scholarly journals Impact of Lidar Data Assimilation on Low-Level Wind Shear Simulation at Lanzhou Zhongchuan International Airport, China: A Case Study

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1342
Author(s):  
Lanqian Li ◽  
Ningjing Xie ◽  
Longyan Fu ◽  
Kaijun Zhang ◽  
Aimei Shao ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Wind Shear ◽  
Three Dimensional ◽  
Wrf Model ◽  
Lidar Data ◽  
Positive Role ◽  
Low Level ◽  
Wind Lidar ◽  
The Impact ◽  
Doppler Wind Lidar

Doppler wind lidar has played an important role in alerting low-level wind shear (LLW). However, these high-resolution observations are underused in the model-based analysis and forecasting of LLW. In this regard, we employed the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-VAR) system to investigate the impact of lidar data assimilation (DA) on LLW simulations. Eight experiments (including six assimilation experiments) were designed for an LLW process as reported by pilots, in which different assimilation intervals, assimilation timespans, and model vertical resolutions were examined. Verified against observations from Doppler wind lidar and an automated weather observing system (AWOS), the introduction of lidar data is helpful for describing the LLW event, which can represent the temporal and spatial features of LLW, whereas experiments without lidar DA have no ability to capture LLW. While lidar DA has an obviously positive role in simulating LLW in the 10–20 min after the assimilation time, this advantage cannot be maintained over a longer time. Therefore, a smaller assimilation interval is favorable for improving the simulated effect of LLW. In addition, increasing the vertical resolution does not evidently improve the experimental results, either with or without assimilation.

scholarly journals An Observing System Simulation Experiment (OSSE) to Assess the Impact of Doppler Wind Lidar (DWL) Measurements on the Numerical Simulation of a Tropical Cyclone

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Zhang ◽  
Zhaoxia Pu
Keyword(s):  
Numerical Simulation ◽  
Tropical Cyclone ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Wrf Model ◽  
Wind Lidar ◽  
Wind Profiles ◽  
The Impact ◽  
Doppler Wind Lidar

The importance of wind observations has been recognized for many years. However, wind observations—especially three-dimensional global wind measurements—are very limited. A satellite-based Doppler Wind Lidar (DWL) is proposed to measure three-dimensional wind profiles using remote sensing techniques. Assimilating these observations into a mesoscale model is expected to improve the performance of the numerical weather prediction (NWP) models. In order to examine the potential impact of the DWL three-dimensional wind profile observations on the numerical simulation and prediction of tropical cyclones, a set of observing simulation system experiments (OSSEs) is performed using the advanced research version of the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system. Results indicate that assimilating the DWL wind observations into the mesoscale numerical model has significant potential for improving tropical cyclone track and intensity forecasts.

Examining Terrain Effects on an Upstate New York Tornado Event Utilizing a High-Resolution Model Simulation

2021 ◽  
Author(s):  
Luke J. LeBel ◽  
Brian H. Tang ◽  
Ross A. Lazear
Keyword(s):  
New York ◽  
High Resolution ◽  
Wind Shear ◽  
Model Simulation ◽  
Wrf Model ◽  
Severe Weather ◽  
Streamwise Vorticity ◽  
Low Level ◽  
Severe Convection ◽  
The Impact

AbstractThe complex terrain at the intersection of the Mohawk and Hudson valleys of New York has an impact on the development and evolution of severe convection in the region. Specifically, previous research has concluded that terrain-channeled flow in the Mohawk and Hudson valleys likely contributes to increased low-level wind shear and instability in the valleys during severe weather events such as the historic 31 May 1998 event that produced a strong (F3) tornado in Mechanicville, New York.The goal of this study is to further examine the impact of terrain channeling on severe convection by analyzing a high-resolution WRF model simulation of the 31 May 1998 event. Results from the simulation suggest that terrain-channeled flow resulted in the localized formation of an enhanced low-level moisture gradient, resembling a dryline, at the intersection of the Mohawk and Hudson valleys. East of this boundary, the environment was characterized by stronger low-level wind shear and greater low-level moisture and instability, increasing tornadogenesis potential. A simulated supercell intensified after crossing the boundary, as the larger instability and streamwise vorticity of the low-level inflow was ingested into the supercell updraft. These results suggest that terrain can have a key role in producing mesoscale inhomogeneities that impact the evolution of severe convection. Recognition of these terrain-induced boundaries may help in anticipating where the risk of severe weather may be locally enhanced.

An Impact Assessment of GPS Radio Occultation Data on Prediction of a Rapidly Developing Cyclone over the Southern Ocean*

2014 ◽  
Vol 142 (11) ◽  
pp. 4187-4206 ◽  
Author(s):  
Shu-Ya Chen ◽  
Tae-Kwon Wee ◽  
Ying-Hwa Kuo ◽  
David H. Bromwich
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Large Scale ◽  
Radio Occultation ◽  
Three Dimensional ◽  
Wrf Model ◽  
The Other ◽  
Gps Radio Occultation ◽  
The Antarctic ◽  
The Impact

Abstract The impact of global positioning system (GPS) radio occultation (RO) data on an intense synoptic-scale storm that occurred over the Southern Ocean in December 2007 is evaluated, and a synoptic explanation of the assessed impact is offered. The impact is assessed by using the three-dimensional variational data assimilation scheme (3DVAR) of the Weather Research and Forecasting (WRF) Model Data Assimilation system (WRFDA), and by comparing two experiments: one with and the other without assimilating the refractivity data from four different RO missions. Verifications indicate significant positive impacts of the RO data in various measures and parameters as well as in the track and intensity of the Antarctic cyclone. The analysis of the atmospheric processes underlying the impact shows that the assimilation of the RO data yields substantial improvements in the large-scale circulations that in turn control the development of the Antarctic storm. For instance, the RO data enhanced the strength of a 500-hPa trough over the Southern Ocean and prevented the katabatic flow near the coast of East Antarctica from an overintensification. This greatly influenced two low pressure systems of a comparable intensity, which later merged together and evolved into the major storm. The dominance of one low over the other in the merger dramatically changed the track, intensity, and structure of the merged storm. The assimilation of GPS RO data swapped the dominant low, leading to a remarkable improvement in the subsequent storm’s prediction.

scholarly journals The Impact of Assimilating Atmospheric Infrared Sounder Observation on the Forecast of Typhoon Tracks

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Chien-Ben Chou ◽  
Huei-Ping Huang
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Atmospheric Infrared Sounder ◽  
Background Error ◽  
Forecasting System ◽  
Typhoon Tracks ◽  
The Impact ◽  
Decorrelation Scale

This work assesses the effects of assimilating atmospheric infrared sounder (AIRS) observations on typhoon prediction using the three-dimensional variational data assimilation (3DVAR) and forecasting system of the weather research and forecasting (WRF) model. Two major parameters in the data assimilation scheme, the spatial decorrelation scale and the magnitude of the covariance matrix of the background error, are varied in forecast experiments for the track of typhoon Sinlaku over the Western Pacific. The results show that within a wide parameter range, the inclusion of the AIRS observation improves the prediction. Outside this range, notably when the decorrelation scale of the background error is set to a large value, forcing the assimilation of AIRS data leads to degradation of the forecast. This illustrates how the impact of satellite data on the forecast depends on the adjustable parameters for data assimilation. The parameter-sweeping framework is potentially useful for improving operational typhoon prediction.

scholarly journals Application of ATOVS Radiance with ARW WRF/GSI Data Assimilation System in the Prediction of Hurricane Katrina

2009 ◽  
Vol 3 (1) ◽  
pp. 13-28 ◽  
Author(s):  
J. Xu ◽  
S. Rugg ◽  
M. Horner ◽  
L. Byerle
Keyword(s):  
Data Assimilation ◽  
Hurricane Katrina ◽  
Three Dimensional ◽  
Wrf Model ◽  
Vortex Center ◽  
Atmospheric Flow ◽  
Infrared Observation ◽  
Westward Movement ◽  
Radiance Data ◽  
The Impact

In this study, we evaluated the impact of directly assimilating radiance on Hurricane Katrina forecasts over the Gulf of Mexico in the southeastern United States in August 2005. The ATOVS (i.e., The Advanced Television and Infrared Observation Satellite (TIROS)-N Operational Vertical Sounder) radiance data, the Gridpoint Statistical Interpolation (GSI) three-dimensional variational analysis (3DVAR) system, and the Advanced Research WRF (ARW WRF) model were employed. The results in a series of experiments show that after radiance data assimilation, the intensity and structure of initial fields including atmospheric flow, temperature and moisture have been modified somehow, especially with instruments using microwave bands such as AMSU-A/B. An anomalous southward pressure gradient has been added behind the hurricane center, which made the easterly flow go through the initial vortex center, accelerating westward movement of the hurricane. All data assimilation experiments obtain a similar forecast for the hurricane track before 36 h of model integration. After 36 h, the hurricane tracks in AMSU-A/B experiments are closer to the best track, but the tracks in HIRS3 and control experiments have a bigger error. However, we note that the improvement is limited, all assimilation experiments did not properly depict the deepening of the hurricane center around 1800 UTC 28 August.

scholarly journals Observation of nocturnal low-level wind shear and particulate matter in urban Beijing using a Doppler wind lidar

2017 ◽  
Vol 10 (6) ◽  
pp. 411-417 ◽  
Author(s):  
Yong Chen ◽  
Jun-Ling An ◽  
Jian Lin ◽  
Ye-Le Sun ◽  
Xi-Quan Wang ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Wind Shear ◽  
Low Level ◽  
Wind Lidar ◽  
Doppler Wind Lidar

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 Lidar Data Assimilation on Analysis and Prediction of Low-level Wind Shears at Lanzhou Zhongchuan International Airport, China

2020 ◽  
Author(s):  
Lanqian Li ◽  
Aimei Shao
Keyword(s):  
Data Assimilation ◽  
Wind Shear ◽  
Weather Conditions ◽  
Experimental Results ◽  
Small Scale ◽  
Lidar Data ◽  
Low Level ◽  
International Airport ◽  
Rainy Weather ◽  
Lidar Observations

<p>Low-level wind shear could occur not only in rainy weather conditions but also in non-rainy weather conditions, which is dangerous to aircraft safety for its rapid changes in wind direction or velocity. Recently, dry wind shear occurred in non-rainy condition has drawn more and more attention. Rain-detecting Doppler radar has no capabilities in detecting dry wind shear occurred in non-rainy condition, while Doppler Lidar observations with higher spatial and temporal resolution provide valuable information for dry wind shear. For this, considering dry wind shear cases reported by pilots at Lanzhou Zhongchuan International Airport as study object, lidar observations (radial velocities) were assimilated along with surface data to improve the prediction skill of dry wind shear events.</p><p>All experiments were conducted with Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-VAR) system. Three-nested domains were employed with 1-km horizontal resolution in the innermost domain. The model was derived by the NCEP FNL data. Lidar data was processed and only assimilated in the innermost domain. Experimental results show that the low-level wind shear can not be found in the experimental results without lidar data assimilation, while lidar data assimilation experiment successfully represented wind shear small-scale characteristics and simulated radial wind pattern was close to lidar observation. In addition, assimilation cycles with short time intervals effectively improved simulation accuracy of wind shear events.</p>

Variational Assimilation of Cloud Liquid/Ice Water Path and Its Impact on NWP

2015 ◽  
Vol 54 (8) ◽  
pp. 1809-1825 ◽  
Author(s):  
Yaodeng Chen ◽  
Hongli Wang ◽  
Jinzhong Min ◽  
Xiang-Yu Huang ◽  
Patrick Minnis ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Liquid Water ◽  
Weather Prediction ◽  
Wrf Model ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Water Path ◽  
Ice Water Path ◽  
Ice Water ◽  
The Impact

AbstractAnalysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

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