Evaluation of the NCEP Global Forecast System at the ARM SGP Site

Yang, Fanlin; Pan, Hua-Lu; Krueger, Steven K.; Moorthi, Shrinivas; Lord, Stephen J.

doi:10.1175/mwr3264.1

Cited by 124 publications

(76 citation statements)

References 38 publications

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“…These include an interactive sea ice model, assimilation of satellite radiances, increased resolution in the atmospheric model, and most importantly, coupling between the atmosphere, ocean, and land surface models. The Global Forecast System (GFS) of Yang et al [2006] constitutes the atmospheric model, which has 38 km horizontal resolution and 64 layers extending from the surface to the 0.26 hPa level. The Geophysical Fluid Dynamic Lab's Modular Ocean Model (MOM) version 4 describes the ocean circulation at 0.25-0.5 resolution with 40 levels extending to 4737 m depth.…”

Section: Climate Forecast System Reanalysis and Preprocessingmentioning

confidence: 99%

Periodicity and patterns of ocean wind and wave climate

Stopa

Cheung

2014

JGR Oceans

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The Climate Forecast System Reanalysis (CFSR) provides a wealth of information spanning 1979-2009 for investigation of ocean wind and wave climate. Preprocessing of the data is necessary to remove the dominant seasonal signals and to create time series of semimonthly averaged wind speed and significant wave height over a 0.5 global grid. We perform an empirical orthogonal function (EOF) analysis to extract the dominant space-time patterns. The results for the three major ocean basins show strong zonal structures in the winds and saturation of the swells corroborating prior works with various data sets. We reexamine the CFSR data in the frequency domain to identify periodic signals associated with published climate indices. The Fourier transform of the preprocessed time series generates spectra ranging from 1 month to 15 years period for an EOF analysis. The results demonstrate the spatial structures and periods of climate phenomena. The Arctic Oscillation dominates the Atlantic basin with a broad range of intra-annual signals off the European coasts. The Indian and Pacific Oceans are strongly influenced by inter-annual cycles of the El Niño Southern Oscillation (ENSO) and the Antarctica Oscillation. The Indian Ocean also has strong intra-annual components ranging from 50 to 80 days period. The ENSO proves to be a ubiquitous signal around the globe, and in particular, saturates the Pacific with strong influences in the equatorial region and the Southern Hemisphere Westerlies. A commonality of all basins is that the magnitude and the spatial structure of the intra-annual and inter-annual signals are similar suggesting a wide range of periods in each of the climate cycles examined.

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Section: Climate Forecast System Reanalysis and Preprocessingmentioning

confidence: 99%

Periodicity and patterns of ocean wind and wave climate

Stopa

Cheung

2014

JGR Oceans

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“…These observations have been used to evaluate a number of numerical weather prediction (NWP) models (e.g. Mace et al, 1998;Hinkelman et al, 1999;Hogan et al, 2001;Morcrette, 2002;Guichard et al, 2003;Sengupta et al, 2004;Yang et al, 2006;Illingworth et al, 2007;Hogan et al, 2009;Bouniol et al, 2010;Paquin-Ricard et al, 2010). In the work presented here, we sample four months and compare short-range forecasts with ground-based observations of liquid and ice cloud fraction at five locations.…”

Section: Introductionmentioning

confidence: 99%

“…These metrics include: mean cloud amount (e.g. Hogan et al, 2001;Yang et al, 2006;Illingworth et al, 2007), cloud frequency of occurrence (e.g. Mace et al, 1998;Hinkelman et al, 1999;Hogan et al, 2001Hogan et al, , 2009Bouniol et al, 2010), cloud fraction histograms (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of two cloud parametrization schemes using ARM and Cloud‐Net observations

Morcrette

O’Connor

Petch

2011

Quart J Royal Meteoro Soc

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† The contribution of these authors was written in the course of their employment at the Met Office, UK, and is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.Ground-based remote-sensing observations from Atmospheric Radiation Measurement (ARM) and Cloud-Net sites are used to evaluate the clouds predicted by a weather forecasting and climate model. By evaluating the cloud predictions using separate measures for the errors in frequency of occurrence, amount when present, and timing, we provide a detailed assessment of the model performance, which is relevant to weather and climate time-scales. Importantly, this methodology will be of great use when attempting to develop a cloud parametrization scheme, as it provides a clearer picture of the current deficiencies in the predicted clouds.Using the Met Office Unified Model, it is shown that when cloud fractions produced by a diagnostic and a prognostic cloud scheme are compared, the prognostic cloud scheme shows improvements to the biases in frequency of occurrence of low, medium and high cloud and to the frequency distributions of cloud amount when cloud is present. The mean cloud profiles are generally improved, although it is shown that in some cases the diagnostic scheme produced misleadingly good mean profiles as a result of compensating errors in frequency of occurrence and amount when present. Some biases remain when using the prognostic scheme, notably the underprediction of mean ice cloud fraction due to the amount when present being too low, and the overprediction of mean liquid cloud fraction due to the frequency of occurrence being too high.

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“…This input included maximum winds and storm positions at 6-h intervals from the NHC best track (Jarvinen et al 1984) and predictors from Reynolds SST (Reynolds et al 2002) and the NCEP Global Forecasting System (GFS) model analyses (Yang et al 2006 Fig. 1, but for negative .…”

Section: Preliminary Forecast Resultsmentioning

confidence: 99%

A Simplified Dynamical System for Tropical Cyclone Intensity Prediction

DeMaria

2009

Monthly Weather Review

114

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A simplified dynamical system for tropical cyclone intensity prediction based on a logistic growth equation (LGE) is developed. The time tendency of the maximum sustained surface winds is proportional to the sum of two terms: a growth term and a term that limits the maximum wind to an upper bound. The maximum wind evolution over land is determined by an empirical inland wind decay formula. The LGE contains four free parameters, which are the time-dependent growth rate and maximum potential intensity (MPI), and two constants that determine how quickly the intensity relaxes toward the MPI. The MPI is estimated from an empirical formula as a function of sea surface temperature and storm translational speed. The adjoint of the LGE provides a method for finding the other three free parameters to make the predictions as close as possible to the National Hurricane Center best-track intensities. The growth rate is assumed to be a linear function of the vertical shear (S), a convective instability parameter (C) determined from an entraining plume, and their product, where both S and C use global model fields as input. This assumption reduces the parameter estimation problem to the selection of six constants. Results show that the LGE optimized for the full life cycle of individual storms can very accurately simulate the intensity variations out to as long as 15 days. For intensity prediction, single values of the six constants are found by fitting the model to more than 2400 Atlantic forecasts from 2001 to 2006. Results show that the observed intensity variations can be fit more accurately with the LGE than with the linear Statistical Hurricane Intensity Prediction Scheme (SHIPS) formulation, and with a much smaller number of constants. Results also show that LGE model solution (and some properties of real storms) can be explained by the evolution in the two-dimensional S-C phase space. Forecast and other applications of the LGE model are discussed.

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Evaluation of the NCEP Global Forecast System at the ARM SGP Site

Cited by 124 publications

References 38 publications

Periodicity and patterns of ocean wind and wave climate

Periodicity and patterns of ocean wind and wave climate

Evaluation of two cloud parametrization schemes using ARM and Cloud‐Net observations

A Simplified Dynamical System for Tropical Cyclone Intensity Prediction

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