Inversion Depth Research Articles

Electrical Resistivity Tomography and Boreholes Data to Investigate the Near-Surface Structure under the Campus Area of Çanakkale Onsekiz Mart University, Çanakkale, Turkey (Türkiye)

We study the soil foundation underneath the Çanakkale Onsekiz Mart University (ÇOMU) campus, Çanakkale, Türkiye by employing the electrical resistivity tomography – ERT supported by 27 boreholes data. The studied area taking place in southwest Marmara region was historically affected by large earthquakes () created by the North Anatolian Fault system. The boreholes data show that the near surface structure beneath the ÇOMU campus is made of mostly silty sands and marls. A high sensitivity resistivity instrument is used to collect the field data in which nine ERT profiles reaching lengths as long as 315 m are utilized. The current geoelectrical measurements are simulated by using two numerical models to estimate the inversion depth sensitivity from which it is found satisfactory in the depth range 0-30 m and then somewhat decreasing. The observed electrical resistivity values are in the range 2-160 W m. The geoelectrical structure corresponding to the silty sands are represented by low resistivities (<20 W m) while the high resistivity (>40 W m) depth sections are associated with the marl units. The resistivity structure beneath the ÇOMU campus is complex where both low and high resistivity depth sections reside side by side. The groundwater and clay mineralogy contribute to the broad changes in the subsurface resistivities. The groundwater flow below the steep terrain of the ÇOMU campus causes low resistivities (<10 W m) deeper than 10-m depth. The boreholes data superimposed on the two-dimensional (2-D) ERT profiles show consistency with the resistivity-depth distributions at corresponding depths.

Three-dimensional displacement and slip distribution of the 2021 Mw 7.4 Maduo (Tibetan Plateau) earthquake determined by GNSS and InSAR

On May 22, 2021, an Mw 7.4 earthquake occurred on the Maduo-Jiangcuo fault, located in Maduo County, Qinghai Province (China). According to the co-seismic deformation and the three-dimensional displacement reached from the interferometric synthetic aperture radar (InSAR) and the global navigation satellite system (GNSS), the rupture trace in the NWW direction was mapped, spanning a length of approximately 160 km and the earthquake happened on a typical sinistral strike-slip fault. In three-dimensional displacement, the displacement in E-W was bigger than that in N-S and vertical, with the RMSE 3.01 mm, 15.28 mm, and 5.16 mm respectively. Moreover, the slip distribution inverted with the homogeneous half-space model and layered half-space model, indicated that the corresponding magnitude was 7.3 Mw and 7.4 Mw, and the corresponding peak slip was 5.53 m and 5.96 m. The correlation coefficients of observed and predicted both exceeded 0.791, meeting the need for slip inversion. What is more, the peak inversion depth was no more than 15 km with strike angle and dip angle of 269° and 82.9° in the homogeneous half-space model and 265° and 83.7° in the layered half-space model, respectively. In addition, the Coulomb stress changes decreased on the Maduo-Jiangcuo fault and accumulated on the East Kunlun fault, which might cause aftershocks and future earthquakes were still possible. In short, the 2021 Mw7.4 Maduo earthquake made it possible to unravel seismic behavior in the Tibet plateau, which is of great significance for seismic hazard research.

An integrated method for gravity gradient inversion and gravity gradient depth imaging

SUMMARY Gravity gradient data can show the structural features of geological bodies in the shallow lithosphere with higher sensitivity and resolution than conventional gravity data. Gravity gradient inversion can be applied to obtain the lithospheric density structures of geological bodies. However, as with gravity data, gravity gradient data have no inherent depth resolution. The methods of gravity gradient depth imaging and gravity gradient inversion are integrated in this study. The depth imaging method is effective for calculations without prior information and iterative computations. As the parameters in the depth weighting function should be chosen from a set of values used in inversion tests of synthetic data, which brings some uncertainties, the depth imaging results of gravity gradient are introduced into the depth weighting function. Several synthetic models are tested to demonstrate the advantages and features of the effective integrated method. Finally, the integrated method is applied to the interpretation of the GOCE satellite gravity gradient tensors over the northeastern margin of the Qinghai–Tibet Plateau. The results reveal that in the crust of the study area, the distribution of density anomalies is more in line with the mechanism of the crustal flow model, in the upper mantle of the study area, the density anomalies are mainly influenced by the high heat flow environment.

The relationship between atmospheric temperature inversion and urban air pollution characteristics: a case study of Tehran, Iran

This study conducts a comprehensive evaluation of air pollution in Tehran, focusing on its origins and potential remedial measures. Data from the Mehrabad station in Tehran were meticulously analyzed to scrutinize the spatiotemporal dynamics over a three-year period. Leveraging the Universal Radio Sound Observation (RAOB) program, we probed atmospheric attributes up to 750 m on specific critical dates: January 22, 2014; January 25, 2015; and November 27, 2016, which represent peak pollution events. Employing mathematical models, we scrutinized air temperature inversion profiles and applied the Heffter method to gauge the impact of atmospheric inversions on Tehran's air quality. Our investigation unequivocally identified January 25, 2015, as the most polluted date, unveiling precise atmospheric conditions during a radiation inversion, encompassing altitudes, temperatures, humidity, wind speed, and inversion depth. This research significantly advances our comprehension of the intricate interplay between atmospheric inversions and air pollution. It provides critical insights that can inform the development of targeted strategies to mitigate air quality issues not only in Tehran but also in analogous urban settings worldwide.

Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review

In this paper, seismic exploration methods are reviewed with a particular emphasis on the use of the reflection seismology to investigate the subsurface structures and characterize active faults. The paper provides a descriptive overview, intended for a non-specialist audience, of the methods and of their recent developments aimed at improving the resolution, accuracy, and computational efficiency of seismic imaging. Techniques such as seismic ray tomography, full-waveform inversion and pre-stack depth migration are briefly introduced, highlighting their potential applications in structural geology studies. The main seismic attributes that have become increasingly important in the interpretation of faults and fractures are also presented, along with some examples of application. Finally, some case studies of active fault characterization are discussed. From these examples, the crucial role played nowadays by the seismic exploration methods for structural studies and for active fault characterization is evident.

Inversion Model of Salt Content in Alfalfa-Covered Soil Based on a Combination of UAV Spectral and Texture Information

This study aimed to investigate how the combination of texture information and spectral index affects the accuracy of the soil salinity inversion model. Taking the Bianwan Farm in Jiuquan City, Gansu Province, China as the research area, the multi-spectral data and soil salinity data at 0–15 cm, 15–30 cm and 30–50 cm depths in the sampling area under alfalfa coverage were collected, and spectral reflectance and texture features were obtained from a multispectral image. Moreover, the red-edge band was introduced to improve the spectral index, and gray correlation analysis was utilized to screen sensitive features. Five types of alfalfa-covered soil salinity machine learning inversion models based on random forest (RF) and extreme learning machine (ELM) algorithms were constructed, using the salinity index (SIs), vegetation index (VIs), salinity index + vegetation index (SIs + VIs), vegetation index + texture feature (VIs + TFs), and vegetation index + texture index (VIs + TIs). The determination coefficient R2, root-mean-square error (RMSE) and mean absolute error (MAE) were used to evaluate each model’s performance. The results show that the VIs model is more accurate than the SIs and SIs +VIs models. Combining texture information with VIs improves the inversion accuracy, and the VIs + TIs model has the best inversion effect. From the perspective of inversion depth, the inversion effect for 0–15 cm soil salinity was significantly better than that for other depths, and was the best inversion depth under alfalfa cover. The average R2 of the RF model was 10% higher than that of the ELM. The RF algorithm has high inversion accuracy and stability and performs better than ELM. These findings can serve as a theoretical basis for the efficient inversion of soil salinity and management of saline–alkali lands.

First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data

Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, we propose a method based on convolutional neural networks (CNNs) that can accurately identify the first arrivals of large-offset seismic data. A time window for linear dynamic correction was established to convert the raw seismic data into rectangular images so as to reduce the amount of invalid sample data and improve the training efficiency. In order to enhance the prediction effect of the far-offset first arrivals, we propose the strategy of adjusting the weight of the far-offset data to increase the weight of the far-offset data in the training dataset and, thus, to improve the first arrival accuracy. The manually picked first arrivals are used as labels and the input to the CNNs for training, and the full-offset first arrivals are the output. The travel time tomography velocity is modeled and compared based on the first arrivals obtained through manual picking, industrial software automatic picking, and CNN prediction. The results show that the application of CNNs to large-offset seismic datasets can help researchers to obtain the first arrivals at different offsets, while the inclusion of far-offset weights can effectively improve the modeling depth of the tomography inversion, and the accuracy of the results is high.

The Influence of Temperature Inversion on the Vertical Distribution of Aerosols

Temperature inversion plays an important role in the accumulation and diffusion of aerosols. In this study, the relationship between temperature inversion and the vertical distribution of aerosols is investigated based on Raman lidar observations taken from January 2010 to September 2015 at the Atmospheric Radiation Measurement site in the Southern Great Plains, USA. First, the diurnal and seasonal variations of the surface-based inversion (SBI) and elevated temperature inversion (EI) are investigated. The results indicate that the occurrence frequency of SBI and EI have different seasonal trends. SBI has the highest frequency in summer, while EI has the highest frequency in winter. The diurnal variation of SBI is obvious, with a higher frequency in nighttime and a lower frequency in daytime. The inversion intensity (ΔT) and inversion depth (ΔZ) of SBI and EI have consistent diurnal and seasonal trends. The effects of SBI and EI on the vertical distribution of aerosols are then analyzed. The mean aerosol optical depth (AOD) below the SBI height shows a clear seasonal variation, which is consistent with the seasonal trends of ΔT and ΔZ. This phenomenon also occurs on the AOD below EI top height. The sensitivity analysis shows that the mean AOD below SBI height or EI top height increases with an increase of the ΔT and ΔZ of SBI (EI). It indicates that ΔT and ΔZ are the key factors affecting the vertical distribution of aerosols. In addition, the variation of AOD below and above EI top height is opposite to that of AOD below and above EI bottom height under different ΔT and ΔZ conditions. The correlation coefficients between ΔT (ΔZ) of EI with AOD in EI were 0.62 (0.65). These results indicate that the space between EI bottom height and EI top height can store aerosols. The larger the ΔZ of EI, the more aerosols are stored. These findings contribute to our understanding of the effect of temperature inversion on the vertical distribution of aerosols.

A 25-year climatology of low-tropospheric temperature and humidity inversions for contrasting synoptic regimes at Neumayer Station, Antarctica.

A 25-year set of daily radiosonde data was used to investigate temperature and humidity inversions at Neumayer Station, coastal Dronning Maud Land, Antarctica. For the first time, inversions were studied differentiating between different synoptic conditions and different height levels. It was shown that, generally, inversions occurred on the majority (78%) of the days, with simultaneous occurrence of humidity and temperature inversions being observed on approximately two thirds of all days. Multiple inversions are common in all seasons for cyclonic and noncyclonic conditions, however, typically occur more frequently under cyclonic conditions. The seasonality of inversion occurrence and features, that is, inversion strength, depth and vertical gradients, was analysed statistically. Different formation mechanisms depending on inversion levels and prevailing weather situations are related to typical annual courses of certain inversion features. Winter maxima were found for the features that are mostly connected to the temperature close to the surface, which is mainly a result of the negative energy balance, thus influencing surface-based inversions. At the second level, both temperature and humidity inversions are often caused by advection of comparably warm and moist air masses related to the passage of cyclones and their frontal systems. Hence, maxima in several inversion features are found in spring and fall, when cyclonic activity is strongest. Monthly mean profiles of humidity and temperature inversions reveal that elevated inversions are often obscured in average profiles due to large variations in inversion height and depth.

Modulation of turbulent Rayleigh-Bénard convection under spatially harmonic heating.

We numerically study turbulent Rayleigh-Bénard (RB) convection under spatial temperature modulation, where the bottom temperature varies sinusoidally around a mean value in space. Both two- and three-dimensional simulations are performed over the Rayleigh number range 10^{7}≤Ra≤10^{10} and the wave number range 1≤k≤120 at fixed Prandtl number Pr=0.7. It is demonstrated that spatial temperature modulation with small wave numbers can enhance the global heat transfer (characterized by the Nusselt number Nu) in the turbulent regime, while Nu is close to that in standard RB convection in the case of large wave numbers. Further, we propose two characteristic modulation length scales: one is the penetration depth δ_{k} above which spatial modulation is negligible, the other is the inversion depth δ_{k2} below which there exists a stable inverse temperature gradient. Based on the relative thickness of the thermal boundary layer (BL) δ_{th} compared with these two length scales, the underlying modulation mechanism is physically explained and three regimes are identified: (1) an unperturbed BL regime (δ_{k}<δ_{th}), in which the modulation effect does not penetrate through the thermal BL and Nu is nearly unchanged; (2) a partially modulated BL regime (δ_{k2}<δ_{th}<δ_{k}), in which hot spots trigger more plume emissions from the thermal BL, resulting in Nu enhancement; and (3) a fully modulated BL regime (δ_{th}<δ_{k2}), in which the stable temperature inversion over the cold phases begins to affect convective flows, which alters the trend of Nu enhancement.

Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning

ABSTRACT Accurate lake depth mapping and estimation of changes in water level and water storage are fundamental significance for understanding the lake water resources on the Tibetan Plateau. In this study, combined with satellite images and bathymetric data, we comprehensively evaluate the accuracy of a multi-factor combined linear regression model (MLR) and machine learning models, create a depth distribution map and compare it with the spatial interpolation, and estimate the change of water level and water storage based on the inverted depth. The results indicated that the precision of the random forest (RF) was the highest with a coefficient of determination (R 2) value (0.9311) and mean absolute error (MAE) values (1.13 m) in the test dataset and had high reliability in the overall depth distribution. The water level increased by 9.36 m at a rate of 0.47 m/y, and the water storage increased by 1.811 km3 from 1998 to 2018 based on inversion depth. The water level change was consistent with that of the Shuttle Radar Topography Mission (SRTM) method. Our work shows that this method may be employed to study the water depth distribution and its changes by combining with bathymetric data and satellite imagery in shallow lakes.

Radar remote sensing-based inversion model of soil salt content at different depths under vegetation.

Excessive soil salt content (SSC) seriously affects the crop growth and economic benefits in the agricultural production area. Prior research mainly focused on estimating the salinity in the top bare soil rather than in deep soil that is vital to crop growth. For this end, an experiment was carried out in the Hetao Irrigation District, Inner Mongolia, China. In the experiment, the SSC at different depths under vegetation was measured, and the Sentinel-1 radar images were obtained synchronously. The radar backscattering coefficients (VV and VH) were combined to construct multiple indices, whose sensitivity was then analyzed using the best subset selection (BSS). Meanwhile, four most commonly used algorithms, partial least squares regression (PLSR), quantile regression (QR), support vector machine (SVM), and extreme learning machine (ELM), were utilized to construct estimation models of salinity at the depths of 0–10, 10–20, 0–20, 20–40, 0–40, 40–60 and 0–60 cm before and after BSS, respectively. The results showed: (a) radar remote sensing can be used to estimate the salinity in the root zone of vegetation (0-30 cm); (b) after BSS, the correlation coefficients and estimation accuracy of the four monitoring models were all improved significantly; (c) the estimation accuracy of the four regression models was: SVM > QR > ELM > PLSR; and (d) among the seven sampling depths, 10–20 cm was the optimal inversion depth for all the four models, followed by 20–40 and 0–40 cm. Among the four models, SVM was higher in accuracy than the other three at 10–20 cm (RP2 = 0.67, RMSEP = 0.12%). These findings can provide valuable guidance for soil salinity monitoring and agricultural production in the arid or semi-arid areas under vegetation.

EV Adoption Influence on Air Quality and Associated Infrastructure Costs

Exploring the system-level interactions within the modern urban transportation system, factors such as human health, vehicle exhaust pollution, air quality, emerging personal transportation technologies, and local weather events, are increasingly expedient considering the growth of human population centers projected in the 21st century. Pollutants often accumulate to unhealthy concentrations during winter inversion events such as those that commonly occur in Utah’s Salt Lake valley and other mountainous regions. This work examines the degree to which replacing conventionally powered vehicles with electric vehicles (EV) could reduce the near-road accumulation of criteria pollutants under various degrees of inversion depth and wind speed. Vehicle emissions data are combined with inversion and wind factors to determine changes in the Air Quality Index, and a first-order estimate of the cost required to build an EV charging infrastructure to support a given EV adoption scenario is also derived. Results are presented in the form of multiple Pareto frontiers and a simplified cost–benefit formula that inform potential public and private EV charging infrastructure investments to drive the EV adoption that would result in optimal air quality improvements during average weather and winter inversion events.

Surface-based nocturnal air temperature inversions in southern Poland and their influence on PM10 and PM2.5 concentrations in Upper Silesia

The frequency, strength and seasonal variations of surface-based temperature inversions (SBTIs) in Upper Silesia (Southern Poland) were examined using data from January 2001 to September 2020. Based on the air temperatures recorded at the meteorological station of the Institute of Earth Sciences in Sosnowiec (263 m a.s.l.) at heights of 2 m and 88 m above the ground, the vertical temperature gradient of the 100-m layer (γSos100 m) was determined. A lapse rate of γSos100 m > 0.5 K was defined as a temperature inversion. The measurements for 00 UTC (midnight) and 12 UTC (midday) were compared with data from the upper air station in Wrocław (116 m a.s.l.) located in the Lower Silesia Lowland, approximately 170 km NW of Sosnowiec. Based on soundings from Wrocław, in addition to the temperature gradient in the lower 100-m layer of air (γWrc100), three other characteristics of SBTIs were calculated: inversion depth (ID) or thickness in metres, inversion strength (ΔTi) in K and vertical temperature gradients across the whole SBTI layer γi in K 100 m−1. On an annual basis, the frequency of nighttime SBTIs (γ > 0.5 K 100 m−1) ranged from 47% in Sosnowiec to almost 59% in Wrocław. At both stations, the fewest SBTIs occurred in winter (23–38%) and the most in summer (64–75%). Moreover, they were more frequent in spring (52–61%) than in autumn (49–59%). The SBTI frequency was very low during the midday hours, amounting to 0.6–0.7% days a year, and it increased to 1–2% only in winter. Annually, the depth of 81% of inversions ranged between 50 and 300 m, varying seasonally from almost 67% in winter to 87% in summer. The presented research shows that SBTIs in winter were among the main factors contributing to a high concentration of particulate matter pollutants in the ground-level atmosphere. During nights with temperature inversions, the annual mean PM10 concentration reached 125% of the mean value, ranging from 114% in summer to 189% in winter.

Is the atmospheric boundary layer altitude or the strong thermal inversions that control the vertical extent of aerosols?

It is well known that the atmospheric boundary layer (ABL) plays a significant role in controlling the variability of atmospheric constituents such as aerosols and trace-gases. Hence, significant diurnal and seasonal variation in these will be observed as the ABL altitude does. However, on several occasions, high aerosol concentration in the lidar measurements is observed even above the ABL altitude. This raised a question that up to what extent ABL altitude acts as a capping layer for these pollutants? From the detailed analysis carried out using long-term (2010–2018) lidar observations and simultaneous radiosonde profiles obtained from Gadanki, India, we show that ‘there exist thermal inversions (TI), which are stronger than the ABL inversions, that fully control the vertical extent’. The detailed characteristics of TI (inversion strength (IS) and inversion depth (ID)) are also obtained. The results revealed that aerosol concentrations below the TI altitude increases with IS (ID) up to 3–4 K (300–400 m) during winter whereas in pre-monsoon it increases up to 2–3 K (100–200 m). Thus, IS of up to 2–4 K is required to fully trap the aerosol concentrations and this TI coincide with the ABL inversions for 51.7% only, particularly during the winter and pre-monsoon seasons. This analysis is further extended to different geographical locations of India using the aerosol profiles obtained from CALIPSO and a network of 23 radiosonde stations. The observed results provided further evidence that the vertical distribution of aerosols is restricted to the maximum extent by the TI but not the ABL altitude. These observations lead us to propose a hypothesis that ‘trapping of aerosols fully occurs up to particular IS and ID only and the ABL altitude is not the deciding factor most of the time for capping the aerosol vertical distribution’. These findings will greatly help in modeling the diffusion and transport of air pollutants in the lower troposphere.

Formation of ferroelectric regular domain structures using shear elastic waves

By simulating the effect of interfering shear waves on a ferroelectric through a layer of a liquid electrode, it was shown that it is possible to form not only micro-, but also submicron domain structures at frequencies below 1000 MHz at room temperature. The frequency dependences of the domain structure period and the angles of incidence of shear waves are obtained for frequencies limited by the framework of the Newtonian fluid model. The method has a short technological cycle, provides the necessary depth of domain inversion, and does not depend on the degree of acoustic transparency.

The Shallowing Surface Temperature Inversions in the Arctic

AbstractTemperature inversions play an important role in various physical processes by affecting the atmospheric stability, regulating the development of clouds and fog, and controlling the transport of heat and moisture fluxes. In the past few decades, previous studies have analyzed the spatiotemporal variability of Arctic inversions, but few studies have investigated changes in temperature inversions. In this study, the changes in the depth of Arctic inversions in the mid-twenty-first century are projected based on a 30-member ensemble from the Community Earth System Model Large Ensemble (CESM-LE) project. The ERA-Interim, JRA-55, and NCEP–NCAR reanalysis were employed to verify the model results. The CESM-LE can adequately reproduce the spatial distribution and trends of present-day inversion depth in the Arctic, and the simulation is better in winter. The mean inversion depth in the CESM-LE is slightly underestimated, and the discrepancy is less than 11 hPa, within a reasonable range. The model results show that during the mid-twenty-first century, the inversion depth will strongly decrease in autumn and slightly decrease in winter. The shallowing of the inversion is most obvious over the Arctic Ocean, and the maximum decrease is over 65 hPa in the Pacific sector in autumn. In contrast, the largest decrease in the inversion depth, which is more than 45 hPa, occurs over the Barents Sea in winter. Moreover, the area where the inversion shallows is consistent with the area where the sea ice is retreating, indicating that the inversion depth over the Arctic Ocean in autumn and winter is likely regulated by the sea ice extent through modulating surface heat fluxes.

A rule-based method for diagnosing radiation fog in an arid region from NWP forecasts

A reliable fog forecast can assist in water resource management and mitigate against hazardous effects on transport networks. However, diagnosing fog from numerical weather prediction models remains a challenge and various methods have been developed to achieve this. One approach is to use a multi rule-based method, where threshold values of multiple variables are employed to define fog conditions in the forecast. Such a method is presented here for next day forecasts of radiation fog at Abu Dhabi in the United Arab Emirates. Variables tested include the dew point depression, wind speed, modified Richardson index, inversion depth, relative humidity and liquid water content. Of these, the best performing rule included relative humidity (≥94%), wind speed (≤3ms−1) and inversion depth (>250 m) with a probability of detection (POD) (false alarm ratio (FAR)) of 0.62 (0.37). The worst performing rule was liquid water content with a POD (FAR) of 0.37 (0.31). Model performance was more sensitive to inversion depth than wind speed, and rules that included inversion depth performed better than rules that included the modified Richardson number. A qualitative spatial analysis showed that rules produced realistic fog patches when compared to satellite images. This suggests that the geophysical parameters chosen to define rules are well suited for defining fog as long as the model bias is sufficiently small to fall within the threshold values. This method worked well for the study region and did not demonstrate any seasonal bias.

Temperature inversions in China derived from sounding data from 1976 to 2015

Temperature inversions inhibit the transfer of momentum, heat and moisture in the atmosphere and have led to severe air pollution in China. This study investigated the spatiotemporal variation in temperature inversions in China using sounding data for the past four decades. Surface-based inversion, elevated inversion, and both in one sounding dataset were analysed. Statistical analyses of inversion parameters included frequency, strength and depth. The annual frequency of total inversions showed no significant increasing or decreasing trend with mean values of 0.78, 0.33, 0.24, 0.28, 0.5 and 0.36 at six stations representing different climate zones—Beijing, Harbin, Haikou, Shaowu, Ruoqiang, and Xining, respectively. The annual inversion strength and depth showed downward trends. Monthly variation in inversion frequency and strength differed among stations. The weakest surface-based inversion was found in summer at Beijing and Harbin with mean values of 1 and 1.3 °C, respectively; the strongest surface-based inversion was found in winter with respective mean values of 3.5 and 3.6 °C. Higher surface temperature in summer and subsidence aloft in winter may explain the monthly variation in inversion depth with a minimum in summer, with mean values of 165, 334, 135, 267, 363 and 420 m, and a maximum in winter, with mean values of 250, 646, 140, 591, 806 and 664 m, at the six respective stations. Total inversion was least frequent in southwestern China (mean 0.15), surface-based inversion was most frequent in the north (mean 0.78), and elevated inversion was most frequent in the southeast (mean 0.42). The strongest, deepest surface-based inversion dominated in the north (mean 3.4 °C and 398 m). Elevated inversion strength did not significantly differ among regions (mean 2.5 °C). The deepest elevated inversion dominated in the southeast (mean 654 m). Future efforts should focus on the interactions between aerosols and temperature inversions and accurate model simulations of temperature inversions.

Characteristics of temperature inversion from radiosonde measurements in the Western Ghats region

The characteristics of temperature inversion (TI) are believed to be dependent on topographical features and synoptic regime. Western Ghats (WGs) mountains located in the west coast of India have a complex topography, and the TI features for this region remain unknown due to the complexity and non-availability of observational datasets. This study investigates TI characteristics in the troposphere using high-resolution radiosonde data (2012–2015). The radiosonde measurements are collected at Mahabaleshwar (17.92°N, 73.6°E, ~1.4 km above mean sea level, AMSL), a high-altitude site in the WGs. The characteristics of TI in terms of height, depth, and strength (i.e., temperature difference between base and top of the inversion layer) are studied. The results showed that TI occurs in all seasons, however, their frequency differs. Winter (Dec.-Feb.) shows the maximum number of inversions followed by pre-monsoon (Mar.-May), post-monsoon (Oct.-Nov.), and monsoon (Jun.-Sep.). In winter, the depth of inversion is higher; however, the inversion strength is weaker as compared to other seasons. The TI strength is always below 5 K. Apart from surface-based inversions, elevated and upper-tropospheric inversions are also observed. Vertical and horizontal wind shear profiles along with ERA-5 data were analyzed to examine the plausible mechanism for TI to occur.