Cyclone track forecast by combining persistence, climatology and synoptic method

lkj & Å".kdfVca/kh; pØokr fouk’kdkjh izkÑfrd vkink gksrs gSaA buls tku eky dh cM+h gkfu gksrh gSA pØokr ds /kjkry ls Vdjkus ds ckn eq[; fouk’k mldh izpaM iouksa rFkk rwQkuh ty rjaxksa ls gksrk gSA pØokr ds /kjkry ls Vdjkus ds lgh LFkku dk iwokZuqeku djuk iwokZuqekudrkZvksa rFkk ,tsfUl;ksa] tks lqj{kk ds mik;ksa vFkok iquokZl dk;ksZa esa yxs gSa] ds fy, vR;ar egRoiw.kZ gksrk gSA bl ’kks/k Ik= esa pØokr ds /kjkry ls Vdjkus ds LFkku rFkk le; dk iwokZuqeku djus dk iz;kl fd;k x;k gSA O;fDrxr daI;wVj ij vk/kkfjr ekxZ iwokZuqeku ekWMy Hkkjr ekSle foKku foHkkx ds fofHkUu iwokZuqeku dk;kZy;ksa esas mi;ksx esa gSA izpfyr ekWMy ds fy, nzks.kh ds pØokr ekxZ dh tyok;q rFkk pØokrksa ds iwoZ dh fLFkfr dh tkudkjh dh vko’;drk gksrh gSA lkekU;r;% pØokr ds /kjkry ls Vdjkus ds 24 ls 36 ?kaVs iwoZ rV ds fdukjs dk ok;qnkc de gks tkrk gSA bl v/;;u esa ekxZ iwokZuqeku ds fy, bl izkpy dk leku egRo ds vU; nks izkpyksa ds la;kstu ds lkFk vFkkZr~ 1@3 ¼LFkkf;Ro + tyok;q + nkc ifjorZu½ mi;ksx fd;k x;k gSA blds ifj.kke dsoy tyok;q ,oa LFkkf;Ro ¼DykbesV ,.M ijflLVsaV½ ds mi;ksx ls izkIr fd, x, ifj.kkeksa dh rqyuk esa T;knk lgh gSaA ;fn pØokr ds /kjkry ls Vdjkus ds 12 ls 24 ?kaVs ds vanj dh mldh izfØ;k ij fopkj fd;k tk; rks 24 ?kaVs ds vanj dk nkc ifjorZu] tyok;q ,oa LFkkf;Ro dh rqyuk esa vf/kd egRoiw.kZ gks tkrk gS rFkk /kjkry ls Vdjkus ds 12 ?kaVs iwoZ dk ?kaVkokj nkc ifjorZu pØokr ds /kjkry ls Vdjkus ds lgh LFkku dk irk yxkus esa enn djrk gSA Tropical cyclones are deadly natural disasters. They came large loss of lives and properties. After the landfall, the main damages from cyclones are due to strong winds and storm surges. The forecast of landfall point is most important to forecasters as well as the agencies who are engaged to take safety measures or rehabilitation works. In this paper an attempt has been made to forecast point and time of landfall. Personnel computer based, track forecast models are already in use, in India Meteorological Department’s (IMD) different forecasting offices. The existing model requires cyclone track climatology of the basin and past positions of cyclones. Generally pressure falls along the coast, 24 to 36 hours in advance of cyclone’s landfall. This parameter, in combination with other two, with equal weightage i.e., 1/3 (Persistence + Climatology + Pressure change) have been used for track forecasting in this study. Results are comparatively superior to the results obtained only by using climatology and persistence. When the system is within 12 to 24 hour prior to landfall, the 24 hour pressure change becomes more important than Climatology and Persistence and 12 hour prior landfall the hourly pressure change helps in pinpointing the landfall point.

Tropical cyclones prediction by numerical models in India Meteorological Department

lkj & bl 'kks/k&Ik= esa Hkkjr ekSle foKku foHkkx ¼Hkk- ekS- fo- fo-½ esa viukbZ xbZ pØokr izfr:fir djus dh dfYir rduhdksa ij ppkZ dh xbZ gSA vDrwcj 1999 esa mM+hlk esa vk, egkpØokr ds izkjfEHkd {ks=ksa esa dkYifud Hkzfeyrk dk mi;ksx djds] pØokr ds fof’k"V ekWMy] Doklh ySaxjfx;u ekWMy ¼D;w- ,y- ,e-½ ls 72 ?kaVs ds iwokZuqeku vkSj Hkkjr ekSle foKku foHkkx ds lhfer {ks= fun’kZ ¼,y- ,- ,e-½ ls 36 ?kaVs ds iwokZuqeku izfr:fir fd, x,A bl 'kks/k esa] 26 ls 28 vDrwcj rd dh izkjafHkd fLFkfr;ksa ds vk/kkj ij D;w- ,y- ,e- ls pØokr ds ekxZ ds iwokZuqeku dh vkSlr =qfV;k¡ 24 ?kaVs ds fy, 21 fd-eh-] 48 ?kaVs ds fy, 91 fd-eh- vkSj 72 ?kaVs ds fy, 179 fd-eh- jghA 1998&2004 rd ds fiNys lkr o"kksZa ds nkSjku D;w- ,y- ,e- ls pØokr ds ekxZ ds iwokZuqeku dh =qfV;ksa ds vk¡dM+ksa ij Hkh blesa ppkZ dh xbZ gSA blds vykok] ,y- ,- ,e- ls fd, x, iwokZuqeku ij izkjafHkd fLFkfr;ksa ds izHkko dh Hkh tk¡p dh xbZA fofHkUu izkjafHkd fLFkfr;ksa ls rS;kj fd, x, vkSlr ¼lesfdr½ iwokZuqeku ls 24 ?kaVs ds iwokZuqeku esa 123 fd-eh- vkSj 36 ?kaVs ds iwokZuqeku esa 81 fd-eh- dh =qfV;k¡ ikbZ xbZ] tks ,dek= iwokZuqeku dh rqyuk esa de jghA bu iz;ksxksa ls ;g irk pyk fd dkYifud Hkzfeyrk okys D;w- ,y- ,e- ekWMy ls pØokr ds ekxZ dk lVhd iwokZuqeku izkIr fd;k tk ldrk gS tks vHkh rd la[;kRed ekWMyksa ls miyC/k gks ikrk FkkA In the present paper, the cyclone bogusing techniques followed in India Meteorological Department (IMD) were discussed. Using the idealized vortex in the initial fields for Orissa super cyclone October 1999, the specialized cyclone model, Quasi-Lagrangian Model (QLM) 72 hours track forecast and also 36 hours forecast with IMD limited area model (LAM) were simulated. In this case, the QLM average track forecast errors based on 26-28 October initial conditions were 21 km for 24 hours, 91 km for 48 hours and 179 km for 72 hours. Also the QLM track forecast error statistics during the last 7 years 1998-2004 are discussed. In addition, the impact of initial conditions on the LAM forecast was examined. It was observed that the mean (ensemble) forecast generated from different initial conditions was shown track error of 123 km in 24 hours and 81 km in 36 hours forecast which is less than individual forecast. These experiments have established that the QLM model, with idealized vortex, provides track forecast within an accuracy level that was currently available from numerical models.

Performance of 2020 Real-Time Atlantic Hurricane Forecasts from High-Resolution Global-Nested Hurricane Models: HAFS-globalnest and GFDL T-SHiELD

The global-nested Hurricane Analysis and Forecast System (HAFS-globalnest) is one piece of NOAA’s Unified Forecast System (UFS) application for hurricanes. In this study, results are analyzed from 2020 real-time forecasts by HAFS-globalnest and a similar global-nested model, the Tropical Atlantic version of GFDL’s System for High-resolution prediction on Earth- to- Local Domains (T-SHiELD). HAFS-globalnest produced the highest track forecast skill compared to several operational and experimental models, while T-SHiELD showed promising track skill as well. The intensity forecasts from HAFS-globalnest generally had a positive bias at longer lead times primarily due to the lack of ocean coupling, while T-SHiELD had a much smaller intensity bias particularly at longer forecast lead times. With the introduction of a modified planetary boundary layer scheme and an increased number of vertical levels, particularly in the boundary layer, HAFS forecasts of storm size had a smaller positive bias than occurred in the 2019 version of HAFS-globalnest. Despite track forecasts that were comparable to the operational GFS and HWRF, both HAFS-globalnest and T-SHiELD suffered from a persistent right-of-track bias in several cases at the 4-5 day forecast lead times. The reasons for this bias were related to the strength of the subtropical ridge over the western North Atlantic and are continuing to be investigated and diagnosed. A few key case studies from this very active hurricane season, including Hurricanes Laura and Delta, were examined.

Are We Reaching the Limit of Tropical Cyclone Track Predictability in the Western North Pacific?

The annual-mean position errors (PE) of tropical cyclone (TC) track forecasts from three forecast agencies (RSMC-Tokyo, CMA, and JTWC) are analyzed to document the past improvements and project future tendency in track forecast accuracy for TCs in the western North Pacific. An improvement of 48 h (2-day) in lead time has been achieved in the past thirty years, but with noticeable stepwise periods of improvements with superposed short-term fluctuations. The stepwise improvement features differ among the three forecast agencies, but are highly related to the development of objective forecast guidance and the application strategy. As demonstrated by an exponential model for the growth of PEs with lead time for TCs of tropical storm category and above, the improvements in the past ten years have mainly been due to the reduction in analysis errors rather than the reduction in the error growth rate. If the current trend continues, a further 2-day improvement in TC track forecast lead times may be projected for the coming fifteen years up to 2035, and we certainly have not reached yet the limit of TC track predictability in the western North Pacific.

A Statistical Analysis of High Frequency Track and Intensity Forecasts from NOAA’s Operational Hurricane Weather Research and Forecast (HWRF) Modeling System

A statistical analysis is performed on the high-frequency (3 1/3 s) output from NOAA’s cloud-permitting, high-resolution operational Hurricane Weather Research and Forecasting (HWRF) model for all tropical cyclones (TCs) in the North Atlantic basin over a 3-year period (2017-2019). High-frequency HWRF forecasts of TC track and 10-m maximum wind speed (Vmax) exhibited large fluctuations that were not captured by traditional low-frequency (6 h) model output. Track fluctuations were inversely proportional to Vmax with average values of 6-8 km. Vmax fluctuations were as high as 20 kt in individual forecasts and were a function of maximum intensity, with a standard deviation of 5.5 kt for category 2 hurricanes and smaller fluctuations for tropical storms and major hurricanes. The radius of Vmax contracted or remained steady when TCs rapidly intensified in high-frequency HWRF forecasts, consistent with observations. Running mean windows of 3-9 h were applied at synoptic times to smooth the high-frequency HWRF output to investigate its utility to operational forecasting. Smoothed high-frequency HWRF output improved Vmax forecast skill by up to 8% and produced a more realistic distribution of 6-h intensity change when compared with low-frequency, instantaneous output. Furthermore, the high-frequency track forecast output may be useful for investigating characteristics of TC trochoidal motions.

Performance Evaluation of Numerical Tools for Hurricane Forecast (NTHF) System during 2020 North Atlantic Tropical Cyclones Season

This study evaluates the performance of the Numerical Tools for Hurricane Forecast (NTHF) system during the 2020 North Atlantic (NATL) tropical cyclones (TCs) season. The system is configured to provide 5-day forecasts with basic input from the National Hurricane Center (NHC) and the Global Forecast System. For the NTHF validation, the NHC operational best track was used. The average track errors for 2020 NATL TCs ranged from 62 km at 12 h to 368 km at 120 h. The NTHF track forecast errors displayed an improvement over 60% above the guidance Climatology and Persistence (CLIPER) model from 36 h to 96 h, although the NTHF was better than the CLIPER in all forecast periods. The forecast errors for the maximum wind speed (minimum central pressure) ranged between 20 km/h and 25 km/h (4 hPa to 8 hPa), but the NTHF model intensity forecasts showed only marginal improvement of less than 20% after 78 h over the baseline Decay Statistical Hurricane Intensity Prediction Scheme (D-SHIPS) model. Nevertheless, the NTHF’s ability to provide accurate intensity forecasts for the 2020 NATL TCs was higher than the NTHF’s average ability during the 2016–2019 period.

Does Increasing Model Resolution Improve the Real-Time Forecasts of Western North Pacific Tropical Cyclones?

In this study, the general impact of high-resolution moving nesting domains on tropical cyclone (TC) intensity and track forecasts was verified, for a total of 107 forecast cases of 33 TCs, using the Weather Research and Forecasting (WRF) model. The experiment, with a coarse resolution of 12 km, could not significantly capture the intensification process, especially for maximum intensities (>60 m s−1). The intense TCs were better predicted by experiments using a moving nesting domain with a horizontal resolution of 4 km. The forecast errors for maximum wind speed and minimum sea-level pressure decreased in the experiment with higher resolution; the forecast of lifetime maximum intensity was improved. For the track forecast, the experiment with a coarser resolution tended to simulate TC tracks deviating rightward to the TC motions in the best-track data; this erroneous deflection was reduced in the experiment with a higher resolution. In particular, the track forecast in the experiment with a higher resolution improved more frequently for intense TCs that were generally distributed at relatively lower latitudes among the test cases. The sensitivity of the track forecast to the model resolution was relatively significant for lower-latitude TCs. On the other hand, the track forecasts of TCs moving to the mid-latitudes, which were primarily influenced by large-scale features, were not sensitive to the resolution.

Development of a track-pattern-based medium-range tropical cyclone forecasting system for the western North Pacific

Despite tremendous advancements in dynamical models for weather forecasting, statistical models continue to offer various possibilities for tropical cyclone (TC) track forecasting. Herein, a track-pattern-based approach was developed to predict a TC track for a lead time of 6–8 days over the western North Pacific (WNP), utilizing historical tracks in conjunction with dynamical forecasts. It is composed of four main steps: (1) clustering historical tracks similar to that of an operational five-day forecast in their early phase into track patterns, and calculating the daily mean environmental fields (500-hPa geopotential height and steering flow) associated with each track; (2) deriving the two environmental variables forecasted by dynamical models; (3) evaluating pattern correlation coefficients between the two environmental fields from step (1) and those from dynamical model for a lead times of 6–8 days; and (4) producing the final track forecast based on relative frequency maps obtained from the historical tracks in step (1) and the pattern correlation coefficients obtained from step (3). TCs that formed in the WNP and lasted for at least seven days, during the 9-year period 2011–2019 were selected to verify the resulting track-pattern-based forecasts. In addition to the performance comparable to dynamical models under certain conditions, the track-pattern-based model is inexpensive, and can consistently produce forecasts over large latitudinal or longitudinal ranges. Machine learning techniques can be implemented to incorporate non-linearity in the present model for improving medium-range track forecasts.

Five-day track forecast skills of WRF model for the western North Pacific tropical cyclones

In this study, the characteristics of simulated tropical cyclones (TCs) over the western North Pacific by a regional model (the WRF model) are verified. We utilize 12 km horizontal grid spacing, and simulations are integrated for 5 days from model initialization. One hundred and twenty-five forecasts are divided into five clusters through the k-means clustering method. The TCs in the cluster 1 and 2 (group 1), which includes many TCs moves northward in subtropical region, generally have larger track errors than for TCs in cluster 3 and 4 (group 2). The optimal steering vector is used to examine the difference in the track forecast skill between these two groups. The bias in the steering vector between the model and analysis data is found to be more substantial for group 1 TCs than group 2 TCs. The larger steering vector difference for group 1 TCs indicates that environmental fields tend to be poorly simulated in group 1 TC cases. Furthermore, the residual terms, including the storm-scale process, asymmetric convection distribution, or beta-related effect, are also larger for group 1 TCs than group 2 TCs. Therefore, it is probable that the large track forecast error for group 1 TCs is a result of unreasonable simulations of environmental wind fields and residual processes in the midlatitudes.