Richard E. Danielson

Job Position: 
Senior Researcher
E-mail: 
Phone: 
+47 465 41 656

Academic Degree:

Doctor in Atmospheric and Oceanic Sciences, McGill University, Canada, 2003

Research interests

Peer Review Publications

2014

``Optimized tropical cyclone winds from QuikSCAT: A neural network approach'', B. W. Stiles, R. E. Danielson, W. L. Poulsen, M. J. Brennan, S. M. Hristova-Veleva, T.-P. J. Shen, and A. J. Fore, 2014, IEEE-Trans. Geosci. Remote Sensing, 52, 7418-7434, doi:10.1109/TGRS.2014.2312333.

2010

``A composite look at short-time-scale sea-surface temperature changes in the western North Pacific based on ships and buoys'', R. E. Danielson and J. R. Gyakum, 2010, Quart. J. Royal Met. Soc., 136, 319-332.

2008

``Objective analysis of marine winds with the benefit of the Radarsat-1 synthetic aperture radar: A nonlinear regression framework'', R. E. Danielson, M. Dowd, and H. Ritchie, 2008, J. Geophys. Res. (Oceans), 113, C05019, doi:10.1029/2007JC004413.

2006

``A case study of downstream baroclinic development over the North Pacific Ocean. Part II: Diagnoses of eddy energy and wave activity'', R. E. Danielson, J. R. Gyakum, and D. Straub, 2006, Mon. Wea. Rev., 134, 1549-1567.

``A case study of downstream baroclinic development over the North Pacific Ocean. Part I: Dynamical impacts'', R. E. Danielson, J. R. Gyakum, and D. Straub, 2006, Mon. Wea. Rev., 134, 1534-1548.

2004

``Examples of downstream baroclinic development among 41 cold-season eastern North Pacific cyclones'', R. E. Danielson, J. R. Gyakum, and D. Straub, 2004, Atmos.-Ocean, 42, 235-250.

 

Background

The successful launch of a new observational platform often marks a shift in focus, from modeling the physical processes to be observed, to exploiting information about such processes in the observations.  Of course, no physical retrieval model is perfect and improved interpretations are needed, but improvements do not occur in isolation.  Theory, models, and observations are all being improved concurrently and the challenge is in part to build on the work of others.  I am interested in incorporating lessons learned from data assimilation into observational oceanography, and suspect that calibration and validation (e.g., by triple collocation) is key.  Although the focus of most work on data assimilation continues to be mainly theoretical and applied to complex numerical models, there is no inconsistency: that's where the bulk of the work is required.  But in order to maintain a smooth interaction between (i.e., feeding from and into) theoretical, modelling, and observational work, it is important to place some emphasis on observational data assimilation.

Observational platforms that provide well calibrated observations can be useful in operational forecasting.  If platforms survive long enough, some become part of the climate data record (and sometimes the full benefit of a platform is only realized after it stops recording).  My focus since 2004 has been mainly on post-launch satellite observations, particularly from active microwave instruments, which provide information about ocean surface processes (even in challenging atmospheric environments).  A diverse variety of near-simultaneous observations tend to permit better distinctions between, say, atmospheric and oceanic processes.  Moreover, there has never been so great an opportunity to employ multiple views, frequencies, and polarizations, observing both actively and passively, often from the same platform.  Thus, one can attempt to put to best use the observations that have already been taken and those that are expected in the space agencies' decadal plans.  (One example would be improvement in the interpretation of a given platform's observations, leading to improvements in analyses of the atmosphere and ocean, which leads to a better interpretation of other platforms in a kind of bootstrapping.)  Fundamental to the goal of designing instruments and exploiting observations is the development of physical models for how the ocean and atmosphere are observed.  In principle, this makes it possible to experiment with strategies for remote sensing even before building an instrument.  In turn, this facilitates a decision of which strategies might be best in terms of their potential impact on forecasts.