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,f’k;k ks= dh xzh"dkyhu ekulwu o"kkZ dk fHkUu fHkUu fnDddkfyd iSekuksa ij iwokZuqeku djuk vHkh Hkh ,d dfBu dk;Z gSA vHkh gky ds o"kksZa esa e/; vakk’k dh rqyuk esa ekWulwu o"kkZ dk e/; vof/k iwokZuqeku nsus dh nkrk esa lekuqikfrd lq/kkj esa deh jgh gSA ekulwu vkSj m".kdfVca/kh; iwokZuqeku esa vkxs vkSj lq/kkj ds fy, Xykscy ekWMyl vkSj vk¡dM+ vkesyu rduhd dk mi;ksx fd;k tk jgk gSSA cgjgky cgq fun’kZ bUlsEcy ¼,e-,e-bZ-½ iwokZuqeku dh yksdfiz;rk c<+ jgh gS D;ksafd iwokZuqeku esa ,d:irk vkSj fun’kZ dh vfuf’prrkvksa esa deh dks ns[krs gq, ikrs gaS fd blesa O;kogkfjd iwokZuqeku djus ds fy, vf/kd lwpuk djkus dh laHkkouk gSA tSlk fd izeq[k dsUnz okLrfod le; ds ekWMy izfrQyksa dk vkil esa vknku iznku djrs gS blesa ,e-,e-bZ- iwokZuqeku dkS’ky dks c<+kusa esa O;kogkfjd :i ls fdQk;rh rjhdk gSA lk/kkj.k bUlsEcy vkSlr dks NksM+dj ,u-lh-,e-vkj-MCY;w-,Q-@i`Foh foKku ea=ky;] Hkkjr kjk o"kZ 2009 ds ekulwu ds nkSjku e/; vof/k ekulwu o"kkZ iwokZuqeku ds fy, ,e-,e-bZ- iwokZuqeku dk vf/kd mi;ksx fd;k x;kA blds fy, izekf.kd fopj rduhd dk iz;ksx fd;k x;kA izkIr fd, x, izfrQyksa dh x.kuk djus ls irk pyk gS fd cgq fun’kZ bUlsEcy iwokZuqeku dh nkrk fdlh ,d ekWMy iwokZuqekuksa dh viskk vf/kd lgh gS vkSj ;g vkerkSj ij lk/kkj bUlsEcy vkSlr ls Hkh vf/kd lgh gSA gkyk¡fd Xykscy ekWMyl dh nkrk rhu fnu ds vkxs rd dh gksrh gS ijUrq ,e-,e-bZ- rduhd dks 5 fnuksa dh vof/k rd ykxw djus ij blesa egRoiw.kZ lq/kkj ns[kk tk ldrk gSA The prediction of Asian summer monsoon rainfall at various space-time scales is still a difficult task. Compared to mid-latitudes, proportional improvement in the skill in prediction of monsoon rainfall in medium range had been less in recent years. Global models and data assimilation techniques are being further improved for monsoon and tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and reducing the model uncertainties. As major centres are exchanging the model output in near real-time, MME is a viable inexpensive way for enhancing the forecast skill. During monsoon 2009, apart from simple ensemble mean ,the MME predictions of large-scale monsoon precipitation in medium range was carried out NCMRWF/MoES, India. The canonical variates technique is used for it. The skill scores are computed, which indicate that multi-model ensemble forecast has higher skill than individual model forecasts and also higher than the simple ensemble mean in general. Although the skill of the global models falls beyond day-3, but a significant improvement could be seen by employing the MME technique up to day-5. , number=2, journal=MAUSAM, year=2013, month=Apr., pages=211–220