Abstract
To enhance the capabilities of the Subseasonal to Seasonal (S2S) database for forecasting Iran’s southwest precipitation from 1 to 4 weeks ahead, we compared observed precipitation and atmospheric variables with the corresponding hindcasts generated by the KMA, UKMO, ECWMF, and Meteo-France (MF) research centers. This analysis involved several deterministic and probabilistic metrics. Our reference datasets included daily precipitation data from 176 rain gauge stations and the NOAA-based atmospheric circulations data for Dec-April 1995–2014. Most hindcasts underestimated wet events in southern and eastern districts but overestimated them in the western and northern regions. Additionally, all hindcasts overforecasted the frequency of wet events across all lead times. The correlation scores were highest in the first week and declined as lead times increased. The ECMWF had the best correlation in all regions, showing superior deterministic and probabilistic forecast skills in western districts. The UKMO hindcasts, whose accuracy has the highest dependancy on precipitation amount, effectively captured signals of the El-Niño Southern Oscillation (ENSO) and Madden Julian Oscillation (MJO) over the study area and the Middle East. They accurately forecasted the 850 hPa moisture transport and 500 hPa vertical velocity features in these regions, particularly during the rainy phases of MJO. These findings offer valuable insights to enhance the accuracy of operational S2S precipitation forecasts for planning and decision-making in Iran and the Middle East.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The dataset on the global S2S precipitation and atmospheric components (850 hPa u-wind and v-wind, 850 hPa specific humidity, and 500 hPa vertical velocity) hindcasts are available at https://apps.ecmwf.int/datasets/data/s2s-reforecasts-daily-averaged-ecmf/levtype=sfc/type=cf/. Daily precipitation data from synoptic stations and rain gauges are available upon request on the National Meteorological Organization website (www.irimo.ir). SST from NOAA Optimum Interpolation v.2 (OISST.v2) is available at https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html. Reference atmospheric components data, including 850 hPa u-wind and v-wind, specific humidity, and 500 hPa vertical velocity generated by the NOAA-CIRES-DOE 20 Century Reanalysis Project version 3 are available at https://www.psl.noaa.gov/data/gridded/data.20thC_ReanV3.html. Daily Outgoing Longwave Radiation (OLR) data Version 1.2 is sourced from NOAA Climate Data Record (CDR) available at https://www.ncei.noaa.gov/thredds/catalog/cdr/olr-daily/catalog.html.
Code availability
The authors used software such as R statistic and Microsoft Excel to prepare or analyze data and produce maps. We also used the command lines of Climate Data Operators (CDO) and NCAR Command Language (NCL) to analyze gridded data (GRIB2 and NetCDF-4 files) and produce some spatial maps.
References
Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6:661–675. https://doi.org/10.1002/joc.3370060607
Best MJ, Pryor M, Clark DB, Rooney GG, Essery RLH, Ménard CB, Edwards JM, Hendry MA, Porson A, Harding RJ (2011) The Joint UK Land Environment Simulator (JULES), model description-part 1: energy and water fluxes. Geosci Model Dev 4:677–699. https://doi.org/10.5194/gmd-4-677-2011
Buishand TA (1982) Some methods for testing the homogeneity of rainfall records. J Hydrol 58:11–27. https://doi.org/10.1016/0022-1694(82)90066-X
Coelho CAS, Firpo MAF, de Andrade FM (2018) A verification framework for south American sub-seasonal precipitation predictions. Meteorol Z 27(6):503–520. https://doi.org/10.1127/metz/2018/0898
de Andrade FM, Coelho CAS, Cavalcanti IFA (2019) Global precipitation hindcast quality assessment of the Subseasonal to Seasonal (S2S) prediction project models. Clim Dyn 52:5451–5475. https://doi.org/10.1007/s00382-018-4457-z
de Andrade FM, Young MP, MacLeod D, Hirons LC, Woolnough SJ, Black E (2021) Subseasonal Precipitation Prediction for Africa: Forecast evaluation and sources of predictability. Weather Forecast 36(1):265–284. https://doi.org/10.1175/WAF-D-20-0054.1
Gao G, Li Y, Zhou XY, Xiang XM, Li JQ, Yin SC (2023) Deep learning-based subseasonal to seasonal precipitation prediction in southwest China: Algorithm comparison and sensitivity to input features. Earth Planet Phys 7:471–486. https://doi.org/10.26464/epp2023049
Ghaedamini H, Morid S, Nazemosadat MJ, Shamsoddini A, Shafizadeh Moghadam H (2021) Validation of the CHIRPS and CPC-Unified products for estimating extreme daily precipitation over southwestern Iran. Theor Appl Climatol 146:1207–1225. https://doi.org/10.1007/s00704-021-03790-y
Gudoshava M, Wanzala M, Thompson E, Mwesigwa J, Endris HS, Segele Z, Hirons L, Kipkogei O, Mumbua C, Artan G (2022) Application of real time S2S forecasts over Eastern Africa in the co-production of climate services. Clim Serv 27. https://doi.org/10.1016/j.cliser.2022.100319
Hagedorn R, Doblas-Reyes FJ, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting–I. Basic concept. Tellus A 57(3):219–233. https://doi.org/10.3402/tellusa.v57i3.14657
Huang B, Liu C, Banzon V, Freeman E, Graham G, Hankins B, Smith T, Zhang HM (2021) Improvements of the Daily Optimum Interpolation Sea Surface temperature (DOISST) version 2.1. J Clim 34(8):2923–2939. https://doi.org/10.1175/JCLI-D-20-0166.1
Jones PW (1999) First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon Wea Rev 127(9):2204–2210. https://doi.org/10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2
Kumi N, Abiodun BJ, Adefisan EA (2020) Performance evaluation of a Subseasonal to Seasonal model in predicting rainfall onset over West Africa. Earth Space Sci 7. https://doi.org/10.1029/2019EA000928. e2019EA000928-T
Lee H-T, NOAA CDR Program (2011) NOAA Climate Data Record (CDR) of daily outgoing Longwave Radiation (OLR), Version 1.2. NOAA Natl Clim Data Cent. https://doi.org/10.7289/V5SJ1HH2. [2023-08-21]
Li S, Robertson AW (2015) Evaluation of submonthly precipitation forecast skill from global ensemble prediction systems. Mon Wea Rev 143(7):2871–2889. https://doi.org/10.1175/MWR-D-14-00277.1
Li W, Chen J, Li L, Chen H, Liu B, Xu CY, Li X (2019) Evaluation and Bias correction of S2S precipitation for hydrological extremes. J Hydrometeorol 20(9):1887–1906. https://doi.org/10.1175/JHM-D-19-0042.1
Lin H, Mo M, Vitart F, Stan C (2019) Eastern Canada flooding 2017 and its Subseasonal predictions. Atmos-Ocean 57(3):195–207. https://doi.org/10.1080/07055900.2018.1547679
Liu S, Li W, Duan Q (2023) Spatiotemporal variations in precipitation forecasting skill of three global subseasonal prediction products over China. J Hydrometeorol 24(11):2075–2090. https://doi.org/10.1175/JHM-D-23-0071.1
MacLeod DA, Dankers R, Graham R, Guigma K, Jenkins L, Todd MC, Kiptum A, Kilavi M, Njogu A, Mwangi E (2021) Drivers and subseasonal predictability of heavy rainfall in equatorial East Africa and relationship with flood risk. J Hydrometeorol 22(4):887–903. https://doi.org/10.1175/JHM-D-20-0211.1
Mani NJJ, Lee Y, Waliser D, Wang B, Jiang X (2014) Predictability of the Madden–Julian oscillation in the Intraseasonal variability Hindcast Experiment (ISVHE). J Clim 27(12):4531–4543. https://doi.org/10.1175/JCLI-D-13-00624.1
Mansouri S, Masnadi-Shirazi MA, Golbahar-Haghighi S, Nazemosadat MJ (2021) An analogy toward the real-time multivariate MJO index to improve the estimation of the impacts of the MJO on the Precipitation variability over Iran in the Boreal Cold months. Asia Pac J Atmos Sci 57:207–222. https://doi.org/10.1007/s13143-020-00188-0
Moron V, Robertson AW (2020) Tropical rainfall subseasonal-to-seasonal predictability types. npj Clim Atmos Sci 3(4). https://doi.org/10.1038/s41612-020-0107-3
Murakami M (1979) Large-scale aspects of deep convective activity over the GATE data. Mon Wea Rev 107(8):994–1013. https://doi.org/10.1175/1520-0493(1979)107<0994:LSAODC>2.0.CO;2
Musonda B, Jing Y, Nyasulu M, Mumo L (2021) Evaluation of sub-seasonal to seasonal rainfall forecast over Zambia. J Earth Syst Sci 130:47. https://doi.org/10.1007/s12040-020-01548-0
Nazemosadat MJ, Cordery I (2000) On the relationships between ENSO and autumn rainfall in Iran. Int J Climatol 20(1):47–61. https://doi.org/10.1002/(SICI)1097-0088(200001)20:1<47::AID-JOC461>3.0.CO;2-P
Nazemosadat MJ, Ghaedamini H (2010) On the relationships between the Madden-Julian oscillation and precipitation variability in Southern Iran and the Arabian Peninsula: atmospheric circulation analysis. J Clim 23(4):887–904. https://doi.org/10.1175/2009JCLI2141.1
Nazemosadat MJ, Ghasemi AR (2004) Quantifying the ENSO-Related shifts in the Intensity and Probability of Drought and Wet periods in Iran. J Clim 17(20):4005–4018. https://doi.org/10.1175/1520-0442(2004)017<4005:QTESIT>2.0.CO;2
Nazemosadat MJ, Shahgholian K (2017) Heavy precipitation in the southwest of Iran: association with the Madden–Julian Oscillation and synoptic scale analysis. Clim Dyn 49:3091–3109. https://doi.org/10.1007/s00382-016-3496-6
Nazemosadat MJ, Shahgholian K, Ghaedamini H, Nazemosadat E (2020) Introducing new climate indices for identifying wet/dry spells within an Madden-Julian Oscillation phase. Int J Climatol 41. https://doi.org/10.1002/joc.6799
Nazemosadat MJ, Shahgholian K, Ghaedamini H (2023) The wet and dry spells within the MJO-phase 8 and the role of ENSO and IOD on the modulation of these spells: a regional to continental-scales analysis. Atmos Res 285:106631. https://doi.org/10.1016/j.atmosres.2023.106631
Pettitt AN A non-parametric approach to the change-point problem. J R, Stat Soc, Series C (1979) (Applied Statistics) 28:126–135. https://doi.org/10.2307/2346729
Pourasghar F, Tozuka T, Ghaemi H, Oettli P, Jahanbakhsh S, Yamagata T (2015) Influences of the MJO on intraseasonal rainfall variability over southern Iran. Atmos Sci Lett 16(2):110–118. https://doi.org/10.1002/asl2.531
Pourasghar F, Oliver ECJ, Holbrook NJ (2019) Modulation of wet-season rainfall over Iran by the Madden–Julian oscillation, Indian Ocean dipole and El Niño-southern oscillation. Int J Climatol 39(10):4029–4040. https://doi.org/10.1002/joc.6057
Robertson AW, Kumar A, Peña M, Vitart F (2014) Improving and promoting subseasonal to seasonal prediction. Bull Am Meteorol Soc 96:ES49–ES53. https://doi.org/10.1175/BAMS-D-14-00139.1
Slivinski LC, Compo GP, Whitaker JS, Sardeshmukh PD, Giese BS, McColl C, Allan R, Yin X, Vose R, Wyszyński P (2019) Towards a more reliable historical reanalysis: improvements for version 3 of the Twentieth Century Reanalysis system. Q J R Meteorol Soc 145(724):2876–2908. https://doi.org/10.1002/qj.3598
Specq D, Batté L (2020) Improving subseasonal precipitation forecasts through a statistical–dynamical approach: application to the southwest tropical Pacific. Clim Dyn 55:1913–1927. https://doi.org/10.1007/s00382-020-05355-7
Tennant W, Beare S (2014) New schemes to perturb sea-surface temperature and soil moisture content in MOGREPS. Q J R Meteorol Soc 140(681):1150–1160. https://doi.org/10.1002/qj.2202
Vaghefi SA, Keykhai M, Jahanbakhshi F, Sheikholeslami J, Ahmadi A, Yang H, Abbaspour KC (2019) The future of extreme climate in Iran. Sci Rep 9. https://doi.org/10.1038/s41598-018-38071-8
Vigaud N, Tippett MK, Robertson AW (2018) Probabilistic skill of subseasonal precipitation forecasts for the East Africa-West Asia sector during September-May. Wea Forecast 33(6):1513–1532. https://doi.org/10.1175/WAF-D-18-0074.1
Vigaud N, Tippett MK, Robertson AW (2019) Deterministic skill of subseasonal precipitation forecasts for the East Africa-West Asia sector from September to May. J Geophys Res Atmos 124(22):11887–11896. https://doi.org/10.1029/2019JD030747
Vitart F, Buizza R, Balmaseda MA, Balsamo G, Bidlot JR, Bonet A, Fuentes M, Hofstadler A, Molteni F, Palmer TN (2008) The new VarEPS-monthly forecasting system: a first step towards seamless prediction. Q J R Meteorol Soc 134(636):1789–1799. https://doi.org/10.1002/qj.322
Vitart F, Robertson AW, Anderson DLT (2012) Subseasonal to seasonal prediction project: bridging the gap between weather and climate. WMO Bull 61(2):23–28. https://public.wmo.int/en/resources/bulletin?tid-type-bulletin=258
Vitart F, Ardilouze C, Bonet A, Brookshaw A, Chen M, Codorean C, Déqué M, Ferranti L, Fucile E, Zhang L (2017) The subseasonal to seasonal (S2S) prediction project database. Bull Amer Meteorol Soc 98(1):163–173. https://doi.org/10.1175/bams-d-16-0017.1
Voldoire A, Sanchez-Gomez E, Salas y Mélia D, Decharme B, Cassou C, Sénési S, Valcke S, Beau I, Alias A, Chauvin F (2013) The CNRM-CM5,1 global climate model: description and basic evaluation. Clim Dyn 40:2091–2121. https://doi.org/10.1007/s00382-011-1259-y
Vuillaume JF, Dorji S, Komolafe A, Herath S (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
Waliser D (2011) Predictability and forecasting. Intraseasonal Variability of the Atmosphere-Ocean Climate System. Lau WKM, Waliser DE, Eds., Springer Praxis, 389–423. https://doi.org/10.1007/3-540-27250-X_12
Wheeler MC, Hendon HH (2004) An all-season real-time multivariate MJO index: development of an index for monitoring and prediction. Mon Weather Rev 132(8):1917–1932. https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2
White CJ, Franks SW, McEvoy D (2015) Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia. Proc Int Assoc Hydrol Sci 370:229–234. https://doi.org/10.5194/piahs-370-229-2015
White CJ, Carlsen H, Robertson AW, Klein RJT, Lazo JK, Kumar A, Vitart F, de Perez EC, Ray AJ, Zebiak SE (2017) Review potential applications of subseasonal-to-seasonal (S2S) predictions. Meteorol Appl 24(3):315–325. https://doi.org/10.1002/met.1654
Wilks DS (2019) Statistical Methods in the Atmospheric Sciences, 4rd eds. Ithaca, NY, USA
Zhou S, L’Heureux M, Weaver S, Kumar A (2011) A composite study of the MJO influence on the surface air temperature and precipitation over the Continental United States. Clim Dyn 38(7–8):1459–1471. https://doi.org/10.1007/s00382-011-1001-9
Acknowledgements
The authors thank the I.R of Iran Meteorology Organization (IRIMO) for providing the daily precipitation data. We are grateful to the ECMWF for providing the hindcasts from the S2S database. We also acknowledge the NOAA/CIRES/DOE 20th Century Reanalysis (V3) and NOAA OI SST V2 High-Resolution datasets provided by the NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov for providing the daily atmospheric variables and SST data that made this study possible. Also, we would like to express our gratitude to NOAA/CDR for supplying the daily OLR data.
Funding
The authors did not receive support from any organization for the submitted work.
Author information
Authors and Affiliations
Contributions
All authors, including HAG, MJN, and SM, contributed to the design and implementation of the research. HAG, MJN, and SM performed the analysis. HG wrote the manuscript’s first draft, and MJN and SM commented on previous versions. HG and MJN read and approved the final manuscript.
Ethics declarations
Ethical approval
The paper is part of the doctoral thesis and is not currently being considered for publication elsewhere. It reflects the author’s research and analysis truthfully and completely credit the meaningful contributions of the co-authors.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ghaedamini, H.A., Nazemosadat, M.J., Morid, S. et al. Comparing the S2S hindcast skills to forecast Iran’s precipitation and capturing climate drivers signals over the Middle East. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-04922-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00704-024-04922-w