Search Results

You are looking at 1 - 10 of 38,317 items for :

  • Error analysis x
  • All content x
Clear All
James L. Hench and Johanna H. Rosman

-contained, none of these supplemental navigation corrections are available. In this paper, we summarize the results of a set of field tests in which the Cobra-Tac was used to map out known paths. The tests show that navigation error results primarily from heading-dependent error in the Cobra-Tac internal compass. An analysis of this error is presented and a postprocessing correction method is proposed that significantly improves Cobra-Tac positional records. Finally, errors before and after the correction are

Full access
Ross N. Hoffman

. 2009 ). These methods are suboptimal, but the impacts on analysis errors can be mitigated with careful tuning. However, the “optimal” tuning for one data sample may not be optimal for another since the true error statistics vary in space and time. A number of authors have described various approaches to thinning, including Purser et al. (2000) , Ochotta et al. (2005) , Ramachandran et al. (2005) , and Lazarus et al. (2010) , and its impact on DA, including Dando et al. (2007) , Li et al

Full access
Jean-François Caron, M. K. Yau, and Stéphane Laroche

1. Introduction The development of tangent linear model (TLM) and its adjoint in numerical weather prediction makes it possible to calculate corrections to the initial conditions that improve the accuracy of short- to medium-range forecasts (e.g., Klinker et al. 1998 ; Pu et al. 1997 ; Gelaro et al. 1998 ). One such technique implemented at the Canadian Meteorological Centre (CMC; see Laroche et al. 2002 ) is the key analysis error algorithm ( Klinker et al. 1998 ). Until recently, it was

Full access
Jean-Marie Beckers, Alexander Barth, Charles Troupin, and Aida Alvera-Azcárate

1. Introduction Spatial analysis of observations, also called gridding, is a common task in oceanography and meteorology, and a series of methods and implementations exists and is widely used. Here N d data points of values d i , i = 1, …, N d at location ( x i , y i ) are generally distributed unevenly in space. Furthermore, the values of d i are affected by observational errors, including representativity errors. From this dataset an analysis on a regular grid is often desired. It

Full access
Simona Ecaterina Ştefănescu, Loïk Berre, and Margarida Belo Pereira

proposed by Houtekamer et al. (1996) . This method relies on the time evolution of some perturbations that are constructed to be consistent with the involved error contributions. There are three basic steps or components that can be involved in the time evolution. First, the analysis scheme is applied to some perturbed observations and to a perturbed background, which provides a perturbed analysis: this simulates the effect of the two information errors (namely, the observation errors and the

Full access
Yanwu Zhang, James G. Bellingham, and Yi Chao

literature, some considerations for flux experiment designs are based on correlation scales of the flux variable, but we have not seen a rigorous error analysis on the flux. In Cuny et al. (2005) , the authors used an array of moorings to estimate the volume, freshwater, and heat fluxes across Davis Strait. They calculated the correlations of temperature, salinity, and current velocity between adjacent moorings. Based on the low correlation values, they determined that the moorings were too sparse to

Full access
Laura Kanofsky and Phillip Chilson

of reflectivity can cause range location errors (due to a signal processing artifact) and developed a technique to correct for these errors. It should be noted that a treatment of error analysis can be found in Atlas et al. (1973) . The work presented by Atlas et al. (1973) , however, focuses on the error in the DSD due to a given error in vertical air motion w. The work presented here examines the error in rainfall rate due to vertical air motion w, different fall speed relationships

Full access
V. N. Bringi, M. A. Rico-Ramirez, and M. Thurai

was estimated from the gauge data. Following Habib and Krajewski (2002) , an error variance separation analysis is used to explain the proportion of variance of the radar–gauge differences that could be attributed to the point-to-area variance of the gauges. The “radar error,” as defined by Habib and Krajewski (2002) , is also calculated along with estimates of the parameterization and measurement errors to see how much of the radar error can be explained by the aforementioned errors. This is

Full access
Yiwen Mei, Emmanouil N. Anagnostou, Efthymios I. Nikolopoulos, and Marco Borga

of East Africa, respectively. They showed that the products are consistent with rainfall estimated from the ground-based gauge networks at a coarse resolution (i.e., 2.5° × 2.5° grid cell at monthly interval). Although the topic of satellite rainfall error analysis has been investigated globally for more than two decades ( Anagnostou et al. 2010 ; Anagnostou 2004 ; Petty and Krajewski 1996 ; Arkin and Ardanuy 1989 ), only a small portion of these studies have focused on the error structure of

Full access
Katie Lean and Roger W. Saunders

Resolution Radiometer (AVHRR) SSTs ( Kilpatrick et al. 2001 ). However, the ARC SSTs are only linked indirectly to in situ data through their use in an SST analysis used as one of the NWP parameters in the cloud detection scheme ( Merchant et al. 2012 ). This project therefore has the potential to provide a virtually independent check on estimates of the rate of warming of the ocean surface covering recent decades. To verify the accuracy (bias, random error, and stability) of the ARC data, validation was

Full access