A Ka-band Solid-state Transmitter Cloud Radar and Data Merging Algorithm for Its Measurements

This study concerns a Ka-band solid-state transmitter cloud radar, made in China, which can operate in three different work modes, with different pulse widths, and coherent and incoherent integration numbers, to meet the requirements for cloud remote sensing over the Tibetan Plateau. Specifically, the design of the three operational modes of the radar（i.e., boundary mode M1, cirrus mode M2, and precipitation mode M3） is introduced. Also, a cloud radar data merging algorithm for the three modes is proposed. Using one month＇s continuous measurements during summertime at Naqu on the Tibetan Plateau,we analyzed the consistency between the cloud radar measurements of the three modes. The number of occurrences of radar detections of hydrometeors and the percentage contributions of the different modes＇ data to the merged data were estimated.The performance of the merging algorithm was evaluated. The results indicated that the minimum detectable reflectivity for each mode was consistent with theoretical results. Merged data provided measurements with a minimum reflectivity of -35 dBZ at the height of 5 km, and obtained information above the height of 0.2 km. Measurements of radial velocity by the three operational modes agreed very well, and systematic errors in measurements of reflectivity were less than 2 dB. However,large discrepancies existed in the measurements of the linear depolarization ratio taken from the different operational modes.The percentage of radar detections of hydrometeors in mid- and high-level clouds increased by 60% through application of pulse compression techniques. In conclusion, the merged data are appropriate for cloud and precipitation studies over the Tibetan Plateau.

A wave energy resource assessment in the China's seas based on multi-satellite merged radar altimeter data

Wave energy resources are abundant in both offshore and nearshore areas of the China＇s seas. A reliable assessment of the wave energy resources must be performed before they can be exploited. First, for a water depth in offshore waters of China, a parameterized wave power density model that considers the effects of the water depth is introduced to improve the calculating accuracy of the wave power density. Second, wave heights and wind speeds on the surface of the China＇s seas are retrieved from an AVISO multi-satellite altim-eter data set for the period from 2009 to 2013. Three mean wave period inversion models are developed and used to calculate the wave energy period. Third, a practical application value for developing the wave energy is analyzed based on buoy data. Finally, the wave power density is then calculated using the wave field data. Using the distribution of wave power density, the energy level frequency, the time variability indexes, the to-tal wave energy and the distribution of total wave energy density according to a wave state, the offshore wave energy in the China＇s seas is assessed. The results show that the areas of abundant and stable wave energy are primarily located in the north-central part of the South China Sea, the Luzon Strait, southeast of Taiwan in the China＇s seas; the wave power density values in these areas are approximately 14.0–18.5 kW/m. The wave energy in the China’s seas presents obvious seasonal variations and optimal seasons for a wave energy utilization are in winter and autumn. Except for very coastal waters, in other sea areas in the China＇s seas, the energy is primarily from the wave state with 0.5 m≤Hs≤4 m, 4 s≤Te≤10 s whereHs is a significant wave height andTe is an energy period; within this wave state, the wave energy accounts for 80% above of the total wave energy. This characteristic is advantageous to designing wave energy convertors （WECs）. The practical application value of the wave energy is higher which can be as an effective supplement for an energy con-sumption in some areas. The above results are consistent with the wave model which indicates fully that this new microwave remote sensing method altimeter is effective and feasible for the wave energy assessment.

A new merged dataset of global ocean chlorophyll a concentration with higher spatial and temporal coverage

Understanding the ocean＇s role in the global carbon cycle and its response to environmental change requires a high spatio-temporal resolution of observation.Merging ocean color data from multiple sources is an effective way to alleviate the limitation of individual ocean color sensors（e.g.,swath width and gaps,cloudy or rainy weather,and sun glint） and to improve the temporal and spatial coverage.Since the missions of Sea-Viewing Wide Field-of-View Sensor（Sea Wi FS） and Medium-spectral Resolution Imaging Spectrometer（MERIS） ended on December 11,2010 and May 9,2012,respectively,the number of available ocean color sensors has declined,reducing the benefits of the merged ocean color data with respect to the spatial and temporal coverage.In present work,Medium Resolution Spectral Imager（MERSI）/FY-3 of China is added in merged processing and a new dataset of global ocean chlorophyll a（Chl a） concentration（2000–2015） is generated from the remote sensing reflectance（Rrs（λ）） observations of MERIS,Moderate-resolution imaging spectra-radiometer（MODIS）-AQUA,Visible infrared Imaging Radiometer（VIIRS） and MERSI.These data resources are first merged into unified remote sensing reflectance data,and then Chl a concentration data are inversed using the combined Chl a algorithm of color index-based algorithm（CIA） and OC3.The merged data products show major improvements in spatial and temporal coverage from the addition of MERSI.The average daily coverage of merged products is approximately 24% of the global ocean and increases by approximately 9% when MERSI data are added in the merging process.Sampling frequency（temporal coverage） is greatly improved by combining MERSI data,with the median sampling frequency increasing from 15.6%（57 d/a） to 29.9%（109 d/a）.The merged Chl a products herein were validated by in situ measurements and comparing them with the merged products using the same approach except for omitting MERSI and Glob Colour and MEa SUREs merged data.Correlation and relative error between the new merged Chl a products and in situ observation are stable relative to the results of the merged products without the addition of MERSI.Time series of the Chl a concentration anomalies are similar to the merged products without adding MERSI and single sensors.The new merged products agree within approximately 10% of the merged Chl a product from Glob Colour and MEa SUREs.

Validation of the multi-satellite merged sea surface salinity in the South China Sea

Sea surface salinity(SSS)is an essential variable of ocean dynamics and climate research.The Soil Moisture and Ocean Salinity(SMOS),Aquarius,and Soil Moisture Active Passive(SMAP)satellite missions all provide SSS measurements.The European Space Agency(ESA)Climate Change Initiative Sea Surface Salinity(CCI-SSS)project merged these three satellite SSS data to produce CCI L4SSS products.We validated the accuracy of the four satellite products(CCI,SMOS,Aquarius,and SMAP)using in-situ gridded data and Argo floats in the South China Sea(SCS).Compared with in-situ gridded data,it shows that the CCI achieved the best performance(RMSD:0.365)on monthly time scales.The RMSD of SMOS,Aquarius,and SMAP(SMOS:0.389;Aquarius:0.409;SMAP:0.391)are close,and the SMOS takes a slight advantage in contrast with Aquarius and SMAP.Large discrepancies can be found near the coastline and in the shelf seas.Meanwhile,CCI with lower RMSD(0.295)perform better than single satellite data(SMOS:0.517;SMAP:0.297)on weekly time scales compared with Argo floats.Overall,the merged CCI have the smallest RMSD among the four satellite products in the SCS on both weekly time scales and monthly time scales,which illustrates the improved accuracy of merged CCI compared with the individual satellite data.

Deep learning-based multi-source precipitation merging for the Tibetan Plateau

Due to its complex and diverse terrain, precipitation gauges in the Tibetan Plateau(TP) are sparse, making it difficult to obtain reliable precipitation data for environmental studies. Data merging is a method that can integrate precipitation data from multiple sources to generate high-precision precipitation data. However, the more commonly used methods, such as regression and machine learning, do not usually consider the local correlation of precipitation, so that the spatial pattern of precipitation cannot be reproduced, while deep learning methods do incorporate spatial correlation. To explore the ability of using deep learning methods in merging precipitation data for the TP, this study compared three methods: a deep learning method—a convolutional neural network(CNN) algorithm, a machine learning method—an artificial neural network(ANN) algorithm, and a statistical method based on Extended Triple Collocation(ETC) in merging precipitation from multiple sources(gauged, grid,satellite and dynamic downscaling) over the TP, as well as their performance for hydrological simulations. Dynamic downscaling data driven by global reanalysis data centered on the TP were introduced in the merging process to better reflect the spatial variability of precipitation. The results show that:(1) in terms of the meteorological metrics, the merged data perform better than the gauge interpolation data. By using data merging, the error between the raw multi-source and gauged precipitation can be reduced, and the precipitation detection capability can be greatly improved;(2) The merged precipitation data also perform well in the hydrological evaluation. The Xin’anjiang(XAJ) model parameter calibration experiments at the source of the Yangtze River(SYR) and the source of the Yellow River(SHR) were repeated 300 times to remove uncertainty in the model parameter results. The median Kling-Gupta Efficiency Coefficients(KGE) of simulated runoff from the merged data of the ANN, CNN and ETC methods for the SYR and the SHR are 0.859, 0.864, 0.838 and 0.835, 0.835, 0.789, respectively. Except for the ETC merging data at the SHR, the performance of other merged data was improved compared to the simulation results of the gauged precipitation(KGE=0.807 at the SYR, KGE=0.828 at the SHR);and(3) In contrast to the machine learning ANN method and the statistical ETC method, the deep learning method, CNN, consistently showed better performance.

Diurnal Variations of Precipitation over the Steep Slopes of the Himalayas Observed by TRMM PR and VIRS

This study investigates diurnal variations of precipitation during May–August, 1998–2012, over the steep slopes of the Himalayas and adjacent regions(flat Gangetic Plains–FGP, foothills of the Himalayas–FHH, the steep slope of the southern Himalayas–SSSH, and the Himalayas-Tibetan Plateau tableland–HTPT). Diurnal variations are analyzed at the pixel level utilizing collocated TRMM precipitation radar and visible infrared data. The results indicate that rain parameters(including rain frequency, rain rate, and storm top altitude) are predominantly characterized by afternoon maxima and morning minima at HTPT and FGP, whereas, maximum rain parameters at FHH typically occur in the early morning. Rain parameters at SSSH are characterized by double peaks;one in the afternoon and one at midnight. Over HTPT and FGP,convective activity is strongest in the afternoon with the thickest crystallization layer. Over FHH, the vertical structure of precipitation develops most vigorously in the early morning when the most intense collision and growth of precipitation particles occurs. Over SSSH, moist convection is stronger in the afternoon and at midnight with strong mixing of ice and water particles. The results of harmonic analysis show that rain bands move southward from lower elevation of SSSH to FHH with apparent southward propagation of the harmonic phase from midnight to early morning. Moreover, the strongest diurnal harmonic is located at HTPT, having a diurnal harmonic percentage variance of up to 90%. Large-scale atmospheric circulation patterns exhibit obvious diurnal variability and correspond well to the distribution of precipitation.

Discussion on several statistical problems in establishing quality standards of standard decoctions

Since 2016,a number of studies have been published on standard decoctions used in Chinese medicine.However,there is little research on statistical issues related to establishing the quality standards for standard decoctions.In view of the currently established quality standard methods for standard decoctions,an improvement scheme is proposed from a statistical perspective.This review explores the requirements for dry matter yield rate data and index component transfer data for the application of two methods specified in‘‘Technical Requirements for Quality Control and Standard Establishment of Chinese Medicine Formula Granules,"which include the average value plus or minus three times the standard deviation (■±3SD) or 70%to 130%of the average value (■±30%■).The square-root arcsine transformation method is used as an approach to solve the problem of unreasonable standard ranges of standard decoctions.This review also proposes the use of merged data to establish a standard.A method to judge whether multiple sets of standard decoction data can be merged is also provided.When multiple sets of data have a similar central tendency and a similar discrete tendency,they can be merged to establish a more reliable quality standard.Assuming that the dry matter yield rate and transfer rate conform to a binomial distribution,the number of batches of prepared slices that are needed to establish the standard decoction quality standard is estimated.It is recommended that no less than 30 batches of prepared slices should be used for the establishment of standard decoction quality standards.