Ground-based Data Research Articles

Separating the causes of O–C variations in an RR Lyrae star with the Blazhko effect

Context. To date, puzzlingly few bona fide RR Lyrae stars have been identified in binaries. Binarity in pulsating stars can be revealed through well-timed photometric data over sufficiently long time bases because of the light travel time effect (LTTE) on the pulsations, which manifests as variation in the timings of maximum light in the O−C (observed minus calculated) diagram. However, O−C variations can also have other causes, such as the Blazhko effect or a sudden or gradual change in the main pulsation period. Aims. We approach this challenge by disentangling the Blazhko effect and period changes from the potential LTTE on V1109 Cas, an RR Lyrae star suspected to be part of a binary system based on its O−C data in the GEOS database. In doing so, we aim to uncover the subtler signals indicative of a companion (LTTE). Methods. We analysed nine years of ground-based photometric data, using Fourier analysis to model the pulsation modulated by the Blazhko effect. From the fit to the observed light curves, we constructed a refined O−C diagram without the scatter caused by Blazhko modulation. Subsequently, we considered different possible scenarios, because distinguishing intrinsic period changes or breaks from the LTTE is challenging. Results. If the remaining O−C variation is due to a period break, refining the O−C diagram can almost entirely remove the trends. If we interpret the variation as an LTTE, we can consider possible configurations. While we currently favour a period break as the explanation for the residual O−C variations, more data on the star in the forthcoming years will help reveal whether the O−C variations are due to the LTTE or intrinsic period changes. Conclusions. Our study, based on a detailed light-curve fitting of V1109 Cas, offers insights into the discernment between Blazhko modulation, intrinsic period breaks, and the LTTE in binary systems. Our results highlight the complexity of determining binarity from O−C data traditionally used for detecting binary motion, calling for caution in this process.

Study of the Correlation between Angstrom Exponent and Fine Mode Fraction in the Indo-Gangetic Plain Using Ground-Based Remote Sensing AERONET Data

Study of the Correlation between Angstrom Exponent and Fine Mode Fraction in the Indo-Gangetic Plain Using Ground-Based Remote Sensing AERONET Data

Analysis of the Samos Earthquake Using Swarm Satellite Data

Making use of the geomagnetic field and the electron density data from the ionosphere observed by the three Swarm satellites that were recorded through 2020, 303 days before and 62 days after the Samos earthquake were investigated, which occurred at 11:51 UTC on Oct 30, 2020, happened in an offshore location around 60 km southwest of Izmir, western Turkey, and 16 km north of Samos Island, Greece 37.897°N 26.784°E. Magnetic and electron density data abnormalities were examined using the Analysis of Magnetic Swarm (AMSW) and the Analysis of Electron Density Swarm (AEDSW) algorithms. The quiet time abnormalities during nighttime within the Dobrovisky region showed exciting results. It showed an enhancement in the cumulative number of tracks (acceleration) approximately 150 days before the mainshock of the earthquake in the magnetic and electron density data. In addition, magnetic and electron density data results matched the ground-based observatory data recorded within the same period and the Dobrovisky region.

Multi-Index Approach to Assess and Monitor Meteorological and Agricultural Drought in the Mediterranean Region: Case of the Upper Oum Er Rabia Watershed, Morocco

Drought is a severe disaster, increasingly exacerbated by climate change, and poses significant challenges worldwide, particularly in arid and semi-arid regions like Morocco. This study aims to assess and monitor drought using a multi-index approach to provide a comprehensive understanding of its spatio-temporal dynamics at both meteorological and agricultural levels. The research focuses on the Upper Oum Er Rabia watershed, which spans 35,000 km2 and contributes approximately a quarter of Morocco’s renewable water resources. We propose a methodology that combines ERA5 temperature data from remote sensing with ground-based precipitation data to analyze drought characteristics. Three meteorological indices were utilized: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Reconnaissance Drought Index (RDI). Additionally, three remote-sensing indices were employed to capture agricultural drought: the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Crop Water Stress Index (CWSI), with a total of 528 NDVI and EVI images and 1016 CWSI images generated through Google Earth Engine (GEE), using machine-learning techniques. Trend analyses were conducted to monitor drought patterns spatio-temporally. Our results reveal that the three-month interval is critical for effective drought monitoring and evaluation. Among the indices, SPEI emerged as the most effective for capturing drought in combination with remote-sensing data, while CWSI exhibited the highest correlation with SPEI over the three-month period, outperforming NDVI and EVI. The trend analysis indicates a significant precipitation deficit, alongside increasing trends in temperature and evapotranspiration over both the short and long term. Furthermore, all drought indices (SPI, SPEI, and RDI) demonstrate an intensification of drought conditions. Adaptation strategies are essential for managing water resources in the Upper Oum Er Rabia watershed under these evolving climate conditions. Continuous monitoring of climate variables and drought indices will be crucial for tracking changes and informing future water management strategies.

Remote sensing and machine learning algorithms to predict soil salinity in southern Kazakhstan

Salinization and land degradation are significant challenges in the southern regions of Kazakhstan. These issues arise due to climate change, unequal water resource distribution, and human impact. The primary concern revolves around water resources, which are influenced by the area’s trans boundary flow of major rivers. The low level of water and food security has pushed the development of new approaches based on remote sensing monitoring and geographic information systems (GIS) to provide solutions for soil salinity. The research aims to focus on utilizing high-resolution radar images. This data type is effective for cloudy weather and can be useful for continued monitoring of some areas. Machine learning methods can solve the problem of automatic mapping of agricultural land salinity in Kazakhstan’s southern regions. The precise mapping of the salinity area helps prevent or decrease salinity’s impact on agriculture. The experiment realized that complex models such as LightGBM do not have significant accuracy performance over simple models on a small dataset compared with Ridge regression. The results allow us to recommend an approach for further improvement with ground-based measurement data and other deep-learning methods for mapping the salinity of agricultural lands.

Estimation of PM2.5 Using Multi-Angle Polarized TOA Reflectance Data from the GF-5B Satellite

The use of satellite data to estimate PM2.5 is an appropriate approach for long-term, substantial monitoring and assessment. To estimate PM2.5, the majority of the algorithms now in use utilize the top-of-atmosphere (TOA) reflectance or aerosol optical depth (AOD) derived from scalar satellite data. However, there is relatively little research on the retrieval of PM2.5 using multi-angle polarized data. With its directional polarimetric camera (DPC), the Chinese new-generation satellite Gaofen 5B (henceforth referred to as GF-5B) offers a unique opportunity to close this gap in multi-angle polarized observation data. In this research, we utilized TOA data from the DPC payload and applied the gradient boosting machine method to simulate the impact of the observation angle, wavelength, and polarization information on the accuracy of PM2.5 retrieval. We identified the optimal conditions for the effective estimation of PM2.5. The quantitative results indicated that, under these optimal conditions, the PM2.5 concentrations retrieved by GF-5B showed a strong correlation with the ground-based data, achieving an R2 of 0.9272 and an RMSE of 7.38 µg·m−3. By contrast, Himawari-8’s retrieval accuracy under similar data conditions consisted of an R2 of 0.9099 and RMSE of 7.42 µg·m−3, indicating that GF-5B offers higher accuracy. Furthermore, the retrieval results in this study demonstrated an R2 of 0.81 when compared to the CHAP dataset, confirming the feasibility and effectiveness of the use of GF-5B for PM2.5 retrieval and providing support for PM2.5 estimation through multi-angle polarized data.

Assessing long-term water storage dynamics in Afghanistan: An integrated approach using machine learning, hydrological models, and remote sensing

Assessment of Terrestrial Water Storage (TWS) components is crucial for understanding regional climate and water resources, particularly in arid and semi-arid regions like Afghanistan. Given the scarcity of ground-based data, this study leverages remote sensing datasets to quantify water storage changes. We integrated Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GRACE-FO) data with WaterGap, Global Land Water Storage (GWLS), Catchment Land Surface Model (CLSM), and climate variables (precipitation, temperature, potential evapotranspiration) using artificial neural networks (ANN) and random forests (RF). Additionally, Ice, Cloud, and Land Elevation Satellite (ICESat-1,2) data were utilized to estimate glacier mass changes. Seasonal trend decomposition using Loess (STL) was applied to assess TWS changes from 2003 to 2022. Our methodology reveals a high correlation (R = 0.90–0.97) between reconstructed and observed TWS anomalies across Afghanistan's major basins. Glacier mass decreased by −0.59 and −1.17 Gt/year during 2003–2009 and 2018–2022, respectively, while overall TWS declined by −2.46 Gt/year. The HRB experienced the largest TWS loss (−1.47 Gt/year), primarily due to groundwater depletion (−1.18 Gt/year). These findings underscore the importance of our approach for assessing water resources, providing vital insights for sustainable management under a changing climate in a data-scarce country.

Оценка возможности использования спутниковой информации для мониторинга засух на территории южных регионов России

A brief description of the automated operational system for 10-day monitoring of droughts developed and operating at the Drought Monitoring Center of the All-Russian Research Institute of Agricultural Meteorology is provided. The system uses meteorological information from the network of weather stations. To obtain more detailed information on the spatial distribution of droughts, it is proposed to use satellite information (VIRS scanner of the SUOMI NPP satellite) along with ground-based data. For this purpose, it is proposed to calibrate satellite information (VCI index) based on ground information (HTC index) using test sites located near weather stations. Calculations of the aridity assessment for the territory of the considered federal districts using the proposed method are presented. Satisfactory convergence with the data obtained from ground information is observed. It is planned to refine the proposed procedure to operational application.

Modeling of Biologically Effective Daily Radiant Exposures over Europe from Space Using SEVIRI Measurements and MERRA-2 Reanalysis

Ultraviolet solar radiation at the Earth’s surface significantly impacts both human health and ecosystems. A biologically effective daily radiant exposure (BEDRE) model is proposed for various biological processes with an analytical formula for its action spectrum. The following processes are considered: erythema formation, previtamin D3 synthesis, psoriasis clearance, and inactivation of SARS-CoV-2 virions. The BEDRE model is constructed by multiplying the synthetic BEDRE value under cloudless conditions by a cloud modification factor (CMF) parameterizing the attenuation of radiation via clouds. The CMF is an empirical function of the solar zenith angle (SZA) at midday and the daily clearness index from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements on board the second-generation Meteosat satellites. Total column ozone, from MERRA-2 reanalysis, is used in calculations of clear-sky BEDRE values. The proposed model was trained and validated using data from several European ground-based spectrophotometers and biometers for the periods 2014–2023 and 2004–2013, respectively. The model provides reliable estimates of BEDRE for all biological processes considered. Under snow-free conditions and SZA < 45° at midday, bias and standard deviation of observation-model differences are approximately ±5% and 15%, respectively. The BEDRE model can be used as an initial validation tool for ground-based UV data.

Global Horizontal Irradiance in Brazil: A Comparative Study of Reanalysis Datasets with Ground-Based Data

Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets.

Field-scale variability and dynamics of soil moisture in Southwestern Nigeria

This research leverages field-based sensors and Geographic Information Systems (GIS) to elucidate soil moisture dynamics across diverse land uses in Southwestern Nigeria, including Cocoa, Cassava, Oil Palm, and Riparian ecosystems. The study synthesizes soil moisture data and geospatial coordinates from strategically selected land covers with satellite-derived soil moisture records from NASA's Prediction of Worldwide Energy Resources (POWER) and the US Geological Survey. Through a robust analytical framework combining cross-correlation functions, wavelet analysis, and inverse distance weighting interpolation, we revealed distinct soil moisture patterns ranging from 0 to 47.9% in arable farms, 0 to 46.8% in built-up areas, 0 to 49.9% in cocoa farms, 0.2 to 49.7% in oil palm farms, and 5.6 to 94.1% in riparian vegetation. The coefficient of determination values between 0.7 and 0.97 (p ≤ 0.05) underscore the significant influence of periodic land use on soil moisture during the study period. A comparative analysis of ground-based and satellite data revealed a potential overestimation of minimum soil moisture values by 66% and an underestimation of maximum values by 4%. This investigation provides critical insights into the interplay between hydrological variables and agro-meteorological factors, informing the management and assessment of agricultural resources.

Maps of agrolandscape and ecological zoning of natural and natural-agricultural ecosystems of the Russian Federation

Agriculture has a large area of coverage and places the greatest burden on land, soil, water and biological resources in many countries of the world. Agricultural lands occupy huge areas in the world (4.8 billion hectares, or 37% of the land) and in Russia (221.9 million hectares, or 13% of the Russian Federation area). The agriculture best adapted to local conditions is the most beneficial one for people and nature. The main direction of agricultural adaptation is agrolandscape-ecological zoning. In order to provide information support for regionally, landscapeand ecologically differentiated agriculture and rational environmental management, an agrolandscape– ecological zoning of natural and natural-agricultural ecosystems was developed for the first time for all 11 natural-economic regions of Russia, namely, Northern, North-Western, Volga–Vyatka, Central, Central Black Earth, Volga, North Caucasus, Ural, West Siberian, East Siberian, and Far Eastern. The zoning was carried out using comparative geographical and agrolandscape-ecological methods, ecological-landscape and agroecological approaches. The contour and information basis for zoning is the map of Soil–Ecological zoning of the Russian Federation from the Faculty of Soil Science of Lomonosov Moscow State University. Maps of other types of zoning, numerous maps and atlases, statistical data, available literary and stock sources, ground-based and remote data were also used. Our sets of zoning materials included: a map, a map legend, 3 databases (land, forage lands, and negative processes), 2 classifications (forage lands, reindeer pastures, if any), articles, monographs, recommendations, and proposals for production. The maps have been compiled on a 1:2,500,000 scale on a modern, highly informative topographic cartographic basis for the map of Russian Federation. The maps show 6 types of boundaries, including 4 types of agrolandscape-ecological ones (taken from the Soil–Ecological Zoning map): 1) zones, 2) mountainous territories, 3) provinces, 4) districts and 2 types of administrative ones (available on the cartographic basis) of: 1) constituent entities of the Russian Federation, and 2) natural-economic regions.

A probabilistic framework for identifying anomalies in urban air quality data.

Just as the value of crude oil is unlocked through refining, the true potential of air quality data is realized through systematic processing, analysis, and application. This refined data is critical for making informed decisions that may protect health and the environment. Perhaps ground-based air quality monitoring data often face quality control issues, notably outliers. The outliers in air quality data are reported as error and event-based. The error-based outliers are due to instrument failure, self-calibration, sensor drift over time, and the event based focused on the sudden change in meteorological conditions. The event-based outliers are meaningful while error-based outliers are noise that needs to be eliminated and replaced post-detection. In this study, we address error-based outlier detection in air quality data, particularly targeting particulate pollutants (PM2.5 and PM10) across various monitoring sites in Delhi. Our research specifically examines data from sites with less than 5% missing values and identifies four distinct types of error-based outliers: extreme values due to measurement errors, consecutive constant readings and low variance due to instrument malfunction, periodic outliers from self-calibration exceptions, and anomalies in the PM2.5/PM10 ratio indicative of issues with the instruments' dryer unit. We developed a robust methodology for outlier detection by fitting a non-linear filter to the data, calculating residuals between observed and predicted values, and then assessing these residuals using a standardized Z-score to determine their probability. Outliers are flagged based on a probability threshold established through sensitivity testing. This approach helps distinguish normal data points from suspicious ones, ensuring the refined quality of data necessary for accurate air quality modeling. This method is essential for improving the reliability of statistical and machine learning models that depend on high-quality environmental data.

Apsidal motion and TESS light curves of three southern close eccentric eclipsing binaries: GM Nor, V397 Pup, and PT Vel

The study of apsidal motion in eccentric eclipsing binaries provides an important observational test of theoretical models of stellar structure and evolution. New ground-based and space-based photometric data have been obtained and archival spectroscopic measurements were used in this study of three detached early-type and southern-hemisphere eccentric eclipsing binaries GM Nor (P = 1d​​.88, e = 0.05), V397 Pup (3d​​.00, 0.30), and PT Vel (1d​​.80, 0.12). Their TESS observations in several sectors have also been included and the corresponding light curves were solved using the PHOEBE code. As a result, new accurate photoelectric times of minimum light have been obtained. The newly completed O − C diagrams were analyzed using all reliable timings found in the literature and calculated using the TESS light curves. New or improved values for the elements of apsidal motion were obtained. Using ESO archive spectroscopy, for V397 Pup, the precise absolute parameters were newly derived: M1 = 3.076(35) M⊙, M2 = 2.306(35) M⊙, and R1 = 2.711(55) R⊙, R2 = 1.680(55) R⊙. For PT Vel the absolute dimensions were improved: M1 = 2.204(25) M⊙, M2 = 1.638(25) M⊙, and R1 = 2.108(30) R⊙, R2 = 1.605(30) R⊙. For GM Nor, the less accurate absolute parameters based on the light curve analysis were evaluated: M1 = 1.94(15) M⊙, M2 = 1.84(14) M⊙, and R1 = 2.27(20) R⊙, R2 = 2.25(20) R⊙. We found more precise and relatively short periods of apsidal motion of about 80, 335, and 160 years, along with the corresponding internal structure constants, log k2, –2.524, –2.361, and –2.563, for GM Nor, V397 Pup, and PT Vel, respectively. Relativistic effects are small but not negligible, making up to 10% of the total apsidal motion rate in all systems. No marks of the presence of the third body were revealed in the light curves, on the O − C diagrams, or in the reduced spectra of the eccentric systems studied here.

Snow runoff modelling in the upper Indus River Basin and its implication to energy water food nexus

Pakistan's hydropower sector depends heavily on glacier and snowmelt water that originates from the Upper Indus Basin (UIB). It is expected that climate change may adversely affect future hydropower generation capacity as a result of fluctuations in the magnitude, seasonality and hydrological extremes of the Indus River flow. This study employed the Degree-Day Snowmelt Runoff Model alongside the Moderate Resolution Imaging Spectroradiometer MODIS and daily ground-based hydro-meteorological data to model the snowmelt runoff response in the UIB. The results indicated a significant increase in the annual and seasonal runoff under both RCP4.5 and RCP8.5 scenarios, suggesting more water availability for hydropower and irrigation. By the end of the century, annual river flow is projected to increase by 28 % to 69 % under the RCP4.5 and RCP8.5 climate scenarios. Consequently, rise in annual river flow is expected to increase the electricity generation capacity of future hydropower projects by 93 % to 167 % under the RCP4.5 and RCP8.5 scenarios, respectively. The construction of robust multipurpose dams may potentially reduce flood risks in downstream areas during peak flows, while also supplying water for hydropower generation and irrigation during low flows. This, in turn, may enhance the resilience of both the hydropower and agriculture sectors in the face of climate change.

Investigating azimuthal propagation of Pc5 geomagnetic pulsations and their equivalent current vortices from ground-based and satellite data

Using phase delays at spaced stations and satellite observations in the magnetosphere during two events, we have studied azimuthal propagation of resonant bursts of geomagnetic pulsations in the Pc5 range. We have also examined propagation of equivalent current vortices during these events. It has been found that the pulsations, observed in the magnetosphere and ionosphere, and the equivalent current vortices in the ionosphere propagate in the azimuthal direction from the dayside to the nightside. Propagation velocities according to ground-based observations are 5–25 km/s; according to satellite observations, 114–236 km/s. Propagation velocities according to satellite observations do not exceed the Alfvén velocity in the magnetosphere, which is 620–1006 km/s. According to data from various instruments, there are signatures of fast magnetosonic and Alfvén waves at a time in one of the events on the satellite. This clearly reflects the transformation of these waves. The geomagnetic latitude of registration of vortex centers coincides with the latitude of the maximum amplitude of geomagnetic pulsations (field line resonances) and decreases by ~15° toward the early hours of MLT. The observed dynamics of Pc5 pulsations and vortices is assumed to reflect MHD wave propagation in the magnetosphere.

An in-depth analysis of the differentially expanding star cluster Stock 18 (Villafranca O-036) using Gaia DR3 and ground-based data

Context. The Villafranca project is combining Gaia data with ground-based surveys to analyze Galactic stellar groups (clusters, associations, or parts thereof) with OB stars. Aims. We want to analyze the poorly studied cluster Stock 18 within the Villafranca project, as it is a very young stellar cluster with a symmetrical and compact H II region around it, Sh 2-170, so it is likely to provide insights into the structure and dynamics of such objects at an early stage of their evolution. Methods. We used Gaia astrometry, photometry, spectrophotometry, and variability data as well as ground-based spectroscopy and imaging to determine the characteristics of Stock 18. We used these data to analyze its core, massive star population, extinction, distance, membership, internal dynamics, density profile, IMF, stellar variability, and Galactic location. Results. Stock 18 is a very young (∼1.0 Ma) cluster located at a distance of 2.91 ± 0.10 kpc and is dominated by the GLS 13 370 system, whose primary (Aa) is an O9 V star. We propose that Stock 18 was in a very compact state (∼0.1 pc) about 1.0 Ma ago and that most massive stars were ejected at that time without significantly affecting the less massive stars as a result of multi-body dynamical interactions. Different age estimates also point toward an age close to 1.0 Ma, indicating that the dynamical interactions took place very shortly after massive star formation. Well-defined expanding stellar clusters have been observed before, but none are as young as this one. If we include all of the stars, the initial mass function is top heavy, but if we discard the ejected ones, it becomes nearly canonical. Therefore, this is another example (in addition to the previous one we found – the Bermuda cluster) of (a) a very young cluster with an already evolved present day mass function (b) that has significantly contributed to the future population of free-floating compact objects. If confirmed in more clusters, the number of such compact objects may be higher in the Milky Way than previously thought. Stock 18 has a variable extinction with an average value of R5495 higher than the canonical one of 3.1. We have discovered a new visual component (Ab) in the GLS 13 370 system. The cluster is above our Galactic mid-plane, likely as a result of the Galactic warp, and it has a distinct motion with respect to its surrounding old population, which is possibly an influence of the Perseus spiral arm.

Evaluation of GPM IMERG satellite precipitation for rainfall–runoff modelling in Great Britain

ABSTRACT Reliable hydrological simulations require accurate precipitation data. However, data uncertainties due to the indirect nature of satellite estimates can propagate through hydrological models and lead to simulation errors. This study assesses the accuracy of Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products, comparing them directly with ground-based precipitation data and evaluating their performance in rainfall–runoff modelling across Great Britain. Three IMERG V06 products (IMERG-Early, IMERG-Late, and IMERG-Final) are examined. Utilizing the simple water balance model (SWBM), the analysis covers 250 basins, revealing that the SWBM performs well in over 50% of the basins. Runoff estimations show that European Observation (E-OBS) ground-based data yield the highest Nash-Sutcliffe efficiency (NSE) score (0.91), followed by IMERG-Final (0.85), IMERG-Late (0.82), and IMERG-Early (0.73). The findings underscore IMERG’s utility in hydrological modelling for ungauged or poorly gauged basins.

Constraints on fifth forces and ultralight dark matter from OSIRIS-REx target asteroid Bennu

It is important to test the possible existence of fifth forces, as ultralight bosons that would mediate these are predicted to exist in several well-motivated extensions of the Standard Model. Recent work indicated asteroids as promising probes, but applications to real data are lacking so far. Here we use the OSIRIS-REx mission and ground-based tracking data for the asteroid Bennu to derive constraints on fifth forces. Our limits are strongest for mediator masses m ~ (10−18-10−17) eV, where we currently achieve the tightest bounds. These can be translated to a wide class of models leading to Yukawa-type fifth forces, and we demonstrate how they apply to U(1)B dark photons and baryon-coupled scalars. Our results demonstrate the potential of asteroid tracking in probing well-motivated extensions of the Standard Model and ultralight bosons near the fuzzy dark matter range.

Assimilation of ground-based GNSS data using a local ensemble Kalman filter

Tropical cyclones become increasingly nonlinear and dynamically unstable in high-resolution models. The initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. Simultaneously, the lack of real ground-based GNSS observations over the ocean poses significant challenges when evaluating the assimilation results in oceanic regions. In this study, an Observation System Simulation Experiment is carried out based on a tropical cyclone case. Assimilation experiments using the WRF-PDAF framework are conducted. Conventional and GNSS observation operators are implemented. A diverse array of synthetic observations, encompassing temperature (T), wind components (U and V), precipitable water (PW), and zenith total delay (ZTD), are assimilated utilizing the Local Error-Subspace Transform Kalman filter (LESTKF). The findings highlight the improvement in forecast accuracy achieved through the assimilation process over the ocean. Multiple observation types further improve the forecast accuracy. The study underscores the crucial role of GNSS data assimilation techniques. The assimilation of GNSS data presents potential for advancing weather forecasting capabilities. Thus, the construction of ground-based GNSS observation stations over the ocean is promising.