Atmospheric Boundary Layer Height Research Articles

Evaporation driven by Atmospheric Boundary Layer Processes over a Shallow Salt-Water Lagoon in the Altiplano

Evaporation driven by Atmospheric Boundary Layer Processes over a Shallow Salt-Water Lagoon in the Altiplano

Four Years of Atmospheric Boundary Layer Height Retrievals Using COSMIC-2 Satellite Data

This work aimed to study the atmospheric boundary layer height (ABLH) from COSMIC-2 refractivity data, endeavoring to refine existing ABLH detection algorithms and scrutinize the resulting spatial and seasonal distributions. Through validation analyses involving different ground-based methodologies (involving data from lidar, ceilometer, microwave radiometers, and radiosondes), the optimal ABLH determination relied on identifying the lowest refractivity gradient negative peak with a magnitude at least τ% times the minimum refractivity gradient magnitude, where τ is a fitting parameter representing the minimum peak strength relative to the absolute minimum refractivity gradient. Different τ values were derived accounting for the moment of the day (daytime, nighttime, or sunrise/sunset) and the underlying surface (land or sea). Results show discernible relations between ABLH and various features, notably, the land cover and latitude. On average, ABLH is higher over oceans (≈1.5 km), but extreme values (maximums > 2.5 km, and minimums < 1 km) are reached over intertropical lands. Variability is generally subtle over oceans, whereas seasonality and daily evolution are pronounced over continents, with higher ABLHs during daytime and local wintertime (summertime) in intertropical (middle) latitudes.

Graph-based Tracing of the Atmospheric Boundary Layer by a Ceilometer: A Case Study

The atmospheric boundary layer (ABL) is an important factor in the atmospheric processes. It serves as an intermediary between the earth's surface and the atmosphere in the exchange of mass, energy, and momentum. ABL height (ABLH) also restricts the dispersion volume of atmospheric pollutants. The ABLH can be retrieved from lidar and ceilometer (low power lidar) backscatter profiles by tracing the increased aerosol loading associated with ABL. Numerous lidar-based algorithms are currently implemented, but their ability to trace ABL evolution is often limited. The main issues are related to the selection of the correct layer when multiple aerosol layers are present. To overcome the problem, we employ a graph-based approach to post-process already identified ABL candidates (determined by the gradient in ceilometer backscatter) in two cases. A simple non-parametric smoothing method was also utilized for comparison. Our findings indicate that the graph-based algorithm is superior to the smoothing method in obtaining a consistent tracing of the diurnal evolution of ABL over Sofia, although some issues were also encountered during the evening ABL transition.

Study of spatiotemporal variation and annual emission of CH4 in Shaoxing Yangtze River Delta, China, Using a portable CH4 detector on the UAV

Methane (CH4) is the second greenhouse gas and has a profound impact on global climate change due to its high global warming potential and concentration. By 2022, the CH4 concentration was approximately 1.9 ppm, which was 264% of the pre-industrial level. The spatiotemporal distribution of CH4 was investigated by a portable CH4 detector on an unmanned aerial vehicle and electric bicycles in Shaoxing, a city situated in the Yangtze River Delta, China. The vertical distribution revealed CH4 concentration generally decreased slowly with height. However, the inversion condition and low atmospheric boundary layer height (ABLH) leaded to the enhancement of CH4 with height. The highest CH4 concentration (2.2 ± 0.1 ppm, n = 1428) was observed in winter and the lowest (2.0 ± 0.2 ppm, n = 1530) in spring. Regarding the daily variation, CH4 concentration peaked at 5:00 local time (LT) and reached its lowest level at 14:00 LT, which was attributed to the daily variation of ABLH, lowest in the early morning and highest in the noon. In urban areas, CH4 concentrations showed higher levels near restaurants, natural gas stations and sewerage well, with a maximum value of 13.1 ppm, which was caused by CH4 emission and natural gas leakage from these places. The annual CH4 emission in Shaoxing were estimated to be approximately 69 ton/(km2·year) by the mass balance approach. Compared with other cities in the world, the CH4 emission is in higher level which imply some control measures should be conducted to reduce CH4 emission in Shaoxing.

Analysis of Spatio-temporal Distribution Characteristics and Influencing Factors of PM2.5 Concentration in Urban Agglomerations on the Northern Slope of Tianshan Mountains

Analysis of the spatial and temporal distribution characteristics and influencing factors of PM2.5 concentrations for the urban agglomeration on the northern slope of Tianshan Mountain is of positive significance for regional economic construction and environmental protection. The spatial and temporal distributions of PM2.5 concentrations in the Tianshan North Slope urban agglomeration from March to November 2015 to 2021 were obtained through the inversion of the MCD19A2 aerosol product combined with meteorological factors using a geographically weighted regression (GWR) model, followed by the analysis of change trends and influencing factors. The results were as follows:① the high PM2.5 concentrations in the study area were mainly distributed in the oasis city cluster between the northern foot of Tianshan Mountain and the Gurbantunggut Desert, showing the spatial distribution characteristics of being "low around and high in the middle" and "low in the west and high in the east." The annual average value of ρ(PM2.5) in the study area was 16.98 μg·m-3, with high values mainly concentrated in the urban part of Urumqi and decreasing towards Changji and Fukang. The monthly average ρ(PM2.5) distribution pattern was consistent with the annual average, but there were seasonal differences as follows:autumn (20.32 μg·m-3) > spring (18.25 μg·m-3) > summer (12.47 μg·m-3). The accumulation phenomenon was more pronounced in spring and winter. ② The study area's annual average PM2.5 concentration showed a decreasing trend from 2015 to 2021, and the average value from March to October also showed a decreasing trend, with only a slight increase in November. From the analysis of the spatial distribution of PM2.5 concentration trends, the decrease was concentrated in the urban parts of major cities, especially in the urban part of Urumqi and its surrounding areas, where the decrease was the largest and the change was the most drastic. ③ Temperature and air pressure were positively correlated with PM2.5 concentrations, whereas relative humidity, wind speed, atmospheric boundary layer height, and precipitation were negatively correlated with PM2.5 concentrations. The degree of influence of each factor was ranked from high to low as follows:atmospheric boundary layer height > relative humidity > air pressure > air temperature > wind speed > precipitation.

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.

Impact of Uncertainties in the Atmospheric Boundary Layer Height on the Numerical Simulation of Chemical Species

Uncertainties in the atmospheric boundary layer height (ABL-H) are associated either with the uncertainties in meteorological fields or the definition of the ABL-H. When the ABL-H is involved in ABL parameterization schemes these uncertainties can have impacts on numerical simulation of chemical species. The impacts are examined numerically by employing a 1-D diffusion model with the K-profile scheme and 3-D air quality forecast model with the turbulent kinetic energy (TKE)-based scheme. The dependency on the ABL-H in the two schemes is very different. Sensitivity tests with the 1-D model show that the over-estimated/under-estimated ABL-H leads to the decrease/increase of concentration of tracers under both stable and unstable conditions due to the increase/decrease of the volume of tracers within the ABL. Under unstable conditions, the over-estimated/under-estimated ABL-H also enhances/weakens the vertical diffusivity, and leads to the decrease/increase of the concentration of tracers for the negative/positive vertical gradient of tracer. These impacts result in a little change with height within the ABL. The impacts of the ABL-H through the counter-gradient term and entrainment flux are much smaller than the impact through changing the volume of the ABL and vertical diffusivity in the ABL. Sensitivity tests with the 3-D numerical model with the TKE scheme show that over urban areas the over-estimated/under-estimated ABL-H leads to the increase/decrease of TKE in the whole ABL but does not always lead to stronger/weaker diffusivity and lower/higher concentrations of chemical species.

Atmospheric Material Boundary Layer Height Retrieval and Analysis in Qingdao Based on Doppler Lidar

Atmospheric Material Boundary Layer Height Retrieval and Analysis in Qingdao Based on Doppler Lidar

Contradictory response of ozone and particulate matter concentrations to boundary layer meteorology

At the present stage, collaborative control of particulate matter and ozone pollution has become a modern challenge. The atmospheric boundary layer height (ABLH) is an important meteorological parameter for the sources and sinks of air pollutants. It is generally recognized that the reduction of boundary layer is conducive to the accumulation of pollutants. However, in recent years, some studies have shown that the relationship between ABLH and ozone is not negatively correlated. Here, we analyzed the spatial distribution characteristics of PM2.5 and ozone exceedance in China from 2015 to 2022. The relationships between particulate pollution and ozone pollution and boundary layer meteorology were discussed. The key to coordinated control is to control the PM2.5 concentration in the winter and ozone in summer. Moreover, the two have different responses to meteorological factors, especially to the ABLH. Low temperature and low ABLH are conducive to the deterioration of particulate pollution, but high temperature and high ABLH are conducive to the occurrence and development of ozone pollution. The response of ozone to ABLH is contrary to previous studies in Europe and the United States. Moreover, an abnormal positive correlation was observed for PM2.5 and ABLH in Southwest China, which was mainly due to the impact of biomass combustion in Southeast Asia.

Evaluation of methods to determine the surface mixing layer height of the atmospheric boundary layer in the central Arctic during polar night and transition to polar day in cloudless and cloudy conditions

Abstract. This study evaluates methods to derive the surface mixing layer (SML) height of the Arctic atmospheric boundary layer (ABL) using in situ measurements inside the Arctic ABL during winter and the transition period to spring. An instrumental payload carried by a tethered balloon was used for the measurements between December 2019 and May 2020 during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Vertically highly resolved (centimeter scale) in situ profile measurements of mean and turbulent parameters were obtained, reaching from the sea ice to several hundred meters above ground. Two typical conditions of the Arctic ABL over sea ice were identified: cloudless situations with a shallow surface-based inversion and cloudy conditions with an elevated inversion. Both conditions are associated with significantly different SML heights whose determination as accurately as possible is of great importance for many applications. We used the measured turbulence profile data to define a reference of the SML height. With this reference, a more precise critical bulk Richardson number of 0.12 was derived, which allows an extension of the SML height determination to regular radiosoundings. Furthermore, we have tested the applicability of the Monin–Obukhov similarity theory to derive SML heights based on measured turbulent surface fluxes. The application of the different approaches and their advantages and disadvantages are discussed.

Study of the Effect of Different Atmospheric Conditions on the Temporal Evolution of the Mixing Layer over Madrid during the Year 2020 by Means of Two Different Methods: Ceilometer Signals and the ECMWF-IFS Meteorological Model

Atmospheric aerosols are one of the main factors that contribute to poor air quality. These aerosols are mostly concentrated within the atmospheric boundary layer (ABL) and mixing layer (ML). The ABL extends from ground level to the lowest level of the troposphere directly affected by surface temperature, solar irradiance, the orography and its proximity to coastal areas, causing turbulence in a daily cycle. This turbulence controls the vertical mixing of aerosols and pollutants and their dispersion in the ML. Therefore, proper characterization of these layers is of crucial importance in numerical weather forecasting and climate models; however, their estimation nowadays presents some spatial and temporal limitations. In order to deal with these limitations and to assess the influence of different meteorological conditions on the temporal evolution of the aforementioned layers, the evolution of the ML over Madrid (Spain) has been studied for the year 2020 by means of ceilometer profiles fed into the STRATfinder algorithm. This algorithm is able to give reliable estimates of the height of the ABL (ABLH) and ML (MLH). The results are compared with the ECMWF-IFS model predictions, which is able to compute the MLH under any meteorological condition. Then, the influence of the meteorology in the estimation of MLHs was established by classifying data based on the season and six different prevalent synoptic meteorological situations defined using ground-level pressure fields, as well as by splitting the days into four periods (morning, daytime, evening and nighttime). Our results show that both datasets, the STRATfinder values and the ECMWF-IFS model computations, are very sensitive to the meteorological conditions that play a main role in the MLH temporal evolution. For instance, high solar irradiance and ground radiation cause high turbulence and convection that lead to a well-developed ML. In cases in which the ML is well developed, both methods show similar results, and there are therefore better correlations between them. On the contrary, the results presented here show that the presence of high relative humidity and low temperatures hamper the growth of the ML, causing different errors in both MLH estimations and poor correlations between them. Furthermore, the ECMWF-IFS model has shown a sharp decrease, identified as an artificial behavior from 16:00 UTC, because of the influence of low solar zenith angles and the temporal interpolation. The STRATfinder algorithm also shows a sharp decrease just before the sunset because of the way the algorithm distinguishes between the ML and the residual layer. Thus, this study concludes that the MLH temporal evolution still needs to be characterized using complementary tools, since the methods presented here are strongly affected by the meteorological conditions and do not show enough reliability to work individually. However, ceilometer measurements offer great potential as a correction tool for ABL heights derived from models involved in air pollution dispersion assessments.

Roles of tropical cyclones with varying intensities in the re-distribution of aerosols

The study analyzes three tropical cyclones (TC) with varying intensities, namely BOB02, a deep depression in the Bay of Bengal; Tauktae, an extremely severe cyclone in the Arabian Sea (AS); and Amphan, a supercyclone in the Bay of Bengal, which developed on October 11, 2020, May 13, 2021, and May 16, 2020, respectively. The analysis focuses on studying the re-distribution of the aerosols over the Indian region using these distinct datasets: Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA2), Integrated Multi-Satellite Retrievals for GPM (IMERG), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). Throughout the cyclonic period, which includes the period from the inception of the low-pressure area to its dissipation as well as the pre-cyclonic and post-cyclonic periods (one week prior to and after the cyclonic period, respectively), various parameters related to aerosols and background meteorological conditions were collected. The primary parameter of interest was the aerosol optical depth (AOD). The analysis revealed significant variations in meteorological parameters such as wind speed, temperature, relative humidity, and rainfall along the cyclone track during both the cyclonic and post-cyclonic conditions compared to the pre-cyclonic conditions of these TCs. The AOD surrounding the cyclone track decreases during cyclonic and post-cyclonic conditions compared to pre-cyclonic conditions. This is due to the scavenging of both anthropogenic and natural (dust) aerosols. Conversely, AOD increases along the cyclone track, mainly due to an increase in natural (sea salt) aerosols. During pre-cyclonic conditions, both natural (dust) and anthropogenic (black carbon, organic carbon, and sulfate) aerosols contribute significantly to the total AOD, whereas during pre-cyclonic conditions, the main contributors are natural (sea-salt) aerosols. The coarse-mode particles are more dominant during cyclonic and post-cyclonic conditions than pre-cyclonic ones. Furthermore, our observations indicate that the atmospheric boundary layer (ABL) height increases by ∼0.1–0.6 km along the cyclone track during cyclonic conditions compared to pre-cyclonic conditions. While AOD from the free troposphere (FT) contributes more to the total AOD, contributions from ABL increase, while those from the FT decrease during cyclonic and post-cyclonic conditions.

Behavior of 222Rn, 220Rn and their progenies along a daily cycle for different meteorological situations: Implications on atmospheric aerosol residence times and Rn daughters' equilibrium factors

The correct assessment of the radiological hazard from radon and daughters, external and internal doses, residence times and equilibrium factors, implies the need to properly determine 222Rn (radon), 220Rn (thoron) and their respective short-lived progenies (214Pb and 214Bi, and 212Pb and 212Bi, respectively), where the precise measurements of both progenies are quite complex due to their very short half-lives. In addition, it is important to study the temporal behavior of all these radionuclides along daily cycles. Therefore, the aim of this study was to analyze the temporal evolution of radon, thoron and their progenies, and of their activity ratios along daily cycles for two different meteorological situations (synoptic and mesoscale processes). Radon and thoron were measured using a radon monitoring system, while their respective progenies were collected onto atmospheric filters using an ASS-500 sampler, and then measured by gamma-ray spectrometry. Furthermore, the different relationships between the concentrations of radionuclides and the different meteorological variables of interest (temperature, ABL height, and speed and direction of the wind) were found. Finally, the atmospheric aerosol residence times and Rn daughters’ equilibrium factors were estimated for each sampling carried out along the two daily cycles, finding results consistent with previous studies.

A Comparison of Atmospheric Boundary Layer Height Determination Methods Using GNSS Radio Occultation Data

The accurate determination of Atmospheric Boundary Layer Height (ABLH) is crucial in various atmospheric studies and practical applications. In this study, we present a comprehensive comparative analysis of five distinct methods for estimating ABLH using Global Navigation Satellite System (GNSS) Radio Occultation (RO) data. These methods encompass the use of bending angle and refractivity profiles, namely Minimum Gradient methods of the Bending Angle (MGBA) and Refractivity (MGR) profiles, breaking point, Wavelet Covariance Transform (WCT), and Double-Parameter Model Function (DPMF). GNSS-RO data from COSMIC-2 and Spire are used. To establish robust validation, radiosonde data are employed as a reference, ensuring the reliability of our findings. The results reveal notable variations in the performances of these ABLH estimation methods. Specifically, the MGBA, MGR, breaking point, and DPMF methods exhibit strong correlations with the reference data. Conversely, the WCT method displays weaker correlations, higher biases, and elevated root-mean-square-errors, suggesting limitations in capturing the true ABLH. Furthermore, we remove outlier screening to facilitate a comparison of the differences among the five methods. The WCT and DPMF methods can detect strong variations in the profiles near the Earth’s surface and consider them as ABLH. However, these variations are caused by errors. The MGBA method emerges as a reliable and stable option, while the WCT and DPMF methods should be used with caution due to the lower quality of the GNSS-RO profiles near the Earth’s surface.

A novel method of estimating atmospheric boundary layer height using a 205 MHz VHF radar

The present study proposes a novel methodology for the estimation of atmospheric boundary layer height (ABLH) using data from a 205 MHz VHF (very high frequency) radar installed at the Advanced Centre for Atmospheric Radar Research, Cochin University of Science and Technology, India. Using a year-long dedicated experimental campaign involving collocated, concurrent radar and radiosonde operations in 2021, clear-sky days were identified for the analysis in order to avoid any further ambiguity in the results. The reference ABLH is derived from the mixing ratio and its gradient profiles from the radiosonde data. A new method has been devised to estimate the boundary layer height using the dataset from radar by combining three parameters, viz., signal-to-noise ratio, wind speed and wind direction. The ABLH is identified as that height at which the sum of the normalized standard deviations of three parameters peaks and subsequently decreases abruptly and substantially above it. This method has been verified against the ABLH estimated from four other variables from the radiosonde data, and a strong correlation (r = 0.91 at 99 % confidence level) between the ABLH derived from both methods has been obtained. This study extends ample opportunity to explore the high temporal variability such as the diurnal cycle of ABLH from the radar, unlike sparsely available radiosonde measurements.

Insights into quantitative evaluation technology of PM2.5 transport at multi–perspective and multi–spatial and temporal scales in the north China plain

Cross-border transport is a crucial factor affecting air quality, while how to quantify the transport contribution through different technologies at multi–perspective and multi–scale have not been fully understood. This study established three quantification techniques, and conducted a systematic assessment of PM2.5 transport over the North China Plain (NCP) based on numerical simulations and vertical observations. Results suggested that the annual local emissions, inter–urban and outer–regional transport contributed 44.5%–64.6%, 15.2%–27.9% and 18.0%–28.2% of total surface PM2.5 concentrations, respectively, with transport intensity stronger in July and April, yet weaker in January and October. The southwest–northeast, northeast–southwest, and southeast–northwest were three prevailing transport directions near the surface. By comparison, the annual PM2.5 transport contribution below the atmospheric boundary layer height increased by 16.8%–24.5% in Beijing, Tianjin and Shijiazhuang, with inter–urban and outer–regional contribution of 29.8%–32.1% and 18.5%–23.1%. Furthermore, observed fluxes from fixed-point and vehicle-based mobile lidar were in good agreement with the simulated flux. PM2.5 net flux intensity varied with height, with generally larger at the middle- and high-altitude layer than that of low-altitude layer. In the early, during and late period of haze peak formation (Stage Ⅰ, Ⅱ, Ⅲ, respectively), the largest absolute flux intensity on average was Stage Ⅱ (566.7 t/d), followed by Stage Ⅲ (307.0 t/d) and Ⅰ (191.4 t/d). Besides, external transport may dominate the second concentration peak, while local emissions may play a more vital role in the first and third peaks. It has been noted that joint prevention and control measures should be proposed 1–2 days before reaching PM2.5 extremes. These findings could improve our understanding of transport influence mechanism of PM2.5 and propose effective emission reduction measures in the NCP region.

Machine Learning for Simulation of Urban Heat Island Dynamics Based on Large-Scale Meteorological Conditions

This study considers the problem of approximating the temporal dynamics of the urban-rural temperature difference (ΔT) in Moscow megacity using machine learning (ML) models and predictors characterizing large-scale weather conditions. We compare several ML models, including random forests, gradient boosting, support vectors, and multi-layer perceptrons. These models, trained on a 21-year (2001–2021) dataset, successfully capture the diurnal, synoptic-scale, and seasonal variations of the observed ΔT based on predictors derived from rural weather observations or ERA5 reanalysis. Evaluation scores are further improved when using both sources of predictors simultaneously and involving additional features characterizing their temporal dynamics (tendencies and moving averages). Boosting models and support vectors demonstrate the best quality, with RMSE of 0.7 K and R2 > 0.8 on average over 21 years. For three selected summer and winter months, the best ML models forced only by reanalysis outperform the comprehensive hydrodynamic mesoscale model COSMO, supplied by an urban canopy scheme with detailed city-descriptive parameters and forced by the same reanalysis. However, for a longer period (1977–2023), the ML models are not able to fully reproduce the observed trend of ΔT increase, confirming that this trend is largely (by 60–70%) driven by megacity growth. Feature importance assessment indicates the atmospheric boundary layer height as the most important control factor for the ΔT and highlights the relevance of temperature tendencies as additional predictors.

A procedure for obtaining forecast turbulence parameters in the atmospheric boundary layer for acoustic propagation

Propagation through turbulence causes a mean reduction in loudness of sonic booms and in some cases can cause significant fluctuations in loudness about that mean. Understanding these effects is important for accurately planning loudness levels during upcoming community noise tests with the X-59 aircraft as well as mitigating the risk for excessive loudness due to turbulence. Current methods for modeling acoustic propagation through turbulence require several atmospheric parameters including the atmospheric boundary layer (ABL) height, friction velocity, mixed-layer velocity scale, surface-layer temperature scale, as well as ambient pressure, temperature, and humidity within the ABL. Accurate forecasts of these parameters are needed for X-59 flight planning and may also be useful for planning future supersonic aircraft certification flight tests. This presentation showcases one method for obtaining the forecast turbulence and ambient atmospheric parameters from freely available Climate Forecast System Version 2 data. Forecast parameters are compared to measurements to assess accuracy and utility.

Evaluation of COSMIC-2 performance in detecting atmospheric boundary layer height and analysis of its variation

The atmospheric boundary layer (ABL) height (ABLH) is an essential index for climate and weather studies in the troposphere. Obtaining abundant and precise observations is crucial for modeling the top of the ABL. The radio occultation can provide observation data of Earth’s atmosphere with high vertical resolution and comprehensive spatial coverage, benefiting from its measurement mode. Thanks to the improved quality of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-2 lower atmosphere observations, its substantial amount and higher-quality atmospheric profile data are well suited for ABLH detection. Therefore, 1.4 million COSMIC-2 refractivity profiles from October 2019 to September 2020 were employed to determine the ABLH, and its retrieval performance is assessed using the official COSMIC-2 product (zbalmax) and radiosonde data. The retrieval results show that most of the effectively detected ABLH occultation events occur over the ocean. Comparing COSMIC-2 ABLH and zbalmax, consistent accuracy is found with a standard deviation of 0.22 km. Similar biases are observed each month. And the inversion accuracy in the Northern Hemisphere is higher than in the Southern Hemisphere. The ABLH derived from radiosonde data and COSMIC-2 has a high correlation coefficient of no less than 0.75. The better accuracy happened in the DJF (i.e. from December to February) for both comparisons with two datasets. Additionally, the distribution and seasonal variations were revealed by modeling verified COSMIC-2 ABLH. The fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) surface temperature and self-calibrated Palmer Drought Severity Index are used to analyze the impact of temperature and drought on ABLH. The results indicate that higher surface temperatures lead to a smaller ABLH, whereas drought at the surface leads to a larger ABLH. Therefore, the higher precision ABLH can be derived from COSMIC-2 after verification, and we can reveal its seasonal variation and influencing factors on this basis.

The characteristics of atmospheric boundary layer height over the Arctic Ocean during MOSAiC

Abstract. The important roles that the atmospheric boundary layer (ABL) plays in the central Arctic climate system have been recognized, but the atmospheric boundary layer height (ABLH), defined as the layer of continuous turbulence adjacent to the surface, has rarely been investigated. Using a year-round radiosonde dataset during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we improve a Richardson-number-based algorithm that takes cloud effects into consideration and subsequently analyze the characteristics and variability of the ABLH over the Arctic Ocean. The results reveal that the annual cycle is clearly characterized by a distinct peak in May and two respective minima in January and July. This annual variation in the ABLH is primarily controlled by the evolution of the ABL thermal structure. Temperature inversions in the winter and summer are intensified by seasonal radiative cooling and warm-air advection with the surface temperature constrained by melting, respectively, leading to the low ABLH at these times. Meteorological and turbulence variables also play a significant role in ABLH variation, including the near-surface potential temperature gradient, friction velocity, and turbulent kinetic energy (TKE) dissipation rate. In addition, the MOSAiC ABLH is more suppressed than the ABLH during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment in the summer, which indicates that there is large variability in the Arctic ABL structure during the summer melting season.