Feedback from IOMASA User Advisory Group after PM1 at DMI, Copenhagen, 24-25 April, 2003

I am head of the atmospheric modelling group of the Finnish Meteorological Institute. My group consists of ten scientists, working in the department of meteorological research. Together with the international HIRLAM project, we develop the HIRLAM NWP system, and we also maintain a national implementation of HIRLAM for operational use.

I hold a Ph. D. in meteorology from the University of Helsinki since 1993. Most of my own research work is spent on the assesment of meteorological data assimilation systems with respect to processes related to the climatic energy and water cycles.

My comments on the IOMASA project focus on the data assimilation development. They deal partly with the methods used to assess the impact and utility of the innovations, and partly with steps that could be taken in order to ensure that the procedures developed within this project are offered for general use within and outside of the HIRLAM community with a minimum of delay.

From the point of view of an NWP agency like FMI, the most interesting potential result of IOMASA are the new procedures for assimilating AMSU-A and AMSU-B data in the context of the HIRLAM NWP system. The channel whereby innovations are distributed among the HIRLAM project is the HIRLAM reference system. Therefore new procedures developed within IOMASA, as well as their evaluation, should ideally be brought to a level of perfection and completeness where they can be accepted into the HIRLAM reference system at the end of the project. This would involve substantial testing using the reference version of HIRLAM rather than national implementations, as is described in the IOMASA document of work. If this goal is too ambitious to be achieved within IOMASA, then the necessary steps whereby it could be achieved later could be described in the IOMASA technology implementation plan (TIP).

The IOMASA document of work describes how to evaluate innovations by their impact on forecast verification statistics and the evolution of selected cases. It is worth considering to look, as well, at the statistics of analysis increments and the short range tendencies of prognostic variables. Such scrutiny might give valuable insight into the interaction between the data assimilation module and the parameterized physics, which is potentially important noting the strong influence of model physics on the humidity distribution. For example, if two different approaches are followed for assimilating AMSU-B moisture data, not only the differences in the ensuing forecasts are of interest. It is also important to understand the mechanisms whereby the different forecasts arise. Three-dimensional arrays of the tendencies of temperature, moisture and cloud condensate, generated by individual parameterized processes, are available from HIRLAM and might be useful.

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Background:

Head of the operational ice charting group (Greenland Waters) at DMI. To ensure and support safe operations the Ice Service provides high resolution ice information (primarily ice charts) to a number of clients, i.e. Danish Coast Guard, commercial and passenger vessels. Most operational products are based on near real time data from Radarsat SAR, NOAA-AVHRR and DMSP- SSM/I.

The Marine users demands to products:

• high quality
• high resolution
• reliability
• near real time
• easy presentation and standard formats
• low communication costs
• continuity
• limitations

Outcome of UAG PM1:

IOMASA is a very ambitious scientific project !!

IOMASA Overall objective:

New products and improved input to weather models in this case the end users are operational weather centers

Problems ­ challenges:

• Role of UAG not clear
• Need for UAG not clear
• Specific end user(s) not clear
• To bring science to operation
• Quantify need for IOMASA
• Quantify expected output and long term goals for IOMASA
• Quantify near real time operational products
• Relations to SAF? GMES? Other?

Focusing at the DMI operational users needs a number of secondary products may be a result of IOMASA, i.e.:

• sea ice edge (user defined, 0%, 1%, 10%...)
• realistic representation of ice concentration near ice edge
• ice types / thickness
• ice surface roughness
• surface winds
• precipitation over water (visibility)

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I represent regional climate modeling in the UAG. In addition to our own work at SMHI in Sweden, we are involved in Nordic and European research projects. Some of these target the Arctic, such as GLIMPSE (EU/FP5-project coordinated by Klaus Detloff).

A basic comment is that even though climate modeling is mentioned in the IOMASA project plan, there is nothing concrete on how the work by IOMASA or its results in practice benefit climate modeling. I suppose that we found some ways in the meeting. I hope that these will be followed up, as appropriate:

• in developing and evaluating climate models for the Arctic (indeed, any region in the polar regions and the marginal sea ice zone), better observations are always useful. This is true for both long-term monitoring (that provides climate statistics) and case studies (that sheds light on processes).
• for results of IOMASA to be of use for climate modeling, data are needed in numerical format (instead of "only" images) and including error statistics/error bars.
• climate modeling in turn could perhaps provide assistance in developing the new algorithms and testing the impact of these in climate-length simulations. The latter could add something to the NWP-applications IOMASA already has.
• in developing and evaluating climate modeling, use is made of observations such as SHEBA and the Swedish ice breaker expeditions (cf. http://www.fysik.lu.se/eriksw/aoe2001/aoe2001.htm ). Models are also being run, in intercomparison mode (ARC-MIP). Perhaps some of these data, findings among these that could be of assistance to the IOMASA-work. Climate models need to handle the underlying surface (ocean surface, sea ice, snow on sea ice, land and snow on land on grid scale but also on sub-grid scale), atmospheric structure (temperature, humidity and wind profiles, clouds etc.) and processes such as radiation and radiation-cloud -interaction. In cases that models perform well, they provided supplementary information to measurements. In cases that models cannot be relied on, they indicate need of better measurements.
• I also mentioned that you might be producing information also without realizing it. Somebody said that occasional melt episodes cause difficulty in retrievals. So, in a way, you localize such episodes. Statistics of melt episodes might be interesting for climate research. You could test this idea with Klaus Detloff.

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Personal background:

Weather forecaster 13 years

Present position:

Regional Director in Tromsoe at met.no's forecasting center for Northern Norway and the Arctic. Responsible for weather forecasts for land areas, sea districts and aviation north of lat. 65 degrees North. National Ice Service located at the Tromsoe office. The ice service issues daily ice and sea surface temperature charts for the European Arctic/Barents Sea. I am also Project Manager for the High Latitude Center of EUMETSATs Ocean & Sea Ice SAF. From February this year I am also Project Manager for ICEMON, a part of the GMES programme, funded by EU and ESA.

Feedback to the IOMASA project:

IOMASA is pointing towards very relevant topics both for weather forecasting and ice charting. What I consider unclear is:

• The identification of IOMASA products Representing an end user organization I find it hard to estimate to what extent the IOMASA results can be to any benefit Better described products would make it easier to give advises for improved products Identification of target users.
• Methods of distributing results and products to users The success of IOMASA will depend on what extent of use the results will have. Distribution, promotion and maybe training is important to make potential users aware of the achievement that are made. There are other distribution chains established that are targeting some of the same users; e.g. the OSI SAF have their distribution system. Synergy with other ongoing projects and activities ICEMON in Phase 2 (if awarded after Phase 1 that ends November 2004); R&D activities towards enhanced products for ice monitoring will take place. Also a distribution network will be developed. OSI SAF already has a distribution system. R&D activities will continue to enhance the existing products.
• These are some of my thoughts after the project meeting in Copenhagen. I think the idea behind IOMASA is very good and I do hope the project will have success. As Manger of the ICEMON project I want to communicate between the projects and possibly be represented mutually in project meetings.

Best regards,
Helge Tangen

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