• Sumant Nigam, Professor
  • Alfredo Ruiz-Barradas, Assoc. Professor
  • Agniv Sengupta, Graduate Student




    Department of Atmospheric and Oceanic Science
    3419 Atlantic Bldg. (224)
    4254 Stadium Drive
    University of Maryland, College Park, MD 20742, USA
    Voice:(301) 405-5381 (5391); Fax:(301) 314-9482
    Email: nigam@umd.edu

  • The Laboratory for Experimental Hydroclimate Prediction seeks to develop subseasonal-to-seasonal forecasts of regional variations in precipitation, surface air temperature, and soil moisture using influential climate system components with large thermal inertia as predictors.

    A distinctive feature of the laboratory's prediction strategy is its statistical approach, rooted in innovative spatiotemporal analysis of the observational record. The deployed strategy is complementary to the commonly pursued dynamical prediction paradigm where similar influences find forecast expression from initialized integrations of the atmospheric and oceanic general circulation models. The skill of statistical forecasts provides an important evaluative benchmark for dynamical forecasting. It is hoped that statistical forecasts generated by the laboratory will be more skillful than previous ones, upping the ante for dynamical forecasting of regional hydroclimate variations.

    Upper ocean temperatures, in particular, meet the criterion of an influential climate system component with large thermal inertia but reliable long-term observations are available mostly at the surface. The influence of sea surface temperature (SST) on regional and faraway hydroclimate is efficiently mined in this prediction effort.


    South Asian Summer Monsoon Rainfall Forecasts

    Press Release on the 2018 Forecast

    Enabling SST Analysis: Seasonal SST anomalies in the 20S-70N global domain were analyzed in the 1900-2015 period using the Extended Empirical Orthogonal Functional (E-EOF) technique (Weare and Nasstrom 1982). The E-EOFs were rotated using the Varimax criterion (Kaiser 1958). A multi-season sampling window was used to identify spatiotemporal recurrence as in Guan and Nigam 2008 (hereafter GN2008). The Hadley Centre Sea Ice and Sea Surface Temperature data (HadISST 1.1; Rayner et al. 2003), available globally at monthly, 1 degree resolution, was analyzed. The physicality of the extracted variability modes was assessed using marine productivity and observational analog counts (as in GN2008). The North Pacific and Bering Sea recruitment records obtained from Hare and Mantua (2000) and the International Pacific Halibut Commission were used to assess the physicality of the decadal modes in the Pacific. The optimal sampling window-width and number of rotated modes was determined from sensitivity analysis reported in Sengupta et al. (2016) who have updated and refined the GN2008 analysis. The E-EOF technique yields, among others, the nonstationary Secular Trend, Pacific and Atlantic decadal variability modes, and ENSO, all without any advance filtering (and potential aliasing) of the SST record. The Indian Ocean Dipole does not emerge as an independent mode, consistent with Zhao and Nigam (2015).

    Statistical Forecast of Summer Monsoon Rainfall: The summer monsoon rainfall forecast is developed from the spatiotemporal structure of the antecedent multi-season SST anomalies. The anomalies were projected on the SST loading vectors, generating the SST principal components (PC) for the central season. As the multi-season SST anomaly is centered several seasons prior to the forecast period (summer), the obtained SST PCs were multiplied by their multi-season-lagged seasonal rainfall regressions, generating the summer (JJA) monsoon rainfall forecast. But for the lagged aspect, the strategy is similar to that deployed in drought reconstruction (Nigam et al. 2011). The rainfall forecast for September is similarly obtained, and the suitably weighted JJA and September forecasts are used in generating the final, interim and initial versions of the June-September forecasts (weblinks provided below). Three forecasts are developed from regressions of different analyses of historical rainfall (GPCC, CRU and Aphrodite); the ensemble forecast with respect to a 50-year climatology (1951-2000) is shown in the right panel of Figure 1. The GPCC-based forecast relative to the TRMM period climatology (1998-2013) is shown in the left panel of Figure 1, to facilitate forecast verification against the TRMM 3b42v7 rainfall anomalies.

    In June 2018, the University of Maryland began forecasting both the seasonal and monthly rainfall departures using the same method, while being aware that the SST-based monthly forecasts are often less reliable than the seasonal one from their significant exposure to intraseasonal variability.


    2018 Forecast of June-September averaged Rainfall Anomaly




  • INITIAL (ver.1a): The Initial forecast is based on antecedent SST anomalies extending up to March; issued on 8 April 2018

  • INTERIM (ver.1b): The Interim forecast based on antecedent SST anomalies extending up to April; issued on 9 May 2018

  • FINAL (ver.1c): The Final forecast is based on antecedent SST anomalies extending up to May; issued on 6 June 2018


    2017 Forecast & Verification of June-September averaged Rainfall Anomaly

    2016 Forecast & Verification of June-September averaged Rainfall Anomaly

  • References
  • Becker, A., and co-authors, 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present. Earth Syst. Sci. Data, 5, 71-99.
  • Guan, B., and S. Nigam, 2008: Pacific sea surface temperatures in the twentieth century: An evolution-centric analysis of variability and trend. Journal of Climate, 21, 2790-2809.
  • Hare, S. R., and N. J. Mantua, 2000: Empirical evidence for North Pacific regime shifts in 1977 and 1989. Prog. Oceanogr., 47, 103-145.
  • Harris, I., P.D. Jones, T.J. Osborn and D.H. Lister, 2014: Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset, International Journal of Climatology, 34, 623-642.
  • Kaiser, H. F., 1958: Varimax criterion for analytic rotations in factor analysis. Psychometrika, 412, 23, 187-200.
  • Nigam, S., B. Guan, and A. Ruiz-Barradas, 2011: Key role of fthe Atlantic Multidecadal Oscillation in 20th century drought and wet periods over the Great Plains. Geophysical Research Letters, 38.
  • Rayner, N. A., and co-authors, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, doi:10.1029/2002JD002670
  • Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer, M. Ziese, and B. Rudolf, 2013: GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theoretical and Applied Climatology, 115, 15-40.
  • Sengupta, A., S. Nigam, and A. Ruiz-Barradas, 2018: Evolution-centric analysis of global sea surface temperature variability from 1900-2015 factoring for inter-basin links. (Manuscript in internal revision).
  • Weare, B., and J. Nasstrom, 1982: Examples of extended empirical orthogonal function analyses. Monthly Weather Review, 110, 481-485.
  • Yatagai, A., and co-authors, 2012: APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bulletin of the American Meteorological Society, 93(9), 1401-1415.
  • Zhao, Y., and S. Nigam, 2015: The Indian Ocean Dipole: A Monopole in SST. J. Climate, 28, 3-19.


  • Acknowledgements: Sumant Nigam, Agniv Sengupta, and Alfredo Ruiz-Barradas thank the US National Science Foundation for supporting the investigation of seasonal predictability of the South Asian summer monsoon rainfall, via Grant AGS1439940. Agniv Sengupta also thanks India's National Monsoon Mission for partially supporting his graduate studies and research at the University of Maryland.