scholarly journals The Use of Composite GOES-R Satellite Imagery to Evaluate a TC Intensity and Vortex Structure Forecast by an FV3GFS-Based Hurricane Forecast Model

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 126
Author(s):  
Shaowu Bao ◽  
Zhan Zhang ◽  
Evan Kalina ◽  
Bin Liu
Keyword(s):  
Satellite Images ◽  
Radiative Transfer Model ◽  
Mature Stage ◽  
Synthetic Image ◽  
Asymmetric Structure ◽  
Transfer Model ◽  
State Variables ◽  
Model Forecast ◽  
High Resolution Data ◽  
Verification Methods

The HAFS model is an effort under the NGGPS and UFS initiatives to create the next generation of hurricane prediction and analysis system based on FV3-GFS. It has been validated extensively using traditional verification indicators such as tracker error and biases, intensity error and biases, and the radii of gale, damaging and hurricane strength winds. While satellite images have been used to verify hurricane model forecasts, they have not been used on HAFS. The community radiative transfer model CRTM is used to generate model synthetic satellite images from HAFS model forecast state variables. The 24 forecast snapshots in the mature stage of hurricane Dorian in 2019 are used to generate a composite model synthetic GOES-R infrared brightness image. The composite synthetic image is compared to the corresponding composite image generated from the observed GOES-R data, to evaluate the model forecast TC vortex intensity, size, and asymmetric structure. Results show that the HAFS forecast TC Dorian agrees reasonably well with the observation, but the forecast intensity is weaker, its overall vortex size smaller, and the radii of its eye and maximum winds larger than the observed. The evaluation results can be used to further improve the model. While these results are consistent with those obtained by traditional verification methods, evaluations based on composite satellite images provide an additional benefit with richer information because they have near-real-times spatially and temporally continuous high-resolution data with global coverage. Composite satellite infrared images could be used routinely to supplement traditional verification methods in the HAFS and other hurricane model evaluations. Note since this study only evaluated one hurricane, the above conclusions are only applicable to the model behavior of the mature stage of hurricane Dorian in 2019, and caution is needed to extend these conclusions to expect model biases in predicting other TCs. Nevertheless, the consistency between the evaluation using composite satellite images and the traditional metrics, of hurricane Dorian, shows that this method has the potential to be applied to other storms in future studies.

scholarly journals All-Sky Radiance Assimilation of ATMS in HWRF: A Demonstration Study

2018 ◽  
Vol 147 (1) ◽  
pp. 85-106 ◽  
Author(s):  
Ting-Chi Wu ◽  
Milija Zupanski ◽  
Lewis D. Grasso ◽  
Christian D. Kummerow ◽  
Sid-Ahmed Boukabara
Keyword(s):  
Single Case ◽  
Control Variable ◽  
Data Availability ◽  
Advanced Technology ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
State Variables ◽  
Initial State ◽  
Clear Sky ◽  
Analysis System

Abstract Satellite all-sky radiances from the Advanced Technology Microwave Sounder (ATMS) are assimilated into the Hurricane Weather Research and Forecasting (HWRF) Model using the hybrid Gridpoint Statistical Interpolation analysis system (GSI). To extend the all-sky capability recently developed for global applications to HWRF, some modifications in HWRF and GSI are facilitated. In particular, total condensate is added as a control variable, and six distinct hydrometeor habits are added as state variables in hybrid GSI within HWRF. That is, clear-sky together with cloudy and precipitation-affected satellite pixels are assimilated using the Community Radiative Transfer Model (CRTM) as a forward operator that includes hydrometeor information and Jacobians with respect to hydrometeor variables. A single case study with the 2014 Atlantic storm Hurricane Cristobal is used to demonstrate the methodology of extending the global all-sky capability to HWRF due to ATMS data availability. Two data assimilation experiments are carried out. One experiment uses the operational configuration and assimilates ATMS radiances under the clear-sky condition, and the other experiment uses the modified HWRF system and assimilates ATMS radiances under the all-sky condition with the inclusion of total condensate update and cycling. Observed and synthetic Geostationary Operational Environmental Satellite (GOES)-13 data along with Global Precipitation Measurement Mission (GPM) Microwave Imager (GMI) data from the two experiments are used to show that the experiment with all-sky ATMS radiances assimilation has cloud signatures that are supported by observations. In contrast, there is lack of clouds in the initial state that led to a noticeable lag of cloud development in the experiment that assimilates clear-sky radiances.

scholarly journals Satellite Data Assimilation in Numerical Weather Prediction Models. Part II: Uses of Rain-Affected Radiances from Microwave Observations for Hurricane Vortex Analysis

2007 ◽  
Vol 64 (11) ◽  
pp. 3910-3925 ◽  
Author(s):  
Fuzhong Weng ◽  
Tong Zhu ◽  
Banghua Yan
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Forecast Model ◽  
Background Information ◽  
Radiative Transfer Model ◽  
Asymmetric Structure ◽  
Transfer Model ◽  
Surface Parameters

Abstract A hybrid variational scheme (HVAR) is developed to produce the vortex analysis associated with tropical storms. This scheme allows for direct assimilation of rain-affected radiances from satellite microwave instruments. In the HVAR, the atmospheric temperature and surface parameters in the storms are derived from a one-dimension variational data assimilation (1DVAR) scheme, which minimizes the cost function of both background information and satellite measurements. In the minimization process, a radiative transfer model including scattering and emission is used for radiance simulation (see Part I of this study). Through the use of 4DVAR, atmospheric temperatures from the Advanced Microwave Sounding Unit (AMSU) and surface parameters from the Advanced Microwave Scanning Radiometer (AMSR-E) are assimilated into global forecast model outputs to produce an improved analysis. This new scheme is generally applicable for variable stages of storms. In the 2005 hurricane season, the HVAR was applied for two hurricane cases, resulting in improved analyses of three-dimensional structures of temperature and wind fields as compared with operational model analysis fields. It is found that HVAR reproduces detailed structures for the hurricane warm core at the upper troposphere. Both lower-level wind speed and upper-level divergence are enhanced with reasonable asymmetric structure.

scholarly journals Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

2017 ◽  
Vol 21 (1) ◽  
pp. 357-375 ◽  
Author(s):  
Anaïs Barella-Ortiz ◽  
Jan Polcher ◽  
Patricia de Rosnay ◽  
Maria Piles ◽  
Emiliano Gelati
Keyword(s):  
Radiative Transfer ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Function Analysis ◽  
Forthcoming Paper ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
State Variables ◽  
Brightness Temperatures

Abstract. L-band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture (SSM) by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm which yields SSM estimates. The work exposed compares brightness temperatures measured by the SMOS mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The two modelled sets were estimated using a radiative transfer model and state variables from two land-surface models: (i) ORCHIDEE and (ii) H-TESSEL. The radiative transfer model used is the CMEM. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations at the moment. Further hypotheses are proposed and will be explored in a forthcoming paper. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies.

scholarly journals Joint retrieval of surface reflectance and aerosol properties with continuous variations of the state variables in the solution space: Part 1: theoretical concept

2017 ◽  
Author(s):  
Yves Govaerts ◽  
Marta Luffarelli
Keyword(s):  
Radiative Transfer ◽  
Solution Space ◽  
The State ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Reflectance ◽  
State Variables ◽  
Atmospheric Scattering ◽  
Acceleration Techniques ◽  
Aerosol Properties

Abstract. This paper presents a new algorithm for the joint retrieval of surface reflectance and aerosol properties with continuous variations of the state variables in the solution space. This algorithm, named CISAR (Combined Inversion of Surface and AeRosol), relies on a simple atmospheric vertical structure composed of two layers and an underlying surface. Surface anisotropic reflectance effects are taken into account and radiatively coupled with atmospheric scattering. For this purpose, a fast radiative transfer model has been explicitly developed, which includes acceleration techniques to solve the radiative transfer equation and to calculate the Jacobians. The inversion is performed within an optimal estimation framework including prior information on the state variable magnitude and regularization constraints on their spectral and temporal variability. In each processed wavelength, the algorithm retrieves the parameters of the surface reflectance model, the aerosol total column optical thickness and single scattering properties. The CISAR algorithm functioning is illustrated with a series of simple experiments.

scholarly journals Simulation of SEVIRI infrared channels: a case study from the Eyjafjallajökull April/May 2010 eruption

2012 ◽  
Vol 5 (5) ◽  
pp. 7783-7813 ◽  
Author(s):  
A. Kylling ◽  
R. Buras ◽  
S. Eckhardt ◽  
C. Emde ◽  
B. Mayer ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Satellite Images ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Framework ◽  
Brightness Temperatures ◽  
Retrieval Technique ◽  
Infrared Radiances

Abstract. Infrared satellite images are widely and successfully used to detect and follow atmospheric ash from erupting volcanoes. We describe a new radiative transfer model framework for the simulation of infrared radiances, which can be compared directly with satellite images. This can be helpful to get insight into the processes that affect the satellite retrievals. As input to the radiative transfer model, the distribution of ash is provided by simulations with the FLEXPART Lagrangian particle dispersion model, meteorological cloud information is adopted from the ECMWF analysis and the radiative transfer modelling is performed with the MYSTIC 3-D radiative transfer model. The model framework is used to study an episode during the Eyjafjallajökull eruption in 2010. It is found that to detect ash by the reverse absorption retrieval technique, accurate representation of the ash particle size distribution is required. Detailed investigation of individual pixels displays the radiative effects of various combinations of ash, liquid water and ice clouds. In order to be clearly detectable, the ash clouds need to be located at some distance above other clouds. If ash clouds are mixed with water clouds or are located only slightly above water clouds, detection of the ash becomes difficult. Simulations were also made using the so-called independent pixel approximation (IPA) instead of the fully 3-D radiative transfer modeling. A comparison between these IPA simulations and the 3-D simulations revealed differences in brightness temperatures of up to ±25 K due to shadow effects. The presented model framework is useful for further studies of the processes that affect satellite imagery and may be used to test both new and existing ash retrieval algorithms.

scholarly journals Estimating and Reporting Uncertainties in Remotely Sensed Atmospheric Composition and Temperature

2019 ◽  
Author(s):  
Thomas von Clarmann ◽  
Douglas A. Degenstein ◽  
Nathaniel J. Livesey ◽  
Stefan Bender ◽  
Amy Braverman ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
A Priori ◽  
Atmospheric Composition ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Detailed Knowledge ◽  
State Variables ◽  
Retrieval Technique ◽  
Retrieval Scheme ◽  
Data Volume

Abstract. Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. Reported characterization data should be intercomparable between different instruments, empirically validatable, grid-independent, usable without detailed knowledge of the instrument or retrieval technique, traceable, and still have reasonable data volume. The latter condition of adequacy may force one to work with representative rather than individual characterization data. The main sources of uncertainty are measurement noise, calibration errors, simplifications and idealizations in the radiative transfer model and retrieval scheme, auxiliary data errors, and uncertainties in atmospheric or instrumental parameters. Some of these errors affect the result in a random way, while others chiefly cause a bias or are of mixed character. Beyond this, it is of utmost importance to know the influence of any constraint and prior information on the solution. While different instruments or retrieval schemes may require different error estimation schemes, we provide a list of recommendations which shall help to unify retrieval error reporting.

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