Calibrating GPM IMERG Late-Run product using ground-based CPC daily precipitation data: a case study in the Beijing-Tianjin-Hebei urban agglomeration

Abstract. The density of rain gauges in many regions is lower than standard. Therefore, there are no precise estimates of precipitation in such regions. Today the use of satellite data to overcome this deficiency is increasing day to day. Unfortunately, the results from different satellite products also show a significant difference. Hence, their evaluation and validation are very important. The main objective of this study is to investigate the accuracy of the daily precipitation data of TRMM-3B42 V7 and PERSIANN-CDR satellites under a case study in the southern slopes of Alborz mountains, Iran. For this purpose, satellite precipitation data were compared with ground measured precipitation data of 12 synoptic stations over a 15- year period. The statistical criteria of MAE, RMSE, and Bias were used to assess error and the statistical indices of POD, FAR, and CSI was used to evaluate the recognition rate of occurrence or non-occurrence of precipitation. The results showed that there is a low correlation between satellite precipitation data and ground measured precipitation data, and the lowest and the highest values of correlation coefficient are from 0.228 to 0.402 for TRMM and from 0.047 to 0.427 for PERSIANN, respectively. However, there is a theoretical consensus on other assessment parameters, so that TRMM data is preferable in terms of the amount of data bias and the False Alarm Ratio (FAR) and PERSIANN data is superior in terms of RMSE, POD, and CSI. Also, it seems that in the study region, both of TRMM and PERSIANN have overestimated the number of daily precipitation events, so that the number of daily precipitation events was estimated about 125% and 200% of ground stations by TRMM and PERSIANN, respectively.

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Assessing Bias Corrected Daily Precipitation Data with Special Focus on Precipitation Climate Indices Using an Ensemble of High-Resolution EURO-CORDEX and Med-CORDEX Simulations for the Carpathian Region

10.1002/essoar.10500442.1 ◽

2019 ◽

Author(s):

Csaba Zsolt Torma ◽

Anna Kis ◽

Rita Pongracz

Keyword(s):

High Resolution ◽

Daily Precipitation ◽

Climate Indices ◽

Special Focus ◽

Precipitation Data ◽

Carpathian Region ◽

Daily Precipitation Data

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

Remote Sensing ◽

10.3390/rs13112040 ◽

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.

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

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.30.21992 ◽

2018 ◽

Vol 7 (4.30) ◽

pp. 5 ◽

Cited By ~ 1

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.

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Global rainfall erosivity changes between 1980 and 2017 based on an erosivity model using daily precipitation data

CATENA ◽

10.1016/j.catena.2020.104768 ◽

2020 ◽

Vol 194 ◽

pp. 104768 ◽

Cited By ~ 1

Author(s):

Yue Liu ◽

Wenwu Zhao ◽

Yanxu Liu ◽

Paulo Pereira

Keyword(s):

Daily Precipitation ◽

Rainfall Erosivity ◽

Precipitation Data ◽

Daily Precipitation Data

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Estimating extremely large amounts of missing precipitation data

Journal of Hydroinformatics ◽

10.2166/hydro.2020.127 ◽

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.

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