Trends and Periodicities in Rainfall, Temperature, and Relative Humidity Datasets Over Ghana

2021 ◽  
Vol 4 (1) ◽  
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Coastal Zone ◽  
Seasonal Rainfall ◽  
Rainfall Time Series ◽  
Cyclic Pattern ◽  
Temperature And Humidity ◽  
Long Time ◽  
Northern Zone

This paper analyzed trends and periodicities in annual and seasonal rainfall, temperature, and relative humidity (RH) over Ghana using climatological datasets for twelve (12) synoptic stations spanning the periods of 30 to 52 years (1961–2010), obtained from the Ghana Meteorological Agency (GMet). The datasets were normalized by dividing with the long time mean, and grouped in decades. Findings show that 1961-1970 and 2001-2010 decades recorded significant wetter years, while 1981-1990 recorded relatively drier years within in the zones with bimodal rainfall seasons. However in the zone with mono-modal rainfall seasons wetter years occurred within the decades of 1981-1990 and 2001-2010. Furthermore, it is realised that, there is a cyclic pattern noted in the rainfall time series and a cycle of about 5 to 8 months for the Northern zone, 6 – 8 month for the middle belt and approximately 8 months for the coastal zone in the rainfall, temperature and humidity datasets suggesting a coherence in their relationship.

scholarly journals Long Time-Series Mapping and Change Detection of Coastal Zone Land Use Based on Google Earth Engine and Multi-Source Data Fusion

2021 ◽  
Vol 14 (1) ◽  
pp. 1 ◽  
Author(s):  
Dong Chen ◽  
Yafei Wang ◽  
Zhenyu Shen ◽  
Jinfeng Liao ◽  
Jiezhi Chen ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Change Detection ◽  
Coastal Zone ◽  
Google Earth ◽  
Coastal Zones ◽  
Long Time Series ◽  
Coastal Aquaculture ◽  
Long Time

Human activities along with climate change have unsustainably changed the land use in coastal zones. This has increased demands and challenges in mapping and change detection of coastal zone land use over long-term periods. Taking the Bohai rim coastal area of China as an example, in this study we proposed a method for the long time-series mapping and change detection of coastal zone land use based on Google Earth Engine (GEE) and multi-source data fusion. To fully consider the characteristics of the coastal zone, we established a land-use function classification system, consisting of cropland, coastal aquaculture ponds (saltern), urban land, rural settlement, other construction lands, forest, grassland, seawater, inland fresh-waters, tidal flats, and unused land. We then applied the random forest algorithm, the optimal classification method using spatial morphology and temporal change logic to map the long-term annual time series and detect changes in the Bohai rim coastal area from 1987 to 2020. Validation shows an overall acceptable average accuracy of 82.30% (76.70–85.60%). Results show that cropland in this region decreased sharply from 1987 (53.97%) to 2020 (37.41%). The lost cropland was mainly transformed into rural settlements, cities, and construction land (port infrastructure). We observed a continuous increase in the reclamation with a stable increase at the beginning followed by a rapid increase from 2003 and a stable intermediate level increase from 2013. We also observed a significant increase in coastal aquaculture ponds (saltern) starting from 1995. Through this case study, we demonstrated the strength of the proposed methods for long time-series mapping and change detection for coastal zones, and these methods support the sustainable monitoring and management of the coastal zone.

Spatiotemporal variability of annual and seasonal rainfall time series in Ho Chi Minh city, Vietnam

2018 ◽  
Vol 10 (3) ◽  
pp. 658-670 ◽  
Author(s):  
Dang Nguyen Dong Phuong ◽  
Vu Thuy Linh ◽  
Tran Thong Nhat ◽  
Ho Minh Dung ◽  
Nguyen Kim Loi
Keyword(s):  
Time Series ◽  
Rainy Season ◽  
Kendall Test ◽  
Dry Season ◽  
Seasonal Rainfall ◽  
Spatiotemporal Variability ◽  
Ho Chi Minh City ◽  
Annual Rainfall ◽  
Rainfall Time Series ◽  
Spatial And Temporal Patterns

Abstract This study analyzed spatial and temporal patterns of rainfall time series from 14 proportionally distributed stations in Ho Chi Minh City for the period 1980–2016. Both parametric and nonparametric approaches, specifically, linear regression, the Mann–Kendall test and Sen's slope estimator, were applied to detect and estimate the annual and seasonal trends after using original and notched boxplots for the preliminary interpretation. The outcomes showed high domination of positive trends in the annual and seasonal rainfall time series over the 37-year period, but most statistically significant trends were observed in the dry season. The results of trend estimation also indicated higher increasing rates of rainfall in the dry season compared to the rainy season at most stations. Even though the total amount of annual rainfall is mainly contributed by rainfall during the rainy season, the pronounced increment in the dry season can be a determining factor of possible changes in annual rainfall. Additionally, the interpolated results revealed a consistently increasing trend in the southeastern parts of the study area (i.e., Can Gio district), where annual rainfall was by far the lowest intensity compared to other regions.

scholarly journals Factors affecting multiscaling analysis of rainfall time series

1997 ◽  
Vol 4 (3) ◽  
pp. 137-156 ◽  
Author(s):  
D. Harris ◽  
A. Seed ◽  
M. Menabde ◽  
G. Austin
Keyword(s):  
Time Series ◽  
Parameter Estimates ◽  
Rainfall Time Series ◽  
Spectral Exponent ◽  
Factors Affecting ◽  
Cascade Models ◽  
Long Time ◽  
Variable Data ◽  
Q Function ◽  
Multiplicative Cascade

Abstract. Simulations based on random multiplicative cascade models are used to investigate the uncertainty in estimates of parameters characterizing the multiscaling nature of rainfall time series. The principal parameters used and discussed are the spectral exponent, β, and the K(q) function which characterizes the scaling of the moments. By simulating a large number of series, the sampling variability of parameter estimates in relation to the length of the time series is assessed and found to be in excess of 10%-20% for fields less than ~104 points in length. The issue of long time series which may consist of physically distinct processes with different statistics is addressed and it is shown that highly variable data mixed with an equal amount of less variable data of similar strength is dominated entirely by the statistics of the highly variable data. The effects on the estimates of β and K(q) with the addition of white noise or the tipping bucket effect (quantization) can also be significant, particularly following gradient transformations. Some high resolution rainfall data are also analyzed to illustrate how a single instrumental glitch can strongly bias results and how mixing physically different processes together can lead to incorrect conclusions.

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

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.

scholarly journals SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 602
Author(s):  
Luisa Martínez-Acosta ◽  
Juan Pablo Medrano-Barboza ◽  
Álvaro López-Ramos ◽  
John Freddy Remolina López ◽  
Álvaro Alberto López-Lambraño
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Autocorrelation Function ◽  
Information Criterion ◽  
Monthly Rainfall ◽  
Statistical Characteristics ◽  
Rainfall Time Series ◽  
River Watershed ◽  
Statistical Measures ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.

scholarly journals Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products

2021 ◽  
Vol 260 ◽  
pp. 112438
Author(s):  
Kai Yan ◽  
Jiabin Pu ◽  
Taejin Park ◽  
Baodong Xu ◽  
Yelu Zeng ◽  
...  
Keyword(s):  
Time Series ◽  
Long Time Series ◽  
Long Time
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