Cooperative Institute for Research in the Atmosphere

About: Cooperative Institute for Research in the Atmosphere is a based out in . It is known for research contribution in the topics: Snow & Data assimilation. The organization has 332 authors who have published 997 publications receiving 38835 citations. The organization is also known as: CIRA.

Reply to Liu et al.: On the importance of US deposition of nitrogen dioxide, coarse particle nitrate, and organic nitrogen

Abstract: In PNAS (1), we highlight changes in the balance of oxidized and reduced nitrogen contributions to US reactive nitrogen (N) deposition. Successful reductions in nitrogen oxides (NOx) emissions combined with modest growth in ammonia emissions have now tipped the balance in most regions toward deposition of reduced N species, ammonia and ammonium.

Improved Flash Flood Predictions

Abstract: Flash floods will continue to be a significant problem in the foreseeable future. In spite of advances in technology and science, society will increase its vulnerability to flash floods. We present predictions on the rapid social change in the development and use of hydrometeorological information. In spite of advances, society continues to move in harm’s way of flash floods.

Non-Gaussian Variational Data Assimilation

Abstract: This chapter introduces a new area of research in variational data assimilation. The theory that we have presented for both variational and ensemble data assimilation schemes has assumed that the errors are Gaussian distributed. In this chapter we relax the Gaussian assumption to allow for errors that are lognormally distributed. We shall derive the full field and increment 3D and 4D VAR cost functions for both lognormal errors and mixed Gaussian-lognormal errors as well. We shall show results comparing the mixed non-Gaussian approach with the logarithmic approach, as well as with just a Gaussian-fits-all scheme. We finish this chapter with interesting new results with respect to minimization algorithms and a Newton-fractal.

Semi-Lagrangian methods for linear advection problems

Abstract: In this chapter we will first introduce the basis of Lagrangian dynamics to highlight the positives of this approach. Given the Lagrangian form of the advection equation, we move on to the semi-Lagrangian approach to show the advantages of combining the Eulerian and Lagrangian forms. We shall also present the performance of the semi-Lagrangian scheme with the advection of a bell curve and a step function for a linear, quadratic, and cubic interpolation polynomial to find the value of the field at the departure point.