Ekaterina Kourzeneva

Global gridded dataset of lake coverage and lake depth for use in numerical weather prediction and climate modelling

ABSTRACT A global dataset of lake coverage and lake depth was developed for use in numerical weather prediction and climate modelling. It provides the global gridded information on lake depth with the resolution of 30 arc sec. (approximately 1 km). It was obtained by mapping data on mean lake depth for ca. 13 000 freshwater lakes. Apart from the mean depth, the bathymetry for 36 large lakes is included. Information for individual lakes was collected from regional databases, water cadastres and public sources. For mapping, the land cover map ECOCLIMAP2 was used. A new automatic probabilistic mapping method was developed and is described here. We discuss also how to project the lake depth data onto the numerical atmospheric model grid and how to achieve the consistency of physiographic datasets when several maps are used in a model.

Data assimilation and parametrisation of lakes in HIRLAM

ABSTRACT When the resolution of numerical weather prediction (NWP) and climate models increases, it becomes more and more important to correctly account for the lake–atmosphere interactions. One possible way to handle lake effects is to use a lake model, which treats the lake surface temperature and ice conditions as prognostic variables. Such a parametrisation eliminates the traditional for NWP need to prescribe lake characteristics based on long-term climate averages. At the same time, new in situ and satellite measurements are becoming available for an operational practice. This offers the possibility to assimilate lake observations into the NWP models. We study the applicability of the prognostic and observation-based approaches and compare both. As a first step towards integrated lake data assimilation and forecasting in NWP, we suggest using the results of the prognostic lake parametrisation as the background for objective analysis (spatialisation) of the lake water surface temperature observations. We run NWP experiments in the Nordic conditions, where the freezing and melting of lakes can significantly influence local weather. Our results indicate that a lake model, usually used in climate studies, works well also in the NWP model even without assimilation of observations. However, it is possible to improve the description of the changing lake surface state by using good observation data. In this case, the lake model provides a better background for the data assimilation than a lake surface temperature climatology.

Impact of satellite-based lake surface observations on the initial state of HIRLAM. Part II: Analysis of lake surface temperature and ice cover

This paper presents results from a study on the impact of remote-sensing Lake Surface Water Temperature (LSWT) observations in the analysis of lake surface state of a numerical weather prediction (NWP) model. Data assimilation experiments were performed with the High Resolution Limited Area Model (HIRLAM), a three-dimensional operational NWP model. Selected thermal remote-sensing LSWT observations provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Along-Track Scanning Radiometer (AATSR) sensors onboard the Terra/Aqua and ENVISAT satellites, respectively, were included into the assimilation. The domain of our experiments, which focussed on two winters (2010–2011 and 2011–2012), covered northern Europe. Validation of the resulting objective analyses against independent observations demonstrated that the description of the lake surface state can be improved by the introduction of space-borne LSWT observations, compared to the result of pure prognostic parameterisations or assimilation of the available limited number of in-situ lake temperature observations. Further development of the data assimilation methods and solving of several practical issues are necessary in order to fully benefit from the space-borne observations of lake surface state for the improvement of the operational weather forecast. This paper is the second part of a series of two papers aimed at improving the objective analysis of lake temperature and ice conditions in HIRLAM.

Impact of partly ice-free Lake Ladoga on temperature and cloudiness in an anticyclonic winter situation - a case study using a limited area model

At the end of January 2012, a low-level cloud from partly ice-free Lake Ladoga caused very variable 2-m temperatures in Eastern Finland. The sensitivity of the High Resolution Limited Area Model (HIRLAM) to the lake surface conditions was tested in this winter anticyclonic situation. The lake appeared to be (incorrectly) totally covered by ice when the lake surface was described with its climatology. Both parametrisation of the lake surface state by using a lake model integrated to the NWP system and objective analysis based on satellite observations independently resulted in a correct description of the partly ice-free Lake Ladoga. In these cases, HIRLAM model forecasts were able to predict cloud formation and its movement as well as 2-m temperature variations in a realistic way. Three main conclusions were drawn. First, HIRLAM could predict the effect of Lake Ladoga on local weather, when the lake surface state was known. Second, the current parametrisation methods of air–surface interactions led to a reliable result in conditions where the different physical processes (local surface processes, radiation and turbulence) were not strong, but their combined effect was important. Third, these results encourage work for a better description of the lake surface state in NWP models by fully utilising satellite observations, combined with advanced lake parametrisation and data assimilation methods.

Assimilation of lake water surface temperature observations using an extended Kalman filter

A new extended Kalman filter (EKF)-based algorithm to assimilate lake water surface temperature (LWST) observations into the lake model/parameterisation scheme Freshwater Lake (FLake) has been developed. The data assimilation algorithm has been implemented into the stand-alone offline version of FLake. The mixed and non-mixed regimes in lakes are treated separately by the EKF algorithm. The timing of the ice period is indicated implicitly: no ice if water surface temperature is measured. Numerical experiments are performed using operational in-situ observations for 27 lakes and merged observations (in-situ plus satellite) for 4 lakes in Finland. Experiments are analysed, potential problems are discussed, and the role of early spring observations is studied. In general, results of experiments are promising: (1) the impact of observations (calculated as the normalised reduction of the LWST root mean square error comparing to the free model run) is more than 90% and (2) in cross-validation (when observations are partly assimilated, partly used for validation) the normalised reduction of the LWST error standard deviation is more than 65%. The new data assimilation algorithm will allow prognostic variables in the lake parameterisation scheme to be initialised in operational numerical weather prediction models and the effects of model errors to be corrected by using LWST observations.

Implementation of a simple thermodynamic sea ice scheme, SICEversion 1.0-38h1, within the ALADIN-HIRLAM numerical weatherprediction 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 m temperature reaches 1.5 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 m temperature mean absolute error on average by 0.5 C in areas that are influenced by sea ice. The new scheme is sensitive to the representation of the form drag. The 10 m wind speed bias increases on average by 0.4 ms−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 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.

Towards improved objective analysis of lake surface water temperature in a NWP model: preliminary assessment of statistical properties

Information about the statistical structure of the lake surface water temperature (LSWT) field is needed for assimilation of lake observations into Numerical Weather Prediction (NWP) models, to describe the lake surface state at each grid-point containing lakes. In this study, we obtain the autocorrelation function for LSWT from two types of observations, in situ and satellite-based. We use summer time measurements during 2010–2014 over selected Fennoscandian lakes and Northern European domain. The estimated autocorrelations decrease exponentially (from 0.99 to 0.73 for in situ and from 0.97 to 0.61 for satellite observations), when the distance between observations increases from zero to one thousand kilometres .A large difference in lake depth leads to a decrease of the correlation. Typical error standard deviation of LSWT observations was found to be 0.9 C for in situ observations and 1.2C for satellite observations. The exponential approximation for the LSWT autocorrelation functions is proposed, which depends on both the distance and the difference in lake depth. These results are directly applicable for the LSWT objective analysis in NWP. New autocorrelation functions, which allow interpolation of observations within and between lakes, were used in numerical experiments with the High-Resolution Limited Area Model (HIRLAM). In this preliminary assessment, we suggest adaptation of the presently used functions by increasing the influence radius and taking into account the lake depth difference. Generalization of the results to cover the melting and freezing seasons, their assessment for different geographical areas as well as their application to other prognostic lake variables within NWP are foreseen.

Thermal regime and components of water balance of lakes in Antarctica at the Fildes peninsula and the Larsemann Hills

Abstract Thermal regime and water balance components of 12 lakes located at two different parts of the Antarctic (the Fildes peninsula in the Maritime Antarctic and the Larsemann Hills in the continental Antarctica) were studied using the observations from three field campaigns in 2012–2014. The morphometric characteristics of the studied lakes were updated with GPS/echo-sounding surveys, and changes in the length, width and volume of the lakes were revealed in comparison with the previous surveys. The thermal regime of the lakes was also studied by modelling, applying the lake model FLake, which is widely used in different environmental applications but was tested for the first time in the Antarctic conditions. In contrast to boreal lakes, for lakes in Antarctica the modelling results by FLake appeared to be sensitive to the light extinction coefficient. According to simulations, all lakes were mixed down to the bottom for the whole summer; however, the reasons for this are different for shallow and deep lakes. The sensitivity of different methods to calculate evaporation, by the Dalton-type empirical equation and by the atmospheric surface layer block of FLake, was studied. For endorheic lakes, the sensitivity appeared to be large, up to 47% of the total seasonal water volume change, which assumes that FLake has the potential to be used in hydrological applications to calculate evaporation. Seasonal variations of the volume of the lakes in the continental Antarctica are larger than in the Maritime Antarctic. Usually, small and medium-sized lakes accumulate or redistribute water during the warm season. However, the systems of big lakes also release the stored water through the mechanism of abrupt jumps, which simultaneously cause the inflow into the sea of huge amounts of fresh water during short time intervals.