the spreading of lake cooling distances (LCDs). However, the development of LCIs, LCDs, and LBCs is asynchronous, with LCIs peaking around mid-morning before the generation of LBCs, and LCDs peaking in the afternoon once LBCs have developed vigorously. Moreover, we propose a planning framework integrating the cooling effects of lake groups to support cooler, healthier, and more sustainable cities.

Variations of Remote-Sensed Forel-Ule Index in the Bohai and Yellow Seas during 1997–2019

Water color, often quantified using the Forel-Ule Index (FUI), is a crucial parameter for assessing the water quality and ecological health of coastal waters. However, there is limited research on the spatiotemporal variations of FUI and the associated influencing factors in the Bohai and Yellow Seas. In this study, we utilized multi-sensor satellite datasets to retrieve monthly FUI products for the Bohai and Yellow Seas spanning the period from September 1997 to December 2019. Subsequently, we examined significant spatial disparities and variations across multiple timescales in the remotely sensed FUI time series. The climatological annual mean FUI map reveals a decreasing trend from nearshore to offshore regions, with similar spatial patterns observed in terms of overall and interannual FUI variability. The annual variations in wind field, sea surface temperature (SST), and ocean stratification play a key role in the seasonal dynamics of FUI by modulating the sediment resuspension process, resulting in low FUI values in summer and high FUI values in winter. Linear regression analysis of FUI anomaly indicates a long-term decreasing trend in FUI for the three bays of the Bohai Sea, while upward trends in FUI predominantly prevail in the central Yellow Sea. Factors related to interannual FUI variations, such as surface winds, SST, river outflow, rainfall, and anthropogenic activities, are qualitatively discussed. The findings of this study provide the first comprehensive evaluation of water color variations and their underlying mechanisms in the Bohai and Yellow Seas.

The acceleration of sea-level rise along the coast of the Netherlands started in the 1960s

Abstract. The global acceleration of sea-level rise (SLR) during the 20th century is now established. On the local scale, this is harder to establish as several drivers of SLR play a role, which can mask the acceleration. Here, we study the rate of SLR along the coast of the Netherlands from the average of six tide gauge records covering the period 1890–2021. To isolate the effects of the wind field variations and the nodal tide from the local sea-level trend, we use four generalised additive models (GAMs) which include different predictive variables. From the sea-level trend estimates, we obtain the continuous evolution of the rate of SLR and its uncertainty over the observational period. The standard error in the estimation of the rate of SLR is reduced when we account for nodal-tide effects and is reduced further when we also account for the wind effects, meaning these provide better estimates of the rate of SLR. A part of the long-term SLR is due to wind forcing related to a strengthening and northward shift of the jet stream, but this SLR contribution decelerated over the observational period. Additionally, we detect wind-forced sea-level variability on multidecadal timescales with an amplitude of around 1 cm. Using a coherence analysis, we identify both the North Atlantic Oscillation and the Atlantic Multidecadal Variability as its drivers. Crucially, accounting for the nodal-tide and wind effects changes the estimated rate of SLR, unmasking an SLR acceleration that started in the 1960s. Our best-fitting GAM, which accounts for nodal and wind effects, yields a rate of SLR of about 1.72.21.3 mm yr−1 in 1900–1919 and 1.51.91.2 mm yr−1 in 1940–1959 compared to 2.93.52.4 mm yr−1 in 2000–2019 (where the lower and upper bounds denote the 5th and 95th percentiles). If we discount the nodal tide, wind and fluctuation effects and assume a constant rate of SLR, then the probability (p value) of finding a rate difference between 1940–1959 and 2000–2019 of at least our estimate is smaller than 1 %. Consistent with global observations and the expectations based on the physics of global warming, our results show unequivocally that SLR along the Dutch coast has accelerated since the 1960s.

A Detailed Analysis on Hydrodynamic Response of a Highly Stratified Lake to Spatio-Temporally Varying Wind Field

Wind is generally considered an important factor driving the transport and mixing processes in stratified enclosed systems such as lakes and reservoirs. Lake Abashiri is one of the instances of such a system. For these systems, typically, the temporally unsteady but spatially uniform nature of wind has been assumed for simplicity. However, the spatial non-uniformity of wind could significantly alter compound hydrodynamic responses. In this study, such responses were investigated under the continuous imposition of different inhomogeneous wind conditions by applying numerical models and integrated analysis. The resultant tracer transport in both uniform and non-uniform wind cases was insignificant for the total study period of 9 days. However, under the short interval of Ti, where Ti is the internal fundamental period, different behaviors of both surface particle transport and the internal wave field were identified. Particularly, surface mass transport responses to higher spatial wind variance were obviously different from those in the uniform case. In addition, internal wave spectra under strong wind magnitude, which has low spatial variances, became identical to that of uniform wind; however, there were some discrepancies in the non-uniform case in the wave spectra under the influence of weak-to-moderate wind of high spatial variances. The results could provide an in-depth understanding of the lake’s hydrodynamic response to inhomogeneous wind which could improve water management in lakes and reservoirs.

SOLVING HIGH-DIMENSIONAL INVERSE PROBLEMS WITH AUXILIARY UNCERTAINTY VIA OPERATOR LEARNING WITH LIMITED DATA

In complex large-scale systems such as climate, important effects are caused by a combination of confounding processes that are not fully observable. The identification of sources from observations of the system state is vital for attribution and prediction, which inform critical policy decisions. The difficulty of these types of inverse problems lies in the inability to isolate sources and the cost of simulating computational models. Surrogate models may enable the many-query algorithms required for source identification, but data challenges arise from high dimensionality of the state and source, limited ensembles of costly model simulations to train a surrogate model, and few and potentially noisy state observations for inversion due to measurement limitations. The influence of auxiliary processes adds an additional layer of uncertainty that further confounds source identification. We introduce a framework based on (1) calibrating deep neural network surrogates to the flow maps provided by an ensemble of simulations obtained by varying sources, and (2) using these surrogates in a Bayesian framework to identify sources from observations via optimization. Focusing on an atmospheric dispersion exemplar, we find that the expressive and computationally efficient nature of the deep neural network operator surrogates in appropriately reduced dimension allows for source identification with uncertainty quantification using limited data. Introducing a variable wind field as an auxiliary process, we find that a Bayesian approximation error approach is essential for reliable source inversion when uncertainty due to wind stresses the algorithm.

A review of wind-driven hydrodynamics in large shallow lakes: Importance, process-based modeling and perspectives

Abstract In many large shallow lakes across the globe, the surface wind field drives the hydrodynamic process directly through the momentum and energy exchange at the air–water interface. Numerous field measurements, experiments and modeling show that wind-driven hydrodynamic disturbances have profound impacts on the structure and function of lake ecosystems. In this article, we review the response of the shallow lake to the wind-driven wave and flow field, which may accelerate the sediment resuspension and nutrient cycling and, in turn, affect the concentrations of nutrients and dissolved oxygen. Furthermore, the life activities of bacterioplankton, plankton and fish in the aquatic ecosystem are closely related to these water-quality factors. Although we have a developed understanding of the physical processes and biogeochemical cycles of lakes by process-based modeling, the most basic wind-driven hydrodynamic process in some lake models is imprecise. Comprehensive results of physical parameterization, including the wind stress and wind drag coefficient, with their mathematical expressions for depicting the wind-driven force in the hydrodynamic model of lakes are synthesized. Some of these expressions are empirically determined without considering the dynamic environment, and expressions based on physical mechanisms have been widely recognized. Additionally, the adaptation standard of wind-driven force parameterizations to inland lake models under light winds is provided. This article highlights the importance of heterogeneous wind field variability and suggests future studies on the wind fields in extreme climates, which could also cause damage to deep lake ecosystems and the biodiversity effects of wind wave turbulence.

Hydrodynamics of a large lake with complex geometry and topography: Lake of the Woods

We developed a high-resolution (250 m) three-dimensional hydrodynamic model of Lake of the Woods (LoW) to study surface seiches, lake circulation, and water temperature. LoW spans over two different geological regions, experiences wind variability, encompasses >14,500 islands and includes twelve hydraulically connected sub-basins. The complex geometry and topography are unique for this large lake that suffers from seasonal algal blooms. The model uses a spatially and temporally variable wind field, heat fluxes, riverine inflows obtained from a watershed model, outflows at Kenora and Norman dams, and was run for the spring-summer of 2017 and 2018. The predominant physical processes in LoW are surface seiches, and wind and rivers drive mean lake-wide circulations and mixing. The observed periods of seiches in the lake are 9.5, 3.4, 2.3, and 1.3 h, attributed to horizontal modes 1, 3, 6, and 12, respectively. Amphidromic structures show that sub-basin seiche's responses to wind may be limited to one sub-basin; however, lake-wide responses were also observed, which suggests the seiche-induced oscillatory velocities can impact transport paths. The mean water temperature is variable spatially, ranging from <15 °C in early June to ∼25 °C in July-August. Sabaskong Bay experienced warmer summer water temperatures with unidirectional mean flows to the lake. Main water transport is towards the northeast. Modelled conservative tracers, consistent with observations, show that the Rainy River plume extent covers Big Traverse, Morson, and Little Traverse Bays. The modelled seasonal hydraulic retention times in southern bays are <150 days and are much longer in northern bays.