Earth and Space Science最新文献

During the first part of the Martian year (L_s = 50°–160°) a phenomenon occurs on Mars in the tropical and equatorial regions (30°N–10°S) known as the Aphelion Cloud Belt (ACB). During this time, there is prominent formation and diurnal variability of water ice clouds. Limited empirical attempts have been made to characterize the magnitude of radiative flux contributions from clouds to nighttime surface temperatures. In this work, we estimated the infrared (IR) flux contribution at ground level from the clouds by comparing surface temperature data from the Thermal Emission Spectrometer (TES) onboard Mars Global Surveyor (MGS) to calculated temperatures using the KRC numerical thermal model. We then generated a database of IR fluxes at the ground contributed by clouds spanning the entirety of the tropical and equatorial regions as a function of Solar Longitude (L_s) on Mars in one degree bins. We compared results with work presented elsewhere in the literature and found good agreement. We also found that temporal trends followed the general established range for the ACB but our analysis demonstrated the peak ACB values occurred at later times (L_s = 100°–140°) than previously published data sets using water ice opacity retrievals (L_s = 90°–110°). This database may be used in comparison to calculated Global Climate Model fluxes as well as a lookup tool for more precise estimation of surface and subsurface thermal environments in these regions.

Solar irradiance received at the lunar surface is crucial for interpreting brightness temperatures detected by orbiters and for understanding the thermal, physical, and dielectric properties of the lunar regolith. We developed a real-time effect solar irradiance (ESI) model that accounts for the influence of surface relief and terrain shading. This model was integrated with a standard thermal model to examine ESI fluctuations and their impacts on the diurnal physical temperature variations. To assess the effects of spatial resolution, we selected four locations with significant ESI disparities for simulation, then compared lunar surface temperatures at various spatial scales, ranging from 20 m to 25 km. Utilizing brightness temperature data obtained from the Chang'E-2 (CE-2) microwave radiometer (MRM), we integrated the shallow physical temperature profiles with the radiative transfer equation to simulate brightness temperatures and determine dielectric loss at different frequencies. In the Von Kármán crater, the received ESI exhibits a cyclical pattern of approximately 18 years and areas with rugged topography may exhibit larger ESI variations (∼7%). We found that the spatial resolution of ESI has a minimal effect on the physical and brightness temperatures at resolutions of 10 km or coarser. At the shallow layer, the average dielectric loss values are 0.0128–0.0170, 0.0083–0.0110, 0.0055–0.0073, and 0.0061–0.0081 for the CE-2 frequencies of 3, 7.8, 19.35, and 37 GHz, respectively. The integration of real-time ESI modeling, thermal dynamics, radiative transfer equations, and observational data enhances our comprehension of the physical temperature profile and thermal characteristics of shallow regolith.

Far infrared (FIR) emission from the Earth's polar regions has become an area of increasing scientific interest and value. FIR emission is important for understanding Earth's radiative balance and improving global climate models, especially in rapidly changing Arctic conditions. Far-infrared emission from Earth is not currently being monitored from space, except as part of broadband emission channels of Earth radiation budget measurements like those from the CERES project, and only limited measurements in the FIR spectrum exist. The Absolute Radiance Interferometer (ARI), developed as a prototype of the infrared spectrometer for CLARREO at the University of Wisconsin-Madison, Space Science and Engineering Center, measures absolute spectrally resolved infrared (IR) radiance from 200 to 2,000 cm⁻¹ (or 5–50 μm) at 0.5 cm⁻¹ resolution with high accuracy (<0.1 K 3-sigma brightness temperature at scene temperature). This instrument was taken into the field in Madison, Wisconsin, USA, during the winters of 2021 and 2022, where the weather can reach polar-like conditions to measure high spectral resolution radiances of various sample types. Sample materials included water, snow, ice, evergreen leaves, dry grass, and sand, all characteristic of high latitude regions. Radiances collected from both a sky view and the sample view in clear-sky conditions were used to retrieve FIR emissivity. This paper describes the ARI instrument configuration and capability for ground-based measurements in the FIR region, and documents retrieved emissivities of various analyzed samples. The retrieved emissivity results are publicly available, and comparisons are made to simulated emissivity estimates.

Satellite-based sensors of ocean color have become the primary tool to infer changes in surface chlorophyll, while BGC-Argo floats are now filling the information gap at depth. Here we use BGC-Argo data to assess depth-resolved information on chlorophyll-a derived from an ocean biogeochemical model constrained by the assimilation of surface ocean color remote sensing. The data-assimilating model replicates well the general seasonality and meridional gradients in surface and depth-resolved chlorophyll-a inferred from the float array in the Southern Ocean. On average, the model tends to overestimate float-based chlorophyll, particularly at times and locations of high productivity such as the beginning of the spring bloom, subtropical deep chlorophyll maxima, and non-iron limited regions of the Southern Ocean. The highest model RMSE in the upper 50 m with respect to the float array is of 0.6 mg Chl m⁻³, which should allow the detection of seasonal changes in float-based biomass (varying between 0.01 and >1 mg Chl m⁻³) but might hinder the identification of subtle changes in chlorophyll at narrow local scales. Both model and float profiling data show good agreement with in situ data from station ALOHA, with model estimates showing a slight accuracy edge in inferring depth-resolved observations. Uncertainties in float bio-optical estimates impede their use as a reliable benchmark for validation, but the general qualitative agreement between model and float data provides confidence in the ability of model to replicate biogeochemical features below the surface, where data is not directly constrained by the assimilation of satellite ocean color.

Convective initiation (CI) nowcasting in subtropical regions often faces challenges, such as complex physical processes and imbalanced samples of CI events, resulting in a high false alarm ratio (FAR). In this paper, we propose a Storm Warning System with Physics-Augmentation (SWASP) based on the random forest algorithm and cloud physical conditions, using Himawari-8 Advanced Himawari Imager data from April to September 2019 in South China. The cloud physical conditions (e.g., cloud-top cooling rates) were investigated to establish regional thresholds for convection occurrence. Ancillary information, including elevation, satellite zenith angle, and latitude, was also incorporated into the SWASP model. Compared to conventional methods, the SWASP model exhibits an improved probability of detection by 0.11 and 0.08 and a decreased FAR by 0.38 and 0.44 for daytime and nighttime forecasts. Moreover, the SWASP model enables the detection of local convective storm systems about 30 min to 1 hr ahead of radar detection in typical convective storm cases. This study contributes to further advancements of the SWASP model by incorporating physical conditions and emphasizes the potential application of geostationary satellites in convective early warnings.

Identifying the origin and conditions of basalt generation is a crucial yet formidable task. To tackle this challenge, we introduce an innovative approach leveraging machine learning. Our methodology relies on a comprehensive database of approximately one thousand major element concentrations derived from glass samples generated through experiments encompassing a wide range of source lithologies, pressure (from 0.28 to 20 GPa) and temperature (850–2100°C). We first applied the XGBoost classification models to assess the compositional characteristics of melts from three principal mantle source categories: peridotitic, transitional, and mafic sources. We obtained an accuracy of approximately 96% on the test data set. Furthermore, we also employ an XGBoost regression model to predict the pressure and temperature conditions of generation of basalts from diverse lithologic sources. Our predictions of temperature and pressure exhibit remarkable precisions, of about 49°C and 0.37 GPa, respectively. To enhance accessibility of our model, we have implemented a user-friendly web browser application, available at (https://huggingface.co/spaces/lilucheng/sourcedetection). The web application allows users to swiftly recover the source lithology as well as pressure and temperature conditions governing basalt generation for a broad array of samples within a matter of seconds.

Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision-making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well-documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision-making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS.

The Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth system models (ESMs). Historical hydrological variables from six ESMs driven by four meteorological forcing data sets (GSWP, WFDEI, CRU-NCEP, and Princeton) in Land Model Intercomparison Project (LMIP) have been extensively evaluated with various high-quality reference data sets over Chinese mainland. Compared with the reference data sets, the multi-model ensemble means (MMEs) of most hydrological variables are underestimated, while their annual trends show high spatial consistency, with sign consistency over 56%–85% of land area. After computing and ranking four statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root-mean-square biases) between simulations and references, it is found that the CLM5 has the best performance, while the GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) is then used to trace sources (model, atmospheric forcing data sets and their interactions) of the uncertainty of those modeling hydrological variables for 1900–2012 (1948–2012 for runoff) over China. The results indicate that the total uncertainty and its composition vary with time and decrease significantly in recent decades, reflecting the enhanced forcing data quality. Larger forcing uncertainty existed during the early twentieth century because less available observation data sets have been adopted to constrain climate variables. For all modeling hydrological variables, the model uncertainty plays the dominant role, suggesting that the quality of LMIP products largely relies on Land surface models.

On 18 June 2023, comprehensive observations were conducted to an altitude-triggered lightning flash. Upward positive leader (UPL) and downward negative leader (DNL) in a bi-directional development system were detected simultaneously by a high-speed camera, together with the coordinated measurements of magnetic field and very-high-frequency (VHF) emissions. High-speed images reveal, for the first time, the enhancement of the UPL's propagation speed by DNL in the bi-directional leader system. Concretely, the upward positive leader initially originates from a suspended wire tip and propagates at a two dimensional (2D) speed of 5.32 × 10⁴ m/s, and after about 6.3 ms, its propagation speed was enhanced to 1.12 × 10⁵ m/s when the stepped DNL started advancing at an average speed of 1.44 × 10⁵ m/s. Additionally, based on the evolution of channel luminosity and the variations of magnetic radiation, it is found that there is a consistency in luminosity variation between the ascending channel and the descending channel in the bi-directional leader system, and the amplitude of the magnetic field increases when the negative discharges start at the bottom wire end with intensive VHF emissions. Those facts indicate that the DNL has an effect, may be a positive one, on the UPL's development in the early stage of altitude-triggered lightning.

We investigated the applicability of miniature microtremor arrays with a radius of a few meters or less to shallow surveys (up to a few tens of meters). It is shown that the upper limit wavelength normalized by the seismometer separation distance s that is analyzable by a miniature array does not depend on the observation instrument as long as the instrument has a self-noise level that is sufficiently lower than the microtremor intensities; however, it generally depends on the average S-wave velocity of the ground. This means that a miniature array is useful at soft-soil sites but not hard-soil sites. A statistical study in central Japan showed that the penetration depth by a miniature array with s = 1 m ranges from 6 to 12 m; specifically, the depth exceeds 12 m in one quarter of the cases but remains below 6 m in one quarter of the cases. This large variation is due to the variation in the average S-wave velocity. A miniature array should thus be used in combination with a larger array with an s value of several meters to 20 m. In urbanized areas with high industrial activity, where the microtremor wavefield is likely isotropic, these arrays can be replaced by a linear array. In environments with extremely low signal-to-noise ratios, a less efficient zero-crossing method should be applied to the larger array. An appropriate observation strategy should be selected for a given geoenvironment. Field examples show that our strategy enables efficient and feasible dense surveys.