Long-term change of warm-season precipitation climatology in South Korea

2020 ◽  
Author(s):  
Hyeon-seok Do ◽  
Joowan Kim
Keyword(s):  
South Korea ◽  
Daily Precipitation ◽  
Warm Season ◽  
Precipitation Intensity ◽  
Precipitation Data ◽  
Precipitation Characteristics ◽  
Daily Precipitation Data ◽  
Long Term Changes ◽  
Precipitation Increase

<div> <div> <div> <p>This study examines long-term changes of precipitation characteristics in South Korea focusing on warm season (June-September). Daily precipitation data are obtained from 15 surface stations that have continuously observed precipitation for 58 years (1961 – 2018). Precipitation characteristics and their long-term changes are examined including trend, amount, and intensity. The warm- season precipitation in South Korea is largely affected by the East Asian Summer Monsoon, which causes rainy season in late July and mid August (these are called “Changma” and “Post-Changma” seasons in Korea). Thus, these characteristics are also analyzed focusing on Changma season.</p> <p>The warm-season precipitation increased roughly by 1.0 mm per day for the last thirty years. The change is particularly pronounced during Changma season, and it shows 1.6 mm of daily precipitation increase. Trend analysis for the 58 years also showed a consistent and significant result. The precipitation change is mostly founded in the intensity of 30 – 110 mm per day implying that the precipitation intensity is increasing in warm season. Multiple regression analysis further suggests that this change is more related to precipitation intensity than precipitation frequency. Global precipitation data reveals the similar change in precipitation over central eastern China presenting a band-like precipitation increase extending to the Korean peninsula. These results are likely caused by near-surface temperature and moisture increase in a warming climate.</p> </div> </div> </div>

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

scholarly journals Decadal changes in extreme daily precipitation in Greece

2008 ◽  
Vol 16 ◽  
pp. 55-62 ◽  
Author(s):  
P. T. Nastos ◽  
C. S. Zerefos
Keyword(s):  
Factor Analysis ◽  
Daily Precipitation ◽  
Time Variability ◽  
Precipitation Data ◽  
Shape Parameters ◽  
Precipitation Characteristics ◽  
Daily Precipitation Data ◽  
Central Regions ◽  
Greek Region ◽  
Of Greece

Abstract. The changes in daily precipitation totals in Greece, during the 45-year period (1957–2001) are examined. The precipitation datasets concern daily totals recorded at 21 surface meteorological stations of the Hellenic National Meteorological Service, which are uniformly distributed over the Greek region. First and foremost, the application of Factor Analysis resulted in grouping the meteorological stations with similar variation in time. The main sub groups represent the northern, southern, western, eastern and central regions of Greece with common precipitation characteristics. For representative stations of the extracted sub groups we estimated the trends and the time variability for the number of days (%) exceeding 30 mm (equal to the 95% percentile of daily precipitation for eastern and western regions and equal to the 97.5% percentile for the rest of the country) and 50 mm which is the threshold for very extreme and rare events. Furthermore, the scale and shape parameters of the well fitted gamma distribution to the daily precipitation data with respect to the whole examined period and to the 10-year sub periods reveal the changes in the intensity of the precipitation.

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 Precipitation Variations under a Changing Climate from 1961–2015 in the Source Region of the Indus River

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1366
Author(s):  
Muhammad Rizwan ◽  
Xin Li ◽  
Kashif Jamal ◽  
Yingying Chen ◽  
Junaid Nawaz Chauhdary ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Source Region ◽  
Summer Precipitation ◽  
Heavy Precipitation ◽  
Winter Precipitation ◽  
Indus Basin ◽  
Indus River ◽  
Precipitation Characteristics ◽  
Daily Precipitation Data ◽  
Increasing Trends

The source region of the Indus River (SRIR), which is located in the Hindukush, Karakoram and Himalayan (HKH) mountainous range and on the Third Pole (TP), is very sensitive to climate change, especially precipitation changes, because of its multifarious orography and fragile ecosystem. Climate changes in the SRIR also have important impacts on social and economic development, as well as on the ecosystems of the downstream irrigation areas in Pakistan. This paper investigates the changes in precipitation characteristics by dividing the daily precipitation rate into different classes, such as light (0–10 mm), moderate (10.1–25 mm) and heavy precipitation (>25 mm). Daily precipitation data from gauging and non-gauging stations from 1961–2015 are used. The results of the analysis of the annual precipitation and rainy day trends show significant (p < 0.05) increases and decreases, respectively, while light and heavy precipitation show significant decreasing and increasing trends, respectively. The analysis of the precipitation characteristics shows that light precipitation has the highest number of rainy days compared to moderate or heavy precipitation. The analysis of the seasonal precipitation trends shows that only 18 stations have significant increasing trends in winter precipitation, while 27 stations have significant increasing trends in summer precipitation. Both short and long droughts exhibit increasing trends, which indicates that the Indus Basin will suffer from water shortages for agriculture. The results of this study could help policymakers cope with floods and droughts and sustain eco-environmental resources in the study area.

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 (&gt;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 Development of a Pressure–Precipitation Transmitter

2019 ◽  
Vol 58 (11) ◽  
pp. 2453-2468
Author(s):  
Masaru Inatsu ◽  
Tamaki Suematsu ◽  
Yuta Tamaki ◽  
Naoto Nakano ◽  
Kao Mizushima ◽  
...  
Keyword(s):  
Regression Model ◽  
Daily Precipitation ◽  
Reanalysis Data ◽  
Precipitation Pattern ◽  
Multilevel Regression ◽  
Extreme Statistics ◽  
Spatial Continuity ◽  
Daily Precipitation Data ◽  
Multilevel Regression Model

AbstractA novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical downscaling methods of the analog ensemble and singular value decomposition (SVD). After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure–precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data.

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