The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Draper, C.; Reichle, Rolf H.

doi:10.5194/hess-19-4831-2015

Cited by 32 publications

(34 citation statements)

References 27 publications

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“…Draper and Reichle (2015) have shown that data assimilation is able to correct modelled soil moisture also at longer time intervals from subseasonal to seasonal scale and seasonal differences in the assimilation effect are reported across many studies, as also shown here. Existing hydrological monitoring systems, such as the US Drought Monitor (Svoboda et al, 2002), the African Flood and Drought Monitor , the German Drought Monitor (Samaniego et al, 2013) or the Australian Water Resource Assessment (Van Dijk et al, 2011;Vaze et al, 2013) all use soil moisture quantiles at grid cell level to characterise different levels of severity and facilitate the comparison of soil moisture levels between grid cells.…”

Section: Discussionmentioning

confidence: 52%

SMOS brightness temperature assimilation into the Community Land Model

Rains

Han²,

Lievens

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010)(2011)(2012)(2013)(2014)(2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5 % is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7 % when updating both the top layers and root zone, and by 4 % when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The longterm assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.

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Section: Discussionmentioning

confidence: 52%

SMOS brightness temperature assimilation into the Community Land Model

Rains

Han²,

Lievens

et al. 2017

Hydrol. Earth Syst. Sci.

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“…On average, the assimilation resulted in slightly higher skill improvements against the surface in situ measurements for DA-NN and DA-L2P-gCDF and slightly higher skill improvements in the root zone for DA-NN-lCDF. Overall, the results suggest that the global rescaling approaches could potentially be very beneficial for soil moisture estimation under the conditions of: (1) a good observation error characterization; (2) rigorous observation quality control; and (3) potential component-wise assimilation [51] to better isolate the reliable satellite information.…”

Section: Discussionmentioning

confidence: 93%

“…To use the DA-NN approach it is thus crucial to accurately characterize the model and observation errors and to apply a rigorous quality control to the observations. Additionally, to better isolate the reliable retrieval information, it might be beneficial to separately assimilate the different temporal components of the retrievals-i.e., the long-term mean, seasonal, sub-seasonal and interannual signatures [51]-with the DA-NN approach.…”

Section: Discussion Of Da-nn and Da-nn-lcdf Resultsmentioning

confidence: 99%

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

et al. 2017

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Abstract:This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the National Aeronautics and Space Administration (NASA) Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill, and reduced the surface and root zone unbiased root-mean-square error (ubRMSE) by 0.005 m 3 m −3 and 0.001 m 3 m −3 , respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m 3 m −3 , but increased the root zone bias by 0.014 m 3 m −3 . Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to skill degradation in other land surface variables.

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“…In this study a land surface model is combined with PMW Tb observations in a data assimilation (DA) approach. The ultimate goal of DA is to yield a merged estimate that is superior to the observations and to the model alone (Draper & Reichle, 2015;Forman & Margulis, 2010;McLaughlin, 2002;Reichle, 2008). The direct assimilation of Tbs (rather than PMW-derived SWE retrievals) is preferable as it avoids inconsistencies in the use of In order to overcome the spatial sparsity in ground-based measurements, other studies assimilated satellite-based retrieval products to characterize SWE or snow depth, which are derived from either physical (e.g., radiative transfer) or statistical (e.g., regression) models.…”

Section: Introductionmentioning

confidence: 99%

Estimating Snow Mass in North America Through Assimilation of Advanced Microwave Scanning Radiometer Brightness Temperature Observations Using the Catchment Land Surface Model and Support Vector Machines

Xue

Forman

Reichle

2018

Water Resources Research

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To estimate snow mass across North America, brightness temperature observations collected by the Advanced Microwave Scanning Radiometer (AMSR‐E) from 2002 to 2011 were assimilated into the Catchment model using a support vector machine as the observation operator and a one‐dimensional ensemble Kalman filter. The performance of the assimilation system is evaluated through comparisons against ground‐based measurements and reference snow products. In general, there are no statistically significant skill differences between the domain‐averaged, model‐only (open loop, or OL) snow estimates and assimilation estimates. The assessment of improvements (or degradations) in snow estimates is difficult because of limitations in the measurements (or products) used for evaluation. It is found that assimilation estimates agree slightly better in terms of root‐mean‐square error and Nash‐Sutcliffe model efficiency with ground‐based snow depth measurements than OL estimates in 82% (56 out of 62) of pixels that are colocated with at least two ground‐based stations. Assimilation estimates tend to agree slightly better in terms of mean difference with reference snow products over tundra snow, alpine snow, maritime snow, and sparsely vegetated, snow‐covered pixels. Changes in snow mass via assimilation translate into improvements (e.g., by 22% on average in terms of root‐mean‐square error, relative to OL) in cumulative runoff estimates when compared against discharge measurements in 11 out of 13 snow‐dominated basins in Alaska. These results suggest that a support vector machine can potentially serve as an effective observation operator for snow mass estimation within a radiance assimilation system, but a better observational baseline is required to document a statistically significant improvement.

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The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Cited by 32 publications

References 27 publications

SMOS brightness temperature assimilation into the Community Land Model

SMOS brightness temperature assimilation into the Community Land Model

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

Estimating Snow Mass in North America Through Assimilation of Advanced Microwave Scanning Radiometer Brightness Temperature Observations Using the Catchment Land Surface Model and Support Vector Machines

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