The Impact of Multisatellite Data on the Initialization and Simulation of Hurricane Lili’s (2002) Rapid Weakening Phase

2007 ◽  
Vol 135 (2) ◽  
pp. 526-548 ◽  
Author(s):  
Xiaoyan Zhang ◽  
Qingnong Xiao ◽  
Patrick J. Fitzpatrick
Keyword(s):  
Data Assimilation ◽  
Satellite Data ◽  
Inner Core ◽  
Surface Wind ◽  
Storm Track ◽  
Outer Core ◽  
Intensity Change ◽  
Track Forecast ◽  
Surface Winds ◽  
The Impact

Abstract Numerical experiments have been conducted to examine the impact of multisatellite data on the initialization and forecast of the rapid weakening of Hurricane Lili (in 2002) from 0000 UTC to landfall in Louisiana on 1300 UTC 3 October 2002. Fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) 4DVAR sensitivity runs were conducted separately with QuikSCAT surface winds, the Geostationary Operational Environmental Satellite-8 (GOES-8) cloud drift–water vapor winds, and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) temperature–dewpoint sounding data to investigate their individual impact on storm track and intensity. The results were compared against a simulation initialized from a Global Forecast System background interpolated to the MM5 grid. Assimilating QuikSCAT surface wind data improves the analyzed outer-core surface winds, as well as the inner-core low-level temperature and moisture fields. Substantial adjustments of winds are noted on the periphery of the hurricane by assimilating GOES-8 satellite-derived upper-level winds. Both track forecasts initialized at 1200 UTC 2 October 2002 with four-dimensional variational data assimilation (4DVAR) of QuikSCAT and GOES-8 show improvement compared to those initialized with the model background. Assimilating Aqua MODIS sounding data improves the outer-core thermodynamic features. The Aqua MODIS data has a slight impact on the track forecast, but more importantly shows evidence of impacting the model intensity predicting by retarding the incorrect prediction of intensification. All three experiments also show that bogusing of an inner-core wind vortex is required to depict the storm’s initial intensity. To properly investigate Lili’s weakening, data assimilation experiments that incorporate bogusing vortex, QuikSCAT winds, GOES-8 winds, and Aqua MODIS sounding data were performed. The 4DVAR satellite-bogus data assimilation is conducted in two consecutive 6-h windows preceding Lili’s weakening. Comparisons of the results between the experiments with and without satellite data indicated that the satellite data, particularly the Aqua MODIS sounding information, makes an immediate impact on the hurricane intensity change beyond normal bogusing procedures. The track forecast with the satellite data is also more accurate than just using bogusing alone. This study suggests that dry air intrusion played an important role in Lili’s rapid weakening. It also demonstrates the potential benefit of using satellite data in a 4DVAR context—particularly high-resolution soundings—on unusual cases like Hurricane Lili.

The Impact of Outer-Core Surface Heat Fluxes on the Convective Activities and Rapid Intensification of Tropical Cyclones

2020 ◽  
Vol 77 (11) ◽  
pp. 3907-3927
Author(s):  
Chin-Hsuan Peng ◽  
Chun-Chieh Wu
Keyword(s):  
Inner Core ◽  
Surface Wind ◽  
Outer Core ◽  
Surface Heat ◽  
Heat Fluxes ◽  
Intensity Change ◽  
Rapid Intensification ◽  
Core Surface ◽  
Surface Heat Fluxes ◽  
The Impact

AbstractThe rapid intensification (RI) of Typhoon Soudelor (2015) is simulated using a full-physics model. To investigate how the outer-core surface heat fluxes affect tropical cyclone (TC) structure and RI processes, a series of numerical experiments are performed by suppressing surface heat fluxes between various radii. It is found that a TC would become quite weaker when the surface heat fluxes are suppressed outside the radius of 60 or 90 km [the radius of maximum surface wind in the control experiment (CTRL) at the onset of RI is roughly 60 km]. However, interestingly, the TC would experience stronger RI when the surface heat fluxes are suppressed outside the radius of 150 km. For those sensitivity experiments with capped surface heat fluxes, the members with greater intensification rate show stronger inner-core mid- to upper-level updrafts and higher heating efficiency prior to the RI periods. Although the outer-core surface heat fluxes in these members are suppressed, the inner-core winds become stronger, extracting more ocean energy from the inner core. Greater outer-core low-level stability in these members results in aggregation of deep convection and subsequent generation and concentration of potential vorticity inside the inner core, thus confining the strongest winds therein. The abovementioned findings are also supported by partial-correlation analyses, which reveal the positive correlation between the inner-core convection and subsequent 6-h intensity change, and the competition between the inner-core and outer-core convections (i.e., eyewall and outer rainbands).

scholarly journals Impact of Satellite Data Assimilation in Atmospheric Reanalysis on the Marine Wind and Wave Climate

2016 ◽  
Vol 29 (17) ◽  
pp. 6351-6361 ◽  
Author(s):  
Wataru Sasaki
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Southern Ocean ◽  
Satellite Data ◽  
North Pacific ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Surface Winds ◽  
Near Surface ◽  
The Impact

Abstract This study investigated the impact of assimilating satellite data into atmospheric reanalyses on trends in ocean surface winds and waves. Two experiments were performed using a numerical wave model forced by near-surface winds: one derived from the Japanese 55-year Reanalysis (JRA-55; experiment A) and the other derived from JRA-55 using assimilated conventional observations only (JRA-55C; experiment B). The results showed that the satellite data assimilation reduced upward trends of the annual mean of wave energy flux (WEF) in the midlatitude North Pacific and southern ocean (30°–60°S), south of Australia, from 1959 to 2012. It was also found that the assimilation of scatterometer winds reduced the near-surface wind speed in the midlatitude North Pacific after the mid-1990s, which resulted in the reduced trend in WEF from 1959 to 2012. By contrast, assimilation of the satellite radiances for 1973–94 increased near-surface wind speed in the southern ocean, south of Australia, whereas the assimilation of the scatterometer winds after the mid-1990s reduced wind speed. The latter led to the reduced trend in WEF south of Australia from 1959 to 2012.

scholarly journals Comparing Impacts of Satellite Data Assimilation and Lateral Boundary Conditions on Regional Model Forecasting: A Case Study of Hurricane Sandy (2012)

2017 ◽  
Vol 32 (2) ◽  
pp. 595-608
Author(s):  
Tong Zhu ◽  
Sid Ahmed Boukabara ◽  
Kevin Garrett
Keyword(s):  
Data Assimilation ◽  
Boundary Conditions ◽  
Satellite Data ◽  
Regional Model ◽  
Hurricane Sandy ◽  
Track Forecast ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
Outer Domain ◽  
The Impact

Abstract The impacts of both satellite data assimilation (DA) and lateral boundary conditions (LBCs) on the Hurricane Weather Research and Forecasting (HWRF) Model forecasts of Hurricane Sandy 2012 were assessed. To investigate the impact of satellite DA, experiments were run with and without satellite data assimilated, as well as with all satellite data but excluding Geostationary Operational Environmental Satellite (GOES) Sounder data. To gauge the LBC impact, these experiments were also run with a variety of outer domain (D-1) sizes. The inclusion of satellite DA resulted in analysis fields that better characterized the tropical storm structures including the warm core anomaly and wavenumber-1 asymmetry near the eyewall, and also served to reduce the forecast track errors for Hurricane Sandy. The specific impact of assimilating the GOES Sounder data showed positive impacts on forecasts of the storm minimum sea level pressure. Increasing the D-1 size resulted in increases in the day 4/5 forecast track errors when verified against the best track and the Global Forecast System (GFS) forecast, which dominated any benefits from assimilating an increased volume of satellite observations due to the larger domain. It was found that the LBCs with realistic environmental flow information could provide better constraints on smaller domain forecasts. This study demonstrated that satellite DA can improve the analysis of a hurricane asymmetry, especially in a shear environment, and then lead to a better track forecast, and also emphasized the importance of the LBCs and the challenges associated with the evaluation of satellite data impacts on regional model prediction.

scholarly journals Assessment of Assimilating ASCAT Surface Wind Retrievals in the NCEP Global Data Assimilation System

2011 ◽  
Vol 139 (11) ◽  
pp. 3405-3421 ◽  
Author(s):  
Li Bi ◽  
James A. Jung ◽  
Michael C. Morgan ◽  
John F. Le Marshall
Keyword(s):  
Data Assimilation ◽  
Geopotential Height ◽  
Surface Wind ◽  
Experimental Simulation ◽  
Surface Winds ◽  
European Organization ◽  
Wind Retrievals ◽  
The Impact ◽  
Global Data ◽  
Assimilation System

Abstract A two-season Observing System Experiment (OSE) was used to quantify the impacts of assimilating the Advanced Scatterometer (ASCAT) surface winds product distributed by the European Organization for the Exploitation of Meteorological Satellites (EUMESAT) and the National Environmental Satellite, Data, and Information Service (NESDIS). The ASCAT wind retrievals were provided by the Royal Netherlands Meteorological Office (KNMI) and the 50-km resolution ASCAT products were assimilated. The impact of assimilating the ASCAT surface wind product in the National Centers for Environmental Prediction (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was assessed by comparing the forecast results through 168 h for the months of August 2008 and January 2009. The NCEP GDAS/GFS was used, at a resolution of T382–64 layers, as the assimilation system and forecast model for these experiments. A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the ASCAT surface winds. Quality control procedures required to assimilate the ASCAT surface winds are discussed. Anomaly correlations (ACs) of geopotential height forecasts as well as geographic distribution of AC of geopotential height forecasts at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of forecast impact (FI) on the wind and temperature fields near the surface is also presented. The results of this study show that assimilation of the surface wind retrievals from the ASCAT sensor improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The ASCAT experiment AC scores show modest forecast improvements from days 4 through 7.

scholarly journals Structure and Evolution of Rainfall in Numerically Simulated Landfalling Hurricanes

2008 ◽  
Vol 136 (10) ◽  
pp. 3822-3847 ◽  
Author(s):  
Sytske K. Kimball
Keyword(s):  
Land Surface ◽  
Inner Core ◽  
Storm Track ◽  
Surface Characteristics ◽  
Outer Core ◽  
Maximum Rainfall ◽  
Dry Air ◽  
Forcing Mechanisms ◽  
The Right ◽  
The Impact

Using an idealized landfalling model hurricane, the impact of different land surface characteristics on hurricane rainfall distribution before, during, and after landfall is investigated. Before landfall, maximum rainfall occurs on the right side of the storm track as a result of dry air intrusion from both the environmental flow behind the vortex and the land surface ahead of the vortex. These sources of dry air combine to destabilize the right side and stabilize the left side of the storm. Upon landfall, the rainfall maximum shifts to the left of the storm track over land, near the coast. Increased friction over land drives a region of convergence in the entire front half of the storm. While mean rainfall rates decrease, localized areas of large rainfall accumulations may occur as a result of this frictional forcing. Over land, the rainfall area broadens and mean rainfall rates decrease. No differences are detected in inner-core rainfall rates between cases, but outer-core rainfall rates and rainfall coverage increase with moister land surfaces. Hence, significant differences in rainfall accumulations occur depending on moisture availability of the land surface. Since reconnaissance planes cannot fly over land, forecasters are often forced to make extrapolations from reconnaissance data over water. They should take extreme caution in doing so, since rainfall distributions may change suddenly upon landfall as different forcing mechanisms take over.

scholarly journals The Effects of Assimilating Conventional and ATOVS Data on Forecasted Near-Surface Wind with WRF-3DVAR

2015 ◽  
Vol 143 (1) ◽  
pp. 153-164 ◽  
Author(s):  
Feimin Zhang ◽  
Yi Yang ◽  
Chenghai Wang
Keyword(s):  
Data Assimilation ◽  
Surface Wind ◽  
Lower Atmosphere ◽  
Observation Data ◽  
Initial Field ◽  
Infrared Observation ◽  
Forecast Performance ◽  
Near Surface ◽  
The Impact ◽  
Positive Effect

Abstract In this paper, the Weather Research and Forecasting (WRF) Model with the three-dimensional variational data assimilation (WRF-3DVAR) system is used to investigate the impact on the near-surface wind forecast of assimilating both conventional data and Advanced Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (ATOVS) radiances compared with assimilating conventional data only. The results show that the quality of the initial field and the forecast performance of wind in the lower atmosphere are improved in both assimilation cases. Assimilation results capture the spatial distribution of the wind speed, and the observation data assimilation has a positive effect on near-surface wind forecasts. Although the impacts of assimilating ATOVS radiances on near-surface wind forecasts are limited, the fine structure of local weather systems illustrated by the WRF-3DVAR system suggests that assimilating ATOVS radiances has a positive effect on the near-surface wind forecast under conditions that ATOVS radiances in the initial condition are properly amplified. Assimilating conventional data is an effective approach for improving the forecast of the near-surface wind.

scholarly journals A Two-Season Impact Study of the WindSat Surface Wind Retrievals in the NCEP Global Data Assimilation System

2010 ◽  
Vol 25 (3) ◽  
pp. 931-949 ◽  
Author(s):  
Li Bi ◽  
James A. Jung ◽  
Michael C. Morgan ◽  
John F. Le Marshall
Keyword(s):  
Data Assimilation ◽  
Surface Wind ◽  
Temperature Fields ◽  
Experimental Simulation ◽  
Naval Research ◽  
Surface Winds ◽  
Control Simulation ◽  
Wind Retrievals ◽  
Global Data ◽  
Assimilation System

Abstract A two-season observing system experiment (OSE) was used to quantify the impacts of assimilating the WindSat surface winds product developed by the Naval Research Laboratory (NRL). The impacts of assimilating these surface winds were assessed by comparing the forecast results through 168 h for the months of October 2006 and March 2007. The National Centers for Environmental Prediction’s (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was used, at a resolution of T382-64 layers, as the assimilation system and forecast model for these experiments. A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the WindSat surface winds. Quality control procedures required to assimilate the surface winds are discussed. Anomaly correlations (ACs) of geopotential heights at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of the forecast impacts (FIs) on the wind field and temperature fields at 10-m height and 500 hPa is also discussed. The results of this study show that assimilating the surface wind retrievals from the WindSat satellite improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests that the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The WindSat experiment AC scores are similar to the control simulation AC scores until the day 6 forecasts, when the improvements in the WindSat experiment become greater for both seasons and in most of the cases.

Reducing TC Position Uncertainty in an Ensemble Data Assimilation and Prediction System: A Case Study of Typhoon Fanapi (2010)

2018 ◽  
Vol 33 (2) ◽  
pp. 561-582 ◽  
Author(s):  
Kuan-Jen Lin ◽  
Shu-Chih Yang ◽  
Shuyi S. Chen
Keyword(s):  
Data Assimilation ◽  
Negative Impact ◽  
Wrf Model ◽  
Track Forecast ◽  
Background Error ◽  
Position Uncertainty ◽  
Background Error Covariance ◽  
Error Covariance ◽  
The Impact

Abstract Ensemble-based data assimilation (EDA) has been used for tropical cyclone (TC) analysis and prediction with some success. However, the TC position spread determines the structure of the TC-related background error covariance and affects the performance of EDA. With an idealized experiment and a real TC case study, it is demonstrated that observations in the core region cannot be optimally assimilated when the TC position spread is large. To minimize the negative impact from large position uncertainty, a TC-centered EDA approach is implemented in the Weather Research and Forecasting (WRF) Model–local ensemble transform Kalman filter (WRF-LETKF) assimilation system. The impact of TC-centered EDA on TC analysis and prediction of Typhoon Fanapi (2010) is evaluated. Using WRF Model nested grids with 4-km grid spacing in the innermost domain, the focus is on EDA using dropsonde data from the Impact of Typhoons on the Ocean in the Pacific field campaign. The results show that the TC structure in the background mean state is improved and that unrealistically large ensemble spread can be alleviated. The characteristic horizontal scale in the background error covariance is smaller and narrower compared to those derived from the conventional EDA approach. Storm-scale corrections are improved using dropsonde data, which is more favorable for TC development. The analysis using the TC-centered EDA is in better agreement with independent observations. The improved analysis ameliorates model shock and improves the track forecast during the first 12 h and landfall at 72 h. The impact on intensity prediction is mixed with a better minimum sea level pressure and overestimated peak winds.

scholarly journals Joint Impact of Forecast Tendency and State Error Biases in Ensemble Kalman Filter Data Assimilation of Inner-Core Tropical Cyclone Observations

2013 ◽  
Vol 141 (9) ◽  
pp. 2992-3006 ◽  
Author(s):  
Tomislava Vukicevic ◽  
Altuğ Aksoy ◽  
Paul Reasor ◽  
Sim D. Aberson ◽  
Kathryn J. Sellwood ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Inner Core ◽  
Systematic Errors ◽  
Secondary Circulation ◽  
Short Term ◽  
Background Error ◽  
High Resolution Data ◽  
Boundary Layer Dynamics ◽  
The Impact

Abstract In this study the properties and causes of systematic errors in high-resolution data assimilation of inner-core tropical cyclone (TC) observations were investigated using the Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS). Although a recent study by Aksoy et al. demonstrated overall good performance of HEDAS for 83 cases from 2008 to 2011 using airborne observations from research and operational aircraft, some systematic errors were identified in the analyses with respect to independent observation-based estimates. The axisymmetric primary circulation intensity was underestimated for hurricane cases and the secondary circulation was systematically weaker for all cases. The diagnostic analysis in this study shows that the underestimate of primary circulation was caused by the systematic spindown of the vortex core in the short-term forecasts during the cycling with observations. This tendency bias was associated with the systematic errors in the secondary circulation, temperature, and humidity. The biases were reoccurring in each cycle during the assimilation because of the inconsistency between the strength of primary and secondary circulation during the short-term forecasts, the impact of model error in planetary boundary layer dynamics, and the effect of forecast tendency bias on the background error correlations. Although limited to the current analysis the findings in this study point to a generic problem of mutual dependence of short-term forecast tendency and state estimate errors in the data assimilation of TC core observations. The results indicate that such coupling of errors in the assimilation would also lead to short-term intensity forecast bias after the assimilation for the same reasons.

The Effects of Sampling Errors on the EnKF Assimilation of Inner-Core Hurricane Observations

2014 ◽  
Vol 142 (4) ◽  
pp. 1609-1630 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Fuqing Zhang ◽  
Yonghui Weng
Keyword(s):  
Data Assimilation ◽  
Hurricane Katrina ◽  
Inner Core ◽  
Doppler Radar ◽  
Mesoscale Model ◽  
Outer Core ◽  
State Variables ◽  
Sampling Errors ◽  
Relaxation Coefficient ◽  
Airborne Doppler Radar

Abstract Atmospheric data assimilation methods that estimate flow-dependent forecast statistics from ensembles are sensitive to sampling errors. This sensitivity is investigated in the context of vortex-scale hurricane data assimilation by cycling an ensemble Kalman filter to assimilate observations with a convection-permitting mesoscale model. In a set of numerical experiments, airborne Doppler radar observations are assimilated for Hurricane Katrina (2005) using an ensemble size that ranges from 30 to 300 members, and a varying degree of covariance inflation through relaxation to the prior. The range of ensemble sizes is shown to produce variations in posterior storm structure that persist for days in deterministic forecasts, with the most substantial differences appearing in the vortex outer-core wind and pressure fields. Ensembles with 60 or more members converge toward similar axisymmetric and asymmetric inner-core solutions by the end of the cycling, while producing qualitatively similar sample correlations between the state variables. Though covariance relaxation has little impact on model variables far from the observations, the structure of the inner-core vortex can benefit from a more optimal tuning of the relaxation coefficient. Results from this study provide insight into how sampling errors may affect the performance of an ensemble hurricane data assimilation system during cycling.

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