NASA Satellite Data Research Articles

Satellite-based estimate of PM2.5 and the association with the risk of death in 505,808 Taiwanese adults

Introduction There are a number of time-series studies showing the acute effects of particulate matter with aerodynamic diameter < 2.5 (PM2.5) on mortality worldwide. However, there is limited information on long-term effects of PM2.5 using large cohort study design in general populations, especially for Asian. We therefore investigate the association between PM2.5 and the risk of death in a prospective cohort with 505,808 Taiwanese adults. Methods This cohort consisted of Taiwanese who participated in a standard medical screening program between 1996 and 2011. The participants’ residential addresses were geo-coded into latitude and longitude data and then the PM2.5 levels at the addresses 1 x 1 kilometer grid were estimated by a remote-sensing algorithm based on the NASA satellite data. Information on health and potential confounders was from the medical checkup and vital information was obtained by matching the death registry system. The Cox proportional regression models were used to investigate the relative risk of death with PM2.5 in this large Asian cohort. Results The mean age of the population was 40 years old, and 52.2% of the participants were female. The participants were followed up between 1996 and 2011 with a mean follow-up period of 9.7 years, and a total of 18350 death occurred. A significantly increased hazard ratio (HR) for PM2.5 of 1.05 (95% CI: 1.03-1.07) per 10 µg/m3 was observed after adjusting for age, sex, education, smoking, drinking, and physical activity. The significant associations remained in the analyses stratified by smoking status (for never smokers, HR: 1.06, 95% CI: 1.04-1.09; for ever smokers, HR: 1.03, 95% CI: 1.00-1.06) Conclusions Long-term exposure to PM2.5 air pollution is associated with higher risk of death in Asian populations.

Generalized Extreme Value Distribution Models for the Assessment of Seasonal Wind Energy Potential of Debuncha, Cameroon

The method of generalized extreme value family of distributions (Weibull, Gumbel, and Frechet) is employed for the first time to assess the wind energy potential of Debuncha, South-West Cameroon, and to study the variation of energy over the seasons on this site. The 29-year (1983–2013) average daily wind speed data over Debuncha due to missing values in the years 1992 and 1994 is gotten from NASA satellite data through the RETScreen software tool provided by CANMET Canada. The data is partitioned into min-monthly, mean-monthly, and max-monthly data and fitted using maximum likelihood method to the two-parameter Weibull, Gumbel, and Frechet distributions for the purpose of determining the best fit to be used for assessing the wind energy potential on this site. The respective shape and scale parameters are estimated. By making use of the P values of the Kolmogorov-Smirnov statistic (K-S) and the standard error (s.e) analysis, the results show that the Frechet distribution best fits the min-monthly, mean-monthly, and max-monthly data compared to the Weibull and Gumbel distributions. Wind speed distributions and wind power densities of both the wet and dry seasons are compared. The results show that the wind power density of the wet season was higher than in the dry season. The wind speeds at this site seem quite low; maximum wind speeds are listed as between 3.1 and 4.2 m/s, which is below the cut-in wind speed of many modern turbines (6–10 m/s). However, we recommend the installation of low cut-in wind turbines like the Savonius or Aircon (10 KW) for stand-alone low energy need.

Assessing U.S. Fire Risks Using Soil Moisture Satellite Data

NASA satellite data and models provide information for mapping fire risks nationwide, giving agencies tools for fire response, say scientists at the AGU Fall Meeting.

Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model

Solar potential of western Himalayan Indian state of Himachal Pradesh is assessed using Artificial Neural Network (ANN) based global solar radiation (GSR) prediction model. J48 algorithm in Waikato Environment for Knowledge Analysis (WEKA)is used for the selection of input parameters for ANN model for predicting GSR. Most relevant input parameters are found to be temperature, altitude and sunshine hours whereas latitude, longitude, clearness index and extraterrestrial radiation are found to be least influencing variables. The usefulness of J48 algorithm in variable selection is checked by developing five ANN models: ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5. The maximum mean absolute percentage error (MAPE) for ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5 are found to be 16.91%, 16.89%, 16.38%, 6.89% and 9.04% respectively. ANN-5 model is used to develop the solar maps of Himachal Pradesh. The estimated GSR varies from 3.59 to 5.38 kWh/m2/day indicating good solar potential for solar energy applications. A correlation is developed between NASA satellite data and ground measured GSR data to find values close to ground measured GSR for different locations. The correlation coefficient is found to be 0.97. Models developed can be used to assess solar potential of any location worldwide.

Economic Status, Air Quality, and Child Health: Evidence from Inversion Episodes

On normal days, the temperature decreases with altitude, allowing air pollutants to rise and disperse. During inversion episodes, a warmer air layer at higher altitude traps pollutants close to the ground. We show how readily available NASA satellite data on vertical temperature profiles can be used to measure inversion episodes on a global scale with high spatial and temporal resolution. Then, we link inversion episode data to ground level pollution monitors and to daily in- and outpatient records for the universe of children in Sweden during a six-year period to provide instrumental variable estimates of the effects of air quality on children's health.The IV estimates show that the respiratory illness health care visit rate increases by 8 percent for each 10 μm/m³ increase in PM10; an estimate four times higher than conventional estimates. Importantly, by linking the health care data to detailed records of parental background characteristics, we show that children from low-income households suffer significantly more from air pollution than children from high income households. Finally, we provide evidence on the importance of several mechanisms that could contribute to the difference in the impact of air pollution across children in rich and poor households.

You’re Fired!: Identifying Electoral Accountability in a Competitive Authoritarian Regime

Do elections in less than free regimes serve as mechanisms of accountability? How do autocratic regimes manipulate elections to attain their most preferred outcomes and maintain power? Recent literature has failed to produce a con- sensus on how voters evaluate and punish politicians under autocracy. In response, this paper looks at the political consequences of an exogenous shock to an autocratic system, the outbreak of destructive wildfires across Russia in 2010. I find that voters punished the ruling party in subsequent elections for its incompetent response to the natural disaster. Furthermore, this paper shows that the party itself removed incumbents from electoral slates in burned areas in order to win back voters. The strategy worked, as the party only suffered losses in proportional representation tallies and not in single-member districts. I test my hypotheses by examining elections to six regional legis- latures in Russia using NASA satellite data to identify fire damage. These findings suggest that electoral institutions not only matter both to citizens and regimes under autocracy, but that these institutions provide an indirect mechanism of accountability to hold poorly performing authorities to task.

Spatio-temporal assessment of dust risk maps for solar energy systems using proxy data

This paper presents a new approach for spatio-temporal assessment of dust risk map for solar energy systems in arid area using proxy open source data. The approach considers the recent NASA satellite data from the Multi-angle Imaging Spectro-Radiometer (MISR) which provides significant information about dust emission, transport, concentration and property evolution, through Aerosol Optical Depth (AOD). The analysis was conducted on Oman as a case study. The dust risk maps were developed based on the spatio-temporal evolution of MISR AOD in blue band (446 nm). These maps showed significant variations of AOD over the year. The summer season presents the highest risk of dust contamination because of the favorable regional weather synoptic conditions for the dispersion of mineral dust and creation of haze conditions. The annual average map of AOD was also developed and compared to the dust emission sources (desert sand locations), and the annual average maps of wind direction and air temperature at 100 m and 2 m above the ground, respectively. These maps show a good correlation between dust emission sources, wind and temperature profiles, and high atmospheric dust concentration captured by AOD. Finally, the impact of dust constraint on solar energy resource assessment and systems siting was investigated for Oman. It was found that a considerable reduction (64%) of the highly suitable land is obtained after consideration of dust concentration constraint.

Terrestrial Ecosystem Carbon Fluxes Predicted from MODIS Satellite Data and Large-Scale Disturbance Modeling

The CASA (Carnegie-Ames-Stanford) ecosystem model based on satellite greenness observations has been used to estimate monthly carbon fluxes in terrestrial ecosystems from 2000 to 2009. The CASA model was driven by NASA Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation cover properties and large-scale (1-km resolution) disturbance events detected in biweekly time series data. This modeling framework has been implemented to estimate historical as well as current monthly patterns in plant carbon fixation, living biomass increments, and long-term decay of woody (slash) pools before, during, and after land cover disturbance events. Results showed that CASA model predictions closely followed the seasonal timing of Ameriflux tower measurements. At a global level, predicting net ecosystem production (NEP) flux for atmospheric CO2 from 2000 through 2005 showed a roughly balanced terrestrial biosphere carbon cycle. Beginning in 2006, global NEP fluxes became increasingly imbalanced, starting from -0.9 Pg C yr-1 to the largest negative (total net terrestrial source) flux of -2.2 Pg C yr-1 in 2009. In addition, the global sum of CO2 emissions from forest disturbance and biomass burning for 2009 was predicted at 0.51 Pg C yr-1. These results demonstrate the potential to monitor and validate terrestrial carbon fluxes using NASA satellite data as inputs to ecosystem models.

Evaluation of NASA satellite and modelled temperature data for simulating maize water requirement satisfaction index in the Free State Province of South Africa

Low density of weather stations and high percentages of missing values of the archived climate data in most places around the world makes it difficult for decision-makers to make meaningful conclusions in natural resource management. In this study, the use of NASA modelled and satellite-derived data was compared with measured minimum and maximum temperatures at selected climate stations in the Free State Province of South Africa. The NASA temperature data-fed Hargreaves evapotranspiration estimate was compared with the Penman–Monteith estimate to obtain regional coefficients for the Free State. The maize water requirement satisfaction index (WRSI) obtained using the NASA temperature data and calibrated Hargreaves equation was evaluated against the WRSI obtained using Penman–Monteith estimate. The data used is mostly from 1999 to 2008. The results of the comparison between measured minimum temperatures and NASA minimum temperatures show overestimation of the NASA values by between a monthly mean of 1.4°C and 4.1°C. NASA maximum temperatures seem to underestimate measured temperatures by monthly values ranging from 2.2 to 3.8°C. NASA-fed Hargreaves equation in its original form underestimates Penman–Monteith evapotranspiration by between 20% and 40% and hence its coefficient was calibrated accordingly. The comparison of the maize WRSI simulated with NASA temperatures showed a good correlation and small deviations from WRSI calculated from measured data. Thus, the use of NASA satellite and modelled data is recommended in the Free State Province in places where there are no meteorological readings, with special consideration of the biasness of the data.

NASA satellite data assist in tornado damage assessments

During the period of 25–28 April 2011, the southeastern United States was raked by several episodes of severe weather. Numerous tornadoes caused extensive damage and, tragically, the deaths of more than 300 people. Meteorologists from the National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS) performed extensive surveys to assess the intensity, duration, and ground track of tornadoes reported during the event. Survey activities included visits to the affected locations, analysis of radar data, aerial surveys, and interviews with eyewitnesses.

Coupling Advanced Modeling and Visualization to Improve High-Impact Tropical Weather Prediction

To meet the goals of extreme weather event warning, this approach couples a modeling and visualization system that integrates existing NASA technologies and improves the modeling system's parallel scalability to take advantage of petascale supercomputers. It also streamlines the data flow for fast processing and 3D visualizations, and develops visualization modules to fuse NASA satellite data.

A Prototype WRF-Based Ensemble Data Assimilation System for Dynamically Downscaling Satellite Precipitation Observations

Abstract In the near future, the Global Precipitation Measurement (GPM) mission will provide precipitation observations with unprecedented accuracy and spatial/temporal coverage of the globe. For hydrological applications, the satellite observations need to be downscaled to the required finer-resolution precipitation fields. This paper explores a dynamic downscaling method using ensemble data assimilation techniques and cloud-resolving models. A prototype ensemble data assimilation system using the Weather Research and Forecasting Model (WRF) has been developed. A high-resolution regional WRF with multiple nesting grids is used to provide the first-guess and ensemble forecasts. An ensemble assimilation algorithm based on the maximum likelihood ensemble filter (MLEF) is used to perform the analysis. The forward observation operators from NOAA–NCEP’s gridpoint statistical interpolation (GSI) are incorporated for using NOAA–NCEP operational datastream, including conventional data and clear-sky satellite observations. Precipitation observation operators are developed with a combination of the cloud-resolving physics from NASA Goddard cumulus ensemble (GCE) model and the radiance transfer schemes from NASA Satellite Data Simulation Unit (SDSU). The prototype of the system is used as a test bed to optimally combine observations and model information to produce a dynamically downscaled precipitation analysis. A case study on Tropical Storm Erin (2007) is presented to investigate the ability of the prototype of the WRF Ensemble Data Assimilation System (WRF-EDAS) to ingest information from in situ and satellite observations including precipitation-affected radiance. The results show that the analyses and forecasts produced by the WRF-EDAS system are comparable to or better than those obtained with the WRF-GSI analysis scheme using the same set of observations. An experiment was also performed to examine how the analyses and short-term forecasts of microphysical variables and dynamical fields are influenced by the assimilation of precipitation-affected radiances. The results highlight critical issues to be addressed in the next stage of development such as model-predicted hydrometeor control variables and associated background error covariance, bias estimation, and correction in radiance space, as well as the observation error statistics. While further work is needed to optimize the performance of WRF-EDAS, this study establishes the viability of developing a cloud-scale ensemble data assimilation system that has the potential to provide a useful vehicle for downscaling satellite precipitation information to finer scales suitable for hydrological applications.

A Quantitative Analysis of Suspected Environmental Causes of MS

Multiple sclerosis (MS) is a disease with purported environmental causes. Consistent correlations have been found in various settings for latitude, smoking exposure, sunlight, and vitamin D deficiency. We analysed the contribution of various environmental factors to the risk of developing MS from a population perspective. We collated global data of MS prevalence from 54 studies over the previous ten years and calculated the degree of risk contributed by latitude, longitude, ultraviolet radiation (from NASA satellite data and formulae for available sunlight hours), population smoking rates (from WHO data), gender, study date, study demographics, and several socioeconomic factors. We report a very significant negative correlation between MS prevalence and available ultraviolet (UV) radiation. The lack of available UV radiation outweighs other factors by at least 20 fold (p < 10⁻⁸) from single variate regression analysis. Multiple regression analysis revealed that latitude and longitude are also significant factors; smoking may also provide a very minimal role. The eight prevalence studies from Scandinavia produced prevalences that were lower than expected, given their global geospatial positioning. The available ultraviolet radiation is a significant environmental factor, more so than all the other factors examined.

Assessing the Cumulative Climate-related Health Risks in the Eastern United States

PP-30-072 Background/Aims: Climate change is likely to impose sizeable future health costs even in highly developed countries, such as the United States, caused by various adverse outcomes including elevated air pollution level. Previous research has focused on the health impact of individual stressors without considering their interactions and confounding factors. Yet a comprehensive approach is needed because environmental and public health authorities and affected communities must address a range of climate-sensitive health outcomes and their causative exposures in developing adaptation and mitigation programs. Methods: We will model health risks associated with 3 groups of climate-related stressors: heat waves, air pollution, and Lyme disease. We will couple the Community Climate System Model and WRF/CMAQ system to generate exposure estimates under current condition and in the 2050s under various IPCC greenhouse gas emission scenarios. Results: We will rely on epidemiological evidence to quantify the conresponse relations of each stressor on the general population and various susceptible subpopulations while controlling for the confounding or effect modification from other stressors. We will apply advanced NASA satellite data to verify simulated current exposures, and analyze the impact of each analytical step to the overall uncertainty in risk estimates. Conclusion: Our analysis will serve as a model cumulative risk assessment characterizing the combined risks of 3 important climate-related stressors with complex interactions in geographic and demographic space. We will develop spatial representations of both hazard overlap and risk overlap. The spatial distribution of vulnerable populations will be examined with an emphasis on identifying potential response locations using geophysical, climatological, and demographic characteristics. Through current collaborations of our team members with local governments, we will not only advance climate-health science, but also develop relevant tools to guide policy decisions regarding the response and preparedness to climate change.

The Beat

The Beat

Reliable Maps of Lightning Thunderstorms for Saudi Arabia

The main objective of this research is to develop reliable maps of isokeraunic level (thunderdays/year, TD) for the country on annual and seasonal bases. New annual thunder day maps for Saudi Arabia are presented. The results in this paper are based on data and records of thunderstorm incidences in Saudi Arabia recorded by the Presidency of Metrology and Environment. These are compared with data from power system fault records and with NASA satellite data. These new maps indicate the location, frequency, and intensity of thunderstorms. Such information is useful for engineers concerned with the design and operation of transmission and distribution lines in the country, and for the more general design of lightning protection systems. Thunderstorms are most frequent in the southwest mountains in the country, reaching a maximum TD of 97; the average TD for the whole country is 26.

NASA satellite data will help conservation

NASA and the World Conservation Union (IUCN), the world's largest environmental knowledge network, signed a joint declaration on 18 November to use NASA satellite data to assist with worldwide conservation efforts.sist with worldwide conservation efforts. The joint declaration is meant to improve IUCN access to NASA data, technology, images, and remote sensing products.

Exergy of solar radiation: global scale

A new concept regarding the exergy of solar radiation [Ecol. Model. 145 (1991) 101] is applied to the analysis of satellite data describing the seasonal (monthly) dynamics of four components of the global radiation balance: incoming and outgoing short- and long-wave solar radiation. Stated in general terms, the incoming and outgoing radiation are described by the energy spectra E in( ν) and E out( ν) for any point of the globe. We assume that there exists a non-linear operator which transforms E in( ν) into E out( ν). Its properties are defined by the characteristics of a reflecting surface (ocean, ice, land, vegetation, etc.). It is suggested that exergy, which is properly a generalised Kullback measure of the information increment, is such an operator. It is also shown that such standard values as the radiation balance and albedo are characteristics of a linear transformation operator. Using NASA satellite data to calculate the global distribution of the annual mean of exergy, we see that the domains with maximal values of exergy correspond to the main upwellings of the ocean. If thermodynamics analogies are applied to the process of interaction of solar radiation with an “active” planetary surface, in particular with vegetation cover, then the difference between the radiation balance and the exergy can be considered as the increment of internal energy. It is shown that the global pattern of this value is very similar to the global vegetation pattern.

Solar disinfection (SODIS): simulation of solar radiation for global assessment and application for point-of-use water treatment in Haiti

Haiti and other developing countries do not have sufficient meteorological data to evaluate if they meet the solar disinfection (SODIS) threshold of 3–5 h of solar radiation above 500 W/m 2, which is required for adequate microbial inactivation in drinking water. We have developed a mathematical model based on satellite-derived daily total energies to simulate monthly mean, minimum, and maximum 5-h averaged peak solar radiation intensities. This model can be used to assess if SODIS technology would be applicable anywhere in the world. Field measurements were made in Haiti during January 2001 to evaluate the model and test SODIS efficacy as a point-of-use treatment option. Using the total energy from a measured solar radiation intensity profile, the model recreated the intensity profile with 99% agreement. NASA satellite data were then used to simulate the mean, minimum, and maximum 5-h averaged peak intensities for Haiti in January, which were within 98.5%, 62.5%, and 86.0% agreement with the measured values, respectively. Most of the discrepancy was attributed to the heterogeneous nature of Haiti's terrain and the spatial resolution of the NASA data. Additional model simulations suggest that SODIS should be effective year-round in Haiti. Actual SODIS efficacy in January was tested by the inactivation of total coliform, E. coli, and H 2S-producing bacteria. Exposure period proved critical. One-day exposure achieved complete bacterial inactivation 52% of the time, while a 2-day exposure period achieved complete microbial inactivation 100% of the time. A practical way of providing people with cold water every morning that has undergone a 2-day exposure would be to rotate three groups of bottles every morning, so two groups are out in the sun and one is being used for consumption.