Ground-based Observations Research Articles

Characterization of non-Gaussianity in the snow distributions of various landscapes

Abstract. Seasonal snowpack is an important predictor of the water resources available in the following spring and early-summer melt season. Total basin snow water equivalent (SWE) estimation usually requires a form of statistical analysis that is implicitly built upon the Gaussian framework. However, it is important to characterize the non-Gaussian properties of snow distribution for accurate large-scale SWE estimation based on remotely sensed or sparse ground-based observations. This study quantified non-Gaussianity using sample negentropy; the Kullback–Leibler divergence from the Gaussian distribution for field-observed snow depth data from the North Slope, Alaska; and three representative SWE distributions in the western USA from the Airborne Snow Observatory (ASO). Snowdrifts around lakeshore cliffs and deep gullies can bring moderate non-Gaussianity in the open, lowland tundra of North Slope, Alaska, while the ASO dataset suggests that subalpine forests may effectively suppress the non-Gaussianity of snow distribution. Thus, non-Gaussianity is found in areas with partial snow cover and wind-induced snowdrifts around topographic breaks on slopes and on other steep terrain features. The snowpacks may be considered weakly Gaussian in coastal regions with open tundra in Alaska and alpine and subalpine terrains in the western USA if the land is completely covered by snow. The wind-induced snowdrift effect can potentially be partitioned from the observed snow spatial distribution guided by its Gaussianity.

The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators

Abstract. Supercooled liquid clouds are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We take advantage of the Macquarie Island Cloud and Radiation Experiment (MICRE) to perform an analysis of observed and simulated cloud processes over the Southern Ocean in a region and season dominated by supercooled liquid clouds. Using a single-column version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), we compare two different cloud microphysical schemes to ground-based observations of cloud, precipitation, and radiation over a 2.5-month period (1 January–17 March 2017). Both schemes are able to reproduce aspects of the cloud and radiation observations during MICRE to within the uncertainty of the data when the thermodynamic profile is prescribed with relaxation. There are differences in water mass and representation of reflectivity between the schemes. A sensitivity study of the cloud microphysics schemes, one a bulk one-moment scheme and the other a two-moment scheme with prediction of mass and number, indicates that several key processes create differences between the schemes. Surface radiative fluxes and total water path are highly sensitive to the formation and fall speed of precipitation. The prediction of hydrometeor number with the two-moment scheme yields a better comparison with observed reflectivity and radiative fluxes, despite predicting higher liquid water contents than observed. With the two-moment scheme, we are also able to test the sensitivity of the results to the input of liquid cloud condensation nuclei (CCN) and ice nuclei (IN). The cloud properties and resulting radiative effects are found to be sensitive to the CCN and IN concentrations. More CCN and IN increase liquid and ice water paths, respectively. Thus, both the dynamic environment and aerosols, integrated through the cloud microphysics, are important for properly representing Southern Ocean cloud radiative effects.

Performance Evaluation of Satellite Precipitation Products During Extreme Events—The Case of the Medicane Daniel in Thessaly, Greece

Mediterranean tropical-like cyclones, or Medicanes, present unique challenges for precipitation estimations due to their rapid development and localized impacts. This study evaluates the performance of satellite precipitation products in capturing the precipitation associated with Medicane Daniel that struck Greece in early September 2023. Utilizing a combination of ground-based observations, reanalysis, and satellite-derived precipitation data, we assess the accuracy and spatial distribution of the satellite precipitation products GPM IMERG, GSMaP, and CMOPRH during the cyclone event, which formed in the Eastern Mediterranean from 4 to 7 September 2023, hitting with unprecedented, enormous amounts of rainfall, especially in the region of Thessaly in central Greece. The results indicate that, while satellite precipitation products demonstrate overall skill in capturing the broad-scale precipitation patterns associated with Medicane Daniel, discrepancies exist in estimating localized intense rainfall rates, particularly in convective cells within the cyclone’s core. Indeed, most of the satellite precipitation products studied in this work showed a misplacement of the highest amounts of associated rainfall, a significant underestimation of the event, and large unbiased root mean square error in the areas of heavy precipitation. The total precipitation field from IMERG Late Run and CMORPH showed the smallest bias (but significant) and good temporal correlation against rain gauges and ERA5-Land reanalysis data as a reference, while IMERG Final Run and GSMaP showed the largest underestimation and overestimation, respectively. Further investigation is needed to improve the representation of extreme precipitation events associated with tropical-like cyclones in satellite precipitation products.

The Impact of Firework Ban Relaxation on Variations in SO2 Emissions in China During the 2023 Chinese New Year

During the 2023 Chinese New Year (CNY), many city governments temporarily relaxed firework restrictions, leading to increased sulfur dioxide (SO2) emissions from the combustion of sulfur-containing fireworks. This study employed the four-dimensional variational (4DVar) assimilation system to examine variations in SO2 emissions in China by assimilating hourly ground-based observations. Two experiments were conducted during CNY in 2022 and 2023 to quantify the variations in SO2 emissions. On CNY’s Eve in 2023, following the relaxation of the firework ban, SO2 emissions surged by 8.22 Gg nationwide compared to the previous day with significant increases in the Energy Golden Triangle (2.037 Gg), the North China Plain (1.709 Gg), and northeast China (0.945 Gg). Emissions peaked on CNY’s Eve and rapidly declined in the following two days but remained elevated compared to the pre-CNY period, indicating lingering effects of firework burning. Compared to the forecasts using the prior emissions, the optimized emissions markedly improved the model forecasts of SO2 during the 2023 CNY period, with an increase in the correlation coefficient (R) from 0.13 to 0.64 and a reduction in the root mean square error (RMSE) by 49.2%, demonstrating the effectiveness of the optimized emissions. These findings will be useful for local governments in formulating strategies for firework burning during CNY.

Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems

Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. The primary objective is to assess how remote sensing can enhance avalanche risk assessment and monitoring systems. A systematic literature review was conducted, focusing on studies published between 2010 and 2025. The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. Optical and radar imagery enable the detection of crucial parameters like snow cover, slope, and vegetation that influence avalanche risks. However, challenges such as limitations in spatial and temporal resolution and real-time monitoring were identified. Emerging technologies, including microsatellites and hyperspectral imaging, offer potential solutions to these issues. The practical implications of these findings underscore the importance of integrating remote sensing data with ground-based observations for more robust avalanche forecasting. Enhanced real-time monitoring and data fusion techniques will improve disaster management, allowing for quicker response times and more effective policymaking to mitigate risks in avalanche-prone regions.

Crescendo beyond the horizon: more gravitational waves from domain walls bounded by inflated cosmic strings

Abstract Gravitational-wave (GW) signals offer a unique window into the dynamics of the early universe. GWs may be generated by the topological defects produced in the early universe, which contain information on the symmetry of UV physics. We consider the case in which a two-step phase transition produces a network of domain walls bounded by cosmic strings. Specifically, we focus on the case in which there is a hierarchy in the symmetry-breaking scales, and a period of inflation pushes the cosmic string generated in the first phase transition outside the horizon before the second phase transition. We show that the GW signal from the evolution and collapse of this string-wall network has a unique spectrum, and the resulting signal strength can be sizeable. In particular, depending on the model parameters, the resulting signal can show up in a broad range of frequencies and can be discovered by a multitude of future probes, including the pulsar timing arrays and space- and ground-based GW observatories. As an example that naturally gives rise to this scenario, we present a model with the first phase transition followed by a brief period of thermal inflation driven by the field responsible for the second stage of symmetry breaking. The model can be embedded into a supersymmetric setup, which provides a natural realization of this scenario. In this case, the successful detection of the peak of the GW spectrum probes the soft supersymmetry breaking scale and the wall tension.

First evaluation of the GEMS glyoxal products against TROPOMI and ground-based measurements

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) on board the GEO-KOMPSAT-2B satellite is the first geostationary satellite launched to monitor the environment. GEMS conducts hourly measurements during the day over eastern and southeastern Asia. This work presents glyoxal (CHOCHO) vertical column densities (VCDs) retrieved from GEMS, with optimal settings for glyoxal retrieval based on sensitivity tests involving reference spectrum sampling and fitting window selection. We evaluated GEMS glyoxal VCDs by comparing them to the TROPOspheric Monitoring Instrument (TROPOMI) and multi-axis differential optical absorption spectroscopy (MAX-DOAS) ground-based observations. On average, GEMS and TROPOMI VCDs show a spatial correlation coefficient of 0.63, increasing to 0.87 for northeastern Asia. While GEMS and TROPOMI demonstrate similar monthly variations in the Indochinese Peninsula regions (R > 0.67), variations differ in other areas. Specifically, GEMS VCDs are higher in the winter and either lower or comparable to TROPOMI and MAX-DOAS VCDs in the summer across northeastern Asia. We attributed the discrepancies in the monthly variation to a polluted reference spectrum and high NO2 concentrations. When we correct GEMS glyoxal VCDs as a function of NO2 SCDs, the monthly correlation coefficients substantially increase from 0.16–0.40 to 0.45–0.72 in high NO2 regions. When averaged hourly, GEMS and MAX-DOAS VCDs exhibit similar diurnal variations, especially at stations in Japan (Chiba, Kasuga, and Fukue).

A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment.

Floods are the most destructive, widespread, and frequent natural hazards. The extent of flood events is accelerating in the context of climate change, where flood management and disaster mitigation remain important long-term issues. Different studies have been utilizing data and images from various types of sensors for mapping, assessment, forecasting, early warning, rescue, and other disaster prevention and mitigation activities before, during, and after floods, including flash floods, coastal floods, and urban floods. These monitoring processes evolved from early ground-based observations relying on in situ sensors to high-precision, high-resolution, and high-coverage monitoring by airborne and remote sensing sensors. In this study, we have analyzed the different kinds of sensors from the literature review, case studies, and other methods to explore the development history of flood sensors and the driving role of floods in different countries. It is found that there is a trend towards the integration of flood sensors with artificial intelligence, and their state-of-the-art determines the effectiveness of local flood management to a large extent. This study helps to improve the efficiency of flood monitoring advancement and flood responses as it explores the different types of sensors and their effectiveness.

INTOMO operator for GNSS multi-source tomography based on 3D ray tracing technique

AbstractThe current GNSS meteorology literature focuses on ground-based and space-based GNSS observations separately, without exploring potential synergies. In this study, we propose combining the two data sources using GNSS tomography to overcome current limitations in (1) horizontal resolution of GNSS space-based, (2) low vertical resolution of GNSS ground-based tropospheric retrievals when the number of GNSS ground-based observations is limited and (3) instability of the tomography system due to a lack of observations traversing the atmosphere horizontally. Our study on the combination of GNSS ground-based and space-based presents an innovative way for data integration based on uncertainty estimation. The developed integrated tomography operator, based on 3D ray tracing principles, is tested on 30 days of simulated data with 101 ground stations and over 240 radio occultation events, using three different station layouts. The a priori data introduced into the tomography processing is from a deterministic model, while ray tracing uses the ERA5 reanalysis wet refractivity field to obtain input data for individual test cases. The results are verified by comparing tomography output to ERA5 reanalysis. We observed a decrease in tomography RMSE between 2% and 16% in the case of an integrated solution, depending on GNSS station layout and the number and geometry of radio occultation ray paths. We show that a single RO event during one processing epoch can shift the wet refractivity estimates by 2 to 5 ppm closer to the correct solution compared to ground-based-only GNSS tomography.

Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost

With Gaia Data Release 3 (DR3), new and improved astrometric, photometric, and spectroscopic measurements for 1.8 billion stars have become available. Alongside this wealth of new data, however, there are challenges in finding efficient and accurate computational methods for their analysis. In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. To this end, we built a stable gradient-boosted random-forest regressor (xgboost), trained on spectroscopic data, capable of producing output parameters with reliable uncertainties from Gaia DR3 data (most notably the low-resolution XP spectra), without ground-based spectroscopic observations. Using Shapley additive explanations, we interpret how the predictions for each star are influenced by each data feature. For the training and testing of the network, we used high-quality parameters obtained from the StarHorse code for a sample of around eight million stars observed by major spectroscopic stellar surveys, complemented by curated samples of hot stars, very metal-poor stars, white dwarfs, and hot sub-dwarfs. The training data cover the whole sky, all Galactic components, and almost the full magnitude range of the Gaia DR3 XP sample of more than 217 million objects that also have reported parallaxes. We have achieved median uncertainties of 0.20 mag in V-band extinction, 0.01 dex in logarithmic effective temperature, 0.20 dex in surface gravity, 0.18 dex in metallicity, and 12% in mass (over the full Gaia DR3 XP sample, with considerable variations in precision as a function of magnitude and stellar type). We succeeded in predicting competitive results based on Gaia DR3 XP spectra compared to classical isochrone or spectral-energy distribution fitting methods we employed in earlier works, especially for parameters AV and Teff, along with the metallicity values. Finally, we showcase some potential applications of this new catalogue, including extinction maps, metallicity trends in the Milky Way, and extended maps of young massive stars, metal-poor stars, and metal-rich stars.

Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements

Abstract. Instruments for air quality observations on geostationary satellites provide multiple observations per day and allow for the analysis of the diurnal variation in important air pollutants such as nitrogen dioxide (NO2). The South Korean instrument GEMS (Geostationary Environmental Monitoring Spectrometer), launched in February 2020, is the first geostationary instrument that is able to observe the diurnal variation in NO2. The measurements have a spatial resolution of 3.5 km × 8 km and cover a large part of Asia. This study compares 1 year of tropospheric NO2 vertical column density (VCD) observations from the operational GEMS L2 product, the scientific GEMS IUP-UB (Institute of Environmental Physics at the University of Bremen) product, the operational TROPOspheric Monitoring Instrument (TROPOMI) product, and ground-based differential optical absorption spectroscopy (DOAS) measurements in South Korea. The GEMS L2 tropospheric NO2 VCDs overestimate the ground-based tropospheric NO2 VCDs with a median relative difference of +61 % and a correlation coefficient of 0.76. The median relative difference is −2 % for the GEMS IUP-UB product and −16 % for the TROPOMI product, with correlation coefficients of 0.83 and 0.89, respectively. The scatter in the GEMS products can be reduced when observations are limited to the TROPOMI overpass time. Diurnal variations in tropospheric NO2 VCDs differ by the pollution level of the analyzed site but with good agreement between the GEMS IUP-UB and ground-based observations. Low-pollution sites show weak or almost no diurnal variation. In summer, the polluted sites show a minimum around noon, indicating the large influence of photochemical loss. Most variation is seen in spring and autumn, with increasing NO2 in the morning, a maximum close to noon, and a decrease towards the afternoon. Winter observations show rather flat or slightly decreasing NO2 throughout the day. Winter observations under low-wind-speed conditions at high-pollution sites show enhancements of NO2 throughout the day. This indicates that under calm conditions, dilution and the less effective chemical loss in winter do not balance the accumulating emissions. Diurnal variation observed at a low-pollution site follows seasonal wind patterns. A weekday–weekend effect analysis shows good agreement between the different products. However, the GEMS L2 product, while agreeing with the other data sets on weekdays, shows significantly less reduction on weekends. The influence of the stratospheric contribution and the surface reflectivity product on the satellite tropospheric NO2 VCD products is investigated. While the TM5 model's stratospheric VCDs, used in the TROPOMI product, are too high, resulting in tropospheric NO2 VCDs that are too low and even negative, when used in the GEMS IUP-UB retrieval, the GEMS L2 stratospheric VCD is too low. Surface reflectivity comparisons indicate that the GEMS L2 reflectivity makes a large contribution to the observed overestimation and scatter.

Diffusion-driven Incomplete Multimodal Learning for Air Quality Prediction

Predicting air quality using multimodal data is crucial to comprehensively capture the diverse factors influencing atmospheric conditions. Therefore, this study introduces a multimodal learning framework that integrates outdoor images with traditional ground-based observations to improve the accuracy and reliability of air quality predictions. However, aligning and fusing these heterogeneous data sources pose a formidable challenge, further exacerbated by pervasive data incompleteness issues in practice. In this paper, we propose a novel incomplete multimodal learning approach (iMMAir) to recovery missing data for robust air quality prediction. Specifically, we first design a shallow feature extractor to capture modal-specific features within the latent representation space. Then we develop a conditional diffusion-driven recovery module to mitigate the distribution gap between the recovered and true data. This module further incorporates two conditional constraints of temporal correlation and semantic consistency for effective modal completion. Finally, we reconstruct incomplete modalities and fuse available data using a multimodal transformer network to predict the air quality. To alleviate the modality imbalance problem, we employ an adaptive gradient modulation strategy to adjust the optimization of each modality. Experimental results demonstrate that iMMAir significantly reduces prediction errors, outperforming baseline models by an average of 5.6% and 2.5% in air quality regression and classification tasks. Our source code and data are available at https://github.com/pestasu/IMMAir.

Sources and Variability of Greenhouse Gases over Greece

This study provides an overview of the atmospheric drivers of climate change over Greece (Eastern Mediterranean), focusing on greenhouse gases (GHG: carbon dioxide, CO2; methane, CH4; etc.). CO2 in Greece is mostly produced by energy production, followed by transport, construction, and industry. Waste management is the largest anthropogenic source of methane, accounting for 47% of total CH4 emissions, surpassing emissions from the agricultural sector in 2017, while the energy sector accounts for the remaining 10.5%. In situ simultaneous observations of GHG concentrations in Greece conducted at three sites with different topologies (urban background; Athens, regional background; Finokalia and free troposphere; and Helmos) during the last 5 years (2019–2023) showed increasing trends of the order of 2.2 ppm·yr−1 and ~15 ppb·yr−1 for CO2 and CH4, respectively, in line with the global trends. These increasing trends were found from both ground-based and satellite-based remote-sensing observations. Finally, during the lockdown period due to the COVID-19 global pandemic, a 58% reduction in CO2 levels was observed in the urban background site of Athens after subtracting the regional background levels from Finokalia, while the respective reduction in CH4 was of only the order of 15%, highlighting differences in emission sources.

Validating global horizontal irradiance retrievals from Meteosat SEVIRI at increased spatial resolution against a dense network of ground-based observations

Validating global horizontal irradiance retrievals from Meteosat SEVIRI at increased spatial resolution against a dense network of ground-based observations

The mystery of water in the atmosphere of τ Boötis b continues: Insights from revisiting archival CRIRES observations

Abstract The chemical abundances of gas-giant exoplanet atmospheres hold clues to the formation and evolution pathways that sculpt the exoplanet population. Recent ground-based high-resolution spectroscopic observations of the non-transiting hot Jupiter τ Boötis b from different instruments have resulted in a tension on the presence of water vapour in the planet’s atmosphere, which impact the planet’s inferred C/O and metallicity. To investigate this, we revisit the archival CRIRES observations of the planet’s dayside in the wavelength range 2.28 to 2.33 μm. We reanalyse them using the latest methods for correcting stellar and telluric systematics, and free-chemistry Bayesian atmospheric retrieval. We find that a spurious detection of CH4 can arise from inadequate telluric correction. We confirm the detection of CO and constrain its abundance to be near solar log10(CO) = –3.44$^{+1.63}_{-0.85}$ VMR. We find a marginal evidence for H2O with log10(H2O) = –5.13$^{+1.22}_{-6.37}$ VMR. This translates to super solar C/O (0.95$^{+0.06}_{-0.31}$), marginally sub-solar metallicity (–0.21 $^{+1.66}_{-0.87}$). Due to the relatively large uncertainty on H2O abundance, we cannot confidently resolve the tension on the presence of H2O and the super-solar atmospheric metallicity of τ Boötis b. We recommend further observations of τ Boötis b in the wavelength ranges simultaneously covering CO and H2O to confirm the peculiar case of the planet’s super-solar C/O and metallicity.

A Review of Satellite-Based CO2 Data Reconstruction Studies: Methodologies, Challenges, and Advances

Carbon dioxide is one of the most influential greenhouse gases affecting human life. CO2 data can be obtained through three methods: ground-based, airborne, and satellite-based observations. However, ground-based monitoring is typically composed of sparsely distributed stations, while airborne monitoring has limited coverage and spatial resolution; they cannot fully reflect the spatiotemporal distribution of CO2. Satellite remote sensing plays a crucial role in monitoring the global distribution of atmospheric CO2, offering high observation accuracy and wide coverage. However, satellite remote sensing still faces spatiotemporal constraints, such as interference from clouds (or aerosols) and limitations from satellite orbits, which can lead to significant data loss. Therefore, the reconstruction of satellite-based CO2 data becomes particularly important. This article summarizes methods for the reconstruction of satellite-based CO2 data, including interpolation, data fusion, and super-resolution reconstruction techniques, and their advantages and disadvantages, it also provides a comprehensive overview of the classification and applications of super-resolution reconstruction techniques. Finally, the article offers future perspectives, suggesting that ideas like image super-resolution reconstruction represent the future trend in the field of satellite-based CO2 data reconstruction.

Advances in Space Astroparticle Physics: Frontier Technologies for Particle Measurements in Space

In the last decades, breakthrough advances in understanding the mechanisms of the Universe and fundamental physics have been achieved through the exploitation of data on cosmic rays and high-energy radiation gathered via orbiting experiments, in a synergic and complementary international effort that combines space-based instrument data with ground-based space observatories, accelerator, and collider experiments [...]

Spatial-temporal seasonal and annual rainfall trends and variability assessment in the Pangani Basin, East Africa

Although in situ rainfall data remains the most accurate, the gauge network density in East Africa is sparse. It lacks continuity, thus making it inadequate to assess the spatial and long-term rainfall trend and variability accurately. As such, rainfall remote sensing data are normally used instead of ground station data. This study evaluates the capabilities and limitations of remote sensing data compared with ground-based observations in Tanzania's Pangani Basin in assessing the seasonal and annual rainfall trends and variability. Data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and twenty-three ground stations were analyzed, comprising a time series from 1990 to 2022. Trend analysis was conducted using the Mann-Kendall test, while the spatial distribution of precipitation was determined using Sen's slope method. CHIRPS annual rainfall showed good agreement with station rainfall data, with NSE, R2, slope, Pbias, MAE, and RMSE values of 0.84, 0.92, 0.92, 7.55%, 297.4, and 397.1, respectively. The coefficient of variation from station data indicated extreme variability, exceeding 30% for annual rainfall, while CHIRPS data showed moderate variability, ranging from 20% to 30%. Both station data and remote sensing data showed an increasing trend for annual and seasonal rainfall at least 10 stations. However, stations like Maji Moshi, Kiungu Primary School, Segera C. Tank, and Kibong'oto exhibited an increasing trend for vuli rainfall with CHIRPS data, while station data indicated a significant decreasing trend.The study highlights the necessity for calibration and validation to avert misinterpretations in climate trend analyses, especially at the basin level.

On the рossibility of multichannel optical backscattering sondes for joint balloon and lidar studies of the aerosol composition of the middle atmosphere

Aerosol backscattering sondes in the practice of aerological sounding, along with lidar observations, are used at night to study and monitor polar stratospheric clouds, tropospheric and stratospheric aerosol, cirrus clouds, pyroconvection, volcanic aerosol, as well as to verify remote methods and means of ground-based and satellite-based aerosol observations. For aerosol sondes, a simple two-wave measurement technique is used, which makes it possible to diagnose changes in aerosol composition by color index. The possibilities of the two-wave technique have limitations, which are discussed in this article. Aerological sounding combined with lidar observations expands the wavelength range for multi-wavelength studies, and direct measurements of atmospheric temperature increase the accuracy of aerosol sensing. The paper considers the application of 3 or more wavelenght techniques. Data from probe measurements using wavelengths of 470, 528, 850 and 940 nm and lidar sensing at wavelengths of 355 and 532 nm are presented.

Inferring the dust emission at submillimeter and millimeter wavelengths using neural networks

The Planck mission provided all-sky dust emission maps in the submillimeter (submm) to millimeter (mm) range at an angular resolution of 5$^ prime $. In addition, some specific sources can be observed at long wavelengths and higher resolution using ground-based telescopes. These observations are limited to small scales and are sometimes not delivered to the community. These ground-based observations require extensive data processing before they become available for scientific analysis, and suffer from extended emission filtering. At present, we are still unable to fully understand the emissivity variations observed in different astrophysical environments at long (submm and mm) wavelengths. Several models have been developed to reproduce the diffuse Galactic medium, and each distinct environment requires an adjustment of the models. It is therefore challenging to estimate any dust emission in the submm-mm at a better resolution than the 5$^ prime $ from Planck . In this analysis, based on supervised deep learning algorithms, we produced dust emission predictions in the two Planck bands centered at 850 mic (353 GHz) and 1.38 mm (217 GHz) at the Herschel resolution (37$^ prime $). Prediction or forecasting is a frequently used term in machine learning or neural network research that refers to the output of an algorithm that has been trained on a given dataset and that is being used for modeling purposes. Herschel data of Galactic environments, ranging from 160 mic to 500 mic and smoothed to an angular resolution of 5$^ prime $, were used to train the neural network. This training aimed to provide the most accurate model for reproducing Planck maps of dust emission at 850 mic and 1.38 mm. Then, using Herschel data only, the model was applied to predict dust emission maps at 37$^ prime The neural network is capable of reproducing dust emission maps of various Galactic environments with a difference of only a few percent at the Planck resolution. Remarkably, it also performs well for nearby extragalactic environments. This could indicate that large dust grains, probed by submm or mm observations, have similar properties in both our Galaxy and nearby galaxies, or at least that their spectral behaviors are comparable in Galactic and extragalactic environments. For the first time, we provide to the community dust emission prediction maps at 850 mic and 1.38 mm at the 37$^ prime $ of several surveys: Hi-GAL, Gould Belt, Cold Cores, HERITAGE, Helga, HerM33es, KINGFISH, and Very Nearby Galaxies. The ratio of these two wavelength brightness bands reveals a derived emissivity spectral index statistically close to 1 for all the surveys, which favors the hypothesis of a flattened dust emission spectrum for wavelengths larger than 850 mic . Neural networks appear to be powerful algorithms that are highly efficient at learning from large datasets and achieving accurate reproductions with a deviation of only a few percent. However, to fully recover the input data during the training, it is essential to sample a sufficiently large range of datasets and physical conditions.