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Water vapor scale height reflects the vertical distribution of water vapor in the atmosphere, which is a vital parameter in tropospheric zenith wet delay (ZWD) modeling and GNSS meteorology. This study accurately calculated the long-term water vapor scale height using 89 radiosonde stations in China, analyzed the characteristics in temporal and spatial variations and explored the impact factors of the water vapor scale height from the perspective of the climate zones. The numerical results indicate that the water vapor scale height in China exhibits annual and semiannual variations with a rising-then-decreasing trend across the four seasons. A relatively larger value for the water vapor scale height were observed in the subtropical monsoon climate and the temperate continental climate zone, in which the values are greater than 2.35 km, while the values in temperate monsoon climate and the alpine plateau climate zone are relatively small. In addition, the water vapor scale height in different climate zones shows different trends, with a downward trend appeared in the subtropical monsoon climate and the alpine plateau climate zone, while an upward trend was observed in the temperate monsoon climate and the temperate continental climate zone. Furthermore, a positive correlation between water vapor scale height and temperature, pressure, and atmospheric precipitable water was found in China, with a varied degree of correlation in different climate zones. [ABSTRACT FROM AUTHOR]

• The IGTmS model takes seasonal and intraday variations into consideration of T m. • The IGTmS model adopts a stratification mode to improve accuracy of T m estimation. • The advantage of the IGTmS model becomes more pronounced for higher altitudes. • Three resolutions of the IGTmS models have been developed. The atmospheric weighted mean temperature (T m) is a key parameter for converting tropospheric wet delay (ZWD) into precipitable water vapor (PWV) and plays a vital role in Global Navigation Satellite System (GNSS) meteorology. Existing T m models suffer from limitations such as the lack of consideration of full periodicity, poor accuracy at high altitudes, and the adoption of single-resolution grid data for modeling. Hence, an improved global T m stratification model is developed based on ERA5 hourly reanalysis data from 2015 to 2019, called IGTmS, which takes seasonal and intraday variations into consideration and adopts a stratification mode to improve the vertical accuracy of T m estimation. The accuracy and applicability of the IGTmS model are evaluated using T m data at 417 radiosonde stations in 2020. The results show that the mean bias and RMS of T m for the IGTmS model are −0.34 K and 3.53 K, and the accuracy is improved by approximately 7.8 %, 4.6 % and 2.5 % compared with that of GPT3, GTm-III and GTrop models on a global scale, respectively. IGTmS model can achieve the mean σ PWV and σ PWV / PWV values of 0.38 mm and 1.3 %, respectively, which shows optimal performance among the four models, especially in the Antarctic and Tibetan Plateau regions. Furthermore, three resolutions of the IGTmS models have been developed and all show high accuracy compared to GPT3 models. Users can choose the suitable model according to the desired accuracy and resolution. The developed stratification model IGTmS can effectively weaken the influence of large height difference on T m estimation and can obtain high-accuracy and stable T m over the globe and provide data support for real-time GNSS PWV retrieval. [ABSTRACT FROM AUTHOR]

Knowledge of the planetary boundary layer height (PBLH) is crucial for various applications in atmospheric and environmental sciences. Lidar measurements are frequently used to monitor the evolution of the PBLH, providing more frequent observations than traditional radiosonde‐based methods. However, lidar‐derived PBLH estimates have substantial uncertainties, contingent upon the retrieval algorithm used. In addressing this, we applied the Different Thermo‐Dynamic Stabilities (DTDS) algorithm to establish a PBLH data set at five separate Department of Energy's Atmospheric Radiation Measurement sites across the globe. Both the PBLH methodology and the products are subject to rigorous assessments in terms of their uncertainties and constraints, juxtaposing them with other products. The DTDS‐derived product consistently aligns with radiosonde PBLH estimates, with correlation coefficients exceeding 0.77 across all sites. This study delves into a detailed examination of the strengths and limitations of PBLH data sets with respect to both radiosonde‐derived and other lidar‐based estimates of the PBLH by exploring their respective errors and uncertainties. It is found that varying techniques and definitions can lead to diverse PBLH retrievals due to the inherent intricacy and variability of the boundary layer. Our DTDS‐derived PBLH data set outperforms existing products derived from ceilometer data, offering a more precise representation of the PBLH. This extensive data set paves the way for advanced studies and an improved understanding of boundary‐layer dynamics, with valuable applications in weather forecasting, climate modeling, and environmental studies. Plain Language Summary: The planetary boundary layer (PBL) is the lowest region of the atmosphere directly influenced by Earth's surface. This layer is vital as it connects the atmosphere to surface processes. Given its importance, accurately determining its height (PBLH) is crucial for weather, climate, and air quality studies. However, current PBLH estimates either have infrequent time intervals, as seen with radiosondes, or face notable uncertainties, like those derived from remote sensing techniques. This research evaluates a new lidar‐based PBLH method at five Department of Energy's Atmospheric Radiation Measurement sites across the globe. The PBLH data set from the new methodology aligns well with radiosonde, with notably smaller biases compared to the existing products. Additionally, this study investigates potential errors in our PBLH data, revealing that varying techniques and definitions can produce different PBLH values, highlighting the complex and dynamic nature of the PBL. Key Points: This study evaluates the performance of the new Different Thermo‐Dynamic Stabilities (DTDS) algorithm at five Department of Energy's Atmospheric Radiation Measurement sitesDTDS‐derived boundary layer data set outperforms existing lidar‐derived products at all five sites, offering a more precise representationThis study provides an extensive boundary layer height data set that paves the way for future investigations in various applications [ABSTRACT FROM AUTHOR]

From the past to the present, weather forecasting holds significant importance for humanity. The precise execution of weather forecasting enables the implementation of precautions against natural disasters such as floods, tsunamis, etc., thereby minimizing the adverse effects that may arise. In this study, weather prediction is conducted using Radiosonde data. Within this prediction, estimations for both the highest and lowest temperatures are made employing machine learning algorithms. Unlike previous temperature prediction studies in the literature, a three-year dataset of Radiosonde observations is utilized. This dataset, measured at intervals of 1mbar up to an altitude of 40 km from the ground, allows for a more accurate modeling of the atmosphere compared to other studies in the literature. In this model, predictions for the highest and lowest temperatures for the next day are made. In this stage, the effects of normalization, feature extraction, or selection on the results are analyzed, and the most suitable model for prediction is determined. The software, implemented in the MATLAB environment, compares different regression methods. As a result of these analyses, utilizing the Gaussian Process Regression (GPR) method, the highest temperature prediction for the next day is achieved with the highest accuracy, with a mean square root deviation of 1.2. Using the same method, the lowest temperature prediction is made with a mean square root deviation ratio of 2.4. The results indicate more successful temperature predictions compared to studies in the literature. [ABSTRACT FROM AUTHOR]

Abstract Precise modeling of weighted mean temperature (T m ) is essential for Global Navigation Satellite System (GNSS) meteorology. In retrieving precipitable water vapor (PWV) from GNSS, T m is a crucial parameter for the conversion of zenith wet delay (ZWD) into PWV. In this study, an improved T m model, named EGWMT, was developed to accurately estimate T m at any site in Egypt. This new model was established using hourly ERA5 reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) covering the period from 2008 to 2019 with a spatial resolution of 0.25° × 0.25°. The performance of the proposed model was evaluated using two types of data sources, including hourly ERA5 reanalysis data from 2019 to 2022 and radiosonde profiles over a six-year period from 2017 to 2022. The accuracy of the EGWMT model was compared to that of four other models: Bevis, Elhaty, ANN and GGTm-Ts using two statistical quantities, including mean absolute bias (MAB) and root mean square error (RMSE). The results demonstrated that the EGWMT model outperformed the Bevis, Elhaty, ANN and GGTm-Ts models with RMSE improvements of 32.5%, 30.8%, 39% and 48.2%, respectively in the ERA5 data comparison. In comparison with radiosonde data, the EGWMT model achieved RMSE improvements of 22.5%, 34%, 38% and 19.5% against Bevis, Elhaty, ANN and GGTm-Ts models, respectively. In order to determine the significance of differences in means and variances, statistical tests, including t-test and F-test, were conducted. The results confirmed that there were significant differences between the EGWMT model and the four other models.

The vertical profiles of specific humidity in the troposphere measured by the millimetre-wave humidity sounder (MHS) onboard of the Earth Observation Satellite (EOS)-07 (Microsoft 2B) are validated using ground-based radiosonde observations during July-August 2023. MHS onboard EOS-07 (hereafter referred as EOS-07/MHS) is a 6-channel cross-track scanning radiometer operating in the 183.31 ± 16.25 GHz band. The EOS-07 observations are used to retrieve three-dimensional specific humidity profiling from 1000 hPa (surface) to 100 hPa (16 km) atmospheric pressure levels with a spatial resolution of 10 km at nadir. The EOS-07/MHS measurements over India have a high correlation coefficient of 0.98 with the radiosonde observations. The mean relative bias is found to be 0.44 ± 0.55 g kg−1 below 350hPa and 2.8 ± 2.3 g kg−1 above 350 hPa. It is also found that above the 350 hPa level, MHS seems to be systematically overestimating the radiosonde measurements. After that, the EOS-07/MHS measurements are used to construct the diurnal variation of humidity during the active phases of the Indian summer monsoon. The observations are consistent with the present understanding of the Indian summer monsoon system. The present results are very encouraging and demonstrate the great potential of EOS-07/MHS observations of humidity for meteorological application, especially in understanding the hydrological cycle. [ABSTRACT FROM AUTHOR]

As part of the program for upgrading a stratospheric radiosonde for cosmic rays, an electronic barorelay was developed using modern circuitry elements. This article considers its design and characteristics. The results of test flights with the electronic barorelay and flights with a mechanical barorelay during similar solar activity in 1978 are also compared. According to the results of all tests, it is recommended to use an electronic barorelay in the radiosonde circuit. [ABSTRACT FROM AUTHOR]

Pressure, temperature and specific humidity profiles from COSMIC-2 are compared with those from ERA5 and radiosonde data from October 2019 to September 2020. The results demonstrate that COSMIC-2 pressure and specific humidity profiles show relatively poor accuracy and stability in the lower troposphere. The 1D-Var solution effectively improves the accuracy of temperature parameters in the troposphere. Three parameters show high accuracy over one climatological year, but show slight seasonal fluctuations, especially for the specific humidity profiles in summer. Furthermore, COSMIC-2 specific humidity data show low consistency with radiosonde and ERA5 in the equatorial region. [ABSTRACT FROM AUTHOR]

• Empirical model using radiosonde (52 yrs), M-100 rocket (17 yrs), SABER (22 yrs) over Indian region. • SAO, TAO, AO, QBO variability is incorporated to the temperature to obtain the empirical model. • Model compares well with observations below (RMSE < 1 K) and differences above 20 km (RMSE ∼ 4–6 K). • First study to use ground and space-based measurements for a long period to develop empirical model. An empirical model of temperature is developed using long-term observations of radiosonde (52 years), M-100 Rocket (17 years), and Sounding of the Atmosphere by Broadband Emission Radiometry (SABER) (22 years) data. The dominant oscillations such as Annual Oscillation (AO), Semi-Annual Oscillation (SAO), Terr-Annual oscillation (TAO), and Quasi-Biennial Oscillation (QBO) variability has been incorporated into the temperature to obtain the empirical atmosphere. The monthly mean of temperature obtained from radiosonde from 0 to 25 km, M-100 rocket from 26 to 80 km and SABER from 26 to 109 km has been subjected to the Fast Fourier Transform technique to retrieve the amplitudes and phases of AO, SAO, TAO, and QBO. The amplitudes and phases obtained are put in an empirical formula along with the temperature obtained from the observations to retrieve the empirical temperature. The empirical model is developed for two epochs (1971–1990, and 2002–2023). The amplitude is similar in both epochs for the TAO, and it is high in the first compared to the second epoch for SAO, AO, and QBO. The peak in the amplitude (3–4 K) is obtained between 80 and 90 km for TAO, and between 70 and 80 km (2–3 K) for SAO. The amplitude is less than 1 K for AO in both the epochs. The peaks in the QBO are observed between 20 and 30 km (2–3 K), another between 60 and 70 km (3–4 K) in the first epoch and above 80 km (2–3 K). Downward phase propagation is dominant for all the oscillations observed below 50 km, and upward propagation above. The empirical temperature obtained compares well with the observations below 20 km (RMSE <1 K) and shows differences above it (RMSE ∼ 4–6 K). This is due to the fact, that the amplitude of the oscillation is more in the stratosphere and mesosphere which results in perturbation of the background temperature. [ABSTRACT FROM AUTHOR]

In this paper, we applied a variety of statistical methods to study gravity waves in the troposphere and lower stratosphere in the Brazilian sector, using a large database from Instituto de Controle do Espaço Aéreo (ICEA) of radiosonde measurements carried out in 2014 at 32 locations in the Brazilian territory totaling 49,652 wind and temperature profiles. The average wind profiles were computed and classified by means of a hierarchical cluster analysis. The kinetic and potential energy densities of gravity waves were determined using a detrending technique based on the Least Squares Method and the Fast Fourier Transform. By analyzing the energy density time series it was found that tropospheric average values are consistently larger in the months of winter, late autumn and early spring. Stratospheric average values of variability and kinetic energy density are also consistently larger in this period. A systematic search for quasi monochromatic waves was carried out and their main characteristics such as horizontal/vertical wavelengths and velocities were determined both in the troposphere and lower stratosphere. A correlation analysis between the troposphere and the lower stratosphere based on the measured parameters was used to investigate the wave coupling between the two layers, and no significant correlation was found. Finally, a spatial correlation analysis between energy densities measured at different aerodromes in the same atmospheric layer was carried out, showing that energy densities are spatially correlated for distances less than 3,000–4,000 km. Plain Language Summary: Like waves in the ocean that can be easily seen by any observer in the beach, the atmosphere is also permeated by waves of similar nature, called Gravity Waves (GWs). These waves transport energy through the atmosphere, eventually breaking, reflecting or dissipating at some point. In this work we investigated the characteristics of these waves using weather data retrieved by weather balloons released from several locations in the Brazilian territory in 2014. By analyzing the measurements, we quantified parameters related to GWs, such as the kinetic and potential energy densities. We also investigated GWs that have well defined frequencies, called monochromatic waves, and determined their wavelengths, phases, amplitudes and phase velocities. We did not find correlations between the wave energies in the troposphere and the low stratosphere, which is an evidence of weak coupling between both layers. This result suggests that GWs characteristics are substantially modified in the perturbed, turbulent and windy region between the troposphere and low stratosphere. Moreover, we also identified the prevailing behavior of the winds in each of the studied locations. Key Points: A comprehensive survey on gravity waves using radiosonde data from 32 locations in the Brazilian territory totaling 49,652 profilesThe energy densities of gravity waves are spatially correlated within a region of approximately 3,000 km of radiusIt was found that gravity waves propagating in the troposphere and low stratosphere are uncorrelated in the studied locations [ABSTRACT FROM AUTHOR]

Significant knowledge gaps exist in our understanding of urban boundary layer processes, particularly the hygrothermal state. The earth system community has successfully used microwave radiometers for several decades. However, the applicability in complex urban environments has never been adequately tested. Here, observations from a microwave radiometer are compared to radiosonde readings in a densely urbanized site in Houston, Texas. The site was influenced by both an urban heat island and the sea breeze phenomenon. The analysis showed significant disagreement between the virtual potential temperature predicted by the microwave radiometer and the radiosonde for all periods within the boundary layer. However, the values were reasonably comparable above the boundary layer. The microwave radiometer incorrectly predicted an inversion layer instead of a mixed layer during convective periods. The microwave radiometer measurements deviated from the radiosonde measurements throughout the lower troposphere for the relative humidity. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Microwave Imager (GMI) is a microwave (MW) radiometer that has near-global coverage and frequent revisit time. To date, operational total column water vapor (TCWV) data records from the GPM GMI sensor have been exclusively offered over oceanic regions. It is challenging to retrieve TCWV over land from satellite MW measurements because of varying land surface characteristics. In this paper, a novel Light Gradient Boosting Machine-based retrieval algorithm is proposed to derive TCWV over land from GMI-sensed MW brightness temperature (BT) observations. The GMI-observed MW BT at 18.7 GHz and 23.8 GHz, differential BT between 18.7 GHz and 23.8 GHz, latitude, longitude, and month are selected and utilized as the input variables of the retrieval approach, because of their strong correlation with satellite-sensed MW TCWV retrievals. Instead of surface emissivity data or radiative transfer model, we take into account the spatial and temporal elements, namely latitude, longitude, and month. The training of the retrieval method is performed based on ground-based TCWV estimates from worldwide 4,471 Global Navigation Satellite System (GNSS) stations in 2017. The performance of the newly proposed retrieval algorithm is independently validated in a worldwide coverage using reference TCWV from additional 4,341 GNSS stations in 2018–2020 and 605 radiosonde stations in 2017–2020. The newly retrieved TCWV estimates over land have a correlation coefficient of 0.76 and 0.83, a root-mean-square error (RMSE) of 5.82 mm and 6.02 mm, a relative RMSE of 34.91% and 34.36%, and a mean bias of 0.02 mm and −0.42 mm compared to reference TCWV from GNSS and radiosonde, respectively. The performance of the retrieval algorithm is satisfactory when compared to that of land-purpose TCWV of other satellite missions, though we have not used either surface emissivity data or radiative transfer model. This result increases confidence in retrieving TCWV over land from satellite-sensed MW BT measurements based on machine learning using ground-based TCWV observations. The newly developed retrieval algorithm has the potential for integration into operational satellite missions or meteorological services, thereby enhancing weather forecasting, climate modeling, and other relevant applications.

This study aims to elucidate the influence of meteorological conditions on particle levels in Cergy-Pontoise. It explores the temporal variability of PM10 pollution days by associating them with the vertical temperature profile derived from conventional radiosondes from 2013 to 2022 (regional station). The results indicate that nearly 80% of exceedance days were associated with thermal inversions, primarily observed in winter and typically lasting 1 to 3 days. Analysis of winter thermal inversion characteristics suggests that those linked to pollution primarily occur near the ground, with higher intensity in December (12.1 °C) and lower in February (10.3 °C). Persistent inversions (extended nocturnal by diurnal inversion) account for 91.4% of the total inversions associated with high concentrations. Captive balloon soundings and temperature measurements at different altitudes were conducted during the winter of 2022/2023 to clarify thermal inversion in the Oise Valley at the center of Cergy-Pontoise. The results highlight three nocturnal wind circulation mechanisms in the valley, including downslope flow, circulation influenced by an urban heat island, and mechanical air evacuation under an inversion layer towards the less steep East side of the valley. Analysis of PM with the temperature gradient in the Oise Valley shows a significant correlation, suggesting an increase in concentrations during locally detected inversions and a decrease during atmospheric disturbance. [ABSTRACT FROM AUTHOR]

Atmospheric boundary-layer studies have applications in lower tropospheric processes, air pollution meteorology, weather prediction and climate monitoring. Despite this, very few studies are available over tropics, especially over central India. Here, we analyse, high resolution vertical profiles of meteorological parameters corresponding to fully developed mixed layer obtained using balloon borne radiosonde to study characteristics of atmospheric boundary layer over Nagpur (21.15 °N, 79.15 °E). Study reveals that, within the mixed layer, variation in RH is very less (within 20%) while above mixed layer, large variations are seen. Back trajectory analysis confirms that the moist days (dry days) are associated with the transport of air masses from the Arabian Sea (from arid locations). Mixed layer heights derived through gradients in Virtual Potential Temperature (VPT) showed highest values during Pre monsoon (2564 ± 867 m) and lowest (788 ± 477 m) in Monsoon season. Specific Humidity varies in the range from 0 to 10 g/kg during pre-monsoon and 0–20 g/kg during monsoon season. D-theta exhibits lowest values in monsoon and highest values during pre-monsoon season while Parcel saturation pressure difference exhibits highest values in monsoon and lowest values during pre-monsoon season. During the monsoon and early post monsoon there is a presence of high moist static energy (Theta-e ~ 365 K) at the surface and moderate during mid-winter. We have examined, for the first time, the spatial (vertical) coherence of VPT using a novel technique of auto correlation analysis. Coherence length scales, derived with this method are found to be Maximum (1129 ± 48 m) in winter and minimum (694 ± 281 m) in Pre monsoon season. [ABSTRACT FROM AUTHOR]

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the Global Navigation Satellite System (GNSS) data collected from 12 stations situated in Yunnan, which are part of the Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) data from 2011 to 2022. The retrieved PWV data at Station KMIN were evaluated by the nearby radiosonde data, and the results show that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm, respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the stations' heights and latitudes. Differences between average PWV in the wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. The amplitudes of the PWV annual cycles are 9.75–20.94 mm. The spatial variation of these amplitudes is similar to that of the average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed. [ABSTRACT FROM AUTHOR]

The characteristics of atmospheric variables over the southern coast of West Java in the presence of Australian Monsoon (AUM) and Mixed Rossby-Gravity (MRG) waves were investigated by conducting a dedicated radiosonde observation campaign from 26 to 29 September 2022 at Pameungpeuk Station (107.7°E, 7.6°S). The vertical profiles of pressure, temperature, relative humidity, and wind in the presence of the AUM and MRG waves were obtained and analyzed. The Global Navigation Satellite System - Radio Occultation (GNSS-RO) and the latest ECMWF climate reanalysis (ERA5) data were used to identify the MRG waves features using Hovmöller, space-time spectral, and wavelet analyses. Furthermore, spatial and time-series analyses were performed to study the wave propagation. The results showed the characteristics of atmospheric variables over Pameungpeuk Station in the presence of the AUM and MRG waves. We found that the presence of AUM significantly reduced the relative humidity, particularly in the region from 2 to 10 km altitude, and enhanced the average wind speed during the campaign compared to the wet season. Moreover, we found the reduction of low-level pressure, the enhancement of temperature and relative humidity in the mid-troposphere, and the weakening of the easterly and southerly winds during the dry phase approaching the wet phase of the MRG waves over the southern coast of West Java following the wave structure and propagation. [ABSTRACT FROM AUTHOR]

Radiosonde measurements of relative humidity (RH) are the main source of uncertainty in precipitable water vapour (PWV) calculation from pressure, temperature, and RH/dewpoint (PTU) data. This paper presents a formula expressing PWV in terms of pressure and temperature as functions of geopotential height (GPH), thereby allowing the PWV to be determined: (1) without any moisture‐related calculations other than those involved in measuring GPH (in radiosondes with a pressor sensor) or pressure (otherwise); (2) without relying on humidity measurements by using Global Positioning System (GPS)‐based GPH according to the gravity field, provided that pressure is directly measured. The numerical instability associated with random data errors or deviations from hydrostatic equilibrium makes the second approach unfeasible on short time scales, revealing discrepancies between the PTU‐ and GPS‐based GPHs; however, the estimation of long‐term average PWV above a location is not hindered. The estimation of PWV without humidity data was tested using high‐resolution data from 62 upper‐air stations operated by the NOAA National Weather Service. The seasonal mean {DJF, MAM, JJA, SON} PWV from the surface to 300 hPa calculated from PT and GPS data over the period 2016–2018, after rejecting individual estimates inconsistent with the 0%–100% RH range, showed a mean bias error of {−0.1, +0.1, −1.4, −0.9} kg·m−2 relative to the PTU‐based values across the stations, and a RMSE ranging from 2.4 (DJF) to 3.2 (JJA) kg·m−2. By restricting the analysis to observations with above‐average matching between the PTU‐ and GPS‐based GPH, the bias magnitude and RMSE reduced respectively to less than 0.5 and 1 kg·m−2 in all seasons. The results indicate that evaluating the long‐term agreement between the two PWV calculation methods at different sites could be useful in detecting systematic observation errors in GPS radiosonde systems using a pressure sensor. [ABSTRACT FROM AUTHOR]

The accurate measurement of water vapor is essential for research about and the applications of meteorology, climatology, and hydrology. Based on the BeiDou PPP-B2b service, real-time precipitable water vapor (PWV) can be retrieved with the precise point positioning (PPP) software (XTW-PPP version 0.0). The experiment was conducted in Beijing in January 2023. Three solutions were designed with PPP using the BeiDou system only, the GPS system only, and the BeiDou-GPS combined solution. Real-time PWVs for the three solutions were validated with the ERA5 reanalysis data. Between the PWV values from the single BeiDou and ERA5, there was a bias of 0.7 mm and an RMSE of 1.8 mm. For the GPS case, the bias was 0.73 mm and the RMSE was 1.97 mm. The biases were less than 1 mm and RMSEs were less than 2 mm. Both the BeiDou and the GPS processing performed very well. But little improvement was found for the BeiDou-GPS combined solution, compared with the BeiDou system-only and the GPS system-only solution. This may be due to the poor handling of two different kinds of errors for the GPS and the BeiDou systems in our PPP software. A better PWV estimation with the two systems is to estimate PWV with a single system at the first step and then obtain the optimization by Bayesian model averaging.

The tropopause region is crucial for the stratosphere‐troposphere exchange (STE) and acts as an indicator of climate change. Double tropopauses (DTs) act to increase the STE process but their driving mechanisms remain an open question. The present assessment offers for the first time the linkage between tropospheric gravity waves (GWs) and DT events by exploring a global data set of multi‐year radiosonde measurements. In the extratropics, the occurrence frequency of DT events keeps a remarkably consistent spatial‐temporal structure with GW total energy. Under the DT scenario, GW total energy has increased by 37.67% compared to single tropopause events. Based on a statistical assessment, the upward propagating GWs throughout the second tropopause region can probably raise Kelvin‐Helmholtz instability or turbulence, leading permanent irregularities in thermodynamic structure, and consequently, increasing the likelihood of DT. Plain Language Summary: Atmospheric disturbances above the troposphere are predominantly associated with waves generated in the troposphere. Among others, gravity waves (GWs) play major roles in transport of energy and momentum from the lower to the upper atmosphere. The double tropopause (DT) caused by irregular temperature changes is a common phenomenon in the lowermost stratosphere, which has an indicative significance for climate change. The upward propagating GWs sourced from the troposphere can probably alter the temperature variability in the tropopause region, and therefore, they are likely associated with the occurrence of DTs. By analyzing a large amount of global radiosonde data, simultaneous measurements of tropospheric GWs and irregularities in the tropopause enable us to argue that GWs sourced from troposphere could increase the likelihood of DTs. Key Points: A good level of agreement in spatial‐temporal variability has been identified between Double tropopause (DT) events and tropospheric gravity wave (GW) total energySubstantial tropospheric GW and Kelvin‐Helmholtz instability frequency enhancements in the tropopause region can be observed with DT phenomenaThe tropospheric GWs may increase the likelihood of DT events by introducing permanent localized irregularities or instabilities [ABSTRACT FROM AUTHOR]

Multi-Global Navigation Satellite Systems (multi-GNSS) (including GPS, BDS, Galileo, and GLONASS) provide a significant opportunity for high-quality zenith tropospheric delay estimation and its applications in meteorology. However, the performance of zenith total delay (ZTD) retrieval from single- or multi-GNSS observations is not clear, particularly from the new, fully operating BDS-3. In this paper, zenith tropospheric delay is estimated using the single-, dual-, triple-, or four-GNSS Precise Point Positioning (PPP) technique from 55 Multi-GNSS Experiment (MGEX) stations over one year. The performance of GNSS ZTD estimation is evaluated using the International GNSS Service (IGS) standard tropospheric products, radiosonde, and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5). The results show that the GPS-derived ZTD time series is more consistent and reliable than those derived from BDS-only, Galileo-only, and GLONASS-only solutions. The performance of the single-GNSS ZTD solution can be enhanced with better accuracy and stability by combining multi-GNSS observations. The accuracy of the ZTD from multi-GNSS observations is improved by 13.8%, 43.8%, 27.6%, and 22.9% with respect to IGS products for the single-system solution (GPS, BDS, Galileo, and GLONASS), respectively. The ZTD from multi-GNSS observations presents higher accuracy and a significant improvement with respect to radiosonde and ERA5 data when compared to the single-system solution. [ABSTRACT FROM AUTHOR]

Water vapour is a critical atmospheric parameter to understand the Earth's climate system and it is characterized by a complex variability in time and space. GNSS observations have become an important source of information of the water vapour, thanks to its high temporal and spatial resolution. However, the lack of meteorological sites collocated with the GNSS site could hamper water vapour retrieval. The empirical blind models can fill this gap. This study analyses the temporal and spatial distribution of the water vapour using nine GNSS sites located on the Atlantic coast of Spain and France, with the empirical blind model GPT3 as the source of meteorological information. The observations were processed with Bernese 5.2 software on a double difference approach and validated with Zenith Total Delay EUREF REPRO2 values. Consequently, four-years series of water vapour was determined and validated using two matched radiosonde sites. The characterization of the water vapour on the area shows clear seasonal characteristics that the technique captures, using an empirical blind model for the whole process. Maximum values are observed in summer season and minimum in winter. The PWV tends to decrease with increasing latitude in the area of the study. The short-term variations can be reproduced by the high temporal resolution of the GNSS-retrieved water vapour and show a different behaviour over the area, but a similar pattern with a peak in the afternoon and minimum at night was found. Also, less variability is observed in winter season and higher in summertime. [ABSTRACT FROM AUTHOR]

Four meteorological components (geopotential height Z, air temperature T, dew point temperature Td, and relative humidity RH) collected from ERA-5 and ERA-Interim were compared with the observations of nine radiosonde stations with different climatic changes, at different isobaric levels (850, 700, 500, and 200 hPa) during the period 2000–2017, in order to assess the accuracy of the aforementioned reanalysis datasets. The results showed that both reanalysis datasets have a strong correlation with the observed variables, except with dew point temperature and relative humidity in the upper troposphere. The mean values of geopotential height and temperature from both grid dataset are generally consistent with the radiosonde values, whereas considerable bias in the mean Td and RH exists and increases upwards. The study clearly proved that the reanalysis datasets can be used to compensate for the lack of radiosonde observation. Furthermore, air temperature (during 1959–2021) showed an increasing trend from the surface to the lower troposphere, while the temperature decreased in the upper troposphere and lower stratosphere. Finally in this study, the impact of the North Atlantic Oscillation Index (NAOI) on the air temperature was also examined, and a negative relationship was found between NAOI and temperature at the levels: surface, 850, 700, and 500 hPa, while a positive relationship was found, only in winter, at 200 hPa. At the level of 100 hPa, the correlation is positive for both seasons. [ABSTRACT FROM AUTHOR]

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The intercomparison between different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde and atmospheric model outputs. Since instruments and/or measures are not perfectly collocated, miss-collocation uncertainty must be considered in related intercomparison uncertainty budgets. This paper is motivated by the comparison of GNSS-RO, the Global Navigation Satellite System Radio Occultation, with ERA5, the version 5 Reanalysis of the European Centre for Medium-range Weather Forecasts. We consider temperature interpolation observed at GNSS-RO pressure levels to the ERA5 levels. We assess the interpolation uncertainty using as 'truth' high-resolution reference data obtained by GRUAN, the Reference Upper-Air Network of the Global Climate Observing System. In this paper, we propose a mathematical representation of the interpolation problem based on the well-known State-space model and the related Kalman filter and smoother. We show that it performs the same (sometimes better) than linear interpolation and, in addition, provides an estimate of the interpolation uncertainty. Moreover, with both techniques, the interpolation error is not Gaussian distributed, and a scaled Student's t distribution with about 4.3 degrees of freedom is an appropriate approximation for various altitudes, latitudes, seasons and times of day. With our data, interpolation uncertainty results larger at the equator, the Mean Absolute Error being M A E ≅ 0.32 K, and smaller at a high latitude, M A E ≅ 0.21 K at −80° latitude. At lower altitudes, it is close to the measurement uncertainty, with M A E < 0.2 K below the tropopause. Around 300 hPa, it starts increasing and reaches about 0.8 K above 100 hPa, except at the equator, where we observed MAE about 1 K. [ABSTRACT FROM AUTHOR]

Knowledge of atmospheric boundary layer height (ABL) during transient monsoon conditions is essential to understand the role of the monsoon on the exchange between ABL and the free troposphere (FT). The role of the Indian summer monsoon on the day-to-day variations of the ABL height over the five selected stations distributed across the northern (New Delhi), central (Nagpur), western (Mumbai), eastern (Kolkata), and southern peninsular (Gadanki) India has been investigated. In this study, radiosonde datasets over 2004–2019 are utilized to obtain the ABL height during the active and break phases of the monsoon. The India Meteorological Department (IMD) gridded rainfall dataset identifies the active and break phases based on the central India rainfall. We have observed the contrasting wind range of spatial variation in the meteorological features during the active and break monsoon conditions over these stations. Within the ABL, the temperature becomes lower and relative humidity (RH) higher during the active than the break phase over different stations across India. The inland stations show pronounced active and break differences compared to the coastal stations. All the stations have a larger active and break difference in surface meteorological parameters during the daytime compared to nighttime. The particulate matter (PM) 2.5 concentration is higher during the break phase when compared to the active phase. The active and break difference in the ABL height strongly depends on the time and space scales besides the meteorological factors. On a day-to-day scale, the relationship between the ABL and active and break phases is not straightforward, as their relationship is nonlinear. The relationship between the ABL height and active and break monsoon phases is stronger for the central inland stations than the coastal stations. Over southern peninsular India, ABL height is more related to wet and dry conditions based on the local rainfall than the central India monsoon rainfall. The ABL height becomes shallower (0.6–0.8 km) over central India and east and west coastal regions in north India, while deeper ABL (~ 1.0–1.8 km) over northwestern India and peninsular inland regions during the active phase and vice versa during the break phase. [ABSTRACT FROM AUTHOR]

Assumptions of linear lapse rates in regions prone to surface-based inversions (SBIs) can generate biases in the prediction of surface air temperature. Although studies of Arctic inversions are common, few regional studies of their characteristics exist in high-latitude regions with mountainous topography. To address this gap, vertical atmospheric temperature profiles for five sites in northwestern Canada were analysed using archived radiosonde data from 1990 to 2016. We present monthly, seasonal, and annual SBI characteristics including the occurrence of transient and persistent SBIs. A novel metric, surface-based inversion impact (SBIimp), was developed by combining the traditional inversion characteristics of depth, strength, and frequency, and was used to quantify the impact of SBIs on cooling the surface air temperature. SBIimp values of >5 °C yr−1 and ∼10 °C winter−1 occur locally. A weak linear relationship between sea ice coverage in the Beaufort Sea and SBIimp manifests across parts of the study area, though this relationship does not persist after detrending the datasets. Topographic analysis of areas surrounding each radiosonde location reveals highly variable SBIimp in complex mountain areas and more consistent SBIimp across areas of low relief. Our results can help interpret the role of inversions in climatic conditions maintaining cryospheric elements such as permafrost.

Abstract The tropopause region is crucial for the stratosphere‐troposphere exchange (STE) and acts as an indicator of climate change. Double tropopauses (DTs) act to increase the STE process but their driving mechanisms remain an open question. The present assessment offers for the first time the linkage between tropospheric gravity waves (GWs) and DT events by exploring a global data set of multi‐year radiosonde measurements. In the extratropics, the occurrence frequency of DT events keeps a remarkably consistent spatial‐temporal structure with GW total energy. Under the DT scenario, GW total energy has increased by 37.67% compared to single tropopause events. Based on a statistical assessment, the upward propagating GWs throughout the second tropopause region can probably raise Kelvin‐Helmholtz instability or turbulence, leading permanent irregularities in thermodynamic structure, and consequently, increasing the likelihood of DT.

The long-term height-resolved wind trend in China under global warming still needs to be discovered. To fill this gap, in this paper we examined the climatology and long-term (1979–2021) trends of the wintertime wind speed at the near-surface and upper atmosphere in China based on long-term radiosonde measurements. At 700, 500, and 400 hPa, much higher wind speed was found over eastern China, compared with western China. At 300, 200, and 100 hPa, maximum wind speed was observed in the latitude zone of around 25–35°N. Furthermore, westerly winds dominated most parts of China between 20°N and 50°N at altitudes from 700 hPa to 100 hPa. A stilling was revealed for the near-surface wind from 1979–2003. From 2004 onward, the near-surface wind speed reversed from decreasing to increasing. This could be largely due to the joint impact of reduced surface roughness length, aerosol optical depth (AOD), and increased sensible heat flux in the ground surface. The decrease of AOD tended to reduce aerosol radiative forcing, thereby destabilizing the planetary boundary layer (PBL). By comparison, the wintertime wind in the upper atmosphere exhibited a significant monotonic upward trend, albeit with varying magnitude for different altitudes. In the upper troposphere, the wintertime maximum wind was observed along a westerly jet stream, with a pronounced upward trend within the zone approximately bounded by latitudes of 25–50°N, particularly above 500 hPa. This accelerating wind observed in the upper troposphere and lower stratosphere could be closely associated with the large planetary-scale meridional temperature trend gradient. Besides, the direction for the wind at the near-surface and lower troposphere (925 and 850 hPa) exhibited a larger variance over the period 1979–2021, which could be associated with the strong turbulence of PBL caused by the heterogeneous land surface. For those pressure levels higher than 850 hPa, large wind directional variance was merely found to the south of 25 °N. The findings from long-term radiosonde measurements in winter over China shed light on the changes in wind speed on the ground and upper atmosphere under global warming from an observational perspective. [ABSTRACT FROM AUTHOR]

The hailstorm of 30 August 2022 in the NE of Catalonia (NE of the Iberian Peninsula) produced the largest hail size, with diameters exceeding 10 cm. Furthermore, hail occurrence exceeded 2 cm in fourteen days and 4 cm in seven days during August 2022. The size and the days number constituted new records in Catalonia for at least the last 30 years. The analysis has compared the thermodynamic values derived from the sounding of Barcelona with the climatic data for 1998–2022 (25 years of data). This fact has allowed the selection and evaluation of different thermodynamic parameters' behaviour during hail days in Catalonia. In this research, the precipitable water mass provided the best results as a hail forecaster. Second, the study has evaluated if the different parameters have a significant trend during the study period. The answer is yes in all cases, but some parameters presented a stepped rise while others increased smoothly. Finally, the research has analysed if the parameter values during August 2022 were extraordinary compared with the rest of the period. In this case, the thermodynamic parameters nature was well correlated with the hail size and occurrence maximums of August 2022. [ABSTRACT FROM AUTHOR]

The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) Radio Occultation (RO) data play an essential role in improving numerical weather prediction (NWP) and monitoring climate change. The NOAA Commercial RO Purchase Program (CDP) purchased RO data provided by Spire Global Inc. To ensure the data quality from Spire Global Inc. is consistent with other RO missions, we need to quantify their accuracy and retrieval uncertainty carefully. In this work, Spire Wet Profile (wet temperature profile) data from 7 September 2021 to 31 October 2022, processed by the University Corporation for Atmospheric Research (UCAR), and COSMIC-2 (Constellation Observing System for Meteorology, Ionosphere, and Climate-2/Formosa Satellite Mission 7) data are evaluated through comparison with NOAA-20 Advanced Technology Microwave Sounder (ATMS) microwave sounder measurements and collocated RS41 radiosonde measurements. Through the Community Radiative Transfer Model (CRTM) simulation, we convert the Spire and COSMIC-2 RO retrievals to ATMS brightness temperature (BT) at sounding channels CH07 to CH14 (temperature channels), with weighting function peak heights from 8 km to 35 km, and CH19 to CH22 (water vapor channels), with weighting function peak heights ranging from 3.2 km to 6.7 km, and compare the simulations with the collocated NOAA-20 ATMS measurements over ocean. Using ATMS observations as references, Spire and COSMIC-2 BTs agree well with ATMS within 0.07 K for CH07-14 and 0.20 K for CH19-22. The trends between Spire and COSMIC-2 are consistent within 0.07 K/year over the oceans for ATMS CH07-CH13 and CH19-22, indicating that Spire/COSMIC-2 wet profiles are, in general, compatible with each other over oceans. The RO retrievals and RS41 radiosonde observation (RAOB) comparison shows that above 0.2 km altitude, RS41 RAOB matches Spire/COSMIC-2 temperature profiles well with a temperature difference of <0.13 K, and the trends between Spire and COSMIC-2 are consistent within 0.08 K/year over land, indicating that Spire/COSMIC-2 wet profiles are overall compatible with each other through RS41 RAOB measurements over land. In addition, the consistency of Spire and COSMIC-2 based on different latitude intervals, local times, and signal-to-noise ratios (SNRs) through ATMS was evaluated. The results show that the performance of Spire is comparable to COSMIC-2, even though COSMIC-2 has a higher SNR. The high quality of RO profiles from Spire is expected to improve short- and medium-range global numerical weather predictions and help construct consistent climate temperature records. [ABSTRACT FROM AUTHOR]

In response to the nonlinear fitting difficulty of the traditional weighted average temperature (Tm) modeling, this paper proposed four machine learning (ML)-based Tm models. Based on the seven radiosondes in the Yangtze River Delta region from 2014 to 2019, four forecasting ML-based Tm models were constructed using Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Tree (CART) algorithms. The surface temperature (Ts), water vapor pressure (Es), and atmospheric pressure (Ps) were identified as crucial influencing factors after analyzing their correlations to the Tm. The ML-based Tm models were trained using seven radiosondes from 2014 to 2018. Then, the mean bias and root mean square error (RMSE) of the 2019 dataset were used to evaluate the accuracy of the ML-based Tm models. Experimental results show that the overall accuracy of the LightGBM-based Tm model is superior to the SVM, CART, and RF-based Tm models under different temporal variations. The mean RMSE of the daily LightGBM-based Tm model is reduced by 0.07 K, 0.04 K, and 0.13 K compared to the other three ML-based models, respectively. The mean RMSE of the monthly LightGBM-based Tm model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean RMSE of the quarterly LightGBM-based Tm model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean bias of the LightGBM-based Tm model is also smaller than that of the other ML-based Tm models. Therefore, the LightGBM-based Tm model can provide more accurate Tm and is more suitable for obtaining GNSS precipitable water vapor in the Yangtze River Delta region. [ABSTRACT FROM AUTHOR]

A back propagation neural network (BPNN)-based calibration method is developed to enhance the all-weather quality of official integrated water vapor (IWV) products from near-infrared (NIR) observations of the Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B satellites. The model utilizes multiple variables that link with the derivation of satellite NIR IWV products, including official OLCI NIR IWV, latitude, land quality and science flag, month, and solar zenith angle. The in situ IWV observations, collected from 100 Global Positioning System (GPS) stations, are employed as the desired IWV estimates. The model is evaluated using one-year water vapor data at additional 114 GPS stations and 97 radiosonde stations from June 1, 2019 to May 31, 2020 in China and its surrounding regions. The results show that the BPNN-based calibration model reduces the root-mean-square error (RMSE) of official OLCI NIR all-weather IWV products by 24.52% from 3.10 to 2.34 mm for Sentinel-3A and by 25.00% from 3.44 to 2.58 mm for Sentinel-3B, when compared with in situ GPS-observed reference IWV. When compared with in situ radiosonde-observed IWV, the RMSE reduces 21.01% from 4.57 to 3.61 mm and 20.81% from 5.19 to 4.11 mm for Sentinel-3A and Sentinel-3B, respectively. [ABSTRACT FROM AUTHOR]

Tropospheric delay is one of the major error sources in global navigation satellite systems (GNSS). The position accuracy determined using GNSS depends on the satellite’s geometry factor. However, in low elevation angle GNSS signals have a high tropospheric slant delay error and need a good mapping function model to cover this error. In this paper, two new tropospheric mapping functions (hydrostatic and non-hydrostatic) were developed based on an artificial neural network (ANN). The first model (Model A) was based on the day of the year (DOY), latitude and longitude. The second model (Model B) added real-time surface pressure to the parameters of Model “A.” The two developed models showed a strong correlation with the ray tracing mapping function, with a significant t-test value.The applicability of these mapping functions was tested over the Egypt region. The comparison was done with ray tracing of 5220 traces for 145 days from 2014 to 2017 as well as by assessing it (Mohamed et al., Vienna and Niell). This evaluation showed that the new model “B” agrees very well with the ray trace value. Model “B” and ray traces are on par with each other for a very low elevation angle 2⁰ the hydrostatic and non-hydrostatic mapping functions. Models “B” and “A” gave hydrostatic mean biases of 32 and 39 mm, respectively, and for non-hydrostatic, 4.5 and 4.9 mm, respectively. Vienna, Niell, and Mohamed et al. gave 46, 45, and 94 mm in hydrostatic, respectively, and 5.4, 5.2 and 9.2 mm in non-hydrostatic, respectively.

Volcanic eruptions are significant aviation hazards due to the formation of airborne volcanic ash clouds. Further, deep eruptions that reach the upper troposphere and lower stratosphere may have significant weather and climate impacts. A key variable for both dispersion model forecasting for aviation hazards and understanding climate impacts is the volcanic plume height. This work presents a method to quickly and reliably estimate the maximum plume heights of volcanic eruptions that interact with the tropical tropopause layer in the tropical western Pacific region. The method uses infrared (11.2μm) data from Himawari-8 to identify ‘stratospheric warm spots’ in optically thick portions of the eruption cloud top by searching for reversals in the local-brightness temperature gradient. The brightness temperature of these warm spots is converted to height using seasonal stratospheric reference temperature profiles derived from 20years of radiosonde data from 17 stations spread throughout the western Pacific. An approach for estimating the height of cold ‘overshooting tops’ is also adopted. Based on the radiosonde data, estimates of the uncertainty in the plume height depend on the height and range within 0.5–5.0km. A case study of the 19 December 2021 eruption of Hunga Tonga-Hunga Ha’apai demonstrates the technique. The heights are robustly determined with this simple technique and compare well with height estimates of eruptions in the literature that use more complex satellite techniques.

Significant knowledge gaps exist in our understanding of urban boundary layer processes, particularly the hygrothermal state. The earth system community has successfully used microwave radiometers for several decades. However, the applicability in complex urban environments has never been adequately tested. Here, observations from a microwave radiometer are compared to radiosonde readings in a densely urbanized site in Houston, Texas. The site was influenced by both an urban heat island and the sea breeze phenomenon. The analysis showed significant disagreement between the virtual potential temperature predicted by the microwave radiometer and the radiosonde for all periods within the boundary layer. However, the values were reasonably comparable above the boundary layer. The microwave radiometer incorrectly predicted an inversion layer instead of a mixed layer during convective periods. The microwave radiometer measurements deviated from the radiosonde measurements throughout the lower troposphere for the relative humidity.

This study aims to elucidate the influence of meteorological conditions on particle levels in Cergy-Pontoise. It explores the temporal variability of PM10 pollution days by associating them with the vertical temperature profile derived from conventional radiosondes from 2013 to 2022 (regional station). The results indicate that nearly 80% of exceedance days were associated with thermal inversions, primarily observed in winter and typically lasting 1 to 3 days. Analysis of winter thermal inversion characteristics suggests that those linked to pollution primarily occur near the ground, with higher intensity in December (12.1 °C) and lower in February (10.3 °C). Persistent inversions (extended nocturnal by diurnal inversion) account for 91.4% of the total inversions associated with high concentrations. Captive balloon soundings and temperature measurements at different altitudes were conducted during the winter of 2022/2023 to clarify thermal inversion in the Oise Valley at the center of Cergy-Pontoise. The results highlight three nocturnal wind circulation mechanisms in the valley, including downslope flow, circulation influenced by an urban heat island, and mechanical air evacuation under an inversion layer towards the less steep East side of the valley. Analysis of PM with the temperature gradient in the Oise Valley shows a significant correlation, suggesting an increase in concentrations during locally detected inversions and a decrease during atmospheric disturbance.

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the Global Navigation Satellite System (GNSS) data collected from 12 stations situated in Yunnan, which are part of the Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) data from 2011 to 2022. The retrieved PWV data at Station KMIN were evaluated by the nearby radiosonde data, and the results show that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm, respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the stations’ heights and latitudes. Differences between average PWV in the wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. The amplitudes of the PWV annual cycles are 9.75–20.94 mm. The spatial variation of these amplitudes is similar to that of the average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed.

• All PWV products under the FY-3D MWRI sensor were inspected. • Two types of ground data were used for the evaluation. • The products were analyzed in terms of space and season. • Two years of global data were selected. • This is useful for the PWV applications and PWV retrieval algorithm improvement of FY-3D microwave imager. Precipitable water vapor (PWV) products from the Microwave Radiation Imager (MWRI) onboard the Fengyun-3D (FY-3D), a second-generation polar orbit meteorological satellite of China, have a high spatiotemporal resolution and play an increasingly key role in the atmosphere and climate analysis. We focus on the FY-3D/MWRI PWV products in the global ocean area and comprehensively evaluate their quality by using the coastal radiosonde data from the Integrated Global Radiosonde Archive (IGRA) along with the PWV reanalysis products from the fifth generation of European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5). With respect to the radiosonde data, the correlation coefficient is 0.97, the root mean squared error (RMSE) is 2.71 mm, and the mean bias and mean relative bias is 0.36 mm and 2.57% respectively. The discrepancy between FY-3D/MWRI and radiosonde PWV is smallest at high latitudes and is biggest in the ocean around Africa. The seasonal variation characteristics perform best in spring and worst in summer. We also find that FY-3D/MWRI PWV products are in consistent with the ERA5 PWV, with a RMSE of 2.39 mm and 2.85 mm for daily and monthly data, respectively. Among globally 551,213 daily oceanic PWV grid data, there are 11.3% of matched points, which are mainly located between 15°S and 30°N latitudes, has a RMSE larger than 3 mm, while 68.5% of points have a RMSE of less than 2 mm, meeting the requirements of climate research. [ABSTRACT FROM AUTHOR]

• A novel approach for regional modeling and forecasting of PWV using LS-SVR is suggested. • The results of the new model are analyzed at control and radiosonde stations. • A comparative analysis between the results of new model, GPS and VBT is made. We propose a new model for spatio-temporal modeling and forecasting of precipitable water vapor (PWV) using least square support vector regression (LS-SVR) method. The LS-SVR uses simple linear equations. As a result, the complexity of the computational algorithm is reduced. In addition, the convergence speed and accuracy of the results increase. The evaluation of the new method has been done with the observations of the GPS networks at north-west and central Alborz in Iran. In the north-west GPS network, observations of 23 GPS stations in the period of 27 October to 10 November 2011 are used. However, in central Alborz network, the observations of 11 GPS stations in the period of 10 to 24 June 2016 have been used. The north-west GPS network is in the mountainous region and its observations are for the winter season. But, the second network is near the coastal area and summer season measurements are used. The latitude, longitude and height of GPS stations, DOY, time, relative humidity, temperature and pressure are considered as an input of the LS-SVR model. The output of the new model is the PWV (LS-SVR PWV). After the training step, the new model is used to estimate the spatio-temporal variation of PWV. The results of the LS-SVR model are compared and evaluated with the standard neural network (SNN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), Saastamoinen, global pressure and temperature 3 (GPT3), voxel-based tomography (VBT) models, as well as with radiosonde measurements. Error evaluation of models has been done in control stations as well as by precise point positioning (PPP) method. For LS-SVR model, the averaged RMSE in the control stations of the north-west GPS network is 1.69 mm, while for the central Alborz GPS network, 1.88 mm is calculated. Also, the averaged relative error of LS-SVR model calculated in the Tabriz and Tehran radiosonde stations are 4.66 % and 6.12 %, respectively. The results of this paper show that the LS-SVR model has a very high capability in forecasting the spatio-temporal variation of PWV at the GPS network territory. The new model can be used for accurate estimation of PWV, meteorological applications and flood forecasting. [ABSTRACT FROM AUTHOR]

Climate change research is an important field of study. Because of the refractivity of the earth's atmosphere, Global Positioning System (GPS) signals have a significant delay, making them a suitable approach for studying atmospheric weather phenomena. The zenith wet delays (ZWD) can be determined using GPS measurements. The capability of computing precipitable water vapour (PWV) from GPS signals is helpful to comprehend unique weather events. There are two objective for this work, firstly, zenith tropospheric delay (ZTD) is estimated for total of 20 GPS stations. Results demonstrated that the average root mean square (RMS) and standard deviation (STD) of ZTD estimate is 2440.1 and 63.6 mm, respectively. We compared the calculated ZTD values with the ZTD products offered by Center of Orbit Determination in Europe (CODE) products. Comparison results showed that the average difference and STD between estimates of ZTD and CODE products is 4.84 and 3.77 mm, respectively. Secondly, estimates of ZWD is used to determine the PWV. According to the PWV estimation results, the average RMS and STD value is 35.89 and 11.14 mm, respectively. PWV estimates for 10 IGS sites are compared with Radiosonde (RS) data. Comparison results show that the GPS-PWV estimates and RS data that the R's squared, bias and difference in STD is, respectively, 0.94, -0.24 and 3.99 mm. In addition, the differences of STD between RS-PWV and GPS-PWV is 3.88 and 4.08 mm, at UTC 00:00 and UTC 12:00, respectively. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) tropospheric tomography is a promising technique to provide high-resolution three-dimensional (3D) water vapor fields for assimilation into weather forecast models. Recently, a novel tomography method based on special mathematical functions, referred to as function-based tropospheric tomography, has been developed. However, this conventional function-based tomography (CFT) method only expresses the water vapor content through a fixed-degree polynomial function in the horizontal direction. This study suggests an adaptive-degree layered function-based tomography (ALFT) approach to overcome the limitation. The novelties of this approach are (1) the establishment of both horizontal and vertical base functions for tomography and (2) the determination of the optimal adaptive degree of the base functions. The proposed method can estimate the atmospheric water vapor at any position and altitude, producing space-continuous atmospheric water vapor distributions. Comparisons against radiosonde and ERA5 data suggest that the ALFT approach yields more accurate solutions than the CFT method, with the root mean square (RMS) error of tomographic results improved by 34% and 33%, respectively. This suggests that the ALFT method may improve the tomographic water vapor products and advance the function-based tomographic technique. [ABSTRACT FROM AUTHOR]