Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication

Lannoy, Gabriëlle J. M. De; Bechtold, Michel; Albergel, Clément; Brocca, Luca; Calvet, Jean‐Christophe; Carrassi, Alberto; Crow, Wade T.; Rosnay, Patricia de; Durand, Michael; Forman, B. A.; Geppert, Gernot; Girotto, Manuela; Franssen, Harrie-Jan Hendricks; Jonas, Tobias; Kumar, Sujay V.; Lievens, Hans; Lu, Yang; Massari, Christian; Pauwels, Valentijn R. N.; Reichle, Rolf H.; Steele‐Dunne, Susan

doi:10.3389/frwa.2022.981745

Cited by 23 publications

(18 citation statements)

References 142 publications

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“…Challenges and priorities related to LSM calibration and parameter estimation, with a particular focus on coupled carbon‐vegetation‐water interactions related to longer‐term biosphere‐climate feedbacks, will be addressed in a separate companion paper (Quaife et al., in preparation). In parallel, a perspective is offered (De Lannoy et al., 2022) on how to address the increased complexity of land DA for either state or parameter estimation (or both) in the future.…”

Section: Introduction and Premisementioning

confidence: 99%

An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space

Kumar

Kolassa

Reichle

et al. 2022

J Adv Model Earth Syst

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The task of quantifying spatial and temporal variations in terrestrial water, energy, and vegetation conditions is challenging due to the significant complexity and heterogeneity of these conditions, all of which are impacted by climate change and anthropogenic activities. To address this challenge, Earth Observations (EOs) of the land and their utilization within data assimilation (DA) systems are vital. Satellite EOs are particularly relevant, as they offer quasi‐global coverage, are non‐intrusive, and provide uniformity, rapid measurements, and continuity. The past three decades have seen unprecedented growth in the number and variety of land remote sensing technologies launched by space agencies and commercial companies around the world. There have also been significant developments in land modeling and DA systems to provide tools that can exploit these measurements. Despite these advances, several important gaps remain in current land DA research and applications. This paper discusses these gaps, particularly in the context of using DA to improve model states for short‐term numerical weather and sub‐seasonal to seasonal predictions. We outline an agenda for land DA priorities so that the next generation of land DA systems will be better poised to take advantage of the significant current and anticipated shifts and advancements in remote sensing, modeling, computational technologies, and hardware resources.

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Section: Introduction and Premisementioning

confidence: 99%

An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space

Kumar

Kolassa

Reichle

et al. 2022

J Adv Model Earth Syst

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“…Based on this description, and in agreement with a recent review from De Lannoy et al . (2022), CaLDAS places among the state‐of‐the‐art LDAS systems currently used in major meteorological and environmental prediction centres. The weak coupling approach, in which the land‐surface and atmospheric data assimilation systems run side by side with frequent bidirectional information exchange, has been used for several decades at ECCC (see Bélair et al ., 2003a).…”

Section: Methodsmentioning

confidence: 99%

Evaluating the impact of land surface on medium‐range weather forecasts using screen‐level analyses

Bélair

Alavi

Carrera

et al. 2023

Quart J Royal Meteoro Soc

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In this study screen‐level analyses of air temperature and humidity are used to objectively evaluate the impact of a new land‐surface package being considered for implementation in Environment and Climate Change Canada (ECCC)'s medium‐range global deterministic numerical weather prediction (NWP) system. Through its control of heat, moisture, and momentum fluxes to the atmosphere, the land surface has a substantial impact on near‐surface meteorology and on the atmospheric boundary layer. The approach examined in this study is based on the comparison between model forecasts and screen‐level analyses. It demonstrates the impact on medium‐range NWP of a new land‐surface package that includes (i) a new set of databases to specify soils and land‐cover characteristics, (ii) improved land‐surface initial conditions obtained by the assimilation of space‐based remote‐sensing observations, and (iii) a more sophisticated scheme for land‐surface modelling. The evaluation method is shown to provide useful information on the impact of the new land‐surface package, including lead‐time‐averaged difference maps as well as plots showing the evolution with lead time of the standard deviation of errors (STDE) and of the temporal correlation between forecasts and analyses. The new land‐surface package has a positive impact on near‐surface forecasts of air temperature and humidity for a summertime period, with smaller STDE and larger temporal correlation for both variables. The improvement is greater for humidity than for air temperature. The maximum impact is found around seven‐day lead time, with substantial gains in absolute and relative values for STDE and temporal correlation. The positive impact is also quantified in terms of prediction hours, with gains of about one day at the medium range. Details of the pros and cons for this objective evaluation approach are discussed.

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“…Hybrid methods that combine the EnKF with particle methods could be used to obtain a DA system that performs well both for state updates and parameter updates (Frei and Künsch, 2013;van Leeuwen et al, 2019;De Lannoy et al, 2022).…”

Section: Alternatives To Rescaling Of Observationsmentioning

confidence: 99%

Effects of a biased LAI data assimilation system on hydrological variables and carbon uptake over Europe

Scherrer

Lannoy

Heyvaert

et al. 2022

Preprint

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Abstract. Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e.\\ without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in either observations or simulations, or both. We perform bias-blind and bias-aware DA of the Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–019 period, and evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture. In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. Bias-blind LAI DA can also lead to unrealistic shifts in soil moisture climatologies, for example when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between update steps. This model drift also propagates to short-term estimates of GPP and ET, and to internal DA diagnostics that indicate a suboptimal DA system performance. The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements of GPP anomalies from the bias-blind DA, but forego improvements in the root mean square deviation (RMSD) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance.

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Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication

Abstract: and Steele-Dunne S ( ) Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication. Front. Water : . doi: .

Cited by 23 publications

References 142 publications

An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space

An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space

Evaluating the impact of land surface on medium‐range weather forecasts using screen‐level analyses

Effects of a biased LAI data assimilation system on hydrological variables and carbon uptake over Europe

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