Assimilation of Satellite Microwave Observations in the Rainband of Hurricanes Using Bayesian Monte Carlo Technique
No abstract available
Moradi, Isaac↗
Engineering topics
Moradi, Isaac
Publications and source records attributed to Moradi, Isaac.
Assimilation of Satellite Microwave Observations in the Rainband of Hurricanes Using Bayesian Monte Carlo Technique
No abstract available
Moradi, Isaac↗
Assimilation of Microwave Cloudy Observations over the Rainband of Hurricanes Using a Novel Bayesian Monte Carlo Technique
We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the rainbands of tropical cyclones (TC). These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated into numerical models to provide accurate initial conditions for the NWP models. The BMCI technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI).
Moradi, Isaac↗
Assimilation of Microwave Cloudy Radiances into NASA GEOS Model Using a Novel Bayesian Monte Carlo Technique
No abstract available
Moradi, Isaac↗
Assimilation of Satellite Microwave Observations in the Rainband of Hurricanes Using Bayesian Monte Carlo Technique
No abstract available
Moradi, Isaac↗
Assimilation of Microwave Observations in the Rainbands of Tropical Cyclones
We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of tropical cyclones (TC). These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated into numerical models to provide accurate initial conditions for the NWP (Numerical Weather Prediction) models. The BMCI technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI). The retrieved profiles are then assimilated into Hurricane WRF (Weather Research and Forecasting) using the GSI (Gridpoint Statistical Interpolation) data assimilation system.
Moradi, Isaac↗
Assimilation of All-Weather Microwave Cloudy Radiances Using a Novel Bayesian Monte Carlo Technique into NASA GEOS-5
No abstract available
Moradi, Isaac↗
CRTM Support to GMAO: Validation and Coefficient Generation
Radiative transfer (RT) models play a very critical role in assimilating satellite radiances into NWP models. Community Radiative Transfer Model (CRTM) developed by Joint Center for Satellite Data Assimilation is widely used in the U.S. as the forward operator for the assimilation of microwave and infrared satellite radiances. This work shows an snapshot of the GMAO radiative transfer modeling activities to advance the assimilation of satellite radiances as well as to facilitate the GMAO activities on Observing System Simulation Experiments (OSSE).
CRTM↗
On the Usage of Recalibrated Radiance in Reanalysis Experiments
Usage of re-calibrated data in reanalysis systems are quite common due to their uniform and continuous data distribution. Recalibrated radiosonde temperature and SSU radiances have already shown positive impact in MERRA and MERRA2 reanalyses. In this study recalibrated AMSU-A radiances are used to study sudden degradation of observation statistics which are noticed with the introduction of new AMSU-A radiances in MERRA2. In particular, the analysis temperature in the upper stratosphere showed large variability due to lack of viable observation at that height in the atmosphere. Our results show that the re-calibrated AMSU-A radiance in a system similar to MERRA2 is capable of mitigating the problem.
Chattopadhyay, Mohar↗
Assimilation of GMI and ATMS Observations in the Rainbands of Hurricanes
We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of tropical cyclones (TC). These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated into numerical models to provide accurate initial conditions for the NWP models. The BMCI technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI). The retrieved profiles are then assimilated into Hurricane WRF using GSI data assimilation system. This talk presents the development of the BMCI retrieval system as well as the preliminary results of assimilating the retrievals into NASA GEOS-5 model.
Moradi, Isaac↗
Assimilation of GMI and ATMS Observations in the Rainbands of Hurricanes
No abstract available
Moradi, Isaac↗
Assimilation of All-Weather GMI and ATMS Observations into HWRF
We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of TCs. These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated to provide accurate initial conditions for the NWP models. The technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI).
Moradi, Isaac↗
On the Limitations of Variational Bias Correction
Satellite radiances are the largest dataset assimilated into Numerical Weather Prediction (NWP) models, however the data are subject to errors and uncertainties that need to be accounted for before assimilating into the NWP models. Variational bias correction uses the time series of observation minus background to estimate the observations bias. This technique does not distinguish between the background error, forward operator error, and observations error so that all these errors are summed up together and counted as observation error. We identify some sources of observations errors (e.g., antenna emissivity, non-linearity in the calibration, and antenna pattern) and show the limitations of variational bias corrections on estimating these errors.
Moradi, Isaac↗
The OSSE Framework at the NASA Global Modeling and Assimilation Office (GMAO)
This abstract summarizes the OSSE framework developed at the Global Modeling and Assimilation Office at the National Aeronautics and Space Administration (NASA/GMAO). Some of the OSSE techniques developed at GMAO including simulation of realistic observations, e.g., adding errors to simulated observations, are now widely used by the community to evaluate the impact of new observations on the weather forecasts. This talk presents some of the recent progresses and challenges in simulating realistic observations, radiative transfer modeling support for the GMAO OSSE activities, assimilation of OSSE observations into data assimilation systems, and evaluating the impact of simulated observations on the forecast skills.
Moradi, Isaac↗
Assimilation of All-Weather GMI and ATMS Observations into HWRF
We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of TCs. These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated to provide accurate initial conditions for the NWP models. The technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI).
Moradi, Isaac↗
Description of the GMAO OSSE for Weather Analysis Software Package: Version 3
The Global Modeling and Assimilation Office (GMAO) at the NASA Goddard Space Flight Center has developed software and products for conducting observing system simulation experiments (OSSEs) for weather analysis applications. Such applications include estimations of potential effects of new observing instruments or data assimilation techniques on improving weather analysis and forecasts. The GMAO software creates simulated observations from nature run (NR) data sets and adds simulated errors to those observations. The algorithms employed are much more sophisticated, adding a much greater degree of realism, compared with OSSE systems currently available elsewhere. The algorithms employed, software designs, and validation procedures are described in this document. Instructions for using the software are also provided.
OSSE↗
CRTM Support to GMAO, Validation and Coefficient Generation
Radiative transfer (RT) models play a very critical role in assimilating satellite radiances into NWP models. The RT models are used as forward operator to simulate satellite radiances from atmopspheric control variables such as pressure, temperature, water vapor, and ozone. However because line-by-line RT models are computationally very expensive, fast RT models have been developed and advanced especially in past two decades to overcome these limitations. Community Radiative Transfer Model (CRTM) developed by Joint Center for Satellite Data Assimilation is widely used in the U.S. as the forward operator for the assimilation of microwave and infrared satellite radiances. This abstract summarizes the GMAO activities in the support of CRTM including generating training coefficients for new instruments as well as developments for assimilating satellite radiances from shortwave infrared channels.
Moradi, Isaac↗