Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1

Sea ice is an important factor affecting weather regimes, especially in polar regions. A lack of its representation in numerical weather prediction (NWP) systems leads to large errors. For example, in the HARMONIE–AROME model configuration of the ALADIN–HIRLAM NWP system, the mean absolute error in 2&thinsp;m temperature reaches 1.5&thinsp;°C after 15 forecast hours for Svalbard. A possible reason for this is that the sea ice properties are not reproduced correctly (there is no prognostic sea ice temperature in the model). Here, we develop a new simple sea ice scheme (SICE) and implement it in the ALADIN–HIRLAM NWP system in order to improve the forecast quality in areas influenced by sea ice. The new parameterization is evaluated using HARMONIE–AROME experiments covering the Svalbard and Gulf of Bothnia areas for a selected period in March–April 2013. It is found that using the SICE scheme improves the forecast, decreasing the value of the 2&thinsp;m temperature mean absolute error on average by 0.5&thinsp;°C in areas that are influenced by sea ice. The new scheme is sensitive to the representation of the form drag. The 10&thinsp;m wind speed bias increases on average by 0.4&thinsp;m s−1 when the form drag is not taken into account. Also, the performance of SICE in March–April 2013 and December 2015–December 2016 was studied by comparing modelling results with the sea ice surface temperature products from MODIS and VIIRS. The warm bias (of approximately 5&thinsp;°C) of the new scheme is indicated for areas of thick ice in the Arctic. Impacts of the SICE scheme on the modelling results and possibilities for future improvement of sea ice representation in the ALADIN–HIRLAM NWP system are discussed.</p

Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

In Norway, 30 % of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of 1 km over an area in southern Norway for 2 years (1 September 2014–31 August 2016). Experiments were carried out using two different forcing data sets: (1) hourly forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing) including post-processed temperature (500 m grid spacing) and wind, and (2) gridded hourly observations of temperature and precipitation (1 km grid spacing) combined with meteorological forecasts from AROME MetCoOp for the remaining weather variables required by SURFEX/Crocus. We present an evaluation of the modelled snow depth and snow cover in comparison to 30 point observations of snow depth and MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snowmelt. Both experiments are capable of simulating the snowpack over the two winter seasons, but there is an overestimation of snow depth when using meteorological forecasts from AROME MetCoOp (bias of 20 cm and RMSE of 56 cm), although the snow-covered area in the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season. When using gridded observations, the simulation of snow depth is significantly improved (the bias for this experiment is 7 cm and RMSE 28 cm), but the spatial snow cover distribution is not well captured during the melting season. Underestimation of snow depth at high elevations (due to the low elevation bias in the gridded observation data set) likely causes the snow cover to decrease too soon during the melt season, leading to unrealistically little snow by the end of the season. Our results show that forcing data consisting of post-processed NWP data (observations assimilated into the raw NWP weather predictions) are most promising for snow simulations, when larger regions are evaluated. Post-processed NWP data provide a more representative spatial representation for both high mountains and lowlands, compared to interpolated observations. There is, however, an underestimation of snow ablation in both experiments. This is generally due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snowmelt and biases in the forcing data

ML, PL, QL in Markov Chain Models

In many spatial and spatial-temporal models, and more generally in models with com- plex dependencies, it may be too difficult to carry out full maximum-likelihood (ML) analysis. Rem- edies include the use of pseudo-likelihood (PL) and quasi-likelihood (QL) (also called the composite likelihood). The present paper studies the ML, PL and QL methods for general Markov chain mod- els, partly motivated by the desire to understand the precise behaviour of the PL and QL methods in settings where this can be analysed. We present limiting normality results and compare perfor- mances in different settings. For Markov chain models, the PL and QL methods can be seen as maximum penalized likelihood methods. We find that QL is typically preferable to PL, and that it loses very little to ML, while sometimes earning in model robustness. It has also appeal and potential as a modelling tool. Our methods are illustrated for consonant-vowel transitions in poetry and for analysis of DNA sequence evolution-type models