Estimation of intensity of tropical cyclone over Bay of Bengal using microwave imagery

caxky dh [kkM+h esa o"kZ 2008&2010 esa ,Q- Mh- ih- vof/k ¼15 vDrwcj ls 30 uoEcj½ ds nkSjku vk, ik¡p pØokrksa ds lw{e rjaxh; es?k fcEckofy;ksa rFkk 85 fxxkgV~tZ vko`fÙk esa izkIr fd, x, mRiknksa dh tk¡p dh xbZ gS ftlls rkieku nhfIr] rkieku nhfIr esa vfu;ferrk] dsUnz dk LFkku] lrg ij vuojr cgus okyk vf/kdre iou ¼,e- ,l- MCY;w-½ rFkk pØokrksa ds fHkUu&fHkUu fLFkfr;ksa esa muds rhozhdj.k ls lacaf/kr djdksa tSls% vonkc ¼Mh-½] xgu vonkc ¼Mh- Mh-½] pØokrh; rwQku ¼lh- ,l-½] rhoz pØokrh; rwQku ¼,l-lh-,l-½] vfr rhoz pØokrh; rwQku ¼oh-,l-lh-,l-½ vkfn dk vkdfyr dsUnzh; nkc ¼bZ- lh- ih-½ dk vkdyu fd;k tk ldsA izf{kr fd, x, nhfIr rkieku vfu;ferrkvksa dh rqyuk lS)kafrd :i ls bZ-lh-ih- ds csLV VªSd vkdyu ij vk/kkfjr nhfIr rkieku vfu;ferrk ,oa bu pØokrksa ds ckgjh nkc ds lkFk Hkh dh xbZ gSA dsUnz ds LFkku] bZ-lh-ih- ,oa lw{erajxh; fcEckoyh ds vk/kkj ij vkdfyr ,e- ,l- MCY;w- dh rqyuk csLV VªSd ,oa Hkkjr ekSle foKku foHkkx ds Mh- oksjkWd ds vkdyu ls dh xbZ gS vkSj mldk fo’ys"k.k fd;k x;k gSA pØokrh; fo{kksHk ¼lh- Mh-½ ds dsUnz ds LFkku esa varZ tSlkfd lw{erjaxh fcEckofy;ksa rFkk csLV VªSd vkdyu ds }kjk vkdfyr fd;k x;k gS] fo{kksHkksa ds rhozhdj.k ds lkFk&lkFk de gksrk tkrk gS vkSj vonkc ¼Mh-½ dh fLFkfr esa yxHkx 25 fd-eh- ls vfr rhoz pØokrh; rwQku ¼oh-,l-lh-,l-½ dh fLFkfr esa 18 fd- eh ds chp cnyrk jgrk gSA tcfd ;g varj Mh oksjkWd ds vkdyu ls dkQh vf/kd gSA lw{erjaxh; vkdyuksa ij vk/kkfjr ,e- ,l- MCY;w- vkdyu oh-,l- lh- ,l- ds nkSjku csLV VªSd vkdyuksa ls yxHkx 28 ukWV~l vf/kd vkdfyr fd;k x;k gS vkSj vonkc ¼Mh-½@pØokrh; rwQku ¼lh-,l-½@rhoz pØokrh; rwQku ¼,l- lh- ,l-½ dh fLFkfr esa ;g 6&8 ukWV~l vkdfyr fd;k x;k gSA csLV VSªd vkdyuksa ls lkisf{kd varj dks ns[kus ls irk pyk gS fd lh-,l- vkSj ,l-lh- dh fLFkfr esa lw{e rajx esa ,e-,l-MCY;w- yxHkx 12&15 izfr’kr vkSj oh-,l-lh-,l- dh fLFkfr esa yxHkx 30 izfr’kr vf/kd vkdfyr gqvk gS tcfd Mh- oksjkWd dk ,e- ,l- MCY;w- vkdyu lh- ,l-] ,l- lh- ,l- vkSj oh- ,l- lh- ,l- dh fLFkfr;ksa esa 15&18 izfr’kr de gks x;k gSA caxky dh [kkM+h ds Åij 230 dsfYou dk nhfIr rkieku vonkc ds cuus ds fy, vuqdwy gksrk gS] 250 dsfYou dk rkieku bldks pØokrh rwQku esa 260 dsfYou rhoz pØokrh rwQku esa vkSj 270 dsfYou vfr izpaM+ pØokrh rwQku esa cny nsrk gSA nhfIr rkieku ds nsgyheku ¼FkszlksYM osY;w½ ds vfHkKku ¼fMVSD’ku½ ls bl iz.kkyh ds rhoz gksus dk iwokZuqeku nsus ds fy, iz;kIr vfxze le; fey ldrk gSA blh izdkj nhfIr rkieku folaxfr 3 dsfYou ls vf/kd gksus ij pØokrh; rwQku rhoz pØokrh; rwQku esa cny tkrk gS vkSj 8 dsfYou dk rkieku bls caxky dh [kkM+h esa vfr izpaM pØokrh; rwQku ds :i esa cny nsrk gSA Microwave cloud imageries and derived products in the frequency of 85 GHz have been examined for five cyclones that occurred during FDP period (15 October- 30 November) of 2008-2010 over the Bay of Bengal to estimate the brightness temperature, brightness temperature anomaly, location of centre, maximum sustained wind (MSW) at surface level and estimated central pressure (ECP) associated with cyclones in their different stages of intensification like depression (D), deep depression (DD), cyclonic storm (CS), severe cyclonic storm (SCS), very severe cyclonic storm (VSCS), etc. Also the observed brightness temperature anomalies are compared with theoretically derived brightness temperature anomalies based on the best track estimates of ECP and outermost pressure for these cyclones. The location of centre, ECP and MSW based on microwave imagery estimates have been compared with those available from the best track and Dvorak’s estimates of India Meteorological Department and analyzed. The difference in location of the centre of cyclonic disturbance (CD) as estimated by microwave imageries and best track estimates decreases with intensification of the disturbances and varies from about 25 km in depression (D) stage to 18 km in VSCS stage whereas the difference is significantly higher in case of Dvorak estimate compared to best track estimate. The MSW based on microwave estimates is higher than that of best track estimates by about 28 knots during VSCS and 6-8 knots during D, CS, SCS stage. Considering relative difference with respect to best track estimates, the MSW is overestimated in microwave by about 12-15% in case of CS and SCS stage and by about 30% in VSCS stage while Dvorak’s MSW overestimation reduced to 15-18% during CS, SCS and VSCS stages. Brightness temperature of the order of 230 K is favourable for genesis (formation of D), 250K for its intensification into CS, 260 K for intensification into SCS and 270K for its further intensification into VSCS stage over the Bay of Bengal. Detection of threshold value of brightness temperature may provide adequate lead time to forecast intensification of the system. Similarly, when brightness temperature anomaly exceeds 3K, CS intensify into SCS and 8K, it intensifies into a VSCS over Bay of Bengal.

Variability of Indian summer monsoon: Relationship with surface air temperature anomalies over northern hemisphere

Thirty year (1950-79) time series of Monsoon Index (MI) is correlated with the gridded surface air temperature data over northern hemisphere land at various time lags of months (i.e., months preceding concurrent and succeeding to the monsoon season) to identify tele-connections of monsoon with the northern hemisphere surface air temperature anomalies. . Out of three key regions identified which show statistically significant relationship of monsoon rainfall, two regions are in the higher latitudinal belt of 40oN- 70oN over North America and Eurasia which show positive correlations with temperatures during northern winter particularly during January and February. The third region is located over northwest India and adjoining Pakistan, where the maximum positive correlation is observed to occur during the pre-li1onsoon months of April and May. These relationships suggest that cooler northern hemisphere during the preceding seasons of winter/spring over certain key regions are generally associated with below normal summer monsoon rainfall over India and vice-versa which could be useful predictors for long-range forecasting of monsoon. There are two large regions in the northern tropics, namely, Asian and African monsoons whose temperatures reveal strong negative correlations with monsoon rainfall during the seasons concurrent and subsequent to the summer monsoon season. However, persistence of this relationship for longer period of about two seasons after the monsoon, suggests the dominant influence of ENSO (El. Nino-Southern Oscillation) on tropical climate.

The 2019 Benguela Niño

High interannual sea surface temperature anomalies of more than 2°C were recorded along the coasts of Angola and Namibia between October 2019 and January 2020. This extreme coastal warm event that has been classified as a Benguela Niño, reached its peak amplitude in November 2019 in the Angola Benguela front region. In contrast to classical Benguela Niños, the 2019 Benguela Niño was generated by a combination of local and remote forcing. In September 2019, a local warming was triggered by positive anomalies of near coastal wind-stress curl leading to downwelling anomalies through Ekman dynamics off Southern Angola and by anomalously weak winds reducing the latent heat loss by the ocean south of 15°S. In addition, downwelling coastal trapped waves were observed along the African coast between mid-October 2019 and early January 2020. Those coastal trapped waves might have partly emanated from the equatorial Atlantic as westerly wind anomalies were observed in the central and eastern equatorial Atlantic between end of September to early December 2019. Additional forcing for the downwelling coastal trapped waves likely resulted from an observed weakening of the prevailing coastal southerly winds along the Angolan coast north of 15°S between October 2019 and mid-February 2020. During the peak of the event, latent heat flux damped the sea surface temperature anomalies mostly in the Angola Benguela front region. In the eastern equatorial Atlantic, relaxation of cross-equatorial southerly winds might have contributed to the equatorial warming in November 2019 during the peak of the 2019 Benguela Niño. Moreover, for the first time, moored velocities off Angola (11°S) revealed a coherent poleward flow in the upper 100 m in October and November 2019 suggesting a contribution of meridional heat advection to the near-surface warming during the early stages of the Benguela Niño. During the Benguela Niño, a reduction of net primary production in the Southern Angola and Angola Benguela front regions was observed.

An Intraseasonal Mode Linking Wintertime Surface Air Temperature over Arctic and Eurasian Continent

Key processes associated with the leading intraseasonal variability mode of wintertime surface air temperature (SAT) over Eurasia and the Arctic region are investigated in this study. Characterized by a dipole distribution in SAT anomalies centered over north Eurasia and the Arctic, respectively, and coherent temperature anomalies vertically extending from the surface to 300hPa, this leading intraseasonal SAT mode and associated circulation have pronounced influences on global surface temperature anomalies including the East Asian winter monsoon region. By taking advantage of realistic simulations of the intraseasonal SAT mode in a global climate model, it is illustrated that temperature anomalies in the troposphere associated with the leading SAT mode are mainly due to dynamic processes, especially via the horizontal advection of winter mean temperature by intraseasonal circulation. While the cloud-radiative feedback is not critical in sustaining the temperature variability in the troposphere, it is found to play a crucial role in coupling temperature anomalies at the surface and in the free-atmosphere through anomalous surface downward longwave radiation. The variability in clouds associated with the intraseasonal SAT mode is closely linked to moisture anomalies generated by similar advective processes as for temperature anomalies. Model experiments suggest that this leading intraseasonal SAT mode can be sustained by internal atmospheric processes in the troposphere over the mid-to-high latitudes by excluding forcings from Arctic sea ice variability, tropical convective variability, and the stratospheric processes.

Drivers of modern New Zealand glacier change

<p>Glaciers across the Southern Alps of New Zealand have been photographed annually since 1977, creating a rare record of Southern Hemisphere glacier change. Here, we revisit these historic photographs and use structure from motion photogrammetry to quantitatively measure glacier change from the images. To establish this new method, it is initially applied to Brewster Glacier (1670 – 2400 m a.s.l.), one of the 50 monitored glaciers. We derive annual equilibrium line altitude (ELA) and length records from 1981 – 2017, and quantify the uncertainties associated with the method. Our length reconstruction shows largely continuous terminus retreat of 365 ± 12 m for Brewster Glacier since 1981. The ELA record, which compares well with glaciological mass-balance data measured between 2005 and 2015, shows pronounced interannual variability. Mean ELAs range from 1707 ± 6 m a.s.l. to 2303 ± 5 m a.s.l. The newly developed ELA chronology from Brewster shows several years since 1981 with especially high mass loss, all of which occurred in the past decade. Investigation using reanalysis data shows that these extreme mass-loss years occur when surface air temperatures, sea surface temperatures, and mean sea level pressure are anomalously high. In particular, the three highest mass-loss years on record, 2011, 2016, and 2018, each had a 2-month mean surface air temperature anomaly of at least +1.7°C between November and March, which is exclusive to these three years over the time investigated (April 1980 – March 2018). Using event attribution — a methodology using climate model simulations with and without human-induced forcings to calculate the anthropogenic influence on extreme events — we calculate the anthropogenic influence on these surface air temperature anomalies. The positive temperature anomalies during extreme mass-loss years have probabilities of 0 – 90% confidence) more likely to occur with anthropogenic forcing, and in once case in 2018 could not have occurred (>90% confidence) without anthropogenic forcing. This increased likelihood is driven by present-day temperatures ~1.0°C above the pre-industrial average, confirming a connection between rising anthropogenic greenhouse gases, warming temperatures, and high annual ice loss.</p>

Drivers of modern New Zealand glacier change

<p>Glaciers across the Southern Alps of New Zealand have been photographed annually since 1977, creating a rare record of Southern Hemisphere glacier change. Here, we revisit these historic photographs and use structure from motion photogrammetry to quantitatively measure glacier change from the images. To establish this new method, it is initially applied to Brewster Glacier (1670 – 2400 m a.s.l.), one of the 50 monitored glaciers. We derive annual equilibrium line altitude (ELA) and length records from 1981 – 2017, and quantify the uncertainties associated with the method. Our length reconstruction shows largely continuous terminus retreat of 365 ± 12 m for Brewster Glacier since 1981. The ELA record, which compares well with glaciological mass-balance data measured between 2005 and 2015, shows pronounced interannual variability. Mean ELAs range from 1707 ± 6 m a.s.l. to 2303 ± 5 m a.s.l. The newly developed ELA chronology from Brewster shows several years since 1981 with especially high mass loss, all of which occurred in the past decade. Investigation using reanalysis data shows that these extreme mass-loss years occur when surface air temperatures, sea surface temperatures, and mean sea level pressure are anomalously high. In particular, the three highest mass-loss years on record, 2011, 2016, and 2018, each had a 2-month mean surface air temperature anomaly of at least +1.7°C between November and March, which is exclusive to these three years over the time investigated (April 1980 – March 2018). Using event attribution — a methodology using climate model simulations with and without human-induced forcings to calculate the anthropogenic influence on extreme events — we calculate the anthropogenic influence on these surface air temperature anomalies. The positive temperature anomalies during extreme mass-loss years have probabilities of 0 – 90% confidence) more likely to occur with anthropogenic forcing, and in once case in 2018 could not have occurred (>90% confidence) without anthropogenic forcing. This increased likelihood is driven by present-day temperatures ~1.0°C above the pre-industrial average, confirming a connection between rising anthropogenic greenhouse gases, warming temperatures, and high annual ice loss.</p>