Forecasting over international borders: limitations and solutions for large-scale or continental forecasting systems

Contributed by Chantal Donnelly (SMHI), member of the SMHI Guest Columnist Team

Global and continental forecasting schemes already exist and are used to inform disaster management in countries without sufficient national forecast systems of their own, as inputs to operational oceanographic models and for the general interest of citizens. I have been lucky enough to have worked with two operational European forecasting systems (setting up of E-HYPE and the WET tool, as an operational EFAS forecaster and testing E-HYPE in EFAS). My colleagues have also just recently set up a forecast system for the Arctic basin, Arctic-HYPE. So, I thought I’d reflect on some of the challenges specific to international forecasting.

Unlike national or subnational forecast systems, which often have access to their own country or region’s collected hydrometeorological data and expertise, international forecasting systems have to do with inhomogenous collations of data from different countries. There can be huge differences in how neighbouring countries contribute to international databases!

Historical collations of daily precipitation and temperature observations are improving, for example the E-OBS product in Europe, but data coverage both in time and space tends to be fairly uneven. Similarly, the global runoff data centre (GRDC) provides a fantastic service in collating and disseminating river discharge data around the world, but again, not all discharge data is available for all periods in this data set either.

So, what are some of the limitations for international forecasting and how can these be solved? Here are just a few:

• There is often no single forcing data set that is consistently better than others. For example, in Europe, variations in precipitation and temperature gauges used in different data sets mean that quality varies regionally (e.g. Fig 1)

• The available historical forcing sets are often only available to a fixed period (e.g. 2013), so can’t be used for hydrological model spin-up and initialisation.
• This leads to a mix of model based forcing (reanalysis/forecast) and observation based forcing (interpolated observations) to calibrate, spin-up/initialise and run forecasts. As a result, potentially 3 or more different forcing sets can be used to make a forecast, e.g. (i) calibration to best available historical forcing, (ii) spin up with reanalysis and/or saved forecasts (iii) forecasting with ensemble or deterministic forecast model.
• Real-time discharge data is generally unavailable for assimilation into the forecast model, so initial states are only as good as the calibrated hydrological model and forcing data.

We have begun testing solutions to these issues in our operational international forecasting at SMHI.

To secure a continuous  forcing data set that can continue from an  historical reference period until near-real-time, we created GFD (or global forcing data). This in an operational product that can flexibly correct a gridded reanalysis or forecast grid to gridded observations. By flexible we mean that either the gridded model data or observations can be interchanged so that when an historical reanalysis such as ERA-INTERIM is not available (e.g. typically at t-3 months), this can be replaced by saved deterministic forecast data (DFD). GFD corrects both ERA-INTERIM and the saved forecasts to gridded monthly mean precipitation (e.g. GPCC), ensuring continuity of the forcing data set from calibration to model initialisation. We are also now testing to see if we could similarly exploit our knowledge of the biases between forecasts (saved) and best available historical forcing data to correct our meteorological forecasts (e.g. Fig. 2).

This could potentially be useful not only for homogenising input forcing data to international forecast systems, but possibly also for bias-correcting forecasts in all hydrological forecasting systems.

We are also investigating remote sensing solution for model initialisation including altimeter measurements of surface water bodies, and satellite derived snow extent and depths. Our ultimate goal is to make international forecasting more useful despite the limitations that international borders sometimes present!

Examples:

Read more here.