scholarly journals Assimilation of Cloud-Top Temperature and Radar Observations of an Idealized Splitting Supercell Using an Observing System Simulation Experiment

2015 ◽  
Vol 143 (4) ◽  
pp. 1018-1034 ◽  
Author(s):  
Christopher A. Kerr ◽  
David J. Stensrud ◽  
Xuguang Wang
Keyword(s):  
Simulation Experiment ◽  
Doppler Radar ◽  
System Simulation ◽  
Radar Data ◽  
Radar Observations ◽  
Relative Value ◽  
Convective Scale ◽  
Temperature Observations ◽  
Observing System Simulation Experiment ◽  
Cloud Top Temperature

Abstract The Geostationary Operational Environmental Satellite-R Series will provide cloud-top observations on the convective scale at roughly the same frequency as Doppler radar observations. To evaluate the potential value of cloud-top temperature observations for data assimilation, an imperfect-model observing system simulation experiment is used. Synthetic cloud-top temperature observations from an idealized splitting supercell created using the Weather Research and Forecasting Model are assimilated along with synthetic radar reflectivity and radial velocity using an ensemble Kalman filter. Observations are assimilated every 5 min for 2.5 h with additive noise used to maintain ensemble spread. Four experiments are conducted to explore the relative value of cloud-top temperature and radar observations. One experiment only assimilates satellite data, another only assimilates radar data, and two more experiments assimilate both radar and satellite observations, but with the observation types assimilated in different order. Results show a rather weak correlation between cloud-top temperature and horizontal winds, whereas larger correlations are found between cloud-top temperature and microphysics variables. However, the assimilation of cloud-top temperature data alone produces a supercell storm in the ensemble, although the resulting ensemble has much larger spread compared to the ensembles of radar inclusive experiments. The addition of radar observations greatly improves the storm structure and reduces the overprediction of storm extent. Results further show that assimilating cloud-top temperature observations in addition to radar data does not lead to an improved forecast. However, assimilating cloud-top temperature can produce reasonable forecasts for areas lacking radar coverage.

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 Comparison between Dual-Doppler and EnKF Storm-Scale Wind Analyses: Observing System Simulation Experiments with a Supercell Thunderstorm

2012 ◽  
Vol 140 (12) ◽  
pp. 3972-3991 ◽  
Author(s):  
Corey K. Potvin ◽  
Louis J. Wicker
Keyword(s):  
System Simulation ◽  
Radar Data ◽  
Model Errors ◽  
Radar Observations ◽  
Low Level ◽  
Flow Evolution ◽  
Supercell Thunderstorm ◽  
Wind Retrievals ◽  
Observing System Simulation Experiment ◽  
Microphysical Parameterization

Abstract Kinematical analyses of mobile radar observations are critical to advancing the understanding of supercell thunderstorms. Maximizing the accuracy of these and subsequent dynamical analyses, and appropriately characterizing the uncertainty in ensuing conclusions about storm structure and processes, requires thorough knowledge of the typical errors obtained using different retrieval techniques. This study adopts an observing system simulation experiment (OSSE) framework to explore the errors obtained from ensemble Kalman filter (EnKF) assimilation versus dual-Doppler analysis (DDA) of storm-scale mobile radar data. The radar characteristics and EnKF model errors are varied to explore a range of plausible scenarios. When dual-radar data are assimilated, the EnKF produces substantially better wind retrievals at higher altitudes, where DDAs are more sensitive to unaccounted flow evolution, and in data-sparse regions such as the storm inflow sector. Near the ground, however, the EnKF analyses are comparable to the DDAs when the radar cross-beam angles (CBAs) are poor, and slightly worse than the DDAs when the CBAs are optimal. In the single-radar case, the wind analyses benefit substantially from using finer grid spacing than in the dual-radar case for the objective analysis of radar observations. The analyses generally degrade when only single-radar data are assimilated, particularly when microphysical parameterization or low-level environmental wind errors are introduced. In some instances, this leads to large errors in low-level vorticity stretching and Lagrangian circulation calculations. Nevertheless, the results show that while multiradar observations of supercells are always preferable, judicious use of single-radar EnKF assimilation can yield useful analyses.

Importance of Horizontally Inhomogeneous Environmental Initial Conditions to Ensemble Storm-Scale Radar Data Assimilation and Very Short-Range Forecasts

2010 ◽  
Vol 138 (4) ◽  
pp. 1250-1272 ◽  
Author(s):  
David J. Stensrud ◽  
Jidong Gao
Keyword(s):  
Data Assimilation ◽  
Short Range ◽  
Doppler Radar ◽  
Environmental Variability ◽  
Initial Conditions ◽  
Radar Data ◽  
Ensemble Member ◽  
Radar Observations ◽  
Convective Scale ◽  
Background Fields

Abstract The assimilation of operational Doppler radar observations into convection-resolving numerical weather prediction models for very short-range forecasting represents a significant scientific and technological challenge. Numerical experiments over the past few years indicate that convective-scale forecasts are sensitive to the details of the data assimilation methodology, the quality of the radar data, the parameterized microphysics, and the storm environment. In this study, the importance of horizontal environmental variability to very short-range (0–1 h) convective-scale ensemble forecasts initialized using Doppler radar observations is investigated for the 4–5 May 2007 Greensburg, Kansas, tornadic thunderstorm event. Radar observations of reflectivity and radial velocity from the operational Doppler radar network at 0230 UTC 5 May 2007, during the time of the first large tornado, are assimilated into each ensemble member using a three-dimensional variational data assimilation system (3DVAR) developed at the Center for Analysis and Prediction of Storms (CAPS). Very short-range forecasts are made using the nonhydrostatic Advanced Regional Prediction System (ARPS) model from each ensemble member and the results are compared with the observations. Explicit three-dimensional environmental variability information is provided to the convective-scale ensemble using analyses from a 30-km mesoscale ensemble data assimilation system. Comparisons between convective-scale ensembles with initial conditions produced by 3DVAR using 1) background fields that are horizontally homogeneous but vertically inhomogeneous (i.e., have different vertical environmental profiles) and 2) background fields that are horizontally and vertically inhomogeneous are undertaken. Results show that the ensemble with horizontally and vertically inhomogeneous background fields provides improved predictions of thunderstorm structure, mesocyclone track, and low-level circulation track than the ensemble with horizontally homogeneous background fields. This suggests that knowledge of horizontal environmental variability is important to successful convective-scale ensemble predictions and needs to be included in real-data experiments.

scholarly journals Monitoring the Northern Current in the Gulf of Lions with an observing system simulation experiment

2012 ◽  
Vol 76 (3) ◽  
pp. 441-453 ◽  
Author(s):  
Aurélie Duchez ◽  
Jacques Verron ◽  
Jean-Michel Brankart ◽  
Yann Ourmières ◽  
Philippe Fraunié
Keyword(s):  
Simulation Experiment ◽  
System Simulation ◽  
Gulf Of Lions ◽  
Observing System Simulation Experiment ◽  
Northern Current

Doppler Radar Analysis of the Eyewall Replacement Cycle of Hurricane Matthew (2016) in Vertical Wind Shear

2021 ◽  
Author(s):  
Ting-Yu Cha ◽  
Michael M. Bell ◽  
Alexander J. DesRosiers
Keyword(s):  
Wind Shear ◽  
Doppler Radar ◽  
Vertical Wind Shear ◽  
Radar Data ◽  
Secondary Circulation ◽  
Vertical Wind ◽  
High Temporal Resolution ◽  
Radar Observations ◽  
Eyewall Replacement Cycle ◽  
Replacement Cycle

AbstractHurricane Matthew (2016) was observed by ground-based polarimetric radars in Miami (KAMX), Melbourne (KMLB), and Jacksonville (KJAX) and a NOAA P3 airborne tail Doppler radar near the coast of the southeastern United States during an eyewall replacement cycle (ERC). The radar observations indicate that Matthew’s primary eyewall was replaced with a weaker outer eyewall, but unlike a classic ERC, Matthew did not reintensify after the inner eyewall disappeared. Triple Doppler analysis was calculated from the NOAA P3 airborne fore and aft radar scanning combined with the KAMX radar data during the period of secondary eyewall intensification and inner eyewall weakening from 19 UTC 6 October to 00 UTC 7 October. Four flight passes of the P3 aircraft show the evolution of the reflectivity, tangential winds, and secondary circulation as the outer eyewall became well-established. Further evolution of the ERC is analyzed from the ground-based single Doppler radar observations for 35 hours with high temporal resolution at a 5-minute interval from 19 UTC 6 October to 00 UTC 8 October using the Generalized Velocity Track Display (GVTD) technique. The single-Doppler analyses indicate that the inner eyewall decayed a few hours after the P3 flight, while the outer eyewall contracted but did not reintensify and the asymmetries increased episodically. The analysis suggests that the ERC process was influenced by a complex combination of environmental vertical wind shear, an evolving axisymmetric secondary circulation, and an asymmetric vortex Rossby wave damping mechanism that promoted vortex resiliency despite increasing shear.

scholarly journals Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1

2019 ◽  
Vol 12 (7) ◽  
pp. 2899-2914
Author(s):  
Yun Liu ◽  
Eugenia Kalnay ◽  
Ning Zeng ◽  
Ghassem Asrar ◽  
Zhaohui Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Simulation Experiment ◽  
System Simulation ◽  
Carbon Fluxes ◽  
Observation Window ◽  
Surface Carbon ◽  
Ensemble Transform Kalman Filter ◽  
Observing System Simulation Experiment ◽  
Ensemble Transform ◽  
Assimilation System

Abstract. We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model, version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variable localization”, and increased observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place” (RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation (Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solution forward or backward within an assimilation window at no cost. In the new scheme a long “observation window” (e.g., 7 d or longer) is used to create a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7 d observations, which improves the analysis and accelerates the spin-up. The assimilation and observation windows are then shifted forward by 1 d, and the process is repeated. This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models, especially in order to make greater use of observations in conjunction with models.

scholarly journals The Impact of Covariance Localization for Radar Data on EnKF Analyses of a Developing MCS: Observing System Simulation Experiments

2013 ◽  
Vol 141 (11) ◽  
pp. 3691-3709 ◽  
Author(s):  
Ryan A. Sobash ◽  
David J. Stensrud
Keyword(s):  
System Simulation ◽  
Three Dimensional ◽  
Radar Data ◽  
Convective System ◽  
State Variables ◽  
Simulation Experiments ◽  
Length Scales ◽  
Convective Scale ◽  
The Impact ◽  
Prior State

Abstract Several observing system simulation experiments (OSSEs) were performed to assess the impact of covariance localization of radar data on ensemble Kalman filter (EnKF) analyses of a developing convective system. Simulated Weather Surveillance Radar-1988 Doppler (WSR-88D) observations were extracted from a truth simulation and assimilated into experiments with localization cutoff choices of 6, 12, and 18 km in the horizontal and 3, 6, and 12 km in the vertical. Overall, increasing the horizontal localization and decreasing the vertical localization produced analyses with the smallest RMSE for most of the state variables. The convective mode of the analyzed system had an impact on the localization results. During cell mergers, larger horizontal localization improved the results. Prior state correlations between the observations and state variables were used to construct reverse cumulative density functions (RCDFs) to identify the correlation length scales for various observation-state pairs. The OSSE with the smallest RMSE employed localization cutoff values that were similar to the horizontal and vertical length scales of the prior state correlations, especially for observation-state correlations above 0.6. Vertical correlations were restricted to state points closer to the observations than in the horizontal, as determined by the RCDFs. Further, the microphysical state variables were correlated with the reflectivity observations on smaller scales than the three-dimensional wind field and radial velocity observations. The ramifications of these findings on localization choices in convective-scale EnKF experiments that assimilate radar data are discussed.

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