Impacts of large scale climate drivers on precipitation in Sindh, Pakistan using machine learning techniques
Sindh province of Pakistan has a long history of severe droughts. Several large scale climate drivers (LSCD) are known for their effect on precipitation worldwide but studies in the Sindh region are missing; wide variety of LSCDs and lagged associative information. This study aimed to identify the s...
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ftmtak:oai:real.mtak.hu:173534 2023-10-09T21:55:53+02:00 Impacts of large scale climate drivers on precipitation in Sindh, Pakistan using machine learning techniques Tajbar, Sapna Khorshiddoust, Ali Mohammad Asl, Saeed Jahanbakhsh 2023 text http://real.mtak.hu/173534/ http://real.mtak.hu/173534/1/127-3-4-tajbar.pdf https://doi.org/10.28974/idojaras.2023.3.4 en eng Hungarian Meteorological Service http://real.mtak.hu/173534/1/127-3-4-tajbar.pdf Tajbar, Sapna and Khorshiddoust, Ali Mohammad and Asl, Saeed Jahanbakhsh (2023) Impacts of large scale climate drivers on precipitation in Sindh, Pakistan using machine learning techniques. IDŐJÁRÁS = QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE, 127 (3). pp. 321-346. ISSN 0324-6329 (print); 2677-187X (online) QE04 Meteorology / meteorológia Article NonPeerReviewed info:eu-repo/semantics/article 2023 ftmtak https://doi.org/10.28974/idojaras.2023.3.4 2023-09-20T23:31:33Z Sindh province of Pakistan has a long history of severe droughts. Several large scale climate drivers (LSCD) are known for their effect on precipitation worldwide but studies in the Sindh region are missing; wide variety of LSCDs and lagged associative information. This study aimed to identify the significant LSCDs in Sindh province of Pakistan and improve the forecast skill of monthly precipitation by employing the principal component analysis (PCA), artificial neural network (ANN), Bayesian regularization neural network (BRNN), and multiple regression analysis (MRA), while considering the 12 months lagged LSCDs such as Nino-1+2, Nino-3, Nino-3.4, Nino-4, Quasi-Biennial Oscillation (QBO) at 30 and 50hPa (QBOI and QBOII), sea surface temperature (SST), 2m air temperature (T2M), 500 hPa and 850 hPa geopotential heights (H500 and H850), surface and 500 hPa zonal velocity (SU and U500), latent and sensible heat fluxes over land (LHFOL and SHFOL), and surface specific humidity (SSH). Global Land Data Assimilation System (GLDAS), Tropical Rainfall Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Application (MERRA-2), NOAA, Freie University Berlin, and Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) datasets were used. Results manifested that significant LSCDs with 99% confidence level were SU, U500, T2M, SST, SHFOL, LHFOL, SSH, and H850. During test period, compared with MR models of 0.39 to 0.64 and principal components of 0.31 to 0.57, the ANN and BRNN models had better predictive skills with correlation coefficients of 0.57 to 0.83 and 0.52 to 0.76, respectively. It can be concluded that the ANN and BRNN models enable us to predict monthly precipitation in Sindh region with lagged LSCDs. Article in Journal/Newspaper Sea ice MTAK: REAL (Library and Information Centre of the Hungarian Academy of Sciences Merra ENVELOPE(12.615,12.615,65.816,65.816) Időjárás 127 3 321 346 |
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Open Polar |
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MTAK: REAL (Library and Information Centre of the Hungarian Academy of Sciences |
op_collection_id |
ftmtak |
language |
English |
topic |
QE04 Meteorology / meteorológia |
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QE04 Meteorology / meteorológia Tajbar, Sapna Khorshiddoust, Ali Mohammad Asl, Saeed Jahanbakhsh Impacts of large scale climate drivers on precipitation in Sindh, Pakistan using machine learning techniques |
topic_facet |
QE04 Meteorology / meteorológia |
description |
Sindh province of Pakistan has a long history of severe droughts. Several large scale climate drivers (LSCD) are known for their effect on precipitation worldwide but studies in the Sindh region are missing; wide variety of LSCDs and lagged associative information. This study aimed to identify the significant LSCDs in Sindh province of Pakistan and improve the forecast skill of monthly precipitation by employing the principal component analysis (PCA), artificial neural network (ANN), Bayesian regularization neural network (BRNN), and multiple regression analysis (MRA), while considering the 12 months lagged LSCDs such as Nino-1+2, Nino-3, Nino-3.4, Nino-4, Quasi-Biennial Oscillation (QBO) at 30 and 50hPa (QBOI and QBOII), sea surface temperature (SST), 2m air temperature (T2M), 500 hPa and 850 hPa geopotential heights (H500 and H850), surface and 500 hPa zonal velocity (SU and U500), latent and sensible heat fluxes over land (LHFOL and SHFOL), and surface specific humidity (SSH). Global Land Data Assimilation System (GLDAS), Tropical Rainfall Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Application (MERRA-2), NOAA, Freie University Berlin, and Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) datasets were used. Results manifested that significant LSCDs with 99% confidence level were SU, U500, T2M, SST, SHFOL, LHFOL, SSH, and H850. During test period, compared with MR models of 0.39 to 0.64 and principal components of 0.31 to 0.57, the ANN and BRNN models had better predictive skills with correlation coefficients of 0.57 to 0.83 and 0.52 to 0.76, respectively. It can be concluded that the ANN and BRNN models enable us to predict monthly precipitation in Sindh region with lagged LSCDs. |
format |
Article in Journal/Newspaper |
author |
Tajbar, Sapna Khorshiddoust, Ali Mohammad Asl, Saeed Jahanbakhsh |
author_facet |
Tajbar, Sapna Khorshiddoust, Ali Mohammad Asl, Saeed Jahanbakhsh |
author_sort |
Tajbar, Sapna |
title |
Impacts of large scale climate drivers on precipitation in Sindh, Pakistan using machine learning techniques |
title_short |
Impacts of large scale climate drivers on precipitation in Sindh, Pakistan using machine learning techniques |
title_full |
Impacts of large scale climate drivers on precipitation in Sindh, Pakistan using machine learning techniques |
title_fullStr |
Impacts of large scale climate drivers on precipitation in Sindh, Pakistan using machine learning techniques |
title_full_unstemmed |
Impacts of large scale climate drivers on precipitation in Sindh, Pakistan using machine learning techniques |
title_sort |
impacts of large scale climate drivers on precipitation in sindh, pakistan using machine learning techniques |
publisher |
Hungarian Meteorological Service |
publishDate |
2023 |
url |
http://real.mtak.hu/173534/ http://real.mtak.hu/173534/1/127-3-4-tajbar.pdf https://doi.org/10.28974/idojaras.2023.3.4 |
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ENVELOPE(12.615,12.615,65.816,65.816) |
geographic |
Merra |
geographic_facet |
Merra |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
http://real.mtak.hu/173534/1/127-3-4-tajbar.pdf Tajbar, Sapna and Khorshiddoust, Ali Mohammad and Asl, Saeed Jahanbakhsh (2023) Impacts of large scale climate drivers on precipitation in Sindh, Pakistan using machine learning techniques. IDŐJÁRÁS = QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE, 127 (3). pp. 321-346. ISSN 0324-6329 (print); 2677-187X (online) |
op_doi |
https://doi.org/10.28974/idojaras.2023.3.4 |
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Időjárás |
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127 |
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3 |
container_start_page |
321 |
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346 |
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1779320100899258368 |