Review of Global 5-km resolution estimates of secondary evaporation including irrigation through satellite data assimilation by Van Dijk et al.

To ameliorate suboptimality in ensemble data assimilation, methods have been introduced that involve expanding the ensemble size. Such expansions can incorporate model space covariance localization and/or estimates of climatological or model error covariances. Model space covariance localization in the vertical overcomes problematic aspects of ensemble-based satellite data assimilation. In the case of the ensemble transform Kalman filter (ETKF), the expanded ensemble size associated with vertical covariance localization would also enable the simultaneous update of entire vertical columns of model variables from hyperspectral and multispectral satellite sounders. However, if the original formulation of the ETKF were applied to an expanded ensemble, it would produce an analysis ensemble that was the same size as the expanded forecast ensemble. This article describes a variation on the ETKF called the gain ETKF (GETKF) that takes advantage of covariances from the expanded ensemble, while producing an analysis ensemble that has the required size of the unexpanded forecast ensemble. The approach also yields an inflation factor that depends on the localization length scale that causes the GETKF to perform differently to an ensemble square root filter (EnSRF) using the same expanded ensemble. Experimentation described herein shows that the GETKF outperforms a range of alternative ETKF-based solutions to the aforementioned problems. In cycling data assimilation experiments with a newly developed storm-track version of the Lorenz-96 model, the GETKF analysis root-mean-square error (RMSE) matches the EnSRF RMSE at shorter than optimal localization length scales but is superior in that it yields smaller RMSEs for longer localization length scales.

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Supplementary material to "Decadal changes in global surface NO<sub><i>x</i></sub> emissions from multi-constituent satellite data assimilation"

10.5194/acp-2016-529-supplement ◽

2016 ◽

Author(s):

Kazuyuki Miyazaki ◽

Henk Eskes ◽

Kengo Sudo ◽

K. Forkert Boersma ◽

Kevin Bowman ◽

...

Keyword(s):

Data Assimilation ◽

Satellite Data ◽

Supplementary Material ◽

Global Surface

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Remotely sensed latent heat fluxes for improving model predictions of soil moisture: a case study

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-6179-2010 ◽

2010 ◽

Vol 7 (4) ◽

pp. 6179-6205

Author(s):

J. M. Schuurmans ◽

F. C. van Geer ◽

M. F. P. Bierkens

Keyword(s):

Spatial Distribution ◽

Soil Moisture ◽

Data Assimilation ◽

Latent Heat ◽

Satellite Data ◽

Hydrological Model ◽

Remotely Sensed ◽

Heat Fluxes ◽

Model Errors ◽

Small Catchment

Abstract. This paper investigates whether the use of remotely sensed latent heat fluxes improves the accuracy of spatially-distributed soil moisture predictions by a hydrological model. By using real data we aim to show the potential and limitations in practice. We use (i) satellite data of both ASTER and MODIS for the same two days in the summer of 2006 that, in association with the Surface Energy Balance Algorithm for Land (SEBAL), provides us the spatial distribution of daily ETact and (ii) an operational physically based distributed (25 m×25 m) hydrological model of a small catchment (70 km2) in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Firstly, model outcomes of ETact are compared to the processed satellite data. Secondly, we perform data assimilation that updates the modelled soil moisture. We show that remotely sensed ETact is useful in hydrological modelling for two reasons. Firstly, in the procedure of model calibration: comparison of modeled and remotely sensed ETact together with the outcomes of our data assimilation procedure points out potential model errors (both conceptual and flux-related). Secondly, assimilation of remotely sensed ETact results in a realistic spatial adjustment of soil moisture, except for the area with forest and deep groundwater levels. As both ASTER and MODIS images were available for the same days, this study provides also an excellent opportunity to compare the worth of these two satellite sources. It is shown that, although ASTER provides much better insight in the spatial distribution of ETact due to its higher spatial resolution than MODIS, they appeared in this study just as useful.

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Improvement in WRF model prediction for heavy rain events over North Sumatra region using satellite data assimilation

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/893/1/012040 ◽

2021 ◽

Vol 893 (1) ◽

pp. 012040

Author(s):

Immanuel Jhonson Arizona Saragih ◽

Huda Abshor Mukhsinin ◽

Kerista Tarigan ◽

Marzuki Sinambela ◽

Marhaposan Situmorang ◽

...

Keyword(s):

Data Assimilation ◽

Initial Data ◽

Satellite Data ◽

Wrf Model ◽

Heavy Rain ◽

Convective System ◽

Large Area ◽

Weather Forecasts ◽

Rain Events ◽

North Sumatra

Abstract Located adjacent to the Indian Ocean and the Malacca Strait as a source of water vapour, and traversed by the Barisan Mountains which raise the air orographically causing high diurnal convective activity over the North Sumatra region. The convective system that was formed can cause heavy rainfall over a large area. Weather Research and Forecasting (WRF) was a numerical weather model used to make objective weather forecasts. To improve the weather forecasts accuracy, especially for predict heavy rain events, needed to improve the output of the WRF model by the assimilation technique to correct the initial data. This research was conducted to compare the output of the WRF model with- and without assimilation on 17 June 2020 and 14 September 2020. Assimilation was carried out using the 3D-Var technique and warm starts mode on three assimilation schemes, i.e. DA-AMSU which used AMSU-A satellite data, DA-MHS which used MHS satellite data, and DA-BOTH which used both AMSU-A and MHS satellite data. Model output verification was carried out using the observational data (AWS, AAWS, and ARG) and GPM-IMERG data. The results showed that the satellite data assimilation corrects the WRF model initial data, so as increasing the accuracy of rainfall predictions. The DA-BOTH scheme provided the best improvement with a final weighted performance score of 0.64.

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