[Show abstract][Hide abstract] ABSTRACT: Previous research has suggested that vitamin D and sunlight are related to cardiovascular outcomes, but associations between sunlight and risk factors have not been investigated. We examined whether increased sunlight exposure was related to improved cardiovascular risk factor status.
[Show abstract][Hide abstract] ABSTRACT: Using a geographic transect in Central Mexico, with an elevation/climate gradient, but uniformity in socio-economic conditions among study sites, this study evaluates the applicability of three widely-used remote sensing (RS) products to link weather conditions with the local abundance of the dengue virus mosquito vector, Aedes aegypti (Ae. aegypti).
Field-derived entomological measures included estimates for the percentage of premises with the presence of Ae. aegypti pupae and the abundance of Ae. aegypti pupae per premises. Data on mosquito abundance from field surveys were matched with RS data and analyzed for correlation. Daily daytime and nighttime land surface temperature (LST) values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua cloud-free images within the four weeks preceding the field survey. Tropical Rainfall Measuring Mission (TRMM)-estimated rainfall accumulation was calculated for the four weeks preceding the field survey. Elevation was estimated through a digital elevation model (DEM). Strong correlations were found between mosquito abundance and
RS-derived night LST, elevation and rainfall along the elevation/climate gradient. These findings show that RS data can be used to predict Ae. aegypti abundance, but further studies are needed to define the climatic and socio-economic conditions under which the correlations observed herein can be assumed to apply.
ISPRS International Journal of Geo-Information. 05/2014; 3(2):732-749.
[Show abstract][Hide abstract] ABSTRACT: Previous studies showed that fine particulate matter (PM2.5, particles smaller than 2.5 μm in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM2.5 concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM2.5 exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM2.5 characteristics that are crucial to population-based PM2.5 health effects research. In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM2.5 concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R2 of 0.83, mean prediction error (MPE) of 1.89 μg/m3, and square root of the mean squared prediction errors (RMSPE) of 2.73 μg/m3 in model fitting, and R2 of 0.67, MPE of 2.54 μg/m3, and RMSPE of 3.88 μg/m3 in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM2.5 concentrations.
Remote Sensing of Environment 01/2014; 140:220–232. · 5.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Due to large footprints of remotely sensed microwave brightness temperatures, accuracy of microwave observations in areas of large surface heterogeneity has always been a technological challenge. Microwave observations in areas dominated by waterbodies typically exhibit observed brightness temperature several tens of kelvins lower than areas having no surface water. The non-linearity between brightness temperature and other geophysical quantities such as soil moisture makes the accuracy of microwave observations a critical element for accurate estimation of these quantities. In retrieving soil moisture estimates, an error of 1 K in remotely sensed microwave brightness temperatures results in about 0.5–1% error in volumetric soil moisture. Large uncertainties in the observed brightness temperatures make such observations unusable in areas of large brightness temperature contrast. In this article, we discuss a deconvolution method to improve accuracy using the overlap in the adjacent microwave observations. We have shown that the method results in improved accuracy of 40% in brightness temperature estimation in regions of high brightness temperature contrast.
International Journal of Remote Sensing 11/2013; 34(21):7811-7820. · 1.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Operational application of NASA satellite observations is identified as a key component of the Weather Focus Area within the NASA Earth Science Division. Thus, next generation NASA instruments, such as the Soil Moisture Active Passive (SMAP) and Global Precipitation Measurement (GPM) missions have an inherent operational objective, and the potential for the data to improve operational forecasts must be explored. This paper describes activities at the Short-term Prediction Research and Transition (SPoRT) Center to develop a suite of high-resolution, near-real-time land surface model (LSM) products integrating these two datasets that will meet the requirements of National Oceanic and Atmospheric Administration (NOAA) and National Weather Service (NWS) local operational forecasting entities. This product suite will use the current operational SPoRT-Land Information System (LIS) platform to assimilate enhanced soil moisture estimates from SMAP L-band observations and incorporate gridded precipitation analysis products from the GPM constellation of satellites to generate improved LSM results. It is anticipated that improvements to both numerical weather prediction and situational awareness for forecast challenges such as drought monitoring, excessive heat during dry-soil conditions, and convective precipitation will result from exploitation of data products from these missions.
[Show abstract][Hide abstract] ABSTRACT: Sunlight may be related to cognitive function through vitamin D metabolism or circadian rhythm regulation. The analysis presented here sought to test whether ground and satellite measures of solar radiation are associated with cognitive decline. The study used a 15-year residential history merged with satellite and ground monitor data to determine sunlight (solar radiation) and air temperature exposure for a cohort of 19,896 cognitively intact black and white participants aged 45+ from the 48 contiguous United States. Exposures of 15, 10, 5, 2, and 1-year were used to predict cognitive status at the most recent assessment in logistic regression models; 1-year insolation and maximum temperatures were chosen as exposure measures. Solar radiation interacted with temperature, age, and gender in its relationships with incident cognitive impairment. After adjustment for covariates, the odds ratio (OR) of cognitive decline for solar radiation exposure below the median vs above the median in the 3rd tertile of maximum temperatures was 1.88 (95 % CI: 1.24, 2.85), that in the 2nd tertile was 1.33 (95 % CI: 1.09, 1.62), and that in the 1st tertile was 1.22 (95 % CI: 0.92, 1.60). We also found that participants under 60 years old had an OR = 1.63 (95 % CI: 1.20, 2.22), those 60-80 years old had an OR = 1.18 (95 % CI: 1.02, 1.36), and those over 80 years old had an OR = 1.05 (0.80, 1.37). Lastly, we found that males had an OR = 1.43 (95 % CI: 1.22, 1.69), and females had an OR = 1.02 (0.87, 1.20). We found that lower levels of solar radiation were associated with increased odds of incident cognitive impairment.
International Journal of Biometeorology 01/2013; · 2.59 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Studies of the effect of air pollution on cognitive health are often limited to populations living near cities that have air monitoring stations. Little is known about whether the estimates from such studies can be generalized to the U.S. population, or whether the relationship differs between urban and rural areas. To address these questions, we used a satellite-derived estimate of fine particulate matter (PM2.5) concentration to determine whether PM2.5 was associated with incident cognitive impairment in a geographically diverse, biracial US cohort of men and women (n = 20,150). A 1-year mean baseline PM2.5 concentration was estimated for each participant, and cognitive status at the most recent follow-up was assessed over the telephone using the Six-Item Screener (SIS) in a subsample that was cognitively intact at baseline. Logistic regression was used to determine whether PM2.5 was related to the odds of incident cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was not reliably associated with an increased odds of incident impairment, after adjusting for temperature, season, incident stroke, and length of follow-up [OR (95% CI): 1.26 (0.97, 1.64)]. The odds ratio was attenuated towards 1 after adding demographic covariates, behavioral factors, and known comorbidities of cognitive impairment. A 10 µg/m(3) increase in PM2.5 concentration was slightly associated with incident impairment in urban areas (1.40 [1.06-1.85]), but this relationship was also attenuated after including additional covariates in the model. Evidence is lacking that the effect of PM2.5 on incident cognitive impairment is robust in a heterogeneous US cohort, even in urban areas.
PLoS ONE 01/2013; 8(9):e75001. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Most of currently reported models for predicting PM(2.5) concentrations from satellite retrievals of aerosol optical depth are global methods without considering local variations, which might introduce significant biases into prediction results. In this paper, a geographically weighted regression model was developed to examine the relationship among PM(2.5), aerosol optical depth, meteorological parameters, and land use information. Additionally, two meteorological datasets, North American Regional Reanalysis and North American Land Data Assimilation System, were fitted into the model separately to compare their performances. The study area is centered at the Atlanta Metro area, and data were collected from various sources for the year 2003. The results showed that the mean local R(2) of the models using North American Regional Reanalysis was 0.60 and those using North American Land Data Assimilation System reached 0.61. The root mean squared prediction error showed that the prediction accuracy was 82.7% and 83.0% for North American Regional Reanalysis and North American Land Data Assimilation System in model fitting, respectively, and 69.7% and 72.1% in cross validation. The results indicated that geographically weighted regression combined with aerosol optical depth, meteorological parameters, and land use information as the predictor variables could generate a better fit and achieve high accuracy in PM(2.5) exposure estimation, and North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields.
Environmental Research 12/2012; · 3.24 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: OBJECTIVE: Examine whether long- and short-term sunlight radiation is related to stroke incidence. METHODS: Fifteen-year residential histories merged with satellite, ground monitor, and model reanalysis data were used to determine sunlight radiation (insolation) and temperature exposure for a cohort of 16,606 stroke and coronary artery disease-free black and white participants aged ≥45 years from the 48 contiguous United States. Fifteen-, 10-, 5-, 2-, and 1-year exposures were used to predict stroke incidence during follow-up in Cox proportional hazard models. Potential confounders and mediators were included during model building. RESULTS: Shorter exposure periods exhibited similar, but slightly stronger relationships than longer exposure periods. After adjustment for other covariates, the previous year's monthly average insolation exposure below the median gave a hazard ratio (HR) of 1.61 (95% confidence interval [CI], 1.15-2.26), and the previous year's highest compared to the second highest quartile of monthly average maximum temperature exposure gave an HR of 1.92 (95%, 1.27-2.92). INTERPRETATION: These results indicate a relationship between lower levels of sunlight radiation and higher stroke incidence. The biological pathway of this relationship is not clear. Future research will show whether this finding stands, the pathway for this relationship, and whether it is due to short- or long-term exposures. ANN NEUROL 2012.
Annals of Neurology 08/2012; · 11.19 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM2.5), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.
Geocarto International 01/2012; · 0.58 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Evidence is mounting regarding the clinically significant effect of temperature on blood pressure.
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.
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.
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.
Environmental Health 01/2011; 10(1):7. · 2.71 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In the near future, data from two microwave remote sensors at L-band will enable estimation of near-surface soil moisture. The European Space Agency's Soil Moisture and Salinity Mission (SMOS) launched in November 2009, and NASA is developing a new L-band soil moisture mission named Soil Moisture Active/Passive (SMAP). Soil moisture retrieval theory is well-established, but many details of its application, including the effects of spatial scale, are still being studied. To support these two L-band missions, studies are needed to improve our understanding of the various error sources associated with retrieval of soil moisture from satellite sensors. The purpose of this study is to quantify the magnitude of the scaling error created by the existence of sub-footprint scale variability in soil and vegetation properties, which have nonlinear relationships with emitted microwave energy. The scaling error is related to different functional relationships between surface microwave emissivity and soil moisture that exist for different soils and land cover types within a satellite footprint. We address this problem using single-frequency, single-polarization passive L-band microwave simulations for an Upper Midwest agricultural region in the United States. Making several simplifying assumptions, the analysis performed here helps provide guidance and define limits for future mission requirements by indicating hydrological and landscape conditions under which large errors are expected, and other conditions that are more conducive to accurate soil moisture estimates. Errors associated with spatial aggregation of highly variable land surface characteristics within 40 km satellite ?footprints? were found to be larger than the baseline mission requirements of 0.04-0.06 Volumetric Soil Moisture (VSM) over much of the study area. Soil moisture estimation errors were especially large and positive over portions of the domain characterized by mixtures of forests, wetlands, and open wate-
r or mixtures of forest and pasture. However, by eliminating from the analysis areas with high vegetation water content or substantial surface water fractions, conditions that have well-documented adverse effects on soil moisture retrieval, we obtained errors that are in line with these mission requirements. We developed a parameterization for effective optical depth (?<sub>eff</sub>) based on the standard deviation of optical depth (?<sub>?</sub>) within a footprint in order to improve soil moisture retrieval in the presence of highly variable vegetation density. Use of the resulting parameterized optical depth in retrievals eliminated almost all of the soil moisture biases in our simulated setting. Operationally, the empirical relationship between ?<sub>eff</sub> and ?<sub>?</sub> would need to be determined a priori based on intensive measurements from ground-based instrumentation networks or via tuning of the algorithm. Due to this issue and other confounding factors, results are not expected to be as good as in the simulated cases presented here. However, the relationship found in this study is likely to be consistent across landscapes, so any correction following this functional form would very likely lead to large improvements over retrievals based simply on weighted mean properties.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 04/2010; · 2.87 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Urbanization has been correlated with hypertension (HTN) in developing countries undergoing rapid economic and environmental transitions.
We examined the relationships among living environment (urban, suburban, and rural), day/night land surface temperatures (LST), and blood pressure in selected regions from the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. Also, the linking of data on blood pressure from REGARDS with National Aeronautics and Space Administration (NASA) science data is relevant to NASA's strategic goals and missions, particularly as a primary focus of the agency's Applied Sciences Program.
REGARDS is a national cohort of 30,228 people from the 48 contiguous United States with self-reported and measured blood pressure levels. Four metropolitan regions (Philadelphia, PA; Atlanta, GA; Minneapolis, MN; and Chicago, IL) with varying geographic and health characteristics were selected for study. Satellite remotely sensed data were used to characterize the LST and land cover/land use (LCLU) environment for each area. We developed a method for characterizing participants as living in urban, suburban, or rural living environments, using the LCLU data. These data were compiled on a 1-km grid for each region and linked with the REGARDS data via an algorithm using geocoding information.
REGARDS participants in urban areas have higher systolic and diastolic blood pressure than do those in suburban or rural areas, and also a higher incidence of HTN. In univariate models, living environment is associated with HTN, but after adjustment for known HTN risk factors, the relationship was no longer present.
Further study regarding the relationship between HTN and living environment should focus on additional environmental characteristics, such as air pollution. The living environment classification method using remotely sensed data has the potential to facilitate additional research linking environmental variables to public health concerns.
Environmental Health Perspectives 12/2009; 117(12):1832-8. · 7.26 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: 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.
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.
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.
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.
Environmental Health 08/2009; 8:34. · 2.71 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This study describes and demonstrates different techniques for surface fitting daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 microm (PM2.5) for the purpose of integrating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC) pilot study of Health and Environment Linked for Information Exchange (HELIX)-Atlanta. It presents a methodology for estimating daily spatial surfaces of ground-level PM2.5 concentrations using the B-Spline and inverse distance weighting (IDW) surface-fitting techniques, leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM2.5 from the EPA database for the year 2003 as well as PM2.5 estimates derived from NASA's satellite data. Hazard data have been processed to derive the surrogate PM2.5 exposure estimates. This paper shows that merging MODIS remote sensing data with surface observations of PM,2. not only provides a more complete daily representation of PM,2. than either dataset alone would allow, but it also reduces the errors in the PM2.5-estimated surfaces. The results of this study also show that although the IDW technique can introduce some numerical artifacts that could be due to its interpolating nature, which assumes that the maxima and minima can occur only at the observation points, the daily IDW PM2.5 surfaces had smaller errors in general, with respect to observations, than those of the B-Spline surfaces. Finally, the methods discussed in this paper establish a foundation for environmental public health linkage and association studies for which determining the concentrations of an environmental hazard such as PM2.5 with high accuracy is critical.
Journal of the Air & Waste Management Association (1995) 08/2009; 59(7):865-81. · 1.20 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Aerosol optical depth (AOD), an indirect estimate of particulate matter using satellite observations, has shown great promise in improving estimates of PM2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 μm) surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM2.5 in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) an approach that integrates satellite measurements with ground measurements in the estimates of pollutants and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect, identifying the optimal temporal scale to correlate AOD with PM2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale: within the last 30 days, displays the best model performance in a southern multi-state area centered in Mississippi using 2004 and 2005 data sets.
[Show abstract][Hide abstract] ABSTRACT: This manuscript presents an assessment of daily regional simulations of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model initialized with high-resolution land sur-face data from the NASA Land Information System (LIS) software versus a control WRF configuration that uses land surface data from the National Centers for Environmental Prediction (NCEP) Eta Model. The goal of this study is to investigate the potential benefits of using the LIS software to improve land surface initialization for regional NWP. Fifty-eight individual nested simulations were integrated for 24 h for both the control and experimental (LISWRF) configurations during May 2004 over Florida and the sur-rounding areas: 29 initialized at 0000 UTC and 29 initialized at 1200 UTC. The land surface initial conditions for the LISWRF runs came from an offline integration of the Noah land surface model (LSM) within LIS for two years prior to the beginning of the month-long study on an identical grid domain to the subsequent WRF simulations. Atmospheric variables used to force the offline Noah LSM integration were provided by the North American Land Data Assimilation System and Global Data Assimilation System gridded analyses. The LISWRF soil states were generally cooler and drier than the NCEP Eta Model soil states during May 2004. Comparisons between the control and LISWRF runs for one event suggested that the LIS land surface initial conditions led to an improvement in the timing and evolution of a sea-breeze circulation over portions of northwestern Florida. Surface verification statistics for the entire month indicated that the LISWRF runs produced a more enhanced and accurate diurnal range in 2-m temperatures compared to the control as a result of the overall drier initial soil states, which resulted from a reduction in the nocturnal warm bias in conjunction with a reduction in the daytime cold bias. Daytime LISWRF 2-m dewpoints were correspondingly drier than the control dewpoints, again a manifestation of the drier initial soil states in LISWRF. The positive results of the LISWRF experiments help to illustrate the importance of initializing regional NWP models with high-quality land surface data generated at the same grid resolution.
Journal of Hydrometeorology 12/2008; 9:1249-1266. · 3.27 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Soil moisture, because of its very high spatial variability and relative paucity of in situ observations, is well suited to take advantage of assimilation of remotely-sensed data. However, the limitations of microwave data for estimating soil moisture (shallow penetration, vegetation and surface roughness effects) complicate data assimilation efforts within land surface models. The goal of this study is to improve the representation of the soil moisture profile in the SHEELS (Simulator for Hydrology and Energy Exchange at the Land Surface) model by assimilating microwave satellite estimates of soil moisture based on X-band (10.65 GHz) brightness temperatures from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) sensor flying on the NASA Aqua satellite. These estimates represent soil moisture for only the top 1-2 cm of soil and are valid only over regions of sparse to moderate vegetation cover, but still may be useful for specifying surface boundary conditions in a modeling system. Further research is needed to better characterize the benefits to numerical weather prediction gained by assimilating AMSR-E soil moisture estimates. SHEELS is a land surface model tailored to accurately characterize soil moisture and temperature profiles, and is designed to facilitate assimilation of soil moisture data. The number and depth of soil layers is user- defined, permitting higher vertical resolution near the surface where temperature and moisture gradients are large. We have integrated SHEELS into the Land Information System (LIS), a framework for running land surface models from a common interface using common inputs (atmospheric model outputs or base forcing, and observational data). In LIS, we have coupled SHEELS with the Weather Research and Forecasting (WRF) model. LIS includes an Ensemble Kalman Filter (EnKF) data assimilation algorithm. We have added AMSR-E soil moisture as a LIS observation type, which enables us to assimilate soil moisture observations with the EnKF algorithm. We will present results of our numerical simulations over a central U.S. study area and discuss the changes in model performance resulting from data assimilation.
[Show abstract][Hide abstract] ABSTRACT: In advance of two upcoming soil moisture satellite missions, it is important to improve our understanding of the error sources associated with satellite-based soil moisture retrievals. Uncertainties in remotely-sensed soil moisture estimates can be attributed to many factors, including errors in measured brightness temperatures and input parameters, deficiencies of the radiative transfer scheme and variability of surface conditions within a satellite footprint. The purpose of this study is to quantify the magnitude of scaling errors attributable to heterogeneity within a satellite footprint of soil and vegetation properties and to present a method to reduce these errors. We address this problem using simulated single-frequency, single-polarization passive L-band microwave observations for an agricultural region of the United States.