IMPROVED ANALYSES AND FORECASTS WITH AIRS TEMPERATURE RETRIEVALS USING THE LOCAL ENSEMBLE TRANSFORM KALMAN FILTER

In this paper we investigate the impact of the Atmospheric Infra-Red Sounder (AIRS) temperature retrievals on data assimilation and the resulting forecasts using the four-dimensional Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme and a reduced resolution version of the NCEP Global Forecast System (GFS).Our results indicate that the AIRS temperature retrievals have a significant and consistent positive impact in the Southern Hemispheric extratropics on both analyses and forecasts,which is found not only in the temperature field but also in other variables.In tropics and the Northern Hemispheric extratropics these impacts are smaller,but are still generally positive or neutral.

An Improved Method for Doppler Wind and Thermo dynamic Retrievals

A variational method is developed to retrieve winds in the first step and then thermodynamic fields in the second step from Doppler radar observations. In the first step, wind fields are retrieved at two time levels: the beginning and ending times of the data assimilation period, simultaneously from two successive volume scans by using the weak form constraints provided by the mass continuity and vorticity equations. As the retrieved wind fields are expressed by Legendre polynomial expansions at the beginning and ending times, the time tendency term in the vorticity equation can be conveniently formulated, and the retrieved winds can be compared with the radar observed radial winds in the cost function at the precise time and position of each radar beam. In the second step, the perturbation pressure and temperature fields at the middle time are then derived from the retrieved wind fields and the velocity time tendency by using the weak form constraints provided by the three momentum equations. The merits of the new method are demonstrated by numerical experiments with simulated radar observations and compared with the traditional least squares methods which consider neither the precise observation times and positions nor the velocity time tendency. The new method is also applied to real radar data for a heavy rainfall event during the 2001 Meiyu season in China.

The Study of In-Orbit Calibration Accuracy of NOAA Satellite Infrared Sounder and Its Effect on Temperature Profile Retrievals

The calibration accuracy of High Resolution Infrared Radiation Sounder Mod. 2 (HIRS / 2) on NOAA-10 satellite is analyzed in this paper. The non-linear effect in the linear calibration curve induces a deviation of 1.5 degrees (k) of brightness temperature in the tenth channel (8.3 um, water vapor absorption) of the HIRS/2 and the non-linear effect affects the other channels to a different extent. Based on analyzing non- linearity in two-point calibration curve, a tri-point calibration equation is given. A numerical test of effects of the linear and non-linear calibration models on the accuracy of atmospheric temperature retrievals is carried out.

Validation of MODIS aerosol retrievals and evaluation of potential cloud contamination in East Asia

MODIS aerosol retrievals onboard Terra/Aqua and ground truth data obtained from AERONET(Aerosol Robtic Network) solar direct radiance measurements are collocated to evaluate the quality of the former in East Asia. AERONET stations in East Asia are separated into two groups according to their locations and the preliminary validation results for each station. The validation results showed that the accuracy of MODIS aerosol retrievals in East Asia is a little worse than that obtained in other regions such as Eastern U.S., Western Europe, Brazil and so on. The primary reason is due to the improper aerosol model used in MODIS aerosol retrieval algorithm, so it is of significance to characterize aerosol properties properly according to long term ground-based remote sensing or other relevant in situ observations in order to improve MODIS retrievals in East Asia. Cloud contamination is proved to be one of large errors, which is demonstrated by the significant relation between MODIS aerosol retrievals versus cloud fraction, as well as notable improvement of linear relation between satellite and ground aerosol data after potential cloud contamination screened. Hence, it is suggested that more stringent clear sky condition be set in use of MODIS aerosol data. It should be pointed out that the improvement might be offset by other error sources in some cases because of complex relation between different errors. Large seasonal variation of surface reflection and uncertainties associated with it result in large intercepts and random error in MODIS aerosol retrievals in northern inland of East Asia. It remains to be a big problem to retrieve aerosols accurately in inland characterized by relatively larger surface reflection than the requirement in MODIS aerosol retrieval algorithm.

A high wind geophysical model fuction for Quik SCAT wind retrievals and application to Typhoon IOKE

The geophysical model function （GMF） describes the relationship between a backscattering and a sea surface wind, and enables a wind vector retrieval from backscattering measurements. It is clear that the GMF plays an important role in an ocean wind vector retrieval. The performance of the existing Ku-band model function QSCAT-1 is considered to be effective at low and moderate wind speed ranges. However, in the conditions of higher wind speeds, the existing algorithms diverge alarmingly, owing to the lack of in situ data required for developing the GMF for the high wind conditions, the QSCAT-1 appears to overestimate the a0, which results in underestimating the wind speeds. Several match-up QuikSCAT and special sensor microwave/imager （SSM/I） wind speed measurements of the typhoons occurring in the west Pacific Ocean are analyzed. The results show that the SSM/I wind exhibits better agreement with the ＂best track＂ analysis wind speed than the QuikSCAT wind retrieved using QSCAT-1. On the basis of this evaluation, a correction of the QSCAT-1 model function for wind speed above 16 m/s is proposed, which uses the collocated SSM/I and QuikSCAT measurements as a training set, and a neural network approach as a multiple nonlinear regression technologytechnology.In order to validate the revised GMF for high winds, the modified GMF was applied to the QuikSCAT observations of Hurricane IOKE. The wind estimated by the QuikSCAT for Typhoon IOKE in 2006 was improved with the maximum wind speed reaching 55 m/s. An error analysis was performed using the wind fields from the Holland model as the surface truth. The results show an improved agreement with the Holland model wind when compared with the wind estimated using the QSCAT-1. However, large bias still existed, indicating that the effects of rain must be considered for further improvement.

Verification and Correction of Cloud Base and Top Height Retrievals from Ka–band Cloud Radar in Boseong,Korea

In this study,cloud base height（CBH） and cloud top height（CTH） observed by the Ka-band（33.44 GHz） cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013（September-November） were verified and corrected.For comparative verification,CBH and CTH were obtained using a ceilometer（CL51） and the Communication,Ocean and Meteorological Satellite（COMS）.During rainfall,the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops,and this difference increased with rainfall intensity.During dry periods,however,the CBH and CTH observed by the cloud radar,ceilometer,and COMS were similar.Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS.In cases of rainfall or missing cloud radar data,the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data.These corrected cloud data were used to classify cloud types,which revealed that low clouds occurred most frequently.

Effect of HITRAN Database Improvement on Retrievals of Atmospheric Carbon Dioxide from Reflected Sunlight Spectra in the 1.61-μm Spectral Window

A large number of experimental and theoretical investigations of carbon dioxide （CO 2 ） spectra have been conducted since the most recent update of the High-Resolution Transmission Molecular Absorption （HITRAN） database. To maintain optimal parameters, the HITRAN 2004 CO 2 line list has been completely replaced by HITRAN 2008 data in the near-infrared region from 4300 cm-1 to 7000 cm-1 . To examine the effect of this change on the retrieval of CO 2 vertical column data from reflected sunlight spectra in the 1.61-μm spectral window, synthetic measurements for a given atmospheric state and instrument setup were generated and compared using radiative transfer model with the line-transition parameters from the HITRAN 2004 and 2008 databases. Simulated retrievals were then performed based on the optimal estimation retrieval theory. The results show that large systematic errors in atmospheric CO 2 column retrievals were induced by the differences in the HITRAN laboratory line parameters in the 1.61-μm region. The retrieved CO 2 columns were underestimated by 10 ppm using the HITRAN 2004 data, and improvements resulting from the use of the improved HITRAN database were more pronounced at a higher spectral resolution.

EFFECTS OF A CLOUD FILTERING METHOD FOR FENGYUN-3C MICROWAVE HUMIDITY AND TEMPERATURE SOUNDER MEASUREMENTS OVER OCEAN ON RETRIEVALS OF TEMPERATURE AND HUMIDITY

For Microwave Humidity and Temperature sounder(MWHTS) measurements over the ocean, a cloud filtering method is presented to filter out cloud-and precipitation-affected observations by analyzing the sensitivity of the simulated brightness temperatures of MWHTS to cloud liquid water, and using the root mean square error(RMSE)between observation and simulation in clear sky as a reference standard. The atmospheric temperature and humidity profiles are retrieved using MWHTS measurements with and without filtering by multiple linear regression(MLR),artificial neural networks(ANN) and one-dimensional variational(1DVAR) retrieval methods, respectively, and the effects of the filtering method on the retrieval accuracies are analyzed. The numerical results show that the filtering method can improve the retrieval accuracies of the MLR and the 1DVAR retrieval methods, but have little influence on that of the ANN. In addition, the dependencies of the retrieval methods upon the testing samples of brightness temperature are studied, and the results show that the 1DVAR retrieval method has great stability due to that the testing samples have great impact on the retrieval accuracies of the MLR and the ANN, but have little impact on that of the 1DVAR.

The Preliminary Discussion of the Potential of GNSS-IR Technology for Terrain Retrievals

The expansion of research and applications of Global Navigation Satellite Systems(GNSS)has revealed the information of reflecting surface in inherent multipath errors.GNSS signals,usually used to measure position,have been demonstrated that they can be used to retrieve water properties including water level,soil moisture,snow depth,and vegetation water content,which are important for climate analysis and water resources monitoring.Reflected GNSS signals with different azimuths can carry information of the corresponding reflecting zone,which means every reflected signal has distinct"signal-tonoise ratio(SNR)characteristics"influenced by specific reflecting zones—and the parameter named"Reflector Height(RH)"deduced from SNR frequency is focused on in this study.Thus,after interpolation of a series of reflector height by coordinates of the footprint,products describing highly detailed terrain over a reflecting footprint can be produced.Data of three GNSS sites in Earth Scope Plate Boundary Observatory,named P025,P351 and P101,was used to evaluate the terrain after calculating the terrain slopes and correcting the footprint following the slopes.A comparison of the results with a digital elevation model(DEM)showed that it is possible to retrieve terrain by GNSS-Interferometric Reflectometry(GNSS-IR);and the comparison with terrain slopes from DEMs in previous research also validated its potential.

Retrievals of Rain-Rate over Oceans from SSM/IData Using SOM Model

A Self-Organizing Feature Mapping (SOM) network model was developed for the retrievals of rain-rate (RR) over oceans from SSM / I measurement, by using the SSM / I and corresponding radar-derived rain-rate data provided by National Space Development Agency of Japan (NASDA). Based on the frequency-distribution of rain-rate samples, the SOM model was constructed in different rain-rate ranges. The model was first trained by five-sixths of the data, and the other data were used to test the retrieval ability of the model. The retrieval results of the SOM model were compared with two statistically-based algorithms. It is shown that the SOM model provides better retrievals of rain-rate.

THEORETICAL ANALYSES AND NUMERICAL EXPERIMENTS FOR TWO-DIMENSIONAL WIND RETRIEVALS FROM SINGLE-DOPPLER DATA

Based on variational adjoint methods,theoretical analyses and numerical experiments are performed for retrievals of two-dimensional winds and related parameters from single-Doppler radar data in polar coordinates.In the method,the reflectivity conservation equation is used as the governing equation and the mass continuity equation as a weak constraint.At the same time,the stable functional of regularization and background field are also introduced in the cost functional.Based on the ideal experiments with artificial data,real two-dimensional wind fields are retrieved with low-altitude data from the Nanjing Doppler Radar Station,and the results are encouraging and promising.

Remote Sensing of Tropical Cyclone Thermal Structure from Satellite Microwave Sounding Instruments: Impacts of Background Profiles on Retrievals

A variational retrieval system often requires background atmospheric profiles and surface parameters in its minimization process. This study investigates the impacts of specific background profiles on retrievals of tropical cyclone(TC) thermal structure. In our Microwave Retrieval Testbed(MRT), the K-means clustering algorithm is utilized to generate a set of mean temperature and water vapor profiles according to stratiform and convective precipitation in hurricane conditions. The Advanced Technology Microwave Sounder(ATMS) observations are then used to select the profiles according to cloud type. It is shown that the cloud-based background profiles result in better hurricane thermal structures retrieved from ATMS observations. Compared to the Global Positioning System(GPS) dropsonde observations, the temperature and specific humidity errors in the TC inner region are less than 3 K and 2.5 g kg^(–1), respectively, which are significantly smaller than the retrievals without using the cloud-based profiles. Further experiments show that all the ATMS observations could retrieve well both temperature and humidity structures, especially within the inner core region. Thus, both temperature and humidity profiles derived from microwave sounding instruments in hurricane conditions can be reliably used for evaluation of the storm intensity with a high fidelity.

Remote Sensing of Tropical Cyclone Thermal Structure from Satellite Microwave Sounding Instruments:Impacts of Optimal Channel Selection on Retrievals

Accurate information on atmospheric temperature of tropical cyclones （TCs） is important for monitoring and pre- diction of their developments and evolution. For hurricanes, temperature anomaly in the upper troposphere can be de-rived from Advanced Microwave Sounding Unit （AMSU） and Advanced Technology Microwave Sounder （ATMS） through either regression-based or variational retrieval algorithms. This study investigates the dependency of TC warm core structure on emission and scattering processes in the forward operator used for radiance computations in temperature retrievals. In particular, the precipitation scattering at ATMS high-frequency channels can significantly change the retrieval outcomes. The simulation results in this study reveal that the brightness temperatures at 183 GHz could be depressed by 30-50 K under cloud ice water path of 1.5 mm, and thus, the temperature structure in hur-ricane atmosphere could be distorted if the ice cloud scattering was inaccurately characterized in the retrieval system. It is found that for Hurricanes Irma, Maria, and Harvey that occurred in 2017, their warm core anomalies retrieved from ATMS temperature sounding channels 4 15 were more reasonable and realistic, compared with the retrievals from all other channel combinations and earlier hurricane simulation results.

Retrievals of underlying surface roughness and moisture from polarimetric pulse echoes in the specular direction through inhomogeneous vegetation canopy

The time-dependent Mueller matrix solution of vector radiative transfer for inhomoge-neous random media of non-spherical scatterers is presented. Co-polarized and cross-polarized bistatic scatterings for a polarized pulse incidence are numerically simulated. Numerical results well demonstrate volumetric and surface scattering mechanism and depict the inhomogeneous fraction profile of random scatterers. The peak tails in polarized echoes due to wave reflections from the underlying surface can be identified. Its co-polarized peaks in the specular direction are applied to simultaneous retrievals of the underlying surface roughness and moisture in the pres-ence of inhomogeneous vegetation canopy.

Antibiotic usage in surgical sperm retrievals among IVF centers

Surgical sperm retrieval(SSR)is currently one of the most common procedures in in vitro fertilization(IVF).However,a gap between the guidelines and routine clinical practice regarding antibiotic use in sSR,which might lead to antibiotic resistance,is a challenging problem worldwide.A cross-sectional survey was conducted from May 1,2021,to July 15,2021,to investigate antibiotic usage by medical professionals when performing SSR in IVF centers in Vietnam.The confidential questionnaire comprised 12 items,including characteristics of the study population,awareness of antimicrobial resistance,attitude toward prescribing antibiotics,and current practice of prescribing antibiotics when performing SSR.Surveys were completed by 30 of 45 registered IVF centers(66.7%).Among 67 physicians working at those centers,the age and work-experience years(mean±standard deviation[s.d.])were 38.6±6.6 years and 11.2±7.0 years,respectively.Over 60%of them held a degree in Obstetrics and Gynecology,and over four-fifths were men.Most respondents"often/very often/always"raised awareness of antimicrobial resistance to their patients(83.3%),but only half of them"often/occasionally"prescribed antibiotics to patients with SSR in cases where the prescription would be optional.About one-tenth of respondents followed the recommendation from the American Urological Association using"prophylaxis only"for SSR patients.For more invasive SSR,physicians tended to prescribe more complicated and sometimes inappropriate regimens.In conclusion,antibiotic usage in SSR was not always appropriate among IVF centers.Further studies may define specific recommendations for regimens,intervention strategies,and programs to promote appropriate antibiotic use for SSR patients among IVF specialists.

Variation in the surface heat flux on the north and south slopes of Mount Qomolangma

The distinctive conditions present on the north and south slopes of Mount Qomolangma,along with the intricate variations in the underlying surfaces,result in notable variations in the surface energy flux patterns of the two slopes.In this paper,data from TESEBS(Topographical Enhanced Surface Energy Balance System),remote sensing data from eight cloud-free scenarios,and observational data from nine stations are utilized to examine the fluctuations in the surface heat flux on both slopes.The inclusion of MCD43A3 satellite data enhances the surface albedo,contributing to more accurate simulation outcomes.The model results are validated using observational data.The RMSEs of the net radiation,ground heat,sensible heat,and latent heat flux are 40.73,17.09,33.26,and 30.91 W m^(−2),respectively.The net radiation flux is greater on the south slope and exhibits a rapid decline from summer to autumn.Due to the influence of the monsoon,on the north slope,the maximum sensible heat flux occurs in the pre-monsoon period in summer and the maximum latent heat flux occurs during the monsoon.The south slope experiences the highest latent heat flux in summer.The dominant flux on the north slope is sensible heat,while it is latent heat on the south slope.The seasonal variations in the ground heat flux are more pronounced on the south slope than on the north slope.Except in summer,the ground heat flux on the north slope surpasses that on the south slope.

A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI:Development and Verification over Asia

The Advanced Geosynchronous Radiation Imager(AGRI)is a mission-critical instrument for the Fengyun series of satellites.AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands,enabling the detection of highly variable aerosol optical depth(AOD).Quantitative retrieval of AOD has hitherto been challenging,especially over land.In this study,an AOD retrieval algorithm is proposed that combines deep learning and transfer learning.The algorithm uses core concepts from both the Dark Target(DT)and Deep Blue(DB)algorithms to select features for the machinelearning(ML)algorithm,allowing for AOD retrieval at 550 nm over both dark and bright surfaces.The algorithm consists of two steps:①A baseline deep neural network(DNN)with skip connections is developed using 10 min Advanced Himawari Imager(AHI)AODs as the target variable,and②sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters.Out-of-station validation shows that the retrieved AOD attains high accuracy,characterized by a coefficient of determination(R2)of 0.70,a mean bias error(MBE)of 0.03,and a percentage of data within the expected error(EE)of 70.7%.A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm,as well as the surface reflectance at 650 nm,are the two largest sources of uncertainty impacting the retrieval.In a case study of monitoring an extreme aerosol event,the AGRI AOD is found to be able to capture the detailed temporal evolution of the event.This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.

Construction of apricot variety search engine based on deep learning

Apricot has a long history of cultivation and has many varieties and types. The traditional variety identification methods are timeconsuming and labor-consuming, posing grand challenges to apricot resource management. Tool development in this regard will help researchers quickly identify variety information. This study photographed apricot fruits outdoors and indoors and constructed a dataset that can precisely classify the fruits using a U-net model (F-score:99%), which helps to obtain the fruit's size, shape, and color features. Meanwhile, a variety search engine was constructed, which can search and identify variety from the database according to the above features. Besides, a mobile and web application (ApricotView) was developed, and the construction mode can be also applied to other varieties of fruit trees.Additionally, we have collected four difficult-to-identify seed datasets and used the VGG16 model for training, with an accuracy of 97%, which provided an important basis for ApricotView. To address the difficulties in data collection bottlenecking apricot phenomics research, we developed the first apricot database platform of its kind (ApricotDIAP, http://apricotdiap.com/) to accumulate, manage, and publicize scientific data of apricot.

Quantifying the chemical composition of weathering products of Hainan basalts with reflectance spectroscopy and its implications for Mars

With the development of the hyperspectral remote sensing technique,extensive chemical weathering profiles have been identified on Mars.These weathering sequences,formed through precipitation-driven leaching processes,can reflect the paleoenvironments and paleoclimates during pedogenic processes.The specific composition and stratigraphic profiles mirror the mineralogical and chemical trends observed in weathered basalts on Hainan Island in south China.In this study,we investigated the laboratory reflectance spectra of a 53-m-long drilling core of a thick basaltic weathering profile collected from Hainan Island.We established a quantitative spectral model by combining the genetic algorithm and partial least squares regression(GA-PLSR)to predict the chemical properties(SiO2,Al2O3,Fe2O3)and index of laterization(IOL).The entire sample set was divided into a calibration set of 25 samples and a validation set of 12 samples.Specifically,the GA was used to select the spectral subsets for each composition,which were then input into the PLSR model to derive the chemical concentration.The coefficient of determination(R2)values on the validation set for SiO2,Al2O3,Fe2O3,and the IOL were greater than 0.9.In addition,the effects of various spectral preprocessing techniques on the model accuracy were evaluated.We found that the spectral derivative treatment boosted the prediction accuracy of the GA-PLSR model.The improvement achieved with the second derivative was more pronounced than when using the first derivative.The quantitative model developed in this work has the potential to estimate the contents of similar weathering basalt products,and thus infer the degree of alteration and provide insights into paleoclimatic conditions.Moreover,the informative bands selected by the GA can serve as a guideline for designing spectral channels for the next generation of spectrometers.

A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.