Search Results
–frontal interactions by removing terrain within a model simulation ( Wang et al. 2005 ; Colle et al. 2002 , among others); however, defining the portion of atmosphere below ground is often arbitrary and can be inconsistent with the real atmosphere above. An idealized initialization removes this problem, since the initialized atmosphere is consistent down to sea level. Many early studies of idealized baroclinic waves used a small-amplitude disturbance of normal-mode form ( Simmons and Hoskins 1976 , 1978
–frontal interactions by removing terrain within a model simulation ( Wang et al. 2005 ; Colle et al. 2002 , among others); however, defining the portion of atmosphere below ground is often arbitrary and can be inconsistent with the real atmosphere above. An idealized initialization removes this problem, since the initialized atmosphere is consistent down to sea level. Many early studies of idealized baroclinic waves used a small-amplitude disturbance of normal-mode form ( Simmons and Hoskins 1976 , 1978
; Houtekamer and Mitchell 1998 ; Miller et al. 1999 ; Anderson and Anderson 1999 ; Mitchell and Houtekamer 2000 ; Hamill and Snyder 2000 ; Anderson 2001 ; Mitchell et al. 2002 ; Whitaker and Hamill 2002 ; Reichle et al. 2002 ; Snyder and Zhang 2003 ; Dowell et al. 2004 ; Zhang et al. 2004 ; Hacker and Snyder 2005 ; Houtekamer et al. 2005 ; Tong and Xue 2005 ; Zhang et al. 2006 ). The present study is aimed at investigating the relative roles of uncertainties in initial conditions and model
; Houtekamer and Mitchell 1998 ; Miller et al. 1999 ; Anderson and Anderson 1999 ; Mitchell and Houtekamer 2000 ; Hamill and Snyder 2000 ; Anderson 2001 ; Mitchell et al. 2002 ; Whitaker and Hamill 2002 ; Reichle et al. 2002 ; Snyder and Zhang 2003 ; Dowell et al. 2004 ; Zhang et al. 2004 ; Hacker and Snyder 2005 ; Houtekamer et al. 2005 ; Tong and Xue 2005 ; Zhang et al. 2006 ). The present study is aimed at investigating the relative roles of uncertainties in initial conditions and model
1. Introduction Creating skillful decadal climate predictions represents a major challenge ( Meehl et al. 2009 ) because decadal prediction lies at the intersection of initial-value problems (as in seasonal forecasts) and boundary-value problems (as in long-term climate projections). To predict near-term climate changes, we have to capture both the externally forced and the internally generated climate signals. For this, the coupled climate models need to be initialized from the best estimate
1. Introduction Creating skillful decadal climate predictions represents a major challenge ( Meehl et al. 2009 ) because decadal prediction lies at the intersection of initial-value problems (as in seasonal forecasts) and boundary-value problems (as in long-term climate projections). To predict near-term climate changes, we have to capture both the externally forced and the internally generated climate signals. For this, the coupled climate models need to be initialized from the best estimate
environment. The evaluation is conducted by comparing the effects on model performance of aggregated to distributed initial conditions and forcing data. 2. Study basin and observations The selected study area was Granger Basin (60°31′N, 135°07′W) which is part of Wolf Creek Research Basin situated 15 km south of Whitehorse, Yukon Territory, Canada ( Fig. 1 ). Granger Basin, drained by Granger Creek, is located in the mountainous headwaters of the Yukon River basin and compromises a drainage area about 8
environment. The evaluation is conducted by comparing the effects on model performance of aggregated to distributed initial conditions and forcing data. 2. Study basin and observations The selected study area was Granger Basin (60°31′N, 135°07′W) which is part of Wolf Creek Research Basin situated 15 km south of Whitehorse, Yukon Territory, Canada ( Fig. 1 ). Granger Basin, drained by Granger Creek, is located in the mountainous headwaters of the Yukon River basin and compromises a drainage area about 8
) and potentially impact storm intensity. For a coupled TC prediction model to accurately forecast intensity evolution, these oceanographic features must be properly initialized in the ocean model with respect to both location and the properties of the water contained within them. Inaccurate initialization of vertical T and S profiles within these features degrades the OML evolution and SST cooling in the ocean model. Data-assimilative ocean nowcast products developed for the Global Ocean Data
) and potentially impact storm intensity. For a coupled TC prediction model to accurately forecast intensity evolution, these oceanographic features must be properly initialized in the ocean model with respect to both location and the properties of the water contained within them. Inaccurate initialization of vertical T and S profiles within these features degrades the OML evolution and SST cooling in the ocean model. Data-assimilative ocean nowcast products developed for the Global Ocean Data
advancements in observations and the rapid increase in computing resources. However, numerical TC forecasts still suffer from considerable errors in both track and intensity due to uncertainties in model physics and initial conditions. One way to reduce TC forecast errors is to improve TC forecast models. There have been a number of efforts in recent years to improve these models in terms of physical processes related to surface flux under TC conditions ( Emanuel 2003 ; Donelan et al. 2004 ; Moon et al
advancements in observations and the rapid increase in computing resources. However, numerical TC forecasts still suffer from considerable errors in both track and intensity due to uncertainties in model physics and initial conditions. One way to reduce TC forecast errors is to improve TC forecast models. There have been a number of efforts in recent years to improve these models in terms of physical processes related to surface flux under TC conditions ( Emanuel 2003 ; Donelan et al. 2004 ; Moon et al
initialization method to the then-current CGCM3.1(T63) model version. This approach, similar to that of Keenlyside et al. (2005) , relaxed model SSTs to observation-based time series during a multiyear period preceding each forecast. Although far from optimal, this method did initialize the crucial equatorial Pacific region with some skill, leading to reasonably skillful El Niño–Southern Oscillation (ENSO) forecasts and global skills competitive with those of the HFP2 system despite the smaller ensemble
initialization method to the then-current CGCM3.1(T63) model version. This approach, similar to that of Keenlyside et al. (2005) , relaxed model SSTs to observation-based time series during a multiyear period preceding each forecast. Although far from optimal, this method did initialize the crucial equatorial Pacific region with some skill, leading to reasonably skillful El Niño–Southern Oscillation (ENSO) forecasts and global skills competitive with those of the HFP2 system despite the smaller ensemble
1. Introduction It is widely admitted that there are two main reasons why predictions in meteorology are limited in time: (i) the amplification in the course of the evolution of small uncertainties in the initial conditions used in a prediction scheme, usually referred to as initial errors , and (ii) the presence of model errors , reflecting the fact that a model is only an approximate representation of nature. While the first kind of error is indicative of the property of sensitivity to the
1. Introduction It is widely admitted that there are two main reasons why predictions in meteorology are limited in time: (i) the amplification in the course of the evolution of small uncertainties in the initial conditions used in a prediction scheme, usually referred to as initial errors , and (ii) the presence of model errors , reflecting the fact that a model is only an approximate representation of nature. While the first kind of error is indicative of the property of sensitivity to the
modeling-based studies have examined the interactions between the land surface and the atmosphere and the sensitivities as a result of variations in the initial land surface properties. Smith et al. (1994) demonstrated the feasibility of obtaining realistic initial soil moisture fields for regional mesoscale model runs of the fifth generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) by running a 1D hydrologic model offline for 4.5 months, forced by
modeling-based studies have examined the interactions between the land surface and the atmosphere and the sensitivities as a result of variations in the initial land surface properties. Smith et al. (1994) demonstrated the feasibility of obtaining realistic initial soil moisture fields for regional mesoscale model runs of the fifth generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) by running a 1D hydrologic model offline for 4.5 months, forced by
1. Introduction The performance of tropical cyclone (TC) prediction relies heavily on model initial conditions. Uncertainties exist in the initial conditions of forecast models in spite of the use of comprehensive data assimilation methods with various observations to improve initial analyses. Over the open ocean where TCs form and develop, the observations, mostly from satellites, are typically scarce and do not provide sufficient information to resolve the TC core structure in global
1. Introduction The performance of tropical cyclone (TC) prediction relies heavily on model initial conditions. Uncertainties exist in the initial conditions of forecast models in spite of the use of comprehensive data assimilation methods with various observations to improve initial analyses. Over the open ocean where TCs form and develop, the observations, mostly from satellites, are typically scarce and do not provide sufficient information to resolve the TC core structure in global
