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, plans call for the SERCAA model to replace the current U.S. Air Force (USAF) operational cloud analysis model, the Real-Time Nephanalysis (RTNEPH), in the summer of 2002. In the coming years, archived cloud analyses from this model will offer an alternative global cloud record for climate studies. As such, it is important to document and describe the retrieval technique in order to better understand and use its cloud products. The RTNEPH operates on two-channel data, generally obtained from Defense
, plans call for the SERCAA model to replace the current U.S. Air Force (USAF) operational cloud analysis model, the Real-Time Nephanalysis (RTNEPH), in the summer of 2002. In the coming years, archived cloud analyses from this model will offer an alternative global cloud record for climate studies. As such, it is important to document and describe the retrieval technique in order to better understand and use its cloud products. The RTNEPH operates on two-channel data, generally obtained from Defense
one singular event, rather it is the culmination of a sustained investment in research and development of numerical models, observations, and data assimilation techniques for providing improved initial states in those models and, above all, multiple media sources for forecast dissemination across the region. Until 2003 forecast methods were subjective at the India Meteorological Department (IMD), with the exception of the aging, coarse-resolution quasi-Lagrangian model (QLM) providing track
one singular event, rather it is the culmination of a sustained investment in research and development of numerical models, observations, and data assimilation techniques for providing improved initial states in those models and, above all, multiple media sources for forecast dissemination across the region. Until 2003 forecast methods were subjective at the India Meteorological Department (IMD), with the exception of the aging, coarse-resolution quasi-Lagrangian model (QLM) providing track
Interannual Prediction (EUROSIP; http://cosmos.enes.org/uploads/media/TStockdale.pdf ) project, which is anticipated to further improve seasonal climate prediction skill and more importantly produce robust estimates of seasonal forecast uncertainty. Much of hydrological forecasting is based on empirical methods, like linear regression, which use initial conditions and information on future climate conditions as predictors ( Rosenberg et al. 2011 ; Pagano et al. 2009 ). However, since the introduction of
Interannual Prediction (EUROSIP; http://cosmos.enes.org/uploads/media/TStockdale.pdf ) project, which is anticipated to further improve seasonal climate prediction skill and more importantly produce robust estimates of seasonal forecast uncertainty. Much of hydrological forecasting is based on empirical methods, like linear regression, which use initial conditions and information on future climate conditions as predictors ( Rosenberg et al. 2011 ; Pagano et al. 2009 ). However, since the introduction of
forecasters, since out of the four frequencies imaged, it alone had sufficient resolution to support high quality images. In particular, these images allow forecasters to locate the cloud-covered eyes or centers of low-level circulations that cannot be detected otherwise. In 85-GHz images the primary signature is the depression of brightness temperature (Tb) caused by ice scattering within deep convection and precipitating anvil clouds ( Spencer et al., 1989 ). Images of lower frequencies are dominated by
forecasters, since out of the four frequencies imaged, it alone had sufficient resolution to support high quality images. In particular, these images allow forecasters to locate the cloud-covered eyes or centers of low-level circulations that cannot be detected otherwise. In 85-GHz images the primary signature is the depression of brightness temperature (Tb) caused by ice scattering within deep convection and precipitating anvil clouds ( Spencer et al., 1989 ). Images of lower frequencies are dominated by
Press, 996 pp . Tang , Z. , B. A. Engel , B. C. Pijanowski , and K. J. Lim , 2005 : Forecasting land use change and its environmental impact at a watershed scale . J. Environ. Manage. , 76 , 35 – 45 . USGS , cited 2009 : Daily streamflow for the nation . U.S. Geological Survey. [Available online at http://waterdata.usgs.gov/nwis/inventory/?site_no=02349500 .] Viger , R. J. , and G. H. Leavesley , 2007 : The GIS Weasel user’s manual . U.S. Geological Survey Techniques and
Press, 996 pp . Tang , Z. , B. A. Engel , B. C. Pijanowski , and K. J. Lim , 2005 : Forecasting land use change and its environmental impact at a watershed scale . J. Environ. Manage. , 76 , 35 – 45 . USGS , cited 2009 : Daily streamflow for the nation . U.S. Geological Survey. [Available online at http://waterdata.usgs.gov/nwis/inventory/?site_no=02349500 .] Viger , R. J. , and G. H. Leavesley , 2007 : The GIS Weasel user’s manual . U.S. Geological Survey Techniques and
is used, there may not be enough statistical power to draw many conclusions about temporal and spatial patterns ( Doswell 2007 ). Most researchers employ spatial smoothing techniques to overcome some of the problems associated with an incomplete and inconsistent dataset; however, there is no clear “best” shape or size for smoothing methods ( Dixon and Mercer 2012 ; Marsh and Brooks 2012 ). The variety of decisions made by researchers suggests that there is also no singular best method for
is used, there may not be enough statistical power to draw many conclusions about temporal and spatial patterns ( Doswell 2007 ). Most researchers employ spatial smoothing techniques to overcome some of the problems associated with an incomplete and inconsistent dataset; however, there is no clear “best” shape or size for smoothing methods ( Dixon and Mercer 2012 ; Marsh and Brooks 2012 ). The variety of decisions made by researchers suggests that there is also no singular best method for
discharge for each year in each simulation. For a particular simulation period, this results in an m × n matrix containing the ensemble of model forecasts, representing m years of annual maximum daily discharges (11 years) and n GCM–scenario combinations (15 members). A log transformation of the annual maximum discharges was used to ensure that discharges generated by the resampling approach would be nonnegative. Thus, the term x i , j represents the natural log of the maximum daily
discharge for each year in each simulation. For a particular simulation period, this results in an m × n matrix containing the ensemble of model forecasts, representing m years of annual maximum daily discharges (11 years) and n GCM–scenario combinations (15 members). A log transformation of the annual maximum discharges was used to ensure that discharges generated by the resampling approach would be nonnegative. Thus, the term x i , j represents the natural log of the maximum daily
vegetation status ( Ji and Peters 2004 ; Jolly et al. 2005 ), scaling-up from species and vegetation communities to ecosystems ( Lin and Dugarsuren 2015 ), and use of more advanced modeling techniques ( Ji and Peters 2004 ). Phenology is influenced by a range of environmental variables. The increased dimensionality of data space leads to a series of problems such as multicollinearity, numerical instability, and overfitting, referred to as the “curse of dimensionality.” The environment
vegetation status ( Ji and Peters 2004 ; Jolly et al. 2005 ), scaling-up from species and vegetation communities to ecosystems ( Lin and Dugarsuren 2015 ), and use of more advanced modeling techniques ( Ji and Peters 2004 ). Phenology is influenced by a range of environmental variables. The increased dimensionality of data space leads to a series of problems such as multicollinearity, numerical instability, and overfitting, referred to as the “curse of dimensionality.” The environment
showed that mesoscale gravity wave signals could possibly be identified and tracked in near–real time using routinely disseminated 20-min interval observation data and existing automated surface observing site (ASOS) density. Such availability of data could allow severe weather forecasters to use either EOF filtering or bandpass filtering to monitor convection-induced or possibly convection-triggering gravity waves. The EOF filtering technique clearly has potential uses in other areas of mesoscale
showed that mesoscale gravity wave signals could possibly be identified and tracked in near–real time using routinely disseminated 20-min interval observation data and existing automated surface observing site (ASOS) density. Such availability of data could allow severe weather forecasters to use either EOF filtering or bandpass filtering to monitor convection-induced or possibly convection-triggering gravity waves. The EOF filtering technique clearly has potential uses in other areas of mesoscale
atmospheric data assimilation and forecasts, ocean reanalysis fields, and coupled climate model projections. The analysis covers thermodynamic and kinematic advection patterns contributed by the atmosphere and the background marine climate governed by the ocean. The intensity of convection in the eastern Antilles region is quantified in the period 24–25 December 2013 using 4-km Geostationary Operational Environmental Satellite (GOES) infrared cloud temperatures at 30-min interval, 25-km multisatellite
atmospheric data assimilation and forecasts, ocean reanalysis fields, and coupled climate model projections. The analysis covers thermodynamic and kinematic advection patterns contributed by the atmosphere and the background marine climate governed by the ocean. The intensity of convection in the eastern Antilles region is quantified in the period 24–25 December 2013 using 4-km Geostationary Operational Environmental Satellite (GOES) infrared cloud temperatures at 30-min interval, 25-km multisatellite
