Remote-sensing based technique to account for sub-grid scale variability of land surface properties

A method has been presented for the representation of sub-grid scale variability of surface properties within a land surface processes model. The method uses remotely-sensed data to directly or indirectly estimate probability density functions (PDF's) or key surface variables. Application of this technique in a coupled land surface-atmosphere model requires only grid-scale values of the variables of interest, obtained from low-resolution satellite imagery or surface/remote sensing data assimilation. The PDF's of each controlling surface property are superimposed on the respective grid-scale values to simulate sub-grid scale heterogeneity. Sensitivity studies will be carried out to ascertain the relative importance of the heterogeneity of several variables, and the degree to which non-linear property-process interactions impact large-scale fluxes

Results from Assimilating AMSR-E Soil Moisture Estimates into a Land Surface Model Using an Ensemble Kalman Filter in the Land Information System

Improve simulations of soil moisture/temperature, and consequently boundary layer states and processes, by assimilating AMSR-E soil moisture estimates into a coupled land surface-mesoscale model Provide a new land surface model as an option in the Land Information System (LIS

Assimilation of SMOS Retrievals in the Land Information System

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in the upper 5 cm with a 30-50 km resolution and a mission accuracy requirement of 0.04 cm(sub 3 cm(sub -3). These observations can be used to improve land surface model soil moisture states through data assimilation. In this paper, SMOS soil moisture retrievals are assimilated into the Noah land surface model via an Ensemble Kalman Filter within the NASA Land Information System. Bias correction is implemented using Cumulative Distribution Function (CDF) matching, with points aggregated by either land cover or soil type to reduce sampling error in generating the CDFs. An experiment was run for the warm season of 2011 to test SMOS data assimilation and to compare assimilation methods. Verification of soil moisture analyses in the 0-10 cm upper layer and root zone (0-1 m) was conducted using in situ measurements from several observing networks in the central and southeastern United States. This experiment showed that SMOS data assimilation significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10 cm layer. Time series at specific stations demonstrate the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared to using a simple uniform correction curve

Assessment of Rainfall Estimates Using a Standard Z-R Relationship and the Probability Matching Method Applied to Composite Radar Data in Central Florida

Precipitation estimates from radar systems are a crucial component of many hydrometeorological applications, from flash flood forecasting to regional water budget studies. For analyses on large spatial scales and long timescales, it is frequently necessary to use composite reflectivities from a network of radar systems. Such composite products are useful for regional or national studies, but introduce a set of difficulties not encountered when using single radars. For instance, each contributing radar has its own calibration and scanning characteristics, but radar identification may not be retained in the compositing procedure. As a result, range effects on signal return cannot be taken into account. This paper assesses the accuracy with which composite radar imagery can be used to estimate precipitation in the convective environment of Florida during the summer of 1991. Results using Z = 30OR(sup 1.4) (WSR-88D default Z-R relationship) are compared with those obtained using the probability matching method (PMM). Rainfall derived from the power law Z-R was found to he highly biased (+90%-l10%) compared to rain gauge measurements for various temporal and spatial integrations. Application of a 36.5-dBZ reflectivity threshold (determined via the PMM) was found to improve the performance of the power law Z-R, reducing the biases substantially to 20%-33%. Correlations between precipitation estimates obtained with either Z-R relationship and mean gauge values are much higher for areal averages than for point locations. Precipitation estimates from the PMM are an improvement over those obtained using the power law in that biases and root-mean-square errors are much lower. The minimum timescale for application of the PMM with the composite radar dataset was found to be several days for area-average precipitation. The minimum spatial scale is harder to quantify, although it is concluded that it is less than 350 sq km. Implications relevant to the WSR-88D system are discussed

The association of remotely-sensed outdoor temperature with blood pressure levels in REGARDS: a cross-sectional study of a large, national cohort of African-American and white participants

<p>Abstract</p> <p>Background</p> <p>Evidence is mounting regarding the clinically significant effect of temperature on blood pressure.</p> <p>Methods</p> <p>In this cross-sectional study the authors obtained minimum and maximum temperatures and their respective previous week variances at the geographic locations of the self-reported residences of 26,018 participants from a national cohort of blacks and whites, aged 45+. Linear regression of data from 20,623 participants was used in final multivariable models to determine if these temperature measures were associated with levels of systolic or diastolic blood pressure, and whether these relations were modified by stroke-risk region, race, education, income, sex hypertensive medication status, or age.</p> <p>Results</p> <p>After adjustment for confounders, same-day maximum temperatures 20°F lower had significant associations with 1.4 mmHg (95% CI: 1.0, 1.9) higher systolic and 0.5 mmHg (95% CI: 0.3, 0.8) higher diastolic blood pressures. Same-day minimum temperatures 20°F lower had a significant association with 0.7 mmHg (95% CI: 0.3, 1.0) higher systolic blood pressures but no significant association with diastolic blood pressure differences. Maximum and minimum previous-week temperature variabilities showed significant but weak relationships with blood pressures. Parameter estimates showed effect modification of negligible magnitude.</p> <p>Conclusions</p> <p>This study found significant associations between outdoor temperature and blood pressure levels, which remained after adjustment for various confounders including season. This relationship showed negligible effect modification.</p

Linking Excessive Heat with Daily Heat-Related Mortality over the Coterminous United States

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. This research seeks to provide historical and future measures of climate-driven extreme heat events to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The focus of research is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM outputs, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons for 2040 and 2090 are compared to the recent past period of 1981- 2000. We characterize regional-scale temperature and humidity conditions using GCM outputs for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM outputs are analyzed to develop a 'heat stress climatology' based on statistics of extreme heat indicators. Differences between the two future and the past period are used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes are combined with the historical meteorological data, which is hourly and at a spatial scale (12 km) much finer than that of GCMs, to create future climate realizations. From these realizations, we compute the daily heat stress measures and related spatially-specific climatological fields, such as the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices, and a new heat stress variable developed as part of this research that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. Outputs are aggregated to the county level, which is a popular scale of analysis for public health interests. County-level statistics are made available to public health researchers by the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This addition of heat stress measures to CDC WONDER allows decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. Through CDC WONDER, users are able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S

Effect of sunlight exposure on cognitive function among depressed and non-depressed participants: a REGARDS cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Possible physiological causes for the effect of sunlight on mood are through the suprachiasmatic nuclei and evidenced by serotonin and melatonin regulation and its associations with depression. Cognitive function involved in these same pathways may potentially be affected by sunlight exposure. We evaluated whether the amount of sunlight exposure (i.e. insolation) affects cognitive function and examined the effect of season on this relationship.</p> <p>Methods</p> <p>We obtained insolation data for residential regions of 16,800 participants from a national cohort study of blacks and whites, aged 45+. Cognitive impairment was assessed using a validated six-item screener questionnaire and depression status was assessed using the Center for Epidemiologic Studies Depression Scale. Logistic regression was used to find whether same-day or two-week average sunlight exposure was related to cognitive function and whether this relationship differed by depression status.</p> <p>Results</p> <p>Among depressed participants, a dose-response relationship was found between sunlight exposure and cognitive function, with lower levels of sunlight associated with impaired cognitive status (odds ratio = 2.58; 95% CI 1.43–6.69). While both season and sunlight were correlated with cognitive function, a significant relation remained between each of them and cognitive impairment after controlling for their joint effects.</p> <p>Conclusion</p> <p>The study found an association between decreased exposure to sunlight and increased probability of cognitive impairment using a novel data source. We are the first to examine the effects of two-week exposure to sunlight on cognition, as well as the first to look at sunlight's effects on cognition in a large cohort study.</p

Relationships Between Excessive Heat and Daily Mortality over the Coterminous U.S

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. Using National Land Data Assimilation System (NLDAS) meteorological reanalysis data, we have developed several measures of extreme heat to enable assessments of the impacts of heat on public health over the coterminous U.S. These measures include daily maximum and minimum air temperatures, daily maximum heat indices and a new heat stress variable called Net Daily Heat Stress (NDHS) that gives an integrated measure of heat stress (and relief) over the course of a day. All output has been created on the NLDAS 1/8 degree (approximately 12 km) grid and aggregated to the county level, which is the preferred geographic scale of analysis for public health researchers. County-level statistics have been made available through the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. We have examined the relationship between excessive heat events, as defined in eight different ways from the various daily heat metrics, and heat-related and all-cause mortality defined in CDC's National Center for Health Statistics 'Multiple Causes of Death 1999-2010' dataset. To do this, we linked daily, county-level heat mortality counts with EHE occurrence based on each of the eight EHE definitions by region and nationally for the period 1999-2010. The objectives of this analysis are to determine (1) whether heat-related deaths can be clearly tied to excessive heat events, (2) what time lags are critical for predicting heat-related deaths, and (3) which of the heat metrics correlates best with mortality in each US region. Results show large regional differences in the correlations between heat and mortality. Also, the heat metric that provides the best indicator of mortality varied by region. Results from this research will potentially lead to improvements in our ability to anticipate and mitigate any significant impacts of extreme heat events on health

Chronic IL-1β-induced inflammation regulates epithelial-to-mesenchymal transition memory phenotypes via epigenetic modifications in non-small cell lung cancer.

Chronic inflammation facilitates tumor progression. We discovered that a subset of non-small cell lung cancer cells underwent a gradually progressing epithelial-to-mesenchymal (EMT) phenotype following a 21-day exposure to IL-1β, an abundant proinflammatory cytokine in the at-risk for lung cancer pulmonary and the lung tumor microenvironments. Pathway analysis of the gene expression profile and in vitro functional studies revealed that the EMT and EMT-associated phenotypes, including enhanced cell invasion, PD-L1 upregulation, and chemoresistance, were sustained in the absence of continuous IL-1β exposure. We referred to this phenomenon as EMT memory. Utilizing a doxycycline-controlled SLUG expression system, we found that high expression of the transcription factor SLUG was indispensable for the establishment of EMT memory. High SLUG expression in tumors of lung cancer patients was associated with poor survival. Chemical or genetic inhibition of SLUG upregulation prevented EMT following the acute IL-1β exposure but did not reverse EMT memory. Chromatin immunoprecipitation and methylation-specific PCR further revealed a SLUG-mediated temporal regulation of epigenetic modifications, including accumulation of H3K27, H3K9, and DNA methylation, in the CDH1 (E-cadherin) promoter following the chronic IL-1β exposure. Chemical inhibition of DNA methylation not only restored E-cadherin expression in EMT memory, but also primed cells for chemotherapy-induced apoptosis

SERVIR and Public Health

Servir is a NASA-USAID partnership to improve environmental management and resilience to climate change by strengthening the capacity of governments and other key stakeholders to integrate Earth observations into development decision-makin