Search Results (158)

The evaluation of cloud distribution, properties, and their interaction with the radiation (longwave and shortwave) is of utmost importance for the proper assessment of future climate. Therefore, this study focuses on the Coupled Model Inter-Comparison Project Phase-6 (CMIP6) historical and future projections using the Shared Socio-Economic Pathways (SSPs) low- (ssp1–2.6), moderate- (ssp2–4.5), and high-emission (ssp5–8.5) scenarios along the South Asian region. For this purpose, a multi-model ensemble mean approach is employed to analyze the future projections in the low-, mid-, and high-emission scenarios. The cloud water content and cloud ice content in the CMIP6 models show an increase in upper and lower troposphere simultaneously in future projections as compared to ERA5 and historical projections. The longwave and shortwave cloud radiative effects at the top of the atmosphere are examined, as they offer a global perspective on radiation changes that influence atmospheric circulation and climate variability. The longwave cloud radiative effect (44.14 W/m²) and the shortwave cloud radiative effect (−73.43 W/m²) likely indicate an increase in cloud albedo. Similarly, there is an expansion of Hadley circulation (intensified subsidence) towards poleward, indicating the shifting of subtropical high-pressure zones, which can influence regional monsoon dynamics and cloud distributions. The impact of future projections on the tropospheric temperature (200–600 hPa) is studied, which seems to become more concentrated along the Tibetan Plateau in the moderate- and high-emission scenarios. This increase in the tropospheric temperature at 200–600 hPa reduces atmospheric stability, allowing stronger convection. Hence, the strengthening of convective activities may be favorable in future climate conditions. Thus, the correct representation of the model physics, cloud-radiative feedback, and the large-scale circulation that drives the Indian Summer Monsoon (ISM) is of critical importance in Coupled General Circulation Models (GCMs). Full article

This study investigates an extreme squall line event that occurred in northern Jiangxi Province, China on 30–31 March 2024. Based on the WRF model, convection-permitting ensemble forecast experiments were conducted using two distinct initial perturbation approaches, namely, the Local Breeding of Growing Modes (LBGM) and the Ensemble Transform Kalman Filter (ETKF), to compare their perturbation structures, spatiotemporal evolution, and precipitation forecasting capabilities. The experiments demonstrated the following: (1) The LBGM method significantly improved the root mean square error (RMSE) of mid-upper tropospheric variables, particularly demonstrating superior performance in low-level temperature field forecasts, but the overall ensemble spread of the system was consistently smaller than that of ETKF. (2) The evolution of dynamical spread within the squall line system confirmed that ETKF generated greater spread growth in low-level wind fields, while LBGM exhibited better spatiotemporal alignment between mid-upper tropospheric wind field spread and the synoptic system evolution. (3) Vertical profiles of total moist energy revealed that ETKF initially exhibited higher total moist energy than LBGM. Both methods showed increasing total moist energy with forecast lead time, displaying a bimodal structure dominated by kinetic energy in upper layers (300–100 hPa) and balanced kinetic energy and moist physics terms in lower layers (1000–700 hPa), with ETKF demonstrating larger growth rates. (4) Kinetic energy spectrum analysis indicated that ETKF exhibited significantly higher perturbation energy than LBGM in the 100–1000 km mesoscale range and superior small- to medium-scale perturbation characterization at the 6–60 km scales initially. Precipitation and radar echo verification showed that ETKF effectively corrected positional biases in precipitation forecasts, while LBGM more accurately reproduced the bow-shaped echo structure near Nanchang due to its precise simulation of leading-edge vertical updrafts and rear-sector low pseudo-equivalent potential temperature regions. Full article

The performance of Precise Point Positioning (PPP) using different Global Navigation Satellite System (GNSS) product sets, including broadcast ephemerides, International GNSS Service Real-Time Service (IGS-RTS) corrections, Galileo High Accuracy Service (HAS) corrections, and precise products from the Center for Orbit Determination in Europe (CODE) Multi-GNSS Experiment (MGEX), has been evaluated. The availability of solutions, convergence time, position accuracy and Zenith Tropospheric Delay (ZTD) estimation across these products were analyzed using simulated real-time and postprocessing static modes, using data from globally distributed stations with a 1 s observation interval. The results indicate that precise products from the MGEX provide the highest accuracy, achieving centimeter-level precision in post-processed mode. Real-time simulated solutions, such as HAS and IGS-RTS, deliver promising results, with Galileo HAS meeting its target accuracy of 20 cm horizontally and 40 cm vertically and a convergence time under 5 min. However, Global Positioning System (GPS) performance within HAS is limited by a significantly lower correction availability—around 67% on average compared to over 95% for Galileo—which negatively impacts PPP performance. ZTD estimation results show that real-time services (HAS, IGS-RTS) achieved errors within 1–3 cm, sufficient for meteorological applications. This study highlights the growing importance of HAS in real-time positioning applications and suggests further improvements in GPS for enhanced performance. Full article

The photochemical production of tropospheric ozone (O₃) is very closely linked to seasonal cycles and peaks in solar radiation occurring during warm seasons. In the Mediterranean Basin, which is a hotspot for climate and air mass transport mechanisms, boreal warm seasons cause a notable increase in tropospheric O₃, which unlike stratospheric O₃ is not beneficial for the environment. At the Lamezia Terme (code: LMT) World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) station located in Calabria, Southern Italy, peaks of tropospheric O₃ were observed during boreal summer and spring seasons, and were consequently linked to specific wind patterns compatible with increased photochemical activity in the Tyrrhenian Sea. The finding resulted in the introduction of a correction factor for O₃ in the O₃/NO_x (ozone to nitrogen oxides) ratio “Proximity” methodology for the assessment of air mass aging. However, some of the mechanisms driving O₃ patterns and their correlation with other parameters at the LMT site remain unknown, despite the environmental and health hazards posed by tropospheric O₃ in the area. In general, the issue of ozone photochemical pollution in the region of Calabria, Italy, is understudied. In this study, the behavior of O₃ at the site is assessed with remarkable detail using nine years (2015–2023) of data and correlations with surface temperature and solar radiation. The evaluations demonstrate non-negligible correlations between environmental factors, such as temperature and solar radiation, and O₃ concentrations, driven by peculiar patterns in local wind circulation. The northeastern sector of LMT, partly neglected in previous works, yielded higher statistical correlations with O₃ than expected. The findings of this study also indicate, for central Calabria, the possibility of heterogeneities in O₃ exposure due to local geomorphology and wind patterns. A case study of very high O₃ concentrations reported during the 2015 summer season is also reported by analyzing the tendencies observed during the period with additional methodologies and highlighting drivers of photochemical pollution on larger scales, also demonstrating that near-surface concentrations result from specific combinations of multiple factors. Full article

Temperature and water vapor density profiles in the troposphere (from the surface to 10 km) can be retrieved from a ground-based microwave radiometer (MWR) at high temporal and moderate vertical resolution. The back-propagation neural network (BPNN) algorithm is commonly deployed in ground-based microwave radiometers. Some studies have shown that the accuracy of the BPNN retrieval algorithm is affected by training data with large deviations. In this paper, an improved 1D-VAR method is proposed, which can effectively correct the bias; the results show that the improved 1D-VAR method can provide more accurate inversion results. Compared to the BPNN and 1D-VAR methods, the root mean square errors of temperature for the improved 1D-VAR method are reduced by $60.8\%$ and $29.4\%$ during daytime and by $54.2\%$ and $49.7\%$ during nighttime, respectively. Full article

Radar observations of tropospheric and lower-stratospheric winds and density-normalized momentum flux made in northern Germany with two 53.5 MHz VHF MST radars over a period of one week in August 1986 are presented. One MST radar was a permanent installation, the SOUSY VHF Harz radar, located in the Harz Mountains, and the other temporarily installed about 27 km away from the Harz. The latter radar, the SOUSY VHF Lindau radar, was operated with a limited number of antennas and much-reduced power, making it effectively a tropospheric radar. Unusually, this small radar was successfully operated in Doppler beam steering (DBS) mode to measure winds and density-normalized momentum fluxes after correcting for biases in the beam look directions resulting from its small antenna aperture. We compared the winds and density-normalized upward fluxes in horizontal momentum measured using these two radars. The mean winds show good agreement between the two radars and with winds from radiosondes launched from Essen and Hannover. Density-normalized zonal momentum fluxes are similar in form between the two radars, but do show an offset when calculated over the entire observational period. Because of the agreement in form, the zonal mean flow accelerations calculated from them are similar, and so these results are consistent between the radars even though the topography is quite different. Although the observations were made many years ago, the results we present here are still of interest, because comparisons of closely spaced wind profiling radar observations are still relatively rare, radar measurements of tropospheric and stratospheric momentum fluxes are sparse, and the successful operation of a very small DBS radar operating in the lower VHF band is of particular interest from a technical perspective. Full article

Accurate measurements of upper troposphere humidity are essential to enhance understanding of contrail formation and guiding mitigation efforts. This study evaluates the ability of the IPRAL Raman Lidar, located south of Paris, to provide high-resolution water vapour mixing ratio (WVMR) profiles at contrail-relevant altitudes. Raman signals are screened on hourly bases, and a universal calibration method, independent of acquisition mode, is proposed towards operational Lidar water vapour profiles, using co-located ERA5 data. Calibration factors are derived from comparisons between 4 and 6 km, and nightly coefficients determined from hourly factors. Instrumental stability is monitored through the temporal evolution of calibration factors, and stable-period medians are adopted as final values. The uncertainty of calibrated WVMR profiles is assessed by comparison with GRUAN processed Meteomodem M10 radiosondes and ERA5 data. Results show a high agreement (>90%), with IPRAL exhibiting a small negative bias (~10%) below 8 km, reducing to ~5% up to 10.5 km to radiosondes. ERA5 systematically underestimates water vapour at cruise altitudes, with a dry bias increasing from 10% at 9 km to >20% at 11 km. Recent IAGOS corrections to ERA5, improving supersaturation representation, are validated over Paris. This calibrated Lidar data set supports improved atmospheric modelling and contributes to future air traffic management strategies. Full article

InSAR technology effectively monitors urban subsidence and evaluates the stability of infrastructure across extensive regions. Atmospheric tropospheric delay constitutes a significant source of error that adversely impacts the accuracy of InSAR deformation measurements. The meteorological conditions in the highland basin region are complex, and there is a notable deficiency of sufficient sounding balloon observations. This study replaces the sounding balloon data in the power-law model with ERA-5 data (PLE5) to correct the InSAR atmosphere phase delay. This method was tested in Dali utilizing Sentinel-1 data. By comparing its performance against the GACOS model, traditional linear model, and ERA-5 correction, the PLE5 model exhibited the lowest phase standard deviation. This method provides an alternative approach for applying the power-law model in regions with limited sounding balloon data, enhancing the accuracy of InSAR tropospheric delay correction. Full article

The Microwave Temperature Sounder-3 (MWTS-3) onboard the Chinese FengYun-3E (FY-3E) satellite is the third generation of Chinese microwave temperature sounder. Based on MWTS-2, the number of MWTS-3 channels has been increased from 13 to 17, which can observe the atmospheric temperature and water vapor profiles from the surface to the lower stratosphere. In this study, two generations of MWTSs onboard FY-3D/3E were inter-calibrated by the Double Difference (DD) method to eliminate bias. The results showed that the biases of tropospheric channels were stable (within 1 K) and the biases of stratospheric channels were relatively large (over 2 K). In addition, the weighting functions of all MWTS channels varied with fields of view (FOVs) due to different optical paths, causing the brightness temperature (TB) observations to display strong scan-dependent features, i.e., the limb effect. This work used a limb correction method to remove scan-dependent patterns so that the underlying weather signals could be uncovered. After inter-calibration and limb correction, this work converted the TB observations from MWTS-2/3 onto a global gridded dataset at 0.5° × 0.5° latitudinal and longitudinal resolutions using a method of nested interpolation. Based on this research, more long-term FengYun series satellite climate datasets can be established in the future. Full article

Empirical tropospheric models can improve the performance of GNSS precise point positioning (PPP) by providing a priori zenith tropospheric delay (ZTD) information. However, existing models experience insufficient ZTD profile refinement, inadequate correction for systematic bias between the ZTD used in empirical modelling and the GNSS ZTD, and low time efficiency in model updating as more data become available. Therefore, a refined spatiotemporal empirical ZTD model was developed in this study on the basis of the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) data and GNSS data. First, an ENM-R profile model was established by refining the modelling height of the negative exponential function model (ENM). Second, a regression kriging interpolation method was designed to model the systematic bias correction between the ERA5 ZTD and the GNSS ZTD. Last, the final refined ZTD model, ENM-RS, was established by introducing systematic bias correction into ENM-R. Experiments suggest that, compared with the ENM-R and GPT3 models, ENM-RS can effectively suppress systematic bias and improve ZTD modelling accuracy by 10~17%. To improve model update efficiency, the idea of updating an empirical model with sequential least square (SLSQ) adjustment is proposed for the first time. When ENM-RS is modelled via 12 years of ERA data, our method can reduce the time consumption to one-fifth of that of the traditional method. The benefits of our ENM-RS model are evaluated with PPP. The results show that relative to PPP solutions with ENM-R- and GPT3-derived ZTD constraints as well as no constraint, the ENM-RS ZTD constraint can decrease PPP convergence time by approximately 10~30%. Full article

This study addresses the challenges associated with single-system long-baseline real-time kinematic (RTK) navigation, including limited positioning accuracy, inconsistent signal reception, and significant residual atmospheric errors following double-difference corrections. This study explores the effectiveness of long-baseline RTK navigation using an integrated system of the BeiDou Navigation Satellite System (BDS), Global Positioning System (GPS), and Galileo Satellite Navigation System (Galileo). A long-baseline RTK approach that incorporates Kalman filtering and partial ambiguity resolution is applied. Initially, error models are used to correct ionospheric and tropospheric delays. The zenith tropospheric and inclined ionospheric delays and additional atmospheric error components are then regarded as unknown parameters. These parameters are estimated together with the position and ambiguity parameters via Kalman filtering. A two-step method based on a success rate threshold is employed to resolve partial ambiguity. Data from five long-baseline IGS monitoring stations and real-time measurements from a ship were employed for the dual-frequency RTK positioning experiments. The findings indicate that integrating additional GNSSs beyond the BDS considerably enhances both the navigation precision and the rate of ambiguity resolution. At the IGS stations, the integration of the BDS, GPS, and Galileo achieved navigation precisions of 2.0 cm in the North, 5.1 cm in the East, and 5.3 cm in the Up direction while maintaining a fixed resolution exceeding 94.34%. With a fixed resolution of Up to 99.93%, the integration of BDS and GPS provides horizontal and vertical precision within centimeters in maritime contexts. Therefore, the proposed approach achieves precise positioning capabilities for the rover while significantly increasing the rate of successful ambiguity resolution in long-range scenarios, thereby enhancing its practical use and exhibiting substantial application potential. Full article

The detection of potential rural mountain landslide displacements using time-series interferometric Synthetic Aperture Radar has been challenged by both atmospheric phase screens and decoherence noise. In this study, we propose the use of a combined distributed scatterer (DS) and the Prophet_ZTD-NEF model to rapidly map the landslide surface displacements in Diqing Tibetan Autonomous Prefecture, China. We conducted tests on 28 full-resolution SENTINEL-1A images to validate the effectiveness of our methods. The conclusions are as follows: (1) Under the same sample conditions, confidence interval estimation demonstrated higher performance in identifying SHPs compared to generalized likelihood ratio test. The density of DS points was approximately eight times and five times higher than persistent scatterer interferometry and small baseline subset methods, respectively. (2) The proposed Prophet_ZTD-NEF model considers the spatial and temporal variability properties of tropospheric delays, and the root mean square error of measured values was approximately 1.19 cm instead of 1.58 cm (PZTD-NEF). (3) The proposed Prophet_ZTD-NEF method reduced the mean standard deviation of the corrected interferograms from 1.88 to 1.62 cm and improved the accuracy of the deformation velocity solution by approximately 8.27% compared to Global Position System (GPS) measurements. Finally, we summarized the driving factors contributing to landslide instability. Full article

High-accuracy geolocation is crucial for high-resolution spaceborne SAR images. Most advanced SAR satellites have a theoretical geolocation accuracy better than 1 m, but this may be unrealizable with less accurate external data, such as atmospheric parameters and ground elevations. To investigate the actual SAR geolocation accuracy in common applications, we analyze the properties of different geolocation errors, propose a geolocation procedure, and conduct experiments on TerraSAR-X images and a pair of Tianhui-2 images. The results show that based on GNSS elevations, the geolocation accuracy is better than 1 m for TerraSAR-X and 2 m/4 m for the Tianhui-2 reference/secondary satellites. Based on the WorldDEM and the SRTM, additional geolocation errors of 2 m and 4 m are introduced, respectively. By comparing the effectiveness of different tropospheric correction methods, we find that the GACOS mapping method has advantages in terms of resolution and computational efficiency. We conclude that tropospheric errors and ground elevation errors are the primary factors influencing geolocation accuracy, and the key to improving accuracy is to use higher-accuracy DEMs. Additionally, we propose and validate a geolocation model for the Tianhui-2 secondary satellite. Full article

Satellite remote sensing is the best way to derive sea surface skin temperature (SST_skin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SST_skin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SST_skin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SST_skin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SST_skin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SST_skin data to study surface ocean processes and climate change in the Arctic. Full article

Meteorological satellite remote sensing is important for numerical weather forecasts, but its accuracy is affected by many things during observation and retrieval, showing that it can be improved. As a standard way to measure wind from space, atmospheric motion vectors (AMVs) are used. They are separate pieces of information spread out in the troposphere, which gives them more depth than regular surface or sea surface wind measurements. This makes rectifying problems more difficult. For error correction, this research builds a deep-learning model that is specific to AMVs. The outcomes show that AMV observational errors are greatly reduced after correction. The root mean square error (RMSE) drops by almost 40% compared to ERA5 true values. Among these, the optimization of solar observation errors exceeds 40%; the discrepancies at varying atmospheric pressure altitudes are notably improved; the degree of optimization for data with low QI coefficients is substantial; and there remains potential for enhancement in data with high QI coefficients. Furthermore, there has been a significant enhancement in the consistency coefficient of the wind’s physical properties. In the assimilation forecasting experiments, the corrected AMV data demonstrated superior forecasting performance. With more training, the model can fix things better, and the changes it makes last for a long time. The results show that it is possible and useful to use deep learning to fix errors in meteorological remote-sensing data. Full article