Seasonal prediction of Indian Summer Monsoon onset with machine learning

2021 ◽  
Author(s):  
Takahito Mitsui ◽  
Niklas Boers
Keyword(s):  
Time Scales ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Water Resource Management ◽  
Prediction Models ◽  
Indian Subcontinent ◽  
Weather Prediction ◽  
Seasonal Prediction ◽  
Onset Date ◽  
Summer Monsoon Onset

<p>The prediction of the onset date of the Indian Summer Monsoon (ISM) is crucial for effective agricultural planning and water resource management on the Indian subcontinent, with more than one billion inhabitants. Existing approaches focus on extended-range to subseasonal forecasts, i.e., provide skillful predictions of the ISM onset date at horizons of 10 to 60 days. Here we propose a method for ISM onset prediction and show that it has high forecast skill at longer, seasonal time scales. The method is based on recurrent neural networks and allows for ensemble forecasts to quantify uncertainties. Our approach outperforms state-of-the-art numerical weather prediction models at comparable or longer lead times. To our knowledge, there is no statistical forecasting approach at comparable, seasonal time scales. Our results suggest that predictability of the ISM onset emerges earlier than previously assumed.</p>

scholarly journals EMPIRICAL ORTHOGONAL FUNCTIONS ASSOCIATED WITH PARAMETERS USED IN LONG RANGE FORECASTING OF INDIAN SUMMER MONSOON

MAUSAM ◽  
2021 ◽  
Vol 44 (1) ◽  
pp. 29-34
Author(s):  
H. N. SRIVASTAVA ◽  
S. S. SINGH
Keyword(s):  
Snow Cover ◽  
Long Range ◽  
Northern Hemisphere ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Monsoon Onset ◽  
Empirical Orthogonal Functions ◽  
Onset Date ◽  
Orthogonal Functions ◽  
Summer Monsoon Onset

EEmpirical Orthogonal Functions (EOF),. associated with the; parameters for long range forecasting of Indian summer monsoon onset and seasonal. rainfall have been discussed. It was found that the percentage of variance explained was 77 and 67 respectively through the first four EOF. The highest correlation coefficient with the onset date was found for the first function which showed the maximum influence of Cobar (Australia) and Darwin (Australia) zonal winds on the onset date. It was interesting to note that for rainfall prediction predominant effect on the first EOF was noticed of 50 hPa ridge over northern hemisphere, Eurasian snow cover, Argentina pressure (negatively correlated) and 500 hpa ridge, 10 hPa Balboa wind, north, central India and east coast  minimum temperatures, and northern hemisphere temperature. However, the Influence of EI-Nino, equatorial pressure and Darwin pressure (Including Tahiti minus Darwin) and Himalayan snow cover was almost negligible. The eigen index for the onset date suggests a complementary method for its application In long range prediction of summer monsoon onset date.

scholarly journals Tracking the Indian Summer Monsoon Onset Back to the Preinstrument Period

2016 ◽  
Vol 29 (22) ◽  
pp. 8115-8127 ◽  
Author(s):  
Paulina Ordoñez ◽  
David Gallego ◽  
Pedro Ribera ◽  
Cristina Peña-Ortiz ◽  
Ricardo García-Herrera
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
India Meteorological Department ◽  
Monsoon Onset ◽  
Onset Date ◽  
Late Nineteenth Century ◽  
Stable Relationship ◽  
Summer Monsoon Onset ◽  
The Pacific ◽  
Precipitation Increase

Abstract The Indian summer monsoon onset is one of the most expected meteorological events of the world, affecting the lives of hundreds of millions of people. The India Meteorological Department has dated the monsoon onset since 1901, but its original methodology was considered subjective and it was updated in 2006. Unfortunately, the new method relies on OLR measurements, which impedes the construction of an objective onset series before the 1970s. An alternative approach is the use of the wind field, but the development of such an index is limited to the period covered by reanalysis products. In this paper historical wind records taken on board ships are used to develop a new onset series using only wind direction measurements, providing an objective record of the onset since the late nineteenth century. The new series captures the rapid precipitation increase associated with the onset, correlates well with previous approaches, and is robust against anomalous (bogus) onsets. A tendency for later-than-average onsets during the 1900–25 and 1970–90 periods and earlier-than-average onsets between 1940 and 1965 was found. A relatively stable relationship between ENSO and Indian monsoon onset dates was found; however, this link tends to be weaker during decades characterized by prevalent La Niña conditions. Furthermore, it was found that the link between the Pacific decadal oscillation (PDO) and the onset date is limited to the phases characterized by a shift from negative to positive PDO phases.

scholarly journals Oceanic factors controlling the Indian summer monsoon onset in a coupled model

2014 ◽  
Vol 44 (3-4) ◽  
pp. 977-1002 ◽  
Author(s):  
Chloé Prodhomme ◽  
Pascal Terray ◽  
Sébastien Masson ◽  
Ghyslaine Boschat ◽  
Takeshi Izumo
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Coupled Model ◽  
Monsoon Onset ◽  
Summer Monsoon Onset

scholarly journals Relationship between monsoon onset over Thiruvananthapuram and Mumbai and rainfall over Kerala and Konkan

MAUSAM ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 445-450
Author(s):  
M. P. SHEWALE ◽  
A. S. PONKSHE
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Significant Relationship ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Summer Monsoon Rainfall ◽  
Monsoon Onset ◽  
Onset Date ◽  
Simultaneous Occurrence

Lkkj & bl ’kks/k&Ik= esa nks LFkkuksa uker% fFk:ouUriqje vkSj eqEcbZ dk p;u djds ;g irk yxkus dk iz;kl fd;k x;k gS fd D;k ekulwu ds vkxeu dh rkjh[k dk Øze’k% dsjy vkSj dksad.k esa gksus okyh o"kkZ ds lkFk dksbZ laca/k gS A lewps ns’k ds fy, Hkkjrh; xzh"edkyhu ekulwu o"kkZ ¼vkbZ- ,l- ,e- vkj-½ ds lkFk ekulwu ds vkxeu dh rkjh[k ds laca/k dh Hkh tk¡p dh xbZ gS A bl v/;;u ds fy, 100 o"kksaZ ¼1901&2000½ ds vk¡dM+ksa dk mi;ksx fd;k x;k gS A             fo’ys"k.k dh lgk;rk ls vk¡dM+ksa dh ,d:irk vkSj o"kkZ ds vU; y{k.kksa dh tk¡p dh xbZ gS A dsjy vkSj dksad.k esa vdky vkSj ekSle laca/kh ck<+ dh ledkfyd ?kVukvksa dh vko`fr dk irk yxk;k x;k gS vkSj mldh tk¡p dh xbZ gS A             bl v/;;u ls ;g irk pyk gS fd ekulwu ds vkxeu ds laca/k esa fFk:ouUriqje vkSj eqEcbZ ds chp egRoiw.kZ laca/k gS A fFk:ouariqje vFkok eqEcbZ vkSj vkbZ- ,l- ,u- vkj- ds chp ekulwu ds vkxeu ds ckjs esa fdlh egRoiw.kZ laca/k dk irk ugha pyk gS A dsjy dh rqyuk eas dksad.k esa ekSle laca/kh ck<+ vf/kd vkrh gS A In this paper, choosing two locations viz., Thiruvananthapuram and Mumbai, an attempt has been made to find whether the onset date of monsoon has any bearing on monsoon rainfall over Kerala and Konkan respectively.   Association of the onset dates with Indian Summer Monsoon Rainfall (ISMR) for the country as a whole has also been explored.  The study utilizes 100 years’ (1901-2000) data. Homogeneity of the data and other rainfall features have been examined with the help of  analysis.  Frequency of simultaneous occurrence of droughts and meteorological floods at Kerala and Konkan have been determined and examined.    The study showed that onset over Thiruvananthapuram and Mumbai are significantly related.  It revealed absence of any significant relationship between onset over Thiruvananthapuram or Mumbai and the ISMR. Meteorological floods seem to be more frequent over Konkan compared to Kerala.

Holocene climate variability and Indian Summer Monsoon: An overview

The Holocene ◽  
2020 ◽  
Vol 30 (5) ◽  
pp. 744-773 ◽  
Author(s):  
Upasana S Banerji ◽  
P Arulbalaji ◽  
D Padmalal
Keyword(s):  
Indian Ocean ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Indian Subcontinent ◽  
Ice Age ◽  
Interglacial Period ◽  
Northern Indian Ocean ◽  
The Holocene ◽  
Indian Landmass ◽  
Holocene Period

The response of the Indian Summer Monsoon (ISM) to forcing factors and climate variables has not yet fully explored, even though the ISM plays a pivotal role in the socio-economics of the Indian subcontinent and nearby areas. The ISM progression over Indian landmass is a manifestation of the Intertropical Convergence Zone (ITCZ) migration over the northern Indian Ocean and the Indian subcontinent. The recent anomalous behaviour of ISM raises the need for a better understanding of its spatio-temporal changes during the ongoing interglacial period termed as the Holocene period. The Holocene period has been classified further based on the globally observed abrupt climatic events at 8.2 and 4.2 ka. The 8.2 ka global cooling events have been recorded from northern Indian Ocean marine archives but limited records from the continental archives of the Indian landmass has demonstrated the 8.2 ka event. At the same time, the 4.2 ka dry climate has been endorsed by both marine as well as continental records and agrees with the global studies. During the ‘Little Ice Age’ (LIA), in the India subcontinent, wet conditions prevailed in the northern, central and western regions while a dry climate existed over the greater part of peninsular India. The present review offers an account of ISM signatures and possible mechanisms associated with the monsoon variability in the Indian subcontinent and the northern Indian Ocean during the Holocene period.

Impact of convective parameterization on the seasonal prediction skill of Indian summer monsoon

2019 ◽  
Vol 53 (9-10) ◽  
pp. 6227-6243 ◽  
Author(s):  
R. Phani Murali Krishna ◽  
Suryachandra A. Rao ◽  
Ankur Srivastava ◽  
Hari Prasad Kottu ◽  
Maheswar Pradhan ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Convective Parameterization
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