An improved four-dimensional variation source term inversion model with observation error regularization

Aiming at the Four-Dimensional Variation source term inversion algorithm proposed earlier,the observation error regularization factor is introduced to improve the prediction accuracy of the diffusion model,and an improved Four-Dimensional Variation source term inversion algorithm with observation error regularization(OER-4DVAR STI model)is formed.Firstly,by constructing the inversion process and basic model of OER-4DVAR STI model,its basic principle and logical structure are studied.Secondly,the observation error regularization factor estimation method based on Bayesian optimization is proposed,and the error factor is separated and optimized by two parameters:error statistical time and deviation degree.Finally,the scientific,feasible and advanced nature of the OER-4DVAR STI model are verified by numerical simulation and tracer test data.The experimental results show that OER-4DVAR STI model can better reverse calculate the hazard source term information under the conditions of high atmospheric stability and flat underlying surface.Compared with the previous inversion algorithm,the source intensity estimation accuracy of OER-4DVAR STI model is improved by about 46.97%,and the source location estimation accuracy is improved by about 26.72%.

Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and T-S constraint

Temperature （T） and salinity （S） profiles from conductivity-temperature-depth data collected during the Northern South China Sea Open Cruise from August 16 to September 13, 2008 are assimilated using Ensemble Kalman Filter （EnKF）. An adaptive observational error strategy is used to prevent filter from diverging. In the meantime, aiming at the limited improvement in some sites caused by the T and S biases in the model, a T-S constraint scheme is adopted to improve the assimilation performance, where T and S are separately updated at these locations. Validation is performed by comparing assimilated outputs with independent in situ data （satellite remote sensing sea level anomaly （SLA）, the OSCAR velocity product and shipboard ADCP）. The results show that the new EnKF assimilation scheme can significantly reduce the root mean square error （RMSE） of oceanic T and S compared with the control run and traditional EnKF. The system can also improve the simulation of circulations and SLA.

Assimilation of ASCAT Sea Surface Wind Retrievals with Correlated Observation Errors

Data assimilation systems usually assume that the observation errors of wind components, i.e., u(the longitudinal component) and v(the latitudinal component), are uncorrelated. However, since wind components are derived from observations in the form of wind speed and direction(spd and dir), the observation errors of u and v are correlated. In this paper, an explicit expression of the observation errors and correlation for each pair of wind components are derived based on the law of error propagation. The new data assimilation scheme considering the correlated error of wind components is implemented in the Weather Research and Forecasting Data Assimilation(WRFDA) system. Besides, adaptive quality control(QC) is introduced to retain the information of high wind-speed observations. Results from real data experiments assimilating the Advanced Scatterometer(ASCAT) sea surface winds suggest that analyses from the new data assimilation scheme are more reasonable compared to those from the conventional one, and could improve the forecasting of Typhoon Noru.

Wind Speed and Altitude Dependent AMDAR Observational Error and Its Impacts on Data Assimilation and Forecasting

Aircraft Meteorological Data Relay(AMDAR)observations have been widely used in numerical weather prediction(NWP)because of its high spatiotemporal resolution.The observational error of AMDAR is influenced by aircraft flight altitude and atmospheric condition.In this study,the wind speed and altitude dependent observational error of AMDAR is estimated.The statistical results show that the temperature and the observational error in wind speeds slightly decrease as altitude increases,and the observational error in wind speed increases as wind speed increases.Pseudo single AMDAR observation assimilation tests demonstrate that the wind speed and altitude dependent observational error can provide more reasonable analysis increment.Furthermore,to assess the performance of wind speed and altitude dependent observational error on data assimilation and forecasting,two-month 3-hourly cycling data assimilation and forecast experiments based on the Weather Research and Forecasting Model(WRF)and its Data Assimilation system(WRFDA)are performed for the period during 1 September-31 October,2017.The results of the two-month 3-hourly cycling experiments indicate that new observational error improves analysis and forecast of wind field and geo-potential height,and has slight improvements on temperature.The Fractions Skill Score(FSS)of the 6-h accumulated precipitation shows that new wind speed and altitude dependent observational error leads to better precipitation forecast skill than the default observational error in the WRFDA does.

ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas： A Target Observation Perspective

Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Ni n？o prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni n？o prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year,increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.

A Modified Regression Estimator for Single Phase Sampling in the Presence of Observational Errors

In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariates were used and a case where the observational errors were in both the survey variable and the covariates was considered. The inclusion of observational errors was due to the fact that data collected through surveys are often not free from errors that occur during observation. These errors can occur due to over-reporting, under-reporting, memory failure by the respondents or use of imprecise tools of data collection. The expression of mean squared error (MSE) based on the obtained estimator has been derived to the first degree of approximation. The results of a simulation study show that the derived modified regression mean estimator under observational errors is more efficient than the mean per unit estimator and some other existing estimators. The proposed estimator can therefore be used in estimating a finite population mean, while considering observational errors that may occur during a study.

Using self-location to calibrate the errors of observer positions for source localization

The uncertainty of observers＇ positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error （LMMSE） estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers＇ positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound （CRLB）.

Quality analysis of crustal tilt and strain observations in China's earthquakes in 2014

This work analyzes the quality of crustal tilt and strain observations during 2014, which were acquired from 269 sets of ground tiltmeters and 212 sets of strainmeters. In terms of data quality, the water tube tiltmeters presented the highest rate of excellent quality,approximately 91%, and the pendulum tiltmeters and ground strainmeters yielded rates of81% and 78%, respectively. This means that a total of 380 sets of instruments produced high-quality observational data suitable for scientific investigations and analyses.

Feasibility of central loop TEM method for prospecting multilayer water-fi lled goaf

With deep mining of coal mines, prospecting multilayer water-filled goaf has become a new content that results from geophysical exploration in coalfields. The central loop transient electromagnetic （TEM） method is favorable for prospecting conductive layers because of the coupling relationship between its field structure and formation. However, the shielding effect of conductive overburden would not only require a longer observation time when prospecting the same depth but also weaken the anomalous response of underlying layers. Through direct time domain numerical simulation and horizontal layered earth forward modeling, this paper estimates the length of observation time required to prospect the target, and the distinguishable criterion of multilayer water-filled goal is presented with observation error according to the effect of noise on observation data. The observed emf curves from Dazigou Coal Mine, Shanxi Province can distinguish multilayer water-filled goaf. In quantitative inversion interpretation of observed curves, using electric logging data as initial parameters restrains the equivalence caused by coal formation thin layers. The deduced three-layer and two-layer water-filled goals are confirmed by the drilling hole. The result suggests that when observation time is long enough and with the anomalous situation of underlying layers being greater than the observation error, the use of the central loop TEM method to orosoect a multilaver water-filled goaf is feasible.

Characteristics of Fengyun-4A Satellite Atmospheric Motion Vectors and Their Impacts on Data Assimilation

The high observation efficiency,scanning speed and observation frequency of the Fengyun-4A(FY-4A)satellite indicates the progress of Chinese geostationary meteorological satellites.The characteristics of FY-4A atmospheric motion vectors(AMVs)derived from the high-level water vapor(WV-High)channel,mid-level water vapor(WV-Mid)channel,and infrared(IR)channel of FY-4A are analyzed,and their corresponding observation errors estimated.Then,the impacts of single-channel and multi-channel FY-4A AMVs on RMAPS-ST(the Rapid-refresh Multi-scale Analysis and Prediction System-Short Term)are evaluated based on one-month data assimilation cycling and forecasting experiments.Results show that the observation errors of FY-4A AMVs from the three channels have an explicit vertical structure.Results from the cycling experiments indicate that the assimilation of AMVs from WV-High produces more apparent improvement of the wind in the upper layer,while a more positive effect in the lower layer is achieved by the assimilation of AMVs from IR.Furthermore,the assimilation of AMVs from IR is more skillful for medium and moderate precipitation than from other channels owing to the good quality of data in the lower layer in the AMVs from IR.Assimilation of FY-4A AMVs from the three channels could combine the advantages of assimilation from each individual channel to improve the wind in the upper,middle and lower layers simultaneously.

Testing the transfer functions for the Geonor T-200B and Chinese standard precipitation gauge in the central Qinghai-Tibet Plateau

Accurately measuring precipitation is integral for understanding water cycle processes and assessing climate change in the Qinghai–Tibet Plateau(QTP).The Geonor T-200B weighing precipitation gauge with a single Alter shield(Geonor)and the Chinese standard precipitation gauge(CSPG)are widely used for measuring precipitation in the QTP.However,their measurements need to be adjusted for wetting loss,evaporation loss and windinduced undercatch.Four existing transfer functions for adjusting the Geonor-recorded and three transfer functions for adjusting the CSPG-recorded precipitation at hourly,daily or event scale has been proposed based on the precipitation intercomparison experiments conducted at a single site in different regions.Two latest transfer functions for the Geonor(which are referred to as K2017a and K2017b)at the half-hour time scale based on the precipitation intercomparison experiments at multiple stations in the northern hemisphere were provided in the World Meteorological Organization Solid Precipitation Intercomparison Experiment.However,the applicability of these transfer functions in the QTP has not been evaluated.Therefore,the current study carried out a precipitation measurement intercomparison experiment between August 2018 and September 2020 at a site in Beiluhe in central QTP.The performance of these transfer functions at this site was also evaluated on the basis of mean bias(MB),root mean squared error(RMSE)and relative total catch(RTC).The results are as follows:First,the unadjusted RTC values of the Geonor for rain,mixed(snow mixed with rain),snow and hail are 92.06%,85.35%,64.11% and 91.82%,respectively,and the unadjusted RTC values of the CSPG for the same precipitation types are 92.59%,81.32%,46.43% and 95.56%,respectively.Second,K2017a has the most accurate adjustment results for the Geonor-recorded snow and mixed precipitation at the half-hour time scale,and the post-adjustment RTC values increased to 98.25% and 98.23%,respectively.M2007e,an event-based transfer function,was found to have the most accurate adjustment results for the Geonorrecorded snow precipitation at the event scale,and the post-adjustment RTC value increased to 96.36%.Third,the existing transfer functions for CSPG underestimate snowfall,while overestimating rainfall.Fourth,hail is a significant precipitation type in central QTP.The catch efficiency of hail precipitation and the temperature when hail precipitation occurs are close to those of rain;moreover,the transfer functions for rain are suitable for hail as well.

Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN

Grain-size distribution data,as a substitute for measuring hydraulic conductivity(K),has often been used to get K value indirectly.With grain-size distribution data of 150 sets of samples being input data,this study combined the Artificial Neural Network technology(ANN)and Markov Chain Monte Carlo method(MCMC),which replaced the Monte Carlo method(MC)of Generalized Likelihood Uncertainty Estimation(GLUE),to establish the GLUE-ANN model for hydraulic conductivity prediction and uncertainty analysis.By means of applying the GLUE-ANN model to a typical piedmont region and central region of North China Plain,and being compared with actually measured values of hydraulic conductivity,the relative error ranges are between 1.55%and 23.53%and between 14.08%and 27.22%respectively,the accuracy of which can meet the requirements of groundwater resources assessment.The global best parameter gained through posterior distribution test indicates that the GLUEANN model,which has satisfying sampling efficiency and optimization capability,is able to reasonably reflect the uncertainty of hydrogeological parameters.Furthermore,the influence of stochastic observation error(SOE)in grain-size analysis upon prediction of hydraulic conductivity was discussed,and it is believed that the influence can not be neglected.

Appendicitis-mimicking presentation in fishbone induced microperforation of the distal duodenum:A case report

BACKGROUND Upper gastrointestinal fishbone microperforations are rare and not commonly reported in medical literature.Despite the increasing use of computer tomography(CT)imaging and the employment of the Alvardo criteria,misdiagnosis of acute appendicitis can still occur.We report the rare case of an elderly Chinese gentleman who had a fish-bone induced microperforation of the duodenum that closely mimicked the symptoms of acute appendicitis.CASE SUMMARY This 79-year-old man presented with migratory lower abdominal pain that localized at his periumbilical region and right lower quadrant.He had associated pyrexia,general malaise and was noted to have an elevated white cell count.CT investigations initially revealed a distended appendix which was resected laparoscopically but showed no obvious signs of gross inflammation.The patient then deteriorated clinically and had increased oxygen requirements immediately after the surgery.This prompted further investigations.A further review of his CT scan revealed a fine fishbone microperforation in the distal duodenum associated with retroperitoneal abscess formation and seepage extending into the right lower quadrant.He was then started on broad spectrum intravenous antibiotics and subsequently underwent a laparotomy 12 h later to manage the obscure aetiology and to drain the abscess.The post-operative course was uneventful and he was discharged 11 d later including a 2-d stay in the intensive care unit.CONCLUSION This case offers an insight into a potential mimic of acute appendicitis and the diagnostic difficulties experienced in such presentations.

Interobserver error in grassland vegetation surveys:sources and implications

Aims Observer error is an unavoidable aspect of vegetation surveys involving human observers.We quantified four components of interobserver error associated with long-term monitoring of prairie vegetation:overlooking error,misidentification error,cautious error and estimation error.We also evaluated the association of plot size with pseudoturnover due to observer error,and how documented pseudochanges in species composition and abundance compared with recorded changes in the vegetation over a 4-year interval.Methods This study was conducted at Tallgrass Prairie National Preserve,Kansas.Monitoring sites contained 10 plots;each plot consisted of a series of four nested frames(0.01,0.1,1 and 10 m^(2)).The herbaceous species present were recorded in each of the nested frames,and foliar cover was visually estimated within seven cover categories at the 10 m^(2)spatial scale only.Three hundred total plots(30 sites)were surveyed,and 28 plots selected at random were resurveyed to assess observer error.Four surveyors worked in teams of two.Important Findings At the 10 m^(2)spatial scale,pseudoturnover resulting from overlooking error averaged 18.6%,compared with 1.4%resulting from misidentification error and 0.6%resulting from cautious error.Pseudoturnover resulting from overlooking error increased as plot size decreased,although relocation error likely played a role.Recorded change in species composition over a 4-year interval(excluding potential misidentification error and cautious error)was 30.7%,which encompassed both pseudoturnover due to overlooking error and actual change.Given a documented overlooking error rate of 18.6%,this suggests the actual change for the 4-year period was only 12.1%.For estimation error,26.2%of the time a different cover class was recorded.Over the 4-year interval,46.9%of all records revealed different cover classes,suggesting that 56%of the records of change in cover between the two time periods were due to observer error.

Random Weighting Estimation Method for Dynamic Navigation Positioning

This paper presents a new random weighting estimation method for dynamic navigation positioning. This method adopts the concept of random weighting estimation to estimate the covariance matrices of system state noises and observation noises for controlling the disturbances of singular observations and the kinematic model errors. It satisfies the practical requirements of the residual vector and innovation vector to sufficiently utilize observation information, thus weakening the disturbing effect of the kinematic model error and observation model error on the state parameter estimation. Theories and algorithms of random weighting estimation are established for estimating the covariance matrices of observation residual vectors and innovation vec- tors. This random weighting estimation method provides an effective solution for improving the positioning accuracy in dynamic navigation. Experimental results show that compared with the Kalman filtering, the extended Kalman filtering and the adaptive windowing filtering, the proposed method can adaptively determine the covariance matrices of observation error and state error, effectively resist the disturbances caused by system error and observation error, and significantly improve the positioning accu- racy for dynamic navigation.