Evaluating meteorological data from weather stations, and from satellites and global models for a multi-site epidemiological study

Abstract

BackgroundLongitudinal and time series analyses are needed to characterize the associations between hydrometeorological parameters and health outcomes. Earth Observation (EO) climate data products derived from satellites and global model-based reanalysis have the potential to be used as surrogates in situations and locations where weather-station based observations are inadequate or incomplete. However, these products often lack direct evaluation at specific sites of epidemiological interest.MethodsStandard evaluation metrics of correlation, agreement, bias and error were applied to a set of ten hydrometeorological variables extracted from two quasi-global, commonly used climate data products – the Global Land Data Assimilation System (GLDAS) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) - to evaluate their performance relative to weather-station derived estimates at the specific geographic locations of the eight sites in a multi-site cohort study. These metrics were calculated for both daily estimates and 7-day averages and for a rotavirus-peak-season subset. 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.ResultsThe 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.ConclusionResearchers 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.

Meteorological Temperature and Humidity Prediction from Fourier-Statistical Analysis of Hourly Data

“…Carson reports 40 different results concerning χ s /ρ s c vs and points that 6 of them are connected with results which deviate from the others. e average of his other 34 measurements is χ s /ρ s c vs � 0.53 cm 2 /s [33][34][35][36][37][38]. It is expected that this work will be useful for people who are interested in studying and researching meteorological temperature and humidity prediction from Fourier-statistical analysis of hourly data.…”

“…where T(y, t) is connected with the daily oscillation of temperature produced by the Sun and transmitted through the atmosphere mainly by convection. e temperature distribution given by equation (34) should reduce at the surface of the ground to equation (27) and satisfy a limiting condition similar to that imposed by equation (28) and is represented by T(y, t) � T p (y), for large positive values of y. (35) e temperature changes at the surface of the ground should propagate upwards, to the atmosphere, and, although having pretty complicated details because of the convectionlike transport, should have, in the average, a simple spacetime behavior.…”

Assessment of Regional and Historical Climate Records for a Water Budget Approach in Eastern Colombia

“…The disadvantages of this source of data include the wide variation in the uncertainty of the estimates [5]. Weather conditions recorded at ground-based stations can still be considered the gold standard for meteorological data but are also subject to limitations such as the lack of maintaining routine record keeping can lead to significant data gaps, forcing researchers either to exclude outcome data reducing statistical power [6]. The current availability of long-term and high-quality instrumental climate data is still very limited in some parts of the world and this reduces the ability to carry out more reliable and long-term climate assessments to better understand, detect, predict, and respond to global climate variability and change [7].…”

The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

“…Research is being carried out to elucidate the interactions and biological mechanisms through which weather influences health. For example, Colston et al [30] conducted research with the aim of selecting climate data and assessing their performance as predictors of rotavirus infections.…”