Abstract
This work demonstrates the potential of extreme cool-season precipitation forecasts in the Arabian Peninsula (AP) at sub-seasonal time scales, identifies regions and periods of forecast opportunity, and investigates the predictability of synoptic-scale forcing at sub-seasonal time scales. To this end, we simulate 18 extreme precipitation events using the convective-permitting Weather Research and Forecasting (CP-WRF) model with lateral boundary forcing from the European Centre of Medium-range Weather Forecasts sub-seasonal to seasonal reforecasts (ECMWF S2S reforecasts). The simulations are initiated at one-, two-, and three-week lead times. At all lead times, the CP-WRF improves the mean accumulated precipitation in the extratropical synoptic regimes over the west coastal and central AP and the central Red Sea compared to ECMWF S2S reforecasts as evaluated against the Global Precipitation Measurement Final (GPMF) and King Abdullah University of Science and Technology reanalysis (KAUST-RA) precipitation products. Based on categorical statistics with a threshold of 20 mm accumulated precipitation over 7 days, the CP-WRF skillfully forecasts the precipitation over Jeddah, the west coast of AP, and the central Red Sea up to three-week lead time. The relative operating characteristic curve reconfirmed the high forecast skill of the CP-WRF, with an area under the curve above 0.5 in most of the events at all lead times. Finally, the correlation coefficients between the ECMWF S2S reforecast and ECMWF reanalysis interim 500 hPa geopotential heights are higher in the events associated with an extratropical synoptic regime than in those associated with a tropical synoptic regime, regardless of lead time. Therefore, the convective-permitting model can potentially improve the accuracy of extreme winter precipitation forecasts at two-and three-week lead times over Jeddah, the west coast of AP, and the central Red Sea in the extratropical synoptic regime.
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reproduced from the datasets of Luong et al (2020a). It shows the synoptic-scale circulation of extratropical (a), transitional (b), and tropical (c) regimes. Black, red, blue, and green contours represent the mean sea level pressure, geopotential heights at 850 hPa, geopotential heights at 500 hPa, and vertical integral water vapor flux, respectively. Extreme events are associated with the Red Sea trough (RST), Arabian anticyclone (AA), upper-level trough (ULT), and moisture flux (MF). The green arrows represent wind speed and direction at 10 m. The yellow star indicates the city of Jeddah
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Data availability
All the data used in this study are stored on the Shaheen supercomputer at the King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. The data are available upon request.
References
Al Saud M (2010) Assessment of flood hazard of Jeddah area 2009, Saudi Arabia. J Water Resour Prot 2:839–847. https://doi.org/10.4236/jwarp.2010.29099
Alkhalaf A, Basset H (2013) Diagnostic study of a severe thunderstorm over Jeddah. Atmos Clim Sci 3:150–164. https://doi.org/10.4236/ACS.2013.31017
Almazroui M (2011) Calibration of TRMM rainfall climatology over Saudi Arabia during 1998–2009. Atmos Res 99:400–414. https://doi.org/10.1016/j.atmosres.2010.11.006
Atlas (1984) Water atlas of Saudi Arabia. Water Resource Department, Ministry of Agriculture and Water, Riyadh, Saudi Arabia, pp 2–19
Ban N, Schmidli J, Schär C (2014) Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations. J Geophys Res Atmos 119:7889–7907. https://doi.org/10.1002/2014JD021478
Ban N, Caillaud C, Coppola E et al (2021) The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation. Clim Dyn 57:275–302. https://doi.org/10.1007/s00382-021-05708-w
Becker EJ (2017) Prediction of short-term climate extremes with a multimodel ensemble. In: Wang S-YS, Yoon J-H, Funk CC, Gillies RR (eds) climate extremes. John Wiley & Sons, Inc, Hoboken. https://doi.org/10.1002/9781119068020.ch21https://agupubs.onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2F9781119068020.ch21
Berrisford P, Dee D, Poli P, Brugge R, Fielding K, Fuentes M, Kallberg P, Kobayashi S, Uppala S, Simmons A (2011) The ERA-Interim archive, version 2.0. ERA report series. 1. Technical Report. ECMWF. Shinfield Park, Reading, Berkshire RG2 9AX, United Kingdom, pp 23
Carrillo CM, Castro CL, Chang HI, Luong TM (2017) Multi-year climate variability in the Southwestern United States within a context of a dynamically downscaled twentieth century reanalysis. Clim Dyn 49:4217–4236. https://doi.org/10.1007/s00382-017-3569-1
Castro CL, Chang H, Dominguez F, Carrillo C, Schemm J, Juang H (2012) Can a regional climate model improve the ability to forecast the North American monsoon? J Clim 25:8212–8237. https://doi.org/10.1175/JCLI-D-11-00441.1
Chan SC, Kendon EJ, Berthou S (2020) Europe-wide precipitation projections at convection permitting scale with the Unified Model. Clim Dyn 55:409–428. https://doi.org/10.1007/s00382-020-05192-8
Chen D, Chen HW (2013) Using the Köppen classification to quantify climate variation and change: an example for 1901–2010. Environ Develop 6:69–79. https://doi.org/10.1016/j.envdev.2013.03.007
Christidis N, Stott PA (2015) Changes in the geopotential height at 500 hPa under the influence of external climatic forcings. Geophys Res Lett 42:10798–10806. https://doi.org/10.1002/2015GL066669
Clark P, Roberts N, Lean H, Ballard SP, Charlton-Perez C (2016) Convection-permitting models: a step-change in rainfall forecasting. Met Apps 23:165–181. https://doi.org/10.1002/met.1538
Coelho CAS, Firpo MAF, de Andrade FM (2018) A verification framework for South American sub-seasonal precipitation predictions. Meteorol Z 27:503–520. https://doi.org/10.1127/metz/2018/0898
Coelho CAS, Brown B, Wilson L, Mittermaier M, Casati B (2019) Forecast verification for S2S timescales. In: Robertson AW, Vitart F (eds) Sub-seasonal to seasonal prediction, the gap between weather and climate forecasting. Elsevier, Amsterdam, p 569. https://www.sciencedirect.com/science/article/pii/B9780128117149000164
Collier JC, Zhang GJ (2007) Effects of increased horizontal resolution on simulation of the North American monsoon in the NCAR CAM3: an evaluation based on surface, satellite, and reanalysis data. J Clim 20:1843–1861
Coppola E, Sobolowski S, Pichelli E et al (2020) A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean. Clim Dyn 55:3–34. https://doi.org/10.1007/s00382-018-4521-8
Crawford AD, Alley KE, Cooke AM, Serreze MC (2020) Synoptic climatology of rain-on-snow events in Alaska. Mon Wea Rev 148:1275–1295. https://doi.org/10.1175/MWR-D-19-0311.1
Da Silva NA, Webber BGM, Matthews AJ, Feist MM, Stein THM, Holloway CE, Abdullah MFAB (2021) Validation of GPM IMERG extreme precipitation in the Maritime Continent by station and radar data. Earth and Space Science 8:e2021EA001738. https://doi.org/10.1029/2021EA001738
Dasari HP, Attada R, Knio O, Hoteit I (2017) Analysis of a severe weather event over Mecca, Kingdom of Saudi Arabia, using observations and high-resolution modelling. Met Apps 24:612–627. https://doi.org/10.1002/met.1662
Dasari HP, Langodan S, Viswanadhapalli Y, Vadlamudi BR, Papadopoulos VP, Hoteit I (2018) ENSO influence on the interannual variability of the Red Sea convergence zone and associated rainfall. Int J Climatol 38:761–775. https://doi.org/10.1002/joc.5208
Dasari HP, Desamsetti S, Langodan S, Attada R, Kunchala RK, Viswanadhapalli Y, Knio O, Hoteit I (2019) High-resolution assessment of solar energy resources over the Arabian Peninsula. Appl Energy 248:354–371. https://doi.org/10.1016/j.apenergy.2019.04.105
Davis SR, Pratt LJ, Jiang H (2015) The Tokar gap jet: regional circulation, diurnal variability, and moisture transport based on numerical simulations. J Clim 28:5885–5907. https://doi.org/10.1175/JCLI-D-14-00635.1
De Haan LL, Kanamitsu M, De Sales F, Sun L (2015) An evaluation of the seasonal added value of downscaling over the United States using new verification measures. Theor Appl Climatol 122:47–57. https://doi.org/10.1007/s00704-014-1278-9
de Vries AJ, Tyrlis E, Edry D, Krichak SO, Steil B, Lelieveld J (2013) Extreme precipitation events in the Middle East: dynamics of the active Red Sea trough. J Geophys Res Atmos 118:7087–7108. https://doi.org/10.1002/jgrd.50569
de Vries AJ, Feldstein SB, Reimer M, Tyrlis E, Sprenger M, Baumgart M, Fnais M, Lelieveld J (2016) Dynamics of tropical-extratropical interactions and extreme precipitation events in Saudi Arabia in autumn, winter and spring. Q J R Meteorol Soc 142:1862–1880. https://doi.org/10.1002/qj.2781
de Vries AJ, Ouwersloot HG, Feldstein SB, Riemer M, El Kenawy AM, McCabe MF, Lelieveld J (2018) Identification of tropical-extratropical interactions and extreme precipitation events in the Middle East based on potential vorticity and moisture transport. J Geophys Res Atmos 123:861–881. https://doi.org/10.1002/2017JD027587
Deng L, McCabe MF, Stenchikov G, Evans JP, Kucera PA (2015) Simulation of flash-flood-producing storm events in Saudi Arabia using the weather research and forecasting model. J Hydrometeor 16:615–630. https://doi.org/10.1175/JHM-D-14-0126.1
Feng Z, Dong X, Xi B, McFarlane SA, Kennedy A, Lin B, Minnis P (2012) Life cycle of midlatitude deep convective systems in a Lagrangian framework. J Geophys Res 117:D23201. https://doi.org/10.1029/2012JD018362
Gao Y, Leung LR, Zhao C, Hagos S (2017) Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions. J Geophys Res Atmos 122:2714–2733. https://doi.org/10.1002/2016JD025896
Grell GA, Freitas SR (2013) A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos Chem Phys Discuss 13:23845–23893. https://doi.org/10.5194/acpd-13-23845-2013
Grimm AM, Hakoyama LR, Scheibe LA (2021) Active and break phases of the South American summer monsoon: MJO influence and subseasonal prediction. Clim Dyn 56:3603–3624. https://doi.org/10.1007/s00382-021-05658-3
Hafez YY, Almazroui M (2016) Study of the relationship between geopotential height anomaly over Europe and extreme abnormal weather over the Eastern Mediterranean and Middle East during December 2013. Arab J Geosci 9:409. https://doi.org/10.1007/s12517-016-2424-8
Haggag M, El-Badry H (2013) Mesoscale numerical study of quasi-stationary convective system over Jeddah in November 2009. Atmos Clim Sci 3:73–86. https://doi.org/10.4236/acs.2013.31010
Hassim MEE, Lane TP, Grabowski WW (2016) The diurnal cycle of rainfall over New Guinea in convection-permitting WRF simulations. Amos Chem Phys 16:161–175. https://doi.org/10.5194/acp-16-161-2016
Hewitson BC, Crane RG (2002) Self-organizing maps: application to synoptic climatology. Clim Res 22:13–26
Hoteit I, Abualnaja Y, Afzal S, Ait-El-Fquih B, Akylas T, Antony C, Dawson C, Asfahani K, Brewin RJ, Cavaleri L, and Coauthors (2021) Towards an end-to-end analysis and prediction system for weather, climate, and marine applications in the Red Sea. Bull Am Meteorol Soc 102:E99–E122. https://doi.org/10.1175/BAMS-D-19-0005.1
Houze RA (1993) Cloud dynamics. Academic Press, San Diego, p 573
Hudson D, Alves O, Hendon HH, Marshall AG (2011) Bridging the gap between weather and seasonal forecasting: intraseasonal forecasting for Australia. Q J R Meteorol Soc 137:673–689. https://doi.org/10.1002/qj.769
Huffman GJ (2019) Caveats for IMERG and the TRMM era. Technical notes. https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_TRMMera-caveats.pdf
Huffman GJ, and Coauthors (2018) Algorithm theoretical basis document version 5.2. NASA Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), pp 31, https://gpm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V5.2_0.pdf
Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeor 8:38–55. https://doi.org/10.1175/JHM560.1
Iacono MJ, Delamere JS, Mlawer EJ, Shephard MW, Clough SA, Collins WD (2008) Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J Geophys Res 113:D13103. https://doi.org/10.1029/2008JD009944
Jie W, Vitart F, Wu T, Liu X (2017) Simulations of the Asian summer monsoon in the sub-seasonal to seasonal prediction project (S2S) database. Q J R Meteorol Soc 143:2282–2295. https://doi.org/10.1002/qj.3085
Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503. https://doi.org/10.1175/1525-7541(2004)005%3c0487:CAMTPG%3e2.0.CO;2
Kendon EJ, Roberts NM, Senior CA, Roberts MJ (2012) Realism of rainfall in a very high-resolution regional climate model. J Clim 25:5791–5806. https://doi.org/10.1175/JCLI-D-11-00562.1
Kendon EJ, Ban N, Roberts NM, Fowler HJ, Roberts MJ, Chan SC, Evans JP, Fosser G, Wilkinson JM (2017) Do convection-permitting regional climate models improve projections of future precipitation change? Bull Am Meteorol Soc 98:79–93. https://doi.org/10.1175/BAMS-D-15-0004.1
Kim K, Park J, Baik J, Choi M (2017) Evaluation of topographical and seasonal feature using GPM IMERG and TRMM 3B42 over Far-East Asia. Atmos Res 187:95–105
Köppen W (1936) Das geographische System de Klimate. In: Koppen W, Geiger R (eds) Handbuch de klimatologie, vol 1. GebrBorntrager, Berlin, pp 1–44
Kouadio K, Bastin S, Konare A et al (2020) Does convection-permitting simulate better rainfall distribution and extreme over Guinean coast and surroundings? Clim Dyn 55:153–174. https://doi.org/10.1007/s00382-018-4308-y
Kubota T, Shige S, Hashizume H, Aonashi K, Takahashi N, Seto S, Hirose M, Takayabu YN, Nakagawa K, Iwanami K, Ushio T, Kachi M, Okamoto K (2007) Global precipitation map using satellite borne microwave radiometers by the GSMaP Project: production and validation. IEEE Trans Geosci Remote Sens 45:2259–2275. https://doi.org/10.1109/TGRS.2007.895337
Langodan S, Cavaleri L, Viswanadhapalli Y, Hoteit I (2014) The Red Sea: a natural laboratory for wind and wave modeling. J Phys Oceano 44:3139–3159. https://doi.org/10.1175/JPO-D-13-0242.1
Langodan S, Cavaleri L, Vishwanadhapalli Y, Pomaro A, Bertotti L, Hoteit I (2017) The climatology of the Red Sea—part 1: the wind. Int J Climatol 37:4509–4517. https://doi.org/10.1002/joc.5103
Liang P, Lin H (2018) Sub-seasonal prediction over East Asia during boreal summer using ECCC monthly forecasting system. Clim Dyn 50:1007–1022. https://doi.org/10.1007/s00382-017-3658-1
Lind P, Belušić D, Christensen OB et al (2020) Benefits and added value of convection-permitting climate modeling over Fenno-Scandinavia. Clim Dyn 55:1893–1912. https://doi.org/10.1007/s00382-020-05359-3
Liu C, Ikeda K, Rasmussen R et al (2017) Continental-scale convection-permitting modeling of the current and future climate of North America. Clim Dyn 49:71–95. https://doi.org/10.1007/s00382-016-3327-9
Livezey RE, Chen WY (1983) Statistical significance and its determination by Monte Carlo techniques. Mon Weather Rev 111:46–59. https://doi.org/10.1175/1520-0493(1983)111%3c0046:SFSAID%3e2.0.CO;2
Lucas-Picher P, Argüeso D, Brisson E, Tramblay Y, Berg P, Lemonsu A, Kotlarski S, Caillaud C (2021) Convection-permitting modeling with regional climate models: latest developments and next steps. Wiley Interdiscip Rev: Clim Change 12:e731. https://doi.org/10.1002/wcc.731
Luong TM, Dasari HP, Hoteit I (2020a) Extreme precipitation events are becoming less frequent but more intense over Jeddah, Saudi Arabia. Are shifting weather regimes the cause? Atmos Sci Lett 21:e981. https://doi.org/10.1002/asl.981
Luong TM, Dasari HP, Hoteit I (2020b) Impact of urbanization on the simulation of extreme rainfall in the city of Jeddah, Saudi Arabia. J Appl Meteor Climatol 59:953–971. https://doi.org/10.1175/JAMC-D-19-0257.1
Mariotti A, Baggett C, Barnes EA, Becker E, Butler A, Collins DC, Dirmeyer PA, Ferranti L, Johnson NC, Jones J, Coauthors (2020) Windows of opportunity for skillful forecasts subseasonal to seasonal and beyond. Bull Am Meteorol Soc 101:E608–E625. https://doi.org/10.1175/BAMS-D-18-0326.1
Mason SJ, Graham NE (2002) Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: statistical significance and interpretation. Q J R Meteorol Soc 128:2145–2166
Merino A, Fernández-Vaquero M, López L, Fernández-González S, Hermida L, Sánchez JL, García-Ortega E, Gascón E (2016) Large-scale patterns of daily precipitation extremes on the Iberian Peninsula. Int J Climatol 36:3873–3891. https://doi.org/10.1002/joc.4601
Merryfield WJ, Baehr J, Batté L, Becker EJ, Butler AH, Coelho CAS, Danabasoglu G, Dirmeyer PA, Doblas-Reyes FJ, Domeisen DIV, Coauthors (2020) Current and emerging developments in subseasonal to decadal prediction. Bull Am Meteorol Soc 101:E869–E896. https://doi.org/10.1175/BAMS-D-19-0037.1
Mitchell DL, Ivanova D, Rabin R, Brown TJ, Redmond K (2002) Gulf of California sea surface temperatures and the North American Monsoon: mechanistic implications from observations. J Clim 15:2261–2281. https://doi.org/10.1175/1520-0442(2002)015%3c2261:GOCSST%3e2.0.CO;2
Moker JM Jr, Castro CL, Arellano AF Jr, Serra YL, Adams DK (2018) Convective-Permitting hindcast simulations during the North American Monsoon GPS transect experiment 2013: establishing baseline model performance without data assimilation. J Appl Meteorol Climatol 57:1683–1710. https://doi.org/10.1175/JAMC-D-17-0136.1
Nakanishi M, Niino H (2006) An Improved Mellor-Yamada level 3 model: its numerical stability and application to a regional prediction of advection fog. Bound-Layer Meteor 119:397–407. https://doi.org/10.1007/s10546-005-9030-8
Nguyen-Le D, Yamada TJ, Tran-Anh D (2017) Classification and forecast of heavy rainfall in northern Kyushu during Baiu season using weather pattern recognition. Atmos Sci Lett 18:324–329. https://doi.org/10.1002/asl.759
Orth R, Seneviratne SI (2017) Variability of soil moisture and sea surface temperatures similarly important for warm-season land climate in the community earth system model. J Clim 30:2141–2162. https://doi.org/10.1175/JCLI-D-15-0567.1
Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci 11:1633–1644. https://doi.org/10.5194/hess-11-1633-2007
Pegion K, Kirtman BP, Becker E, Collins DC, LaJoie E, Burgman R, Bell R, DelSole T, Min D, Zhu Y, Li W, Sinsky E, Guan H, Gottschalck J, Metzger EJ, Barton NP, Achuthavarier D, Marshak J, Koster RD, Lin H, Gagnon N, Bell M, Tippett MK, Robertson AW, Sun S, Benjamin SG, Green BW, Bleck R, Kim H (2019) The subseasonal experiment (SubX): a multimodel subseasonal prediction experiment. Bull Am Meteorol Soc 100:2043–2060. https://doi.org/10.1175/BAMS-D-18-0270.1
Powers JG, Klemp JB, Skamarock WC, Davis CA, Dudhia J, Gill DO, Coen JL, Gochis DJ, Ahmadov R, Peckham SE, Coauthors (2017) The weather research and forecasting model: overview, system efforts, and future directions. Bull Am Meteorol Soc 98:1717–1737. https://doi.org/10.1175/BAMS-D-15-00308.1
Prein, AF, Langhans W, Fosser G, Ferrone A, Ban N, Goergen K, Keller M, Tölle M, Gutjahr O, Feser F, Coauthors (2015) A review on regional convection-permitting climate modeling: demonstrations, prospects, and challenges. Rev Geophys 53:323–361. https://doi.org/10.1002/2014RG000475
Risanto CB, Castro CL, Moker JM Jr, Arellano AF Jr, Adams DK, Fierro LM, Minjarez Sosa CM (2019) Evaluating forecast skills of moisture from convective-permitting WRF-ARW Model during 2017 North American Monsoon Season. Atmosphere 10:694. https://doi.org/10.3390/atmos10110694
Risanto CB, Castro CL, Arellano AF Jr, Moker JM Jr, Adams DK (2021) The impact of assimilating GPS precipitable water vapor in convective-permitting WRF-ARW on North American Monsoon precipitation forecasts over Northwest Mexico. Mon Wea Rev 149:3013–3035. https://doi.org/10.1175/MWR-D-20-0394.1
Robertson AW, Vitart F, Camargo SJ (2020) Subseasonal to seasonal prediction of weather to climate with application to tropical cyclones. J Geophys Res 125:e2018JD029375. https://doi.org/10.1029/2018JD029375
Schumacher RS, Rasmussen KL (2020) The formation, character and changing nature of mesoscale convective systems. Nat Rev Earth Environ 1:300–314. https://doi.org/10.1038/s43017-020-0057-7
Shukla S, Lettenmaier DP (2013) Multi-RCM ensemble downscaling of NCEP CFS winter season forecasts: Implications for seasonal hydrologic forecast skill. J Geophys Res Atmos 118:10770–10790. https://doi.org/10.1002/jgrd.50628
Skamarock WC, Coauthors (2008) A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, National Center for Atmospheric Research, Boulder, Colorado, USA pp 113. https://doi.org/10.5065/D68S4MVH
Smith AB, Katz RW (2013) US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases. Nat Hazards 67:387–410. https://doi.org/10.1007/s11069-013-0566-5
Sorooshian S, Hsu KL, Gao X, Gupta HV, Imam B, Braithwaite D (2002) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull Am Meteorol Soc 81:2035–2046. https://doi.org/10.1175/1520-0477(2000)081%3c2035:EOPSSE%3e2.3.CO;2
Stratton RA, Senior CA, Vosper SB, Folwell SS, Boutle IA, Earnshaw PD et al (2018) A Pan-African convection-permitting regional climate simulation with the met office unified model: CP4-Africa. J Clim 31:3485–3508. https://doi.org/10.1175/JCLI-D-17-0503.1
Sun R, Subramanian AC, Miller AJ, Mazloff MR, Hoteit I, Cornuelle BD (2019) SKRIPS v1.0: a regional coupled ocean–atmosphere modeling framework (MITgcm–WRF) using ESMF/NUOPC, description and preliminary results for the Red Sea. Geosci Model Dev 12:4221–4244. https://doi.org/10.5194/gmd-12-4221-2019
Tan ML, Duan Z (2017) Assessment of GPM and TRMM precipitation products over Singapore. Remote Sens 9:720. https://doi.org/10.3390/rs9070720
Tewari M, Coauthors (2004) Implementation and verification of the unified Noah land surface model in the WRF Model. In: 20th Conf. on Weather Analysis and Forecasting/16th Conf. on Numerical Weather Prediction, Seattle, WA, Amer Meteorol Soc 14.2a. [Available online at https://ams.confex.com/ams/84Annual/techprogram/paper_69061.htm.]
Thompson G, Field PR, Rasmussen RM, Hall WD (2008) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: implementation of a new snow parameterization. Mon Weather Rev 136:5095–5115. https://doi.org/10.1175/2008MWR2387.1
Viswanadhapalli Y, Srinivas CV, Langodan S, Hoteit I (2016) Predicting extreme rainfall events over Jeddah, Saudi Arabia: impact of data assimilation with conventional and satellite observations. Q J R Meteorol Soc 142:327–348. https://doi.org/10.1002/qj.2654
Viswanadhapalli Y, Dasari HP, Langodan S, Challa VS, Hoteit I (2017) Climatic features of the Red Sea from a regional assimilative model. Int J Climatol 37:2563–2581. https://doi.org/10.1002/joc.4865
Vitart F, Robertson AW (2018) The sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events. Npj Clim Atmos Sci. https://doi.org/10.1038/s41612-018-0013-0
Vitart F, Ardilouze C, Bonet A, Brookshaw A, Chen M, Codorean C, Déqué M, Ferranti L, Fucile E, Coauthors (2017) The Subseasonal to seasonal (S2S) prediction project database. Bull Am Meteorol Soc 98:163–173. https://doi.org/10.1175/BAMS-D-16-0017.1
Vuillaume JF, Dorji S, Komolafe A, Coauthors (2018) Sub-seasonal extreme rainfall prediction in the Kelani River basin of Sri Lanka by using self-organizing map classification. Nat Hazards 94:385–404. https://doi.org/10.1007/s11069-018-3394-9
Wilks DS (2011) Statistical methods in the Atmospheric Sciences. Academic Press, Oxford, (UK), p 676
Zhang C, Chen X, Shao H, Chen S, Liu T, Chen C, Ding Q, Du H (2018) Evaluation and intercomparison of high-resolution satellite precipitation estimates—GPM, TRMM, and CMORPH in the Tianshan Mountain Area. Remote Sens 10:1543. https://doi.org/10.3390/rs10101543
Zou KH, O’Malley J, Mauri L (2007) Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation 115:654–657. https://doi.org/10.1161/CIRCULATIONAHA.105.594929
Funding
This work is supported by a competitive research grant from the King Abdullah University of Science and Technology with sub-award agreement OSR-2018-CRG7-3706.2. This work is based on S2S data. S2S is a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). The original S2S database is hosted at ECMWF as an extension of the TIGGE database. We thank the editor and the two anonymous reviewers for their constructive feedback.
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Risanto, C.B., Chang, HI., Luong, T.M. et al. Retrospective sub-seasonal forecasts of extreme precipitation events in the Arabian Peninsula using convective-permitting modeling. Clim Dyn 62, 2877–2906 (2024). https://doi.org/10.1007/s00382-022-06336-8
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DOI: https://doi.org/10.1007/s00382-022-06336-8