Search Results (134)

Onboard the MetOp satellite series, Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform spectrometer based on the Michelson interferometer. IASI acquires interferograms, which are processed to provide high-resolution atmospheric emission spectra. These spectra enable the derivation of temperature and humidity profiles, among other parameters, with exceptional spectral resolution. In this study, we evaluate a novel, rapid retrieval approach in the interferogram domain, aiming for near-real-time (NRT) analysis of large spectral datasets anticipated from next-generation tropospheric sounders, such as MTG-IRS. The Partially Sampled Interferogram (PSI) method, applied to trace gas retrievals from IASI, has been sparsely explored. However, previous studies suggest its potential for high-accuracy retrievals of specific gases, including CO, CO₂, CH₄, and N₂O at the resolution of a single IASI footprint. This article presents the results of a study based on retrieval in the interferogram domain. Furthermore, the optical pathway differences sensitive to the parameters of interest are studied. Interferograms are generated using a fast Fourier transform on synthetic IASI spectra. Finally, the relationship to the total column of carbon monoxide is explored using three different algorithms—from the most intuitive to a complex neural network approach. These algorithms serve as a proof of concept for interferogram classification and rapid predictions of surface temperature, as well as the abundances of H₂O and CO. IASI spectra simulations were performed using the LATMOS Atmospheric Retrieval Algorithm (LARA), a robust and validated radiative transfer model based on least squares estimation. The climatological library TIGR was employed to generate IASI interferograms from LARA spectra. TIGR includes 2311 atmospheric scenarios, each characterized by temperature, water vapor, and ozone concentration profiles across a pressure grid from the surface to the top of the atmosphere. Our study focuses on CO, a critical trace gas for understanding air quality and climate forcing, which displays a characteristic absorption pattern in the 2050–2350 ${\mathrm{cm}}^{-1}$ wavenumber range. Additionally, the study explores the potential of correlating interferogram characteristics with surface temperature and H₂O content, aiming to enhance the accuracy of CO column retrievals. Starting with intuitive retrieval algorithms, we progressively increased complexity, culminating in a neural network-based algorithm. The results of the NN study demonstrate the feasibility of fast interferogram-domain retrievals, paving the way for operational applications. Full article

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is a key metric for air quality, weather forecasting, and climate modeling. The novelty of this study lies in estimating PBLH using only surface-based meteorological observations. This approach is validated against remote sensing measurements (e.g., LIDAR, ceilometer, and wind profilers), which are seldom available in the Amazon region. The dataset includes various meteorological features, though substantial missing data for the latent heat flux (LE) and net radiation (Rn) measurements posed challenges. We addressed these gaps through different data-cleaning strategies, such as feature exclusion, row removal, and imputation techniques, assessing their impact on model performance using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and $r^2$ metrics. The best-performing strategy achieved an RMSE of 375.9 m. In addition to the RF model, we benchmarked its performance against Linear Regression, Support Vector Regression, LightGBM, XGBoost, and a Deep Neural Network. While all models showed moderate correlation with observed PBLH, the RF model outperformed all others with statistically significant differences confirmed by paired t-tests. SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability, revealing hour of the day, air temperature, and relative humidity as the most influential predictors for PBLH, underscoring their critical role in atmospheric dynamics in Central Amazonia. Despite these optimizations, the model underestimates the PBLH values—by an average of 197 m, particularly in the spring and early summer austral seasons when atmospheric conditions are more variable. These findings emphasize the importance of robust data preprocessing and higtextight the potential of ML models for improving PBLH estimation in data-scarce tropical environments. Full article

The impacts of the solar eclipse that occurred on 8 April 2024 over the United States on various atmospheric parameters are investigated. We analyzed surface and vertical profiles of temperature and humidity to understand how this eclipse affected the atmosphere from the ground to the stratosphere. Our findings show a significant response throughout the atmospheric range. The eclipse caused a decrease in shortwave radiation, leading to cooler Earth surfaces and a subsequent drop in surface temperature. This cooling effect also resulted in high relative humidity and lower wind speeds at the surface. Furthermore, GPS radio occultation data from COSMIC-2 revealed a decrease in tropospheric temperature and increase in stratospheric temperature during the eclipse. We also observed a reduction in both the temperature and height of the tropopause. The uniqueness of the present investigations lies in delineating the solar eclipse’s effects on the land and ocean. Our analysis indicates that land regions experienced a more pronounced temperature change compared to ocean regions. Full article

Accurate prediction of Cloud Liquid Water Content (CLWC) is critical for understanding and forecasting weather phenomena, particularly in regions with complex microclimates. This study integrates high-resolution ERA5 climatic data from the European Centre for Medium-Range Weather Forecasts (ECMWF) with radiosonde observations from the Hong Kong area to address data accuracy and resolution challenges. Machine learning (ML) models—specifically Fine Tree regressors—were employed to interpolate radiosonde data, resolving temporal and spatial discrepancies and enhancing data coverage. A metaheuristic algorithm was also applied for data cleansing, significantly improving correlations between input features (temperature, pressure, and humidity) and CLWC. The methodology was tested across multiple ML algorithms, with ensemble models such as Bagged Trees demonstrating superior predictive accuracy and robustness. The approach substantially improved CLWC profile reliability, outperforming traditional methods and addressing the nonlinear complexities of atmospheric data. Designed for scalability, this methodology extends beyond Hong Kong’s unique conditions, offering a flexible framework for improving weather prediction models globally. By advancing CLWC estimation techniques, this work contributes to enhanced weather forecasting and atmospheric science in diverse climatic regions. Full article

The planetary boundary layer height (PBLH) in coastal Arctic regions is influenced by sea breeze circulation. However, the specific mechanisms through which sea breeze affects PBLH evolution remain insufficiently explored. This study uses meteorological data, micro-pulse lidar (MPL) data, and sounding profiles from 2014 to 2021 to investigate the annual and polar day PBLH evolution driven by sea breezes in the Barrow region of Alaska, as well as the specific mechanisms. The results show that sea breeze events significantly suppress PBLH, especially during the polar day, when prolonged solar radiation intensifies the thermal contrast between land and ocean. The cold, moist sea breeze stabilizes the atmospheric conditions, reducing net radiation and sensible heat flux. All these factors inhibit turbulent mixing and PBLH development. Lidar and sounding analyses further reveal that PBLH is lower during sea breeze events compared to non-sea-breeze conditions, with the peak of its probability density distribution occurring at a lower PBLH range. The variable importance in projection (VIP) analysis identifies relative humidity (VIP = 1.95) and temperature (VIP = 1.1) as the primary factors controlling PBLH, highlighting the influence of atmospheric stability in regulating PBLH. These findings emphasize the crucial role of sea breeze in modulating PBL dynamics in the Arctic, with significant implications for improving climate models and studies on pollutant dispersion in polar regions. Full article

The temperature and humidity profiles within the planetary boundary layer (PBL) are crucial for Earth’s climate research. The Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST) measures downward thermal radiation in the atmosphere with high temporal and spectral resolution continuously during day and night. The physics-based retrieval method, utilizing iterative optimization, can obtain solutions that align with the true atmospheric state. However, the retrieval is typically an ill-posed problem and is affected by noise, necessitating the introduction of regularization. To achieve high-precision detection, a systematic evaluation was conducted on the retrieval performance of temperature and humidity profiles using ASSIST by regularization methods based on the Gauss–Newton framework, which include Fixed regularization factor (FR), L-Curve (LC), Generalized Cross-Validation (GCV), Maximum Likelihood Estimation (MLE), and Iterative Regularized Gauss–Newton (IRGN) methods, and the Levenberg–Marquardt (LM) method based on a damping least squares strategy. A five-day validation experiment was conducted under clear-sky conditions at the Anqing radiosonde station in China. The results indicate that for temperature profile retrieval, the IRGN method demonstrates superior performance, particularly below 1.5 $\mathrm{k}\mathrm{m}$ altitude, where the mean BIAS, mean RMSE, mean Degrees of Freedom for Signal (DFS), and mean residual reach 0.42 $\mathrm{K}$, 0.80 $\mathrm{K}$, 3.37, and $3.01\times {10}^{-13}$ $\left(\mathrm{W}/{\mathrm{c}\mathrm{m}}^2 \mathrm{s}\mathrm{r} {\mathrm{c}\mathrm{m}}^{-1}\right)$, respectively. In contrast, other regularization methods exhibit over-regularization, leading to degraded information content. For humidity profile retrieval, below 1.5 $\mathrm{k}\mathrm{m}$ altitude, the LM method outperforms all regularization-based methods, with the mean BIAS, mean RMSE, mean DFS, and mean residual of 3.65$\%$, 5.62$\%$, 2.05, and $4.36\times {10}^{-12}$ $\left(\mathrm{W}/{\mathrm{c}\mathrm{m}}^2 \mathrm{s}\mathrm{r} {\mathrm{c}\mathrm{m}}^{-1}\right)$, respectively. Conversely, other regularization methods exhibit strong prior dependence, causing retrieval to converge results toward the initial guess. Full article

As China’s second operational Geostationary Interferometric Infrared Sounder, Fengyun-4B/GIIRS can provide temporally and spatially continuous atmospheric temperature profile (ATP) and atmospheric humidity profile (AHP) information, which can be used in cold wave monitoring and other meteorological applications. In this study, radiosonde observations and ERA5 reanalysis are used to assess the quality of operational Fengyun-4B/GIIRS ATP and AHP products released by the National Satellite Meteorological Centre (NSMC). The results are as follows: (1) Compared to Fengyun-4A/GIIRS, due to the improvement in the instruments, the usability of Fengyun-4B/GIIRS is enhanced, and the influence of clouds and land surfaces reduces its usability under clear-sky conditions and below 900 hPa. (2) The current operational quality-flagged algorithm can identify the Fengyun-4B/GIIRS ATP and AHP products with different accuracies well, providing beneficial information to users. Taking radiosonde observations as a reference, the RMSEs of the Fengyun-4B/GIIRS ATP and AHP products with the best quality (with the quality flag of “very good”) are around 1.5K and below 2 kg/kg, respectively, which is better than those of the Fengyun-4A/GIIRS ATP product. (3) Compared to the ERA5 reanalysis, due to the different coefficients in the retrieval algorithm, systematic overestimation and underestimation occur for the Fengyun-4B/GIIRS ATP product under clear-sky conditions and cloudy-sky conditions, respectively. (4) The biases and RMSEs of the Fengyun-4B/GIIRS ATP and AHP products have significant dependence on the satellite zenith angles when the angles are larger than 50°, but when the angles are smaller than 50°, the dependence is negligible. Full article

Temperature and humidity profile lidar is one of the important means of urban atmospheric environment monitoring, which can capture atmospheric elements such as lidar ratio, color ratio, depolarization ratio, Ångström exponent, and temperature and humidity profile with research values. This study was based on the observation results of temperature and humidity profile lidar in Harbin and discusses the changes in the urban atmospheric environment under different conditions. The interaction processes between water vapor, temperature, and particulate matter, including aggregation, diffusion, phase transition, and transport, were explored under the main factor of anthropogenic pollution. This article analyzes the mutual influence of these atmospheric parameters in different environments, highlighting the important impact of temperature and humidity on the formation and diffusion of pollutants during pollution events. It supplements more data on urban atmospheric environment monitoring in the region and provides more data support for urban environmental governance. Full article

In this study, a drone-based measurement system equipped with miniaturized optical and condensation particle counters was deployed to investigate the vertical distribution of particulate matter and meteorological variables up to 2.5 km in altitude. Measurements captured at various altitudes demonstrated notable vertical variations in particle concentration and significant correlations with meteorological factors, particularly relative humidity (RH). Near the surface, within a well-mixed boundary layer, particle concentrations remained stable despite RH changes, indicating both anthropogenic and natural influences. At higher altitudes, a clear positive relationship between RH and particle number concentration emerged, particularly for smaller particles, while temperature inversions and distinct wind patterns influenced aerosol dispersion. The unmanned aerial vehicle system’s robust performance, validated against standard meteorological tower data, underscores its potential for high-resolution atmospheric profiling. These insights are crucial for understanding particle behavior in diverse atmospheric layers and have implications for refining air quality monitoring and climate models. Future work should incorporate chemical analysis of aerosols to further expand these findings and assess their environmental impact. Full article

Freezing precipitation and the resultant ice glaze can have catastrophic impacts on urban infrastructure, the environment, forests, and various industries, including transportation, energy, and agriculture. In this study, we develop and evaluate regional algorithms for detecting freezing precipitations in the Far East, utilizing the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts, along with standard meteorological observations for 20 cold seasons (September–May) from 2004 to 2024. We propose modified diagnostic algorithms based on vertical atmospheric temperature and humidity profiles, as well as near-surface characteristics. Additionally, we apply a majority voting ensemble (MVE) technique to integrate outputs from multiple algorithms, thereby enhancing classification accuracy. Evaluation of detection skills shows significant improvements over the original method developed at the Finnish Meteorological Institute and the ERA5 precipitation-type product. The MVE-based method demonstrates optimal verification statistics. Furthermore, the modified algorithms validly reproduce the spatially averaged inter-annual variability of freezing precipitation activity in both continental (mean correlation of 0.93) and island (correlation of 0.54) regions. Overall, our findings offer a more effective and valuable tool for operational activities and climatological assessments in the Far East. Full article

This study evaluated the reliability of satellite-derived atmospheric temperature and humidity profiles derived from occultations of Fengyun-3D (FY-3D), the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), the Meteorological Operational Satellite program (METOP), and the microwave observations of NOAA Polar Orbital Environmental Satellites (POES) using various conventional sounding datasets from 2020 to 2021. Satellite-derived profiles were also used to explore the precursors of severe convective precipitations in terms of the atmospheric boundary layer (ABL) characteristics and convective parameters. It was found that the satellite-derived temperature profiles exhibited high accuracy, with RMSEs from 0.75 K to 2.68 K, generally increasing with the latitude and decreasing with the altitude. Among these satellite-derived profile sources, the COSMIC-2-derived temperature profiles showed the highest accuracy in the middle- and low-latitude regions, while the METOP series had the best performance in high-latitude regions. Comparatively, the satellite-derived relative humidity profiles had lower accuracy, with RMSEs from 13.72% to 24.73%, basically increasing with latitude. The METOP-derived humidity profiles were overall the most reliable among the different data sources. The ABL temperature and humidity structures from these satellite-derived profiles showed different characteristics between severe precipitation and non-precipitation regions and could reflect the evolution of ABL characteristics during a severe convective precipitation event. Furthermore, some convective parameters calculated from the satellite-derived profiles showed significant and rapid changes before the severe precipitation, indicating the feasibility of using satellite-derived temperature and humidity profiles as precursors to severe convective precipitation. Full article

Near space refers to the atmospheric region 20–100 km above Earth’s surface, encompassing the stratosphere, mesosphere, and part of the thermosphere. This region is susceptible to surface and upper atmospheric disturbances, and the atmospheric temperature and humidity profiles can finely characterize its complex environment. To analyze the relationship between changes in temperature and humidity profiles and natural activities, this study utilizes 18 years of temperature and water vapor data from the TIMED/SABER and AURA/MLS instruments to investigate the variations in temperature and humidity with altitude, time, and spatial distribution. In addition, multiple linear regression analysis is used to examine the impact mechanisms of solar activity, the El Niño–Southern Oscillation (ENSO), and the Quasi-Biennial Oscillation (QBO) on temperature and humidity. The results show that in the mid- and low-latitude regions, temperature and water vapor reach their maxima at an altitude of 50 km, with values of 265 K and 8–9 × 10⁻⁶ ppmv, respectively; the variation characteristics differ across latitudes and altitudes, with a clear annual cycle; the feedback effects of solar activity and the ENSO index on temperature and humidity in the 20–40 km atmospheric layer are significantly different. Among these factors, solar activity is the most significant influence on temperature and water vapor, with response coefficients of −0.2 to −0.16 K/sfu and 0.8 to 4 × 10⁻⁶ ppmv/sfu, respectively. Secondly, in the low-latitude stratospheric region, the temperature response to ENSO is approximately −1.5 K/MEI, while in the high-latitude region, a positive response of 3 K/MEI is observed. The response of water vapor to ENSO varies between −1 × 10⁻⁷ and −4 × 10⁻⁷ ppmv/sfu. In the low-latitude stratospheric region, the temperature and humidity responses to the QBO index exhibit significant differences, ranging from −1.8 to −0.6 K/10 m/s. Additionally, there are substantial differences in responses between the polar regions and the low-latitude equatorial region. Finally, a three-dimensional model coefficient was constructed to illustrate the influence of solar activity, ENSO, and QBO on temperature and humidity in the near space. The findings of this study contribute to a deeper understanding of the temperature and humidity variation characteristics in near space and provide valuable data and model references for predicting three-dimensional parameters of temperature and humidity in this region. Full article

During the process of infrared remote sensing monitoring, obtaining real-time measurements of sky background radiation is extremely inconvenient. The current methods incur a certain amount of lag. In this study, within the existing theoretical framework, a fast transmittance calculation method using interpolation was adopted, and a simplified transmission model was established. This led to the development of a new and simplified method for rapid temperature and humidity retrieval. Compared to the line-by-line integration method, the interpolation method significantly improves the speed of transmittance calculation by several tens of times, while maintaining a high level of accuracy. The relative deviation between the results obtained using the interpolation method and those obtained through line-by-line integration is less than 1 ‱. With the proposed method, temperature and humidity profile information can be retrieved from measured spectra within 5 min and corresponding background spectra can be obtained. The differences between the calculated background radiation and the measured spectra using the new method are smaller, making it more suitable for calculating sky background radiation. Additionally, the rapid retrieval results of the temperature profiles in the lower atmosphere have a certain level of accuracy (the mean deviation is less than 2 K). Full article

Obtaining temperature and humidity profiles with high vertical resolution is essential for describing and predicting atmospheric motion, and, in particular, for understanding the evolution of medium- and small-scale weather processes, making short-range and near-term weather forecasting, and implementing weather modifications (artificial rainfall, artificial rain elimination, etc.). Ground-based microwave radiometers can acquire vertical tropospheric atmospheric data with high temporal and spatial resolution. However, the accuracy of temperature and relative humidity retrieval is still not as accurate as that of radiosonde data, especially in cloudy conditions. Therefore, improving the observation and retrieval accuracy is a major challenge in current research. The focus of this study was to further improve the accuracy of atmospheric temperature and humidity profile retrieval and investigate the specific effects of cloud information (cloud-base height and cloud thickness) on temperature and humidity profile retrieval. The observation data from the ground-based multichannel microwave radiometer (GMR) and the millimeter-wave cloud radar (MWCR) were incorporated into the retrieval process of the atmospheric temperature and relative humidity profiles. The retrieval was performed using the backpropagation neural network (BPNN). The retrieval results were quantified using the mean absolute error (MAE) and root mean square error (RMSE). The statistical results showed that the temperature profiles were less affected by the cloud information compared with the relative humidity profiles. Cloud thickness was the main factor affecting the retrieval of relative humidity profiles, and the retrieval with cloud information was the best retrieval method. Compared with the retrieval profiles without cloud information, the MAE and RMSE values of most of the altitude layers were reduced to different degrees after adding cloud information, and the relative humidity (RH) errors of some altitude layers were reduced by approximately 50%. The maximum reduction in the RMSE and MAE values for the retrieval of temperature profiles with cloud information was about 1.0 °C around 7.75 km, and the maximum reduction in RMSE and MAE values for the relative humidity profiles was about 10%, which was obtained around 2 km. Full article

Due to the influence of geometric factors, the temperature and humidity profile of lidar’s near-field signal was warped when sensing the air environment. In order to perform geometric factor correction on near-field signals, this article proposes different correction solutions for the Mie and Raman scattering channels. Here, the Mie scattering channel used the Raman method to invert the aerosol backscatter coefficient and correct the extinction coefficient in the transition zone. The geometric factor was the ratio of the measured signal to the forward-computed vibration Raman scattering signal. The aerosol optical characteristics were reversed using the corrected echo signal, and the US standard atmospheric model was added to the missing signal in the blind zone, reflecting the aerosol evolution process. The stability and dependability of the proposed algorithm were validated by the consistency between the visibility provided by the Environmental Protection Agency and the visibility acquired via lidar retrieval data. The near-field humidity data were supplemented by the interpolation method in the Raman scattering channel to reflect the water vapor transfer process in the temporal dimension. The measured transmittance curve of the filter, the theoretical normalized spectrum, and the sounding data were used to compute the delay geometric factor. The temperature was retrieved and the near-field signal distortion issue was resolved by applying the corrected quotient of the temperature channel. The proposed algorithm exhibited robustness and universality, enhancing the system’s detection accuracy compared to the temperature and humidity data constantly recorded by the probes in the meteorological gradient tower, which have a high correlation with the lidar observation data. The comparison between lidar data and instrument monitoring data showed that the proposed algorithm could effectively correct distorted echo signals in the transition zone, which was of great value for promoting the application of lidar in the meteorological monitoring of the urban canopy layer. Full article