2.3 Using a Spectrum of OSSEs of Varying Complexity to Support Satellite Mission Design

Monday, 8 January 2018: 11:15 AM
Ballroom G (ACC) (Austin, Texas)
Derek J. Posselt, JPL, Pasadena, CA; and H. Su, L. Wu, H. Nguyen, W. McCarty, and R. Atlas

Earth observing satellite missions are designed around a set of pre-defined measurement needs, and aimed at addressing a specific set of science questions. As such, mission design necessarily incorporates considerations of: measurement accuracy and uncertainty, spatial and temporal sampling, mission science impact, and impact of observations on numerical weather prediction. The ability of a mission to meet its requirements is assessed, and, if possible, quantified, at each stage of design and development.

Observing system simulation experiments (OSSEs) have been traditionally used to measure the anticipated impact of a new set of measurements on weather prediction. Forecast OSSEs are useful, in that they measure the effectiveness of a set of measurements in the context of the current global observing system. However, forecast OSSEs also have a number of key limitations. First, they require simulation of all current measurements, along with calibration of their errors. Second, they rely on the availability of a forecast and data assimilation system that is capable of ingesting the new measurements. If measurement uncertainties are not properly calibrated, the measure of impact of a new observing system will be incorrect. If a data assimilation system is not capable of assimilating a new type of measurement, a forecast OSSE is not possible. In fact, there is an inherent paradox: measurements are needed for processes that are not well understood, while poorly understood processes are generally not realistically simulated by numerical models.

In this presentation, we propose a hierarchy of OSSEs that range from simple to complex, and include:

  1. Experiments that explore satellite spatial and temporal sampling
  2. Quantification of retrieval (and forward model) uncertainty
  3. Assessment of mission science goals
  4. Observation impact on numerical weather prediction

We use as an example the measurement of the three-dimensional distribution of atmospheric winds from space, which has been highlighted as a key observational need during the coming decade.

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