Data processing and forecasting systems meeting of the joint expert teams on long-range forecasting

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  1. Canada’s contribution

Two main tasks have been assigned to RSMC Montreal (Canadian Meteorological Centre – CMC):

1- Access to verification datasets on the SVSLRF web site:

It is reported that the verification datasets Xie-Arkin (Xie et al, 1997), GPCP (Huffman et al, 1997), UKMO/CRU (Jones et al, 1999) and Reynolds OI (Reynolds et al , 1994; Smith et al, 1996) have all been converted to a standard 2.5 by 2.5 degree grid in GRIB1 format. These data are available on an ftp site, accessible via the SVSLRF Lead Centre “datasets” page:

2- Provide and maintain software to calculate the verification scores:

It is reported that the development of software to calculate the verification scores is nearing completion. These programs will be placed on an ftp site, accessible via the SVSLRF Lead Centre “scores” page:

  1. Australia’s contribution

Three main tasks have been assigned to WMC Melbourne (Australian Bureau of Meteorology – BoM):

1– To develop the structure of, and to host, the SVSLRF web site

It is reported that the SVSLRF web site has been established and is being further developed and expanded. It may be viewed via the URL: (see Figure 1.)

It is envisaged that once the Lead Centre web site is fully operational it may be moved to a new URL if it is thought that the current URL is not suitable. It is also proposed that the full website not be password protected, only that part of the web site which contains the verification maps. This would allow easier access for users of the web site and encourage those who view the system to become active participants (currently none of the website is password protected).

2– To define formats for the data to be sent to the Lead Centre, and hence to develop infrastructure to generate and display graphics posted on the web site. It is reported that naming conventions and standard formats for the submission of the data have been listed on the SVSLRF web site, as part of a “Users Guide”:

A system to automatically generate a standard graphical output based upon the standardised names and formats is at the panning stage.

A system to display the graphical output has been developed. The prototype is available via the SVSLRF web site, or directly at:

3– To develop a structured database to store the digital data

It is planned for the digital data to be stored in a cascading directory structure database as per the graphics files, and will be made available via the web site.

Annex to paragraph 6.1

Statement of Guidance for

Seasonal-to-Interannual Forecasts (updated Nov 2003)
This Statement of Guidance (SOG) was developed through a process of consultation to document the observational data requirements to support seasonal-to-interannual (SIA) climate prediction. This version was prepared originally by the ET-ODRRGOS with experts from the NWP community, and was subsequently updated in consultation with a number of experts from the climate community through AOPC. It is expected that the statement will be reviewed at appropriate intervals by the OPAG on Data Processing Forecasting Systems to ensure that it remains consistent with the current state of the relevant science and technology

The ET on Long-Range Forecasting reviewed the Statement of Guidance. Comments are inserted in bold.

1. Introduction

Coupled atmosphere-ocean models are used to produce seasonal-to-inter-annual forecasts of climate. While empirical and statistical methods are also used to predict climate conditions a season ahead, the present assessment of how well observational requirements are met relates only to the coupled model inputs. It is noted that historical data sets also play an important role in SIA prediction by supporting calibration and verification activities.

Whilst such forecasting is still subject to much research and development, many seasonal forecast products are now widely available. The complexity of the component models ranges from simple baroclinic models to full general-circulation-model representations of both the ocean and atmosphere. There is also large variation in the approach to the assimilation of initial data, with some of the simpler models assimilating only wind information while the more complex models usually assimilate subsurface temperature information and satellite surface topography and temperature data. Indeed, major challenges remain in the development of assimilation techniques that optimise the use of observations in initialising models. At present, useful forecast skill (as measured against ocean and atmosphere indices) is restricted to around 6-8 months lead-time and is confined primarily to the tropical Pacific and those regions directly impacted by El Niño, although useful skill has also been demonstrated in some extratropical regions.

The time and space scales associated with seasonal-to-interannual variability (large scale, low frequency) suggest the key information for forecasts will derive mostly from the slow parts of the climate system, in particular the ocean. The initial conditions for the atmospheric model component are not so significant. When considering impacts such as rainfall deficiencies or increased temperatures over land, however, there are very good reasons for considering variables associated with the land surface conditions. In particular, land surface moisture and vegetation health should be specified and predicted. The models should also include up-to-date radiative forcing (e.g. greenhouse forcing), which may be important for maximising skill in forecasts of land surface air temperature anomalies relative to recent historical reference-normal periods.

Comprehensive statements on requirements of AOPC, OOPC, TOPC, WCP, WCRP and CCl have appeared in several places, most recently in the proceedings of First International Conference on Ocean Observing Systems for Climate, and published separately in Observing the Oceans in the 21st Century (published by the GODAE Office and the Australian Bureau of Meteorology). In terms of key variables, the priorities have changed little since the Tropical Oceans-Global Atmosphere (TOGA) Experiment of 1985-1994. These requirements are being entered into the CEOS/WMO data base. The above references also provide details of ocean-based and space-based platforms capable of meeting these requirements. Further, the report of the IGOS Ocean Theme Team provides a consolidated and integrated perspective for the oceans that embraces SIA forecasts explicitly.

In this SOG, the requirements for SIA forecasts are based on a consensus of the coupled atmosphere-ocean modelling community. It builds on the requirements for Global NWP and represents in addition those variables that are known to be important for initialising models or for testing and validating models. For the most part, aspects that remain purely experimental (i.e. unproven) are not included. There is some attempt to capture the impacts aspects; that is, those variables that are needed for downscaling and/or regional interpretation.

2. Data Requirements
The following terminology has been adhered to as much as possible: marginal (minimum user requirements are being met), acceptable (greater than minimum but less than optimum requirements are being met), and good (near optimum requirements are being met).
2.1 Sea surface temperature

Accurate SST determinations, especially in the tropics, are important for SIA forecast models. Ships and moored and drifting buoys provide observations of good temporal frequency and acceptable accuracy, but coverage is marginal or worse over large areas of the Earth. Instruments on polar satellites provide information with global coverage in principle, good horizontal and temporal resolution and acceptable accuracies (once they are bias-corrected using in situ data), except in areas that are persistently cloud-covered (which includes significant areas of the tropics). Geostationary imagers with split window measurements are helping to expand the temporal coverage by making measurements hourly and thus creating more opportunities for finding cloud-free areas and characterising any diurnal variations (known to be up to 4 degrees C in cloud free regions with relatively calm seas). Microwave measurements provide acceptable resolution and accuracy and have the added value of being able to ’see through’ clouds. Blended products from the different satellites and in-situ data can be expected to be good for SIA forecasts.

There is a requirement for high quality, fast delivery SST (ideally with accuracy < 0.1 deg C on 100 km spatial scale, available within 24h ( by SST we mean eg bulk temperature at 2m depth).

2.2 Ocean wind stress

Ocean wind stress is a key variable for driving ocean models. It is important to recognise the complementarity between surface wind and surface topography measurements. Current models use winds derived from Numerical Weather Prediction (NWP), from specialist wind analyses or, in some cases, winds inferred from atmospheric models constrained by current SST fields. The tropical moored buoy network has been a key contributor for surface winds over the last decade, particularly for monitoring and verification, providing both good coverage and accuracy in the equatorial Pacific. Fixed and drifting buoys and ships outside the tropical Pacific provide observations of marginal coverage and frequency; accuracy is acceptable.

Satellite surface wind speed and direction measurements are now the dominant source of this information. Currently their data reach SIA models mostly through the assimilated surface wind products of NWP, where their positive impact is acknowledged. Overall, a two-satellite scatterometer system, or its equivalent, would provide good coverage and acceptable frequency, and it would complement the ocean-based systems. At this time, continuity and long-term commitment are a concern. Improved integration of the data streams and operational wind stress products from NWP and other sources will be needed to achieve acceptable or better coverage, frequency and accuracy.

High quality scatterometer winds are the best products available at the moment and need to be maintained operationally. Additional data would always be useful. For example data to allow better estimates of heat-fluxes and P-E could help give a better definition of the mixed layer structure.

2.3 Subsurface temperature and salinity profiles

Many, but not all, SIA forecast models assimilate subsurface temperature data, at least in the upper ocean (down to ~500 m depth). No current model assimilates salinity data (subsurface or surface), principally because of the paucity of data and inadequate knowledge of the variability. The Tropical Atmosphere Ocean (TAO) / TRITON moored buoy network provides data of good frequency and accuracy, and acceptable spatial resolution, of subsurface temperature for the tropical Pacific, at least for the current modeling capability. The tropical moored network in the Atlantic (PIRATA) is better than marginal but does not yet have the long-term resource commitments and stability to be classified as acceptable. There is no array in the Indian Ocean. The Ships-of-Opportunity Programme (SOOP) provides data of acceptable spatial resolution over some regions of the globe but the temporal resolution is marginal. It is noted that SOOP is evolving to provide enhanced temporal resolution along some specific lines. The Argo Project is providing increasing global coverage of temperature and salinity profiles to ~2000 m, mostly with acceptable-to-good spatial resolution, but only marginal temporal resolution in the tropics. In all cases the accuracy is acceptable for SIA purposes.

Ocean observation system over Equatorial Atlantic is deficient in moorings. Moorings at and near the equator are likely to be most important. Equatorial moorings in the Indian Ocean are also likely to be useful.

2.4 Ocean topography

Ocean altimetry provides a measure of the sea surface topography relative to some (largely unknown) geoid (or mean sea surface position) that in turn is a reflection of thermodynamic changes over the full-depth ocean column. In principle, the combination of altimetry, tropical mooring and Argo will provide a useful system for initialising the thermodynamic state of SIA models. Commitments are improving for long-term operation of satellite altimeters (e.g. through the Jason series). Research satellites are providing a mix of data with acceptable accuracy and resolution and data with good spatial resolution (along the satellite tracks) but marginal accuracy and frequency. The "synoptic" global coverage, particularly beyond the tropical Pacific, is an important requisite.

2.5 Surface heat flux

There are a few sites in the tropical ocean where the data on surface heat flux are of some value for validation. At a selected number of reference sites the accuracy and temporal resolution will be good. NWP products, in principle, have good resolution but the accuracy is at best marginal. Satellite data provide prospects for several of the components of heat flux, particularly shortwave radiation, but at present none is used on a routine basis for SIA forecasts. Precipitation estimates are important for validation because of the fundamental role of the hydrological cycle in SIA impacts. They also have potential importance in initialisation because of the links to salinity. However, there remain significant uncertainties in estimates of rainfall over the oceans.

2.6 Ocean current data

No model currently assimilates ocean current data. However, because of the central importance of dynamics and advection, current data are important for testing and validation. For example, experimental fields of surface current for the tropical Pacific and Atlantic are now being produced routinely by blending geostrophic estimates from altimetry with Ekman estimates from remotely-sensed wind observations. Inferred surface currents from drifting buoys are acceptable in terms of accuracy and temporal resolution but marginal in spatial coverage. Satellite altimetry is also being used to infer the distribution of ocean currents. Moored buoys are good in temporal coverage and accuracy, but marginal otherwise.

2.7 Sea level

In-situ sea level measurements provide an additional time-series approach (good temporal resolution and accuracy; marginal spatial coverage), particularly for testing models and validating altimetry.

2.8 Atmospheric data

Since several SIA systems are driven by winds and, in several cases, surface heat flux products from operational analyses, the global (atmospheric) observing system is important for SIA forecasts and their verification.

2.9 Other data

There are many other data sets that may play a role in future-generation SIA forecast models. Because these roles are largely unknown, it is premature to discuss the adequacy of observing systems to meet these needs; generally speaking, they are not expected to rank near the above data in terms of priority. These data sets include:

  • Surface salinity (particularly from new space-based approaches). No present model uses surface salinity.

  • Snow cover. Research suggests snow cover may be important, particularly at short lead times (intraseasonal-to-seasonal).

  • Ice cover. Ice cover is important for high latitudes. It is implicitly included in the leading SST products.

  • Soil moisture and terrestrial properties. Research suggests proper initialisation of soil moisture is important. There are also indications that terrestrial properties, such as the state of vegetation, may be important, particularly in downscaling and impacts/applications.
  • Ocean colour. Ocean transparency is already included in several ocean models and is thought to be a factor in SIA models (helping to determine where radiation is absorbed). Ocean colour measurements provide a means to estimate transparency.

  • Clouds. Poor representation of clouds remains a key weakness of most SIA models. Better data are needed to improve parameterisations but these needs are adequately specified under NWP and elsewhere.

Ice cover, Ice thickness, Snow cover depth and mass both for real time analyses and consistent analysis of the past are important and should be given same priority as data in above sections.

In order to have a realistic representation of the land surface conditions the following are necessary:

An accurate soil moisture analysis (together with consistent analyses of the past) in order to provide realistic soil moisture initial conditions to the LRF. This is very important for 1-2 month range forecasts in some parts of the world. Data describing soil water content, river run off, soil type, vegetation, land use (and its changes) are also needed in order to validate and improve the surface schemes.

Aerosols data such as volcanic ash is also required.

Global data that can be used to validate the Long Range Forecast. This is particularly important for rainfall, where high quality, high density and readily available data would be of great value both for assessing model quality, and, more importantly, empirical downscaling global model output for local use.
3. Summary

The following key points summarise the SOG for Seasonal to Interannual forecasts:

  • The data requirements for seasonal-to-interannual modelling and forecasts are now entered in the CEOS/WMO data base (as well as available in several GCOS and WCRP documents; see Section 1);
  • The WCRP has concluded that models show useful skill in predicting variability of the El Niño-Southern Oscillation but there is less useful predictability beyond the Pacific. The exploitation of skill is dominated by the signal of El Niño;

  • Integrated and complementary approaches to the atmospheric and oceanic observing systems is required, exploiting synergies with other areas;

  • The TAO/TRITON Array of moored buoys (SST and winds; subsurface temperature; currents) provides the backbone of the ENSO Observing System in place today and its continuation is essential;

  • Enhancements from satellite wind vector and surface topography estimates, from autonomous systems such as Argo, and from enhanced surface flux reference sites, are providing a substantial contribution.

The key observational problems affecting improvements in seasonal to inter-annual forecasting are:

  • The transition of research networks and outputs (especially ocean-based) to operational status (i.e. with sustained institutional support);

  • The timely operational acquisition of data from research and non-governmental systems/sources;

  • The lack of long-term commitment to (a) a two-satellite scatterometer system, (b) tropical moored arrays in the Atlantic and Indian Oceans, (c) operational satellite altimetry, and (d) a network of surface flux reference sites.

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