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

Kourzeneva, Ekaterina; Asensio, Hermann; Martin, Éric; Faroux, Stéphanie

doi:10.3402/tellusa.v64i0.15640

Cited by 77 publications

(73 citation statements)

References 11 publications

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“…But despite recent efforts to gather and map lake depth on a global scale Kourzeneva et al, 2012a), in East Africa information on lake depth and water transparency is only available for the largest lakes, adding uncertainty to FLake's outcome when it is applied to the entire region. Furthermore, the effect of the forcing data source -e.g.…”

Section: Sensitivity Studymentioning

confidence: 99%

Understanding the performance of the FLake model over two African Great Lakes

Thiery

Martynov

Darchambeau³

et al. 2014

Geosci. Model Dev.

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Abstract. The ability of the one-dimensional lake model FLake to represent the mixolimnion temperatures for tropical conditions was tested for three locations in East Africa: Lake Kivu and Lake Tanganyika's northern and southern basins. Meteorological observations from surrounding automatic weather stations were corrected and used to drive FLake, whereas a comprehensive set of water temperature profiles served to evaluate the model at each site. Careful forcing data correction and model configuration made it possible to reproduce the observed mixed layer seasonality at Lake Kivu and Lake Tanganyika (northern and southern basins), with correct representation of both the mixed layer depth and water temperatures. At Lake Kivu, mixolimnion temperatures predicted by FLake were found to be sensitive both to minimal variations in the external parameters and to small changes in the meteorological driving data, in particular wind velocity. In each case, small modifications may lead to a regime switch, from the correctly represented seasonal mixed layer deepening to either completely mixed or permanently stratified conditions from ∼ 10 m downwards. In contrast, model temperatures were found to be robust close to the surface, with acceptable predictions of near-surface water temperatures even when the seasonal mixing regime is not reproduced. FLake can thus be a suitable tool to parameterise tropical lake water surface temperatures within atmospheric prediction models. Finally, FLake was used to attribute the seasonal mixing cycle at Lake Kivu to variations in the near-surface meteorological conditions. It was found that the annual mixing down to 60 m during the main dry season is primarily due to enhanced lake evaporation and secondarily to the decreased incoming long wave radiation, both causing a significant heat loss from the lake surface and associated mixolimnion cooling.

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Section: Sensitivity Studymentioning

confidence: 99%

Understanding the performance of the FLake model over two African Great Lakes

Thiery

Martynov

Darchambeau³

et al. 2014

Geosci. Model Dev.

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“…Figure 2 illustrates the set of climate data and the system to extract information for the particular atmospheric model grid provided with the lake depth. The lake depth information may be provided by the lake database described in Kourzeneva et al (2012). For a NWP system, climatology is usually needed on the target model grid for the specific day of the year.…”

Section: Projection Onto the Target Gridmentioning

confidence: 99%

“…To represent each individual lake of this size is a too ambitious task. Sometimes we do not even know if the individual lake is represented on our map or not, land cover datasets are not precise enough Kourzeneva et al, 2012). However, we may be satisfied even with quite approximate information, as variables will be in future corrected either by a lake model or by observations.…”

Section: Introductionmentioning

confidence: 99%

Climate data for parameterisation of lakes in Numerical Weather Prediction models

Kourzeneva

Martin²,

Batrak

et al. 2012

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TIn Numerical Weather Prediction (NWP), to initialise lake variables for parameterisation of lakes, lake climatology is required. We obtained model lake climatology through offline runs of a lake model FLake (Freshwater Lake model) with different values of lake depth parameter. As a result, a global dataset with the resolution of 18 was developed. To project lake climatology onto a particular NWP model grid, data are extracted from the dataset depending on the lake depth value provided on the target grid. To prevent drifting of the bottom temperature in warm deep lakes, relaxation to the long-term mean air temperature was applied in FLake. Lake model climatology was validated against observations for different types of lakes. We suppose that detected errors for boreal lakes in spring are connected with inaccuracies in forcing data, but further study of errors is needed.

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“…The behaviour of FLake may vary between individual grid columns because of their different lake depths. For large lakes such as Ladoga, an approximate bathymetry is available in HIRLAM (Kourzeneva et al, 2012a). However, to a large extent, the conditions over the lake remain homogeneous, also from the point of view of the atmospheric forcing.…”

Section: April 2011 Comparisonmentioning

confidence: 99%

“…Lake depths were obtained from the lake data base for NWP and climate models (Kourzeneva et al, 2012a). Fraction of lakes was taken from the HIRLAM physiography description (Unde´n et al, 2002).…”

Section: Description Of the Analysis-forecast System And Setup Of Expmentioning

confidence: 99%

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

Pour¹,

Rontu²,

Duguay

et al. 2014

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T 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 (2010Á2011 and 2011Á2012), 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.

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

Cited by 77 publications

References 11 publications

Understanding the performance of the FLake model over two African Great Lakes

Understanding the performance of the FLake model over two African Great Lakes

Climate data for parameterisation of lakes in Numerical Weather Prediction models

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

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