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.

Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from2Dimages.This approach reconstructs color and density fields from 2D images using Neural Radiance Field(NeRF)and improves image quality using frequency regularization.The NeRF model is obtained via joint training ofmultiple artificial neural networks,whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel.In addition,customized physics-informed neural network(PINN)with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations and convection-diffusion equations to reconstruct the velocity field.The velocity uncertainties are also evaluated through ensemble learning.The effectiveness of the proposed algorithm is demonstrated through numerical examples.The presentmethod is an important step towards downstream tasks such as reliability analysis and robust optimization in engineering design.

Assimilation of the FY-4A AGRI Clear-Sky Radiance Data in a Regional Numerical Model and Its Impact on the Forecast of the“21·7”Henan Extremely Persistent Heavy Rainfall

Assimilation of the Advanced Geostationary Radiance Imager(AGRI)clear-sky radiance in a regional model is performed.The forecasting effectiveness of the assimilation of two water vapor(WV)channels with conventional observations for the“21·7”Henan extremely heavy rainfall is analyzed and compared with a baseline test that assimilates only conventional observations in this study.The results show that the 24-h cumulative precipitation forecast by the assimilation experiment with the addition of the AGRI exceeds 500 mm,compared to a maximum value of 532.6 mm measured by the national meteorological stations,and that the location of the maximum precipitation is consistent with the observations.The results for the short periods of intense precipitation processes are that the simulation of the location and intensity of the 3-h cumulative precipitation is also relatively accurate.The analysis increment shows that the main difference between the two sets of assimilation experiments is over the ocean due to the additional ocean observations provided by FY-4A,which compensates for the lack of ocean observations.The assimilation of satellite data adjusts the vertical and horizontal wind fields over the ocean by adjusting the atmospheric temperature and humidity,which ultimately results in a narrower and stronger WV transport path to the center of heavy precipitation in Zhengzhou in the lower troposphere.Conversely,the WV convergence and upward motion in the control experiment are more dispersed;therefore,the precipitation centers are also correspondingly more dispersed.

Assimilation of FY-3D MWTS-Ⅱ Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

Precipitation detection is an essential step in radiance assimilation because the uncertainties in precipitation would affect the radiative transfer calculation and observation errors.The traditional precipitation detection method for microwave only detects clouds and precipitation horizontally,without considering the three-dimensional distribution of clouds.Extending precipitation detection from 2D to 3D is expected to bring more useful information to the data assimilation without using the all-sky approach.In this study,the 3D precipitation detection method is adopted to assimilate Microwave Temperature Sounder-2(MWTS-Ⅱ)onboard the Fengyun-3D,which can dynamically detect the channels above precipitating clouds by considering the near-real-time cloud parameters.Cycling data assimilation and forecasting experiments for Typhoons Lekima(2019)and Mitag(2019)are carried out.Compared with the control experiment,the quantity of assimilated data with the 3D precipitation detection increases by approximately 23%.The quality of the additional MWTS-Ⅱradiance data is close to the clear-sky data.The case studies show that the average root-mean-square errors(RMSE)of prognostic variables are reduced by 1.7%in the upper troposphere,leading to an average reduction of4.53%in typhoon track forecasts.The detailed diagnoses of Typhoon Lekima(2019)further show that the additional MWTS-Ⅱradiances brought by the 3D precipitation detection facilitate portraying a more reasonable circulation situation,thus providing more precise structures.This paper preliminarily proves that 3D precipitation detection has potential added value for increasing satellite data utilization and improving typhoon forecasts.

浅探运用Radiance模拟天然采光

美国伯克利实验室开发的专业计算机光学模拟软件Radiance是目前国际公认的能够较为精确地模拟室内天然采光的光学模拟软件。但Radiance掌握起来并不容易,除需要建立适合天然采光模拟的数字模型外,还需要设置大量的相关参数,往往很小的参数设定会导致完全不同的模拟结果,从而达不到精确模拟的效果。本文尝试运用实际建筑的室内采光为参照物,通过实测数据与模拟结果相比较,来学习如何快速、准确的运用Radiance模拟室内天然采光。

浅谈Radiance在建筑光环境设计中的应用

对建筑光环境分析软件Radiance进行介绍,从Radiance软件的原理、算法和应用进行论述,介绍了Radiance软件在建筑光环境设计和采光评估的应用,推动Radiance软件在国内建筑光环境设计领域中的应用。

基于Radiance软件模拟的高校教室平面进深优化设计研究——以内蒙古大学南校区主楼为例

文章通过资料搜集与现场测试,建立内蒙古大学南校区主楼标准层南向侧窗采光教室的模型,采用基于Windows系统的采光软件Desktop Radiance作为室内天然光计算的分析工具,对教室模型进行采光模拟。基于采光模拟结果,设定7.2m、7.5m、7.8m、8.1m共四种进深尺寸对室内光环境进行优化设计模拟。结果显示,室内光环境质量随着教室进深尺寸的减小而提高,当教室进深为7.5m时的室内采光质量最好,这一研究结果为今后的教学楼教室单元的平面设计提供设计依据。

CONVECTIVE-STRATIFORM RAINFALL PARTITION BY RADIANCE-DERIVED CLOUD CONTENT:A MODELING STUDY

A new scheme that separates convective-stratiform rainfall is developed using threshold values of liquid water path(LWP) and ice water path(IWP).These cloud contents can be predicted with radiances at the Advanced Microwave Sounding Unit(AMSU) channels(23.8,31.4,89,and 150 GHz) through linear regression models.The scheme is demonstrated by an analysis of a two-dimensional cloud resolving model simulation that is imposed by a forcing derived from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment(TOGA COARE).The rainfall is considered convective if associated LWP is larger than 1.91 mm or IWP is larger than1.70 mm.Otherwise,the rainfall is stratiform.The analysis of surface rainfall budget demonstrates that this new scheme is physically meaningful.

Effect of the Instrument Slit Function on Upwelling Radiance from a Wavelength Dependent Surface Reflectance

The Radiance Enhancement (RE) method was introduced for efficient detection of clouds from the space. Recently, we have also reported that due to high reflectance of combustion-originated smokes, this approach can also be generalized for detection of the forest fires by retrieving and analyzing datasets collected from a space orbiting micro-spectrometer operating in the near infrared spectral range. In our previous publication, we have performed a comparison of observed and synthetic radiance spectra by developing a method for computation of surface reflectance consisting of different canopies by weighted sum based on their areal coverage. However, this approach should be justified by a method based on corresponding proportions of the upwelling radiance. The results of computations we performed in this study reveal a good match between areal coverage of canopies and the corresponding proportions of the upwelling radiance due to effect of the instrument slit function.

Application of Direct Assimilation of ATOVS Microwave Radiances to Typhoon Track Prediction

In order to solve the difficult problem of typhoon track prediction due to the sparsity of conventional data over the tropical ocean, in this paper, the No. 0205 typhoon Rammasun of 4-6 July 2002 is studied and an experiment of the typhoon track prediction is made with the direct use of the Advanced TIROS-N Operational Vertical Sounder (ATOVS) microwave radiance data in three-dimensional variational data assimilation. The prediction result shows that the experiment with the ATOVS microwave radiance data can not only successfully predict the observed fact that typhoon Rammasun moves northward and turns right, but can also simulate the action of the fast movement of the typhoon, which cannot be simulated with only conventional radiosonde data. The skill of the typhoon track prediction with the ATOVS microwave radiance data is much better than that without the ATOVS data. The typhoon track prediction of the former scheme is consistent in time and in location with the observation. The direct assimilation of ATOVS microwave radiance data is an available way to solve the problem of the sparse observation data over the tropical ocean, and has great potential in being applied to typhoon track prediction.

Application of ATOVS Microwave Radiance Assimilation to Rainfall Prediction in Summer 2004

Experiments are performed in this paper to understand the influence of satellite radiance data on the initial field of a numerical prediction system and rainfall prediction. First, Advanced Microwave Sounder Unit A （AMSU-A） and Unit B （AMSU-B） radiance data are directly used by three-dimensional variational data assimilation to improve the background field of the numerical model. Then, the detailed effect of the radiance data on the background field is analyzed. Secondly, the background field, which is formed by application of Advanced Television and Infrared Observation Satellite Operational Vertical Sounder （ATOVS） microwave radiance assimilation, is employed to simulate some heavy rainfall cases. The experiment results show that the assimilation of AMSU-A （B） microwave radiance data has a certain impact on the geopotential height, temperature, relative humidity and flow fields. And the impacts on the background field are mostly similar in the different months in summer. The heavy rainfall experiments reveal that the application of AMSU-A （B） microwave radiance data can improve the rainfall prediction significantly. In particular, the AMSU-A radiance data can significantly enhance the prediction of rainfall above 10 mm within 48 h, and the AMSU-B radiance data can improve the prediction of rainfall above 50 mm within 24 h. The present study confirms that the direct assimilation of satellite radiance data is an effective way to improve the prediction of heavy rainfall in the summer in China.

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.

Satellite All-sky Infrared Radiance Assimilation:Recent Progress and Future Perspectives

Satellite infrared(IR)sounder and imager measurements have become one of the main sources of data used by data assimilation systems to generate initial conditions for numerical weather prediction(NWP)models and atmospheric analysis/reanalysis.This paper reviews the development of satellite IR data assimilation in NWP in recent years,especially the assimilation of all-sky satellite IR observations.The major challenges and future directions are outlined and discussed.

A Method for Retrieving Water-leaving Radiance from Landsat TM Image in Taihu Lake, East China

The visible and infrared bands of Landsat Thematic Mapper （TM） can be used for inland water studies. A method of retrieving water-leaving radiance from TM image over Taihu Lake in Jiangsu Province of China was investigated in this article. To estimate water-leaving radiance, atmospheric correction was performed in three visible bands of 485nm, 560nm and 660rim. Rayleigh scattering was computed precisely, and the aerosol contribution was estimated by adopting the clear-water-pixels approach. The clear waters were identified by using the Landsat TM middle-infrared band （2.1 μm）, and the water-leaving radiance of clear water pixels in the green band was estimated by using field data. Aerosol scattering at green band was derived for six points, and interpolated to match the TM image. Assuming the atmospheric correction coefficient was 1.0, the aerosol scattering image at blue and red bands were derived. Based on a simplified atmospheric radiation transfer model, the water-leaving radiance for three visible bands was retrieved. The water-leaving radiance was normalized to make it comparable with that estimated from other remotely sensed data acquired at different times, and under different atmospheric conditions. Additionally, remotely sensed reflectance of water was computed. To evaluate the atmospheric correction method presented in this article, the correlation was analyzed between the corrected remotely sensed data and the measured water parameters based on the retrieval model. The results show that the atmospheric correction method based on the image itself is more effective for the retrieval of water parameters from Landsat TM data than 6S （Second Simulation of the Satellite Signal in the Solar Spectrum） code based on standard atmospheric and aerosol models.

Application of ATOVS Radiance-Bias Correction to Typhoon Track Prediction with Ensemble Kalman Filter Data Assimilation

In this paper, firstly, the bias between observed radiances from the Advanced TIROS-N Operational Vertical Sounder （ATOVS） and those simulated from a model first-guess are corrected. After bias correction, the observed minus calculated （O-B） radiances of most channels were reduced closer to zero, with peak values in each channel shifted towards zero, and the distribution of O-B closer to a Gaussian distribution than without bias correction. Secondly, ATOVS radiance data with and without bias correction are assimilated directly with an Ensemble Kalman Filter （EnKF） data assimilation system, which are then adopted as the initial fields in the forecast model T106L19 to simulate Typhoon Prapiroon （2006） during the period 2-4 August 2006. The prediction results show that the assimilation of ATOVS radiance data with bias correction has a significant and positive impact upon the prediction of the typhoon＇s track and intensity, although the results are not perfect.

Assimilation of All-sky Geostationary Satellite Infrared Radiances for Convection-Permitting Initialization and Prediction of Hurricane Joaquin(2015)

Intensity forecasting is one of the most challenging aspects of tropical cyclone(TC) forecasting. This work examines the impact of assimilating high-resolution all-sky infrared radiance observations from geostationary satellite GOES-13 on the convection-permitting initialization and prediction of Hurricane Joaquin(2015) with an ensemble Kalman filter(EnKF)based on the Weather Research and Forecasting(WRF) model. Given that almost all operational global and regional models struggled to capture Hurricane Joaquin(2015)'s intensity, this study examines the potential in improving Joaquin's prediction when assimilating all-sky infrared radiances from GOES-13's water vapor channel. It is demonstrated that, after a few 3-hour cycles assimilating all-sky radiance, the WRF model was able to forecast reasonably well Joaquin's intensity,including its rapid intensification(RI). The improvement was largely due to a more realistic initial hurricane structure with a stronger, warmer, and more compact inner-core. Ensemble forecasts were used to further explore the important physical mechanisms driving the hurricane's RI. Results showed that the RI forecasts were greatly impacted by the initial inner-core vortex structure.

A Validation of the Multivariate and Minimum Residual Method for Cloud Retrieval Using Radiance from Multiple Satellites

The Multivariate and Minimum Residual （MMR） cloud detection and retrieval algorithm,previously developed and tested on simulated observations and Advanced Infrared Sounder radiance,was explored and validated using various radiances from multiple sensors.For validation,the cloud retrievals were compared to independent cloud products from CloudSat,MODIS （Moderate Resolution Imaging Spectroradiometer）,and GOES （Geostationary Operational Environmental Satellites）.We found good spatial agreement within a single instrument,although the cloud fraction on each pixel was estimated independently.The retrieved cloud properties showed good agreement using radiances from multiple satellites,especially for the vertically integrated cloud mask.The accuracy of the MMR scheme in detecting mid-level clouds was found to be higher than for higher and lower clouds.The accuracy in retrieving cloud top pressures and cloud profiles increased with more channels from observations.For observations with fewer channels,the MMR solution was an ＂overly smoothed＂ estimation of the true vertical profile,starting from a uniform clear guess.Additionally,the retrieval algorithm showed some meaningful skill in simulating the cloudy radiance as a linear observation operator,discriminating between numerical weather prediction （NWP） error and cloud effects.The retrieval scheme was also found to be robust when different radiative transfer models were used.The potential application of the MMR algorithm in NWP with multiple radiances is also discussed.

Effects of Aerosol Vertical Inhomogeneity on the Upwelling Radiance and Satellite Remote Sensing of Surface Reflectance

There are two widely used radiative models without consideration of aerosol inhomogeneity for satellite remote sensing application, the Homogeneous Model and the Two-layer Model with aerosol in the lower layer. In this paper, effects of the aerosol vertical inhomogeneity on upwelling radiance and satellite remote sensing of surface reflectance are analyzed through numerical simulations by using two models. As shown in the simulations by using 24 representative aerosol models, there is often a considerably large error in upwel-ling radiance calculated by two models (Homogeneous and Two-layer) for the short wavelength channel with strong molecular scattering, owing to the difference between molecular and aerosol scattering proper-ties. For the long wavelength channel, the error is small if aerosol optical parameters are less variable with height, but it could also be significant if there are aerosol layers with different scattering phase functions and single scattering albedo. The radiance errors by the Homogeneous Model and the Two-layer Model can be up to 31.4% and 31.5% for the clean atmosphere, and in case of turbid atmosphere 67.8% and 59.2%, respectively. The radiance error could result in a large uncertainty of surface reflectance retrievals, especially for the short wavelength channel and the strongly absorbing aerosol. For the turbid atmosphere with strong-ly absorbing aerosol, the Homogeneous Model and the Two-layer Model are not suitable for atmospheric correction application. Key words Satellite remote sensing - Aerosol inhomogeneity - Surface reflectance - Radiance

Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation： A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level（surface-sensitive）channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets.Here, we used an improved land use and leaf area index（LAI） dataset in the WRF-3 DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels（e.g., channel 3）,the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.

Chlorophyll Fluorescence Extraction from Water-Leaving Radiance of Algae-Containing Water Through Polarization

When measuring reflectance spectra, it is very important to accurately extract chlorophyll fluorescence from elastic-scattering light in water-leaving radiance. The elastic scattering of light by water particles produces partially polarized light. In contrast, chlorophyll fluorescence in planktonic algae yields completely unpolarized light. These properties can be used to separate fluorescent signals from the water-leaving radiance and thus to determine chlorophyll concentration. The algal species Aureococcus anophagefferens was used to conduct a laboratory polarization experiment. For the tests, we used a field spectroradiometer and a polarizer; measurements were collected using two different observation modes. The chlorophyll fluorescence curve extracted through polarization shows an excellent match with the results obtained using the fluorospectro photometer for both measurement modes, suggesting that polarization-based chlorophyll fluorescence extraction may be feasible. The extracted fluorescence is more reliable at incident zenith angles ranging from 30? to 60?. For algae-containing water, the results improve with increasing chlorophyll concentration. This method could help improve chlorophyll concentration measurement and the remote-sensing detection of resulting harmful algae blooms.