Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission

Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates

SMAP (Soil Moisture Active and Passive) radiometer observations at similar to 40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model to generate the 9 km SMAP Level-4 Soil Moisture product. This study demonstrates that adding high-resolution radar observations from Sentinel-1 to the SMAP assimilation can increase the spatiotemporal accuracy of soil moisture estimates. Radar observations were assimilated either separately from or simultaneously with radiometer observations. Assimilation impact was assessed by comparing 3-hourly, 9 km surface and root-zone soil moisture simulations with in situ measurements from 9 km SMAP core validation sites and sparse networks, from May 2015 to December 2016. The Sentinel-1 assimilation consistently improved surface soil moisture, whereas root-zone impacts were mostly neutral. Relatively larger improvements were obtained from SMAP assimilation. The joint assimilation of SMAP and Sentinel-1 observations performed best, demonstrating the complementary value of radar and radiometer observations

SMAP Data Assimilation at the GMAO

The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. These observations are sensitive to surface(0-5 cm) soil moisture. Several of the key applications targeted by SMAP, however, require knowledge of deeper-layer, root zone (0-100 cm) soil moisture, which is not directly measured by SMAP. The NASA Global Modeling and Assimilation Office (GMAO) contributes to SMAP by providing Level 4 data, including the Level 4 Surface and Root Zone Soil Moisture(L4_SM) product, which is based on the assimilation of SMAP Tb observations in the ensemble-based NASA GEOS-5 land surface data assimilation system. The L4_SM product offers global data every three hours at 9 km resolution, thereby interpolating and extrapolating the coarser- scale (40 km) SMAP observations in time and in space (both horizontally and vertically). Since October 31, 2015, beta-version L4_SM data have been available to the public from the National Snow and Ice Data Center for the period March 31, 2015, to near present, with a mean latency of approx. 2.5 days

Soil Moisture Data Assimilation

Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydro-meteorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission

SMAP Level 4 Surface and Root Zone Soil Moisture

The SMAP Level 4 soil moisture (L4_SM) product provides global estimates of surface and root zone soil moisture, along with other land surface variables and their error estimates. These estimates are obtained through assimilation of SMAP brightness temperature observations into the Goddard Earth Observing System (GEOS-5) land surface model. The L4_SM product is provided at 9 km spatial and 3-hourly temporal resolution and with about 2.5 day latency. The soil moisture and temperature estimates in the L4_SM product are validated against in situ observations. The L4_SM product meets the required target uncertainty of 0.04 m(exp. 3)m(exp. -3), measured in terms of unbiased root-mean-square-error, for both surface and root zone soil moisture

Treatment of Porphyromonas gulae infection and downstream pathology in the aged dog by lysine-gingipain inhibitor COR388.

COR388, a small-molecule lysine-gingipain inhibitor, is currently being investigated in a Phase 2/3 clinical trial for Alzheimer's disease (AD) with exploratory endpoints in periodontal disease. Gingipains are produced by two species of bacteria, Porphyromonas gingivalis and Porphyromonas gulae, typically associated with periodontal disease and systemic infections in humans and dogs, respectively. P. gulae infection in dogs is associated with periodontal disease, which provides a physiologically relevant model to investigate the pharmacology of COR388. In the current study, aged dogs with a natural oral infection of P. gulae and periodontal disease were treated with COR388 by oral administration for up to 90 days to assess lysine-gingipain target engagement and reduction of bacterial load and downstream pathology. In a 28-day dose-response study, COR388 inhibited the lysine-gingipain target and reduced P. gulae load in saliva, buccal cells, and gingival crevicular fluid. The lowest effective dose was continued for 90 days and was efficacious in continuous reduction of bacterial load and downstream periodontal disease pathology. In a separate histology study, dog brain tissue showed evidence of P. gulae DNA and neuronal lysine-gingipain, demonstrating that P. gulae infection is systemic and spreads beyond its oral reservoir, similar to recent observations of P. gingivalis in humans. Together, the pharmacokinetics and pharmacodynamics of COR388 lysine-gingipain inhibition, along with reduction of bacterial load and periodontal disease in naturally occurring P. gulae infection in the dog, support the use of COR388 in targeting lysine-gingipain and eliminating P. gingivalis infection in humans

Assimilation of Freeze–Thaw Observations into the NASA Catchment Land Surface Model

The land surface freeze–thaw (F/T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, an F/T assimilation algorithm was developed for the NASA Goddard Earth Observing System, version 5 (GEOS-5), modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F/T state in the GEOS-5 Catchment land surface model. The F/T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F/T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F/T observations. The assimilation of perfect (error free) F/T observations reduced the root-mean-square errors (RMSEs) of surface temperature and soil temperature by 0.206° and 0.061°C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7% and 3.1%, respectively). For a maximum classification error CEmax of 10% in the synthetic F/T observations, the F/T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178° and 0.036°C, respectively. For CEmax = 20%, the F/T assimilation still reduces the RMSE of model surface temperature estimates by 0.149°C but yields no improvement over the model soil temperature estimates. The F/T assimilation scheme is being developed to exploit planned F/T products from the NASA Soil Moisture Active Passive (SMAP) mission

Joint Assimilation of SMOS Brightness Temperature and GRACE Terrestrial Water Storage Observations for Improved Soil Moisture Estimation

Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0 - 5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle

Sentinel-1 detects firn aquifers in the Greenland ice sheet

Firn aquifers in Greenland store liquid water within the upper ice sheet and impact the hydrological system. Their location and area have been estimated with airborne radar sounder surveys (Operation IceBridge, OIB). However, the OIB coverage is limited to narrow flight lines, offering an incomplete view. Here, we show the ability of satellite radar measurements from Sentinel-1 to map firn aquifers across all of Greenland at 1 km(2) resolution. The detection of aquifers relies on a delay in the freezing of meltwater within the firn above the water table, causing a distinctive pattern in the radar backscatter. The Sentinel-1 aquifer locations are in very good agreement with those detected along the OIB flight lines (Cohen's kappa = 0.84). The total aquifer area is estimated at 54,800 km(2). With continuity of Sentinel-1 ensured until 2030, our study lays a foundation for monitoring the future response of firn aquifers to climate change