Improved Diurnal Cycle of Precipitation on Land in a Global Non-Hydrostatic Model Using a Revised NSAS Deep Convective Scheme

In relatively coarse-resolution atmospheric models,cumulus parameterization helps account for the effect of subgridscale convection,which produces supplemental rainfall to the grid-scale precipitation and impacts the diurnal cycle of precipitation.In this study,the diurnal cycle of precipitation was studied using the new simplified Arakawa-Schubert scheme in a global non-hydrostatic atmospheric model,i.e.,the Yin-Yang-grid Unified Model for the Atmosphere.Two new diagnostic closures and a convective trigger function were suggested to emphasize the job of the cloud work function corresponding to the free tropospheric large-scale forcing.Numerical results of the 0.25-degree model in 3-month batched real-case simulations revealed an improvement in the diurnal precipitation variation by using a revised trigger function with an enhanced dynamical constraint on the convective initiation and a suitable threshold of the trigger.By reducing the occurrence of convection during peak solar radiation hours,the revised scheme was shown to be effective in delaying the appearance of early-afternoon rainfall peaks over most land areas and accentuating the nocturnal peaks that were wrongly concealed by the more substantial afternoon peak.In addition,the revised scheme enhanced the simulation capability of the precipitation probability density function,such as increasing the extremely low-and high-intensity precipitation events and decreasing small and moderate rainfall events,which contributed to the reduction of precipitation bias over mid-latitude and tropical land areas.

The Assessment of Soil Quality on the Arable Land in Yellow River Delta Combined with Remote Sensing Technology

Soil quality assessment is essential to improve the understanding of soil quality and make proper agricultural practices. However, soil quality assessments are extremely difficult to implement in a large-scale area, since they are time and labor consuming. Remote sensing technique gained more attention in plant and soil information monitoring recently for its high effi-ciency and convenience. But seldom studies tested the applicability of remote sensing techniques before implementing. This study conducted the soil quality assessment in a typical agricultural county in the Yellow River delta (Kenli). We found the soil quality in Kenli was dominantly in the low grade (71.85%), with deficient nutrient (SOM and TN), poor structure (high BD) and high EC. Salinity is the primary limiting factor for soil quality in Kenli, and adjustment of soil salinization through suitable farming practices such as organic fertilizers application, irrigation for leaching, and salt-tolerant crop planting is the key point for soil quality improvement. We obtained the normalized difference vegetation index (NDVI) of the study area by remote sensing technique, and found the high correlation between NDVI and soil quality indicator (SOM, TN and EC) and yield. The NDVI can help to study the soil conditions as a soil quality assessment indicator. More studies about the ap-plication of remote sensing technique on soil quality detecting are expected.

Impact of Revised Trigger and Closure of the Double-Plume Convective Parameterization on Precipitation Simulations over East Asia

A double-plume convective parameterization scheme is revised to improve the precipitation simulation of a global model(Global-to-Regional Integrated Forecast System;GRIST).The improvement is achieved by considering the effects of large-scale dynamic processes on the trigger of deep convection.The closure,based on dynamic CAPE,is improved accordingly to allow other processes to consume CAPE under the more restricted convective trigger condition.The revised convective parameterization is evaluated with a variable-resolution model setup(110–35 km,refined over East Asia).The Atmospheric Model Intercomparison Project(AMIP)simulations demonstrate that the revised convective parameterization substantially delays the daytime precipitation peaks over most land areas,leading to an improved simulated diurnal cycle,evidenced by delayed and less frequent afternoon precipitation.Meanwhile,changes to the threshold of the trigger function yield a small impact on the diurnal amplitude of precipitation because of the consistent setting of dCAPE-based trigger and closure.The simulated mean precipitation remains reasonable,with some improvements evident along the southern slopes of the Tibetan Plateau.The revised scheme increases convective precipitation at the lower levels of the windward slope and reduces the large-scale precipitation over the upper slope,ultimately shifting the rainfall peak southward,which is in better agreement with the observations.

Combination of artificial zeolite and microbial fertilizer to improve mining soils in an arid area of Inner Mongolia,China

Restoration of mining soils is important to the vegetation and environment.This study aimed to explore the variations in soil nutrient contents,microbial abundance,and biomass under different gradients of substrate amendments in mining soils to select effective measures.Soil samples were collected from the Bayan Obo mining region in Inner Mongolia Autonomous Region,China.Contents of soil organic matter(SOM),available nitrogen(AN),available phosphorus(AP),available potassium(AK),microbial biomass carbon/microbial biomass nitrogen(MBC/MBN)ratio,biomass,and bacteria,fungi,and actinomycetes abundance were assessed in Agropyron cristatum L.Gaertn.,Elymus dahuricus Turcz.,and Medicago sativa L.soils with artificial zeolite(AZ)and microbial fertilizer(MF)applied at T0(0 g/kg),T1(5 g/kg),T2(10 g/kg),and T3(20 g/kg).Redundancy analysis(RDA)and technique for order preference by similarity to ideal solution(TOPSIS)were used to identify the main factors controlling the variation of biomass.Results showed that chemical indices and microbial content of restored soils were far greater than those of control.The application of AZ significantly increases SOM,AN,and AP by 20.27%,23.61%,and 40.43%,respectively.AZ significantly increased bacteria,fungi,and actinomycetes abundance by 0.63,3.12,and 1.93 times of control,respectively.RDA indicated that AN,MBC/MBN ratio,and SOM were dominant predictors for biomass across samples with AZ application,explaining 87.6%of the biomass variance.SOM,MBC/MBN ratio,and AK were dominant predictors with MF application,explaining 82.9%of the biomass variance.TOPSIS indicated that T2 was the best dosage and the three plant species could all be used to repair mining soils.AZ and MF application at T2 concentration in the mining soils with M.sativa was found to be the most appropriate measure.

Assessment of theoretical approaches to derivation of internal solitary wave parameters from multi-satellite images near the Dongsha Atoll of the South China Sea

This study assesses the accuracy and the applicability of the Korteweg-de Vries(KdV)and the nonlinear Schr?dinger(NLS)equation solutions to derivation of dynamic parameters of internal solitary waves(ISWs)from satellite images.Visible band images taken by five satellite sensors with spatial resolutions from 5 m to 250 m near the Dongsha Atoll of the northern South China Sea(NSCS)are used as a baseline.From the baseline,the amplitudes of ISWs occurring from July 10 to 13,2017 are estimated by the two approaches and compared with concurrent mooring observations for assessments.Using the ratio of the dimensionless dispersive parameter to the square of dimensionless nonlinear parameter as a criterion,the best appliable ranges of the two approaches are clearly separated.The statistics of total 18 cases indicate that in each 50%of cases,the KdV and the NLS approaches give more accurate estimates of ISW amplitudes.It is found that the relative errors of ISW amplitudes derived from two theoretical approaches are closely associated with the logarithmic bottom slopes.This may be attributed to the nonlinear growth of ISW amplitudes as propagating along a shoaling thermocline or topography.The test results using three consecutive satellite images to retrieve the ISW propagation speeds indicate that the use of multiple satellite images(>2)may improve the accuracy of retrieved phase speeds.Meanwhile,repeated multi-satellite images of ISWs can help to determine the types of ISWs if mooring data are available nearby.

The analysis of gales over the “Maritime Silk Road” with remote sensing data

The 21st century ＂Maritime Silk Road＂ strategy is a significant part of the belt and road initiatives of China. The cognition and investigation of ocean environment is essential and necessary in these regions which will provide scientific reference for many fields such as navigation, ocean engineering, and disaster prevent and reduction. A high-resolution cross-calibrated multi-platform wind product is used to analyze gales over the Maritime Silk Road. The yearly mean speed and space distribution of gale, and the frequencies and trends of gale and extreme wind speed are analyzed. The results show that relatively high pools of gale are mainly located in the waters of the Arabian Sea, the Somali Sea, Indo-China Peninsula sea area, and Bay of Bengal in the summer. The gale frequency of the Somali Sea is more than 90%. Overall, the gale days increase year by year in the majority of the South China Sea and the northern Indian Ocean, especially in the autumn and the winter.

Automated Delineation of Smallholder Farm Fields Using Fully Convolutional Networks and Generative Adversarial Networks

Accurate boundaries of smallholder farm fields are important and indispensable geo-information that benefits farmers,managers,and policymakers in terms of better managing and utilizing their agricultural resources.Due to their small size,irregular shape,and the use of mixed-cropping techniques,the farm fields of smallholder can be difficult to delineate automatically.In recent years,numerous studies on field contour extraction using a deep Convolutional Neural Network(CNN)have been proposed.However,there is a relative shortage of labeled data for filed boundaries,thus affecting the training effect of CNN.Traditional methods mostly use image flipping,and random rotation for data augmentation.In this paper,we propose to apply Generative Adversarial Network(GAN)for the data augmentation of farm fields label to increase the diversity of samples.Specifically,we propose an automated method featured by Fully Convolutional Neural networks(FCN)in combination with GAN to improve the delineation accuracy of smallholder farms from Very High Resolution(VHR)images.We first investigate four State-Of-The-Art(SOTA)FCN architectures,i.e.,U-Net,PSPNet,SegNet and OCRNet,to find the optimal architecture in the contour detection task of smallholder farm fields.Second,we apply the identified optimal FCN architecture in combination with Contour GAN and pixel2pixel GAN to improve the accuracy of contour detection.We test our method on the study area in the Sudano-Sahelian savanna region of northern Nigeria.The best combination achieved F1 scores of 0.686 on Test Set 1(TS1),0.684 on Test Set 2(TS2),and 0.691 on Test Set 3(TS3).Results indicate that our architecture adapts to a variety of advanced networks and proves its effectiveness in this task.The conceptual,theoretical,and experimental knowledge from this study is expected to seed many GAN-based farm delineation methods in the future.

Wind speed inversion and in-orbit assessment of the imaging altimeter on Tiangong-2 space station

Imaging altimeter(IALT)is a new type of radar altimeter system.In contrast to the conventional nadir-looking altimeters,such as HY-2 A altimeter,Jason-1/2,and TOPEX/Poseidon,IALT observes the earth surface at low incident angles(2.5°–8°),so its swath is much wider and its spatial resolution is much higher than the previous altimeters.This paper presents a wind speed inversion method for the recently launched IALT onboard Tiangong-2 space station.Since the current calibration results of IALT do not agree well with the well-known wind geophysical model function at low incidence angles,a neural network is used to retrieve the ocean surface wind speed in this study.The wind speed inversion accuracy is evaluated by comparing with the ECMWF reanalysis wind speed,buoy wind speed,and in-situ ship measurements.The results show that the retrieved wind speed bias is about–0.21 m/s,and the root-mean-square(RMS)error is about 1.85 m/s.The wind speed accuracy of IALT meets the performance requirement.

Exploring changes of precipitation extremes under climate change through global variable-resolution modeling

Understanding the responses of precipitation extremes to global climate change remains limited owing to their poor representations in models and complicated interactions with multi-scale systems.Here we take the record-breaking precipitation over China in 2021 as an example,and study its changes under three different climate scenarios through a developed pseudo-global-warming(PGW)experimental framework with 60-3 km variable-resolution global ensemble modeling.Compared to the present climate,the precipitation extreme under a warmer(cooler)climate increased(decreased)in intensity,coverage,and total amount at a range of 24.3%-37.8%(18.7%-56.1%).With the help of the proposed PGW experimental framework,we further reveal the impacts of the multi-scale system interactions in climate change on the precipitation extreme.Under the warmer climate,large-scale water vapor transport converged from double typhoons and the subtropical high marched into central China,enhancing the convective energy and instability on the leading edge of the transport belt.As a result,the mesoscale convective system(Mcs)that directly contributed to the precipitation extreme became stronger than that in the present climate.On the contrary,the cooler climate displayed opposite changing characteristics relative to the warmer climate,ranging from the large-scale systems to local environments and to the Mcs.In summary,our study provides a promising approach to scientifically assess the response of precipitation extremes to climate change,making it feasible to perform ensemble simulations while investigating the multi-scale system interactions over the globe.

The decrease of salinity in lakes on the Tibetan Plateau between 2000 and 2019 based on remote sensing model inversions

Salinity is an essential factor of lake water environments and aquatic systems.It is also sensitive to climatic changes and human activities based on concentration variations of solved minerals.However,there are few consecutively temporal studies on lake salinity variations on the Tibetan Plateau because the harsh environmental conditions make it diffcult to carry out in-situ observations for several lakes.In this study.we constructed a remote sensing retrieval model for lake salinity based on 87 in-situ lake investigations;moreover,interannual lake salinity and associated variations from 152 lakes larger than 50 km2 were analyzed on the Tibetan Plateau.A significant decreasing trend in lake salinity was observed between 2000 and 2019(p<0.01).The spatial variation of lake salinity was negatively correlated with lake area changes,and the optical characteristics of salt mineral solutions were generally positively correlated with mineral concentration based on the absorption coefficients of ionic solutions.The decreasing trend of lake salinity was not directly affected by the.precipitation,but was,potentially dominated by the expanding lake water volume.This study improves the understanding of regional water environmental changes and management efficacy of water resources.

A knowledge-based approach for estimating the distribution of urban mixed land use

Estimating the proportion of land-use types in different regions is essential to promote the organization of a compact city and reduce energy consumption.However,existing research in this area has a few limitations:(1)lack of consideration of land-use distribution-related factors other than POIs;(2)inability to extract complex relations from heterogeneous information;and(3)overlooking the correlation between land-use types.To overcome these limitations,we propose a knowledge-based approach for estimating land-use distributions.We designed a knowledge graph to display POIs and other related heterogeneous data and then utilized a knowledge embedding model to directly obtain the region embedding vectors by learning the complex and implicit relations present in the knowledge graph.Region embedding vectors were mapped to land-use distributions using a label distribution learning method integrating the correlation between land-use types.To prove the reliability and validity of our approach,we conducted a case study in Jinhua,China.The results indicated that the proposed model outperformed other algorithms in all evaluation indices,thus illustrating the potential of this method to achieve higher accuracy land-use distribution estimates.

Fengyun-3D MERSI True Color Imagery Developed for Environmental Applications

Many techniques were developed for creating true color images from satellite solar reflective bands, and the so-derived images have been widely used for environmental monitoring. For the newly launched Fengyun-3 D(FY-3 D)satellite, the same capability is required for its Medium Resolution Spectrum Imager-II(MERSI-II). In processing the MERSI-II true color image, a more comprehensive processing technique is developed, including the atmospheric correction, nonlinear enhancement, and image splicing. The effect of atmospheric molecular scattering on the total reflectance is corrected by using a parameterized radiative transfer model. A nonlinear stretching of the solar band reflectance is applied for increasing the image contrast. The discontinuity in composing images from multiple orbits and different granules is eliminated through the distance weighted pixel blending(DWPB) method. Through these processing steps, the MERSI-II true color imagery can vividly detect many natural events such as sand and dust storms, snow, algal bloom, fire, and typhoon. Through a comprehensive analysis of the true color imagery, the specific natural disaster events and their magnitudes can be quantified much easily, compared to using the individual channel data.

Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data

Wheat is a major staple food crop in China.Accurate and cost-effective wheat mapping is exceedingly critical for food production management,food security warnings,and food trade policy-making in China.To reduce confusion between wheat and non-wheat crops for accurate growth stage wheat mapping,we present a novel approach that combines a random forest(RF)classifier with multi-sensor and multi-temporal image data.This study aims to(1)determine whether an RF combined with multi-sensor and multi-temporal imagery can achieve accurate winter wheat mapping,(2)to find out whether the proposed approach can provide improved performance over the traditional classifiers,and(3)examine the feasibility of deriving reliable estimates of winter wheat-growing areas from medium-resolution remotely sensed data.Winter wheat mapping experiments were conducted in Boxing County.The experimental results suggest that the proposed method can achieve good performance,with an overall accuracy of 92.9%and a kappa coefficient(κ)of 0.858.The winter wheat acreage was estimated at 33,895.71 ha with a relative error of only 9.3%.The effectiveness and feasibility of the proposed approach has been evaluated through comparison with other image classification methods.We conclude that the proposed approach can provide accurate delineation of winter wheat areas.

Estimation of wind speeds inside Super Typhoon Nepartak from AMSR2 low-frequency brightness temperatures

Accurate estimations of typhoon-level winds are highly desired over the westem Pacific Ocean.A wind speed retrieval algorithm is used to retrieve the wind speeds within Super Typhoon Nepartak (2016)using 6.9- and 10.7-GHz brightness temperatures from the Japanese Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor on board the Global Change Observation Mission-Water 1 (GCOM-Wl)satellite.The results show that the retrieved wind speeds clearly represent the intensification process of Super Typhoon Nepartak.A good agreement is found between the retrieved wind speeds and the Soil Moisture Active Passive wind speed product.The mean bias is 0.51 m/s,and the root-mean-square difference is 1.93 m/s between them.The retrieved maximum wind speeds are 59.6 m/s at 04:45 UTC on July 6 and 71.3 m/s at 16:58 UTC on July 6.The two results demonstrate good agreement with the results reported by the China Meteorological Administration and the Joint Typhoon Warning Center.In addition,Feng-Yun 2G (FY-2G) satellite infrared images,Feng-Yun 3C (FY-3C)microwave atmospheric sounder data,and AMSR2 brightness temperature images are also used to describe the development and structure of Super Typhoon Nepartak.

Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm

This paper presents an approach to process raw unmanned aircraft vehicle(UAV)image-derived point clouds for automatically detecting,segmenting and regularizing buildings of complex urban landscapes.For regularizing,we mean the extraction of the building footprints with precise position and details.In the first step,vegetation points were extracted using a support vector machine(SVM)classifier based on vegetation indexes calculated from color information,then the traditional hierarchical stripping classification method was applied to classify and segment individual buildings.In the second step,we first determined the building boundary points with a modified convex hull algorithm.Then,we further segmented these points such that each point was assigned to a fitting line using a line growing algorithm.Then,two mutually perpendicular directions of each individual building were determined through a W-k-means clustering algorithm which used the slop information and principal direction constraints.Eventually,the building edges were regularized to form the final building footprints.Qualitative and quantitative measures were used to evaluate the performance of the proposed approach by comparing the digitized results from ortho images.

Data science for oceanography:from small data to big data

The rapid development of ocean observation technology has resulted in the accumulation of a large amount of data and this is pushing ocean science towards being data-driven.Based on the types and distribution of oceanographic data,this paper analyzes the present and makes predictions for the future regarding the use of big and small data in ocean science.The ocean science has not fully entered the era of big data.There are two ways to expand the amount of oceanographic data to better understanding and man-agement of the ocean.On the data level,fully exploit the potential value of big and small ocean data,and transform the limited,small data into rich,big data,will help to achieve this.On the application level,oceanographic data are of great value if realize the federation of the core data owners and the consumers.The oceanographic data will provide not only a reliable scientific basis for climate,ecological,disaster and other scientific research,but also provide an unprecedented rich source of information that can be used to make predictions of the future.

Ensemble forecast of tropical cyclone tracks based on deep neural networks

A nonlinear artificial intelligence ensemble forecast model has been developed in this paper for predicting tropical cyclone(TC)tracks based on the deep neural network(DNN)by using the 24-h forecast data from the China Meteorological Administration(CMA),Japan Meteorological Agency(JMA)and Joint Typhoon Warning Center(JTWC).Data from a total of 287 TC cases over the Northwest Pacific Ocean from 2004 to 2015 were used to train and validate the DNN based ensemble forecast(DNNEF)model.The comparison of model results with Best Track data of TCs shows that the DNNEF model has a higher accuracy than any individual forecast center or the traditional ensemble forecast model.The average 24-h forecast error of 82 TCs from 2016 to 2018 is 63 km,which has been reduced by 17.1%,16.0%,20.3%,and 4.6%,respectively,compared with that of CMA,JMA,JTWC,and the error-estimation based ensemble method.The results indicate that the nonlinear DNNEF model has the capability of adjusting the model parameter dynamically and automatically,thus improving the accuracy and stability of TC prediction.