Harald Schyberg

Assimilation of ERS SAR wave spectra in an operational wave model

Inverted wave spectra from ERS-1 synthetic aperture radar (SAR) image and wave mode products have been assimilated in the operational wave model for the ocean wave forecast service at the Norwegian Meteorological Institute. Elements of the operational system are explained briefly, and the impact of including the SAR wave data in the operational wave model runs is shown both for individual cases and as overall statistics. Although individual cases clearly show that the satellite observations are able to influence the forecast in a generally positive way, the average improvement is minor for the areas covered by the wave model. Reasons for this are the intermittency of the data, on the average small differences between inverted SAR and model first-guess wave spectra, and to some extent, limitations in the analysis method.

Assimilating Advanced Scatterometer Winds in a High-Resolution Limited Area Model Over Northern Europe

Satellite-based scatterometer ocean surface wind measurements have been shown to improve global weather forecasts through data assimilation. However, these scatterometer data are not yet widely assimilated operationally in high-resolution regional models. This paper demonstrates the impact of assimilating Advanced Scatterometer (ASCAT) winds on the analysis and forecasts in observation system experiments using the convection-resolving operational HARMONIE-AROME model system over Northern Europe. At high latitudes, ASCAT provides dense observational data of meteorological phenomena, such as cold-air mesocyclones, particular in this region. Observation errors for ASCAT used in assimilation were found to be consistent with what was found in analysis of observation versus model background statistics, and no spatial error correlations were found on the 50 km separation distances. The largest impact of the assimilation of ASCAT winds was found over the ocean and in the coastal regions. Forecast verification against synoptic observations at coastal stations showed on average improvements for mean sea-level pressure and to some extent for 10-m wind speed on short forecast range. This varied only a little when changing the assimilation settings. Decreasing the data thinning distance from 100 to 50 km further improved forecasts, while shortening the assimilation window from 3 to 1 h did not yield a consistent forecast impact. The observation system experiments have confirmed that scatterometer winds contribute to improved analysis and forecasts in high-resolution regional modeling. This demonstrates general applicability of scatterometer observations for improving weather forecasts at high latitudes.

Future sea ice conditions and weather forecasts in the Arctic: Implications for Arctic shipping

The ability to forecast sea ice (both extent and thickness) and weather conditions are the major factors when it comes to safe marine transportation in the Arctic Ocean. This paper presents findings focusing on sea ice and weather prediction in the Arctic Ocean for navigation purposes, in particular along the Northeast Passage. Based on comparison with the observed sea ice concentrations for validation, the best performing Earth system models from the Intergovernmental Panel on Climate Change (IPCC) program (CMIP5—Coupled Model Intercomparison Project phase 5) were selected to provide ranges of potential future sea ice conditions. Our results showed that, despite a general tendency toward less sea ice cover in summer, internal variability will still be large and shipping along the Northeast Passage might still be hampered by sea ice blocking narrow passages. This will make sea ice forecasts on shorter time and space scales and Arctic weather prediction even more important.

The EUMETSAT OSI SAF near 50 GHz sea ice emissivity model

ABSTRACT A sea ice thermal microwave emission model for 50 GHz was developed under EUMETSATs Ocean and Sea Ice Satellite Application Facility (OSI SAF) programme. The model is based on correlations between the surface brightness temperature at 18, 36 and 50 GHz. The model coefficients are estimated using simulated data from a combined thermodynamic and emission model. The intention with the model is to provide a first guess sea ice surface emissivity estimate for atmospheric temperature sounding applications in the troposphere in numerical weather prediction (NWP) models assimilating Advanced Microwave Sounding Unit (AMSU) and Special Sensor Microwave Imager/Sounder (SSMIS) data. The spectral gradient ratio is defined as the difference over the sum of the SSMIS brightness temperatures at 18 and 36 GHz vertical linear polarisation (GR1836). The GR1836 is related to the emissivity at the atmospheric temperature sounding channels at around 50 GHz. Furthermore, the brightness temperatures and the polarisation ratio (PR) at the neighbouring 18, 36 and 50 GHz channels are highly correlated. Both the gradient ratio at 18 and 36 GHz and the PR at 36 GHz measured by SSMIS are input into the model predicting the 50 GHz emissivity for horizontal and vertical linear polarisations and incidence angles between 0° and 60° The simulated emissivity is compared to the emissivity derived with alternative methods. The fit to real AMSU observations is investigated using the different emissivity estimates for simulating the observations with atmospheric data from a regional weather prediction model.

Objective analysis combining observation errors in physical space and observation space

A generalization of the traditional observation-error model in objective analysis is presented. It is suggested that for observations with nonlinear observation operators, the observation errors should be split up into one contribution in the measurement domain and one contribution in the domain of the model physical quantities interpolated to the measurement locations (physical space). In particular, errors of representativeness are better modelled in physical space. Analysis equations are derived for this generalized problem. The approach suggested has important applications in the assimilation of satellite observations. For special cases with a wide application this generalization does not introduce an inhibiting computational complexity. Copyright

IOMASA-Integrated Observing and Modelling of the Arctic Surface and Atmosphere

IOMASA aims to improve the analysis and forecast of the Arctic weather and sea ice conditions using an integrated approach including remote sensing of the atmospheric parameters temperature, humidity and cloud liquid water, improved remote sensing of sea ice with more accurate and higher resolved ice concentrations, and improved numerical atmospheric models by assimilating the results. The usefulness of the concept will be shown in a demonstration phase with near-real time processing and online data distribution.