1. FY10-11 GIMPAP Project Proposal Title Page • Title: Testing of New Height Assignment Methodology for GOES Atmospheric Motion Vectors (AMVs) • Project Type: Product Improvement • Status: New – continuing work from GIMPAP FY08-09 project • Duration: 2 Years • Leads: • Chris Velden (CIMSS) • Howard Berger (CIMSS) • Other Participants: • Jaime Daniels (STAR) • Mary Forsythe (UKMet Office) • Iliana Genkova (ECMWF)
2. Project Summary • Although AMVs have had positive impacts on NWP, the representative vector heights have proven to be a relatively large source of observation uncertainty, because in most cases the satellite imagers actually sense radiation emitted from a finite layer of the troposphere rather than just one specific level. Thus, problems in data assimilation can arise from the difficulty in accurately placing the height of the tracer, and/or adequately representing the measured motion of a layer by a single-level value. • This height-assignment issue was addressed in previous GIMPAP-sponsored research, and the results published by Velden and Bedka, 2009, Weather and Forecasting. The potential impact of these findings on NWP when assimilating GOES AMVs using this novel height-assignment methodology is the primary motivation for this proposed research. • To this end, we propose to work with data assimilation colleagues to address the issue of exploiting this new AMV height assignment information in numerical model simulations to determine the potential forecast impact.
3. Motivation/Justification • Supports NOAA Mission Goal(s): Climate, Weather and Water • It is well known that accurate numerical weather prediction (NWP) requires upper-air observations for representing the initial state of the atmosphere and for updating the model analyses through data assimilation. In particular, the proper specification of tropospheric winds is an important prerequisite to accurate numerical model forecasts. • Over oceanic regions, where significant weather is common, conventional data sources are especially scarce. Thus, atmospheric motions vectors (AMVs) derived from satellites are useful for NWP because they can provide wind information in these important regions. • Advances in data assimilation and NWP in recent years have placed an increasing demand on data quality. With remotely-sensed observations dominating the initialization of NWP models over regions of the globe that are traditionally data-sparse, the motivation is clear: the importance of providing high-quality AMVs becomes crucial to their relevance and contributions toward realizing superior model predictability. • We continue to strive to improve the AMV product through new innovations, that when successful can be transitioned to the operational production. • One such new innovation we propose to investigate is a new method for vector height assignment that we believe can lead to improved AMV accuracies.
4. Methodology • The novel concept of considering layer-mean heights for AMVs, and the relationship of this approach to improving vector accuracy, was developed and successfully demonstrated using large AMV datasets over several domains (Velden and Bedka 2009). Basically, the vector RMSE can be considerably reduced with the proper assignment of a layer-averaged height vs. the traditional single-level approach, especially in high impact shear environments. This information can be calculated and tagged to each AMV record. • Various approaches to minimize the height-assignment problems in data assimilation have been previously investigated, however, an optimal forward operator for AMVs has remained elusive because the height assignment uncertainties and the vertical representativeness of the AMVs have not been previously provided by AMV data providers. • To this end, we propose to work with data assimilation colleagues at UKMET and ECMWF, whom are in a position to immediately investigate these issues. We hope to address the issue of exploiting this new AMV height assignment information in numerical model simulations to determine the potential forecast impact.
5. Expected Outcomes • If the research is successful and validated, we will see an improved ability for NWP to effectively assimilate AMVs and to positively impact analyses and forecasts. • Pending the successful experimental testing in the UKMET and ECMWF data assimilation systems, this research has two possible paths to operational use in NOAA: 1) Joint Center for Satellite Data Assimilation (JCSDA) – Test and transfer the assimilation methods and results found in the UKMET/ECMWF studies/systems to the GFS system, 2) PSDI – Transfer the new height assignment methodology in the AMV processing software to STAR, for testing/implementation into the NESDIS operational AMV processing algorithm.
6. FY10-11 Milestones • Jan – July 2010 • Continue the previous exploratory work on the layer-mean height assignment method • Implement the new findings into the CIMSS AMV processing code • July – Dec 2010 • Begin delivery of new product to NWP centers for assimilation testing • Jan – July 2011 • Continue experimentation and conduct model forecast impact experiments with UKMET and ECMWF • July – Dec 2011 • Evaluate experimental results • Provide final report and analysis; recommend for operational transition