Data assimilation for constructing long-term gridded daily rainfall time series over Southeast Asia

2019 ◽  
Vol 53 (5-6) ◽  
pp. 3289-3313 ◽  
Author(s):  
Vishal Singh ◽  
Qin Xiaosheng
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Southeast Asia ◽  
Daily Rainfall ◽  
Rainfall Time Series

scholarly journals Cascade Rainfall Disaggregation Application in U.S. Central Plains

2017 ◽  
Vol 7 (4) ◽  
pp. 30 ◽  
Author(s):  
Jurgen D. Garbrecht ◽  
Rabi Gyawali ◽  
Robert W. Malone ◽  
John C. Zhang
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Rainfall Time Series ◽  
Random Component ◽  
Rainfall Characteristics ◽  
Hourly Rainfall ◽  
Replicated Observations

Long-term observations of daily rainfall are common and routinely available for a variety of hydrologic applications. In contrast, observations of 10 or more years of continuous hourly rainfall are rare. Yet, sub-daily rainfall data are required in rainfall-runoff models. Rainfall disaggregation can generate sub-daily time-series from available long term daily observations. Herein, the performance of Multiplicative Random Cascade (MRC) model at disaggregating daily-to-hourly rainfall was investigated. The MRC model was parameterized and validated with 15 years of continuous observed daily and hourly rainfall data at three weather stations in Oklahoma. Model performance, or degree to which the disaggregated rainfall time series replicated observations, was assessed using 46 variables of hourly rainfall characteristics, such as longest wet spell duration, average number of rainfall hours per year, and largest hourly rainfall. Findings include: a) average-type hourly rainfall characteristics were better replicated than single value characteristics such as longest, maximum, or peak hourly rainfall; b) the large number of sub-trace hourly rainfall values (<0.254 mm h-1) generated by the MRC model were not supported by observations; c) the random component of the MRC model led to a variation under 15% of the average value for most rainfall characteristics with the exceptions of the “longest wet spell duration” and “maximum hourly rainfall”; and d) the MRC model produced fewer persistent rainfall events compared to those in the observed rainfall record. The large number of generated trace rainfall values and difficulties to replicate reliably extreme rainfall characteristics, reduces the number of potential hydrologic applications that could take advantage of the MRC disaggregated hourly rainfall. Nevertheless, in most cases, the disaggregated rainfall generated by the MRC model replicated observed average-type rainfall characteristics well.

scholarly journals A multiple threshold method for fitting the generalized Pareto distribution and a simple representation of the rainfall process

2010 ◽  
Vol 7 (4) ◽  
pp. 4957-4994 ◽  
Author(s):  
R. Deidda
Keyword(s):  
Time Series ◽  
Distribution Function ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Daily Rainfall ◽  
Rainfall Time Series ◽  
Threshold Method ◽  
Generalized Pareto ◽  
Multiple Threshold ◽  
Optimum Threshold

Abstract. Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution function to reliably describe the exceedances of daily rainfall records above a proper optimum threshold, which should be selected as small as possible to retain the largest sample while assuring an acceptable fitting. Such an optimum threshold may differ from site to site, affecting consequently not only the GPD scale parameter, but also the probability of threshold exceedance. Thus a first objective of this paper is to derive some expressions to parameterize a simple threshold-invariant three-parameter distribution function which is able to describe zero and non zero values of rainfall time series by assuring a perfect overlapping with the GPD fitted on the exceedances of any threshold larger than the optimum one. Since the proposed distribution does not depend on the local thresholds adopted for fitting the GPD, it will only reflect the on-site climatic signature and thus appears particularly suitable for hydrological applications and regional analyses. A second objective is to develop and test the Multiple Threshold Method (MTM) to infer the parameters of interest on the exceedances of a wide range of thresholds using again the concept of parameters threshold-invariance. We show the ability of the MTM in fitting historical daily rainfall time series recorded with different resolutions. Finally, we prove the supremacy of the MTM fit against the standard single threshold fit, often adopted for partial duration series, by evaluating and comparing the performances on Monte Carlo samples drawn by GPDs with different shape and scale parameters and different discretizations.

scholarly journals Modelling of monsoon rainfall for a mesoscale catchment in North-West India II: stochastic rainfall simulations

2006 ◽  
Vol 10 (6) ◽  
pp. 807-815 ◽  
Author(s):  
E. Zehe ◽  
A. K. Singh ◽  
A. Bárdossy
Keyword(s):  
Time Series ◽  
Meteorological Data ◽  
Daily Rainfall ◽  
Time Variability ◽  
Western India ◽  
Data Sets ◽  
Rainfall Time Series ◽  
North West ◽  
Monthly Scale ◽  
Circulation Patterns

Abstract. Within this study we present a robust method for generating precipitation time series for the Anas catchment in North Western India. The method employs a multivariate stochastic simulation model that is driven by a time series of objectively classified circulation patterns (CPs). In a companion study (Zehe et al., 2006) it was already shown that CPs classified from the 500 or 700 Hpa levels are suitable to explain space-time variability of precipitation in that area. The model is calibrated using observed rainfall time series for the period 1985–1992 for two different CP time series, one from the 500 Hpa level and the over from the 700 Hpa level, and 200 realizations of daily rainfall are simulated for the period 85–94. Simulations using the CPs from the 500 Hpa level as input yield a good match of the observed averages and standard deviations of daily rainfall. They show furthermore good performance at the monthly scale. When used with the 700 Hpa level CPs as inputs the model clearly underestimates the standard deviation and performs much worse at the monthly scale, especially in the validation period 93–94. The presented results give evidence that CPs from the 500 Hpa, level in combination with a multivariate stochastic model, make up a suitable tool for reducing the sparsity of precipitation data in developing regions with sparse hydro-meteorological data sets.

scholarly journals Automatic Curve Extraction for Digitizing Rainfall Strip Charts

2011 ◽  
Vol 28 (7) ◽  
pp. 891-906 ◽  
Author(s):  
H. E. van Piggelen ◽  
T. Brandsma ◽  
H. Manders ◽  
J. F. Lichtenauer
Keyword(s):  
Time Series ◽  
Batch Process ◽  
Rainfall Time Series ◽  
Graphical Interface ◽  
The Core ◽  
Cumulative Rainfall ◽  
Color Detection ◽  
Meta Information

Abstract A method has been developed that largely automates the labor-intensive extraction work for large amounts of rainfall strip charts and paper rolls. The method consists of the following five basic steps: 1) scanning the charts and rolls to high-resolution digital images, 2) manually and visually registering relevant meta information from charts and rolls and preprocessing rolls to locate day transitions, 3) applying automatic curve extraction software in a batch process to determine the coordinates of cumulative rainfall lines on the images, 4) postprocessing the curves that were not correctly determined in step 3, and 5) aggregating the cumulative rainfall in pixel coordinates to the desired time resolution. The core of the method is in step 3. Here a color detection procedure is introduced that automatically separates the background of the charts and rolls from the grid and subsequently the rainfall curve. The rainfall curve is detected by minimization of a cost function. In total, 321 station years of locations in the Netherlands have successfully been digitized and transformed to long-term rainfall time series with 5-min resolution. In about 30% of the cases, semiautomatic postprocessing of the results was needed using a purpose-built graphical interface application. This percentage, however, strongly depends on the quality of the recorded curves and the charts and rolls. Although developed for rainfall, the method can be applied to other elements as well.

Hybrid of ARIMA-GARCH Modeling in Rainfall Time Series

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Fadhilah Yusof ◽  
Ibrahim Lawal Kane ◽  
Zulkifli Yusop
Keyword(s):  
Time Series ◽  
Daily Rainfall ◽  
Rainfall Series ◽  
Empirical Modeling ◽  
Dependence Structure ◽  
Rainfall Time Series ◽  
Arima Models ◽  
Starting Point ◽  
Garch Modeling ◽  
Squared Residuals

The dependence structure of rainfall is usually very complex both in time and space. It is shown in this paper that the daily rainfall series of Ipoh and Alorsetar are affected by nonlinear characteristics of the variance often referred to as variance clustering or volatility, where large changes tend to follow large changes and small changes tend to follow small changes. In most empirical modeling of hydrological time series, the focus was on modeling and predicting the mean behavior of the time series through conventional methods of an Autoregressive Moving Average (ARMA) modeling proposed by the Box Jenkins methodology. The conventional models operate under the assumption that the series is stationary that is: constant mean and either constant variance or season-dependent variances, however, does not take into account the second order moment or conditional variance, but they form a good starting point for time series analysis. The residuals from preliminary ARIMA models derived from the daily rainfall time series were tested for ARCH behavior. The autocorrelation structure of the residuals and the squared residuals were inspected, the residuals are uncorrelated but the squared residuals show autocorrelation, the Ljung-Box test confirmed the results. McLeod-Li test and a test based on the Lagrange multiplier (LM) principle were applied to the squared residuals from ARIMA models. The results of these auxiliary tests show clear evidence to reject the null hypothesis of no ARCH effect. Hence indicates that GARCH modeling is necessary. Therefore the composite ARIMA-GARCH model captures the dynamics of the daily rainfall series in study areas more precisely. On the other hand, Seasonal ARIMA model became a suitable model for the monthly average rainfall series of the same locations treated.

scholarly journals Rainfall variability in Malay Peninsula region of Southeast Asia using gridded data

2019 ◽  
Vol 81 ◽  
pp. 01002
Author(s):  
Vishal Singh ◽  
Xiaosheng Qin
Keyword(s):  
Time Series ◽  
Southeast Asia ◽  
Rainfall Variability ◽  
Rainfall Time Series ◽  
Malay Peninsula ◽  
Gap Filling ◽  
Mathematical Functions ◽  
Rainfall Analysis ◽  
The Past ◽  
Filling Analysis

Southeast Asia is recognized as a climate-change vulnerable region as it has been significantly affected by many extreme events in the past. This study carried out a rainfall analysis over the Malay Peninsula region of Southeast Asia utilizing historical (1981-2007) gridded rainfall datasets (0.5°×0.5°). The rainfall variability was analyzed in an intra-decadal time series duration. The uncertainty involved in all datasets was also checked based on the comparison of multiple global rainfall datasets. Rainfall gap filling analysis was conducted for producing more accurate rainfall time series after testing multiple mathematical functions. Frequency-based rainfall extreme indices such as Dry Days and Wet days are generated to assess the rainfall variability over the study area. Our results revealed a notable variation existed in the rainfalls over Malay Peninsula as per the long historical duration (1981-2007).

scholarly journals A multiple threshold method for fitting the generalized Pareto distribution to rainfall time series

2010 ◽  
Vol 14 (12) ◽  
pp. 2559-2575 ◽  
Author(s):  
R. Deidda
Keyword(s):  
Time Series ◽  
Distribution Function ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Daily Rainfall ◽  
Rainfall Time Series ◽  
Threshold Method ◽  
Generalized Pareto ◽  
Multiple Threshold ◽  
Optimum Threshold

Abstract. Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution function to reliably describe the exceedances of daily rainfall records above a proper optimum threshold, which should be selected as small as possible to retain the largest sample while assuring an acceptable fitting. Such an optimum threshold may differ from site to site, affecting consequently not only the GPD scale parameter, but also the probability of threshold exceedance. Thus a first objective of this paper is to derive some expressions to parameterize a simple threshold-invariant three-parameter distribution function which assures a perfect overlapping with the GPD fitted on the exceedances over any threshold larger than the optimum one. Since the proposed distribution does not depend on the local thresholds adopted for fitting the GPD, it is expected to reflect the on-site climatic signature and thus appears particularly suitable for hydrological applications and regional analyses. A second objective is to develop and test the Multiple Threshold Method (MTM) to infer the parameters of interest by using exceedances over a wide range of thresholds applying again the concept of parameters threshold-invariance. We show the ability of the MTM in fitting historical daily rainfall time series recorded with different resolutions and with a significative percentage of heavily quantized data. Finally, we prove the supremacy of the MTM fit against the standard single threshold fit, often adopted for partial duration series, by evaluating and comparing the performances on Monte Carlo samples drawn by GPDs with different shape and scale parameters and different discretizations.

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