Diagnosis of Tropical Cyclone Intensity and Structure Using Upper Tropospheric Atmospheric Motion Vectors

Ryo OYAMA; Masahiro SAWADA; Kazuki SHIMOJI

doi:10.2151/jmsj.2017-024

Impact of Four-Dimensional Variational Data Assimilation of Atmospheric Motion Vectors on Tropical Cyclone Track Forecasts

Weather and Forecasting ◽

10.1175/waf940.1 ◽

2006 ◽

Vol 21 (4) ◽

pp. 663-669 ◽

Cited By ~ 11

Author(s):

Dongliang Wang ◽

Xudong Liang ◽

Yihong Duan ◽

Johnny C. L. Chan

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Mesoscale Model ◽

State University ◽

Motion Vectors ◽

Geostationary Meteorological Satellite ◽

Atmospheric Motion ◽

Atmospheric Motion Vectors ◽

Data Assimilation Technique ◽

The Impact

Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

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Tropical Cyclone Forecasts Impact Assessment from the Assimilation of Hourly Visible, Shortwave, and Clear-Air Water Vapor Atmospheric Motion Vectors in HWRF

Weather and Forecasting ◽

10.1175/waf-d-18-0072.1 ◽

2019 ◽

Vol 34 (1) ◽

pp. 177-198 ◽

Cited By ~ 7

Author(s):

Agnes H. N. Lim ◽

James A. Jung ◽

Sharon E. Nebuda ◽

Jaime M. Daniels ◽

Wayne Bresky ◽

...

Keyword(s):

Water Vapor ◽

Tropical Cyclone ◽

Tropical Cyclones ◽

Positive Impact ◽

Data Types ◽

Motion Vectors ◽

Atmospheric Motion ◽

Shortwave Infrared ◽

Atmospheric Motion Vectors ◽

The Impact

Abstract The assimilation of atmospheric motion vectors (AMVs) provides important wind information to conventional data-lacking oceanic regions, where tropical cyclones spend most of their lifetimes. Three new AMV types, shortwave infrared (SWIR), clear-air water vapor (CAWV), and visible (VIS), are produced hourly by NOAA/NESDIS and are assimilated in operational NWP systems. The new AMV data types are added to the hourly infrared (IR) and cloud-top water vapor (CTWV) AMV data in the 2016 operational version of the HWRF Model. In this study, we update existing quality control (QC) procedures and add new procedures specific to tropical cyclone assimilation. We assess the impact of the three new AMV types on tropical cyclone forecasts by conducting assimilation experiments for 25 Atlantic tropical cyclones from the 2015 and 2016 hurricane seasons. Forecasts are analyzed by considering all tropical cyclones as a group and classifying them into strong/weak storm vortices based on their initial model intensity. Metrics such as track error, intensity error, minimum central pressure error, and storm size are used to assess the data impact from the addition of the three new AMV types. Positive impact is obtained for these metrics, indicating that assimilating SWIR-, CAWV-, and VIS-type AMVs are beneficial for tropical cyclone forecasting. Given the results presented here, the new AMV types were accepted into NOAA/NCEP’s operational HWRF for the 2017 hurricane season.

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Assimilation of Himawari-8 Rapid-Scan Atmospheric Motion Vectors on Tropical Cyclone in HWRF System

Atmosphere ◽

10.3390/atmos11060601 ◽

2020 ◽

Vol 11 (6) ◽

pp. 601

Author(s):

Masahiro Sawada ◽

Zaizhong Ma ◽

Avichal Mehra ◽

Vijay Tallapragada ◽

Ryo Oyama ◽

...

Keyword(s):

Tropical Cyclone ◽

Lead Time ◽

Forecast Skill ◽

Track Forecast ◽

Motion Vectors ◽

Substantial Impact ◽

Rapid Scan ◽

Forecast Lead Time ◽

Atmospheric Motion ◽

Atmospheric Motion Vectors

This study investigates the assimilation impact of rapid-scan (RS) atmospheric motion vectors (AMVs) derived from the geostationary satellite Himawari-8 on tropical cyclone (TC) forecasts. Forecast experiments for three TCs in 2016 in the western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). An ensemble-variational hybrid data assimilation system is used as an initialization. The results show that the assimilation of RS-AMVs can improve the track forecast skill, while the weak bias or slow intensification bias increases at the shorter forecast lead time. A vortex initialization in HWRF has a substantial impact on TC structure, but it has neutral impacts on the track and intensity forecasts. A thinning of AMVs mitigates the weak bias caused by RS-AMV assimilation, resulting in the reduction of intensity error. However, it degrades the track forecast skill for a longer lead time. A decomposition of the TC steering flows demonstrated that the change in TC-induced flow was a primary factor for reducing the track forecast error, and the change in environmental flow has less impact on the track forecast. The investigation of the structural change from the assimilation of RS-AMV revealed that the following two factors are likely related to the intensity forecast degradation: (1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and (2) a drying around and outside the RMW.

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Relationship between Tropical Cyclone Intensification and Cloud-Top Outflow Revealed by Upper-Tropospheric Atmospheric Motion Vectors

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-17-0058.1 ◽

2017 ◽

Vol 56 (10) ◽

pp. 2801-2819 ◽

Cited By ~ 5

Author(s):

Ryo Oyama

Keyword(s):

Tropical Cyclone ◽

Inner Core ◽

Secondary Circulation ◽

Infrared Band ◽

Motion Vectors ◽

In Situ Data ◽

Core Convection ◽

Technological Advances ◽

Atmospheric Motion ◽

Atmospheric Motion Vectors

AbstractThe temporally dense geostationary satellite observations made possible by recent technological advances enable atmospheric motion vectors (AMVs) to be derived that are suitable for capturing atmospheric flows even of mesoscale phenomena, for which in situ data are scarce. Tropical cyclone (TC) outflows around the cloud top, reflecting TC secondary circulation, were computed by using AMVs derived from successive Multifunctional Transport Satellite (MTSAT) imagery, and the relationship between TC intensification rate (defined as the change of the best-track maximum sustained wind in the previous 24 h) and the outflow was investigated for 44 TCs occurring during 2011–14. During the TC intensification phase, temporal changes in the outflow were generally synchronous with changes in the cloud-top temperature of TC inner-core convective clouds detected by MTSAT infrared band. It was noteworthy that the intensification rates of 66% of the TCs peaked 0–36 h after outflow maximization and that the intensification rate for TCs with a maximum rate of >15 m s−1 day−1 peaked after the outflow maximum. Furthermore, TCs with a large intensification rate and latent-heat release around the midlevel tended to have a large outflow during the intensification phase. A comparison of TCs with and without convective bursts (CBs) revealed that the correlation between outflow and the TC intensification rate was higher for TCs accompanied by CBs than for those without CBs, implying that a rapid deepening of inner-core convection is important for intensification of a TC’s secondary circulation. The outflow tended to be most correlated with the TC intensification rate 0–6 h earlier.

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Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

Annales Geophysicae ◽

10.5194/angeo-33-805-2015 ◽

2015 ◽

Vol 33 (7) ◽

pp. 805-828 ◽

Cited By ~ 12

Author(s):

M. M. Greeshma ◽

C. V. Srinivas ◽

V. Yesubabu ◽

C. V. Naidu ◽

R. Baskaran ◽

...

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Bay Of Bengal ◽

Weather Research And Forecasting ◽

Motion Vectors ◽

Arw Model ◽

Atmospheric Motion ◽

Atmospheric Motion Vectors ◽

Radiance Data ◽

Weather Research

Abstract. The tropical cyclone (TC) track and intensity predictions over Bay of Bengal (BOB) using the Advanced Research Weather Research and Forecasting (ARW) model are evaluated for a number of data assimilation experiments using various types of data. Eight cyclones that made landfall along the east coast of India during 2008–2013 were simulated. Numerical experiments included a control run (CTL) using the National Centers for Environmental Prediction (NCEP) 3-hourly 0.5 × 0.5° resolution Global Forecasting System (GFS) analysis as the initial condition, and a series of cycling mode variational assimilation experiments with Weather Research and Forecasting (WRF) data assimilation (WRFDA) system using NCEP global PrepBUFR observations (VARPREP), Atmospheric Motion Vectors (VARAMV), Advanced Microwave Sounding Unit (AMSU) A and B radiances (VARRAD) and a combination of PrepBUFR and RAD (VARPREP+RAD). The impact of different observations is investigated in detail in a case of the strongest TC, Phailin, for intensity, track and structure parameters, and finally also on a larger set of cyclones. The results show that the assimilation of AMSU radiances and Atmospheric Motion Vectors (AMV) improved the intensity and track predictions to a certain extent and the use of operationally available NCEP PrepBUFR data which contains both conventional and satellite observations produced larger impacts leading to improvements in track and intensity forecasts. The forecast improvements are found to be associated with changes in pressure, wind, temperature and humidity distributions in the initial conditions after data assimilation. The assimilation of mass (radiance) and wind (AMV) data showed different impacts. While the motion vectors mainly influenced the track predictions, the radiance data merely influenced forecast intensity. Of various experiments, the VARPREP produced the largest impact with mean errors (India Meteorological Department (IMD) observations less the model values) of 78, 129, 166, 210 km in the vector track position, 10.3, 5.8, 4.8, 9.0 hPa deeper than IMD data in central sea level pressure (CSLP) and 10.8, 3.9, −0.2, 2.3 m s−1 stronger than IMD data in maximum surface winds (MSW) for 24, 48, 72, 96 h forecasts respectively. An improvement of about 3–36 % in track, 6–63 % in CSLP, 26–103 % in MSW and 11–223 % in the radius of maximum winds in 24–96 h lead time forecasts are found with VARPREP over CTL, suggesting the advantages of assimilation of operationally available PrepBUFR data for cyclone predictions. The better predictions with PrepBUFR could be due to quality-controlled observations in addition to containing different types of data (conventional, satellite) covering an effectively larger area. The performance degradation of VARPREP+RAD with the assimilation of all available observations over the domain after 72 h could be due to poor area coverage and bias in the radiance data.

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Assimilation of High-Resolution Satellite-Derived Atmospheric Motion Vectors: Impact on HWRF Forecasts of Tropical Cyclone Track and Intensity

Monthly Weather Review ◽

10.1175/mwr-d-16-0229.1 ◽

2017 ◽

Vol 145 (3) ◽

pp. 1107-1125 ◽

Cited By ~ 24

Author(s):

Christopher Velden ◽

William E. Lewis ◽

Wayne Bresky ◽

David Stettner ◽

Jaime Daniels ◽

...

Keyword(s):

Quality Control ◽

High Resolution ◽

Tropical Cyclone ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

Motion Vectors ◽

Atmospheric Motion ◽

Atmospheric Motion Vectors ◽

The Impact ◽

Atlantic Tropical Cyclone

It is well known that global numerical model analyses and forecasts benefit from the routine assimilation of atmospheric motion vectors (AMVs) derived from meteorological satellites. Recent studies have also shown that the assimilation of enhanced (spatial and temporal) AMVs can benefit research-mode regional model forecasts of tropical cyclone track and intensity. In this study, the impact of direct assimilation of enhanced (higher resolution) AMV datasets in the NCEP operational Hurricane Weather Research and Forecasting Model (HWRF) system is investigated. Forecasts of Atlantic tropical cyclone track and intensity are examined for impact by inclusion of enhanced AMVs via direct data assimilation. Experiments are conducted for AMVs derived using two methodologies (“HERITAGE” and “GOES-R”), and also for varying levels of quality control in order to assess and inform the optimization of the AMV assimilation process. Results are presented for three selected Atlantic tropical cyclone events and compared to Control forecasts without the enhanced AMVs as well as the corresponding operational HWRF forecasts. The findings indicate that the direct assimilation of high-resolution AMVs has an overall modest positive impact on HWRF forecasts, but the impact magnitudes are dependent on the 1) availability of rapid scan imagery used to produce the AMVs, 2) AMV derivation approach, 3) level of quality control employed in the assimilation, and 4) vortex initialization procedure (including the degree to which unbalanced states are allowed to enter the model analyses).

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