scholarly journals Analysis of Tropical Storm Formation Based on Ensemble Data Assimilation and High-Resolution Numerical Simulations of a Nondeveloping Disturbance

2016 ◽  
Vol 144 (10) ◽  
pp. 3631-3649 ◽  
Author(s):  
Andrew B. Penny ◽  
Joshua P. Hacker ◽  
Patrick A. Harr
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Simulations ◽  
Doppler Radar ◽  
Vertical Wind Shear ◽  
Heating Rates ◽  
Low Level ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
The Impact

A nondeveloping tropical disturbance, identified as TCS025, was observed during three intensive observing periods during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC)/Tropical Cyclone Structure-2008 (TCS-08) field experiment. The low-level circulation of the disturbance was relatively weak, asymmetric, and displaced a considerable distance from the midlevel circulation. An ensemble of high-resolution numerical simulations initialized from global model analyses was used to further examine TCS025. These simulations tended to unrealistically overdevelop the TCS025 disturbance. This study extends that work by examining the impact of assimilating in situ observations of TCS025 and dual-Doppler radial velocities from the airborne Electra Doppler Radar (ELDORA) using the Data Assimilation Research Testbed (DART) ensemble data assimilation system. The assimilation of observations results in a more accurate vortex structure that is consistent with the observational analysis. In addition, forecasts initialized from the state of the ensemble after data assimilation exhibit less development than both the control simulation and an ensemble of forecasts without prior data assimilation. A composite analysis of developing and nondeveloping forecasts from the ensemble reveals that convection was more active in developing simulations, especially near the low-level circulation center. This led to larger diabatic heating rates, spinup of the low-level circulation from vorticity stretching, and greater alignment of the low- and midlevel vorticity centers. In contrast, nondeveloping simulations exhibited less convection, and the circulation was more heavily impacted by vertical wind shear.

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.

scholarly journals Impact of Doppler Radar Wind Observations on Australian High-Resolution Numerical Weather Prediction

2020 ◽  
Vol 35 (2) ◽  
pp. 309-324
Author(s):  
Susan Rennie ◽  
Lawrence Rikus ◽  
Nathan Eizenberg ◽  
Peter Steinle ◽  
Monika Krysta
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Limited Area ◽  
Observation Error ◽  
Numerical Weather ◽  
Skill Scores ◽  
The Impact

Abstract The impact of Doppler radar wind observations on forecasts from a developmental, high-resolution numerical weather prediction (NWP) system is assessed. The new 1.5-km limited-area model will be Australia’s first such operational NWP system to include data assimilation. During development, the assimilation of radar wind observations was trialed over a 2-month period to approve the initial inclusion of these observations. Three trials were run: the first with no radar data, the second with radial wind observations from precipitation echoes, and the third with radial winds from both precipitation and insect echoes. The forecasts were verified against surface observations from automatic weather stations, against rainfall accumulations using fractions skill scores, and against satellite cloud observations. These methods encompassed verification across a range of vertical levels. Additionally, a case study was examined more closely. Overall results showed little statistical difference in skill between the trials, and the net impact was neutral. While the new observations clearly affected the forecast, the objective and subjective analyses showed a neutral impact on the forecast overall. As a first step, this result is satisfactory for the operational implementation. In future, upgrades to the radar network will start to reduce the observation error, and further improvements to the data assimilation are planned, which may be expected to improve the impact.

Comparing Limited-Area 3DVAR and Hybrid Variational-Ensemble Data Assimilation Methods for Typhoon Track Forecasts: Sensitivity to Outer Loops and Vortex Relocation

2013 ◽  
Vol 141 (12) ◽  
pp. 4350-4372 ◽  
Author(s):  
Craig S. Schwartz ◽  
Zhiquan Liu ◽  
Xiang-Yu Huang ◽  
Ying-Hwa Kuo ◽  
Chin-Tzu Fong
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Limited Area ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Typhoon Track ◽  
The Mean ◽  
The Impact

Abstract The Weather Research and Forecasting Model (WRF) “hybrid” variational-ensemble data assimilation (DA) algorithm was used to initialize WRF model forecasts of three tropical cyclones (TCs). The hybrid-initialized forecasts were compared to forecasts initialized by WRF's three-dimensional variational (3DVAR) DA system. An ensemble adjustment Kalman filter (EAKF) updated a 32-member WRF-based ensemble system that provided flow-dependent background error covariances for the hybrid. The 3DVAR, hybrid, and EAKF configurations cycled continuously for ~3.5 weeks and produced new analyses every 6 h that initialized 72-h WRF forecasts with 45-km horizontal grid spacing. Additionally, the impact of employing a TC relocation technique and using multiple outer loops (OLs) in the 3DVAR and hybrid minimizations were explored. Model output was compared to conventional, dropwindsonde, and TC “best track” observations. On average, the hybrid produced superior forecasts compared to 3DVAR when only one OL was used during minimization. However, when three OLs were employed, 3DVAR forecasts were dramatically improved but the mean hybrid performance changed little. Additionally, incorporation of TC relocation within the cycling systems further improved the mean 3DVAR-initialized forecasts but the average hybrid-initialized forecasts were nearly unchanged.

Impact of CASA Radar and Oklahoma Mesonet Data Assimilation on the Analysis and Prediction of Tornadic Mesovortices in an MCS

2011 ◽  
Vol 139 (11) ◽  
pp. 3422-3445 ◽  
Author(s):  
Alexander D. Schenkman ◽  
Ming Xue ◽  
Alan Shapiro ◽  
Keith Brewster ◽  
Jidong Gao
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Radar Data ◽  
Wind Data ◽  
Accurate Analysis ◽  
Low Level ◽  
Regional Prediction ◽  
Oklahoma Mesonet ◽  
The Impact ◽  
Gust Front

Abstract The impact of radar and Oklahoma Mesonet data assimilation on the prediction of mesovortices in a tornadic mesoscale convective system (MCS) is examined. The radar data come from the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere’s (CASA) IP-1 radar network. The Advanced Regional Prediction System (ARPS) model is employed to perform high-resolution predictions of an MCS and the associated cyclonic line-end vortex that spawned several tornadoes in central Oklahoma on 8–9 May 2007, while the ARPS three-dimensional variational data assimilation (3DVAR) system in combination with a complex cloud analysis package is used for the data analysis. A set of data assimilation and prediction experiments are performed on a 400-m resolution grid nested inside a 2-km grid, to examine the impact of radar data on the prediction of meso-γ-scale vortices (mesovortices). An 80-min assimilation window is used in radar data assimilation experiments. An additional set of experiments examines the impact of assimilating 5-min data from the Oklahoma Mesonet in addition to the radar data. Qualitative comparison with observations shows highly accurate forecasts of mesovortices up to 80 min in advance of their genesis are obtained when the low-level shear in advance of the gust front is effectively analyzed. Accurate analysis of the low-level shear profile relies on assimilating high-resolution low-level wind information. The most accurate analysis (and resulting prediction) is obtained in experiments that assimilate low-level radial velocity data from the CASA radars. Assimilation of 5-min observations from the Oklahoma Mesonet has a substantial positive impact on the analysis and forecast when high-resolution low-level wind observations from CASA are absent; when the low-level CASA wind data are assimilated, the impact of Mesonet data is smaller. Experiments that do not assimilate low-level wind data from CASA radars are unable to accurately resolve the low-level shear profile and gust front structure, precluding accurate prediction of mesovortex development.

scholarly journals A Case Study on the Impact of Ensemble Data Assimilation with GNSS-Zenith Total Delay and Radar Data on Heavy Rainfall Prediction

2020 ◽  
Vol 148 (3) ◽  
pp. 1075-1098 ◽  
Author(s):  
Shu-Chih Yang ◽  
Zih-Mao Huang ◽  
Ching-Yuang Huang ◽  
Chih-Chien Tsai ◽  
Ta-Kang Yeh
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Radar Data ◽  
Total Delay ◽  
Ensemble Data Assimilation ◽  
Zenith Total Delay ◽  
Ensemble Data ◽  
Convective Scale ◽  
The Impact

Abstract The performance of a numerical weather prediction model using convective-scale ensemble data assimilation with ground-based global navigation satellite systems-zenith total delay (ZTD) and radar data is investigated on a heavy rainfall event that occurred in Taiwan on 10 June 2012. The assimilation of ZTD and/or radar data is performed using the framework of the WRF local ensemble transform Kalman filter with a model grid spacing of 2 km. Assimilating radar data is beneficial for predicting the rainfall intensity of this local event but produces overprediction in southern Taiwan and underprediction in central Taiwan during the first 3 h. Both errors are largely overcome by assimilating ZTD data to improve mesoconvective-scale moisture analyses. Consequently, assimilating both the ZTD and radar data show advantages in terms of the location and intensity of the heavy rainfall. Sensitivity experiments involving this event indicate that the impact of ZTD data is improved by using a broader horizontal localization scale than the convective scale used for radar data assimilation. This optimization is necessary in order to consider more fully the network density of the ZTD observations and the horizontal scale of the moisture transport by the southwesterly flow in this case.

Multi-scale shear impacts during the genesis of Hagupit (2008)

2020 ◽  
Author(s):  
Chaehyeon C. Nam ◽  
Michael M. Bell
Keyword(s):  
Tropical Cyclone ◽  
Doppler Radar ◽  
Negative Impact ◽  
Early Stage ◽  
Spatial Scales ◽  
Deep Convection ◽  
Vertical Wind Shear ◽  
Low Level ◽  
Multi Scale ◽  
The Impact

AbstractThe impact of vertical wind shear (VWS) on tropical cyclogenesis is examined from the synoptic to meso scales using airborne Doppler radar observations of pre-depression Hagupit during the Tropical Cyclone Structure 2008 (TCS08) / THORPEX Pacific Area Regional Campaign (T-PARC) field campaigns. The high temporal and spatial resolution observations reveal complex localized convective and vortical characteristics of a pre-depression in a sheared environment. Pre-depression Hagupit interacted with an upper-tropospheric trough during the observation period. The strong deep-layer VWS (> 20 m s−1) had a negative impact on the development through misalignment of the low and mid-level circulations and dry air intrusion. However, the low-level circulation persisted and the system ultimately formed into a tropical cyclone after it left the high-shear zone. Here we propose that a key process that enabled the pre-depression to survive through the upper-tropospheric trough interaction was persistent vorticity amplification on the meso-γ scale that was aggregated on the meso-α scale within the wave pouch. Multi-Doppler wind analysis indicates that cumulus congestus tilted the low-level horizontal vorticity into the vertical in the early stage of convective life-cycle, followed by stretching from maturing deep convection. Variations in low-level VWS on the meso-β scale affect convective organization and horizontal vorticity generation. The results provide new insights into multi-scale processes during TC genesis and the interactions of a pre-depression with VWS at various spatial scales.

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