A model for the estimation of total dust production for the United States is discussed. Its primary use will be in the inventory of alkaline elements for use in acid/base balance studies of atmospheric precipitation by the National Acid Precipitation Assessment Program (NAPAP). The model is a summation of the expected dust production caused by wind erosion for individual sampling units of the detailed soil and land use inventory of the National Resources Inventory, compiled by the U.S. Department of Agriculture. The model is based on a dust emission function derived theoretically and verified by experiment. An extremely important parameter is the threshold velocity for dust production; this parameter is dependent on effects of vegetative residue, roughness of the soil, live standing plants, soil texture and the effect of atmospheric precipitation. Experimentation has supplied values of this parameter for the calculation. Wind data used in the model were obtained from the Wind Energy Resource Information System (WERIS). The model was calibrated with dust emission data for the area, including the panhandles of Texas and Oklahoma.
We consider the problem of estimating systematic errors in a network of magnetic direction finders using bearing data from a historical record containing a large number of lightning flash observations. It is clear that this problem is linked with the flash location estimation problem and that the two are statistically confounded in the sense that the effect of one cannot be separated from the other. We formalize mathematically a connection between the two problems to arrive at a self‐consistent system. In this paper we parameterize the site errors as two‐cycle sinusoidal functions and show that the estimation equations can be described by a model which separates into linear and nonlinear parameters. We take advantage of this separation to decouple large‐dimensional equations into smaller dimensional equations. Through these equations we show the link between the site error and flash location estimation problems. Validation of our modeling procedures is done by heuristic arguments and analysis of residuals. The latter reveals that not all of the residuals have been explained by sinusoidal site errors but that some residuals might result from the geometry of the network. This appears to be an important problem and is presently under study. Also, there is a need to test the site error estimation algorithm developed in this paper against some ground truth.
The performance of an algorithm for the recovery of site‐specific errors of direction finder (DF) networks is tested under controlled simulated conditions. The simulations show that the algorithm has some inherent shortcomings for the recovery of site errors from the measured azimuth data. These limitations are fundamental to the problem of site error estimation using azimuth information. Several ways for resolving or ameliorating these basic complications are tested by means of simulations. From these it appears that for the effective implementation of the site error determination algorithm, one should design the networks with at least four DFs, improve the alignment of the antennas, and increase the gain of the DFs as much as it is compatible with other operational requirements. The use of a nonzero initial estimate of the site errors when working with data from networks of four or more DFs also improves the accuracy of the site error recovery. Even for networks of three DFs, reasonable site error corrections could be obtained if the antennas could be well aligned.
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