Earth and Space Science最新文献

To improve the radar data assimilation scheme for the high-resolution Tropical Regional Atmospheric Model System (TRAMS) model, this study investigates the sensitivity of simulating a warm-sector rainfall event in southern China to different radar reflectivity retrieval methods and incremental updating strategies. The findings indicate that the ice cloud retrieval (ICR) method yields more reasonable cloud hydrometeors. However, the impact of different retrieval methods is minimal without corresponding adjustments to the dynamic field. Further assimilation of the wind field effectively reduced the overestimated south winds and successfully simulated the observed low-level convergence in northern Guangdong, significantly improving precipitation forecasts. Both incremental analysis update (IAU) and Nudging methods were able to adjust the forecast to better match the observations, with IAU performing slightly better. These findings are beneficial for further improving the forecast accuracy of precipitation intensity. Extending the IAU relaxation time from 4 to 10 min has almost no impact on the actual forecasting. However, prioritizing the adjustment of the wind field through time-dependent IAU weighting factors, the impact of cloud particle adjustments on the dynamical field can be avoided (e.g., the drag caused by the sinking of cloud particles may offset the upward motion induced by dynamical convergence adjustments). This allows for more realistic low-level wind convergence and precipitation forecasts to be obtained. Overall, the ICR method for retrieving cloud hydrometeors, combined with the IAU method using time-dependent distribution weighting factors appears to be a more suitable option for the radar data assimilation scheme in TRAMS model.

This study reviews the progress made in modeling precipitations in four generations of reanalyses from the European Center for Medium-Range Weather Forecasts, using traditional metrics and a new set of regional metrics. Regional metrics at oceanic basin scales and large land catchment areas over the continents allow for a more comprehensive analysis of the performance of the reanalyses. This leads to the conclusion that significant progress has been made in the past several decades in both the atmospheric model and the assimilation system at the ECMWF, leading to more realistic precipitation. The most recent ERA5 reanalysis outperforms ERA-Interim and its predecessors by all metrics considered. ERA5 is then used to force a modern ocean general circulation model, and the results show an improvement in terms of the freshwater budget, particularly after the year 2000. However, uncertainties remain about the magnitude and trends of the modeled evaporation.

The correlation and physical interconnection between space weather indices and cosmic ray flux has been well-established with extensive literature on the topic. Our investigation is centered on the relationships among the solar radio flux, geomagnetic field activity, and cosmic ray flux, as observed by the Neutron Monitor at the Lomnický štít Observatory in Slovakia. We processed the raw neutron monitor data, generating the first publicly accessible data set spanning 42 years. The curated continuous data are available in.csv format in hourly resolution from December 1981 to July 2023 and in minute resolution from January 2001 to July 2023 (Institute of Experimental Physics SAS, 2024, https://doi.org/10.5281/zenodo.10790915). Validation of this processed data was accomplished by identifying distinctive events within the data set. As part of the selection of events for case studies, we report the discovery of TGE-s visible in the data. Applying the Pearson method for statistical analysis, we quantified the linear correlation of the data sets. Additionally, a prediction power score was computed to reveal potential non-linear relationships. Our findings demonstrate a significant anti-correlation between cosmic ray and solar radio flux with a correlation coefficient of −0.74, coupled with a positive correlation concerning geomagnetic field strength. We also found that the neutron monitor measurements correlate better with a delay of 7–21 hr applied to the geomagnetic field strength data. The correlation between these data sets is further improved when inspecting periods of extreme solar events only. Lastly, the computed prediction power score of 0.22 for neutron flux in the context of geomagnetic field strength presents exciting possibilities for developing real-time geomagnetic storm prediction models based on cosmic ray measurements.

A rational three-dimensional (3D) geological model with complex characteristics generated on a small amount of data is a crucial data infrastructure for scientific research and many applications. However, reconstructing structures with multi-Z values on a single point caused by folding or overthrusting is still one of the bottlenecks in 3D geological modeling. Combined with the multi-point statistics (MPS) method and fully connected neural networks (FCNs), this study presented a hybrid framework for 3D geological modeling. The loss functions of FCN and the conventional MPS method jointly form the kernel function of the proposed method, which is constrained by stratigraphic sequence and stratum thickness. The input and output parameters of the FCN are the coordinates and corresponding elevations of geological contacts, respectively. To solve the kernel function, the initial model, in which geological surfaces are generated by the FCNs, is generated using a sequential process. An iterative MPS process with an Expectation Maximization-like (EM-like) algorithm is carried out to illuminate the artifacts in the initial model. Ten orthogonal cross-sections are extracted from the overthrust model created by SEG/EAGE as the modeling data source. The results illustrated that the geometry and spatial relationships of strata and faults are retained well with the geological constraints. The comparison of virtual boreholes from the results and the real model shows that the accuracy of the geological object reaches 75%. The presented method provides a new idea for simulating 3D structures with multi-Z values, which overcomes the limitations of the conventional MPS-based 3D modeling method.

Satellite column formaldehyde (HCHO) is an indicator of regional volatile organic compounds (VOC) emissions as HCHO is a short-lived intermediate oxidation product. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in 2020, is the first geostationary satellite to monitor hourly HCHO. GEMS offers unprecedented potential to reveal the diurnal variations of VOC emissions in Asia. Here, we present the first study to evaluate year-round GEMS HCHO retrievals using TROPOMI satellite and ground-based Pandora spectrometers. Our study shows that GEMS HCHO aligns with TROPOMI (r = 0.59–0.85; differences within 20% for most areas). Moreover, GEMS captures monthly and diurnal HCHO variations observed by Pandora spectrometers across Asia with differences overall within 15% (r ∼ 0.85). Diurnally, we find strong HCHO variations over urban areas but not in forests. During the fire season of mainland Southeast Asia, GEMS HCHO increases in the afternoon, in line with diurnal emission estimates from the Global Fire Emissions Database Version 4 with small fires (GFED4s) and GEOS-Chem simulations. GEMS also captures the spatial patterns of fire emissions in GFED4s. GEMS HCHO shows negative bias when observing with a high (>60°) viewing zenith angle (VZA) and overly relies on model correction for observations to the north of 30°N.

Streamflow in the Colorado River Basin (CRB) is significantly altered by human activities including land use/cover alterations, reservoir operation, irrigation, and water exports. Climate is also highly varied across the CRB which contains snowpack-dominated watersheds and arid, precipitation-dominated basins. Recently, machine learning methods have improved the generalizability and accuracy of streamflow models. Previous successes with LSTM modeling have primarily focused on unimpacted basins, and few studies have included human impacted systems in either regional or single-basin modeling. We demonstrate that the diverse hydrological behavior of river basins in the CRB are too difficult to model with a single, regional model. We propose a method to delineate catchments into categories based on the level of predictability, hydrological characteristics, and the level of human influence. Lastly, we model streamflow in each category with climate and anthropogenic proxy data sets and use feature importance methods to assess whether model performance improves with additional relevant data. Overall, land use cover data at a low temporal resolution was not sufficient to capture the irregular patterns of reservoir releases, demonstrating the importance of having high-resolution reservoir release data sets at a global scale. On the other hand, the classification approach reduced the complexity of the data and has the potential to improve streamflow forecasts in human-altered regions.

Hurricane Nicholas was classified as a Category 1 tropical cyclone (TC) at 0000 UTC on 14 September 2021 and made landfall along the upper Texas Gulf Coast at 0530 UTC with maximum sustained winds of 33 m s⁻¹. Much of the electrical activity during Nicholas was monitored by the Houston Lightning Mapping Array (HLMA) network. Thunderstorm activity developed in the rainband at 1700 UTC on 13 September, diminished by 2030 UTC, and re-intensified after 2200 UTC. At 2004 UTC (13 September), a curved megaflash (∼220 km) was observed by the HLMA in the stratiform precipitation region of the outer rainband. By 0130 UTC on 14 September 2021, vigorous storm cells developed in the eastern eyewall region and propagated cyclonically to the western eyewall region. At least four “jet-like” transient luminous events (TLEs) were observed by the HLMA emanating from a storm cell in the western eyewall region between 0230 and 0300 UTC with VHF source points ranging from 30 to 45 km in altitude. Moreover, the TLEs occurred within a region of strong wind shear, upper-level graupel-ice crystal collisions (∼15 km), and strong cloud top divergence. Charge analysis of the thunderstorm activity during Nicholas revealed an overall normal dipole structure, while the megaflash and TLE cases exhibited inverted dipole charge structures. Dissipation of the upper-level screening charge layer resulting from cloud top divergence likely played a role in the observed TLE VHF sources escaping to altitudes exceeding 30 km.

The Tibetan Plateau (TP) is covered by numerous lakes, and lake surface water temperature (LSWT) is an essential indicator of climate change, while few observations hinder our understanding of LSWT variation and its causes over TP. This study aims to simulate the summer LSWT long-term trends of 81 TP lakes during 1980–2018 and quantify the impacts and contributions of atmospheric variables. Results show that TP lakes warmed with 0.32°C decade⁻¹ on average. Northern TP lakes warmed faster than the southern ones (0.44 vs. 0.16°C decade⁻¹) due to stronger trends of atmospheric variables and higher sensitive of colder lakes to atmospheric changes. 55 (67.9%) lakes of the total lakes studied in current work warmed slower than air due to weakened shortwave radiation (SW_↓). Attribution analysis suggests that the air warming and wetting over TP dominate lakes' warming. Regarding synthesis contributions, air warming contributed 79.3%, with increased surface air temperature (SAT) and downward longwave radiation (LW_↓) accounting for 41.6% and 37.7%, respectively, and air wetting indicated by increased surface specific humidity (SSH) contributed 39.0%, followed by a positive contribution (16.8%) from declined wind speed (WS). The negative contribution (−35.1%) from weakened SW_↓ nearly counterbalances the positive effects of increased LW_↓. 55.1% of the total synthesis contribution arises from the cross contribution through interactions among atmospheric variables and is mainly reflected in SAT and SSH, accounting for 26.8% and 24.8%, respectively. The findings enhance understanding of climate change impacts on lake systems and offer insights for lake resource management.