Validation of the Atmospheric Infrared Sounder Retrieval Products over China and Their Application in Numerical Model

The atmospheric infrared sounder (AIRS) instrument onboard Aqua Satellite is a high spectral resolution infrared sounder. In recent years, AIRS has gradually become the primary method of atmospheric vertical observations. To examine the validation of AIRS retrieval products (V3.0) over China, the AIRS surface air temperature retrievals were compared with the ground observations obtained from 540 meteorological stations in July 2004 and January 2005, respectively. The sources of errors were considerably discussed. Based on the error analysis, the AIRS retrieved surface air temperature products were systemi-cally corrected. Moreover, the AIRS temperature and humidity profile retrievals were compared with T213 numerical forecasting products. Because T213 forecasting products are not the actual atmospheric states, to further verify the validation, the AIRS temperature and humidity profile products were assimilated into the MM5 model through the analysis nudging. In this paper, the case on February 14, 2005 in North China was simulated in detail. Then, we investigated the effects of AIRS retrievals on snowfall, humidity field, vertical velocity field, divergence field, and cloud microphysical processes. The major results are: (1) the errors of AIRS retrieved surface air temperature products are largely systematic deviations, for which the influences of terrain altitude and surface types are the major reasons; (2) the differences between the AIRS atmospheric profile retrievals and T213 numerical prediction products in temperature are generally less than 2 K, the differences in relative humidity are generally less than 25%; and (3) the AIRS temperature and humidity retrieval products can adjust the model initial field, and thus can improve the capacity of snowfall simulation to some extent.     

Wet Refractivity Tomography with an Improved Kalman-Filter Method

An improved retrieval method, which uses the solution with a Gaussian constraint as the initial state variables for the Kalman Filtering （KF） method, was developed to retrieve the wet refractivity profiles from slant wet delays （SWD） extracted by the double-differenced （DD） GPS method. The accuracy of the GPS-derived SWDs is also tested in this study against the measurements of a water vapor radiometer （WVR） and a weather model. It is concluded that the GPS-derived SWDs have similar accuracy to those measured with WVR and are much higher in quality than those derived from the weather model used. The developed method is used to retrieve the 3D wet refractivity distribution in the Hong Kong region. The retrieved profiles agree well with the radiosonde observations, with a difference of about 4 mm km^- 1 in the low levels. The accurate profiles obtained with this method are applicable in a number of meteorological applications.     

Retrieval of atmospheric and oceanic parameters and the relevant numerical calculation

It is well known that retrieval of parameters is usually ill-posed and highly nonlinear, so parameter retrieval problems are very difficult. There are still many important theoretical issues under research, although great success has been achieved in data assimilation in meteorology and oceanography. This paper reviews the recent research on parameter retrieval, especially that of the authors. First, some concepts and issues of parameter retrieval are introduced and the state-of-the-art parameter retrieval technology in meteorology and oceanography is reviewed briefly, and then atmospheric and oceanic parameters are retrieved using the variational data assimilation method combined with the regularization techniques in four examples： retrieval of the vertical eddy diffusion coefficient; of the turbulivity of the atmospheric boundary layer; of wind from Doppler radar data, and of the physical process parameters. Model parameter retrieval with global and local observations is also introduced.     

Ensemble retrieval of atmospheric temperature profiles from AIRS

ABSTRACT Satellite-based observations provide great opportunities for improving weather forecasting. Physical retrieval of atmo spheric profiles from satellite observations is sensitive to the uncertainty of the first guess and other factors. In order to improve the accuracy of the physical retrieval, an ensemble methodology was developed with an emphasis on perturbing the first guess. In the methodology, a normal probability density function （PDF） is used to select the optimal profile from the ensemble retrievals. The ensemble retrieval algorithm contains four steps： （1） regression retrieval for original first guess; （2） perturbation of the original first guess to generate new first guesses （ensemble first guesses）; （3） using the ensemble first guesses and nonlinear iterative physical retrieval to generate ensemble physical results; and （4） the final optimal profile is selected from the ensemble physical results by using PDE Temperature eigenvectors （EVs） were used to generate the pertur- bation and generate the ensemble first guess. Compared with the regular temperature profile retrievals from the Atmospheric InfraRed Sounder （AIRS）, the ensemble retrievals RMSE of temperature profiles selected by the PDF was reduced between 150 and 320 hPa and below 400 hPa, with a maximum improvement of 0.3 K at 400 hPa. The bias was also reduced in many layers, with a maximum improvement of 0.69 K at 460 hPa. The combined optimal （CombOpt） profile and a mean optimal （MeanOpt） profile of all ensemble physical results were improved below 150 hPa. The MeanOpt profile was better than the CombOpt profile, and was regarded as the final optimal （FinOpt） profile. This study lays the foundation for improving temperature retrievals from hyper-spectral infrared radiance measurements.     

Atmospheric profile retrieval with AIRS data and validation at the ARM CART site

The physical retrieval algorithm of atmospheric temperature and moisture distribution from the Atmospheric InfraRed Sounder （AIRS） radiances is presented. The retrieval algorithm is applied to AIRS clear-sky radiance measurements. The algorithm employs a statistical retrieval followed by a subsequent nonlinear physical retrieval. The regression coefficients for the statistical retrieval are derived from a dataset of global radiosonde observations （RAOBs） comprising atmospheric temperature, moisture, and ozone profiles. Evaluation of the retrieved profiles is performed by a comparison with RAOBs from the Atmospheric Radiation Measurement （ARM） Program Cloud And Radiation Testbed （CART） in Oklahoma, U. S. A.. Comparisons show that the physically-based AIRS retrievals agree with the RAOBs from the ARM CART site with a Root Mean Square Error （RMSE） of 1K on average for temperature profiles above 850 hPa, and approximately 10% on average for relative humidity profiles. With its improved spectral resolution, AIRS depicts more detailed structure than the current Geostationary Operational Environmental Satellite （GOES） sounder when comparing AIRS sounding retrievals with the operational GOES sounding products.     

RETRIEVING ATMOSPHERIC SOUNDING PROFILES AROUND TYPHOON YUNNA USING INFRARED HYPERSPECTRAL MEASUREMENTS AIRS

In this study,we derived atmospheric profiles of temperature,moisture,and ozone,along with surface emissivity,skin temperature,and surface pressure,from infrared-sounder radiances under clear sky (cloudless) condition.Clouds were detected objectively using the Atmospheric Infrared Sounder under a relatively low spatial resolution and cloud-mask information from the Moderate Resolution Imaging Spectroradiometer under a high horizontal resolution;this detection was conducted using space matching.Newton’s nonlinear physical iterative solution technique is applied to the radiative transfer equation (RTE) to retrieve temperature profiles,relative humidity profiles,and surface variables simultaneously.This technique is carried out by using the results of an eigenvector regression retrieval as the background profile and using corresponding iterative forms for the weighting functions of temperature and water-vapor mixing ratio.The iterative forms are obtained by applying the variational principle to the RTE.We also compared the retrievals obtained with different types of observations.The results show that the retrieved atmospheric sounding profile has great superiority over other observations by accuracy and resolution.Retrieved profiles can be used to improve the initial conditions of numerical models and used in areas where conventional observations are sparse,such as plateaus,deserts,and seas.     

Dynamical and Microphysical Retrieval from Simulated Doppler Radar Observations Using the 4DVAR Assimilation Technique

Based on a cloud model and the four-dimensional variational (4DVAR) data assimilation method developed by Sun and Crook (1997), simulated experiments of dynamical and microphysical retrieval from Doppler radar data were performed. The 4DVAR data assimilation technique was applied to a cloud scale model with a warm rain parameterization scheme. The 3D wind, thermodynamical, and microphysical fields were determined by minimizing a cost function, defined by the difference between both radar observed radial velocities and reflectivities and their model predictions. The adjoint of the numerical model was used to provide the gradient of the cost function with respect to the control variables. Experiments have demonstrated that the 4DVAR assimilation method is able to retrieve the detailed structure of wind, thermodynamics, and microphysics by using either dual-Doppler or single-Doppler information. The quality of retrieval depends strongly on the magnitude of constraint with respect to the variables. Retrieving the temperature field, cloud water and water vapor is more difficult than the recovery of the wind field and rainwater. Accurate thermodynamic retrieval requires a longer assimilation period. The inclusion of a background term, even mean fields from a single sounding, helped reduce the retrieval errors. Less accurate velocity fields were obtained when single-Doppler data were used. It was found that the retrieved velocity is sensitive to the location of the retrieval domain relative to the radars while the other fields have very little changes. Two radar volumetric scans are generally adequate for providing the evolution, although the use of additional volumes improves the retrieval. As the amount of the observations decreases, the performance of the retrieval is degraded. However, the missing observations can be compensated by adding a background term to the cost function. The technique is robust to random errors in radial velocity and calibration errors in reflectivity. The boundary conditions from the dual-Doppler synthesized winds are sufficient for the retrieval. When the retrieval is mainly controlled by the observations in the regions away from the boundaries, the simple boundary conditions from velocity azimuth display (VAD) analysis are also available. The microphysical retrieval is sensitive to model errors.     

Validation of the Polar MM5 for use in the simulation of the Arctic river basins

The simulations were performed using a modified mesoscale model,the Polar MM5,which was adapted for use within polar regions.The objective of the study was to illustrate the skill of the Polar MM5 in simulating atmospheric behavior over the Arctic river basins.Automatic weather station data,global atmospheric analyses,as well as near-surface and upper-air observations were used to verify the simulation. Parallel simulations of the Polar MM5 and the original MM5 within the period 19-29 April 1997 simula- tions revealed that Polar MM5 reproduced better near-surface variables forecasts than the original MM5 for the region located over the North American Arctic regions.The well predicted near-surface temperature and mixing ratio by the Polar MM5 confirmed the modified physical parameterization schemes that were used in this model are appropriate for the Arctic river regions.Then the extended evaluations of the Polar MM5 simulations over both the North American and Eurasian domains during 15 December 2002 to 15 May 2003 were then carried out.The time series plots and statistical analyses from the observations and the Polar MM5 simulations at 16 stations for the near-surface and vertical profiles at 850 hPa and 500 hPa variables were analyzed.The model was found to reproduce the observed atmospheric state both at magnitude and variability with a high degree of accuracy,especially for temperature and near-surface winds,although there was a slight cold bias that existed near the surface.     

Retrieval of Upper Tropospheric Relative Humidity by the GMS-5 Water Vapor Channel： A Study of the Technique

This paper presents an analysis of a technique for retrieving upper tropospheric relative humidity through the GMS-5 satellite‘s 6.7-micron water vapor channel brightness temperature. NCEP analysis shows that a critical assumption of the retrieval theory, namely the constant temperature lapse rate,matches only in the tropical atmosphere. By statistical analyses of brightness temperature simulated by a radiative transfer model and of relative humidity, we examine the effect of lapse rate on this retrieval method and obtain retrieval parameters and error estimates applicable to the GMS-5 satellite over East Asia. If the retrieval parameters are properly chosen, the relative error of retrieving the upper tropospheric relative humidity in this region is less than 10%, and if applied to the low-latitude summer atmosphere, it is less than 5%.     

0-10 KM TEMPERATURE AND HUMIDITY PROFILES RETRIEVAL FROM GROUND-BASED MICROWAVE RADIOMETER

Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 are inputted into the MonoRTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234GHz to 58.8GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model’s effect by comparing its output with the real measured data and the microwave radiometer’s own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1K and 2.0K; the water vapor density’s RMS error is between 0.2 g/m3 and 1.93g/m3; and the relative humidity’s RMS error is between 2.5% and 18.6%.     

Comparison of COSMIC radio occultation refractivity profiles with radiosonde measurements

In recent years, radio occultation (RO) technology making use of global positioning system (GPS) signals has been exploited to obtain profiles of atmospheric parameters in the neutral atmosphere. In this paper, the RO refractivity profiles obtained from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) mission are statistically compared with the observations of 38 radiosonde stations provided by the Australian Bureau of Meteorology during the period from 15 July 2006 through 15 July 2007. Different collocation criteria are compared at first, and COSMIC RO soundings that occur within 3 hours and 300 km of radiosonde measurements are used for the final statistical comparison. The overall results show that the agreements between the COSMIC refractivity profiles and the radiosonde soundings from the 38 stations are very good at 0--30 km altitude, with mean absolute relative refractivity deviations of less than 0.5%. Latitudinal comparisons indicate that there are negative refractivity deviations in the lower troposphere over the low latitude and middle latitude regions and large standard deviations exist in the lower troposphere of low latitude regions, which can reach up to ~6%. The comparisons of COSMIC RO refractivity profiles and radiosonde observations for 3 polar stations in four different seasons indicate that the accuracy of GPS RO profiles is better in the Austral summer and autumn than in the Austral spring and winter during the year from September 2006 to August 2007.     

The impact of Global Positioning System data on the prediction of an extratropical cyclone: an observing system simulation experiment

In this paper, we report a series of observing system simulation experiments that we conducted to assess the potential impact of Global Positioning System/meteorology (GPS/MET) refractivity data on short-range numerical weather prediction. We first conducted a control experiment using the Penn State/NCAR mesoscale model MM5 at 90-km resolution on an extratropical cyclone known as the ERICA (Experiment on Rapidly Intensifying Cyclones over the Atlantic) IOP 4 storm. The results from the control experiment were then used to simulate GPS/MET refractivity observations with different spatial resolution and measurement characteristics. The simulated refractivity observations were assimilated into an 180-km model during a 6-h period, which was followed by a 48-h forecast integration. Key findings can be summarized as follows:

• The assimilation of refractivity data at the 180-km resolution can recover important atmospheric structures in temperature and moisture fields both in the upper and lower troposphere, and, through the internal model dynamical processes, also the wind fields. The assimilation of refractivity data led to a considerably more accurate prediction of the cyclone.

• Distributing the refractivity randomly in space and applying a line averaging did not alter the results significantly, while reducing the spatial resolution from 180 km to 360 km produced a moderately degraded result. Even at the 360-km resolution, the GPS-type refractivity data still have a notable positive impact on cyclone prediction.

• Restricting the refractivity data to altitude 3 km and above considerably degraded its impact on cyclone prediction. This degradation was greater than the combined effects of distributing the refractivity data randomly, performing line averaging, and reducing the resolution to 360 km.

These results showed that the GPS/MET refractivity data is likely to have a significant impact on short-range operational numerical weather prediction. The random distribution and line averaging associated with the inherent GPS occultation do not pose a problem for effective assimilation. On the other hand, these results also argue that we need to improve the GPS/MET retrieval algorithm in order to recover useful data in the lower troposphere, and to increase the number of low-earth-orbiting satellites carrying GPS receivers in order to increase the density of GPS soundings, so that the potential impact of GPS/MET refractivity data on numerical weather prediction can be fully realized.     

Wet Refractivity Tomography with an hnproved Kalman-Filter Method

An improved retrieval method, which uses the solution with a Gaussian constraint as the initial state variables for the Kalman Filtering (KF) method, was developed to retrieve the wet refractivity profiles from slant wet delays (SWD) extracted by the double-differenced (DD) GPS method. The accuracy of the GPS-derived SWDs is also tested in this study against the measurements of a water vapor radiometer (WVR) and a weather model. It is concluded that the GPS-derived SWDs have similar accuracy to those measured with WVR and are much higher in quality than those derived from the weather model used. The developed method is used to retrieve the 3D wet refractivity distribution in the Hong Kong region. The retrieved profiles agree well with the radiosonde observations, with a difference of about 4 mm km-1 in the low levels. The accurate profiles obtained with this method are applicable in a number of meteorological applications.     

全谱反演技术在山基掩星数据处理中的应用

山基掩星观测是一种探测特定区域气象数据的新技术。该文从山基掩星探测技术的概念出发,阐述了山基掩星探测技术的特点和优势,介绍了山基掩星观测数据几何光学反演方法; 在详细介绍全谱反演技术的基础上,提出了采用全谱反演技术来处理山基掩星观测数据的新方法。采用该方法对2005年8月在河北兴隆雾灵山获取的山基掩星观测试验探测数据进行了处理,成功获得了大气折射率剖面, 将全谱反演结果与几何光学反演结果和同时获取的时空匹配的探空数据进行了比对分析。结果表明:全谱反演结果与几何光学反演结果平均相对偏差小于2%,全谱反演结果偏小,标准偏差低于3%;全谱反演结果与常规探空结果的平均相对偏差为8.15%,全谱反演结果偏小,标准偏差为1.4%。     

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.     

Validation of the Atmospheric Infrared Sounder Retrieval Products over China and Their Application in Numerical Mode

The atmospheric infrared sounder (AIRS) instrument onboard Aqua Satellite is a high spectral resolution infrared sounder. In recent years, AIRS has gradually become the primary method of atmospheric vertical observations. To examine the validation of AIRS retrieval products (V3.0) over China, the AIRS surface air temperature retrievals were compared with the ground observations obtained from 540 meteorological stations in July 2004 and January 2005, respectively. The sources of errors were considerably discussed. Based on the error analysis, the AIRS retrieved surface air temperature products were systemically corrected. Moreover, the AIRS temperature and humidity profile retrievals were compared with T213 numerical forecasting products. Because T213 forecasting products are not the actual atmospheric states,to further verify the validation, the AIRS temperature and humidity profile products were assimilated into the MM5 model through the analysis nudging. In this paper, the case on February 14, 2005 in North China was simulated in detail. Then, we investigated the effects of AIRS retrievals on snowfall, humidity field,vertical velocity field, divergence field, and cloud microphysical processes. The major results are: (1) the errors of AIRS retrieved surface air temperature products are largely systematic deviations, for which the influences of terrain altitude and surface types are the major reasons; (2) the differences between the AIRS atmospheric profile retrievals and T213 numerical prediction products in temperature are generally less than 2 K, the differences in relative humidity are generally less than 25%; and (3) the AIRS temperature and humidity retrieval products can adjust the model initial field, and thus can improve the capacity of snowfall simulation to some extent.     

COSMIC-2掩星反演数据质量分析

为评价COSMIC-2掩星反演数据精度,利用探空站点资料,对比验证了基于COSMIC-2附加相位数据由无线电掩星数据处理软件ROPP计算得到的大气折射率及温湿廓线。实验结果表明:COSMIC-2数据经反演后的大气廓线质量较高,大气折射率相对偏差标准差为1.5%~4.3%,大气温度和相对湿度均方根误差分别为1.3~2.2 K、10%~15%;低纬地区样本精度总体优于中纬地区; 季节性差异不明显,但冬春季样本精度总体略优于夏秋季。反演数据与COSMIC-2二级产品相比,折射率与温度在部分高度层精度更优,相对湿度在总体上精度更优。