Ground-based Observations Research Articles

Nuance: Efficient Detection of Planets Transiting Active Stars

The detection of planetary transits in the light curves of active stars, featuring correlated noise in the form of stellar variability, remains a challenge. Depending on the noise characteristics, we show that the traditional technique that consists of detrending a light curve before searching for transits alters their signal-to-noise ratio and hinders our capability to discover exoplanets transiting rapidly rotating active stars. We present nuance, an algorithm to search for transits in light curves while simultaneously accounting for the presence of correlated noise, such as stellar variability and instrumental signals. We assess the performance of nuance on simulated light curves as well as on the Transiting Exoplanet Survey Satellite light curves of 438 rapidly rotating M dwarfs. For each data set, we compare our method to five commonly used detrending techniques followed by a search with the Box-Least-Squares algorithm. Overall, we demonstrate that nuance is the most performant method in 93% of cases, leading to both the highest number of true positives and the lowest number of false-positive detections. Although simultaneously searching for transits while modeling correlated noise is expected to be computationally expensive, we make our algorithm tractable and available as the JAX-powered Python package nuance, allowing its use on distributed environments and GPU devices. Finally, we explore the prospects offered by the nuance formalism and its use to advance our knowledge of planetary systems around active stars, both using space-based surveys and sparse ground-based observations.

Optical observations of the potentially hazardous asteroid (4660) Nereus at opposition 2021

On 11 December 2021 at 11 UTC, the near-Earth asteroid (4660) Nereus passed the Earth at a distance of 0.0263 au, reaching about 13th magnitude that made it an excellent target for ground-based observations. We observed the asteroid photometrically from 11 October 2021, when it was visible at V = 17.7 mag, until 16 February 2022 (at V = 18.0 mag), and spectroscopically on 10 December 2021 (V ∼ 13.1 mag).Lightcurve analysis of (4660) Nereus gave a rotational period of 15.184245±0.000015 h with amplitude of 0.796 ± 0.052 mag. Fitting the magnitude-phase curve with the standard HG, HG1G2, HG12 functions yielded the following values: HR = 18.68−0.13+0.07 mag, G = 0.30−0.11+0.05; HR = 18.64−0.84+0.42 mag, G1 = 0.21−0.11+0.05, G2 = 0.46−0.23+0.22; HR = 18.61−0.07+0.13 mag, G12 = 0.18−0.18+0.18. The color indices of the asteroid at phase angle of 72.9° were found to be as follows: (B−V) = 0.688 ± 0.084, (B−R) = 1.046 ± 0.073, (V−R) = 0.358 ± 0.047, (V−I) = 0.718 ± 0.050. Its equivalent diameter was estimated as 0.34−0.10+0.07 km. These values, as well as the computed values of G1 and G2 coefficients, indicate that (4660) Nereus belongs to E-class asteroids. The same conclusion was made based on the spectra obtained.

Characteristics and seasonal variations of cirrus clouds from ground-based lidar and satellite observations over Shouxian Area, China

This study investigates the macroscopic and optical properties of cirrus clouds in the 32N region from July 2016 to May 2017, leveraging data from ground-based lidar observations and CALIOP to overcome the inconsistencies in detected cirrus cloud samples. Through extensive data analysis, statistical characteristics of cirrus clouds were discerned, revealing lidar ratio values of 28.5 ± 10.8 from ground-based lidar and 27.4 ± 11.2 from CALIOP. Validation with a decade of CALIOP data (2008-2018) confirmed these findings, presenting a consistent lidar ratio of 27.4 ± 12.0. A significant outcome of the analysis was the identification of a positive correlation between the lidar ratio and cloud centroid temperature, indicating a gradual decrease in the lidar ratio as temperatures dropped. The study established a fundamental consistency in their macroscopic properties, including cloud base height, cloud top height, cloud thickness, cloud centroid height, and cloud centroid temperature. The results for ground-based lidar (CALIOP) are: 10.0 ± 2.1 km (10.0 ± 2.2 km), 11.8 ± 2.1 km (11.5 ± 2.3 km), 1.87 ± 0.83 km (1.52 ± 0.71 km), and 10.5 ± 2.2 km, -46.9 ± 9.7°C (-47.1 ± 10.0°C).These properties exhibited seasonal variations, with cirrus clouds reaching higher altitudes in summer and lower in winter, influenced by the height of the tropopause. The optical properties of cirrus clouds were also analyzed, showing an annual average optical depth of 0.31 ± 0.35 for ground-based lidar and 0.32 ± 0.44 for CALIOP. The study highlighted the distribution of subvisible, thin, and thick cirrus clouds, with a notable prevalence of subvisible clouds during summer, suggesting their frequent formation above 14 km. Furthermore, the study observed linear growth in geometric thickness and optical depth up to 2.5 km from CALIOP and 2.9 km from ground-based lidar. Maximum optical depth was observed at cloud centroid temperatures of -35°C for CALIOP and -40°C for ground-based lidar, with optical depth decreasing as temperatures fell. This suggests that fully glaciated cirrus clouds exhibit the highest optical depth at warmer temperatures, within the complete glaciation temperature range of -35°C to -40°C.

Spatio-temporal characterization of drought variability in data-scarce regions using global precipitation data: a case study in Cauto river basin, Cuba.

Drought is considered the most severe water-related disaster in the Cauto river basin, which is the longest river and the main agricultural producer in Cuba. Better understanding of drought characteristics is crucial to drought management. Given the sparsity of ground-based precipitation observations in the Cauto, this study aims at using gridded global precipitation to analyze the spatio-temporal variations of drought in this river basin. Firstly, the monthly Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was calibrated with the gauged precipitation using the Thiessen polygon-based method and linear least squares regression equations. Then, the gridded standardized precipitation index (SPI) with time scales of 3, 6, 9months and drought characteristics, namely, drought frequency, duration and intensity were calculated using the calibrated CHIRPS. Finally, the spatio-temporal analysis was performed to investigate the variations of drought in the Cauto river basin in time and space. The obtained results show that the calibrated CHIRPS is highly consistent with the gauged observations and is capable of determining the magnitude, time, and spatial extent of drought events in the Cauto river basin. The trend analysis by the Mann-Kendall test reveals that although the trend is not statistically significant, the SPI tends to decrease with time in the dry season, which indicates the more severe drought. The spatial analysis indicates that the lower altitude area of the Cauto river basin is suffered from longer drought duration and higher drought intensity than the upper one. This study expresses the importance of open global precipitation data sources in monitoring and quantifying drought characteristics in data-scarce regions.

An Innovative Correction–Fusion Approach for Multi-Satellite Precipitation Products Conditioned by Gauge Background Fields over the Lancang River Basin

Satellite precipitation products can help improve precipitation estimates where ground-based observations are lacking; however, their relative accuracy and applicability in data-scarce areas remain unclear. Here, we evaluated the accuracy of different satellite precipitation datasets for the Lancang River Basin, Western China, including the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals (GPM IMERG), and Fengyun 2G (FY-2G) datasets. The results showed that GPM IMERG and FY-2G are superior to TRMM 3B42RT for meeting local research needs. A subsequent bias correction on these two datasets significantly increased the correlation coefficient and probability of detection of the products and reduced error indices such as the root mean square error and mean absolute error. To further improve data quality, we proposed a novel correction–fusion method based on window sliding data correction and Bayesian data fusion. Specifically, the corrected FY-2G dataset was merged with GPM IMERG Early, Late, and Final Runs. The resulting FY-Early, FY-Late, and FY-Final fusion datasets showed high correlation coefficients, strong detection performances, and few observation errors, thereby effectively extending local precipitation data sources. The results of this study provide a scientific basis for the rational use of satellite precipitation products in data-scarce areas, as well as reliable data support for precipitation forecasting and water resource management in the Lancang River Basin.

Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin

This study evaluates the long-term accuracy of six daily aerosol optical depth (AOD) datasets derived from the Moderate Resolution Imaging Spectroradiometers (MODIS) Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), combined DT and DB datasets (DTB) and MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms and the Multi-angle Imaging SpectroRadiometer (MISR) Version 23, by comparing them with ground-based AOD observations in 18 Aerosol Robotic Network (AERONET) sites over the Amazon basin between 2000 and 2022. The AOD retrieval accuracy was also assessed under varying seasons, aerosol loading, particle size, elevation and land cover type. Overall results showed that Terra and Aqua-MODIS DB and MISR algorithms perform better, with about 88% of AOD retrievals within the expected error range. However, MISR presented a systematic high negative bias. MODIS DT 3 km overestimated AOD values and indicated unsatisfactory retrieval accuracy in almost all scenarios. MODIS and MISR retrievals showed improved performance under low aerosol loading, whereas Aqua-MODIS MAIAC under intermediate conditions (AOD>0.2). MISR indicated a large dependence on a coarse mode-dominated environment with considerable negative bias. Terra-MODIS MAIAC retrievals exhibited a smaller dependence for aerosol particle size distribution, while Terra-MODIS DB performed better for fine mode and Aqua-MODIS DB for coarse dominance. MAIAC also performed well under polluted scenarios in the dry season in contrast to the majority of satellite-based AOD retrievals, which showed good agreement under clean background conditions in the wet season. Almost all satellite-based retrievals showed higher uncertainty under extremely high altitudes (>3000 m) followed by non-satisfactory correlation and significant biases. Additionally, almost all satellite-based AOD retrievals indicated poor performance as vegetation coverage increases. On the contrary, MISR showed higher accuracy under forest type. In general, MODIS and MISR retrieval accuracy demonstrated distinct levels of dependencies that require further improvement in the Amazon Basin.

Initial evaluation of humidity profiles retrieved by the EOS-07 millimetre-wave humidity sounder

ABSTRACT The vertical profiles of specific humidity in the troposphere measured by the millimetre-wave humidity sounder (MHS) onboard of the Earth Observation Satellite (EOS)-07 (Microsoft 2B) are validated using ground-based radiosonde observations during July-August 2023. MHS onboard EOS-07 (hereafter referred as EOS-07/MHS) is a 6-channel cross-track scanning radiometer operating in the 183.31 ± 16.25 GHz band. The EOS-07 observations are used to retrieve three-dimensional specific humidity profiling from 1000 hPa (surface) to 100 hPa (16 km) atmospheric pressure levels with a spatial resolution of 10 km at nadir. The EOS-07/MHS measurements over India have a high correlation coefficient of 0.98 with the radiosonde observations. The mean relative bias is found to be 0.44 ± 0.55 g kg−1 below 350hPa and 2.8 ± 2.3 g kg−1 above 350 hPa. It is also found that above the 350 hPa level, MHS seems to be systematically overestimating the radiosonde measurements. After that, the EOS-07/MHS measurements are used to construct the diurnal variation of humidity during the active phases of the Indian summer monsoon. The observations are consistent with the present understanding of the Indian summer monsoon system. The present results are very encouraging and demonstrate the great potential of EOS-07/MHS observations of humidity for meteorological application, especially in understanding the hydrological cycle.

Methodological advancement in deriving primary productivity and ecosystem respiration fluxes across different biomes

In this paper, we introduce a methodology that can improve the estimations of Gross Primary Productivity (GPP) and ecosystem Respiration (Reco) processes at a regional scale. This method is based on a satellite data-driven approach which is suitable for regions like India where there exists a serious shortage of ground-based observations of biospheric carbon fluxes (e.g., Eddy Covariance (EC) flux measurements). We relied on the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance for capturing vegetation dynamics in the Light-Use Efficiency (LUE)-based vegetation model. Further, we utilised recently available satellite-based Solar-Induced Fluorescence (SIF) and other variables such as Soil Moisture (SM) and Soil Temperature (ST) to refine the predictions of GPP and Reco. The methodology involves establishing a relationship between SIF and GPP for different vegetation classes over India. The SIF-GPP relationship established across the biomes was then used to correct the GPP fluxes simulated by the LUE-based model. Similarly, the ecosystem respiration estimations by the model have undergone refinement by incorporating ST and SM information. This innovative method shows remarkable potential to improve biospheric CO2 uptake and release, especially for in situ data-constrained regions like India.• SIF-based information is introduced to a light-use efficiency-based vegetation model.• SIF-GPP relationship is established for major biomes across India.• SM and ST information is incorporated into the Reco simulations in the model.

Orbit determination of asteroid (469219) Kamo‘oalewa using a combination of historical and new observations

ABSTRACT The orbit solution of near-Earth asteroids heavily relies on ground-based optical observations. The orbit uncertainty is limited by the insufficient observation quantity and quality. The Chinese Tianwen-2 mission targets a near-Earth asteroid (469219) Kamo‘oalewa and a main-belt comet, 311P/PANSTARRS. To accurately determine the orbit of Kamo‘oalewa, more optical observations are needed. Autonomous observation experiments focusing on Kamo‘oalewa were carried out using the 2.16-m telescope at the Xinglong Observatory of the National Astronomical Observatory of China. We found that this telescope could observe near-Earth asteroids as faint as a magnitude of 22.8. Based on the stacking method and trailed star extraction algorithm, 14 optical observations were obtained from 2022 to 2024. The orbit for Kamo‘oalewa was determined by combining our observations with historical observations between 2004 and 2024. The inclusion of our observations results in improved orbital uncertainties of Kamo‘oalewa by 18.67, 7.93, and 11.12 km (1σ) in the X, Y, and Z directions, respectively. Furthermore, by combining all existing and simulated observations, the uncertainty of the orbital determination of Kamo‘oalewa was analysed. When using an additional 180 group observations from 3 Chinese observatories over 2 yr, the orbital uncertainties of Kamo‘oalewa in the 3 directions could be reduced to 30 km (1σ).

Ground-based cloud microphysical observations at Mount Lu in the East Asian monsoon region from 2015 to 2020

Ground-based cloud microphysical observations were continuously conducted at Mount (Mt.) Lu site from 2015 to 2020, which is located in the East Asian monsoon areas of eastern China. The statistical characteristics of cloud microphysics under different meteorological conditions were analyzed by using visibility, cloud droplet and raindrop size distribution data, and meteorological variables. The mean cloud droplet number concentration (Nc), liquid water content (LWC) and effective diameter (De) are 151 cm−3, 0.08 g m−3 and 11.4 μm, respectively. Nc and LWC observed at Mount Lu are lower than the observation results of cloud from other mountain stations all over the world. Broader cloud droplet spectra are correlated with higher temperatures and lower wind speeds. The clouds are classified into nonprecipitating, lightly-raining and raining clouds. Statistics show that lightly-raining and raining clouds account for 66% of all clouds. Nc and LWC of lightly-raining (drizzling) clouds are highest, while spectral width of raining clouds is largest. Spectral dispersion of raining clouds converges to the narrow range of 0.4–0.7. Notably, medium sized cloud droplets (11–40 μm) promote the formation of large cloud droplets (40–50 μm, pre-drizzle size) and drizzle drops which initiate the auto-conversion from condensation to collision-coalescence growth. This study relying on in-situ continuous cloud observation from mountain station are helpful to understand cloud microphysical structures in East Asian monsoon region, and fitting parameters also provide observational evidence for numerical models.

Intensity correlation imaging design for geostationary satellite inspection

Recent advances in the reduction of the integration time required of intensity correlation imaging (ICI) have opened the possibility for significant improvements in astronomical imaging. This paper discusses the application of ICI to the fine-resolution imaging of geostationary satellites conducted by ground-based observatories.

Aerosols in the central Arctic cryosphere: satellite and model integrated insights during Arctic spring and summer

Abstract. The central Arctic cryosphere is influenced by the Arctic amplification (AA) and is warming faster than the lower latitudes. AA affects the formation, loss, and transport of aerosols. Efforts to assess the underlying processes determining aerosol variability are currently limited due to the lack of ground-based and space-borne aerosol observations with high spatial coverage in this region. This study addresses the observational gap by making use of total aerosol optical depth (AOD) datasets retrieved by the AEROSNOW algorithm over the vast cryospheric region of the central Arctic during Arctic spring and summer. GEOS-Chem (GC) simulations combined with AEROSNOW-retrieved data are used to investigate the processes controlling aerosol loading and distribution at different temporal and spatial scales. For the first time, an integrated study of AOD over the Arctic cryosphere during sunlight conditions was possible with the AEROSNOW retrieval and GC simulations. The results show that the spatial patterns observed by AEROSNOW differ from those simulated by GC. During spring, which is characterized by long-range transport of anthropogenic aerosols in the Arctic, GC underestimates the AOD in the vicinity of Alaska in comparison with AEROSNOW retrieval. At the same time, it overestimates the AOD along the Bering Strait, northern Europe, and the Siberian central Arctic sea-ice regions, with differences of −12.3 % and 21.7 %, respectively. By contrast, GC consistently underestimates AOD compared with AEROSNOW in summer, when transport from lower latitudes is insignificant and local natural processes are the dominant source of aerosol, especially north of 70° N. This underestimation is particularly pronounced over the central Arctic sea-ice region, where it is −10.6 %. Conversely, GC tends to overestimate AOD along the Siberian and Greenland marginal sea-ice zones by 19.5 % but underestimates AOD along the Canadian Archipelago by −9.3 %. The differences in summer AOD between AEROSNOW data products and GC-simulated AOD highlight the need to integrate improved knowledge of the summer aerosol process into existing models in order to constrain its effects on cloud condensation nuclei, on ice nucleating particles, and on the radiation budget over the central Arctic sea ice during the developing AA period.

People’s Perception of Climate Change Impacts on Subtropical Climatic Region: A Case Study of Upper Indus, Pakistan

In developing countries like Pakistan, the preservation of the environment, as well as people’s economies, agriculture, and way of life, are believed to be hampered by climate change. Understanding how people perceive climate change and its signs is essential for creating a variety of adaptation solutions. In this study, we aim to bridge the gap in current research within this area, which predominantly relies on satellite data, by integrating qualitative assessments of people’s perceptions of climate change, thereby providing valuable ground-based observations of climate variability and its impacts on local communities. Field-based data were collected at different altitudes (upstream (US), midstream (MS), and downstream (DS)) of the Upper Indus Basin using both quantitative and qualitative assessments in 2017. The result shows that these altitudes are highly variable in many contexts: socioeconomic indicators of education, agriculture, income, women empowerment, health, access to basic resources, and livelihood diversifications are highly variable in the Indus Basin. The inhabitants of the Indus Basin perceive the climate changing around them and report impacts of this change as increase in overall temperatures (US 96.9%, MS 97%, DS 93.6%) and erratic rainfall patterns (US 44.1%, MS 73.3%, DS 51.0%) resulting in increased water availability for crops (US 38.6%, MS 39.7%, DS 54.8%) but also increasing number of dry days (US 56.7%, MS 85.5%, DS 67.1%). Communities at these altitudes said that agriculture was their primary source of income, making them particularly vulnerable to the effects of climate change and the dangers that go along with it. The insights are useful for determining what information and actions are required to support local climate-related hazard management in subtropical climate regions. Moreover, it is vital to launch a campaign to raise awareness of potential hazards, as well as to provide training and an early warning system.

Evaluation of Satellite-Based Rainfall Estimates against Rain Gauge Observations across Agro-Climatic Zones of Nigeria, West Africa

Satellite rainfall estimates (SREs) play a crucial role in weather monitoring, forecasting and modeling, particularly in regions where ground-based observations may be limited. This study presents a comprehensive evaluation of three commonly used SREs—African Rainfall Climatology version 2 (ARC2), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) and Tropical Application of Meteorology using SATellite data and ground-based observation (TAMSAT)— with respect to their performance in detecting rainfall patterns in Nigeria at daily scales from 2002 to 2022. Observed data obtained from the Nigeria Meteorological Agency (NiMet) are used as reference data. Evaluation metrics such as correlation coefficient, root mean square error, mean error, bias, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are employed to assess the performance of the SREs. The results show that all the SREs exhibit low bias during the major rainfall season from May to October, and the products significantly overestimate observed rainfall during the dry period from November to March in the Sahel and Savannah Zones. Similarly, over the Guinea Zone, all the products indicate overestimation in the dry season. The underperformance of SREs in dry seasons could be attributed to the rainfall retrieval algorithms, intensity of rainfall occurrence and spatial-temporal resolution. These factors could potentially lead to the accuracy of the rainfall retrieval being reduced due to intense stratiform clouds. However, all the SREs indicated better detection capabilities and less false alarms during the wet season than in dry periods. CHIRPS and TAMSAT exhibited high POD and CSI values with the least FAR across agro-climatic zones during dry periods. Generally, CHIRPS turned out to be the best SRE and, as such, would provide a useful dataset for research and operational use in Nigeria.

Global retrieval of stratospheric and tropospheric BrO columns from the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) on board the Suomi-NPP satellite

Abstract. Quantifying the global bromine monoxide (BrO) budget is essential to understand ozone chemistry better. In particular, the tropospheric BrO budget has not been well characterized. Here, we retrieve nearly a decade (February 2012–July 2021) of stratospheric and tropospheric BrO vertical columns from the Ozone Mapping and Profiling Suite Nadir Mapper (OMPS-NM) on board the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite. In quantifying tropospheric BrO enhancements from total slant columns, the key aspects involve segregating them from stratospheric enhancements and applying appropriate air mass factors. To address this concern and improve upon the existing methods, our study proposes an approach that applies distinct BrO vertical profiles based on the presence or absence of tropospheric BrO enhancement at each pixel, identifying it dynamically using a satellite-derived stratospheric-ozone–BrO relationship. We demonstrate good agreement for both stratosphere (r = 0.81–0.83) and troposphere (r = 0.50–0.70) by comparing monthly mean BrO vertical columns from OMPS-NM with ground-based observations from three stations (Lauder, Utqiaġvik, and Harestua). Although algorithm performance is primarily assessed at high latitudes, the OMPS-NM BrO retrievals successfully capture tropospheric enhancements not only in polar regions but also in extrapolar areas, such as the Rann of Kutch and the Great Salt Lake. We also estimate random uncertainties in the retrievals pixel by pixel, which can assist in quantitative applications of the OMPS-NM BrO dataset. Our BrO retrieval algorithm is designed for cross-sensor applications and can be adapted to other space-borne ultraviolet spectrometers, contributing to the creation of continuous long-term satellite BrO observation records.

Deep Sky Objects Detection with Deep Learning for Electronically Assisted Astronomy

Electronically Assisted Astronomy is a fascinating activity requiring suitable conditions and expertise to be fully appreciated. Complex equipment, light pollution around urban areas and lack of contextual information often prevents newcomers from making the most of their observations, restricting the field to a niche expert audience. With recent smart telescopes, amateur and professional astronomers can capture efficiently a large number of images. However, post-hoc verification is still necessary to check whether deep sky objects are visible in the produced images, depending on their magnitude and observation conditions. If this detection can be performed during data acquisition, it would be possible to configure the capture time more precisely. While state-of-the-art works are focused on detection techniques for large surveys produced by professional ground-based observatories, we propose in this paper several Deep Learning approaches to detect celestial targets in images captured with smart telescopes, with a F1-score between 0.4 and 0.62 on test data, and we experimented them during outreach sessions with public in Luxembourg Greater Region.

Joule Heating rate at high-latitudes by Swarm and ground-based observations compared to MHD simulations

We compare Joule Heating rates as derived from ground-based magnetic field and all-sky camera data, from Low Earth Orbit satellite data (ESA Swarm) and from a MHD simulation (GUMICS-5) with each other in a case study of an auroral arc system. The observational estimates of Joule Heating rates provide information on regional scales and with high spatial resolution (10–100 km). Their comparison with global MHD results is conducted for a quiet time interval of a few minutes, just before a magnetic substorm. Analysis of the ground-based observations yields electric field with dominating North-South component pointing towards the arcs and having maxima values in the range 20–35 V/km. Combining these values with Pedersen conductance estimates from optical data (5–10 S) yields Joule Heating rates in the range 2.5–3.5 mW/m2. Swarm electric field measurements are consistent in their direction and intensity with the ground-based estimates. They also show that heating is increased particularly in the region where the conductance is low. The total amount of Joule heating in the area between the Swarm A and C satellite footprints while crossing the all-sky camera field of view is estimated to be 46 MW and the total amount energy dissipation during the 80 s overflight is around 3.6 GJ (1000 kWh). GUMICS-5 estimate of the peak Joule Heating in the magnetic local time sector of the arc system is smaller than that from the ground-based data with a factor of 2.9. Comparisons of GUMICS-5 results with Space Weather Modeling Framework (SWMF), shows that the latter gives on average larger heating rates being thus more consistent with our regional observations. However, both MHD-codes yield smaller Joule Heating rates around the time of the arcs and during the following substorm than the CTIP-e code. CTIP-e has a more detailed description of ionosphere-thermosphere interactions than the MHD-codes and its convection electric field is enhanced with a randomly varying additional component mimicking small scale structures. GUMICS-SWMF comparisons of global Joule Heating patterns in the Northern polar area reveal that the two simulations have significant differences in their spatial distribution of heating rates. Main cause for these deviations is the difference in the derivation of ionospheric Pedersen conductance. Our results emphasize the fact that future estimates of the global energetics in the magnetosphere–ionosphere–thermosphere system require better knowledge on ionospheric conductivities, both by new measurement concepts and by better understanding on the background physics controlling conductivity variations.

A dusty rain falls on the nova V959 Monocerotis

ABSTRACT We present archival and ground-based infrared observations of the γ-ray-emitting nova V959 Mon, covering the period 100–4205 d after the 2012 eruption. We use these data to determine that the secondary in the nova system is a G5 main sequence star. Data from the NEOWISE survey reveal a significant increase in the emission at 3.4 and 4.6 $\, \mu$m at late (≳600 d) times, which we interpret as emission by dust. Other interpretations are considered but cannot be reconciled with the data. The presence of such late dust emission, and in particular its variation with time, are unprecedented in the context of novae. The behaviour of the dust emission suggests a qualitative interpretation in which ejecta from the 2012 eruption encounter denser pre-eruption circumbinary material, giving rise to Rayleigh–Taylor instabilities that cause clumps of dust-bearing material to fall back towards the central binary, the dust undergoing destruction by chemisputtering as it does so. The observed rise in the dust temperature, the decline in the nova-dust distance, and in the dust mass are consistent with this interpretation. Not all novae are expected to show this behaviour, but inspection of resources such as NEOWISE might reveal other novae post-eruption that do.

Earthquakes Three-Stage Early Warning and Short-Term Prediction

Among the anomalous geophysical phenomena observed preceding earthquakes, specific attention has been given to VLF/LF, ULF (very low frequency/low frequency, ultra-low frequency) electromagnetic (EM) emissions, recorded before earthquakes, because of an interesting correlation between them and seismic activity. Numerous scientific papers have been published on this topic, providing convincing evidence of these processes observed using ground-based and satellite ground-based observations during the preparation for the earthquake. These phenomena are detectable both at laboratory and geological scales. The authors of this paper have used an avalanche-like unstable geological model of fault formation and a model of the generation of electromagnetic emissions detected before the earthquakes to prove the prediction capabilities of VLF/LF EM emissions. The first gives a clear imagination of earthquake preparation in all stages in the focal area from the beginning of the micro-cracks appearing to the final formation of fault. Another one, based on electrodynamics, explains the EM emissions origination process and offers a formula that analytically connects the observed frequency of EM radiation with the linear size of the emitted body (fault). It is worth emphasizing the synthesis and rather harmonious relation of the mentioned models. Based on the above models conducted studies clearly show that the description of a qualitative avalanche-unstable geological model of fault formation using VLF/LF EM radiation data made it possible to quantitatively characterize the full cycle of preparation and occurrence of earthquake process. Namely, in the case of VLF/LF emissions monitoring, the beginning of the “avalanche process” of ruptures is considered the first stage of early warning of an incoming earthquake. The so-called EM emissions “silence” period is assessed as the second stage of early warning. The third early warning or an alarm about an incoming earthquake may be announced immediately at the moment of the first anomaly appearance subsequently the “silence” period. This article offers the method of earthquake three-step early warning and short-term (hourly) prediction.

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.