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 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 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 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 Impacts of Some Climatic Variables on the Seasonal Productivity of Aman Rice at Dhaka Region, Central Part of Bangladesh

2016 ◽  
Vol 8 (2) ◽  
pp. 7-10
Author(s):  
SMSA Tuhin ◽  
MA Farukh ◽  
BS Nahar ◽  
MA Baten
Keyword(s):  
Crop Production ◽  
Climatic Variability ◽  
Climatic Variables ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Climatic Data ◽  
Average Maximum ◽  
Average Maximum Temperature ◽  
Daily Sunshine

An agro-climatic study was conducted at Dhaka region of Bangladesh using 43 years (1970-2012) of climatic data (daily maximum temperature, seasonal total rainfall, daily average humidity, and daily sunshine hour) to observe the climatic variability and their impacts on the productivity of Aman rice. The average maximum temperature increased by 0.04°C in Aman season in Dhaka region. The average sunshine hours decreased by 0.05 in the season. The average humidity decreased by 0.14% in the season. The average seasonal rainfall increased slightly by 0.09 mm in the season. The Aman rice production increased by 0.03 t ha-1 in the region. The production year 2003 shows highest productivity due to less climatic devastation impact on the seasonal productivity of the rice. The climatic variables impact ( Savg > Havg > Tmax ) implies the seasonal productivity of Aman rice was mostly and inversely correlated with average sunshine (Savg) hour. However, most of the time the production showed increasing trend except some devastating natural calamities in the year of 1988 and 1998 which affected crop production seriously.J. Environ. Sci. & Natural Resources, 8(2): 7-10 2015

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 The Effect of Maximum Average Temperature on Suicide Rates in California, 2008-2017: An Ecological Study

2020 ◽  
Author(s):  
Mavra Qamar ◽  
Sierra Cheng ◽  
Rebecca Plouffe ◽  
Stephanie Nanos ◽  
David N Fisman ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Suicide Rates ◽  
Binomial Regression ◽  
The Relationship ◽  
Heat Events

Abstract Background: Suicide prevention is a salient public health responsibility, as it is one of the top ten leading causes of premature mortality in the United States. Risk factors of suicide transcend the individual and societal level as risk can increase based on climatic variables. Previous studies have been country-based. Currently, studies focused solely on regions, provinces, or states, such as California, are limited. The present study holds two purposes: i) to assess the effect of maximum temperature on suicides, and ii) to evaluate the effect of number of monthly heat events on suicide rates, in California from 2008-2017.Methods: The exposure was measured as the average Californian daily maximum temperature within each month, and the number of monthly heat events, which was calculated as a count of the days exhibiting a >15% increase from the historical monthly temperature. The outcome was measured as California’s monthly suicide rate. Negative binomial regression models assessed the relationship between maximum temperature and suicides, and heat events and suicide. A seasonal decomposition of a time series and auto-correlogram further analyzed the seasonality of suicide and the trend from 2008-2017. Results: There were 40,315 deaths by suicide in California between 2008-2017. Negative binomial regression indicated a 6.1% increase in suicide incidence rate ratio (IRR) per 10°F increase in maximum temperature (IRR=1.00590 per 1°F, 95% CI: 1.00387, 1.00793, p<0.0001) and a positive, non-significant association between suicide rates and number of heat events adjusted for month of occurrence (IRR 1.00148 per heat event, 95% CI: 0.99636, 1.00661, p=0.572). The time series analysis and auto-correlogram suggested seasonality of deaths by suicide.Conclusion: The present study provided preliminary evidence that will generate future directions for research. We must seek to further illuminate the relationship of interest and apply our findings to public health interventions that will lower the rates of death by suicide as we are confronted with the effects of climate change.

scholarly journals On forecasting daily summer maximum temperature at Madras

MAUSAM ◽  
2021 ◽  
Vol 49 (1) ◽  
pp. 95-102
Author(s):  
Y. E. A. RAJ
Keyword(s):  
Zonal Wind ◽  
Lead Time ◽  
Optimal Number ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Statistical Techniques ◽  
Daily Maximum ◽  
Skill Scores ◽  
Summer Maximum ◽  
Maximum Temperatures

Forecasting schemes based on statistical techniques have been developed to forecast daily summer (March-May) maximum temperatures of Madras. A set of optimal number of predictors were chosen from a large number of parameters by employing stepwise forward screening. Separate forecasting schemes for Madras city and airport, with lead time of 24 and 9 hr were developed from the data of 12 years and tested in an independent sample of 4 years. Maximum temperature of the previous day, normal daily maximum temperature, temperature advection index and morning zonal wind at Madras at 900 hPa level were among the predictors selected. The schemes yielded good results providing 77-87% correct, forecasts with skill scores of 0.29-0.57.

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