Atmospheric Modeling, Data Assimilation and Predictability by Eugenia Kalnay

By Eugenia Kalnay

This accomplished textual content and reference paintings on numerical climate prediction covers for the 1st time, not just equipment for numerical modeling, but in addition the $64000 comparable parts of knowledge assimilation and predictability. It accommodates all elements of environmental laptop modeling together with an ancient evaluation of the topic, equations of movement and their approximations, a contemporary and transparent description of numerical tools, and the selection of preliminary stipulations utilizing climate observations (an very important new technological know-how referred to as facts assimilation).

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New planetary boundary layer (PBL) based on nonlocal diffusion (Hong and Pan, 1996). 2. ) Year Operational model 5 Nov 1997 New observational error statistics. ). Improved physics. 5 days). Improved physics. 3D ozone data assimilation and forecast. Nonlinear increments in 3D-Var. Resolution reduced to T62/28levels on Oct. 1998 and upgraded back in Jan. 2000 Ensemble resolution increased to T126 for the first 60 h Tropical cyclones relocated to observed position every 6 h 13 Jan 1998 June 1998 June 2000 July 2000 Computer IBM SV2 256 processors in 1985 with the implementation of the first comprehensive package of physical parameterizations from GFDL (Geophysical Fluid Dynamics Laboratory).

The S1 score measures the average relative error in the pressure gradient (compared to a verifying analysis chart). Experiments comparing two independent subjective analyses of the same data-rich North American region made by two experienced analysts suggested that a “perfect” forecast would have an S1 score of about 20%. It was also found empirically that forecasts with an S1 score of 70% or more were useless as synoptic guidance. Shuman pointed out some of the major system improvements that enabled NWP forecasts to overtake and surpass subjective forecasts.

When the “conservation” equations are discretized over a given grid size (typically from a few to several hundred kilometers) it is necessary to add “sources and sinks” terms due to small-scale physical processes that occur at scales that cannot be explicitly resolved by the models. 1) where q is the ratio between water vapor and dry air mass, x and y are horizontal coordinates with appropriate map projections, p is pressure, t is time, u and v are the horizontal air velocity (wind) components, ω = d p/dt is the vertical velocity in pressure coordinates, and the product of primed variables represents turbulent transports of moisture on scales unresolved by the grid used in the discretization, with the overbar indicating a spatial average over the grid of the model.

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