Long Time-Series Simulation of Water Quality in Distribution Systems

The period between 1955 and 1988 was the time of reconstruction in the destroyed Federal Republic of Germany, marked by booming industrial development and - with some delay -by the rise and spread of ecological awareness and the idea of water conservation. This is the background against which the analysis of water quality and of pollution load trends should be seen. The study of a long-term load trend presupposes the following requirements:–sufficiently large number of measured data;–reliable, reproducible analytical methods which produce comparable results over long time series; and–hydrological interpretation and evaluation of results. The continuous efforts to improve analytical methods and the elimination of distorting substances and matrix effects led to the situation that long time series of data are not directly comparable. In some cases, summative analyses (aggregate parameters) have been replaced by newly developed substance-specific methods so that the continuity of records has been broken. Furthermore, there is a general underestimation of the necessity to consider and evaluate analytical data in a space-time continuum. However, on the whole, we are able to give a satisfying interpretation of trends for the classical parameters. In the case of the trace substances detected by modem methods, this is possible only for the past two decades and with some reservations.

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A numerical implementation of the Dynamic Thermal Network method for long time series simulation of conduction in multi-dimensional non-homogeneous solids

International Journal of Heat and Mass Transfer ◽

10.1016/j.ijheatmasstransfer.2013.02.016 ◽

2013 ◽

Vol 61 ◽

pp. 475-489 ◽

Cited By ~ 10

Author(s):

Simon J. Rees ◽

Denis Fan

Keyword(s):

Time Series ◽

Numerical Implementation ◽

Thermal Network ◽

Long Time Series ◽

Network Method ◽

Long Time ◽

Thermal Network Method ◽

Time Series Simulation

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Consistent, accurate, high resolution, long time-series mapping of built-up land in the North China Plain

GIScience & Remote Sensing ◽

10.1080/15481603.2021.1948275 ◽

2021 ◽

pp. 1-17

Author(s):

Jinzhu Wang ◽

Michalis Hadjikakou ◽

Brett A. Bryan

Keyword(s):

Time Series ◽

High Resolution ◽

North China Plain ◽

North China ◽

Long Time Series ◽

The North ◽

The North China Plain ◽

Long Time

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Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

Remote Sensing ◽

10.3390/rs13112174 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2174

Author(s):

Lijian Shi ◽

Sen Liu ◽

Yingni Shi ◽

Xue Ao ◽

Bin Zou ◽

...

Keyword(s):

Time Series ◽

Sea Ice ◽

Ocean Circulation ◽

The Arctic ◽

Retrieval Algorithm ◽

Atmospheric Effects ◽

Observation Data ◽

Long Time Series ◽

Ice Concentration ◽

Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

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