Earth and Space Science最新文献

In July 2022, the National Aeronautics and Space Administration (NASA) funded an airborne lidar data acquisition campaign over the central Arctic Ocean to evaluate ICESat-2 ATLAS (Ice, Cloud, and Land Elevation Satellite, Advanced Topographic Laser Altimeter System) retrievals of summer sea ice heights and melt pond characteristics. A Leica Chiroptera-4x (CHIR) was mounted on a Gulfstream V aircraft with a glass viewport, alongside NASA's Land, Vegetation, and Ice Sensor (LVIS). Despite the operational constraints—including CHIR's low-altitude, slow-cruise constraints, and other logistical and environmental challenges—measurements nearly coincident with ICESat-2 observations were successfully collected. In total, 138 min of CHIR lidar data and four-band aerial imagery were acquired at 500 m altitude, mapping 11,000 km² of sea ice. Cross-check validation between CHIR and LVIS over a 31-km long swath demonstrated strong agreement (R² > 0.98, RMSE = 0.045 m), confirming both the spatial accuracy and redundancy of the airborne measurements. A novel algorithm was developed to compare lidar data sets in a tide-free system by repositioning CHIR measurements to align with ICESat-2 observed points, accounting for drift speed (m/s) and heading (degrees), which significantly improved consistency. Comparisons between CHIR's near-infrared returns and ATL07 strong-beam products yielded an absolute height difference of 0.015 m with an agreement of R² = 0.73. Additionally, ATL03 photons showed a slight bias of 0.01 m and strong correspondence (R² = 0.84) with CHIR green-wavelength returns for coincident melt pond and lead depths in the height domain.

The Cross-track Infrared Sounder (CrIS) radiance data plays a crucial role in numerical weather prediction (NWP) models by providing essential atmospheric sounding information through data assimilation. However, challenges arise in handling subpixel cloud contamination within CrIS fields of view (FOVs), which can impact the accuracy of radiance simulations. To address this, the Visible Infrared Imaging Radiometer Suite (VIIRS) Radiances Cluster analysis within the CrIS FOVs is developed to characterize subpixel scene homogeneity. This paper describes the algorithms and data processing procedures for this cluster analysis. A fast and accurate collocation method was developed to directly align VIIRS measurements within CrIS FOVs using line-of-sight (LOS) pointing vectors. This method supports both terrain-corrected and non-terrain-corrected VIIRS geolocation data sets as inputs. The K-means clustering method is used to group collocated VIIRS radiance within CrIS FOVs into seven (7) clusters based on their radiance values. The mean, standard deviation, and coverage of each cluster are output for each CrIS FOV. Comparisons with the Infrared Atmospheric Sounding Interferometer cluster analysis demonstrate similar performance, confirming the validity of the CrIS-VIIRS approach. Data assimilation experiments at the European Centre for Medium-Range Weather Forecasts indicate that the VIIRS radiance cluster data can be effectively integrated into NWP models, aiding in cloud detection and improving data quality. These findings highlight the potential of CrIS-VIIRS clustering for enhancing data thinning, quality control, and assimilation of cloudy radiance observations in operational NWP systems.

Low-magnitude earthquakes occur far more frequently than major quakes and often go unnoticed by the public. These tremors rarely cause any damage, yet they play an important role in advancing our understanding of Earth's seismicity. Accurate detection of low-magnitude earthquakes is crucial to develop complete earthquake catalogs and improve seismic hazard forecasting models. However, conventional detection algorithms such as the short-time-average/long-time-average (STA/LTA) method struggle to identify these events because of their inherently low signal-to-noise ratio (SNR). Additionally, lack of labeled waveforms for low-magnitude earthquakes further complicates the training of effective deep-learning models. In this study, we use an Auxiliary Classifier Generative Adversarial Network (AC-GAN) to produce synthetic yet realistic three-component waveforms of low-magnitude earthquakes. The AC-GAN is trained on fixed-length (60-s) waveform segments conditioned by predefined SNR classes. All selected events have magnitudes lower than 3 and are categorized into 10 distinct SNR classes. Our results indicate that the AC-GAN model generates realistic three-component waveforms that effectively capture essential characteristics of real seismic signals. To evaluate the quality of these synthetic waveforms, we employ both quantitative and qualitative assessments. Quantitative analysis using Pearson's correlation coefficient yield relatively low correlations (ranging from 0.01 to 0.04); however, correlation values noticeably improve as SNR increases. Qualitatively, a user-based visual inspection experiment demonstrate remarkable similarities in general seismic features between the synthetic and authentic waveforms. We also test their effectiveness for data augmentation in binary deep-learning classifier designed for detecting low-magnitude earthquakes. Our result show improved classification performance with the addition of synthetic data.

This study presents the detection and characterization of co-volcanic ionospheric disturbances (CVIDs) associated with Mt. Etna's large-scale lava fountain (Italy). Leveraging a dense and proximal GNSS network, we identify local Total Electron Content (TEC) perturbations extending up to $��\sim �$200 km south/southwest of the vent. The observed anomalies exhibit quasi-periodic signatures with amplitudes of $��\sim �$0.6 TECU, periods of 15–25 min, and horizontal propagation velocities of 135–300 m$��\cdot �$s⁻¹, with dominant spectral power in the 0.5–1.5 mHz range, consistent with internal gravity waves. These signatures emerge gradually, 20–30 min after the seismo-acoustic onset of the eruption, coinciding with the rise of the volcanic plume. Eruption chronology is independently constrained using seismo-acoustic and thermal/visible imagery. Detection robustness is ensured via complementary spectral analyses (FFT and Empirical Mode Decomposition) and confirmed across multiple GNSS stations. The results suggest that open-conduit eruptive dynamics may facilitate sustained, gravity-dominated atmospheric forcing, generating subtle TEC disturbances that are otherwise difficult to detect under natural ionospheric variability. Comparison with prior studies, that did not detect such signals, highlights the critical role of near-field GNSS coverage. These findings contribute to a better understanding of CVID typologies and open new avenues for integrating ionospheric observations into multi-sensor volcanic monitoring frameworks. Further multi-event analyses are needed to generalize the proposed mechanisms and assess their utility in hazard forecasting.

We present evidence that the time delay between the multiple rings of elves is not caused by the ground reflection of the electromagnetic pulse produced by intracloud lightning. To investigate temporal differences of multi-elves, we analyzed data from four storms occurring at various times and distances from the Pierre Auger Observatory in Malargüe, Argentina. The Auger fluorescence detector's high temporal resolution of 100 ns enabled the frequent observation of multi-elves, accounting for approximately 23% of the events. By examining the traces of 70 double and 24 triple elves, we demonstrate that the time delay between the rings remains relatively constant regardless of the arc distance to the lightning. These results deviate from the trend expected from the electromagnetic pulse (EMP) ground reflection model, which predicts a decreasing time delay with increasing arc distance from an intracloud lightning at a given height. The first emission ring is due to a direct path of the EMP to the ionosphere, with the reflected EMP creating the second ring. Simulations conducted with this model demonstrate that short energetic in-cloud pulses can generate four-peak elves, and a temporal resolution of at least 25 $��\mu �$s is required to separate them. Therefore, temporal resolution is crucial in the study of multi-elves. Our observations in the Córdoba province, central Argentina, indicate that the current understanding of the mechanism generating these phenomena may be incomplete, and further studies are needed to assess whether multi-elves are more likely related to the waveform shape of the lightning than to its altitude.

Atmospheric water vapor is an important factor in the formation and evolution of extreme weather events, such as heavy rainfall, typhoons, and major droughts and floods. We analyzed the applicability of precipitable water vapor (PWV) values from the Global Navigation Satellite System Meteorology (GNSS/MET) stations in northeastern China. We examined the potential of PWV for precipitation forecasting using an enhanced bidirectional long short-term memory (BiLSTM) network. Using radiosonde data, the accuracy of GNSS/MET PWV was evaluated, and the diurnal and seasonal differences in retrieval errors were analyzed. Results show that diurnal differences in retrieval errors are insignificant, while seasonal differences are pronounced, which can be attributed to the seasonal distribution of precipitation. Through case analyses of rainstorms and severe convective events, this study concludes that GNSS PWV varies several hours ahead of precipitation. Building on the earlier analyses of applicability assessment and precipitation warning signal identification, the improved BiLSTM framework is employed to investigate the application of GNSS PWV in hourly precipitation forecasting. Feature extraction and data resampling were utilized to enhance the physical interpretability of the binary nature of precipitation prediction and improve the model's generalization capability. Validation with 873 randomly split testing samples revealed a classification accuracy of 86.3% for precipitation prediction, with a regression RMSE of 2.73 mm. The intelligent precipitation forecasting methodology developed in this research can be applied to public-sector precipitation monitoring and early warning services.

Cloud plays a crucial role in modulating the energy budget of the earth-atmosphere system. Cloud-resolving models have been demonstrated to be capable of better simulating the microphysical processes within clouds. This study assesses the super-parameterized version of Community Atmosphere Model Version 6 (SPCAM6) in simulating clouds, radiation, and precipitation. The results indicate that SPCAM6 effectively reduces the overestimation of cloud amount in CAM6, particularly in equatorial and mid-high latitude regions. The improvement in cloud amount simulation further enhances the simulation of cloud radiative forcing and precipitation. Through further research on the North Pacific, Southern Ocean regions and Maritime Continent, it is found that with the coupling of the cloud-resolving model, cloud amount at high, middle, and low levels all exhibits a decreasing trend. However, liquid water content and ice water content (IWC) display different characteristics. Despite a decrease in cloud amount, IWC increases due to the heterogeneity of IWC in sub-grid scales, which is closer to actual observations. SPCAM6 can simulate sub-grid in-cloud processes and capture the in-cloud heterogeneity distribution, showing potential as a tool for understanding sub-grid cloud processes.

The NASA Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument is hosted on a geostationary commercial communications satellite. TEMPO is an imaging spectrometer with primary mission to measure trace-gas concentrations from the observed spectra of reflected sunlight over the Continental United States and parts of Canada, Mexico, and the Caribbean. TEMPO produces an ultraviolet (UV, 293–494 nm) and a visible (538–741 nm) spectrum for each spatial pixel. TEMPO saw first light in August 2023. At night, TEMPO can observe city lights, gas flaring, maritime lights from fishing and offshore oil platforms, clouds and snow in the moonlight, lightning, aurorae, and nightglow without interfering with its primary daytime air quality/chemistry mission. This paper describes the capabilities of TEMPO to make nighttime observations and surveys of some early results. Repetitive coverage of North America enables production of clearest-sky composites that are similar to VIIRS Day-Night Band (DNB) “Black Marble” products. Spectra of urban areas contain spectral signatures of artificial lighting of various types that allow the radiance from each class of lighting to be estimated. Moonlight imaging of clouds provides a useful capability for discriminating clouds and fog. Lightning illuminating cloud tops from below is seen with distinct spectral features. Gas flares, associated with oil production, are observed and flare temperatures can be estimated from their spectra. Known auroral and nightglow spectral lines of atomic oxygen and molecular nitrogen are seen in the UV and visible spectra. The sodium d-layer is also observed.

First observations on the characteristics of vertical velocity during the Asian Summer Monsoon (ASM) months, over the central Himalayan region using 206.5 MHz Stratosphere-Troposphere radar are presented. Clear air zenith observation data have been extracted and analyzed to determine the distribution of vertical velocity occurrence and the mean vertical velocity profiles up to 16 km from June to October. Results show that the mean updraft and downdraft characteristic velocity ∼4 km did not generally exceed 5 cm s⁻¹ for all the months. An exception to this feature is observed as a layer of persistent downdraft between 10 and 11 km with a maximum mean vertical velocity of ∼−7 cm s⁻¹ in August, which is new observation. Consistent downdrafts in the lower troposphere and consistent updrafts in the upper troposphere above 12 km show that direct transport of airmass from lower to upper level and vice versa is not climatologically supported. In turn, such profile shows similarity to the two-step process of upliftment of air mass in the tropical region. Notably this height corresponds to the lower level of ASM Anticyclone and gives insight in to the entrapment and slow upliftment of airmass toward stratosphere. Further analysis of inter-period variability of vertical velocity for forenoon, afternoon, and evening periods for all the months show notable variations in the mid-tropospheric region with barely any change in the upper troposphere above 12 km, indicating that the slow upward transport does not directly depend on the inter-period variability for any of the months.

The Polar Radiant Energy in the Far-InfraRed Experiment (PREFIRE) was selected by NASA to fly two miniaturized Thermal InfraRed Spectrometers (TIRS) capable of distinguishing the spectral signatures of surface and atmospheric properties in Earth's polar regions. A trade study examining spectral sampling as well as separation of cloudy and clear scenery at 20 km scales highlighted the possibility to utilize ambient (uncooled) detector technologies in a miniaturized spectrometer that could facilitate low-cost and rapid access to space. This work describes the design, implementation, testing and performance of two TIRS systems, as well as the challenges and acceptable limitations of the cost-constrained effort, that feature the novel joining of compact thermopile array technologies with concentric imaging spectrometry methods. The TIRS systems presented here each have 2.7 kg mass, draw 4.3 W power, and provide spectral resolution of 1.71 $��\mu �$m below 35 $��\mu �$m sampled at 0.86 $��\mu �$m increments.