scholarly journals New Techniques for the Detection and Adjustment of Shifts in Daily Precipitation Data Series

2010 ◽  
Vol 49 (12) ◽  
pp. 2416-2436 ◽  
Author(s):  
Xiaolan L. Wang ◽  
Hanfeng Chen ◽  
Yuehua Wu ◽  
Yang Feng ◽  
Qiang Pu
Keyword(s):  
Daily Precipitation ◽  
Precipitation Amount ◽  
Data Series ◽  
Precipitation Series ◽  
Precipitation Data ◽  
Transformation Procedure ◽  
Precipitation Regimes ◽  
Daily Precipitation Data ◽  
Non Gaussian ◽  
Gaussian Data

Abstract This study integrates a Box–Cox power transformation procedure into a common trend two-phase regression-model-based test (the extended version of the penalized maximal F test, or “PMFred,” algorithm) for detecting changepoints to make the test applicable to non-Gaussian data series, such as nonzero daily precipitation amounts or wind speeds. The detection-power aspects of the transformed method (transPMFred) are assessed by a simulation study that shows that this new algorithm is much better than the corresponding untransformed method for non-Gaussian data; the transformation procedure can increase the hit rate by up to ∼70%. Examples of application of this new transPMFred algorithm to detect shifts in real daily precipitation series are provided using nonzero daily precipitation series recorded at a few stations across Canada that represent very different precipitation regimes. The detected changepoints are in good agreement with documented times of changes for all of the example series. This study clarifies that it is essential for homogenization of daily precipitation data series to test the nonzero precipitation amount series and the frequency series of precipitation occurrence (or nonoccurrence), separately. The new transPMFred can be used to test the series of nonzero daily precipitation (which are non Gaussian and positive), and the existing PMFred algorithm can be used to test the frequency series. A software package for using the transPMFred algorithm to detect shifts in nonzero daily precipitation amounts has been developed and made freely available online, along with a quantile-matching (QM) algorithm for adjusting shifts in nonzero daily precipitation series, which is applicable to all positive data. In addition, a similar QM algorithm has also been developed for adjusting Gaussian data such as temperatures. It is noticed that frequency discontinuities are often inevitable because of changes in the measuring precision of precipitation, and that they could complicate the detection of shifts in nonzero daily precipitation data series and void any attempt to homogenize the series. In this case, one must account for all frequency discontinuities before attempting to adjust the measured amounts. This study also proposes approaches to account for detected frequency discontinuities, for example, to fill in the missed measurements of small precipitation or the missed reports of trace precipitation. It stresses the importance of testing the homogeneity of the frequency series of reported zero precipitation and of various small precipitation events, along with testing the series of daily precipitation amounts that are larger than a small threshold value, varying the threshold over a set of small values that reflect changes in measuring precision over time.

scholarly journals A Novel Combined Model for Prediction of Daily Precipitation Data using Instantaneous Frequency Feature and Bidirectional Long Short Time Memory Networks

2021 ◽  
Author(s):  
Levent Latifoğlu
Keyword(s):  
Instantaneous Frequency ◽  
Daily Precipitation ◽  
Human Life ◽  
Absolute Error ◽  
Precipitation Data ◽  
Precipitation Forecasting ◽  
Precipitation Regimes ◽  
Daily Precipitation Data ◽  
Long Time ◽  
Short Time

Abstract In the developing world, to learn nature better, to get the maximum benefit from nature is being studied. Meteorological events constantly affect human life. The occurrence of excessive precipitation in a short time causes important events such as floods. However, in case of insufficient precipitation for a long time, drought occurs. In recent years, significant changes in precipitation regimes have been observed and these changes cause socioeconomic and ecological problems. Therefore, it is of great importance to correctly predict and analyze these variables. In this study, reliable and accurate precipitation forecasting model is proposed. Ensemble of instantaneous frequency (IF) Bidirectional Long Short Time Memory Networks (biLSTM) model was employed for the aim of forecasting of daily precipitation data. To compare the performance of biLSTM model, Long Short Time Memory Networks (LSTM) and Gated Recurrent Unit (GRU) model was applied for forecasting of daily precipitation data. The performance of the proposed IF-biLSTM model was evaluated using Mean absolute error (MAE), Mean square Error (MSE), Correlation Coefficient (R) and Determination Coefficient (R2) performance parameter. According to numerical results, IF-biLSTM model has the best forecasting performance for daily precipitation data. Especially six ahead precipitation forecasting is noteworthy.

scholarly journals A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

2021 ◽  
Vol 13 (11) ◽  
pp. 2040
Author(s):  
Xin Yan ◽  
Hua Chen ◽  
Bingru Tian ◽  
Sheng Sheng ◽  
Jinxing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Daily Precipitation ◽  
Machine Learning Algorithms ◽  
Precipitation Data ◽  
Large Area ◽  
Tree Model ◽  
Daily Precipitation Data ◽  
Precipitation Estimates

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.

scholarly journals Identifying the Ideal Number Components of the Bayesian Principal Component Analysis Model for Missing Daily Precipitation Data Treatment

2018 ◽  
Vol 7 (4.30) ◽  
pp. 5 ◽  
Author(s):  
Zun Liang Chuan ◽  
Azlyna Senawi ◽  
Wan Nur Syahidah Wan Yusoff ◽  
Noriszura Ismail ◽  
Tan Lit Ken ◽  
...  
Keyword(s):  
Time Series ◽  
Daily Precipitation ◽  
Principal Component ◽  
Precipitation Data ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Data Treatment ◽  
Daily Precipitation Data ◽  
Ideal Number ◽  
The Ideal

The grassroots of the presence of missing precipitation data are due to the malfunction of instruments, error of recording and meteorological extremes. Consequently, an effective imputation algorithm is indeed much needed to provide a high quality complete time series in assessing the risk of occurrence of extreme precipitation tragedy. In order to overcome this issue, this study desired to investigate the effectiveness of various Q-components of the Bayesian Principal Component Analysis model associates with Variational Bayes algorithm (BPCAQ-VB) in missing daily precipitation data treatment, which the ideal number of Q-components is identified by using The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The effectiveness of BPCAQ-VB algorithm in missing daily precipitation data treatment is evaluated by using four distinct precipitation time series, including two monitoring stations located in inland and coastal regions of Kuantan district, respectively. The analysis results rendered the BPCA5-VB is superior in missing daily precipitation data treatment for the coastal region time series compared to the single imputation algorithms proposed in previous studies. Contrarily, the single imputation algorithm is superior in missing daily precipitation data treatment for an inland region time series rather than the BPCAQ-VB algorithm.   

scholarly journals Estimating extremely large amounts of missing precipitation data

2020 ◽  
Vol 22 (3) ◽  
pp. 578-592
Author(s):  
Héctor Aguilera ◽  
Carolina Guardiola-Albert ◽  
Carmen Serrano-Hidalgo
Keyword(s):  
Missing Values ◽  
Daily Precipitation ◽  
Learning Algorithm ◽  
Accurate Estimation ◽  
Precipitation Data ◽  
Rain Gauges ◽  
Error Measures ◽  
Predictive Mean Matching ◽  
Daily Precipitation Data ◽  
Spatio Temporal

Abstract Accurate estimation of missing daily precipitation data remains a difficult task. A wide variety of methods exists for infilling missing values, but the percentage of gaps is one of the main factors limiting their applicability. The present study compares three techniques for filling in large amounts of missing daily precipitation data: spatio-temporal kriging (STK), multiple imputation by chained equations through predictive mean matching (PMM), and the random forest (RF) machine learning algorithm. To our knowledge, this is the first time that extreme missingness (>90%) has been considered. Different percentages of missing data and missing patterns are tested in a large dataset drawn from 112 rain gauges in the period 1975–2017. The results show that both STK and RF can handle extreme missingness, while PMM requires larger observed sample sizes. STK is the most robust method, suitable for chronological missing patterns. RF is efficient under random missing patterns. Model evaluation is usually based on performance and error measures. However, this study outlines the risk of just relying on these measures without checking for consistency. The RF algorithm overestimated daily precipitation outside the validation period in some cases due to the overdetection of rainy days under time-dependent missing patterns.

scholarly journals Bias corrections of long-term (1973-2004) daily precipitation data over the northern regions

2005 ◽  
Vol 32 (19) ◽  
pp. n/a-n/a ◽  
Author(s):  
Daqing Yang ◽  
Douglas Kane ◽  
Zhongping Zhang ◽  
David Legates ◽  
Barry Goodison
Keyword(s):  
Daily Precipitation ◽  
Precipitation Data ◽  
Daily Precipitation Data ◽  
Northern Regions ◽  
Bias Corrections

An error analysis for the hybrid gridding of Texas daily precipitation data

2009 ◽  
Vol 30 (4) ◽  
pp. 601-611 ◽  
Author(s):  
Andreas J. Rupp ◽  
Barbara A. Bailey ◽  
Samuel S.P. Shen ◽  
Christine K. Lee ◽  
B. Scott Strachan
Keyword(s):  
Error Analysis ◽  
Daily Precipitation ◽  
Precipitation Data ◽  
Daily Precipitation Data
Sign in / Sign up

Export Citation Format

Share Document