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.

Forecasting Severe Weather with Random Forests

Using nine years of historical forecasts spanning April 2003–April 2012 from NOAA’s Second Generation Global Ensemble Forecast System Reforecast (GEFS/R) ensemble, random forest (RF) models are trained to make probabilistic predictions of severe weather across the contiguous United States (CONUS) at Days 1–3, with separate models for tornado, hail, and severe wind prediction at Day 1 in an analogous fashion to the Storm Prediction Center’s (SPC’s) convective outlooks. Separate models are also trained for the western, central, and eastern CONUS. Input predictors include fields associated with severe weather prediction, including CAPE, CIN, wind shear, and numerous other variables. Predictor inputs incorporate the simulated spatiotemporal evolution of these atmospheric fields throughout the forecast period in the vicinity of the forecast point. These trained RF models are applied to unseen inputs from April 2012 to December 2016, and their forecasts are evaluated alongside the equivalent SPC outlooks. The RFs objectively make statistical deductions about the relationships between various simulated atmospheric fields and observations of different severe weather phenomena that accord with the community’s physical understandings about severe weather forecasting. Using these quantified flow-dependent relationships, the RF outlooks are found to produce calibrated probabilistic forecasts that slightly underperform SPC outlooks at Day 1, but significantly outperform their outlooks at Days 2 and 3. In all cases, a blend of the SPC and RF outlooks significantly outperforms the SPC outlooks alone, suggesting that use of RFs can improve operational severe weather forecasting throughout the Day 1–3 period.