Overview of Researches on All-Sky Satellite Microwave Data Variational Assimilation

As the important components of the earth’s atmospheric system, cloud and precipitation strongly affect the global hydrology and energy cycles through the interaction of solar and infrared radiation with cloud droplets and the release of latent heat in precipitation development. The microwave observations in cloudy and rainy conditions have a large amount of information closely related to the development of weather systems, especially the severe weather systems like typhoon and rainstorm. Nevertheless, satellite microwave observations are usually only assimilated in clear-sky above the ocean and their cloud and precipitation content is discarded. Over the past two decades, several Numerical Weather Prediction (NWP) centers have gradually developed the “all-sky” approach to make use of the cloud- and precipitation-affected microwave radiances. It’s been proved that the all-sky assimilation can be used to improve the first guessed mass, wind, humidity, cloud and precipitation through the tracer effect. For providing an investigated reference for the future research of all-weather assimilation in domestic numerical weather forecast, this paper reviewed the all-sky assimilation methods using microwave observation data, analyzed the advantages and disadvantages of each method, and discussed the key technical problems and the existing difficulties and challenges in this field. With the development and application of the new generation of NWP model in China, advancing the domestic research of all-weather data assimilation technology will bring more scientific and practical benefits in the future.     

Polarized microwave forward model simulations for tropical storm <Emphasis Type="Italic">Fanoos</Emphasis>

In the present study, forward radiative transfer simulations are carried out for the tropical cyclone Fanoos that hit the coast off south India in December 2005. The in-house radiative transfer package used for this study employs the doubling and adding method to calculate radiances leaving the top of the one dimensional precipitating atmosphere. The particle drop size distribution is assumed to follow a modified gamma distribution in respect of the cloud liquid water and cloud ice water content. For precipitation, the Marshall-Palmer particle size distribution is used. All the hydrometeor particles are assumed to be spherical and Lorentz Mie theory is used to evaluate the interaction parameters like absorption, scattering coefficients and polarized scattering matrix. In order to validate the drop size distributions and interaction parameter calculations, the simulated brightness temperatures are compared with the TMI measured brightness temperatures for all the channels. For carrying out this exercise, vertical hydrometeors retrieved by TMI are used as input. The differences between simulated and measured brightness temperatures are found to be within ±10%. The maximum difference in the brightness temperatures between the present work and the Eddington model which the TRMM algorithm employs is about 4.5K. This may become significant when retrieval of precipitation is attempted by combining the forward model with a suitable retrieval strategy, under tropical conditions.     

Retrieval of hydrometeors from microwave radiances with a polarized radiative transfer model

This paper reports the results of a Bayesian-based algorithm for the retrieval of hydrometeors from microwave satellite radiances. The retrieval technique proposed makes use of an indigenously developed polarized radiative transfer (RT) model that drives a data driven optimization engine (Bayesian) to perform retrievals of rain and other hydrometeors in a multi-layer, plane parallel raining atmosphere. For the sake of completeness and for the purposes of comparison, retrievals with Artificial Neural Networks (ANN) have also been done. Retrievals have been done first with a simplified two-layer atmosphere, where assumed values of hydrometeors are given to the forward model and these are taken as ‘measured radiances’. The efficacy of the two retrieval strategies is then tested for this case in order to establish accuracy and speed. The highlight of the work is however, the case study wherein a tropical storm in the Bay of Bengal is taken up, to critically examine the performance of the retrieval algorithm for an extreme event wherein a 14-layer realistic, raining atmosphere has been considered and retrievals are done against Tropical Rainfall Measuring Mission (TRMM) measured radiances. The key novelties of the work are:

Radiative transfer simulations for the MADRAS imager of Megha-Tropiques

This paper reports the radiative transfer simulations for the passive microwave radiometer onboard the proposed Indian climate research satellite Megha-Tropiques due to be launched in 2011. These simulations have been performed by employing an in-house polarized radiative transfer code for raining systems ranging from depression and tropical cyclones to the Indian monsoon. For the sake of validation and completeness, simulations have also been done for the Tropical Rainfall Measuring Mission (TRMM)’s Microwave Imager (TMI) of the highly successful TRMM mission of NASA and JAXA. The paper is essentially divided into two parts: (a) Radiometer response with specific focus on high frequency channels in both the radiometers is discussed in detail with a parametric study of the effect of four hydrometeors (cloud liquid water, cloud ice, precipitating water and precipitating ice) on the brightness temperatures. The results are compared with TMI measurements wherever possible. (b) Development of a neural network-based fast radiative transfer model is elucidated here. The goal is to speed up the computational time involved in the simulation of brightness temperatures, necessitated by the need for quick and online retrieval strategies. The neural network model uses hydrometeor profiles as inputs and simulates spectral microwave brightness temperature at multiple frequencies as output. A huge database is generated by executing the in-house radiative transfer code for seven different cyclones occurred in North Indian Ocean region during the period 2001–2006. A part of the dataset is used to train the network while the remainder is used for testing purposes. For the purpose of testing, a typical scene from the southwest monsoon rain is also considered. The results obtained are very encouraging and show that the neural network is able to mimic the underlying physics of the radiative transfer simulations with a correlation coefficient of over 99%.     

Real-time prediction of a severe cyclone ��Jal�� over Bay of Bengal using a high-resolution mesoscale model WRF (ARW)

Real-time predictions for the JAL severe cyclone formed in November 2010 over Bay of Bengal using a high-resolution Weather Research and Forecasting (WRF ARW) mesoscale model are presented. The predictions are evaluated with different initial conditions and assimilation of observations. The model is configured with two-way interactive nested domains and with fine resolution of 9?km for the region covering the Bay of Bengal. Simulations are performed with NCEP GFS 0.5° analysis and forecasts for initial/boundary conditions. To examine the impact of initial conditions on the forecasts, eleven real-time numerical experiments are conducted with model integration starting at 00, 06, 12, 18 UTC 4 Nov, 5?Nov and 00, 06, 12 UTC 6 Nov and all ending at 00 UTC 8 Nov. Results indicated that experiments starting prior to 18 UTC 04 Nov produced faster moving cyclones with higher intensity relative to the IMD estimates. The experiments with initial time at 18 UTC 04 Nov, 00 UTC 05 Nov and with integration length of 78?h and 72?h produced best prediction comparable with IMD estimates of the cyclone track and intensity parameters. To study the impact of observational assimilation on the model predictions FDDA, grid nudging is performed separately using (1) land-based automated weather stations (FDDAAWS), (2) MODIS temperature and humidity profiles (FDDAMODIS), and (3) ASCAT and OCEANSAT wind vectors (FDDAASCAT). These experiments reduced the pre-deepening period of the storm by 12?h and produced an early intensification. While the assimilation of AWS data has shown meagre impact on intensity, the assimilation of scatterometer winds produced an intermittent drop in intensity in the peak stage. The experiments FDDAMODIS and FDDAQSCAT produced minimum error in track and intensity estimates for a 90-h prediction of the storm.     

Assimilation of satellite observations of the atmosphere

Measurements of the atmosphere by satellite were first collected in the 1960s. However, it was not until the mid-1990s that these observations were translated into systematic improvements of numerical weather forecasts. We present here the data and methodology of data assimilation that enabled this achievement. Data assimilation is essentially a filtering processing that exploits the (assumed) known error statistical properties of the observations and of the underlying numerical model in which data are assimilated. It is also a process which corrects the state of the numerical model with physical observations of the real world. This part relies on (assumed) known physical laws to relate meteorological quantities (such as temperature, humidity, pressure, and wind) to observables. Atmospheric data collected by satellite all come from the interaction of electromagnetic waves with the atmosphere. Satellite data assimilation has greatly supported the progress in numerical weather prediction and holds promises for climate studies, for example via reanalysis.     

Improvements in medium range weather forecasting system of India

Medium range weather forecasts are being generated in real time using Global Data Assimilation Forecasting System (GDAFS) at NCMRWF since 1994. The system has been continuously upgraded in terms of data usage, assimilation and forecasting system. Recently this system was upgraded to a horizontal resolution of T574 (about 22 km) with 64 levels in vertical. The assimilation scheme of this upgraded system is based on the latest Grid Statistical Interpolation (GSI) scheme and it has the provision to use most of available meteorological and oceanographic satellite datasets besides conventional meteorological observations. The new system has an improved procedure for relocating tropical cyclone to its observed position with the correct intensity. All these modifications have resulted in improvement of skill of medium range forecasts by about 1 day.     

Analysis and characterization of the synergistic AIRS and MODIS cloud-cleared radiances

The Atmospheric Infrared Sounder (AIRS) and MODerate-Resolution Imaging Spectroradiometer (MODIS) on board NASA Earth Observing System (EOS) Aqua spacecraft measure the upwelling infrared radiance used for numerous remote-sensing- and climate-related applications. AIRS provides high spectral resolution infrared radiances, while MODIS provides collocated high spatial resolution radiances at 16 broad infrared bands. An optimal algorithm for cloud-clearing has been developed for AIRS cloudy soundings at the University of Wisconsin-Madison, where the spatially and spectrally collocated AIRS and MODIS data has been used to analyze the characteristic of this algorithm. An analysis and characterization of the global AIRS cloud-cleared radiances using the bias and the standard deviation between the cloud-cleared and the nearby clear measurements are studied. Scene inhomogeneity for both land- and water-surface types has been estimated to account for the assessed error. Both monthly and seasonal changes of global AIRS/MODIS cloud-clearing performance also have been analyzed.     

A new ensemble-based data assimilation algorithm to improve track prediction of tropical cyclones

This paper proposes a new ensemble-based algorithm that assimilates the vertical rain structure retrieved from microwave radiometer and radar measurements in a regional weather forecast model, by employing a Bayesian framework. The goal of the study is to evaluate the capability of the proposed technique to improve track prediction of tropical cyclones that originate in the North Indian Ocean. For this purpose, the tropical cyclone Jal has been analyzed by the community mesoscale weather model, weather research and forecasting (WRF). The ensembles of prognostic variables such as perturbation potential temperature (θ, k), perturbation geopotential (?, m²/s²), meridional (U) and zonal velocities (V) and water vapor mixing ratio (q _v , kg/kg) are generated by the empirical orthogonal function technique. An over pass of the tropical rainfall-measuring mission (TRMM) satellite occurred on 06th NOV 0730 UTC over the system, and the observations from the radiometer and radar on board the satellite(1B11 data products) are inverted using a combined in-home radiometer-radar retrieval technique to estimate the vertical rain structure, namely the cloud liquid water, cloud ice, precipitation water and precipitation ice. Each ensemble is input as a possible set of initial conditions to the WRF model from 00 UTC which was marched in time till 06th NOV 0730 UTC. The above-mentioned hydrometeors from the cloud water and rain water mixing ratios are then estimated for all the ensembles. The Bayesian filter framework technique is then used to determine the conditional probabilities of all the candidates in the ensemble by comparing the retrieved hydrometeors through measured TRMM radiances with the model simulated hydrometeors. Based on the posterior probability density function, the initial conditions at 06 00 UTC are then corrected using a linear weighted average of initial ensembles for the all prognostic variables. With these weighted average initial conditions, the WRF model has been run up to 08th Nov 06 UTC and the predictions are then compared with observations and the control run. An ensemble independence study was conducted on the basis of which, an optimum of 25 ensembles is arrived at. With the optimum ensemble size, the sensitivity of prognostic variables was also analyzed. The model simulated track when compared with that obtained with the corrected set of initial conditions gives better results than the control run. The algorithm can improve track prediction up to 35 % for a 24 h forecast and up to 12 % for a 54 h forecast.     

Retrieval of humidity and temperature profiles over the oceans from INSAT 3D satellite radiances

In this study, retrieval of temperature and humidity profiles of atmosphere from INSAT 3D-observed radiances has been accomplished. As the first step, a fast forward radiative transfer model using an Artificial neural network has been developed and it was proven to be highly effective, giving a correlation coefficient of 0.97. In order to develop this, a diverse set of physics-based clear sky profiles of pressure (P), temperature (T) and specific humidity (q) has been developed. The developed database was further used for geophysical retrieval experiments in two different frameworks, namely, an ANN and Bayesian estimation. The neural network retrievals were performed for three different cases, viz., temperature only retrieval, humidity only retrieval and combined retrieval. The temperature/humidity only ANN retrievals were found superior to combined retrieval using an ANN. Furthermore, Bayesian estimation showed superior results when compared with the combined ANN retrievals.     

Artificial neural network approach for estimation of surface specific humidity and air temperature using multifrequency scanning microwave radiometer

Microwave sensor MSMR (Multifrequency Scanning Microwave Radiometer) data onboard Oceansat-1 was used for retrieval of monthly averages of near surface specific humidity (Q _a) and air temperature (T _a) by means of Artificial Neural Network (ANN). The MSMR measures the microwave radiances in 8 channels at frequencies of 6.6, 10.7, 18 and 21 GHz for both vertical and horizontal polarizations. The artificial neural networks (ANN) technique is employed to find the transfer function relating the input MSMR observed brightness temperatures and output (Q _a andT _a) parameters. Input data consist of nearly 28 months (June 1999 – September 2001) of monthly averages of MSMR observed brightness temperature and surface marine observations ofQ _a andT _a from Comprehensive Ocean-Atmosphere Data Set (COADS). The performance of the algorithm is assessed with independent surface marine observations. The results indicate that the combination of MSMR observed brightness temperatures as input parameters provides reasonable estimates of monthly averaged surface parameters. The global root mean square (rms) differences are 1.0‡C and 1.1 g kg⁻¹ for air temperature and surface specific humidity respectively.     

Real-time nowcast of a cloudburst and a thunderstorm event with assimilation of Doppler weather radar data

Extreme weather events such as cloudburst and thunderstorms are great threat to life and property. It is a great challenge for the forecasters to nowcast such hazardous extreme weather events. Mesoscale model (ARPS) with real-time assimilation of DWR data has been operationally implemented in India Meteorological Department (IMD) for real-time nowcast of weather over Indian region. Three-dimensional variational (ARPS3DVAR) technique and cloud analysis procedure are utilized for real-time data assimilation in the model. The assimilation is performed as a sequence of intermittent cycles and complete process (starting from reception, processing and assimilation of DWR data, running of ARPS model and Web site updation) takes less than 20 minutes. Thus, real-time nowcast for next 3 h from ARPS model is available within 20 minutes of corresponding hour. Cloudburst event of September 15, 2011, and thunderstorm event of October 22, 2010, are considered to demonstrate the capability of ARPS model to nowcast the extreme weather events in real time over Indian region. Results show that in both the cases, ARPS3DVAR and cloud analysis technique are able to extract hydrometeors from radar data which are transported to upper levels by the strong upward motion resulting in the distribution of hydrometeors at various isobaric levels. Dynamic and thermodynamic structures of cloudburst and thunderstorm are also well simulated. Thus, significant improvement in the initial condition is noticed. In the case of cloudburst event, the model is able to capture the sudden collisions of two or more clouds during 09–10 UTC. Rainfall predicted by the model during cloudburst event is over 100 mm which is very close to the observed rainfall (117 mm). The model is able to predict the cloudburst with slight errors in time and space. Real-time nowcast of thunderstorm shows that movement, horizontal extension, and north–south orientation of thunderstorm are well captured during first hour and deteriorate thereafter. The amount of rainfall predicted by the model during thunderstorm closely matches with observation with slight errors in the location of rainfall area. The temporal and spatial information predicted by ARPS model about the sudden collision/merger and broken up of convective cells, intensification, weakening, and maintaining intensity of convective cells has added value to a human forecast.     

On improving tropical cyclone track forecasts using a scale-selective data assimilation approach: a case study

A dynamical downscaling approach based on scale-selective data assimilation (SSDA) is applied to tropical cyclone (TC) track forecasts. The results from a case study of super Typhoon Megi (2010) show that the SSDA approach is very effective in improving the TC track forecasts by fitting the large-scale wind field from the regional model to that from the global forecast system (GFS) forecasts while allowing the small-scale circulation to develop freely in the regional model. A comparison to the conventional spectral-nudging four-dimensional data assimilation (FDDA) indicates that the SSDA approach outperforms the FDDA in TC track forecasts because the former allows the small-scale features in a regional model to develop more freely than the latter due to different techniques used. In addition, a number of numerical experiments are performed to investigate the sensitivity of SSDA’s effect in TC track forecasts to some parameters in SSDA, including the cutoff wave number, the vertical layers of the atmosphere being adjusted, and the interval of SSDA implementation. The results show that the improvements are sensitive in different extent to the above three parameters.     

联合机载PLMR微波辐射计和MODIS产品反演黑河中游张掖绿洲土壤水分研究

土壤水分是气候、水文学研究中的重要变量,微波遥感是获取区域地表土壤水分的重要手段,而L波段更是微波土壤水分反演的最优波段。依托HiWATER黑河中游绿洲试验区的地面观测及机载PLMR微波辐射计亮温数据,利用微波辐射传输模型L-MEB,并将MODIS地表温度产品(MOD11A1)和叶面积指数产品(MYD15A2)作为模型及反演中的先验辅助信息,借助LM优化算法,通过PLMR双极化多角度的亮温观测,针对土壤水分、植被含水量(VWC)和地表粗糙度这3个主要参数,分别进行土壤水分单参数反演、土壤水分与VWC或粗糙度的双参数反演以及这3个参数的同时反演。通过对不同反演方法的比较可以得出结论,多源辅助数据及PLMR双极化、多角度信息的应用可以显著降低反演的不确定性,提高土壤水分反演精度。证明在合理的模型参数和反演策略下,SMOS的L-MEB模型和产品算法可以达到0.04 cm3/cm3的反演精度,另外无线传感器网络可以在遥感产品真实性检验中起到重要作用。     

Estimation of surface latent heat fluxes from IRS-P4/MSMR satellite data

The brightness temperatures of the Microwave sensor MSMR (Multichannel Scanning Microwave Radiometer) launched in May 1999 onboard Indian Oceansat-1 IRS-P4 are used to develop a direct retrieval method for latent heat flux by multivariate regression technique. The MSMR measures the microwave radiances at 8 channels at frequencies of 6.6, 10.7, 18 and 21 GHz at both vertical and horizontal polarizations. It is found that the surface LHF (Latent Heat Flux) is sensitive to all the channels. The coefficients were derived using the National Centre for Environmental Prediction (NCEP) reanalysis data of three months: July, September, November of 1999. The NCEP daily analyzed latent heat fluxes and brightness temperatures observed by MSMR were used to derive the coefficients. Validity of the derived coefficients was checked within situ observations over the Indian Ocean and with NCEP analyzed LHF for global points. The LHF derived directly from the MSMR brightness temperature (Tb) yielded an accuracy of 35 watt/m². LHF was also computed by applying bulk formula using the geophysical parameters extracted from MSMR. In this case the errors were higher apparently due to the errors involved in derivation of the geophysical parameters.     

Numerical simulation of cyclonic storms FANOOS, NARGIS with assimilation of conventional and satellite observations using 3-DVAR

In this work, the impact of assimilation of conventional and satellite data is studied on the prediction of two cyclonic storms in the Bay of Bengal using the three-dimensional variational data assimilation (3D-VAR) technique. The FANOOS cyclone (December 6?C10, 2005) and the very severe cyclone NARGIS (April 28?CMay 2, 2008) were simulated with a double-nested weather research and forecasting (WRF-ARW) model at a horizontal resolution of 9?km. Three numerical experiments were performed using the WRF model. The back ground error covariance matrix for 3DVAR over the Indian region was generated by running the model for a 30-day period in November 2007. In the control run (CTL), the National Centers for Environmental Prediction (NCEP) global forecast system analysis at 0.5° resolution was used for the initial and boundary conditions. In the second experiment called the VARCON, the conventional surface and upper air observations were used for assimilation. In the third experiment (VARQSCAT), the ocean surface wind vectors from quick scatterometer (QSCAT) were used for assimilation. The CTL and VARCON experiments have produced higher intensity in terms of sea level pressure, winds and vorticity fields but with higher track errors. Assimilation of conventional observations has meager positive impact on the intensity and has led to negative impact on simulated storm tracks. The QSCAT vector winds have given positive impact on the simulations of intensity and track positions of the two storms, the impact is found to be relatively higher for the moderate intense cyclone FANOOS as compared to very severe cyclone NARGIS.     

非均匀月壤介质的被动微波辐射传输模拟

基于非均匀月壤物理模型和辐射传输方程,模拟月壤介质中的微波辐射传输特性,探讨频率、月壤厚度等与月表亮温的关系。结果表明:在低频段,月壤微波辐射亮温的动态变化范围较大,可探测的月壤厚度大,3 GHz时的最大可探测月壤厚度达12.4 m;在高频段对应的可探测月壤厚度较小,特别是从50GHz往后的频率段内,最大可探测月壤厚度均小于2 m。不同频率的亮温-厚度变化曲线没有交叉点,且频率越高,所能探测的月壤厚度越小。根据模拟结果,建立了月壤厚度与亮温的查找表。基于查找表,利用单个波段的亮温数据即可得到月壤厚度信息。     

Impact of 3D Var GSI-ENKF hybrid data assimilation system

The hybrid two-way coupled 3DEnsVar assimilation system was tested with the NCMRWF global data assimilation forecasting system. At present, this system consists of T574L64 deterministic model and the grid-point statistical interpolation analysis scheme. In this experiment, the analysis system is modified with a two-way coupling with an 80 member Ensemble Kalman Filter of T254L64 resolution and runs are carried out in parallel to the operational system for the Indian summer monsoon season (June–September) for the year 2015 to study its impact. Both the assimilation systems are based on NCEP GFS system. It is found that hybrid assimilation marginally improved the quality of the forecasts of all variables over the deterministic 3D Var system, in terms of statistical skill scores and also in terms of circulation features. The impact of the hybrid system in prediction of extreme rainfall and cyclone track is discussed.     

Optimizing the routing of ship’s tropical cyclone avoidance based on the numerical forecasts

In this paper, an algorithm to design a shortest-time route for a ship to avoid a tropical cyclone (TC) is proposed. The proposed algorithm takes into account the influence of the changing winds and sea waves on ship’s speed and the corresponding risk using the forecasts from a numerical weather prediction model. Experimental results show that the new algorithm is able to save more time comparing with the traditional sector diagram typhoon avoidance method. In the application of the new algorithm to the navigation practice, the distance between adjacent alternative waypoints should be adjusted to meet the navigational needs, and the route should be updated simultaneously with TC forecasts from a numerical weather prediction model.     

Assimilation of Indian Doppler Weather Radar observations for simulation of mesoscale features of a land-falling cyclone

In this paper, impact of Indian Doppler Weather Radar (DWR) data, i.e., reflectivity (Z), radial velocity (Vr) data individually and in combination has been examined for simulation of mesoscale features of a land-falling cyclone with Advance Regional Prediction System (ARPS) Model at 9-km horizontal resolution. The radial velocity and reflectivity observations from DWR station, Chennai (lat. 13.0°N and long. 80.0°E), are assimilated using the ARPS Data Assimilation System (ADAS) and cloud analysis scheme of the model. The case selected for this study is the Bay of Bengal tropical cyclone NISHA of 27–28 November 2008. The study shows that the ARPS model with the assimilation of radial wind and reflectivity observations of DWR, Chennai, could simulate mesoscale characteristics, such as number of cells, spiral rain band structure, location of the center and strengthening of the lower tropospheric winds associated with the land-falling cyclone NISHA. The evolution of 850 hPa wind field super-imposed vorticity reveals that the forecast is improved in terms of the magnitude and direction of lower tropospheric wind, time, and location of cyclone in the experiment when both radial wind and reflectivity observations are used. With the assimilation of both radial wind and reflectivity observations, model could reproduce the rainfall pattern in a more realistic way. The results of this study are found to be very promising toward improving the short-range mesoscale forecasts.