MOS, Perfect Prog, and Reanalysis

Marzban, Caren; Sandgathe, Scott; Kalnay, Eugenia

doi:10.1175/mwr3088.1

Cited by 44 publications

(32 citation statements)

References 13 publications

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“…In this context, downscaling is very close to model post-processing or re-calibration methodssuch as the perfect prog method (Klein, 1971), model output statistics (Glahn and Lowry, 1972) or the automated forecasting method (Ghil et al, 1979) -in the sense that these approaches apply a similar class of statistical (Klein, 1971;Glahn and Lowry, 1972) or physics-based (Ghil et al, 1979) models (see below) to describe relationships between largescale situations and local-scale variables (Marzban et al, 2005;Friederichs and Hense, 2008).…”

Section: Bridging Time and Space Scalesmentioning

confidence: 99%

Extreme events: dynamics, statistics and prediction

Ghil

Yiou

Hallegatte

et al. 2011

Nonlin. Processes Geophys.

228

176

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Abstract. We review work on extreme events, their causes and consequences, by a group of European and American researchers involved in a three-year project on these topics. The review covers theoretical aspects of time series analysis and of extreme value theory, as well as of the deterministic modeling of extreme events, via continuous and discrete dynamic models. The applications include climatic, seismic and socio-economic events, along with their prediction.Two important results refer to (i) the complementarity of spectral analysis of a time series in terms of the continuous and the discrete part of its power spectrum; and (ii) the need for coupled modeling of natural and socio-economic systems. Both these results have implications for the study and prediction of natural hazards and their human impacts.

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Section: Bridging Time and Space Scalesmentioning

confidence: 99%

Extreme events: dynamics, statistics and prediction

Ghil

Yiou

Hallegatte

et al. 2011

Nonlin. Processes Geophys.

228

176

View full text Add to dashboard Cite

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“…However, they still have considerable biases [ Christensen et al , ; Herrera et al , ; Turco et al , ] which are typically adjusted in practical applications using a variety of Model Output Statistics (MOS) methods. Formally, in the MOS downscaling approach, the target variable (e.g., precipitation) simulated by the RCM is directly corrected against the available local‐scale observations using appropriate statistical techniques [ Marzban et al , ; Maraun et al , ; Ruiz‐Ramos et al , ].…”

Section: Introductionmentioning

confidence: 99%

Bias correction and downscaling of future RCM precipitation projections using a MOS‐Analog technique

et al. 2017

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In this study we assess the suitability of a recently introduced analog‐based Model Output Statistics (MOS) downscaling method (referred to as MOS‐Analog) for climate change studies and compare the results with a quantile mapping bias correction method. To this aim, we focus on Spain and consider daily precipitation output from an ensemble of Regional Climate Models provided by the ENSEMBLES project. The reanalysis‐driven Regional Climate Model (RCM) data provide the historical data (with day‐to‐day correspondence with observations induced by the forcing boundary conditions) to conduct the analog search of the control (20C3M) and future (A1B) global climate model (GCM)‐driven RCM values. First, we show that the MOS‐Analog method outperforms the raw RCM output in the control 20C3M scenario (period 1971–2000) for all considered regions and precipitation indices, although for the worst‐performing models the method is less effective. Second, we show that the MOS‐Analog method broadly preserves the original RCM climate change signal for different future periods (2011–2040, 2041–2070, 2071–2100), except for those indices related to extreme precipitation. This could be explained by the limitation of the analog method to extrapolate unobserved precipitation records. These results suggest that the MOS‐Analog is a spatially consistent alternative to standard bias correction methods, although the limitation for extreme values should be taken with caution in cases where this aspect is relevant for the problem.

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“…In this manner, the initial condition is tuned to have the lowest possible error to help keep it closer to the solution that could be represented by the model. (See Daley 1991or Kalnay 2005 for more discussion. )…”

Section: Prefacementioning

confidence: 99%