Impact of cyclone bogusing and regional assimilation on tropical cyclone track and intensity predictions

bl v/;;u dk mÌs’; vYi vof/k iwokZuqeku esa pØokr ds iFk vkSj mldh rhozrk dk iwokZuqeku yxkus ds fy, MCY;w-vkj-,Q- lehdj.k vkSj iwokZuqeku iz.kkyh esa m".kdfVca/kh; dkYifud pØokr ds vk/kkj ij mlds izHkko dk fu/kkZj.k djuk gSA bl izHkko dks pØokr ds izHkko dh =qfV] dsUnzh; nkc vkSj vf/kdre lrr iou xfr ds :i esa crk;k x;k gSA ;g v/;;u o"kZ 2010 esa cus rhu pØokrksa uker% ‘ySyk’ ¼caxky dh [kkM+h½] ‘fxjh’ ¼caxky dh [kkM+h½ vkSj ‘QsV’ ¼vjc lkxj½ ij vk/kkfjr gSA MCY;w- vkj- ,Q- ekWMy izpkyukRed ,u-lh-,e- vkj-MCY;w-,Q- Vh- 382 ,y 64 ds fo’ys"k.k vkSj iwokZuqekuksa dk mi;ksx djrk gS vkSj bl ekWMy dks pØokr ds iFk vkSj bldh rhozrk dk iwokZuqeku yxkus ds fy, 72 ?kaVs rd lekdfyr fd;k x;k gSA bl ijh{k.k ds pkj lSVksa dh tk¡p dh xbZ ¼i½ fu;a=.k ijh{k.k ¼lh-,u-Vh-,y-½ ftlesa uk rks lehdj.k vkSj uk gh dkYifud pØokr dks vk/kkj ekuk x;k gSA bl ekWMy dk vkjaHk varoZsf’kr HkweaMyh; ekWMy fo’ys"k.k dk mi;ksx djrs gq, fd;k x;kA ¼ii½ lehdj.k ijh{k.k ¼oh-,-vkj-½ esa MCY;w- vkj- ,Q- oh- ,- vkj- vk¡dM+k lehdj.k iz.kkyh ¼fcuk dkYifud vk/kkj ij ekuk x;k pØokr½ dk mi;ksx djrs gq, ekWMy dh vkjafHkd fLFkfr;k¡ rS;kj dh xbaZA ¼iii½ pØokr ds ijh{k.k ¼ch-vks-th-½ lehdj.k ds fcuk dsoy dkYifud pØokr dks ekurs gq, dkYifud vk/kkj ij pØokr ds iz;ksx fd, x, gSaA bl ekeys esa dkYifud vk?kkj ij pØokr dk mi;ksx djrs gq, ekWMy ds izFke vuqeku dks la’kksf/kr fd;k x;k vkSj bldk vkjafHkd fLFkfr;ksa ds :i esa mi;ksx fd;k x;k gSA ¼iv½ pkSFks ijh{k.k esa dkYifud vk/kkj ij pØokr ds ckn MCY;-w vkj- ,Q- vk¡dM+k lehdj.k ¼ch- vks- th- oh- ,- vkj-½ nksuksa dk mi;ksx djrs gq, ekWMy dh vkjafHkd fLFkfr;k¡ rS;kj dh xbZA buls izkIr gq, ijh.kkeksa ls vkjafHkd fLFkfr;ksa esa dkYifud pØokr ds mYys[kuh; izHkko dk irk pyk gSA ;s rhuksa gh pØokr dkYifud ¼ch-vks-th- vkSj oh-,-vkj-½ iz;ksxksa dh vkjafHkd fLFkfr;ksa ¼0000 ;w- Vh- lh-½ esa ik, x, tk ldrs gSa tks vU;Fkk dkYifud vk/kkj ij rS;kj fd, x, pØokrksa ds vHkko esa ¼oh- ,-vkj- vkSj lh- ,u- Vh- ,y-½ iz;ksx esa ugha gksrh gSA ch- vks- th- oh- ,- vkj- ijh{k.k ds iFk =qfV;ksa esa mYys[kuh; deh ns[kh xbZ gSA oh- ,- vkj- dh rqyuk esa ch- vks- th- oh- ,- vkj- esa iFk =qfV esa vf/kdre deh Øe’k% ‘ySyk’ esa 76-8 izfr’kr] ‘fxjh’ esa 87-3 izfr’kr vkSj ‘QsV’ esa 51-5 izfr’kr jghA ‘ySyk’ vkSj ‘fxjh’ ds fy, oh-,-vkj- dh rqyuk esa ch-vks-th-oh-,-vkj- esa fy, x, izs{k.k vf/kdre lrr@Øfed iou xfr vkSj vf/kdre dsUnzh; nkc ds fudV gSaA The aim of this study is to assess the impact of tropical cyclone bogusing in WRF assimilation and forecast system for cyclone track and intensity prediction in short range forecast. The impact is demonstrated in terms of track error, central pressure, and maximum sustained wind speed. The study is based on the three cyclones; namely 'LAILA' (Bay of Bengal), 'GIRI' (Bay of Bengal) and 'PHET' (Arabian Sea), formed in the year 2010. The WRF model makes use of the operational NCMRWF T382L64 analysis and forecasts and the model is integrated upto 72 hrs for producing the cyclone track and intensity forecast. Four sets of experiments were performed: (i) The control experiment (CNTL) in which neither assimilation nor cyclone bogusing is done. The model is initialized using interpolated global model analysis. (ii) In assimilation experiment (VAR), model initial condition is prepared using WRF VAR data assimilation system (without cyclone bogusing). (iii) The cyclone bogusing experiment (BOG) featuring cyclone bogusing alone without assimilation. In this case the model first guess is modified using cyclone bogusing and used as the initial condition. (iv) In the forth experiment, the initial condition of the model is prepared with both cyclone bogusing followed with WRF data assimilation (BOGVAR). Results indicate remarkable impact of cyclone bogusing on the initial condition. All three cyclones can be located in the initial conditions (0000 UTC) of bogus (BOG and BOGVAR) experiments which were otherwise absent in no-bogus (VAR and CNTL) experiments. Significant reductions in track errors occurred in BOGVAR experiment. The maximum reduction in track error in BOGVAR compare to VAR is 76.8 % in 'LAILA', 87.3 % in 'GIRI' and 51.5 % in 'PHET' respectively. Maximum sustained wind speed and minimum central pressure are close to observations in BOGVAR compared to VAR for 'LAILA' and 'GIRI'.

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The Impact of Dropwindsonde Data on Typhoon Track Forecasts in DOTSTAR

Weather and Forecasting ◽

10.1175/2007waf2006062.1 ◽

2007 ◽

Vol 22 (6) ◽

pp. 1157-1176 ◽

Cited By ~ 80

Author(s):

Chun-Chieh Wu ◽

Kun-Hsuan Chou ◽

Po-Hsiung Lin ◽

Sim D. Aberson ◽

Melinda S. Peng ◽

...

Keyword(s):

Tropical Cyclone ◽

Error Reduction ◽

Track Forecast ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

Global Spectral Model ◽

Track Error ◽

Typhoon Track ◽

The Mean ◽

The Impact

Abstract Starting from 2003, a new typhoon surveillance program, Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR), was launched. During 2004, 10 missions for eight typhoons were conducted successfully with 155 dropwindsondes deployed. In this study, the impact of these dropwindsonde data on tropical cyclone track forecasts has been evaluated with five models (four operational and one research models). All models, except the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model, show the positive impact that the dropwindsonde data have on tropical cyclone track forecasts. During the first 72 h, the mean track error reductions in the National Centers for Environmental Prediction’s (NCEP) Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS) of the Fleet Numerical Meteorology and Oceanography Center (FNMOC), and the Japanese Meteorological Agency (JMA) Global Spectral Model (GSM) are 14%, 14%, and 19%, respectively. The track error reduction in the Weather Research and Forecasting (WRF) model, in which the initial conditions are directly interpolated from the operational GFS forecast, is 16%. However, the mean track improvement in the GFDL model is a statistically insignificant 3%. The 72-h-average track error reduction from the ensemble mean of the above three global models is 22%, which is consistent with the track forecast improvement in Atlantic tropical cyclones from surveillance missions. In all, despite the fact that the impact of the dropwindsonde data is not statistically significant due to the limited number of DOTSTAR cases in 2004, the overall added value of the dropwindsonde data in improving typhoon track forecasts over the western North Pacific is encouraging. Further progress in the targeted observations of the dropwindsonde surveillances and satellite data, and in the modeling and data assimilation system, is expected to lead to even greater improvement in tropical cyclone track forecasts.

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Impact of Enhanced Satellite-Derived Atmospheric Motion Vector Observations on Numerical Tropical Cyclone Track Forecasts in the Western North Pacific during TPARC/TCS-08

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-019.1 ◽

2011 ◽

Vol 50 (11) ◽

pp. 2309-2318 ◽

Cited By ~ 34

Author(s):

Howard Berger ◽

Rolf Langland ◽

Christopher S. Velden ◽

Carolyn A. Reynolds ◽

Patricia M. Pauley

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

North Pacific ◽

Western North Pacific ◽

Forecast Errors ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

Rapid Scan ◽

Atmospheric Motion ◽

The Impact

AbstractEnhanced atmospheric motion vectors (AMVs) produced from the geostationary Multifunctional Transport Satellite (MTSAT) are assimilated into the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) to evaluate the impact of these observations on tropical cyclone track forecasts during the simultaneous western North Pacific Ocean Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (TPARC) and the Tropical Cyclone Structure—2008 (TCS-08) field experiments. Four-dimensional data assimilation is employed to take advantage of experimental high-resolution (space and time) AMVs produced for the field campaigns by the Cooperative Institute for Meteorological Satellite Studies. Two enhanced AMV datasets are considered: 1) extended periods produced at hourly intervals over a large western North Pacific domain using routinely available MTSAT imagery and 2) limited periods over a smaller storm-centered domain produced using special MTSAT rapid-scan imagery. Most of the locally impacted forecast cases involve Typhoons Sinlaku and Hagupit, although other storms are also examined. On average, the continuous assimilation of the hourly AMVs reduces the NOGAPS tropical cyclone track forecast errors—in particular, for forecasts longer than 72 h. It is shown that the AMVs can improve the environmental flow analyses that may be influencing the tropical cyclone tracks. Adding rapid-scan AMV observations further reduces the NOGAPS forecast errors. In addition to their benefit in traditional data assimilation, the enhanced AMVs show promise as a potential resource for advanced objective data-targeting methods.

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A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

Remote Sensing ◽

10.3390/rs12081243 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1243 ◽

Cited By ~ 3

Author(s):

Xuanli Li ◽

John R. Mecikalski ◽

Timothy J. Lang

Keyword(s):

Wind Speed ◽

Tropical Cyclone ◽

Data Assimilation ◽

Positive Impact ◽

Specular Reflection ◽

Surface Wind ◽

Wrf Model ◽

Precipitation Forecast ◽

Wind Speed Data ◽

The Impact

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a ±35° latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated.

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The Impact of Convective Momentum Transport on Tropical Cyclone Track Forecasts Using the Emanuel Cumulus Parameterization

Monthly Weather Review ◽

10.1175/mwr3365.1 ◽

2007 ◽

Vol 135 (4) ◽

pp. 1195-1207 ◽

Cited By ~ 15

Author(s):

Timothy F. Hogan ◽

Randal L. Pauley

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Tropical Cyclones ◽

Momentum Transport ◽

Cumulus Parameterization ◽

Transport Parameter ◽

Medium Range Forecast ◽

Medium Range ◽

Convective Momentum Transport ◽

The Impact

Abstract The influence of convective momentum transport (CMT) on tropical cyclone (TC) track forecasts is examined in the Navy Operational Global Atmospheric Prediction System (NOGAPS) with the Emanuel cumulus parameterization. Data assimilation and medium-range forecast experiments show that for 35 tropical cyclones during August and September 2004 the inclusion of CMT in the cumulus parameterization significantly improves the TC track forecasts. The tests show that the track forecasts are very sensitive to the magnitude of the Emanuel parameterization’s convective momentum transport parameter, which controls the CMT tendency returned by the parameterization. While the overall effect of this formulation of CMT in NOGAPS data assimilation/medium-range forecasts results in the surface pressure of tropical cyclones being less intense (and more consistent with the analysis), the parameterization is not equivalent to a simple diffusion of winds in the presence of convection. This is demonstrated by two data assimilation/medium-range forecast tests in which a vertical diffusion algorithm replaces the CMT. Two additional data assimilation/medium-range forecast experiments were conducted to test whether the skill increase primarily comes from the CMT in the immediate vicinity of the tropical cyclones. The results show that the inclusion of the CMT calculation in the vicinity of the TC makes the largest contribution to the increase in forecast skill, but the general contribution of CMT away from the TC also plays an important role.

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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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Northeast Pacific annual accumulated cyclonic energy rank-profile

International Journal of Modern Physics C ◽

10.1142/s0129183121500832 ◽

2021 ◽

pp. 2150083

Author(s):

Elio Roca-Flores ◽

Gerardo G. Naumis

Keyword(s):

Wind Speed ◽

Tropical Cyclone ◽

Sliding Window ◽

Human Society ◽

Energy Index ◽

Northeast Pacific ◽

Hurricane Activity ◽

Tropical Cyclone Season ◽

Maximum Sustained Wind Speed ◽

Dissipation Processes

The ranking of events is a powerful way to study the complexity of rare catastrophic events as earthquakes and hurricanes. Hurricane activity can be quantified by the annual accumulated cyclone energy index (ACE), which contains the information of the maximum sustained wind speed, duration and frequency of the tropical cyclone season. Here, the ranking of the Northeast Pacific annual ACE is obtained and fitted using nonlinear regression with several two- and three-parameter ranking laws that fit the tail and head of the data, where lives the information of relevant events for human society. The logarithmic like function [Formula: see text] overperforms all other fits. A sliding window analysis of the parameters [Formula: see text] and [Formula: see text] of such a function shows that forcing and dissipation processes are anticorrelated.

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Seasonal and Inter-Annual Variability of Sea Surface Temperature and Its Correlation with Maximum Sustained Wind Speed in Bay of Bengal

Climate Change Impacts on Water Resources - Water Science and Technology Library ◽

10.1007/978-3-030-64202-0_23 ◽

2021 ◽

pp. 253-265

Author(s):

Jiya Albert ◽

Prasad K. Bhaskaran

Keyword(s):

Wind Speed ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Bay Of Bengal ◽

Sea Surface ◽

Annual Variability ◽

Maximum Sustained Wind Speed

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Impact of Resolution and Design on the U.S. Navy Global Ensemble Performance in the Tropics

Monthly Weather Review ◽

10.1175/2011mwr3546.1 ◽

2011 ◽

Vol 139 (7) ◽

pp. 2145-2155 ◽

Cited By ~ 22

Author(s):

Carolyn A. Reynolds ◽

Justin G. McLay ◽

James S. Goerss ◽

Efren A. Serra ◽

Daniel Hodyss ◽

...

Keyword(s):

Wind Speed ◽

Tropical Cyclone ◽

Kinetic Energy ◽

Ensemble Prediction ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

Ensemble Performance ◽

Ensemble Mean ◽

The Tropics ◽

The U.S

Abstract The performance of the U.S. Navy global atmospheric ensemble prediction system is examined with a focus on tropical winds and tropical cyclone tracks. Ensembles are run at a triangular truncation of T119, T159, and T239, with 33, 17, and 9 ensemble members, respectively, to evaluate the impact of resolution versus the number of ensemble member tradeoffs on ensemble performance. Results indicate that the T159 and T239 ensemble mean tropical cyclone track errors are significantly smaller than those of the T119 ensemble out to 4 days. For ensemble forecasts of upper- and lower-tropospheric tropical winds, increasing resolution has only a small impact on ensemble mean root-mean-square error for wind speed, but does improve Brier scores for 10-m wind speed at the 5 m s−1 threshold. In addition to the resolution tests, modifications to the ensemble transform initial perturbation methodology and inclusion of stochastic kinetic energy backscatter are also evaluated. Stochastic kinetic energy backscatter substantially increases the ensemble spread and improves Brier scores in the tropics, but for the most part does not significantly reduce ensemble mean tropical cyclone track error.

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