Теоретичні основи побудови багаточастотних радіометричних комплексів для виявлення БпЛА на тлі атмосферного випромінювання

Due to the impossibility of hiding the unmanned aerial vehicles (UAV) own radiothermal radiation or reducing its contrast against the background of atmospheric radiation, it is advisable to use highly sensitive radiometric receivers to solve the detection problem. The optimal method for complexing the results of measurements in multichannel radiometric receivers and identifying different types and classes of UAV against the sky in X, Ka, and W wave ranges under different meteorological conditions has been developed. end-to-end optimization of methods and algorithms will reveal the theoretical foundations of the construction of radiometric systems, ranging from the field of registration of electromagnetic fields to the final stages. In cloudless and clear weather, radiometric measurements in the W range will allow to obtain high-precision estimates of the spatial position of UAVs, in the X range of reliable observations in rain, snow, fog. The use of the Ka-band receiver in the radiometric complex will allow to realize the best sensitivity due to the technical achievements of domestic production in the creation of broadband radiometric receivers in this waveband. Studies of the main parameters of UAV detection have been conducted, namely, the probability of erroneous detection alarm and the probability of correct detection. The obtained theoretical results allow to determine signal processing algorithms and optimal structures of radiometric receivers, to analyze the maximum measurement error and to develop recommendations for experiments. Having received a database of radiometric contrasts, it is possible to further implement technical solutions to increase the capabilities of airspace monitoring for UAV detection. Recommendations are given for the practical choice of the UAV detection threshold to ensure the probability of correct detection is not worse than 0.9 for different angles of observation, atmospheric state, size and material of manufacture.

An Aerosol Extinction Coefficient Retrieval Method and Characteristics Analysis of Landscape Images

Images based on RGB pixel values were used to measure the extinction coefficient of aerosols suspended in an atmospheric state. The pixel values of the object-image depend on the target-object reflection ratio, reflection direction, object type, distances, illumination intensity, atmospheric particle extinction coefficient, and scattering angle between the sun and the optical axes of the camera, among others. Therefore, the imaged intensity cannot directly provide information on the aerosol concentration or aerosol extinction coefficient. This study proposes simple methods to solve this problem, which yield reasonable extinction coefficients at the three effective RGB wavelengths. Aerosol size information was analogized using the RGB Ångström exponent measured at the three wavelengths for clean, dusty, rainy, Asian dust storm, and foggy days. Additionally, long-term measurements over four months showed reasonable values compared with existing PM2.5 measurements and the proposed method yields useful results.

Correction to: Correlation Structures between Satellite All-Sky Infrared Brightness Temperatures and the Atmospheric State at Storm Scales

The article “Correlation Structures between Satellite All-Sky Infrared Brightness Temperatures and the Atmospheric State at Storm Scales”, written by Yunji ZHANG, Eugene E. CLOTHIAUX, and David J. STENSRUD was originally published electronically on the publisher’s internet portal on 30 of April 2021 without open access. With the author(s)’ decision to opt for Open Choice, the copyright of the article changed on 26 of October 2021 to © The Author(s), 2021 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0.The original article has been corrected.

Relative humidity gradients as a key constraint on terrestrial water and energy fluxes

Earth's climate and water cycle are highly dependent on terrestrial evapotranspiration and the associated flux of latent heat. Although it has been hypothesized for over 50 years that land dryness becomes embedded in atmospheric conditions through evaporation, underlying physical mechanisms for this land–atmosphere coupling remain elusive. Here, we use a novel physically based evaporation model to demonstrate that near-surface atmospheric relative humidity (RH) fundamentally coevolves with RH at the land surface. The new model expresses the latent heat flux as a combination of thermodynamic processes in the atmospheric surface layer. Our approach is similar to the Penman–Monteith equation but uses only routinely measured abiotic variables, avoiding the need to parameterize surface resistance. We applied our new model to 212 in situ eddy covariance sites around the globe and to the FLUXCOM global-scale evaporation product to partition observed evaporation into diabatic vs. adiabatic thermodynamic processes. Vertical RH gradients were widely observed to be near zero on daily to yearly timescales for local as well as global scales, implying an emergent land–atmosphere equilibrium. This equilibrium allows for accurate evaporation estimates using only the atmospheric state and radiative energy, regardless of land surface conditions and vegetation controls. Our results also demonstrate that the latent heat portion of available energy (i.e., evaporative fraction) at local scales is mainly controlled by the vertical RH gradient. By demonstrating how land surface conditions become encoded in the atmospheric state, this study will improve our fundamental understanding of Earth's climate and the terrestrial water cycle.

Spectrally resolved outgoing longwave radiation and its applications for the study of climate

Spectrally resolved outgoing longwave radiation and its applications for the study of climate The ERC advanced “IASI-FT” project exploits the space-based instantaneous spectrally resolved observations provided by the family of IASI thermal infrared instruments to (1) monitor atmospheric composition changes and (2) establish climate records. More than 3.5 million of data are available each day, from which near-real-time information on the atmospheric state can be inferred.

Evaluation of simulated cloud liquid water in low clouds over the Beaufort Sea in the Arctic System Reanalysis using ARISE airborne in situ observations

Arctic low clouds and the water they contain influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Atmospheric models struggle to accurately simulate the occurrence and properties of Arctic low clouds, stemming from errors in both the simulated atmospheric state and the dependence of cloud properties on the atmospheric state. Knowledge of the contributions from these two factors to the model errors allows for the isolation of the process contributions to the model–observation differences. We analyze the differences between the Arctic System Reanalysis version 2 (ASR) and data taken during the September 2014 Arctic Radiation–IceBridge Sea and Ice Experiment (ARISE) airborne campaign conducted over the Beaufort Sea. The results show that ASR produces less total and liquid cloud water than observed along the flight track and is unable to simulate observed large in-cloud water content. Contributing to this bias, ASR is warmer by nearly 1.5 K and drier by 0.06 g kg−1 (relative humidity 4.3 % lower) than observed. Moreover, ASR produces cloud water over a much narrower range of thermodynamic conditions than shown in ARISE observations. Analyzing the ARISE–ASR differences by thermodynamic conditions, our results indicate that the differences are primarily attributed to disagreements in the cloud–thermodynamic relationships and secondarily (but importantly) to differences in the occurrence frequency of thermodynamic regimes. The ratio of the factors is about 2/3 to 1/3. Substantial sampling uncertainties are found within low-likelihood atmospheric regimes; sampling noise cannot be ruled out as a cause of observation–model differences, despite large differences. Thus, an important lesson from this analysis is that when comparing in situ airborne data and model output, one should not restrict the comparison to flight-track-only model output.

The interior-atmosphere coupling of rocky worlds

<p>Earth has been the only known habitable world and thus used as a reference to understand habitability. The origin of life on Earth is not yet clearly understood, but known traces are up-to the Archean (∼ 3.5Ga, Ga-billion years). Earth had water and continents from the Hadean Earth (> 4.0Ga), which had different atmospheric conditions compared to the Archean Earth. Similarly, the current state and composition of atmosphere does not represent its future state. Climate changes are partly attributed to feedback mechanism between the internal processes and the atmosphere. And as such, each atmospheric state is depictive of an instance a long a trajectory path of a coupled evolution of Earth system. Venus was thought to be habitable until into the 1960s, when its surface was observed to be oven-hot with surface pressure a hundred times that of Earth. Why and when the evolutionary paths of Venus and Earth, which are similarly sized and should have similar internal compositions, started to diverge? Moreover, known exoplanets, planets and moons have very different geophysical characteristic from Earth. This implies exotic life might vary substantially from what we know. As a result, understanding evolution of rocky planets, that is their interior structure, atmospheres and climate regardless of their habitability is of great importance. In this work we study the relation between a rocky planet’s internal properties and its observable surface and atmosphere properties over time.   We explore the different convection regimes (stagnant lid, episodic-lid and tectonic), studying the relation between a planet’s viscous state, its interior composition and structure. Focusing on the effects of mantle convection on volatile recycling processes such as CO2 outgassing that influence the atmospheric state and climatic conditions over time. The computed models are then used to compute observables, that ultimately can be tested with observations.</p>

Testing the reliability of interpretable neural networks in geoscience using the Madden–Julian oscillation

We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying them to a commonly studied geophysical phenomenon, the Madden–Julian oscillation. The Madden–Julian oscillation is a multi-scale pattern within the tropical atmosphere that has been extensively studied over the past decades, which makes it an ideal test case to ensure the interpretability methods can recover the current state of knowledge regarding its spatial structure. The neural networks can, indeed, reproduce the current state of knowledge and can also provide new insights into the seasonality of the Madden–Julian oscillation and its relationships with atmospheric state variables. The neural network identifies the phase of the Madden–Julian oscillation twice as accurately as a linear regression approach, which means that nonlinearities used by the neural network are important to the structure of the Madden–Julian oscillation. Interpretations of the neural network show that it accurately captures the spatial structures of the Madden–Julian oscillation, suggest that the nonlinearities of the Madden–Julian oscillation are manifested through the uniqueness of each event, and offer physically meaningful insights into its relationship with atmospheric state variables. We also use the interpretations to identify the seasonality of the Madden–Julian oscillation and find that the conventionally defined extended seasons should be shifted later by 1 month. More generally, this study suggests that neural networks can be reliably interpreted for geoscientific applications and may thereby serve as a dependable method for testing geoscientific hypotheses.