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forecast), can then be computed. The analysis increment is relevant where there are sufficient observations. In regions of few observations, the analysis increment will be small because unless the observations are very accurate, there will not be much difference between the model first guess and the analysis. The data assimilation (DA) system itself has been used to monitor observations and data quality control ( Hollingsworth et al. 1986 ) by computing statistics involving observations, such as
forecast), can then be computed. The analysis increment is relevant where there are sufficient observations. In regions of few observations, the analysis increment will be small because unless the observations are very accurate, there will not be much difference between the model first guess and the analysis. The data assimilation (DA) system itself has been used to monitor observations and data quality control ( Hollingsworth et al. 1986 ) by computing statistics involving observations, such as
1. Introduction Operational numerical weather (or climate) predictions deteriorate as a function of lead time because of the presence of modeling and initial condition errors. To partly correct this decrease of skill postprocessors are commonly used based on (linear or nonlinear) statistical methods (see, e.g., Casaioli et al. 2003 ; Kalnay 2003 ; Marzban 2003 ; Wilks 2006 ). These are usually referred to as model output statistics (MOS) techniques. One of the most popular approaches
1. Introduction Operational numerical weather (or climate) predictions deteriorate as a function of lead time because of the presence of modeling and initial condition errors. To partly correct this decrease of skill postprocessors are commonly used based on (linear or nonlinear) statistical methods (see, e.g., Casaioli et al. 2003 ; Kalnay 2003 ; Marzban 2003 ; Wilks 2006 ). These are usually referred to as model output statistics (MOS) techniques. One of the most popular approaches
et al. 2001 ; Newman and Sardeshmukh 2008 ), and the competitiveness of week-2 and week-3 linear forecast models with comprehensive numerical weather prediction (NWP) models ( Winkler et al. 2001 ; Newman et al. 2003 ). Even on the time scales of daily weather, linear stochastically forced (LSF) models of the form (1) , although not as accurate as NWP models for daily predictions, are realistic enough to capture many features of the second-order statistics of observed synoptic variability
et al. 2001 ; Newman and Sardeshmukh 2008 ), and the competitiveness of week-2 and week-3 linear forecast models with comprehensive numerical weather prediction (NWP) models ( Winkler et al. 2001 ; Newman et al. 2003 ). Even on the time scales of daily weather, linear stochastically forced (LSF) models of the form (1) , although not as accurate as NWP models for daily predictions, are realistic enough to capture many features of the second-order statistics of observed synoptic variability
(likely owing to the scarcity of moisture near the troposphere), so we cannot analyze the range between 300 and 100 hPa for this variable. Petoukhov et al. (2008) looked at this variable. b. Methodology: Non-Gaussian statistics 1) Higher statistical moments In statistics, distributions can be described quantitatively by the central moments, or moments about the mean. The central moments are calculated using the anomalies from a mean state of a time series. Therefore, the first central moment
(likely owing to the scarcity of moisture near the troposphere), so we cannot analyze the range between 300 and 100 hPa for this variable. Petoukhov et al. (2008) looked at this variable. b. Methodology: Non-Gaussian statistics 1) Higher statistical moments In statistics, distributions can be described quantitatively by the central moments, or moments about the mean. The central moments are calculated using the anomalies from a mean state of a time series. Therefore, the first central moment
1. Introduction A major challenge for the climate community is to provide information that decision makers can directly apply to reduce vulnerability to climate risk. While probabilistic forecasts of seasonal mean quantities (such as precipitation and surface temperature over the conterminous United States) have proven utility, they do not address questions relating to the specific character of the daily weather statistics within the season. User requests for products that expand beyond
1. Introduction A major challenge for the climate community is to provide information that decision makers can directly apply to reduce vulnerability to climate risk. While probabilistic forecasts of seasonal mean quantities (such as precipitation and surface temperature over the conterminous United States) have proven utility, they do not address questions relating to the specific character of the daily weather statistics within the season. User requests for products that expand beyond
equivalent to a succession of random triangle storms. This type of equivalence defines the probabilistic structure of the ETS model, which depends on wave data only via the observed significant wave height exceedance P ( h ) = Pr( H s > h ) and the conditional average duration b ( a ) = B | A = a , both estimated via regression. Then, the estimates of wave extremes and their associated statistics simply follow from the density p A with no need for data fitting. In particular, Boccotti (2000
equivalent to a succession of random triangle storms. This type of equivalence defines the probabilistic structure of the ETS model, which depends on wave data only via the observed significant wave height exceedance P ( h ) = Pr( H s > h ) and the conditional average duration b ( a ) = B | A = a , both estimated via regression. Then, the estimates of wave extremes and their associated statistics simply follow from the density p A with no need for data fitting. In particular, Boccotti (2000
and energy transport processes relative to the total cloud amount and basic statistics on the vertical velocities in these clouds are lacking. The representation of fair-weather cumuli clouds in numerical models is challenging because of the scale of these clouds and their intimate involvement with small-scale processes in the boundary layer, which are also difficult to parameterize. The vertical transport due to cumulus clouds in large-scale models is often parameterized using a mass
and energy transport processes relative to the total cloud amount and basic statistics on the vertical velocities in these clouds are lacking. The representation of fair-weather cumuli clouds in numerical models is challenging because of the scale of these clouds and their intimate involvement with small-scale processes in the boundary layer, which are also difficult to parameterize. The vertical transport due to cumulus clouds in large-scale models is often parameterized using a mass
1. Introduction The statistics of wave crest elevation are fundamental to the design of both deep-water offshore structures and shallow-water coastal structures. In the case of fixed structures, deck elevations are typically set to maintain an effective air gap, thereby preventing the impact of the largest wave crests on the underside of the structure. In addition, individual members must be designed to support the applied loads, with the maximum drag forces arising beneath the largest wave
1. Introduction The statistics of wave crest elevation are fundamental to the design of both deep-water offshore structures and shallow-water coastal structures. In the case of fixed structures, deck elevations are typically set to maintain an effective air gap, thereby preventing the impact of the largest wave crests on the underside of the structure. In addition, individual members must be designed to support the applied loads, with the maximum drag forces arising beneath the largest wave
statistics (EMOS) or nonhomogeneous Gaussian regression ( Gneiting et al. 2005 ; Thorarinsdottir and Gneiting 2010 ). The EMOS technique transforms a raw ensemble forecast into a predictive probability density function, and simultaneously corrects for biases and dispersion errors. EMOS methods have been developed for temperature and surface pressure ( Gneiting et al. 2005 ; Hagedorn et al. 2008 ; Kann et al. 2009 ), where the predictive density is normal and the method is often referred to as
statistics (EMOS) or nonhomogeneous Gaussian regression ( Gneiting et al. 2005 ; Thorarinsdottir and Gneiting 2010 ). The EMOS technique transforms a raw ensemble forecast into a predictive probability density function, and simultaneously corrects for biases and dispersion errors. EMOS methods have been developed for temperature and surface pressure ( Gneiting et al. 2005 ; Hagedorn et al. 2008 ; Kann et al. 2009 ), where the predictive density is normal and the method is often referred to as
Satellite Observations (CALIPSO; Winker et al. 2009a ) project in April 2006 have allowed the scientific community to investigate global statistics of optically thin cirrus cloud optical properties to the limit of signal attenuation. Ground-based and airborne elastic backscatter lidar measurements are essential for improving the accuracy of knowledge about cloud optical properties, such as extinction-to-backscatter ratio, from current space-based lidar systems. The extinction-to-backscatter ratio, also
Satellite Observations (CALIPSO; Winker et al. 2009a ) project in April 2006 have allowed the scientific community to investigate global statistics of optically thin cirrus cloud optical properties to the limit of signal attenuation. Ground-based and airborne elastic backscatter lidar measurements are essential for improving the accuracy of knowledge about cloud optical properties, such as extinction-to-backscatter ratio, from current space-based lidar systems. The extinction-to-backscatter ratio, also
