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Providing reliable and accurate high-resolution meteorological data is very significant to guide the rapid response to extreme weather conditions. However, due to the constraints of computing power and simulation time, the spatial resolution of existing all global climate models is low, and it is unable to provide meteorological data with more precise resolution at local scale. In this research, a deep learning downscaling model called two-stage multi-scale feature extraction network (TSMFN) is proposed. By combining ERA5 reanalysis data and terrain data, spatial downscaling of global precipitation measurement mission precipitation data is carried out. Specifically, in the first stage of the network, several multi-scale residual Inception blocks are used to extract multi-scale features of low-resolution precipitation data; several residual-based residual multi-scale cross blocks are used to fully excavate multi-scale features after the fusion of multiple data. In the second stage of the TSMFN, the output feature map after the fusion of the previous stage is fused with the high-resolution monthly average precipitation data, and several progressive multistage Swin Transformer blocks are constructed to overcome the problems that the reconstructed image of a general convolutional neural network is smooth and cannot reflect the real spatial distribution of precipitation. Finally, a hybrid loss function combining L 1 loss function and focal frequency loss function is proposed to alleviate the ill-posedness of the downscaling. By comparing the proposed algorithm with some advanced deep learning downscaling algorithms, the results of the experiment show that the TSMFN model is significantly better in terms of generated image quality and multiple evaluation indexes, and the model has stronger generalization ability. [ABSTRACT FROM AUTHOR]

This paper presents the first method for synthesizing seasonal transition of terrain textures for an input heightfield. Our method reproduces a seamless transition of terrain textures according to the seasons by learning measured data on the earth using a convolutional neural network. We attribute the main seasonal texture transition to vegetation and snow, and control the texture synthesis not only with the input heightfield but also with the annual temperature and precipitation based on Köppen's climate classification as well as insolation at the location. We found that month-by-month synthesis yields incoherent transitions, while a naïve conditioning with explicit temporal information (e.g., month) degrades generalizability due to the north–south hemisphere difference. To address these issues, we introduce a simple solution—periodic conditioning on the annual data without explicit temporal information. Our experiments reveal that our method can synthesize plausible seasonal transitions of terrain textures. We also demonstrate large-scale texture synthesis by tiling the texture output. [ABSTRACT FROM AUTHOR]

Skilful and localised daily weather forecasts for upcoming seasons are desired by climate-sensitive sectors. Various General circulation models routinely provide such long lead time ensemble forecasts, also known as seasonal climate forecasts (SCF), but require downscaling techniques to enhance their skills from historical observations. Traditional downscaling techniques, like quantile mapping (QM), learn empirical relationships from pre-engineered predictors. Deep-learning-based downscaling techniques automatically generate and select predictors but almost all of them focus on simplified situations where low-resolution images match well with high-resolution ones, which is not the case in ensemble forecasts. To downscale ensemble rainfall forecasts, we take a two-step procedure. We first choose a suitable deep learning model, very deep super-resolution (VDSR), from several outstanding candidates, based on an ensemble forecast skill metric, continuous ranked probability score (CRPS). Secondly, via incorporating other climate variables as extra input, we develop and finalise a very deep statistical downscaling (VDSD) model based on CRPS. Both VDSR and VDSD are tested on downscaling 60 km rainfall forecasts from the Australian Community Climate and Earth-System Simulator Seasonal model version 1 (ACCESS-S1) to 12 km with lead times up to 217 days. Leave-one-year-out testing results illustrate that VDSD has normally higher forecast accuracy and skill, measured by mean absolute error and CRPS respectively, than VDSR and QM. VDSD substantially improves ACCESS-S1 raw forecasts but does not always outperform climatology, a benchmark for SCFs. Many more research efforts are required on downscaling and climate modelling for skilful SCFs. [ABSTRACT FROM AUTHOR]

Remote sensing has emerged as a new technique for collecting farmland data due to its rapid advancement, rising popularity, and application in social production practice. In order to understand and manage farmland resources in China, it is essential to account for and monitor high-standard farmland and its usage. Therefore, this work used satellite remote sensing empowered with various abilities for monitoring high-standard farmland by employing GF-2 high-resolution satellite images to identify targets and objects in Hebei and Guangdong provinces. Farmland occupation and utilization were analyzed by detecting destructions, underutilization, and overutilization, and converting farmland for other economic activities registered on a special field sheet for quantification. A statistical summary was compiled for the two provinces, and the results reveal that high-standard farmland irregularities were detected in both Hebei and Guangdong provinces. However, in Hebei province, this was due to domestic purposes, such as building home shelters and domestic factories. On a contract, the result shows that in Guangdong province, farmland was being converted for economic purposes on an industrial scale, such as high residential apartment blocks and new industrial zones, and environmental destruction. Furthermore, the results reveal that there is still a steady and continuous decline in arable land due to accelerated industrialization and population pressure, especially in the Guangdong provinces, which is a threat to national food security. The high interpretation accuracy demonstrates that high-resolution remote sensing is an effective farmland monitoring tool that can be used to advance policy formulation. [ABSTRACT FROM AUTHOR]

In the semiarid Bulal transboundary catchment of southern Ethiopia, groundwater is the only reliable drought-resilient water source. The central and southern parts of the catchment are dominantly overlain by the transboundary aquifers of the Bulal basalts, while the basement rocks outcrop in the eastern part. This study uses an integrated geographic information system (GIS), remote sensing (RS), and analytical hierarchal process (AHP) to identify and delineate the groundwater potential zones of the semiarid Bulal catchment within the Ethiopian territory. Based on their relative importance to groundwater occurrence and movement, ten input parameters were chosen. According to Saaty's AHP approach, the input themes and each of their distinct features were given normalized weights. A composite groundwater potential zone index (GWPZI) map was generated by integrating all the input layers employing the GIS-overlay analysis technique. The map was validated using the yield of wells from the catchment. The GWPZI map depicts four groundwater potential zones: high (representing 27% of the total area), moderate (20%), low (28%), and very low (25%). The geological feature has the greatest influence on the distribution of groundwater potential. Areas with high potential are mainly overlain by the Bulal basaltic flow, while low groundwater potential zones are in the regolith over the basement rocks. Unlike conventional methods, our novel approach is effective in identifying relatively shallow GWPZs throughout the catchment, and it can be applied in similar semiarid regions. The GWPZI map serves as a quick guide for effectively planning, managing, and developing the catchment's groundwater resources. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Salt precipitation in rocks is a strongly coupled physico-chemical process, which has implications on natural gas extraction efficiency. The paper examines the effect of salt precipitation on porosity, permeability, permeability-stress sensitivity and elastic wave velocities of the rock matrix in tight sandstone with high salinity. Results show that the porosity and permeability decrease significantly after salt precipitation, and the permeability-stress sensitivity was aggravated by the salt precipitation. Following salt precipitation, the elastic wave velocities decreased significantly, the dynamic Young's modulus of the rock decreased, and the dynamic Poisson's ratio increased. Microstructural analysis suggests that following salt precipitation, the smaller pores and pore throats become the main seepage channels. The crystalline salt was deposited in the pores, the micro-fractures and on the surface of clay minerals, which not only reduces porosity and permeability, but contributes to the development of secondary cracks that weaken the rock matrix. [ABSTRACT FROM AUTHOR]

Some important characteristics of the November-April heavy precipitation in southwestern parts of Iran and their linkages to the Madden-Julian Oscillation (MJO) were assessed for the period of 1975-2011. Daily precipitation data in nine meteorological stations spread in various parts of the study area and the corresponding MJO indices were analyzed. For each station, precipitation data were sorted in descending order and those values that fell within 5% of the highest records were categorized as the heavy precipitation. Besides this, the 10% threshold was also analyzed as an axillary assessment. The considered heavy precipitation data (5% threshold) accounted from about 26-35% of total annual precipitation. About half of the heavy precipitation occurred during December-January period and the other half distributed within the months of March, February, November and April by about 17, 14, 13and 6%, respectively. The highest frequency of heavy precipitation was related to the MJO phase 8. After this, the more frequent precipitation events were respectively associated to the phases 2, 7, 1, 6, 5 and 4 of the MJO. For the phases 1, 2, 7 and 8 frequency of the heavy precipitation statistically increased when the MJO amplitude was greater than unity. In contrast, for phases 4 and 5, heavy precipitation was generally linked to the spells that the amplitude size was lower than unity. Formation of a strong north-south oriented cold front mainly in Saudi Arabia and west-east oriented warm fronts in the southwest of Iran were realized as the key elements for initiating heavy precipitation over the study area. Although development of the Mediterranean-based cyclonic circulation is essential for the formation of these fronts, moisture transport mostly originates from northern parts of the Arabian Sea, southern parts of the Red Sea and the Persian Gulf. [ABSTRACT FROM AUTHOR]

In this study we observed the microphysical properties, including the vertical and horizontal distributions of ice particles, liquid water content and ice habit, in different regions of a slightly supercooled stratiform cloud. Using aircraft instrument and radar data, the cloud top temperature was recorded as higher than −15°C, behind a cold front, on 9 September 2015 in North China. During the flight sampling, the high ice number concentration area was located in the supercooled part of a shallow convective cloud embedded in a stratiform cloud, where the ambient temperature was around −3°C. In this area, the maximum number concentrations of particles with diameter greater than 100 μm and 500 μm ( N and N ) exceeded 300 L and 30 L, respectively, and were related to large supercooled water droplets with diameter greater than 24 μm derived from cloud-aerosol spectrometer probe measurements. The ice particles types in this region were predominantly columnar, needle, graupel, and some freezing drops, suggesting that the occurrence of high ice number concentrations was likely related to the Hallett-Mossop mechanism, although many other ice multiplication processes cannot be totally ruled out. The maximum ice number concentration obtained during the first penetration was around two to three orders of magnitude larger than that predicted by the Demott and Fletcher schemes when assuming the cloud top temperature was around −15°C. During the second penetration conducted within the stratiform cloud, N and N decreased by a factor of five to ten, and the presence of columnar and needle-like crystals became very rare. [ABSTRACT FROM AUTHOR]

The aim of this study is to improve classification results of multispectral satellite imagery for supporting flood risk assessment analysis in a catchment area in Cyprus. For this purpose, precipitation and ground spectroradiometric data have been collected and analyzed with innovative statistical analysis methods. Samples of regolith and construction material were in situ collected and examined in the spectroscopy laboratory for their spectral response under consecutive different conditions of humidity. Moreover, reflectance values were extracted from the same targets using Landsat TM/ETM+ images, for drought and humid time periods, using archived meteorological data. The comparison of the results showed that spectral responses for all the specimens were less correlated in cases of substantial humidity, both in laboratory and satellite images. These results were validated with the application of different classification algorithms (ISODATA, maximum likelihood, object based, maximum entropy) to satellite images acquired during time period when precipitation phenomena had been recorded. [ABSTRACT FROM AUTHOR]

Inter- and intraspecific effects of climate change were assessed for the dominant conifers of Siberia (60-140°E and 48-75°N): Larix spp. (L. sibirica, L. dahurica, and L. sukaczewii) and Pinus sylvestris. The approach employed a tri-variate (degree-days above 5 °C, degree-days below 0 °C, and a moisture index) estimate of the climatic envelope within which exists the actual ecological distribution of a species and their constituent climatypes (genotypes physiologically attuned to similar environments). Limits of the actual ecological distribution were approximated by reducing the climatic envelope according to effects of permafrost and interspecific competition. Climatypes were mapped within the climatic envelope according to the climatic interval that must separate populations for reasonable assurance of genetic differentiation. This interval was calculated from response functions that related 13-year growth and survival of a species to the difference in climate between the provenance of a climatype and the climate of numerous test sites distributed across Russia. Mapping species' distributions and their climatypes was done for the contemporary climate and for future climates predicted by the HadCM3GGa1 scenario of Hadley Centre. The results showed that if the forests of the future are to reflect the adaptedness of today, the distribution of species will shift and genotypes within species will be redistributed. Some contemporary climatypes are projected to disappear from Siberia while others common elsewhere would evolve. To mitigate these effects, climatypes should be transferred today to the expected future location of their climatic optima, a distance that is likely to approach 700-1200 km for these species. [ABSTRACT FROM AUTHOR]

Climate conditions in the Last Glacial Maximum (LGM) were remarkably different from the present ones. Adopting a regional climate model (RCM) which has included a detailed land surface scheme, LGM climate of East Asia has been simulated. The effects of vegetation changes on LGM climate have been diagnosed by adding forces of LGM paleovegetation reconstructed from the geological records. The results of the simulations by RCM indicate that large decreases in whole year temperature of East Asia continent caused strongly enhanced winter monsoon and weakened summer monsoon. The strengthening and westward-stretching of the Subtropical High of West-Pacific are the key reasons of decreases of LGM summer precipitation in eastern China. Precipitation and effective precipitation were increased in the Tibetan Plateau and Middle-Asia, while the humid condition in the Tibetan Plateau was mainly caused by increase of precipitation. Accumulated snow of LGM was also increased in the Tibetan Plateau, which was helpful to developing glacier and permafrost. This experiment has simulated that the frozen soil areas extend southward to 30° N. In LGM climate simulation, climate effects caused by external forces were amplified by added paleovegetation, therefore, decreases of temperature, changes of precipitation and snowfall, and other climatic parameters were further strengthened, making the simulation results more approach to geological evidences. [ABSTRACT FROM AUTHOR]

The wind-evaporation-convection feedback in the tropics is demonstrated to strongly affect the mean state of surface climate in atmospheric general circulation models. The feedback is shown to be very effective in channeling perturbations from one component of the climate system to other components, e.g., from evaporation to surface wind and from atmospheric convective activity to evaporation. It also provides an effective channel to pass on atmospheric perturbations in the middle and upper troposphere to the surface. As an illustration, it is shown that surface evaporation over tropical oceans is connected with cloud absorption of shortwave radiation through this feedback. Insufficient shortwave cloud absorption, causing excessive shortwave radiation at the surface as is common in most of the climate models, leads to excessive evaporation. Quantitatively, sensitivity of evaporation to short-wave cloud absorption, when averaged over the whole tropics, can be described by an approximate balance of variations in atmospheric radiative cooling and latent heating. This balance is achieved by the impact of radiation on convection, and then on the surface wind and evaporation. This mechanism calls for the need to include atmospheric processes far beyond the surface for improvements of the quality of surface climate simulation. [ABSTRACT FROM AUTHOR]

We present concept of a new multi-satellite Universat-SOCRAT project aimed to study transient phenomena in the upper atmosphere such as transient luminous events (TLEs) and terrestrial gamma-ray flashes (TGFs). It is a new space project of Lomonosov Moscow State University based on the use of a few satellites in the near-Earth orbit for real-time monitoring of radiation environment, natural (asteroids, meteoroids) and artificial (space debris) potentially dangerous objects, electromagnetic transients including cosmic gamma-ray bursts, terrestrial gamma-ray flashes, and optical and ultraviolet bursts in the Earth's atmosphere.Study of TLEs and TGFs remains an important and demanding task despite of a multitude of recently acquired data for these phenomena. This might be explained by the absence of comprehensive theoretical understanding of physical nature of high-energy processes in the Earth's atmosphere. Multi-wavelength synchronous observations with moderate accuracy of localization of TGF and TLE events are necessary to gain an insight of physics governing these high-energy processes in the Earth's atmosphere. In the article, we present results of TLE observations in space experiments of Moscow State University and discuss advanced instruments for optical observations of TLEs, as well as gamma-ray burst monitor and tracking gamma spectrometer for TGFs observations. [ABSTRACT FROM AUTHOR]