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Michael A. Hollan and Brian C. Ancell

; Weisman et al. 2008 ). Another very important advancement in NWP is the use of ensembles. Ensemble forecasting is a probabilistic technique designed to account for unavoidable errors encountered in NWP (e.g., in the initial conditions, or model physics), aiming to quantify the inherent uncertainties involved with atmospheric prediction ( Leutbecher and Palmer 2008 ). By using an ensemble of forecasts that are all slightly altered by perturbed initial conditions, for example, one can simulate a wide

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Paolo Reggiani and Oleksiy Boyko

1. Introduction A wide series of forecasting applications require forcing hydrological or reservoir models by means of numerical weather predictions. Examples include river stage and flow forecasting, coastal flood forecasting, as well as irrigation or reservoir operations. Predictions of meteorological forcing variables are inherently uncertain due to the internal structure of specific atmospheric models, the high nonlinearity of the underlying physical processes, as well as the selection and

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Lawrence L. Takacs, Max J. Suárez, and Ricardo Todling

not included in the original run, or to test, inexpensively, how model changes affect forecasts, albeit without feedback from the analysis. At NASA’s Global Modeling and Assimilation Office (GMAO) the means of running the model from preexisting analyses is referred to as “replay.” The GMAO replay is fundamentally dependent on IAU-based strategies used for initialization. When the replay strategy was initially applied by W. Putman (2017, personal communication) in an attempt to downscale the MERRA

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Elizabeth Satterfield and Istvan Szunyogh

varying adaptive covariance inflation technique, such as described in Anderson (2009) or a localized version of Li et al. (2009) may lead to an improvement of the analyses and the short-term ensemble forecasts. Fig . 7. The time average of the ratio d k in the leading direction for the temperature at 850 hPa. Results are shown for (left) the analysis time and (right) the 5-day forecast for experiments that assimilate (top) randomly distributed simulated observations, (middle) simulated

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Thomas M. Hamill and Michael Scheuerer

Ensemble Forecast System (GEFS) was skillful, and POPs were skillful and also reliable, there were several reasons to consider further modifications to the procedure. First, the postprocessing algorithm of H17 combined information from all potential prediction systems at an early stage of the processing, forming a superensemble of quantile-mapped amounts. Such a procedure, especially applying data-informed weighting techniques discussed below, would be challenging if the size of the ensemble varied

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Xiaohao Qin and Mu Mu

. Many objective adaptive techniques have been used to identify sensitive regions. The singular vectors (SVs; Palmer et al. 1998 ; Buizza and Montani 1999 ) and the ensemble transform Kalman filter (ETKF; Bishop et al. 2001 ) techniques are two broadly utilized methods for field programs in the midlatitudes. The SV technique was used by the European Centre for Medium-Range Weather Forecasts (ECMWF), the U.S. Naval Research Laboratory (NRL), and Météo-France for the Fronts and Atlantic Storm Track

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F. Anthony Eckel and Luca Delle Monache

distinct simulation of a possible atmospheric flow within a forecast period. The dispersion deficiencies of the NWP ensemble can be ameliorated via postprocessing calibration, which is performed in this study using the logistic regression technique. The AnEn starts from only one possible flow (albeit at higher resolution) for the forecast period and then relies on the analogs to reveal the error growth. Multiple possible flows are discovered, and thus the variable error growth, by linking the single

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Justin R. Davis, Vladimir A. Paramygin, David Forrest, and Y. Peter Sheng

WW3), which are currently using the ensemble parameters discussed herein. Background on how probabilistic ensembles are created within the SCOOP program is presented in section 2 , followed by how these methods are optimized for a limited-resource environment in section 3 . Implications for how this priority system affects forecast surge and inundation products for Hurricane Charley is presented in section 4 . Related efforts focusing on individual aspects of the forecasting techniques and

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John Bjørnar Bremnes

several spline configurations. To further increase robustness forecasts from these could potentially be combined. Averaging forecasts generated by splines with one and two interior knots gave approximately 0.1% improvement relative to the former. Thus, only minor improvements on the overall performance could be expected. The bootstrap is another averaging technique which is applied in many situations to reduce estimation uncertainty and thereby also increasing the forecast skill. A test with 50

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Constantin Junk, Luca Delle Monache, and Stefano Alessandrini

quantiles from the cumulative distribution function at levels for ( Gneiting et al. 2005 ). c. Analog ensemble An analog ensemble can be estimated using a set of M past verifying measurements that correspond to the M past forecasts (analogs), which are most similar to a current forecast. While Hamill and Whitaker (2006) used the analog ensemble as a technique to calibrate an existing ensemble forecast, Delle Monache et al. (2013b) proposed the analog ensemble as a mean to generate an

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