2018
DOI: 10.1007/s00703-018-0589-2
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Predicting summer monsoon of Bhutan based on SST and teleconnection indices

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Cited by 5 publications
(4 citation statements)
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“…Due to differences in methods and data sources, different reanalysis datasets have demonstrated a wide range of estimated results, ranging from 1000 mm/year (for instance, He et al [11] estimated the LYZV annual mean precipitation at 1001 mm/year by combining upstream ground station data with satellite reanalysis data) to 4000 mm/year (the estimation by Ma et al [12] is 3011 mm/year using interpolation of downstream gauge data; the estimation by Li et al [8] is from 1000 to 4000 mm/year at different altitudes using grid data from the Global Precipitation Measurement, GPM). We tend to use the higher precipitation estimates for LYZV because they are more consistent with precipitation characteristics in the southern Himalayas, which are close to the same altitude regions in Bhutan [45] and corroborate early findings by Yang et al [91]. Nonetheless, given the QTP's extensive area and complex topography, different reanalysis datasets offer distinct advantages across various regions [20].…”
Section: Availability Of Reanalysis Precipitationsupporting
confidence: 82%
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“…Due to differences in methods and data sources, different reanalysis datasets have demonstrated a wide range of estimated results, ranging from 1000 mm/year (for instance, He et al [11] estimated the LYZV annual mean precipitation at 1001 mm/year by combining upstream ground station data with satellite reanalysis data) to 4000 mm/year (the estimation by Ma et al [12] is 3011 mm/year using interpolation of downstream gauge data; the estimation by Li et al [8] is from 1000 to 4000 mm/year at different altitudes using grid data from the Global Precipitation Measurement, GPM). We tend to use the higher precipitation estimates for LYZV because they are more consistent with precipitation characteristics in the southern Himalayas, which are close to the same altitude regions in Bhutan [45] and corroborate early findings by Yang et al [91]. Nonetheless, given the QTP's extensive area and complex topography, different reanalysis datasets offer distinct advantages across various regions [20].…”
Section: Availability Of Reanalysis Precipitationsupporting
confidence: 82%
“…Because of water vapor transport by monsoons and the uplift effect of the valley, precipitation in LYZV is greater than in other regions [44]. Although LYZV is an ungauged region, we can estimate its annual precipitation ranging from 2500 mm to 3500 mm by using observations from similar neighborhoods in the south of the Himalayas and early investigation findings [43,[45][46][47]. Thus, it is essential to provide reasonable estimations in LYZV.…”
Section: Definition and Division Of Study Areamentioning
confidence: 99%
“…The point predictions generated from linear or nonlinear regression models were also converted to probabilistic predictions that are often of greater usefulness in decisionmaking. Similar to current operational forecasts such as those from the South Asian Climate Outlook Forum [58], the probabilities generated were those for placing in each tercile of precipitation from the training period. The point predictions were converted to per-tercile probabilities that minimized the Kullback-Leibler divergence from an equal-chances climatology probability distribution while having the mean indicated by the point prediction (a 'maximum entropy' approach [59,60]), but with the tercile probabilities all constrained to the range 25%p50%.…”
Section: Probabilistic Prediction and Information Skill Score (Iss)mentioning
confidence: 99%
“…Numerous teleconnection studies have revealed that South Asian Monsoon (SAM) is modulated by many large-scale circulation patterns, including the El Niño-Southern Oscillation (ENSO) [64,63,47,40,37], Indian Ocean Dipole (IOD) [56,8,9,69], Atlantic Multidecadal Oscillation (AMO) [79,23,36], and Pacific Decadal Oscillation (PDO) [34,35,38,60,59]. Among these patterns, ENSO generally affects the SAM variability through the Walker circulation, subject to the change in spatial configurations of sea surface temperature (SST) anomalies in the Pacific Ocean [19]. However, weaker ENSO influence has been recently reported due to more active IOD events [56,74,69], the south-eastward shift in the Walker circulation anomalies [40,41], and forcing from the Atlantic circulations [16].…”
Section: Introductionmentioning
confidence: 99%