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M. T. Yilmaz, W. T. Crow, and D. Ryu


Soil moisture datasets vary greatly with respect to their time series variability and signal-to-noise characteristics. Minimizing differences in signal variances is particularly important in data assimilation to optimize the accuracy of the analysis obtained after merging model and observation datasets. Strategies that reduce these differences are typically based on rescaling the observation time series to match the model. As a result, the impact of the relative accuracy of the model reference dataset is often neglected. In this study, the impacts of the relative accuracies of model- and observation-based soil moisture time series—for seasonal and subseasonal (anomaly) components, respectively—on optimal model–observation integration are investigated. Experiments are performed using both well-controlled synthetic and real data test beds. Investigated experiments are based on rescaling observations to a model using strategies with decreasing aggressiveness: 1) using the seasonality of the model directly while matching the variance of the observed anomaly component, 2) rescaling the seasonality and the anomaly components separately, and 3) rescaling the entire time series as one piece or for each monthly climatology. All experiments use a simple antecedent precipitation index model and assimilate observations via a Kalman filtering approach. Synthetic and real data assimilation results demonstrate that rescaling observations more aggressively to the model is favorable when the model is more skillful than observations; however, rescaling observations more aggressively to the model can degrade the Kalman filter analysis if observations are relatively more accurate.

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F. Chen, W. T. Crow, L. Ciabatta, P. Filippucci, G. Panegrossi, A. C. Marra, S. Puca, and C. Massari


Satellite-based precipitation estimates (SPEs) are generally validated using ground-based rain gauge or radar observations. However, in poorly instrumented regions, uncertainty in these references can lead to biased assessments of SPE accuracy. As a result, at regional or continental scales, an objective basis to evaluate SPEs is currently lacking. Here, we evaluate the potential for large-scale, spatially continuous evaluation of SPEs over land via the application of collocation-based techniques [i.e., triple collocation (TC) and quadruple collocation (QC) analyses]. Our collocation approach leverages the Soil Moisture to Rain (SM2RAIN) rainfall product, derived from the time series analysis of satellite-based soil moisture retrievals, in combination with independent rainfall datasets acquired from ground observations and climate reanalysis to validate four years of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) H23 daily rainfall product. Large-scale maps of the H23 correlation metric are generated using both TC and QC analyses. Results demonstrate that the SM2RAIN product is a uniquely valuable independent product for collocation analyses, because other available large-scale rainfall datasets are often based on overlapping data sources and algorithms. In particular, the availability of SM2RAIN facilitates the large-scale evaluation of SPE products like H23—even in areas that lack adequate ground-based observations to apply traditional validation approaches.

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Rolf H. Reichle, Gabrielle J. M. De Lannoy, Qing Liu, Randal D. Koster, John S. Kimball, Wade T. Crow, Joseph V. Ardizzone, Purnendu Chakraborty, Douglas W. Collins, Austin L. Conaty, Manuela Girotto, Lucas A. Jones, Jana Kolassa, Hans Lievens, Robert A. Lucchesi, and Edmond B. Smith


The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and related land surface variables from 31 March 2015 to present with ~2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (OF) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of ~0.37 K for the OF Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the OF residuals (under ~3 K), the soil moisture increments (under ~0.01 m3 m−3), and the surface soil temperature increments (under ~1 K). Typical instantaneous values are ~6 K for OF residuals, ~0.01 (~0.003) m3 m−3 for surface (root zone) soil moisture increments, and ~0.6 K for surface soil temperature increments. The OF diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The OF autocorrelations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

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