155 research outputs found
Longitudinal multimodal imaging in mild to moderate Alzheimer disease: a pilot study with memantine
Data Assimilation to Extract Soil Moisture Information From SMAP Observations
Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, they can be used to reduce the need for localized bias correction techniques typically implemented in data assimilation (DA) systems that tend to remove some of the independent information provided by satellite observations. Here, we use a statistical neural network (NN) algorithm to retrieve SMAP (Soil Moisture Active Passive) surface soil moisture estimates in the climatology of the NASA Catchment land surface model. Assimilating these estimates without additional bias correction is found to significantly reduce the model error and increase the temporal correlation against SMAP CalVal in situ observations over the contiguous United States. A comparison with assimilation experiments using traditional bias correction techniques shows that the NN approach better retains the independent information provided by the SMAP observations and thus leads to larger model skill improvements during the assimilation. A comparison with the SMAP Level 4 product shows that the NN approach is able to provide comparable skill improvements and thus represents a viable assimilation approach
The SMAP Level-4 ECO Project: Linking the Terrestrial Water and Carbon Cycles
The SMAP (Soil Moisture Active Passive) Level-4 projects aims to develop a fully coupled hydrology-vegetation data assimilation algorithm to generate improved estimates of modeled hydrological fields and carbon fluxes. This includes using the new NASA Catchment-CN (Catchment-Carbon-Nitrogen) model, which combines the Catchment land surface hydrology model with dynamic vegetation components from the Community Land Model version 4 (CLM4). As such, Catchment-CN allows a more realistic, fully coupled feedback between the land hydrology and the biosphere. The L4 ECO project further aims to inform the model through the assimilation of Soil Moisture Active Passive (SMAP) brightness temperature observations as well as observations of Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of absorbed photosynthetically active radiation (FPAR). Preliminary results show that the assimilation of SMAP observations leads to consistent improvements in the model soil moisture skill. An evaluation of the Catchment-CN modeled vegetation characteristics showed that a calibration of the model's vegetation parameters is required before an assimilation of MODIS FPAR observations is feasible
The SMAP Level-4 ECO Project: Improving Terrestrial Flux Estimates Through Coupled Hydrology-Vegetation Data Assimilation
Simulations of hydrologic and vegetation states as well as water, energy and carbon fluxes from the land surface to the atmosphere are crucial for a wide range of applications, including agricultural advisories, forecasts of (short-term) atmospheric behavior and seasonal weather predictions including forecasts of extreme events, such as heatwaves or droughts. The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Eco-Hydrology (L4-ECO) project aims to improve modeled estimates of the terrestrial water, energy and carbon fluxes and states by developing a fully-coupled hydrology-vegetation data assimilation system. This system is developed around the NASA Goddard Earth Observing System (GEOS) Catchment-CN land surface model, which combines land hydrology and energy balance components of the GEOS Catchment model with dynamic vegetation components of the Community Land Model version 4. Catchment-CN fully couples the terrestrial water, energy and carbon cycles, allowing feedbacks from the land hydrology to the biosphere and vice versa.Here, we implement a calibration of the Catchment-CN vegetation parameterization against observations of the fraction of absorbed photosynthetically active radiation (FPAR) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve the model's standalone skill. Later, the DA algorithm used to produce the SMAP L4 soil moisture product will be adapted to Catchment-CN to assimilate SMAP brightness temperatures and inform the model's land hydrology component. Finally, the DA system will be further extended to assimilate MODIS FPAR observations in order to constrain the model's dynamic vegetation component.In this presentation, we demonstrate that the Catchment-CN parameter calibration leads to more realistic vegetation simulations and reduces the root mean squared error between modeled and observed vegetation states across the model's various plant functional types. We also show that the assimilation of SMAP observations is able to improve the average correlation, bias and unbiased RMSE between the modeled surface and root zone soil moisture estimates, and ground observations from the SMAP core validation sites
Global downscaling of remotely sensed soil moisture using neural networks
Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e.,
of the order of 1 km) is necessary in order to quantify its role in regional
feedbacks between the land surface and the atmospheric boundary layer.
Moreover, several applications such as agricultural management can benefit
from soil moisture information at fine spatial scales. Soil moisture
estimates from current satellite missions have a reasonably good temporal
revisit over the globe (2–3-day repeat time); however, their finest spatial
resolution is 9 km. NASA's Soil Moisture Active Passive (SMAP) satellite has
estimated soil moisture at two different spatial scales of 36 and 9 km since
April 2015. In this study, we develop a neural-network-based downscaling
algorithm using SMAP observations and disaggregate soil moisture to 2.25 km
spatial resolution. Our approach uses the mean monthly Normalized Differenced
Vegetation Index (NDVI) as ancillary data to quantify the subpixel
heterogeneity of soil moisture. Evaluation of the downscaled soil moisture
estimates against in situ observations shows that their accuracy is better
than or equal to the SMAP 9 km soil moisture estimates.</p
Mean-Based Forecasting Error Measures for Intermittent Demand
To compare different forecasting methods on demand series we require an error
measure. Many error measures have been proposed, but when demand is
intermittent some become inapplicable, some give counter-intuitive results, and
there is no agreement on which is best. We argue that almost all known measures
rank forecasters incorrectly on intermittent demand series. We propose several
new error measures with wider applicability, and correct forecaster ranking on
several intermittent demand patterns. We call these "mean-based" error measures
because they evaluate forecasts against the (possibly time-dependent) mean of
the underlying stochastic process instead of point demands
Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence
A new global estimate of surface turbulent fluxes,
latent heat flux (LE) and sensible heat flux (H), and gross primary
production (GPP) is developed using a machine learning approach informed by
novel remotely sensed solar-induced fluorescence (SIF) and other radiative
and meteorological variables. This is the first study to jointly retrieve LE,
H, and GPP using SIF observations. The approach uses an artificial neural
network (ANN) with a target dataset generated from three independent data
sources, weighted based on a triple collocation (TC) algorithm. The new
retrieval, named Water, Energy, and Carbon with Artificial Neural Networks
(WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at
1° × 1° spatial resolution and at monthly time
resolution. The quality of ANN training is assessed using the target data,
and the WECANN retrievals are evaluated using eddy covariance tower estimates
from the FLUXNET network across various climates and conditions. When compared to
eddy covariance estimates, WECANN typically outperforms other products,
particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals
across three extreme drought and heat wave events demonstrates the capability
of the retrievals to capture the extent of these events. Uncertainty
estimates of the retrievals are analyzed and the interannual variability in
average global and regional fluxes shows the impact of distinct climatic
events – such as the 2015 El Niño – on surface turbulent fluxes and
GPP
Global Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using Assimilation Diagnostics
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 (O-F) 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 O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under ~3 K), the soil moisture increments (under ~0.01 cu.m/cu.m), and the surface soil temperature increments (under ~1 K). Typical instantaneous values are ~6 K for O-F residuals, ~0.01 (~0.003) cu.m/cu.m for surface (root-zone) soil moisture increments, and ~0.6 K for surface soil temperature increments. The O-F 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 O-F auto-correlations 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
Molecular Variation at the SLC6A3 Locus Predicts Lifetime Risk of PTSD in the Detroit Neighborhood Health Study
Recent work suggests that the 9-repeat (9R) allele located in the 3′UTR VNTR of the SLC6A3 gene increases risk of posttraumatic stress disorder (PTSD). However, no study reporting this association to date has been based on population-based samples. Furthermore, no study of which we are aware has assessed the joint action of genetic and DNA methylation variation at SLC6A3 on risk of PTSD. In this study, we assessed whether molecular variation at SLC6A3 locus influences risk of PTSD. Participants (n = 320; 62 cases/258 controls) were drawn from an urban, community-based sample of predominantly African American Detroit adult residents, and included those who had completed a baseline telephone survey, had provided blood specimens, and had a homozygous genotype for either the 9R or 10R allele or a heterozygous 9R/10R genotype. The influence of DNA methylation variation in the SLC6A3 promoter locus was also assessed in a subset of participants with available methylation data (n = 83; 16 cases/67 controls). In the full analytic sample, 9R allele carriers had almost double the risk of lifetime PTSD compared to 10R/10R genotype carriers (OR = 1.98, 95% CI = 1.02–3.86), controlling for age, sex, race, socioeconomic status, number of traumas, smoking, and lifetime depression. In the subsample of participants with available methylation data, a significant (p = 0.008) interaction was observed whereby 9R allele carriers showed an increased risk of lifetime PTSD only in conjunction with high methylation in the SLC6A3 promoter locus, controlling for the same covariates. Our results confirm previous reports supporting a role for the 9R allele in increasing susceptibility to PTSD. They further extend these findings by providing preliminary evidence that a “double hit” model, including both a putatively reduced-function allele and high methylation in the promoter region, may more accurately capture molecular risk of PTSD at the SLC6A3 locus
Facial expressions depicting compassionate and critical emotions: the development and validation of a new emotional face stimulus set
Attachment with altruistic others requires the ability to appropriately process affiliative and kind facial cues. Yet there is no stimulus set available to investigate such processes. Here, we developed a stimulus set depicting compassionate and critical facial expressions, and validated its effectiveness using well-established visual-probe methodology. In Study 1, 62 participants rated photographs of actors displaying compassionate/kind and critical faces on strength of emotion type. This produced a new stimulus set based on N = 31 actors, whose facial expressions were reliably distinguished as compassionate, critical and neutral. In Study 2, 70 participants completed a visual-probe task measuring attentional orientation to critical and compassionate/kind faces. This revealed that participants lower in self-criticism demonstrated enhanced attention to compassionate/kind faces whereas those higher in self-criticism showed no bias. To sum, the new stimulus set produced interpretable findings using visual-probe methodology and is the first to include higher order, complex positive affect displays
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