Radiosonde Research Articles

A New Grid Zenith Tropospheric Delay Model Considering Time-Varying Vertical Adjustment and Diurnal Variation over China

Improving the accuracy of zenith tropospheric delay (ZTD) models is an important task. However, the existing ZTD models still have limitations, such as a lack of appropriate vertical adjustment function and being unsuitable for China, which has a complex climate and great undulating terrain. A new approach that considers the time-varying vertical adjustment and delicate diurnal variations of ZTD was introduced to develop a new grid ZTD model (NGZTD). The NGZTD model employed the Gaussian function and considered the seasonal variations of Gaussian coefficients to express the vertical variations of ZTD. The effectiveness of vertical interpolation for the vertical adjustment model (NGZTD-H) was validated. The root mean squared errors (RMSE) of the NGZTD-H model improved by 58% and 22% compared to the global pressure and temperature 3 (GPT3) model using ERA5 and radiosonde data, respectively. The NGZTD model’s effectiveness for directly estimating the ZTD was validated. The NGZTD model improved by 22% and 31% compared to the GPT3 model using GNSS-derived ZTD and layered ZTD at radiosonde stations, respectively. Seasonal variations in Gaussian coefficients need to be considered. Using constant Gaussian coefficients will generate large errors. The NGZTD model exhibited outstanding advantages in capturing diurnal variations and adapting to undulating terrain. We analyzed and discussed the main error sources of the NGZTD model using validation of spatial interpolation accuracy. This new ZTD model has potential applications in enhancing the reliability of navigation, positioning, and interferometric synthetic aperture radar (InSAR) measurements and is recommended to promote the development of space geodesy techniques.

Cold Surge Impacts on the Structure, Energy Budget, and Turbulence of the South China Sea Boundary Layer

Abstract Episodic cold surges in the East Asia winter monsoon can penetrate deep into the South China Sea (SCS), enhance consequent tropical rainfall, and further strengthen the East Asia meridional overturning circulation. These cold surges can promote strong surface fluxes and lead to a deeper marine boundary layer (MBL). However, there is a lack of boundary layer studies over the SCS, unlike many other well-studied regions such as the north Atlantic Ocean and the central-eastern Pacific Ocean. In this study, we use high resolution radiosonde data of temperature and humidity profiles over Dongsha Island (116.69E, 20.70N) to identify the inversion layer, mixed layer, cloud base, cloud top, and factors controlling low cloud cover for the period of December-January-February from 2010 to 2020. We perform an energy budget analysis with ERA-5 meteorological variables and surface fluxes. Here we show a strong turbulent flux convergence of both heat and moisture within the SCS MBL during cold surges, which leads to a lifting of the mixed layer to ~1.0 km and inversion layer to ~2.0 km and associated cloud development over Dongsha Island. The cold and dry horizontal advection is balanced by this vertical turbulent flux convergence in the energy budget. Overall, cold surges over the SCS enhance lower branch of winter monsoon meridional overturning circulation with stronger inversion and higher low cloud covers.

Improving atmospheric pressure vertical correction model using Gaussian function

The Zenith Hydrostatic Delay (ZHD) is essential for high-precision Global Navigation Satellite System (GNSS) and Very Long Baseline Interferometry (VLBI) data processing. Accurate estimation of ZHD relies on in situ atmospheric pressure, which is primarily variable in the vertical direction. Current atmospheric pressure is either site-specific or has limited spatial coverage, necessitating vertical corrections for broader applicability. This study introduces a model that uses a Gaussian function for the vertical correction of atmospheric pressure when in situ meteorological observations are unavailable. Validation with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) reveals an average Bias and RMS for the new model of 0.31 hPa and 2.96 hPa, respectively. This corresponds to improvements of 37.5% and 80.3% in terms of RMS compared to two commonly used models (T0 and Tv models) that require in situ meteorological observations, respectively. Additional validation with radiosonde data shows an average Bias and RMS of 1.85 hPa and 4.87 hPa, corresponding to the improvement of 42.8% and 71.1% in RMS compared with T0 and Tv models, respectively. These accuracies are sufficient for calculating ZHD to an accuracy of 1 mm by performing atmospheric pressure vertical correction. The new model can correct atmospheric pressure from meteorological stations or numerical weather forecasts to different heights of the troposphere.

A model-based study of the dynamics of Arctic low-level jet events for the MOSAiC drift

Low-level jets (LLJs) are studied for the period of the ship-based experiment MOSAiC 2019/2020 using the regional climate model Consortium for Small-scale Model—Climate Limited area Mode (CCLM). The model domain covers the whole Arctic with 14 km resolution. CCLM is run in a forecast mode (nested in ERA5) and with different configurations of sea ice data for the winter. The focus is on the study of LLJs for the MOSAiC site. LLJs are detected using model output every 1 h. We define LLJ events as LLJs that last at least 6 h. Case studies of LLJ events are shown using wind lidar and radiosonde data as well as CCLM simulations. LLJs are not local events but are embedded in large jet structures extending for hundreds of kilometers that are advected toward the MOSAiC site. CCLM simulations are used to study the statistics of LLJs of all profiles and of LLJ events as well as the dynamics. LLJs are found in about 40% of the hourly profiles, but only 26% of the hourly profiles are associated with LLJ events. Strong LLJs (≥15 m/s) are detected in 13% of the hourly profiles, which is about the same fraction as for strong LLJ events. The mean duration of events is about 12 h. The LLJ events are characterized using dynamical criteria for the wind speed profile and the evolution of the jet core. A fraction of 35% of the LLJ events are baroclinic, but more than 40% of the LLJ events show a large contribution of advection to the initial generation as well as for the evolution of the jet core. Only very few events fulfill the criteria of inertial oscillations. LLJ events occur for all months, but strong events have a higher frequency during winter. The turbulent kinetic energy in the lower atmospheric boundary layer (ABL) is twice (4 times) as large for LLJs (strong LLJs) than for situations without LLJs, which underlines the impact of LLJs on turbulent processes in the ABL.

Error Analysis and Correction of FENGYUN-4A GIIRS Temperature Profile Products in Summer over the Qinghai–Tibet Plateau

To understand the applicability of the temperature profile product of the FENGYUN-4A (FY-4A) geostationary interferometric infrared detector (GIIRS) in summer over the Qinghai–Tibet Plateau and to improve product quality, the error of GIIRS temperature products was analyzed based on radiosonde data. A long short-term memory network model (LSTM) was used to correct the GIIRS temperature profile product in summer over the plateau at a high altitude (above 500 hPa), and further evaluation of the corrected product was conducted. The results show that summertime GIIRS temperature retrievals over the Qinghai–Tibet Plateau had a positive bias above 150 hPa and a negative bias below 150 hPa, resulting in an overall negative bias. The root mean square error was between 2 and 2.9 K, and the root mean square error was relatively large at 100 hPa and above. The LSTM established in this study could effectively correct the GIIRS temperature over the plateau. The correlation and root mean square error of the corrected GIIRS temperature and the radiosonde observation temperature were significantly improved. Using a trained LSTM correction model to correct the hourly GIIRS temperature can improve the accuracy and usability of the product. After correction, the average bias of the GIIRS temperature compared to the ERA5 temperature product was reduced from −0.26 K to 0.06 K, and the root mean square error was reduced from 2.25 K to 1.25 K. The correction model can be applied to different seasons, and it can also correct the GIIRS temperature in larger areas based on other high-precision and high-resolution data, achieving good results, thus indicating that the correction model has universal applicability.

Investigating Stagnant Air Conditions in Almaty: A WRF Modeling Approach

This study investigates stagnant atmospheric conditions in Almaty, Kazakhstan, a city nestled within a complex terrain. These conditions, characterized by weak local winds and inversion layers, trap pollutants within the city, particularly during winter. The Weather Research & Forecasting (WRF) model was employed to simulate atmospheric conditions using Local Climate Zone data. Verification of the model’s accuracy was achieved through comparisons with data from weather stations and the Landsat-9 satellite. The model successfully reproduced the observed daily temperature variations and weak winds during the testing period (13–23 January 2023). Comparisons with radiosonde data revealed good agreement for morning temperature profiles, while underestimating the complexity of the evening atmospheric structure. The analysis focused on key air quality factors, revealing cyclical patterns of ground-level and elevated inversions linked to mountain-valley circulation. The model effectively captured anabatic and katabatic flows. The study further examined the urban heat island (UHI) using a virtual rural method. The UHI exhibited daily variations in size and temperature, with heat transported by prevailing winds and katabatic flows. Statistical analysis of temperature and wind patterns under unfavorable synoptic situations revealed poor ventilation in Almaty. Data from three Januaries (2022/2023/2024) were used to create maps showing average daytime and nighttime air temperatures, wind speed, and frequency of calm winds.

Construction of a meteorological application system based on BDS ground-based augmentation network and water vapor products validation

The national Beidou Navigation Satellite System (BDS) ground-based augmentation network (BGAN) of China is constructed with the existing GNSS observation resources of industrial sectors and local governments, based on the concept of joint building and sharing with sustainable development. This study provides a detailed introduction to the design, construction and operation of a meteorological application system based on BGAN, and validation of its water vapor products. BDS and GPS real-time observation of atmospheric water vapor is achieved nationwide in China and multi-GNSS applications. Through the application of multi-GNSS data and validation of the water vapor products from 2018 to 2020, the accuracy of precipitable water vapor (PWV) derived from BDS only is equivalent to that from GPS only. The root mean square error (RMSE) between them is about 2 mm with high correlation coefficient. Based on radiosonde data, the validation is conducted with the products of BDS-PWV, GPS-PWV, and Combined-PWV derived with multi-GNSS of BDS and GPS. The error characteristics of the three products show a consistent trend over the months. The bias is relatively small. The RMSE of the three products is in the range of 2.18–2.73 mm. The BDS-PWV has the largest RMSE, followed by GPS-PWV, and Combined-PWV has the smallest RMSE.

Higher accuracy estimation of the weighted mean temperature (Tm) using GPT3 model with new grid coefficients over China

The weighted mean temperature (Tm) is an essential parameter in the field of Global Navigation Satellite System (GNSS) meteorology, as it enables the conversion of zenith wet delay to atmospheric precipitable water vapor (PWV). The existing Tm models, such as the GPT3 model, utilize the empirical annual and semi-annual amplitudes of Tm at grid points along with corresponding trigonometric functions to estimate Tm, making it difficult to provide a more detailed description of the daily variation of Tm. Therefore, the measured surface temperature and water vapor pressure were introduced to calculate the new series of grid coefficients using the least-square method, which enables the GPT3 model to estimate Tm with higher accuracy. In this process, the ERA5 reanalysis data derived from ECMWF from 2001 to 2010 was utilized to compute Tm values, and the new grid coefficient obtained by introducing only surface temperature was considered as scheme #1 and that achieved by introducing both surface temperature and surface water vapor pressure was regarded as scheme #2. The Tm data of 2023 calculated both by the radiosonde and ERA5 reanalysis data were utilized to assess the new grid coefficients for Tm estimation, and the results show that the grid coefficients based on these two surface parameters outperformed those only using the temperature. In the compassion using the ERA5 data, the bias/MAE/RMSE with the two types of new grid coefficients are −0.09/1.89/2.41 K and − 0.07/1.82/2.31 K, respectively, and the improvements reach to 50%/32.5%/34.2% and 61.1%/35%/36.9% compared with GPT3 model. In the comparison with radiosonde data, the scheme #1 and #2 performed a 0.55 K(13.8%) and 0.68 K(17.1%) improvement in the mean RMSE of stations compared with the GPT3 model. Note that the proposed two schemes can effectively improve the accuracy of estimating Tm in the comparison of regions, epochs and seasons with the Bevis and GPT3 model. Furthermore, the impact of Tm on PWV can be improved by the proposed two schemes, which was demonstrated using the theoretical RMSE and relative error of PWV.

A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest

Abstract. Various ground-based observing techniques provide precipitable water vapor (PWV) products with different spatial resolutions. To effectively integrate these products, especially in terms of vertical orientation, spatial interpolation is essential. In this context, we have developed a model to characterize PWV variation with altitude over our study area. Our model, known as RF-PWV (a PWV vertical correction grid model with a 1° × 1° resolution), is constructed using random forest based on the relationship between the differences in different pressure level PWV data from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) monthly average hourly data and corresponding differences in their height differences over time. When validated against 1 h ERA5 PWV profiles, RF-PWV exhibits a 99.84 % reduction in bias and a 63.41 % decrease in the RMSE compared with the most recent model, C-PWVC1. Furthermore, when validated against radiosonde data, RF-PWV shows a 96.36 % reduction in bias and a 5 % decrease in the RMSE compared with C-PWVC1. Additionally, RF-PWV outperforms C-PWVC1 in terms of resistance to seasonal and height difference interference. The model eliminates the need for meteorological parameters, allowing for high-precision PWV vertical correction by inputting only time and height differences. Consequently, RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.

Extending the wind profile beyond the surface layer by combining physical and machine learning approaches

Abstract. Accurate estimation of the wind profile, especially in the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. Nevertheless, the Monin–Obukhov similarity theory fails above the surface layer over a heterogeneous underlying surface, causing an unreliable wind profile to be obtained from conventional extrapolation methods. To solve this problem, we propose a novel method called the PLM-RF method that combines the power-law method (PLM) with the random forest (RF) algorithm to extend wind profiles beyond the surface layer. The underlying principle is to treat the wind profile as a power-law distribution in the vertical direction, with the power-law exponent (α) determined by the PLM-RF model. First, the PLM-RF model is constructed based on the atmospheric sounding data from 119 radiosonde (RS) stations across China and in conjunction with other data such as surface wind speed, land cover type, surface roughness, friction velocity, geographical location, and meteorological parameters from June 2020 to May 2021. Afterwards, the performance of the PLM-RF, PLM, and RF methods over China is evaluated by comparing them with RS observations. Overall, the wind speed at 100 m from the PLM-RF model exhibits high consistency with RS measurements, with a determination coefficient (R2) of 0.87 and a root mean squared error (RMSE) of 0.92 m s−1. By contrast, the R2 and RMSE of wind speed results from the PLM (RF) method are 0.75 (0.83) and 1.37 (1.04) m s−1, respectively. This indicates that the estimates from the PLM-RF method are much closer to observations than those from the PLM and RF methods. Moreover, the RMSE of the wind profiles estimated by the PLM-RF model is relatively large for highlands, while it is small for plains. This result indicates that the performance of the PLM-RF model is affected by the terrain factor. Finally, the PLM-RF model is applied to three atmospheric radiation measurement sites for independent validation, and the wind profiles estimated by the PLM-RF model are found to be consistent with Doppler wind lidar observations. This confirms that the PLM-RF model has good applicability. These findings have great implications for the weather, climate, and renewable energy sector.

A study of evaporation duct characteristics in the South China Sea during the Winter of 2017

The evaporation duct (ED) near the sea surface is the most frequent type of duct that traps and redirects electromagnetic waves over longer or shorter distances. This study uses radiosonde data from the South China Sea Two-Island Monsoon Experiment (SCSTIMX) in 2017 and the Weather Research and Forecasting (WRF) model to study the ED and ED height (EDH). We enhanced the vertical resolution to 26 layers in the lowest 134 m. The simulations indicate that EDs were prevalent throughout the South China Sea (SCS) area in winter 2017, where the prevailing surface winds are strong northeasterly. The EDH exhibits diurnal variations and significant variability across the SCS. The WRF simulations of ED and EDH generally agree with observations of the trajectory of Taiwan-O.R.1. Our results indicate that the WRF model can simulate the pattern of ED and EDH over SCS. Results reveal that Yonsei University (YSU) boundary layer (BL) parameterization more accurately simulates the vertical temperature and humidity variations in the lower atmosphere, with stronger winds and higher EDH in most areas. The YSU and the Medium Range Forecast Model (MRF) BL schemes show similar ED patterns to the Taiwan-O.R.1 SCS radiosonde observations but consistently underestimate the EDH. While strong surface winds in the winter SCS may contribute to a higher EDH, the strong surface wind-induced vertical mixing also modifies sea surface air temperature, relative humidity, and vapor pressure, affecting the vertical refractivity. Our results indicate that sea surface air temperature is a more reliable factor affecting EDH in the simulations.

A New Empirical Model of Weighted Mean Temperature Combining ERA5 Reanalysis Data, Radiosonde Data, and TanDEM-X 90m Products over China

Weighted mean temperature (Tm) is an important parameter in the water vapor inversion of global navigation satellite systems (GNSS). High-precision Tm values can effectively improve the accuracy of GNSS precipitable water vapor. In this study, a new regional grid Tm empirical model called the RGTm model over China and the surrounding areas was proposed by combining ERA5 reanalysis data, radiosonde data, and TanDEM-X 90m products. In the process of model establishment, we considered the setting of the reference height in the height correction formula and the bias correction for the Tm lapse rate. Tm values derived from ERA5 and radiosonde data in 2019 were used as references to validate the performance of the RGTm model. At the same time, the GPT3, GGNTm, and uncorrected seasonal model were used for comparison. Results show that compared with the other three models, the accuracy of the RGTm model’s Tm was improved by approximately 12.21% (15.32%), 1.17% (3.09%), and 2.31% (5.05%), respectively, when ERA5 (radiosonde) Tm data were used as references. In addition, the introduction of radiosonde data prevented the accuracy of the Tm empirical model from being entirely dependent on the accuracy of the reanalysis data.

Application of wetPf2 Data for Investigating Characteristics of Temperature and Humidity of Air Masses over Paracel and Spratly Islands

This article uses data from the second-generation Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) satellites (wetPf2) to study the temperature and humidity properties of the air masses over Paracel and Spratly Islands in the Vietnam East Sea (South China Sea). The satellite observational data were validated with the radiosonde data from three stations in Vietnam: Hanoi, Danang, and Ho Chi Minh City. Subsequently, the wetPf2 data are used to analyze the characteristics of temperature and relative humidity variations of the air masses over the Paracel and Spratly regions. Results show that the mean error of the satellite observational data for temperature ranges from −0.06°C to −0.02°C, with standard deviations ranging from 0.73°C to 1.04°C. The mean error of relative humidity fluctuates between 11.6% and 12.5%, with standard deviations ranging from 15.1% to 19.1%. The values are reasonable and comparable to those in previous studies. Seasonal variations of temperature and humidity show that the air mass over the Paracel Islands exhibits a larger annual temperature with an annual variation of approximately 5.0°C, significantly higher than the value of 2.2°C in the air mass over the Spratly Islands. The difference may be due to the greater influence of continental and seasonal wind systems in the northern region. Within both air masses, the annual temperature variation in the boundary layer is much larger than that in the free atmosphere. Annual relative humidity variation is higher in summer and autumn than in winter and spring. The significant changes in the relative humidity with height during winter and no significant change of the relative humidity with height during summer may be related to the important role of strong convective activity carrying moist air upward to higher atmospheric levels during the summer time.

Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements

Abstract. In this paper a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. First, a review of the literature on the two-way transmittance method is presented. This method is a well-known lidar inversion method used to retrieve the optical properties of an aerosol–cloud layer between two molecular (i.e. aerosol and cloud-free) regions below and above, without the need to make any a priori assumptions about their optical and/or microphysical properties. Second, a simple mathematical expression of the two-way transmittance method is proposed for both ground-based and spaceborne lidar systems. This approach of the method allows the retrieval of the cloud optical depth, the cloud column lidar ratio and the vertical profile of the cloud backscatter coefficient. The method is illustrated for a cirrus cloud using measurements from the ground-based MPL and from the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Third, the database is then filtered with a cirrus identification criterion based on (and compared to) the literature using only lidar and radiosonde data. During the period from November 2018 to September 2022, 367 high-altitude cirrus clouds were identified at 00:00 and 12:00 UTC, of which 203 were successfully inverted with the two-way transmittance method. The statistical results of these 203 high-altitude cirrus clouds show that the cloud thickness is 1.8 ± 1.1 km, the mid-cloud temperature is −51 ± 8 ∘C and the linear cloud depolarization ratio is 0.32 ± 0.13. The application of the transmittance method yields an average cloud optical depth (COD) of 0.36 ± 0.45 and a mean effective column lidar ratio of 30 ± 19 sr. Statistical results of the errors associated with the two-way transmittance method retrievals are also provided. The highest occurrence of cirrus is observed in spring and the majority of cirrus clouds (48 %) are visible (0.03 < COD < 0.3), followed by opaque (COD > 0.3) with a percentage of 38 %. Together with results from other sites, possible latitudinal dependencies have been analysed together with correlations between cirrus cloud properties. For example, we noted that in Barcelona the COD correlates positively with the cloud base temperature, effective column lidar ratio and linear cloud depolarization ratio and negatively with the cloud base height.

Irrigated Agriculture Significantly Modifies Seasonal Boundary Layer Atmosphere and Lower-Tropospheric Convective Environment

Abstract Modification of grasslands into irrigated and nonirrigated agriculture in the Great Plains resulted in significant impacts on weather and climate. However, there has been lack of observational data–based studies solely focused on impacts of irrigation on the PBL and convective conditions. The Great Plains Irrigation Experiment (GRAINEX), conducted during the 2018 growing season, collected data over irrigated and nonirrigated land uses over Nebraska to understand these impacts. Specifically, the objective was to determine whether the impacts of irrigation are sustained throughout the growing season. The data analyzed include latent and sensible heat flux, air temperature, dewpoint temperature, equivalent temperature (moist enthalpy), PBL height, lifting condensation level (LCL), level of free convection (LFC), and PBL mixing ratio. Results show increased partitioning of energy into latent heat relative to sensible heat over irrigated areas while average maximum air temperature was decreased and dewpoint temperature was increased from the early to peak growing season. Radiosonde data suggest reduced planetary boundary layer (PBL) heights at all launch sites from the early to peak growing season. However, reduction of PBL height was much greater over irrigated areas than over nonirrigated croplands. Relative to the early growing period, LCL and LFC heights were also lower during the peak growing period over irrigated areas. Results note, for the first time, that the impacts of irrigation on PBL evolution and convective environment can be sustained throughout the growing season and regardless of background atmospheric conditions. These are important findings and applicable to other irrigated areas in the world. Significance Statement To meet the ever-increasing demand for food, many regions of the world have adopted widespread irrigation. The High Plains Aquifer (HPA) region, located within the Great Plains of the United States, is one of the most extensively irrigated regions. In this study, for the first time, we have conducted a detailed irrigation-focused land surface and atmospheric data collection campaign to determine irrigation impacts on the atmosphere. This research demonstrates that irrigation significantly alters lower atmospheric characteristics and creates favorable cloud and convection development conditions during the growing season. The results clearly show first-order impacts of irrigation on regional weather and climate and hence warrant further attention so that we can minimize negative impacts and achieve sustainable irrigation.

Analysis of Extreme Weather Causes of Flooding in Manado North Sulawesi

On January 27 and 28 2023, Manado City was hit by heavy rain which caused several areas to experience significant flooding. The high intensity of rain during that period reached extreme levels, resulting in the inundation of several areas and submerging hundreds of residents' houses in 22 villages or sub-districts and 7 sub-districts in Manado. In studying this event, this research investigates the factors that influence extreme weather in Manado, observing sea surface temperature data, streamlines, air observations, the Southern Oscillation Index (SOI), and the Indian Ocean Dipole (IOD). The analysis shows that the positive SOI and negative IOD phenomena significantly impact extreme weather in the region. Cumulonimbus (Cb) clouds drove the atmospheric conditions formed on January 27 and 28 2023 and air flows from the Indian Ocean which brought wet air masses into Indonesian territory. High sea surface temperatures, reaching 28-29°C, also play a role in the intensive growth of rain clouds. Based on atmospheric conditions seen from radiosonde observation data, the stability index value shows that unstable conditions trigger heavy rain which causes flooding

Validation of Aeolus L2B products over the tropical Atlantic using radiosondes

Abstract. Since its launch by the European Space Agency in 2018, the Aeolus satellite has been using the first Doppler wind lidar in space to acquire three-dimensional atmospheric wind profiles around the globe. Especially in the tropics, these observations compensate for the currently limited number of other wind observations, making an assessment of the quality of Aeolus wind products in this region crucial for numerical weather prediction. To evaluate the quality of the Aeolus L2B wind products across the tropical Atlantic Ocean, 20 radiosondes corresponding to Aeolus overpasses were launched from the islands of Sal, Saint Croix, and Puerto Rico during August–September 2021 as part of the Joint Aeolus Tropical Atlantic Campaign. During this period, Aeolus sampled winds within a complex environment with a variety of cloud types in the vicinity of the Intertropical Convergence Zone and aerosol particles from Saharan dust outbreaks. On average, the validation for Aeolus Rayleigh-clear revealed a random error of 3.8–4.3 m s−1 between 2 and 16 km, and 4.3–4.8 m s−1 between 16 and 20 km, with a systematic error of -0.5±0.2 m s−1. For Mie-cloudy, the random error between 2 and 16 km is 1.1–2.3 m s−1 and the systematic error is -0.9±0.3 m s−1. It is therefore concluded that Rayleigh-clear winds do not meet the mission's random error requirement, while Mie winds most likely do not fulfil the mission bias requirement. Below clouds or within dust layers, the quality of Rayleigh-clear observations are degraded when the useful signal is reduced. In these conditions, we also noticed an underestimation of the L2B estimated error. Gross outliers, defined as large deviations from the radiosonde data, but with low error estimates, account for less than 5 % of the data. These outliers appear at all altitudes and under all environmental conditions; however, their root cause remains unknown. Finally, we confirm the presence of an orbital-dependent bias observed with both radiosondes and European Centre for Medium-Range Weather Forecasts model equivalents. The results of this study contribute to a better characterisation of the Aeolus wind product in different atmospheric conditions and provide valuable information for further improvement of the wind retrieval algorithm.

Planetary Boundary Layer Flow over Complex Terrain during a Cold Surge Event: A Case Study

Planetary boundary layer (PBL) flow over complex terrain during a cold surge event was investigated using 3-hourly radiosonde measurements in upwind, near ridge, and downwind locations of mountains in the northeastern part of Republic of Korea and using a high-resolution (333-m) numerical simulation. A cold surge occurred on 23 January 2018 and lasted for 4 days. We analyzed onset day of the cold surge when air temperature dropped rapidly. Analysis of the radiosonde data shows that the PBL was characterized by an adiabatic layer with strong capping inversion in early morning and evening as well as during daytime in the upwind and near-ridge sites. The PBL flow at the near-ridge site was strongest among three sites except at 0600 local standard time (LST) when the PBL flow in the lee was strongest. We performed high-resolution (333-m) numerical simulations using the Weather Research and Forecasting (WRF) model. The adiabatic PBL in the upwind site at 0600 LST was simulated, although its depth was underestimated. The model reproduced the strong low-level wind at 0600 LST and large wind shear during the daytime in the lee, but it did not capture the exact timing of the large wind shear. The model showed overall good performance in simulating the vertical profile of the virtual potential temperature and wind below 2 km above ground level at the three sites, with a high index of agreement (IOA) except for the wind at 1200 and 1500 LST in the lee. To examine the cause for the different behavior of PBL flow in the lee of mountains between 0600 LST and the daytime, we calculated the Froude number for PBL flow using radiosonde measurements based on reduced gravity shallow water (RGSW) theory. At 0600 LST, the upwind Froude number F0 was close to 1, while during the daytime, it was much lower than 1. The observed lee flow behavior was consistent with the flow regime change of a single layer over an obstacle with changing F0; the flow with a propagating lee jump changes into that with a stationary lee jump with decreasing F0. Numerical simulation shows that the steepening of streamlines of lee-wave field leads to a jump-like structure in the lee of mountains during the daytime.

Seasonal variations of gravity‐wave parameters over the northern and southern Tibetan plateau radiosonde observatory

AbstractSeasonal variations of gravity‐wave parameters over the northern and southern Tibetan Plateau are studied using high vertical‐resolution L‐band radiosonde data during the period 2014 to 2019. The wave parameters include wavelength, frequency, phase speed, group velocity, perturbation energies and momentum fluxes. The synoptic effects on the gravity‐wave parameters are discussed and assessed with the Pearson correlation. The tropospheric vertical wavelengths as estimated from the zonal wind perturbation were found to show a pronounced decrease over the southern Tibetan Plateau in the warm season. The tropospheric (stratospheric) horizontal wavelength is shortest (longest) in summer, and the stratospheric horizontal wavelength is longer than the tropospheric horizontal wavelength. The ratio of the intrinsic frequency to the Coriolis parameter is 2.5–5 (2–4.5) with a summer (winter) maximum in the troposphere (lower stratosphere). The ratio is generally higher in the north than in the south. The typical value of the mean tropospheric (stratospheric) intrinsic‐phase speed is 3.5–6 (2–11) m·s−1. The mean intrinsic‐phase speed in the south is mostly larger than that in the north. The tropospheric zonal ground‐based group velocities are dominantly eastward in all seasons. The stratospheric zonal ground‐based group velocity shifts from eastward in summer to westward in winter. In the troposphere, the seasonal cycles of the kinetic energy (KE) and potential energy (PE) exhibit regional differences. The tropospheric vertical fluctuation energy (VE) is independent of both season and region. The stratospheric KE, PE and VE are smaller in the warm season. The negative zonal momentum flux and positive meridional momentum flux are strongest in winter and summer.

Multiscale Spatiotemporal Variations of GNSS-Derived Precipitable Water Vapor over Yunnan

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.