Multifrequency Retrieval of Cloud Ice Particle Size Distributions
Abstract
There are many sources of uncertainty in remote sensing retrievals. This is particularly true where complex parameters such as liquid or ice hydrometeors must be retrieved. Many of the uncertainties are the direct result of assumptions made in the retrieval process to address the ill-posed nature of the inverse problem namely that there are more variables than measurements. In this paper, an optimal estimation retrieval technique is applied to a multi-frequency data set from the Wakasa Bay AMSR-E validation experiment. First, airborne radar observations at 13.4, 35.6 and 94.9 GHz are integrated to retrieve all three parameters of a normalized gamma ice particle size distribution (PSD), N (sub o)*, mu, and D(sub m). This retrieved PSD was validated against the near simultaneous coincident in situ cloud probe observations. The differences between the retrieved and in situ measured PSDs were explored through sensitivity analysis and the sources of uncertainty were found to be the ice particle density and the aspect ratio of the nonspherical particles modeled as oblate spheroids in the forward radiative transfer model. The optimal estimation technique was then applied to retrieve an optimal density and aspect ratio for the cloud under study through integration of the in situ and remote sensing observations. The optimal particle size-density relationship was found to be p(D) = 0.07 * D-(exp 1.58) and the oblate spheroid aspect ratio was found to be 0.53. The use of these optimal values as improved assumptions in the PSD retrieval reduced the uncertainty in the retrieved reflectivity of the three radars from +1-6dB to +1-2 dB. Next, the retrieval technique is expanded to include passive microwave observations and retrieve a full atmospheric column vertical hydrometeor profile.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2005
- Accession Number
- ADA444212
Entities
People
- Brian D. Griffith
Organizations
- Colorado State University