scholarly journals A Quantile Mapping Method to Fill in Discontinued Daily Precipitation Time Series

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2304
Author(s):  
Manolis G. Grillakis ◽  
Christos Polykretis ◽  
Stelios Manoudakis ◽  
Konstantinos D. Seiradakis ◽  
Dimitrios D. Alexakis
Keyword(s):  
Time Series ◽  
Missing Values ◽  
Climate Models ◽  
Daily Precipitation ◽  
Mapping Method ◽  
Precipitation Time Series ◽  
Quantile Mapping ◽  
Step Procedure ◽  
Mediterranean Island ◽  
Data Statistics

We present and assess a method to estimate missing values in daily precipitation time series for the Mediterranean island of Crete. The method involves a quantile mapping methodology originally developed for the bias correction of climate models’ output. The overall methodology is based on a two-step procedure: (a) assessment of missing values from nearby stations and (b) adjustment of the biases in the probability density function of the filled values towards the existing data of the target. The methodology is assessed for its performance in filling-in the time series of a dense precipitation station network with large gaps on the island of Crete, Greece. The results indicate that quantile mapping can benefit the filled-in missing data statistics, as well as the wet day fraction. Conceptual limitations of the method are discussed, and correct methodology application guidance is provided.

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2016 ◽  
Vol 20 (4) ◽  
pp. 1387-1403 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Ole Bøssing Christensen ◽  
Karsten Arnbjerg-Nielsen ◽  
Peter Steen Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a spatio-temporal Neyman–Scott rectangular pulses weather generator (WG). Precipitation time series used as input to the WG are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 km  ×  60 km model domain. The WG simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from regional climate models (RCMs) with spatial resolutions of 25 and 8 km, respectively. Six different RCM simulation pairs are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

scholarly journals Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes

2017 ◽  
Vol 21 (1) ◽  
pp. 345-355 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Stylianos Georgiadis ◽  
Ida Bülow Gregersen ◽  
Karsten Arnbjerg-Nielsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Future Climate ◽  
High Temporal Resolution ◽  
Urban Water ◽  
Precipitation Time Series ◽  
Event Scale ◽  
The Impact

Abstract. Urban water infrastructure has very long planning horizons, and planning is thus very dependent on reliable estimates of the impacts of climate change. Many urban water systems are designed using time series with a high temporal resolution. To assess the impact of climate change on these systems, similarly high-resolution precipitation time series for future climate are necessary. Climate models cannot at their current resolutions provide these time series at the relevant scales. Known methods for stochastic downscaling of climate change to urban hydrological scales have known shortcomings in constructing realistic climate-changed precipitation time series at the sub-hourly scale. In the present study we present a deterministic methodology to perturb historical precipitation time series at the minute scale to reflect non-linear expectations to climate change. The methodology shows good skill in meeting the expectations to climate change in extremes at the event scale when evaluated at different timescales from the minute to the daily scale. The methodology also shows good skill with respect to representing expected changes of seasonal precipitation. The methodology is very robust against the actual magnitude of the expected changes as well as the direction of the changes (increase or decrease), even for situations where the extremes are increasing for seasons that in general should have a decreasing trend in precipitation. The methodology can provide planners with valuable time series representing future climate that can be used as input to urban hydrological models and give better estimates of climate change impacts on these systems.

HHT ANALYSIS OF THE GLOBAL AVERAGE MONTHLY PRECIPITATION DATA

2012 ◽  
Vol 04 (03) ◽  
pp. 1250018 ◽  
Author(s):  
SAMUEL S. P. SHEN ◽  
DAVID NEW ◽  
THOMAS M. SMITH ◽  
PHILLIP A. ARKIN
Keyword(s):  
Time Series ◽  
Annual Cycle ◽  
Climate Models ◽  
Southern Oscillation ◽  
Frequency Variation ◽  
Frequency Change ◽  
Monthly Precipitation ◽  
Precipitation Time Series ◽  
Time Frequency ◽  
The Mean

This paper uses the Hilbert–Huang transform (HHT) method to make time–frequency diagnostic analyses of four monthly time series of the global precipitation: MERG (1900–2008), REOF (1900–2008), GPCP (1979–2009), and CMAP (1979–2009). All these data are the global land and ocean average of precipitation anomalies with respect to the mean of the entire data period. The MERG and REOF are spectral reconstructions based on historical data. The GPCP and CMAP are based on station gauge data and satellite remote sensing data. We have made the following analysis of the four datasets: (a) extract intrinsic mode functions (IMF) by HHT empirical model decomposition (EMD) sifting, (b) calculate the mean frequency and energy of each IMF, (c) calculate the Fourier spectra to compare with the IMF spectral properties, (d) calculate the Hilbert spectra and display the time–frequency variation of the precipitation time series, and (e) calculate the basic statistics of the four datasets, including mean, standard deviation, skewness, kurtosis and inter-correlation among the datasets. Our analysis results indicate the following: (i) IMFs may contain physical signals of MJO (Madden–Julian oscillation), monsoon, annual cycle, and ENSO (El Nino southern oscillation), (ii) Hilbert spectra appears to be an effective tool to display the time-frequency change of a precipitation time series and can help identify critical characteristics for improving data aggregation method and climate models, (iii) among the four datasets, MERG is the smoothest data and has the smallest variance and hence the smallest IMF energies, while the CMAP has the largest, followed by GPCP and REOF, and (iv) the nonlinear and nonstationary annual cycle is the IMF3 for all the four datasets, which is modulated by ENSO signals.

scholarly journals Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Alefu Chinasho ◽  
Bobe Bedadi ◽  
Tesfaye Lemma ◽  
Tamado Tana ◽  
Tilahun Hordofa ◽  
...  
Keyword(s):  
Time Series ◽  
Missing Values ◽  
Daily Rainfall ◽  
Substantial Contribution ◽  
Ratio Method ◽  
Rainfall Time Series ◽  
Gap Filling ◽  
Quantile Mapping ◽  
South Ethiopia ◽  
Skill Scores

Meteorological stations, mainly located in developing countries, have gigantic missing values in the climate dataset (rainfall and temperature). Ignoring the missing values from analyses has been used as a technique to manage it. However, it leads to partial and biased results in data analyses. Instead, filling the data gaps using the reference datasets is a better and widely used approach. Thus, this study was initiated to evaluate the seven gap-filling techniques in daily rainfall datasets in five meteorological stations of Wolaita Zone and the surroundings in South Ethiopia. The considered gap-filling techniques in this study were simple arithmetic means (SAM), normal ratio method (NRM), correlation coefficient weighing (CCW), inverse distance weighting (IDW), multiple linear regression (MLR), empirical quantile mapping (EQM), and empirical quantile mapping plus (EQM+). The techniques were preferred because of their computational simplicity and appreciable accuracies. Their performance was evaluated against mean absolute error (MAE), root mean square error (RMSE), skill scores (SS), and Pearson’s correlation coefficients (R). The results indicated that MLR outperformed other techniques in all of the five meteorological stations. It showed the lowest RMSE and the highest SS and R in all stations. Four techniques (SAM, NRM, CCW, and IDW) showed similar performance and were second-ranked in all of the stations with little exceptions in time series. EQM+ improved (not substantial) the performance levels of gap-filling techniques in some stations. In general, MLR is suggested to fill in the missing values of the daily rainfall time series. However, the second-ranked techniques could also be used depending on the required time series (period) of each station. The techniques have better performance in stations located in higher altitudes. The authors expect a substantial contribution of this paper to the achievement of sustainable development goal thirteen (climate action) through the provision of gap-filling techniques with better accuracy.

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