scholarly journals CHARAKTERYSTYKA OPADÓW ATMOSFERYCZNYCH W GORZOWIE WIELKOPOLSKIM W LATACH 1951–2016

1970 ◽  
pp. 133-149
Author(s):  
KATARZYNA SZYGA-PLUTA ◽  
DOMINIKA WOJTKOWIAK
Keyword(s):  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Precipitation Data ◽  
Atmospheric Front ◽  
Precipitation Occurrence ◽  
Daily Precipitation Data ◽  
Synoptic Conditions ◽  
Average Precipitation ◽  
Research Period

The purpose of the work is to characterize the precipitation occurrence and the synoptic conditions of the extreme cases in Gorzów Wielkopolski. In the paper the daily precipitation data from IMGW-PIB for Gorzów Wielkopolski station in years 1951–2016 were used. The average monthly, annual and seasonal sums were calculated and the intensity of precipitation was analyzed. Days without any precipitation were also included. Special attention was paid to extreme precipitation cases and their synoptic conditions. The average precipitation in the research period was 547,1 mm. On average, as much as 80% during the year were days with very low (0.1–1.0 mm) and low (1.1–5.0 mm) precipitation, and 13% with moderate (5.1–10.0 mm). Extreme daily precipitation totals in Gorzów Wielkopolski (95th percentile) occur mainly in summer and spring. They are associated with the transition of the atmospheric front or with the development of convection over heated land.

scholarly journals Identifying hydro-meteorological events from precipitation extremes indices and other sources over northern Namibia, Cuvelai Basin

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Frans C. Persendt ◽  
Christopher Gomez ◽  
Peyman Zawar-Reza
Keyword(s):  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Economic Losses ◽  
Precipitation Data ◽  
Rainfall Station ◽  
Extreme Precipitation Events ◽  
Daily Precipitation Data ◽  
Dry Spell ◽  
Flood Disasters ◽  
Precipitation Indices

Worldwide, more than 40% of all natural hazards and about half of all deaths are the result of flood disasters. In northern Namibia flood disasters have increased dramatically over the past half-century, along with associated economic losses and fatalities. There is a growing concern to identify these extreme precipitation events that result in many hydro-meteorological disasters. This study presents an up to date and broad analysis of the trends of hydrometeorological events using extreme daily precipitation indices, daily precipitation data from the Grootfontein rainfall station (1917–present), regionally averaged climatologies from the gauged gridded Climate Research Unit (CRU) product, archived disasters by global disaster databases, published disaster events in literature as well as events listed by Mendelsohn, Jarvis and Robertson (2013) for the data-sparse Cuvelai river basin (CRB). The listed events that have many missing data gaps were used to reference and validate results obtained from other sources in this study. A suite of ten climate change extreme precipitation indices derived from daily precipitation data (Grootfontein rainfall station), were calculated and analysed. The results in this study highlighted years that had major hydro-meteorological events during periods where no data are available. Furthermore, the results underlined decrease in both the annual precipitation as well as the annual total wet days of precipitation, whilst it found increases in the longest annual dry spell indicating more extreme dry seasons. These findings can help to improve flood risk management policies by providing timely information on historic hydro-meteorological hazard events that are essential for early warning and forecasting.

Global scaling of observed sub-daily precipitation extremes to dewpoint temperature

2020 ◽  
Author(s):  
Haider Ali ◽  
Hayley Fowler ◽  
Geert Lenderink ◽  
Elizabeth Lewis
Keyword(s):  
Air Temperature ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
The United States ◽  
Precipitation Extremes ◽  
Precipitation Data ◽  
Lower Altitude ◽  
Dewpoint Temperature ◽  
Extreme Precipitation Events ◽  
Daily Precipitation Data

<p>The intensity and frequency of extreme precipitation events have increased globally and are likely to rise further under the warming climate. The Clausius-Clapeyron (CC) relationship (scaling) provides a physical basis to understand the relationship of precipitation extremes with temperature. Recent studies have used global sub-daily precipitation data from satellite, reanalysis and climate model outputs (due to the limited availability of long term observed sub-daily data at global scales) and have reported a higher sensitivity of sub-daily precipitation extremes to surface air temperature than for daily extremes. Moreover, at higher temperatures, moisture availability becomes the dominant driver of extreme precipitation, therefore, dewpoint temperature can be a better scaling variable to overcome humidity limitations as compared to air temperature. Here, we used hourly precipitation data from the Global Sub-daily Rainfall (GSDR) dataset and daily dewpoint temperature data (DPT) from the Met Office Hadley Centre observations dataset (HadISD) at 6695 locations across the United States of America, Australia, Europe, Japan, India and Malaysia. We found that more than 60% of locations (scaling estimated for individual location) show scaling greater than 7%/K (CC rate). Moreover, more than 55% of locations across Europe, Japan, Australia and Malaysia show scaling greater than 1.5CC. Furthermore, when locations across selected regions are pooled within similar climatic zones (based on Koppen Geiger classification), scaling curves show around 7%/K scaling. The scaling curves for locations at greater altitude (>400m MSL) are flat compared to locations at relatively lower altitude. The difference in scaling rates at-station and for pooled regions highlight the importance of understanding the thermodynamic and dynamic processes governing precipitation extremes at different spatial scales and indicate that local processes are driving the super-CC sensitivities in most regions.</p>

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

scholarly journals Bias Correction of Zero-Inflated RCM Precipitation Fields: A Copula-Based Scheme for Both Mean and Extreme Conditions

2019 ◽  
Vol 20 (4) ◽  
pp. 595-611 ◽  
Author(s):  
Rajib Maity ◽  
Mayank Suman ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Extreme Precipitation ◽  
Joint Distribution ◽  
Daily Precipitation ◽  
Extreme Values ◽  
Entire Study ◽  
Daily Precipitation Data ◽  
Zero Values ◽  
The Mean

Abstract Changes in extreme precipitation due to climate change often require the application of methods to bias correct simulated atmospheric fields, including extremes. Most existing bias correction techniques (i) only focus on the bias in the mean value or on the extreme values separately, and (ii) exclude zero values from analysis, even though their presence is significant in daily precipitation. We developed a copula-based bias correction scheme that is suitable for zero-inflated daily precipitation data to correct the bias in mean as well as in extreme precipitation at any specific statistical quantile. In considering the whole of Germany as a test bed, the proposed scheme is found to work well across the entire study area, including the German Alpine regions. The joint distribution between observed and regional climate model (RCM)-derived precipitation is developed through copulas. In particular, the joint distribution is modified to make it discrete at zero in order to account for zero values. The benefit of considering zero precipitation values is revealed through the improved performance of bias correction both in the mean and extreme values. Second, the quantile that best captures the bias (whether in the mean or any extreme value) is determined for a specific location and varies spatially and seasonally. This relaxation in selecting the location-specific optimal quantile renders the proposed methodology spatially transferable. By acknowledging possible changes in extreme precipitation due to climate change, the proposed scheme is expected to be suitable for climate change impact assessments for extreme events worldwide.

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