Theoretical and Applied Climatology

, Volume 132, Issue 1–2, pp 181–207 | Cite as

An intercomparison of observational precipitation data sets over Northwest India during winter

  • M. M. Nageswararao
  • U. C. Mohanty
  • S. S. V. S. Ramakrishna
  • A. P. Dimri
Original Paper

Abstract

Winter (DJF) precipitation over Northwest India (NWI) is very important for the cultivation of Rabi crops. Thus, an accurate estimation of high-resolution observations, evaluation of high-resolution numerical models, and understanding the local variability trends are essential. The objective of this study is to verify the quality of a new high spatial resolution (0.25° × 0.25°) gridded daily precipitation data set of India Meteorological Department (IMD1) over NWI during winter. An intercomparison with four existing precipitation data sets at 0.5° × 0.5° of IMD (IMD2), 1° × 1° of IMD (IMD3), 0.25° × 0.25° of APHRODITE (APRD1), and 0.5° × 0.5° of APHRODITE (APRD1) resolution during a common period of 1971–2003 is done. The evaluation of data quality of these five data sets against available 26 station observations is carried out, and the results clearly indicate that all the five data sets reasonably agreed with the station observation. However, the errors are relatively more in all the five data sets over Jammu and Kashmir-related four stations (Srinagar, Drass, Banihal top, and Dawar), while these errors are less in the other stations. It may be due to the lack of station observations over the region. The quality of IMD1 data set over NWI for winter precipitation is reasonably well than the other data sets. The intercomparison analysis suggests that the climatological mean, interannual variability, and coefficient of variation from IMD1 are similar with other data sets. Further, the analysis extended to the India meteorological subdivisions over the region. This analysis indicates overestimation in IMD3 and underestimation in APRD1 and APRD2 over Jammu and Kashmir, Himachal Pradesh, and NWI as a whole, whereas IMD2 is closer to IMD1. Moreover, all the five data sets are highly correlated (>0.5) among them at 99.9% confidence level for all subdivisions. It is remarkably noticed that multicategorical (light precipitation, moderate precipitation, heavy precipitation, and very heavy precipitation) skill score of accuracy (>0.8) for the four data sets against IMD1 is good for all the subdivisions as well as NWI and is more in IMD2. IMD1 performs well in capturing the relationships of winter precipitation with climate indices such as Nino 3.4 region sea surface temperature, Southern Oscillation Index, Arctic Oscillation, and North Atlantic Oscillation. The results conclude that IMD1 is useful to understand the variability trends at the local climate scale and its global teleconnections.

Notes

Acknowledgements

This research has been conducted as part of the project entitled “Development and Application of Extended Range Forecast System for Climate Risk Management in Agriculture Phase II” at IIT Bhubaneswar sponsored by the Department of Agriculture and Cooperation, Government of India. We are thankful to Dr. B. S. L. Vidhyadhari, Assistant Professor, NIMS, Utkal University, Bhubaneswar, and Mr. P. Praveen, School of Earth Ocean and Climate Sciences (SEOCS), IIT Bhubaneswar, for providing support for this study. We are also thankful to the National Data Center, IMD Pune, and the Climate Data Guide, Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) for providing the gridded precipitation data sets used for study. We are also thankful to the anonymous reviewers for their valuable comments and suggestions for improving the quality of the manuscript.

References

  1. Agnihotri CL, Singh MS (1982) Satellite study of western disturbances. Mausam 33:249–254Google Scholar
  2. Bhaskara Rao NS, Morey PE (1971) Cloud systems associated with western disturbances—a preliminary study. Indian J Meteorol Geophys 22:413–420Google Scholar
  3. Collins M, AchutaRao K, Ashok K, Bhandari S, Mitra AK, Prakash S, Srivasatva R, Turner A (2013) Observational challenges in evaluating climate models. Nat Clim Chang 3(11):940–941. doi: 10.1038/nclimate2012 CrossRefGoogle Scholar
  4. Dimri AP (2013) Relationship between ENSO phases with Northwest India winter precipitation. Int J Clim 33(8):1917–1923CrossRefGoogle Scholar
  5. Dimri AP, Mohanty UC (2009) Simulation of mesoscale features associated with intense western disturbances over western Himalayas. Meteorol Appl 16:289–308CrossRefGoogle Scholar
  6. Dimri AP, Niyogi D, Barros AP, Ridley J, Mohant UC, Yasunari T (2015) Western disturbances: a review. Rev Geophys 53:225–246. doi: 10.1002/2014RG000460 CrossRefGoogle Scholar
  7. Dutta RK, Gupta MG (1967) Synoptic study of the formation and movement of western depressions. Indian J MeteorolGeophys 18:45–50Google Scholar
  8. Ebert EE, Janowiak JE, Kidd C (2007) Comparison of near real–time precipitation estimates from satellite observations and numerical models. Bull Am Meteorol Soc 88:47–64. doi: 10.1175/BAMS-88-1-47 CrossRefGoogle Scholar
  9. Ghajarnia N, Liaghat A, Daneshkar Arasteh P (2015) Comparison and evaluation of high–resolution precipitation estimation products in Urmia Basin–Iran. Atmos Res 158–159:50–65. doi: 10.1016/j.atmosres.2015.02.010 CrossRefGoogle Scholar
  10. Han K, Lee C, Lee J, Kim J, Song C (2011) A comparison study between model–predicted and OMI–retrieved tropospheric NO2 columns over the Korean peninsula. Atmos Environ 45:2962–2971CrossRefGoogle Scholar
  11. Hartman DL, Michelsen ML (1989) Interaseasonal periodicities in Indian rainfall. J Atmos Sci 46:2838–2862CrossRefGoogle Scholar
  12. Heidke P (1926) Calculation of the success and goodness of strong wind forecasts in the storm warning service. Geogr Ann Stockholm 8:301–349Google Scholar
  13. Hoskins B (2013) The potential for skill across the range of the seamless weather–climate prediction problem: a stimulus for our science. Q J R Meteorol Soc 139:573–584. doi: 10.1002/qj.1991 CrossRefGoogle Scholar
  14. Jayaraman T (2011) Climate change and agriculture: a review article with special reference to India. Rev Agrar Stud 1 (2) (at available: http://www.ras.org.in/climate_change_and_agriculture)
  15. Krishna kumar K, Rajagopalan B, Cane MA (1999) On the weakening relationship between Indian monsoon and ENSO. Science 284:2156–2159. doi: 10.1126/science.284.5423.2156 CrossRefGoogle Scholar
  16. Krishna Kumar K, Rupa Kumar K, Ashrit RG, Deshpade NR, Hansen JW (2004) Climate impact on India agriculture. Int J Climatol 24:1375–1393. doi: 10.1002/joc.1081 CrossRefGoogle Scholar
  17. Krishnamurthy V, Shukla J (2000) Intra–seasonal and inter–annual variability of rainfall over India. J Clim 13:4366–4377. doi: 10.1175/1520-0442(2000)013<0001:IAIVOR>2.0.CO;2 CrossRefGoogle Scholar
  18. Krishnamurthy V, Shukla J (2007) Intra–seasonal and seasonally persisting patterns of Indian monsoon rainfall. J Clim 20:3–20. doi: 10.1175/JCLI3981.1 CrossRefGoogle Scholar
  19. Krishnamurthy V, Shukla J (2008) Seasonal persistence and propagation of intra–seasonal patterns over the Indian summer monsoon region. Clim Dyn 30:353–369. doi: 10.1007/s00382-007-0300-7 CrossRefGoogle Scholar
  20. Kumar P, Rupakumar K, Rajeevan M, Sahai AK (2007) On the recent strengthening of the relationship between ENSO and northeast monsoon rainfall over South Asia. Clim Dyn 28(6):649–660. doi: 10.1007/s00382-006-0210-0 CrossRefGoogle Scholar
  21. Mason IB (2003) Binary events. In: Jolliffe IT, Stephenson DB (eds) Forecast verification – a practitioner’s guide in a tmospheric science. Wiley, Hoboken 240 ppGoogle Scholar
  22. Mitra AK, Rajagopal EN, Iyengar GR, Mohapatra DK, Momin IM, Gera A, Sharma K, George JP, Ashrit R, Dasgupta M, Mohandas S, Prasad VS, Basu S, Arribas A, Milton SF, Martin GM, Barker D, Martin M (2013) Prediction of monsoon using a seamless coupled modeling system. Curr Sci 104(10):1369–1379Google Scholar
  23. Mooley DA (1957) The role of western disturbances in the production of weather over India during different seasons. Ind J Met Geophy 8:253–260Google Scholar
  24. Mooley DA, Parthasarathy B (1984) Indian summer monsoon and east equatorial Pacific SST. Atmosphere-Ocean V22:23–35CrossRefGoogle Scholar
  25. Murphy AH, Winkler RL (1987) A general framework for forecast verification. Mon Weather Rev 115:1330–1338. doi: 10.1175/1520-0493 (1987)115<1330: AGFFFV>2.0.CO; 2 CrossRefGoogle Scholar
  26. Murphy AH, Brown BG, Chen YS (1989) Diagnostic verification of temperature forecasts. Weather Forecast 4:485–501CrossRefGoogle Scholar
  27. Nageswararao MM, Mohanty UC, Kiran Prasad S, Osuri KK, Ramakrishna SSVS (2015a) Performance evaluation of NCEP climate forecast system for the prediction of winter temperatures over India. Theor Appl Climatol 121(3):1–15. doi: 10.1007/s00704-015-1588-6 Google Scholar
  28. Nageswararao MM, Mohanty UC, Nair A, Ramakrishna SSVS (2015b) Comparative evaluation of performances of two versions of NCEP climate forecast system in predicting winter precipitation over India. Pure Appl Geophys. doi: 10.1007/s00024-015-1219-2 Google Scholar
  29. Nageswararao MM, Mohanty UC, Osuri KK, Ramakrishna SSVS (2015c) Prediction of winter precipitation over Northwest India using ocean heat fluxes. Clim Dyn. doi: 10.1007/s00382-015-2962-x Google Scholar
  30. Nageswararao MM, Mohanty UC, Ramakrishna SSVS, Nair A, Kiran Prasad S (2016a) Characteristics of winter precipitation over Northwest India using high-resolution gridded dataset (1901–2013). Glob Planet Chang 147:67–85. doi: 10.1016/j.gloplacha.2016.10.017 CrossRefGoogle Scholar
  31. Nageswararao MM, Dhekale BS, Mohanty UC (2016b) Impact of climate variability on various Rabi crops over Northwest India. Theor Appl Climatol. doi: 10.1007/s00704-016-1991-7 Google Scholar
  32. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models, part I – a discussion of principles. J Hydrol 10:282–290. doi: 10.1016/0022-1694(70)90255-6 CrossRefGoogle Scholar
  33. Pai DS, Sridhar Latha, Rajeevan M, Sreejith OP, Satbhai NS, Mukhopadhyay B (2014a) Development of a new high spatial resolution (0.25° × 0.25°) long period (1901–2010) daily gridded rainfall dataset over India and its comparison with existing datasets over the region. Mausam 65(1):1–18Google Scholar
  34. Pai DS, Sridhar Latha, Badwaik MR, Rajeevan M (2014b) Analysis of the daily rainfall events over India using a new long period (1901–2010) high–resolution (0.25° × 0.25°) gridded rainfall dataset. Clim Dyn. doi: 10.1007/s00382-014-2307-1 Google Scholar
  35. Pal RK, Murty NS, Rao MMN (2012) The response of wheat to temperatures as simulated with CERES–wheat model in Tarai region. J Agrometeorol 14(2):163–166Google Scholar
  36. Pant GB, Parthasarathy B (1981) Some aspects of the association between southern oscillation and Indian summer monsoon. Arch Meteorology Geophysics Book 229:245–252CrossRefGoogle Scholar
  37. Parthasarathy B, Mooley DA (1978) Some features of a long homogenous series of Indian summer monsoon rainfall. MonWeaRev 106:771–781Google Scholar
  38. Pisharoty PR, Desai BN (1956) Western disturbances and Indian weather. Indian J Meteor Geophys 8:333–338Google Scholar
  39. Rajeevan M, Bhate J (2009) A high–resolution daily gridded rainfall dataset (1971–2005) for mesoscale meteorological studies. Curr Sci 96(4):558–562Google Scholar
  40. Rajeevan M, Bhate J, Kale JD, Lal B (2006) High resolution daily gridded rainfall data for the Indian region: analysis of break and active monsoon spells. Curr Sci 91(3):296–306Google Scholar
  41. Rajeevan M, Bhate J, Jaswal AK (2008) Correction to analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data. Geophys Res Lett 35:L23701. doi: 10.1029/ GL036105 CrossRefGoogle Scholar
  42. Satya Prakash, Mitra AK, Imran MM, Rajagopal EN, Basu S, Collins M, Turner AG, Achuta Rao K, Ashok K (2014) Seasonal inter–comparison of observational rainfall datasets over India during the southwest monsoon season. Int J Climatol. doi: 10.1002/joc.4129 Google Scholar
  43. Savage NH, Agnew P, Davis LS, Ordóñez C, Thorpe R, Johnson CE, O’Connor FM, Dalvi M (2013) Air quality modelling using the Met Office unified model (AQUM OS24–26): model description and initial evaluation. Geosci Model Dev 6:353–372. doi: 10.5194/gmd-6-353-2013 CrossRefGoogle Scholar
  44. Shepard D (1968) A two–dimensional interpolation function for irregularly spaced data. Proc. 1968 ACM Nat. Conf, 517–524Google Scholar
  45. Singh MS (1963) Upper air circulation associated with western disturbance. Indian J. Meteorol. Geophys. 1:156–172Google Scholar
  46. Singh MS (1979) Westerly upper air troughs and development of western depression over India. Mausam 30(4):405–414Google Scholar
  47. Singh MS, Kumar S (1977) Study of western disturbances. Indian J Meteorol Hydrol Geophys 28(2):233–242Google Scholar
  48. Slingo J, Palmer T (2011) Uncertainty in weather and climate prediction. Philos Trans R Soc A 369:4751–4767. doi: 10.1098/rsta.2011.0161 CrossRefGoogle Scholar
  49. Sorooshian S, AghaKouchak A, Arkin P, Eylander J, Foufoula-Georgiou E, Harmon R, Hendrickx JMH, Imam B, Kuligowski R, Skahill B, Skofronick-Jackson G (2011) Advancing concepts of remote sensing of precipitation at multiple scales. Bull Am Meteorol Soc 92(10):1353–1357. doi: 10.1175/2011BAMS3158.1 CrossRefGoogle Scholar
  50. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106:7183–7192CrossRefGoogle Scholar
  51. Thompson DWJ, Wallace JM (2001) Regional climate impacts of the northern hemisphere annular mode. Science 293:85–89. doi: 10.1126/science.1058958 CrossRefGoogle Scholar
  52. Walker GT (1910) On the meteorological evidence for supposed changes of climate in India. Indian Meteor Memo 21(Part –I):1–21Google Scholar
  53. Wallace JM (2000) North Atlantic oscillation/northern hemisphere annular mode: two paradigms—one phenomenon. Q J R Meteorol Soc 126:791–805. doi: 10.1256/smsqj.56401 CrossRefGoogle Scholar
  54. Webster PJ, Yang S (1992) Monsoon and ENSO: selectively interactive systems. Quart J Roy Meteor Soc 118:877–926. doi: 10.1002/qj.49711850705 CrossRefGoogle Scholar
  55. Wilks DS (2011) Statistical methods in the atmospheric sciences, 3rd edn. Academic Press, OxfordGoogle Scholar
  56. Willmot CJ (1981) On the validation of models. Phys Geogr 2:184–194Google Scholar
  57. Willmott CJ, Rowe CM, Mintz Y (1985) Climatology of the terrestrial seasonal water cycle. J Climatol 5(6):589–606Google Scholar
  58. Xie P, Yatagai A, Chen M, Hayasaka Fukushima Y, Liu C, Yang S (2007) A gauge–based analysis of daily precipitation over East Asia. J Hydrol 8:607–627. doi: 10.1175/JHM583.1 Google Scholar
  59. Yadav RK, Rupa Kumar K, Rajeevan M (2009) Increasing influence of ENSO and decreasing influence of AO/NAO in the recent decades over Northwest India winter precipitation. J Geophys Res 114:D12112CrossRefGoogle Scholar
  60. Yadav RK, Yoo JH, Kucharski F, Abid MA (2010) Why is ENSO influencing Northwest India winter precipitation in recent decades? J Clim 23:1979–1993. doi: 10.1175/2009JCLI3202.1 CrossRefGoogle Scholar
  61. Yadav RK, Rupa Kumar K, Rajeevan M (2012) Characteristic features of winter precipitation and its variability over Northwest India. J Earth Syst Sci 121(3):611–623CrossRefGoogle Scholar
  62. Yatagai A, Kenji K, Osamu A, Atsushi H, Natsuko Y, Kitoh A (2012) APHRODITE: constructing a long–term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull Amer Meteor Soc 93:1401–1415. doi: 10.1175/BAMS-D-11-00122.1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2017

Authors and Affiliations

  • M. M. Nageswararao
    • 1
    • 2
  • U. C. Mohanty
    • 1
  • S. S. V. S. Ramakrishna
    • 2
  • A. P. Dimri
    • 3
  1. 1.School of Earth, Ocean and Climate Sciences (SEOCS)Indian Institute of Technology (IIT) BhubaneswarBhubaneswarIndia
  2. 2.Department of Meteorology and OceanographyAndhra UniversityVisakhapatnamIndia
  3. 3.School of Environmental SciencesJawaharlal Nehru UniversityNew DelhiIndia

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