Predicting extreme rainfall events over Jeddah, Saudi Arabia: Impact of data assimilation with conventional and satellite observations

Handle URI:
http://hdl.handle.net/10754/575935
Title:
Predicting extreme rainfall events over Jeddah, Saudi Arabia: Impact of data assimilation with conventional and satellite observations
Authors:
Viswanadhapalli, Yesubabu; Srinivas, C.V.; Langodan, Sabique ( 0000-0003-0513-1790 ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
The impact of variational data assimilation for predicting two heavy rainfall events that caused devastating floods in Jeddah, Saudi Arabia is studied using the Weather Research and Forecasting (WRF) model. On 25 November 2009 and 26 January 2011, the city was deluged with more than double the annual rainfall amount caused by convective storms. We used a high resolution, two-way nested domain WRF model to simulate the two rainfall episodes. Simulations include control runs initialized with National Center for Environmental Prediction (NCEP) Global Forecasting System (GFS) data and 3-Dimensional Variational (3DVAR) data assimilation experiments conducted by assimilating NCEP prepbufr and radiance observations. Observations from Automated Weather Stations (AWS), synoptic charts, radar reflectivity and satellite pictures from the Presidency of Meteorology and Environment (PME), Jeddah, Saudi Arabia are used to assess the forecasting results. To evaluate the impact of the different assimilated observational datasets on the simulation of the major flooding event of 2009, we conducted 3DVAR experiments assimilating individual sources and a combination of all data sets. Results suggest that while the control run had a tendency to predict the storm earlier than observed, the assimilation of profile observations greatly improved the model's thermodynamic structure and lead to better representation of simulated rainfall both in timing and amount. The experiment with assimilation of all available observations compared best with observed rainfall in terms of timing of the storm and rainfall distribution, demonstrating the importance of assimilating different types of observations. Retrospective experiments with and without data assimilation, for three different model lead times (48, 72 and 96-h), were performed to examine the skill of WRF model to predict the heavy rainfall events. Quantitative rainfall analysis of these simulations suggests that 48-h lead time runs with assimilation of all observational data provide best statistical scores.
KAUST Department:
Earth Science and Engineering Program
Citation:
Predicting extreme rainfall events over Jeddah, Saudi Arabia: Impact of data assimilation with conventional and satellite observations 2015:n/a Quarterly Journal of the Royal Meteorological Society
Publisher:
Wiley-Blackwell
Journal:
Quarterly Journal of the Royal Meteorological Society
Issue Date:
20-Aug-2015
DOI:
10.1002/qj.2654
Type:
Article
ISSN:
00359009
Additional Links:
http://doi.wiley.com/10.1002/qj.2654
Appears in Collections:
Articles; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorViswanadhapalli, Yesubabuen
dc.contributor.authorSrinivas, C.V.en
dc.contributor.authorLangodan, Sabiqueen
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-08-25T07:56:55Zen
dc.date.available2015-08-25T07:56:55Zen
dc.date.issued2015-08-20en
dc.identifier.citationPredicting extreme rainfall events over Jeddah, Saudi Arabia: Impact of data assimilation with conventional and satellite observations 2015:n/a Quarterly Journal of the Royal Meteorological Societyen
dc.identifier.issn00359009en
dc.identifier.doi10.1002/qj.2654en
dc.identifier.urihttp://hdl.handle.net/10754/575935en
dc.description.abstractThe impact of variational data assimilation for predicting two heavy rainfall events that caused devastating floods in Jeddah, Saudi Arabia is studied using the Weather Research and Forecasting (WRF) model. On 25 November 2009 and 26 January 2011, the city was deluged with more than double the annual rainfall amount caused by convective storms. We used a high resolution, two-way nested domain WRF model to simulate the two rainfall episodes. Simulations include control runs initialized with National Center for Environmental Prediction (NCEP) Global Forecasting System (GFS) data and 3-Dimensional Variational (3DVAR) data assimilation experiments conducted by assimilating NCEP prepbufr and radiance observations. Observations from Automated Weather Stations (AWS), synoptic charts, radar reflectivity and satellite pictures from the Presidency of Meteorology and Environment (PME), Jeddah, Saudi Arabia are used to assess the forecasting results. To evaluate the impact of the different assimilated observational datasets on the simulation of the major flooding event of 2009, we conducted 3DVAR experiments assimilating individual sources and a combination of all data sets. Results suggest that while the control run had a tendency to predict the storm earlier than observed, the assimilation of profile observations greatly improved the model's thermodynamic structure and lead to better representation of simulated rainfall both in timing and amount. The experiment with assimilation of all available observations compared best with observed rainfall in terms of timing of the storm and rainfall distribution, demonstrating the importance of assimilating different types of observations. Retrospective experiments with and without data assimilation, for three different model lead times (48, 72 and 96-h), were performed to examine the skill of WRF model to predict the heavy rainfall events. Quantitative rainfall analysis of these simulations suggests that 48-h lead time runs with assimilation of all observational data provide best statistical scores.en
dc.language.isoenen
dc.publisherWiley-Blackwellen
dc.relation.urlhttp://doi.wiley.com/10.1002/qj.2654en
dc.rightsThis is the peer reviewed version of the following article: Yesubabu, V., C. V. Srinivas, Sabique Langodan, and Ibrahim Hoteit. "Predicting extreme rainfall events over Jeddah, Saudi Arabia: Impact of data assimilation with conventional and satellite observations." Quarterly Journal of the Royal Meteorological Society (2015)., which has been published in final form at http://doi.wiley.com/10.1002/qj.2654. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.en
dc.subjectFlash Flooden
dc.subjectJeddahen
dc.subjectWRF modelen
dc.subjectData Assimilationen
dc.subject3DVARen
dc.titlePredicting extreme rainfall events over Jeddah, Saudi Arabia: Impact of data assimilation with conventional and satellite observationsen
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.identifier.journalQuarterly Journal of the Royal Meteorological Societyen
dc.eprint.versionPost-printen
dc.contributor.institutionRadiological Safety and Environment Group, Indira Gandhi Centre for Atomic Research, Kalpakkam, Indiaen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorHoteit, Ibrahimen
kaust.authorViswanadhapalli, Yesubabuen
kaust.authorLangodan, Sabiqueen
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