scholarly journals Reconstruction of Typhoon Structure Using 3-Dimensional Doppler Radar Radial Velocity Data with the Multigrid Analysis: A Case Study in an Idealized Simulation Context

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Hongli Fu ◽  
Xinrong Wu ◽  
Wei Li ◽  
Yuanfu Xie ◽  
Guijun Han ◽  
...  
Keyword(s):  
Radial Velocity ◽  
Doppler Radar ◽  
Multiple Scale ◽  
Wind Data ◽  
Strong Constraint ◽  
3 Dimensional ◽  
Typhoon Structure ◽  
Analysis Scheme ◽  
Analysis System ◽  
The Impact

Extracting multiple-scale observational information is critical for accurately reconstructing the structure of mesoscale circulation systems such as typhoon. The Space and Time Mesoscale Analysis System (STMAS) with multigrid data assimilation developed in Earth System Research Laboratory (ESRL) in National Oceanic and Atmospheric Administration (NOAA) has addressed this issue. Previous studies have shown the capability of STMAS to retrieve multiscale information in 2-dimensional Doppler radar radial velocity observations. This study explores the application of 3-dimensional (3D) Doppler radar radial velocities with STMAS for reconstructing a 3D typhoon structure. As for the first step, here, we use an idealized simulation framework. A two-scale simulated “typhoon” field is constructed and referred to as “truth,” from which randomly distributed conventional wind data and 3D Doppler radar radial wind data are generated. These data are used to reconstruct the synthetic 3D “typhoon” structure by the STMAS and the traditional 3D variational (3D-Var) analysis. The degree by which the “truth” 3D typhoon structure is recovered is an assessment of the impact of the data type or analysis scheme being evaluated. We also examine the effects of weak constraint and strong constraint on STMAS analyses. Results show that while the STMAS is superior to the traditional 3D-Var for reconstructing the 3D typhoon structure, the strong constraint STMAS can produce better analyses on both horizontal and vertical velocities.

Application of IVAP-Based Observation Operator in Radar Radial Velocity Assimilation: The Case of Typhoon Fitow

2017 ◽  
Vol 145 (10) ◽  
pp. 4187-4203 ◽  
Author(s):  
Feng Chen ◽  
Xudong Liang ◽  
Hao Ma
Keyword(s):  
Spatial Distribution ◽  
Radial Velocity ◽  
Doppler Radar ◽  
Tangential Velocity ◽  
Distribution Characteristics ◽  
Observation Operator ◽  
Spatial Distribution Characteristics ◽  
Tangential Wind ◽  
Analysis System ◽  
Background Fields

An improved Doppler radar radial velocity assimilation observation operator is proposed based on the integrating velocity–azimuth process (IVAP) method. This improved operator can ingest both radial wind and its spatial distribution characteristics to deduce the two components of the mean wind within a given area. With this operator, the system can be used to assimilate information from tangential wind and radial wind. On the other hand, because the improved observation operator is defined within a given area, which can be uniformly chosen in both the observation and analysis coordinate systems, it has a thinning function. The traditional observation operator and the improved observation operator, along with their corresponding data processing modules, were implemented in the community Gridpoint Statistical Interpolation analysis system (GSI) to demonstrate the superiority of the improved operator. The results of single analysis unit experiments revealed that the two operators are comparable when the analysis unit is small. When the analysis unit becomes larger, the analysis results of the improved operator are better than those of the traditional operator because the former can ingest more wind information than the latter. The results of a typhoon case study indicated that both operators effectively ingested radial wind information and produced more reasonable typhoon structures than those in the background fields. The tangential velocity relative to the radar was retrieved by the improved operator through ingesting tangential wind information from the spatial distribution characteristics of radial wind. Because of the improved vortex intensity and structure, obvious improvements were seen in both track and intensity predictions when the improved operator was used.

scholarly journals Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002

2007 ◽  
Vol 135 (10) ◽  
pp. 3381-3404 ◽  
Author(s):  
Qingnong Xiao ◽  
Juanzhen Sun
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Doppler Radar ◽  
Radar Data ◽  
Squall Line ◽  
Convective System ◽  
Precipitation Forecasting ◽  
Doppler Radar Data ◽  
The Impact ◽  
Radar Data Assimilation

Abstract The impact of multiple–Doppler radar data assimilation on quantitative precipitation forecasting (QPF) is examined in this study. The newly developed Weather Research and Forecasting (WRF) model Advanced Research WRF (ARW) and its three-dimensional variational data assimilation system (WRF 3DVAR) are used. In this study, multiple–Doppler radar data assimilation is applied in WRF 3DVAR cycling mode to initialize a squall-line convective system on 13 June 2002 during the International H2O Project (IHOP_2002) and the ARW QPF skills are evaluated for the case. Numerical experiments demonstrate that WRF 3DVAR can successfully assimilate Doppler radial velocity and reflectivity from multiple radar sites and extract useful information from the radar data to initiate the squall-line convective system. Assimilation of both radial velocity and reflectivity results in sound analyses that show adjustments in both the dynamical and thermodynamical fields that are consistent with the WRF 3DVAR balance constraint and background error correlation. The cycling of the Doppler radar data from the 12 radar sites at 2100 UTC 12 June and 0000 UTC 13 June produces a more detailed mesoscale structure of the squall-line convection in the model initial conditions at 0000 UTC 13 June. Evaluations of the ARW QPF skills with initialization via Doppler radar data assimilation demonstrate that the more radar data in the temporal and spatial dimensions are assimilated, the more positive is the impact on the QPF skill. Assimilation of both radial velocity and reflectivity has more positive impact on the QPF skill than does assimilation of either radial velocity or reflectivity only. The improvement of the QPF skill with multiple-radar data assimilation is more clearly observed in heavy rainfall than in light rainfall. In addition to the improvement of the QPF skill, the simulated structure of the squall line is also enhanced by the multiple–Doppler radar data assimilation in the WRF 3DVAR cycling experiment. The vertical airflow pattern shows typical characteristics of squall-line convection. The cold pool and its related squall-line convection triggering process are better initiated in the WRF 3DVAR analysis and simulated in the ARW forecast when multiple–Doppler radar data are assimilated.

scholarly journals Assimilation of Synthetic GOES-R ABI Infrared Brightness Temperatures and WSR-88D Radar Observations in a High-Resolution OSSE

2016 ◽  
Vol 144 (9) ◽  
pp. 3159-3180 ◽  
Author(s):  
Rebecca M. Cintineo ◽  
Jason A. Otkin ◽  
Thomas A. Jones ◽  
Steven Koch ◽  
David J. Stensrud
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Radial Velocity ◽  
Doppler Radar ◽  
Forecast Accuracy ◽  
Central Plains ◽  
Radar Observations ◽  
Brightness Temperatures ◽  
Observing System Simulation Experiment ◽  
The Impact

This study uses an observing system simulation experiment to explore the impact of assimilating GOES-R Advanced Baseline Imager (ABI) 6.95-μm brightness temperatures and Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity observations in an ensemble data assimilation system. A high-resolution truth simulation was used to create synthetic radar and satellite observations of a severe weather event that occurred across the U.S. central plains on 4–5 June 2005. The experiment employs the Weather Research and Forecasting Model at 4-km horizontal grid spacing and the ensemble adjustment Kalman filter algorithm in the Data Assimilation Research Testbed system. The ability of GOES-R ABI brightness temperatures to improve the analysis and forecast accuracy when assimilated separately or simultaneously with Doppler radar reflectivity and radial velocity observations was assessed, along with the use of bias correction and different covariance localization radii for the brightness temperatures. Results show that the radar observations accurately capture the structure of a portion of the storm complex by the end of the assimilation period, but that more of the storms and atmospheric features are reproduced and the accuracy of the ensuing forecast improved when the brightness temperatures are also assimilated.

Impacts of 4DVAR Assimilation of Airborne Doppler Radar Observations on Numerical Simulations of the Genesis of Typhoon Nuri (2008)

2014 ◽  
Vol 53 (10) ◽  
pp. 2325-2343 ◽  
Author(s):  
Zhan Li ◽  
Zhaoxia Pu ◽  
Juanzhen Sun ◽  
Wen-Chau Lee
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Numerical Simulations ◽  
Doppler Radar ◽  
Initial Conditions ◽  
Radar Data ◽  
Radar Observations ◽  
Wind Analysis ◽  
The Impact ◽  
Airborne Doppler Radar

AbstractThe Weather Research and Forecasting Model and its four-dimensional variational data assimilation (4DVAR) system are employed to examine the impact of airborne Doppler radar observations on predicting the genesis of Typhoon Nuri (2008). Electra Doppler Radar (ELDORA) airborne radar data, collected during the Office of Naval Research–sponsored Tropical Cyclone Structure 2008 field experiment, are used for data assimilation experiments. Two assimilation methods are evaluated and compared, namely, the direct assimilation of radar-measured radial velocity and the assimilation of three-dimensional wind analysis derived from the radar radial velocity. Results show that direct assimilation of radar radial velocity leads to better intensity forecasts, as this process enhances the development of convective systems and improves the inner-core structure of Nuri, whereas assimilation of the radar-retrieved wind analysis is more beneficial for tracking forecasts, as it results in improved environmental flows. The assimilation of both the radar-retrieved wind and the radial velocity can lead to better forecasts in both intensity and tracking, if the radial velocity observations are assimilated first and the retrieved winds are then assimilated in the same data assimilation window. In addition, experiments with and without radar data assimilation led to developing and nondeveloping disturbances in numerical simulations of Nuri’s genesis. The improved initial conditions and forecasts from the data assimilation imply that the enhanced midlevel vortex and moisture conditions are favorable for the development of deep convection in the center of the pouch and eventually contribute to Nuri’s genesis. The improved simulations of the convection and associated environmental conditions produce enhanced upper-level warming in the core region and lead to the drop in sea level pressure.

Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System

2008 ◽  
Vol 136 (1) ◽  
pp. 335-351 ◽  
Author(s):  
Yanqiu Zhu ◽  
Ronald Gelaro
Keyword(s):  
Zonal Wind ◽  
Highly Nonlinear ◽  
Analysis Scheme ◽  
Linear Version ◽  
Development Approach ◽  
Analysis System ◽  
Environmental Prediction ◽  
The Impact ◽  
Statistical Interpolation ◽  
Observing Systems

Abstract The adjoint of a data assimilation system provides an efficient way of estimating sensitivities of analysis or forecast measures with respect to observations. The NASA Global Modeling and Assimilation Office (GMAO) has developed an exact adjoint of the Gridpoint Statistical Interpolation (GSI) analysis scheme developed at the National Centers for Environmental Prediction (NCEP). The development approach is unique in that the adjoint is derived from a line-by-line tangent linear version of the GSI. Availability of the tangent linear scheme provides an explicit means of assessing not only the fidelity of the adjoint, but also the effects of nonlinear processes in the GSI itself. In this paper, the development of the tangent linear and adjoint versions of the GSI are discussed and observation sensitivity results for a near-operational version of the system are shown. Results indicate that the GSI adjoint provides accurate assessments of the sensitivities with respect to observations of wind, temperature, satellite radiances, and, to a lesser extent, moisture. Sensitivities with respect to ozone observations are quite linear for the ozone fields themselves, but highly nonlinear for other variables. The sensitivity information provided by the adjoint is used to estimate the contribution, or impact, of various observing systems on locally defined response functions based on the analyzed increments of temperature and zonal wind. It is shown, for example, that satellite radiances have the largest impact of all observing systems on the temperature increments over the eastern North Pacific, while conventional observations from rawinsondes and aircraft dominate the impact on the zonal wind increments over the continental United States. The observation impact calculations also provide an additional means of validating the observation sensitivities produced by the GSI adjoint.

scholarly journals Application of the Multigrid Method to the Two-Dimensional Doppler Radar Radial Velocity Data Assimilation

2010 ◽  
Vol 27 (2) ◽  
pp. 319-332 ◽  
Author(s):  
Wei Li ◽  
Yuanfu Xie ◽  
Shiow-Ming Deng ◽  
Qi Wang
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Multigrid Method ◽  
Doppler Radar ◽  
Wind Data ◽  
In Situ Observation ◽  
Velocity Data ◽  
Significant Information ◽  
Tangential Wind ◽  
Radial Velocity Data

Abstract In recent years, the Earth System Research Laboratory (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) has developed a space and time mesoscale analysis system (STMAS), which is currently a sequential three-dimensional variational data assimilation (3DVAR) system and is developing into a sequential 4DVAR in the near future. It is implemented by using a multigrid method based on a variational approach to generate grid analyses. This study is to test how STMAS deals with 2D Doppler radar radial velocity and to what degree the 2D Doppler radar radial velocity can improve the conventional (in situ) observation analysis. Two idealized experiments and one experiment with real Doppler radar radial velocity data, handled by STMAS, demonstrated significant improvement of the conventional observation analysis. Because the radar radial wind data can provide additional wind information (even it is incomplete: e.g., missing tangential wind vector), the analyses by assimilating both radial wind data and conventional data showed better results than those by assimilating only conventional data. Especially in the case of sparse conventional data, radar radial wind data can provide significant information and improve the analyses considerably.

scholarly journals Assimilation of Satellite Infrared Radiances and Doppler Radar Observations during a Cool Season Observing System Simulation Experiment

2013 ◽  
Vol 141 (10) ◽  
pp. 3273-3299 ◽  
Author(s):  
Thomas A. Jones ◽  
Jason A. Otkin ◽  
David J. Stensrud ◽  
Kent Knopfmeier
Keyword(s):  
Radial Velocity ◽  
Simulation Experiment ◽  
Doppler Radar ◽  
Mesoscale Model ◽  
System Simulation ◽  
Radar Data ◽  
Cool Season ◽  
Radar Observations ◽  
Observing System Simulation Experiment ◽  
The Impact

Abstract An observing system simulation experiment is used to examine the impact of assimilating water vapor–sensitive satellite infrared brightness temperatures and Doppler radar reflectivity and radial velocity observations on the analysis accuracy of a cool season extratropical cyclone. Assimilation experiments are performed for four different combinations of satellite, radar, and conventional observations using an ensemble Kalman filter assimilation system. Comparison with the high-resolution “truth” simulation indicates that the joint assimilation of satellite and radar observations reduces errors in cloud properties compared to the case in which only conventional observations are assimilated. The satellite observations provide the most impact in the mid- to upper troposphere, whereas the radar data also improve the cloud analysis near the surface and aloft as a result of their greater vertical resolution and larger overall sample size. Errors in the wind field are also significantly reduced when radar radial velocity observations were assimilated. Overall, assimilating both satellite and radar data creates the most accurate model analysis, which indicates that both observation types provide independent and complimentary information and illustrates the potential for these datasets for improving mesoscale model analyses and ensuing forecasts.

scholarly journals The Implementation of the Ice-Phase Microphysical Process into a Four-Dimensional Variational Doppler Radar Analysis System (VDRAS) and Its Impact on Parameter Retrieval and Quantitative Precipitation Nowcasting

2016 ◽  
Vol 73 (3) ◽  
pp. 1015-1038 ◽  
Author(s):  
Shao-Fan Chang ◽  
Yu-Chieng Liou ◽  
Juanzhen Sun ◽  
Sheng-Lun Tai
Keyword(s):  
Doppler Radar ◽  
Deep Convection ◽  
Optimal Solution ◽  
Adjoint Equations ◽  
Scale Model ◽  
Microphysical Process ◽  
Analysis System ◽  
The Impact ◽  
Phase Process ◽  
Ice Phase

Abstract The microphysical process of a cloud-scale model used by a four-dimensional Variational Doppler Radar Analysis System (VDRAS) is extended from its original warm rain parameterization scheme to a cold rain process containing ice and snow. The development of the adjoint equations for the additional control variables related to ice physics is accomplished by utilizing the existing four-dimensional variational (4DVar) minimization framework employed by VDRAS. Experiments are conducted to examine the accuracy of the new 4DVar system with the ice physics scheme implemented and to explore the impact of the ice-phase process on numerical simulations, parameter retrievals, and the model’s quantitative precipitation nowcasting (QPN) capability. It is shown that the ice-phase microphysical process can significantly alter the kinematic and thermodynamic structure of deep convection and provide a better description of the contents of the hydrometeors. During the 4DVar minimization, using the VDRAS-predicted freezing level after the previous assimilation cycle to replace the true but unknown 0°C line is found to be a feasible approach for separating the rain and snow and, at the same time, allowing the 4DVar minimization algorithm to converge to an optimal solution. A real case study from intensive observation period 8 of the 2008 Southwest Monsoon Experiment shows that, with the added ice-phase process, VDRAS is more capable of capturing the actual evolution of the reflectivity field than the original scheme. The model’s QPN skill is also improved significantly. Thus, the benefits of adding the ice-phase process into a 4DVar radar data assimilation system on the convective-scale weather analysis and forecast are demonstrated.

scholarly journals Exoplanet mass estimation for a sample of targets for the Ariel mission

2021 ◽  
Author(s):  
J. R. Barnes ◽  
C. A. Haswell
Keyword(s):  
Radial Velocity ◽  
Input Parameter ◽  
Noise Sources ◽  
Time Commitment ◽  
Low Mass ◽  
Time Required ◽  
Fixed Input ◽  
The Impact ◽  
Quadrature Sampling

AbstractAriel’s ambitious goal to survey a quarter of known exoplanets will transform our knowledge of planetary atmospheres. Masses measured directly with the radial velocity technique are essential for well determined planetary bulk properties. Radial velocity masses will provide important checks of masses derived from atmospheric fits or alternatively can be treated as a fixed input parameter to reduce possible degeneracies in atmospheric retrievals. We quantify the impact of stellar activity on planet mass recovery for the Ariel mission sample using Sun-like spot models scaled for active stars combined with other noise sources. Planets with necessarily well-determined ephemerides will be selected for characterisation with Ariel. With this prior requirement, we simulate the derived planet mass precision as a function of the number of observations for a prospective sample of Ariel targets. We find that quadrature sampling can significantly reduce the time commitment required for follow-up RVs, and is most effective when the planetary RV signature is larger than the RV noise. For a typical radial velocity instrument operating on a 4 m class telescope and achieving 1 m s−1 precision, between ~17% and ~ 37% of the time commitment is spent on the 7% of planets with mass Mp < 10 M⊕. In many low activity cases, the time required is limited by asteroseismic and photon noise. For low mass or faint systems, we can recover masses with the same precision up to ~3 times more quickly with an instrumental precision of ~10 cm s−1.

Computational Analysis of Olfactory Airspace in Patients With Unilateral Cleft Lip Nasal Deformity

2020 ◽  
pp. 105566562098275
Author(s):  
Reanna Shah ◽  
Jeffrey R. Marcus ◽  
Dennis O. Frank-Ito
Keyword(s):  
Cleft Lip ◽  
Computational Analysis ◽  
Normal Subjects ◽  
Nasal Deformity ◽  
Normal Anatomy ◽  
3 Dimensional ◽  
Computed Tomography Images ◽  
High Prevalence ◽  
Dimensional Reconstruction ◽  
The Impact

Objectives: To evaluate the magnitude of olfactory recess opacity in patients with unilateral cleft lip nasal deformity (uCLND). Design: Subject-specific 3-dimensional reconstruction of the nasal airway anatomy was created from computed tomography images in 11 (4 males and 7 females) subjects with uCLND and 7 (3 males, and 4 females) normal subjects. The volume and surface area of each subject’s unilateral and bilateral olfactory airspace was quantified to assess the impact of opacification. Qualitatively speaking, patients with 75% to 100% olfactory recess opacification were classified as extreme, 50% to 75% as severe, 25% to 50% as moderate, and 0% to 25% as mild. Results: Of the 11 subjects with uCLND, 5 (45%) were classified as having extreme olfactory recess opacification, 3 (27%) subjects had severe opacification, and 3 (27%) subjects had moderate opacification. Mean (±SD) bilateral olfactory recess volume was significantly greater in normal subjects than in subjects with uCLND (0.9668 cm3 ± 0.4061 cm3 vs 0.3426 cm3 ± 0.1316 cm3; P < .001). Furthermore, unilateral olfactory airspace volumes for the cleft and non-cleft sides in subjects with uCLND were considerably less than unilateral olfactory volume in subjects with normal anatomy (uCLND cleft side = 0.1623 cm3 ± 0.0933 cm3; uCLND non-cleft side = 0.1803 cm3 ± 0.0938 cm3; normal = 0.4834 cm3 ± 0.2328 cm3; P < .001). Conclusions: Our findings indicate a high prevalence of olfactory recess opacification among subjects with uCLND when compared to subjects with normal anatomy. The majority of subjects with uCLND had extreme olfactory recess opacity, which will likely influence their sense of smell.

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