Communicating Satellite MW Ocean Product Errors to a Variety of Users
These ocean products are widely used by scientists, educators, students, governmental organizations and climate researchers. The extensive use of these ocean products requires proper communication of product errors for best results. However, error determination is a complex process, and will be the topic of this poster.
Ocean products can be retrieved to a high degree of accuracy in rain-free conditions because of the unique relationship between brightness temperature and the wind, vapor, cloud and SST. The product retrievals are based on a radiative transfer model for the ocean surface and the intervening atmosphere. Thus, the accuracy of the products depends both on the accuracy of the model and on the precise calibration of the measured brightness temperatures. Determination of product error is usually completed by comparison with in situ observations, other satellite retrievals, and operational weather analyses. From these activities, we assign error estimates to each MW retrieval, estimate the long-term errors in specifying interannuual and decadal trends, propagate these errors through the algorithms used to produce gridded and merged products, and identify the most important sources of error. Error analysis considers measurement noise, algorithm sensitivity, geophysical model accuracy, and the influence of contamination parameters such as rain, land, sea ice, and radio frequency interference.
The addition of error information into DISCOVER ocean products can greatly increase their usefulness to users, but only if the error values are understood. The availability of error estimates will enable the accurate assimilation of the measurements into derived products and the assessment of the statistical significance of conclusions reached when using the products in research applications. But most important to proper data use is the clear communication of the data errors. How can we best express where we, as data producers, are more or less confident in our data products? And how can we communicate this to our users so that they produce meaningful research results?
This poster will provide examples of our error assessments and discuss the communication of the errors to different types of users, whether they are scientists, government workers, or the general public.