Journal of Geophysical Research: Atmospheres最新文献

During the Uncrewed Aircraft Systems Demonstration Campaign (UAS-DC), led by the World Meteorological Organization (WMO), twice-daily meteorological profiling was conducted for 2 months at the Meteorological Research Institute in Tsukuba City (Japan), which is identified as a densely inhabited district. This campaign was instigated to assess the feasibility of obtaining continuous daily measurements for the long term (over a period of more than 1 month), to distribute the data in a format designated for numerical weather prediction, and to evaluate data quality compared to conventional meteorological data. Three types of uncrewed aircraft systems were utilized, that is, a meteorological medium-sized hexacopter and medium- and small-sized commercial drones with meteorological sensors attached. The maximum flight height was limited to 900 m above ground level owing to airspace regulations around the observation site. Compared with routine radiosonde data, the bias of air temperature, relative humidity, and wind speed measurements was less than 0.3 K, 1.5%, and 0.6 m $��s^{�-�1�}�$, respectively, thereby meeting WMO requirements. Moreover, data transfer to the WMO-prepared repository was completed within 30 min after measurement acquisition. Based on user experience, several aspects regarding the UAS-DC campaign were discussed from the perspective of sustainable operation and atmospheric boundary layer research.

Surface microlayer at freshwater (rivers, lakes, ponds, streams, and groundwater) and seawater is abundant with organic compounds compared to subsurface water. These organics adsorbed at the air-water interface can interact with the atmospheric oxidants and influence the exchange of organic materials between the water and the atmosphere. Here, we assess the chemical interaction between gaseous NO₂ and authentic surface microlayers collected at the lake water (Dianchi Lake) situated in China. The formation of the gas-phase product compounds was evaluated in real time using a novel secondary electrospray ionization ultrahigh-resolution quadrupole Orbitrap mass spectrometer (SESI-UHR-MS) upon exposure of surface microlayer to gaseous NO₂ (20 or 50 ppb) in dark and under simulated sunlight irradiation at two different temperatures: 5°C and 25°C. The obtained results revealed that the sampling sites of the lake impacted by human activities (municipal sewage and agricultural activities) significantly impact the number and the composition of the formed gas-phase product compounds. The formation of nitrogen (N)-containing compounds was observed as well, which contain most likely nitro or amino functional groups, or alternatively, they could be aromatic compounds. The observed N-containing compounds may contribute to the “brown carbon” which act as light-absorbing compounds, thus influencing the radiative forcing of aerosols in the atmosphere.

We introduce a new type of cloud class, which we call “active cloud regime” (ACR), owing to its provenance from active (lidar and cloud radar) spaceborne cloud observations. ACRs intend to provide a climatological description based on cloud vertical structure (CVS) of the most prevalent monthly CVS mixtures encountered at large spatial scales of ∼400 km. ACRs are thus a way to create a gridded data set of a vertically resolved cloud mask that can facilitate joint analysis with other gridded data sets. The detailed 2D cloud mask comes from the 2B-CLDCLASS-LIDAR CloudSat data set fusing CALIPSO (lidar) and CloudSat (cloud radar) cloud detections. We show that the global classification of cloudiness under the ACR framework provides valuable insights on how the world's dominant cloud systems regulate the two major components of atmospheric energetics, precipitation and radiative cooling. NASA's GEOS model allows us to demonstrate the feasibility of applying the ACR concept in Earth System Models that have the capability to produce subgrid cloudiness obeying pre-specified vertical overlap rules. Comparison of observed and simulated ACRs provides thus another means to assess the realism of modeled clouds.

Arctic sea ice prediction is critical for exploring climate change, resource extraction, and shipping route planning. This paper introduces a novel neural network model, Ice Graph Attention neTwork (IceGAT), that is trained to predict sea ice concentration (SIC) from a number of atmospheric, oceanic, and land surface measurements. It is based on two design principles: (a) the complex spatial interactions in weather dynamics are captured via a series of graphs corresponding to different spatial resolutions and (b) the incorporation of the physical conservation laws for moisture and potential vorticity. We devise two main variants with 1 hr and 24 hr temporal resolution and determine the optimal input horizon to be 5 days. IceGAT features leading accuracy (96.7%; +2.4% over the current state-of-the-art) and low inference time (1/4 s, on a single GPU). An online implementation (based on data from ERA5) alongside supplementary videos and our shared code are accessible at: https://lannwei.github.io/IceGAT/.

Due to its sensitivity to water vapor, high resolution, and global availability, interferometric satellite radar (InSAR) has a large but unexploited potential for the improvement of regional NWP models. A relatively straightforward approach is to exploit the exact instantaneous character of the InSAR data in data assimilation to improve the timing of NWP model realizations. Here we show the potential impact of InSAR data on the NWP model timing and subsequently on improved model performance. By time-shifting the model to find the best match with the InSAR data we show that we can achieve a model error reduction (one-sigma) of up to 40% in cases where weather fronts are present, while other cases show more modest improvements. Most model performance gain due to time-shifts can therefore be achieved in cases where weather fronts are present over the study area. The model-timing errors related to the maximum model error reduction for these cases are in the order of $��\sim �$30 min.

Hydroclimate and terrestrial hydrology greatly influence the local community, ecosystem, and economy in Alaska and Yukon River Basin. A high-resolution simulation of the historical climate in Alaska can provide an important benchmark for climate change studies. In this study, we utilized the Regional Arctic System Model (RASM) and conducted coupled land-atmosphere modeling for Alaska and Yukon River Basin at 4-km grid spacing. In RASM, the land model was replaced with the Community Terrestrial Systems Model (CTSM) given its comprehensive process representations for cold regions. The microphysics schemes in the Weather Research and Forecast (WRF) atmospheric model were manually tuned for optimal model performance. This study aims to maintain good model performance for both hydroclimate and terrestrial hydrology, especially streamflow, which was rarely a priority in coupled models. Therefore, we implemented a strategy of iterative testing and optimization of CTSM. A multi-decadal climate data set (1990–2021) was generated using RASM with optimized land parameters and manually tuned WRF microphysics. When evaluated against multiple observational data sets, this data set well captures the climate statistics and spatial distributions for five key weather variables and hydrologic fluxes, including precipitation, air temperature, snow fraction, evaporation-to-precipitation ratios, and streamflow. The simulated precipitation shows wet bias during the spring season and simulated air temperatures exhibit dampened seasonality with warm biases in winter and cold biases in summer. We used transfer entropy to investigate the discrepancy in connectivity of hydrologic and energy fluxes between the offline CTSM and coupled models, which contributed to their discrepancy in streamflow simulations.

In this study, the role of the wind-induced surface heat exchange (WISHE) in rapid intensification (RI) is investigated in a numerical model. During the development of Hinnamnor, its energy growth rate (EGR) continuously increases as RI progresses. After Hinnamnor reaches its maximum intensity, although its EGR weakens a little, it remains relatively large. If it had not been for the influence of the external environment (such as the tropical depression), its maximum intensity would have been far greater than the actual maximum intensity (140 knots). As the WISHE effect progressively weakens, the number of convective bursts (CBs) gradually diminishes. This, in turn, gives rise to a corresponding weakening of the warm core and a subsequent delay in the start time of the axisymmetrization of the inner core, thereby affecting the intensification rate of the vortex and the final maximum intensity. Consequently, the start time of RI is also correspondingly postponed. Differing from the maximum potential intensity theory, when the EGR approaches zero, a TC does not immediately reach its maximum intensity. Instead, it attains its peak intensity approximately 12 hr later. During this additional 12 hr period, the number of CBs continues to increase, the warm core keeps on strengthening and the inner core continues its progress toward axisymmetric until the end of the RI process. This indicates that the dynamical and thermodynamical processes are also of great importance during the RI stage.

The Qinghai-Tibet Plateau (TPL), crucial for the global climate, lacks a comprehensive understanding of nitrogen-containing organic compound (NOCs) emissions and their impact on light absorption and radiative forcing through atmospheric oxidation. This study examined NOCs from dung and bitumite combustion in the TPL and their atmospheric oxidation by hydroxyl (·OH) and nitrate (·NO₃) radicals using an integrated experimental and theoretical approach. Dung produced higher NOC emissions, mainly methyl types, while bitumite emitted more fused-ring NOCs. Exposure to intense solar radiation resulted in substantial photobleaching of methyl NOCs through hydroxyl (·OH) reactions, reducing the maximum molar absorption at 300–400 nm wavelengths by 76.9%–96.4%. Moreover, nitrate radical (·NO₃) oxidation maintained spectral characteristics while producing minor absorption decreases of 48.9%–58.8%. The oxidative aging of fused-ring NOCs exhibited structure-dependent responses, wherein both ·NO₃ and ·OH oxidation induced photo-enhancement effects proportional to the number of aromatic rings in the molecular structure. Oxidation generally reduced radiative forcing for methyl NOCs but enhanced it for fused-ring NOCs, particularly through ·OH reactions, which increased simple forcing efficiency at 300–400 nm by 43.7%. This study provides crucial insights into NOCs' effects on regional climate and air quality, emphasizing the need for source-specific considerations in atmospheric models for TPL's unique high-altitude environment.

The sub-grid terrain solar radiative effect (STSRE) significantly affects the heterogeneity of the surface downward solar radiation (SDSR) over mountainous areas, which further exerts remarkable influences on simulations of surface energy budgets and hydrological processes. Given that two thirds of China is mountainous, we have systematically revealed the noticeable STSRE impacts on the performance of the Common Land Model (CoLM) in simulating land surface thermal and hydrological processes over China. Validations indicate that adopting the three dimensional (3D) STSRE scheme clearly improves the CoLM's ability in simulating the land thermal and moist characteristics over China in almost all seasons, and the improvements increase with the terrain complexity increasing. Over the regions with the most rugged terrain, adopting the 3D STSRE scheme can remarkably reduce the overestimated SDSR and thereafter land surface temperature (LST) warm biases in the CoLM with the plane-parallel radiative scheme. The modeled snow cover increases corresponding to the reduced LST with the Taylor score improved the most by 52.84% in summer, and the modeled evapotranspiration (ET) over southeastern Tibet and the Hengduan Mountains is also notably improved in summer. Further analysis indicates that the surface net radiation well corrected by the 3D STSRE scheme firstly reduces the warm biases of LST simulation using the plane-parallel radiative scheme over strong terrain shaded areas, directly leading to the depressed sensible heat flux and ET and more snow accumulation, then the inhibited ET and increased snow accumulation jointly affect the runoff and soil moisture simulations and thereafter latent heat simulation.

An unprecedented extreme rainfall event occurred in Zhengzhou, Henan Province, China, in July 2021. To understand the impact of local topography on this extreme rainfall event, the Weather Research and Forecasting model is configured with 27 and 9 km model grid spacings (MGS), along with United States Geological Survey (USGS) topography data at 8.3 and 0.9 km resolutions, called MGS27_USGS8.3, MGS9_USGS8.3, and MGS9_USGS0.9. Results show that the 9 km MGS, permitting activities with coarse γ scales (∼20 km), successfully reproduces the generation of mesoscale cyclones. However, the finer topography enables a more accurate representation of orographic blocking and lifting effects, thereby adjusting the position of the mesoscale cyclone. It can depict the location of adiabatic processes, local circulation, and vertical pressure gradient forces more accurately, thereby adjusting the position of topography-induced vertical motions. The turbulence diagnostics show that the topography-induced lifting motion enhances clouds that block longwave radiation, leading to local environment warming, which in turn enhances turbulence and further amplifies the updrafts, ultimately improving the spatial distribution and temporal variation of precipitation. This study provides insights for an in-depth understanding of the mechanisms of topography on extreme rainfall.