Algorithms for effective objective analysis of surface weather variables
This paper will describe methods we have devised to deal with these and other challenges. For instance, our BCDG analysis package can compute an expected change in elevation from either the surface data being analyzed, upper air data from a model forecast, or a combination of both. A different radius of influence can be used for each data point, or a constant one that depends only on pass number. There are four choices for the first guess, and several options, such as throw-out criteria in quality controlling the data, vary by first guess choice. Land and water can be treated together or separately. There are several options for smoothing the grid, including a terrain-following smoother, a spot remover, and a ray smoother for water areas that can variably smooth depending on distance from the coast. The number of corrective passes can vary, and there are three types of correction that can be made in combination with bi-linear, bi-quadratic, or terrain-related interpolation. Provision is made to give special emphasis to the range of values of interest to users; for instance, the careful analysis of ceiling heights below 1000 ft is much more important that a 1000-ft range up at 10,000 ft.
Grids of observations and of MOS and LAMP forecasts are provided in the National Digital Guidance Database. The techniques used to produce these grids will be presented.