This paper presents the Global Atmospheric Profiles derived from Reanalysis Information (GAPRI) database, which was designed for earth surface temperature retrieval. GAPRI is a comprehensive compilation of selected atmospheric vertical profiles at global scale which can be used for radiative transfer simulation in order to obtain generalized algorithms to estimate land surface temperature (LST). GAPRI includes information on geopotential height, atmospheric pressure, air temperature, and relative humidity derived from the European Centre for Medium-Range Weather Forecasts Re-Analysis data from year 2011. The atmospheric profiles are structured for 29 vertical levels and extracted from a global spatial grid of about 0.75° × 0.75° latitude–longitude with a temporal resolution of 6 hours. The selection method is based in the extraction of clear sky profiles over different atmospheric weather conditions such as tropical, mid-latitude summer, subarctic, and arctic, while also considering sea and land areas and day- and night-time conditions. The GAPRI database was validated by comparing land and sea surface temperature values derived from it to those obtained using other existing atmospheric profile databases and in situ measurements. Moreover, GAPRI was also compared to previous radiosonde atmospheric profiles using simulated split-window algorithms. Results show good agreement between GAPRI and previous atmospheric databases, thus demonstrating the potential of GAPRI for studies related to forward simulations in the thermal infrared range. GAPRI is a freely available database that can be modified according to the user’s needs and local atmospheric conditions.

A set of Essential Climate Variables (ECV) have been defined to be monitored by current and new remote sensing missions. The ECV retrieved at global scale need to be validated in order to provide reliable products to be used in remote sensing applications. For this, test sites are required to use in calibration and validation of the remote sensing approaches in order to improve the ECV retrievals at global scale. The southern hemisphere presents scarce test sites for calibration and validation field campaigns that focus on soil moisture and land surface temperature retrievals. In Chile, remote sensing applications related to soil moisture estimates have increased during the last decades because of the drought and water use conflicts that generate a strong interest on improved water demand estimates. This work describes the Laboratory for Analysis of the Biosphere (LAB)—NETwork, called herein after ‘LAB-net’, which was designed to be the first network in Chile for remote sensing applications. The test sites were placed in four sites with different cover types: vineyards and olive orchards located in the semi-arid region of Atacama, an irrigated raspberry crop in the Mediterranean climate zone of Chimbarongo, and a rainfed pasture in the south of Chile. Over each site, well implemented meteorological and radiative flux instrumentation was installed and continuously recorded the following parameters: soil moisture and temperature at two ground levels (10 and 20 cm), air temperature and relative humidity, net radiation, global radiation, radiometric temperature (8–14 µm), rainfall and soil heat flux. The LAB-net data base post-processing procedure is also described here. As an application, surface remote sensing products such as soil moisture data derived from the Soil Moisture Ocean Salinity (SMOS) and Land Surface Temperature (LST) extracted from the MODIS-MOD11A1 and GOES LST from Copernicus products were compared to in situ data in Oromo LAB-net site. Moreover, land surface energy flux estimation is also shown as an application of LAB-net data base. These applications revealed a good performance between in situ and remote sensing data. LAB-net data base also contributes to provide suitable information for land surface energy budget and therefore water resources management at cultivars scale. The data based generated by LAB-net is freely available for any research or scientific purpose related to current and future remote sensing applications.

Snowfall is a key component to the Antarctic region, contributing significantly to the surface mass balance and influencing mean sea level changes. The intricate nature of ice particle microphysics, encompassing type, size, and structure, presents a great challenge in comprehending the processes of solid precipitation in Antarctica. The characteristics of individual ice crystals as they fall from clouds are crucial for understanding their formation and evolution along the vertical profile. Mechanisms such as aggregation, fragmentation, and riming play a pivotal role in accurately representing precipitation in numerical weather prediction models [1]. Despite their importance, the scarcity of observations for evaluating and validating these processes, particularly in the Southern Ocean and Antarctica, adds complexity. To address this gap, a comprehensive set of precipitation observations occurred during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016-2017 was carried out, utilizing diverse sensors aboard the research vessel Akademik Tryoshnikov. The observational toolkit included a snow particle counter (SPC), two total particle counters (Wenglors), vertical precipitation profiles from 24-GHz micro rain radar (MRR) observations, and manually collected Formvar samples. The Formvar technique, preserving ice particle shapes, offers insights into microphysical properties of ice crystals and snowflakes. SPC and Formvar were employed for particle size distribution (PSD) characterization and quantitative precipitation estimations (QPE) [2]. Precipitation was derived from MRR using the existing reflectivity (Ze)-snowfall (S) relationship for Antarctica [3,4,5]. During ACE, primary observations related to snowfall were near the coasts of the Antarctic Peninsula, Western Antarctica, and Adélie Land (Eastern Antarctica). In the last region, a large-scale event was observed by both the ACE expedition and a Multi-angle Snowflake Camera (MASC) at Dumont d’Urville station. Results showed good agreement between Formvar, SPC (size < 500µm), and MASC (size > 500µm) PSDs. Notably, the 20-µm resolution Formvar images exhibited significantly better performance for particles smaller than 500µm compared to MASC (35-µm resolution). Regarding QPE, all sources exhibited a large spread, particularly MRR estimations, sensitive to Ze-S relationship parameters. The use of PSD observations proved useful in making informed choices about these parameters. In monitoring snowfall precipitation, developing a multi-instrumental approach to overcome individual system limitations is crucial, reducing uncertainty.References:[1] Grazioli, J. et al. MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall. Sci Data 9, 186 (2022).[2] Sugiura, K. et al., Application of a snow particle counter to solid precipitation measurements under Arctic conditions. CRST, 58: 77-83, 2009.[3] Grazioli, J. et al., Measurements of precipitation in Dumont d'Urville, Adélie Land, East Antarctica. TC 11, 1797–1811, 2017.[4] Souverijns, N. et al., Estimating radar reflectivity – snowfall rate relationships and their uncertainties over Antarctica by combining disdrometer and radar observations. AR, 196: 211–223, 2017.[5] M.S. Kulie and R. Bennartz, Utilizing Spaceborne Radars to Retrieve Dry Snowfall. JAMC, 48, 2564-2580.Acknowledgements: PROPOLAR APMAR-2024, FCT ATLACE (CIRCNA/CAC/0273/2019) and ANR-APRES3. ACE was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals.