Gleckler, J. Lanzante, J. Perlwitz, S. Solomon, P. Stott, K. Taylor, L. Terray, P. Thorne, M. Wehner, F.
Wentz, T. Wigley, L.
Wilcox and C. Caldwell, P. Gleckler, T. Wigley, S. Solomon, N. Gillett, D. Ivanova, T. Karl, J. Lanzante, G. Meehl, P. Taylor, P. Wehner and F.
Gleckler, C. Bonfils, T. Barnett, D. Pierce, T. Wigley, C. Mears, F.
Climate Data Record
Wentz, W. Bruggemann, N. Gillett, S. Klein, S. Stott and M. Thorne, L. Haimberger, K.
Taylor, T. These metrics were calculated for both daily estimates and 7-day averages and for a rotavirus-peak-season subset.
- Code 61!
- Handbook for Blast Resistant Design of Buildings!
- In collections.
- C# 2005 For Dummies (For Dummies (Computer/Tech))!
- Climate Data Records from Environmental Satellites: Interim report (2004);
- Chemistry of Heterocyclic Compounds: Small Ring Heterocycles, Part 3: Oxiranes, Arene Oxides, Oxaziridines, Dioxetanes, Thietanes, Thietes, Thiazetes, and Others, Volume 42.
Then the variables from the two sources were each used as predictors in longitudinal regression models to test their association with rotavirus infection in the cohort after adjusting for covariates. The availability and completeness of station-based validation data varied depending on the variable and study site. The performance of the two gridded climate models varied considerably within the same location and for the same variable across locations, according to different evaluation criteria and for the peak-season compared to the full dataset in ways that showed no obvious pattern.
They also differed in the statistical significance of their association with the rotavirus outcome. For some variables, the station-based records showed a strong association while the EO-derived estimates showed none, while for others, the opposite was true. Researchers wishing to utilize publicly available climate data — whether EO-derived or station based - are advised to recognize their specific limitations both in the analysis and the interpretation of the results.
Epidemiologists engaged in prospective research into environmentally driven diseases should install their own weather monitoring stations at their study sites whenever possible, in order to circumvent the constraints of choosing between distant or incomplete station data or unverified EO estimates. Climate and weather influence population health through a number of interrelated pathways.
Extreme weather events such as heatwaves, coastal floods and storm surges can both cause mortality directly and can compromise water sources and crop production, leading to widespread food and water insecurity, illness, undernutrition and other morbidities World Health Organization, Moreover, climate is one of the primary constraints on the geographic and seasonal distribution of pollutants Fann et al.
The growth, survival and dispersal of microorganisms and the viable range of their intermediary hosts and vectors is determined by environmental and hydrometeorological conditions Hellberg and Chu, An increased awareness of the knowledge gaps surrounding these relationships, as well as the urgency of the climate change threat and greater understanding of its likely impact on public health has spurred calls for a research agenda to elucidate the interactions and biological mechanisms through which weather influences health Xu et al.
A major barrier to this is the scarcity of empirical data linking climate and health at a sufficient level of spatiotemporal disaggregation for use in longitudinal and time series regression analyses Kolstad and Johansson, Until recently, such analyses were hindered by the difficulty of accessing accurate and complete data on hydrometeorological predictors at high temporal resolution. Researchers wishing to include climate variables as predictors in analyses of health outcomes generally have two options: to use either EO-derived or station-based data.
The former have the advantage of completeness, both temporal and spatial. Estimates may be available at a daily or even sub-hourly resolution Fang et al. Many also offer a larger suite of mutually consistent variables than are typically available from weather stations, and the data are often freely available to access online.
Disadvantages include the wide variation in the uncertainty of the estimates Hamm et al.
U.S. Satellite System May Soon Create Gaps in Earth-Monitoring Data - Scientific American
Weather conditions recorded at ground-based stations may be considered the gold standard for meteorological data, insofar as one exists, but are also subject to limitations. Lack of capacity to maintain routine record keeping may lead to significant data gaps, forcing researchers either to exclude outcome data for which no coincident exposure measures are available thus reducing statistical power, or to rely on summary measures such as moving mean values or binned aggregates, reducing variability and temporal resolution.
- Climate Data Records from Environmental Satellites: Interim Report | The National Academies Press?
- Observational Needs for Climate Prediction and Adaptation.
- Jean Arthur: The Actress Nobody Knew.
- Orca: The Whale Called Killer;
- Exploring IBM Server & Storage Technology: A Laymens Guide to the IBM eServer and TotalStorage Families (Exploring IBM series).
- Get Copyright Permission!
- The Leper of Saint Giles (Chronicles of Brother Cadfael, Book 5) (UK Edition)!
Furthermore, weather stations are often situated in locations key to their primary uses in aviation or in monitoring weather for large population centers i. Epidemiological surveillance sites may lie many kilometers from their nearest weather stations, distances greater than those over which localized meteorological conditions vary, introducing further error.
Accessing data may be a challenge and, while the US National Oceanic and Atmospheric Administration NOAA offers a substantial online repository of historical data for some stations around the globe, for less well-served locations coordination with local meteorological agencies and organizations on the ground may be required National Oceanic and Atmospheric Administration, Finally, weather stations vary in their accuracy and generally only record a small subset of variables — often only temperature, rainfall, pressure and wind speed - and more technically demanding measures, such as humidity and solar radiation, may be lacking.
- Sleazy Stories: Confessions of an Infamous Modern Seducer of Women;
- Observational Needs for Climate Prediction and Adaptation | World Meteorological Organization;
- Bacterial and Bacteriophage Genetics?
- Looking for other ways to read this?!
The aim of this paper is to report on an exercise in selecting climate data products and assessing their performance both in characterizing meteorological conditions at the specific locations of epidemiological study sites and as predictors of a known climate-sensitive outcome — namely rotavirus infection episodes. The hypothesis that we aim to test is that gridded, EO-derived climate data products can be used as valid surrogates in longitudinal analyses where ground-based measurements are unavailable or incomplete to predict health outcomes at particular locations.
The MAL-ED project was established in to investigate risk factors for enteric infection, diarrheal disease, undernutrition and other related adverse outcomes. This network of institutions recruited and monitored birth cohorts in eight communities, each in a different low- and middle-income country — Bangladesh, Brazil, India, Nepal, Pakistan, Peru, South Africa and Tanzania — across three continents. While the sites were originally selected to be characteristic of a variety of epidemiological contexts, they also vary in the type of climate that they experience, offering a representative range of the kinds of weather patterns that prevail across the developing regions of the world.
Because they are situated at different latitudes and are divided equally between the northern and southern hemispheres, they also experience their rainy seasons and annual peaks in temperature at different times of the year and at different intensities. Similarly, the type of settlement and the altitude and topography of their locations — factors which may either have a direct effect on the weather they experience, or mediate the effect on EID incidence — all vary between sites.
The MAL-ED project sites were selected as an illustrative example not only because they allow for the assessment of weather data quality and availability over a representative range of contexts, but also so that this information could be linked temporally and geographically with data on an outcome of public health importance. The first step in this analysis was to compile a list of hydrometeorogical variables that either have been demonstrated or are hypothesized to be associated with EID transmission in general and rotavirus incidence in particular.
Next, available data sources were reviewed for daily estimates of these parameters for the eight MAL-ED study site locations over the period of follow-up. GLDAS derives meteorological fields from the Global Data Assimilation System GDAS , an operational atmospheric analysis system that merges a global climate model — a numerical representation of the physical processes and energy fluxes occurring in the earth's atmosphere, oceans and land surfaces — with a diverse suite of in situ and satellite-derived observations Intergovernmental Panel on Climate Change IPCC Working Group, , Rodell et al.
The system applies bias correction to GDAS precipitation and radiation estimates and employs the adjusted surface meteorology fields to drive advanced land surface models LSMs that simulate surface hydrological conditions. Its products have been applied in numerous studies of climate, hydrology, agriculture, and ecology, as well as, more recently, public health outcomes Grace et al.
It is internally consistent across locations and between variables although GLDAS version 1 can suffer from temporal discontinuities as input datasets change over time Kato et al. Although GLDAS does offer precipitation estimates, it employs a standard correction for bias in the GDAS precipitation field, whereas the Climate Hazards Group Infrared Precipitation with Stations CHIRPS, version 2 product, which was developed solely to estimate rainfall, calibrates cloud-top temperature estimates and gauge-satellite data by interpolating observation data and weighting it according to proximity to the five closest weather stations Funk et al.
Precipitation estimates from both sources were evaluated to determine the better-performing estimate. For the GLDAS variables, the 3-hourly estimates were aggregated to daily averages, totals or maximum and minimum as appropriate, while the daily estimated rainfall totals were taken from the CHIRPS product. The following variables were extracted from the two gridded products. In the next stage, sources of ground-based observational data were sought that contained equivalent variables to the EO-derived measured at the nearest weather station to each MAL-ED site and covering as much of the MAL-ED follow-up period — as was available.
To maintain consistency between sites, only the one nearest weather station to each site was considered.