Climate data for parameterisation of lakes in Numerical Weather
                        Prediction models

Kourzeneva, Ekaterina; Martin, Éric; Batrak, Yurii; Moigne, Patrick Le

doi:10.3402/tellusa.v64i0.17226

Cited by 30 publications

(42 citation statements)

References 9 publications

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“…Even in more studied areas, information on the spatial and temporal variability of water transparency is mostly lacking (Kirillin, 2010;Kourzeneva et al, 2012b;Rontu et al, 2012). Therefore, the first need is to collect more observations of k and to gain more insight into the relationship between water transparency and seasonal mixing cycles in deep tropical lakes.…”

Section: Discussionmentioning

confidence: 99%

“…1). One of FLake's main external parameters is the lake depth (Mironov, 2008;Kourzeneva et al, 2012b). However, for most of the deep African Great Lakes, their actual lake depth cannot be used, since FLake only describes the mixed layer and thermocline, whereas in reality a monimolimnion is found below the thermocline of these meromictic lakes.…”

Section: Model Configuration Evaluation and Sensitivitymentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Model Configuration Evaluation and Sensitivitymentioning

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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“…Thirdly, the LWST field produced by the prognostic lake parameterisation was used as the background state for analysis of observations. Fourthly, we utilised a model climatology of prognostic lake variables developed by Kourzeneva et al (2012). Finally, we also studied the behaviour of HIRLAM over lakes during the snow/ice melting period.…”

Section: P U B L I S H E D B Y T H E I N T E R N a T I O N A L M E T mentioning

confidence: 99%

Data assimilation and parametrisation of lakes in HIRLAM

Rontu¹,

Eerola²,

Kourzeneva³

et al. 2012

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T When the resolution of numerical weather prediction (NWP) and climate models increases, it becomes more and more important to correctly account for the lakeÁatmosphere 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.

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“…Zolfaghari et al: Satellite-derived light extinction coefficient ited to 40-60 m depth; Kourzeneva et al, 2012a), located in both temperate and warm climate regions Martynov et al, 2010Martynov et al, , 2012Mironov, 2008;Mironov et al, 2010Mironov et al, , 2012Samuelsson et al, 2010;Kourzeneva et al, 2012a, b).…”

Section: Introductionmentioning

confidence: 99%

Satellite-derived light extinction coefficient and its impact on thermal structure simulations in a 1-D lake model

Zolfaghari

Duguay

Pour

2017

Hydrol. Earth Syst. Sci.

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Abstract.A global constant value of the extinction coefficient (K d ) is usually specified in lake models to parameterize water clarity. This study aimed to improve the performance of the 1-D freshwater lake (FLake) model using satellite-derived K d for Lake Erie. The CoastColour algorithm was applied to MERIS satellite imagery to estimate K d . The constant (0.2 m −1 ) and satellite-derived K d values as well as radiation fluxes and meteorological station observations were then used to run FLake for a meteorological station on Lake Erie. Results improved compared to using the constant K d value (0.2 m −1 ). No significant improvement was found in FLake-simulated lake surface water temperature (LSWT) when K d variations in time were considered using a monthly average. Therefore, results suggest that a timeindependent, lake-specific, and constant satellite-derived K d value can reproduce LSWT with sufficient accuracy for the Lake Erie station.A sensitivity analysis was also performed to assess the impact of various K d values on the simulation outputs. Results show that FLake is sensitive to variations in K d to estimate the thermal structure of Lake Erie. Dark waters result in warmer spring and colder fall temperatures compared to clear waters. Dark waters always produce colder mean water column temperature (MWCT) and lake bottom water temperature (LBWT), shallower mixed layer depth (MLD), longer ice cover duration, and thicker ice. The sensitivity of FLake to K d variations was more pronounced in the simulation of MWCT, LBWT, and MLD. The model was particularly sensitive to K d values below 0.5 m −1 . This is the first study to assess the value of integrating K d from the satellite-based CoastColour algorithm into the FLake model. Satellite-derived K d is found to be a useful input parameter for simulations with FLake and possibly other lake models, and it has potential for applicability to other lakes where K d is not commonly measured.

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Climate data for parameterisation of lakes in Numerical Weather Prediction models

Cited by 30 publications

References 9 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

Data assimilation and parametrisation of lakes in HIRLAM

Satellite-derived light extinction coefficient and its impact on thermal structure simulations in a 1-D lake model

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