Explore the vast range of titles available.

This study examines the progress made by reanalyses and satellite products in the estimation of cloud cover over China: the ECMWF reanalyses ERA5 and ERA-Interim, geostationary satellite observation Himawari-8 (H8) and the International Satellite Cloud Climatology Project H-series (ISCCP) product. There is great similarity in spatial patterns of cloud cover in reanalyses and satellite observations, especially between ERA5 and H8. Distinct characteristics of the seasonal evolution of cloud cover are shown over the Tibetan Plateau (TP), the southeast (SE) and northeast (NE) of China. Differences in magnitudes of cloud cover exist. Overestimations are about 10% for reanalyses and about 20% for ISCCP in compared with certain cloud cover in H8. When probable cloud (about 10%) in H8 is included in the estimation, biases reduce the most in ERA5. The cloud hit rate (CHR) and false alarm rate (FAR) in against H8 and ISCCP reveal that simulated clouds in ERA5 have been improved especially over eastern China, but with limited improvement over TP in compared with ERA-Interim. Diurnal variations of cloud cover are characterized by increases during daytime over those three regions. Amplifications of diurnal variation vary over different regions and months. Satellite observations and ERA5 indicate distinguished diurnal cycle of cloud cover over TP, while further investigation based on ERA5 reveals coherent diurnal cycle in meteorological environment. Long-term changes of cloud cover highlight decreasing trends over TP and particular during March in past decades based on ISCCP and ERA5, which require further investigation in future.

Land surface temperature (LST) plays a critical role in land surface processes. However, as one of the effective means for obtaining global LST observations, remote sensing observations are inherently affected by cloud cover, resulting in varying degrees of missing data in satellite-derived LST products. Here, we propose a solution. First, the data interpolating empirical orthogonal functions (DINEOF) method is used to reconstruct invalid LSTs in cloud-contaminated areas into ideal, clear-sky LSTs. Then, a cumulative distribution function (CDF) matching-based method is developed to correct the ideal, clear-sky LSTs to the real LSTs. Experimental results prove that this method can effectively reconstruct missing LST data and guarantee acceptable accuracy in most regions of the world, with RMSEs of 1–2 K and R values of 0.820–0.996 under ideal, clear-sky conditions and RMSEs of 4–7 K and R values of 0.811–0.933 under all weather conditions. Finally, a spatiotemporally continuous MODIS LST dataset at 0.05° latitude/longitude grids is produced based on the above method.Measurement(s)land surface temperatureTechnology Type(s)satellite imagingSample Characteristic - Environmentplanetary surfaceSample Characteristic - Locationglobal

Based on Argo sea surface salinity ( SSS ) and the related precipitation ( P ), evaporation ( E ), and sea surface height data sets, the climatological annual mean and low-frequency variability in SSS in the global ocean and their relationship with ocean circulation and climate change were analyzed. Meanwhile, together with previous studies, a brief retrospect and prospect of seawater salinity were given in this work. Freshwater flux ( E-P ) dominated the mean pattern of SSS , while the dynamics of ocean circulation modulated the spatial structure and low-frequency variability in SSS in most regions. Under global warming, the trend in SSS indicated the intensification of the global hydrological cycle, and featured a decreasing trend at low and high latitudes and an increasing trend in subtropical regions. In the most recent two decades, global warming has slowed down, which is called the “global warming hiatus”. The trend in SSS during this phase, which was different to that under global warming, mainly indicated the response of the ocean surface to the decadal and multi-decadal variability in the climate system, referring to the intensification of the Walker Circulation. The significant contrast of SSS trends between the western Pacific and the southeastern Indian Ocean suggested the importance of oceanic dynamics in the cross-basin interaction in recent decades. Ocean Rossby waves and the Indonesian Throughflow contributed to the freshening trend in SSS in the southeastern Indian Ocean, while the increasing trend in the southeastern Pacific and the decreasing trend in the northern Atlantic implied a long-term linear trend under global warming. In the future, higher resolution SSS data observed by satellites, together with Argo observations, will help to extend our knowledge on the dynamics of mesoscale eddies, regional oceanography, and climate change.

Surface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia–Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016 to 2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy, and high-resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher, but the product accuracy is also higher than that of those products. In particular, hourly and daily root-mean-square errors of the new SWDR are 104.9 and 31.5 W m−2, respectively, which are much smaller than those of CERES (at 121.6 and 38.6 W m−2, respectively), ERA5 (at 176.6 and 39.5 W m−2, respectively), and GLASS (daily of 36.5 W m−2). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 W m−2, respectively, which are comparable to that of CERES and ERA5, and even better over high-altitude regions.

In power fingerprint identification, feature information is insufficient when using a single feature to identify equipment, and small load data of specific customers, difficult to meet the refined equipment classification needs. A power fingerprint identification based on the improved voltage-current(V-I) trajectory with color encoding and transferred CBAM-ResNet34 is proposed. First, the current, instantaneous power, and trajectory momentum information are added to the original V-I trajectory image using color coding to obtain a color V-I trajectory image. Then, the ResNet34 model was pre-trained using the ImageNet dataset and a new fully-connected layer meeting the device classification goal was used to replace the fully-connected layer of ResNet34. The Convolutional Block Attention Module (CBAM) was added to each residual structure module of ResNet34. Finally, Class-Balanced (CB) loss is introduced to reweight the Softmax cross-entropy (SM-CE) loss function to solve the problem of data imbalance in V-I trajectory identification. All parameters are retrained to extract features from the color V-I trajectory images for device classification. The experimental results on the imbalanced PLAID dataset verify that the method in this paper has better classification capability in small sample imbalanced datasets. The experimental results show that the method effectively improves the identification accuracy by 4.4% and reduces the training time of the model by 14 minutes compared with the existing methods, which meets the accuracy requirements of fine-grained power fingerprint identification.

The parameterization of thermal roughness length z0h plays a key role in land surface modeling. Previous studies have found that the daytime land surface temperature (LST) on dry land (arid and semiarid regions) is commonly underestimated by land surface models (LSMs). This paper presents two improvements of Noah land surface modeling for China's dry‐land areas. The first improvement is the replacement of the model's z0h scheme with a new one. A previous study has validated the revised Noah model at several dry‐land stations, and this study tests the revised model's performance on a regional scale. Both the original Noah and the revised one are driven by the Global Land Data Assimilation System (GLDAS) forcing data. The comparison between the simulations and the daytime Moderate Resolution Imaging Spectroradiometer‐ (MODIS‐) Aqua LST products indicates that the original LSM produces a mean bias in the early afternoon (around 1330, local solar time) of about −6 K, and this revision reduces the mean bias by 3 K. Second, the mean bias in early afternoon is further reduced by more than 2 K when a newly developed forcing data set for China (Institute of Tibetan Plateau Research, Chinese Academy of Sciences (ITPCAS) forcing data) is used to drive the revised model. A similar reduction is also found when the original Noah model is driven by the new data set. Finally, the original Noah model, when driven by the new forcing data, performs satisfactorily in reproducing the LST for forest, shrubland and cropland. It may be sensible to select the z0h scheme according to the vegetation type present on the land surface for practical applications of the Noah LSM. Key Points Improved modeling of land surface temperature in dry land of China Use of newly developed forcing data Improved modeling of land surface energy budget

Satellite remote sensing has advanced the understanding of the climate system in the short period of observations. This study highlights remote sensing discoveries that were not detected by climate models or conventional observations, and suggests future challenges for the robust determination of climate trends. Satellite remote sensing has provided major advances in understanding the climate system and its changes, by quantifying processes and spatio-temporal states of the atmosphere, land and oceans. In this Review, we highlight some important discoveries about the climate system that have not been detected by climate models and conventional observations; for example, the spatial pattern of sea-level rise and the cooling effects of increased stratospheric aerosols. New insights are made feasible by the unparalleled global- and fine-scale spatial coverage of satellite observations. Nevertheless, the short duration of observation series and their uncertainties still pose challenges for capturing the robust long-term trends of many climate variables. We point out the need for future work and future systems to make better use of remote sensing in climate change studies.

The land surface temperature (LST) changes in North America are very abnormal recently, but few studies have systematically researched these anomalies from several aspects, especially the influencing forces. After reconstructing higher quality MODIS monthly LST data (0.05° * 0.05°) in 2002–2018, we analyzed the LST changes especially anomalous changes and their driving forces in North America. Here we show that North America warmed at the rate of 0.02 °C/y. The LST changes in three regions, including frigid region in the northwestern (0.12 °C/y), the west coast from 20°N–40°N (0.07 °C/y), and the tropics south of 20°N (0.04 °C/y), were extremely abnormal. The El Nino and La Nina were the main drivers for the periodical highest and lowest LST, respectively. The North Atlantic Oscillation was closed related to the opposite change of LST in the northeastern North America and the southeastern United States, and the warming trend of the Florida peninsula in winter was closely related to enhancement of the North Atlantic Oscillation index. The Pacific Decadal Oscillation index showed a positive correlation with the LST in most Alaska. Vegetation and atmospheric water vapor also had a profound influence on the LST changes, but it had obvious difference in latitude.

Accurate quantitative global scale snow water equivalent information is crucial for meteorology, hydrology, water cycle and global change studies, and is of great importance for snow melt-runoff forecast, water resources management and flood control. With land surface process model and snow process model, the snow water equivalent can be simulated with certain accuracy, with the forcing data as input. However, the snow water equivalent simulated using the snow process models has large uncertainties spatially and temporally, and it may be far from the needs of practical applications. Thus, the large scale snow water equivalent information is mainly from remote sensing. Beginning with the launch of Nimbus-7 satellite, the research on microwave snow water equivalent remote sensing has developed for more than 30 years, researchers have made progress in many aspects, including the electromagnetic scattering and emission modeling, ground and airborne experiments, and inversion algorithms for future global high resolution snow water equivalent remote sensing program. In this paper, the research and progress in the aspects of electromagnetic scattering/emission modeling over snow covered terrain and snow water equivalent inversion algorithm will be summarized.

The climate warming over the Third Pole is twice as large as that in other regions and glacier mass loss is considered to be more intensive in the region. However, due to the vast geographical differences, the characteristics of glacier mass loss might be very different between different parts of the Third Pole, such as between the southern and northern Third Pole. It is, therefore, very important to clarify the characteristics of glacier mass loss between different parts of the Third Pole, particularly between the southern and northern Third Pole. We selected the Yala Glacier in the Central Himalayas and the Qiyi Glacier in the Qilian Mountains to study the different characteristics of glacier mass loss between the southern and northern Third Pole using remote sensing data and in situ data. Based on the results, we found that the Yala Glacier has not only been in a status of mass loss but also in a status of intensive and accelerating mass loss. Our analysis showed that the average multi-year mass loss of the Yala Glacier is −736 mm w.e.a−1, with a maximum of −1815 mm w.e.a−1. At the same time, the Qiyi Glacier has experienced a mild glacier mass loss process compared with the Yala Glacier. The Qiyi Glacier’s mass loss is −567 mm w.e.a−1 with a maximum of −1516 mm w.e.a−1. Our results indicate that the mass loss of the Yala Glacier is much stronger than that of the Qiyi Glacier. The major cause of the stronger mass loss of the Yala Glacier is from the decrease of glacier accumulation associated with precipitation decrease under the weakening Indian monsoon. Other factors have also contributed to the more intensive mass loss of the Yala Glacier.