Assimilation Of Radiance Data Research Articles

Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall

Ground-based microwave radiometer (GMWR) data with high spatial and temporal resolution can improve the accuracy of weather forecasts when effectively assimilated into numerical weather prediction. Nowadays, the major method to assimilate these data is via indirect assimilation by assimilating the retrieved profiles, which introduces large retrieval errors and cannot easily be represented by an error covariance matrix. Direct assimilation, on the other hand, can avoid this issue. In this study, the ground-based version of the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-gb) was selected as the observation operator, and a direct assimilation module for GMWR radiance data was established in the Weather Research and Forecasting Model Data Assimilation (WRFDA). Then, this direct assimilation module was applied to assimilate GMWR data. The results were compared to the indirect assimilation experiment and demonstrated that direct assimilation can more effectively improve the model’s initial fields in terms of temperature and humidity than indirect assimilation while avoiding the influence of retrieval errors. In addition, direct assimilation performed better in the precipitation forecast than indirect assimilation, making the main precipitation center closer to the observation. In particular, the improvement in the precipitation forecast with a threshold of 60 mm/6 h was obvious, and the corresponding TS score was significantly enhanced.

Combined assimilation of hourly rainfall data and every 10-min AHI radiance with WRF 4DVar for the short-range heavy rainfall forecast in Eastern China

Rainfall observations and geostationary satellite water vapor radiances account for complementary humidity information over rainy areas and large-scale environments, respectively. This study investigates the simultaneous assimilation of China Hourly Merged Precipitation Analysis (CHMPA) data and clear-sky water vapor radiances provided by the Himawari-8 Advanced Himawari Imager (AHI) with Four-Dimensional Variational (4DVar) data assimilation method using Weather Research and Forecasting (WRF) model with an hourly rapid-update cycling data assimilation configuration. The rapid-update 4DVar assimilation is configured as 10-min interval for AHI radiances and 1-h interval for CHMPA rainfall data in a same 1-h assimilation time window. Generally positive impacts from assimilating CHMPA rainfall and AHI water vapor radiance observations are achieved for the 12-h short-term precipitation forecast. Error reductions are found after assimilating CHMPA rainfall and AHI water vapor radiance observations for both analyses and forecasts when compared to ERA5 reanalysis. It is found that combined assimilation of CHMPA rainfall and AHI water vapor radiance data has clearly positive impacts on heavy rainfall forecasts. A heavy rainfall case that occurred in Jiangsu Province on 28 June 2020 is examined in details. The combined assimilation of infrared radiances from AHI water vapor channels together with CHMPA rainfall data improves the vertical and horizontal transport of humidity conditions, which better adjusts the humidity distribution. The over-estimation of rainfall forecast caused by the assimilation of CHMPA is alleviated. This result can provide reference for quantitative precipitation forecasts of regional numerical weather model.

Effects of joint assimilation of FY-4A AGRI and ground-based microwave radiometer on heavy rainfall prediction

As the latest generation of Chinese Geostationary Weather Satellites, Fengyun-4 carries the Advanced Geosynchronous Radiation Imager (AGRI), which has more spectral bands and higher temporal and spatial resolution than the Visible Infrared Spin-Scan Radiometer (VISSR) onboard geostationary satellite FY-2. Direct assimilation of the FY-4A AGRI datasets has been proved to be an efficient way to improve heavy rainfall simulation. We aim to assess the joint assimilation of AGRI infrared radiance and ground-based MWR (Microwave Radiometer) data on short-duration heavy rainfall prediction. RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) is used as the observational operator for FY-4A AGRI data assimilation. The data assimilation interface is built in WRFDA 4.3 to achieve direct assimilation of FY4A AGRI radiance. The forecasting effectiveness of the joint assimilation for a typical heavy rainfall event over northern China is analyzed with four simulation experiments. The main conclusions are: 1) Assimilating MWR data can improve the initial humidity condition in the middle-lower layers, while AGRI radiance assimilation favors the initial humidity correction in the middle-upper layers. The joint assimilation of two datasets can remarkably improve the initial humidity condition in the entire column. 2) Data assimilation effectively improves the 6-h accumulated rainfall simulation. The joint assimilation of AGRI radiance and MWR data is superior to assimilating either of them. The joint assimilation significantly improves the rainfall forecast over the Beijing area, where the seven MWRs are distributed. 3) Data assimilation experiments present similar effects on predicted and initial humidity conditions. The MWR_DA experiment (only assimilate MWR data) markedly improves the humidity forecast in the middle-lower layers, while AGRI_DA (only assimilate AGRI data) is effective in the middle-upper layers. The joint assimilation of AGRI radiance and MWR data could skillfully correct the humidity distribution in the entire layers, allowing for more accurate heavy rainfall prediction. This paper provides a valuable basis for further improving the application of FY-4A AGRI radiance in numerical weather models.

Impacts of 3DEnVar-Based FY-3D MWHS-2 Radiance Assimilation on Numerical Simulations of Landfalling Typhoon Ampil (2018)

The module for assimilating radiance data of the Microwave Humidity Sounder-2 (MWHS-2) onboard the Feng Yun 3D (FY-3D) satellite is built in the Weather Research and Forecasting (WRF) model data assimilation (WRFDA) system. The CONV, 3DVar, and EnVar experiments are conducted to investigate the impact of assimilating the new humidity sounder based on Typhoon Ampil (2018). Both the 3DVar and EnVar experiments assimilate FY-3D MWHS-2 radiance data on top of the conventional data, while the CONV experiment only applies conventional data. In the EnVar experiment, notable geopotential height increment is observed around the typhoon, leading the typhoon to move northeast. In addition, the moisture field is improved to some extent. Finally, from the analysis of the dynamic field of the typhoon, it can be found that the EnVar experiment can adjust the dynamic structure of the typhoon. Furthermore, the assimilation of FY-3D MWHS-2 radiance data reduces the forecast error of the typhoon track and intensity. Additionally, the precipitation skill is improved in terms of rainfall pattern and the verification score. This improvement in the precipitation may be closely related to the features of the circulation structure concerning the evolution of the typhoon. The improved prediction of the position and intensity of rainbands in the FY-3D MWHS-2 radiance data assimilation experiment corresponds to a better prediction of typhoon structure.

Impacts of Multi-Source Microwave Satellite Radiance Data Assimilation on the Forecast of Typhoon Ampil

This study investigates the impacts of the joint assimilation of microware temperature sensor, Advanced Microwave Sounding Unit-A (AMSUA), and microware humidity sensors, Microwave Humidity Sounder (MHS) and Microwave Humidity Sounder-2 (MWHS2), on the analyses and forecasts for the tropical cyclone (TC) system. Experiments are conducted using a three-dimensional variation (3DVAR) algorithm in the framework of the weather research and forecasting data assimilation (WRFDA) system for the forecasting of Typhoon Ampil (2018). The results show that the assimilation of MWHS2 radiance data improves the analyses better in terms of TC’s structure and moisture conditions than those of the MHS experiment. To some extent, the experiment with only AMSUA radiance delivers some positive impacts of the precipitation, track, and intensity forecast than the other two experiments do. In addition, the skill of the precipitation forecast is notably enhanced with higher equitable threat score (ETS) by the simultaneous assimilation of the MHS, MWHS2, and AMSUA radiance. Generally, assimilation of radiance from all sources of MHS, MWHS2, and AMSUA could combine the advantages of assimilating each type of sensors rather than individually. The consistent improvement is also confirmed for the TC’s track forecast with reduced error on average, whereas the improvement of intensity forecast is not obvious.

Assimilation of Radiance Data from the AMSU-A Sensor in the GSI: Overview of its Importance for the Quality of Initial Conditions in the CPTEC NWP. Assimilation of Radiance Data from the AMSU-A Sensor in the GSI/BAM/CPTEC: Description and Importance for the NWP

Resumo O Gridpoint Statistical Interpolation (GSI) é o sistema de assimilação de dados utilizado no Centro de Previsão de Tempo e Estudos Climáticos (CPTEC) do Instituto Nacional de Pesquisas Espaciais (INPE) desde 2013 e atualmente integra, de maneira acoplada ao Brazilian Global Atmospheric Model (BAM), o Sistema de Modelagem Global (SMG). Por meio da abordagem variacional aplicada ao SMG, esse sistema permite assimilar de forma direta dados de radiância, os quais são atualmente o sistema de observação que apresenta o maior impacto positivo para a melhoria da qualidade das Previsão Numérica de Tempo (PNT) em escala sinótica. Com intuito de detalhar as particularidades necessárias para a assimilação desses dados e evidenciar sua importância para a qualidade dos produtos obtidos ao utilizar o SMG, o presente estudo destaca os principais pontos do tratamento dos dados de radiância dentro do GSI e apresenta resultados de um experimento usando dados do sensor AMSU-A a bordo de diversos satélites para descrever o comportamento espacial e temporal dos dados assimilados, em termos quantitativos e qualitativos. Usando os dados de radiância como referência, em uma comparação da previsão de curto prazo do modelo com as análises, observa-se que essas últimas apresentam uma diminuição dos erros indicando a eficiência do processo de assimilação de radiância, com impacto positivo na assimilação de outras bases de dados, como os da Rádio Ocultação GNSS aqui avaliados. Esse impacto deve contribuir para a melhoria da qualidade dos produtos de PNT gerados pelo CPTEC, o que deverá ser avaliado na continuidade dessa pesquisa.

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 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

Evaluation of the Simulation of Typhoon Lekima (2019) Based on Different Physical Parameterization Schemes and FY-3D Satellite’s MWHS-2 Data Assimilation

In this study, the case of super typhoon Lekima, which landed in Jiangsu and Zhejiang Province on 4 August 2019, is numerically simulated. Based on the Weather Research and Forecasting (WRF) model, the sensitivity experiments are carried out with different combinations of physical parameterization schemes. The results show that microphysical schemes have obvious impacts on the simulation of the typhoon’s track, while the intensity of the simulated typhoon is more sensitive to surface physical schemes. Based on the results of the typhoon’s track and intensity simulation, one parameterization scheme was further selected to provide the background field for the following data assimilation experiments. Using the three-dimensional variational (3DVar) data assimilation method, the Microwave Humidity Sounder-2 (MWHS-2) radiance data onboard the Fengyun-3D satellite (FY-3D) were assimilated for this case. It was found that the assimilation of the FY-3D MWHS-2 radiance data was able to optimize the initial field of the numerical model in terms of the model variables, especially for the humidity. Finally, by the inspection of the typhoon’s track and intensity forecast, it was found that the assimilation of FY-3D MWHS-2 radiance data improved the skill of the prediction for both the typhoon’s track and intensity.

Assimilation of Himawari-8 imager radiance data with the WRF-3DVAR system for the prediction of Typhoon Soudelor

Abstract. Himawari-8 is a next-generation geostationary meteorological satellite launched by the Japan Meteorological Agency. It carries the Advanced Himawari Imager (AHI) on board, which can continuously monitor high-impact weather events with high frequency in space and time. The assimilation of AHI radiance data was implemented with the three-dimensional variational data assimilation system (3DVAR) of the Weather Research and Forecasting Model for the analysis and prediction of Typhoon Soudelor (2015) in the Pacific typhoon season. The effective assimilation of AHI radiance data in improving the forecast of the tropical cyclone during its rapid intensification has been realized. The results show that, after assimilating the AHI radiance data under clear-sky conditions, the typhoon position in the background field of the model was effectively corrected compared with the control experiment without AHI radiance data assimilation. It is found that the assimilation of AHI radiance data is able to improve the analyses of the water vapor and wind in a typhoon's inner-core region. The analyses and forecasts of the minimum sea level pressure, the maximum surface wind, and the track of the typhoon are further improved.

Assimilation of GPM Microwave Imager Radiance data with the WRF hybrid 3DEnVar system for the prediction of Typhoon Chan-hom (2015)

The impact of assimilating radiances from the global satellite precipitation program (GPM) Microwave Imager (GMI) on the track prediction of typhoon Chan-hom was studied with a hybrid three-dimensional ensemble-variational (3DEnVar) data assimilation (DA) system. It is found that the data assimilation of clear-sky GMI radiance is able to depict the structure of typhoon well by warming the typhoon inner core area. The bias correction coefficients obtained by the off-line model along with the variational bias correction method can reduce the bias. GMI radiance data assimilation experiment can effectively adjust the core area of Typhoon “Chan-hom” and systematically correct the typhoon position in the background of the model. The sensitivity of the hybrid 3DEnVar DA behaviors on the choice of the ensemble members is also investigated. Results show that the 3DEnVar Global Ensemble Forecast System (3DEnVar_GEFS)-based ensemble spread is essentially dominated in the vicinity of Typhoon Chan-hom, which is less disturbed by random errors. In general, the track forecasts from 3DEnVar_RCV match better with the best track by adding Gaussian noise randomly based on the background error than those from the 3DVAR, and 3DEnVar_GEFS.

Impact of INSAT-3D/3DR Radiance Data Assimilation in Predicting Tropical Cyclone Titli Over the Bay of Bengal

This is the first study concerning the assimilation of the INSAT-3D/3DR radiance in the Hurricane Weather Research and Forecasting (HWRF) model and assesses its credibility to improve track, intensity, and precipitation forecasts of tropical cyclone (TC) Titli that occurred over the Bay of Bengal (BoB), which showed rapid intensification (RI) and weakening through its lifetime. The inbuilt Gridpoint Statistical Interpolation (GSI) method is used with a 3-D variational (3DVAR) configuration. Three sets of numerical experiments such as control (CNTL) (no assimilation), Global Telecommunication System (GTS) (observations from GTS network), and INSAT-3D/3DR (INSAT-3D/3DR sounder radiance data and GTS observations) were carried out with seven different initializations. The radiance analysis reproduced the initial vortex and the prominent synoptic scale features associated with TC Titli. The average root-mean-square errors (RMSE) of the analysis were relatively lower in the INSAT-3D/3DR compared to the CNTL and GTS. The HWRF performance is enhanced for track simulation, with improvements in mean landfall position errors by 40%–70% and 26%–52% for the INSAT-3D/3DR and GTS runs, respectively. The assimilation of radiance data has a positive impact on the simulation of warm core and thermodynamic structures, which has led to a more accurate intensity prediction (by 30–47%) over the CNTL. The assimilation run could realistically simulate the RI and weakening phases of the TC. A cold dry air intrusion is also observed when associated with the weakening. The study highlights the need to incorporate INSAT-3D/3DR radiances for improved TC predictions over the BoB basin.

Assimilation of MWHS and MWTS radiance data from the FY-3A satellite with the POD-3DEnVar method for forecasting heavy rainfall

An ensemble three-dimensional variational assimilation method based on Proper Orthogonal Decomposition (referred to as POD-3DEnVar) can not only provide flow-dependent covariances through the evolving ensemble of short-range forecasts, but also obtain directly the analysis field without an iterative process. Using the POD-3DEnVar method, a regional hybrid variational ensemble data assimilation system (referred to as POD-HVEDAS) is constructed with the Community Radiative Transfer Model (CRTM) as the observation operator. Observations from Microwave Humidity Sounder (MWHS) and Microwave Temperature Sounder (MWTS) onboard Fengyun-3A are simultaneously assimilated into the Weather Research and Forecasting (WRF) model by the POD-HVEDAS. Bias correction and quality control schemes for MWHS and MWTS microwave radiance are applied in the system. Three experiments (Con, Hybrid-DA, and POD-DA) are designed to investigate the assimilation ability of the POD-HVEDAS for heavy rainfall over the Yangtze River. The assimilation effects of POD-3DEnVar and WRFDA-Hybrid assimilation method are compared. The results show that the POD-HVEDAS can assimilate MWHS and MWTS microwave radiance effectively, and give a better precipitation forecast than that of the WRFDA-Hybrid method. And it is found that the assimilation increments of both POD-3DEnVar and WRFDA-Hybrid methods all show flow dependent characteristic. However, the improvements in the background wind, temperature and humidity fields by the POD-DA experiment are more obvious than those of the Hybrid-DA experiment, which play key roles in improving the accuracy of precipitation forecast. These promising results suggest initiulizing limited-area models with POD-3DEnVar method that assimilate microwave radiances is beneficial. In the future, POD-3DEnVar method will be used to study multiple cases to fully assess the robustness of POD-HVEDAS.

Impact of Radiance Data Assimilation on the Prediction of Heavy Rainfall in RMAPS: A Case Study

Herein, a case study on the impact of assimilating satellite radiance observation data into the rapid-refresh multi-scale analysis and prediction system (RMAPS) is presented. This case study targeted the 48 h period from 19–20 July 2016, which was characterized by the passage of a low pressure system that produced heavy rainfall over North China. Two experiments were performed and 24 h forecasts were produced every 3 h. The results indicated that the forecast prior to the satellite radiance data assimilation could not accurately predict heavy rainfall events over Beijing and the surrounding area. The assimilation of satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) improved the skills of the quantitative precipitation forecast to a certain extent. In comparison with the control experiment that only assimilated conventional observations, the experiment with the integrated satellite radiance data improved the rainfall forecast accuracy for 6 h accumulated precipitation after about 6 h, especially for rainfall amounts that were greater than 25 mm. The average rainfall score was improved by 14.2% for the 25 mm threshold and by 35.8% for 50 mm of rainfall. The results also indicated a positive impact of assimilating satellite radiances, which was primarily reflected by the improved performance of quantitative precipitation forecasting and higher spatial correlation in the forecast range of 6–12 h. Satellite radiance observations provided certain valuable information that was related to the temperature profile, which increased the scope of the prediction of heavy rainfall and led to an improvement in the rainfall scoring in the RMAPS. The inclusion of satellite radiance observations was found to have a small but beneficial impact on the prediction of heavy rainfall events as it relates to our case study conditions. These findings suggest that the assimilation of satellite radiance data in the RMAPS can provide an overall improvement in heavy rainfall forecasting.

Assimilation of Himawari-8 Clear Sky Radiance Data in JMA's Global and Mesoscale NWP Systems

Assimilation of Himawari-8 Clear Sky Radiance Data in JMA's Global and Mesoscale NWP Systems

Impacts from assimilation of one data stream of AMSU‐A and MHS radiances on quantitative precipitation forecasts

Since the launch of the NOAA‐15 satellite in 1998, the observations from microwave temperature and humidity sounders have been routinely disseminated to user communities through two separate data streams. In the Advanced Microwave Sounding Unit‐A (AMSU‐A) data stream, brightness temperatures in 15 channels are available primarily for profiling atmospheric temperature from the Earth's surface to the low stratosphere. In the Advanced Microwave Sounding Unit‐B (AMSU‐B) or Microwave Humidity Sounder (MHS) data stream, the brightness temperatures in five channels are included for sounding water vapour in the low troposphere. Assimilation of microwave radiance data in numerical weather prediction systems has also been carried out with AMSU‐A and AMSU‐B (MHS) data in two separate data streams. A new approach is to combine AMSU‐A and MHS radiances into one data stream for their assimilation. The National Centers for Environmental Prediction Gridpoint Statistical Interpolation analysis system and the Advanced Research Weather Research and Forecast model are employed for testing the impacts of the combined datasets. It is shown that the spatial collocation between MHS and AMSU‐A fields of view in the one data stream experiment allows for an improved quality control of MHS data, especially over the conditions where the liquid‐phase clouds are dominant. As a result, a closer fit of analyses to AMSU‐A and MHS observations is obtained, especially for AMSU‐A surface‐sensitive channels. The quantitative precipitation forecast skill is improved over a 10‐day period when Hurricane Isaac made landfall.

Assimilation of MWHS radiance data from the FY‐3B satellite with the WRF Hybrid‐3DVAR system for the forecasting of binary typhoons

AbstractChan‐hom and Linfa were binary typhoons that occurred in the western North Pacific in 2015. In this study, the impacts of FY‐3B satellite Microwave Humidity Sounder (MWHS) radiance observations on the analyses and forecasts of Linfa and Chan‐hom are assessed. The regional Weather Research and Forecasting model and its data assimilation (DA) systems, using three‐dimensional variational (3DVAR) and Hybrid (ensemble/3DVAR) methods are used. Assimilation of the FY‐3B MWHS data using the 3DVAR method slightly improves the descriptive wind and temperature fields. Positive impacts on the specific humidity forecasts, for levels higher than 850 hPa, are also obvious. 3DVAR adjusts the typhoons' initial positions and their dynamic structures favorably, yielding better tracks, intensities, and precipitation forecasts, compared to the experiment run without MWHS data (control). With the Hybrid method, the water vapor information from the MWHS data better improve the analyses through multivariable correlations with the flow‐dependent background error. The Hybrid method further improves the track, intensities, and precipitation forecasts. For Typhoon Linfa, with the coexistence of Typhoon Chan‐hom, the Hybrid method provides a more descriptive background error covariance matrix, than using 3DVAR. Experiments on multiple binary typhoon cases are also provided to further validate the robustness of the results on the FY‐3B satellite MWHS radiance data assimilation.

Impact of Satellite Radiance Data on Simulations of Bay of Bengal Tropical Cyclones Using the WRF-3DVAR Modeling System

This study attempts to evaluate whether assimilating radiance observations in the Weather Research and Forecasting (WRF) model could improve track, intensity, and precipitation forecasts of tropical cyclones (TCs) that occurred over the Bay of Bengal. The bias correction coefficients obtained from offline statistics, along with the quality control for radiances, were computed in the variational assimilation system. For this study, three numerical experiments named CNTL (no assimilation), GTS (with Global Telecommunication System observations), and RAD (radiance data along with GTS observations) were carried out with ten different model initial conditions for two TCs. The averaged root-mean-square errors of the analysis were relatively lower in the RAD experiments in comparison to the GTS experiments for all assimilation cycles of the meteorological variables. The mean initial position errors of TCs were reduced by 30%–47% in the RAD runs over the other runs. The results indicate that the assimilation of radiance data has a positive impact on the prediction of track, intensity, thermodynamic structures, and reflectivity associated with the storms. Improvements in mean landfall position errors were shown to range from 40% to 70% in the RAD experiments as compared to the CNTL and GTS simulations. This is because the RAD analyses are able to successfully reproduce the initial vortex and vertical structures as well as the prominent synoptic features associated with TC storms; therefore, the performance of the WRF modeling system is enhanced for simulations of track, structures, and intensity of TCs.

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.

Reduction of Middle-Atmospheric Forecast Bias through Improvement in Satellite Radiance Quality Control

Abstract This article discusses a practical problem faced in operational atmospheric forecasting and data assimilation, and efforts to improve forecast quality through the choice of quality control parameters. The need to utilize as much data as possible must be carefully balanced against the need to reject observations deemed erroneous because they are far from the background value. Alleviation of forecast bias in the middle atmosphere for a global atmospheric prediction system is attempted via improvement of the quality control and bias correction of the satellite radiance data; in particular, the sensitivity of the analysis to the satellite radiance outlier check parameters for the Naval Research Laboratory’s three-dimensional variational data assimilation system [Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] is investigated. A series of forecast experiments are performed with an extended-top (0.04 hPa or ∼65 km) version of the U.S. Navy’s Operational Global Atmospheric Prediction System (NOGAPS) for the month of January 2007. The experiments vary the prescribed radiance observation error variance for the Advanced Microwave Sounding Unit-A (AMSU-A) and the tolerance factors for the AMSU-A and NAVDAS quality control processes. The biases of geopotential height, temperature, and wind in the middle atmosphere are significantly reduced when the observation error limit for the highest-altitude AMSU-A channel (i.e., 14) is relaxed from 0.95 to 3 K and the tolerance factors for the AMSU-A and NAVDAS quality control processes are relaxed from 3 to 4. The improvement is due to assimilation of more high quality AMSU-A radiance data from the highest-peaking channel.

Direct assimilation of satellite radiance data in GRAPES variational assimilation system

Variational method is capable of dealing with observations that have a complicated nonlinear relation with model variables representative of the atmospheric state, and so make it possible to directly assimilate such measured variables as satellite radiance, which have a nonlinear relation with the model variables. Assimilation of any type of observations requires a corresponding observation operator, which establishes a specific mapping from the space of the model state to the space of observation. This paper presents in detail how the direct assimilation of real satellite radiance data is implemented in the GRAPES-3DVar analysis system. It focuses on all the components of the observation operator for direct assimilation of real satellite radiance data, including a spatial interpolation operator that transforms variables from model grid points to observation locations, a physical transformation from model variables to observed elements with different choices of model variables, and a data quality control. Assimilation experiments, using satellite radiances such as NOAA17 AMSU-A and AMSU-B (Advanced Microwave Sounding Unit), are carried out with two different schemes. The results from these experiments can be physically understood and clearly reflect a rational effect of direct assimilation of satellite radiance data in GRAPES-3DVar analysis system.