Services for Data Assimilation Component of the Regional Ice Prediction System.

Environment and Climate Change Canada The Canadian Ice Service (CIS) in collaboration with the Meteorological Research Division (MRD) has developed the Regional Ice Prediction System (RIPS), a fully automated sea ice analysis and forecasting system that provides high resolution (5km) ice information in the waters surrounding North America and the Arctic Ocean. The analyses and forecasts from this system are intended to provide guidance to analysts and forecasters in CIS Operations for preparation of ice products. The analysis component of RIPS employs data assimilation techniques (3-dimensional variational data assimilation, or 3DVar), to optimally combine satellite and other observations of ice and water information with a background state to produce an analysis of current ice concentration. The latest version of RIPS (version 2.2) was implemented on a supercomputer network at the Canadian Centre for Meteorological and Environmental Prediction (CCMEP) fall 2015. Ongoing developments, including the addition of new satellite observations to the system, the refinement of quality control, ice model cycling and improved assimilation techniques are planned. In the most recent version, the system uses “tie points”, which are static observed values for ice and water that allow the conversion between ice concentration and observation measurement space. An approach currently being developed, but not yet implemented operationally, is to use a “characteristic value” system, which automatically produces similar values to the tie points but that captures the variability of the measurements under different and evolving conditions. Because this is more accurate than static values, it is expected to be a significant improvement to the system. This subsystem must be well designed to fit in with the rest of the data assimilation code, and should provide the architecture for adding quality control as well. A first version of this code exists, but requires support for continued development and maintenance, and to be implemented into an operational version of the full system. There are also plans to develop and improve the use of various remote sensing observations, in terms of quality control and data assimilation tests. The challenge of this work will be in the context of sea ice data assimilation, and the difficulty in accurately converting the original observational data into ice and water information. Areas requiring further work are the development and implementation of AVHRR data assimilation, and the application of similar techniques to assimilate VIIRS data. Passive microwave data from SSM/I, SSM/IS, and AMSR2 also require an improved approach through use of the characteristic value system, more refined quality control, and possibly different algorithms for conversion of observations to ice and water. Synthetic Aperture Radar (SAR) data assimilation is another ongoing project that will require continued refinement in the strategies for assimilation, as this is a complex observation source that can be approached in many ways. A new direction of the analysis is to also start assimilating ice thickness information, and work using SMOS is in initial stages. Contractor’s Requirement The Individual Learning Branch of the ECCC wishes to establish Standing Offers with a number of contractors to provide specialized expertise services in order to further develop and implement new versions of the RIPS analysis on an “as and when requested” basis.  Contractors qualified under the Standing Offer Agreement (SOA) shall support the Canadian Ice Service with the provision of services in relation to development of the RIPS analysis. As described in any resultant call-up(s) issued by ECCC. The specific activities and deliverables to be provided by the Qualified Resource will be set out in the Call-Up. The activities and deliverables set out in a Call-Up may include, but are not limited to, the activities described below. Develop and maintain the Fortran-coded automated characteristic value system, to replace the static tie points approach being used in the current version of the RIPS analysis, including but not limited to: Further develop and refine code to suit ongoing needs for the various remote sensing applications Support and troubleshoot issues with code as it is used Design changes to fit into the larger data assimilation system, and provide assistance when the characteristic value system is implemented into an operational version of RIPS

Develop and implement the assimilation of Visible/Infrared satellite data (AVHRR/VIIRS), including but not limited to: Investigate feasibility of using characteristic values to assimilate AVHRR and VIIRS, and develop necessary quality control for this approach Develop a cloud masking technique for VIIRS data Adapt methods used for AVHRR to VIIRS – either ice/no-ice approach or characteristic value approach Explore advantages of using the full set of channels available with VIIRS

Investigate improvements to the assimilation of Passive Microwave data (SSM/I, SSM/IS, AMSR2), including but not limited to: Investigate use of higher resolution (high frequency) channels and any necessary quality control in using these channels Further develop the use of numerical weather prediction data to eliminate problematic retrievals of ice and water Depending on earlier results, further develop use of characteristic values to assimilate passive microwave data If necessary, investigate other passive microwave algorithms for sea ice, and use as appropriate

Develop the assimilation of ice thickness data (SMOS), including but not limited to: Determine a method for evaluating the impact of assimilating ice thickness data Refine quality control parameters that determine when to assimilate the SMOS data Explore the use of other types of ice thickness data

Refine techniques for use of SAR data (Radarsat2), including but not limited to: Expand SAR data assimilation to analyze different ice types Enhance quality control by using numerical weather prediction data Conduct study on choices for optimal characteristic value categories