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Wind shear has important implications for Kelvin–Helmholtz instability (KHI) and gravity waves (GWs) in the mesosphere–lower thermosphere (MLT) region where its momentum transport process is dominated by short-period (<1 h) GWs. However, the sub-hourly variation in wind shear is still not well quantified. This study aims to improve current understanding of vertical wind shear by analyzing multi-year meteor radar measurements at the Mohe (MH, 53.5°N, 122.3°E), Beijing (BJ, 40.3 ° N , 116.2 ° E ), Wuhan (WH, 30.5 ° N , 114.6 ° E ), and Fuke (FK, 19.5°N, 109.1°E) stations in China. The wind field is estimated by a new algorithm, e.g., the damped least squares fitting. Taking the wind shear estimated by normal products as a criterion, the shear produced by the new algorithm has more statistical convergence as compared to the traditional algorithm, e.g., the least squares fitting. Therefore, we argue that the 10 min DLSA wind probably produces a more reasonable vertical shear. Both intensive wind shears and GW kinetic energy can be simultaneously captured during the 0600–1600 UTs of May at MH and during the 1300–2400 UTs of March at FK, possibly implying that the up-propagation of GWs could contribute to the production of large wind shears. The sub-hourly variation in wind shears is potentially valuable for understanding the interrelationship between shear (or KHI) and GWs. [ABSTRACT FROM AUTHOR]

Atmospheric gravity waves (GWs) in the mesosphere-lower thermosphere (MLT) are crucial for the understanding of general circulation. However, their dynamical characteristics are hardly retrieved due to the difficulty in the high-resolution observation of wind. Therefore, this paper uses eight years (2013–2020) of meteor radar measurements in the MLT region at Mohe station (53.5°N, 122.3°E), China, to retrieve high-temporal-resolution mesospheric wind data and further evaluate the temporal variation of GW kinetic energy. As the detected meteor trails exceed 6, the wind velocity is recalculated using the least square algorithm method, significantly increasing the temporal resolution of wind from 1 h up to 5 min. This resolution is sufficiently high for the investigation of GW kinetic energy, which exhibits a high spatial-temporal variability. For instance, it is enhanced in the winter season during the period of 0200–1400 UT and in the spring season during the period of 0800–1300 UT. The similarity between the climatological characteristics of GWs in MLT and the seasonal variation of GW total energy in the troposphere, determined from high-resolution radiosondes near to Mohe station, suggests that the meteorology in the lower atmosphere could be an important source of GWs in the MLT region. [ABSTRACT FROM AUTHOR]

Background: Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag Sandy Land (OSL) resulted in the reduction in land productivity and serious ecological/environmental consequences. Although many ecological restoration projects have been carried out, the vegetation restoration of OSL and the impact mechanism of climate and human activities on desertification remain unclear. Methods: Taking OSL as the research area, this paper constructs the desertification index by using the remote sensing images and meteorological and socio-economic data, between 1986 and 2016, and analyzes the spatio-temporal evolution process and driving factors of desertification by using trend analysis and spearman rank correlation. Results: The results showed that: (1) Desertification in the OSL has fluctuated greatly during the past 30 years. Desertification recovered between 1986 and 1990, expanded and increased between 1990 and 2000, reduced between 2000 and 2004, developed rapidly between 2004 and 2007, and recovered again between 2007 and 2016; (2) The desertification of OSL is dominated by a non-significant change trend, accounting for 73.27%. In the significant change trend, the area of desertification rising trend is 20.32%, which is mainly located in the north and east, and the area of declining trend is 6.41%, which is mainly located in the southwest; (3) Desertification is the result of the superposition of climate and human activities. Climate change is the main influencing factor, followed by human activities, and the superposition effects of the two are spatio-temporal differences. Conclusions: These results shed light on the development of desertification in OSL and the relative importance and complex interrelationship between human activities and climate in regulating the process of desertification. Based on this, we suggest continuing to implement the ecological restoration policy and avoid the destruction of vegetation by large-scale animal husbandry in order to improve the situation of desertification. [ABSTRACT FROM AUTHOR]

In this study, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was evaluated for the assessment of long-term drought monitoring in Huaihe River Basin using daily gauge observation data for the period from 1983 to 2017. The evaluation results show that the PERSIANN-CDR algorithm has a good detection ability for small precipitation events over the whole basin, but a poor ability for extreme precipitation events (>50 mm/day). Daily PERSIANN-CDR estimates perform relatively better in areas with abundant precipitation, while the monthly and yearly PERSIANN-CDR estimates are highly consistent with gauge observations both in magnitude and space. The Standardized Precipitation Index (SPI) at various time scales (3, 6, and 12 months) was calculated based on PERSIANN-CDR and gauge observation, respectively. Grid-based values of statistics derived from those SPI values demonstrate that PERSIANN-CDR has a good ability to capture drought events of each time scale across the basin. However, caution should be applied when using PERSIANN-CDR estimates for basin-scale drought trend analysis. Furthermore, three drought events with long duration and large extent were selected to test the applicability of PERSIANN-CDR in drought monitoring. The results show that it has a good ability to capture when and where droughts occur and how far they spread. Due to the overestimation of small precipitation events, PERSIANN-CDR tends to overestimate the number of extreme droughts and their extents. This needs to be considered in future algorithm improvement. [ABSTRACT FROM AUTHOR]

Probable Maximum Precipitation (PMP) is an essential prerequisite in designing dams, spillways, and reservoirs in order to minimize the risk of overtopping infrastructure collapse, especially under today's changing climate. This study investigates conventional PMP estimation approach by using both scarce in-situ observations and mainstream satellite precipitation products in the Dadu River basin, where plenty of reservoirs and dams are being built. The satellite data include Climate Prediction Center (CPC) MORPHing algorithm (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Tropic Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The evaluation of satellite products shows that CMORPH and 3B42V7 agree well with gauge-based dataset for the period of 1998-2013 at both the grid and basin scales, also capturing the extreme precipitation events, with high Correlation Coefficients (CC) in terms of 0.68 and 0.71, respectively. Also, CMORPH and 3B42V7 show better performance for the magnitude and spatial distribution of 24-h PMP in such complex terrains. PERSIANN-CDR shows an overestimation in the upstream and an underestimation in the downstream. As among the first studies of satellite precipitation-based PMP estimation, this work sheds lights on the suitability of satellite precipitation in PMP estimation and could provide a reference for future extended spatially-distributed PMP estimation in vast ungauged regions. [ABSTRACT FROM AUTHOR]