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The construction of automatic meteorological observation stations in China has been continuously improved.Currently， more than 60， 000 automatic meteorological observation stations have been built， providing abundant information of surface meteorological variables for weather and climate research.However， the practical application of ground automatic station data has always been constrained by high uncertainty in the quality of observation data.Strict quality control is a prerequisite for the effective application of automatic station data， but the high spatiotemporal resolution characteristics of automatic station observations bring more difficulties to quality control researches.How to accurately distinguish local small-scale weather information and local variation caused by erroneous data in high-resolution automatic station data has always been a difficult point in the research of quality control methods for spatiotemporal resolution automatic station data.On the basis of analyzing the spatial correlation scale and error characteristics of surface temperature， this study established a quality control method for temperatures from surface automatic station based on EOF （Empirical Orthogonal Function） analysis method， which only relies on observation data.The study conducted quality control experiments using surface automatic station temperature observations from January to May 2022， and compared the differences in surface temperature between the automatic station observation data and the Chinese reanalysis data CRA40 （CMA's global atmospheric Re-Analysis） before and after quality control.The results indicate that the established autonomous quality control method for observation data can effectively identify erroneous observation data， relying solely on the observation data itself， effectively avoiding the impact of background errors on quality control effectiveness.The quality control sub regions determined on the basis of correlation scale analysis further enhance the quality control method's ability to identify small-scale temperature changes in observation data， effectively preserving the reject of temperature extremum data corresponding to extreme events in small areas， the number of quality control modes determined by actual data characteristics can well separate the principal and residual terms of the observed data， significantly improving the accuracy of erroneous extreme value recognition.Further introducing sliding detection methods and overlap rejection standards can also retain as much valuable observation data as possible in areas with steep terrain.The quality control results of 1 month data show that the new quality control method can obviously and stably improve the spatial correlation coefficient between the surface temperature of automatic station data and the corresponding variable of CRA40 （CMA's global atmospheric Reanalysis） reanalysis data， and the average deviation is also reduced.Although the average data rejection rate is only about 8%， the spatial correlation coefficient can reach a maximum increase of about 0.02， which fully proves that proposed quality control method can effectively eliminate erroneous data and improve the spatial continuity of automatic station data.

The luminescent spectra of boron–nitrogen (BN) superatoms under the influence of small molecule excitation remain unexplored, yet hold promising prospects for application in luminescent materials. This study employs density functional theory to investigate the absorption and fluorescence emission spectra of small molecules (pyrazine, pyridine, and benzene) adsorbed on B12N12 superatoms. The findings reveal the formation of stable chemisorption structures, namely pyrazine-B12N12 and pyridine-B12N12, while benzene forms a physisorption structure benzene-B12N12. Interestingly, the adsorbed benzene enhances the absorption spectrum intensity of B12N12, while pyrazine and pyridine adsorbed significantly amplify the emission spectrum intensity of B12N12. Moreover, this study discusses the impact of variation in the number of adsorbed small molecules on spectral characteristics. Results indicate that the absorption spectra intensity of 2pyrazine-B12N12, 2pyridine-B12N12, and 2benzene-B12N12 is relatively robust, with 2benzene-B12N12 exhibiting a stronger emission spectrum intensity compared to benzene-B12N12 and 4benzene-B12N12. These computational findings offer valuable insights for the exploration of luminescent materials and serve as theoretical reference for experimental investigations.

As a hub of the “Belt and Road” （B&R）， Central and West Asia （CWA） was significantly threatened by droughts.The study of drought that occurs in CWA is of great value to the successful implementation of B&R.This work analyzed the main characteristics of drought in CWA using the self-calibrating Palmer Drought Severity Index （scPDSI） and ERA5 reanalysis data.The result has shown that the drought in CWA has a noticeable seasonal difference.The drought almost started from the boreal winter， rapidly intensified in spring， and reached the peak at the end of spring， then relieved slowly in summer.By using the composite analyses method， this work examined the differences in climate systems between extreme droughts and humid events in CWA.The results show that the tropical Pacific Sea surface temperatures anomaly （SSTA） exhibited a La Niña pattern in the early stage of extreme drought events， which results in the continued northwards winds anomaly in the lower layers of southern CWA have reduced water vapor transport from the Indian Ocean.At the same time， the atmospheric circulation in the meddle troposphere of the Eurasian continent is characterized by EU2 type teleconnection， resulting in positive height field anomaly in the region and continuous enhancement.The air movement driven by the positive height field anomaly was mainly subsidence.Besides， the reduction of water vapor transport was not conducive to the precipitation in CWA， which led to the rapid development of drought in CWA.

Observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard polar-orbiting satellites are considered to be the most effective satellite data in terms of obviously reducing operational prediction errors. However, there are still significant difficulties in the application of AMSU-A low-level channel data assimilation over land. One of them is the inaccurate surface skin temperature (SKT) of the background on land areas, which leads to significant uncertainty in the accuracy of simulating brightness temperature (BT) in these channels. Therefore, improving the accuracy of SKT in the background field is a direct way to improve the assimilation effect of these low-level channel data over land. In this study, both high-spatio-temporal-resolution automatic weather station (AWS) observation data from China in September 2021 and the AMSU-A observation data from NOAA-15/18/19 and MetOp-A were used. Based on the Advanced Research version of the Weather Research and Forecast model (WRF-ARW) and Gridpoint Statistical Interpolation (GSI) assimilation system, we first analyzed the differences in SKT between AWS observations and model simulations and then attempted to directly replace the simulated SKT with the observation data. On this basis, the differences in BT simulation effects over the land area of Southwest China before and after replacement were meticulously analyzed and compared. In addition, the impacts of SKT replacement in areas with different terrain elevations and in cloudy areas were also evaluated. The results indicate that the SKTs of background fields were generally lower than the surface observations, whereas the diurnal variation in SKT was not well simulated. After replacing the SKT of the background field with station observations, the BT differences between the observation and background (O–B, observation minus background) were remarkably reduced, especially for channels 3–5 and 15 of the AMSU-A. The volume of data passing the GSI quality control significantly increased, and the standard deviation of O–B decreased. Further analysis showed that the improvement effect was better in areas at an elevation above 1600 m. Moreover, introducing SKT observations leads to a significant and stable improvement over BT simulations in cloudy areas over land.

Ground temperature is an important variable affecting the thermal interaction between atmosphere and land.However， due to its strong small-scale variation characteristics， the study of ground temperature variation has been restricted by the insufficient spatial and temporal resolution of the observations.Based on the observation data of automatic stations with high spatial-temporal resolution in China in January and July 2019， the reproduction ability of two reanalysis data， GFS （Global Forecast System） and era5 （European Center for medium range weather forecasts fifth reanalysis data）， to the variation characteristics of ground temperature in eastern China is analyzed.The results show that the surface 2m temperature and ground temperature of the two reanalysis data have obvious diurnal variation characteristics， but the difference between the observation and the reanalysis data has obvious diurnal characteristics.Further analysis shows that the diurnal characteristics of the difference is mainly due to the systematic phase bias of the two reanalysis data in the simulation of the diurnal variation of ground temperature in China， and the phase bias also has an obvious longitude dependence.The possible causes of the diurnal variation of the errors are also discussed by comparative analysis of the errors in each latitude band.

On 5 July 2021, China launched the world’s first early morning orbit meteorological satellite, which was equipped with China’s newest-generation microwave instrument, the Microwave Temperature Sounder-3 (MWTS-3). MWTS-3 has 17 detection channels, which can detect temperature profiles from near the ground to approximately 2 hPa. However, similar to the newest-generation microwave temperature instruments in other countries, MWTS-3 may also suffer from striping noise. In this paper, a principal component analysis (PCA) is combined with ensemble empirical mode decomposition (EEMD) to extract the striping noise from the brightness temperatures observed by MWTS-3, and a comparative analysis is performed with its predecessor (MWTS-2). This paper analyzes the characteristics of the striping noise of both instruments and its possible impact on their observations. The results show that the striping noise of MWTS-3 has been significantly reduced in most of the 17 channels. In particular, the striping noise of MWTS-3’s channel 3 is reduced by nearly half compared to the channel of MWTS-2 with the same frequency. Furthermore, the interchannel correlations for MWTS-3 are significantly lower than those for MWTS-2. After noise mitigation, the interchannel correlations of both MWTS-2 and MWTS-3 are obviously reduced. In addition, striping noise mitigation can reduce observation errors to some extent, especially for channels above the middle troposphere.

Na3ScF6: 20% Yb3+, 2% Er3+ samples were synthesized with different reaction times and reaction temperatures using the solvothermal method. We carried out a series of tests on Na3ScF6 crystals. The XRD patterns showed that the monoclinic phases of the Na3ScF6 samples could be synthesized under different reaction conditions, and doping with Yb3+ ions and Er3+ ions did not change the crystal structures. The SEM images showed that the sizes of the samples gradually increased with reaction time and reaction temperature. The fluorescence spectra showed that the emission peaks of the prepared samples under 980 nm near-infrared (NIR) excitation were centered at 520 nm/543 nm and 654 nm, corresponding to the 2H11/2/4S3/2→4I15/2 and 4F9/2→4I15/2 transitions, respectively. With the increasing size of the samples, the emission intensities at 654 nm increased and the luminescence colors changed from green to red; at the same time, the red-to-green luminescence intensity ratios (IR/IG ratios) increased from 0.435 to 15.106—by as much as ~34.7 times. Therefore, this paper provides a scheme for tuning the IR/IG ratios of Na3ScF6: 20% Yb3+, 2% Er3+ samples by changing their sizes, making it possible to enhance the intensity of red upconversion, which has great potential for the study of color displays and lighting.

The new generation of geostationary environmental operational satellite imager, the Himawari-8 Advanced Himawari Imager (AHI), adds two more water vapour channels and four more other channels than its predecessor, MTSAT-2. But except for the three water vapour channels, AHI channels are often not assimilated over land due to large uncertainty in surface parameters. Using the relative adjoint sensitivity analysis method, we show that the brightness temperature of AHI channel 16 is much more sensitive to the low-tropospheric atmosphere than to the surface emissivity, similar to those high-level water vapour channels. We thus assimilated AHI channel 16 brightness temperature observations together with AHI water vapour channels over land, and assessed the added benefits on short-range quantitative precipitation forecasts for several convection-induced rainfall cases. Results show that adding channel 16 over land to AHI data assimilation further improves short-range rainfall forecasts. Assimilation of AHI channel 16 improves the upstream near surface atmospheric temperature analysis and influences the development of downstream precipitating weather systems.

FengYun-3E (FY-3E), the fifth satellite in China’s second-generation polar-orbiting satellite FY-3 series, was launched on 5 July 2021. FY-3E carries a third-generation microwave temperature sounder (MWTS-3). For the first time, this study demonstrates that MWTS-3 radiances data assimilation can improve the China Meteorological Administration Global Forecast System (CMA-GFS). By establishing a cloud detection module based on the retrieval results of the new channels of MWTS-3, a quality control module according to the error characteristics of MWTS-3 data, and a bias correction module considering the scanning position of satellite and weather systems, the effective assimilation of MWTS-3 data in the CMA-GFS has been realized. Through one-month cycling experiments of assimilation and forecasts, the error characteristics and assimilation effects of MWTS-3 data are carefully evaluated. The results show that the observation errors in MWTS-3 data are similar to those in advanced technology microwave sounder (ATMS) data within the same frequency channel, are slightly larger than those in the advanced microwave-sounding unit-A (AMSU-A) data, and are much better than those in the MWTS-2 data. The validation of the assimilation and prediction results demonstrate the positive contribution of MWTS-3 data assimilation, which can remarkably reduce the analysis errors in the Northern and Southern Hemispheres. Specifically, the error growth on the upper layer of the model is obviously suppressed. When all other operational satellite observations are included, the assimilation of MWTS-3 data has a neutral or slightly positive contribution to the analysis and forecast results, and the improvement is mainly found in the Southern Hemisphere. The relevant evaluation results indicate that the MWTS-3 data assimilation has good application prospects for operation.

Waterlogging induced by rain in urban areas has a potential risk impact on property and safety. This paper focuses on the impact of rain on waterlogging and evaluates the waterlogging risk in the central city of Shanghai. A simplified waterlogging depth model is developed in different areas with different drainage capacity and rainfall in consumption of simplifying the effect of complex terrain characteristics and hydrological situation. Based on urban waterlogging depth and its classification collection, a Rain-induced Urban Waterlogging Risk Model (RUWRM) is further established to evaluate waterlogging risk in the central city. The results show that waterlogging depth is closely linked with rainfall and drainage, with a linear relationship between them. More rainfall leads to higher waterlogging risk, especially in the central city with imperfect drainage facilities. Rain-induced urban waterlogging risk model can rapidly gives the waterlogging rank caused by rainfall with a clear classification collection. The results of waterlogging risk prediction indicate that it is confident to get the urban waterlogging risk rank well and truly in advance with more accurate rainfall prediction. This general study is a contribution that allows the public, policy makers and relevant departments of urban operation to assess the appropriate management to reduce traffic intensity and personal safety or strategy to lead to less waterlogging risk.

Spaceborne microwave radiometer observations play vital roles in surface parameter retrievals and data assimilation, but widespread radio-frequency interference (RFI) signals in the C-band channel result in a lack of valuable data over large areas. Establishing repaired data based on existing observation information is crucial. In this study, Advanced Microwave Scanning Radiometer (AMSR)-2 C-band data affected by RFI were accurately repaired through the iterative principal component analysis (PCA) method in 2016 over the U.S. land area. The standard deviation (STD) and bias characteristics of the brightness temperature in the C-band vertical polarization channel were compared and analyzed before and after the restoration to verify the assimilation application prospect of the repaired data. Not only was the spatial continuity of the microwave imager observations significantly improved following restoration; the STD and bias of the observation minus background (OMB) of the restored data were basically consistent with those of the RFI-free data. The STD of OMB exhibited obvious seasonal variations, which were approximately 4.0 K from January to May and 3.0 K from June to December, whereas the biases were near zero in winter but negative (approximately −2.0 K) in summer. The surface type and terrain height also critically affected the STD and bias. The STD decreased with increasing terrain height, whereas the bias exhibited the opposite trend. The STD was largest in low-vegetation areas (4.0 K) but only approximately 2.0–3.0 K in pine forest and brush areas. These results show that the restored data have a high prospect for retrieval application and assimilation, and the STD and bias estimation results also provide a reference for land-based AMSR-2 data assimilation.

Observation bias and error characteristics are the preconditions for the effective assimilation of observation data. In this paper, the bias and error characteristics of the AHI (Advanced Himawari Imager (AHI) and AGRI (Multi-Channel Advanced Geostationary Orbit Radiation Imager (AGRI) data are compared and analyzed, with an emphasis on the observations obtained from land areas. The results show that the observation errors of the two instruments for the ocean area have a good channel consistency over ocean areas, which are all with errors of about 0.6 K; however, the bias and error are significantly affected by the land-surface types and terrain heights over land. For most of the AHI channels, the bias in urban-area bias is smaller than that of those of other surface types, while that of the AGRI data exhibits just the opposite trend, with obviously larger biases in urban areas. However, the observation errors of these two instruments in urban areas are significantly smaller than those of other surface types. The biases of the two instruments do not extensively change much with the terrain height, only slightly decreasing when the height is above 1000 m; however, the observation errors increase obviously with the increase of terrain heights. The difference between the two instruments is that the observation error of the AHI data tends to be stable and stabilizes above 1000 m, while that of the AGRI data is relatively stable below 500 m. The observation errors of the CO2-sensitive channels of the two instruments over the land areas are obviously smaller than those of other near-surface channels, which may indicate that the data obtained in these two CO2 channels have good application prospects for assimilation over land.

Soil moisture is a key variable of the climate system. Microwave remote sensing has become an essential means of obtaining soil moisture because of the unique advantages of its all-day and all-weather observation capability. Theoretically, low-frequency C-band observations are highly suitable for soil moisture retrieval because of their high sensitivity to soil moisture at vegetation roots. However, the quality of C-band observations suffers from radio frequency interference (RFI) over the United States. This paper used the iterative principal component analysis (PCA) method to repair RFI-contaminated second-generation Advanced Microwave Scanning Radiometer (AMSR-2) C-band observations, and the results of soil moisture retrieval based on restored data were evaluated. It was found that RFI could lead to nonconvergence in the retrieval of a large amount of data, and the application of repaired data in retrieval could result in the recovery of more than 80% of nonconvergent data, especially in spring and autumn. The retrieval results based on restored data attained a satisfactory correlation with ERA5 reanalysis data and European Space Agency Climate Change Initiative (ESA-CCI) soil moisture data and suitably agreed with precipitation observation data. Soil moisture generally exhibited a gradual increase from west to north and east. This feature was weakened due to the influence of monsoons in the east in summer. The western side of the Cascade Mountains is the wettest area of the United States, with soil moisture exceeding 0.4 m3 m−3. The driest region of the United States is located between the Rockies and Cordillera Mountains, and the soil moisture value is lower than 0.1 m3 m−3.

Fengyun-3E, which is equipped with MicroWave Humidity Sounder 2 (MWHS-2) for detecting both temperature and humidity, is China’s latest polar orbiting meteorological satellite and China’s first satellite in early-morning orbit. The observation bias and observation error characteristics of MWHS-2 are evaluated by using ERA-5 as the background field. The results show that the biases range from −4 to 2 K, and the observation errors are within 1.5 K except for the window channels. A further analysis of the dependence on the scanning angles indicates that observation errors for the window channels and the 118-GHz channels increase with decreasing scanning angles. Observation errors of the window channels and water vapor channels are also latitudinally dependent, and the maximum errors in the high latitudes reach 2.0 K, while those in the tropical regions are approximately 0.8 K. In addition, the observed brightness temperature of FY-3E MWHS-2 is accompanied by striping noises along the track and high-frequency oscillation noises along the scanlines. The noise mitigation results show that both noises are approximately 0.2 K.

The FY-3E satellite was successfully launched on 5 July 2021 and carries on board the Microwave Temperature Sounder-III (MWTS-III). In this study, the biases of MWTS-III data with respect to simulations are analyzed according to the instrument field of view and location latitude over the Pacific region. The cloud liquid water path (CLWP) over oceans is retrieved from two new window channels at 23.8 and 31.4 GHz and is used for detecting the clouds-affected microwave sounding data. The absolute bias between the observed and simulated brightness temperature (O–B) under the clear sky point is, in general, less than 2.0 K, depending on the MWTS-III channel. The standard deviations of O-B in most channels are less 1.0 K, but they are 1–1.5 K in channels 1–4 and 17. The average and the standard deviation of O−B from the channels 1–10 shows an obvious symmetrical variation with FOV. The evaluation results all indicate good prospects for the assimilation application of FY-3E microwave sounding data.

Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) data have been widely assimilated in operational forecasting systems. However, effective distinction between cloudy and clear-sky data is still an essential prerequisite for the assimilation of microwave observations. Cloud detection over the Tibetan Plateau has long been a challenge owing to the influence of low temperatures, terrain height, surface vegetation, and inaccurate background fields. Based on the variations in the response characteristics of different channels of AMSU-A to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) are chosen to establish a cloud detection index. Combined with the existing MHS cloud detection index, a cloud detection scheme over the Tibetan Plateau is proposed. Referring to VISSR-II (Stretched Visible and Infrared Spin Scan Radiometer-II) and CALIPSO (The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) cloud classification products, the detection rate of cloudy data and the rejection rate of clear-sky data under different cloud index thresholds are evaluated. Results show that the new cloud detection scheme can identify more than 80% of cloudy data on average, but this decreases to 72% for area with terrain higher than 5 km, and the false deletion rate remains stable at 45%. The detection rates of mixed clouds and cumulonimbus are higher than 90%, but it is lower than 50% for altostratus with an altitude of about 7–8 km. Comparative analysis shows that the new method is more suitable for areas with terrain higher than 700 m. Based on the cloud detection results, the effects of terrain height on the characteristics of observation error and bias are also discussed for AMSU-A channels 5 and 6.

GOES-R satellite was successfully launched on November 19, 2016 with an Advanced Baseline Imager (ABI) on board. ABI is a new imager and provides more advanced capabilities for weather applications through more channels and higher temporal and spatial resolutions than the earlier GOES imagers. In this study, impacts from assimilating ABI brightness temperatures in NWP models on quantitative precipitation forecasts (QPFs) are presented and compared with those from assimilating GOES-13/-15 imager data. Cloudy and precipitation-affected brightness temperatures are removed in assimilation through using an infrared (IR) cloud algorithm in quality control. This quality control can be applied for ABI data both at day and at night. Assimilation of the ten ABI IR channels improves the 24-h forecast accuracy of both temperature and specific humidity fields when compared with radiosonde observations. It is also shown that the assimilation of ABI IR channels produces positive impacts on QPFs. The impacts of ABI data assimilation on QPFs are slightly larger than those from assimilation of GOES-13 and -15 imager observations.

Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin.

Satellite data are the main source of information for operational data assimilation systems, and Advanced Microwave Sounding Unit-A (AMSU-A) data are one of the types of satellite data that contribute most to the reduction of numerical forecast errors. However, the assimilation of AMSU-A data over land lags behind that over the ocean. In this respect, the accuracy of cloud detection over land is one of the factors affecting the assimilation of AMSU-A data, especially for the window and low-peaking channel (23–53.59 GHz and 89 GHz) data. Strong surface emissivity and high spatial and temporal variability make it difficult to distinguish between the radiative contributions of clouds and the atmosphere. Based on the differences in the response characteristics of different channels to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) were selected to develop a new index for cloud detection over land. Case studies showed that the AMSU-A cloud index can detect most of the convective clouds; additionally, by further matching the MHS (Microwave Humidity Sounder) cloud detection index, we can effectively distinguish between cloudy and clear-sky observations. Batch test results also verified the accuracy and stability of the new cloud detection method. By referring to the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud product, the POD (probability of detection) of the cloud fields of view with the new method was nearly 84%. By using the new cloud detection method to remove the cloudy data, the bias and standard deviation of the observation-minus-simulated brightness temperature (O−B) were significantly reduced, with the bias of O−B for channels 2–4 being below 1.0 K and the standard deviation of channels 5 and 6 being nearly 1.0 K.

Clouds affect the assimilation of microwave data from satellites and therefore the detection of clouds is important under both clear sky and cloudy conditions. We introduce a new cloud detection method based on the assimilation of data from the advanced microwave sounder unit A (AMSU-A) and the microwave humidity sounder (MHS) into the global and regional assimilation and prediction system (GRAPES) and use forecast experiments to evaluate its impact. The new cloud detection method can retain more observational data than the current method in GRAPES, thereby improving the assimilation of AMSU-A data. Verification of the method showed that, by improving the forecast of the lower-level air temperature and geopotential height, the new cloud detection method improved the forecast of the track of two typhoons. The forecast of a large-scale weather system in GRAPES was also improved by the new method in the later period of the forecast.

In this study, we prepared cubic-phase oleic-acid-coated KMnF3: Yb3+,Er3+ nanoparticles (NPs) and NaYF4:Yb3+,Er3+ NPs, which were about 23 nm. From the down-shifting emissions spectra of the two NPs obtained by 980 nm excitation, we observed the fact that the KMnF3: 18%Yb3+,1%Er3+ NPs were a luminescent material with a broadband near-infrared emission of 1.5 μm, and full-width at half-maximum (FWHM) of 55 cm−1, which was wider than that of the NaYF4: 18%Yb3+,1% NPs. Therefore, we believe that the oleic-acid-coated KMnF3:Yb3+,Er3+ NPs have great potential in fabricating broadband waveguide amplifiers. Through epitaxial growth of a KMnF3: Yb3+ active-shell on the core NPs, we compounded KMnF3:Yb3+,Er3+@KMnF3:Yb3+ core-active-shell NPs whose 1.5-μm infrared emissions intensity was 3.4 times as strong as that of the core NPs. In addition, we manufactured waveguide amplifiers using KMnF3:18%Yb3+,1%Er3+@KMnF3:2%Yb3+ NPs as the core materials of the waveguide amplifiers. When the input signal power was 0.2 mW and the pump power was 200 mW, we achieved a relative gain of 0.6 dB at 1534 nm in a 10-mm long waveguide.

Small fluoride nanoparticles (NPs) with strong down-conversion (DC) luminescence at 1.5 μm are quite desirable for optical fiber communication systems. Nevertheless, a problem exists regarding how to synthesize small fluoride NPs with strong DC emission at 1.5 μm. Herein, we propose an approach to improve 1.5 μm emission of BaLuF5:Yb3+,Er3+ NPs by way of combining doping Ce3+ ions and coating multiple BaLuF5: Yb3+ active-shells. We prepared the BaLuF5:18%Yb3+,2%Er3+,2%Ce3+ NPs through a high-boiling solvent method. The effect of Ce3+ concentration on the DC luminescence was systematically investigated in the BaLuF5:Yb3+,Er3+ NPs. Under a 980 nm laser excitation, the intensities of 1.53 μm emission of BaLuF5:18%Yb3+,2%Er3+,2%Ce3+ NPs was enhanced by 2.6 times comparing to that of BaLuF5:18%Yb3+,2%Er3+ NPs since the energy transfer between Er3+ and Ce3+ ions: Er3+:4I11/2 (Er3+) + 2F5/2 (Ce3+) → 4I13/2 (Er3+) + 2F7/2 (Ce3+). Then, we synthesized BaLuF5:18%Yb3+,2%Er3+,2%Ce3+@BaLuF5:5%Yb3+@BaLuF5:5%Yb3+ core-active-shell-active-shell NPs via a layer-by-layer strategy. After coating two BaLuF5:Yb3+ active-shell around BaLuF5:Yb3+,Er3+,Ce3+ NPs, the intensities of the 1.53 μm emission was enhanced by 44 times compared to that of BaLuF5:Yb3+,Er3+ core NPs, since the active-shells could be used to not only suppress surface quenching but also to transfer the pump light to the core region efficiently through Yb3+ ions inside the active-shells.

Data assimilation is an essential approach to improve the predictions of land surface models. Due to the characteristics of single-column models, assimilation of land surface information has mostly focused on improving the assimilation of single-point variables. However, land surface variables affect short-term climate more through large-scale anomalous forcing, so it is indispensable to pay attention to the accuracy of the anomalous spatial structure of land surface variables. In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The ERA5_Land soil moisture reanalysis data are used as ideal observation for the preliminary effectiveness validation of the image assimilation system. The results show that the new image assimilation system is able to well absorb the spatial structure information of the observed data and has a remarkable ability to adjust the spatial structure of soil moisture in the land model. The spatial correlation coefficient between model surface soil moisture and observation has increased from 0.39 to about 0.67 after assimilation. By assimilating the surface soil moisture data and combining with the model physical processes, the image assimilation system can also gradually improve the spatial structure of deep soil moisture, with the spatial correlation coefficient between model soil moisture and observation increased from 0.35 to about 0.57. The forecast results show that the positive assimilation effect could be maintained for more than 30 days. The results of this study adequately demonstrate the application potential of image assimilation system in the short-term climate prediction. [ABSTRACT FROM AUTHOR]