Search Results

You are looking at 11 - 20 of 3,109 items for :

  • Forecasting techniques x
  • Journal of Climate x
  • All content x
Clear All
Timothy DelSole and Arindam Banerjee

compute the eigenvectors using the Green’s function technique of DelSole and Tippett (2015) . The resulting eigenfunctions are orthogonal with respect to an area-weighted norm and normalized to unit-area-weighted norm. The leading Laplacian eigenvectors in the North Pacific are shown in Fig. 1 . The first eigenfunction, not shown, is merely a constant and corresponds to the mean over the North Pacific basin. The second and third eigenfunctions measure the east–west and north–south gradients across

Full access
Terry C. K. Lee, Francis W. Zwiers, Xuebin Zhang, and Min Tsao

forecast large-scale climate change on decadal time scales. Given their potential applications, many of which would involve some type of hedging, it is desirable that any decadal-scale forecast should be probabilistic rather than deterministic. One approach for obtaining such forecasts would be to produce large ensembles of forced climate simulations that would then be interpreted in a probabilistic manner, either directly or after some type of postprocessing to adjust for model biases. Unfortunately

Full access
Timothy DelSole

. Wea. Forecasting , 9 , 457 – 465 . Yun , W. T. , L. Stefanova , and T. N. Krishnamurti , 2003 : Improvement of the multimodel superensemble technique for seasonal forecasts. J. Climate , 16 , 3834 – 3840 . APPENDIX Asymptotics of R:MM+R This appendix shows that (26) approaches (24) in the limit as λ →∞. First, note that the matrix is idempotent , which means that 𝗛 2 = 𝗛. By invoking standard properties of idempotent matrices, it can be shown that 𝗛 has rank K − 1, and

Full access
Nipa Phojanamongkolkij, Seiji Kato, Bruce A. Wielicki, Patrick C. Taylor, and Martin G. Mlynczak

series using the variance reduction technique ( Ross 2006 ). The average of the simulation solutions has to be an unbiased estimator of its corresponding truth. This average value changes as the number of simulations M increases. We evaluate the sensitivity of using different numbers of simulations to simulation error (the standard deviation of the average). We would expect simulation error to decrease as the number of simulations increases. Simulation error generally can be calculated from The

Full access
Gerd Bürger

realistic models) dynamical evolutions of the phenomenon in question (here BSISO) that are not captured by the index. For the heavily smoothed OMI and PII indices this involves forecast data well beyond the subseasonal range, which are often not available. Following Kikuchi et al. (2012) , Kiladis et al. (2014) and Wang (2020) therefore derive real-time variants of OMI and PII by employing a special windowing technique that is tapered to each respective target date. This real-time index is then

Restricted access
Á. G. Muñoz, L. Goddard, S. J. Mason, and A. W. Robertson

1. Introduction Extreme events are difficult to forecast, but many locations of the world exhibit some regional predictability of seasonal amount and frequency of extreme precipitation that is still useful for decision-making. The impacts of extreme rainfall events are of key socioeconomic importance for southeast South America (SESA; Muñoz et al. 2015 ; Bettolli et al. 2009 ; Mechoso et al. 2001 ), especially for the rainy season. The skill of seasonal rainfall forecasts in this part of the

Full access
Stefan Siegert, David B. Stephenson, Philip G. Sansom, Adam A. Scaife, Rosie Eade, and Alberto Arribas

the basis for commonly used postprocessing techniques in seasonal forecasting (e.g., Feddersen et al. 1999 ). In section 3f , we will compare the simple linear regression approach with a fully Bayesian posterior predictive approach that accounts for parameter uncertainty. Eade et al. (2014) use the relation between signal-plus-noise interpretation and linear regression in their postprocessing technique for the ensemble mean and then adjust the distribution of the ensemble members around the

Full access
David Chapman, Mark A. Cane, Naomi Henderson, Dong Eun Lee, and Chen Chen

implement a VAR model. The VAR is a classical time series analysis technique that carries with it a substantial body of literature, particularly for financial and economic forecasting ( Yule 1927 ; Zellner 1962 ; Stock and Watson 2001 ; Box et al. 2008 ). Furthermore, being less general than EMR, it is a conceptually simpler model with a more compact definition. 2. VAR model The VAR( L ) model is a linear–stochastic time series model for causal stationary processes. The model is “vector” in that it

Full access
Philip G. Sansom, Christopher A. T. Ferro, David B. Stephenson, Lisa Goddard, and Simon J. Mason

-validation procedure for comparing different recalibration methods and training periods; section 4 applies the methodology developed in the previous sections to data from the CMIP5 near-term experiments; and section 5 discusses how widely the conclusions of this analysis might be expected to apply and how the methodology can be extended. 2. Recalibrating probability forecasts This study is concerned with techniques for obtaining calibrated probability forecasts of a climate variable y τ observed at time τ

Full access
Umberto Triacca, Antonello Pasini, Alessandro Attanasio, Alessandro Giovannelli, and Marco Lippi

of the weighting of several influences on the recent global temperature time series by means of multilinear regressions (see, e.g., Lean and Rind 2008 ; Lean 2010 ; Foster and Rahmstorf 2011 ). This led to the development of empirical models that allowed the identification of the components of temperature variability due to various factors in in-sample investigations. Here, instead, the focus is on the predictive capabilities of GHGs and ENSO and the use of empirical forecast models (to be

Full access