Assimilating synthetic land surface temperature in a coupled land–atmosphere model

Sgoff, Christine; Schomburg, Annika; Schmidli, Juerg; Potthast, Roland

doi:10.1002/qj.3883

Cited by 2 publications

(3 citation statements)

References 51 publications

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“…It was able to reduce near surface atmospheric temperature RMSE by up to 60% and the temperature of the upper soil levels by up to 40% within the OSSE framework. Near-surface observations, such as LST or soil moisture, can increase their influence on the overall system through the fully coupled land-atmosphere assimilation system, which can have a positive impact on the weather forecasts (Sgoff et al, 2020;Lin and Pu, 2020).…”

Section: Discussion Of Resultsmentioning

confidence: 99%

“…This thesis, particularly the main parts of Chapter 7, were the foundation of the publication by Sgoff et al (2020). Thus, note that parts of the abstract, of Chapter 2 to 7, as well as parts of the conclusion in Chapter 9 are published in Sgoff et al (2020).…”

Section: Outlinementioning

confidence: 95%

“…In conclusion, there is potential to benefit from assimilation of LST into soil and atmosphere models. To further enhance this impact of LST assimilation a possible step is to use a fully coupled land-atmosphere assimilation framework (Sgoff et al, 2020), where soil moisture and soil temperature are as well updated within the analysis step as the atmospheric variables. Another fully coupled land-atmosphere assimilation system has already been successfully implemented for soil moisture (Weather Research and Forecasting Model coupled with the NOAH land surface model, Lin and Pu, 2018.…”

Section: Background Of Lst Assimilationmentioning

confidence: 99%

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Assimilating synthetic land surface temperature in a fully coupled land-atmosphere system

Sgoff¹

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The weather of the atmospheric boundary layer significantly affects our life on Earth. Thus, a realistic modelling of the atmospheric boundary layer is crucial. Hereby, the processes of the atmospheric boundary layer depend on an accurate representation of the land-atmosphere coupling in the model. In this context the land surface temperature (LST) plays an important role. In this thesis, it is examined if the assimilation of LST can lead to improved estimates of the boundary layer and its processes. To properly assimilate the LST retrievals, a suitable model equivalent in the weather prediction model is necessary. In the weather forecast model of the German Weather Service used here, the LST is modelled without a vegetation temperature. To compensate for this deficit, two different vegetation parameterizations were investigated and the better one, a conductivity scheme, was implemented. In order to make optimal use of the influence of the assimilation of the LST observation on the model system, it is useful to pass on the information of the observation to land and atmosphere already in the assimilation step. For that reason, a fully coupled land-atmosphere prediction model was used. Therefore, the existing control vector of the assimilation system, a local ensemble transform Kalman filter, was extended by the soil temperature and moisture. In two-day case studies in March and August 2017, different configurations of the augmented assimilation system were evaluated based on observing system simulation experiments (OSSE). LST was assimilated hourly over two days in the weakly and strongly coupled assimilation system. In addition, every six hours a free 24-hour forecast was simulated. The experiments were validated with the simulated truth (a high-resolution model run) and compared against an experiment without assimilation. It was shown that the prediction of the boundary layer temperature, especially during the day, and the prediction of the soil temperature, during the whole day and night, could be improved. The best impact of LST assimilation was achieved with the fully coupled system. The humidity variables of the model benefited only partially from the LST assimilation. For this reason, covariances in the model ensemble were investigated in more detail. To check their compatibility with the high-resolution model run the ensemble consistency score was introduced. It was found that the covariances between the LST and the temperatures of the high-resolution model run were better represented in the ensemble than those between the LST and the humidity variables.

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Section: Discussion Of Resultsmentioning

confidence: 99%

Section: Outlinementioning

confidence: 95%

Section: Background Of Lst Assimilationmentioning

confidence: 99%

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Assimilating synthetic land surface temperature in a fully coupled land-atmosphere system

Sgoff¹

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The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model

et al. 2022

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Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST.

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Assimilating synthetic land surface temperature in a coupled land–atmosphere model

Cited by 2 publications

References 51 publications

Assimilating synthetic land surface temperature in a fully coupled land-atmosphere system

Assimilating synthetic land surface temperature in a fully coupled land-atmosphere system

The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model

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