Assimilating FY-4A AGRI Radiances with a Channel-Sensitive Cloud Detection Scheme for the Analysis and Forecasting of Multiple Typhoons

This paper presents an attempt at assimilating clear-sky FY-4A Advanced Geosynchronous Radiation Imager(AGRI)radiances from two water vapor channels for the prediction of three landfalling typhoon events over the West Pacific Ocean using the 3DVar data assimilation(DA)method along with the WRF model.A channel-sensitive cloud detection scheme based on the particle filter(PF)algorithm is developed and examined against a cloud detection scheme using the multivariate and minimum residual(MMR)algorithm and another traditional cloud mask–dependent cloud detection scheme.Results show that both channel-sensitive cloud detection schemes are effective,while the PF scheme is able to reserve more pixels than the MMR scheme for the same channel.In general,the added value of AGRI radiances is confirmed when comparing with the control experiment without AGRI radiances.Moreover,it is found that the analysis fields of the PF experiment are mostly improved in terms of better depicting the typhoon,including the temperature,moisture,and dynamical conditions.The typhoon track forecast skill is improved with AGRI radiance DA,which could be explained by better simulating the upper trough.The impact of assimilating AGRI radiances on typhoon intensity forecasts is small.On the other hand,improved rainfall forecasts from AGRI DA experiments are found along with reduced errors for both the thermodynamic and moisture fields,albeit the improvements are limited.

Formation of Assimilating Surface Areas and Photosynthetic Potential of Various Assimilating Parts of Wheat Species under Drought Conditions

Some of environmental factors (weather sharp fluctuations) may accrue stress within a few minutes, while others may not be long for this period of stress in this situation. It has formed stress in organisms, to eliminate the stress, there starts over various biochemical and physiological mechanisms for protection. Therefore, a large variety of plants to examine ways of increasing the resistance against stress factors have a scientific and practical importance. Thus, the primary processes of photosynthesis, based on the results of the study and its corresponding morphophysiological higher photochemical activity, has been found in a range of genotypes. Their leaves and plants assimilate the biological productivity of the intensity of the symptoms associated with the use of photosynthetic learned of the opportunity to create a new perspective varieties. This allows the research to prove the expansion of the electronic delivery of high-yielding genotypes and phosphorised chloroplast high speed, as well as the pH of thylacoid membranes are characterized by a great price, also photosynthetic electron transport, CO2 assimilation and it was approved to be the link between productivity.

Critique on the Bourgeoisie:Laura’s Assimilating and Interference of Aes⁃thetics and Politics in The Garden Party

As Mansfield’s representative masterpiece,The Garden Party contains her critique on the bourgeoisie.This essay argues that Laura Sheridan is assimilating into the bourgeoisie,taking up Mrs.Sheridan’s role as a matriarch,while she is struggling with the waking sympathies on working class.By depicting the process of assimilating,along with the interference of aesthetics and politics,Mansfield reveals her critique on the bourgeoisie which shows indifference towards the lower class.

Estimating the Soil Moisture Profile by Assimilating Near-Surface Observations with the Ensemble Kalman Filter (EnKF)

The paper investigates the ability to retrieve the true soil moisture profile by assimilating near-surface soil moisture into a soil moisture model with an ensemble Kalman filter （EnKF） assimilation scheme, including the effect of ensemble size, update interval and nonlinearities in the profile retrieval, the required time for full retrieval of the soil moisture profiles, and the possible influence of the depth of the soil moisture observation. These questions are addressed by a desktop study using synthetic data. The ＂true＂ soil moisture profiles are generated from the soil moisture model under the boundary condition of 0.5 cm d^-1 evaporation. To test the assimilation schemes, the model is initialized with a poor initial guess of the soil moisture profile, and different ensemble sizes are tested showing that an ensemble of 40 members is enough to represent the covariance of the model forecasts. Also compared are the results with those from the direct insertion assimilation scheme, showing that the EnKF is superior to the direct insertion assimilation scheme, for hourly observations, with retrieval of the soil moisture profile being achieved in 16 h as compared to 12 days or more. For daily observations, the true soil moisture profile is achieved in about 15 days with the EnKF, but it is impossible to approximate the true moisture within 18 days by using direct insertion. It is also found that observation depth does not have a significant effect on profile retrieval time for the EnKF. The nonlinearities have some negative influence on the optimal estimates of soil moisture profile but not very seriously.

Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China

The prediction of sea surface temperature （SST） is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea （BYECS）. One is based on a surface net heat flux correction, named as Qcorrection （QC）, which nudges the flux correction to the model equation; the other is ensemble optimal interpolation （EnOI）, which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis （OSTIA） system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error （RMSE） of the four experiments is 1.74, 1.16, 1.30 and 0.91~C, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively. Although both two methods are effective in assimilating the SST, the EnOI shows more advantages than the QC, and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions.

Assimilating Surface Observations in a Four-Dimensional Variational Doppler Radar Data Assimilation System to Improve the Analysis and Forecast of a Squall Line Case

This paper examines how assimilating surface observations can improve the analysis and forecast ability of a four- dimensional Variational Doppler Radar Analysis System （VDRAS）. Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational （4DVAR） data assimilation system. A squall-line case observed during a field campaign is selected to investigate the performance of the technique. A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions. The surface-based cold pool, divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation. Three experiments--assimilating radar data only, assimilating radar data with surface data blended in a mesoscale background, and assimilating both radar and surface observations with a 4DVAR cost function--are conducted to examine the impact of the surface data assimilation. Independent surface and wind profiler observations are used for verification. The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations. It is also shown that the additional surface data can help improve the analysis and forecast at low levels. Surface and low-level features of the squall line-- including the surface warm inflow, cold pool, gust front, and low-level wind--are much closer to the observations after assimilating the surface data in VDRAS.

Assimilating AMSU-A Radiance Data with the WRF Hybrid En3DVAR System for Track Predictions of Typhoon Megi(2010)

The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A（AMSU-A） on the track prediction of Typhoon Megi（2010） was studied using the Weather Research and Forecasting（WRF） model and a hybrid ensemble threedimensional variational（En3DVAR） data assimilation（DA） system.The influences of tuning the length scale and variance scale factors related to the static background error covariance（BEC） on the track forecast of the typhoon were studied.The results show that,in typhoon radiance data assimilation,a moderate length scale factor improves the prediction of the typhoon track.The assimilation of AMSU-A radiances using 3DVAR had a slight positive impact on track forecasts,even when the static BEC was carefully tuned to optimize its performance.When the hybrid DA was employed,the track forecast was significantly improved,especially for the sharp northward turn after crossing the Philippines,with the flow-dependent ensemble covariance.The flow-dependent BEC can be estimated by the hybrid DA and was capable of adjusting the position of the typhoon systematically.The impacts of the typhoon-specific BEC derived from ensemble forecasts were revealed by comparing the analysis increments and forecasts generated by the hybrid DA and 3DVAR.Additionally,for 24 h forecasts,the hybrid DA experiment with use of the full flow-dependent background error substantially outperformed 3DVAR in terms of the horizontal winds and temperature in the lower and mid-troposphere and for moisture at all levels.

Improved simulation of carbon and water fluxes by assimilating multi-layer soil temperature and moisture into process-based biogeochemical model

Background: Soil temperature and moisture are sensitive indicators in soil organic matter decomposition because they control global carbon and water cycles and their potential feedback to climatic variations. Although the Biome-Biogeochemical Cycles (Biome-BGC) model is broadly applied in simulating forest carbon and water fluxes, its single-layer soil module cannot represent vertical variations in soil moisture. This study introduces the Biome-BGC MuSo model, which is composed of a multi-layer soil module and new modules pertaining to phenology and management for simulations of carbon and water fluxes. Although this model considers soil processes among active layers, estimates of soil-related variables might be biased, leading to inaccurate estimates of carbon and water fluxes. Methods: To improve the estimations of soil-related processes in Biome-BGC MuSo, this study assimilates ground-measured multi-layer daily soil temperature and moisture at the Changbai Mountains forest flux site by using the Ensemble Kalman Filter algorithm. The modeled estimates of water and carbon fluxes were evaluated with measurements using determination coefficient (R2) and root mean square error (RMSE). The differences in the RMSEs from Biome-BGC MuSo and the assimilated Biome-BGC MuSo were calculated (ΔRMSE), and the relationships between ΔRMSE and the climatic and biophysical factors were analyzed. Results: Compared with the original Biome-BGC model, Biome-BGC MuSo improved the simulations of ecosystem respiration (ER), net ecosystem exchange (NEE) and evapotranspiration (ET). Data assimilation of the soil-related variables into Biome-BGC MuSo in real time improved the accuracies of the simulated carbon and water fluxes (ET: R^2=0.81, RMSE=0.70 mm·d^-1;ER: R^2=0.85, RMSE=1.97 gC·m^-2·d^-1;NEE: R^2=0.70, RMSE=1.16 gC·m^-2·d^-1). Conclusions: This study proved that seasonal simulation of carbon and water fluxes are more accurate when using Biome-BGC MuSo with a multi-layer soil module than using Biome-BGC with a single-layer soil module. Moreover, assimilating the observed soil temperature and moisture data into Biome-BGC MuSo improved the modeled estimates of water and carbon fluxes via calibrated soil-related simulations. The assimilation strategy is applicable to various climatic and biophysical conditions, particularly densely forested areas, and for local or regional simulation.

Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China

Although radar observations capture storm structures with high spatiotemporal resolutions, they are limited within the storm region after the precipitation formed. Geostationary satellites data cover the gaps in the radar network prior to the formation of the precipitation for the storms and their environment. The study explores the effects of assimilating the water vapor channel radiances from Himawari-8 data with Weather Research and Forecasting model data assimilation system(WRFDA) for a severe storm case over north China. A fast cloud detection scheme for Advanced Himawari imager(AHI)radiance is enhanced in the framework of the WRFDA system initially in this study. The bias corrections, the cloud detection for the clear-sky AHI radiance, and the observation error modeling for cloudy radiance are conducted before the data assimilation. All AHI radiance observations are fully applied without any quality control for all-sky AHI radiance data assimilation. Results show that the simulated all-sky AHI radiance fits the observations better by using the cloud dependent observation error model, further improving the cloud heights. The all-sky AHI radiance assimilation adjusts all types of hydrometeor variables, especially cloud water and precipitation snow. It is proven that assimilating all-sky AHI data improves hydrometeor specifications when verified against the radar reflectivity. Consequently, the assimilation of AHI observations under the all-sky condition has an overall improved impact on both the precipitation locations and intensity compared to the experiment with only conventional and AHI clear-sky radiance data.

Assimilating Amounts of Precipitation Using a New Four-Dimensional Variational Method

Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard fourdimensional variational data assimilation at a much lower computational cost.

Influence of assimilating ground-based microwave radiometer data into the WRF model on precipitation

Ground-based microwave radiometers profilers(MWRPs)have been used in numerical weather prediction(NWP)systems and show different impacts on forecasts.Currently,there are around hundreds of ground-based MWPRs used in weather stations over China;however,the application of MWPRs in NWP systems is rather limited.In this work,two MWRP retrieved profiles were assimilated into the Weather Research and Forecasting(WRF)model for a rainstorm event that occurred in Beijing,China.The quality of temperature and humidity profiles retrieved from the MWRP was evaluated against radiosonde observations and showed the reliability of the two MWRP products.Then,comparisons between the measurements of ground-based rain gauges and the corresponding forecasted precipitation in different periods of the rainstorm were investigated.The results showed that assimilating the two MWRPs affected the distribution and intensity of rainfall,especially in the early stage of the rainstorm.With the development of the rainstorm,adding MWRP data showed only a slight influence on the precipitation during the stable and mature period of the rainstorm,since the two MWRP observations were too limited to affect the large area of heavy rainfall.

APPLICATION EXPERIMENT OF ASSIMILATING RADAR-RETRIEVED WATER VAPOR IN SHORT-RANGE FORECAST OF RAINFALL IN THE ANNUALLY FIRST RAINY SEASON OVER SOUTH CHINA

A scheme of assimilating radar-retrieved water vapor is adopted to improve the quality of NWP initial field for improvement of the accuracy of short-range precipitation prediction. To reveal the impact of the assimilation of radar-retrieved water vapor on short-term precipitation forecast, three parallel experiments, cold start, hot start and hot start plus the assimilation of radar-retrieved water vapor, are designed to simulate the 31 days of May, 2013 with a fine numerical model for South China. Furthermore, a case of heavy rain that occurred from 8-9 May 2013 over the region from the southwest of Guangdong province to Pearl River Delta is analyzed in detail. Results show that the cold start experiment is not conducive to precipitation 12 hours ahead; the hot start experiment is able to reproduce well the first6 hours of precipitation, but badly for subsequent prediction; the experiment of assimilating radar-retrieved water vapor is not only able to simulate well the precipitation 6 hours ahead, but also able to correctly predict the evolution of rain bands from 6 to 12 hours in advance.

Improvement of short-term forecasting in the northwest Pacific through assimilating Argo data into initial fields

The impact of assimilating Argo data into an initial field on the short-term forecasting accuracy of temper- ature and salinity is quantitatively estimated by using a forecasting system of the western North Pacific, on the base of the Princeton ocean model with a generalized coordinate system （POMgcs）. This system uses a sequential multigrid three-dimensional variational （3DVAR） analysis scheme to assimilate observation da- ta. Two numerical experiments were conducted with and without Argo temperature and salinity profile data besides conventional temperature and salinity profile data and sea surface height anomaly （SSHa） and sea surface temperature （SST） in the process of assimilating data into the initial fields. The forecast errors are estimated by using independent temperature and salinity profiles during the forecasting period, including the vertical distributions of the horizontally averaged root mean square errors （H-RMSEs） and the horizontal distributions of the vertically averaged mean errors （MEs） and the temporal variation of spatially averaged root mean square errors （S-RMSEs）. Comparison between the two experiments shows that the assimila- tion of Argo data significantly improves the forecast accuracy, with 24% reduction of H-RMSE maximum for the temperature, and the salinity forecasts are improved more obviously, averagely dropping of 50% for H-RMSEs in depth shallower than 300 m. Such improvement is caused by relatively uniform sampling of both temperature and salinity from the Argo drifters in time and space.

The Impact of Assimilating Radar-estimated Rain Rates on Simulation of Precipitation in the 17-18 July 1996 Chicago Floods

Rainfall prediction remains one of the most challenging problems in weather forecasting. In order to improve high-resolution quantitative precipitation forecasts （QPF）, a new procedure for assimilating rainfall rate derived from radar composite reflectivity has been proposed and tested in a numerical simulation of the Chicago floods of 17–18 July 1996. The methodology is based on the one-dimensional variation scheme （1DVAR） assimilation approach introduced by Fillion and Errico but applied here using the Kain-Fritsch convective parameterization scheme （KF CPS）. The novel feature of this work is the continuous assimilation of radar estimated rain rate over a three hour period, rather than a single assimilation at the initial （analysis） time. Most of the characteristics of this precipitation event, including the propagation, regeneration of mesoscale convective systems, the frontal boundary across the Midwest and the evolution of the low-level jet are better captured in the simulation as the radar-estimated precipitation rate is assimilated. The results indicate that precipitation assimilation during the early stage can improve the simulated mesoscale feature of the convection system and shorten the spin-up time significantly. Comparison of precipitation forecasts between the experiments with and without the 1DVAR indicates that the 1DVAR scheme has a positive impact on the QPF up to 36 hours in terms of the bias and bias equalized threat scores.

Reconstruction of eddies by assimilating satellite altimeter data into Princeton Ocean Model

An optimal interpolation assimilation model for satellite altimetry data is developed based on Princeton Ocean Model （POM）, which is applied in a quasi-global domain, by the method of isotropic correlation between sea level anomaly （SLA） and sea temperature anomaly. The performance of this assimilation model is validated by the modeled results of SLA and the current patterns. Comparisons between modeling and satellite data show that both the magnitudes and distribution patterns of the sinmlated SLA are improved by assimilation. The most significant improvement is that meso-scale systems, e.g., eddies, are well reconstructed. The evolution of an eddy located in the northwest Pacific Ocean is traced by using the assimilation model. Model results show that during three months the eddy migrated southwestward for about 6 degrees before merging into the Kuroshio. The three dimensional structure of this eddy on 12 August 2001 is further analyzed. The strength of this warm, cyclonic eddy decreases with the increase of depth. The eddy shows different horizontal patterns at different layers, and the SLA and temperature fields agree with each other well. This study suggests that this kind of data assimilation is economic and reliable for eddy reconstruction, and can be used as a promising technique in further studies of ocean eddies as well as other fine circulation structures.

A Basis for Improving Numerical Forecasting in the Gulf Area by Assimilating Doppler Radar Radial Winds

An approach to assimilate Doppler radar radial winds into a high resolution Numerical Weather Prediction (NWP) model using 3D-Var system is described. We discuss the types of errors that occur in radar radial winds. Some related problems such as nonlinearity and sensitivity of the forecast to possible small errors in initial conditions, random observation errors, and the background states are also considered. The technique can be used to improve the model forecasts, in the Gulf area, at the local scale and under high aerosol (dust/sand/pollution) conditions.

Impact of assimilating FY-3C MWHS2 data in the RMAPS-ST forecast system on its rainfall forecasts

In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder(MWHS2)data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term(RMAPS-ST)operational system,which is developed by the Institute of Urban Meteorology of the China Meteorological Administration,four experiments were carried out in this study:(i)Coldstart(no observations assimilated);(ii)CON(assimilation of conventional observations);(iii)FY3(assimilation of FY-3C MWHS2 only);and(iv)FY3+CON(simultaneous assimilation of FY-3C MWHS2 and conventional observations).A precipitation process that took place in central-eastern China during 4–6 June 2019 was selected as a case study.When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system,data quality control and bias correction were performed so that the O-B(observation minus background)values of the five humidity channels of MWHS2 became closer to a normal distribution,and the data basically satisfied the unbiased assumption.The results showed that,in this case,the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments.Meanwhile,the prediction of atmospheric parameters for the mesoscale field was also improved,and the RMSE of the specific humidity forecast at the 850–400 hPa height was reduced.This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.

A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge

In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.

The Impact of Assimilating FY-3C GNOS GPS Radio Occultation Observations on GRAPES Forecasts

In the present study, a gross quality control (QC) procedure is proposed for the Global Navigation Satellite System Occultation Sounder (GNOS) Global Positioning System radio occultation (GPS RO) refractivity data to remove abnormal data before they are assimilated. It consists of a climate extreme check removing data outside the range of the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) climate maxima and minima over approximately five years, and a vertical gradient check that rejects profiles containing super-refractions. These two QC steps were applied sequentially to identify outliers in GNOS GPS RO refractivity data during boreal winter 2013/2014.All of the abnormal refractivity profiles and the outliers at each level of the GNOS GPS RO observations were effectively removed by the proposed QC procedure. The post-QC GNOS GPS RO refractivity observations were then assimilated in the Global/Regional Analysis and PrEdiction System (GRAPES) using the three-dimensional variational(3D-Var) system. The impacts of the GNOS refractivity observation on GRAPES analysis and forecasting were evaluated and analyzed using an observation system experiment run over one whole winter season of 2013/2014. The experiment results demonstrated a positive impact of GNOS GPS RO data on analysis and forecast quality. The root mean squared error of GRAPES analysis temperature was reduced by 1%in the Southern Hemisphere (SH) extratropics and in the tropics, and the anomaly correlation scores of the forecasted 500-hPa geopotential height over the SH increased significantly during days 1 to 5. Overall, the benefits of using GNOS GPS RO data are significant in the SH and tropics.

Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter

The Localized Weighted Ensemble Kalman Filter(LWEnKF)is a new nonlinear/non-Gaussian data assimilation(DA)method that can effectively alleviate the filter degradation problem faced by particle filtering,and it has great prospects for applications in geophysical models.In terms of operational applications,along-track sea surface height(AT-SSH),swath sea surface temperature(S-SST)and in-situ temperature and salinity(T/S)profiles are assimilated using the LWEnKF in the northern South China Sea(SCS).To adapt to the vertical S-coordinates of the Regional Ocean Modelling System(ROMS),a vertical localization radius function is designed for T/S profiles assimilation using the LWEnKF.The results show that the LWEnKF outperforms the local particle filter(LPF)due to the introduction of the Ensemble Kalman Filter(EnKF)as a proposal density;the RMSEs of SSH and SST from the LWEnKF are comparable to the EnKF,but the RMSEs of T/S profiles reduce significantly by approximately 55%for the T profile and 35%for the S profile(relative to the EnKF).As a result,the LWEnKF makes more reasonable predictions of the internal ocean temperature field.In addition,the three-dimensional structures of nonlinear mesoscale eddies are better characterized when using the LWEnKF.