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

Kumar, Sujay V.; Kolassa, Jana; Reichle, Rolf H.; Crow, Wade T.; Lannoy, Gabriëlle De; Rosnay, Patricia de; MacBean, Natasha; Girotto, Manuela; Fox, Andy; Quaife, Tristan; Draper, C.; Forman, B. A.; Balsamao, Gianpaolo; Steele‐Dunne, Susan; Albergel, Clément; Bonan, Bertrand; Calvet, Jean‐Christophe; Dong, Jianzhi; Liddy, Hannah M.; Ruston, Benjamin

doi:10.1029/2022ms003259

Cited by 20 publications

(17 citation statements)

References 369 publications

(488 reference statements)

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“…Looking ahead, an even tighter integration of the land and atmospheric analysis components into a single, strongly coupled land–atmosphere analysis may result in further progress (Kumar et al ., 2022). Nevertheless, the weakly coupled system as used in the present study already shows clear benefits and should be adopted in a future upgrade of the GEOS FP quasi‐operational system.…”

Section: Discussionmentioning

confidence: 99%

A weakly coupled land surface analysis with SMAP radiance assimilation improves GEOS medium‐range forecasts of near‐surface air temperature and humidity

Reichle

Zhang

Kolassa

et al. 2023

Quart J Royal Meteoro Soc

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The NASA Goddard Earth Observing System (GEOS) employs a hybrid four‐dimensional ensemble‐variational (Hybrid‐4DEnVar) atmospheric data assimilation system to provide global, near‐real time weather analysis and forecast products. This study introduces a land analysis into the GEOS Hybrid‐4DEnVar system to additionally assimilate L‐band (1.4 GHz) brightness temperature observations over land from the NASA Soil Moisture Active Passive (SMAP) satellite mission, which are highly sensitive to surface (∼0–5 cm) soil moisture. This weakly coupled land analysis impacts the simulated and forecast atmosphere through the land–atmosphere exchange processes encoded in the atmospheric model. Retrospective assimilation experiments for boreal summer 2017 were conducted with the system at 50 km horizontal resolution. The SMAP assimilation is shown to mitigate errors in screen‐level (2 m) specific humidity (q2m) and temperature (T2m), with regional reductions in the root‐mean‐square error (RMSE) of q2m and maximum daily T2m by up to 0.4 g·kg−1 and 0.3 K, respectively. These improvements are somewhat smaller than those found in a precursor study that used the atmospheric analysis in the simpler, 3‐dimensional variational (3DVar) atmospheric assimilation configuration of the current GEOS reanalysis; Hybrid‐4DEnVar provides overall better near‐surface background estimates than 3DVar and therefore leaves less room for improvement. Finally, in the Hybrid‐4DEnVar system with SMAP assimilation, forecasts of q2m and T2m have significantly improved anomaly correlation and RMSE (99% confidence) at lead times out to 5 days compared to the Hybrid‐4DEnVar system without SMAP assimilation, with medium‐range lead times extended by ∼3 hr for q2m and ∼2 hr for T2m. Slight but nevertheless significant improvements are also seen for temperature forecasts at the 925 and 850 hPa levels and lead times out to 4 days. Humidity forecasts at 925 and 850 hPa are improved out to 1.5‐day lead time with 90% confidence.

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Section: Discussionmentioning

confidence: 99%

A weakly coupled land surface analysis with SMAP radiance assimilation improves GEOS medium‐range forecasts of near‐surface air temperature and humidity

Reichle

Zhang

Kolassa

et al. 2023

Quart J Royal Meteoro Soc

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“…Data assimilation (DA) methods are employed to do this. With the proliferation of high-resolution datasets, often at resolutions higher than that of the forecast models, otherwise useful data is regularly ignored and not assimilated into operational models due to time or computational constraints (Eyre et al, 2022;Kumar et al, 2022). Assimilation of a subset of available satellite data has improved forecasts, making it likely that leveraging currently unused data could generate further improvements (Eyre et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Augmented Data Assimilation Method for High-Resolution Observation

Howard

Subramanian

Hoteit

2023

Preprint

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The accuracy of initial conditions is an important driver of the forecast skill of numerical weather prediction models. Increases in the quantity of available measurements, in particular high-resolution remote sensing observational data products from satellites, are valuable inputs for improving those initial condition estimates. However, the data assimilation methods used for integrating observations into forecast models are computationally expensive. This makes incorporating dense observations into operational forecast systems challenging, and it is often prohibitively time-consuming. As a result, large quantities of data are discarded and not used for state initialization. We demonstrate, using the Lorenz-96 system for testing, that a simple machine learning method can be trained to assimilate high-resolution data. Using it to do so improves both initial conditions and forecast accuracy. Compared to using the Ensemble Kalman Filter with high-resolution observations ignored, our augmented method has an average root-mean-squared error reduced by 15%. Ensemble forecasts using initial conditions generated by the augmented method are more accurate and reliable at up to 10 days of forecast lead time.

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“…The other significant challenges they present are related to land-surface modelling (including parameter estimation) necessary to provide a first guess in LDAS, and to assimilation methods, calling attention to spatial scale incompatibility between the several sources of information put together to build land-surface analyses, and to the coupling between LDAS and atmospheric data-assimilation systems. Kumar et al (2022) is of particular interest since the agenda they lay out for LDAS considers gaps specific to NWP.…”

Section: Introductionmentioning

confidence: 99%

“…On the data-assimilation side, Xia et al (2019) and Kumar et al (2022) provide recent reviews with extensive literature surveys. They highlight the current challenges related to LDAS inputs, that is, surface observations, retrievals from space-based observations, atmospheric forcing (most notably precipitation), and surface databases for soils and vegetation.…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 20 publications

References 369 publications

A weakly coupled land surface analysis with SMAP radiance assimilation improves GEOS medium‐range forecasts of near‐surface air temperature and humidity

A weakly coupled land surface analysis with SMAP radiance assimilation improves GEOS medium‐range forecasts of near‐surface air temperature and humidity

A Machine Learning Augmented Data Assimilation Method for High-Resolution Observation

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

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