Earth and Space Science最新文献

This study evaluates the integration of an explicit electrification module in the Weather Research and Forecasting model to predict lightning over North-Eastern India, using inductive and non-inductive charging mechanisms for hydrometeors and charge exchange during collisions. A multigrid solver computes electric field components, while a bulk discharge scheme reduces charge in regions where the field exceeds the breakdown threshold. Simulations employed nested domains (27 km, 9 km, and 3 km), allowing detailed storm analysis via explicit microphysics. Pre-monsoon simulations included lightning data assimilation (LDA) from Earth Networks Total Lightning Network sensors, adjusting moisture fields through nudging within the 3 km grid. Over 43 pre-monsoon days, including 3 April 2017, have been chosen for this study, LDA improved model accuracy, particularly in high-density regions like Assam and Meghalaya, achieving high Probability of Detection (POD) and Equitable Threat Score (ETS) with low False Alarm Ratio (FAR). LDA enhanced 3–6 hr forecasts (91%–100% accuracy) by refining water vapor fields but struggled beyond 6 hr, where POD dropped and FAR rose. Challenges emerged in low-density areas like Mizoram, where overprediction increased FAR. The model captured core lightning zones but performance decreases surrounding areas, reducing spatial accuracy. A positive bias in flash density over central Assam and Bangladesh suggests adjusting module parameters to improve performance across varied lightning conditions.

In an attempt to improve the model-derived coastal circulation around India, we explore three different frameworks to assimilate surface currents from HF-Radars into an ocean model for the Indian Ocean. The first approach involves correcting the winds through Ekman Theory using the discrepancy between the model surface currents and the HF-Radar observations. The second approach entails direct assimilation of the HF-Radar surface currents into the ocean model. The third approach combines the first two frameworks. We show that all three approaches improve coastal circulation but in varying degrees. The improvements in analysis are least in the wind-correction approach and most prominent in the combined approach. We show that the combined approach of wind correction and assimilation of HF-Radar currents significantly improves the coastal circulation around India with root-mean-squared error at par with the precision of the HF-Radar measurements. This approach yields a correlation of ∼0.8–0.9 between the surface current analysis and the observation. We also show that this approach improves the subsurface currents. The surface current forecasts from the combined approach with a lead time of a day outperforms the free model run by a large margin thereby proving its mettle for operational adoption.

Surface wave dispersion analysis is crucial for investigating crustal and mantle structures on Earth and other celestial bodies. On Mars, Rayleigh and Love waves have been detected, with Rayleigh waves exhibiting vertical and radial motion and Love waves polarized transversely to the source-receiver azimuth. Accurate dispersion measurements require rotating three-component seismic data with an accurate back azimuth. However, large back azimuth (BAZ) uncertainties of marsquakes, due to single-station recordings and low signal-to-noise ratios, can cause rotation errors. These errors result in Rayleigh wave energy leaking into the transverse component, complicating dispersion extraction. In addition, the surface wave signal may be disturbed by multipath waves. To address these challenges, we develop a two-stage framework for measuring marsquake surface wave group velocity dispersion, which has not been utilized in previous studies. First, we perform a back azimuth correction based on surface wave analysis to optimize the rotation angle by scanning back-azimuths in a given range relative to the cataloged value. Second, we apply continuous wavelet transform (CWT) and phase-matched filter (PMF) to suppress noise and enhance the weak surface wave signals in the dispersion spectrogram. We validate this approach using synthetic and observed data on Earth and the S1222a marsquake.

The dominant external forcing to Earth's climate system is solar radiation, referred to as the solar spectral irradiance (SSI) and the total solar irradiance (TSI), which is the integral of SSI over all wavelengths. Accurate measurements of TSI and SSI are made from space because solar radiation is absorbed within Earth's atmosphere and that absorption would otherwise need to be corrected for, with added uncertainty, in ground or airborne solar irradiance observations. Composite solar irradiance records, developed from multiple instruments, can represent solar forcing over timescales longer than the lifetime of any individual instrument. We develop a new TSI composite record from pairs of overlapping individual measurement records with the highest relative stability over the longest timescales as determined from Allan Deviation analysis. Allan Deviation is a time-domain measurement standard analysis technique that was originally established by the metrology community to quantify the stability of atomic clocks. Version 0 of the Time Variance TSI (TV TSI) Composite begins in early 2003 and includes all Total Irradiance Monitor (TIM) instrument records beginning with the NASA Solar Radiation and Climate Experiment (SORCE) mission and continuing through today with the Total and Spectral Solar Irradiance Sensor (TSIS-1) mission. In future work, we plan to use Allan Deviation analysis to guide the incorporation of additional TSI measurement records and their time-dependent expression of uncertainties in order to develop a new version of the TV TSI composite that spans an even longer period of time for climate studies.

Global Climate Models (GCMs) are useful tools for simulating the dynamics of Mars atmosphere on a planetary scale. However their coarse resolution ($��\sim �$100 km) does not allow to capture small-scale phenomena such as slope winds which can occur over scales of just a few kilometers. These local slope winds can be crucial in Mars climate modeling, since they can significantly contribute to dust lifting affecting the amount of dust in the atmosphere, which is the major factor that governs the variability in Mars atmosphere. These winds can also affect the sublimation of water and $��{\text{CO}}_2�$ ice on the surface, both playing a key role in the Martian climate. In this study, we aim to develop a simple and efficient numerical model to simulate slope winds on Mars in single column and global climate models. We compare the model results with those from a mesoscale model with a resolution of a few kilometers capable of resolving these small scale winds (but only for a few days and with a high computational cost). This simple model of slope winds has been included in the Mars Climate Database (MCD v6.1) tool.

Apollo passive seismic data provide a unique resource for studying the lunar interior structure. However, the raw data are frequently contaminated by various artifacts, either related to the seismic sensors and the acquisition system or to issues during data transmission to the Earth. Understanding and exploiting the data set as a whole for seismic analyses remains therefore challenging. In this article, we present different artifacts observed in Apollo passive seismic data. This includes a systematic acquisition error at the Apollo 14 station, which was not investigated in previous studies but could affect most of the records of this station. We describe methods for the removal of this acquisition error as well as the numerous glitches. We conclude by illustrating the efficiency of these cleaning techniques on the collection of major lunar impacts and provide the associated data for future research.

State space models are well-suited for time series in which the evolution of variables cannot be well represented by deterministic basis functions. A key challenge in using state space models is the selection of the noise variance parameters. To better understand their impact on the model's filtering behavior, we derive the frequency response of several commonly used state space models by utilizing the connection between the Kalman smoother and regularized least squares problems. We show that the frequency response reveals distinguishing spectral features between competing state space models and explains the flattening of the log-likelihood function when the ratio of observation to disturbance variance becomes large. Using Dutch tide gauge data, we illustrate how the frequency response can be used to make informed decisions about the variance parameters, which could in turn support more reliable interpretations of time series data.

Coastal watersheds impacted by wildfires experience higher erosion resulting in increased sediment delivery to the ocean that alters limiting factors (i.e., light) for marine organisms. With increasing wildfire magnitude and severity, it is critical to explore changes in riverine discharges to the ocean to assess cascading hazards associated with wildfires. In situ data, remotely sensed turbidity data, and hydrological model (Soil and Water Assessment Tool “SWAT”) simulations have been adapted to capture and investigate fire-related land use change impacts on Malibu Creek, California, USA. Modifying SWAT land cover inputs using burn severity data had minimal impact on simulations, requiring additional parameterization for acceptable model performance. Remotely sensed turbidity, in situ discharge, rating curve sediment loads, and SWAT simulated discharge and sediment loads increased following the Woolsey Fire. When compared to in situ and rating curve data in similar non-fire water years, the 2019 Woolsey Fire water year in situ discharge was 1.8 times higher, SWAT simulated discharges were 1.4–1.7 times higher, and rating curve sediment load was 1.3 times higher. However, the SWAT simulated sediment loads were slightly lower (0.8–0.9 times) than rating curve sediment loads in similar non-fire water years. Mean coastal turbidity increased to 18.2 Formazin Nephelometric Unit (FNU) during the first storm post-fire (mean background value of 4.3 FNU). Synergies between methods demonstrated rapid coastal sediment exports (remote sensing) and ongoing erosion post-fire (SWAT). These data are essential to understanding fire-related marine ecological changes and implementing effective management and conservation initiatives.

Gridded precipitation data sets have become essential for understanding climate variability and long-term trends; however, their accuracy and reliability strongly depend on the availability and the spatial and temporal distribution of in situ meteorological observations. Here, we evaluate the performance of four gridded precipitation products: the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) v2, the Global Precipitation Climatology Centre (GPCC) Full Data Monthly Product v2022, the Climatic Research Unit (CRU) TS 4.07, and the ERA5-Land (ERA5-L) reanalysis, against a network of weather stations across Central America compiled from the regional meteorological service agencies. Using a point (station)-to-pixel comparison and a grid-by-grid spatial decorrelation analysis, we assess gridded data set accuracy and examine how station coverage affects precipitation trend detection. Results from the point (station)-to-pixel analysis show that CHIRPS consistently outperforms ERA5-Land, GPCC, and CRU across all standard statistical metrics (including correlation coefficient, bias, and root mean square error). CRU exhibits the largest spatial decorrelation distances, suggesting inflated spatial coherence likely resulting from interpolation over data-sparse regions. We find disagreement between the spatial representation of precipitation trends between reanalysis-based and observation-based data sets and show that the observed regional drying trend in eastern Honduras and Nicaragua in the GPCC and CHIRPS products may reflect the influence of one station rather than a broader, spatially coherent climate signal. These findings highlight the importance of considering both spatial station density and temporal data availability when using gridded precipitation products for studies of climate variability and change, especially in data-sparse regions such as Central America.

Saline lakes are expected to have been extensively present on ancient Mars, particularly as the planet dried or cooled. Such lakes likely deposited sulfate salts, as these salts have been widely identified from orbital and in situ Mars data. However, the relationship between martian sulfates and the environmental conditions that formed them (including whether conditions were warm, cool, drying, or freezing), remains under-characterized. To evaluate the relationship between sulfates and climate, we investigated the hypersaline, sulfate-bearing Basque Lakes in British Columbia, Canada, which serve as an analog for both “cold and wet” and “warm and wet” early Mars. We use the rover and orbiter relevant instrument techniques of Raman, Near Infrared, and X-Ray Fluorescence spectroscopy to evaluate seasonal lake mineralogy. We find that temperature-dependent, multi-cation salts form widely within the Basque Lakes' efflorescent crusts in the fall, which transform to meridianiite, mirabilite, a metastable Na-sulfate 7-hydrate, and epsomite in the wintertime. In both seasons, salt assemblages are metastable and persist beyond expected thermodynamic stability fields, suggesting ongoing climatic changes can prevent saline systems from settling into equilibrium phases. Coupled with sedimentological evidence, intimately mixed Mg-sulfates of different hydration states could be an indicator of surface fluids that interacted with an atmosphere, while formation of Na-sulfates could be evidence for brine freezing. Curiosity's exploration of the Gale crater sulfate-bearing unit and Perseverance's exploration of Jezero crater on Mars offer excellent chances to investigate the influence of climate on Mg-sulfate formation.