scholarly journals Neural Networks for Environmental Problems: Data Quality Control and Air Pollution Nowcasting

2013 ◽  
Vol 2 (3) ◽  
pp. 281-292 ◽  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Quality Control ◽  
Air Pollutant ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Data Quality Control ◽  
Distributed Information ◽  
Pollutant Concentrations

This work illustrates the use and some related results of Artificial Neural Networks (ANNs) for data quality control of environmental time series and for reconstruction of missing data. ANNs are applied to the following problems: i) short and medium-term predicting of air pollutant concentrations in urban areas, ii) interpolating and extrapolating daily maximum temperature, iii) replacing time distribution with spatial distributed information (pollutant concentrations at different measuring sites). Observed versus predicted data are compared to test the efficacy of ANNs in simulating environmental processes. Results confirm ANNs as an improvement of classical models and show the utility of ANNs for restoration of time series..

scholarly journals Monthly Temperature Observations for Uganda

2013 ◽  
Vol 52 (10) ◽  
pp. 2363-2372 ◽  
Author(s):  
John R. Christy
Keyword(s):  
Time Series ◽  
Public Access ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Record Keeping ◽  
Monthly Average ◽  
Weather Observations ◽  
Temperature Observations

AbstractThe International Surface Temperature Initiative is a worldwide effort to locate weather observations, digitize them for public access, and attach provenance to them. As part of that effort, this study sought documents of temperature observations for the nation of Uganda. Although scattered reports were found for the 1890s, consistent record keeping appears to have begun in 1900. Data were keyed in from images of several types of old forms as well as accessed electronically from several sources to extend the time series of 32 stations with at least 4 yr of data back as far as data were available. Important gaps still remain; 1979–93 has virtually no observations from any station. Because many stations were represented by more than one data source, a scheme is described to extract the “best guess” values for each station of monthly averages of the daily maximum, minimum, and mean temperature. A preliminary examination of the national time series indicates that, since the early twentieth century, it appears that Uganda experienced essentially no change in monthly-average daily maximum temperature but did experience a considerable rise in monthly-average daily minimum temperature, concentrated in the last three decades. Because there are many gaps in the data, it is hoped that readers with information on extant data that were not discovered for this study will contact the author or the project so that the data may be archived.

scholarly journals Time Series Construction of Summer Surface Temperatures for Alabama, 1883–2014, and Comparisons with Tropospheric Temperature and Climate Model Simulations

2016 ◽  
Vol 55 (3) ◽  
pp. 811-826 ◽  
Author(s):  
John R. Christy ◽  
Richard T. McNider
Keyword(s):  
Time Series ◽  
Climate Model ◽  
Spatial Scales ◽  
Heat Content ◽  
Time Frame ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Tropospheric Temperature

AbstractThree time series of average summer [June–August (JJA)] daily maximum temperature (TMax) are developed for three interior regions of Alabama from stations with varying periods of record and unknown inhomogeneities. The time frame is 1883–2014. Inhomogeneities for each station’s time series are determined from pairwise comparisons with no use of station metadata other than location. The time series for the three adjoining regions are constructed separately and are then combined as a whole assuming trends over 132 yr will have little spatial variation either intraregionally or interregionally for these spatial scales. Varying the parameters of the construction methodology creates 333 time series with a central trend value based on the largest group of stations of −0.07°C decade−1 with a best-guess estimate of measurement uncertainty from −0.12° to −0.02°C decade−1. This best-guess result is insignificantly different (0.01°C decade−1) from a similar regional calculation using NOAA’s divisional dataset based on daily data from the Global Historical Climatology Network (nClimDiv) beginning in 1895. Summer TMax is a better proxy, when compared with daily minimum temperature and thus daily average temperature, for the deeper tropospheric temperature (where the enhanced greenhouse signal is maximized) as a result of afternoon convective mixing. Thus, TMax more closely represents a critical climate parameter: atmospheric heat content. Comparison between JJA TMax and deep tropospheric temperature anomalies indicates modest agreement (r2 = 0.51) for interior Alabama while agreement for the conterminous United States as given by TMax from the nClimDiv dataset is much better (r2 = 0.86). Seventy-seven CMIP5 climate model runs are examined for Alabama and indicate no skill at replicating long-term temperature and precipitation changes since 1895.

scholarly journals The Influence of Heat on Daily Police, Medical, and Fire Dispatches in Boston, Massachusetts: Relative Risk and Time-Series Analyses

2020 ◽  
Vol 110 (5) ◽  
pp. 662-668 ◽  
Author(s):  
Augusta A. Williams ◽  
Joseph G. Allen ◽  
Paul J. Catalano ◽  
Jonathan J. Buonocore ◽  
John D. Spengler
Keyword(s):  
Time Series ◽  
Relative Risk ◽  
Emergency Services ◽  
Extreme Heat ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Emergency Medical ◽  
Time Series Analyses ◽  
The Impact

Objectives. To examine the impact of extreme heat on emergency services in Boston, MA. Methods. We conducted relative risk and time series analyses of 911 dispatches of the Boston Police Department (BPD), Boston Emergency Medical Services (BEMS), and Boston Fire Department (BFD) from November 2010 to April 2014 to assess the impact of extreme heat on emergency services. Results. During the warm season, there were 2% (95% confidence interval [CI] = 0%, 5%) more BPD dispatches, 9% (95% CI = 7%, 12%) more BEMS dispatches, and 10% (95% CI = 5%, 15%) more BFD dispatches on days when the maximum temperature was 90°F or higher, which remained consistent when we considered multiple days of heat. A 10°F increase in daily maximum temperature, from 80° to 90°F, resulted in 1.016, 1.017, and 1.002 times the expected number of daily BPD, BEMS, and BFD dispatch calls, on average, after adjustment for other predictors. Conclusions. The burden of extreme heat on local emergency medical and police services may be agency-wide, and impacts on fire departments have not been previously documented. Public Health Implications. It is important to account for the societal burden of extreme heat impacts to most effectively inform climate change adaptation strategies and planning.

scholarly journals Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data

2020 ◽  
Vol 18 (2) ◽  
pp. 207-223
Author(s):  
Salima Sultana Daisy ◽  
A. K. M. Saiful Islam ◽  
Ali Shafqat Akanda ◽  
Abu Syed Golam Faruque ◽  
Nuhu Amin ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Lead Time ◽  
Climatic Variables ◽  
Disease Spread ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Forecasting Model ◽  
Daily Maximum ◽  
Auto Regressive

Abstract Cholera, an acute diarrheal disease spread by lack of hygiene and contaminated water, is a major public health risk in many countries. As cholera is triggered by environmental conditions influenced by climatic variables, establishing a correlation between cholera incidence and climatic variables would provide an opportunity to develop a cholera forecasting model. Considering the auto-regressive nature and the seasonal behavioral patterns of cholera, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was used for time-series analysis during 2000–2013. As both rainfall (r = 0.43) and maximum temperature (r = 0.56) have the strongest influence on the occurrence of cholera incidence, single-variable (SVMs) and multi-variable SARIMA models (MVMs) were developed, compared and tested for evaluating their relationship with cholera incidence. A low relationship was found with relative humidity (r = 0.28), ENSO (r = 0.21) and SOI (r = −0.23). Using SVM for a 1 °C increase in maximum temperature at one-month lead time showed a 7% increase of cholera incidence (p < 0.001). However, MVM (AIC = 15, BIC = 36) showed better performance than SVM (AIC = 21, BIC = 39). An MVM using rainfall and monthly mean daily maximum temperature with a one-month lead time showed a better fit (RMSE = 14.7, MAE = 11) than the MVM with no lead time (RMSE = 16.2, MAE = 13.2) in forecasting. This result will assist in predicting cholera risks and better preparedness for public health management in the future.

scholarly journals Trends of the daily maximum temperatures in relation with the climatic change and the urbanization in the Athens basin

2013 ◽  
Vol 8 (3) ◽  
pp. 186-194
Keyword(s):  
Time Series ◽  
Comparative Study ◽  
Statistical Significance ◽  
Extreme Temperature ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Societal Implications ◽  
Daily Maximum ◽  
Time Period ◽  
Maximum Temperatures

It is well known that the studies that associate the climatic changes with the greenhouse effect, as a sequence of uninterruptedly ongoing figures in the concentration mainly of carbon dioxide, have been focused on the trends of the mean temperature. On the other hand the variability and the trends of the extreme temperature values have not been considered sufficiently. We notice that the variability of the maximum and minimum temperature values and generally of the extreme weather has direct economic and societal implications. The interest in this paper is focused on the study of the trends of the daily and the monthly maximum temperature during the warm months July and August for the time period from 1955 to 2000 in the wide Athens area and specifically measurements of the Nea Philadelphia and Helliniko meteorological stations. Nea Philadelphia represents an immiscibly urban area station, while Helliniko a coastal suburban area one. The specific sites were selected for the comparative study of the temperature maximum trends in a time period which covers the population growth in the area of Athens. For the whole time period, the differences of the daily maximum temperature from the corresponding 10-days period mean maximum temperatures per month were calculated for each site. Then, the days with positive difference per month and per year as well as the trends of the time-series for each station were recorded along with the statistical significance of the regression slope’s value using the Student t-test distribution. Furthermore, in order to identify the “warmest decade” in the time-series, a study of the daily maximum temperature trend for the months July and August was performed for each decade followed by a test for the statistical significance of the slope coefficient. It is known that the presumable differences of the temperature time-series depend on the influence of the urbanization, the modification of the natural suburban environment and / or on the stations’ displacement. Based on these facts, we present more in this paper the conclusions of a comparative study of the results regarding each station analytically as well as the interpretation of the results concerning all the stations as an ensemble.

scholarly journals Identification of droughts and heatwaves in Germany with regional climate networks

2021 ◽  
Vol 28 (2) ◽  
pp. 231-245
Author(s):  
Gerd Schädler ◽  
Marcus Breil
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Regional Climate ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Edge Density ◽  
Daily Maximum ◽  
Published Data ◽  
Data Set ◽  
Climate Networks

Abstract. Regional climate networks (RCNs) are used to identify heatwaves and droughts in Germany and two subregions for the summer half-years and summer seasons of the period 1951 to 2019. RCNs provide information for whole areas (in contrast to the point-wise information from standard indices), the underlying nodes can be distributed arbitrarily, they are easy to construct, and they provide details otherwise difficult to access, like temporal and spatial extent and localisation of extreme events; this makes them suitable for the statistical analysis of climate model output. The RCNs were constructed on the regular 0.25∘ grid of the E-OBS data set. The season-wise correlation of the time series of daily maximum temperature Tmax and precipitation were used to construct the adjacency matrix of the networks. Based on the results of a sensitivity study, we used the edge density, which increases significantly during extreme events, as the main metrics to characterise the network structure. The standard indices for comparison were the Effective Drought Index and Effective Heat Index (EDI and EHI), respectively, based on the same time series and complemented by other published data. Our results show that the RCNs are generally able to identify severe and moderate extremes and can differentiate between regions and seasons.

scholarly journals Social media reveal ecoregional variation in how weather influences visitor behavior in U.S. National Park Service units

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Emily J. Wilkins ◽  
Peter D. Howe ◽  
Jordan W. Smith
Keyword(s):  
National Park ◽  
National Park Service ◽  
Spatial Behavior ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Daily Weather ◽  
Visitor Behavior ◽  
Park Service ◽  
The Impact

AbstractDaily weather affects total visitation to parks and protected areas, as well as visitors’ experiences. However, it is unknown if and how visitors change their spatial behavior within a park due to daily weather conditions. We investigated the impact of daily maximum temperature and precipitation on summer visitation patterns within 110 U.S. National Park Service units. We connected 489,061 geotagged Flickr photos to daily weather, as well as visitors’ elevation and distance to amenities (i.e., roads, waterbodies, parking areas, and buildings). We compared visitor behavior on cold, average, and hot days, and on days with precipitation compared to days without precipitation, across fourteen ecoregions within the continental U.S. Our results suggest daily weather impacts where visitors go within parks, and the effect of weather differs substantially by ecoregion. In most ecoregions, visitors stayed closer to infrastructure on rainy days. Temperature also affects visitors’ spatial behavior within parks, but there was not a consistent trend across ecoregions. Importantly, parks in some ecoregions contain more microclimates than others, which may allow visitors to adapt to unfavorable conditions. These findings suggest visitors’ spatial behavior in parks may change in the future due to the increasing frequency of hot summer days.

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